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Energy 66 (2014) 950e958

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Energy

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Regional disparities and carbon “outsourcing”: The political economyof China’s energy policy

Huimin Li a, Tong Wub,*, Xiaofan Zhao b, Xiao Wang c, Ye Qi b

aBeijing Climate Change Response Research and Education Centre, Beijing University of Civil Engineering and Architecture, Beijing 100044, ChinabClimate Policy Institute and School of Public Policy and Management, Tsinghua University, Beijing 100875, ChinacChina Energy Conservation and Environmental Protection Group Consulting Co., Ltd., Beijing 100082, China

a r t i c l e i n f o

Article history:Received 23 July 2013Received in revised form28 December 2013Accepted 4 January 2014Available online 28 January 2014

Keywords:Energy policyPolicy implementationPolitical economyEnergy intensityChinaClimate change

* Corresponding author. Present address: ecoSERSciences, Arizona State University, Tempe, AZ 852878927; fax: þ1 480 965 8330.

E-mail addresses: tongwu86@gmail.com, Tong.Wu

0360-5442/$ e see front matter � 2014 Elsevier Ltd.http://dx.doi.org/10.1016/j.energy.2014.01.013

a b s t r a c t

Since 2007, gross domestic product (GDP) growth in China’s inland provinces has exceeded that of theaffluent coastal provinces. Concurrently, they have also been given more lenient energy intensityreduction targets to provide latitude for continued growth. The regional unevenness of economicdevelopment and energy policy has implications for the ability of the country to achieve its energysavings target e an objective that has become the key part of China’s climate change mitigation strategy.This study shows that there is an explicit trend in which changes in regional economic structure ismoving towards increasing national energy intensity. This is due, in large part, to carbon leakage be-tween provinces. Changes in regional economic structure increased national energy intensity by 0.13%during the 11th five-year plans (FYP) period, and is on track to cause a further increase of 1.35% duringthe 12th FYP period. In formulating national energy policy, the existing “Target Responsibility System”

(TRS) of policy implementation may need to be improved. Regional economic disparities must be takendirectly into account in policymaking, as inland provinces should be assigned higher, not lower, energyintensity reduction targets. This will increase the likelihood that national targets, and hence China’sbroader climate change mitigation goals, will be met.

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1. Introduction and overview

Energy saving has been central to China’s development strategy,due initially to persistent shortages since the 1980s [1]. Morerecently, however, it has become the main plank of China’s climatechange mitigation efforts. Since the 1980s, the government hasadopted a series of policies to promote energy efficiency in an effortto partially decouple energy and economic growth. These measureshave included: setting strict savings target in the five-year plans(FYP), channeling investment into energy efficiency projects, con-trolling enterprise energy intensity and supplies through quotas,phasing out energy-intensive mechanical and electrical devices,creating energy efficiency standards for buildings and residentialappliances, supporting the research and development of energyefficient technology, and establishing national, local, and sectoralenergy saving service centers throughout China [2,3]. With theimplementation of such policies from 1980 to 2002, China’s energy

VICES Group, School of Life-4801, USA. Tel.: þ1 480 965

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use per 10,000 U of gross domestic product (GDP) dropped from3.40 ton coal equivalent (tce) to 1.16 tce (2005 U), a decrease of65.82% (see Fig. 1). However, this declining trend was reversed in2002e2004, as energy intensity increased from 1.16 tce/10,000U to1.28 tce/10,000U, owing to the rapid expansion of energy-intensiveindustries [4].

To stem this recrudescence of out-of-control growth in energydemand, China’s 11th FYP (2006e2010) set a legally binding energyintensity reduction target of 20% compared with the 2005 level.China also enacted and strengthened initiatives such as the “Top-1000 Enterprise Energy Saving Program”, “Ten Key Energy SavingProject”, and “Elimination of Outdated Facilities Program” [6].Concurrent to these energy policies, China accelerated thecontinuing modernization of its development model by reformingand upgrading traditional industries, fostering and strengtheningstrategic and newly emerging industries, and accelerating thedevelopment of the service industry [7].

