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Energy intensive infrastructure investments with retrofits in continuous time : effects of uncertainty on energy use and carbon emissions

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  • Framstad, Nils Christian
  • Strand, Jon

Abstract

Energy-intensive infrastructure may tie up fossil energy use and carbon emissions for a long time after investments, making the structure of such investments crucial for society. Much or most of the resulting carbon emissions can often be eliminated later, through a costly retrofit. This paper studies the simultaneous decision to invest in such infrastructure, and retrofit it later, in a model where future climate damages are uncertain and follow a geometric Brownian motion process with positive drift. It shows that greater uncertainty about climate cost (for given unconditional expected costs) then delays the retrofit decision by increasing the option value of waiting to invest. Higher energy intensity is also chosen for the initial infrastructure when uncertainty is greater. These decisions are efficient given that energy and carbon prices facing the decision maker are (globally) correct, but inefficient when they are lower, which is more typical. Greater uncertainty about future climate costs will then further increase lifetime carbon emissions from the infrastructure, related both to initial investments, and to too infrequent retrofits when this emissions level is already too high. An initially excessive climate gas emissions level is then likely to be worsened when volatility increases.

Suggested Citation

  • Framstad, Nils Christian & Strand, Jon, 2013. "Energy intensive infrastructure investments with retrofits in continuous time : effects of uncertainty on energy use and carbon emissions," Policy Research Working Paper Series 6430, The World Bank.
  • Handle: RePEc:wbk:wbrwps:6430
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    1. Strand, Jon, 2014. "Implications of a lowered damage trajectory for mitigation in a continuous-time stochastic model," Energy Economics, Elsevier, vol. 42(C), pages 43-49.
    2. Nils Chr. Framstad, 2014. "When can the environmental profile and emissions reduction be optimised independently of the pollutant level?," Journal of Environmental Economics and Policy, Taylor & Francis Journals, vol. 3(1), pages 25-45, March.
    3. Bernard Lapeyre & Emile Quinet, 2017. "A Simple GDP-based Model for Public Investments at Risk," Post-Print hal-01666574, HAL.
    4. Lingyun He & Fang Yin & Zhangqi Zhong & Zhihua Ding, 2017. "The impact of local government investment on the carbon emissions reduction effect: An empirical analysis of panel data from 30 provinces and municipalities in China," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-20, July.
    5. Strand, Jon & Miller, Sebastian & Siddiqui, Sauleh, 2014. "Long-run carbon emission implications of energy-intensive infrastructure investments with a retrofit option," Energy Economics, Elsevier, vol. 46(C), pages 308-317.
    6. Chiu, Yi-Bin, 2017. "Carbon dioxide, income and energy: Evidence from a non-linear model," Energy Economics, Elsevier, vol. 61(C), pages 279-288.

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    More about this item

    Keywords

    Climate Change Mitigation and Green House Gases; Climate Change Economics; Transport Economics Policy&Planning; Energy Production and Transportation; Environmental Economics&Policies;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • R42 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Government and Private Investment Analysis; Road Maintenance; Transportation Planning

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