IDEAS home Printed from https://ideas.repec.org/a/eee/enepol/v147y2020ics0301421520305796.html
   My bibliography  Save this article

A modified and improved method to measure economy-wide carbon rebound effects based on the PDA-MMI approach

Author

Listed:
  • Li, Ding
  • Gao, Ming
  • Hou, Wenxuan
  • Song, Malin
  • Chen, Jiandong

Abstract

Although energy technological progress has been regarded as an important driver for reducing carbon emissions, the existence of carbon rebound effect prevents a portion of the potential carbon reductions to be realized. Compared with the energy rebound effect, research on the carbon rebound effect is scarce because it is always equated with the energy rebound effect. However, the carbon rebound effect is more complex. Given that the traditional method for carbon rebound effect assessment only reflects energy rebound effects, our study proposed an improved production-theoretical decomposition analysis (PDA)-Meta-frontier Malmquist index (MMI)-based method and explored carbon rebound effects in China from 2006 to 2015. Our results show that (1) the eastern and western regions faced fewer carbon rebound effect risks compared with those of the central region due to decreasing emission intensity associated with energy technological progress; (2) the reductions in emission intensity in the eastern region relied both on coal and non-coal technology, whereas the western region only relied on coal technology; and (3) the non-coal technology in the eastern region was at the meta-frontier, whereas the non-coal technology of other regions exhibited catch-up effects.

