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Pollution from the electric power sector in Japan and efficient pollution reduction

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  • Matsushita, Kyohei
  • Yamane, Fumihiro

Abstract

Under the scheme of the Kyoto Protocol, there are plans for the efficient reduction of carbon dioxide emissions. In the electric power sector, nuclear power generation, which emits no carbon dioxide in the process of generating electricity, has come under scrutiny. However, this energy produces a new environmental issue: the disposal of radioactive waste. First, we derive shadow prices of carbon dioxide and low-level waste as marginal abatement costs in the case of the electric power sector in Japan, employing a directional output distance function. It is found that the shadow prices are US$39 per tonne for carbon dioxide and US$1531 per liter for low-level waste. Secondly, we calculate the indirect Morishima elasticity between carbon dioxide and low-level waste in order to identify their substitutability, and it is found that the substitution of low-level waste for carbon dioxide is easier than the reverse. This result suggests that, with the amount of generated electricity fixed, carbon dioxide can be substituted more easily by low-level waste when the relative price of carbon dioxide increases, for example, as a result of implementation of a carbon dioxide tax or an emissions trading system.

Suggested Citation

  • Matsushita, Kyohei & Yamane, Fumihiro, 2012. "Pollution from the electric power sector in Japan and efficient pollution reduction," Energy Economics, Elsevier, vol. 34(4), pages 1124-1130.
  • Handle: RePEc:eee:eneeco:v:34:y:2012:i:4:p:1124-1130
    DOI: 10.1016/j.eneco.2011.09.011
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    References listed on IDEAS

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    Cited by:

    1. Yongrok Choi & Hyoung Seok Lee, 2016. "Are Emissions Trading Policies Sustainable? A Study of the Petrochemical Industry in Korea," Sustainability, MDPI, Open Access Journal, vol. 8(11), pages 1-13, October.
    2. Zhou, X. & Fan, L.W. & Zhou, P., 2015. "Marginal CO2 abatement costs: Findings from alternative shadow price estimates for Shanghai industrial sectors," Energy Policy, Elsevier, vol. 77(C), pages 109-117.
    3. Jianjun Wang & Li Li & Fan Zhang & Qiannan Xu, 2014. "Carbon Emissions Abatement Cost in China: Provincial Panel Data Analysis," Sustainability, MDPI, Open Access Journal, vol. 6(5), pages 1-17, May.
    4. repec:spr:nathaz:v:91:y:2018:i:1:d:10.1007_s11069-017-3129-3 is not listed on IDEAS
    5. Emrouznejad, Ali & Yang, Guo-liang, 2016. "A framework for measuring global Malmquist–Luenberger productivity index with CO2 emissions on Chinese manufacturing industries," Energy, Elsevier, vol. 115(P1), pages 840-856.
    6. Ruiz-Fuensanta, María J., 2016. "The region matters: A comparative analysis of regional energy efficiency in Spain," Energy, Elsevier, vol. 101(C), pages 325-331.
    7. Du, Limin & Hanley, Aoife & Wei, Chu, 2015. "Estimating the Marginal Abatement Cost Curve of CO2 Emissions in China: Provincial Panel Data Analysis," Energy Economics, Elsevier, vol. 48(C), pages 217-229.
    8. Kejia Yang & Yalin Lei, 2017. "The carbon dioxide marginal abatement cost calculation of Chinese provinces based on stochastic frontier analysis," 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. 85(1), pages 505-521, January.
    9. Kyohei Matsushita & Kota Asano, 2014. "Reducing CO 2 emissions of Japanese thermal power companies: a directional output distance function approach," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 16(1), pages 1-19, January.
    10. Ke Wang & Yujiao Xian & Chia-Yen Lee & Yi-Ming Wei & Zhimin Huang, 2017. "On selecting directions for directional distance functions in a non-parametric framework: A review," CEEP-BIT Working Papers 99, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
    11. Lee, Myunghun & Zhang, Ning, 2012. "Technical efficiency, shadow price of carbon dioxide emissions, and substitutability for energy in the Chinese manufacturing industries," Energy Economics, Elsevier, vol. 34(5), pages 1492-1497.
    12. Du, Limin & Mao, Jie, 2015. "Estimating the environmental efficiency and marginal CO2 abatement cost of coal-fired power plants in China," Energy Policy, Elsevier, vol. 85(C), pages 347-356.
    13. Ke Wang & Yujiao Xian & Yi-Ming Wei & Zhimin Huang, 2016. "Sources of carbon productivity change: A decomposition and disaggregation analysis based on global Luenberger productivity indicator and endogenous directional distance function," CEEP-BIT Working Papers 91, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
    14. Zhou, Yishu & Huang, Ling, 2016. "Have U.S. power plants become less technically efficient? The impact of carbon emission regulation," Energy Economics, Elsevier, vol. 58(C), pages 105-115.
    15. repec:eee:energy:v:147:y:2018:i:c:p:297-307 is not listed on IDEAS
    16. Tang, Kai & Yang, Lin & Zhang, Jianwu, 2016. "Estimating the regional total factor efficiency and pollutants’ marginal abatement costs in China: A parametric approach," Applied Energy, Elsevier, vol. 184(C), pages 230-240.
    17. Zhou, P. & Zhou, X. & Fan, L.W., 2014. "On estimating shadow prices of undesirable outputs with efficiency models: A literature review," Applied Energy, Elsevier, vol. 130(C), pages 799-806.
    18. Lee, Chia-Yen & Zhou, Peng, 2015. "Directional shadow price estimation of CO2, SO2 and NOx in the United States coal power industry 1990–2010," Energy Economics, Elsevier, vol. 51(C), pages 493-502.

    More about this item

    Keywords

    Directional output distance function; Carbon dioxide; Low-level waste; Shadow price; Indirect Morishima elasticity;

    JEL classification:

    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q51 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Valuation of Environmental Effects
    • Q52 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Pollution Control Adoption and Costs; Distributional Effects; Employment Effects
    • Q53 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Air Pollution; Water Pollution; Noise; Hazardous Waste; Solid Waste; Recycling

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