A Preliminary Evaluation of China s Implementation Progress in Energy Intensity Targets

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1 A Preliminary Evaluation of China s Implementation Progress in Energy Intensity Targets Yahua Wang and Jiaochen Liang Abstract China proposed an ambitious goal of reducing energy consumption per unit of GDP by 20% from 2006 to This paper evaluates the progress of provincial governments implementing energy conservation targets assigned by the central government. The empirical analysis of this paper is divided into two parts, a static analysis and a dynamic analysis. In the static analysis, we established a multiple linear regression model based on provincial cross-sectional data, to explore factors that affect the reduction of energy intensity. In the dynamic analysis, we established a fixed group and time effect model based on provincial panel data, to explain the annual changes in energy intensity. The analysis results show that the framework of the energy conservation policy introduced by the Chinese government is quite robust, and provincial governments respond positively to the instructions from the central government. Keywords China Climate change policy Energy intensity Fixed group and time effect model Panel data analysis 1 Introduction Global climate change will create enormous challenges to human development in terms of ecological, economic and social disasters. Stern (2007) says, Climate change will affect the basic elements of life for people around the world access to water, food production, health and the environment. The Chinese government has taken active actions to address serious domestic energy issues and the challenges from climate change. In March of 2006, the Chinese government propounded the Y. Wang (*) and J. Liang School of Public Policy and Management, Tsinghua University, Beijing 10084, China wangyahua@tsinghua.edu.cn; ljc08@mails.tsinghua.edu.cn Y. Zhou and D.D. Wu (eds.), Modeling Risk Management for Resources and Environment in China, Computational Risk Management, DOI / _47, # Springer-Verlag Berlin Heidelberg

2 426 Y. Wang and J. Liang ambitious targets in its 11th 5-Year Plan from 2006 to 2010: energy intensity per unit of GDP should be reduced by 20%, and total major pollutants emission volume should be reduced by 10%. These have been regarded as obligatory indicators incorporated into the performance appraisal system for local officials. To achieve these goals, the State Council assigned targets for energy conservation and emission reduction to various provinces and released a series of policies to urge the provinces to implement the energy conservation targets. Due to the energy conservation work done in the first 3 years of the 11th 5-Year period, the energy intensity nationwide has been reduced by an accumulative percentage of 10.1%, equivalent to 50.4% of the goal set in the 11th 5-Year Plan, being still in arrear of the expected schedule. There is a world of difference amongst various provinces in fulfilling the task of reducing energy intensity. Out of 30 provinces excepting Tibet, 16 have not fulfilled the energy conservation targets assigned by the central government. In fact, the overall target completion has not yet reached 60 percent. Beijing has best fulfilled the task of reducing energy intensity, accounting for 87.6% of all tasks assigned in the 11th 5-Year Plan; whereas Qinghai has fulfilled the least of the tasks, making up only 28.7% of all tasks. If we take a look at the implementation of tasks dynamically, the energy intensity reduction has been accelerating year by year, with intensity down 1.79% countrywide in 2006, 4.04% in 2007, and 4.59% in And in the past 3 years most of the provinces have reduced their energy intensity at an accelerated pace. Many provinces that had not done well in reducing energy intensity 1 or 2 years before have evidently accelerated the reduction of their energy intensity in the second or third year; these include Ningxia, Qinghai and Shanxi. Nevertheless, several provinces that had done well in fulfilling the tasks in the first 2 years, such as Shanghai and Sichuan, have slowed down their pace in the third year. The above-mentioned situation suggests that the implementation of the energy conservation target in China is both gratifying and worrying, driving us to make an evaluation of the energy conservation policy framework established during the 11th 5-Year period. In the past 2 years some scholars have commenced probing into- China s energy conservation policy from the public management perspective. For instance, Zhang et al. (2008) have examined the behavioral patterns of Chinese local governments addressing climate change and implementing the energy conservation policy. Wang and Yu (2009) have probed into the interest-driven factors for local government to develop low-carbon economies. But so far there has been a shortage of systematic assessments of China s newly-established energy conservation policy framework. This research is an attempt to move in this direction. What we mainly care about is whether China s policy framework for reducing energy intensity in recent years is effective or workable. In the current policy framework of energy conservation, the assignment of energy intensity targets from the central government to provincial governments is the crucial part.

