Journal
JOURNAL OF CLEANER PRODUCTION
Volume 167, Issue -, Pages 1401-1414Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2017.02.097
Keywords
Consequential LCA; Attributional LCA; Corporate GHG inventory; Bioenergy; Project-level GHG accounting; Policy-level GHG accounting
Categories
Funding
- UK's Economic and Social Research Council (ESRC) [ES/L002698/1]
- Society for the Advancement of Management Studies (SAMS)
- UK Commission for Employment and Skills (UKCES)
- Eden Estuary Energy Limited
- Scottish Funding Council
- ESRC [ES/L002698/1] Funding Source: UKRI
- Economic and Social Research Council [ES/L002698/1] Funding Source: researchfish
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In order to avoid dangerous climate change greenhouse gas accounting methods are needed to inform decisions on mitigation action. This paper explores the differences between 'attributional' and 'consequential' greenhouse gas accounting methods, focusing on attributional corporate greenhouse gas inventories, consequential life cycle assessment, and project/policy greenhouse gas accounting. The case study of a 6 MW bioheat plant is used to explore the different results and information these methods provide. The findings show that attributional corporate inventories may not capture the full consequences of the decision in question, even with full scope 3 reporting and are therefore not sufficient for mitigation planning. Although consequential life cycle assessment and the project/policy level method both aim to show the full consequences of the decision, the project/policy level method has a number of advantages, including the provision of a transparent baseline scenario and the distribution of emissions/removals over time. The temporal distribution of emissions/removals is important as the carbon debt of the bioheat plant can exceed 100 years, making the intervention incompatible with 2050 reduction targets. An additional contribution from the study is the use of normative decision theory to further develop the idea that the uncertainty associated with bioenergy outcomes is itself a highly decision relevant finding. (C) 2017 The Author. Published by Elsevier Ltd.
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