On the administrative front, China strengthened the re-sponsibility of local governments in energy management bydecomposing the national target into provincial-level mandatesusing the target responsibility system [8]. Consequently, the energysaving policies of the 11th FYP achieved a 19.1% reduction in energy

Fig. 1. China’s energy use and energy intensity, 1980e2011. Note: Energy intensities are calculated based on data from the China statistical yearbook 2012 [5], using 2005 constantprice.

H. Li et al. / Energy 66 (2014) 950e958 951

intensity. According to officially-released information, during the11th FYP period, the “Top-1000 Enterprise Energy Saving Program”

saved 150 million tce [9], the “Ten Key Energy Saving Project”3.4 million tce [10], and the “Elimination of Outdated FacilitiesProgram” 1.1 million tce [11], all exceeding their targets. From aregional perspective, all provinces except Xinjiang achieved theirtargets for energy intensity reduction, with 28 provinces exceedingtheir targets [12].

Industry structure and technology exert significant influence onnational energy demand [13]. The contributions of technologicaland structural improvement to China’s energy efficiency have beenextensively surveyed in the recent literature. For instance, Ma et al.(2010) reviewed 36 studies that decomposed China’s energy in-tensity into macroeconomic drivers, and found that although theresults of different studies were mixed, there is a trend from the1990s onward in which technology played a dominant role inChina’s energy intensity change, although in the 1980s or earlier,structural shifts played a larger role [14]. Recent studies havefocused on the 11th FYP (2006e2010) e the first time climatechange mitigationwas directly addressed in official planning e andshowed that technical gains from the decrease of physical energyintensity was the primary driver owing to the wide utilization ofthe low-cost and high-return energy-saving technologies, whilestructural effect was less important [15,16].

In China, both economic development and energy intensity arehighly imbalanced at the provincial level. The more affluent coastalprovinces are already moving to a lower-carbon developmentpathways, but the less developed inland provinces are still heavilyreliant on energy intensive industries for growth. In different re-gions, social and economic factors such as gross regionalproduct (GRP) per capita, industrial structure, population, urbani-zation and technology level have different impacts on regionalenergy and energy-related CO2 emissions [17]. These regional dis-parities greatly affected China’s national energy use and its energy-related CO2 emission. Liu et al. explored China’s regional and sec-toral GHG (greenhouse gases) emission and showed that lessdeveloped provinces had contributed most of the national emis-sions increase during 1997e2009, moving the whole country in amore carbon intensive direction [18]. In China, per-capita energyuse is dramatically different across provinces, ranging from 6.80 tce

in Inner Mongolia to 1.42 tce in Jiangxi in 2010; the magnitude indifference between highest and lowest provinces is as high as 479%[5]. Ito et al. (2010) showed that if the energy use per capita in lessdeveloped regions increases due to the improvement of the livingstandard, the total use in China will also increase significantly [19].

Except for per-capita energy use, energy intensity also differsdramatically across provinces. In 2010, the energy intensity of thehighest-ranking province, Ningxia, reached 3.31 tce per 10,000U ofGDP, while that of the lowest province, Beijing, was only 0.58 tceper 10,000 U of GDP. The magnitude of the difference in energyintensity between highest- and lowest-ranking provinces was ashigh as 570% [5]. There is an obvious pattern in which the energyintensity of the less affluent inland is higher than that of coast. Fenget al. (2013) showed that within China, the more affluent provinceshave “outsourced” large shares of their emissions to their lessdeveloped, inland counterparts, which helped the former decreasetheir energy intensity [20].

However, almost all analytical breakdowns of China’s energyand CO2 emissions have focused on industry structure, and tech-nology disparities, while the impact of the regional economicstructure e e.g., disparities in economic performance and man-agement among provinces e on national energy intensity haslargely been neglected. This means important policy-relevant is-sues have been overlooked, leaving considerable scope for furtherexploration. In recent years, developing provinces with higher en-ergy intensity have grown their GRP at faster rates than developedprovinces. This difference in economic development, coupled withdifferent levels of energy intensity, call for a more thoroughconsideration of the relationship between potential carbon leak-ages (“outsourcing”) and administration, to better inform thedesign and implementation of energy policies.

