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Overlapping carbon pricing and renewable support schemes under political uncertainty: Global lessons from an Australian case study

Shahnazari, M., McHugh, A., Maybee, B. and Whale, J. (2017) Overlapping carbon pricing and renewable support schemes under political uncertainty: Global lessons from an Australian case study. Applied Energy, 200 . pp. 237-248.

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Link to Published Version: https://doi.org/10.1016/j.apenergy.2017.05.038
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Abstract

The translation of a greenhouse gas (GHG) emissions reduction policy objective to the required investment in low emissions technologies may be hindered by political contest over the policy instruments employed to achieve it. Political contest may also result in enactment of overlapping policy instruments which, from a ‘policy purist’ perspective, may not appear well calibrated to a shared GHG emissions reduction objective. This paper reports insights gained from an integrated real options and portfolio optimisation model of electricity generation investment behaviour under political uncertainty over the futures of interacting carbon pricing and renewable portfolio standard (RPS) instruments. We compare modelling results and actual outcomes in Australia, where an emission reduction target has had bipartisan support but the means to achieve it has not, to test the assertion that overlapping policy instruments must always increase the social costs of GHG abatement. Results suggest that overlapping a politically contested carbon pricing policy with an RPS may result in a lower risk, renewable energy (RE) investment environment, as the overlap allows investors to hedge their portfolio against political uncertainty through RE additions. Consequently, GHG abatement objectives may be achieved at lower cost than would be the case without the policy interaction. The policies overlap can provide a ‘safety valve’ or ‘hedge’ to both private investors and policymakers when deep uncertainties over the future of energy and climate policies influence investment strategies.

Publication Type: Journal Article
Murdoch Affiliation: School of Engineering and Information Technology
Publisher: Elsevier B.V.
Copyright: © 2017 Elsevier Ltd
URI: http://researchrepository.murdoch.edu.au/id/eprint/37123
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