3 A Preliminary Evaluation of China s Implementation Progress 427 What merits our further attention is the question: under this policy framework, have provincial governments responded positively to the instructions from the central government? Why is there a disparity of performance for different provinces in implementing the energy conservation instructions? What factors have decided the performances of various provinces in energy intensity reduction? The study of these questions will be conducive to an evaluation of China s energy conservation policy and help us to indentify the characteristics of this initially established policy framework. 2 Methodology This study will make a preliminary analysis of the implementation of the energy conservation policy on the provincial level in China. The analysis will be conducted in both static and dynamic ways. The static analysis is focused on the study of why there is such a world of difference in the energy conservation outcomes of various provinces? What factors affect the outcome of energy intensity reduction in various provinces? The dynamic analysis is centered on how to explain the annual changes in the outcomes of energy intensity reduction of various provinces? Will the provinces adjust their behaviors according to the previous outcomes of energy intensity reduction? These two analyses will jointly reveal the intrinsic mechanisms that provide impetus for the provincial governments to carry out the energy conservation policy. In the static analysis conducted in Sect. 3, we have established a multiple linear regression model. Taking the rate of energy intensity reduction in each province as the dependent variable and the exogenous variables as the independent variables, we have identified the independent variables that can be used to explain the outcome of energy conservation tasks through econometric analysis. Considering that the exogenous variables are numerous and that multiple collinearity exists among these variables, we have introduced the factor analysis technique to deal with the groups of possible independent variables so as to pick up the main factors and take them as the possible independent variables for the analysis in the regression model. In the dynamic analysis in Sect. 4, we set up a panel data model. As we consider that there was a general trend of changes in energy intensity in the 30 provinces during the period 2006 to 2008, we have chosen the two-way panel model with fixed group and time effect. The dependent variable of this model is the rate of energy intensity reduction of each province during 2006 to For independent variables, we have examined the influence from the rate of energy intensity reduction during the previous year as well as trying other possible variables, such as the GDP growth rates of each province in the same year and the growth rate of the added value of the secondary industry.

4 428 Y. Wang and J. Liang 3 A Static Analysis 3.1 Model Here we use the rate in energy intensity reduction I, to represent the real outcome of a province s implementation of energy conservation policy, which can be defined as, I ¼ Fðy i Þ (1) Where y i are the exogenous variables that may exert influence on I. Considering that the effects of the exogenous variables interact with each other, we assume y i sin(1) are in multiplication form, and the expression of I can be written as, I ¼ A Y i y i a i (2) Where A is constant, and a i, the exponential values of y i, are unknown coefficients that need to be estimated. We get logarithm on both sides of (2) to have a linear equation: LnðIÞ ¼LnðAÞþ X i a i Lnðy i Þ (3) Next, we need to identify the possible exogenous variables y i that may influence the dependent I, and then to use these y i s to estimate (3) with the Ordinary Least Square Method in order to find out the factors that have significant impact on the rate of a province s energy intensity reduction. 3.2 Data In order to find out the independent variables y i that have effect on I, we have collected the exogenous variables concerned. We have selected the following groups of data as alternative independent variables: GDP, GDP per capita, the percentage of the added value of the secondary industry in the GDP, the percentage of the added value of the heavy industry in the GDP, the initial energy intensity in 2005, and the energy conservation targets assigned to each province. The dependent variable is the accumulative rate of energy intensity reduction of each province during 2006 to The energy conservation targets assigned by the central government to each province during the 11th 5-Year period and the initial energy intensity in 2005 come from the Written Reply of the State Council to the Plan for Energy Intensity Reduction Targets per Unit GDP Allocated to Various Provinces During the

