ECONOMIC RESEARCH CENTERDISCUSSION PAPER
E-Series

No.E15-7

Target Responsibility System and CO2
Emissions Convergence: Mechanism andEmpirical Testby
Sun Yongping

November 2015

ECONOMIC RESEARCH CENTERGRADUATE SCHOOL OF ECONOMICSNAGOYA UNIVERSITY

Target Responsibility System and CO2 Emissions Convergence:
Mechanism and Empirical Test
Sun Yongping1

Abstract
The Chinese government had already taken a string of policies and actions for CO2
abatement, the most important of which is the introduction of energy conservation andcarbon abatement into the target responsibility system. First, this paper elaborates theinfluencing mechanism of target responsibility system for the convergence of CO2
emissions and considers that the bottom-up political system, investment impulse andthe nature of CO2 abatement have major impacts on the convergence of China’s CO2
emissions. Second, using interprovincial panel data and following the grouping
methods of eastern, central and western regions, this paper compares China’s CO2
emissions convergence between the 10th Five-year Plan period (2001-2005) and the11th Five-year Plan period (2006-2010). Analysis indicates that the effect of energy
conservation and CO2 abatement target tends to be subjected to GDP growth targets.
Therefore, this paper considers that in the context of growing international pressures,
China needs to take the absolute value of CO2 emissions decreasing into the target
responsibility system.
Keywords: target responsibility system; energy conservation and emissions
abatement; convergence of CO2 emissions

1. Introduction
Since 1999, China had experienced a new round of heavy industrialization andthe output value, investment, profit and proportion of heavy industry have overtaken
light industry (Jian Xinhua, 2005). Renewed heavy industrialization and rapid
economic growth had led to China’s environmental degradation and CO2 emissions
increase. In 2009, China replaced the United States as the world’s largest CO2 emitter.
According to the statistics of the World Bank, China’s per capita CO2 emissions
was 2.7 tons which represented 66% of the world per capita CO2 emissions in 2000
1

Associate Professor, Hubei University of Economics, Wuhan, Hubei Province, China. The
author is grateful to Economic Research Center of Graduate School of Economics, Nagoya
University for giving me an opportunity to conduct the research during April 1 to September 30,
2014.
1

0

.25

.5

.75

1

1.25

1.5

and reached 4.8 tons which exceeded 4.7 tons of the world per capita CO2 emissions.
China’s per capita CO2 emissions reached 7.35 tons which represented 145.9% of theworld per capita CO2 emissions in 2013. In comparison, China’s per capita GDP
represented only 17.56% of the world per capita GDP in 2000 and although this figure
kept on the increase, it only reached 65.9% in 2013. In 2013, the proportion ofChina’s per capita CO2 emissions was 2.2 times that of China’s per capita GDP.
Accordingly, in 2013, China’s CO2 emissions accounted for 27.6% of the world’s
CO2 emissions while its GDP accounted for 12.5% of the world’s GDP. China haveconfronted tremendous international pressures in CO2 emissions and climate change
has become a key topic in China’s political, economic and diplomatic negotiations.

2000

2002

2004

2006

proportions of per capita GDP
proportions of GDP

2008

2010

2012

proportions of per capita CO2
proportions of CO2

Figure 1: Proportions of China’s GDP and Carbon Emissions
Data source: the World Bank, the World Development Indicators Database, http://databank.worldbank.org/.

The Chinese government attaches great importance to climate change andadopted a series of policies and actions, the most important of which is the inclusionof energy conservation and emissions abatement into the evaluation indicators of thetarget responsibility system. In 2005, the central government decided to initiateenergy conservation and emissions abatement on a nationwide scale and introduced
the reducing energy intensity by 20% as one of eight binding indicators for the
performance evaluation of government officials during the 11th Five-year Plan period(2006-2010). Under fiscal decentralization system, target responsibility system isconsidered as the most powerful policy arrangement because the evaluation indicators
imposed by the central government have a major influence on the decision-making of
local officials.

2

According to the Global Carbon Budget 2014,39.4% of the CO2 emissions
comes from coal combustion, 30.0% from oil, 16.7% from natural gas, and in total
86.1% CO2 emissions from fossil energy combustion（Le Quere et al., 2014). Thus
energy conservation and CO2 abatement have a strong collaborative effect. How
should we evaluate the contributions of energy conservation to CO2 abatement?
Answering these questions is of great significance for the timely adjustment of
performance evaluation system for local government officials, low carbon andsustainable development and international climate negotiations.