As a key driver of economic development in the less developedwestern, central and northeastern provinces, the growth of energy-intensive industries derived not only from breakneck industriali-zation and urbanization, but also from the eastern provinces’ car-bon “outsourcing”. Since 1978, when the “Reform and Open”development strategy was adopted, China has launched measuressuch as the creation of special economic zones to promote growthin the coastal provinces. After three decades of rapid development,the eastern provinces have become China’s economic center and

H. Li et al. / Energy 66 (2014) 950e958952

the leader in industrial technology. As a by-product of this strategy,regional economic disparities between eastern and less inlandprovinces widened significantly. Since 2000, China has embarkedupon major campaigns such as “West Development”, “RevivingNortheastern Old Industrial Base” and “Rise of Central China” topromote development inland. Consequently, economic growth incentral, western and northeastern China has exceeded that ofeastern China since 2007. The trend continues, and these provincesare now accounting for an increasing proportion of China’s eco-nomic structure (see Fig. 2). The implication this has for thecountry’s energy policy is explored in this study.

This study quantitatively assesses the consequences of regionalpolitical and economic disparities for national energy policy. Wefirst present our methodology and data sources. Following, therelative contributions of regional structure to the change in energyintensity during 11th and 12th FYP periods are analyzed. Finally,we discuss the implications for China’s energy policy goingforward.

2. Methodology and data

2.1. Methodology

The index decomposition analysis (IDA) and the structuraldecomposition analysis (SDA) are the most popular decompositiontechniques in analyzing changes in energy use and CO2 emissionsover time. There are similarities as well as differences between IDAand SDA in terms of study scope, method formulation, and datarequirements [23,24]. The fundamental difference between thesetwo techniques regards data requirement e that is, SDA relies oninputeoutput modeling while IDA only requires aggregate sectordata. As a result, SDA is capable of more refined decompositions ofeconomic and technological effects but IDA is capable of moredetailed time and country studies because of the availability of data.Both SDA and IDA had been used in to decomposition analyses ofChina’s energy and CO2 emissions [13,25], but IDA methods havebeen more often employed partly due to the flexibility in problemformulation and data requirement [26]. Considering study scopeand data requirements, this study employed the IDA technique toevaluate the contributions of regional economic structure tochanges in energy intensity.

Fig. 2. The share of regional economic on total national GDP, 2005e2015.Sources: NBS [21], NDRC [22].

Theoretically, in a given year, a country’s total energy use can beexactly decomposed as the sum of all provincial energy use; pro-vincial energy use can be calculated with a province’s GRP and itsenergy intensity:

E ¼Xi

ðGi$IiÞ (1)

where E is national energy use, Gi is GRP for provincial unit i, and Iiis energy intensity for provincial unit i. Then, the national energyintensity in this period can be calculated as:

F ¼ EGDP

(2)

where GDP is nationwide gross domestic product. Further, inte-grating equations (1) and (2), F can be written as:

F ¼Xi

�Gi

GDP$Ii

�(3)

Note Gi/GDP as stri, which represents the share of GRP ofprovince i in total GDP, and its change with time captures the dis-parities in economic performance and management among prov-inces. We use the equation DF ¼ Ft � Ft�1 to indicate the totalchange of national energy intensity F for year t compared with theprevious year. Obviously, DF is caused by the change of both str andI since they are the only variables. In additive decomposition, DFcan be decomposed as:

DF ¼ DFstr þ DFI (4)

where DFstr represents the contribution of variations in regionaleconomic structure to national energy intensity change, and DFImeans the impact of provincial technical improvement on nationalenergy intensity change.

