energy, environment and economic growth

1 23

Environment, Development andSustainabilityA Multidisciplinary Approach to theTheory and Practice of SustainableDevelopment ISSN 1387-585X Environ Dev SustainDOI 10.1007/s10668-013-9459-8

Energy, environment and growth nexus inSouth Asia

Muhammad Zeshan & Vaqar Ahmed

1 23

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REVIEW

Energy, environment and growth nexus in South Asia

Muhammad Zeshan • Vaqar Ahmed

Received: 27 January 2013 / Accepted: 17 April 2013� Springer Science+Business Media Dordrecht 2013

Abstract The present study investigates the energy, environment and growth nexus for a

panel of South Asian countries including Bangladesh, India, Pakistan, Sri Lanka and

Nepal. The simultaneous analysis of real GDP, energy consumption and CO2 emissions is

conducted for the period 1980–2010. Levin panel unit root test and Im test panel unit root

both indicate that all the variables are I (1). In addition, Kao’s panel Cointegration test

specifies a stable long-term relationship between all these variables. Empirical findings

show that a 1 % increase in energy consumption increases output by 0.81 % in long run

whereas for the same increase in CO2 emission output falls by 0.17 % in long run. Panel

Granger causality tests report short-run causality running from energy consumption to CO2

emissions and from CO2 emissions to GDP.

Keywords Energy � Environment � Economic Growth � South Asia

1 Introduction

Rising economic growth in South Asia is escalating the energy demand, and more energy

inputs are required to cater this demand. This region witnesses a positive growth trend over

the last three decades, from 1981 to 2010. It is interesting to note that most of the countries

are following the same growth pattern indicating a strong impact of regional policies on

growth. During the period of analysis, the highest average growth rate was observed in

India that was 6.2 %, whereas Pakistan observed the second highest average growth rate

that was 5 %. On the other hand; Sri Lanka, Bangladesh and Nepal witnessed 4.9, 4.8 and

4.6 % growth rates, respectively (please see Fig. 1 for details).

There are serious concerns about rising demand for energy inputs and the volume of

greenhouse gas (GHG) emissions (Zeshan 2013, Shahbaz and Dube 2012; Shahbaz et al. 2012).

M. Zeshan (&) � V. AhmedSustainable Development Policy Institute, Islamabad, Pakistane-mail: [email protected]; [email protected]

V. Ahmede-mail: [email protected]

123

Environ Dev SustainDOI 10.1007/s10668-013-9459-8

Author's personal copy

Correspondingly, the countries with higher usage of energy consumption are adding more

CO2 emissions in the environment. The rising energy demand has created energy crisis and

environmental degradation. On the one hand, energy resources are depleting quickly,

whereas on the other hand, it is causing environmental degradation. Around the globe,

these problems have forced the governments to closely monitor and supervise energy

markets (ECSSR 2004).

At this stage, universal environment friendly energy policies are essential because the

rising CO2 emissions might bleak the future prospects of the sustainable development.

South Asia requires such energy efficient measures that could ensure the minimum CO2

emissions. Ozturk (2010) argues that higher energy consumption increases CO2 emissions

in the environment, however, the use of efficient production technology might reduce these

emissions over time (Shahbaz et al. 2010; Chang 2010). Almost all the South Asian

countries follow the same pattern for energy consumption but with the exception of

Bangladesh. Nonetheless, sometimes it tends to follow the same pattern but with much

variation. Throughout the period of analysis, average growth rate for the energy con-

sumption in Bangladesh was the highest, 4.5 %. India and Pakistan had the second highest,

4.3 %, whereas Nepal and Sri Lanka observed 2.8 and 2.6 %, respectively (please see

Fig. 2 for details).

The analysis of a causal relationship between the energy consumption, CO2 emissions

and economic growth provides important findings to policy makers. In empirical literature,

the long-term and short-term causal relationships have much importance for energy

assessment policies. The direction of causality suggests the relevant energy policies that

Fig. 1 Economic growth in South Asia 1981–2010

Fig. 2 Energy consumption in South Asia 1981–2010

M. Zeshan, V. Ahmed

123


might be helpful for sustained economic growth (Payne 2010; Ozturk 2010, Squalli 2007).