2. Literature Review
Climate change brought about by CO2 emissions has universal and extensive
impacts on the world. However, due to their global and indirect impact, CO2 have notbeen regarded as a pollutant by various countries over the years. In the recent decade,scholars have paid growing attention to the environmental problems caused by thechanging concentration of greenhouse gases such as CO2 and mainly employedempirical analysis and mathematical modeling methodologies in their respective
studies.
Research results of many scholars have confirmed the existence of EKC curve
for CO2 emissions, particularly for OECD countries (Richmond and Kaufmann, 2006;
Strazicich and List 2003; Brock and Taylor 2010). However, other studies indicatethat under the situation of large samples, convergence does not exist (Nguyen Van,
2005; Aldy 2006). Studies by some scholars have confirmed the existence of the EKC
curve of China’s CO2 emissions, believing that an obvious inverted U-shaped
relationship exists between CO2 emissions and economic growth (Xu and Song, 2010;
Li and Li, 2010; Zheng and Zhu, 2012; Du, 2010). Some scholars believe that there isan N-shaped relationship between economic growth and carbon emissions (Hu et al.,2008; He and Zhang, 2012). However, other scholars believe that the EKC curve ofChina’s CO2 emissions has not been demonstrated (Lin and Jiang, 2009; Hu, Liu andTang, 2013).
There are significant differences in the research conclusions of convergencestudies. According to the study by Li and Feng (2014), there is a significant
divergence in the national carbon total factor productivity (TFP) but “clubconvergence” is demonstrated with regards to the technology efficiency between
eastern and western regions. Wang, Zhang and Cai (2014) consider that China’s
carbon emissions intensity is significantly divergent on the whole but the clubconvergence also exists. Zhou (2014) believes that absolute β convergence of carbon
intensity does not exist on a nationwide basis and for central and western regions but
conditional β convergence exists and club convergence exists for eastern andnorth-eastern regions. Xu (2010) considers that absolute β convergence does not exist
for China’s per capita carbon emissions but conditional β convergence exists and clubconvergence exists for eastern, central and western regions. Lin and Huang (2011)
consider that China’s regional carbon emissions demonstrate the characteristic of3

“convergence of clubs”. Wang, Zhou and Zhou (2010) believe that convergence exists
in the CO2 emissions performance.
In terms of political incentives, GDP growth has been the first and foremost
indicator under China’s existing performance evaluation system. Local governments
are highly incentivized to support polluting enterprises that generate significant output
value and tax revenues rather than imposing environmental regulation upon them.
Nevertheless, the target responsibility system of energy conservation and emissions
abatement comprises an important basis for the evaluation of provincial-levelgovernment leadership, which is subject to the one-ballot veto system. Li, Ma and Qi(2011) believe that the target responsibility system serves as a real and effective
environmental constraint on local governments. However, Jia (2014) maintains that
local government officials, whose promotion hinges upon rapid economic growth, arehighly motivated to attract polluting and energy-intensive industries. Yang et al.
(2007) tested the relationship between fiscal decentralization and environmental
quality and discovered that an increase in the level of fiscal decentralization has asignificant negative impact on environmental quality and that the reform of fiscal
decentralization is likely to have reduced local government efforts of environmental
regulation. Kevin (2014) holds that although the target responsibility system of energy
conservation and emissions abatement have provided local governments with politicalincentives to pursue low-carbon development, unreasonable configuration of
objectives and imperfect statistical system have undermined the policy effectivenessof CO2 abatement. Central government policies often failed to be fully implemented
and in some cases, were even distorted by local governments. Zhang et al. (2011)
investigated the relationship between fiscal decentralization and environmental
pollution from the perspective of CO2 emissions. They discovered that the increase in
the level of decentralization is unfavorable to the reduction of carbon emissions andthat decentralization is likely to compromise government efforts of environmental
regulation. Nevertheless, significant differences exist in the impacts of fiscal
decentralization on carbon emissions for different provinces with different energy
consumption structures, locations and environmental policies.
Target responsibility system as one of the most political system with Chinesecharacteristics has an important impact on the local officials. With CO2 as the main
body of the greenhouse gases has not been identified as pollutants, action to reduce
emissions has extensive externalities. So, target responsibility system has a significant
effect in promoting local economic growth target responsibility system, whether asignificant impact has on CO2 emission reduction is very worthy research topics. thatis the starting point and the innovation points of this article.

