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Using decomposition analysis to evaluate the performance of China’s 30 provinces in CO 2 emission reductions over 2005–2009

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  • Jidong Kang
  • Tao Zhao
  • Xiaosong Ren
  • Tao Lin

Abstract

This paper aims to evaluate the carbon dioxide (CO 2 ) emissions reduction performance of 30 mainland provinces in China over 2005–2009. First, the log-mean Divisia index (LMDI) technique is used to decompose the changes in CO 2 emissions at the provincial level into 4 effects that are carbon intensity effect, energy mix effect, energy intensity effect and gross domestic product (GDP) effect. Next, two indicators, decoupling index and rescaled decoupling index, are implemented to evaluate the performance of 30 provinces in CO 2 emission reduction from 2005 to 2009. The decomposition result shows that the GDP growth is mainly responsible for the CO 2 emissions increase, while the energy intensity effect is the key factor for the decrease in CO 2 emissions in each province. Moreover, according to the evaluation, the performance of each province in what concerns the CO 2 emission reduction varies significantly. Most provinces in China made significant efforts in emissions reduction during 2005–2009, while some provinces only made weak efforts or even no efforts in decoupling progress. Copyright Springer Science+Business Media B.V. 2012

Suggested Citation

  • Jidong Kang & Tao Zhao & Xiaosong Ren & Tao Lin, 2012. "Using decomposition analysis to evaluate the performance of China’s 30 provinces in CO 2 emission reductions over 2005–2009," 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. 64(2), pages 999-1013, November.
  • Handle: RePEc:spr:nathaz:v:64:y:2012:i:2:p:999-1013
    DOI: 10.1007/s11069-012-0212-7
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    2. Roinioti, Argiro & Koroneos, Christopher, 2017. "The decomposition of CO2 emissions from energy use in Greece before and during the economic crisis and their decoupling from economic growth," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 448-459.
    3. Bo Li & Xuejing Liu & Zhenhong Li, 2015. "Using the STIRPAT model to explore the factors driving regional CO 2 emissions: a case of Tianjin, 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. 76(3), pages 1667-1685, April.
    4. Chen, Jiandong & Cheng, Shulei & Song, Malin & Wang, Jia, 2016. "Interregional differences of coal carbon dioxide emissions in China," Energy Policy, Elsevier, vol. 96(C), pages 1-13.
    5. Li, Tianxiang & Baležentis, Tomas & Makutėnienė, Daiva & Streimikiene, Dalia & Kriščiukaitienė, Irena, 2016. "Energy-related CO2 emission in European Union agriculture: Driving forces and possibilities for reduction," Applied Energy, Elsevier, vol. 180(C), pages 682-694.
    6. Yalan Zhao & Yaoqiu Kuang & Ningsheng Huang, 2016. "Decomposition Analysis in Decoupling Transport Output from Carbon Emissions in Guangdong Province, China," Energies, MDPI, vol. 9(4), pages 1-23, April.

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