assessing the performance of korea’s ghg emissions …

Basic Research Report 19-09

Assessing the Performance of Korea’s GHG Emissions Trading Scheme in Phase Ⅰ

Insung Son

Research Staff

Head Researcher: Insung Son, Associate Research Fellow

Outside Participants: Young-Hwan Ahn, Sookmyung Women’s University

Su-Yol Lee, Chonnam National University

ABSTRACT

The human capacity has increased significantly compared to before the Industrial Revolution. The expanded human capacity requires a lot of energy, and we have mainly relied on fossil fuels to supply a lot of energy cheaply and easily. However, the use of fossil fuels results in social costs due to acid rain caused by nitrogen oxides (NOx) and sulfur oxides (SOx), and climate change caused by greenhouse gas emissions such as carbon dioxide. Greenhouse gases, in particular, accumulate in the Earth's atmosphere, causing climate change across the planet in the long run. Therefore, one nation's efforts alone cannot solve the problem of the entire planet. Climate change mitigation has the properties of a public good. This makes it difficult for any country to make voluntary and proactive efforts to reduce greenhouse gas emissions.

Countries from all over the world began to discuss reduction of greenhouse gases and solutions to climate change, and as a result, the United Nations Framework Convention on Climate Change (UNFCCC) in 1992 and the Kyoto Protocol in 1997 were adopted.

Meanwhile, in line with such international efforts, Korea introduced the possibility of an emissions trading scheme through the Framework Act on Low Carbon and Green Growth in 2010, with actual introduction taking place in 2015 after five years of preparation. Since 2015 was before the Paris Agreement was adopted and entered into force, Korea was not obliged to reduce greenhouse gas emissions as a UNFCCC non-Annex I country. Nevertheless, Korea decided to introduce its emissions trading scheme to proactively respond to climate change and greenhouse gas reduction and to foster and support low-carbon industries as new growth engines.

The adoption of the Paris Agreement in 2015 and its early entry into force in 2016 has led to a major change in the international framework for mitigating climate change. Under the Paris Agreement, reducing greenhouse gas emissions is no longer the only responsibility of developed countries. All countries have to nationally set and faithfully implement greenhouse gas reduction targets. Korea also presented an ambitious goal to reduce its greenhouse gas emission forecast by 37 percent in 2030 from the Business As Usual (BAU) through the INDC (Integrated Nationally Determined Distribution) submitted to the UNFCCC in 2015.

Given that emissions from companies subject to emissions trading account for about 70 percent of the country's total emissions, the emissions trading scheme is one of the most important means to achieve the 2030 greenhouse gas reduction target.

Currently, the country's emissions trading scheme has wrapped up the first phase from 2015 to 2017, and the second phase has been in operation since 2018. At the end of the first phase, the results of the emissions trading scheme, its impact on greenhouse gas emissions, and its impact on covered companies will be analyzed to assess the operational performance of the emissions trading scheme in the first phase so that the implications for its future operation can be derived.

1. Purpose for analysis

Greenhouse gas reduction activities always involve the costs of installing and operating reduction facilities, replacing facilities to convert low-carbon fuels, and streamlining production processes. In addition, the cost of reducing greenhouse gas emissions includes profits abandoned due to reduced production activities as opportunity costs.

Greenhouse gas reduction policy, such as the emissions trading scheme, due to the cost of reducing greenhouse gas emissions causes many concerns and discussions about its impact on the national economic and industrial sector competitiveness. Therefore, it is important to minimize the costs of reducing greenhouse gas emissions or achieving emission targets to minimize the negative impact on the competitiveness of the national economy and the industrial sector.

The greenhouse gas emissions trading scheme is one of the ways to achieve a set amount of emissions or reductions for minimum cost. However, an efficient, fully competitive market is needed to achieve cost-effective greenhouse gas reduction, which is the purpose of introducing the emission trading scheme.

This study therefore set the greenhouse gas reduction performance of the emission trading scheme (related to (A) in [Figure Summary-1], the efficiency of the emission trading market (relevant to (B) in [Figure Summary-1]) as a precondition for achieving cost effectiveness, and the impact on the competitiveness of the allocation companies in introducing the emission trading scheme (C) in [Figure Summary-1].

Figure Summary-1. Subject of Analysis: Conceptual Map

In addition, each analysis target was statistically verified of the greenhouse gas reduction performance resulting from the introduction of the emission trading scheme, and analyzed whether the emission trading market was operated efficiently and how the emission trading scheme affected corporate competitiveness. Finally, based on the results analyzed from three perspectives, the implications for the efficient operation of the future greenhouse gas emissions trading scheme (see Figure Summary-2).

Figure Summary-2. Research Structure and Questions

2. Analysis results

2.1. Greenhouse gas reduction performance

According to the ‘Basic Plan for Emissions’, which was announced in 2014, the first phase was aimed at accumulating experience in the emission trading scheme and settling the system (Planning Finance Ministry, 2014). During the first phase, the total emissions of the companies to be allocated stood at 98.76% of the total emissions allowance (1,689.9 million CO2e. tons) during the first phase. This can also be assessed to have played a major role in reducing greenhouse gas emissions and curbing the increase in greenhouse gas. However, it is not sufficient to assess whether the emissions trading scheme has contributed to the reduction of greenhouse gases by simply comparing the total emission allowance and the certified emissions.

In this study (Chapter 2) for assessing the performance of greenhouse gas reduction during the first phase of the emission trading scheme, the company chose a method to confirm quantitatively whether the relationship between adjusted tangible assets and greenhouse gas emissions as surrogate variables for energy use, sales and capital of each industry has improved since the introduction of the emission trading scheme.

Although the statistical analysis of all industries could not be performed due to limitations of data, the transition team, steel, petrochemicals, semiconductors, displays, electronics, and automobile industries checked whether there had been statistically significant reduction results since the introduction of the emission trading scheme. The analysis results were very different for different sectors and industries. First of all, the relationship between energy use and sales volume of greenhouse gas emissions has worsened since the introduction of the emission trading scheme, while the effect of adjusted tangible assets has been improved in a way that contributes to the reduction of greenhouse gas emissions. In the case of petrochemical industries, which are representative multitem industries, no statistically significant changes have been found in the effects of major variables on greenhouse gas emissions since the introduction of the emission trading scheme.

Meanwhile, the steel industry and the semi-d-autonomous industries were the ones with the most clear reduction results. The steel sector saw its impact on greenhouse gas emissions improved both in energy use and sales, but the impact of adjusted tangible assets worsened, the report showed. The impact of energy use on greenhouse gas emissions has improved in semi-d, digital and industrial sectors, and the impact of sales has worsened. However, considering both the greenhouse gas emission characteristics and the data used in this analysis, it is deemed that the reduction in greenhouse gas emissions per unit energy in the semi-d, de-d, and specialty sectors is attributable to the reduction of process emissions.

Finally, it was found that the effect of sales and adjustment tangible assets has

changed statistically significantly since the introduction of the emission trading scheme. But each direction of change was reversed. While the impact of sales on greenhouse gas emissions has been improved, the impact of adjusted assets has been shown to have worsened

There is something to be noted about the interpretation of the analysis results in this chapter. First of all, it should not be interpreted that industries that do not statistically significantly show the effect of introducing emission trading schemes in terms of greenhouse gas emissions have made any effort to reduce emissions. It should be considered that the companies to be allocated have been directly regulated from the greenhouse gas and energy target management system before the emission trading scheme. Therefore, what can be found in the analysis in this chapter is whether the transition from a target management system to an emission trading scheme resulted in additional reduction results.

2.2. Emissions Market Efficiency

Next, the biggest reason why emissions trading schemes are preferred over other direct regulations is that it is possible to achieve cost-effective reduction targets. In order to achieve the cost effectiveness of the emissions trading scheme, the emissions and reductions of all companies must be determined so that the marginal costs of reducing among the allocated companies are all equal. Under the emission trading scheme, the cost-effectiveness of all companies is met because the cost of limit reduction is equal based on the market price of the emission rights. However, this requires an efficient emission trading market.

It is impossible to assess the cost effectiveness achieved by assessing the marginal cost of all allocated companies. Instead, it is possible to assess the achievement of cost effectiveness indirectly by analyzing whether the emission trading market was efficient and assessing whether the prerequisites for achieving the cost effectiveness of the emission trading scheme have been achieved. Chapter 3 evaluated whether the emission trading market for the first phase was efficient through statistical verification methods.

弱形 Efficient Market Hypothesis (EMH) was approved using the Variance Ratio (VR) test to verify the efficiency of the local emission trading market. In the process, the first emission trading market in Korea reflected various characteristics of a thin market, which does not have many transactions. In this study, we conducted a separate analysis based on the price at the opening date as well as the price at which the transaction actually occurred, applied a modified AR(1) process on revenue, analyzed weekly data as well as daily data, and conducted a separate analysis on 2017 when transactions were relatively active during the first period. Lastly, the KAU18

transaction was also analyzed to see if there was any change in the efficiency of the emission trading market in the second period.

As a result, it was found that during the first phase, there was no support for the hypothesis that the emission trading market was efficient. Although the first implementation year of the second phase, 2018 was not fully supported, it was analyzed that support for Random Walk Hypothesis (RWH) was relatively high compared to the first phase. The relatively higher efficiency in 2018 compared to the first phase (2015-2017) is attributed to the relatively lower uncertainty over the price of emission rights.

2.3. Effects of ETS on Firms’ Competitiveness

Although emissions trading is known as a more flexible system than carbon tax or reduction regulations, companies and industries have been perceived as just another form of regulation, raising concerns about rising regulatory compliance costs and lower competitiveness in the market. Similar debates continued in the EU ETS and various studies were conducted on the impact of ETS on industry and corporate competitiveness. EU studies have shown that ETS’ impact on businesses and industries at micro-levels is in a wide variety of forms, with positive, negative, or insignificant consequences depending on industry, product and time, and thus fail to produce a consistent conclusion.

There was the same debate when Korea introduced the ETS, and the negative effects of the ETS were largely highlighted in the pre-predictive. The end of the first phase is a good opportunity to demonstrate the effect of ETS on the industrial and corporate competitiveness.

To this end, this study (Chapter 4) analyzed the impact of ETS on businesses and industries in three aspects. First, changes in the financial performance and output indicators of regulated entities before and after the ETS was implemented were statistically verified. Contrary to the negative concerns about the implementation of the ETS, the analysis showed that the financial situation of the company did not deteriorate after the implementation of the ETS. Many financial indicators have improved from a significant level. The reduced cost ratio of manufacturing costs to sales increased efficiency, while the total assets increased, while the debt-to-equity ratio decreased, improving overall financial conditions.

Second, regression analysis was performed to analyze the net effect of ETS. Statistical verification of the ETS' explanatory power by controlling high-impact descriptive variables has shown that the effect of ETS is not statistically significant. The effect of ETS was significant in terms of the ratio of sales costs, which contributed

to improving the efficiency of the company’s operations.

Third, a double-difference analysis was performed to verify the effectiveness of the ETS policy. According to the analysis of ETS regulated companies (experimental groups) and non-regulated companies (comparative groups) as the population of listed companies in Korea, the effect of the ETS system was not statistically significant. There are limitations that cannot be called a rigorous analysis of the effectiveness of a plan because the comparator group for analysis does not exist in reality.

3. Implications and Policy Proposals

3.1. Inducing investment in reducing greenhouse gas emissions

To reduce greenhouse gas emissions, the cost of reducing greenhouse gases, including direct and opportunity costs, are incurred. Given both the opportunity cost of shrinking production activities and the economic impact on regional and fore-aft industries, investment for the introduction of reduction facilities or technologies must be brisk in order to reduce greenhouse gas emissions.

Therefore, the government's support for reducing greenhouse gas emissions should be made in three aspects. First, the government should support development of facilities and technologies that can be introduced by businesses to reduce greenhouse gas emissions, such as facilities and technologies for reducing greenhouse gas emissions, high-efficiency energy facilities, and processes for producing low-emission gases. Next, the government's policy should provide sufficient incentives to help greenhouse gas reduction investments actively take place. All investment activities are determined by comparing the investment costs with the benefits that will arise from the investment. Therefore, the government should set the direction of support to reduce the cost burden from investments in reducing greenhouse gas emissions and maximize the benefits from investments in reduction.

3.1.1. Development of Greenhouse Gas Reduction Technology

First of all, the government should set short and medium- and long-term directions for development of facilities and technologies for reducing greenhouse gas emissions and implement them separately. First, in the short term, support for technology development that is immediately applicable is needed to reflect the demand for technology development by allocating companies. The government can support the development of technologies and facilities that can be developed and applied in a short period of time by reflecting the demand for technology development by the companies

to be allocated, contributing to the actual performance of reducing greenhouse gas emissions and easing the burden on the companies to develop technologies, reduce greenhouse gas emissions, and purchase emission rights.

In the mid- to long-term, support for source technologies is needed. The nation relies mostly on overseas production facilities, processes and devices. In this case, the problem is that it is difficult for allocating companies to arbitrarily change facilities and processes to reduce greenhouse gas emissions. This is because if the companies to be allocated arbitrarily change their production facilities and processes, they will not receive guarantees and services from suppliers in the event of problems with future production facilities and processes. Therefore, securing original technologies for production facilities and processes in the long term could be the basis for the introduction of active greenhouse gas reduction facilities and technologies in the future.

3.1.2. Support for Investment Costs

Next, the government should expand the size of the greenhouse gas reduction support project, which is currently being carried out through the government offices, by utilizing the proceeds from the auction of paid dividends. On the other hand, for some projects, the upper limit should be mitigated or removed as it may limit support for projects that are costly but have significant reduction effects.

3.1.3. Reinforce incentives to invest in greenhouse gas reduction

The allocation method should be changed in such a way that it would benefit companies that have reduced emissions by proactively implementing greenhouse gas reduction investments. To that end, the government plans to increase the benchmark allocation. However, there is also something to be noted about benchmarked allocation. When benchmarks are set for each industry, the number of companies to be allocated for each industry is often limited. We need to think more about how to set up benchmarks for a small number of companies. And setting benchmarks by process that are common across industries, rather than by industry benchmarks, could also be an alternative.

In the application of benchmarking methods, equity among industries should also be considered. In setting benchmarks, some industries will be able to benefit a relatively large number of companies, while others will not be able to set relatively high benchmarks to allow many in the industry to be pressured to reduce.

Flexible operation of the system is required to effectively utilize the incentive system for investment in greenhouse gas reduction introduced from the second phase in order to increase the benefits of investment in reducing greenhouse gases and induce investment in reducing them under the emission trading scheme. This reduction incentive scheme increases the allocation of emission rights by providing the certified reduction performance in the last phase in addition to the expected emissions when allocating the emission rights for the next phase. However, controversy has been raised over the effectiveness of the method for verifying the reduction performance, saying that the certification rate of the actual reduction performance is not high due to excessive strictness. Therefore, it is necessary to flex its methodology for certifying reduction performance through close communication with companies subject to allocation.

In addition, the recognized reduction results will not be recognized during the next phase because the benefits will be applied only to the next phase. However, rather than uniformly applying the duration of the application of the reduction performance, more long-term benefits should be provided in consideration of the life of the reduction facilities and technologies, the size of the investment in the reduction, and the duration of the reduction performance.

Finally, most newly introduced facilities use high-efficiency equipment at the time of introduction, or most of them introduce reduction facilities. Nevertheless, the current allocation method for new facilities requires a certain percentage reduction by applying the adjustment factor. However, this would weaken the need to introduce high-efficiency or reduction facilities in the introduction of new facilities, thus delaying investment to the future for real greenhouse gas reduction. Therefore, the guidance should be changed so that new facilities can be excluded from the application of the adjustment factor considering the facility efficiency and the introduction of the facility.

3.2. Improving Emissions Market Efficiency

To increase the efficiency of the local emissions trading market, the government should increase liquidity and lower information costs in the market. So far, the government has mainly increased liquidity through changes to market rules, such as restrictions on carryovers. However, frequent changes in market rules reduce the transparency and consistency of information, thereby increasing information costs for companies participating in the market, which can eventually lead to market inefficiencies. Therefore, in the future, it is desirable to improve market efficiency by actively utilizing government holdings, expanding the role of market makers, introducing emission price caps, and introducing futures markets rather than changing market rules.

3.3. Changing the perception of policymakers and businesses

The analysis results provide policymakers and business practitioners with the following policy and strategic implications: First, consistent implementation of the emissions trading scheme is required. In addition, the ETS can induce innovation and improvement in the targeted enterprise and should orientate the implementation of the scheme in that direction. The continuing concern about the economic impact of the ETS, especially its possible impact on the industry, could undermine the consistency of the implementation of the scheme. Pre-predictive models based on static modeling can overemphasize the negative effects of ETS, encouraging excessive policy changes and intervention. There was no negative impact of ETS on businesses and industries during the first phase. The direct effects of the ETS have not been confirmed, but it has been shown that the operating efficiency of the target companies has improved steadily during the implementation of the plan. If the ETS can become an innovation engine for regulated businesses like the system features, it is also fully possible to pursue the intended reduction of greenhouse gas emissions and corporate competitiveness at the same time. Porter and van der Linder (95) is leaving open the possibility of being confirmed in carbon policy.

Second, a shift in perception of ETS by businesses and industries is required. A strategic approach is needed to recognize ETS as another environmental regulation that can be used as an opportunity for improvement and innovation, rather than a negative and passive response. The government should develop and implement strategies to secure competitiveness by reducing manufacturing costs through improved process efficiency, increasing sales through development of low-carbon products, improving reputation through active response to climate change and reducing potential risks. Efforts should also be made to integrate ETS with the entity’s original management activities so that it can take full advantage of the flexible system utilizing market mechanisms.

Table of Contents

ABSTRACT .................................................................................................... 3

1. Purpose for analysis .................................................................................... 4

2. Analysis results ........................................................................................... 6

2.1. Greenhouse gas reduction performance ............................................... 6

2.2. Emissions Market Efficiency ............................................................... 7

2.3. Effects of ETS on Firms’ Competitiveness .......................................... 8

3. Implications and Policy Proposals .............................................................. 9

3.1. Inducing investment in reducing greenhouse gas emissions ................ 9

3.2. Improving Emissions Market Efficiency ........................................... 11

3.3. Changing the perception of policymakers and businesses ................. 12

Chapter I. Introduction .................................................................................. 21

1. Preface................................................................................................... 21

2. Subject of Analysis ............................................................................... 24

3. Research Structure and Questions ......................................................... 25

Chapter II. Performance of GHG Emissions Reduction ............................... 29

1. Introduction ............................................................................................... 29

1.1 Research Purpose and Background ..................................................... 29

1.2. Analytical Approach and Composition of the Chapter ...................... 30

2. Allocation of Emission Permits and Trend in Amounts of Certified Emissions ...................................................................................................... 31

2.1. Transition Sector ................................................................................ 32

2.2. Industrial Sector ................................................................................. 35

3. Model of Analysis ..................................................................................... 43

3.1. Literature Survey ............................................................................... 43

3.2. Model of Analysis .......................................................................... 45

3.2. Data .................................................................................................... 47

4. Results of Analysis ............................................................................... 50

4.1. Transition Sector ................................................................................ 51

4.2. Industrial Sector.............................................................................. 57

5. Chapter Conclusion ............................................................................... 74

Chapter III. Market Efficiency under the First Phase of the Korean ETS .... 77

1. Introduction ........................................................................................... 77

2. Transaction Volume of the First Phase and Literature Review ............ 78

2.1. Volume and Characteristics of Transactions in the First Phase ..... 78

2.2. Literature Review ........................................................................... 84

3. Efficiency of the Korean ETS during the First Phase ........................... 86

3.1. Method ............................................................................................ 86

3.2. Data ................................................................................................ 89

3.3. Results ............................................................................................ 90

3.4. Discussion ...................................................................................... 93

4. Chapter Conclusion ............................................................................... 97

Chapter IV. Impact of the ETS on Business Competitiveness ...................... 98

1. Research Purpose and Background ....................................................... 98

4. Literature Review ...................................................................................... 98

2.1. EU ETS .............................................................................................. 98

2.2. Korean ETS ...................................................................................... 101

3. Impact of the Korean ETS on Business Competitiveness ....................... 102

3.1. Method of Analysis .......................................................................... 102

3.2. Findings............................................................................................ 104

5. Chapter Conclusion ................................................................................. 111

Chapter V. Conclusion ................................................................................ 113

1. Results ................................................................................................. 114

1.1. Performance of GHG Emissions Reduction ................................. 114

1.2. Efficiency of the Emissions Trading Market ................................ 115

1.3. Effect on Business Competitiveness ............................................ 115

2. Policy Implications ............................................................................. 116

2.1. Fostering Investment towards Reducing GHG Emissions ........... 116

2.2. Enhancing Efficiency of the Emissions Market ........................... 119

2.3. Changing the Perception of Policymakers and Businesses .......... 120

References ................................................................................................... 121

Appendix ..................................................................................................... 128

List of Tables

Table 2- 1. Original and Revised Lists of ETS-Targeted Sectors and Industries ...... 31

Table 2- 2. Final Emissions Allowances and Certified Emissions: Transition Sector ............................................................................................................................. 32

Table 2- 3. Emission Permit Trading, Submissions and Carryforwards: Transition Sector (2015-2017) .............................................................................................. 33

Table 2- 4. Trend in Certified Emissions: Transition Sector ..................................... 34

Table 2- 5. Final Emissions Allowances and Certified Emissions by Industry: Industrial Sector (1) ............................................................................................. 36

Table 2- 6. Final Emissions Allowances and Certified Emissions by Industry: Industrial Sector (2) ............................................................................................. 36

Table 2- 7. Emission Permit Trading, Submissions and Carryforwards: Industrial Sector (1) (2015-2017) ........................................................................................ 38

Table 2- 8. Emission Permit Trading, Submissions and Carryforwards: Industrial Sector (2) (2015-2017) ........................................................................................ 39

Table 2- 9. Trend in Certified Emissions: Industrial Sector (1) ................................ 41

Table 2- 10. Trend in Certified Emissions: Industrial Sector (2) .............................. 42

Table 2- 11. Trend in Certified Emissions: Industrial Sector (3) .............................. 43

Table 2- 12. Dataset for Analysis .............................................................................. 48

Table 2- 13. Emissions from Subject Businesses as Indicated in Different Sources 49

Table 2- 14. Analysis of Estimated Coefficients ....................................................... 51

Table 2- 15. Major Variables and Descriptive Statistics: Transition Sector .............. 52

Table 2- 16. Model Test Results: Transition Sector .................................................. 53

Table 2- 17. Analysis Results: Transition Sector ...................................................... 55

Table 2- 18. Sources of Energy for Power Generation in Korea ............................... 57

Table 2- 19. Descriptive Statistics of Major Variables: Steel Industry ..................... 58

Table 2- 20. Model Test Results: Steel Industry ....................................................... 59

Table 2- 21. Analysis Results: Steel Industry ........................................................... 61

Table 2- 22. Descriptive Statistics of Major Variables: Petrochemical Industry ...... 62

Table 2- 23. Model Test Results: Petrochemical Industry ........................................ 63

Table 2- 24. Analysis Results: Petrochemical Industry ............................................. 64

Table 2- 25. Descriptive Statistics of Major Variables: SDE Industry ...................... 66

Table 2- 26. Model Test Results: SDE Industry ........................................................ 67

Table 2- 27. Analysis Results: SDE Industry ............................................................ 69

Table 2- 28. Descriptive Statistics of Major Variables: Automobile Industry ........... 71

Table 2- 29. Model Test Results: Automobile Industry ............................................ 72

Table 2- 30. Analysis Results: Automobile Industry ................................................ 73

Table 3- 1. Emission Permit Trade by Year .............................................................. 79

Table 3- 2. Average Prices of Emission Permits Per Unit Weight by Year ............... 79

Table 3- 3. Number of Transactions by Year ............................................................ 80

Table 3- 4. Average Quantity of Emissions Per Transaction ..................................... 81

Table 3- 5. Quantity of Emissions Traded in KAUs by Year .................................... 82

Table 3- 6. Number of KAUs Traded by Year .......................................................... 82

Table 3- 7. Average Price of Emissions Per Unit Weight: KAU Transactions .......... 83

Table 3- 8. Summary of Major ETS Studies ............................................................. 86

Table 3- 9. Summary Statistics 1 .............................................................................. 89

Table 3- 10. Summary Statistics 2 ............................................................................ 90

Table 3- 11. KAU15-17 (Number of Open Days): VR Test Results ......................... 91

Table 3- 12. KAU15-17 (Number of Transaction Days): VR Test Results ............... 91

Table 3- 13. KAU15-17 (Number of Open Weeks): VR Test Results....................... 92

Table 3- 14. KAU17 (Number of Transaction Days): VR Test Results .................... 92

Table 3- 15. KAU17 (Number of Transaction Days): VR Test Results .................... 93

Table 4- 1. Sample Distribution by Sector and Industry ......................................... 103

Table 4- 2. Variables and Datasets .......................................................................... 104

Table 4- 3. Pre- and Post-ETS Financial Performance of Targeted Businesses (T-Test) ........................................................................................................................... 105

Table 4- 4. Pre- and Post-ETS Financial Performance (T-Test): Transition Sector 106

Table 4- 5. Pre- and Post-ETS Financial Performance (T-Test): Manufacturing Sector ........................................................................................................................... 107

Table 4- 6. Pre- and Post-ETS Average Revenue: Manufacturing Sector ............... 107

Table 4- 7. Impact of the ETS on Revenue and Cost: Regression Analysis ............ 108

Table 4- 8. Impact of the ETS on Financial Performance and Employment: Regression Analysis ............................................................................................................. 109

Table 4- 9. Compared Groups for DID Analysis on Effects of the ETS ................. 110

Table 4- 10. Net Effect of the ETS: DID Analysis .................................................. 111

Appendix Table 1. Model Test Results (Fixed- or Random-Effect): F&B ............. 128

Appendix Table 2. Equation (2) Estimates: F&B ................................................... 129

Appendix Table 3. Model Test Results (Fixed- or Random-Effect): Paper ............ 130

Appendix Table 4. Equation (2) Estimates: Paper .................................................. 131

Appendix Table 5. Model Test Results (Fixed- or Random-Effect): Glass/Ceramics ........................................................................................................................... 132

Appendix Table 6. Equation (2) Estimates: Glass/Ceramics .................................. 133

Appendix Table 7. Model Test Results (Fixed- or Random-Effect): Cement ......... 134

Appendix Table 8. Equation (2) Estimates: Cement ............................................... 135

Appendix Table 9. Model Test Results (Fixed- or Random-Effect): Non-Iron Metals ........................................................................................................................... 136

Appendix Table 10. Equation (2) Estimates: Non-Iron Metals ............................... 137

Appendix Table 11. Statistical Thresholds for Non-Revenue-Adjusting Analysis: KAU15-17, Number of Open Days (n=880) ..................................................... 138






Appendix Table 17. Statistical Thresholds for Non-Revenue-Adjusting Analysis: KAU17, Number of Open Days (n=249) .......................................................... 144

Appendix Table 18. Statistical Thresholds for Revenue-Adjusting Analysis: KAU17, Number of Open Days (n=248) ......................................................................... 145

Appendix Table 19. Statistical Thresholds for Non-Revenue-Adjusting Analysis: KAU18, Number of Open Days (n=153) .......................................................... 146

Appendix Table 20. Statistical Thresholds for Revenue-Adjusting Analysis: KAU18, Number of Open Days (n=152) ......................................................................... 147

List of Figures

Figure 1- 1. Subject of Analysis: Conceptual Map .................................................. 24

Figure 1- 2. Research Structure and Questions ........................................................ 26

Figure 2- 1. Average GHG Emissions and Energy Demand per Business ............... 50

Figure 2- 2. Electricity Demand in Korea by Sector ................................................ 56

Figure 2- 3. GHG Emissions and Energy Demand: SDE Industry........................... 68

Figure 2- 4. GHG Emissions and Energy Demand Trend Since 2014: SDE Industry ............................................................................................................................ 70

Figure 3- 1. EU ETS1: Trend and Distribution of Return Rates .............................. 94

Figure 3- 2. EU ETS 2: Trend and Distribution of Return Rates ............................. 94

Figure 3- 3. Korean ETS 1: Trend and Distribution of Return Rates ....................... 95

Figure 3- 4. Korean ETS 1: Trend and Distribution of Return Rates in KAU18 ..... 95

Figure 5- 1. Policy Support for Reducing GHG Emissions ................................... 117

Chapter I. Introduction

1. Preface

The human capacity to productively create things has been growing without limits since the Industrial Revolution. The relentless advancement of technology has enabled humans to do things they never imagined prior to industrialization. The radical growth of human capacity, however, has also dramatically increased the demand for energy to an increasingly unsustainable extent. Humankind has resorted to abundant and relatively cheap fossil fuels to satiate this growing demand for energy.

The growth of human capacity, aided by technology, and concomitant increases in dependence on fossil fuels have benefitted humankind to an unprecedented extent. Such benefit, though, has not come free of cost. In addition to direct expenses—mining, processing, and transporting—involved in their use, fossil fuels also extract an environmental cost to entire societies, including acid rain and climate change. Only recently have people begun to consider these long-overlooked, societal costs.

Governments worldwide have responded to the new concern about the rising societal cost of fossil fuels through a variety of policy measures. These include regulating the amounts of chemicals that can be used so as to prevent acid rain, and also requiring manufacturers to install devices that reduce harmful emissions such as nitrogen oxides (NOx) and sulfur oxides (SOx). The US government and others have also adopted emissions trading schemes (ETSs) on sulfur dioxides (SO2) and other such gaseous chemicals in an attempt to financially incentivize solutions.

Greenhouse gas (GHG) emissions accumulate in the atmosphere and cause long-term climate change around the globe. A problem of this magnitude cannot be resolved by a single state’s efforts. Reducing GHG emissions and crafting a variety of responses to climate change should therefore be regarded as a public good, i.e., alleviating the adverse impact of climate change for the global public. This means that, when climate change worldwide is abated through the conscious efforts of one state to reduce GHG emissions, this will also benefit other states that have not made such efforts. It is not possible for the effort-making state to prevent other states from benefitting from its work.

The public-good nature of such responses to climate change, paradoxically, is what prevents states from taking the initiative in leading the worldwide movement to reduce GHG emissions.

For this reason, states worldwide launched a series of dialogues in a concerted search for solutions to climate change, culminating in the 1992 United Nations Framework Convention on Climate Change (UNFCCC). The UNFCCC provides an overarching framework to guide individual state efforts and international talks on handling climate change, laying down long-term goals, differentiated the responsibilities of developed and developing countries, and facilitating the support needed to help developing countries reduce and adjust their GHG emissions (UN, 1992).

The UNFCCC, however, lacks specific and substantial measures on how GHG emissions are to be reduced. Accordingly, governments worldwide continued with follow-up talks in search for such measures, adopting the Kyoto Protocol in 1997. The Kyoto Protocol determined the target amounts by which developed states were to reduce their GHG emissions (UN, 1998). The Clean Development Mechanism (CDM), the Joint Implementation (JI), and market mechanisms have also been introduced to enable developed states to achieve cost-effective reductions in emissions (UN, 1998). With the goal of fulfilling the emissions reduction quotas imposed by the Kyoto Protocol, the states listed on Annex I to the UNFCCC1 began to introduce a series of novel measures. Those in the European Union (EU), in particular, introduced the EU Emissions Trading Scheme (EU-ETS) in 2005 to that end (EU, 2003).

The South Korean government hinted at adopting a similar measure when it promulgated the Framework Act on Low-Carbon Green Growth (FALCGG) in 2010.2 After five years of preparation, the Korean government launched its own ETS in 2015. At the time, the Paris Climate Agreement (PCA) had not yet been proposed, and Korea was therefore a non-Annex I country that was not bound by the international treaties to reduce GHG emissions. Nevertheless, the Korean government decided to implement the ETS in an

1 Annex I lists the developed countries that bore the obligation to reduce GHG emissions, through policy and other necessary actions, and the countries transitioning into the market economy at the time. 2 Paragraph (1), Article 46 (Introduction of Cap and Trade System): “The Government may operate a system for trading emissions of greenhouse gases by utilizing market functions in order to accomplish the State’s target of reducing greenhouse gases” (NLIC, 2019a).

effort to take a more proactive approach to climate change and foster low-carbon industries as new engines of the country’s economic growth (NLIC, 2019a).

Introduction of the PCA in 2015 and its effectuation in 2016 transformed the international framework on climate change actions. Under the PCA, reducing GHG emissions was no longer the exclusive lot of developed countries. All nations were urged to set their GHG emissions reduction targets and carry out effective actions to that end voluntarily.

The Korean government responded to this change by announcing an ambitious intended nationally determined contribution (INDC) to the UNFCCC in 2015 that it would reduce business-as-usual (BAU) GHG emissions by 37 percent by 2030. This was followed this up in 2016 with announcement of the National Roadmap on Reducing Greenhouse Gas Emissions 2030. The roadmap, after some revision, was confirmed and adopted in 2018 (Republic of Korea Government, 2016 and 2018). The emissions reduction targets were centrally featured on subsequent energy-related national initiatives, including the Eighth Electricity Demand and Supply Master Plan and the Third Energy Master Plan (MOTIE, 2017 and 2019).

Since setting the emissions reduction target for 2030, the Korean government has been implementing a broad array of measures to promote and support this reduction. The Korean ETS, in particular, is one of the most important means to achieving the 2030 reduction target, as the target businesses together emit nearly 70 percent of all nationwide GHG emissions.

Phase 1 of the Korean ETS began in 2015 and came to a close in 2017. The second phase has been in effect since 2018. Now that the first phase is over, we are able to assess and analyze how the ETS fared during that period of time, in terms of the outcomes of trading, the effect on GHG emissions, and the effect on the target businesses, with a view to finding implications for improving the ETS in the future. This study statistically analyzes the emissions-reducing performance of the Korean ETS, and assesses how efficiently it was run and what effects it exerted on the competitiveness of Korean businesses during its first phase. This study reviews the findings of this analysis and identifies policy implications for more efficient management of the Korean ETS in the future.

2. Subject of Analysis

The performance of an ETS can be assessed using a variety of metrics, including its effect on reducing GHG emissions, its cost-effectiveness, its effect on encouraging investment in emissions-reducing technology, and its effect on the competitiveness of subject businesses. This study analyzes the performance of the Korean ETS during its first phase in terms of the effect on reducing GHG emissions (Box (A) of Figure 1-1), its efficiency as a precondition for cost-effectiveness (Box (B)), and the effect on the competitiveness of the target businesses (Box (C)).

Figure 1- 1. Subject of Analysis: Conceptual Map

(A)온실가스 감 감 (A) GHG reduction 감 감 감 감 감 감 감 감 Cost of reduction 감 감 감 감 감 감 감 감 Direct cost of reduction activities 감 감 감 감 감 감 감 감 감 감 Opportunity cost of production

forgone (B)감 감 감 감 감 감 감 -감 감 감

감 감 감 감 (B) ETS – Market price of emission

permits

감 감 감 감 감 Cost-effectiveness

Activities to reduce GHGs inevitably extract costs. These activities include installing and operating emissions-reducing devices, switching from high-emission to low-carbon fuels, optimizing the production process to minimize energy demand, and so forth. These activities, whether they directly or indirectly reduce GHG emissions, involve the expenses of introducing,

replacing, and improving machinery.

Businesses that generate so much in emissions that cannot be reduced by these direct reduction measures may have to readjust their production to reduce emissions to the required extent. The net revenue they lose by scaling down or forgoing production constitute the opportunity cost of GHG reduction.

