Study on Dynamic Total Factor Carbon Emission Efficiency in China’s Urban Agglomerations

Sustainability - Tập 12 Số 7 - Trang 2675
Fan Zhang1,2, Gui Jin3, Junlong Li4,5, Chao Wang6, Ning Xu7
1Center for Chinese Agricultural Policy, Chinese Academy of Sciences, Beijing, 100101, China
2Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
3College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China
4Research Center of Low Carbon Economy, Research Base of Humanities and Social Sciences of Fujian Institutions of Higher Learning, Sanming 365004, China
5School of Economics and Management, Sanming University, Sanming 365004, China;
6State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China
7College of Geography and Environment, Shandong Normal University, Jinan, 250358, China

Tóm tắt

The scale effect of urbanization on improving carbon emission efficiency and achieving low-carbon targets is an important topic in urban research. Using dynamic panel data from 64 prefecture-level cities in four typical urban agglomerations in China from 2006 to 2016, this paper constructed a stochastic frontier analysis model to empirically measure the city-level total-factor carbon emission efficiency index (TCEI) at different stages of urbanization and to identify rules governing its spatiotemporal evolution. We quantitatively analyzed the influences and functional characteristics of TCEI in the four urban agglomerations of Pearl River Delta, Beijing-Tianjin-Hebei, the Yangtze River Delta, and Chengdu-Chongqing. Results show that the TCEI at different stages of urbanization in these urban agglomerations is increasing year by year. The overall city-level TCEI was ranked as follows: Pearl River Delta > Beijing-Tianjin-Hebei > Yangtze River Delta > Chengdu-Chongqing. Improvements in the level of economic development and urbanization will help achieve low-carbon development in a given urban agglomeration. The optimization of industrial structure and improvement of ecological environment will help curb carbon emissions. This paper provides decision-making references for regional carbon emission reduction from optimizing industrial and energy consumption structures and improving energy efficiency.

Từ khóa


Tài liệu tham khảo

Roland, 2013, Megacities and large urban agglomerations in the coastal zone: Interactions between atmosphere, land, and marine ecosystems, AMBIO, 42, 13, 10.1007/s13280-012-0343-9

Tao, 2017, Dynamics of green productivity growth for major Chinese urban agglomerations, Appl. Energy, 196, 170, 10.1016/j.apenergy.2016.12.108

Zhuang, 2019, Environmental performance and regulation effect of china’s atmospheric pollutant emissions: Evidence from “Three regions and ten urban agglomerations”, Environ. Resour. Econ., 74, 211, 10.1007/s10640-018-00315-6

Wang, 2018, Overview of research on china’s transition to low-carbon development: The role of cities, technologies, industries and the energy system. Renew, Sust. Energy Rev., 81, 1350, 10.1016/j.rser.2017.05.099

Bai, 2019, How does urbanization affect residential CO2 emissions? An analysis on urban agglomerations of China, J. Clean. Prod., 209, 876, 10.1016/j.jclepro.2018.10.248

Jin, 2019, Trade-offs in land-use competition and sustainable land development in the north China plain, Technol. Forecast. Soc. Change, 141, 36, 10.1016/j.techfore.2019.01.004

Central Compilation & Translation Press (2019, December 19). The 13th Five-Year Plan for Economic and Social Development of the People’s Republic of China, Available online: https://en.ndrc.gov.cn/newsrelease_8232/201612/P020191101481868235378.pdf.

GOV.CN (2019, December 22). The Report of the 19th National Congress of the CPC, 18 October 2017, Available online: http://www.gov.cn/zhuanti/2017-10/27/content_5234876.htm.

Wang, S., Wang, H., Zhang, L., and Dang, J. (2019). Provincial carbon emissions efficiency and its influencing factors in China. Sustainability, 11.

