Rapid economic growth in Northwest China has been accompanied by a dramatic increase in carbon emissions. Based on the two-level Logarithmic Mean Divisia Index (LMDI) method, this study decomposes changes in energy-related carbon emissions in Northwest China during 1995-2012 from the regional and provincial perspectives. Further, by constructing an expanded decomposition model of the decoupling index, this paper quantitatively analyzes delinking indicators of economic activity and environmental pressure in Northwest China. The results indicate that: (1) at both regional and provincial levels, economic activity effects play a crucial role in increasing carbon emissions, whereas improvements of energy efficiency appear as the main factor in curbing carbon missions; (2) the significance of influencing factors of CO 2 emissions varies across provinces. The role of economic activity in Shannxi is more pronounced compared to that of the other four provinces, as well as the role of population in Xinjiang; (3) when the decoupling relationship is considered, "relative decoupling" and "no decoupling" are the main characteristics under investigation during the examined period. Whereas "strong decoupling" was only identified in 2007 and 2009; (4) the current extensive pattern of economic growth in Northwest China poses a serious threat to the decoupling process. Furthermore, the coal-based energy structure also hinders the decoupling process. According to these results, some policy recommendations are proposed.

Abstract:With the rapid economic development of the Xinjiang Uygur Autonomous Region, the area's transport sector has witnessed significant growth, which in turn has led to a large increase in carbon dioxide emissions. As such, calculating of the carbon footprint of Xinjiang's transportation sector and probing the driving factors of carbon dioxide emissions are of great significance to the region's energy conservation and environmental protection. This paper provides an account of the growth in the carbon emissions of Xinjiang's transportation sector during the period from 1989 to 2012. We also analyze the transportation sector's trends and historical evolution. Combined with the STIRPAT (Stochastic Impacts by Regression on Population, Affluence and Technology) model and ridge regression, this study further quantitatively analyzes the factors that influence the carbon emissions of Xinjiang's transportation sector. The results indicate the following: (1) the total carbon emissions and per capita carbon emissions of Xinjiang's transportation sector both continued to rise rapidly during this period; their average annual growth rates were 10.8% and 9.1%, respectively; (2) the carbon emissions of the transportation sector come mainly from the consumption of diesel and gasoline, which accounted for an average of 36.2% and 2.6% of carbon emissions, respectively; in addition, the overall carbon emission intensity of the transportation sector showed an "S"-pattern trend within the study period; (3) population density plays a dominant role in increasing carbon dioxide emissions. Population is then followed by per capita GDP and, finally, energy intensity. Cargo turnover has a more significant potential impact on and role in emission reduction than do private vehicles. This is because road freight is the primary form of transportation used across Xinjiang, and this form of transportation has low energy efficiency. These findings have important implications for future efforts to reduce the growth of transportation-based carbon dioxide emissions in Xinjiang and for any effort to construct low-carbon and sustainable environments.

With China's rapid economic growth, energy-related CO 2 emissions have experienced a dramatic increase. Quantification of energy-related CO 2 emissions that occur in China is of serious concern for the policy makers to make efficient environmental policies without damaging the economic growth. Examining 33 productive sectors in China, this paper combined the extended "Kaya identity" and "IPAT model" with the Log-Mean Divisia Index Method (LMDI) to analyze the contribution of various factors driving of energy-related CO 2 emissions in China during 1995-2009. Empirical results show that the main obstacle that hinders China's transition to a green energy economy is the economic structure characterized by high carbon emissions. In contrast, the increased proportion of renewable energy sources (RES) and the improvement of energy efficiency play a more important role in reducing carbon emissions. Moreover, the power sector has a pivotal position in CO 2 emissions reduction, primarily because of the expansion of electricity consumption. These findings suggest that policies and measures should be considered for various industrial sectors to maximize the energy efficiency potential. In addition, optimizing the industrial structure is more urgent than adjusting the energy structure for China.

This paper identifies the driving forces of CO 2 emissions from 1990 to 2014 in Xinjiang's transport sector based on the logarithmic mean divisia index (LMDI) method. Then we introduce the decoupling index to further quantitatively analyze the delinking indicators on the transport sector's growth and environmental pressures. The results indicate that: 1) CO 2 emissions increased significantly with an average annual growth rate of 8.7%. On the contrary, energy intensity has declined constantly over the study period. 2) Economic growth, population size, industrial structure, internal structural and energy mix have proven to contribute to CO 2 emissions increases. Moreover, economic growth plays a critical role in the increment with a contribution of 13.23 million tons, followed by population size and internal structure. 3) Xinjiang's transport witnessed a fluctuating decoupling progress with weak decoupling as the theme. In particular, the decoupling state moved from weak decoupling in 1991-2000 with short-term volatility to weak decoupling in 2001-2010. However, the coupling relationship was strengthened during 2011-2014. 4) Energy intensity is the most important factor for explaining the dissociation in Xinjiang's transport sector. However, internal structural, industrial structure, and population size has turned out to be the obstacles in decoupling progress.