لینک های پویا بین میزان انتشار CO2، رشد اقتصادی و مصرف زغال سنگ در چین و هند
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Applied Energy, Volume 104, April 2013, Pages 310–318
In this study, we employ recent and robust estimation techniques of cointegration to provide more conclusive evidence on the nexus of CO2 emissions, economic growth and coal consumption in China and India. Furthermore, the causal relationships among the variables are further examined using the Granger causality test. Our empirical results suggest that the variables are cointegrated in the case of China but not India. In other words, there is a long-run relationship between CO2 emissions, economic growth and coal consumption in China. Granger causality test for China reveal a strong evidence of uni-directional causality running from economic growth to CO2 emissions. Moreover, there is a bi-directional causality between economic growth and coal consumption as well as CO2 emissions and coal consumption in the short and long run. In the case of India, only a short-run causality is detected. Causality between economic growth and CO2 emissions as well as CO2 emissions and coal consumption are bi-directional. Nonetheless, there is only a uni-directional Granger causality running from economic growth to coal consumption in India. The implications of the results are further discussed.
In achieving rapid developmental goals, countries at large face conflicting policy choices from rapid economic growth, significant consumption of resources and environmental deterioration  and . It is more so in emerging economies such as China and India where both the countries have recorded higher economic growths and significant increases in the consumption of coal ,  and . China and India, together, showed significant increases in their percentage of energy use of total world energy consumption, from 10% in 1990 to 21% in 2008 and are expected to increase their energy use to 31% in 2035 . Robust growth in these two countries, even during recession, is expected to increase the coal consumption and consequently influence the CO2 emissions. If these countries decide to pursue sustainable developmental goals, it might require a reduction in energy consumption, specifically coal, and an increase in the proportion of renewable energy in primary energy supply. In other words, fear of climatic change may limit the use of coal in the future . Indeed, coal consumption contributes more carbon per tonne of oil equivalent than other resources like natural gas and oil. Nevertheless, currently, coal still plays an important role in economic growth and is the second largest source of world CO2 emissions  and . Although reduction in energy consumption seems to be a viable option in reducing CO2 emissions, its impact on economic development can be negative. For instance, China’s coal consumption in terms of percentage of total energy consumption is nearly 69% and any attempt to reduce it may have potential reciprocal influence on economic growth. Therefore, there is an urgent need to understand the dynamic links between coal consumption, CO2 emissions and economic development in these countries. This study is timely given the fact that both countries have recorded high economic development and the consumption of coal is becoming an essential energy mix. On the contrary, pressure to reduce the CO2 is mounting, forcing policymakers to find alternatives to reduce per capita CO2 emissions. In addition, as part of the Kyoto Protocol, participating countries are required to reduce CO2 emissions collectively, about 5% on average over 2008–2012 . Nevertheless, in reality, CO2 emissions between 1992 and 2007 have increased by 38% . The real GDP, coal consumption and CO2 emissions of China and India over the periods of 1965–2009 showed an increasing trend (see Fig. 1). China and India recorded a remarkable growth and in the period of 2001–2009, these economies have been growing at an average rate of 10.5% and 7.4% per annum respectively (see Table 1). China and India being the most populous developing countries have significant influences on global coal consumption and emissions and are expected to have an increasing influence in the future , , ,  and . China’s and India’s per annum average growth for the same periods for coal consumption are 10% and 6.4% respectively and CO2 emissions are 8.5% and 5.7% respectively. In both countries, with large domestic coal reserves, the coal use for electricity power and industrial processes has increased. Moreover, with increasing coal-fired generation capacity in China and India, coal consumption is expected to increase. Industrial coal consumption from 2008 to 2035 is expected to grow by 67% in China and 94% in India . Between 2003 and 2008, China’s coal consumption increased by 71% . Full-size image (72 K)In 2008, China was the top total CO2 emitter in the world surpassing the United States while India ranked at third place. China’s per capita CO2 emissions increased by two and a half times, while India’s per capita CO2 emissions increased two times . Future forecast shows that annual CO2 emissions will increase 2.5 times from 2005 to 2030 reaching 3084 million tons (Mt) of CO2 in 2030, recording an annual growth of 3.7% in India . In 1996 China accounted for 13.8% of CO2 world emissions and the share had increased to 21% in 2007 . The biggest challenge for leaders in China and India is on how to maintain economic growth, while keeping CO2 emissions as well as coal consumption at an acceptable level so that it will not harm economic growth especially if there is a long-run causal relationship between economic growth, coal consumption and CO2 emissions. Nevertheless, in terms of CO2 emissions per unit of GDP, both countries have shown significant reductions between 1990 and 2009 and this might indicate that there is a potential decoupling of CO2 emissions from economic growth . However, more investigation is needed to understand the link between CO2 emissions and economic growth. Indeed, with both countries’ rapid development, increasing environmental awareness may force the government to reduce CO2 emissions and coal consumption. China’s recent efforts to develop renewable energy sectors may also have implications to reduce coal consumption. Therefore, a wide range of interesting questions emerge in these emerging markets. Will the economic growth stall if coal consumption is controlled or if the policymakers move to alternative sources of energy? Can the control of coal consumption significantly reduce the CO2 emissions? How do economic progress and coal consumption affect CO2 emissions and what are their influences? These questions are pertinent to policymakers and require a careful investigation as it will allow a better formulation of strategies and policies. Thus, this study attempts to provide some insights with regards to the above questions. Although numerous studies (e.g. , ,  and ) are available in analysing the nexus between energy consumption, economic growth and CO2 emissions, two fundamental gaps exist in the literature. First, there is limited evidence available on analysing the coal consumption and its implication for economic growth and CO2 emissions using recent and