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Systematic analysis of the evolution of electricity and carbon markets under deep decarbonization

Blyth, W., Bunn, D., Chronopoulos, M. & Munoz, J. (2016). Systematic analysis of the evolution of electricity and carbon markets under deep decarbonization. Journal of Energy Markets, 9(3), pp. 59-94. doi: 10.21314/jem.2016.150


The decarbonization of electricity generation presents policy makers in many countries with the delicate task of balancing initiatives for technological change with a commitment to market liberalization. Despite the theoretical attractions, it has become doubtful whether carbon markets by themselves can offer the desired solution. We address this question through an integrated modeling framework, stylized for the Great Britain (GB) power market within the EU ETS, which includes three distinct components: (a) long-term least-cost capacity planning, similar in functionality to many used in policy analysis, but innovative in providing the endogenous calculation of carbon prices; (b) short-term price risk analysis producing hourly dispatch and pricing outputs, which are used to test the annual financial performance metrics implied by the longer-term investments; and (c) agent-based computational learning to derive pricing behavior in imperfect markets. The results indicate that the risk/return profile of electricity markets may deteriorate substantially as a result of decarbonization, reducing the propensity of companies to invest in the absence of increased government support and/or more beneficial market circumstances. If allowed, markets may adjust by deferring investment until conditions improve, consolidating to increase market power, or operating in a tighter market with reduced spare capacity. To the extent that each of these “market-led” solutions may be politically unpalatable, policy design will need to sustain a delicate regulatory regime, moderating the possible increased market power of companies while maintaining low-carbon subsidies for longer than expected. This paper considers some of the modeling implications for this compromise.

Publication Type: Article
Additional Information: © 2016 Incisive Risk Information (IP) Limited.
Subjects: H Social Sciences > HG Finance
Departments: Bayes Business School > Actuarial Science & Insurance
SWORD Depositor:
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