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How uncertainty reduces greenhouse gas emissions

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  • Schenker, Oliver

Abstract

China has becoming in 2006 the world’s largest emitter of greenhouse gases (GHG), responsible for one-fifth of world’s emissions from power generation. And further strong growth in this sector is to be expected. To provide these additional power generation capacities substantial investments in China’s energy infrastructure are necessary. But the potential investors are confronted with uncertainty in the design of China’s future climate policy, which might affect the profitability of GHG emitting power plants. It is the aim of this paper to investigate the role of uncertainty in China’s climate policy on investments in the electricity sector and its consequences for GHG emissions. We analyze the topic with a stochastic dynamic computable general equilibrium model with an extended energy sector and calibrated with Chinese data. The results show that uncertainty about the timing and extent of China’s climate policy lowers emissions compared to a world with perfect information. Uncertainty lowers the present value of coal-fired electricity in pre-policy periods and has so a positive effect for the environment.

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  • Schenker, Oliver, 2011. "How uncertainty reduces greenhouse gas emissions," MPRA Paper 29591, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:29591
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    References listed on IDEAS

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    Cited by:

    1. Irina Dolgopolova & Bo Hu & Armin Leopold & Stefan Pickl, 2014. "Economic, institutional and technological uncertainties of emissions trading—a system dynamics modeling approach," Climatic Change, Springer, vol. 124(3), pages 663-676, June.
    2. S. Scrieciu & Valerie Belton & Zaid Chalabi & Reinhard Mechler & Daniel Puig, 2014. "Advancing methodological thinking and practice for development-compatible climate policy planning," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 19(3), pages 261-288, March.
    3. Mohajan, Haradhan, 2011. "Greenhouse gas emissions increase global warming," MPRA Paper 50839, University Library of Munich, Germany, revised 18 Apr 2011.

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    More about this item

    Keywords

    China; Energy Policy; Climate Policy; Investment under Uncertainty; Stochastic and Dynamic CGE Model;
    All these keywords.

    JEL classification:

    • O41 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - One, Two, and Multisector Growth Models
    • C68 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computable General Equilibrium Models
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • D58 - Microeconomics - - General Equilibrium and Disequilibrium - - - Computable and Other Applied General Equilibrium Models
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General

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