بررسی ضریب تاثیر تولید گازهای گلخانه ای CO2 مربوط به انرژی با استفاده از مدل STIRPAT در استان گوانگدونگ، چین
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Applied Energy, Volume 106, June 2013, Pages 65–71
To find the key impact factors of CO2 emissions to realize the carbon intensity target, this paper examined the impact factors of population, economic level, technology level, urbanization level, industrialization level, service level, energy consumption structure and foreign trade degree on the energy-related CO2 emissions in Guangdong Province, China from 1980 to 2010 using an extended STIRPAT model. We employed ridge regression to fit the extended STIRPAT model. Empirical results indicate that factors such as population, urbanization level, GDP per capita, industrialization level and service level, can cause an increase in CO2 emissions. However, technology level, energy consumption structure and foreign trade degree can lead to a decrease in CO2 emissions. The estimated elastic coefficients suggest that population is the most important impact factor of CO2 emissions. Industrialization level, urbanization level, energy consumption structure, service level and GDP per capita are also significant impact factors, but the other factors such as technology level and foreign trade degree are less important impact factors. Some policy recommendations are also given on how to mitigate the growth of CO2 emissions.
There is a global consensus that climate change is being driven by an increase in atmospheric greenhouse gases, most notably CO2 emissions. To deal with this issue, Chinese government proposed targets in December 2009 aimed at controlling greenhouse gas emissions. More specifically, it was decided that CO2 emissions per unit gross domestic product (GDP) (carbon intensity) should be cut by 40–45% in 2020 compared to that in 2005. This is a binding target in the mid-long term plans for the national economic and social development. In addition, ‘‘the Outline of National Economic and Social Development Plan in the Twelfth Five-year (2011–2015)’’ clearly pointed out that energy consumption must be reduced by 16% and carbon intensity must be decreased by 17% during the period its validity. Moreover, in January 2012, ‘‘China’s controlling greenhouse gas emissions scheme for the Twelfth Five-Year Plan’’ required Guangdong Province to reduce its carbon intensity by 19.5%. To realize the carbon intensity reduction targets, we need to examine the influence factors of CO2 emissions, and then find the appropriate CO2 emissions reduction paths for Guangdong Province. As the most affluent and populous province in China, Guangdong Province intends to take the lead in relieving and adapting to the challenges posed by climate change. In the past 30 years, the GDP of Guangdong Province has steadily increased with an average annual increase in excess of 18%, reaching 4600 billion Yuan in 2010. Moreover, as the economic development is dependent on energy consumption, the latter has also increased in Guangdong Province. In fact, the province consumed up to 21.9 million tons of standard coal in 2010, with an annual growth rate in energy consumption approaching 11% from 2000 to date. Accordingly, CO2 emissions have also increased greatly, which poses a significant problem as far as energy saving and emission reduction are concerned. However, because Guangdong is extremely deficient in energy resources, energy restrictions present a choke effect on her economic and social development, which makes the cost of economic growth largely increase. Therefore, energy saving and emission reductions are imperative for Guangdong Province, but in such a manner as to augment, rather than impede, socio-economic growth. Currently, many different methods are used to examine the impact factors of CO2 emissions. Among them, the logarithmic mean Divisia index (LMDI) and stochastic impact by regression on population, affluence, and technology (STIRPAT) models are the two most well-known methods used for examining such factors. Wang et al. , Zhu et al. , Elif et al.  and Tan et al.  used the LMDI model to decompose Chinese CO2 emissions into population, GDP per capita, energy consumption intensity and energy consumption structure. Guo et al. , Zhao and Long  and Liu et al.  applied the LMDI model to examine the impact factors of CO2 emissions in Shanghai, Jiangsu Province and Beijing, respectively. These workers decomposed CO2 emissions into the impact factors of GDP, industrial structure, energy consumption intensity, energy consumption structure and carbon emission coefficient. More recently, Song  used the LMDI model to discuss CO2 emissions in Shandong Province in terms of population, GDP per capita, industrial structure, energy consumption intensity, energy consumption structure and carbon emission coefficient. These studies provide some useful scientific supports for making out effective CO2 reduction strategies. However, within the LMDI model, the impact factors considered only are population, GDP per capita, industrial structure, energy consumption intensity, energy consumption structure and carbon emission coefficient. As a result, the LMDI model is a rather poor method of examining more detailed influence factors, so it can only provide limited supportive information for shaping CO2 emissions reduction strategies. Moreover, the STIRPAT model can examine much more impact factors than the LMDI model, which makes its conclusions much more reliable. Consequently, the STIRPAT model has become an increasingly dominant method in examining the impact factors of CO2 emissions. In recent years, the STIRPAT model has been widely applied by more and more researchers. York et al.  and Shi  studied the relationship between CO2 emissions and population using the STIRPAT model. Fan et al.  used the STIRPAT model to examine the impact factors of CO2 emissions in countries with different income levels. Lin et al. , Zhu et al. , Li et al. , Song et al. , Li et al. , Zhu and Peng  and Wei , used the STIRPAT model to make an analysis of the impact factors of CO2 emissions in China. Shao et al.  and Wang et al.  also applied the STIRPAT model to make an analysis of the impact factors of CO2 emissions in Shanghai. More recently, Wang et al.  empirically studied the influences of urbanization level, economic level, industry proportion, tertiary industry proportion, energy intensity and R&D output on CO2 emissions in Beijing using an improved STIRPAT model incorporating partial least square regression. All of the studies outlined above prove that STIRPAT is an efficient model for examining the impact factors of CO2 emissions. However, existing studies have inevitable shortcomings. Firstly, the theory for impact factors of CO2 emissions is relatively weak, focused mainly on population, economic level and technology level, and seldom on energy structure, industrial structure, urbanization level, industrialized level and foreign trade degree. These additional factors should be explored for their