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Liu, C.X.; Tang, R.; Yaqi, G.; Sun, Y.; , . Carbon Emission Efficiency. Encyclopedia. Available online: https://encyclopedia.pub/entry/23342 (accessed on 22 December 2024).
Liu CX, Tang R, Yaqi G, Sun Y,  . Carbon Emission Efficiency. Encyclopedia. Available at: https://encyclopedia.pub/entry/23342. Accessed December 22, 2024.
Liu, Chen Xu, Ruien Tang, Guo Yaqi, Yuhan Sun,  . "Carbon Emission Efficiency" Encyclopedia, https://encyclopedia.pub/entry/23342 (accessed December 22, 2024).
Liu, C.X., Tang, R., Yaqi, G., Sun, Y., & , . (2022, May 25). Carbon Emission Efficiency. In Encyclopedia. https://encyclopedia.pub/entry/23342
Liu, Chen Xu, et al. "Carbon Emission Efficiency." Encyclopedia. Web. 25 May, 2022.
Carbon Emission Efficiency
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This entry is adapted from the peer-reviewed paper 10.3390/su14106114

Carbon emission efficiency is an important concept in environmental science; it refers to the economic benefits generated by production activities that produce carbon emissions at the same time. The less carbon emissions generated per unit of economic output, the more carbon emission efficient it is.

carbon emission efficiency spatial connection social network analysis influencing factors

1. Introduction

Climate change and its impacts have become one of the most serious environmental problems facing the world today [1]. In its fourth Global Climate Assessment, the United Nations Intergovernmental Panel on Climate Change (IPCC) noted that it is an indisputable fact that human activities and massive greenhouse gas emissions are the major causes of global climate change. As CO2 is one of the most important greenhouse gases, it is closely related to global warming [2]. As the main source of carbon emissions, cities have a profound impact on the realization of carbon emission reduction targets [3]. Establishing low-carbon cities is an inevitable choice for China in order to deal with climate change and to develop a low-carbon economy [4].
Based on the above, carbon emission and carbon emission reduction issues have been given extensive attention, and studies on carbon emission estimation methods [5], influencing factors [6][7][8], emission intensity [9][10], and emission efficiency [11] have been carried out successively. Carbon emission efficiency is an important concept in environmental science; it refers to the economic benefits generated by production activities that produce carbon emissions at the same time [12]. The less carbon emissions generated per unit of economic output, the more carbon emission efficient it is. Carbon emission efficiency considers the promotion and inhibitory effect of carbon emissions on economic growth, and measures the level of economic growth under carbon emission restrictions—this has been widely studied. In the context of tightening carbon dioxide emission constraints, the currently used crude economic development approach is unsustainable, and improving carbon efficiency is an important way to promote a change to the development approach. At present, most studies focus on regional differences in carbon emission efficiency, and less attention has been paid to region-specific carbon emission efficiency correlations.
As a typical representative of the world’s major urban agglomerations and metropolitan areas, from an early stage, the Yangtze River Delta region advanced industrialization and urbanization, and is home to a large number of high-emission industries such as petrochemicals, metallurgy, paper making, and automobiles. The energy and carbon emissions brought about by the high-intensity development of industry have put enormous environmental pressure on the Yangtze River Delta urban agglomerations (Figure 1), affecting the sustainable development of the region and the fulfilment of emission reduction commitments.
可持续性 14 06114 g001 550
Figure 1. Carbon emissions in the Yangtze River Delta urban agglomeration from 2001 to 2019 (source: extracted from the DMSP/OLS night light data).
At the same time, the gradual formation of a networked transportation system, the continuous development of information technology, and the continuous advancement of regional economic integration have made the spatial connection between the production factors of each city in the Yangtze River Delta increasingly close, and the carbon emission efficiency among the regions has also shown significant spatial correlation characteristics. It is of great theoretical significance and application value to examine the spatial correlation structure and influencing factors of energy carbon emission efficiency in the cities of the Yangtze River Delta urban agglomeration from a network perspective, and to explore the status and role of carbon emission efficiency of each region in the spatial correlation network, in order to build a cross-regional carbon emission efficiency synergy mechanism under the new economic normal and to formulate carbon emission reduction policies that are both targeted and systematic. It can also fill the gap in the current academic field in the study of the spatial correlation of regional carbon emission efficiency.