However, most of the empirical studies have focused on the developed countries (Hossain

2011; Apergis and Payne 2009a), and a scanty literature is available for developing

countries (Huang et al. 2008). The prevailing gaps in literature focusing on regional

analysis especially for South Asian have motivated us in examining the causal linkages

among energy consumption, CO2 emissions and GDP in South Asia.

The empirical literature follows a country by country analysis which is not robust

because of less number of observations, on the other hand, the panel data provide a robust

analysis (Arouri et al. 2012; Wang et al. 2011). Hence, the present study plans to inves-

tigate the cointegrating relationship and the causal links among the variables in a panel

framework. The causality tests between economic growth, energy consumption and CO2

emissions would be performed in three steps. First it examines order of integration in

variables and employs panel unit root tests offered by Levin et al. (2002) and Im et al.

(2003). Second Kao’s (1999) panel cointegration test is used to find the long-term rela-

tionship between variables. Third it applies Panel Granger causality tests to determine the

direction of causality and adjustment mechanism in the system. The rest of the study is as

follows. Section II provides a literature review, and Section III presents data and an

account of the econometric methodology. Section IV discusses the results, whereas Section

V concludes the study and provides policy recommendations.

2 Literature review

Over the last few decades, extensive efforts are directed to discover the impact of energy

consumption and CO2 emissions on the economic growth. It works through two well-

established directions of empirical literature. One of them discusses the relationship

between CO2 emissions and economic growth discussed in the context of Environmental

Kuznet curve (EKC) hypothesis. In this scenario, an initial rise in income causes envi-

ronmental degradation. However, when income level reaches a specific point, people

become more conscious about their environmental responsibilities and environmental

degradation starts falling (Fodha and Zaghdoud 2010; Shahbaz et al. 2010). In contrast,

Dinda (2004) argues that these findings are not universal because the direction of causality

is not a like for each country. If CO2 emissions are causing economic growth, then CO2

emissions might be the result of the production process.

On the other hand, the seminal work of Kraft and Kraft (1978) provides important

insights into energy consumption and economic growth. It finds a unidirectional causal

relationship between the energy consumption and economic growth; the direction of

causality was from economic growth to energy consumption. This piece of work paved the

way for voluminous literature on finding causal linkages between energy consumption and

economic growth (Abosedra and Baghestani 1989; Bentzen and Engsted 1993).

The literature on energy consumption and economic growth is extended under four

different hypotheses that are based on direction of causality. First one is growth hypothesis

which argues that energy consumption is imperative for economic development. Energy

inputs facilitate production process and are complements to factors of production. An

economy would be energy dependent if higher economic growth is obvious in response to

rising energy consumption. To be more specific, it suggests unidirectional causality run-

ning from energy consumption to economic growth (Akarca and Long 1980).

Second one is known as conservation hypothesis which specifies that a country should

adopt conservation policy if higher energy consumption is unable to boost the economic

Energy, environment and growth

123


growth. In this case, direction of causality is from economic growth to energy consumption

showing rising energy consumption owing to higher income level. For Taiwan, Cheng and

Lai (1997) applied cointegration method to examine the interaction between economic

growth and energy consumption. It took data for the period 1955–1993 and confirmed the

conservation hypothesis. Using the same methodology, Wietze and Van Montfort (2007)

worked for Turkey and ended with the same conclusion.

Third one is neutrality hypothesis which asserts no causal relationship between energy

consumption and economic growth. In this case, energy conservation policies would not be

harmful for sustainable economic growth (Akarca and Long 1980). Finally, the fourth one

is feedback hypothesis. It presumes the bidirectional causal relationship between energy

consumption and economic growth, in this case both might be considered as complements.

It implies that any change in energy policies might cause significant effect on economic

growth and vice versa (Yang 2000; Paul and Bhattacharya 2004).

The following table provides the brief literature summary which considers two criterion.

First as most of the recent economic studies are working with panel data because it provide

robust results as compared to time series data (Lee and Chang 2008; Apergis and Payne

2009b) that is why present study has focused mainly on panel data studies. However, some

important time series studies are also reviewed. Second keeping in view the objectives of

the present study, it analyzes the collective relationship between energy consumption, CO2

emissions and economic growth (see Table 1).