3. Influence Mechanism
According to the scope of influence, environmental quality can be divided into
local public goods and global public goods. In the views of environmental federalism,
environmental quality as local public goods should be provided by local governments
4

given that local governments are the most knowledgeable about local constraints andpreferences of residents and thus able to internalize environmental quality and costs
within the regional scope and provide local residents with environmental quality at thelowest cost (Oates, 2002). Environmental quality as global public goods should be
provided by the central government. In this case, environmental quality lacks
competitiveness and exclusivity because beneficiaries including all residents of acountry and even the world and that cost and benefit internalization cannot be realized
for a certain region, local governments tend to take the “free ride”. As typical global
public goods, CO2 abatement must be provided by the central government since local
governments are not motivated to do so. However, no one is exempt from suchenvironmental problems as climate change, increasing frequencies of extreme weather,rising sea levels, reducing grain output and extinction of species.
Qian and Roland (1998) believe that administrative decentralization and fiscal
decentralization are important drivers of local economic growth. Zhou (2007) offeredanother explanation that in the promotion championship of local officials, GDP
growth rates is the key factor. Fiscal decentralization and the promotion championshipare bottom-up political incentives that have compromised local government efforts ofenvironmental regulation and exacerbated environmental pollution (Wang, Zhang etal., 2007; Yang et al., 2007; Tao et al., 2009). Therefore, although energy
conservation is covered by the one-ballot veto system, over-fulfillment of thisobjective cannot add scores to political performance. Rather, it may sacrifice local
GDP growth. As a result, local officials still focus on GDP growth as their primaryobjective and the political incentive of “one-ballot veto” is difficult to be converted
into the internal incentive of CO2 abatement for local governments.
Investment impulse is an important factor for the China’s CO2 emissions growth
while high saving rate provides the foundation for investment impulse. Focusing on
CO2 emissions, Francesconi and Pieroni (2012) created an extended Solow modelwith interest rate as the only policy variable in order to analyze on the differences
between steady states under green rules and golden rules and conclude that without
policy intervention, the optimal savings rate under golden rules exceeds the optimalsavings rate under green rules. Therefore, they argued that in order to achievesustainable development, savings rate must be adjusted. Based on the statistics of
Latin American and the Caribbean countries, the empirical study indicates thatcountries with high debt levels have high per capita CO2 emissions and vice versa andconcluded that reducing liability level can lead to CO2 abatement(Aubourg, Good andKrutilla,2008).
Seven provinces have currently initiated pilot carbon emission trading system
programs and the national carbon market is under preparation. Emission trading
system allows enterprises to trade carbon emission allowances in the national carbon
market. However, this does not change the nature of CO2 abatement as global public
goods and political incentives of the local officials for carbon abatement. Therefore,
the central government must set a cap for each province based on current target
responsibility system as the basis for evaluating the performance of local officials. Cai
5

et al. (2008) also believe that environmental quality requires the institutional designof the central government.

4. Model and Data
4.1 Model
Empirical model of this paper is based on the Green Solow model of Brock andTaylor (2010). In the Brock and Taylor (2010) model, CO2 is regarded as a byproductof production activity. The dynamic form of carbon emission function can be
expressed as follows:

ec ec   g A  y c y c
The growth rate of per capita CO2 emissions equals to the difference between percapita income growth rate and the advancement rate of emissions abatement
technology. Based on the above equation, the logarithmic mean value of discrete time
section N can be arrived at:

(1 / N ) log(e c etc N )   g A  (1 / N ) log( y c ytc N )After logarithmic linearization, we may arrive at the following absoluteconvergence regression equation:(1 / N ) log(eitc eitc  N )   0  1 log(eitc  N )  it

Given that the individual fixed effects such as energy endowment andgeographical location of various provinces do not change with time and have certain
impacts on CO2 emissions, we have included known individual effects that do notchange with time into the model. Meanwhile, considering that the central government
continuously introduces new policies to regulate the environmental quality, we haveintroduced the yearly dummy variable into the model. In order to comprehensively
analyze the determinants of growth rate of per capita CO2 emissions, we haveintroduced other control variables into the model. In addition, limited by the
regression method of fixed effect model, we are unable to get the coefficient ofknown individual effect variables. Therefore, based on the research objectives of thispaper, we configure the conditional convergence regression equation as follows:(1 / N ) log(e c / eitc  N )   0  1 log(eitc  N )   2 X it   i  t  it6

Where, X is the vector set of the control variables, i denotes cross-section data
and t denotes the time sequence data,
the fixed effect of time and

 it

i

denotes individual fixed effect,

tdenotesdenotes the stochastic disturbance term.