5 A Preliminary Evaluation of China s Implementation Progress 429 Eleventh Five-Year Period. 1 The data of 2007 of the other four variables, come from the China Statistics Yearbook As dependent variables, the accumulative rate of energy intensity reduction of each province during 2006 to 2008 is calculated based on the energy intensity data of each province released by the National Bureau of Statistics in As the data in (3) is in a logarithm form, the logarithms of the above variables have first been taken to set up an SPSS data file. Through analysis, we find that different groups of the alternative independent variables have high correlation. Thus, we are unable to use the above variables to estimate (3) directly. In order to avoid the adverse influence of the multiple collinearity, we use factor analysis in the alternative independent variables before the regression analysis. 3.3 Factor Analysis We adopt the method of factor analysis to extract factors from the six groups of alternative independent variables. The testing shows that the KMO indicator of the samples is 0.502, basically suitable for the factor analysis. According to such determining methods as the Eigen Value greater than 1, the Scree Plot, and the Accumulative Explaining Ratio of Variance, we have picked up three factors with the aggregate explanatory ratio of 84.87%. In order to better understand the meaning of these factors, we have adopted the Varimax Orthogonal Rotation in data processing to conduct the Varimax rotation of the Component Matrix. See Table 1 for the results. The main purpose of the Varimax rotation is to focus each variable s load in one and only one factor. Table 1 Rotated component matrix in factor analysis Indicators Factor GDP GDP per-capital Initial energy intensity (2005) Targets of energy intensity reduction Percentage of secondary industry in GDP Percentage of heavy industry in GDP Extraction method: principle component analysis; rotation method: varimax with Kaiser normalization. 1 China s Central Government, The Written Reply of the State Council to the Plan for Energy intensity Reduction Targets per Unit GDP assigned to Various Regions during the 11th 5-year Plan Period. September 17, China s Central Government, Energy intensity Targets per Unit GDP of Various Provinces in June 30, 2009.

6 430 Y. Wang and J. Liang Table 2 Descriptions for the factors extracted Factors Indicators Descriptions Capability factor Factor 1 GDP The potential of a province in reducing energy GDP per capital Initial energy intensity (2005) intensity, which mainly presents a province s economic and fiscal capabilities, as well as the abilities to overcome the path dependence of highcarbon economy development. Rules factor Factor 2 Targets of energy intensity reduction Structure factor Factor 3 Percentage of secondary industry in GDP Percentage of heavy industry in GDP The pressure facing by provincial governments in policy implementation of energy conservation policy from the central government. The economic and industrial structure of various provinces. Table 2 gives further descriptions to the factors extracted. Factor 1 contains three variables, namely, GDP, GDP per capita, and the Initial Energy Intensity. We call Factor 1 the Capability Factor, which reflects the potential capacity of a province to reduce energy intensity. On one hand, it has a positive correlation with the GDP and GDP per capita. Where the GDP is larger and the economic development level is higher, more resources can be mobilized to realize the policy goals. On the other hand, Factor 1 has a negative correlation with the Initial Energy Intensity, because the provinces with higher energy intensity are usually the regions that are much more dependent on highly energy-consuming industries and less efficient in utilizing their energy resources, thus making it more difficult to reduce energy intensity. Factor 2 contains one variable, the targets of energy intensity reduction assigned to various provinces by the central government, which reflects the instructions set by the central government, and we interpret it as the Rules Factor. Factor 3 contains two variables, namely, the percentage of the GDP contributed by the secondary industry and by the heavy industry, respectively, which reflect the economic and industrial structure of various provinces, and we interpret this combination as the Structure Factor. 3.4 Results of the Multivariate Regression By taking the accumulative rate of energy intensity reduction (logarithm value) of the 30 provinces from 2006 to 2008 as the dependent variable and the three factors 3 obtained from factor analysis as the independent variables, we fit (3) and find that the Structure Factor is not significant from zero at the 5% level of significance. 3 With SPSS software, the regression method can be used to work out the scores of the three factors instead of the observations.