To exactly divide the contribution of every variable to thechange of the dependent variable, Laspeyres index approach andDivisia index approach are the most frequently-used decomposi-tion methods. Ang (2004) [27] compared various index decompo-sition analyses, and supported the logarithmic mean Divisia index(LMDI) as the preferred method, due to its theoretical foundation,

H. Li et al. / Energy 66 (2014) 950e958 953

ease of use and result interpretation, along with some otherdesirable properties in the context of decomposition analysis.Moreover, the LMDI method can deal with zero values in thedataset while other methods cannot [28]. Since 2000, a growingnumber of studies have used LMDI to investigate energy use andcarbon emissions at regional and national scales, such as Wang[29], Zhang [30] and Zhao [31].

In this study, LMDI was employed to analyze the contributionsof str and I to changes of energy intensity because it leaves no re-siduals. For details of the LMDI method, please consult Ang (2001)[32] and Liu and Ang et al. (1998) [33]. For ease of expression, wenote Fi ¼ Gi/GDP$Ii. According to the practical guide by Ang (2005)[34], DFstr and DFI can be derived thusly:

DFstr ¼Xi

�Fti ; F

t�1i

�$LN

strti

strt�1i

!(5)

DFI ¼Xi

�Fti ; F

t�1i

�$LN

Iti

It�1i

!(6)

where function ðFti ; Ft�1i Þ is the logarithmic average of two positive

numbers Fti and Ft�1i , given by

�Fti ; F

t�1i

�¼ Fti � Ft�1

i

LN�Fti�� LN

�Ft�1i

� (7)

From Eqs. (5)e(7), the LMDI formulas contain logarithmic terms.The variables cannot have zero or negative values, although zeroand negative values seldom occur when dealing with the energyand CO2 emissions analyses. The strategy of dealing with zero andnegative values for the LMDI approach can be found in Ang and Liu(2007), which gives a set of guidelines to deal with all possible casesof changes that involve negative and/or zero values using theanalytical limit strategy [35].

1 The decrease rate is inconsistent with the decrease rate of 19.1% official releasedby the NDRC, since this study employs provincially-accounted economic and energydata to calculate national energy intensity.

2.2. Data

This study employs provincially-accounted GRP and energy usedata to estimate the impact of regional structure on national energyintensity. The provincially-accounted GRP are from “Gross RegionalProduct byThreeStrata of Industry”of the China Statistical Yearbook[5,21], and the energy data are from “Total Energy Consumption byRegion” of the China Energy Statistical Yearbook [36e38]. Accordingto the National Bureau of Statistics (NBS), “Total Energy Consump-tion” refers to the total use of energy of various kinds by the pro-duction sectors and the households in a region in a given period oftime. Total energyconsumption canbedivided into threeparts: end-use energy consumption; loss during the process of energy con-version; and energy loss. Indeed, energy is never destroyed duringits “consumption” process; it changes fromone form to another. Thereal essenceof “energyconsumption” is its available energy,which isreferred as “exergy”. Usually, exergy can be calculated as the netcaloric value of energy of various kinds,which is the energy releasedas heat when a compound undergoes complete combustion withoxygen under standard conditions. In China, energy of various kindsare usually translated into standard coal equivalent, and 1 kg coalequivalent corresponds to a value specified as 7000 kcal.

It is worth mentioning that there is a widening gap betweennational and aggregate provincial numbers for bothGDP and energyuse, owing to different statistical scope. According to the NBS [21],the sum of all provincial GRP at constant price is greater than thenationally-reported level by 6.95% in 2005 and by nearly 17.99% in2011,while the sumof all provincial energy use exceeds the national

figure by11.64% in 2005 and 21.35% in 2011(Table 1). AlthoughChinahas made reforms to clarify national and provincial statistics, thegaps seem to have expanded in recent years. According to officialinterpretations, separation of national and provincial accountingsystems is the fundamental reason. Specifically, repeated statisticsfrom growing numbers of cross-regional enterprises, inconsistentestimation methods for the service industry and different scalesused to revise GDP and energy data are considered primary causes.Even though the gaps between national and provincial statistics arelarge, the differences for energy intensity are smaller than those forGDP and energy use data, ranging from 2.85% to 5.13%.

