Chen, Y;
Tanaka, M;
Siddiqui, AS;
(2018)
Market Power with Tradable Performance-Based CO₂ Emission Standards in the Electricity Sector.
Energy Journal
, 39
(6)
10.5547/01956574.39.6.yche.
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Abstract
The U.S. Clean Power Plan stipulates a state-specific performance-based CO2 emission standard, delegating states with considerable flexibility for using either a tradable performance-based or a mass-based permit program. This paper analyzes these two standards under imperfect competitive. We limit our attention to (1) short-run analyses and (2) a situation in which all states are subject to the same type of standard. We show that while the cross-subsidy inherent in the performance-based standard might effectively reduce power prices, it could also inflate energy consumption. A dominant firm with a relatively clean endowment under the performance-based standard would be able to manipulate the electricity market as well as to elevate permit prices, which might worsen market outcomes compared to its mass-based counterpart. On the other hand, the "cross-subsidy" could be the dominant force leading to a higher social welfare if the leader has a relatively dirty endowment.
Type: | Article |
---|---|
Title: | Market Power with Tradable Performance-Based CO₂ Emission Standards in the Electricity Sector |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.5547/01956574.39.6.yche |
Publisher version: | https://doi.org/10.5547/01956574.39.6.yche |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
Keywords: | Climate policy, Electricity industry, Mathematical program with equilibrium constraints, Performance-based standards |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10055190 |
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