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Record W4400520588 · doi:10.1016/j.jclepro.2024.143124

What drives companies’ progress on their emission reduction targets?

2024· article· en· W4400520588 on OpenAlexaff
Anne-France Bolay, Anders Bjørn, Laure Patouillard, Olaf Weber, Manuele Margni

Bibliographic record

VenueJournal of Cleaner Production · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsUniversity of WaterlooConcordia UniversityPolytechnique Montréal
Fundersnot available
KeywordsClimate change mitigationPaceGreenhouse gasBusinessScope (computer science)ShareholderClimate changeRemunerationCorporate governanceAccountingFinanceComputer science

Abstract

fetched live from OpenAlex

As the importance of non-state mitigation actions in the transition to a low-carbon economy becomes firmly established, a rapidly growing number of companies are setting corporate climate mitigation targets. Shareholders increasingly value these commitments, conveying the impression of good future carbon performance. However, a critical question emerges: why do some companies progress better than others toward their climate mitigation targets? There is currently a lack of empirical literature assessing companies’ progress against their mitigation targets. Using a new indicator to evaluate the progress against individual corporate climate mitigation targets in a comparable manner, this study presents an explanatory analysis of 120 determinants applied to 4341 climate mitigation targets (scope 1 and 2 emissions) of 2975 companies reporting to the 2020 CDP questionnaire. The target progress assessment shows that 30% of targets have increased emissions since their base year, 15% have reduced their emissions but not at a sufficient pace, while 55% were on track to achieving or had already achieved their targets. In addition, 18% of targets were already achieved the year the target was set, which may be due to choosing a base year with unusually high emissions. The findings reveal 19 key determinants significantly associated with the progress against corporate targets and highlight future research orientation. Our results indicate better progression by companies having absolute targets with longer timeframes and disclosing additional, as well as remuneration links to climate-related issues. Companies with more ambitious targets progress less than others, except when the ambitious targets are approved by the Science-Based Targets initiative. The latter implies ambitious targets from some firms may only be symbolic, and that investors should consider both target ambition and progress. Clear guidance and regulations should be implemented by policymakers to prevent misleading target information. Future research should address limitations related to reliance on self-reported data and exclusion of scope 3 emissions targets, along with the research directions suggested by the findings.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.908
Threshold uncertainty score0.867

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.004
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.282
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations17
Published2024
Admission routes1
Has abstractyes

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