China established a statistical communiqué system for regionalenergy use per 10,000 U GDP in 2005. Since then, the NBS andNDRC (National Development and Reform Commission) havereleased data for annual regional energy use per 10,000 U GDP,electricity use per 10,000 U GDP, and energy use per 10,000 Uvalue-added for industrial enterprises above a designated size,which has become a benchmark for evaluating provincial govern-ment performance. However, there is still a gap between the na-tional statistical communiqué and provincial accounting, althoughChina’s energy intensity statistics is moving toward a comprehen-sive and transparent indicator [39]. Fig. 3 shows the differencebetween the rate of provincial energy intensity decrease and thenational statistical communiqué. To maintain data consistency, thisstudy employs the energy intensity data calculated from provincialGRP and energy use.

3. Results and analysis

3.1. Impacts of regional economic disparities on national energyintensity

During the 11th FYP period, the energy intensities of all regionsdecreased dramatically. Those of the less developed western, cen-tral and northeast regions decreased by 18.5%, 21.0% and 20%,respectively, while that of the developed eastern region decreasedby 20.0%. Nonetheless, the energy intensity of inland provincesremained higher than that of their eastern counterpart (Fig. 4). GRPin the inland grew faster than that of the eastern region. Inparticularly, the western, central and northeastern regions wit-nessed an 88.4%, 83.0% and 85.0% growth between 2005 and 2010,respectively, while the increase in the eastern region was 81.3%.Less developed provinces and municipalities such as InnerMongolia, Jilin and Chongqing increased their GDP at an annual rateof 17.4%, 14.9% and 14.8%, while the annual growth rate of devel-oped provinces such as Beijing, Shanghai, Zhejiang and Guangdongwere 11.1%, 10.9%, 11.9% and 12.5%, lower than the national averageduring the 11th FYP (Fig. 5).

The regional patterns of energy intensity changes and GRPgrowth had important impacts on national energy intensity (Fig. 6).Changes in regional economic structure had a subtly positive effecton the decline of national energy intensity in 2005e2007, butplayed a negative role in 2007e2010. There is an explicit trend ofthe regional structure effect increasing with rising national energyintensity year by year. Absent the regional structural effect, nationalenergy intensity should have decreased by 19.54% during 11th FYPcompared to the actual decline of 19.41%.1 Changes in regionaleconomic structure is therefore an important reason why the na-tional target was not met, despite the fact that almost all provincesoverachieved their targets.

Table 1Differences in data for GDP, energy use and energy intensity between national accounting and provincial accounting in China.

2005 2006 2007 2008 2009 2010 2011

GDP National accounting (trillion U) 18.49 20.84 23.79 26.08 28.49 31.42 34.36Aggregate provincial accounting (trillion U) 19.78 22.50 25.73 28.75 32.09 36.29 40.54Gap (%) 6.95 7.95 8.16 10.22 12.63 15.47 17.99

Energy use National accounting (billion tce) 2.36 2.59 2.81 2.91 3.07 3.25 3.48Aggregate provincial accounting (billion tce) 2.63 2.91 3.19 3.38 3.57 3.90 4.22Gap (%) 11.64 12.32 13.71 15.87 16.50 19.87 21.35

Energy intensity National accounting (tce/104 U) 1.276 1.241 1.179 1.117 1.076 1.034 1.013Aggregate provincial accounting (tce/104 U) 1.332 1.292 1.240 1.175 1.113 1.073 1.042Gap (%) 4.38 4.04 5.13 5.13 3.43 3.81 2.85

Note: GDP and energy intensity are calculated at 2005 constant price. Energy use in Tibet is not included in this table since it is not released in the China Statistical Yearbook.Source: NBS [21].

H. Li et al. / Energy 66 (2014) 950e958954

3.2. Projecting the impact of regional economic disparity ineconomic development

In the 12th FYP (2011e2015), China set a binding target ofdecreasing energy intensity by 16% compared with its 2005 level. InAugust 2011, China disaggregated this target to provinces andmunicipalities [42]. Moreover, each province or municipalityannounced its own economic growth target in the first half of 2011(see Table 2). Based on these enumerated targets, we analyzed thepotential impact of changes in regional economic structure on na-tional energy intensity.

