مصرف زغال سنگ و رشد اقتصادی در چین

The aim of this paper is to re-examine the relationship between coal consumption and real GDP of China with the use of panel data. This paper applies modern panel data techniques to help shed light on the importance of the heterogeneity among different regions within China. Empirical analyses are conducted for the full panel as well as three subgroups of the panel. The empirical results show that coal consumption and GDP are both I(1) and cointegrated in all regional groupings. Heterogeneity is found in the GDP equation of the full panel. The regional causality tests reveal that the coal consumption–GDP relationship is bidirectional in the Coastal and Central regions whereas causality is unidirectional from GDP to coal consumption in the Western region. Thus, energy conservation measures will not adversely affect the economic growth of the Western region but such measures will likely encumber the economy of the Coastal and Central regions, where most of the coal intensive industries are concentrated.

Coal is the principal primary energy source in China and it is given a strategic role in the economic growth of the country. According to the official figures from the National Bureau of Statistics, in 2009, coal accounted for 70% of the total energy consumed and 77% of the total energy produced in China. Because of its abundance in proven reserves and its stability in supply, coal will continue to be a key component of the primary energy mix in the country at least over the next few decades. However, coal also accounts for a large share of greenhouse gas (GHG) emissions generated by anthropogenic activities, and coal is the most carbon intensive fossil fuel. GHG emissions reduction in this carbon-constrained global environment will prove to be inevitable and the Chinese coal industry may experience significant impact from GHG emissions reduction policies. The relationship between coal consumption and economic growth is an important issue regardless of the direction of causality. First, as coal is an input to production processes, the consumption of coal may influence economic growth. As noted by some other authors (e.g. Apergis and Payne, 2010), if causality flows this way, then attempts to curb GHG emissions through energy conservation may be harmful to economic growth. On the other hand, based on the theory of demand, causality can be expected to run from economic growth to coal consumption through the income effect. In this case the estimated relationship (together with GDP forecasts) may be useful for the projection of future coal consumption. If the causal relationship is bidirectional and positive, then energy conservation policies may retard economic growth and the consumption of coal may be reduced further. The Chinese leaders may well be aware of the potential negative impacts of energy conversation to economic growth—as stated in the Eleventh Five-Year Plan of the People's Republic of China, one of the targets is to reduce energy consumption per unit of GDP by 20% in five years (Yan, 2006a). A plausible interpretation of this target is that there was no plan to massively reduce energy consumption, but the efficient use of energy should be promoted. In fact, the key projects outlined in the Five-Year Plan for revitalizing China's equipment manufacturing industry showed the intention of the Chinese leaders to improve the overall energy efficiency in the country (Yan, 2006b). Since the seminal work of Kraft and Kraft (1978), the relationship between energy consumption and economic growth has been subject to extensive and continuous scrutiny. No consensus can be reached in the huge body of empirical work regarding the causal relationship between the two variables. The relationship between energy consumption and economic growth in China has also received considerable attention at both the aggregated and disaggregated levels of energy consumption. For example, Soytas and Sari (2006) find no causal relationship between total energy consumption and GDP. Zou and Chau (2006) find unidirectional causality from oil consumption to GDP in the short-run and bidirectional causality in the long-run. Despite the differences in time period and detailed modeling strategy, Shiu and Lam (2004) and Yuan et al. (2007) both find unidirectional causality from electricity consumption to real GDP. Regarding the causal relationship between coal consumption and GDP in China, Li et al. (2008) and Wolde-Rufael (2010) both find Granger causality from GDP to coal consumption but not in the other direction. Yuan et al. (2008) also find unidirectional short-run causality from GDP to coal consumption, but causality is bidirectional in the long-run. There is also an extensive literature on the relationship between coal consumption and GDP in countries other than China, including Yang (2000), Sari and Soytas (2004), Lee and Chang (2005), Yoo (2006), Hu and Lin (2008) and Apergis and Payne (2010). The conclusions from these studies are mixed. For countries being examined by more than one study (e.g. Japan, India, Korea and South Africa), the findings often appear to be conflicting. We must point out that with the exception of Apergis and Payne (2010), all of the above studies regarding the GDP–coal consumption relationship relied on the use of time-series methods. The above brief literature review shows that the estimated relationships appear to depend on the chosen time span, geographic region and data structure as well as econometric methodology. To the best of our knowledge, no study in the literature has attempted to investigate the GDP–coal consumption relationship in China using panel data. Since China itself is a big country with different economic structures in different regions, the use of provincial level panel data for China enables us to take into consideration the presence of heterogeneity among the provinces. Panel data opens up the potential to reveal heterogeneous relationships, which will normally be “aggregated away” through the use of national level time-series data. Also, the power property of unit root and cointegration tests can be improved by bringing in a cross-section dimension to the time-series data. The results of this study will be of interest to researchers in the field because the results show that differences in the economic structure and stage of economic development may influence the relationship between energy consumption and economic growth. Also, our results may provide inputs to policymakers as they develop policies related to energy saving, emissions reduction and economic growth. The aim of this paper is to investigate the relationship between coal consumption and GDP in China with the use of provincial level panel data. In order to shed light on the potentially heterogeneous relationships across different regions, causal relationships are examined in the overall panel as well as specific groups within the panel. The rest of this paper is structured as follows. Section 2 briefly reviews the economic structure in various regions of China. Section 3 describes our empirical methodology. Section 4 presents the empirical results. Section 5 further discusses the results and concludes.