The inevitable cost of GHG reduction has been a subject of much contention and debate because of its substantial impact on industry and the national economy. In meeting reduction quotas, it is therefore critical to minimize the cost of GHG reduction so as to minimize adverse impact on the national economy and industrial competitiveness.

The ETS is widely understood as one of the best ways to reduce GHGs at a minimal cost. To reduce GHG emissions in a cost-effective way, which is the aim of the ETS, it is important to provide the target businesses with an efficient and perfectly free emissions trading market. In order to reduce emissions in a given nation or system to a specific extent at minimum cost, the amount by which each business is to reduce its emissions should be decided so that the marginal cost of emissions reduction is equal between businesses. On an efficient and perfectly competitive emissions market, all participating actors would decide the amount of emissions they will generate and reduce that amount so that their marginal reduction costs become equal to the market price of emissions. In other words, the nation- or system-wide cost of GHG reduction becomes minimal when the marginal reduction costs of each target business equal the market price of emissions.

With this theory in the background, this study sets out to analyze the first phase of the Korean ETS with respect to: the effect on reducing GHG emissions, the principal aim of the ETS; efficiency of the emissions trading market as a measure of cost-effectiveness; and the competitiveness of subject companies as a measure of the cost of reducing GHGs (Figure 1-1).

3. Research Structure and Questions

Figure 1-2 sums up the subject of this analysis and the main questions around which it is structured.

Figure 1- 2. Research Structure and Questions

Phase 1 of the Korean ETS: Performance Analysis

(A) GHG reduction (B) ETS (C) Reduction cost

Has the ETS created a structural difference in

GHG emissions?

Has emissions trading been efficient?

How has the ETS affected the

competitiveness of target businesses?

Implications for a more efficient ETS

According to the Master Plan for Emission Permits (2014), the foremost

objective of the first phase of the ETS was to help participants experience emissions trading and solidify the ETS as an institution (MOSF, 2014). During this period, target businesses together emitted 98.76 percent of the total emissions allowance for the first phase, which was 1.6889 billion tCO2eq. Emissions trading thus appears to not only have achieved the stated objective of the first phase, but to also have played a significant role in inhibiting increases in GHG emissions. Nevertheless, we should not fixate on a simple

comparison of the total emissions allowance and the certified emissions (emission permits submitted). First, there are numerous factors that may have hindered participating companies from generating less in emissions than the allowance. Second, the drop in emissions may have more to do with decreases in production rather than the ETS. There is, of course, the possibility that participating businesses were able to reduce their GHG emissions or slow down the rate of increase in their emissions through conscious and sustained effort.

We need to examine data on the amount of GHGs emitted by individual businesses and control production-related variables in order to determine whether the ETS has indeed made a statistically significant difference. In Chapter II, we shall therefore examine the correlation between the GHG emissions of target businesses on the one hand, and their energy demand or business record, on the other, and verify whether the difference in total emissions indeed is due to the ETS.

The ETS is also preferred to other, direct forms of regulation because it enables society to meet its emissions reduction target in a cost-effective manner. In order for an ETS to facilitate this cost-effectiveness, the emissions and reduction quotas of individual businesses should be decided in a way that their marginal reduction costs all become equal. Under an ETS, the marginal reduction costs of all participating businesses become equal based on the market price of emissions, insofar as the scheme is backed by an efficient emissions trading market.

It is beyond the scope of this study to assess whether the marginal cost reduction has been set for every single target business and satisfied the requirement of cost-effectiveness. Instead, this study analyzes whether the emissions trading market in Korea was efficient during the first phase of the ETS, and whether the precondition for cost-effectiveness of the ETS was thus achieved. Chapter III introduces statistical tests to that end and discusses their results.

The numerous discussions held in the lead-up to introduction of the ETS focused mainly on the policy’s potential effect on the economy. Under the ETS, target businesses can generate GHG emissions corresponding to their emission permits. Target businesses should therefore either strive to reduce their emissions internally or trade emission permits on the emissions trading market. Because costs will be incurred either way, the ETS has been expected

to exert at least some effect on the financial performance of the target businesses. The ideal method to verify whether such effects have been exerted would involve estimating or calculating the direct costs of emissions reduction that each business and/or industry has had to bear and thereby analyze how the ETS has affected the competitiveness of these businesses and industries. It is impossible, however, to estimate the direct cost of reduction in a reliable manner on the basis of presently available data. Chapter IV therefore traces and estimates the effect of the ETS on participating business competitiveness in inference from the indicators of business financial performance.

The last chapter of this study renders a comprehensive summary of the findings and conclusions of the preceding chapters, and delineates policy implications for the future and more efficient management of the ETS in Korea.

Chapter II. Performance of GHG Emissions Reduction

1. Introduction

1.1 Research Purpose and Background

Technological progress and economic growth have radically increased demand for energy, compelling humankind to resort to fossil fuels for the most part to satisfy that demand. Fossil fuels have indeed broadened the horizon of human activity to an unprecedented extent, but also generated immense societal costs in the form of acid rain due to the nitrogen and sulfur oxides they produce as byproducts, and climate change due to carbon dioxide and global warming.

The burning of fossil fuels, widely blamed for today’s climate crisis, is not the only source of greenhouse gases (GHGs). Technological progress has enabled humankind to invent and apply a wide variety of manmade substances that did not exist in nature. Of these, perfluorocarbons (PFCs), hydrofluorocarbons (HFCs), and sulfur hexafluorides (SF6) have played far greater roles in accelerating global warming than carbon dioxide.

Acid rain, climate change, and GHG emissions from industrial facilities are all negative externalities of fossil fuel demand that the market has failed to correct. Governments can resort to a variety of measures to control and regulate behavior or substances that cause negative externalities. Examples include setting limiting standards on the chemical compounds that may be produced as byproducts of burning fossil fuels, and requiring manufacturers to install devices that reduce nitrogen and sulfur oxide emissions. The US government introduced an emissions trading scheme (ETS) primarily to reduce the amount of sulfur dioxide emitted into the atmosphere and thereby reduce acid rain.

Economists have been leading proponents of financial and economic incentives to change economic actor behavior and thereby resolve negative externalities. The ETS and emission charges are examples of such incentives. Between these two, the Korean government opted for the former as they afford a chance to regulate total emissions more directly, while the latter could potentially be perceived as a new tax the public would likely resist.

Upon promulgating the Framework Act on Low-Carbon, Green Growth in 2010, the Korean government revealed to the public that it could very well introduce an ETS to limit the total quantity of GHG emissions being generated.3 Enactment of the Act on the Allocation and Trading of Greenhouse Gas Emission Permits (AATGGEP) and its Enforcement Decree ensued in May 2012, paving the way for launching the ETS. Pursuant to the Enforcement Decree to the AATGGEP, the Korean government established a Master Plan for the Emission Trading Scheme (MPETS) in January 2014, and the National Emission Permit Allocation Plan for the First Phase (2015 to 2017) of the ETS in September the same year (MOSF, 2014; ME, 2014). The first phase of the Korean ETS thus began in 2015, targeting 525 businesses.

According to the MPETS of 2014, the foremost objective of the scheme’s first phase was to enable participating businesses to experience emissions trading and solidify the scheme as a new institution (MOSF, 2014). Of the 1.6899 billion tCO2eq permitted as the total emissions allowance during the first phase, 1.6863 billion tCO2eq were emitted through allocation and an additional 4.9 million tCO2eq through public bidding on emission permits as part of efforts to stabilize the emissions trading market. Target businesses also managed to reduce emissions by 15.4 million tCO2eq through external projects, 3 Paragraph (1), Article 46 (Introduction of Cap and Trade System): “The Government may operate a system for trading emissions of greenhouse gases by utilizing market functions in order to accomplish the State’s target of reducing greenhouse gases” (NLIC, 2019a).

which they converted into additional emission permits. In the first phase of the ETS, emission permits totaling 1.7066 billion tCO2eq were circulated on the market. Of these, 1.6689 billion tCO2eq were submitted as certified emissions, while 37.7 million tCO2eq were carried forward into the next phase. The amount of emissions for which businesses failed to produce permits during the first phase amounted to a mere 34,000 tCO2eq.

During the first phase, target businesses together generated only 98.76 percent of the 1.6899 billion tCO2eq permitted as the total emissions allowance. The first phase of the ETS therefore appears to have achieved more than the objective stated in the MPETS and indeed contributed to reducing GHG emissions in Korea.

A simple comparison of the total emissions allowance and the certified amount of emissions generated, of course, does not give us the whole picture of how the ETS has affected GHG emissions in Korea. There can be a variety of factors accounting for this outcome. First, the total emissions allowance may have been more than what the target businesses were capable of generating any way. Second, the drop in GHG emissions may be attributable more to the decrease in production than to the ETS itself. Finally, it is also possible that GHG emissions, or the rate at which they grew, took a drop thanks to the conscious and sustained efforts of the target businesses.

In this chapter, we shall examine whether the ETS indeed made a statistically significant difference to target business GHG emissions when production-related variables at those businesses, identified on the basis of their emissions data, are controlled.

1.2. Analytical Approach and Composition of the Chapter

The performance of an ETS can be assessed according to a variety of metrics. Existing literature on the EU ETS, for example, measures ETS performance as the difference between the counterfactual BAU emissions that would have been generated in the absence of the scheme and actual BAU emissions.

There are numerous other indirect indicators with which we can evaluate the emissions-reducing performance of an ETS, such as the amount of emissions generated, the rate of increase, the amount of emissions per unit of energy, the amount of emissions per unit of output, and the like. These indicators are used to confirm whether introducing an ETS has made a structural difference to emissions, and, if it did, whether it has consistently served to reduce emissions.

Given the limits to the available data and the Korean ETS practice that also includes indirect emissions, this study takes the latter approach. The correlation between amount of emissions and business activity indicators (energy demand and revenue) was examined, and whether the ETS made structural differences to both the amount of emissions and the business activity indicators analyzed to arrive at an indirect assessment of Korean ETS performance.

Whereas much of the existing literature focuses on measuring and evaluating how the ETS has reduced overall emissions, this study looks into how the ETS differed in effect on emissions for each industry. In setting up an equation on the correlation between emissions and business activity, this study considers the differences in production and emission processes across industries. This means setting up multiple equations to account for the different characteristics between industries. A single equation based on the averages of different industries would be ill-suited to reflecting their differences.

This chapter is structured as follows. Section 2 discusses the allocation of emission permits across the transition and major manufacturing industries during the first phase of the ETS, and the amount of certified emissions they generate. Section 3 applies panel data on individual businesses to estimation of

the correlation between GHG emissions and the main indicators of business activity, and verifies whether being subjected to the ETS made a statistically significant difference to that correlation. This study concludes with a summary of the findings of the statistical analysis and identification of policy implications for future management of the ETS.

2. Allocation of Emission Permits and Trend in Amounts of Certified Emissions

Businesses subject to the ETS were initially divided into five sectors and 26 industries and were allocated emission permits reflecting the emission projections for their respective industries. The changes to the emission permit allocation plan in 2017 involved refining industrial classification, with some industries changing sectors. For ease of analysis, this study focuses on the transition and industrial sectors. Of the latter, the focus is on manufacturing and does not include mining and communications. Industrial clusters, which are also part of the industrial sector, were reassigned to the transition sector as their energy use bears greater similarity to others in that sector, such as power generation and collective energy. In this section, we shall compare the final emissions allowances and the certified emissions by industry to examine the pressure each industry faced toward reducing emissions during the first phase of the Korean ETS. Next, we shall evaluate the trend in certified emissions by industry.

Table 2- 1. Original and Revised Lists of ETS-Targeted Sectors and Industries

Original Revised

Sector Industry Sector Industry

Transition Power generation, energy Transition Power generation energy

Collective energy

Industrial

n/a

Industrial

Industrial clusters

Mining Mining

Food and beverage (F&B) F&B

Textile Textile

Wood Wood Paper Paper

Oil refining Oil refining Petrochemical Petrochemical

Glass and ceramics Glass

Ceramics

Cement Cement

Steel Steel Non-iron metal Non-iron metal

Machinery Machinery Semiconductor Semiconductor

Display Display

Electronics Electronics Automobile Automobile

Shipbuilding Shipbuilding

Construction Communications Communications

Construction Construction Construction

Public/waste Water and wastewater

Public/waste Water and wastewater

Waste Waste

Transportation Aviation Transportation Aviation

Source: Government of the Republic of Korea (2017).

2.1. Transition Sector

Table 2-2 shows the final emissions allowance and certified emissions of the transition sector during the first phase. The transition sector produced more certified emissions than the final allowance throughout the first phase, generating 777.1 million tCO2eq in total emissions, three percent more than the final allowance. This is because the power generation energy industry, which accounts for the bulk of business activity in the sector, generated more emissions than the allowance. It produced 714.9 million tCO2eq in emissions in the first phase, 3.8 percent greater than the final allowance. The collective energy industry, by contrast, managed to keep its emissions at 23.3 million tCO2eq, 18.8 percent short of the final allowance. Industrial clusters, too, exceeded the final allowance by 4.9 percent, producing 38.9 million tCO2eq in certified emissions (Table 2-2).

Table 2- 2. Final Emissions Allowances and Certified Emissions: Transition Sector

(Unit: Million tCO2eq)

Year

Power generation energy Collective energy Industrial clusters Sector-wide

Final allowance

Certified emissions

Final allowance

Certified emissions

Final allowance

Certified emissions

Final allowance

Certified emissions

2015 229.3 231.2 7.1 6.0 10.9 11.9 247.3 249.1

2016 226.0 237.4 8.9 7.6 10.7 12.7 245.6 257.8

2017 233.3 246.2 12.7 9.6 15.5 14.3 261.5 270.2

Total 688.6 714.9 28.7 23.3 37.1 38.9 754.4 777.1

Note: Industrial clusters were reassigned to the transition sector from the industrial sector for this analysis. Sources: GIR (2019), pp. 37-39 and ETRS (2019), compiled and edited by the author.

The excess in certified emissions likely exerted pressure on target businesses, particularly those in the power generation energy industry, to lower their emissions. Businesses in this industry thus solved the problem by purchasing additional emission permits on the emissions trading market and also converting their external reductions into Korean Credit Units (KCUs) (Table 2-3).

During the first phase, the power generation industry needed permits for an additional 26.3 million tCO2eq. The industry thus purchased Korean Allowance Units (KAUs) for 46.3 million tCO2eq and converted their reduction records into KCUs as well as purchasing KCUs. The industry ended up selling its surplus KAUs worth 9.2 million tCO2eq to other industries. Overall, the industry bought itself room for 10.9 million tCO2eq, slightly less than the combined amount of its KCU conversions and purchases, by the end of the first phase, which was then carried forward to the first year (2018) of the second phase (Table 2-3). The power generation energy industry accounted for significant amounts of both emission permit inflows in the form of KAU purchases and KCU conversions and also emission permit outflows by way of KAU sales and carryforwards. These transactions suggest that some businesses in the industry

struggled to secure permits for their emissions, while others had more permits than necessary.

The collective energy industry ended up generating certified emissions that fell 5.4 million tCO2eq short of the final allowance, 5.0 million tCO2eq of which it traded to other industries. Industrial clusters needed permits for 1.8 million tCO2eq more than their final allowance. Industrial clusters appear to have solved this problem by purchasing 2.1 million tCO2eq in KAUs (Table 2-3).

Table 2- 3. Emission Permit Trading, Submissions and Carryforwards: Transition Sector (2015-2017)


Power generation energy Collective energy Industrial clusters Sector-wide

Certified emissions 714.9 23.3 38.9 777.1

Allotted/traded

KAUs

Final allowance 688.6 28.7 37.1 754.4

Bought 34.7 0.3 2.5 37.5

Sold -9.2 -5.0 -0.4 -14.6

Subtotal 714.1 24.0 39.2 777.3

KCUs

Converted 9.5 0.01 0.1 9.61

Bought 2.1 - 0.1 2.2

Sold - - - -

Subtotal 11.6 0.01 0.2 11.8

Submitted

KAUs General1 695.4 23.2 35.6 755.2

Borrowed2 7.9 0.1 3.2 13.2

KCUs 11.6 0.01 0.1 11.7

Subtotal 714.9 23.3 38.9 777.1

Carried forward3 KAUs 10.9 0.6 0.4 14.9

KCUs - - 0.004 0.004

Note: Figures have been rounded up to the closest 100,000 tCO2eq and may not add up. 1. “General” refers to the amount of emission permits allocated and returned (submitted) in the given year. 2. “Borrowed” refers to the amount of emission permits allocated and returned (submitted) for the whole phase. 3. Carryforwards indicate the amount of unused emission permits carried forward into the succeeding year, i.e., 2017. Source: GIR (2019), p. 45 and p. 61, compiled and edited by the author.

As Table 2-2 shows, the amount of certified emissions grew steadily across all industries in the transition sector during the first phase of the ETS. Table 2-2, however, shows the overall certified emissions of both businesses that were newly subject to the ETS as well as businesses whose industry categorization changed over the course of the first phase. In other words, it reflects the effect from the increase in the number of target businesses. We should therefore look to other data in order to ascertain the exact trend of certified emissions by industry.

We can control the effects of newly subject businesses or those whose industrial categorization changed by narrowing our analysis to businesses targeted by the ETS in every year of the first phase and identifying their total and average certified emissions. The power generation energy industry, for one, retained the same number of businesses, 15, throughout the first phase. The industry-wide total certified emissions can therefore tell us about the emissions trend of these businesses. The industry’s total and average certified emissions grew 2.7 percent from 2015 to 2017, and again by 3.7 percent from 2016 to 2017 (Table 2-4).

The collective energy industry had 14 target businesses in 2015, which increased by two in 2016, and again by four in 2017. Without the number of newly subject businesses controlled, the industry’s total certified emissions grew 27.4 percent from 2015 to 2016, and by another 26.1 percent from 2016 to 2017. When the analysis was narrowed to the original 14 businesses, however, total certified emissions rose 21.6 percent from 2015 to 2016, and only 7.0 percent from 2016 to 2017 (Table 2-4). In other words, although the industry’s total certified emissions continued to grow from year to year, the rate of increase slowed in the latter two years of the first phase. The apparently significant rise in the industry’s total certified emissions from 2016 to 2017 owes to the fact that the number of targeted businesses in the industry increased significantly.

With all targeted businesses counted, the average certified emissions per business in the collective energy industry grew by 11.7 percent and 12.1 percent in 2016 and 2017, respectively. Average emissions per business among the original 14, however, grew by 21.7 percent and 6.9 percent, respectively, over the same period (Table 2-4). The rate of increase in average certified emissions per business industrywide was smaller than the rate for the original 14 businesses in 2016 thanks to the addition of new businesses. Conversely, the rate of increase industrywide was larger than the rate for the original 14 businesses in 2017 because of the base effect, i.e., the increase in the number of businesses lowering the average certified emissions per businesses in 2016.

There were 10 industrial clusters targeted in 2015, with three more clusters added in 2017. Total industry-wide certified emissions, without controlling for the change in the number of businesses, grew by 6.6 percent and 12.8 percent in 2016 and 2017, respectively. Of the 10 original industrial clusters counted since 2016, however, total certified emissions grew by 6.6 percent from 2015 to 2017, and fell 1.6 percent from 2016 to 2017 (Table 2-4). Industrywide emissions grew over time because of the addition of new clusters.

The newly added industrial clusters played a substantial role in changing the industrywide average certified emissions per cluster. Specifically, average certified emissions per cluster was down, significantly, 13.3 percent, between 2016 and 2017. Of the 10 original clusters, however, the average dropped by a mere 1.7 percent (Table 2-4). The actual decrease in certified emissions per cluster, coupled with the increase in the number of clusters, has done much to lower average emissions per cluster industrywide drastically.

Table 2-4. Trend in Certified Emissions: Transition Sector

(Unit: Thousand tCO2eq)

Industry Subject 2015 2016 2017

Power generation energy

Yearly

Number of businesses 15 15 15

Total certified emissions 231,234 237,435 246,237

n/a (2.7%) (3.7%)

Average certified emissions per business

15,416 15,829 16,416

n/a (2.7%) (3.7%)

Overall



n/a (2.7%) (3.7%)


15,416 15,829 16,416

n/a (2.7%) (3.7%)

Collective energy

Yearly



n/a (21.6%) (7.0%)


428 521 557

n/a (21.7%) (6.9%)

Overall



n/a (27.4%) (26.1%)


428 478 536

n/a (11.7%) (12.1%)

Industrial clusters

Yearly



n/a (6.6%) (-1.6%)


1,192 1,270 1,249

n/a (6.5%) (-1.7%)

Overall



n/a (6.6%) (12.8%)


1,192 1,270 1,101

n/a (6.5%) (-13.3%)

Note: Figures in parentheses indicate the rate of increase over the preceding year. Source: ETRS (2019), compiled and edited by the author.

2.2. Industrial Sector

Tables 2-5 and 2-6 show the final emissions allowance and certified emissions by industry across the industrial sector, except for industrial clusters and communications. Of the 18 industries considered, three—ceramics, cement, and non-iron metals—produced more certified emissions than their final allowances. The other 15 industries managed to keep their certified emissions below their final allowances. Specifically, during the first phase of the ETS, the ceramics, cement, and non-iron metal industries exceeded their allowances by 0.2 million, 1.4 million, and 2.1 million tCO2eq, respectively. Fourteen of the other industries, on the other hand, together fell short of their combined final allowance by 41.6 million tCO2eq. The steel industry, in particular, had the greatest room, at 17.0 million tCO2eq, in emissions allowance, followed by petrochemicals (5.3 million tCO2eq), semiconductors (3.4 million tCO2eq), and display (3.0 million tCO2eq). As a result, the entire industrial sector, except industrial clusters, mining and communications,4 fell short of the sector-wide final allowance by 37.9 million tCO2eq (Tables 2-5 and 2-6).

With industrial clusters, mining, and communications excluded, the industrial sector as a whole had a final emissions allowance of 869.6 million tCO2eq for the first phase of the ETS. Of this, 318.2 million tCO2eq had been set aside for the steel industry. Another 155.8 million tCO2eq (17.9 percent) went toward the petrochemical industry. The cement industry also had a sizable allowance at 134.0 million 4 During the first phase of the ETS, the mining industry produced 1.5 million tCO2eq in certified emissions against a final allowance of 1.6 million tCO2eq, while the communications industry produced 9.7 million tCO2eq against an allowance of KRW 10.1 million tCO2eq.

tCO2eq, or 15.4 percent of the overall allowance. These three industries together held 69.9 percent of the overall final allowance for the sector. The oil refining, semiconductor, display, paper, and non-iron metal industries had the next largest cuts of the final allowance, in that order. Certified emissions followed a similar pattern. The steel, petrochemical and cement industries generated 301.2 million, 153.8 million, and 135.4 million tCO2eq in certified emissions, respectively, accounting for 71.0 percent of the total sector-wide emissions. Petrochemicals, semiconductors, display, non-iron metals and paper were the industries that produced the next largest quantities of certified emissions, in that order (Tables 2-5 and 2-6).

Table 2- 5. Final Emissions Allowances and Certified Emissions by Industry: Industrial Sector (1)


Industry Subject 2015 2016 2017 Total

F&B Final allowance 2.7 2.8 3.5 9.0


Textile Final allowance 4.5 5.1 4.8 14.4


Wood Final allowance 0.4 0.4 0.4 1.2


Paper Final allowance 7.4 8.6 7.5 23.5


Oil refining Final allowance 19.3 23.4 20.2 62.9


Petrochemical Final allowance 47.9 54.0 53.9 155.8


Glass Final allowance 3.7 3.9 4.0 11.6


Ceramics Final allowance 2.4 2.3 2.5 7.2


Cement Final allowance 43.8 45.2 45.0 134.0


Source: GIR (2019), pp. 37-39 and pp. 59-61, compiled and edited by the author.

Table 2- 6. Final Emissions Allowances and Certified Emissions by Industry: Industrial Sector (2)


Industry Subject 2015 2016 2017 Total

Steel Final allowance 102.6 104.9 110.7 318.2


Non-iron metal Final allowance 6.8 7.2 8.0 22.0


Machinery Final allowance 1.4 1.5 1.5 4.4


Semiconductor Final allowance 11.5 12.9 16.1 40.5


Display Final allowance 10.0 9.6 11.0 30.6


Electronics Final allowance 3.2 4.0 4.9 12.1


Automobile Final allowance 4.3 4.8 4.6 13.7


Shipbuilding Final allowance 2.6 3.2 2.7 8.5


Source: GIR (2019), pp. 37-39 and pp. 59-61, compiled and edited by the author.

The difference between certified emissions and final allowance can help us gauge the level of emissions-reducing pressure each industry faced during the first phase. The non-iron metal, ceramic, and cement industries were short of emission permits, as their certified emissions exceeded their final allowances. Specifically, these industries needed 9.5 percent, 2.8 percent, and 1.0 percent more, respectively, than their respective final allowances (Tables 2-5 and 2-6).

The steel industry, on the other hand, boasted the largest room in its emission allowance, as its certified emissions fell short of its final allowance by 17.0 million tCO2eq (5.3 percent). In terms of the absolute differences between certified emissions and final allowances, oil refining had the next largest room (5.3 million tCO2eq), followed by semiconductors (3.4 million tCO2eq), display (3.0 million tCO2eq), paper (2.3 million tCO2eq), electronics (2.1 million tCO2eq), and petrochemicals (2.0 million tCO2eq). The remaining industries also managed to keep their certified emissions around 1.0 million tCO2eq short of their respective final allowances (Tables 2-5 and 2-6).

Percentage-wise, the electronics industry kept its certified emissions by the largest percentage below its final allowance at 17.4 percent. The absolute difference between certified emissions and the final allowance amounted to a mere 0.2 million tCO2eq for the wood industry, but that difference translated into 16.7 percent, as the industry had a small final allowance to begin with. Shipbuilding (1.4 million tCO2eq or 16.5 percent of the final allowance) followed next, leading machinery (0.7 million tCO2eq, or 15.9 percent) and F&B (1.2 million tCO2eq or 13.3 percent) (Tables 2-5 and 2-6). From these results, we may surmise that industries that typically produce relatively little in emissions and that had relatively small differences between their respective final allowances and certified emissions likely faced less pressure than other industries to reduce those emissions.

The ceramics, cement and non-iron metal industries appear to have overcome their emission permit deficiencies by purchasing more KAUs and KCUs (or converting their external reduction performances into KCUs) (Table 2-7). The steel, petrochemical, semiconductor and display industries that had relatively larger room in their allowances, consequently, outperformed the other industries in terms of the number of KAUs they sold. The steel, semiconductor and petrochemical industries also had the three highest numbers of KAU carryforwards (Tables 2-7 and 2-8).

Table 2- 7. Emission Permit Trading, Submissions and Carryforwards: Industrial Sector (1) (2015-2017)

(Unit: Million tCO2eq) Mining F&B Textile Wood Paper Oil ref.

Certified emissions 1.5 7.7 13.2 1.0 21.1 57.6

Allotted/traded

KAUs

Final allowance 1.6 9.0 14.4 1.2 23.5 62.9

Bought 0.03 0.04 0.2 - 0.3 -

Sold -0.2 -0.8 -0.9 -0.1 -1.9 -3.6 Subtotal 1.4 8.2 13.7 1.1 21.9 59.3

KCUs

Converted - 0.01 - - - -

Bought - - - - - -

Sold - - - - - - Subtotal - 0.01 - - - -

Submitted KAUs

General1 1.5 7.6 13.2 1.0 21.0 57.4

Borrowed2 0.02 0.04 0.1 - 0.2 0.1 KCUs - 0.01 - - - - Subtotal 1.5 7.7 13.2 1.0 21.1 57.6

Carried forward3 KAUs 0.03 0.5 0.5 0.1 0.8 1.6 KCUs - - - - - -

Petro-chemical Glass Ceramics Cement Steel Non-iron

metal Certified emissions 153.8 11.1 7.4 135.5 301.1 24.1

Allotted/traded

KAUs

Final allowance 155.8 11.7 7.2 134.0 318.1 21.9

Bought 6.3 0.2 0.5 4.7 0.7 2.1

Sold -5.3 -0.4 - -1.0 -8.9 -0.06 Subtotal 156.8 11.5 7.7 137.7 309.9 23.9

KCUs

Converted 1.6 0.01 - 0.7 0.1 0.5

Bought 0.03 0.003 - 0.2 - 0.1

Sold -1.1 - - - -0.1 - Subtotal 0.5 0.01 - 1.0 - 0.6

Submitted

KAUs General1 148.4 11.0 7.4 130.7 300.2 22.3

Borrowed2 4.8 0.1 0.03 3.8 1.0 1.2

0.5 0.01 - 1.0 - 0.6 153.8 11.1 7.4 135.5 301.1 24.1

Carried forward3 3.5 0.4 0.3 3.1 8.8 0.5

KCUs - - - - - 0.004 Note: Figures have been rounded up to the nearest 100,000 tCO2eq and may not add up. 1. “General” refers to the amount of emission permits allocated and returned (submitted) in the given year. 2. “Borrowed” refers to the amount of emission permits allocated and returned (submitted) for the whole phase. 3. Carryforwards indicate the amount of unused emission permits carried forward into the succeeding year, i.e., 2017. Source: GIR (2019), p. 45 and p. 61, compiled and edited by the author.

Table 2- 8. Emission Permit Trading, Submissions and Carryforwards: Industrial Sector (2) (2015-2017)


Machinery Semi-conductor Display Elec-tronics Auto Ship-building

Certified emissions 3.6 37.1 27.6 10.0 12.4 7.2

Allotted/ traded

KAUs

Final allowance 4.3 40.6 30.7 12.1 13.7 8.5

Bought 0.05 0.7 0.01 0.2 0.1 0.002

Sold -0.4 -3.7 -3.2 -1.7 -0.8 -1.0

Subtotal 4.0 37.6 27.5 10.6 13.0 7.5

KCUs

Converted 0.001 0.1 0.8 0.01 0.004 -

Bought - 0.9 - 0.01 0.002 -

Sold - - -0.4 - - -

Subtotal 0.001 1.0 0.4 0.02 0.006 -

Submitted

KAUs General1 3.6 35.8 27.0 9.9 12.3 7.1

Borrowed2 0.03 0.4 0.2 0.1 0.1 0.04

0.001 0.9 0.5 0.02 0.01 - 3.6 37.1 27.6 10.0 12.4 7.2

Carried forward3 0.2 1.4 0.3 0.6 0.6 0.3

KCUs - - - - - - Note: Figures have been rounded up to the nearest 100,000 tCO2eq and may not add up. 1. “General” refers to the amount of emission permits allocated and returned (submitted) in the given year. 2. “Borrowed” refers to the amount of emission permits allocated and returned (submitted) for the whole phase. 3. Carryforwards indicate the amount of unused emission permits carried forward into the succeeding year, i.e., 2017. Source: GIR (2019), p. 45 and p. 61, compiled and edited by the author.

There are industries in which businesses engaged simultaneously in efforts both to increase the emission permits available to them (through purchases of KAUs and KCUs and conversion into KCUs) and capitalize on their surplus permits (trading them on the market or carrying them forward to the succeeding year). This mixed behavior appears to reflect the significant differences in the emission permits allocated to individual businesses. The cement industry struggled with shortages of energy permits, and ended up purchasing a considerable number of KAUs. However, it also carried more KAUs forward into the succeeding year (2018) than did other industries (Table 2-7). The petrochemical industry produced less in certified emissions than its final allowance, but the surplus was not substantial. Yet the petrochemical industry ended up both purchasing more KAUs and selling/carrying forward more KAUs than did many other industries.

The steel, petrochemical, and cement industries, typically considered to be heavy emitters, carried forward KAUs worth 8.8 million, 3.5 million, and 3.1 million tCO2eq, respectively, into 2018. Together these three industries carried forward 37.9 million KAUs or 40.0 percent of the entire final allowance allotted to the industrial sector (Tables 2-5, 2-6, and 2-7).

Now we need to examine the trend of certified emissions across the industrial sector by industry. As with the transition sector, we need to control the effect of new businesses added each year to the industries in order to gain a better grasp of how the sector-wide emissions trend moved during the first phase of the ETS. Accordingly, we shall limit our focus on businesses in the industrial sector that were subject to the ETS since the beginning in 2015. The wood, oil refining and machinery industries, however, had no new businesses added to them throughout the first phase. As a result, their industrywide certified emissions are analyzed as they are.

Of the 17 industries subject to analysis, 11—F&B, textile, wood, oil refining, petrochemicals, glass, ceramics, cement, non-iron metals, semiconductors, and electronics—saw their total industrywide emissions and average certified emissions per business increase from 2015 to 2017. Seven of these industries—F&B, textile, petrochemicals, ceramics, non-iron metals, semiconductors, and electronics—also saw their certified emissions increase from year to year over these three years. The wood industry’s certified emissions dropped marginally between 2015 and 2016, but the increase between 2016 and 2017 easily offset that drop. The petrochemical, glass and cement industries, on the other hand, produced more certified emissions in 2016 than in 2015, but managed to decrease emissions between 2016 and 2017. Yet the emissions from these three industries increased so much from 2015 to 2016 that their final certified emissions as of 2017 were greater than those of 2015 (Tables 2-9, 2-10, and 2-11).

The petrochemical industry’s total certified emissions grew the most between 2015 and 2017—up 2.289 million tCO2eq. Average certified emissions per business in the petrochemical industry also grew by 29,000 tCO2eq from 2015 to 2017, but this average was not significantly greater than in other industries that also saw their total certified emissions grow. Because the industry had the largest number of businesses (the difference being 16-fold in some cases, such as against the oil refining industry that had only five businesses), its total certified emissions overwhelmed those of other industries (Table 2-9).

The oil refining industry saw the largest increase, from 2015 to 2017, in average certified emissions per business, at 137,000 tCO2eq. Although the industry has only a few businesses (five in total), this overwhelmingly large average amount of emissions per business added to the substantial increase in the industry’s total certified emissions over the three years (Table 2-9).

The paper, steel, machinery, automobile, and shipbuilding industries, by contrast, saw decreases in both their total certified emissions and average certified emissions per business between 2015 and 2017. The machine, automobile and shipbuilding industries, in particular, managed to decrease their emissions on both fronts over all three years. The paper industry’s emissions rose from 2015 to 2016, but dropped significantly enough in 2017 to bring the final certified emissions in 2017 lower than the level seen in 2015. The steel and display industries, conversely, had reduced their emissions so much from 2015 to 2016 that, notwithstanding the yearly increase in their emissions from 2016 to 2017, their certified emissions as of 2017 fell short of what they were in 2015 (Tables 2-9, 2-10, and 2-11).

Of all the industries in this sector, the steel industry managed to cut back on its total certified emissions between 2015 and 2017 by the widest margin, at 1.799 million tCO2eq. Two extremely heavy emitters account for 91 percent of the industrywide total. Aside from these two businesses, the combined certified emissions of the rest actually increased from 2015 to 2017. The decrease in certified emissions from the two major businesses was large enough to countervail the increase in emissions from the other businesses (Table 2-10).

The display industry boasted the second largest decrease in industrywide total certified emissions, which amounted to 8.895 million tCO2eq in 2017, 1.385 million tCO2eq less than what it was in 2015. The industry had four businesses in 2017, with two of them accounting for 99 percent of industrywide emissions. While one of these two maintained its certified emissions more or less constant, the other’s certified emissions drastically decreased from 2015 to 2017, bringing down the overall industry’s emissions (Table 2-11).