Given the strong incentives for economic growth in all prov-inces, meeting energy intensity reduction targets is challenging.While the national GDP growth target is set at 7%, all provincialunits have targets above 7%. Specifically,16 provincial units in Chinaare above 10%, while the rest range from 8% to 10%. Even the mostprosperous provincial units such as Beijing, Shanghai and Guang-dong, whose GDP per capita is comparable to developed countries,are aiming for a growth rate of 8%. Among the 16 provincial unitswith the highest economic growth targets, almost all are located in

Fig. 3. Differences in the rate of provincial energy intensity decrease between thenational statistical communique and accounting by provincial GDP and energy use.Sources: NBS [21], NBS and NDRC [40].

western, northeastern and central China, except for Tianjin andHainan province (a tropical island situated in South China Sea andone of the most economically laggard provinces). On average, thenortheastern, western and central provinces set higher GDP growthtargets than their eastern counterparts. This is not surprising, asthese regions are far more underdeveloped than the east, and thushave the strongest ambition for further economic growth. It is alsoworth noticing that historically, GRP growth targets have alwaysbeen overachieved.

Concurrently, inland provincial units (provinces and munici-palities) were assigned much lower energy intensity targets thaneastern ones in the 12th FYP. All inland provincial units except forJiangxi and Liaoning were given targets at or below the nationaltarget of 16%, while the targets for eastern provincial units exceptHainan range from 16 to 18%. This differentiated target assignmentleaves ample room for economic development in underdevelopedregions, and takes into account their level of industrial technology.Moreover, this assignment accounts for policy performance duringthe 11th FYP period: it is exactly for this reason why Xinjiang wasgiven a target as low as 10%, which aroused controversy over therationality of the target distribution. The booming economies of theunderdeveloped regions, coupled with their “leeway” in energyintensity reduction, suggests that China’s regional economicstructure is changing in a way that drives up national energyintensity.

In order to gain a more accurate understanding of the effect ofthis changing economic structure on China’s energy intensityreduction in the 12th FYP, we performed a hypothetical calculationbased on provincial economic growth targets and energy intensitytargets. The result is that national energy intensity will onlydecrease by 14.9%, much less than the national target of 16%. This isstrong evidence that the negative effect of regional structure isgrowing, causing the underperformance from the 11th FYP to beeven worse in the 12th FYP period (Fig. 6). In contrast, if theregional economic structure remains as it was during the 11th FYP,the national energy intensity will decrease by 16.25%, which is justenough to meet the national target.

4. Discussion

4.1. Regional economic disparities and interregional carbon“outsourcing”

Changes in regional economic structure drove up national en-ergy intensity by 0.13% during 11th FYP, and will increase nationalenergy intensity by 1.35% during the 12th FYP should trendscontinue. Since 2006, the beginning of the 11th FYP, China hasenacted a series of policies with the intention of reducing energy

Fig. 4. Regional energy intensities in China from 2005 to 2010. Note: statistics for the coastal, central, western and northeastern regions are based on NBS [41].Source: NBS [21].

Fig. 5. Regional annual GRP growth in China from 2005 to 2010.Source: NBS [21]

H. Li et al. / Energy 66 (2014) 950e958 955

Fig. 6. Impact of regional economic structure on national energy intensity (2005e2015). Note: Green bars represent the actual impact, and orange bars are estimates based onprovincial GRP growth and energy intensity decrease plans (For interpretation of the references to color in this figure legend, the reader is referred to the web version of thisarticle.).

