Abstract
China is experiencing a high speed economic development which may exert great pressure on the environment and energy systems. To measure the environmental and energy performance during the economic development process, this paper selected 30 provinces, cities or autonomous regions as the decision making unit (DMU), and proposed a Green GDP index (GGI) in view of energy intensity and pollution intensity using the generalized Data Envelopment Analysis (DEA) method, and the developing trends of integrated energy and environment efficiency of DMUs from 2006 to 2010 are also demonstrated by the Malmquist index. Results show that the integrated energy and environment efficiency varies for each DMU. GGI were both 1 in Beijing and Shanghai. GGI values for the developed cities in Eastern China, such as Guangdong, Fujian, Zhejiang, Tianjin, Jiangsu, and Hainan, ranked high, while those in the Northeast and Middle China remained relatively low. Moreover, there is a positive relationship between the GGI and per capita GDP with a correlation coefficient of 0.75. Increases in GGI are also observed in the results, representing great achievements are acquired in energy conservation and emission reduction. However, the GGIs do not converge to the green frontier across the provinces.
Highlights
Nowadays, constrained by the development stage, resource endowment, technical capabilities, and development mechanisms, China is experiencing a scale-driven development characterized by high pollution, extensive energy consumption and low efficiency, which leads to inefficient natural resource utilization and energy use in the production process, as well as high volumes of pollution emissions.To cope with these resource and environmental challenges, we should pay much attention to strategies for optimizing the use of resources and environment in a more efficient way
We describe the establishment of the Green GDP index (GGI)
The Malmquist index, which has been widely used in measuring total factor productivity, is introduced to analyze the change of GGI for each decision making unit (DMU) and decompose it into two parts [14], i.e., the change of green frontier and relative change of GGI, as shown in Equation 3: m( yt +1, xt +1, yt, xt ) = [
Summary
Nowadays, constrained by the development stage, resource endowment, technical capabilities, and development mechanisms, China is experiencing a scale-driven development characterized by high pollution, extensive energy consumption and low efficiency, which leads to inefficient natural resource utilization and energy use in the production process, as well as high volumes of pollution emissions To cope with these resource and environmental challenges, we should pay much attention to strategies for optimizing the use of resources and environment in a more efficient way. Among the wide spectrum of energy and environmental modeling techniques, Data Envelopment Analysis (DEA), a relatively new non-parametric approach for efficiency evaluation, has attracted much attention [8]. Based on the panel data, the Malmquist index is employed to analyze the temporal changes of DMUs from 2006 to 2009 and to decide whether the GGIs are convergent
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