Driving forces in energy-related carbon dioxide emissions in east and south coastal China: commonality and variations

Abstract As the world's top carbon dioxide emitter, China is expected to reach its emissions peak by 2030. East and south coastal China contribute nearly one-third of the emissions in China, and therefore play a critical role in achieving the national goal of emission control. This study analyzes the driving forces of east and south coastal China's energy-related emissions and their provincial characteristics by applying the logarithmic mean divisia index method. The emissions in this region were found to double from 2000 to 2012, along with three and two folds increase in the economy and energy consumption. The result suggests a persistent connection between economic growth and emission even in this socioeconomically advanced region. The per capita emissions are lower than most regions of China at a given economic level, and are expected to be lower than select developed nations when reaching their corresponding economic levels. Energy efficiency has been the leading force in reducing emission growth, and we differentiate the provinces into three distinct low-carbon developmental stages. There is no significant influence from either the economic or energy structure change, indicating great emission reduction potential from structure decarbonization especially when compared to advanced nations. These results suggest that the dual effort of enhancing energy efficiency and decarbonizing the economic and energy structure would probably serve the goal of total emission control more effectively and efficiently, and factor driven emission reduction strategies are needed in these geographically and socioeconomically similar regions. The study expands on the current knowledge by analyzing the interprovincial commonality and variation of this pilot region in China, and therefore provides a stepwise view of the emission driving forces for emerging economies.

[1]  Malin Song,et al.  Driving effect analysis of energy-consumption carbon emissions in the Yangtze River Delta region , 2015 .

[2]  Xianchun Tan,et al.  China’s regional CO2 emissions reduction potential: A study of Chongqing city , 2016 .

[3]  Y. Kaya.  Impact of carbon dioxide emission control on GNP growth : Interpretation of proposed scenarios , 1989 .

[4]  Elif Akbostancı,et al.  A decomposition analysis of CO2 emissions from energy use: Turkish case , 2009 .

[5]  Glen P. Peters,et al.  Construction of Chinese Energy and Emissions Inventory , 2006 .

[6]  Zhongfu Tan,et al.  Examining the driving forces for improving China’s CO2 emission intensity using the decomposing method , 2011 .

[7]  C. Weber,et al.  China's growing CO2 emissions--a race between increasing consumption and efficiency gains. , 2007, Environmental science & technology.

[8]  Yong Geng,et al.  The gigatonne gap in China’s carbon dioxide inventories , 2012 .

[9]  Yi-Ming Wei,et al.  Exploring the regional characteristics of inter-provincial CO2 emissions in China: An improved fuzzy clustering analysis based on particle swarm optimization , 2012 .

[10]  B. W. Ang,et al.  Decomposition of aggregate CO2 emissions: A production-theoretical approach , 2008 .

[11]  K. Hubacek,et al.  Lifestyles, technology and CO2 emissions in China: A regional comparative analysis , 2009 .

[12]  Peng Zhou,et al.  The driving forces of change in energy-related CO2 emissions in Ireland: A multi-sectoral decomposition from 1990 to 2007 , 2012 .

[13]  Yi-Ming Wei,et al.  China’s regional industrial energy efficiency and carbon emissions abatement costs , 2014 .

[14]  P. Eng.  CO2 emissions from fuel combustion: highlights , 2009 .

[15]  Klaus Hubacek,et al.  Driving forces of CO2 emissions in the G20 countries: An index decomposition analysis from 1971 to 2010 , 2015, Ecol. Informatics.

[16]  T. Wei,et al.  Household Energy Use and Carbon Emissions in China: A decomposition analysis , 2015 .

[17]  Ujjaini Mukhopadhyay,et al.  Identifying the major players behind increasing global carbon dioxide emissions: a decomposition analysis , 2010 .

[18]  Ming Zhang,et al.  Accounting for energy-related CO2 emission in China, 1991-2006 , 2009 .