2. Carbon Emission Efficiency

At present, the definition of carbon emission efficiency can be divided into two types: single factor carbon emission efficiency and total factor carbon emission efficiency. Kaya and Yokobori first defined carbon emission efficiency as carbon productivity from a single-factor perspective; that is, the ratio of GDP to carbon emissions in the same period [13]. Yamaji defined the ratio of total CO2 emissions to GDP as CO2 productivity when studying the level of carbon emissions in Japan [14]. Mielnik, Goldember, and Ang used carbon dioxide emissions per unit of energy consumption as an important measure of carbon emission efficiency [15][16]. The single factor only takes into account the proportion between GDP or energy consumption and carbon emissions, but ignores the substitution between factors when multiple factors combined are input into the actual production process. Ramanatha believed that the definition of carbon emission efficiency should be integrated into the three frameworks of energy consumption, economic development, and carbon emission, so that the evaluation results are comprehensive and reasonable [17]. Zaim and Taskin defined carbon emissions as a non-expected output variable, and proposed the concept of the comprehensive efficiency index, and applied this index to the OECD national research [18].
The current research on carbon emission efficiency can be divided into industries and regions according to the research objects. In the research of industry carbon emission efficiency, scholars have used different measurement models to measure the carbon emission efficiency of different industries in the national economy. Wang Kai and Wang Kun used the SBM model, and found that the carbon emission efficiency of China’s tourism showed a significant spatial imbalance [19]; Dwyer et al. measured carbon emissions from tourism in Australia using both the production and expenditure approaches [20]; Hampf proposed a new DEA analysis method based on an efficiency analysis perspective to investigate the standard of CO2 emissions in the U.S. electric power industry [21]; and Erwin et al. used a sample of Indonesian manufacturing firms to explore the determinants of carbon emissions [22]. In terms of regional carbon emission efficiency research, Ramanathan used the data envelope analysis (DEA) to build an input−output index system containing carbon dioxide emissions, energy consumption, and economic activity variables, to compare the carbon emission performance level of various countries [17]. Zhang et al. developed a spatial regression model to study the convergence characteristics and influencing factors of carbon emission intensity in Chinese cities and major strategic regions [23]. Meng et al. used the RAM-DEA model to estimate the low-carbon economic efficiency of the Chinese industrial sector from 2001 to 2013, and found that most industries of low-carbon economic efficiency are still at a low level; however, the carbon emission efficiency was greatly improved during the study period [24].
In addition, many academic studies have confirmed that carbon-emission-related problems do not exist independently among regions, but they have some spatial correlations between them [25][26]. Grunewald and others explored the driving factors of spatial differences in carbon emissions and pointed out that energy intensity and energy structure are the main reasons for the spatial differences in carbon emissions [27]. Marbuah and Mensah performed a statistical test of the spatial association of several pollutants, including CO2, using 290 Swedish urban areas as the study areas, showing that spatial spillover effects were the main driver of the environmental Kuznets curve [28]. Wu studied the spatial pattern and evolution mechanism of carbon emission reduction in China through spatial econometrics, and analyzed the emission reduction characteristics of key provinces [29]. Zhou determined the determinants and spatial relationship of CO2 emissions at an urban level in China [30].
Previous studies have also discussed and analyzed the influence mechanism of regional carbon emission efficiency in depth. Wang et al. used the window SBM analysis method to measure the carbon emission efficiency and emission reduction potential of various provinces and cities in China from 2003 to 2016, and analyzed the impact of resource endowment on emission efficiency using the panel Tobit model. The results show an inverse relationship between resource endowment and emission efficiency [31]. Liu et al. proposed the ideal point cross efficiency (IPCE) model, and used this model to analyze the carbon emission efficiency of the top ten urban agglomerations in China in 2008–2015. The results showed that the population effect and economic effect promoted the emission efficiency of mature urban agglomerations, while reducing the efficiency of emerging urban agglomerations [32]. Zhou et al. measured the carbon efficiency of the top 18 global carbon emitting countries from 1997 to 2004 based on a DEA model, and found that technological progress had a significant effect on the improvement of carbon efficiency [33]. Ma Y. and Lu Y. used the ultra-efficiency SBM model to calculate carbon emission efficiency at a provincial level in China from 1995 to 2012, and examined the impact of independent innovation, FDI, and international trade. The results found that FDI could significantly improve the carbon emission efficiency, while independent innovation and import had inhibitory effects [34].