The display industry also saw the largest decrease in average certified emissions per business, followed by shipbuilding, steel, machinery, automobiles, and paper. The shipbuilding industry recorded the second largest margin of drop in average certified emissions per business. Yet, because the industry had only five businesses, the industrywide total did not decrease as noticeably as was the case with the steel and display industries (Tables 2-9, 2-10, and 2-11).

Table 2- 9. Trend in Certified Emissions: Industrial Sector (1)

(Units: No. of businesses, thousand tCO2eq)

Industry Subject 2015 2016 2017 2017 - 2015

Avg. annual rate of

increase

F&B

Number of businesses 22 22 22 - -

Total certified emissions

2,483 2,528 2,551 69 2.8%

- (1.8%) (0.9%)


113 115 116 3 2.8%

- (1.8%) (0.9%)

Textile



4,146 4,433 4,540 394 9.5%

- (6.9%) (2.4%)


276 296 303 26 9.5%

- (6.9%) (2.4%)

Wood



334 333 344 10 2.9%

- (-0.2%) (3.1%)


48 48 49 1 2.9%

- (-0.2%) (3.1%)

Paper



6,864 6,938 6,769 -95 -1.4%

- (1.1%) (-2.4%)


172 173 169 -2 -1.4%

- (1.1%) (-2.4%)

Oil refining



18,719 19,459 19,406 687 3.7%

- (4%) (-0.3%)


3,744 3,892 3,881 137 3.7%

- (4%) (-0.3%)

Petrochemical



48,596 49,623 50,885 2,289 4.7%

- (2.1%) (2.5%)


607 620 636 29 4.7%

- (2.1%) (2.5%)





Avg. annual rate of increase

Glass



2,744 2,866 2,856 112 4.1%

- (4.4%) (-0.3%)


172 179 179 7 4.1%

- (4.4%) (-0.3%)

Ceramics



2,388 2,479 2,489 101 4.2%

- (3.8%) (0.4%)


478 496 498 20 4.2%

- (3.8%) (0.4%)

Cement



44,412 46,016 44,647 235 0.5%

- (3.6%) (-3%)


2,115 2,191 2,126 11 0.5%

- (3.6%) (-3%)

Steel



101,850 98,974 100,051 -1,799 -1.8%

- (-2.8%) (1.1%)


2,829 2,749 2,779 -50 -1.8%

- (-2.8%) (1.1%)

Non-iron metal



7,530 8,060 8,150 620 8.2%

- (7%) (1.1%)


342 366 370 28 8.2%

- (7%) (1.1%)

Machinery



1,271 1,195 1,180 -91 -7.2%

- (-6%) (-1.3%)


67 63 62 -5 -7.2%

- (-6%) (-1.3%)





Avg. annual rate of

increase

Semiconductor



11,647 11,924 13,037 1,390 11.9%

(2.4%) (9.3%)


613 628 686 73 11.9%

(2.4%) (9.3%)

Display



10,280 8,412 8,895 -1,385 -13.5%

(-18.2%) (5.7%)


2,570 2,103 2,224 -346 -13.5%

(-18.2%) (5.7%)

Electronics



2,964 2,986 3,858 894 30.2%

(0.8%) (29.2%)


135 136 175 41 30.2%

(0.8%) (29.2%)

Automobile



4,103 4,052 4,006 -96 -2.3%

(-1.2%) (-1.1%)


152 150 148 -4 -2.3%

(-1.2%) (-1.1%)

Shipbuilding



2,515 2,415 2,021 -494 -19.6%

(-4%) (-16.3%)


359 345 289 -71 -19.6%

(-4%) (-16.3%)


3. Model of Analysis

3.1. Literature Survey

Much of the existing literature on the ETS and its effect on reducing GHG emissions concerns itself with the EU ETS, introduced in 2005 (Anderson and Di Maria (2011); Arbell et al. (2011); Bel and Joseph (2015); Delarue et al. (2008); Ellerman and Buchner (2008); Ellerman et al. (2010); Jaraité and Di Maria (2016); Klemetsen et al. (2016)). Murray et al. (2014) is exceptional in that it focuses on the Regional Greenhouse Gas Initiative (RGGI) of power plants in 10 northeastern states of the United States.

Ellerman and Buchner (2008) analyzes the over-allocation of emission permits and GHG emissions reduction under the EU ETS in its first two years—2005 and 2006. Applying the projected trend in growth of the gross domestic product (GDP) and carbon intensity to the pre-ETS GHG emission level, the authors estimate the counterfactual BAU emissions that would have been generated in absence of the ETS, and measure the difference between the counterfactual BAU emissions and actual emissions to gauge the effect of the ETS. Whereas Ellerman and Buchner (2008) bases its analysis on the emissions data provided by the National Allocation Plans (NAPs), Ellerman et al. (2010) analyzes official emission data collected under the UNFCCC. The latter study thus concludes that the EU ETS helped to lower emissions by 210 million tCO2eq during its first phase (2005-2007).

Anderson and Di Maria (2011) introduces a dynamic panel model involving GHG emissions, demand levels for major sources of energy (electricity, oil, natural gas, and coal), and major climate variables (heating and cooling degree days). The authors also estimate the counterfactual BAU emissions that would have been produced in absence of the EU ETS to assess the effect of its first phase (2005 to 2007) on reducing GHG emissions. Bel and Joseph (2015) introduces two models, one based on the demand for major sources of energy and the other based on the prices of such energy, to evaluate the effect of the first two phases of the EU ETS (2005 to 2012) on reducing GHG emissions.

Unlike the other studies that pin the EU ETS’ performance on estimated counterfactual BAU emissions, Arbell et al. (2011) seeks to verify whether the EU ETS managed to accelerate the rate of decrease in GHG emissions between the first phase and the second.

Delarue et al. (2008) departs from the foregoing studies, which all examine the effect of the EU ETS on overall GHG emissions reduction, and turns to examine how the EU ETS has induced the power generation sector to reduce GHG emissions by catalyzing energy transition. Jaraitė and Di Maria (2016) and Klemetsen et al. (2016) shift their focus from the entire EU to individual states—Lithuania and Norway, respectively—to assess the impact of the EU ETS on local businesses.

There are studies by Korean authors on the performance and impact of the Korean ETS, including Yu et al. (2017), Lee et al. (2017), and Oh et al. (2018). Yu et al. (2017) analyzes the GHG emissions and energy breakdowns from individual businesses (or their installations) spanning the years 2007 through 2015 to verify whether the Korean ETS has made a statistically significant difference to the amounts of GHG emissions they produced. The authors confirm that the ETS indeed altered emission levels in almost all industries, except for a few (including those not legally subjected to the ETS) with statistical significance.

Lee et al. (2017) similarly analyzes the GHG emission and energy breakdowns to compare and analyze the impacts of the GHG & Energy Management System, on the one hand, and the ETS, on the other. The authors sample only the top 10 percent of heavy emitters from each industry and regularize emissions per business based on industrywide average emissions, and analyze how emissions per capita have changed in comparison to industrywide average emissions. Because the ETS had been in place for less than two years at the time the authors conducted this study, they could not arrive at a decisive conclusion on whether the ETS was more effective than the management system in reducing emissions.

Oh et al. (2018) tracks and analyzes post-ETS changes in emission levels, carbon and energy intensities, and national emissions per capita to assess the ETS’ impact on lowering GHG emissions. The authors also analyze the impact the ETS had on businesses, inflation and GDP, and employment at the macro-level.

The majority of studies on the EU ETS estimate the counterfactual BAU emissions that would have been generated in absence of the ETS, and uses the difference between these estimates and actual emissions to measure the emission-reducing effect of the ETS. To this end, they set up equations on the correlation between the pre-ETS emission levels and other related variables.

The Korean ETS reflects indirect GHG emissions from the use of electricity, heating and steam. The emissions data published for each target business explicitly indicates indirect emissions from these sources. Because the transition sector that supplies power and heating for other industries is also subject to the ETS, total certified emissions from all other target industries or sectors redundantly include emissions generated by the transition sector. This structure of the Korean ETS makes it difficult to estimate and apply counterfactual BAU emission levels like Ellerman and Buchner (2008) and Ellerman et al. (2010).

Alternatively, we may consider resorting to a quantitative model, as did Anderson and Di Maria (2011) and Bel and Joseph (2015), to analyze GHG emissions by industry and estimate counterfactual BAU emissions that would have been generated in Korea in the absence of an ETS. Setting up such a model, however, would require developing industry-by-industry panel data based on individual businesses’ data. It is, however, nearly impossible to obtain all businesses’ data on their demand for different sources of energy, energy costs, and other key variables influencing their emissions. Counterfactual BAU emissions estimated without taking these variables and data into account would not be robust enough to support our analysis.

In this section, therefore, we shall attempt to devise an equation on the correlation between industry-by-industry GHG emissions, on the one hand, and the main indicators of business activity (energy demand and revenue), on the other, and statistically verify whether the ETS has altered the coefficients of the major variables. Applying a single equation across all industries would deal short shrift with the critical differences in the emission-producing structures of industries. Accordingly, we shall devise an emission equation for each industry.

3.2. Model of Analysis

In this section, we resort to the following basic model to set up an equation on the correlation between GHG emissions and the main indicators of business activity for each industry.

ghgi,t = α + δ0ETSi,t + ∑ γt2017t=2012 DYeart +β1energyi,t + δ1energyi,t ∗ ETSi,t + β2revenuei,t +

δ2revenuei,t ∗ ETSi,t + β3adjusted tangible asseti,t + δ3adjusted tangible asseti,t ∗ ETSi,t + νi +ɛi,t Equation (1)

조정유형자산 Adjusted tangible asset (ATA)

Here, ghgi,t stands for the amount of GHG emissions produced by business 𝑖𝑖 in year t; energyi,t, business i′s energy demand in year t ; revenuei,t , business i′s revenue in year t . Adjusted tangible asset (ATA)i,t consists of the total appraised value of business 𝑖𝑖 ’s total tangible assets in year t, including machinery and facilities, but excluding land and buildings. This variable is used, as it was in Kim and Roh (2016), as a proxy variable for each business’ capital. As tangible assets include land, buildings, machinery and facilities, the ATA, as an approximation of the capital that went into actual production, would reflect the appraised value of the business’ tangible assets except for its land and buildings (Kim and Roh (2016), p. 33). DYeart is the dummy variable for each year. ETSi,t is the dummy for whether the business is subject to the ETS. If business i was subject in year t, it would equal one (1); if not, it would equal zero (0).

The focus of our analysis using this model are the coefficients (δ0, δ1, δ2, δ3) related to the dummy

variable, ETSi,t . Coefficient δ1 in particular, on the interaction between ETSi,t and energyi,t, represents the structural difference in GHG emissions per unit of energy when all other variables have been controlled. Before a business is legally subject to the ETS, its GHG emissions would increase by β1 for every unit increase (1 TJ) in its energy demand, assuming other variables remain constant. After the business becomes subject to the ETS, however, its GHG emissions would increase by β1 + δ1 for every unit increase in energy demand (1 TJ), assuming other variables remain constant. When δ1 is less than zero and statistically significant, we can infer that the business has undergone structural change in energy demand and GHG emissions since it became an ETS-subject business.

Coefficient δ2 , on the interaction between ETSi,t and revenuei,t , and coefficient δ3 , on the interaction between ETSi,t and ATAi,t , can be understood in the same way. δ2 represents the structural change in the amount of increase in GHG emissions (β2) in response to every unit increase in revenue (KRW 1 billion, as of 2015) under the ETS, all other variables held equal. δ3 represents the structural change in the amount of increase in GHG emissions (β3) in response to every unit increase in ATA (KRW 1 billion, as of 2015) under the ETS, all other variables held equal.

However, caution is advised in analyzing β1 , β2 , and β3 that form the basis of analysis on the estimated coefficients, particularly δ1, δ2, and δ3. Since 2011, long before the ETS came into being, the Korean government has been implementing the GHG & Energy Management System (GEMS). All ETS-subject businesses were already being regulated under this system before the ETS arose, making efforts to reduce their GHG emissions and energy demand accordingly. The GHG emissions and energy demand breakdowns underlying our analysis date back to 2011, while no data on the years before then is available. The β1, β2, and β3 for analyzing δ1, δ2, and δ3 thus represents not the absence of any regulation of GHG emissions, but rather the efforts that had accumulated since the GEMS was introduced. If we had access to pre-2011 (pre-GEMS) data, we would have been able to separate the respective effects of the GEMS and the ETS using our model of analysis, and use the unregulated state as our reference point. Because there is no data on Korean businesses’ GHG emissions and energy demand prior to 2011, the β1, β2, and β3 should be understood as already reflecting some effect of the GEMS’s regulation on emissions.

The model of analysis introduced herein utilizes business-level panel data. Fixed-effect or random-effect models can be used, depending on the assumed and unobserved characteristics (νi), to estimate linear correlations in panel data. As fixed- and random-effect models use different approaches to tracing, we need to conduct pre-tests to decide which model to use. The Hausman test is commonly used, but it is known to be vulnerable to the heteroscedasticity or autocorrelation of error terms (Han, 2017). This study therefore uses a test proposed by Mundlak (1978) instead, as it is known to be more robust against such problems, to decide which model to use (Han, 2017; Wooldridge, 2010).

This study will not go into detail about fixed- and random-effect models, as detailed explanations can already be found in a number of studies, including Han (2017), Wooldridge (2010), and Green (2012). However, the test that has been chosen in place of the Hausman test to help decide whether to use a fixed-effect model or a random-effect one merits a brief explanation here.

A fixed-effect model assumes that the unobserved business-level attribute, νi, is correlated to the explanatory variables (energyi,t, revenuei,t, 𝐴𝐴𝐴𝐴𝐴𝐴i,t in our case). A random-effect model, on the other hand, assumes νi to be a random effect that has no relation to the explanatory variables. According to Mundlak (1978), an unobserved attribute of businesses, νi , that is correlated to the explanatory variables of those businesses may be expressed as the linear function of the averages of those variables (energyi, revenuei, ATAi), as shown below:

vi = η0 + η1energyi + η2revenuei + η3adjusted tangible asseti + ui.

In this equation, the linear correlation between νi and the explanatory variables is captured by the first four terms (η0 + η1energyi + η2revenuei + η3 ATAi), leaving the remaining ui to stand for the random effect part of (νi), i.e., the part that is not correlated to the explanatory variables. This equation can be applied to the basic equation of our model as follows:

ghgi,t = α + η0 + δ0ETSi,t +∑ γt2017t=2012 DYeart +β1energyi,t + δ1energyi,t ∗ ETSi,t + η0 +

η1energyi +β2revenuei,t + δ2revenuei,t ∗ ETSi,t + η2revenuei +β3adjusted tangible asseti,t +δ3adjusted tangible asseti,t ∗ ETSi,t + η3adjusted tangible asseti +ui + ɛi,t.

Because ui has no correlation to the explanatory variable, this equation can now be applied using a random-effect model. By verifying the result of the random-effect model against the hypothesis, H0 : η1 = η2 = η3 = 0, we can finally determine whether a fixed-effect model should be used in our analysis or a random-effect model. Rejection of the null hypothesis means that νi bears a statistically significant and linear correlation to the explanatory variables, and a fixed-effect model is therefore in order for our analysis. If the null hypothesis is accepted, on the other hand, it means that νi bears no statistically significant or linear correlation to the explanatory variables, and a random-effect model should therefore be used.

3.2. Data

Our model of analysis requires business-level information on GHG emissions, energy demand, revenue, and ATAs. The National Greenhouse Gases Management System (NGMS) provides breakdowns of GHG emissions and energy demand of the businesses or installations that were subject to the first phase of the ETS (2015 to 2017), spanning the years 2011 through 2017.5 NICE Information Service also provides financial data on these businesses through its database (KisValue). The database, however, does not provide financial information on some of the subject businesses. The number of businesses per industry used in the analysis therefore falls short of the actual number of businesses per industry that were subject to the ETS. It should also be noted that businesses on which only single-year data was available were omitted from the analysis. Table 2-12 lists the number of businesses in each industry used in the analysis. The dataset constructed for our analysis may not include all the subject businesses, but accounts for most of them. For ease of analysis and making the most extensive possible use of available data, businesses in the collective energy industry and industrial clusters were grouped with those in the power generation energy industry to form the transition sector. Glass and ceramics, and semiconductors, display and electronics were merged into two industries for analysis.

It would have been ideal if we could set up a model of analysis for every single industry and verify the effect of the ETS on it with statistical tests. Some industries, however, contained too few businesses to allow for the development of a reliable equation. Accordingly, we limit our analysis to the transition sector (power generation energy, collective energy, industrial clusters), F&B, paper, petrochemicals, glass and ceramics, cement, steel, non-iron metals, semiconductor/display/electronics, and automobiles as the sector and each of these industries contained 20 or more businesses as of 2017 (Table 2-12).

5 Paragraph (1), Article 44 of the FALCGG requires businesses that generate certain amounts of GHG emissions and consume certain amounts of energy to produce and submit detailed breakdowns of their emissions and energy demand.

Table 2- 12. Dataset for Analysis

(Unit: No. of businesses)

ETS-subject businesses ETS-subject businesses included in analysis

’15 ’16 ’17 ’11 ’12 ’13 ’14 ’15 ’16 ’17

Tran-sition sector

Power generation

energy 15 15 15 14 15 15 15 15 15 15

Collective energy 14 16 18 7 11 12 16 13

(3) 14 (2) 16

Industrial clusters 10 10 13 7 7 7 11 8

(3) 8 (3) 11

F&B 22 23 25 21 21 22 23 21 (3)

22 (2) 24

Textile 15 16 16 12 12 14 15 14 (1) 15 15

Wood 7 7 7 6 6 6 6 6 6 6

Paper 42 42 41 39 40 40 40 40 40 40

Oil refining 5 5 5 5 5 5 5 5 5 5

Petrochemical 85 88 93 69 74 78 85 76 (10)

81 (7) 88

GC Glass 19 19 21 16 16 19 19 16

(2) 19 (2) 21

Ceramics 5 6 6 3 5 5 5 5 5 5

Cement 23 22 24 18 19 20 21 18 (2)

19 (2) 21

Steel 36 39 40 29 34 35 38 35 (3) 38 38

Non-iron metal 23 24 25 20 22 24 25 23 (2)

24 (1) 25

Machinery 19 19 19 13 18 18 18 18 18 18

SDE

Semiconductor 20 23 23 12 16 17 20 17 (3) 20 20

Display 5 5 4 2 2 3 3 3 3 3

Electronics 22 24 26 13 17 18 21 19 (3)

21 (1) 22

Automobile 27 30 33 14 22 23 29 25 (4)

27 (2) 29

Shipbuilding 8 12 11 7 7 7 11 7 (4) 11 11

Note: The numbers of businesses subject to the ETS are according to ETRS (2019). Figures in parentheses for 2011 through 2014 indicate the numbers of GEMS-subject businesses.

Source: ETRS (2019), compiled and edited by the author.

Some explanation is needed as to why the present analysis focuses on GHG emissions as indicated on the NGMS breakdowns, instead of the certified emissions data published on the Emission Trade Registry System (ETRS). In order to apply the model of analysis introduced in Section 3.2, we need GHG emissions data on individual businesses prior to introduction of the ETS. The ETRS, however, provides certified emissions from businesses dating back only to 2015, when the scheme was

introduced. This study thus turns to the NGMS breakdowns instead for GHG emissions data gathered using the same metrics pre-ETS. The NGMS breakdowns, moreover, also provide data on individual business’ energy demand, the main source of their emissions.

Nevertheless, there are some discrepancies between certified emissions and emissions in the breakdowns because the scopes of subject businesses do not exactly match. The NGMS breakdowns encompass businesses and installations that report their emissions and energy demand, but that are not legally subject to the ETS and its certification requirement. Table 2-13 thus shows emissions on breakdowns to be greater than certified emissions recorded under the ETS. Yet this study relies on the breakdowns because their broader scope of reporting could give us a more comprehensive picture of actual GHG emissions from businesses.

Table 2- 13. Emissions from Subject Businesses as Indicated in Different Sources

(Units: No. of businesses, million tCO2eq)

Year

ETS (ETRS) NGMS breakdowns

Number of certified businesses Emissions Number of reported

businesses Emissions

2015 522 542.7 516 564.4

2016 560 554.3 560 578.1

2017 591 571.9 591 600.2

Total - 1,668.9 - 1,742.7

Sources: GIR (2019) and NGMS (2019), compiled and edited by the author.

Some caution is needed in using this data on GHG emissions and energy demand from the NGMS breakdowns. Examining the trend in emissions per unit of energy used reveals a noticeable change in the year in 2014 on the amounts of emissions generated and energy used (Figure 2-1). This is because the Guidelines on the GHG & Energy Management System (“GEMS Guidelines”) were overhauled in 2014, leading to major changes in how emissions and energy demand are calculated and reported.6

Amendment of the GEMS Guidelines in 2014 was mainly intended to improve and update the way in which emissions were to be measured, as part of preparation for the ETS. The amended Guidelines introduced a more reliable and accurate method for measuring emissions and energy demand, in addition to those chosen and used by businesses themselves, and required suppliers of other byproduct fuels to develop an emissions coefficient. The amended Guidelines also introduced additional categories of emissions-producing activities, such as the production of magnesium/phosphoric acid/caprolactam and fuel cells, and the methods for calculating emissions they produced. The Guidelines corrected some of the errors identified with the ways in which activity data was gathered and GHG emissions measured (NLIC, 2019b).

6 For this reason, Oh et al. (2018) uses details of the breakdowns to correct the rupture in the time series. Because the same details were not made available to this study, which had access only to the data on GHG emissions and energy demand, this study directly controls the time series rupture as part of its model of analysis instead.

Figure 2- 1. Average GHG Emissions and Energy Demand per Business

Source: NGMS (2019), compiled and edited by the author.

단위: 감 CO2eq.감 Unit: Thousand tCO2eq 감 감 : 감 TJ Unit: Thousand TJ 감 감 감 감 감 감 감 감 감 Avg. GHG emissions 감 감 감 감 감 감 감 감 Avg. energy demand

The drastic decrease in GHG emissions and energy demand per business between 2013 and 2014 therefore reflects not just the active efforts made by businesses, but also major changes in how that data is measured and reported. The model of analysis used in this study directly controls for the effect of these changes, which is explained in the following section.

4. Results of Analysis

The significant difference in emissions and energy demand data in 2014 (Figure 2-1), resulting from amendment of government guidelines on how such data is to be measured and reported, is controlled by adding a new dummy variable to Equation (1) introduced in Section 3.2. The following equation was thus applied to each industry.

ghgi,t = α + ρ0Af2014 + δ0ETSi,t +∑ γtt=2012∼13,2015∼17 DYeart +β1energyi,t + ρ1energyi,t ∗Af2014 + δ1energyi,t ∗ ETSi,t +β2revenuei,t + δ2revenuei,t ∗ ETSi,t +β3adjusted tangible asseti,t + δ3adjusted tangible asseti,t ∗ ETSi,t + νi + ɛi,t

Equation (2)

The new dummy, Af2014, equals one for the years 2014 through 2017, and zero for the years 2011 through 2013. In order to avoid the risk of multicollinearity that can be raised by adding this new variable, the existing dummy relating to the year 2014 was excluded from the model of analysis. An interaction term between Af2014 and energy demand per business, energyi,t, was also added to the equation to control for the effect of the amended guidelines on the correlation between energy demand and GHG emissions. In analyzing δ1 , which is the central subject of analysis in this section, the reference point therefore switches from β1 to β1 + ρ1 (Table 2-14). Keep in mind that, insofar as

variables δ1, δ2, and δ3 bear statistically significant negative values, it still means that the upward trend in GHG emissions has weakened since the businesses were newly subject to the ETS.

Table 2- 14. Analysis of Estimated Coefficients

Variable 2011 – 2013 2014 2015 – 2017

Subject to ETS Not subject to ETS

Energy β1 β1+ρ1 β1+ρ1+δ1 β1+ρ1

Revenue β2 β2+δ2 β2

ATA β3 β3 + δ3 β3

Note: Each coefficient indicates the change in GHG emissions in response to a unit difference in the given variable, with all other variables held constant.

The equation used to choose between a fixed-effect model and a random-effect one was similarly altered, on the basis of Equation (2), as shown below:

ghgi,t = α′+ ρ0AF2014 + δ0ETSi,t + ∑ γtt=2012∼13,2015∼17 DYeart +β1energyi,t + ρ1energyi,t ∗AF2014 + δ1energyi,t ∗ ETSi,t +η0 + η1energyi + η11energy ∗ Af2014i + η12energy ∗ ETSi +β2revenuei,t + δ2revenuei,t ∗ ETSi,t +η2revenuei + η21revenue ∗ ETSi +β3adjusted tangible asseti,t + δ3adjusted tangible asseti,t ∗ ETSi,t +η3adjusted tangible asseti + η31adjusted tangible asset ∗ ETSi +η4Af2014 + η5ETSi + ui +ɛi,t Equation (3)

4.1. Transition Sector

4.1.1. Descriptive Statistics and Model Test

Table 2-15 shows the descriptive statistics on major variables of the transition sector. GHG emissions and energy demand per business are shown to have declined steadily from 2011 to 2015, before increasing for the following two years. Revenue, on the other hand, managed to grow only marginally from 2011 to 2013, plummeted in 2014, and continued decreasing thereafter. Adjusted tangible asset (ATA), the total value of tangible assets minus the appraised values of land and buildings, rose slightly in 2013, but has been otherwise on the decline since 2011.

A test was then performed to determine whether to apply a fixed-effect or a random-effect model to this data. The average value of each time-varying variable per business was added to the basic equation as shown as in Equation (3). Equation (3) was then applied to a random-effect analysis and statistical significance of the estimated coefficients of the added variables was tested. Table 2-16 summarizes the results of this test. Rows 1 and 2 of the table list the results of the explanatory variable included in the model of analysis (Equation 2). Rows 3 and 4 list the variables added to test the linear correlation between the business attributes (νi) and explanatory variables, as well as their test results. The following null hypothesis was tested next.

H0 ∶ η1 = η11 = η12 = η2 = η21 = η3 = η31 = η4 = η5 = 0

The null hypothesis was rejected at a significance level below one percent (χ2(9) = 601.39, p −value = 0.0000). In other words, there appeared to be no correlation between the unobserved business attributes (νi ) and the explanatory variables. Accordingly, a fixed-effect model was chosen for this analysis.

Table 2- 15. Major Variables and Descriptive Statistics: Transition Sector

(Units: Thousand tCO2eq, TJ, KRW billion (prices effective as of 2015))

Year Variable Obs. Mean Deviation Min. Max.

2011

ghg 28 8,581.4 15,809.0 68.1 50,991.5

energy 28 112,175.4 199,098.8 1,040.0 574,254.0

revenue 28 4,288.7 10,011.5 9.0 45,941.6

adjusted tangible asset 25 4,780.4 11,182.3 44.7 48,475.0

2012

ghg 33 7,778.0 15,100.8 0.8 53,056.6

energy 33 100,833.9 191,082.5 14.0 601,227.0

revenue 33 4,344.0 10,698.7 8.6 51,891.6


2013

ghg 34 7,753.3 15,149.9 0.9 51,523.8

energy 34 99,805.6 190,902.0 17.0 582,364.0

revenue 34 4,373.8 11,350.5 7.1 55,905.1


2014

ghg 42 6,238.2 13,359.0 0.8 56,552.3

energy 42 78,283.9 164,134.5 16.0 636,876.0

revenue 42 3,596.7 10,656.7 0.8 59,160.7


2015

ghg 42 6,006.5 13,138.3 0.8 59,101.4

energy 42 72,903.6 155,723.6 16.0 658,805.0

revenue 42 3,103.2 9,750.1 14.1 58,540.4


2016

ghg 42 6,203.9 13,528.7 0.3 59,734.3

energy 42 75,497.3 160,247.0 6.0 670,228.0

revenue 42 2,939.2 9,558.7 16.9 59,115.6


2017

ghg 42 6,427.2 13,814.8 0.7 58,140.7

energy 42 77,522.4 161,156.8 14.0 652,401.0

revenue 42 2,906.4 9,311.4 17.3 57,519.4


Sources: NGMS (2019) and NICE Information Service (KisValue, 2019), compiled and edited by the author.

Table 2- 16. Model Test Results: Transition Sector

Dependent variable: ghg

energyi,t 0.0625***

energyi -0.0326**

(0.0040) (0.0111)

energyi,t ∗ Af2014 0.000999

energy ∗ Af2014i -0.0353

(0.0006) (0.0268)

energyi,t ∗ ETSi,t 0.00281***

energy ∗ ETSi

0.174***

(0.0300)

(0.0007)

revenuei,t 0.00846

revenuei -0.224***

(0.0103) (0.0537)

revenuei,t ∗ ETSi,t 0.0181***

revenue ∗ ETSi

0.798***

(0.1651)

(0.0053)

adjusted tangible asseti,t -0.156*

adjusted tangible asseti 1.456**

(0.0791) (0.5287)

adjusted tangible asseti,t ∗ ETSi,t -0.0118***

adjusted tangible asset ∗ ETSi -3.222**

(0.0032) (1.2367)

Af2014 192.1

Af2014 830.4

(134.7879) (675.1915)

ETSi,t -123.9

ETSi

-244.1

(632.4430)

(87.3911)

DYear2012 62.90

-

(98.8652)

DYear2013 183.7

(148.5903)

DYear2015 30.19

(43.5924)

DYear2016 68.21

(59.9074)

DYear2017 166.8

(136.9710)

Constant term -826.9

(522.3539)

Number of businesses 39 Total obs. 240

Note: Figures in parentheses indicate standard errors. * p < 0.05, ** p < 0.01, *** p < 0.001.

4.1.2. Results

Equation (2) was subjected to a fixed-effect analysis to determine whether being subject to the ETS made statistically significant differences to the correlation between businesses’ GHG emissions and their business activity (as measured by their energy demand and revenue). Table 2-17 provides a summary of the results. The table provides estimated results for four different scenarios or models. Model (1) is based on Equation (2) as it is without alterations. Models (2) to (4) show the results of applying Equation (2) with the major variables (energy, revenue, and ATA) taken out in turns so as to test and confirm robustness of the model of analysis used. The following discussion will focus on the results of Model (1).

The analysis revealed a strong positive correlation between energy demand and GHG emissions in the transition sector. The statistical significance and estimates of energy demand remained consistent irrespective of whether other variables (revenue, ATA) were added in the analysis. The analysis, however, did not produce a statistically significant conclusion on what kind of correlation existed between businesses’ revenue and ATA, on the one hand, and their GHG emissions, on the other, before they became subject to the ETS.

The analysis showed, through estimated coefficients of the variables, that GHG emissions per unit of energy rather increased (energyi,t ∗ ETSi,t in Table 2-17, δ1 in Equation (2)) after the ETS took effect. The statistical significance and estimated coefficients remained consistent irrespective of whether other variables (revenue, ATA) were included in the analysis. With other variables held equal, GHG emissions for every terajoule (TJ) increase in energy demand actually grew under the ETS.

Model (1) also shows that the effect of revenue on GHG emissions also grew under the ETS. With other variables held constant, GHG emissions for every unit increase (KRW 1 billion) in revenue per business actually grew by 0.0181 thousand tCO2eq under the ETS. The effect of the ETS on the correlation between revenue and GHG emissions, however, lacked statistical significance in Models (2) through (4). That correlation appears to be lacking in robustness (Table 2-17).

Under Model (1), introduction of the ETS improved the correlation between businesses’ ATA and their GHG emissions. With other variables held constant, GHG emissions for every unit increase (KRW 1 billion) in ATA decreased by 0.0117 thousand tCO2eq. As in the case of revenue, the correlation between ATA and GHG emissions failed to show statistical significance under Models (2) through (4), suggesting that the correlation is not robust (Table 2-17).

Why GHG emissions per unit of energy actually grew, contrary to expectations, after the ETS took effect merits further exploration. As the power generation energy industry accounts for much of energy usage and emissions in the transition sector, we can focus our attention on that industry. Although it would be ideal to examine the power output and energy demand by energy type of ETS-subject businesses in this industry, limited data is available. Let us examine the final power demand of each industry and the fuel demand of the power generation industry in Korea instead.

Table 2- 17. Analysis Results: Transition Sector

Model Model (1) Model (2) Model (3) Model (4)

Dependent variable ghg ghg ghg ghg

energyi,t 0.0625*** 0.0646*** 0.0628*** -

(0.0039) (0.0042) (0.0039) -

energyi,t ∗ Af2014 0.000998 0.000550 0.000943 - (0.0006) (0.0007) (0.0006) -

energyi,t ∗ ETSi,t 0.00282*** 0.00287*** 0.00278*** -

(0.0007) (0.0007) (0.0006) -

revenuei,t 0.00833 -0.0124 - 0.141 (0.0101) (0.0072) - (0.0844)

revenuei,t ∗ ETSi,t 0.0181** -0.000970 - 0.0540 (0.0052) (0.0017) - (0.0330)

adjusted tangible asseti,t -0.156 - -0.137 -0.538

(0.0775) - (0.0710) (0.3327)

adjusted tangible asseti,t ∗ ETSi,t -0.0117*** - 0.00360 -0.0315 (0.0031) - (0.0056) (0.0176)

ETSi,t -125.5 -132.5 -124.3 -210.3

(85.4394) (79.0234) (85.7537) (219.7746)

Af2014 190.9 136.7 185.0 636.7

(133.1173) (121.9679) (132.6246) (343.6794)

DYear2012 60.45 -59.00 56.34 486.5*

(97.0479) (107.1037) (95.6319) (215.0598)

DYear2013 182.5 31.13 174.3 790.3**

(145.7675) (150.0159) (145.1617) (289.0245)

DYear2015 28.53 24.21 28.42 -8.700

(42.8796) (39.4219) (43.0745) (129.4122)

DYear2016 66.55 45.76 62.73 235.2

(58.9225) (56.5378) (56.5562) (173.1619)

DYear2017 165.2 143.0 158.6 501.9

(134.4348) (124.4031) (130.7317) (322.3257)

Constant term 1750.8*** 1161.0*** 1689.6*** 8293.6*** (387.2122) (305.6871) (371.9090) (1144.5354)

σν 3601.9 2966.3 3518.8 14868.5 σɛ 443.7 466.4 443.4 1144.4

Number of businesses 39 42 39 39

Total obs. 240 263 240 240


Electricity demand in Korea constantly grew from 39,136,000 TOE in 2011 to 43,666,000 TOE in 2017. The rate of increase slowed from 2011 to 2014, before rising again in 2015 and afterward. Much of the increase in electricity demand is attributable to the growing industrial demand, particularly in 2011 through 2014. Since 2015, however, households, commerce and the public sector have also played a significant role in increasing electricity demand. In fact, the latter sectors did more to raise electricity demand than did industry from 2015 onward (Figure 2-2).

Figure 2- 2. Electricity Demand in Korea by Sector

Source: KESIS (2019), “Energy Balance.” 산업부문 Industry 감 감 Households 감 감 Commerce 감 감 Public

We should also examine the changing trend of Korea’s dependency on different sources of energy over the years. The share of nuclear energy, for example, decreased from 2011 through 2013. This meant the shares of other sources of energy had to increase to compensate for the loss of nuclear power. As a result, the amounts of oil, natural gas, and new/renewable energy used to generate electricity grew. Coal input dropped in 2011, but stayed more or less constant thereafter (Table 2-18).

After the use of nuclear power began to grow in 2014 and 2015, before dropping slightly, the oil and natural gas inputs shrank drastically in the same two years, before beginning to rise again in 2016. Power output from nuclear energy and the share of nuclear energy in the energy mix decreased in 2017 at a greater rate than they did in 2016, with much of the loss being offset by the increasing coal input (Table 2-18).