4.2 Data
There is not a direct statistics system for CO2 emission. As a result, we can only
indirectly calculate the CO2 emissions of each province based on the energy statistics
system and emission coefficients of various energy released by the IPCC or the
Department of Climate Change of the National Development and Reform
Commission (NDRC). Current discussions on the measurement of carbon emissions
fall into the following three categories: measurement at energy extraction side,
measurement at energy production side and measurement at energy consumption side.
There are great controversies in the academia regarding which method should prevail.
Nevertheless, measurement at energy consumption side is a common approach widelyadopted by various countries. Therefore, this paper has also followed themeasurement at the consumption side for both primary energy and secondary energy.
Final data of this paper derives from China Statistical Yearbook between 2000 and
2014.
The most critical element in the calculation of CO2 emissions is the emission
coefficients of various energy because which is subject to such as the fuel type,combustion technology, operational conditions, control technology, oxidation factor
and maintenance quality and equipment age. These data all require site measurement
at the corporate level. Therefore, this paper has adopted the reference methodology
and parameters in the second volume (energy) of the IPCC Guidelines for National
Greenhouse Gas Inventories. Table 1 is a summary of various energy emission
coefficients employed in this paper.
Table 1: CO2 Emission Coefficients of Energies
Energy Types

Raw
coal

Crude
oil

Naturalgas

Coke

Gasoline Diesel Kerosene Fuel oil

IPCC Emission
factor(kgCO2/TJ)

94600

73300

56100

107000

69300

74100

71900

77400

20908

41816

38931

28435

43070

42652

43070

41816

1.98

3.07

2.18

3.04

2.98

3.16

3.10

3.24

Net calorificvalue in China(kJ/kg)
CO2 emission
coefficient(kgCO2/kg)

In order to investigate the effect of regional on the convergence of CO2
emissions, this paper divides China into regions as eastern, central and western.
Different from traditional method is of classification of Liaoning into eastern region
7and the inclusion of Heilongjiang and Jilin into central region. Eastern region includes
the following 11 provinces: Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu,
Zhejiang, Fujian, Shandong, Guangdong and Hainan. Central region includes thefollowing eight provinces: Heilongjiang, Jilin, Shanxi, Anhui, Jiangxi, Henan, Hubei
and Hunan. And western region includes the following 12 provinces: Inner Mongolia,
Guangxi, Sichuan, Guizhou, Yunnan, Tibet, Shaanxi, Gansu, Qinghai, Ningxia andXinjiang.

4.3 Variables
Per capita CO2 emissions (perCO2): calculated by dividing the CO2 emissions by
the quantity of permanent residents of each province.
Average growth rate of per capita CO2 emissions (agr_perCO2): In a strict sense,
convergence of economic growth takes into account the convergence of growth rates
rather than per capita GDP. Therefore, in study of CO2 emissions convergence,
growth rates rather than absolute quantities should be adopted. Referencing theapproach of convergence studies of economic growth, this paper has adopted thefollowing equation:

agr _ perCO2  (1 / N ) log(e c / etc N )Savings rate (saving): These is the absence of savings rate in China’s statisticalsystem, although this figure can be obtained by per capita disposable income minus
consumption and then divided by per capita disposable income, the disposable
incomes of urban and rural residents are accounted separately and therefore, theweighted average statistics are not accurate. Hence, this paper adopts capitalformation rate as the proxy variable for savings rate.
Capital shallowing (n+g+δ): n is natural growth rate of population. g is theadvancement rate of technology, denoted by the growth rate of domestic patentapplication authorizations. δ is depreciation rate, denoted by the share of fixed asset
depreciation in GDP.
Dummy variable (year): Successive introduction of various environmental
regulation policies by the Chinese government at different levels will inevitably affectCO2 emissions. Therefore, we employ the yearly dummy variable to capture such acontinuous policy effect.
Energy intensity (energy/GDP): It is denoted by the standard coal equivalentconsumed for the production of each unit of GDP to reflect energy utilization
efficiency.
Energy intensity decreasing target (target): It is used to stand for the pressure ofenergy conservation which is denoted by the energy intensity decreasing target set
central government for local government in target responsibility system.