This paper investigates the relationship between coal consumption and real GDP of China under a panel cointegration and error-correction modeling framework. Twenty three provinces of China are included in the study and the causality between the variables is examined. The empirical findings show that the variables are I(1) and cointegrated in the full panel as well as the regional groups. The causality tests indicate bidirectional causality between GDP and coal consumption in the full panel, the Coastal region and the Central region, whereas unidirectional causality from GDP to coal consumption is found in the Western region. The long-run relationship between the two variables is estimated to be positive for all three regions. The joint Hausman tests do not reject long-run homogeneity in any of the regional equations, lending support to our regional grouping based on the Seventh Five-year Plan of China. It must be noted that our causality outcomes are in agreement with our prediction in Section 2. China is a vast country with quite distinct regional characteristics. Given the differences between the Western region and the Coastal and Central regions in terms of economic development, industry structure and coal consumption pattern, it is normal for the causal relationships to differ across these regions. In particular, since coal is the principal primary energy source for the manufacturing and industrial sectors, higher coal consumption implies more activities in these sectors and these activities will ultimately lead to higher GDP. This positive relationship is confirmed by the significant long-run PMG estimated coefficients. The coal intensive manufacturing and industrial sectors are most developed in the Coastal and Central regions of China and it is natural that we find causality from coal consumption to GDP in these regions only. Employing national level time-series data, Li et al. (2008) and Wolde-Rufael (2010) both find unidirectional causality from GDP to coal consumption. Although we find bidirectional causation in the Coastal and Central regions, our results are to some extent compatible with these studies. As long as some goods in a region are produced (directly or indirectly) with the use of coal and these goods are normal goods in economic sense, a rise in GDP (or income) will be associated with a rise in coal consumption. Our income elasticity estimates support such a relationship. This positive causal relationship does not require a strong and prosperous coal intensive sector in the region. Therefore, the use of aggregated national level data should not matter substantially. Because economic growth leads to higher coal consumption, if the Chinese economy keeps growing at a high rate in the future, GHG emissions will follow and the Chinese leaders will continue to face the economic growth–emissions dilemma. As coal is the main contributor to GHG emissions in China, cutting the consumption of coal seems to be an effective way to curb GHG emissions. While attempts to restrict the use of coal may be able to reduce GHG emissions, the reduction in coal consumption may impede economic growth in the Coastal and Central regions. To avoid the possible interference with economic growth, GHG emissions reduction may be achieved by further promotion of the efficient use of coal and adoption of more advanced carbon capture technologies. These measures involve the revitalization of existing equipment and import of new technologies, which may, however, lead to a rise in the cost of coal usage and ultimately a decrease in the attractiveness of the resource. Another possibility is for China to taper off its reliance on coal and switch to lower emission fuel sources. This may lead to technological and energy security issues and so this option may not be viable in the short-run. However, the gradual diversification of energy sources may actually be able to enhance energy supply security in the long-run.