In other words, there is an inverse correlation between fossil fuel inputs (coal, oil, and natural gas) and nuclear energy. The drop in nuclear energy, accompanied by the overall rise in demand for electricity, during the first phase of the ETS led to greater demand for fossil fuels. As a result, GHG emissions produced by ETS-subject businesses also grew.

Table 2- 18. Sources of Energy for Power Generation in Korea

(Unit: Thousand TOE)

2011 2012 2013 2014 2015 2016 2017

Coal 50,827 49,619 49,941 49,201 50,552 49,158 52,817

(46.7%) (45.6%) (45.5%) (45.2%) (45.9%) (44.3%) (47.5%)

Oil 2,255 3,517 3,608 1,729 2,015 3,026 1,227 (2.1%) (3.2%) (3.3%) (1.6%) (1.8%) (2.7%) (1.1%)

Natural gas 19,186 21,037 23,500 21,261 19,000 20,221 20,366 (17.6%) (19.3%) (21.4%) (19.5%) (17.3%) (18.2%) (18.3%)

Urban gas 928 411 336 305 304 293 335

(0.9%) (0.4%) (0.3%) (0.3%) (0.3%) (0.3%) (0.3%)

Hydropower 1,684 1,615 1,771 1,650 1,223 1,400 1,490 (1.5%) (1.5%) (1.6%) (1.5%) (1.1%) (1.3%) (1.3%)

Nuclear energy 33,266 31,719 29,283 33,002 34,765 34,181 31,615 (30.5%) (29.1%) (26.7%) (30.3%) (31.6%) (30.8%) (28.4%)

New/renewable 784 912 1,208 1,777 2,216 2,639 3,327

(0.7%) (0.8%) (1.1%) (1.6%) (2.0%) (2.4%) (3.0%) Source: KESIS (2019), “Energy Balance.”

4.2. Industrial Sector

In this subsection, let us examine the results of analysis on the industrial sector, which includes the F&B, paper, petrochemical, glass and ceramics, cement, steel, non-iron metal, semiconductor/display/electronics (SDE), and automobile industries. Of these, we focus on heavy emitters, i.e., steel, petrochemicals, and SDE, and Korea’s key industry, automobiles. The cement industry is another heavy emitter, but the fitness of our model of analysis to that industry is rather low. The results of analysis on that industry are thus provided in the Annex, along with the results for the F&B, paper, glass and ceramics, and non-iron metal industries.

4.2.1. Steel

Table 2-19 summarizes the descriptive statistics of the major variables relating to the steel industry. Average emissions per business (ghg) in the industry continued decreasing steadily from 2011 to 2016, but rose slightly to 2,685,300 tCO2eq. Average energy demand per business (energy) has also been in decline, from 43,663.7 TJ in 2011 to 17,659.4 TJ in 2017, except for a brief increase in 2015. Average revenue per business, too, has been dwindling from KRW 3.168 trillion in 2011 to KRW 1.5292 trillion in 2016, before rising a little to KRW 1.7303 trillion in 2017. ATA per business, too, steadily decreased from KRW 1.3383 trillion in 2011 to KRW 0.9415 trillion in 2017, except for a brief rise in 2013.

Table 2- 19. Descriptive Statistics of Major Variables: Steel Industry

(Units: Thousand tCO2eq., TJ, KRW billion (prices effective as of 2015))


2011

ghg 29 3,535.6 14,422.5 33.4 77,124.6

energy 29 43,663.7 164,117.8 577.0 863,564.0

revenue 27 3,168.0 8,347.1 57.3 41,717.3


2012

ghg 34 2,987.4 13,119.3 18.4 75,793.8

energy 34 37,575.8 150,334.0 366.0 852,676.0

revenue 33 2,401.9 6,843.2 63.5 37,513.2


2013

ghg 35 2,867.2 12,571.4 25.4 73,380.2

energy 35 35,193.8 140,222.4 468.0 802,818.0

revenue 34 2,049.7 5,754.7 39.2 31,802.2


2014

ghg 38 2,775.2 12,597.6 26.4 76,138.8

energy 38 22,824.1 78,632.1 498.0 389,943.0

revenue 38 1,881.1 5,420.6 62.5 30,149.6


2015

ghg 38 2,680.7 12,142.7 24.1 73,056.1

energy 38 23,263.5 83,013.8 509.0 417,949.0

revenue 38 1,617.1 4,651.2 56.8 25,607.2


2016

ghg 38 2,599.0 11,808.1 24.9 71,017.3

energy 38 22,723.8 81,396.2 449.0 407,730.0

revenue 38 1,529.2 4,380.9 51.3 23,851.2


2017

ghg 38 2,685.3 11,953.1 16.6 71,340.2

energy 38 17,659.4 66,384.5 335.0 397,789.0

revenue 38 1,730.3 5,040.4 57.7 27,389.0

adjusted tangible asset 38 941.5 3,485.8 12.9 17,080.8


Equation (3) was used to test and determine which model should be applied to the steel industry. Table 2-20 provides a summary of the results from applying a random-effect model. Rows 1 and 2 of the table list the explanatory variables in the original model of analysis (Equation (2)) and their estimates. Rows 3 and 4 list the variables that were added to the original model to test linearity between the unobserved attribute of businesses (νi) and the explanatory variables, and their estimates. These estimates were then applied to Equation (3) to test the following null hypothesis:

H0 ∶ η1 = η11 = η12 = η2 = η21 = η3 = η31 = η4 = η5 = 0.

The null hypothesis was rejected at a significance level under one percent (χ2(9) = 1.2e + 05, p −value = 0.0000). As this indicated there was no correlation between the unobserved business attributes (νi) and the explanatory variables, a fixed-effect model was used.

Table 2- 20. Model Test Results: Steel Industry


energyi,t 0.00158

energyi 0.267***

(0.0024) (0.0085)

energyi,t ∗ Af2014 0.00925***

energy ∗ Af2014i -0.0854

(0.0019) (0.0723)

energyi,t ∗ ETSi,t -0.0251***

energy ∗ ETSi

-0.307**

(0.1136) (0.0038)

revenuei,t 0.362***

revenuei -0.370**

(0.0798) (0.1233)

revenuei,t ∗ ETSi,t -0.191***

revenue ∗ ETSi

0.0980

(0.2320) (0.0324)

adjusted tangible asseti,t 0.256

adjusted tangible asseti -1.777***

(0.1371) (0.2561)

adjusted tangible asseti,t ∗ ETSi,t 0.740***

adjusted tangible asset ∗ ETSi 1.964***

(0.0615) (0.3769)

Af2014 50.73

Af2014 110.4

(37.4121) (93.8113)

ETSi,t 91.16

ETSi

172.6

(260.0427) (61.3445)

DYear2012 51.17

-

(33.7140)

DYear2013 87.07*

(41.4611)

DYear2015 63.43***

(18.8773)

DYear2016 -13.97

(73.5218)

DYear2017 30.29

(72.9623)

Constant term -257.8*

(116.8671)



Table 2-21 shows the results of fixed-effect analysis using Equation (2). It shows estimates for four different scenarios or models. Under Model (1) are listed the estimates obtained by applying the basic model of analysis, Equation (2). Models (2) to (4) show the results of testing robustness of the model of analysis by excluding major variables (energy, revenue, and ATA) in turns from Equation (2). The following discussion focuses on the results for Model (1) only.

Applying Equation (2) to the steel industry revealed a strong positive correlation between energy demand and revenue, on the one hand, and GHG emissions (ghg), on the other. These two variables retained their statistical significance irrespective of whether other variables were included in the equation. No such statistical significance was found, however, in the pre-ETS correlation between ATA and ghg. The 2014 reform of government guidelines on reporting and calculating emissions has also influenced the correlation between energy and ghg. The estimate for energyi,t ∗ Af2014 in Table 2-21 (ρ1 of Equation (2)) bore a statistically significant positive value.

Estimates for the coefficients of variables added to test the effect of the ETS show that ghg per unit of energy (energyi,t ∗ ETSi,t on Table 2-21, δ1 of Equation (2)) indeed decreased with statistical significance under the ETS. This was consistently reaffirmed under Models (2) and (3) irrespective of whether other variables were included. With other variables held equal, GHG emissions per unit increase (1 TJ) in energy demand have indeed decreased since the ETS was introduced.

Revenue ( revenuei,t ∗ ETSi,t on Table 2-21, δ2 of Equation (2)) did decrease with statistical significance under Model (1) after the ETS was introduced. With other variables held constant, GHG emissions per unit increase (KRW 1 billion) in revenue did drop under the ETS. This correlation, however, lost statistical significance under Models (2) and (4). The seeming improvement in the correlation between revenue and emissions under the ETS, in other words, lacks statistical robustness (Table 2-17).

Finally, the correlation between ATA (δ3 of Equation (2)) and ghg, under Model (1), deteriorated after the ETS was introduced. With other variables held equal, GHG emissions per unit increase (KRW 1 billion) in ATA rather increased under the ETS. The correlation between ATA and ghg retained statistical significance under Models (3) and (4) as well.

Table 2- 21. Analysis Results: Steel Industry

Model (1) (2) (3) (4)


energyi,t 0.00156 0.00373*** 0.0116*** -

(0.0024) (0.0007) (0.0014) -

energyi,t ∗ Af2014 0.00924*** 0.0112*** 0.0128*** -

(0.0018) (0.0019) (0.0036) -

energyi,t ∗ ETSi,t -0.0251*** -0.0132*** -0.0417*** - (0.0037) (0.0024) (0.0044) -

revenuei,t 0.363*** 0.461*** - 0.376*** (0.0781) (0.0926) - (0.1053)

revenuei,t ∗ ETSi,t -0.192*** 0.193 - -0.232 (0.0317) (0.1159) - (0.1223)

adjusted tangible asseti,t 0.255 - 0.168 0.401*

(0.1343) - (0.1517) (0.1564)

adjusted tangible asseti,t ∗ ETSi,t 0.740*** - 0.781*** 0.396*** (0.0602) - (0.0345) (0.0888)

ETSi,t 91.52 8.005 59.37 23.32

(60.1743) (71.3707) (65.0696) (78.9850)

Af2014 53.23 105.0 -140.7 269.2*

(36.9074) (93.6316) (74.1207) (127.6050)

DYear2012 50.80 103.4 -68.68 55.45

(33.3661) (60.5525) (47.4653) (34.8320)

DYear2013 90.72* 207.3 -113.7 81.98

(41.3017) (140.2177) (88.2773) (74.3599)

DYear2015 63.31** 9.734 26.48 -34.44 (18.6657) (19.9246) (16.4577) (58.2536)

DYear2016 -14.11 -15.74 -66.86 -101.1

(72.1435) (34.6817) (82.0860) (120.5899)

DYear2017 30.17 -51.85 -6.802 8.096

(71.5728) (66.2336) (67.3075) (78.8628)

Constant term 1667.7*** 1599.1*** 2352.3*** 1519.4** (196.6166) (284.1839) (154.5310) (426.5718)

σν 9099.1 9020.6 10205.3 8866.9 σɛ 181.3 360.2 272.0 348.9


Total obs. 246 246 246 246


4.2.2 Petrochemicals

Table 2-22 provides descriptive statistics used in the analysis of petrochemicals. The GHG emissions per business (ghg) in the industry steadily decreased from 656,600 tCO₂eq in 2011 to 583,300 tCO₂eq in 2015, before starting to rise back up in 2016, reaching 610,200 tCO₂eq in 2017. Energy demand per business (energy) similarly decreased in 2011 through 2015 and began to rise again in 2016 and 2017. Revenue per business (revenue) showed a similar pattern, consistently falling from 2011 to 2016, and rising again in 2017. ATA per business, on the other hand, continued to grow in 2011 through 2013, and

fell steadily from 2014 and onward.

Equation (3) was applied to determine whether to use a fixed-effect or a random-effect model in analyzing the petrochemical industry. Table 2-23 lists the results of that application. Columns 1 and 2 indicate the explanatory variables of the base model of analysis (Equation 2) and the estimated results. Columns 3 and 4 show the variables added to test linearity between the attributes of businesses (νi) and the explanatory variables, and their estimates. The null hypothesis, H0 ∶ η1 = η11 = η12 = η2 =η21 = η3 = η31 = η4 = η5 = 0 was tested in relation to Equation (3). The null hypothesis was rejected at a significance level below one percent (χ2(9) = 88.66, p − value = 0.0000). This suggests that there is no correlation between the unobserved attributes of businesses (νi) and the explanatory variables, and that a fixed-effect model should therefore be used.

Table 2- 22. Descriptive Statistics of Major Variables: Petrochemical Industry



2011

ghg 69 652.6 1,195.4 3.9 5,893.6

energy 69 11,098.4 22,377.7 73.0 112,549.0

revenue 64 2,027.3 3,820.4 36.8 21,097.3

adjusted tangible asset 62 413.5 769.0 2.3 4,570.8

2012

ghg 74 615.8 1,196.8 3.8 6,421.3

energy 74 10,898.6 23,136.4 74.0 123,796.0

revenue 71 1,921.1 3,644.2 11.7 21,502.2


2013

ghg 78 602.6 1,236.5 3.7 7,098.8

energy 78 10,845.0 23,825.5 72.0 135,192.0

revenue 75 1,900.6 3,628.3 15.1 21,090.7


2014

ghg 85 583.3 1,213.8 18.0 7,127.0

energy 85 10,706.9 23,875.4 356.0 135,742.0

revenue 82 1,781.4 3,435.3 10.0 20,302.8


2015

ghg 86 583.3 1,212.0 11.1 7,294.6

energy 86 10,796.7 24,182.5 385.0 142,035.0

revenue 83 1,480.5 2,739.2 22.4 17,334.1


2016

ghg 88 591.7 1,238.7 3.3 7,504.6

energy 88 10,833.8 24,664.5 65.0 149,533.0

revenue 86 1,368.0 2,560.9 24.3 16,928.6


2017

ghg 88 610.2 1,292.1 3.4 7,965.9

energy 88 11,301.9 25,624.1 67.0 153,368.0

revenue 87 1,549.7 3,018.7 22.0 20,028.4



Table 2- 23. Model Test Results: Petrochemical Industry


energyi,t 0.0493***

energyi 0.0546***

(0.0069) (0.0135)

energyi,t ∗ Af2014 -0.000825

energy ∗ Af2014i -0.321

(0.0010) (0.1663)

energyi,t ∗ ETSi,t 0.000673

energy ∗ ETSi

0.308

(0.1963)

(0.0011)

revenuei,t 0.0141

revenuei -0.0516

(0.0193) (0.0566)

revenuei,t ∗ ETSi,t -0.0000359

revenue ∗ ETSi

0.0455

(0.1073)

(0.0059)

adjusted tangible asseti,t 0.0372

adjusted tangible asseti 0.411*

(0.0643) (0.1654)

adjusted tangible asseti,t ∗ ETSi,t -0.0331

adjusted tangible asset ∗ ETSi -0.849*

(0.0374) (0.4140)

Af2014 -27.60**

Af2014 355.8

(10.7018) (203.3358)

ETSi,t -15.34

ETSi

-328.1

(271.7425)

(16.3000)

DYear2012 -21.71**

-

(8.1369)

DYear2013 -20.52*

(10.4289)

DYear2015 20.66

(16.1082)

DYear2016 31.00

(17.0368)

DYear2017 25.11

(18.8176)

Constant term 13.36

(56.3327)



Table 2-24 shows the results of estimating the variables in Equation (2) in relation to the petrochemical industry based on the fixed-effect model. Specifically, the table shows estimates for four different scenarios or models. Under Model (1) are listed the estimates obtained by applying the basic model of analysis, Equation (2). Models (2) to (4) show the results of testing robustness of the model of analysis by excluding major variables (energy, revenue, and ATA) by turns from Equation (2). The following discussion focuses on the results for Model (1) only.

Of the three variables, only energy demand (energy) maintained a statistically significant correlation to emissions (ghg) across all models, while revenue and ATA failed to retain significant correlations to energy in Models (1) through (3). Under Model (4), where the energy variable is not included, both variables emerged with statistical significance. In other words, energy demand is the single-most decisive factor in explaining GHG emissions from the petrochemical industry (Table 2-24).

Under all models with energy demand included, including Model (1), there was no statistically significant impact of the ETS. Under Model (4), which does not include energy demand as a variable, however, the effect of the ETS was manifest on ATA (ATAi,t ∗ ETSi,t in Table 2-24, δ3 of Equation (2)), and in intercepts (ETSi,t in Table 2-24, δ0 in Equation (2)).

Table 2- 24. Analysis Results: Petrochemical Industry

Model (1) (2) (3) (4)


energyi,t 0.0493*** 0.0496*** 0.0514***

(0.0068) (0.0054) (0.0068)

energyi,t ∗ Af2014 -0.000822 -0.000808 -0.000992 (0.0010) (0.0009) (0.0011)

energyi,t ∗ ETSi,t 0.000650 -0.000147 0.000151 (0.0011) (0.0012) (0.0010)

revenuei,t 0.0140 0.0121 0.0782*** (0.0192) (0.0186) (0.0170)

revenuei,t ∗ ETSi,t -0.00000374 -0.00182 0.00603 (0.0059) (0.0061) (0.0114)

adjusted tangible asseti,t 0.0371 0.0318 0.272*** (0.0638) (0.0693) (0.0691)

adjusted tangible asseti,t ∗ ETSi,t -0.0323 -0.0302 0.231*** (0.0368) (0.0358) (0.0400)

ETSi,t -15.67 -9.194 -16.53 -64.87** (15.9533) (8.8063) (16.1152) (21.1587)

Af2014 -27.47* -24.72* -29.03** 20.99 (10.6207) (9.5525) (10.4943) (21.5632)

DYear2012 -21.57** -19.82** -23.46** 3.534 (8.0729) (7.2829) (8.0702) (11.3170)

DYear2013 -20.11 -17.65 -22.47* 8.651

(10.3241) (9.4155) (10.0155) (18.7069)

DYear2015 20.76 10.78 21.72 -6.946 (15.7686) (8.7801) (15.7670) (21.6582)

DYear2016 31.19 20.79 30.61 34.98 (16.6913) (10.5611) (16.7653) (25.4716)

DYear2017 25.11 15.21 26.86 41.43 (18.5317) (12.8800) (17.9544) (30.1517)

Constant term 52.01 65.97 60.38 342.4*** (59.3015) (52.7740) (57.1842) (33.9487)

σν 167.9 162.7 162.9 777.6 σɛ 57.54 56.44 57.79 104.6


Total obs. 522 548 522 522


In sum, energy demand is the sole determinant of the petrochemical industry’s GHG emission levels. Energy demand as a variable is, in turn, dependent on the business cycle and production. The ETS does not appear to have made a statistically significant difference to either the correlation between emissions and energy demand or the correlation between energy demand and production.

4.2.2. Semiconductor, Display, and Electronics

Table 2-25 provides a summary of the descriptive statistics used in the analysis of semiconductor, display and electronics (SDE), herein treated as a single industry. The industry’s average GHG emissions continued to decrease steadily from 2011 to 2016, before rising slightly again in 2017 to reach 641,600 tCO₂eq. Energy demand per business (energy), on the other hand, decreased in 2011 through 2014, before resuming an upward pattern in 2015. Revenue rose temporarily in 2013, but otherwise continued decreasing from 2011 to 2016, before climbing slightly in 2017. ATA continued to drop in 2011 through 2015, before drastically rising in 2016 and 2017 thanks to large-scale investment in new facilities.

Equation (3) was applied to determine whether to apply a fixed-effect or a random-effect model to the SDE industry. Table 2-26 lists the results of applying Equation (3) using a random-effect model. Columns 1 and 2 indicate the explanatory variables of the base model of analysis (Equation 2) and the estimated results. Columns 3 and 4 show the variables added to test linearity between the attributes of businesses (νi) and the explanatory variables, and their estimates. The null hypothesis, H0 ∶ η1 = η11 =η12 = η2 = η21 = η3 = η31 = η4 = η5 = 0 was tested in relation to Equation (3). The null hypothesis was rejected at a significance level below one percent (χ2(9) = 450.72 , p − value =0.0000). This suggests that there is no correlation between the unobserved attributes of businesses (νi) and the explanatory variables, and that a fixed-effect model should therefore be used.

Table 2- 25. Descriptive Statistics of Major Variables: SDE Industry



2011

ghg 27 878.7 1,764.2 29.8 5,927.1

energy 27 11,327.7 20,393.1 577.0 76,886.0

revenue 24 9,035.8 26,624.5 168.0 128,667.3


2012

ghg 35 723.1 1,696.2 26.5 6,161.4

energy 35 10,071.2 21,450.3 544.0 91,328.0 revenue 33 8,322.6 26,330.4 150.4 148,523.8


2013

ghg 38 701.2 1,734.1 27.4 6,921.5

energy 38 9,615.0 21,515.3 558.0 96,235.0

revenue 35 8,551.8 28,397.0 110.4 164,898.7 adjusted tangible asset 34 1,902.2 5,354.5 17.1 28,291.6

2014

ghg 44 645.9 1,749.4 26.3 7,694.7 energy 44 8,510.7 20,798.4 539.0 101,320.0

revenue 42 6,627.3 22,678.2 10.7 142,216.2


2015

ghg 45 594.1 1,638.2 9.7 7,348.5

energy 45 8,533.7 21,838.5 198.0 110,573.0 revenue 44 6,127.6 21,164.6 8.6 135,205.0


2016

ghg 45 563.9 1,512.8 2.7 6,897.2

energy 45 8,786.2 22,493.5 56.0 115,605.0

revenue 44 5,904.8 20,481.1 2.6 131,338.6 adjusted tangible asset 43 1,599.1 5,099.1 5.9 29,000.1

2017

ghg 45 641.6 1,737.3 3.7 8,589.5 energy 45 9,569.8 24,970.3 75.0 130,834.0

revenue 45 6,911.8 23,982.7 3.7 155,309.7



Table 2- 26. Model Test Results: SDE Industry


energyi,t 0.0510***

energyi -0.215

(0.0138) (0.1125)

energyi,t ∗ Af2014 0.00553

energy ∗ Af2014i 0.330

(0.0042) (0.3541)

energyi,t ∗ ETSi,t -0.0141**

energy ∗ ETSi

0.328

(0.2348)

(0.0051)

revenuei,t 0.0148

revenuei 0.208

(0.0108) (0.1130)

revenuei,t ∗ ETSi,t 0.0184***

revenue ∗ ETSi

-0.492

(0.2607)

(0.0047)

𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i,t -0.00952

𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i -0.665*

(0.0746) (0.3093)

𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i,t ∗ ETSi,t -0.0436

adjusted tangible asset ∗ ETSi 0.240

(0.0436) (0.5586)

Af2014 -16.46

Af2014 -35.85

(12.9720) (242.1751)

ETSi,t 10.11

ETSi

35.72

(213.8895) (15.5659)

DYear2012 -16.51

-

(12.8588)

DYear2013 -8.001

(11.0479)

DYear2015 12.11

(7.7574)

DYear2016 -13.36

(22.1516)

DYear2017 22.49

(21.1207)


(110.4702)



Table 2-27 shows the results of applying the fixed-effect model to Equation (2) in relation to the SDE industry. It shows estimates for four different models. Under Model (1) are listed the estimates obtained by applying the basic model of analysis, Equation (2). Models (2) to (4) show the results of testing the robustness of the model of analysis by excluding major variables (energy, revenue, and ATA) in turns from Equation (2). The following discussion focuses on the results for Model (1) only.

When Equation (2) was applied, the energy demand (energy) of the SDE industry was revealed to bear a strong positive correlation to its emissions (ghg). The statistical significance of the correlation remained intact whether other variables were added or not. No such statistical significance was found, however, between either revenue or ATA and GHG emissions.

Examining the estimated coefficients for the variables further reveals that emissions per unit of energy (energyi,t ∗ ETSi,t in Table 2-27, δ1 in Equation (2)) did decrease with significance after the ETS was introduced. This pattern remained consistent under Models (2) and (3) as well. In other words, with other variables held equal, the SDE industry’s GHG emissions for every one-terajoule (TJ) increase in energy demand decreased with statistical significance under the ETS.

The correlation between revenue (revenuei,t ∗ ETSi,t in Table 2-27, δ2 in Equation (2)) and GHG emissions, on the other hand, rather deteriorated under the ETS. This pattern, too, emerged steadfastly under all models regardless of other variables.

ATA (δ3 in Equation (2)) did not manifest a statistically significant effect under Model (1), the baseline of Equation (2). Under Model (3), with revenue as a variable absent, ATA emerged with an effect similar to revenue.

The fact that the SDE industry’s emissions per unit of energy has fallen since the ETS was introduced is apparent in Figure 2-3, which shows the trend in the industry’s emissions and energy demand. Although other variables were not controlled for this graph, the slopes of the fitted lines of the two variables confirm that the ETS did slow down the rate of rise in the slope.

Figure 2- 3. GHG Emissions and Energy Demand: SDE Industry


020

0040

0060

0080

0010

000

ghg

0 50000 100000 150000energy

Before ETS Fitted before ETS After ETS Fitted after ETS

Table 2- 27. Analysis Results: SDE Industry

Model (1) (2) (3) (4)


energyi,t 0.0520*** 0.0387** 0.0594***

(0.0137) (0.0126) (0.0110)

energyi,t ∗ Af2014 0.00548 0.00661 0.00419 (0.0042) (0.0046) (0.0033)

energyi,t ∗ ETSi,t -0.0136* -0.0198*** -0.0213*** (0.0052) (0.0011) (0.0039)

revenuei,t 0.0144 0.0140 0.0343**

(0.0106) (0.0085) (0.0100)

revenuei,t ∗ ETSi,t 0.0187*** 0.0148*** 0.0248* (0.0046) (0.0009) (0.0098)

𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i,t -0.00750 -0.0701 0.0403 (0.0737) (0.0452) (0.0860)

𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i,t ∗ ETSi,t -0.0476 0.0593** -0.0696 (0.0439) (0.0193) (0.0489)

ETSi,t 6.946 21.93 26.62 -60.48*

(15.6715) (25.9719) (17.4452) (25.4965)

Af2014 -17.57 -5.350 -1.307 103.9

(12.8245) (19.4594) (13.6606) (69.4963)

DYear2012 -18.44 -2.758 -8.539 20.38

(12.7301) (18.4252) (17.3575) (26.6199)

DYear2013 -8.850 11.22 3.086 35.85

(11.0041) (39.2492) (14.0774) (34.6297)

DYear2015 13.70 12.58 18.67 -45.42

(7.4420) (6.5332) (11.8664) (25.2820)

DYear2016 -11.06 -18.59 -23.88 -50.94

(22.0562) (36.0363) (33.6642) (40.4286)

DYear2017 25.03 6.828 28.35 -21.25

(21.4242) (39.0133) (30.6665) (26.8751)

Constant term 107.8 197.2** 248.2* 314.6**

(121.8277) (60.3642) (103.4768) (96.9158) σν 571.6 701.8 760.5 1046.9 σɛ 127.0 128.4 143.4 177.5


Total obs. 256 267 257 256


However, caution is advised in interpreting the apparent improvement in GHG emissions per unit of energy from the SDE industry. Process emissions account for 30 to 40 percent of all GHG emissions from the industry. The remainder also mostly comes from indirect sources, i.e., the use of power and heating, rather than direct burning of fuels. We need to be mindful of this fact and note that GHG emissions from the SDE industry fell in 2014 through 2016, before rising dramatically again in 2017, while the industry’s energy demand has been steadily on the rise since 2014 (Figure 2-4).

Figure 2- 4. GHG Emissions and Energy Demand Trend Since 2014: SDE Industry


Assuming that the SDE industry as a whole obtains the energy it needs from electricity and heating, and that the emission factors of these two sources of energy remain fixed, the industry’s emissions from indirect sources should increase. The contrary fact that the industry’s GHG emissions rather dropped indicates that it has been successful in reducing process emissions sufficiently to offset increases in indirect emissions. The costs of electricity and heating directly increase manufacturing costs, prompting businesses to invest considerably in increasing energy efficiency. Although it is still too early to conclude that the significant drop in the SDE industry’s emissions owes entirely to the cut in process emissions, reduced process emissions likely played an important role in keeping the entire industry’s general emissions relatively low.

4.2.3. Automobiles

A summary of the descriptive statistics used in analysis of the automobile industry is found in Table 2-28. In this industry, all the variables—emissions (ghg), energy demand (energy), revenue, and ATA—have been moving in a similar fashion. All four variables steadily decreased in 2011 through 2014. Since 2015, they have either remained stagnant at 2014 levels (ghg, energy and ATA) or continued to decline slowly and steadily (revenue).

Equation (3) was used to determine whether a fixed-effect or a random-effect model should be used to analyze the auto industry. Table 2-29 presents the results of applying a random-effect model with Equation (3). The first two columns show the results for the explanatory variables originally included in the base model (Equation (2)). The third and last columns show the variables added to test linearity between the attributes of businesses (νi) and the explanatory variables, as well as their results. Equation (3) was thus applied to test the null hypothesis, H0 ∶ η1 = η11 = η12 = η2 = η21 = η3 = η31 = η4 =η5 = 0 . The null hypothesis was rejected at a significance level of less than one percent (χ2(9) =159.73 , p − value = 0.0000 ), indicating that there was no correlation between the unobserved attributes of businesses (νi) and the explanatory variables. The analysis thus required the fixed-effect model.

Table 2- 28. Descriptive Statistics of Major Variables: Automobile Industry



2011

ghg 14 261.9 434.9 21.1 1,562.7

energy 14 5,046.4 8,457.6 344.0 30,385.0

revenue 14 7,666.3 13,681.9 127.7 45,553.8


2012

ghg 22 186.4 357.5 0.5 1,552.5

energy 22 3,745.6 7,243.3 9.0 31,475.0

revenue 22 5,368.4 11,267.2 97.2 45,399.2


2013

ghg 23 172.5 339.1 0.4 1,501.8

energy 23 3,461.1 6,874.8 7.0 30,458.0

revenue 22 5,049.0 10,982.8 96.0 43,409.3


2014

ghg 29 147.9 307.7 0.4 1,536.1

energy 29 2,964.9 6,194.3 8.0 30,894.0

revenue 27 4,257.7 10,194.8 10.0 44,417.1


2015

ghg 29 148.3 306.1 0.5 1,533.4

energy 29 2,980.6 6,203.7 9.0 31,100.0

revenue 29 4,041.6 10,042.4 90.0 44,439.7


2016

ghg 29 147.2 298.4 0.4 1,494.3

energy 29 2,956.8 6,049.3 8.0 30,314.0

revenue 29 3,886.3 9,368.3 52.7 40,901.3


2017

ghg 29 146.4 302.6 0.4 1,524.6

energy 29 2,923.8 6,091.2 8.0 30,669.0

revenue 29 3,763.2 9,162.1 38.5 39,907.6



Table 2- 29. Model Test Results: Automobile Industry


energyi,t 0.0482***

energyi -0.0114

(0.0039) (0.0072)

energyi,t ∗ Af2014 0.000477

energy ∗ Af2014i 0.0216*

(0.0003) (0.0097)

energyi,t ∗ ETSi,t -0.00179

energy ∗ ETSi

0.000523

(0.0122)

(0.0009)

revenuei,t 0.00511***

revenuei 0.00221

(0.0014) (0.0044)

revenuei,t ∗ ETSi,t -0.00268*

revenue ∗ ETSi

-0.0103

(0.0079)

(0.0012)

𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i,t -0.0246***

𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i 0.00685

(0.0047) (0.0185)

𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i,t ∗ ETSi,t 0.0234*

adjusted tangible asset ∗ ETSi -0.00331

(0.0098) (0.0321)

Af2014 -7.393**

Af2014 -20.01*

(2.4990) (9.3552)

ETSi,t -0.0928

ETSi

-12.37

(12.1131) (0.7095)

DYear2012 -8.477**

-

(3.0843)

DYear2013 -7.099**

(2.7471)

DYear2015 0.648

(0.7791)

DYear2016 1.107

(0.9012)

DYear2017 2.111

(1.4709)

Constant term 25.50**

(8.6649)



Estimates obtained by applying Equation (2), with a fixed-effect model, to the auto industry are presented in Table 2-30. The Model (1) column lists the results of applying Equation (2) as it is. Models (2) to (4) show the results of testing the robustness of the model of analysis by excluding major variables (energy, revenue, and ATA) in turns from Equation (2). The following discussion focuses on the results for Model (1) only.

The analysis reveals that the energy demand (energy) of the auto industry bears a strong positive correlation to its GHG emissions (ghg). The statistical significance of the correlation remains intact irrespective of other variables. Revenue and ATA, too, emerge with a statistically significant and positive correlation to emissions under Model (1). The two variables, however, have the strongest power of explanation when they are used simultaneously (Models (1) and (4)). When only one or the other is used, the statistical significance either disappears or decreases (Models (2) and (3)).

An examination of the coefficients of the variables reveals that, since the ETS was introduced, the changes in GHG emissions per unit of energy (energyi,t ∗ ETSi,t in Table 2-30, δ1 in Equation (2)) took on negative values, but not with great statistical significance (p − value = 0.0992).

The inverse correlation between revenue (revenuei,t ∗ ETSi,t in Table 2-30), δ2 in Equation (2)) and GHG emissions has grown stronger since introduction of the ETS. Insofar as the other variables are held constant, the additional amount of GHG emissions per unit increase in revenue (by KRW 1 billion) has declined.

By contrast, the correlation between ATA (ATAi,t ∗ ETSi,t in Table 2-30, δ3 in Equation (2)) has rather deteriorated under the ETS. In other words, the additional amount of GHG emissions generated has been increasing per unit increase (KRW 1 billion) in ATA.

Table 2- 30. Analysis Results: Automobile Industry

Model (1) (2) (3) (4)


energyi,t 0.0478*** 0.0440*** 0.0550***

(0.0038) (0.0052) (0.0016)

energyi,t ∗ Af2014 0.000470 -0.000344 0.000307 (0.0003) (0.0002) (0.0003)

energyi,t ∗ ETSi,t -0.00155 0.00140 -0.00269 (0.0009) (0.0013) (0.0013)

revenuei,t 0.00522*** 0.00241 0.0185***

(0.0014) (0.0017) (0.0016)

revenuei,t ∗ ETSi,t -0.00274* -0.00101 -0.00908*** (0.0012) (0.0009) (0.0011)

𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i,t -0.0243*** -0.0184* -0.00873 (0.0044) (0.0082) (0.0073)

𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i,t ∗ ETSi,t 0.0228* 0.00977 0.0556*** (0.0095) (0.0052) (0.0078)

ETSi,t -0.107 -0.842 0.563 0.313

(0.6621) (0.9537) (0.6265) (2.4597)

Af2014 -7.647** -8.048* -9.260** -2.924 (2.4684) (3.3879) (3.2521) (6.3198)

DYear2012 -8.612** -9.406* -10.68* 0.430 (3.0050) (3.5956) (3.9703) (4.9245)

DYear2013 -7.329* -9.613* -9.424** -2.848 (2.7158) (4.1063) (3.3075) (6.0931)

DYear2015 0.737 0.291 0.0307 3.864

(0.7447) (0.5614) (0.5501) (2.1047)

DYear2016 1.193 0.471 0.250 4.113

(0.8698) (0.6540) (0.5981) (2.5921)

DYear2017 2.193 1.342 1.103 4.703

(1.4337) (1.2005) (1.1077) (3.6147)

Constant term 6.537 17.67 4.995 87.41***

(9.3623) (11.5099) (9.3744) (9.2095) σν 7.548 14.88 7.120 145.7 σɛ 4.083 4.800 4.523 9.845

Number of businesses 29 29 29 29 Total obs. 171 172 172 171


5. Chapter Conclusion

As part of assessing the performance of the first phase of the ETS in Korea, this chapter provides an econometric analysis of whether the correlation between energy demand, revenue, ATA (as a proxy variable of capital) in each industry and the industry’s GHG emissions has improved under the ETS.