Jin, 2020, Is there a decoupling relationship between CO2 emission reduction and poverty alleviation in China?, Technol. Forecast. Soc. Change, 151, 119856, 10.1016/j.techfore.2019.119856

Zhu, 2017, CO2 emissions from the industrialization and urbanization processes in the manufacturing center Tianjin in China, J. Clean. Prod., 168, 867, 10.1016/j.jclepro.2017.08.245

Liu, 2018, China’s energy revolution strategy into 2030, Resour. Conserv. Recycl., 128, 78, 10.1016/j.resconrec.2017.09.028

Li, 2009, Urbanisation and its impact on building energy consumption and efficiency in China, Renew. Energy, 34, 1994, 10.1016/j.renene.2009.02.015

Lin, 2014, Energy demand in China: Comparison of characteristics between the US and China in rapid urbanization stage, Energy Convers. Manag., 79, 128, 10.1016/j.enconman.2013.12.016

Wang, 2016, Exploring the relationship between urbanization, energy consumption, and CO2 emissions in different provinces of China, Renew. Sustain. Energy Rev., 54, 1563, 10.1016/j.rser.2015.10.090

GOV.CN (2019, December 22). China Submitted Intended Nationally Determined Contributions File to UNFCCC, 30 June 2015, Available online: http://www.gov.cn/xinwen/2015-06/30/content_2887337.htm.

Dong, 2016, Analysis of carbon emission intensity, urbanization and energy mix: Evidence from China, Nat. Hazards, 82, 1375, 10.1007/s11069-016-2248-6

Chen, 2017, Accounting carbon emission changes under regional industrial transfer in an urban agglomeration in China’s pearl river delta, J. Clean. Produc., 167, 110, 10.1016/j.jclepro.2017.08.041

Wang, 2019, Decarbonizing china’s urban agglomerations, Ann. Am. Assoc. Geogr., 109, 266

Kaya, Y., and Yokobori, K. (1998). Environment, Energy and Economy: Strategies for Sustainability, Brookings Institution.

Mielnik, 1999, Communication the evolution of the “carbonization index” in developing countries, Energy Policy, 27, 307, 10.1016/S0301-4215(99)00018-X

Sun, 2005, The decrease of CO2 emission intensity is decarbonization at national and global levels, Energy Policy, 33, 975, 10.1016/j.enpol.2003.10.023

Ramanathan, 2006, A multi-factor efficiency perspective to the relationships among world GDP, energy consumption and carbon dioxide emissions, Technol. Forecast. Soc. Change, 73, 483, 10.1016/j.techfore.2005.06.012

Ramanathan, 2002, Combining indicators of energy consumption and CO2 emissions: A cross-country comparison, Int. J. Global Energ. Issues, 17, 214, 10.1504/IJGEI.2002.000941

Ramanathan, 2005, An analysis of energy consumption and carbon dioxide emissions in countries of the middle east and north Africa, Energy, 30, 2831

Zhou, 2006, Slacks-based efficiency measures for modeling environmental performance, Ecol. Econ., 60, 111, 10.1016/j.ecolecon.2005.12.001

Zhou, 2008, A survey of data envelopment analysis in energy and environmental studies, Eur. J. Oper. Res., 189, 1, 10.1016/j.ejor.2007.04.042

Zhou, 2010, Total factor carbon emission performance: A Malmquist index analysis, Energy Econ., 32, 194, 10.1016/j.eneco.2009.10.003

Reinhard, 2000, Environmental efficiency with multiple environmentally detrimental variables: Estimated with SFA and DEA, Eur. J. Oper. Res., 121, 287, 10.1016/S0377-2217(99)00218-0

Dong, 2013, Regional carbon emission performance in China according to a stochastic frontier model, Renew. Sustain. Energy Rev., 28, 525, 10.1016/j.rser.2013.08.009

Guo, 2011, Evaluation of potential reductions in carbon emissions in Chinese provinces based on environmental DEA, Energy Policy, 39, 2352, 10.1016/j.enpol.2011.01.055

Zhang, 2015, The decomposition of energy-related carbon emission and its decoupling with economic growth in China, Renew. Sustain. Energy Rev., 41, 1255, 10.1016/j.rser.2014.09.021

Ma, H.Z., Bai, Y.P., and Che, L. (2019, December 26). Spatio-Temporal Pattern Changes of Chinese Provincial Carbon Dioxide Emission Efficiency. Destech Transactions on Economics, Business and Management, 2017 (icem). Available online: http://www.dpi-proceedings.com/index.php/dtem/article/view/13212/12735.