8

Fuel price index (price): rising fuel price will increase the production cost andthus incentivize manufacturers to increase energy efficiency and ultimately reduce
CO2 emissions. This indicator reflects the impact of fuel price variations on CO2emissions.
4.4 Estimation Method
Panel data possess the advantages of expanded information quantity, increased
freedom of test statistics and reflection of the gradual changes of economic structureand economic system, which have drawn extensive attention from researchers. In theconfiguration and application of panel data, it is necessary to assess and test whether
error decomposition satisfies fixed effect or stochastic effect. However, the academia
never stopped arguing about which method should be adopted. Before conducting
regression, this paper also configures stochastic effect regression model for B-P test
(chi-square value is 116.45) and result rejects the null hypothesis Var(u) = 0, whichindicates the existence of disturbance and justifies the employment of stochastic effectmodel.

5. Regression Results
5.1βConvergence
In Table 2, we have presented the regression results of absolute convergence andrelative convergence. In the first column, the beginning per capita CO2 emissions are
the only explanatory variable, which has a significantly negative coefficient andpasses 1% significance level test. This indicates that provinces with high beginningper capita CO2 emissions have relatively low average growth rates and β absoluteconvergence exists in the per capita CO2 emissions. From equation (2) to equation (5),
we have respectively introduced such control variables as savings rate, capitalshallowing, energy intensity, target indicators, fuel price index and dummy variable oftime without altering the coefficient and significance of logperCO2_2000. Thus, βconditional convergence exists in China’s per capita CO2 emissions.
Table 2: Regression Results of Absolute Convergence and Relative
Convergence
Equations

（1）

（2）

（3）

（4）

Explained variables

agr_perCO2

agr_perCO2

agr_perCO2

agr_perCO2

logperCO2_2000-0.013***(-6.28)-0.021***(-7.06)***logsavinglog(n+g+δ)energy/GDP-0.021***(-12.06)
***

0.069

0.065

(5.39)(8.84)-0.013-0.015**(-1.22)(-2.39)-0.024***(-11.71)
***

0.045

(4.53)-0.020***(-3.41)
**

0.005

9

(2.52)**0.001

target(2.5)-0.005logprice(-0.12)02000.year

(.)
0.004

2001.year(0.83)*

0.009

2002.year(1.91)***

0.022

2003.year(4.47)***

0.032

2004.year(6.63)***

2005.year

2006.year

2007.year

0.043

0

(8.8)

(.)

***

0.051

0.005

(10.57)(1.16)

***

0.060

0.008

(12.29)(1.58)***

2008.year

2009.year

0.009(13.04)(2.29)

0.068

***

0.009

(13.93)(1.07)***

2010.year***

Constants
Samples
Wald chi

2

**

***

0.075

0.012

(15.34)(2.87)***

2011.year

**

0.063

***

0.083

0.015

(17.0)(3.67)***

0.050-0.153(17.01)(-2.44)(-4.82)(-0.26)

356

356

356

209

39.47

66.07

950.53239.70-0.173-0.05

Note: Numbers in brackets are z values; *denotes p<0.1, **denotes p<0.05 and *** denotes p<0.01; ln denotesthat natural logarithm is adopted for the variable. Software employed in this paper is Stata SE 13.1.