The limited available data has prevented us from analyzing statistics on all the subject industries. Our analysis thus focuses on determining whether the ETS has made a statistically significant difference to the emissions of select industries, i.e., the transition sector, and the steel, petrochemical, SDE, and automobile industries. Our analysis reveals substantial variation from industry to industry. In the transition sector, the correlation between energy demand and revenue, on the one hand, and GHG emissions, on the other, has, in fact, deteriorated under the ETS, while increases in ATA have contributed to reducing emissions. In the petrochemical industry, a typical heavy-emitter, the ETS has failed to exert a statistically significant effect on the correlation between major variables and emissions.

The steel and SDE industries, on the other hand, exhibited the most dramatic decreases in their respective emissions since the ETS was introduced. In the former, the emissions-reducing effect of both energy demand and revenue has improved, while that of ATA has dropped. In the latter, the inverse correlation between energy demand and emissions continued to progress, while the emissions-reducing effect of revenue has rather deteriorated. Considering the characteristics of emissions from the SDE industry and the attributes of the data used in this analysis, the decrease in emissions per unit of energy in the SDE industry appears to stem largely from the drastic cut in process emissions.

In the auto industry, the ETS has made a statistically significant difference to the effects of revenue and ATA on emissions, but in contrasting directions. Whereas emissions per unit of revenue have decreased, the opposite was true of correlation between emissions and ATA.

A word of caution is needed before drawing a definitive conclusion from the analysis provided in this chapter. First, industries that have seemingly not shown a statistically significant difference in their emissions under the ETS should not be regarded as not having made meaningful effort toward reducing emissions. We must remember that the businesses targeted by the ETS had been under direct regulation, compelling them to limit their emissions and energy demand, prior to introduction of the ETS. The pre-

ETS state that forms the baseline of our analysis, in other words, was already being regulated by the GEMS. All that we can surmise from the analysis in this chapter is, therefore, whether the ETS has made an additional contribution to reducing GHG emissions from Korean industries that had already reduced emissions under the GEMS.

The shortcomings of the data that were subjected to analysis should also be mentioned. This chapter draws upon the breakdowns of business performance voluntarily submitted by Korean businesses to the NGMS. Comprehensive amendment of the GEMS Guideline in 2014, however, has created a rupture in the time series of the NGMS data. Failure to control or correct this rupture in analysis would lead us to attribute the apparently radical drop in GHG emissions and energy demand in 2014 simply to the efforts of businesses only, thereby overestimating the impact of the ETS.

Due to the lack of publicly circulated information on this fact, no other Korean studies, except Oh et al. (2018), takes it into account. The rupture in time series does not appear to have made a statistically significant difference to industries, except for steel, but it must nonetheless be accounted for in analysis.

A more thoroughgoing analysis of the emissions-reducing efforts made by businesses would require more varied and refined variables, such as emissions from the direct consumption of fuels, process emissions from the manufacturing process, and indirect emissions from the use of electricity and heating, at the least. As for energy demand, we would also need to distinguish between the use of direct sources of energy and electricity/heating.

More detailed and accurate data are crucial to a more reliable evaluation of the performance of the ETS and the affirmation of how Korean businesses have adapted their behavior to the ETS and toward reducing GHG emissions.

The principal objective of the first phase of the ETS is in establishing and solidifying the ETS as an institution. That period has done more than this stated objective in a number of industries, where it helped lower GHG emissions with statistical difference. The fact that the ETS has not been equally effective on all subject industries should not be a problem in itself. Insofar as the ETS helps the entire nation control nationwide emissions at a certain level, with individual and target businesses submitting emission permits corresponding to their emission quotas, it is unnecessary for all businesses in all industries to curtail their emissions. The outcome of the first phase therefore tells us that it exerted an emission-reducing pressure that could easily be met with the efforts of a few heavy-emitter industries rather than all industries. The emission-reducing pressure exerted by the ETS, however, will continue to grow in future phases.

The Korean government will keep decreasing the total energy permitted under the ETS in the coming years to facilitate the country meeting the emissions reduction target by 2030. This means that the emission permits allotted to target businesses will, too, continue to decrease, and all, rather than just a few, industries will have to make conscious efforts toward that end. There are costs involved in reducing GHG emissions, such as those for acquiring emissions-reducing facilities, switching to low-carbon fuels and high-efficiency facilities, and making improvements to the production process. Businesses that will not or cannot make such investments can reduce their GHG emissions to the necessary level only by reducing their output. Considering the opportunity costs as well as the ripple effects of output losses on local economies and the chain of industrial activities, conscious reduction of output is hardly a recommended solution. Businesses, in other words, have to invest actively in acquiring emissions-reducing facilities and technologies. It is therefore the task of the government to induce and encourage such investments, whether via the ETS or using other policy measures.

The Korean government now faces three specific tasks in supporting businesses’ efforts to reduce GHG emissions. First, it should support the research and development (R&D) of emissions-reducing facilities, high-efficiency energy systems, low-emission processes and other such technologies that businesses can actually acquire and apply. Next, it should provide sufficient incentives for active

investment in emissions reduction. All investment decisions are made on the basis of anticipated costs and benefits. The government should thus reduce the financial burden of investment in emissions reduction, while redesigning the policy support system with a view to maximizing possible benefits from such investment.

Chapter III. Market Efficiency under the First Phase of the Korean ETS

1. Introduction

Our objective in this chapter is to evaluate whether the ETS in Korea was effective during 2015 to 2017—its first phase. One main advantage the ETS has over more direct, command-and-control forms of regulation like the GEMS is that it is, at least in theory, capable of leading to reduction in GHG emissions in a much more cost-effective way systemwide.

To achieve systemwide and cost-effective reduction of emissions, it is critical to determine how much each participating business can emit to the extent that the marginal cost of emissions reduction becomes equal for all participating businesses. This can be achieved only with an efficient system of emissions trading (Daskalakis and Markellos, 2008). The prevailing opinion in Korea is that emissions trading was insufficient during the first phase of the Korean ETS. This opinion, however, is based on the untested observations of market actors and experts and a few select cases, and is not a product of rigorous analysis (Ahn, 2018).

The EU-ETS, first introduced in 2005, is now in its third phase (2013 to 2020), after the first (2005 to 2007) and second (2008 to 2012). Numerous studies have been undertaken on whether and how efficient the EU-ETS was in its first two phases. Researchers have thus formed a general opinion that, although the emissions trading market in Europe remained inefficient during the first phase (Daskalakis and Karkellos, 2008; Montagnoli and de Vries, 2010), this significantly improved by the second (Daskalakis, 2013, Ibikunle et al., 2016). Chinese researchers have also begun to assess the efficiency of the emissions trading market in China, a country into which an ETS was introduced later than in Korea (Cong and Lo, 2017).

All existing studies on the efficiency of the ETS test the efficient market hypothesis (EMH), a conceptual tool most commonly used in analysis of financial markets, against the given ETS. The EMH holds that, if a market were efficient, all the information available on that market would be immediately and perfectly reflected in prices (Cho, 1985). Theorists have postulated that there can be three types of EMH, depending on the types of information available to market participants (Fama, 1970). The weak form of EMH, first, holds that prices reflect only past information, making it impossible for investors to reap profits greater than the long-term market return rate based on past market information. Second, the semi-strong form holds that investors cannot perform better than the market rate of return in the long run because the long-term market prices already reflect all the information that has been made public, including not only data on past transactions, but also disclosed information on the prospects of the firms in which they invest. Third, the strong form holds that market prices reflect all existing information, including information circulated only internally within firms and not made public. Efficiency of the ETS is closer to the weak-form hypothesis at best. As a result, all researchers testing the EMH against ETS have done so in the weak form. This study, too, tests the weak EMH against the Korean ETS.

The weak-form EMH is consistent with the random walk hypothesis (RWH) (Cho, 1985). The RWH holds that, in an efficient market, prices move completely randomly without any order. This means that, even if we had information on how prices moved in the past, it is impossible for us to produce systematic predictions of how those prices will move in the future (Cho, 1985). If the RWH were to apply to the ETS, it would mean that the prices of emission permits reflect all the information existing as of the current point in time, and that no amount of technical analysis could predict future prices, thereby making it impossible for investors to generate surplus returns (Daskalakis and Markellos, 2008). We can use either a generalized autoregressive conditional heteroskedastic (GARCH) model or a variance

ratio (VR) test to test the RWH (Mills and Markellos, 2008). However, the VR test has been a more popular choice in the literature on financial markets as well as recent ETS studies.

In this chapter, we, too, shall apply the VR to test the weak-form EMH against the Korean ETS. An approach similar to Montagnoli and de Vries (2010) is applied here because the first-phase Korean ETS was a thin market without much transaction volume, and Montagnoli and de Vries (2010) analyzes the thin market from various perspectives. Accordingly, this study analyzes not only the reference prices of emission permits on the opening date, but also the prices on days when actual transactions took place.

In line with the thin-market nature of our subject, we also apply a modified adjusted return model (AR (1)), as suggested in Miller et al. (1994), to our analysis of the returns. As with Kim and Shamsuddin (2008) and Montagnoli and de Vries (2010), we analyze not only daily data, but also weekly data. Furthermore, we analyze the year 2017 separately, in addition to our analysis of the entire phase, because there were relatively more transactions conducted in that particular year. Finally, with the goal of determining whether the efficiency of the Korean emissions market has changed between the two phases of the Korean ETS, we also analyze the prices at which emission permits were traded in 2018, the first year of the second phase.

2. Transaction Volume of the First Phase and Literature Review

2.1. Volume and Characteristics of Transactions in the First Phase

There are mainly three types of emission permits found in the Korean ETS, i.e., Korean Allowance Units (KAUs), Korean Credit Units (KCUs) and Korean Offset Credits (KOCs) (Ahn, 2018). KAUs are allocated to eligible businesses according to the government’s allocation plans, either free of charge or through a competitive bidding process (Ahn, 2018). KCUs are emission permits converted from KOCs with government approval (Ahn, 2018). KOCs are credits that businesses can earn additionally for their contributions to reducing GHG emissions, such as external projects or clean development mechanism (CDM) projects. In order to trade these on the market, businesses must convert them into KCUs with the government’s approval (Ahn, 2018). KCUs are to make up no more than 10 percent of total certified emissions from each business.

From January 2015 to September 2018, emission permits amounting to 86,183,000 tCO₂eq were traded in Korea (Table 3-1). The Korean government allocated KAUs totaling 1,704,200 tCO₂eq7 in the first phase of the ETS, with an extremely low turnover rate of 5.1 percent. Of the emission permits traded, 44 were traded on the market, and the rest, off the market. As for the former, 13 percent were traded via competitive processes; 25 percent, subject to private negotiations; and six percent, via bidding. Note that the quantity of permits traded in a competitive manner, without designated transaction partners, makes up a relatively small share, as much of the transactions on the market took place between predefined parties. KAUs, KCUs, and KOCs made up 77 percent, four percent, and 19 percent of the total trade volume, respectively (Table 3-1). As KAUs made up a significant portion of overall emission permits in the first place, and given the limit on the amount of KCUs that could be used, KAUs accounted for the bulk of transactions.

7 The total number of emission permits is equal to the total number of KAUs issued by the government during the first

phase of the ETS. The total emission permit count includes KAUs amounting to 17.066 million tCO2eq in total that were allocated free of charge or auctioned off, as discussed in Chapter II, as well as KAUs distributed to stabilize the market and converted KCUs alike.

Table 3- 1. Emission Permit Trade by Year

(Unit: Thousand tCO₂eq)

Permit type Transaction type

Year Total Percentage

2015 2016 2017 2018

KAU

On-market

Competitive 13 918 3,641 4,552 9,125 11% Negotiated 308 1,370 10,067 7,035 18,780 22%

Bidding - 274 - 4,665 4,939 6% Off-market 78 1,561 10,564 21,511 33,714 39%

Subtotal 400 4,123 24,272 37,763 66,558 77%

KCU

On-market

Competitive 38 482 296 - 816 1% Negotiated 883 1,401 27 - 2,311 3%

Off-market - 296 - - 296 0% Subtotal 921 2,180 323 - 3,424 4%

KOC

On-market

Competitive - 662 703 145 1,510 2% Negotiated - - - 13 13 0%

Off-market 4,934 4,026 1,305 14,678 17% Subtotal 5,596 4,729 1,463 16,201 19%

Overall

On-market

Competitive 51 2,062 4,640 4,697 11,451 13% Negotiated 1,191 2,771 10,094 7,048 21,105 25%

Bidding - 274 - 4,665 4,939 6% Off-market 6,791 14,590 22,816 48,689 56%

Total 11,899 29,324 39,226 86,183 100% Source: GIR (2019), p. 65.

Trade volume grew considerably from year to year, from 5,734,000 tCO₂eq in 2015 to 11,899,000 tCO₂eq in 2016 (up 208 percent), to 29,324,000 tCO₂eq (up 246 percent), and to 39,226,000 tCO₂eq in 2018 (up 134 percent) (GIR, 2019, p. 64).

The average price of emissions per unit weight also continued rising, from KRW 11,007 in 2015 to KRW 17,179 in 2016, to KRW 20,879 in 2017, and to KRW 22,127 in 2018. The average price during the first phase was KRW 20,279 (Table 3-2). The prices did not vary significantly between permit types.

Table 3- 2. Average Prices of Emission Permits Per Unit Weight by Year

(Unit: KRW/tCO₂eq)


Average price each year Average

2015 2016 2017 2018

KAU

On-market

Competitive 10,998 17,712 21,314 22,199 21,378

Negotiated 12,073 17,366 21,065 22,073 21,025

Bidding - 16,221 - 22,500 22,152

Off-market 17,955 20,691 22,145 21,469

Subtotal 17,590 20,940 22,182 21,382

KCU

On-market

Competitive 12,637 18,173 20,605 - 18,800

Negotiated 10,815 18,082 20,813 - 15,337

Off-market 10,763 - - 10,763

Subtotal 17,108 20,622 - 15,767

KOC

On-market

Competitive - 18,127 21,617 23,413 20,259

Negotiated - - - 25,255 25,255

Off-market 16,739 20,405 20,364 16,330

Subtotal 16,903 20,585 20,710 16,703

Overall

On-market

Competitive 12,208 17,953 21,315 22,236 21,047

Negotiated 11,140 17,728 21,064 22,079 20,405 Bidding - 16,221 - 22,500 22,152

Off-market 16,758 20,612 22,044 19,855 Total 17,179 20,879 22,127 20,279

Source: GIR (2019), p. 66.

Table 3-3 shows the number of transactions by year. A total of 2,733 transactions took place over the analyzed years, 86 percent of which occurred on the market, and the remainder, off the market. KAUs made up the vast majority at 2,244 transactions or 82 percent. KCUs were involved in 150 transactions or five percent, and KOCs, in 339 transactions or 12 percent. The number of transactions also grew rapidly over the years, from only 126 in 2015 to 494 in 2016, to 974 in 2017, and to 1,139 in 2018. Also note that the number of transactions for 2018 account for the first nine months of the year only. The total for the year would have been significantly larger than the figure presented here.

Table 3- 3. Number of Transactions by Year


Number of transactions per year Total Percentage

2015 2016 2017 2018

KAU

On-market

Competitive 23 173 695 839 1,730 63% Negotiated 3 21 120 131 275 10%

Bidding - 23 - 56 79 3% Off-market 24 51 82 160 6%

Subtotal 241 866 1,108 2,244 82%

KCU

On-market

Competitive 31 79 21 - 131 5% Negotiated 6 7 3 - 16 1%

Off-market 3 - - 3 0% Subtotal 89 24 - 150 5%

KOC

On-market

Competitive - 29 62 17 108 4% Negotiated - - - 3 3 0%

Off-market 135 22 11 228 8% Subtotal 164 84 31 339 12%

Overall

On-market

Competitive 54 281 778 856 1,969 72% Negotiated 9 28 123 134 294 11%

Bidding - 23 - 56 79 3% Off-market 162 73 93 391 14%

Total 494 974 1,139 2,733 100% Source: GIR (2019), p. 71.

On average, 27,470 tCO₂eq of emissions were traded per transaction, although the average quantity of emissions per transaction varied significantly by permit type and year (Table 3-4). Whereas 5,816 tCO₂eq of emissions were traded per transaction in competitive on-market transactions, the averages

per transaction multiplied steeply to 70,169 tCO₂eq, 62,513 tCO₂eq, and 97,333 tCO₂eq for negotiated on-market, bidding on-market, and off-market transactions, respectively. The average amount of emissions per transaction is relatively small for the sheer number of transactions for the competitive on-market category most likely because of the relatively low cost for transactions in that category. Much of the large-scale transactions, on the other hand, appear to have taken place on the market via negotiations between predefined parties or off the market.

Table 3- 4. Average Quantity of Emissions Per Transaction

(Unit: tCO₂eq)


Average quantity per transaction per year Average

2015 2016 2017 2018

KAU

On-market

Competitive 582 5,307 5,239 5,426 5,274 Negotiated 102,667 65,237 83,891 50,078 66,564

Bidding - 11,910 - 83,296 62,513 Off-market 65,040 177,146 151,332 144,269

Subtotal 17,108 26,261 25,439 24,711

KCU

On-market

Competitive 1,217 6,106 14,115 - 6,233 Negotiated 147,167 200,200 8,957 - 144,454

Off-market 98,716 - - 98,716 Subtotal 24,493 13,470 - 22,826

KOC

On-market

Competitive - 22,821 11,339 8,516 13,978 Negotiated - - - 4,406 4,406

Off-market 36,547 182,991 118,644 64,378 Subtotal 34,120 56,296 47,196 47,791

Overall

On-market

Competitive 946 7,339 5,964 5,487 5,816 Negotiated 132,333 98,978 82,063 49,055 70,169

Bidding - 11,910 - 83,296 62,513 Off-market 41,920 178,908 147,466 97,333

Total 24,086 28,536 26,031 27,470 Source: GIR (2019), p. 71.

In this study, we focus on KAUs, which represent the vast bulk of emission permits that were traded during the first phase of the ETS. Specifically, KAUs made up 77 percent of the total amount of emissions traded throughout the phase, or 58 percent of on-market transactions and 42 percent of off-market transactions (Tables 3-1 and 3-5). Of the 55,451,000 tCO₂eq that were traded in total, KAUs-15 made up three percent; KAUs-16, 24 percent; and KAUs-17, 72 percent (Table 3-5). The volume of KAU trade started out as insignificant, but multiplied by leaps over the years.

Table 3- 5. Quantity of Emissions Traded in KAUs by Year

(Unit: Thousand tCO₂eq)


Quantity of emissions traded per year Total Percentage

2015 2016 2017 2018

KAU 15

On-market

Competitive 13 323 - - 336 1% Negotiated 308 702 - - 1,010 2%

Bidding - 274 - - 274 0% Off-market 208 - - 286 1%

Subtotal 1,506 - - 1,906 3%

KAU 16

On-market

Competitive - 595 1,855 - 2,450 4% Negotiated - 668 5,875 - 6,543 12%

Off-market 713 3,860 - 4,573 8% Subtotal 1,977 11,590 - 13,567 24%

KAU 17

On-market

Competitive - - 1,786 4,552 6,338 11% Negotiated - - 4,192 6,560 10,752 19%

Bidding - - - 4,665 4,665 8% Off-market 640 5,175 12,409 18,224 33%

Subtotal 640 11,152 28,186 39,978 72%

Overall

On-market

Competitive 13 918 3,641 4,552 9,125 16% Negotiated 308 1,370 10,067 6,560 18,305 33%

Bidding - 274 - 4,665 4,939 9% Off-market 1,561 9,034 12,409 23,083 42%

Subtotal 4,123 22,742 28,186 55,451 100% Source: GIR (2019), p. 76.

A total of 2,244 transactions involving KAUs took place. Of these, 202 occurred in 2015 (KAUs-15), 511 in 2016 (KAUs-16), and 1,531 in 2017 and 2018 (KAUs-17). As with the quantity of emissions, the number of KAU-involving transactions also multiplied over the years (Table 3-6).

Table 3- 6. Number of KAUs Traded by Year


Number of transactions per year Total Percentage

2015 2016 2017 2018

KAU 15

On-market

Competitive 23 124 - - 147 7%

Negotiated 3 13 - - 16 1%

Bidding 23 - - 23 1%

Off-market 13 - - 16 1%

Subtotal 173 - - 202 9%

KAU 16

On-market

Competitive - 49 358 - 407 18%

Negotiated - 8 60 - 68 3%

Off-market 8 28 - 36 2%

Subtotal 65 446 - 511 23%

KAU 17

On-market

Competitive - - 337 839 1,176 52%

Negotiated - - 60 131 191 9%

Bidding - - - 56 56 2%

Off-market 3 23 82 108 5%

Subtotal 3 420 1,108 1,531 68%

Overall

On-market

Competitive 23 173 695 839 1,730 77%

Negotiated 3 21 120 131 275 12%

Bidding - 23 - 56 79 4%

Off-market 24 51 82 160 7%

Subtotal 241 866 1,108 2,244 100%

Source: GIR (2019), p. 77.

The average price of emissions traded in KAUs per unit weight was KRW 21,303/ton, approximately five percent higher than the average price per ton for all types of emission permits. This is likely due to the fact that KAUs can be used with considerably greater freedom than either KCUs or KOCs (Tables 3-2 and 3-7).

Table 3- 7. Average Price of Emissions Per Unit Weight: KAU Transactions

(Unit: KRW/tCO₂eq)


Average price of emissions per ton Average

2015 2016 2017 2018

KAU 15

On-market

Competitive 10,998 17,855 - - 17,582

Negotiated 12,073 17,593 - - 15,910

Bidding 16,221 - - 16,221

Off-market 16,879 - - 15,196

Subtotal 17,301 - - 16,142

KAU 16

On-market

Competitive - 17,634 21,220 - 20,349

Negotiated - 17,127 20,876 - 20,493

Off-market 17,205 20,832 - 20,266

Subtotal 17,308 20,916 - 20,390

KAU 17

On-market

Competitive - - 21,411 22,199 21,977

Negotiated - - 21,331 22,117 21,810

Bidding - - - 22,500 22,500

Off-market 19,140 20,702 22,223 21,683

Subtotal 19,140 21,052 22,240 21,859

Overall

On-market

Competitive 10,998 17,712 21,314 22,199 21,378

Negotiated 12,073 17,366 21,065 22,117 21,014

Bidding - 16,221 - 22,500 22,152

Off-market 17,955 20,757 22,223 21,322

Subtotal 17,590 20,983 22,240 21,303

Source: GIR (2019).

2.2. Literature Review

2.2.1. EU ETS

The EU adopted an ETS out of the belief that a market-based approach encouraging the trading of emission rights would be the most efficient and cost-effective solution for reducing GHG emissions (Montgomery, 1972). This belief can be realized when the emissions trading market is efficient, even in the weak form (Fama, 1970). Like other new markets, however, the EU ETS, too, faced a non-competitive and relatively inactive market in its first years (e.g., Wirl, 2009). The lack of transactions was a major obstacle to realization of the mission of the ETS (Liski, 2001). It also hindered risk management efforts of market participants, while denying researchers sufficient data for significant statistical analysis.

Daskalakis and Markellos (2008) was the first study that examined the efficiency of the EU ETS market. The authors applied the VR test, proposed by Lo and Mackinlay (1988), to the daily closing price data from 2005 to 2006, and rejected the weak-form EMH in conclusion. They analyzed the spot and futures markets during the first two years of the EU ETS, and confirmed that investors were able to generate continued returns by employing a simple transaction strategy. They attribute this success to the fact that the market was in its nascent stage and to the trade-limiting policy that was in effect during that phase. The European Commission came to abolish the trade-limiting policy in 2009, and the authors predicted that such a decision would mark a turning point in emissions trading.

Daskalakis (2013) additionally analyzed the futures trade to overcome the insufficient amount of data from the early-stage EU ETS due to the lack of trade. In Europe, futures trading amounted to 40 times spot trading as of 2011 (Kossov and Guigon, 2012). Without the cost of storage, the price of futures would move in tandem with the spot price (Daskalakis et al., 2009). The author analyzed the daily closing prices on the futures market from January 2008 to December 2011, evaluated returns generated by a simple technical analysis and a naïve forecast, and concluded that the EU ETS was gradually maturing and on the path toward achieving efficiency after the second phase. Daskalakis concluded that, although the EU ETS was not an efficient market from 2008 to 2009 because investors were able to generate continued surplus returns even by relying on simple technical analysis, it had been becoming an efficient market since 2010 thanks to the policy change.

Montagnoli and de Vries (2010) applied the VR to test the weak-form EMH against the daily transaction data from the first phase (June 2005 to December 2007) and the first part of the second phase (February 2008 to December 2009) of the EU ETS. The authors found that the EMH did not hold during the first phase, but was partially accepted during the second phase. In addition to using the VR test introduced by Wright (2000) to control autocorrelation, the authors also noted that a thin market like the early-stage EU ETS should be corrected using the moving average process in order to account for days without transactions.

De Manual Aramendía (2011) applied an augmented Dickey-Fuller test and found that the price of emissions had been autocorrelated during the first phase of the ETS and on decline since then. The author advises caution before generalizing the findings of his study, as they concern only the first two years of the second phase of the ETS. Nevertheless, he concludes that, in order for the ETS to bring about substantial decreases in GHG emissions, emission permits should be allocated more stringently. The author also demonstrated, using a GARCH model, that the price of emission permits was tied to market fundamentals, including energy prices. Seifert et al. (2008) demonstrated, through his autocorrelation analysis, that the time-series data on emission prices were non-stationary, while Paolella and Taschini (2008) confirmed the heteroskedasticity of the emissions spot market.

Much of the literature on the EU ETS concludes that the prices of emissions do not follow the normal time-series pattern, and that it allows investors to generate surplus returns by making systematic projections based on past information. The researchers nonetheless conclude that the EU ETS has not

been a consistently efficient market, and that valuations differed significantly depending on the policy factors involved, the relative maturity (or lack thereof) of the market, and the awareness (or lack thereof) of market participants (Zhao et al., 2017). We ought to note that the act of evaluating market efficiency itself affects the market perceptions of participants and ends up affecting the efficiency of the market (Daskalakis and Markellos, 2008).

2.2.2. Chinese ETS

The ETS that was recently introduced into China was a novelty not just to Chinese businesses, but also the Chinese government. Liu and Chen (2015) argued that excessive state intervention would distort the ETS just as it has distorted all other Chinese markets. Inefficiency of the ETS would ultimately defeat the purpose for which it was introduced. Zhou and Chen (2011) identify the absurd prices, misguided initial allocation of energy permits, and the incompetency of governmental oversight as the three main issues facing the Chinese ETS. Zhao et al. (2017) assessed that the Chinese ETS was not yet on a path toward becoming an efficient market because of its failure on three major fronts, i.e., rational participants, opportunity cost, and cost of information. The authors argued that the prevailing asymmetry of information on the Chinese emissions trading market raised the risk of adverse selection, encouraging businesses either to refuse to disclose emissions data or to falsify their emissions data. Accordingly, there was a risk that unproductive and heavy-emitter businesses could end up with a large portion of allocated emission permits. Moreover, the authors found that the high cost of transaction stood in the way of liquidity, preventing the price of emission permits from accurately reflecting available information.

Zhao et al. (2017) performed unit root and runs tests to analyze the emissions trading markets of four major Chinese cities. The authors found that, while the four markets attained to weak-form efficiency in 2014 and 2015, the Shenzhen market failed to do so in 2013,8 and that the efficiency of these markets would improve as market size grow along with increasing volumes of trade. Wang and Wang (2014), assessing the trial runs of the Chinese ETS using the same method, concluded that the Chinese trial ETS was efficient during its run. By contrast, Zhao et al. (2016), taking into account a comprehensive range of variables, including prices, the transparency of information and trade volumes, found that the Chinese emissions market as a whole was far from being efficient.

Applying an ARMA-GARCH-M model to the ETS of Shenzhen, a major city in south China, and the average daily prices of emissions, Cong and Lo (2017) found the higher the risk, the lower the rate of returns on assets (emission permits), and the less likely the market would behave normally. The Shenzhen ETS remained a thin market for the most part, but the trade volumes would spike radically on certain occasions on which transactions between certain institutions appear to have taken place. Cong and Lo (2017) argued that, although the Shenzhen ETS was the most market-friendly and active of the six Chinese emissions markets (Environomist, 2016), it was still inefficient due to misguided state intervention, the limitation of trade to spot only, and the uncertainty of information. The authors, furthermore, emphasized that there were numerous changes that had to be made to ensure the consistency of Chinese emissions data. Table 3-8 provides a summary of the ETS studies mentioned so far, comparing them in terms of datasets, methods, subjects of analysis, and findings.

8 Shenzhen was the only Chinese city with an ETS in 2013.

Table 3- 8. Summary of Major ETS Studies

Study Data and method Subject of analysis Findings

de Manuel Aramendía

(2011)

Daily data, 2008-2012/ Augmented Dickey-

Fuller test EU ETS Emission price on decline.

Daskalakis and Markellos

(2008)

Daily data, 2005-2006/ VR test and analysis of return

rates based on technical analysis and naïve forecasts

EU ETS

Failure to attain weak-form efficiency throughout given period,

with returns deteriorating in the later part.

Daskalakis (2013)

Daily data, 2008-2011/ VR EU ETS

Failure to attain weak-form efficiency, but close to realizing it in

the latter part of the second phase (2010 and onward).

Montagnoli and de Vries

(2010)

Daily data, 2005-2007, 2008-2009/VR EU ETS

Failure to attain weak-form efficiency in the first phase; partial

success in the second.

Zhao et al. (2017)

Daily data, 2013-2015 /ADF test, Runs Test

4 major Chinese cities

(Beijing, Tianjin, Shanghai, and

Shenzhen)

Weak-form efficiency on four markets in 2014 and 2015; Shenzhen

market inefficient in 2013.

Cong and Lo (2017) Daily data, 2013-2015/ARMA-GARCH-M Shenzhen, China

The higher the risk, the lower the return on assets (emission permits), and the more inefficient the market.

3. Efficiency of the Korean ETS during the First Phase

3.1. Method

In this chapter, we apply the VR test to efficiency of the Korean emissions trading market. A number of studies, including Aggarwal and Sundararaghavan (1987), Lo and MacKinlay (1988), Jegadeesh (1990), Liu and He (1991), and Lee, Gleason and Mathur (2000), applied this test to analyze efficiency of the financial market. The VR test has been used in analysis of traditional commodity markets for agricultural produce, crude oil and metals (Mittal and Thakral, 2018), the power market (Proietti, 2012) and even the art market (Aye et al., 2016). Since the ETS was introduced into Europe and China, researchers worldwide have also begun to apply the test to emissions trading. Like the non-parametric test, the VR test offers the benefit of analysis free from any specific distributions.

The underlying assumption of the VR test is that, when prices move randomly, the return rate (xt) either remains independent between points in time or lacks autocorrelation. This means that the distribution of return rates (rates of price change) is linearly proportional to the sampling interval. Dividing the given distribution by the holding period or sampling interval-k will thus yield a distribution equal to the sample distribution whose holding period equals one. The VR(k) necessary to calculate this is defined as follows:

VR(k) =var(xt + xt−1 + ⋯+ xt−k+1)

kvar(xt)

= { 1Tk∑ (Tt=k+1 xt + xt−1 + ⋯+ xt−k+1 − kμ�)} ÷ {1

T∑ (Tt=1 xt − μ�)}.

Here, T refers to the entire sampling interval or the total number of samples, and μ�, the sampling mean, T−1 ∑ xtT

t=1 . The null hypothesis used to test the RWH is shown below:

H0: VR(k) = 1.

Lo and MacKinlay (1988) demonstrated that, when certain weak additional conditions are met and xt is independent and identically distributed (IID), statistics would approximate a normal distribution, asymptomatically, under the null hypothesis that VR(k) equals one, as shown below:

T1/2(VR(k)− 1) →d N(0, 2(2k−1)(k−1)3k

).

Accordingly, the following statistics would also approximate a standard normal distribution:

M1(k) = (VR(k)− 1)(2(2k−1)(k−1)3kT

)−1/2.

However, M1(k) would not approximate a standard normal distribution when the return rate, xt, is conditionally heteroskedastic. To avoid this problem, Lo and MacKinlay (1988) proposed the following additional equation:

M2(k) = (VR(k) − 1)(∑ [k−1j=1

2(k−j)k

]2δj)−12.

Here, δj is expressed as follows:

δj = {∑ (Tt=j+1 xt − μ�)2(xt−j − μ�)2} ÷ {[∑ (T

t=1 xt − μ�)2]2}.

Because the Lo-MacKinlay test is based on the asymptotic distribution, too finite a sample could seriously distort the test results, including size distortion and low power (Hoque et al., 2007). In an effort to avoid the possible size distortion, Wright (2000) proposed the following statistics based on ranks and signs:

R1(k) = (1

Tk∑ (Tt=k+1 r1t + r1t−1 + ⋯+ r1t−k)2

1T∑ r1t2T

t=1

− 1) × (2(2k − 1)(k − 1)

3kT)−

12

R2(k) = (1

Tk∑ (Tt=k+1 r2t + r2t−1 + ⋯+ r2t−k)2

1T∑ r2t2T

t=1

− 1) × (2(2k − 1)(k − 1)

3kT)−

12

S1(k) = (1

Tk∑ (Tt=k+1 st + st−1 + ⋯+ st−k)2

1T∑ st2T

t=1

− 1) × (2(2k − 1)(k − 1)

3kT)−

12

Here, r1t and r2t to the right of R1(k) and R2(k) , respectively, represent two types of standardized ranks shown below:

r1t = (r(xt) −T + 1

2)/�

(T − 1)(T + 1)12

r2t = Φ−1(r(xt)/(T + 1))

Here, r(xt) is a function that represents the rank of xt in the entire sample {x1, x2,⋯ , xt,⋯ , xT}, and Φ represents the standard normal cumulative distribution function.

On the right hand side of S1(k) , st equals 2u(xt, 0) , and u(xt, 0) equals 1(xt〉 0) − 0.5 . 1(xt감 0) is an indicator function equaling one when xt is greater than zero. Wright’s statistics, too, asymptomatically approximate a standard normal distribution under the null hypothesis. This study, however, looks to the finite sample distribution of these statistics to present more precise threshold values that can be used in the test.

In this study, we apply the second statistic of the Lo-MacKinlay model, M2(k), and Wright’s R1(k), R2(k), and S1(k) to test the RWH. As the Korean ETS during its first phase was a relatively inactive market, the following five analyses are additionally performed. First, as Miller et al. (1994) suggested regarding thin market analysis, a modified AR (1) is applied to returns to decide an adjusted return rate necessary for analysis. Second, because the number of transaction days falls short of the number of open days, return rates of both open days and transaction days are analyzed. Third, as with Kim and Shamsuddin (2008) and Montagnoli and de Vries (2010), weekly data is analyzed alongside daily data. Fourth, the year 2017 is given separate treatment as it was the year in which trade volume grew noticeably over the foregoing two years of the first phase. Fifth and finally, in order to verify whether there was any change to the efficiency of the Korean emissions trading market between the first phase

and the second, emission permits allocated in 2018, KAUs-18, are also analyzed.