Yan, 2017, Carbon emission efficiency and spatial clustering analyses in China’s thermal power industry: Evidence from the provincial level, J. Clean. Prod., 156, 518, 10.1016/j.jclepro.2017.04.063

Cheng, 2018, Total-factor carbon emission efficiency of china’s provincial industrial sector and its dynamic evolution, Renew. Sustain. Energy Rev., 94, 330, 10.1016/j.rser.2018.06.015

Wang, 2018, Agglomeration effect of CO2 emissions and emissions reduction effect of technology: A spatial econometric perspective based on China’s province-level data, J. Clean. Prod., 204, 96, 10.1016/j.jclepro.2018.08.243

Wang, 2007, Decomposing energy productivity change: A distance function approach, Energy, 32, 1326, 10.1016/j.energy.2006.10.001

Rahman, 2017, Carbon emissions, energy consumption and industrial growth in Bangladesh: Empirical evidence from ARDL cointegration and granger causality analysis, Energy Policy, 110, 600, 10.1016/j.enpol.2017.09.006

Zhang, 2014, The impact of economic growth, industrial structure and urbanization on carbon emission intensity in China, Natural Hazards, 73, 579, 10.1007/s11069-014-1091-x

Cheng, 2013, Spatial econometric analysis of carbon emission intensity and its driving factors from energy consumption in China, Acta Geogr. Sin., 68, 418

Xing, 2018, Apparel color preferences for different regions in China: The connection to personal values, Color Res. Appl., 43, 423, 10.1002/col.22204

Jin, B. (2020). Seeking balance: Resource conservation, environmental protection and industrial development. China’s Path of Industrialization, Springer.

Kumbhakar, 2000, Estimation and decomposition of productivity change when production is not efficient: A paneldata approach, Econom. Rev., 19, 312, 10.1080/07474930008800481

Lin, 2014, Exploring energy efficiency in China’s iron and steel industry: A stochastic frontier approach, Energy Policy, 72, 87, 10.1016/j.enpol.2014.04.043

Debreu, 1951, ‘The coefficient of resource utilization’, Econometrica, 19, 273, 10.2307/1906814

Farrell, 1957, ‘The measurement of productive efficiency’, J. R. Stat. Soc., 3, 253

Bai, 2017, Measuring environmental performance of industrial sub–sectors in China: A stochastic metafrontier approach, Phys. Chem. Earth Part A/B/C, 101, 3, 10.1016/j.pce.2016.12.007

Bai, 2018, Exploring the relationship between urbanization and urban eco–efficiency: Evidence from prefecture–level cities in China, J. Clean. Prod, 195, 1487, 10.1016/j.jclepro.2017.11.115

Grosskopf, 1989, Multilateral productivity comparisons when some outputs are undesirable: A nonparametric approach, Rev. Econ. Stat., 71, 90, 10.2307/1928055

Grosskopf, 2005, Characteristics of a polluting technology: Theory and practice, J. Econ., 126, 469, 10.1016/j.jeconom.2004.05.010

Herold, 2018, The influence of internal and external pressures on carbon management practices and disclosure strategies, Australas. J. Environ. Manag., 26, 63, 10.1080/14486563.2018.1522604

Herold, 2018, The interaction between institutional and stakeholder pressures: Advancing a framework for categorising carbon disclosure strategies, Bus. Strategy Environ., 2, 1

Thông tin
Thông tin xuất bản

Sustainability

Tập 12 Số 7

2675

Thông tin tác giả