Extending the attributional-consequential distinction to provide a categorical framework for greenhouse gas accounting methods

Research output: ThesisDoctoral Thesis

Abstract / Description of output

As part of the response to the threat of dangerous climate change a variety of methods have emerged for measuring greenhouse gas emissions to the atmosphere, assigning responsibility for those emissions, and informing decisions on mitigation actions. Many of these greenhouse gas accounting methods have developed in semi-isolated fields of practice, and this raises questions about how these different methods relate to each other, and whether they form ‘families’ of conceptually similar approaches.

A useful distinction has developed within the field of life cycle assessment (LCA) between attributional and consequential methods, and this thesis explores the possibility of extending that distinction to categorise other forms of greenhouse gas accounting. Broadly, attributional methods are inventories of emissions/removals for a defined inventory boundary, while consequential methods aim to estimate system-wide changes in emissions that result from a decision or action.

This thesis suggests that national greenhouse gas inventories, city inventories, corporate inventories, and attributional LCA are all attributional in nature, while project-level assessments, policy-level assessments, and consequential LCA are all consequential in nature. The potential benefits from creating this categorical framework include ensuring that individual methods are conceptually coherent, transposing lessons between methods of the same categorical type, and ensuring that the correct type of method is used for a given purpose.

These various benefits are explored conceptually through the analysis of existing greenhouse gas accounting standards, and also empirically with the use of a bioenergy case study. The findings suggest that the attributional-consequential distinction is highly useful for conceptualising and developing greenhouse gas accounting methods, which is important, ultimately, for addressing dangerous climate change.
Original languageEnglish
Awarding Institution
  • University of Edinburgh
  • Ascui, Francisco, Supervisor
  • Mackenzie, Craig, Supervisor
Publication statusPublished - 1 May 2016


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