Suggested Citation

  • Li, Ding & Gao, Ming & Hou, Wenxuan & Song, Malin & Chen, Jiandong, 2020. "A modified and improved method to measure economy-wide carbon rebound effects based on the PDA-MMI approach," Energy Policy, Elsevier, vol. 147(C).
  • Handle: RePEc:eee:enepol:v:147:y:2020:i:c:s0301421520305796
    DOI: 10.1016/j.enpol.2020.111862
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301421520305796
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.enpol.2020.111862?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Brookes, Len, 1990. "The greenhouse effect: the fallacies in the energy efficiency solution," Energy Policy, Elsevier, vol. 18(2), pages 199-201, March.
    2. Chang, Tzu-Pu & Hu, Jin-Li, 2010. "Total-factor energy productivity growth, technical progress, and efficiency change: An empirical study of China," Applied Energy, Elsevier, vol. 87(10), pages 3262-3270, October.
    3. Li, Ke & Zhang, Ning & Liu, Yanchu, 2016. "The energy rebound effects across China’s industrial sectors: An output distance function approach," Applied Energy, Elsevier, vol. 184(C), pages 1165-1175.
    4. Chen, Zhenni & Du, Huibin & Li, Jianglong & Southworth, Frank & Ma, Shoufeng, 2019. "Achieving low-carbon urban passenger transport in China: Insights from the heterogeneous rebound effect," Energy Economics, Elsevier, vol. 81(C), pages 1029-1041.
    5. Chen, Wenying & Li, Hualin & Wu, Zongxin, 2010. "Western China energy development and west to east energy transfer: Application of the Western China Sustainable Energy Development Model," Energy Policy, Elsevier, vol. 38(11), pages 7106-7120, November.
    6. Dariush Khezrimotlagh & Yao Chen, 2018. "Data Envelopment Analysis," International Series in Operations Research & Management Science, in: Decision Making and Performance Evaluation Using Data Envelopment Analysis, chapter 0, pages 217-234, Springer.
    7. Liu, Yu & Tan, Xiu-Jie & Yu, Yang & Qi, Shao-Zhou, 2017. "Assessment of impacts of Hubei Pilot emission trading schemes in China – A CGE-analysis using TermCO2 model," Applied Energy, Elsevier, vol. 189(C), pages 762-769.
    8. Brannlund, Runar & Ghalwash, Tarek & Nordstrom, Jonas, 2007. "Increased energy efficiency and the rebound effect: Effects on consumption and emissions," Energy Economics, Elsevier, vol. 29(1), pages 1-17, January.
    9. Broberg, Thomas & Berg, Charlotte & Samakovlis, Eva, 2015. "The economy-wide rebound effect from improved energy efficiency in Swedish industries–A general equilibrium analysis," Energy Policy, Elsevier, vol. 83(C), pages 26-37.
    10. Ang, B. W., 2005. "The LMDI approach to decomposition analysis: a practical guide," Energy Policy, Elsevier, vol. 33(7), pages 867-871, May.
    11. Ma, Chunbo & Stern, David I., 2008. "China's changing energy intensity trend: A decomposition analysis," Energy Economics, Elsevier, vol. 30(3), pages 1037-1053, May.
    12. Zhou, P. & Ang, B.W., 2008. "Decomposition of aggregate CO2 emissions: A production-theoretical approach," Energy Economics, Elsevier, vol. 30(3), pages 1054-1067, May.
    13. Zha, Donglan & Yang, Guanglei & Wang, Qunwei, 2019. "Investigating the driving factors of regional CO2 emissions in China using the IDA-PDA-MMI method," Energy Economics, Elsevier, vol. 84(C).
    14. Wang, Ke & Wei, Yi-Ming, 2014. "China’s regional industrial energy efficiency and carbon emissions abatement costs," Applied Energy, Elsevier, vol. 130(C), pages 617-631.
    15. Lin, Boqiang & Liu, Xia, 2012. "Dilemma between economic development and energy conservation: Energy rebound effect in China," Energy, Elsevier, vol. 45(1), pages 867-873.
    16. Jiandong Chen & Ming Gao & Ding Li & Malin Song, 2020. "Analysis of the rebound effects of fossil and nonfossil energy in China based on sustainable development," Sustainable Development, John Wiley & Sons, Ltd., vol. 28(1), pages 235-246, January.
    17. Cheng, Zhonghua & Li, Lianshui & Liu, Jun, 2018. "Industrial structure, technical progress and carbon intensity in China's provinces," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2935-2946.
    18. Feng Dong & Ruyin Long & Zhuolin Li & Yuanju Dai, 2016. "Analysis of carbon emission intensity, urbanization and energy mix: evidence from China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 82(2), pages 1375-1391, June.
    19. Wang, Qunwei & Chiu, Yung-Ho & Chiu, Ching-Ren, 2015. "Driving factors behind carbon dioxide emissions in China: A modified production-theoretical decomposition analysis," Energy Economics, Elsevier, vol. 51(C), pages 252-260.
    20. Shao, Shuai & Guo, Longfei & Yu, Mingliang & Yang, Lili & Guan, Dabo, 2019. "Does the rebound effect matter in energy import-dependent mega-cities? Evidence from Shanghai (China)," Applied Energy, Elsevier, vol. 241(C), pages 212-228.
    21. Zhang, Ning & Choi, Yongrok, 2013. "Total-factor carbon emission performance of fossil fuel power plants in China: A metafrontier non-radial Malmquist index analysis," Energy Economics, Elsevier, vol. 40(C), pages 549-559.
    22. Du, Kerui & Xie, Chunping & Ouyang, Xiaoling, 2017. "A comparison of carbon dioxide (CO2) emission trends among provinces in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 19-25.
    23. van der Zwaan, B. C. C. & Gerlagh, R. & G. & Klaassen & Schrattenholzer, L., 2002. "Endogenous technological change in climate change modelling," Energy Economics, Elsevier, vol. 24(1), pages 1-19, January.