3.2. Data

Tables 3-9 and 3-10 present summary statistics of the data analyzed for this study. The data subjected to our test concerns differentiated log-prices, i.e., continuously compounded returns. Assuming a continuously compounded return, pt , represents the price of emission permits, 9 the formula, ln(pt/pt−1), can be applied to calculate it. As a result, the number of observed return rates falls short of the observed number of emission permit prices by one. Table 3-9, for example, shows that there were 880 KAU15-17 (number of open days), meaning that the Korean emissions trading market was open for 881 days in total during the first phase of the ETS. On the other hand, KAU15-17 (number of transaction days) equals 385, meaning that transactions took place on 386 days during the first phase. KAU15-17 (number of open weeks) shows the results of analyzing the weekly closing prices of emissions observed on Fridays. From the table, we can infer that, during the first phase, the Korean emissions market was open for 172 weeks in total. On Table 3-10, KAU17 (number of transaction days) equals 249, meaning that KAUs were traded on 250 days in 2017. As it marked the final year of the first phase, the year 2017 saw a noticeably greater volume of trade than the preceding two years. KAU18 (number of transaction days) equals 153, meaning that emissions trading took place on 154 days in 2018.

The average rate of return for KAU15-17 (number of open days) is lower than those for KAU15-17 (number of transaction days), KAU15-17 (number of open weeks), and KAU18 (number of transaction days) presumably because the daily closing prices did not change on days when there were no transactions. The average rate of return for KAU17, on the other hand, is negative because there were more observed transactions with negative than positive return rates in 2017. No trend is apparent in standard deviations (SD). The kurtosis is the highest, at 23.57, for KAU15-17 (number of open days), while it falls sharply to 12.12, 11.94, and 11.24, respectively, for KAU15-17 (number of transaction days), KAU15-17 (number of open weeks), and KAU17 (number of transaction days). It falls still further to 6.65 for KAU18. The kurtosis is the highest for KAU15-17 (number of open days) likely because there were more days without transactions than with transactions in those years, multiplying the number of observed return rates near zero.

Table 3- 9. Summary Statistics 1

KAU15-17 (number of open days)

KAU15-17 (number of transaction days)

KAU15-17 (number of open weeks)

Mean 0.0010 Mean 0.0024 Mean 0.0048

Standard error 0.0006 Standard error 0.0014 Standard error 0.0035

Median 0.0000 Median 0.0000 Median 0.0000

Mode 0.0000 Mode 0.0000 Mode 0.0000

SD 0.0165 SD 0.0278 SD 0.0461

Variance 0.0003 Variance 0.0008 Variance 0.0021

Kurtosis 23.5663 Kurtosis 12.1224 Kurtosis 11.9498

Skewness -0.2812 Skewness 0.8230 Skewness 0.9878

9 This study analyzes prices as of the end of each day.

Range 0.2007 Range 0.2884 Range 0.4694

Minimum -0.1054 Minimum -0.1264 Minimum -0.2098

Maximum 0.0953 Maximum 0.1620 Maximum 0.2595

Total 0.9163 Total 0.9163 Total 0.8145

N 880 N 385 N 171

Source: KRX (2019).

Table 3- 10. Summary Statistics 2

KAU17 (number of transaction days) KAU18 (number of transaction days)

Mean -0.0007 Mean 0.0044

Standard error 0.0015 Standard error 0.0014

Median 0.0000 Median 0.0017

Mode 0.0000 Mode 0.0000

SD 0.0244 SD 0.0177

Variance 0.0006 Variance 0.0003

Kurtosis 11.2395 Kurtosis 6.6529

Skewness -1.2802 Skewness 0.8601

Range 0.2217 Range 0.1525

Minimum -0.1264 Minimum -0.0572

Maximum 0.0953 Maximum 0.0953

Total -0.1815 Total 0.6678

N 249 N 153

Source: KRX (2019).

3.3. Results

Tables 3-11 and 3-15 show the results of applying the chosen statistics (M2, R1, R2 and S1) to the data with respect to unadjusted and adjusted returns, respectively.10 As with Hoque et al. (2007) and Montagnoli and de Vries (2010), we assume that, should the hypothesis be rejected twice or more at any significance level, the null hypothesis (RWH) that prices change randomly is also rejected. For KAU15-17 (number of open days), the RWH is seemingly rejected. All the statistics concerning the unadjusted returns fall into the critical region in relation to two or more holding periods, k. As for adjusted returns, all the statistics except M2 also lead to rejection of the hypothesis in relation to two or more holding periods (k = 2). As noted, the M2 test tends to be low in power when given a finite sample. The null hypothesis should be seen as rejected for KAU15-17 (number of transaction days).

KAU15-17 (number of transaction days) appears to provide very weak support for the RWH. The RWH is rejected in relation to all holding periods that are two or more (k = 2) under all statistics. As for adjusted returns, however, only the S1 test results in rejection of the null hypothesis across all holding periods. The R1 test leads to rejection of the null hypothesis at a 10-percent significance level only 10 See the Appendix for results on the critical values defining critical regions for the R1, R2 and S1 statistics, obtained by applying Wright (2000)’s method.

when k = 2. As for adjusted returns, in other words, the M2, R1 and R2 tests do not lead to rejection of the RWH.

Table 3- 11. KAU15-17 (Number of Open Days): VR Test Results

k M2 R1 R2 S1

Unadjusted

2 2.459** 6.977** 7.326** 17.529**

5 2.764** 10.979** 10.731** 30.810**

10 2.534** 12.201** 11.407** 43.526**

20 1.233 10.161** 8.885** 59.065**

40 0.229 7.567** 6.004** 79.561**

Adjusted

2 0.023 -6.068** -5.058** 13.795**

5 0.151 -3.602** -2.660** 25.568**

10 0.296 -1.756* -1.093 37.368**

20 -0.276 -1.380 -1.081 51.826**

40 -0.698 -1.153 -1.171 70.449**

Note: The asterisks, ** and *, represent rejection at 5-percent and 10-percent significance levels, respectively.

Table 3- 12. KAU15-17 (Number of Transaction Days): VR Test Results

k M2 R1 R2 S1

Unadjusted

2 2.626** 5.288** 5.439** 7.390**

5 2.761** 8.423** 7.994** 12.617**

10 2.495** 8.967** 8.060** 16.293**

20 -0.051 6.106** 4.536** 18.602**

40 -0.652 3.308** 2.054** 20.812**

Adjusted

2 -0.174 -2.006* -1.666 4.389**

5 0.079 0.177 0.248 8.404**

10 0.172 0.859 0.691 11.378**

20 -1.109 0.155 -0.286 13.336**

40 -1.232 -0.676 -0.934 15.624**


Table 3-13 presents the analysis of weekly data from KAU15-17 (number of open weeks). The results regarding unadjusted returns all fall into the critical region in relation to holding intervals of two or more (k = 2) except for M2. Adjusted returns except S1, on the other hand, all support the RWH. Table 3-14 on KAU17 (number of transaction days) shows the same patterns.

Table 3- 13. KAU15-17 (Number of Open Weeks): VR Test Results

k M2 R1 R2 S1

Unadjusted

2 0.005 2.391** 1.988** 4.512**

5 -1.049 2.346** 1.573* 5.864**

10 -0.868 1.955** 1.383 6.527**

20 -0.935 0.548 0.349 6.219**

40 -1.200 -1.168 -1.010 4.127**

Adjusted

2 -0.034 -0.267 -0.103 4.602**

5 -1.064 0.228 -0.017 6.805**

10 -0.877 -0.060 -0.200 8.550**

20 -0.938 -0.984 -0.898 8.515**

40 -1.191 -1.466** -1.365** 5.472**


Table 3- 14. KAU17 (Number of Transaction Days): VR Test Results

k M2 R1 R2 S1

Unadjusted

2 2.220** 3.914** 4.272** 6.020**

5 1.287 4.867** 4.700** 9.812**

10 -0.150 4.129** 3.348** 12.114**

20 -1.186 1.697** 0.802 12.904**

40 -1.153 1.484** 0.735 15.267**

Adjusted

2 0.168 -1.247 -0.787 2.540**

5 -0.413 -0.139 -0.050 5.217**

10 -1.032 -0.214 -0.448 7.365**

20 -1.498 -1.011 -1.324 10.038**

40 -1.333 -0.915 -1.111 13.421**


Table 3-15 shows the results of applying the test to the KAU18 (number of transaction days) data. The M2 and R2 results concerning unadjusted returns support the RWH, while the hypothesis also remains intact under R1 and S1 at a 10-percent significance level. The results on adjusted returns also support the RWH, except for the S1 results. As three of the tests, S1 being the exception, do not lead to rejection of the null hypothesis, this is by far the strongest support for the RWH.

Table 3- 15. KAU17 (Number of Transaction Days): VR Test Results

k M2 R1 R2 S1

Unadjusted

2 -0.738 1.660* 0.914 1.859*

5 -0.471 1.930** 1.343 1.889*

10 -0.453 1.244* 0.791 1.518

20 -0.571 0.202 -0.025 0.631

40 -0.174 0.082 0.005 -0.425

Adjusted

2 -0.057 0.371 -0.005 3.244**

5 0.090 1.311 0.973 4.887**

10 -0.157 0.877 0.588 6.304**

20 -0.519 -0.134 -0.232 7.804**

40 -0.373 -0.146 -0.127 9.948**


3.4. Discussion

The analysis shows that the RWH is strongly rejected on the basis of KAU15-17 (number of open days). Robustness of the rejection, however, weakens under the additional analysis performed for the thin market, i.e., on the basis of KAU15-17 (number of transaction days), KAU15-17 (number of open weeks), and KAU17 (number of transaction days). Concerning adjusted return rates, the statistics M2, R1 and R2 support the RWH. Analysis of KAU18 (number of transaction days), part of the second phase, does not fully support the RWH, but suggests that the Korean emissions trading market was more efficient in 2018 than at any point during its first phase.

Applying a similar VR test method, Montagnoli and de Vries (2010) found that the EU ETS was inefficient during its first phase, but became efficient in its second. In this study, we compare the return trend and distribution of the EU ETS in its first and second phases to the return rates and trend of the Korean ETS in its first phase and part of its second phase. Figures 3-1 and 3-2 visualize the return rates observed under the EU ETS in the first two phases. Figures 3-3 and 3-4 illustrate the return rates observed under the Korean ETS in its first phase and 2018 (KAU18).

Comparing Figures 3-1 and 3-2 reveals that return rates in the second phase (EU ETS 2) were more homogenous in terms of size of return, with a lower kurtosis of distribution, than they were in the first phase (EU ETS 1). Comparing Figures 3-3 and 3-4 also reveals that returns in 2018 were more homogenous and had a lower kurtosis of distribution than they did in the first phase of the Korean ETS. Although further research is needed for confirmation, the Korean ETS appears to be evolving in a trajectory similar to that of the EU ETS.

Figure 3- 1. EU ETS1: Trend and Distribution of Return Rates

Source: Montagnoli and de Vries (2010)

Figure 3- 2. EU ETS 2: Trend and Distribution of Return Rates

Source: Montagnoli and de Vries (2010)

Figure 3- 3. Korean ETS 1: Trend and Distribution of Return Rates

Figure 3- 4. Korean ETS 1: Trend and Distribution of Return Rates in KAU18

While the distributions of return rates in the first phase and early second phase (2018) of the Korean ETS show similar patterns to their counterparts under the EU ETS as analyzed by Montagnoli and de Vries (2010), there is one key difference. The statistical difference between the adjusted and unadjusted return rates in Montagnoli and de Vries (2010) was not so significant, while the unadjusted return rates in Korea far more robustly reject the null hypothesis than do adjusted return rates, as shown in Tables 3-11 to 3-14. This study adjusted the Korean return rates to countervail the effect of the thin market. The substantial difference in results between adjusted and unadjusted return rates, however, suggests that the effect of the thin market is more significant than assumed. It also suggests that the Korean ETS faces a more severe liquidity problem than does the EU ETS.

Daskalakis and Markellos (2008) attribute inefficiency of the EU ETS in its first phase partly to the compromise of liquidity, resulting from the ban on short selling on the emissions spot market, which

had the effect of barring the participation of diverse investors. Daskalakis (2013) confirmed that EU ETS had become efficient by the 2010s, since the second phase began, and argued that this was mainly thanks to the policy change in 2009 that reinforced liquidity and the public’s confidence in the European emissions trading market.

The Korean ETS similarly faced a serious liquidity issue in its first phase, which the Korean government has been taking measures to address, providing liquidity around the times for settling and clearing emission trade records as the price of emissions continued to rise steeply from year to year (Ahn, 2018). Particularly noteworthy of these measures is the one introduced in April 2017 to stabilize the market. The Korean government limited carryforwards from the first phase to the second with the goal of increasing supply on the market. Furthermore, the government also raised limits on borrowings, pledged to recognize emission decreases via overseas CDM projects as credits, made plans to organize emission permit bidding, and allowed market makers to participate in emissions trade (Ahn, 2018). These measures served to increase liquidity to an extent, improving market efficiency as a result. In order to increase liquidity further and promote even greater efficiency, the Korean government should again consider limiting carryforwards within the given phase.

Abruptly altering the rules of the market during a given phase, however, could backfire and undermine efficiency by raising the cost of information for market participants. Changing the rules during a given phase should therefore be avoided as much as possible. Rather than introducing new limits on carryforwards during the phase, policymakers should consider defining specific conditions for invoking such limits ahead of time. The key is to allow market participants to predict when carryforward limits will be invoked, based on the information they have on trade volumes and the balance of supply and demand, and behave accordingly.

Other additional measures may also be considered to increase liquidity. First, the Korean government needs to relax its own rules on the allocation of emission permits in reserve to actively increase supply. Second, Kiup Bank and the Industrial Bank of Korea are currently participating in the market as market makers, but they need to play more active roles, with more diverse market makers also encouraged to participate. Third, a ceiling may have to be put on emission prices in order to ensure price signal stability in the long run. Finally, policymakers should actively contemplate introducing a futures market. The current spot market may effectively be disincentivizing actors from engaging actively in emissions trade due to the difficulty of hedging prices. The Chinese ETS, also relying solely on spot trading, has received the same criticism (Cong and Lo, 2017).

The assumption underlying the EMH should also be revisited as part of identifying all the possible causes of inefficiency in the Korean ETS during its first phase. There are three key underlying principles to the EMH: rational participants, no information costs, and no transaction costs (Zhao et al., 2017). The Korean ETS appears to have failed to cater to the first of these principles. According to Ahn (2018), the majority of Korean businesses viewed emissions trading not as a new opportunity, but as another form of regulation, and focused on maintaining their emissions low during the first phase. These businesses were not very interested in the emissions market. The same has been said of Chinese businesses (Cong and Lo, 2017). Ahn (2018) also points out that Korean businesses participating in the ETS are prone to a cognitive bias, and that the misinformed reward system within businesses tends to reinforce this cognitive bias in employees more than in the general public. This tendency likely persisted into 2018 even after the first phase ended. Yet market participants acted more rationally concerning KAU18 than in the first phase because the uncertainty over the price of emissions had lessened.

The Korean ETS, furthermore, also appears to have fared poorly with respect to the information cost. The transparency of information and consistency of market rules are two decisive factors in information cost. Lack of consistency in market rules can give rise to information asymmetry, favoring actors who participate actively in policy talks with government officials (Zhao et al., 2017). During the first phase of the ETS, the Korean government frequently changed market rules in an effort to boost liquidity, effectively raising the information cost for businesses. The outcome, paradoxically, was the loss of

liquidity and market efficiency, at least to a partial extent.

The Korean ETS also likely had a high transaction cost because it was a brand new market. The cost of transactions, fortunately, appears to be waning and is not expected to cause long-lasting structural problems.


In this chapter, we apply the VR test to the weak-form EMH to determine whether the Korean ETS was efficient in its first phase, making sure to account for the fact that it was a thin market during that phase with not much in trade volume. The analysis here therefore takes into account not only the prices of emissions on open days, but also on actual transaction days, applying a modified AR (1) model to returns, while also analyzing weekly data along with daily data. This study also analyzes the emissions trade of 2017, the final year of the first phase in which trade volume grew significantly over the two preceding years. Finally, this study also analyzed the KAU18 data to determine whether there was a meaningful change in efficiency of the Korean emissions trading market between its first two phases.

This study finds that the EMH is essentially rejected with respect to the first phase of the Korean ETS. Our analysis of the year 2018, the first year of the second phase, does not yet fully support the RWH, but supports the hypothesis more than our analysis of the first phase. Market efficiency grew in 2018 compared to the first phase thanks to the slight decrease in uncertainty over the prices of emissions.

In order to strengthen efficiency of the Korean emissions trade market, the Korean government ought to increase liquidity and lower information cost. The Korean government has so far sought to increase liquidity by actively altering market rules, such as by imposing limits on carryforwards. Frequently changing market rules, however, compromises the transparency and consistency of information, effectively raising the information cost for market participants and ultimately compromising the market efficiency. It is therefore important for the Korean government to resort to more indirect means of increasing liquidity from now on, such as making more active use of its reserve emission permits, increasing the role of market makers, introducing a ceiling on the price of emissions, and allowing a futures market to open.

Chapter IV. Impact of the ETS on Business Competitiveness

1. Research Purpose and Background

The seed for the Korean ETS was sown in 2008 when the Korean government declared its National

Vision for Green Growth. The Green Growth Commission was launched the following year. The GHG (greenhouse gas) emission reduction targets for the entire nation to meet by 2020 were announced, implying introduction of the ETS as one of the important routes to achieving that goal. This series of initiatives sparked a public debate on how the new environmental measures would affect the economy. In theory, the ETS is seen as capable of reducing GHG emissions in a cost-effective way. It decides, in reference to the total GHG emissions allowed system-wide, the amount of GHG emissions each regulated business may generate. Target businesses are therefore urged to reduce their emission levels, either through internal innovation or by purchasing emission permits on the emissions trading market. Because both options involve additional cost, the ETS has been expected to affect the financial performance of target businesses.

The economic impact of the ETS can be analyzed via ex-ante assessment and/or ex-post evaluation. Ex-ante assessment involves projecting and estimating, using a specific model of analysis, the potential impact of a policy change before it takes effect. The majority of ex-ante assessments on economic effects of the ETS share a few underlying assumptions or hypotheses: Namely, that the ETS would increase the cost of production by forcing target businesses to strive to meet their emissions quota; that it would weaken the competitiveness of businesses and reduce their market share by encouraging the market to favor businesses not subject to it; and that the loss of output would ultimately lead to job losses (Martin et al., 2016). However, these assessments make use of static models that fail to capture the changing response in company behavior as a result of being subject to the ETS.

The EU, which was the first government in the world to adopt an ETS, has been encouraging empirical analyses, using accumulated data, of the impact of the ETS on European industries and firms. However, no definitive consensus has yet emerged from these numerous studies, leaving the controversial debate on the effects of the ETS intact. The first phase of the Korean ETS, 2015 to 2017, has come to a close, and Korean businesses are now in the second phase. Although sufficient longitudinal data has yet to be accumulated, we can no longer delay analyzing how the first three years of the ETS have affected Korean companies and their financial performance. We also need an empirical analysis to help us determine whether the predictions made by pre-ETS studies have materialized.

2. Literature Review

2.1. EU ETS

Much of the global literature on the economic impact of the ETS, particularly on industries and

businesses, originates from the EU because the region has accumulated a considerable amount of data since 2005. Ex-post evaluations of the impact of the EU-ETS are regarded as demonstrative of the effects of the ETS with greater reliability and validity than ex-ante assessments. The EU literature can be divided into the following themes or categories.

2.1.1. Cost Transfers onto Product Prices

The additional cost the ETS imposes on the cost of production can lead to two outcomes. First, the

target business, assuming it is not in direct competition with foreign companies, may seek to offset the cost increase by transferring it onto the price of its product. Second, the target business, assuming it is in direct competition with foreign companies, may find the additional cost imposed by the ETS adversely affecting its market share and related performance.

The literature analyzing the effect of the ETS on companies transferring their cost increase onto their customers through higher product prices is mostly focused on the power industry. The EU-ETS requires power companies to purchase emission permits, thus exerting a significant effect on their production cost. Much of the energy sector in Europe is also privatized, which means that it is entirely up to companies to decide the prices of electricity. Most importantly, the prices of emission permits and electricity are published, allowing researchers to access the data they need with ease. Sijm et al. (2006) analyzed the prices of emission permits and electricity in Germany and the Netherlands during the early stage of the EU ETS. The authors found that power companies offset between 60 percent and 100 percent of the increased cost of carbon emissions by spiking electricity rates accordingly. Moreover, the authors suggest that, by so transferring the cost, power companies may have reaped unexpected windfall profits. Zachmann and von Hirschhausen (2008) reached a similar conclusion. Analyzing the weekly fluctuations in electricity prices in Europe in the first two years of the EU-ETS, the authors found that power companies transferred the abrupt rise in the cost of carbon emissions onto electricity prices. By failing to lower their prices by an amount equivalent to the drop in cost of carbon emissions, and raising their prices when the cost of emissions rose, these companies deliberately used the cost of emissions as a new instrument for revenue creation. In contrast, Fabra and Reguant (2014) focused their analysis on power plants in Spain and revealed that only 80 percent or so of the cost of emissions was transferred onto electricity prices, and that short-term increases in the emission permit prices did not affect the price of electricity.

There are also studies on industries other than power. De Bruyn et al. (2010) traces the stochastic relationships between comparable indicators of price and the cost of carbon emissions for American and EU industries. The authors’ analysis of the monthly data from 2001 to 2009 showed that, compared to their American counterparts, the European steel, metal, and oil refinery industries—all heavy emitters—transferred significant portions of the cost of emissions onto their product prices. Of particular note is that the EU oil refinery industry transferred the entire cost of emissions onto their retail oil prices between 2005 and 2007 (Alexeeva-Talebi, 2011). This transfer of emissions cost onto retail prices was observed in multiple regions, with British companies transferring 50 to 75 percent, in particular (Oberndorfer et al., 2010). Obendorfer et al. (2010) also found that cost transfers were evident on the retail prices of glass and ceramics products in the United Kingdom.

2.1.2. Impact on Output and Financial Performance

The European literature also analyzes the disclosed financial data of businesses to determine how the

EU-ETS affected the productivity and performance of subject businesses. Abrell et al. (2011) analyzed the effect on added value and revenue increases, and found no statistically significant correlation. Commins et al. (2011) analyzed a total of 162,771 firm years from 1996 to 2007, showing that the energy tax and the ETS exerted a statistically significant and negative effect on the returns on capital for target businesses. However, the authors found no significant effect on either total factor productivity or investment. These two studies do not control the industry effect, and thereby make it impossible to determine whether the effects they observed originated from factors inherent to the given industries or the EU-ETS alone. Chan et al. (2013) analyzed the performance of businesses in the power, cement and steel industries in 10 European countries from 2001 to 2009, finding that the ETS exerted a significant

effect on only the steel industry during the analyzed period. The cost of materials for the power industry rose five percent in the first phase of the ETS, and eight percent in the second phase, while the industry’s turnover improved by 30 percent in the latter phase as well. Anger and Oberndorfer (2008) focused on emission permits that were allocated free of charge, specifically seeking to find how the percentage of freely allocated emission permits affected the revenue and employment of 419 ETS-subject German companies in 2004 and 2005. The authors found no statistically significant correlation. Petrick and Wagner (2014) analyzed the internal documents of ETS-subject German companies and concluded that the turnover and exports of these companies increased significantly during the early half of the second phase (until 2010) of the EU-ETS. The insufficient robustness of these findings, however, make it difficult to generalize them onto other countries and industries. Wagner et al. (2013), in analyzing the ETS’ impact on individual plants in France, found no statistically significant difference in exports during the second phase.

Studies on the impact of the EU-ETS on stock prices have not produced a consensus, either. However, they have noted a general trend in which financial investors reacted favorably to the ETS. Veith et al. (2009), for example, researched the daily returns on investment in power companies during the first phase of the EU-ETS, and found a significant positive correlation between the prices of emission permits and stock price fluctuations. The authors suggested that the stock prices of these companies rose because investors assumed that the companies would transfer the cost of emissions (including freely allocated permits) onto their product prices, thereby generating windfall profits. Bushnell et al. (2013) analyzed how the prices of emission permits on the EU-ETS market affected power companies. Observing that the stock prices of power companies plummeted in 2006 when the price of carbon emissions on the ETS market fell, the authors surmised that the financial market in general responded positively to the ETS. These studies on stock market responses improved our understanding of how the EU-ETS has affected the financial markets, but further research is needed as existing studies focus solely on the power industry and deal with a relatively limited sample.

Arbell et al. (2011) discovered that businesses subject to the EU-ETS from 2004 to 2008 reduced the number of workers they hired by 0.9 percent with statistical significance. The contraction was especially evident in the non-iron metal and mining industries. While the authors confirmed the statistical significance of job losses at ETS-subject companies, their finding falls short of confirming that said job losses were the direct result of the ETS. The authors compared the job-creating performances of companies with more emission permits than necessary and others with fewer permits than necessary, and found no significant difference between the two groups. In other words, the shortage of emission permits allocated did not seem to have affected employment directly. Researchers have not yet found specific evidence demonstrating that the EU-ETS has affected employment in addition to company returns on assets. Studies analyzing internal documents of targeted businesses are similarly inconclusive. Petrick and Wagner (2014), using internal data from German companies, concluded that the EU-ETS did not significantly affect employment by German companies. Wagner et al. (2013), by contrast, concluded that employment by French manufacturers took an eight percent hit, with statistical significance, during the second phase of the ETS.

Opinion polls on those managing ETS-subject businesses and case studies can provide some important insights into how industry insiders actually experience the ETS. Kenber et al. (2009), using interviews with executives at six large corporations, reported that the EU-ETS did not significantly influence either the cost of production or the strategic decision-making of those companies. Lacombe (2008), through a case study of five companies, reached a similar conclusion. The study attributed the seeming failure of the ETS to affect management and strategy to factors like organizational inertia, emission permit prices being too low (incapable of incentivizing concrete changes in behavior), and regulatory constraints. Martin et al. (2014) analyzed the results of a survey on executives at 761 companies across Europe, including those subject to the ETS and those that were not, and concluded that even the disappearance of freely allocated emission permits would not affect these businesses drastically (e.g., causing them to downsize their operations). The majority of survey participants believed the price of carbon emissions would not move them to relocate or otherwise change their existing operations.

2.1.3. Impact on Trade

Much of the concern over the EU-ETS preceding its introduction was that targeted businesses would

lose competitiveness on the global market. Critics worried that the cost of manufacturing for these businesses would rise and consequently disadvantage them. Studies have since followed suit, testing this hypothesis against empirical data. Constantini and Mazzanti (2012), for example, analyzed the amount of exports from the 15 countries participating in the EU-ETS during its first phase and noticed a general decline in exports across all industries except the low- and medium technology ones. However, analyzing the aluminum industry, Reinaud (2008) found that, the higher the price of emissions, the lower the imports. This inverse correlation contradicted the prevailing belief that the ETS would compromise the competitiveness of domestic industries and increase imports. The conclusion of this study, however, cannot be generalized because the level of control it applied to variables was not rigorous enough to demonstrate a causal relationship between emission prices and imports.

2.1.4. Impact on Innovation

Policymakers hope that introducing the ETS will motivate and incentivize internal innovation in

businesses. Porter and Van der Linde (1995) explained that a well-designed environmental regulatory regime could promote such innovation and ultimately help enhance business competitiveness. Studies have been conducted to test this so-called Porter hypothesis against the empirical data of the EU-ETS. Calel and Dechezlepretre (2015), for example, analyzed whether and how the EU-ETS prompted the development of low-carbon technologies by tracing and comparing data from ETS-subject businesses and their non-subject, non-European counterparts from 1979 to 2009. The authors confirmed that, since the EU-ETS came into being in 2005, the number of low-carbon patent applications filed by subject businesses increased. The difference from non-regulated companies was not dramatic in absolute terms, but the authors did find statistical significance in the difference. Borghesi et al. (2012), in an analysis of data on innovation by Italian businesses, also found a positive correlation between innovation in environmental technology and the ETS. Yet the authors cautioned that too strong a regulatory grip could inverse the correlation. Martin et al. (2013), on the other hand, reached a different conclusion. Their opinion poll revealed that the difference between ETS-subject businesses and others in their performance on low-carbon innovations in process and product was marginal at best. Through a panel analysis of heavy-emitters and other businesses in general in Sweden from 2002 to 2008, Lofgren et al. (2013) also found that the EU-ETS did not significantly increase R&D investment in Swedish companies.

2.2. Korean ETS

Ex-post evaluations of the economic effects of the Korean ETS have only recently begun, after

conclusion of the first phase. This survey therefore includes ex-post evaluations and ex-ante assessments alike.

Han et al. (2010) sought to predict how the Korean ETS would affect the competitiveness of major Korean industries, setting up different scenarios on indirect effects of the scheme depending on different costs of adopting emission-reducing technologies, purchasing emission permits, and electricity. The authors estimated that, in the short run, the ETS would not significantly increase the cost of production for Korean manufacturers, but those in the steel, cement and non-iron metal industries would see their costs swell more than those in other industries. The authors predicted that, in the long run, the overall cost of production would continue to rise. The rate of cost increase was not so significant under the

scenario of free allocations of emission permits. Under the scenario in which 50 percent of permits were allocated for a charge, however, the cost of production would rise by one percent, or even by four to six percent for the cement, steel, and non-iron metal industries.

Using the input-output tables of Korean industries, Lee (2011) analyzed the effect of emission regulation on the cost of production, price, revenue, and exports. Estimating the rise in the costs of energy based on the price of emissions, the author projected the product price by multiplying the increase in production cost by the rate of cost-to-price transfer (between zero and one), while also estimating changes in revenue and exports to gauge the elasticity of consumption. The biggest flaw in this study is that it bases its estimates of increased production costs not on the permits allocated under the ETS, but on multiplying the total amount of GHG emissions by the price of emission permits. Under a scenario where an emission permit is sold for KRW 25,000, the consumer price would rise by 0.01 to 0.91, revenue would fall by KRW 6.6 trillion (at a rate of 0.0069), and exports would drop by KRW 3.6 trillion (at a rate of 0.0016), assuming that 100 percent of emission permits are traded for a price and the businesses buy up all those permits. In other words, the study ends up grossly overestimating the adverse impact of the ETS on the Korean economy.

Oh et al. (2018) is an ex-post evaluation of the Korean ETS. The authors analyzed the effect of the first phase on the manufacturing cost and R&D investment of subject businesses. Using a differential dynamic panel model, the authors conclude that the first phase of the Korean ETS did not make a significant difference to the cost of production (as a unit of revenue). They also found that the ETS increased business investment in R&D at a significance level of 10 percent.

3. Impact of the Korean ETS on Business Competitiveness

3.1. Method of Analysis

This analysis specifically focuses on how the Korean ETS affected the competitiveness of targeted

businesses, as manifested in their financial performance, during its first phase. First, we verify statistical changes in the economic output of ETS-subject businesses pre- and post-ETS using t-tests. Second, we perform a regression analysis, with the ETS as the dummy explanatory variable, to determine how the ETS directly affected subject business financial performance. Third, we single out public companies of the ETS-subject businesses and subject KOSPI-listed and ETS-subject companies to a differences-in-difference (DID) analysis in comparison to KOSPI-listed and non-ETS-subject ones.

3.1.1. Sample

We compiled a list of ETS-subject businesses in the manufacturing, building, transportation and

transition sectors in Korea. Public enterprises, local governments, and other businesses and installations without accessible financial data were omitted. A total of 349 businesses were included in the final sample. Of these, 169 (48 percent) are KOSPI-listed public companies. Table 4-1 provides a summary of the sample.

Table 4- 1. Sample Distribution by Sector and Industry

Sector N (Percentage) Industry N (Percentage)

Manufacturing 309 (89%)

Industrial clusters 8 (2%)

Mining 1 (0.3%)

Food & beverage (F&B) 16 (5%)

Textile 11 (3%)

Paper 5 (1%)

Pulp 36 (10%)

Oil refining 5 (1%)

Petrochemical 72 (21%)

Glass 15 (4%)

Ceramic 4 (1%)

Cement 15 (4%)

Steel 31 (9%)

Non-iron metal 19 (5%)

Machinery 14 (4%)

Semiconductor 12 (3%)

Display 3 (1%)

Electronics 14 (4%)

Automobile 18 (5%)

Shipbuilding 6 (2%)

Communications 4 (1%)

Building 13 (4%) Building 13 (4%)

Transportation 5 (1%) Aviation 5 (1%)

Transition 22 (6%) Power generation 13 (4%)

Collective energy 9 (3%)

Total 349 (100%) Total 349 (100%)

3.1.2. Variables and Datasets

There are mainly three datasets with which this study analyzes the impact of the Korean ETS on

business competitiveness: namely, data on emissions trading (with the ETS dummy as the independent variable); financial data as proxies of competitiveness (revenue, manufacturing cost, rate of increase in revenue, rate of increase in manufacturing cost, total return on assets) and financial data necessary for controlling variables (operating cash flow, business size, debt, debt ratio, number of employees); and industry dummies. See Table 4-2 for the variables and datasets used.

Table 4- 2. Variables and Datasets

Category Variable Description Source (dataset)

ETS ETS dummy ETS-effective years (ETS=1), Non-

effective years (ETS=0) GIR Emission

Breakdown Statistics, 2011-2018

(NGMS, 2019) Emissions (volume) Per business

Financial information

Revenue Total annual revenue

NICE (2019) (KisValue) for public

companies; FSS (2019) and investor relations

reports cross-referenced for non-public

businesses

Rate of increase in revenue (Revenuet+1-Revenuet)/Revenuet

Manufacturing cost (MC) Annual manufacturing cost

Rate of increase in MC (MCt+1-MCt)/MCt

MC ratio Manufacturing cost/revenue

Advertising expense ratio (AER) Advertising expenses/revenue

Total assets Annual total assets

Operating cash flow (CFO) Annual CFO

Total debts Annual total debts

Asset-to-debt ratio Total debts/total assets

Return on assets (ROA) Net return on total assets

Number of employees Full-time employees

Business size Total assets(log)

Other Industry dummies Nine groups (transition, steel,

petrochemical, cement, collective energy, electronics, oil refining, building, other)

3.2. Findings

3.2.1. Pre- and Post-ETS Financial Performance (T-Test)

Other researchers (e.g., Lee, 2011) providing ex-ante assessments of the economic impact of the

Korean ETS predicted that the scheme would increase the manufacturing cost, causing manufacturers to raise the prices of their products and suffer decreases in revenue in consequence. Numerous empirical studies on the EU-ETS data, however, suggest that much of the ex-ante assessment may not materialize in reality. This study compares the pre- and post-ETS financial performance of ETS-subject businesses in Korea and verifies, with a t-test, whether there have been any changes in business output and employment. This analysis alone does not confirm whether the ETS directly affected Korean business competitiveness. Nevertheless, it can tell us whether there has been a significant change in business performance as evidenced by the financial data from each business. This study statistically verifies whether there were meaningful differences in the indicators of business output over the four-year span surrounding introduction of the ETS in 2015.