References

  1. Su, Y.; Chen, X.; Ye, Y.; Wu, Q.; Zhang, H.; Huang, N.; Kuang, Y. Characteristics and mechanism of energy consumption in China based on night lighting data. J. Geogr. 2013, 68, 1513–1526.
  2. Liu, Q.; Wang, S.; Zhang, W.; Li, J.; Kong, Y. Examining the effects of income inequality on CO2 emissions: Evidence from non-spatial and spatial perspectives. Appl. Energy 2019, 236, 163–171.
  3. Liu, Z.; Chen, L. Demonstration value and dynamic mechanism of regional integration development of the Yangtze River Delta. Reform 2018, 65–71. Available online: https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CJFD&dbname=CJFDLAST2019&filename=REFO201812006&uniplatform=NZKPT&v=3HzzqpA_AsORVP5FzlwfgPZBL5PJkQbSMrejO4VToCA43Zv9M1d3zf5-GHQzBhOn (accessed on 20 March 2022).
  4. Zhang, G.; Wang, J.; Wu, K.; Xu, Z. Spatiotemporal characteristics and influencing factors of economic and environmental coordination among the three major urban agglomerations in China. Geogr. Res. 2020, 39, 272–288.
  5. Liu, Z.; Geng, Y.; Xue, B.; Xi, F.; Jiao, J. A calculation method of CO2 emission from urban energy consumption. Resour. Sci. 2011, 33, 1325–1330.
  6. Zhao, R.; Huang, X.; Peng, B. Research on carbon cycle and carbon balance of Nanjing urban system. Acta Geogr. Sin. 2012, 67, 758–770.
  7. Xu, S.; Xi, R.; He, Z. Influential factors and policy implications of carbon emissions for energy consumption in China. Resour. Sci. 2012, 34, 2–12.
  8. Li, J.; Huang, X.; Chuan, X.; Sun, S. Analysis of the spatio-temporal characteristics and influencing factors of carbon emission efficiency in the Yangtze River Delta region. Resour. Environ. Yangtze River Basin 2020, 29, 1486–1496.
  9. Zhao, R.; Huang, X.; Zhong, T. Research on carbon emission intensity and carbon footprint of different industrial spaces in China. Acta Geogr. Sin. 2010, 65, 1048–1057.
  10. Chen, H.; Qi, S.; Tan, X. Decomposition and prediction of China’s carbon emission intensity towards carbon neutrality: From perspectives of national, regional and sectoral level. Sci. Total Environ. 2022, 8, 5–18.
  11. Fang, G.; Gao, Z.; Tian, L.; Fu, M. What drives urban carbon emission efficiency?—Spatial analysis based on nighttime light data. Appl. Energy 2022, 312, 118772.
  12. Xu, Q.; Zhong, M.; Cao, M. Does digital investment affect carbon efficiency? Spatial effect and mechanism discussion. Sci. Total Environ. 2022, 3, 4–25.
  13. Kaya, Y.; Yokobori, K. Global Environment, Energy and Economic Development; United Nations University: Tokyo, Japan, 1993.
  14. Sun, J.W. The decrease of CO2 emission intensity is decarbonization at national and global levels. Energy Policy 2005, 33, 957–978.
  15. Mielnik, O.; Goldemberg, J. Communication the evolution of the Carbon Index in developing countries. Energy Policy 1999, 27, 307–318.
  16. Ang, B.W. Is the energy intensity a less useful indicator than the carbon factor in the study of climate change. Energy Policy 1999, 27, 943–946.
  17. Ramanathan, V. Combining indicators of energy consumption and CO2 emissions: Across country comparison. Int. J. Glob. Energy Issues 2002, 17, 214–227.