10

In Table 2, we may see that with the gradual inclusion of control variables, thecoefficient of savings rate remains significantly positive and relatively stable andpasses 1% significance level test. Under the political system of fiscal decentralizationand political performance championship, local governments have a strong investment
impulse and investment level of provinces with high capital formation rate, isrelatively high, which is consistent with the conclusions of Brock and Taylor (2010).
For regression equations (3) to (5), the coefficient of capital shallowing issignificantly negative, which indicates that CO2 emissions of provinces with a highcapital shallowing are relatively limited.
5.2Target Responsibility SystemIn equations (4), energy intensity with significantly positive coefficient, which
demonstrates that for provinces with high energy intensity, the growth rate of percapita CO2 emissions is high as well. The reason is that for provinces with highenergy intensity, the production of each unit of GDP requires more resources, i.e.
energy efficiency is relatively low. Table 2 shows that target of energy intensity
decreasing is significantly positive, which means the greater target, the higher per
capita CO2 emissions growth rate. This is because the target setting is to consider the
result of many factors. In regression equation (4), we have introduced fuel price index,
which has no significance. Theoretically speaking, with the rise of resource prices,
enterprises tend to conserve energy consumption under cost considerations. However,central government guidance price for energy is the reason of increasing energy prices
has no significant effect on carbon emissions.
In regression equations (3) to (4), we have included the dummy variable of yearand given that statistics for energy intensity indicator are available only after 2005,
the dummy variable of year in equation (4) is 2005-2011. In equation (3), the year of2000 is taken as the benchmark with the value of zero. From 2003 to 2011, thecoefficient of dummy variables of year is significantly positive, which indicates thatcompared with 2000, the growth rate between 2003 and 2011 is on the increase. In
order to more accurately reflect the changes of coefficient, we have calculated thefirst-order difference of the dummy variable coefficient, i.e. Δcoeft=coeft-coeft-1, with
relevant results shown in Figure 2. Although the coefficient between 2003 and 2008 is
positive compared with 2000, the first-order difference of coefficient kept on thedecrease, which indicates a decreasing time trend. In the context of maintaining
growth as the top political priority after the financial crisis, many energy intensive
projects have re-emerged and the first-order difference of the coefficient swerved toincreasing time trend.

11

.014
.012
Difference Value.006 .008 .01
.004
.002
0

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Figure 2: Difference Value of the Coefficient of Time Variables
Although the coefficient is positive, first-order difference of coefficient shows
decreasing time trends from 2003 to 2008. During the "Tenth Five-year Plan" period,
from 2002 to 2003, the per capita CO2 emissions increasing trend is very obvious.
During the “Eleventh Five-year Plan”, due to the implementation of the target
responsibility system for energy conservation, there is obvious decline from 2005 to
2006. After the financial crisis, the growth was taken as the first priority political goal,a lot of energy project resurge, first-order difference of coefficient turn around to risefrom 2009 to 2011. Evidently, CO2 emissions are not only the energy problem but
also a development issue. In conclusion, the energy intensity could reduce the growthrate of per capita CO2 emissions, which has remarkable effect on the convergence of
the CO2 emissions. However, the target of energy intensity decreasing does notpromote the convergence of per capita CO2 emissions, which is a departure from theprinciple of design.

6. Robustness Test
Analytical conclusions of the econometric model could be sensitive to thevariations of model and data, which makes is necessary to conduct robustness test.
Robustness test means the discussion of whether regression results will change withsample capacity, model configuration and variable selection. Therefore, the basic
approach of robustness test in this paper is to test whether significant changes when
adjust sample capacity. As we have already observed from Table 4, altering the time
scope of samples or including additional control variables will not change the symbol
12and significance of the logperCO2_2000 and logsaving.
Neumayer (2004) discovered that geographical location has a major impact on
the convergence of CO2 emissions. Results of some studies have also confirmed theeffects of different geographical locations on the convergence of CO2 emissions (Li etal., 2014; Wang, et al., 2014; Zhou, 2014; Xu, 2010). Therefore, following the latestclassification method of the NBS, this paper has divided China into eastern, central
and western regions. In Table 3 and Table 4, we have demonstrated the regression
results of absolute convergence and conditional convergence. As far as regionalgrouping is concerned, the significance of beginning per capita CO2 emissions
logperCO2_2000 is the highest for the eastern region, followed by the central andwestern region with a diminishing trend from the eastern to western regions.
Nevertheless, the coefficient is insignificant for the western region, which indicatesthat the eastern and central regions demonstrate absolute convergence, which is not
the case for the western region.
Table 3: Regression Results of Absolute Convergence Based on Regional
Grouping Methods
Explained variable

agr_perCO2

agr_perCO2

agr_perCO2

RegionEastern

Eastern

WesternlogperCO2_2000-0.025***(-8.62)***

Constants
Samples2

Wald chi*

0.004(-1.80)(0.83)-0.006

***

***

0.066

0.038

0.035

（14.10）（8.35）（6.32）

130

96

130

74.33

3.23

0.69

Note: Numbers in brackets are z values; *denotes p<0.1, **denotes p<0.05 and *** denotes p<0.01; ln denotesthat natural logarithm is adopted for the variable. Software employed in this paper is Stata SE 13.1.