7 A Preliminary Evaluation of China s Implementation Progress 431 Table 3 Linear regression results ith two factors Estimator Standard T Sig. Error Statistics Constant Capability Rules-in-use factor R² ¼ 0.589, adj-r² ¼ 0.559, F ¼ Therefore, we reject it from the equation. By taking the other two significant factors, the Capability Factor and the Rules Factor as independent variables, we evaluate (3) again with the Ordinary Least Square Method. The results are shown in Table 3. The F value of this regression model is , which is significant. And the regression coefficients of the Capability Factor and the Rules Factor are significantly positive, suggesting that the conditions reflecting potential capability and the pressure from the central government produce a positive influence on the implementation of energy conservation policy. The R-Square of this model is close to 0.589, indicating that these two factors explain quite a large part of the outcomes of policy implementation. 4 A Dynamic Analysis 4.1 Model This section mainly illustrates why annual changes took place in the rate of energy intensity reduction of each province in the first 3 years of the 11th 5-Year period. From the data of energy intensity reduction during 2006 to 2008, it can be seen that there is an overall trend towards a rising rate of energy reduction each year. For this reason, we adopt the fixed group and time effect model as (4) to estimate the dynamic mechanism of implementation of the energy conservation targets. I it ¼ C þ a i þ g t þ X it b þ u it (4) Where I it denotes the rate of energy intensity reduction of province i in year t; a i is the intercept of province i; g t is the time fixed effect of year t; X it is the vector formed by a number of independent variables of province i in year t, to which we have collected three alternative variables: the GDP growth rate in year t, the growth rate of the added value of the secondary industry in year t, and the completion percentage of the energy intensity reduction by year t. (Specifically, that is the ratio of the province s accumulative rate in energy intensity reduction by year t to the expected completion rate according to the target assigned to the province). Finally, u it is the residual.

8 432 Y. Wang and J. Liang In panel data analysis, the correctness of the model determines the effectiveness of the estimation. Hence, we should firstly test whether (4) has been established correctly. For the fixed group and time effect model in (4), we use the F statistics to test the following hypothesis (Bai 2008): H 3 0 : b ¼ 0 and g 2007 ¼ g 2008 ¼ 0 If the hypothesis H0 3 is rejected, we can accept the model established in (4) as correct. The test is carried out through following the F test: F 3 ¼ ðrrss URSSÞ=ðN þ T 2Þ F½N þ T 2; ðn 1ÞðT 1Þ K þ 1Š URSS=½ðN 1ÞðT 1Þ K þ 1Š (5) where the RRSS is the residual sum of squares acquired through the mixed regression model, and the URSS is the residual sum of squares acquired from the regression of (4). 4.2 Data and Regression Results This study uses 90 groups of data from the 30 provinces in Mainland China (not including Tibet) recorded in the first 3 years of the 11th 5-Year period from 2006 to 2008 to conduct econometric analysis. The completion percentage in energy intensity reduction by year t for province i is derived from dividing the accumulative energy intensity reduction by year t by the expected total reduction according to the target assigned to the province. As the data for the first year cannot be calculated, it is assumed to be 100% for all provinces in In addition, the GDP growth rate and the growth rate of the added value of the secondary industry of various provinces come from China Statistics Yearbook By using these data to conduct the F test in (5) and the regression results in (4), we can obtain the regression coefficients and the significant level of the independent variables. We will reject the variables with the minimum t value one by one from the independent variables that are not significant at the level of 5%, in order to find out the independent variables that have significant influence on the dependent variable in (4). According to this principle, both the growth rate of the added value of the secondary industry and the GDP growth rate are rejected, leaving the variable of the accumulative completion percentage in energy intensity reduction by year t as significant. It suggests that the two variables (the growth rate of the added value of the secondary industry and the GDP growth rate) do not produce significant influence on the implementation of energy conservation policy in the same year. Based on the analysis above, (4) can be written as: I it ¼ C þ a i þ g t þ br it þ u it (6)