longer time series data (e.g. 1965–2009). It is important to note that longer series are essential to establish a more robust estimation using unit root and cointegration test. Some studies even used very short time series of 26 years (e.g. ). By re-examining the issue, we can provide a more conclusive evidence for policymakers. Second, despite coal being the major energy mix, most of the studies analysing the issues used aggregate energy consumption data and not coal consumption. In fact, in the case of India, evidence is scarce and little data is available for any meaningful insight toward policy direction. Additionally, analysing China and India in a single paper provides insight on whether there are any differences in the links despite both countries being heavy consumers of coal. Indeed, the study will be able to establish reasons of why the results might differ. It may also indicate that country specific information is important since results differ among countries. In this study, we use recent datasets (longer time series data from 1965–2009) to re-investigate the issues. In terms of methodology, we employ the Augmented Dickey–Fuller (ADF) and Ng–Perron unit root tests to determine the order of integration of each series. Additionally, we have also performed the Lumsdaine and Papell  unit root test with two structural breaks on each variable. This will be useful in examining the environmental Kuznets curve (EKC) hypothesis that posited a non-linear relationship between CO2 emissions and the level of outputs. Likewise, this study uses the most recently developed combined cointegration tests suggested by Bayer and Hanck  to determine the presence of long run equilibrium relationship between CO2 emissions, economic growth and coal consumption. On the basis of the results of the Monte Carlo experiment, Bayer and Hanck  suggested that the combined cointegration tests were more powerful and robust than the existing individual cointegration tests (e.g. , ,  and ). Indeed, due to increasing number of studies using conventional methods, Karanfil  indicated the need to use new techniques of analysis in examining the energy-growth nexus. There are two main advantages of using the combine cointegration tests. First, the combine cointegration tests have superior performances in small samples. The Engle–Granger, Johansen–Juselius and ECM-based tests for cointegration are not robust and lead to spurious cointegration results particularly when the sample size is less than 100 observations  and . Second, the combine cointegration tests provide more conclusive result. Gregory et al.  found that the p-values for different single cointegration tests are usually not strongly correlated. Moreover, Pesavento  showed that the testing powers vary among the single cointegration tests. Hence, the single cointegration tests tend to provide inconclusive results. Owing to these advantages, we choose to use the combined cointegration tests. Finally, the Granger causality test is used to verify the direction of causality among the variables of interest. In doing so, our empirical results will be more comprehensive and robust. Although, some studies use panel data techniques, in this study we limit the use of panel approach due to the following reasons. First, panel data analysis is based upon the homogenous relationship assumption across countries. This assumption leaves the analysis to focus on one perspective but ignoring the complexity and dynamism of an economic behaviour. Solow  suggested that an economic model should be dynamic in nature in order to observe the evolution of economic behaviour over time. Likewise, Athukorala and Sen  postulated that homogenous relationship may not always exist due to the different nature of economic structure, income and also due to demographic reasons. On the basis of Monte Carlo experiments, Robertson and Symons  and Pesaran and Smith  revealed that when heterogeneous exist in a panel model with a small cross-section (N) dimension as the case of our study (N = 2), the estimation results are likely to be biased. Maddala et al.  added that panel data estimation may not be accurate when the problem of cross-section heterogeneous occurs. Second, Deaton  claimed that the quality of data varies across countries, therefore estimation with cross-sectional and panel data studies are more likely not to yield robust results. Therefore, country-specific study with time series data analysis is more suitable when cross-section dimension of a panel data is very small as the case of our study (N = 2). 1 Subsequently, separate policies can be suggested for China and India. The rest of the paper is organised as follows. Section 2 briefly reviews the previous empirical studies. Section 3 deliberates the data, empirical model and econometric methods. Section 4 discusses the empirical results whereas the conclusion and policy implications are presented in Section 5.
نتیجه گیری انگلیسی
This study attempts to investigate the relationship between CO2 emissions, economic growth and coal consumption in China and India using newly-developed cointegration analysis for an extended time period, i.e. from 1965–2009. There exists a long-run relationship between the variables in the case of China but not for India. Bi-directional causality, in the short and long run, is detected between economic growth and coal consumption as well as between coal consumption and CO2 emissions in China. In addition, uni-directional causality is detected from economic growth to CO2 emissions. For India, a short-run bi-directional causality exists between economic growth and CO2 emissions and CO2 and coal consumption. However, economic growth Granger causes coal consumption in the short run in India. Since it is confirmed that there are consensus in results (our study and others), the policy message is clear for both China and India. China should be cautious in implementing any conservation policy while India should implement the policy without a destabilisation of a long run economic growth. As a whole, China may have to put in more effort to devise alternative choices of policy options than India. Since coal consumption impacts the CO2 emissions and economic growth in the long run, any coal conservation policy might reduce CO2 emissions but has negative consequences on economic growth in China. Since China’s electricity generation is mostly from coal (78% of the total capacity in 2007: ), any reduction in coal consumption will adversely affect its electricity supply. One policy option is to improve coal utilisation efficiency. However, this viable policy option will be challenging in that the Chinese government has to devise policies to improve the efficiency options to increase the GDP-coal intensity. In this way, China will be able to reduce the CO2 emissions without any adverse effect on economic growth. Another viable policy option is to increase the consumption of renewable energy. Fang  claimed that China has the potential of increasing renewable energy; however, it requires market openness, institutional innovation and policy integration. With respect to this, investment and institutional arrangements should be intensified to speed up the development of renewable energy sectors.