H. Li et al. / Energy 66 (2014) 950e958956

intensity, including phasing out small and inefficientmanufacturing facilities and limiting the development of energyintensive industries. However, these policies have not beenimplemented equally effectively across regions [43]. The moredeveloped eastern provinces implemented these policies moreeffectively by exerting significant pressure on energy intensiveenterprises. Since these provinces have already reached a high levelof prosperity, they are paying growing attention to environmentalconcerns, including the transition to a lower-carbon economy. Onthe other hand, less developed regions still rely heavily on energyintensive industries for growth and thus have relaxed regulations.The significantly weaker enforcement of energy policies in theseregions incentivized energy intensive enterprises in the easternprovinces to migrate their operations there. In this way, the richerprovinces have been able to more readily meet their energy-savingtargets by “outsourcing” carbon inland. In the process, enterprisesnot only upgraded their technologies, but also vigorously expanded

Table 2Regional economic growth and energy intensity decrease targets during the 12th FYP pe

Province Economic growthtargeta

Energy intensitydecreasing targetb

Province

Beijing 8 17 GuangxiTianjin 12 18 Inner MongHebei 8.5 17 ChongqingJiangsu 10 18 SichuanZhejiang 8 18 GuizhouShanghai 8 18 YunnanShandong 9 16 TibetFujian 10 16 ShaanxiGuangdong 8 18 GansuHainan 13 10 QinghaiEastern 9.45 16.6 Ningxia

XinjiangShanxi 13 16 WesternAnhui 10 16Jiangxi 11 17 LiaoningHenan 9 16 JilinHubei 10 16 HeilongjiangHunan 10 16 NortheasterCentral 10.5 16.17China 7 16

Sources: adata from NDRC [22]; bdata from SC [42].

their production capacity as a means to improve marketcompetitiveness.

This westward shift facilitated a decrease in energy use perunit of product, but also contributed to a further expansion ofenergy-intensive sectors in China. From a regional perspective,this has been a winewin situation for both the developed andunderdeveloped regions: energy intensity in eastern regions waslowered while energy intensity in less developed regions alsowent down because the incoming enterprises from the east aremore energy efficient. As a result, when viewed separately, thisprocess of industrial reorganization and “outsourcing” has playeda positive role in decreasing energy intensity for both developedregions and developing regions, but from a holistic perspectiveresulted in an increase in overall national energy intensity. Inenergy-saving terms, the whole, so to speak, is less than the sumof the parts. This has important implications for China’s energypolicies.

riods. Unit: %.

Economic growth targeta Energy intensity decreasing targetb

10 15olia 12 15

12.5 1612 1612 1510 1512 1012 1612 1512 1012 1510 1011.54 14

11 1712 1612 16

n 11.67 16.33

H. Li et al. / Energy 66 (2014) 950e958 957

4.2. Implications for energy saving and the TRS

Perhaps the most important implication of the abovementionedphenomenon is for China’s policy implementation system. TheTRS is the central pillar of China’s political economy, especially forenergy policy [8]. TRS is a typical policy enforcement mechanismbased on China’s centralized governance system, usually applied tohigh-level social issues such as pubic security, family planning, andpoverty alleviation [44]. The theoretical underpinning of TRS is akinto “teleocracy” (literally an organization designed to fulfill a specificpurpose), a concept first coined by political philosopher MichaelOakeshott and later popularized by Nobel Laureate Friedrich Hayek,referring to the type of governance where societal resources arecollectively allocated and citizens’ actions are deliberately directedtoward particular political ends [45]. TRS captures the essence ofteleocracy: meeting a well-articulated set of national targetsthrough direct mobilization of resources and personnel. The corepieces include a concrete and measurable policy target and a per-formance evaluation system with a corresponding program of re-wards and punishments. Put simply, TRS increases the incentive forlocal governments to achieve their policy assignment by linking theimplementation of central policies to financial bonuses and careeradvancement [46,47].

Under TRS, each provincial unit was assigned an energy-savingtarget in terms of percentage reduction in energy intensity by thecentral government. For the 11th FYP period (2005e2010), thenational target was disaggregated when the provincial govern-ments submitted their voluntary energy-saving targets to theNDRC, as designated by the State Council. Consequently, 15 prov-inces proposed targets equal to the national target, 12 provincesproposed targets lower than the national target, and only fourprovinces proposed targets above the national average. To ensurethat the national target would be met, the NDRC attempted toconvince the 12 provinces that proposed the lowest targets to raisetheir standards. Taking into account the stage of development, in-dustrial structure, historical energy intensity, total energy use,energy use per capita and self-sufficiency of energy resources, theNDRC eventually raised targets for 11 out of the 12 provinces (Tibetwas the exception) [48]. In September 2006, the NDRC officiallyannounced the results: provinces were grouped into eight energyintensity reduction categories, ranging from 12% to 30%. The fourprovinces of Shanxi, Shandong, Jilin and Inner Mongolia wereassigned targets higher than 20%, 20 provinces were assigned a 20%target, and seven provinces, including developed Guangdong andless-developed Guangxi, Qinghai, Yunnan and Tibet, were giventargets below 20%.