    24. Jin, Taeyoung & Kim, Jinsoo, 2019. "A new approach for assessing the macroeconomic growth energy rebound effect," Applied Energy, Elsevier, vol. 239(C), pages 192-200.
    25. Du, Kerui & Lu, Huang & Yu, Kun, 2014. "Sources of the potential CO2 emission reduction in China: A nonparametric metafrontier approach," Applied Energy, Elsevier, vol. 115(C), pages 491-501.
    26. Zhang, Ning & Wang, Bing, 2015. "A deterministic parametric metafrontier Luenberger indicator for measuring environmentally-sensitive productivity growth: A Korean fossil-fuel power case," Energy Economics, Elsevier, vol. 51(C), pages 88-98.
    27. J. Daniel Khazzoom, 1980. "Economic Implications of Mandated Efficiency in Standards for Household Appliances," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 21-40.
    28. Zhang, Ning & Wang, Bing & Liu, Zhu, 2016. "Carbon emissions dynamics, efficiency gains, and technological innovation in China's industrial sectors," Energy, Elsevier, vol. 99(C), pages 10-19.
    29. Dong-hyun Oh & Jeong-dong Lee, 2010. "A metafrontier approach for measuring Malmquist productivity index," Empirical Economics, Springer, vol. 38(1), pages 47-64, February.
    30. Liu, Bingquan & Shi, Junxue & Wang, Hui & Su, Xuelin & Zhou, Peng, 2019. "Driving factors of carbon emissions in China: A joint decomposition approach based on meta-frontier," Applied Energy, Elsevier, vol. 256(C).
    31. Saunders, Harry D., 2013. "Historical evidence for energy efficiency rebound in 30 US sectors and a toolkit for rebound analysts," Technological Forecasting and Social Change, Elsevier, vol. 80(7), pages 1317-1330.
    32. Liu, Yu & Lu, Yingying, 2015. "The Economic impact of different carbon tax revenue recycling schemes in China: A model-based scenario analysis," Applied Energy, Elsevier, vol. 141(C), pages 96-105.
    33. Richard York, 2012. "Do alternative energy sources displace fossil fuels?," Nature Climate Change, Nature, vol. 2(6), pages 441-443, June.
    34. Saunders, Harry D., 2008. "Fuel conserving (and using) production functions," Energy Economics, Elsevier, vol. 30(5), pages 2184-2235, September.
    35. Wang, Qunwei & Hang, Ye & Su, Bin & Zhou, Peng, 2018. "Contributions to sector-level carbon intensity change: An integrated decomposition analysis," Energy Economics, Elsevier, vol. 70(C), pages 12-25.
    36. Dubey, Rameshwar & Gunasekaran, Angappa & Childe, Stephen J. & Papadopoulos, Thanos & Luo, Zongwei & Wamba, Samuel Fosso & Roubaud, David, 2019. "Can big data and predictive analytics improve social and environmental sustainability?," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 534-545.
    37. Lin, Boqiang & Chen, Yufang & Zhang, Guoliang, 2017. "Technological progress and rebound effect in China's nonferrous metals industry: An empirical study," Energy Policy, Elsevier, vol. 109(C), pages 520-529.
    38. Wang, Wenwen & Liu, Xiao & Zhang, Ming & Song, Xuefeng, 2014. "Using a new generalized LMDI (logarithmic mean Divisia index) method to analyze China's energy consumption," Energy, Elsevier, vol. 67(C), pages 617-622.
    39. Yue-Jun Zhang & Ya-Bin Da, 2013. "Decomposing the changes of energy-related carbon emissions in China: evidence from the PDA approach," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 69(1), pages 1109-1122, October.
    40. Fare, Rolf, et al, 1989. "Multilateral Productivity Comparisons When Some Outputs Are Undesirable: A Nonparametric Approach," The Review of Economics and Statistics, MIT Press, vol. 71(1), pages 90-98, February.
    41. Chen, Jiandong & Gao, Ming & Mangla, Sachin Kumar & Song, Malin & Wen, Jie, 2020. "Effects of technological changes on China's carbon emissions," Technological Forecasting and Social Change, Elsevier, vol. 153(C).
    42. Druckman, Angela & Chitnis, Mona & Sorrell, Steve & Jackson, Tim, 2011. "Missing carbon reductions? Exploring rebound and backfire effects in UK households," Energy Policy, Elsevier, vol. 39(6), pages 3572-3581, June.
    43. Zhang, Yue-Jun & Liu, Zhao & Qin, Chang-Xiong & Tan, Tai-De, 2017. "The direct and indirect CO2 rebound effect for private cars in China," Energy Policy, Elsevier, vol. 100(C), pages 149-161.
    44. Dhakal, Shobhakar, 2009. "Urban energy use and carbon emissions from cities in China and policy implications," Energy Policy, Elsevier, vol. 37(11), pages 4208-4219, November.
    45. Ji, Xi & Yao, Yixin & Long, Xianling, 2018. "What causes PM2.5 pollution? Cross-economy empirical analysis from socioeconomic perspective," Energy Policy, Elsevier, vol. 119(C), pages 458-472.
    46. Fan, Meiting & Shao, Shuai & Yang, Lili, 2015. "Combining global Malmquist–Luenberger index and generalized method of moments to investigate industrial total factor CO2 emission performance: A case of Shanghai (China)," Energy Policy, Elsevier, vol. 79(C), pages 189-201.
    47. Zhang, Ning & Wang, Bing & Chen, Zhongfei, 2016. "Carbon emissions reductions and technology gaps in the world's factory, 1990–2012," Energy Policy, Elsevier, vol. 91(C), pages 28-37.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Liu, Xiao & Hang, Ye & Wang, Qunwei & Chiu, Ching-Ren & Zhou, Dequn, 2022. "The role of energy consumption in global carbon intensity change: A meta-frontier-based production-theoretical decomposition analysis," Energy Economics, Elsevier, vol. 109(C).
