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Hitting emissions targets with (statistical) confidence in multi-instrument Emissions Trading Schemes

Shipworth, D. (2003) Hitting emissions targets with (statistical) confidence in multi-instrument Emissions Trading Schemes. Climate Policy, 352 (Special Su). pp. 575-587. ISSN 1469-3062

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To link to this item DOI: 10.1016/j.clipol.2003.09.007


A means of assessing, monitoring and controlling aggregate emissions from multi-instrument Emissions Trading Schemes is proposed. The approach allows contributions from different instruments with different forms of emissions targets to be integrated. Where Emissions Trading Schemes are helping meet specific national targets, the approach allows the entry requirements of new participants to be calculated and set at a level that will achieve these targets. The approach is multi-levelled, and may be extended downwards to support pooling of participants within instruments, or upwards to embed Emissions Trading Schemes within a wider suite of policies and measures with hard and soft targets. Aggregate emissions from each instrument are treated stochastically. Emissions from the scheme as a whole are then the joint probability distribution formed by integrating the emissions from its instruments. Because a Bayesian approach is adopted, qualitative and semi-qualitative data from expert opinion can be used where quantitative data is not currently available, or is incomplete. This approach helps government retain sufficient control over emissions trading scheme targets to allow them to meet their emissions reduction obligations, while minimising the need for retrospectively adjusting existing participants’ conditions of entry. This maintains participant confidence, while providing the necessary policy levers for good governance.

Item Type:Article
Divisions:Science > School of the Built Environment > Construction Management and Engineering
ID Code:12375
Uncontrolled Keywords:Kyoto Protocol, Emissions Trading Scheme, Baseline, Cap-and-trade , Emissions intensity, Baseline-and-credit, Bayesian graphical modelling

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