For the 12th FYP period (2011e2015), instead of asking indi-vidual provinces to voluntarily propose targets, the NDRC built adisaggregation methodology based on research and recommen-dations by energy experts. In August 2011, the final target assign-ment for each province was officially announced. According toseveral indices, including total energy use, energy intensity, fiscalrevenue, population, urbanization, industrial structure, energysaving potential, environmental capacity, provincial targets wereclassified into five categories, ranging from 10% to 18%. On average,provincial units in western, northeastern and central China wereassigned much lower targets than eastern provinces (see Table 2),implying that the less-developed regions will have laxer policiesthan their affluent counterparts. The purpose of setting lower tar-gets for less-developed provinces is to leave sufficient room forcontinued economic growth, according to the NDRC, but the rela-tively soft energy policies may hinder China’s efforts optimize in-dustrial structure.

From the perspective of most Chinese policy makers, TRS is aleakage-free system that guarantees national target attainment e

meaning national targets will be achieved without uncertainties aslong as every provincial unit achieves its target. However, this studyhas shown that regardless of the rationality of target assignment forprovincial units and the actual target attainment, the lack ofconsideration for the effect of changing regional economic struc-ture on national energy intensity makes TRS susceptible to carbon“outsourcing”. That is to say, TRS alone is not sufficient forachieving the national energy intensity target since regional eco-nomic structure is playing an increasingly negative role in China’senergy profile. According to the NDRC [49], in the first two years of12th FYP period (2010e2012), national energy intensity haddecreased by 7.7% based on provincial energy and GDP data, while itonly decreased by 5.5% based on national energy and GDP data.

Regional disparities in economic development must be moresystematically considered when disaggregating the national energysavings target into provincial units. We argue that less-developedregions with higher energy intensities and faster economicgrowth should set more ambitious energy savings targets, sup-ported by increased technological and financial aid from the centralgovernment. As discussed in our results, the negative trend in theimpact of regional economic structure on national energy intensitybodes ill for national goals of energy saving (and consequentlyclimate change mitigation). China should undertake policy reformto arrest, and potentially reverse, this trend.

Last but not least, energy intensity e currently China’s primaryenergy-savings index e is expressed as energy use per unit of GDP,and thus not only provides multiple avenues for reduction (e.g.,either through accelerating growth or lowering energy use), butalso makes it more complex to integrate provincial data into na-tional accounts. On the one hand, multiple avenues for reductionmake the energy savings measure more flexible for local govern-ments; on the other hand, the index of energy intensity is an in-direct statistical index, and is thus difficult to observe currently bycentral and local governments. At the same time, the indexmakes iteasy for interregional leakage to occur because of regional eco-nomic disparities. We argue that China should use a more simpleand direct index, such as energy use, to benchmark its energypolicy.

5. Concluding remarks

This study quantitatively assesses the relationship betweenregional economic structure, and the changes thereto over the pastdecade, and the ability of China to achieve its national energysavings target. There is an explicit trend in which changes inregional structure, by creating carbon leakage between provinces,is moving towards increasing national energy intensity year byyear. Changes in regional economic structure increased nationalenergy intensity by 0.13% during the 11th FYP period, and is ontrack to increase national energy intensity by 1.35% during the 12thFYP period. This structural effect is confirmed to be a significantreason why the national energy intensity target was not met in the11th FYP, and will be the reason why it will also not be met in the12th FYP without renovations in measurement and policyimplementation.

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