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Can we trust corporates GHG inventories? An investigation among Canada's large final emitters

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  • Talbot, David
  • Boiral, Olivier

Abstract

In the public sphere and the literature on climate strategies, the measurability of corporate GHG emissions tends to be taken for granted, and few empirical studies have examined the reliability of such data. The present case study, which was conducted among 10 Canadian companies considered as large final emitters and three auditing firms, focuses on the factors which could affect the perceived credibility of GHG inventories and the strategic implications of these. The qualitative, inductive study allows identifying three main factors which affect trust in business inventories: technical issues and complexity of GHG measurements, lack of transparency on the part of the companies and unreliability of verification mechanisms. The study also makes it possible to evaluate the implications of uncertainties concerning GHG inventories which are of strategic importance for companies and policy makers. While the reliability of GHG measurement is taken for granted at the political level, uncertainties in this area can in fact have a huge impact on the establishment of the cap and trade system. The study also contributes to the literature on carbon accounting by shedding light on underexplored ethical issues, including the lack of independence of auditors and its implications.

Suggested Citation

  • Talbot, David & Boiral, Olivier, 2013. "Can we trust corporates GHG inventories? An investigation among Canada's large final emitters," Energy Policy, Elsevier, vol. 63(C), pages 1075-1085.
    • Handle: RePEc:eee:enepol:v:63:y:2013:i:c:p:1075-1085
    DOI: 10.1016/j.enpol.2013.09.054
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    2. Spash, Clive L. & Theine, Hendrik, 2016. "Voluntary Individual Carbon Trading," SRE-Discussion Papers 2016/04, WU Vienna University of Economics and Business.
      • Clive L. Spash & Hendrik Theine, 2016. "Voluntary Individual Carbon Trading," SRE-Disc sre-disc-2016_04, Institute for Multilevel Governance and Development, Department of Socioeconomics, Vienna University of Economics and Business.
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    6. Karim Nadim & Ahmed Ragab & Mohamed-Salah Ouali, 2023. "Data-driven dynamic causality analysis of industrial systems using interpretable machine learning and process mining," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 57-83, January.
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