3 Methodology and data

Levin panel unit root test operates under the null of a collective unit root in all the variables

in panel against the collective stationary. It is as follows,

Dxi;t ¼ ai þ dit þ #t þ qixi;t�1 þ ei;t; i ¼ 1; 2; 3; . . .;N; t ¼ 1; 2; 3; . . .; T ð1Þ

It captures cross-sectional fixed affects with the help of a, whereas unit-specific time

trend is denoted with #. As the equation carries lagged dependent variable which presumes

slope homogeneity for all the units, it becomes important to capture unit-specific fixed

effects. Null of this test is specifies qi = 0 (for each i) against the alternative of qi = q\0.

It develops a correction factor to produce standard distribution of pooled OLS estimates.

The assumption of collective stationarity of all variables is the major shortcoming of this

test. Having all the variables integrated or stationary is not a necessary condition in

econometrics, a fraction of variables might be integrated, while other might be stationary.

Im panel unit root test overcomes this problem and it discusses heterogeneity in qi under

different hypothesis. For Eq. (1), its works under the following null and alternative

hypotheses;

H0 : qi ¼ 08i

HA : qi\0; i ¼ 1; 2; . . .;N1; i ¼ N1þ 1;N1þ 2; . . .;N:

In this scenario, null assumes that all the variables are non-stationary against the

alternative that a part of variables are stationary. If each variable in hand is non-stationary,

but there exits such a linear combination between the variables that make the system

stationary then the set of variables must be cointegrated. Using modified Dickey-Fuller

(DF) type and Augmented Dickey-Fuller (ADF) type tests, it employs Kao’s (1999) panel

cointegration test for finding a unique cointegrating vector. Given that all the variables are

I (1), this test is investigates long-run relationship between the variables. It is as follows,

M. Zeshan, V. Ahmed

123


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123


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M. Zeshan, V. Ahmed

123


yit ¼ ai þ xitbþ uit ð2Þ

where ai indicates country-specific constant term, b is slope of parameter, uit specifies

stationary error term, yit and xit both are unit root processes such that I (1). Both the DF

type test and ADF type test can be conducted in the following from:

uit ¼ q uit�1 þ vit ð3Þ

and

uit ¼ q uit�1 þXp

j¼1

u D uit�1 þ vit ð4Þ

where residuals uit can be retained from Eq. (2); null and alternative hypotheses can be

specified as; H0 : q ¼ 1; HA : q\1. Kao (1999) suggested different DF tests which are

based on the assumption of exogeneity of regressor. It also suggested its extended version

similar to ADF type test. All these tests work with nuisance parameters of long-run

conditional variance X. Asymptotic distribution of these tests converges to standard normal

distribution as N ? ? and T ? ?.

After specifying the long-term relationship between the variables, present study aspires

to investigate the direction of causality between the variables. If two integrated variables

are cointegrated, dynamic error correction mechanism can be utilized to discover the

direction of causality. Technically speaking, it is specified in the form of traditional vector

autoregression (VAR) framework augmented with one time period lagged error term

recovered from cointegrated vector. It is as follows,

DGDPit ¼ c1i þX

ph11 ip DGDPi t�pþ

Xph12 ip DECi t�pþ

�X

ph13 ip DCO2i t�pþ l1iECTi t�1 þ e1t

ð5Þ

DECit ¼ c2i þX



�X


ð6Þ

DCO2it ¼ c3i þX



�X


ð7Þ

where D is first difference operator, ECT is error correction term and p specifies lag length.

ECTit is the estimated residual derived from long run Eq. (2), lit shows the speed of

convergence parameter for each variable in the system. To measure granger causality, it

takes the help of F-test with a collective null that all the coefficients of another variable are

zero against the alternative of at least one of the coefficients in nonzero one by one for each

variable. A stable system requires at least one significant coefficient for all the error

correction terms in the system. It measures the speed of convergence if there is some

exogenous shock in the system.

The present study uses annual panel data that cover the period 1980–2010. The panel of

South Asian countries comprises Bangladesh, India, Pakistan, Sri Lanka and Nepal.

Following Al-mulali (2011) and Chang (2010), it uses three variables approach including

GDP (real GDP, constant 2005 international $), EC (energy consumption, constant 2005 kt

of oil equivalent) and CO2 (CO2 Emissions, kg per 2005 PPP $ of GDP). All the variables


123


are transformed in natural logarithm. Our data source is World Development Indicators

(WDI).