9 A Preliminary Evaluation of China s Implementation Progress 433 Table 4 Results of the fixed group and time effect model used panel data Estimator Standard error t statistics Sig. Constant b Time fixed effect g t 2006 C C C R² ¼ , adj-r² ¼ , D.W. ¼ , F ¼ where I it represents the accumulative completion percentage of energy intensity reduction for province i by year t. The F 3 statistics of model (6) is calculated through (5) to be 12.23, which is greater than the critical value at the 0.5% level of significance. Therefore, the hypothesis H0 3 can be rejected and thus model (6) is deemed to be correct. The results obtained from the regression analysis in model (6) are shown in Table 4. The estimated result of the parameter b in Table 4 is negative, suggesting that r it produced significant negative feedback impact on the implementation of energy conservation policy. That is, the provinces which had fulfilled less of the energy intensity reduction targets suffered from greater pressures and intensified their efforts, which would thus increase the rate of energy intensity reduction in the following year. It can also be found from g t, the estimated results of the time fixed effect in each year, that I it increased in 2007 and 2008 as compared with previous years. As we only have 3 years to look at so far, we are still unable to identify the time fixed effect in a longer period of time. However, in view of the implementation of the energy conservation targets over past few years, we suppose that this trend in the time fixed effect was mainly incurred by the macro political environment in China. During 2007 and 2008, the central government has suffered more and more pressures to promote the energy conservation policy. These pressures include domestic factors such as the slow progress made towards energy conservation goal in 2006 and the threat of an energy supply shortage, as well as international factors like the wild price rise in oil on the international market and the pressure to mitigate carbon dioxide emissions. All these factors may form political pressures on the central government, which drive it to bring out more stringent measures and thereby result in the acceleration in energy intensity reduction of various provinces. 5 Conclusion Based on empirical analysis, this paper conducted an evaluation on the operation of China s initially-established energy conservation policy framework since 2006 at the provincial level. This paper conducted static and dynamic analyses by applying the method of econometric models to analyze the implementation of energy

10 434 Y. Wang and J. Liang conservation targets by the provincial governments in China. The main conclusions drawn by this study can be summed up as follows. Firstly, the framework of the energy conservation policy that China initially established is robust. Starting from its own national conditions, China has adopted a framework that breaks the responsibilities down to various levels. The empirical analysis of the implementation of this policy at the provincial level shows that the provincial governments have responded positively to the instructions of the central government. Although we are not able to distinguish the difference in the extent of efforts made by different provinces, quantitative analysis shows that the obligatory targets set by the central government significantly influenced the energy intensity reduction in various provinces, and the provincial governments have been intensifying their efforts to implement the energy conservation tasks year by year. Secondly, the outcomes of energy intensity reduction on the provincial level are restrained by provincial conditions. The quantitative analysis of this paper shows that variables such as GDP, GDP per capita, and initial energy intensity of each province had a significant impact on overall energy intensity reduction, which can explain, to a large extent, why there was such variation in the outcomes of energy intensity reduction among various provinces. It shows that the implementation of the energy conservation targets in various provinces not only relies on subjective efforts, but is also limited by the objective factors the level of economic development, the resources that can be mobilized, and the initial energy intensity. However, some variables, such as economic growth rate and industrial structure, have not had a significant impact on the rate of energy intensity reduction in this study. Thirdly, the provincial governments have strong motivations to follow the instructions of the central government for better relative performance. The quantitative analysis of this paper found that the energy conservation tasks fulfilled by various provinces produce pronounced impacts on subsequent implementation, and the rates of energy intensity reduction of some provinces have been annually increasing. This implies that the provincial governments are facing pressure from the central government. The provincial governments in China have attached importance to and worked hard at their energy conservation tasks, but, in essence, it is an administrative reaction to the call from the central government. Acknowledgments The funding supports come from the National Science Foundation of China ( ) and the Center for Industrial Development and Environmental Governance, School of Public Policy and Management, Tsinghua University References Bai, Z (2008) Econometric analysis of panel data, the Nankai University Press. Nankai University Press (in Chinese) Han Z-y, Wei Y-m, Fan Y (2003) Research on change features of Chinese energy intensity and economic structure. Appl Stat Manage 23(1):1 6, In Chinese

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