[19]  J. C. J. M. Bergh,et al.  Comparing structural decomposition analysis and index , 2003 .

[20]  Tommy Dalgaard,et al.  Potential greenhouse gas emission reductions in soybean farming: a combined use of Life Cycle Assessment and Data Envelopment Analysis , 2013 .

[21]  Jan Christoph Steckel,et al.  Feasible mitigation actions in developing countries , 2014 .

[22]  B. W. Ang,et al.  Total factor carbon emission performance: A Malmquist index analysis , 2010 .

[23]  Zhifeng Yang,et al.  A spatio-temporal decomposition analysis of energy-related CO2 emission growth in China , 2015 .

[24]  S. Paul,et al.  CO2 emission from energy use in India: a decomposition analysis , 2004 .

[25]  B. W. Ang,et al.  Factorizing changes in energy and environmental indicators through decomposition , 1998 .

[26]  Jian Chai,et al.  China’s regional CO2 emissions: Characteristics, inter-regional transfer and emission reduction policies , 2011 .

[27]  Hailin Mu,et al.  Analysis of regional difference on impact factors of China’s energy – Related CO2 emissions , 2012 .

[28]  E. Rosa,et al.  Effects of population and affluence on CO2 emissions. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[29]  Yi-Ming Wei,et al.  China’s regional energy and environmental efficiency: A Range-Adjusted Measure based analysis , 2013 .

[30]  Yong Geng,et al.  Determinants of stagnating carbon intensity in China , 2014 .

[31]  D. Brownstone,et al.  The Impact of Residential Density on Vehicle Usage and Energy Consumption , 2005 .

[32]  Ruyin Long,et al.  Factors that influence carbon emissions due to energy consumption based on different stages and sectors in China , 2016 .

[33]  B. W. Ang,et al.  The LMDI approach to decomposition analysis: a practical guide , 2005 .

[34]  Ruyin Long,et al.  Factors that influence carbon emissions due to energy consumption in China: Decomposition analysis using LMDI , 2014 .

[35]  Yi-Ming Wei,et al.  Residential carbon emission evolutions in urban-rural divided China: An end-use and behavior analysis , 2013 .

[36]  Shinji Kaneko,et al.  Driving forces behind the stagnancy of China's energy-related CO2 emissions from 1996 to 1999: The relative importance of structural change, intensity change and scale change , 2005 .

[37]  Klaus Hubacek,et al.  A "carbonizing dragon": China's fast growing CO2 emissions revisited. , 2011, Environmental science & technology.

[38]  B. W. Ang,et al.  Decomposition analysis for policymaking in energy:: which is the preferred method? , 2004 .

[39]  Dequn Zhou,et al.  Optimal path for controlling CO2 emissions in China: A perspective of efficiency analysis , 2014 .

[40]  Kebin He,et al.  Energy policy: A low-carbon road map for China , 2013, Nature.

[41]  Yong Geng,et al.  Uncovering China’s greenhouse gas emission from regional and sectoral perspectives , 2012 .

[42]  Yi-Ming Wei,et al.  How can China reach its CO2 intensity reduction targets by 2020? A regional allocation based on equity and development , 2011 .

[43]  J. Moore,et al.  Recent advances in carbon emissions reduction: policies, technologies, monitoring, assessment and modeling , 2015 .

[44]  J. Greet,et al.  Trends in global CO2 emissions: 2012 report , 2012 .

[45]  Jonathan E. Sinton,et al.  Accuracy and reliability of China's energy statistics , 2001 .

[46]  中華人民共和国上海市統計局.  Shanghai statistical yearbook , 1988 .

[47]  D.M. Mount,et al.  An Efficient k-Means Clustering Algorithm: Analysis and Implementation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[48]  António Carrizo Moreira,et al.  The driving forces of change in energy-related CO2 emissions in Eastern, Western, Northern and Southern Europe: The LMDI approach to decomposition analysis , 2015 .