4 Results

Standard econometric techniques require the stationary data for empirical analysis. If a

variable is non-stationary, first difference makes it stationary, but this procedure wipes out

long-run information in the data. Kao’s (1999) panel cointegration technique preserves the

long-run information in data and provides robust results. Levin and Im both tests indicate

that all the three variables in this regression are integrated at levels (see Table 2 below).

Furthermore, first difference of all the three variables makes them stationary specifying

that all these series are I (1).1

Kao’s (1999) panel cointegration test portrays a unique cointegrating relationship

between the variables. As all the variables are in natural logarithms, so estimated coeffi-

cients represent elasticities. Long-run energy consumption elasticity of income is 0.81

indicating that a 1 % increase in energy consumption will bring 0.81 percent increase in

GDP in long run (See Table 3 below). It signifies that higher energy consumption might

contribute to economic growth significantly in emerging economies. On the other hand,

higher CO2 emissions are also affecting economic growth significantly. A 1 % increase in

CO2 emission reduces the GDP growth by 0.17 % in the long run. It indicates that CO2

emissions are much more detrimental for the South Asia because of its deteriorating

implications. There is a need for regional policy making to address this issue of rising CO2

emission.

VECM tests unearth the direction of short-term and long-term causality in the system.

Results illustrate that short-term causality runs from energy consumption to CO2 emissions

specifying that higher energy consumption results in more CO2 emissions, this fact is also

consistent with the Fig. 1. On the other hand, the short-term causality is running from CO2

emissions to GDP indicating that these emissions are detrimental for the sustained eco-

nomic development in the short run.

The absence of any causal relationship between energy consumption and economic

growth assures the presence of neutrality hypothesis in South Asia. Zeshan (2013) argues

that if energy does not granger cause economic growth, it implies that energy is operating at

sub-optimal level. In such a situation, conservation policies can bring the society back to the

Table 2 Results of panel unit root tests

Name of variable Levin and Lin test (see foot note 1) Im–Pesaran–Shin test

Level Level First difference

Unadjusted t-statistic Adjusted t-statistic w-t-bar statistic

GDP -0.90 1.24 3.05 -3.28***

EC -3.77 1.15 1.26 -1.44*

CO2 -3.23 0.88 2.27 -3.51***

*** and * indicate 1 and 10 % level of significance

1 As Levin panel unit root test requires strongly balanced panel data so it is unable to operate with firstdifference.

M. Zeshan, V. Ahmed

123


optimal path. It implies that South Asia should adopt the conservation policies which would

also reduce the CO2 emissions in environment without impeding the economic growth.

Finally, the coefficients of error correction terms associated with the variables DEC and

DCO2 portray that the system is convergent in long run. The long-run causality indicates

that all the short-term disturbances in the system are corrected by adjusting the energy

consumption and CO2 emissions. However, GDP is unable to response in short run and one

possible factor might be inertia in GDP (please see last column of Table 4 for details).

5 Conclusion

Increasing energy demand is causing not only the energy crisis but is also depleting the

energy resources. Higher energy consumption escalates the proportion of CO2 emissions in

environment which causes pollution. Over the globe, governments are closely monitoring

this CO2 emission and are trying to supervise energy markets, same is the situation in

South Asia.

In the policy making process, causal linkages between the macro-social indicators are

very important. The information about long-run and short-run causality between energy

consumption, CO2 emission and economic growth is much helpful in devising energy

policies. For this purpose, the present study has focused on a panel of South Asian

countries that include Bangladesh, India, Pakistan, Sri Lanka and Nepal. It employs real

GDP, energy consumption CO2 emissions for the period of 1980–2010.

Both Levin and Im panel unit root tests specify that all the variables are I (1). In this

situation, the use of panel cointegration would be beneficial because it preserves the long-

run information in data. Kao’s panel cointegration test finds a stable long-run relationship

between the variables. It illustrates that CO2 emissions are affecting South Asia signifi-

cantly and a 1 % increase in carbon emission might reduce GDP growth by 0.17 % in the

long run. However, energy consumption positively affects the economic growth and a 1 %

increase in energy consumption escalated the economic growth by 0.81 % in the long run.

