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Modeling the dynamics of carbon emission performance in China: A parametric Malmquist index approach

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  • Lin, Boqiang
  • Du, Kerui

Abstract

This paper contributes to the existing literature on the methodology of modeling the dynamic of carbon emission performance. Based on the analytical framework of Zhou et al. (Energy Economics, 32, 194–201, 2010), we develop a parametric Malmquist index approach that takes into account statistical noises. Moreover, the fixed-effect panel stochastic frontier model is employed to deal with regional heterogeneity. The proposed approach is applied to analyze the dynamics of carbon emission performance in 30 Chinese provinces during the period of 2000–2010. The main findings are as follows. First, the carbon emission performances of 30 provinces as a whole improved by 4.1% annually during the sample period, which was mainly driven by efficiency change component. Second, the east area shows the best performance with an average Malmquist CO2 emissions performance index (MCPI) of 1.108, followed by the central area (1.039). Unlike the east and central areas, the west area experienced deterioration in carbon emission performance. More effective environmental policies should be implemented to change the situation. Third, compared with the proposed approach, the nonparametric approach tends to underestimate China's MCPI and gives rise to volatile results.

Suggested Citation

  • Lin, Boqiang & Du, Kerui, 2015. "Modeling the dynamics of carbon emission performance in China: A parametric Malmquist index approach," Energy Economics, Elsevier, vol. 49(C), pages 550-557.
  • Handle: RePEc:eee:eneeco:v:49:y:2015:i:c:p:550-557
    DOI: 10.1016/j.eneco.2015.03.028
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. repec:eee:energy:v:147:y:2018:i:c:p:197-207 is not listed on IDEAS
    4. repec:eee:enepol:v:109:y:2017:i:c:p:479-487 is not listed on IDEAS
    5. Khalili-Damghani, Kaveh & Tavana, Madjid & Santos-Arteaga, Francisco J. & Mohtasham, Sima, 2015. "A dynamic multi-stage data envelopment analysis model with application to energy consumption in the cotton industry," Energy Economics, Elsevier, vol. 51(C), pages 320-328.
    6. 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.

    More about this item

    Keywords

    Carbon emission performance; Malmquist index; Fixed effect; SFA;

    JEL classification:

    • Q53 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Air Pollution; Water Pollution; Noise; Hazardous Waste; Solid Waste; Recycling
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • Q55 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Technological Innovation
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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