    2. Liang, Ting & Zhang, Yue-Jun & Qiang, Wei, 2022. "Does technological innovation benefit energy firms’ environmental performance? The moderating effect of government subsidies and media coverage," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    3. Jia, Zhijie & Lin, Boqiang, 2022. "Is the rebound effect useless? A case study on the technological progress of the power industry," Energy, Elsevier, vol. 248(C).
    4. Zhang, Yixiang & Zhou, Weiyi & Liu, Meiling, 2022. "Driving factors of enterprise energy-saving and emission reduction behaviors," Energy, Elsevier, vol. 256(C).
    5. Gao, Ming, 2023. "The impacts of carbon trading policy on China's low-carbon economy based on county-level perspectives," Energy Policy, Elsevier, vol. 175(C).
    6. Ke Liu & Mingxue Zhao & Xinyue Xie & Qian Zhou, 2022. "Study on the Decoupling Relationship and Rebound Effect between Economic Growth and Carbon Emissions in Central China," Sustainability, MDPI, vol. 14(16), pages 1-19, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jiandong Chen & Ming Gao & Ke Ma & Malin Song, 2020. "Different effects of technological progress on China's carbon emissions based on sustainable development," Business Strategy and the Environment, Wiley Blackwell, vol. 29(2), pages 481-492, February.
    2. Zha, Donglan & Yang, Guanglei & Wang, Qunwei, 2019. "Investigating the driving factors of regional CO2 emissions in China using the IDA-PDA-MMI method," Energy Economics, Elsevier, vol. 84(C).
    3. Chen, Jiandong & Gao, Ming & Mangla, Sachin Kumar & Song, Malin & Wen, Jie, 2020. "Effects of technological changes on China's carbon emissions," Technological Forecasting and Social Change, Elsevier, vol. 153(C).
    4. Chen, Jiandong & Gao, Ming & Shahbaz, Muhammad & Cheng, Shulei & Song, Malin, 2021. "An improved decomposition approach toward energy rebound effects in China: Review since 1992," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    5. Li, Guohao & Niu, Miaomiao & Xiao, Jin & Wu, Jiaqian & Li, Jinkai, 2023. "The rebound effect of decarbonization in China’s power sector under the carbon trading scheme," Energy Policy, Elsevier, vol. 177(C).
    6. Rongxin Wu & Boqiang Lin, 2022. "Does Energy Efficiency Realize Energy Conservation in the Iron and Steel Industry? A Perspective of Energy Rebound Effect," IJERPH, MDPI, vol. 19(18), pages 1-20, September.
    7. Miao, Zhuang & Chen, Xiaodong, 2022. "Combining parametric and non-parametric approach, variable & source -specific productivity changes and rebound effect of energy & environment," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    8. Feng, Chao & Zhang, Hua & Huang, Jian-Bai, 2017. "The approach to realizing the potential of emissions reduction in China: An implication from data envelopment analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 859-872.
    9. Liu, Xiao & Hang, Ye & Wang, Qunwei & Chiu, Ching-Ren & Zhou, Dequn, 2022. "The role of energy consumption in global carbon intensity change: A meta-frontier-based production-theoretical decomposition analysis," Energy Economics, Elsevier, vol. 109(C).
    10. Cheng, Zhonghua & Li, Lianshui & Liu, Jun & Zhang, Huiming, 2018. "Total-factor carbon emission efficiency of China's provincial industrial sector and its dynamic evolution," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 330-339.
    11. Lin, Boqiang & Zhu, Runqing, 2022. "How does market-oriented reform influence the rebound effect of China’s mining industry?," Economic Analysis and Policy, Elsevier, vol. 74(C), pages 34-44.
    12. Sicen Liu & Xiaodong Chen & Zhiyang Shen & Tomas Baležentis, 2022. "Industrial energy consumption and pollutant emissions: Combined decomposition of relative performance and absolute changes," Business Strategy and the Environment, Wiley Blackwell, vol. 31(7), pages 3454-3469, November.
    13. Figge, Frank & Thorpe, Andrea Stevenson, 2019. "The symbiotic rebound effect in the circular economy," Ecological Economics, Elsevier, vol. 163(C), pages 61-69.
    14. Yao, Xin & Guo, Chengwen & Shao, Shuai & Jiang, Zhujun, 2016. "Total-factor CO2 emission performance of China’s provincial industrial sector: A meta-frontier non-radial Malmquist index approach," Applied Energy, Elsevier, vol. 184(C), pages 1142-1153.
    15. Xu, Mengmeng & Lin, Boqiang & Wang, Siquan, 2021. "Towards energy conservation by improving energy efficiency? Evidence from China’s metallurgical industry," Energy, Elsevier, vol. 216(C).
    16. Du, Kerui & Li, Jianglong, 2019. "Towards a green world: How do green technology innovations affect total-factor carbon productivity," Energy Policy, Elsevier, vol. 131(C), pages 240-250.
    17. Khoshkalam Khosroshahi, Musa & Sayadi, Mohammad, 2020. "Tracking the sources of rebound effect resulting from the efficiency improvement in petrol, diesel, natural gas and electricity consumption; A CGE analysis for Iran," Energy, Elsevier, vol. 197(C).
    18. Jarke-Neuert, Johannes & Perino, Grischa, 2020. "Energy efficiency promotion backfires under cap-and-trade," Resource and Energy Economics, Elsevier, vol. 62(C).
    19. Sueyoshi, Toshiyuki & Yuan, Yan & Goto, Mika, 2017. "A literature study for DEA applied to energy and environment," Energy Economics, Elsevier, vol. 62(C), pages 104-124.
    20. David Font Vivanco & Serenella Sala & Will McDowall, 2018. "Roadmap to Rebound: How to Address Rebound Effects from Resource Efficiency Policy," Sustainability, MDPI, vol. 10(6), pages 1-17, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:enepol:v:147:y:2020:i:c:s0301421520305796. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/enpol .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.