Table 3 Results of Kao’s panelcointegration test

*** and ** indicate 1 and 5 %significance level, respectively

ADF t-statistics p value

-3.75** 0.03

Long-run coefficients: (GDP is dependent variable)

EC 0.81***

CO2 -0.17***

Intercept 0.33

Table 4 Results of causality tests

Dependent variable Short-run causality Long-run causality

DGDP DEC DCO2

DGDP – 1.15 2.71* -0.55

DEC 0.32 – 1.18 2.14**

DCO2 1.96 6.37** – 2.14**

** and * indicate 5 and 10 % significance level, respectively


123


Panel Granger causality tests report short-run causality running from energy con-

sumption to CO2 emissions which shows that higher energy consumption results in more

CO2 emissions in South Asia. Furthermore, it indicates that casualty is running from CO2

emissions to GDP indicating that CO2 emissions are detrimental to economic growth.

Moreover, the absence of any causal relationship between the energy consumption and

economic growth assures the evidence of neutrality hypothesis. Error correction coeffi-

cients portray that the system is convergent in long run and energy consumption and CO2

emissions would adjust them to rectify any short-run disturbance in the system.

6 Policy implications

• The CO2 emissions are adversely affecting the economic growth, and there is a need to

invest in environment friendly technologies.

• The South Asian countries should set regional environment protection targets to

overcome the increasing pollution in the region.

• The South Asian countries should meet at least once a year to discuss the devastating

impact of rising CO2 emissions in the region and also to devise strategies to cope with

these environmental challenges.

• The absence of any causal relationship between energy consumption and economic

growth assures the presence of neutrality hypothesis. The adoption of conservation

policies might reduce CO2 emission without impeding the economic growth.

• The South Asian countries use obsolete energy production technologies that are less

economic and are impeding the economic growth. It should gradually move to the

environment friendly technologies which are more efficient.

• The idea of regional energy market and open regional trade between the South Asian

countries would result in economies of scale and also more secure energy supplies.

• This regional interdependency will also reduce the hostile tendency of conflict which is

impeding the regional economic growth.

References

Abosedra, S., & Baghestani, H. (1989). New evidence on the causal relationship between United Statesenergy consumption and Gross National Product. Journal of Energy and Development, 14, 285–392.

Akarca, A. T., & Long, T. V. (1980). On the relationship between energy and GNP: a reexamination.Journal of Energy and Development, 5, 326–331.

Al-Mulali, U. (2011). Oil Consumption, CO Emission and Economic Growth in MENA Countries. Energy,36(10), 6165–6171.

Ang, J. B. (2007). CO2 emissions, energy consumption, and output in France. Energy Policy, 35(10),4772–4778.

Apergis, N., & Payne, J. E. (2009a). CO2 emissions, energy usage and output in Central America. EnergyPolicy, 37(8), 3282–3286.

Apergis, N., & Payne, J. E. (2009b). Energy consumption and economic growth in Central America:evidence from a panel cointegration and error correction model. Energy Economics, 31, 211–216.

Apergis, N., & Payne, J. E. (2010). The emissions, energy consumption, and growth nexus: Evidence fromthe commonwealth of independent states. Energy Policy, 38(1), 650–655.

Arouri, M.H., Ben Youssef, A., M’Henni, H., & Rault, C. (2012). Energy consumption, economic growthand CO2 emissions in Middle East and North African Countries. CESifo Group Munich, WorkingPaper Series, 3726.

Bentzen, J., & Engsted, T. (1993). Short- and long-run elasticities in energy demand : a Cointegrationapproach. Energy Economics, 15, 9–16.

M. Zeshan, V. Ahmed

123


Chang, C. C. (2010). A multivariate causality test of carbon dioxide emissions, energy consumption andeconomic growth in China. Applied Energy, 87(11), 3533–3537.

Cheng, B. S., & Lai, T. W. (1997). An investigation of co-integration and causality between energyconsumption and economic activity in Taiwan. Energy Economics, 19, 435–444.

Coondoo, D., & Dinda, S. (2002). Causality between income and emission: a country Group specificeconometric analysis. Ecological Economics, 40(3), 351–367.