Our analysis confirms that the ETS did make some differences to the financial performance of targeted businesses. However, the diversity of paths and directions of those differences prevents a uniform conclusion. First, the ETS’ effect on lowering revenue was not statistically significant. Revenue

decreased slightly, but the difference was not significant enough to be attributable to the ETS. By contrast, the ETS’ effect on increasing revenue showed statistical significance (significance level = 5%). In fact, the rate of increase in revenue in ETS-subject businesses accelerated after the ETS was introduced. Third, the manufacturing cost dropped under the ETS, with statistical significance. However, the rate of increase in manufacturing cost rose, indicating that, notwithstanding the drop in manufacturing cost, businesses whose cost increased saw their cost increase even more rapidly. Fourth, the MC ratio (percentage of manufacturing cost in revenue) dropped with statistical significance (significance level = 1%). These results prove the pessimism of ex-ante assessments (e.g., Lee, 2011) and assumptions rooted in economic theories wrong. Fifth, ETS-subject businesses saw their total assets grow and debt ratio fall. Their debts, too, grew to an extent, but debt ratio fell because their assets grew more. Sixth, businesses’ net return increased at a weak significance level (significance level = 10%). This increase in net return improved business ROA slightly, but the higher rate of growth in assets limited the statistical significance of the ROA. Seventh, the ETS seldom made a difference to the number of employees hired by targeted businesses. Overall, the financial performance and output of ETS-subject businesses in Korea improved under the ETS. It is too early to attribute these improvements to the ETS only, but these findings at least support our conclusion that the ETS did not undermine Korean business competitiveness by raising their costs and reducing their revenue and profits. Table 4-3 provides a summary of the findings of the analysis.

Table 4- 3. Pre- and Post-ETS Financial Performance of Targeted Businesses (T-Test)

Variable Pre-ETS (2011-2014)

Post-ETS (2015-2018) t-Value

Revenue 3.322E12 3.179E12 -1.20 Rate of increase in revenue 0.0029 0.0261 2.57*

MC 2.73E12 2.43E12 -2.50* Rate of increase in MC 0.007 0.023 1.82+

MC ratio 0.844 0.829 -4.78** Total assets 3.771E12 4.355E12 3.10** Total debts 1.757E12 1.870E12 1.74+ Debt ratio 0.509 0.484 -3.08** Net return 1.413E11 1.969E11 1.73+

ROA 0.029 0.030 0.49 Number of employees 2738 2766 0.66

Note: N = 349, +, p < 0.10; *, p < 0.05; **, p < 0.01.

We can also analyze the ETS-caused differences in the indicators of business financial performance particularly with respect to the transition and manufacturing sectors. Table 4-4 summarizes findings regarding the former. The transition sector accounts for a vast bulk of Korea’s GHG emissions, and predictions have been that the ETS would exert a significant impact on that sector. Numerous studies on the EU-ETS, too, focused on power companies (e.g., Chan et al., 2013; Bushnell et al., 2013).

Our analysis reveals that, first, the difference in Korean power generation company revenue lacks statistical significance. However, note that the rate of increase in revenue did drop with statistical significance. This contrasts the pattern concerning all subject businesses in general (Table 4-3). Second, changes in manufacturing cost and the rate of increase therein have been minuscule. The ETS, in other words, has not affected manufacturing cost significantly. Rather, the MC ratio to revenue decreased significantly. It is not yet clear whether improvement in the MC ratio owes to business efforts for innovation, prompted by the ETS. This is a subject that warrants further research and case studies. The power generation and collective energy industries making up the transition sector are largely subjected to policy influences. Short-term changes in revenue and cost should therefore not be generalized as

effects of the ETS only. It is nonetheless important to note that the ETS did not at least worsen the manufacturing cost of a sector for which ETS critics feared the worst possible outcome. Third, the sector saw assets increase with statistical significance. Debt increased, too, but the fact that assets grew more suggests that the financial performance of businesses in the sector has improved. Fourth, little difference was made to the ROA. Finally, the number of employees increased in the sector with statistical significance. Overall, the ETS appears to have improved performance of the transition sector.

Table 4- 4. Pre- and Post-ETS Financial Performance (T-Test): Transition Sector



Revenue 5.994E12 5.716E12 -0.40

Rate of increase in revenue 0.0826 -0.0246 -3.93*

MC 5.643E12 5.188E12 0.83

Rate of increase in MC 0.044 -0.000 -1.58

MC ratio 0.909 0.870 -2.12*

Total assets 1.075E13 1.225E13 2.62*

Total debts 6.408E12 6.914E12 1.68

Debt ratio 0.733 0.731 -0.06

Net return 2.830E10 2.907E11 1.12

ROA 0.003 0.017 0.99

Number of employees 2116 2432 2.51*

Note: N = 22, +, p < 0.10; *, p < 0.05; **, p < 0.01.

Table 4-5 presents the results of our analysis of the manufacturing sector. This sector alone accounts for 89 percent of all sampling units in our analysis, and is a major subject of any analysis of economic impact from the ETS. First, revenue in the industrial sector decreased with statistical significance. The rate of growth in revenue, however, was statistically significant.

This contrasts with the outcome of the transition sector. The manufacturing sector’s revenue fell briefly around the time the ETS was introduced, before resuming its upward trend. This fluctuation may explain the significant fall in revenue and the significant rise in the rate of increase in revenue. Second, manufacturing costs also took a statistically significant drop, while the rate of increase in costs grew with statistical significance. The overall MC ratio decreased, suggesting that the sector’s overall cost management improved under the ETS. This again contrasts with the experience of the transition sector, which saw little change in its cost. This analysis alone cannot determine whether the improved MC ratio reflects companies’ internal efforts for innovation. Further research is needed to answer that question. In conclusion, the kind of cost increase feared by ETS critics did not occur in the manufacturing sector, either. Third, assets increased with statistical significance, while the debt ratio fell. Fourth, no major change occurred in either the ROA or the number of employees. In general, the manufacturing sector suffered some loss in revenue, but its MC ratio and financial performance have improved since the ETS was introduced.

Table 4- 5. Pre- and Post-ETS Financial Performance (T-Test): Manufacturing Sector



Revenue 3.102E12 2.896E12 -1.79*

Rate of increase in revenue -0.005 0.020 2.89**

MC 2.536E12 2.245E12 -2.94**

Rate of increase in MC -0.002 0.020 2.24**

MC ratio 0.848 0.836 -3.80**

Total assets 3.154E12 3.560E12 2.18*

Total debts 1.339E12 1.357E12 0.35

Debt ratio 0.485 0.461 -2.76**

Net return 1.477E11 1.942E11 1.45

ROA 0.031 0.032 0.15

Number of employees 2549 2527 -0.33

Note: N = 365 to 389, +, p < 0.10; *, p < 0.05; **, p < 0.01.

Table 4- 6. Pre- and Post-ETS Average Revenue: Manufacturing Sector

Year Average revenue (KRW thousand)

2013 2,751,399,712

2014 2,615,898,950

2015 2,384,886,868

2016 2,293,342,024

2017 2,492,581,654

2018 2,767,521,629 Note: N = 365.

3.2.2. Impact of the ETS on Financial Performance: Regression Analysis

The t-tests discussed in the preceding section were meant to determine whether any changes observed

in the financial performance of ETS-subject companies after the ETS was introduced (2015) had statistical significance. These tests can show an overall trend, but cannot confirm whether the trend is a direct result of the ETS. Stratified regression analysis enables us to control important variables other than the independent one that could affect the dependent variable, and therefore can help us determine how the ETS itself has affected Korean business performance. We set up a model of regression with business size and other financial variables to be controlled, and added the ETS dummy (independent variable) to it to confirm whether the addition would increase the mode’s explanatory power. Below is our regression model:

Equation 1: Revenuet = β0 + β1ETS dummy + β2business sizet + β3CFOt-1 + β4ROAt-1 + β5debt

ratiot-1 + β6AERt + β7year dummy + β8industry dummy

Equation 2: Rate of increase in revenuet = β0 + β1ETS dummy + β2business sizet + β3ROAt-1 + β4debt ratiot-1 + β5year dummy + β6industry dummy

Equation 3: MCt = β0 + β1ETS dummy + β2business sizet + β3ROAt-1 + β4debt ratiot-1 + β5year dummy + β6industry dummy

Equation 4: Rate of increase in MCt = β0 + β1ETS dummy + β2business sizet + β3ROAt-1 + β4debt ratiot-1 + β5year dummy + β6industry dummy

Equation 5: MC ratiot = β0 + β1ETS dummy + β2business sizet + β3ROAt-1 + β4debt ratiot-1 + β5year dummy + β6industry dummy

Equation 6: ROAt = β0 + β1ETS dummy + β2business sizet + β3rate of increase in revenuet + β4CFOt-1 + β5debt ratiot-1 + β6AERt + β7year dummy + β8industry dummy

Equation 7: Debt ratiot = β0 + β1ETS dummy + β2business sizet + β3rate of increase in revenuet + β4CFOt-1 + β5ROAt-1 + β6AERt + β7year dummy + β8industry dummy

Equation 8: Number of employeest = β0 + β1ETS dummy + β2business sizet + β3rate of increase in revenuet + β4CFOt-1 + β5ROAt-1 + β6debt ratiot + β7year dummy + β8industry dummy

Tables 4-7 and 4-8 present the results of applying the last equation, which includes all variables.

Table 4- 7. Impact of the ETS on Revenue and Cost: Regression Analysis

Variable Revenue Rate of

increase in revenue

MC Rate of increase in MC MC ratio

Business size 0.249** 0.0493 0.557** 0.017 -0.167** CFO (t-1) 0.811** - - - -

ROA (t-1) -0.037** 0.096** 0.014 0.191** -0.245** Debt ratio (t-1) 0.002 0.015 0.021 0.056 -0.001

AER -0.043** - - - -

ETS dummy -0.014 -0.137* -0.022 -0.114+ -0.082+ Year dummy Year dummy included

Industry dummy Industry dummy included

Adj.R2 0.889 0.018 0.308 0.032 0.010 F-value 982.75** 4.67** 185.03** 7.51** 44.56**

N 862 1010 2066 1093 2066 Note: Standardized coefficients, +, p < 0.10; *, p < 0.05; **, p < 0.01, N decreases when AER is added.

The regression analysis reveals a statistically significant correlation between the ETS, on the one

hand, and the rate of increase in revenue, the rate of increase in the MC, and the MC ratio, on the other (Table 4-7). The findings can be summarized as follows. First, business size and operating cash flow (CFO) of the previous year are the two main decisive factors behind the revenue of ETS-subject businesses. Controlling these variables along with the debt ratio causes the significance of the ETS dummy’s effect disappear. Second, the ETS does exert a statistically significant adverse effect on the rate of increase in revenue. Introducing the ETS, in other words, has slowed down the growth of business revenue. Third, the ETS also adversely affects the MC ratio, i.e., the percentage of manufacturing cost in revenue, with statistical significance. This means that the company can produce the same output with less input. Similarly, the ETS also adversely affects the rate of increase in manufacturing cost. Contrary to pre-ETS concerns, the ETS helped participating businesses improve efficiency and rein in increases in manufacturing costs.

Table 4-8 shows the results of our analysis on the correlation between the ETS, on the one hand, and the financial performance of businesses and employment, on the other. Return on assets (ROA), along with Tobin’s Q, is a commonly used indicator of financial performance. Tobin’s Q applies to the performance of public companies on the financial market. As our sample includes public and private companies alike, we apply ROA instead. Our analysis shows that, first, the rate of increase in revenue and the CFO of the previous year—both control variables—are the most decisive factors of financial performance. Previous-year debt ratio is another factor that exerts a statistically significant negative effect. The effects of these control variables have been affirmed in the general literature on financial analysis and make it necessary to control those variables. When these variables are controlled, the ETS’ impact on financial performance loses statistical significance. Second, the debt ratio increases in proportion to business size and in inverse proportion to the previous year’s CFO and ROA. Its correlation to the ETS did not show statistical significance. Third, when the business size and the previous year’s CFO are controlled, the correlation between the number of employees and the ETS also loses statistical significance. Our analysis does not affirm that the ETS affects business financial performance and employment (Table 4-8). Compared to the significant and positive role the ETS plays in lowering the manufacturing cost, the paths via which the ETS affects business revenue, debt and employment remain more complex and ambiguous.

Table 4- 8. Impact of the ETS on Financial Performance and Employment: Regression Analysis

Variable ROA Debt ratio Number of employees

Business size 0.053 0.220** 0.351**

Rate of increase in revenue 0.182** 0.022 0.012

CFO (t-1) 0.076* -0.134** 0.636**

ROA (t-1) - -0.407** -0.029

Deb ratio (t-1) -0.210** - -0.016

AER 0.013 -0.020 -

ETS dummy 0.020 -0.053 -0.024

Year dummy Year dummy included

Industry dummy Industry dummy included

Adj.R2 0.058 0.201 0.726

F-value 9.80** 37.13** 363.95**

N 862 862 961

Note: Standardized coefficients, +, p < 0.10; *, p < 0.05; **, p < 0.01.

3.2.3. Direct Effects of the ETS on Business Performance: DID Analysis

Differences-in-difference (DID) analysis can help us verify the direct effect of a new institution or

policy. This approach involves dividing the control group and the experiment group and comparing them, both before and after the given policy change, so as to measure the net effect of policy change (experiment) in the absence of the time effect. The t-tests we performed can tell us whether the changes occurring in business financial performance and output after introduction of the ETS are statistically

significant or not, but cannot tell us whether such changes originate from time or from the policy itself. We can isolate the policy effect when we have an experiment group (affected by the policy) to which to compare the control group.

DID analysis requires two or more groups that are sufficiently similar so as to be compared. In our context, it means that we need a group of businesses that are similar to the ETS-subject ones, but that are not regulated by the same scheme. It is difficult to find such a group comparable enough to support a very rigorous analysis. The ETS targets manufacturing businesses that consume energy heavily and generate heavy GHG emissions. Almost all businesses that meet these criteria are inevitably bound by the ETS. Acknowledging these limitations, we can still attempt to explore with a DID analysis, even if simply to check overall orientation of the policy effect that the ETS has had on Korean businesses.

For this analysis, we include ETS-subject businesses that are now listed on KOSPI into our treatment group. The control group consists of KOSPI-listed companies that are not subject to the ETS. Being on the same financial market is the minimum condition of comparability we apply to this analysis.

Table 4- 9. Compared Groups for DID Analysis on Effects of the ETS

Pre-ETS (Time effect = 0)

Post-ETS (Time effect = 1)

Treatment group (KOSPI-listed, ETS-subject

companies) ETS effect = 1

Treatment group not under ETS effect (a)

Treatment group under direct ETS effect (a’)

Control group (KOSPI-listed, non-ETS-

subject companies) ETS effect = 0

Control group not under ETS effect (b) Control group not under ETS effect (b’)

Statistically significant differences in financial performance of both groups that can be observed after

introduction of the ETS reflect both time and policy effects. In such a case, the net policy (ETS) effect would be found if the treatment group’s change (from a to a’) is either smaller or greater than the control group’s (from b to b’), with statistical significance and the time effect removed (Table 4-9). That net effect should equal the effect of interaction between the time effect and the policy effect. We can verify statistical significance of the coefficients of such interaction effect by adding the interaction variable (time dummy x ETS dummy) to the time dummy (time effect) and the ETS dummy (policy effect) in our regression model.

Table 4-10 presents the results of the analysis. First, the effect of the ETS on the revenue of ETS-subject businesses has not been significant. Neither were the effects of the time dummy and the ETS dummy. Second, the time and ETS effects on net return and the operating cash flow (CFO) have also not been statistically significant. Third, there was no net ETS effect on companies’ ROA, a leading indicator of financial performance. Our DID analysis also does not reveal a significant policy effect from the ETS on the financial performance (competitiveness) of Korean businesses.

Table 4- 10. Net Effect of the ETS: DID Analysis

Variable Revenue Net return CFO ROA

Business size 0.52** 0.28* 0.35* 0.20**

Time dummy -0.02 -0.01 -0.02 0.02

ETS dummy 0.00 -0.02 -0.01 -0.06

Time x ETS 0.01 0.04 0.04 0.03

Adj.R2 0.275 0.079 0.129 0.034

F-value 122.53** 28.49** 48.35** 12.18**

N 574 574 574 574

Note: Standardized coefficients, +, p < 0.10; *, p < 0.05; **, p < 0.01.


As a market-based approach to reducing carbon emissions, the ETS is widely regarded as one of the

most cost-effective ways in which a nation can reduce its GHG emissions. Following the EU’s example, the Korean ETS was introduced in 2015, and its first phase came to a close three years later. Although the ETS is recognized as a more flexible and market-friendly approach than either the carbon tax or the direct emissions control quota, businesses and industries still largely perceive it as one more form of regulation, feeding worries that its introduction will raise the cost of compliance and weaken competitiveness. A similar controversy surrounded the start of the EU-ETS as well, prompting researchers to conduct diverse studies on how the ETS affects the competitiveness of European industries and businesses. Those on the economic impact, particularly at the micro-level, reveal that this impact has varied widely depending on industry, product, and time, so much so as to defy a consistent consensus. Reflecting the same controversy in Korea, early researchers who conducted ex-ante assessments of the potential effects of the ETS generally highlighted its negative impact. Now that the first phase of the Korean ETS is over, we have ground to analyze the effect of the policy empirically. This section presents the results of empirical tests and analyses of how the Korean ETS has affected the competitiveness of Korean businesses.

This study analyzes the impact of the ETS on Korean businesses and industries in three ways. First, it statistically verifies changes that occurred in the financial performance and output of businesses after the ETS was introduced. Contrary to concerns over the possibly negative effect of the ETS, most Korean businesses subject to the ETS maintained sound financial performance. Some financial indicators even improved with statistical significance. Manufacturing costs and the ratio to revenue decreased, enhancing business efficiency and contributing to growth of their total assets. Debt ratios also fell, indicating a general improvement in business financial stability. Second, this study analyzes the net effect of the ETS with a regression model. Controlling influential explanatory variables reveals that the effect of the ETS alone has not been statistically significant. However, it did exert a statistically significant impact on lowering the MC ratio, helping to improve business efficiency. Third, this study verifies the policy effect of the ETS with a DID analysis. Comparing ETS-targeted companies listed on KOSPI to other listed companies reveals that the ETS has not had a statistically significant effect on the performance of Korean businesses. However, the fact that this study could not find a sufficiently comparable group to which it could compare ETS-subject businesses limits the generalizability of this finding.

The findings of this study carry the following policy and strategic implications for policymakers and corporate decision-makers. First, consistency in execution of the ETS is critical. It is also crucial to tailor policy execution in a way so as to encourage innovation by targeted businesses. Constant worries about the potentially negative impact of the ETS on the economy and industries can stand in the way of consistently enforcing the scheme. Ex-ante assessments based on static models can overestimate the harm of the ETS, prompting unwarranted policy changes and intervention. Our analysis demonstrates that the ETS in Korea exerted no negative effects on businesses and industries during its first phase. Although it is still too early to determine what direct effects the ETS has had, its first phase has coincided with steady improvements in efficiency for targeted businesses. Insofar as the ETS can serve as an impetus for innovation in such businesses, it will also be possible to refine it so that it can lead to both reductions of emissions and business innovation. There is room for acceptance of the Porter hypothesis (Porter and van der Linder, 1995) in carbon policy. Second, businesses and industries need to change their views of the ETS. Rather than simply reacting to it as another form of regulation, businesses and industries can instead actively embrace it with strategies for innovation and reform. They can take signs from the ETS to develop more efficient and cost-saving processes, low-carbon products capable of boosting sales, and actively responding to climate change toward strengthening their market image and managing risk. Businesses and industries should seize upon the market-based advantages of the ETS and capitalize upon them to enhance their own competitiveness.

This study is limited to the outcomes of the first phase of the Korean ETS only. Given the brevity of the period under analysis, caution is advised before generalizing the findings of this study. Once more longitudinal data is accumulated through the second and subsequent phases of the Korean ETS, we will be able to perform a more rigorous analysis of the micro-level effects of the policy and refine our policy and strategic recommendations accordingly.

Chapter V. Conclusion

The human capacity to productively create things has been growing seemingly without limits since the Industrial Revolution. The radical growth in human productivity has also dramatically increased the demand for energy, deepening our dependency on relatively cheap and abundant fossil fuels. The increasing use of fossil fuels, however, accounts for extreme growth in GHG emissions, which have culminated in climate change. A global crisis of this scale cannot be resolved by the efforts of a single nation. However, because the benefits of reducing GHG emissions and mitigating climate change are public goods of sorts, no nation has been willing to take the lead in drastically reducing GHG emissions.

For this reason, states worldwide launched a series of dialogues in concerted search for solutions to climate change, culminating in the United Nations Framework Convention on Climate Change (UNFCCC) of 1992 and the Kyoto Protocol of 1997.

The South Korean government hinted at adopting a similar measure when it promulgated the Framework Act on Low-Carbon Green Growth (FALCGG) in 2010.11 After five years of preparation, the Korean government launched its own ETS in 2015. At the time, the Paris Climate Agreement (PCA) had not yet been proposed, and Korea was therefore a non-Annex I country, unbound by international treaties to reduce GHG emissions. Nevertheless, the Korean government decided to implement the ETS in an effort to take a more proactive approach to climate change and foster low-carbon industries as new engines for national economic growth.

Introduction of the PCA in 2015 and its effectuation in 2016 transformed the international framework on climate change actions. Under the PCA, reducing GHG emissions was no longer the exclusive lot of developed countries. All nations were urged to set GHG emission reduction targets and carry out effective actions to that end voluntarily.

The Korean government responded to this change by announcing an ambitious intended nationally determined contribution (INDC) to the UNFCCC in 2015 that it would reduce business-as-usual (BAU) GHG emissions by 37 percent by 2030 (Government of Republic of Korea, 2016). Considering the fact that emissions from ETS-subject businesses account for nearly 70 percent of all nationwide emissions in Korea, the ETS will prove to be pivotal in the realization of the Korean government’s reduction plan for 2030.

Phase 1 of the Korean ETS began in 2015 and came to an end in 2017. The second phase has been in effect since 2018. This study statistically analyzes the emissions-reducing performance of the Korean ETS, and assesses how efficiently it was run and what effects it exerted on the competitiveness of Korean businesses during its first phase. Based on the three types of analysis discussed in the previous chapter, we can now identify and summarize implications for efficient operation of the Korean ETS in the future.

11 Paragraph (1), Article 46 (Introduction of Cap and Trade System): “The Government may operate a system for trading emissions of greenhouse gases by utilizing market functions in order to accomplish the State’s target of reducing greenhouse gases.”

1. Results

1.1. Performance of GHG Emissions Reduction

According to the Master Plan on Emission Permits in 2014, the main objective of the first phase of the Korean ETS was to establish it as an institution, while allowing participants to gain experience with trading emission permits (MOSF, 2014). During this phase, total emissions amounted to 98.76 percent of the total permitted amount (16.899 million tCO2eq). The Korean ETS appears to have contributed significantly, even in its first phase, to slowing down the growth of GHG emissions. Of course, we need more than a simple comparison of the total emissions allowance and certified emissions in order to assess how the ETS affected emission levels in Korea.

Chapter II of this study thus introduced an econometric model for evaluating the emissions-reducing effect of the Korean ETS in its first phase, analyzing how the correlation between energy demand, revenue, and adjusted tangible assets (ATA, as a proxy variable of capital), on the one hand, and GHG emissions, on the other, changed under the ETS. The insufficiency of available data prevented a statistical analysis of all eligible industries, but we were able to analyze whether statistically significant decreases were observed in emissions from the transition sector, and the steel, petrochemical, semiconductor/display/electronics (SDE), and automobile industries. The results of the analysis varied widely from sector to sector and industry to industry. In the transition sector, the correlation between emissions, on the one hand, and energy demand and revenue, on the other, rather deteriorated under the ETS, while the correlation between the ATA and emissions improved toward reducing emissions. As for petrochemicals, a heavy-emitter industry, the ETS did not make statistically significant differences to any of the major variables and their effects on emissions.

The steel and SDE industries, on the other hand, displayed the clearest signs of reduced emissions. The correlations between energy demand and revenue, on the one hand, and emissions, on the other, improved in the steel industry under the ETS, while the correlation between the ATA and emissions diminished. The SDE industry saw the correlation between its energy demand and emissions improve, while the correlation between revenue and emissions deteriorated. Given the characteristics of the industry’s emissions and particularities of the data used in our analysis, much of the effect of energy demand on emissions from the SDE industry, i.e., the decrease in emissions per unit of energy used, appears to owe to reduced process emissions.

The ETS did make a statistically significant difference to the effects of revenue and ATA on emissions in the automobile industry, but in opposite directions. Whereas the correlation between revenue and emissions improved, the correlation between ATA and emissions deteriorated.

Caution is needed before generalizing the findings of this analysis. First, the effect of the ETS on emissions is absent in relation to some industries, but that should not be taken as a sign that these industries made no effort to reduce their emissions. It must be remembered that the majority of businesses subject to the ETS had already been under direct emissions regulation prior to introduction of the ETS. This particular analysis can therefore only demonstrate whether the shift from the GHG & Energy Management System (GEMS) to the ETS made any additional contributions to lowering emissions further.

1.2. Efficiency of the Emissions Trading Market

The ETS is preferred to more direct forms of emission control and regulation mainly because it is perceived as a cost-effective way of realizing reduction targets. In order for an ETS to be cost-effective, the amount of emissions allocated to every participating business should be decided so that the marginal cost of emissions reduction would become equal for all participating businesses. In theory, this applies to the market price of emission permits as well. For this to happen, however, the emissions trading market must be efficient.

It is impossible to determine whether a given ETS is cost-effective by measuring the marginal cost of emissions reduction concerning every target business. Instead, we may evaluate whether the existing emissions trading market has been efficient, and whether the conditions for a cost-effective ETS have been met in order to evaluate, indirectly, whether the ETS is cost-effective. Chapter III of this study thus employed statistical verifications to determine whether the Korean emissions trading market was efficient during the first phase of the ETS.

Korean market efficiency was verified by testing the weak-form efficient market hypothesis (EMH) using a variance ratio (VR) test. In this process, the fact that the market was thin (with little transaction volume) during the first phase of the ETS was taken into account in multiple ways. This study thus analyzed not only the reference prices of open days, but also the prices of permits on days when actual transactions took place. Furthermore, a modified AR(1) process was applied to returns, while both daily and weekly transaction data were analyzed. The year 2017, the final year of the first phase and one that attracted more transactions than the preceding years, was also given a separate analysis. In order to determine whether market efficiency changed in the second phase of the ETS, the KAU18 transaction data was also analyzed.

The chapter’s analysis reveals that, at least in relation to the first phase of the ETS, the EMH does not stand. The year 2018, which was the first year of the second phase, revealed relatively stronger support for the random walk hypothesis (RWH) than did the first phase. Market efficiency improved in 2018 over the first phase (2015 to 2017), most likely because the amount of uncertainty over the prices of emission permits lessened somewhat.

1.3. Effect on Business Competitiveness

The ETS is known to be a more flexible policy measure than either the carbon tax or direct emission control. Businesses and industries nonetheless still perceive the ETS as another regulatory obstacle to business, worrying that its introduction will raise the cost of compliance and weaken their market competitiveness. A similar controversy surrounded the start of the EU-ETS as well, prompting researchers to conduct diverse studies on how the ETS affects such competitiveness of European industries and businesses. Studies on the economic impact of the ETS on the EU, particularly at the micro-level, reveal that the effects of the ETS have varied widely by industry, product, and time, so much so as to defy consistent consensus.

Reflecting the same controversy in Korea, early researchers who conducted ex-ante assessments of the potential effects of the ETS generally highlighted its negative impact. Now that the first phase of the Korean ETS is over, we have the opportunity to analyze the effect of the policy empirically.

Chapter IV thus analyzes the impact of the ETS on Korean businesses and industries in three ways.

First, it statistically verifies changes that occurred in the financial performance and output of businesses after the ETS was introduced. Contrary to concerns over the possibly negative effect of the ETS, most Korean businesses subject to the ETS maintained sound financial performance. Some of the financial indicators even improved with statistical significance. Manufacturing costs and their ratio to revenue decreased, enhancing business efficiency and contributing to the growth of total assets. The debt ratio also fell, indicating a general improvement in financial stability.

Second, this study analyzes the net effect of the ETS with a regression model. Controlling influential explanatory variables reveals that the effect of the ETS alone has not been statistically significant. However, the ETS did exert a statistically significant impact on lowering the MC ratio, helping to improve business efficiency.

Third, this study verifies the policy effect of the ETS with a differences-in-difference (DID) analysis. Comparing ETS-subject companies listed on KOSPI as the treatment group to other listed companies as the control group reveals that the ETS has not had a statistically significant effect on the performance of Korean businesses. Yet the fact that this study could not find a sufficiently comparable group to which it could compare ETS-subject businesses limits the generalizability of this finding.

2. Policy Implications

2.1. Fostering Investment towards Reducing GHG Emissions

The first phase of the Korean ETS started with a humble object—that is, to establish the scheme as a new institution. Yet it did make statistically significant differences to GHG emissions generated by certain industries. That the effect was not observed across all industries cannot be counted as a fault of the ETS. As the goal of the ETS is to lower nationwide emission levels, insofar as the total emissions allowance is not exceeded and all targeted businesses trade and submit the emission permits they need, there is no reason for all industries to strive to reduce their emissions. Of course, the total emissions allowance of the Korean ETS will continue to decrease until the country achieves its target reduction level by 2030, and pressure will keep building for businesses participating in emissions trading to reduce their emissions. In the future, all industries may have to reduce their emissions to stay within the total allowance.

Reducing GHG emissions entails direct and opportunity costs. Considering the downsizing effect efforts to lower emissions could have on production and economic effects on the regional economy as well as the chain of related industries, it will be crucial for government and businesses to make increasing investment in effective technologies towards reducing emissions.

Policy support for reducing GHG emissions should thus encompass three approaches. First, it should be directed to foster the R&D on solutions that businesses can actually adopt toward reducing emissions, such as emission-controlling facilities, high-efficiency energy facilities, and manufacturing processes minimizing emissions. Second, policy should also provide sufficient incentives for continued investment in reducing emissions. All investors make their decisions by comparing the anticipated costs and benefits of their investment. Government can help reduce the cost of investment and maximize the benefits (Figure 5-1).

Figure 5- 1. Policy Support for Reducing GHG Emissions

온실가스 감 감 Reducing emissions 감 감 감 감 감 감 감 감 Investment in reducing emissions 감 감 감 감 감 감 Cost of investment 감 감 감 감 감 감 Benefit of investment 감 감 감 감 감 감 감 감 Support for R&D 감 감 감 감 감 감 감 감 감 감 Incentives for investment 감 감 감 감 감 감 감 감 감 감 감 감 감 Subsidies for emission-lowering solutions

2.1.1. Support for R&D

Before supporting R&D on solutions for reducing GHG emissions, the Korean government should first decide short- and mid-to-long-term goals to be met, and allocate policy support accordingly. In the short run, support should be directed to immediately applicable solutions that meet the identified needs of businesses. A major problem ETS-subject businesses face today is that, although the ETS and the gradual decrease in emissions allowances impose growing burdens on their operations, they do not have many emission-reducing options to choose from. The Korean ETS, in particular, targets indirect emissions from the use of electricity and heat energy as well. The best way to reduce indirect emissions is to adopt high-efficiency energy facilities. The use of electricity and heat, however, is directly related to the cost of production. Most businesses are therefore eager to adopt energy systems with the highest-possible level of efficiency. This means that, in order to help businesses already with such high-efficiency systems lower their emissions further, better energy systems that can readily be applied to actual production must be developed.

The Korean government should ascertain the R&D needs of ETS-subject businesses and prioritize the development of solutions that can be applied in the short run. This will help reduce GHG emissions substantially, and alleviate the significant burdens businesses face in terms of R&D, lowering emissions, and purchasing emission permits.

In the intermediate to long run, government support should be directed to developing original technologies. The steel, petrochemical, semiconductor and display industries are pillars of the Korean economy that involve massive facilities with massive processes of extreme complexity. Korean manufacturers, however, rely mostly on imported machinery, processes and devices. This limits the extent to which they can modify their existing facilities and processes to reduce emissions. Such modifications may cause problems with the existing facilities and processes that suppliers may not be willing to service. It is therefore critical to support the development of emission-lowering facilities,

processes and other such technologies to enable Korean businesses to adopt such solutions actively in the future.

2.1.2. Support for Investment

The Act on the Allocation and Trading of Greenhouse Gas Emission Permits and the Enforcement Decree thereof require the government to provide fiscal and tax support for investment in reducing emissions. Various ministries and agencies have introduced investment support programs for their respective industries/sectors accordingly. The Ministry of Trade, Industry and Energy (MOTIE), for example, provides the GHG Emission-Reducing Facility Support Program for ETS-Participating Businesses for small and medium-sized enterprises (SMEs) in the transition and power generation sectors. The Ministry of Environment (ME) provides a similar program for waste-handling businesses.

The level of support under these individual programs is nothing impressive, though. The MOTIE’s program, for example, had an annual budget of KRW 2.234 billion in 2018 and 2019 each, which pales in comparison to the share of the transition and manufacturing sectors in the ETS.

Beginning in 2019, the Korean government began to allocate paid-for emission permits via bidding for part of emission allowances. This was already announced in the ETS Master Plan (MOSF, 2014). However, policymakers have not yet determined how to use the proceeds from the bids. Using them to expand the investment support programs of individual ministries will enable more businesses to benefit from these programs. Some programs place a ceiling on the amount of fiscal support each business may receive. Such limits, however, should be lifted or at least raised so as not to curb investment in costly, but effective, emissions projects.

2.1.3. Stronger Incentives for Investment

The Korean ETS currently bases emission permits on past emission records. However, this means that heavy-emitter businesses are allocated more free emission permits than non-heavy emitters, consequently facing relatively little pressure to reduce their emissions. The current incentive structure should therefore be reformed so as to benefit businesses that have been proactively reducing their emissions by investing actively in new solutions. The part of emission allowances based on benchmarks should be increased, with one caveat. Setting benchmarks by industry could end up distorting the market because some heavy-emitting industries contain relatively few businesses. The mining, oil refinery, lumber, ceramics, display and shipbuilding industries, for example, consist of 10 or fewer businesses each, necessitating careful deliberation in determining benchmarks therein. One alternative to product-by-product benchmarks may be heat- or energy-based benchmarks used in the EU-ETS.

Ensuring equity between industries is another important matter of consideration. In setting benchmarks, businesses in some industries may benefit more than others, while those in other industries with relatively high benchmarks may face undue increases in pressure to reduce their emissions. This is an outcome that should be avoided.

With the goal of increasing benefits for businesses investing in emissions-reducing solutions and fostering such investment, the Korean government introduced new incentives starting with the second phase of the ETS. Under the Guidelines on Allocating, Adjusting and Revoking GHG Emission Permits,

the certified amounts by which businesses managed to reduce their emissions in the preceding phase are to be added to projected emissions that underline emission allowances for subsequent phases so that businesses that reduced emissions can receive a greater number of allowances.

However, only a few methods—CDM, external business, and KVER—can be used to verify and certify reduction in GHG emissions by businesses. The excessive rigor of the manner in which these methods are applied has meant that the number of businesses that actually succeeded in having their emission-lowering efforts certified has remained relatively small. The new incentive therefore invites questions over its projected utility, notwithstanding its intent. The Korean government should engage businesses in communication and find more applicable methods for certifying the amounts of GHG emissions they actually decrease.

Furthermore, certified reductions in emissions can be carried forward only into the immediately succeeding phase, and not into the phases further down. More long-term benefits should be provided for businesses in light of the lifespans of the new solutions they adopt, the size of investments they make, and the duration of the effects of their investments.

