Dataset: Emissions, Energy Use and Climate Targets of Fortune G500 companies in the SBTi and RE100 initiatives

Datacite citation style:
Ruiz Manuel, Ivan (2021): Dataset: Emissions, Energy Use and Climate Targets of Fortune G500 companies in the SBTi and RE100 initiatives. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/16616965.v1
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Dataset
choose version: version 3 - 2022-12-12 (latest) version 2 - 2022-06-16
version 1 - 2021-09-16
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Several studies have posed that international cooperative initiatives lead by businesses could aid in mitigating global warming in ways that are additional to national policies. However, evaluations on their progress are rare due to many informational issues. This research gap is worrisome since such initiatives can also have negative effects on global commitment due to effort fragmentation and greenwashing.

This thesis evaluates progress in two initiatives that have featured prominently in the literature: the Science Based Targets initiative (SBTi) and RE100. The group of companies evaluated was reduced to the Fortune Global 500, a ranking of the largest firms by revenue.

Large datasets were created with the targets set by these companies, their emissions and energy use, among others. Progress was evaluated in four aspects: the ambition of targets, the robustness of their disclosure of sustainability metrics, the implementation of changes to their energy profiles, and finally the substantive progress seen in their collective carbon footprint.

It was found that among the two initiatives, SBTi appears posed for larger emissions mitigation, while RE100 is better at promoting more effective renewable energy purchasing practices. However, direct emission reductions are mostly concentrated on a reduced number of firms in energy intensive sectors, with the remaining members focusing on reductions through indirect methods that might not be additional at the global level. Similarly, it was found that informational barriers remain high even in these firms, with a plethora of inconsistencies that complicate year-by-year comparisons.
history
  • 2021-09-16 first online, published, posted
publisher
4TU.ResearchData
format
.CSV Pandas (Python)
organizations
TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Electrical Sustainable Energy

DATA

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