Dinda, S. (2004). Environmental Kuznets curve hypothesis: a survey. Ecological Economics, 49, 431–455.Emirates Center for Strategic Studies and Research, ECSSR. (2004). Asian energy markets: Dynamics and

trend. Emirates Center for Strategic Studies and Research.Fodha, M., & Zaghdoud, O. (2010). Economic Growth and Pollutant Emissions in Tunisia: An empirical

analysis of the environmental Kuznets curve. Energy Policy, 38(2), 1150–1156.Hossain, M. S. (2011). Panel estimation for CO2 emissions, energy consumption, economic growth, trade

openness and urbanization of newly industrialized countries. Energy Policy, 39(11), 6991–6999.Huang, B. N., Hwang, M. J., & Yang, C. W. (2008). Causal relationship between energy consumption and

GDP growth revisited: A dynamic panel data approach. Ecological Economics, 67(1), 41–54.Im, K. S., Pesaran, M. H., & Shin, Y. (2003). Testing For Unit Roots in Heterogeneous Panels. Journal of

Econometrics, 115(1), 53–74.Kao, C. (1999). Spurious Regression and Residual-Based Tests for Cointegration in Panel Data. Journal of

Econometrics, 90, 1–44.Kraft, J., & Kraft, A. (1978). On the relationship between energy and GNP. Journal of Energy and

Development, 3, 401–403.Lee, C. C., & Chang, C. P. (2008). New evidence on the convergence of per capita carbon dioxide emissions

from panel seemingly unrelated regressions augmented Dickey-Fuller tests. Energy, 33, 1468–1475.Levin, A., Lin, C. F., & Chu, C. (2002). Unit root test in panel data: Asymptotic and finite sample properties.

Journal of Econometrics, 108, 1–24.Niu, S., Ding, Y., Niu, Y., Li, Y., & Luo, G. (2011). Economic growth, energy conservation and emissions

reduction: A comparative analysis based on panel data for 8 Asian-Pacific countries. Energy Policy,39(4), 2121–2131.

Ozturk, I. (2010). A Literature Survey on Energy-Growth Nexus. Energy Policy, 38, 340–349.Paul, S., & Bhattacharya, R. N. (2004). Causality between energy consumption and economic growth in

India: a note on conflicting results. Energy Economics, 26, 977–983.Payne, J. E. (2010). Survey of the international evidence on the causal relationship between energy con-

sumption and growth. Journal of Economic Studies, 37, 53–95.Shahbaz, M., & Dube, S. (2012). Revisiting the relationship between coal consumption and economic

growth: cointegration and causality analysis in Pakistan. Applied Econometrics and InternationalDevelopment, 12(1).

Shahbaz, M., Lean, H.H., & Shabbir, M.S. (2010). Environmental Kuznets curve and the role of energyconsumption in Pakistan. Development Research Papers.

Shahbaz, M., Zeshan, M., & Afza, T. (2012). Is energy consumption effective to spur economic growth inPakistan? New evidence from bounds test to level relationships and granger causality tests. EconomicModelling, 29(6), 2310–2319.

Soytas, U., & Sari, R. (2009). Energy consumption, economic growth, and carbon emissions: Challengesfaced by an EU candidate member. Ecological Economics, 68(6), 1667–1675.

Soytas, U., Sari, R., & Ewing, B. T. (2007). Energy consumption, income, and carbon emissions in theUnited States. Ecological Economics, 62, 482–489.

Squalli, J. (2007). Electricity consumption and economic growth: bounds and causality analyses of OPECcountries. Energy Economics, 29, 1192–1205.

Wang, S. S., Zhou, D. Q., Zhou, P., & Wang, Q. W. (2011). CO2 emissions, energy consumption andeconomic growth in China: A panel data analysis. Energy Policy, 39(9), 4870–4875.

Wietze, L., & Van Montfort, K. (2007). Energy consumption and GDP in Turkey: Is there a co-integrationrelationship? Energy Economics, 29, 1166–1178.

Yang, H. Y. (2000). A note on the causal relationship between energy and GDP in Taiwan. EnergyEconomics, 22, 309–317.

Zeshan, M. (2013). Finding the cointegration and causal linkages between the electricity production andeconomic growth in Pakistan. Economic Modeling, 31, 344–350.

Zhang, X. P., & Cheng, X. M. (2009). Energy Consumption, Carbon Emissions, and Economic Growth inChina. Ecological Economics, 68(10), 2706–2712.


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