  18. Zaim, O.; Taskin, F. Environmental efficiency in carbon dioxide emissions in the OECD: A non-parametric approach. J. Environ. Manag. 2000, 58, 95–107.
  19. Wang, K.; Shao, H.; Zhou, T.; Liu, H. Carbon emission efficiency and its spatial correlation characteristics of Chinese tourism industry. Resour. Environ. Yangtze River Basin 2018, 27, 473–482.
  20. Dwyer, L.; Forsyth, P.; Spurr, R.; Hoque, S. estimating the carbon footprint of Australian tourism. J. Sustain. Tour. 2010, 18, 355–376.
  21. Hampf, B.; Rødseth, K.L. Carbon dioxide emission standards for U.S. power plants: An efficiency analysis perspective. Energy Econ. 2015, 50, 140–153.
  22. Erwin, S.; Ridha, S.; Tubandryah, H. Determinants of Carbon Emission Disclosure in Indonesia Manufacturing Company. Asian J. Econ. Bus. Account. 2021, 22, 1–9.
  23. Zhang, Z.Q.; Zhang, T.; Feng, D.F. Regional differences, dynamic evolution and convergence of carbon emission intensity in China. Res. Quant. Econ. Technol. 2022, 39, 67–87.
  24. Meng, M.; Fu, Y.; Wang, T.; Jing, K. Analysis of low-carbon economy efficiency of Chinese industrial sectors based on a RAM model with undesirable outputs. Sustainability 2017, 9, 451.
  25. Zheng, H.; Ye, A. The spatial correlation network structure of carbon emission in the Pearl River Delta urban agglomeration. Environ. Sci. China 2022, 4, 1–13.
  26. Li, A.; Wang, Y.; Li, M.; Wang, B.; Chen, W. Study on the structural characteristics and influencing factors of the spatial correlation network of carbon emission: Take the three major urban agglomerations in China as an example. Environ. Sci. Technol. 2021, 44, 186–193.
  27. Grunewald, N.; Jakob, M.; Mouratiadou, I. Decomposing inequality in CO2 emissions: The role of primary energy carriers and economic sectors. Ecol. Econ. 2014, 100, 183–194.
  28. Marbuah, G.; Amuakwa-Mensah, F. Spatial analysis of emissions in Sweden. Energy Econ. 2017, 68, 383–394.
  29. Wu, H. Chinese provincial carbon emission reduction: Spatiotemporal pattern, evolution mechanism and policy Suggestions—Is based on the theory and method of spatial econometrics. Manag. World 2015, 3–10.
  30. Zhou, C.; Wang, S. Examining the determinants and the spatial nexus of city-level CO2 emissions in China: A dynamic spatial panel analysis of China’s cities. J. Clean. Prod. 2018, 171, 917–926.
  31. Wang, K.; Wu, M.; Sun, Y.P.; Shi, X.; Sun, A.; Zhang, P. Resource abundance, industrial structure, and regional carbon emissions efficiency in China. Resour. Policy 2019, 60, 203–214.
  32. Liu, B.; Tian, C.; Li, Y.; Song, H.; Ma, Z. Research on the effects of urbanization on carbon emissions efficiency of urban agglomerations in China. J. Clean. Prod. 2018, 197, 1374–1381.
  33. Zhou, P.; Ang, B.W.; Poh, K.L. Slacks-based efficiency measures for modeling environmental performance. Ecol. Econ. 2010, 60, 111–118.
  34. Ma, Y.; Lu, Y. Different sources of technological advances and carbon dioxide emission efficiency—Based on space panel data model empirical. Res. Dev. Manag. 2017, 29, 33–41.
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Chen Xu Liu
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Update Date: 26 May 2022
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