influences on CO2 emissions. Secondly, existing studies often adopt the input–output method and the structure decomposition method, but seldom use the econometric analysis method, which may lead to their results remaining less than convincing. Nevertheless, as the number of impact factors of CO2 emissions is considerable, there may be multicollinearities among them. Currently, the STIRPAT model most widely used in studies involves ordinary least squares (OLS) regression, which may lead to unreliable regression coefficients. Thirdly, most existing studies focus on the national, and seldom on the provincial, level: due this benign neglect, the inter-provincial differences result in different conclusions, thereby necessitating further research. Fourthly, to the best of our knowledge, there are no reports in the literature that use the STIRPAT model to examine the impact factors of CO2 emissions in Guangdong Province. Moreover, to avoid multicollinearity, we employed ridge regression to fit the extended STIRPAT model. The innovation in, and contribution of, this paper lies in its examination of the following impact factors: population, economic level, technology level, urbanization level, industrialization level, service level, energy consumption structure, and foreign trade degree on the energy-related CO2 emissions in Guangdong Province, China using an extended STIRPAT model incorporating ridge regression, to help policy makers design appropriate energy saving and emission reduction measures for Guangdong Province. Moreover, this paper can also be viewed as a prime example of how to examine the impact factors of CO2 emissions of a province region as a whole. The remainder of this paper is organized as follows: Section 2 describes the study area; Section 3 describes the extended STIRPAT model incorporating ridge regression, data are presented in Section 4; results and discussion are given in Section 5, and the conclusions and policy implications are summarized in Section 6.
نتیجه گیری انگلیسی
Using the energy consumption, economic and social development related data in Guangdong Province for the period 1980–2010, we dynamically estimated the energy-related CO2 emissions in Guangdong Province in compliance with the IPCC National Greenhouse Gas Inventories. The estimation results show that CO2 emissions in Guangdong Province are maintaining a trend of rapid growth. Further, we examined the impact factors of CO2 emissions with the extended STIRPAT model incorporating ridge regression and led to the following conclusions. We find that population scale, urbanization level, GDP per capita, industrialization level and service level have promoting effects on CO2 emissions. Conversely, carbon emission intensity, energy consumption structure and foreign trade degree produce negative, inhibitory effects. In terms of decreasing magnitude of importance, their degrees of influence can be ranked as: population scale, industrial level, urbanization level, energy consumption structure, service level, GDP per capita, carbon emission intensity, and foreign trade degree. Population has a strong, positive influence on CO2 emissions, which is closely connected to the rapid growth of population scale and urbanization in Guangdong Province in the past 30 years. Hence, Guangdong Province should continue to control population scale, promote stable population urbanization and pay attention to optimizing population structure and quality. In addition, it should also make more effort to improve the public low-carbon awareness, strengthen the generalization of a low-carbon economy, low-carbon consumption and green consumption, promoting households to keep to a sustainable consumption mode. GDP per capita, industrialization level and service level constitute strong, positive influences on CO2 emissions. This is directly related to the fact that Guangdong Province’s GDP, especially from secondary industry, has increased in value. Growth rate has continuously kept above 10%. Thus, in order to mitigate such a positive influence, it will be necessary to optimize industrial structure, make an appropriate reduction in secondary industrial proportion, greatly develop the tertiary industry, develop the emerging low-carbon industry and boost the upgrade and cluster development of the traditional high energy consumption industry. In addition to that, it will also be necessary to reduce the GDP growth rate. This would lower the rapid growth in CO2 emissions, contributing to a coordinated development of economy, society and environment. Energy consumption structure has a rather prominent negative influence on CO2 emissions. This is directly attributed to the diligent attention of Guangdong Province to energy consumption structure optimization. In the future, Guangdong Province needs to gradually decrease the proportion of high-carbon energy such as coal and coke that it uses. Moreover, it needs to fully develop and use renewable low-carbon energy resources such as solar energy, wave energy and wind energy. It also needs to greatly develop new energy sources to carry on optimization of its energy consumption structure. Carbon emission intensity contributes a small, negative influence on CO2 emissions. This is mostly due to Guangdong Province’s great attention to upgrading industrial structure, energy structure adjustment and technical level advancements made in recent years. In the future, Guangdong Province should continue to target cuts in energy consumption per unit GDP. It should also create a target responsibility system and take various measures such as accelerating research and development in low-carbon technology, popularizing new energy-saving products and new technologies, and implementing incentive policies and so on to strengthen energy saving and emission reduction, thus improving energy efficiency. Foreign trade degree also has a small negative influence on CO2 emissions. This has been due to Guangdong Province’s attention to its import and export balance and to the upgrade and transformation of nature of the products exported. It is recommended that Guangdong Province should carry on expanding domestic demands to gradually develop a balance between foreign trade and domestic demand. Meanwhile, it should also accelerate the technological upgrading of its export products and realize schemes of renewal and reconstruction based on energy-saving technologies. The conclusions drawn from this study have an important reference value for policy makers in assisting their design and implementation of appropriate energy saving and emission reduction measures for Guangdong Province, and also have academic value in terms of enriching low carbon economy research systems in China. However, this study is still preliminary, and many aspects such as population structure, energy intensity and consumption mode are worthy of further study. Moreover, policy modeling and pathway choice for the realization of carbon intensity targets are valuable, and thus are also worthy of close research attention.