As can be seen from Table 3, the eastern, central and western regions have all
displayed relative convergence. The coefficient of savings rate demonstrates asignificant positive correlation in the eastern and western region. The coefficient ofsavings rate is positive but insignificant for the central region. Capital shallowing
demonstrates a significant negative correlation in all groupings. The coefficient ofenergy intensity coefficient displays a positive significance in the eastern and central
region but insignificance in the western region. The coefficients of energy intensity
decreasing target are positive and significant in the western region. Thus, in theeastern and central region, energy intensity is more binding, while in the westernregion energy intensity decreasing target is relatively more binding. Price index ofenergy products has no significance as well. Coefficient of the dummy variables ofyear basically has positive significance since 2006. Regression results based ongroupings demonstrate once again that the model’s robustness is relatively high anddoes not change greatly with the alteration of sample quantity.

13

Table 4: Relative Convergence Regression Results by Regional Groups
Explained variable

agr_perCO2

agr_perCO2

agr_perCO2

RegionEastern

Eastern

Western***logperCO2_-0.041

2000(-15.84)

logsavinglog(n+g+δ)energy/GDP

targetlogprice

2006.year

2007.year

2009.year***(-9.15)

**

0.005

0.163

(2.47)(0.49)(14.74)0.030-0.044***-0.025***(-4.08)(-4.73)

***

***

***

-0.076***(-13.35)

0.046

0.026

0.0004

(7.9)

(9.9)

(0.18)

0.001

0.001

0.009

(1.57)(0.33)(10.45)-0.035-0.043-0.015(-0.82)(-1.47)(-0.44)

**

***

*

0.005

0.007

0.005

（1.25）（2.3）（1.71）

***

*

0.01

0.014

0.008

（1.75）（3.2）（1.82）

***

***

0.014

0.010

0.012

（3.13）（3.73）（3.57）

0.009

0.021

0.01

（0.94）（3.13）（1.57）***

2010.year-0.047(-14.22)***

2008.year-0.031

***

***

***

***

0.019

0.018

0.013

（4.13）（6.0）（3.97）

***

***

***

0.026

0.028

0.017

（5.19）（8.22）（4.86）-0.089-0.113-0.393(-0.40)(-0.73)(-2.32)

Samples

77

56

70

Wald chi2

472.27292.48368.982011.year

Constants**

Note: Numbers in brackets are z values; * denotes p<0.1, ** denotes p<0.05 and *** denotes p<0.01; ln denotesthat natural logarithm is adopted for the variable. Software employed in this paper is Stata SE 13.1.

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7. Concluding Remarks
This paper first elaborates the mechanism of relationship between target
responsibility system and the convergence of CO2 emissions. China’s political system
is characterized by administrative decentralization, fiscal decentralization and thepromotion championship. Because of the high capital formation rate and global public
goods, the target responsibility system including energy intensity in evaluation
indicator has a limited contribution to the convergence of China’s per capita CO2
emissions, which remains subjected to economic growth targets. Later, using
interprovincial panel data, this paper compared the convergence of CO2 emissions
during the “10th Five-year Plan” (2001-2005) and the “11th Five-year Plan”(2006-2010) and arrived at the following conclusions:
China’s per capita CO2 emissions demonstrate absolute β convergence andconditional β convergence. The eastern and central regions demonstrate absoluteconditional convergence while the western region displays conditional convergencebut does not satisfy absolute convergence. Although the coefficient is significantly
positive, energy intensity as an indicator in the target responsibility system largely
subjects to the GDP growth target. The provinces which capital formation rate isrelatively high, the growth rate of per capita CO2 emissions is high. Investment
impulse is a major factor affecting the CO2 emissions convergence.
The Chinese government attaches great importance to the work on climatechange and successively introduced such policies as pilot programs of low-carbon
provinces, low-carbon cities and carbon trading. In 2006, the Chinese government
introduced energy intensity into the target responsibility system. Nevertheless, on
account of CO2 abatement as typical global public goods, the costs and benefits ofabatement cannot be internalized in specific administrative regions and local
governments do not have strong political and economic impetus to engage in CO2abatement effort. In order to achieve low-carbon development, the Chinese
government must improve the current evaluation indicator of target responsibility
system by changing the relative indicator of carbon intensity into the absolute ones
and set cap on the CO2 emissions growth for local governments.

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