The majority of ETS-subject businesses in Korea have already adopted highly efficient energy facilities. Many do so upon acquiring new facilities in the first place. However, the current system of emissions allocation enforces certain coefficients of adjustment, requiring businesses across the board to reduce their emissions by certain percentages. This actually leads businesses to operate their high-efficiency energy facilities less than their capacities warrant or otherwise purchase additional emission permits. This, in turn, reduces their need to adopt more energy-efficient facilities and solutions, causing them to postpone investment in emissions-reducing technologies. It is therefore important for the government to revisit its policy so that businesses acquiring new facilities can be exempt from the set coefficients of adjustment when the efficiency of their new facilities meet certain emissions-related criteria.

2.2. Enhancing Efficiency of the Emissions Market

In order to enhance the efficiency of the Korean emissions market, it is important to increase liquidity and lower the cost of information. The liquidity problem came to the fore during the first phase of the Korean ETS, leading the government to take steps to boost liquidity each year in response to the soaring prices of emission permits around the time of their settlement (Ahn, 2018). The leading example is the measures for market stability announced in April 2017, which limited the amount of emission permits that could be carried forward from the first phase into the second so as to increase the supply of permits on the market. The measures regarding the second phase also included raising the ceiling on the amount businesses could take out in loans, recognizing CDM performances abroad, auctioning off emission permits, and allowing market developers to participate (Ahn, 2018). These measures enhanced the efficiency of the Korean emissions market to an extent. In order to boost liquidity and market efficiency further, policymakers should consider limiting carryforwards for a certain period of time.

However, suddenly changing market rules during a given phase may rather increase the cost of information for market participants, constraining liquidity and compromising market efficiency in the end. The Korean government should therefore avoid making such changes while a phase is running. It can do so by defining, in advance, specific conditions and circumstances in which the limit on carryforwards is to be invoked. Market participants should have sufficient information on transaction volumes and supply and demand so that they can reasonably predict when the limit on carryforwards would come into effect.

The following additional measures may be considered to boost liquidity. First, the Korean government needs to relax its own rules on the allocation of emission permits in reserve to increase supply actively. Second, Kiup Bank and the Industrial Bank of Korea are currently participating in the market as market makers, but they need to play more active roles, with more diverse market makers also encouraged to participate. Third, a ceiling may have to be put on emission prices to ensure stable price signals in the long run. Finally, policymakers should actively contemplate introducing a futures market. The current spot market may effectively be disincentivizing actors from engaging actively in the emissions trade due to the difficulty of hedging prices. The Chinese ETS, also relying solely on spot trading, has been the target of the same criticism (Cong and Lo, 2017).

Transparency of information and consistency of market rules are two decisive factors in information cost. Lack of consistency in market rules can give rise to asymmetry of information, favoring actors who participate actively in policy talks with government officials (Zhao et al., 2017). During the first phase of the ETS, the Korean government frequently changed market rules in an effort to boost liquidity, effectively raising the information cost for businesses. The outcome, paradoxically, was a loss of liquidity and market efficiency, at least to a partial extent.

Until now, the Korean government has mostly relied on abrupt changes of market rules, such as limiting carryforwards, in an effort to increase liquidity on the emissions market. Frequent changes to the rules of the market, however, undermine the transparency and consistency of information, raising the cost of information for participants and potentially exacerbating market inefficiency. It is therefore important for the Korean government to resort to other measures, such as active use of emission permits in reserve, increasing participation of market developers, placing a ceiling on the prices of emission permits, and creation of a futures market, as ways to enhance liquidity and efficiency.

2.3. Changing the Perception of Policymakers and Businesses

The findings of this study carry the following policy and strategic implications for policymakers and corporate decision-makers. First, consistency in execution of the ETS is critical. It is also crucial to tailor policy execution in a way so as to encourage innovation by target businesses. Constant worries about the potentially negative impact of the ETS on the economy and industries could stand in the way of consistently enforcing the scheme. Ex-ante assessments based on static models can overestimate the harm of the ETS, prompting unwarranted policy change and intervention. Our analysis demonstrates that the ETS in Korea exerted no negative effect on businesses and industries during its first phase. Although it is still too early to determine what direct effects the ETS has had, its first phase has coincided with steady improvements in the efficiency of the targeted businesses. Insofar as it can serve as an impetus of innovation in those businesses, it will also be possible to refine the ETS so that it can lead to both reducing emissions and bringing innovation to businesses. There is room for acceptance of the Porter hypothesis (Porter and van der Linder, 1995) in carbon policy.

Second, businesses and industries need to change their perspective on the ETS. Rather than merely reacting to it as another form of regulation, businesses and industries should actively embrace it with strategies for innovation and reform. They should take signs from the ETS to develop more efficient and cost-saving processes, low-carbon products capable of boosting sales, and actively respond to climate change toward strengthening their market image and managing risk. Businesses and industries should seize upon the market-based advantages of the ETS and capitalize upon them to enhance their own competitiveness.

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Appendix

1. Estimates by Industry

In this section can be found the results of applying, for the purpose of testing the fixed- and random-effect models, Equation (3) to the F&B, paper, glass and ceramics, cement, and non-iron metal industries, as well as the results of applying to the same industries Equation (2), the standard model of analysis described in Chapter II, and removing important variables from that equation.

Appendix Table 1. Model Test Results (Fixed- or Random-Effect): F&B


energyi,t 0.0181

energyi 0.0185

(0.0126) (0.0225)

energyi,t ∗ Af2014 -0.00316 energy ∗ Af2014i 0.176

(0.0017) (0.1471)


energy ∗ ETSi

-0.203

(0.1323) (0.0027)

revenuei,t 0.00560 revenuei

-0.00894 (0.0065) (0.0129)

revenuei,t ∗ ETSi,t 0.00557

revenue ∗ ETSi

-0.00546

(0.0163) (0.0036)

𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i,t 0.0279

𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i -0.0286

(0.0246) (0.0233)



(0.0116) (0.0556)

Af2014 3.555 Af2014

-135.6 (3.7421) (124.3110)

ETSi,t -1.746

ETSi

89.70

(57.5673) (3.4153)

DYear2012 -0.0212

-

(3.4708)

DYear2013 -4.644 (2.7595)

DYear2015 -2.988 (2.6613)

DYear2016 -1.288 (2.5029)

DYear2017 -2.514 (3.6771)

Constant term 41.49 (49.6545)

N 23 Total obs. 144

Note: Figures in parentheses indicate standard errors. *p < 0.05, **p < 0.01, ***p < 0.001.

Appendix Table 2. Equation (2) Estimates: F&B

Model (1) (2) (3) (4)


energyi,t 0.0181 0.0428** 0.0196

(0.0122) (0.0124) (0.0129)

energyi,t ∗ Af2014 -0.00313 -0.00779** -0.00310 (0.0016) (0.0023) (0.0016)

energyi,t ∗ ETSi,t -0.000276 0.00255 0.00150 (0.0027) (0.0031) (0.0030)

revenuei,t 0.00551 -0.00133 0.0123 (0.0064) (0.0061) (0.0113)

revenuei,t ∗ ETSi,t 0.00544 0.00151 0.00665 (0.0035) (0.0023) (0.0045)

𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i,t 0.0279 0.0265 0.0520*

(0.0238) (0.0291) (0.0205)

𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i,t ∗ ETSi,t -0.00440 0.00523 -0.0138 (0.0112) (0.0154) (0.0094)

ETSi,t -2.402 -2.297 -2.375 -2.922

(3.3215) (3.0865) (3.7072) (4.4939)

Af2014 3.459 9.416 3.698 -1.234

(3.5980) (5.4131) (3.7076) (2.4724)

DYear2012 -0.00616 -0.233 0.685 1.680 (3.3523) (3.1488) (3.7341) (4.6019)

DYear2013 -4.661 -7.249* -4.361 -1.144

(2.6636) (3.4300) (2.6352) (2.7478)

DYear2015 -2.219 -3.045 -1.088 -1.743

(2.2557) (3.0700) (1.6406) (2.1496)

DYear2016 -0.507 -2.205 -0.376 1.837

(2.2302) (2.7002) (1.6951) (2.5427)

DYear2017 -1.601 -3.318 -0.604 0.775

(3.4362) (3.2529) (2.8414) (3.8865)

Constant term 55.49** 24.69 59.18** 77.21***

(17.9977) (23.4825) (20.5199) (11.6412) σν 44.71 24.48 45.56 64.58 σɛ 8.952 13.29 9.149 10.38 N 23 24 23 23

Total obs. 144 153 144 144


Appendix Table 3. Model Test Results (Fixed- or Random-Effect): Paper


energyi,t 0.0135*

energyi 0.133*

(0.0054) (0.0533)

energyi,t ∗ Af2014 0.00269

energy ∗ Af2014i -0.0932

(0.0014) (0.2369)


energy ∗ ETSi

-0.132

(0.2621)

(0.0072)

revenuei,t 0.144

revenuei 0.859

(0.0799) (0.6973)


revenue ∗ ETSi

-1.988

(1.6479)

(0.0165)



(0.1177) (1.1792)

𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i,t ∗ ETSi,t 0.209


(0.2018) (3.4983)

Af2014 0.535

Af2014 -230.4*

(5.1238) (94.0738)

ETSi,t -0.786

ETSi

0

(.)

(6.2777)

DYear2012 1.619

-

(2.9422)

DYear2013 0.384

(3.7357)

DYear2015 2.400

(4.9256)

DYear2016 8.637

(5.8493)

DYear2017 0

(.)

Constant term 171.0**

(61.8163)



Appendix Table 4. Equation (2) Estimates: Paper



energyi,t 0.0136* 0.0144** 0.0144*

(0.0053) (0.0044) (0.0054)

energyi,t ∗ Af2014 0.00270 0.00234 0.00288** (0.0014) (0.0015) (0.0010)

energyi,t ∗ ETSi,t -0.00912 -0.00507 -0.0107 (0.0071) (0.0048) (0.0062)

revenuei,t 0.144 0.198** 0.234**

(0.0787) (0.0634) (0.0746)

revenuei,t ∗ ETSi,t 0.00838 0.0335 0.0143 (0.0162) (0.0190) (0.0251)

𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i,t 0.139 0.231 0.258

(0.1162) (0.1553) (0.2867)

𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i,t ∗ ETSi,t 0.207 0.267 0.0228

(0.1993) (0.1586) (0.0766)

ETSi,t -0.846 -5.378 2.137 -10.91*

(6.1929) (5.1024) (4.9169) (5.0678)

Af2014 0.539 2.850 -2.717 4.950

(5.0477) (4.6758) (4.8337) (4.3321)

DYear2012 1.630 1.922 0.387 4.246

(2.8975) (2.9582) (3.0643) (3.8349)

DYear2013 0.389 0.828 -1.006 3.633

(3.6798) (3.7040) (3.9898) (3.4130)

DYear2015 2.434 6.363 -0.261 3.348

(4.8439) (6.1191) (4.9456) (5.3175)

DYear2016 8.657 9.234 7.143 9.954

(5.7609) (6.1555) (6.0112) (6.1540)

DYear2017 0 0 0 0 (.) (.) (.) (.)

Constant term 80.63** 75.68*** 108.7*** 86.39*

(25.4527) (15.3837) (17.2409) (33.2858) σν 109.9 110.8 122.6 124.8 σɛ 19.27 19.83 19.73 22.21



Appendix Table 5. Model Test Results (Fixed- or Random-Effect): Glass/Ceramics


energyi,t 0.0776***

energyi -0.202*

(0.0034) (0.0788)

energyi,t ∗ Af2014 0.0000183

energy ∗ Af2014i 2.945*

(0.0040) (1.2295)


energy ∗ ETSi

-3.230*

(1.4596)

(0.0033)

revenuei,t -0.0141

revenuei 2.763***

(0.0239) (0.6215)


revenue ∗ ETSi

-6.281***

(1.6153)

(0.0318)



(0.0928) (2.7311)

𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i,t ∗ ETSi,t -0.0444*


(0.0183) (7.5227)

Af2014 -13.36**

Af2014 -700.3

(5.0656) (380.8715)

ETSi,t 0.302

ETSi

1713.2**

(587.4465)

(2.3020)

DYear2012 -12.91**

-

(4.5675)

DYear2013 -18.25*

(8.0030)

DYear2015 -0.784

(3.1394)

DYear2016 3.688

(3.5283)

DYear2017 0.109

(3.6055)

Constant term -414.6**

(137.5415)



Appendix Table 6. Equation (2) Estimates: Glass/Ceramics



energyi,t 0.0776*** 0.0765*** 0.0801***

(0.0033) (0.0030) (0.0034)

energyi,t ∗ Af2014 0.0000183 0.000481 0.000379 (0.0039) (0.0036) (0.0035)

energyi,t ∗ ETSi,t -0.00375 -0.000927 0.00219 (0.0032) (0.0025) (0.0016)

revenuei,t -0.0141 0.0123 0.0335 (0.0229) (0.0072) (0.0229)

revenuei,t ∗ ETSi,t 0.0613 0.0271 0.151***

(0.0305) (0.0190) (0.0138)

𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i,t 0.0844 0.0416 -0.0139

(0.0891) (0.0460) (0.0807)

𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i,t ∗ ETSi,t -0.0444* -0.00587 -0.119*** (0.0176) (0.0232) (0.0298)

ETSi,t 0.308 5.145 0.519 -21.99***

(2.2091) (5.1384) (1.5946) (5.0243)

Af2014 -13.35* -10.13* -12.49** -26.28 (4.8633) (4.0974) (4.2314) (16.8094)

DYear2012 -12.91** -10.16* -11.51* -22.52 (4.3850) (3.7248) (4.0627) (18.4279)

DYear2013 -18.25* -14.86* -17.00* -28.86 (7.6835) (6.0833) (6.5780) (17.0927)

DYear2015 -0.792 -6.121 -0.783 -4.812

(3.0146) (4.5617) (3.0084) (7.4582)

DYear2016 3.682 -2.116 2.363 7.025

(3.3879) (4.7822) (2.6761) (5.9911)

DYear2017 0.103 -5.271 -1.141 5.091

(3.4619) (5.1075) (3.8383) (4.3890)

Constant term 26.46 17.08 16.47 291.1***

(16.3580) (12.4694) (16.3370) (18.7649) σν 321.0 267.7 313.1 522.8 σɛ 17.86 16.61 18.22 36.02



Appendix Table 7. Model Test Results (Fixed- or Random-Effect): Cement


energyi,t 0.00553

energyi -0.00376

(0.0061) (0.0068)

energyi,t ∗ Af2014 -0.000409

energy ∗ Af2014i 1.222*

(0.0050) (0.5882)


energy ∗ ETSi

-1.183

(0.7895)

(0.0055)

revenuei,t -0.202

revenuei -2.363***

(0.6899) (0.7167)


revenue ∗ ETSi

1.523

(1.4785)

(0.4646)


𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i 8.401***

(3.1257) (2.2390)


adjusted tangible asset ∗ ETSi -10.73

(0.7678) (6.1285)

Af2014 54.86

Af2014 536.3

(52.1977) (602.4809)

ETSi,t -7.141

ETSi

1899.5

(1390.0779)

(49.1686)

DYear2012 -13.13

-

(61.9021)

DYear2013 27.81

(64.2993)

DYear2015 -40.76

(32.3301)

DYear2016 19.65

(44.3554)

DYear2017 -27.59

(51.6972)


(936.4331)



Appendix Table 8. Equation (2) Estimates: Cement



energyi,t 0.00572 0.00616 0.00538

(0.0059) (0.0064) (0.0058)

energyi,t ∗ Af2014 -0.000563 0.00134 -0.000834 (0.0047) (0.0044) (0.0044)

energyi,t ∗ ETSi,t -0.00371 -0.00172 -0.000773 (0.0054) (0.0076) (0.0071)

revenuei,t -0.162 -0.162 -0.0225

(0.6839) (0.5939) (0.6883)

revenuei,t ∗ ETSi,t 0.274 0.223 0.0871

(0.4668) (0.3110) (0.5102)

𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i,t -1.510 -1.591 -1.520

(2.9706) (2.7798) (3.0035)

𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i,t ∗ ETSi,t -0.0191 0.161 -0.00209 (0.7413) (0.5455) (0.7615)

ETSi,t 4.906 4.970 28.65 18.72

(48.5171) (43.1767) (46.0808) (58.6531)

Af2014 62.65 83.41 53.41 25.26

(48.2289) (80.6756) (54.7367) (44.8195)

DYear2012 -4.975 15.01 -11.08 -19.19

(59.0669) (74.8374) (61.7682) (63.3505)

DYear2013 36.08 73.39 26.72 7.248

(61.2342) (111.4609) (72.9828) (58.0300)

DYear2015 -48.99 -46.94 -50.72 -48.83

(30.6675) (28.8324) (31.0653) (28.2710)

DYear2016 28.44 36.85 23.90 23.26

(42.7018) (30.8182) (48.5058) (39.5072)

DYear2017 -20.48 -26.70 -30.83 -28.86

(50.7563) (56.6705) (68.6949) (35.4725)

Constant term 2392.5*** 2129.2*** 2372.4*** 2451.5*** (383.4949) (246.0685) (406.6630) (400.2849)

σν 3543.1 3150.8 3514.7 3581.7 σɛ 230.3 231.0 228.6 230.4



Appendix Table 9. Model Test Results (Fixed- or Random-Effect): Non-Iron Metals


energyi,t 0.0899***

energyi -0.0778*

(0.0124) (0.0341)

energyi,t ∗ Af2014 -0.00104

energy ∗ Af2014i -0.0696

(0.0015) (0.1083)


energy ∗ ETSi

0.213**

(0.0702)

(0.0021)

revenuei,t 0.000819

revenuei -0.000139

(0.0031) (0.0146)

revenuei,t ∗ ETSi,t -0.000815

revenue ∗ ETSi

-0.00397

(0.0333)

(0.0012)


𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i -0.794**

(0.1606) (0.2715)


adjusted tangible asset ∗ ETSi 1.835***

(0.0558) (0.4136)

Af2014 -7.371

Af2014 82.62

(8.7562) (63.0549)

ETSi,t 6.215

ETSi

-152.5***

(42.0123)

(6.9043)

DYear2012 -20.66*

-

(8.8270)

DYear2013 -14.35

(7.8701)

DYear2015 4.151

(3.3080)

DYear2016 -2.055

(4.1381)

DYear2017 7.854

(6.1451)

Constant term 19.08

(37.6167)



Appendix Table 10. Equation (2) Estimates: Non-Iron Metals



energyi,t 0.0899*** 0.0897*** 0.0898***

(0.0121) (0.0155) (0.0120)

energyi,t ∗ Af2014 -0.00109 -0.00125* -0.00111 (0.0015) (0.0006) (0.0015)

energyi,t ∗ ETSi,t -0.000818 -0.00235 -0.000416 (0.0020) (0.0021) (0.0016)

revenuei,t 0.000779 0.000882 0.0154 (0.0030) (0.0034) (0.0169)

revenuei,t ∗ ETSi,t -0.000829 -0.00182 -0.00704 (0.0012) (0.0011) (0.0050)

𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i,t -0.0217 -0.0198 0.441** (0.1553) (0.1559) (0.1327)

𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑡𝑡𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎i,t ∗ ETSi,t -0.0550 -0.0704 0.275** (0.0541) (0.0578) (0.0910)

ETSi,t 6.527 4.774 6.494 -15.86

(6.7543) (5.6596) (6.7447) (9.1869)

Af2014 -7.188 -7.800 -7.484 2.024

(8.4682) (10.8379) (8.3375) (10.1359)

DYear2012 -20.97* -21.53* -21.17* 2.715 (8.6011) (7.7896) (8.7411) (8.7015)

DYear2013 -15.16 -16.26 -15.57 6.621

(7.6643) (10.2299) (7.8731) (13.8450)

DYear2015 4.009 4.073 3.676 6.802

(3.2266) (2.8199) (3.0458) (14.0537)

DYear2016 -2.195 -0.563 -2.817 39.71

(4.0299) (5.6002) (4.5258) (25.9550)

DYear2017 7.723 9.978 7.047 40.05

(5.9831) (7.8365) (5.7996) (29.7640)

Constant term -95.18 -97.73 -93.67 176.6**

(83.5996) (76.9565) (83.2968) (54.4412) σν 160.9 163.7 160.6 505.0 σɛ 34.74 34.64 34.50 78.64



4. Non-Revenue-Adjusting Statistical Thresholds

Below can be seen the statistical thresholds used in the evaluation, in Chapter III, of efficiency of the Korean emissions market during the first phase of the ETS.

Appendix Table 11. Statistical Thresholds for Non-Revenue-Adjusting Analysis: KAU15-17, Number of Open Days (n=880)

Duration Cumulative probability R1 R2 S1

2

0.5% -2.698 -2.724 -2.495

2.5% -2.025 -2.006 -2.023

5.0% -1.638 -1.665 -1.689

95.0% 1.601 1.524 1.618

97.5% 1.929 1.883 2.023

99.5% 2.556 2.481 2.428

5

0.5% -2.505 -2.573 -2.302 2.5% -1.961 -2.052 -1.957

5.0% -1.676 -1.713 -1.712 95.0% 1.604 1.593 1.539

97.5% 2.044 2.084 1.884 99.5% 2.665 2.661 2.597

10

0.5% -2.616 -2.584 -2.548

2.5% -1.911 -1.935 -1.977 5.0% -1.656 -1.704 -1.650

95.0% 1.522 1.480 1.570 97.5% 1.834 1.947 1.941

99.5% 2.759 2.523 2.732

20

0.5% -2.442 -2.398 -2.442 2.5% -1.879 -1.917 -1.931

5.0% -1.724 -1.716 -1.616 95.0% 1.483 1.569 1.559

97.5% 1.800 1.916 2.054

99.5% 2.583 2.448 2.835

40

0.5% -2.306 -2.285 -2.320

2.5% -1.886 -1.888 -1.805 5.0% -1.681 -1.669 -1.615

95.0% 1.284 1.271 1.504 97.5% 1.686 1.716 1.965

99.5% 2.497 2.495 3.058



2

0.5% -2.551 -2.640 -2.733

2.5% -2.056 -2.047 -1.857

5.0% -1.748 -1.782 -1.585

95.0% 1.483 1.494 1.518

97.5% 1.765 1.749 1.923

99.5% 2.652 2.686 2.597

5

0.5% -2.517 -2.524 -2.562

2.5% -2.046 -1.978 -2.069

5.0% -1.748 -1.735 -1.601

95.0% 1.484 1.447 1.527

97.5% 1.858 1.845 1.897

99.5% 2.382 2.493 2.661

10

0.5% -2.656 -2.736 -2.368

2.5% -1.994 -2.012 -1.969

5.0% -1.716 -1.729 -1.673

95.0% 1.486 1.449 1.622

97.5% 1.774 1.770 1.916

99.5% 2.243 2.353 2.584

20

0.5% -2.441 -2.421 -2.263

2.5% -1.916 -1.978 -1.886

5.0% -1.756 -1.750 -1.632

95.0% 1.398 1.433 1.559

97.5% 1.709 1.821 1.846

99.5% 2.390 2.568 2.423

40

0.5% -2.250 -2.266 -2.218

2.5% -1.927 -1.912 -1.802

5.0% -1.772 -1.750 -1.598

95.0% 1.327 1.419 1.462

97.5% 1.760 1.803 1.814

99.5% 2.335 2.420 2.567



2

0.5% -2.709 -2.857 -2.804

2.5% -2.198 -2.179 -1.990

5.0% -1.880 -1.865 -1.682

95.0% 1.566 1.503 1.580

97.5% 1.839 1.840 1.886

99.5% 2.294 2.381 2.702

5

0.5% -2.467 -2.435 -2.643

2.5% -2.130 -2.070 -1.974

5.0% -1.886 -1.889 -1.675

95.0% 1.476 1.483 1.638

97.5% 1.823 1.819 1.935

99.5% 2.446 2.499 2.680

10

0.5% -2.404 -2.288 -2.388

2.5% -2.044 -2.017 -1.821

5.0% -1.748 -1.829 -1.603

95.0% 1.278 1.325 1.573

97.5% 1.710 1.635 2.008

99.5% 2.492 2.291 2.594

20

0.5% -2.351 -2.265 -2.316

2.5% -2.012 -1.998 -1.756

5.0% -1.771 -1.800 -1.581

95.0% 1.165 1.169 1.572

97.5% 1.567 1.590 1.865

99.5% 2.190 2.342 2.656

40

0.5% -1.992 -2.003 -1.930

2.5% -1.793 -1.776 -1.686

5.0% -1.689 -1.687 -1.512

95.0% 1.107 1.076 1.452

97.5% 1.503 1.539 1.878

99.5% 2.485 2.707 2.544



2

0.5% -2.678 -2.784 -2.552

2.5% -2.071 -2.134 -1.939

5.0% -1.805 -1.820 -1.633

95.0% 1.501 1.544 1.633

97.5% 1.797 1.730 1.840

99.5% 2.441 2.396 2.552

5

0.5% -2.624 -2.646 -2.292

2.5% -1.998 -2.124 -1.883

5.0% -1.757 -1.770 -1.623

95.0% 1.452 1.495 1.584

97.5% 1.872 1.874 1.883

99.5% 2.560 2.585 2.292

10

0.5% -2.352 -2.386 -2.292

2.5% -1.905 -1.913 -1.863

5.0% -1.718 -1.745 -1.578

95.0% 1.470 1.441 1.614

97.5% 1.772 1.841 1.929

99.5% 2.402 2.640 2.594

20

0.5% -2.204 -2.175 -2.154

2.5% -1.824 -1.895 -1.773

5.0% -1.721 -1.742 -1.645

95.0% 1.341 1.261 1.646

97.5% 1.647 1.779 2.033

99.5% 2.820 2.609 2.888

40

0.5% -1.932 -1.952 -1.911

2.5% -1.787 -1.791 -1.681

5.0% -1.665 -1.678 -1.525

95.0% 1.132 1.058 1.650

97.5% 1.444 1.411 2.064

99.5% 2.384 2.621 2.948



2

0.5% -2.409 -2.584 -2.371

2.5% -1.985 -2.029 -1.916

5.0% -1.782 -1.779 -1.759

95.0% 1.456 1.486 1.606

97.5% 1.822 1.794 1.759

99.5% 2.319 2.300 2.371

5

0.5% -2.400 -2.330 -2.346

2.5% -1.966 -2.002 -2.010

5.0% -1.720 -1.748 -1.675

95.0% 1.498 1.474 1.620

97.5% 1.726 1.755 2.010

99.5% 2.457 2.517 2.625

10

0.5% -2.165 -2.155 -2.134

2.5% -1.927 -1.908 -1.889

5.0% -1.681 -1.708 -1.653

95.0% 1.383 1.283 1.582

97.5% 1.775 1.623 2.025

99.5% 2.396 2.345 2.967

20

0.5% -1.919 -1.901 -1.902

2.5% -1.752 -1.706 -1.711

5.0% -1.631 -1.632 -1.573

95.0% 1.168 1.053 1.336

97.5% 1.524 1.429 1.988

99.5% 2.222 2.337 3.148

40

0.5% -1.611 -1.597 -1.565

2.5% -1.540 -1.524 -1.468

5.0% -1.470 -1.460 -1.404

95.0% 0.605 0.545 1.269

97.5% 1.112 0.994 1.663

99.5% 1.896 1.850 3.225



2

0.5% -2.671 -2.701 -2.762

2.5% -2.121 -2.074 -1.994

5.0% -1.766 -1.786 -1.687

95.0% 1.526 1.514 1.534

97.5% 1.883 1.800 1.841

99.5% 2.628 2.621 2.301

5

0.5% -2.569 -2.519 -2.325

2.5% -2.078 -2.062 -1.822

5.0% -1.756 -1.765 -1.596

95.0% 1.570 1.489 1.540

97.5% 2.009 1.930 1.764

99.5% 2.566 2.567 2.493

10

0.5% -2.208 -2.237 -2.290

2.5% -1.966 -1.915 -1.717

5.0% -1.802 -1.762 -1.545

95.0% 1.292 1.369 1.655

97.5% 1.859 1.774 1.891

99.5% 2.426 2.462 2.645

20

0.5% -1.977 -1.978 -1.954

2.5% -1.759 -1.711 -1.645

5.0% -1.655 -1.635 -1.525

95.0% 1.013 1.045 1.494

97.5% 1.532 1.444 1.902

99.5% 2.281 2.334 2.521

40

0.5% -1.605 -1.609 -1.582

2.5% -1.515 -1.510 -1.476

5.0% -1.448 -1.443 -1.410

95.0% 0.493 0.622 1.317

97.5% 0.987 0.973 1.589

99.5% 1.855 1.863 2.663

Appendix Table 17. Statistical Thresholds for Non-Revenue-Adjusting Analysis: KAU17, Number of Open Days (n=249)


2

0.5% -2.734 -2.757 -2.598

2.5% -2.052 -2.164 -2.091

5.0% -1.812 -1.779 -1.711

95.0% 1.554 1.531 1.458

97.5% 1.925 1.850 1.711

99.5% 2.452 2.654 2.091

5

0.5% -2.460 -2.392 -2.314

2.5% -2.045 -2.005 -1.990

5.0% -1.753 -1.734 -1.668

95.0% 1.450 1.418 1.527

97.5% 1.733 1.734 1.851

99.5% 2.521 2.618 2.315

10

0.5% -2.287 -2.263 -2.286

2.5% -1.894 -1.907 -1.821

5.0% -1.682 -1.680 -1.641

95.0% 1.347 1.321 1.506

97.5% 1.652 1.690 1.889

99.5% 2.306 2.207 2.692

20

0.5% -2.095 -2.093 -2.046

2.5% -1.888 -1.878 -1.780

5.0% -1.705 -1.755 -1.622

95.0% 1.057 1.000 1.464

97.5% 1.500 1.395 1.928

99.5% 2.103 2.236 2.719

40

0.5% -1.844 -1.855 -1.726

2.5% -1.696 -1.708 -1.617

5.0% -1.594 -1.617 -1.491

95.0% 0.704 0.681 1.278

97.5% 1.147 1.119 1.813

99.5% 1.931 1.926 2.445

Appendix Table 18. Statistical Thresholds for Revenue-Adjusting Analysis: KAU17, Number of Open Days (n=248)


2

0.5% -2.675 -2.647 -2.414

2.5% -2.062 -2.123 -1.905

5.0% -1.699 -1.727 -1.778

95.0% 1.564 1.567 1.651

97.5% 1.947 1.836 1.905

99.5% 2.617 2.452 2.922

5

0.5% -2.502 -2.548 -2.435

2.5% -2.014 -2.064 -1.878

5.0% -1.769 -1.727 -1.646

95.0% 1.592 1.571 1.646

97.5% 1.873 2.012 2.017

99.5% 2.659 2.865 2.945

10

0.5% -2.311 -2.278 -2.257

2.5% -1.989 -1.985 -1.881

5.0% -1.725 -1.765 -1.566

95.0% 1.417 1.447 1.648

97.5% 1.881 1.794 1.874

99.5% 2.463 2.508 2.768

20

0.5% -2.080 -2.050 -2.164

2.5% -1.836 -1.840 -1.743

5.0% -1.677 -1.654 -1.577

95.0% 1.185 1.203 1.523

97.5% 1.600 1.637 1.884

99.5% 2.308 2.415 2.794

40

0.5% -1.781 -1.754 -1.736

2.5% -1.635 -1.643 -1.573

5.0% -1.548 -1.564 -1.473

95.0% 0.789 0.774 1.437

97.5% 1.156 1.373 1.946

99.5% 1.716 2.096 2.513

Appendix Table 19. Statistical Thresholds for Non-Revenue-Adjusting Analysis: KAU18, Number of Open Days (n=153)


2

0.5% -2.488 -2.551 -2.668

2.5% -2.037 -2.125 -2.021

5.0% -1.653 -1.711 -1.698

95.0% 1.443 1.375 1.698

97.5% 1.745 1.866 2.021

99.5% 2.639 2.619 2.506

5

0.5% -2.230 -2.377 -2.480

2.5% -1.956 -1.965 -1.889

5.0% -1.755 -1.752 -1.653

95.0% 1.456 1.450 1.597

97.5% 1.794 1.799 2.127

99.5% 2.334 2.504 2.658

10

0.5% -2.201 -2.223 -2.150

2.5% -1.863 -1.861 -1.806

5.0% -1.706 -1.699 -1.624

95.0% 1.238 1.211 1.597

97.5% 1.584 1.572 2.104

99.5% 2.255 2.208 3.616

20

0.5% -1.872 -1.887 -1.881

2.5% -1.713 -1.737 -1.656

5.0% -1.610 -1.617 -1.529

95.0% 1.080 0.939 1.390

97.5% 1.422 1.420 1.985

99.5% 2.215 2.043 3.076

40

0.5% -1.535 -1.525 -1.504

2.5% -1.483 -1.488 -1.434

5.0% -1.437 -1.442 -1.366

95.0% 0.479 0.427 1.065

97.5% 0.826 0.746 1.505

99.5% 1.790 1.709 2.885

Appendix Table 20. Statistical Thresholds for Revenue-Adjusting Analysis: KAU18, Number of Open Days (n=152)


2

0.5% -2.584 -2.584 -2.596

2.5% -2.130 -2.064 -1.947

5.0% -1.831 -1.857 -1.622

95.0% 1.475 1.433 1.460

97.5% 1.746 1.752 1.784

99.5% 2.516 2.346 2.434

5

0.5% -2.484 -2.456 -2.162

2.5% -1.971 -2.013 -1.866

5.0% -1.794 -1.766 -1.629

95.0% 1.328 1.334 1.510

97.5% 1.702 1.729 1.925

99.5% 2.335 2.258 2.636

10

0.5% -2.292 -2.253 -2.037

2.5% -1.947 -1.946 -1.730

5.0% -1.748 -1.770 -1.595

95.0% 1.104 1.162 1.596

97.5% 1.448 1.528 1.884

99.5% 2.367 2.373 2.991

20

0.5% -1.929 -1.950 -1.854

2.5% -1.769 -1.757 -1.678

5.0% -1.659 -1.659 -1.528

95.0% 1.055 1.069 1.414

97.5% 1.381 1.464 2.027

99.5% 2.280 2.129 3.124

40

0.5% -1.561 -1.566 -1.495

2.5% -1.508 -1.515 -1.425

5.0% -1.457 -1.451 -1.364

95.0% 0.617 0.612 1.121

97.5% 0.971 0.914 1.896

99.5% 1.776 1.724 2.764

Insung Son

Current: Junior Research Fellow, KEEI

Major publications:

A Study on the Timing of Peak Greenhouse Gas Emissions in Korea, Basic Research Report, 18-13, KEEI, 2018 (co-author).

“Prices Versus Quantities Versus Hybrids in the Presence of Co-pollutants”, J.K. Stranlund, I. Son, Environmental and Resource Economics, 2018.

Basic Research Report 2019-09

Assessing the Performance of Korea’s GHG Emissions Trading Scheme in Phase Ⅰ

Printed on: December 30, 2010

Issued on: December 31, 2019

Written by: Insung Son Published by: Yongseong Cho

Publishing company: Korea Energy Economics Institute

405-11 Jongga-ro, Jung-gu, Ulsan, 44543 Tel: (052) 714-2114 (Main) / Fax: (052)-714-2028 Registration No. 369-2016-000001 (January 22, 2016)

Printed by: Design Bumshin

ⓒ Korea Energy Economics Institute 2019 ISBN 978-89-5504-736-3

93320

* Copies with any damages or flaws may be exchanged. Price: KRW 7,000

Policy recommendations and other such suggestions herein are exclusively the author’s own opinion, and do not necessarily represent the position of the KEEI.

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