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Record W2900529094 · doi:10.1111/1758-5899.12618

The Policy Role of Corporate Carbon Management: Co‐regulating Ecological Effectiveness

2018· article· en· W2900529094 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGlobal Policy · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSustainable Supply Chain Management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCorporate governanceCorporate social responsibilityClimate changeBusinessTransformative learningEconomicsEnvironmental resource managementEcologyPolitical sciencePublic relationsFinanceSociology

Abstract

fetched live from OpenAlex

Abstract The United Nations Intergovernmental Panel on Climate Change ( IPCC ) has called for private sector participation in global carbon governance and corporations now seem to be heeding the call at an unprecedented scale. Both critics and proponents of corporate social responsibility ( CSR ) interpret this as a necessary but uncertain development. Business response has demonstrably failed in the past. Contributing to the CSR and private environmental governance effectiveness literature, this article argues that while voluntary corporate climate governance efforts are essential and improving, they are far from sufficient for meaningful decarbonization. Through an evaluation of the three main underlying corporate carbon management practices (target setting, carbon pricing and carbon reporting), the article highlights how company efforts create business advantage (e.g. risk management) but fall short on ecological effectiveness (i.e. absolute carbon reduction). In response, the paper argues the importance of greater climate policy co‐regulation. This includes indirect enabling by governments and the IPCC to encourage incremental improvements in company efforts. It also includes more direct, state‐led prescriptive interventions coordinated across supply chains and supported by international organizations, to ensure corporate participation and deeper transformative change to business models, industry structures and consumptive patterns at the root of the global climate crisis.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.474
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.010
GPT teacher head0.255
Teacher spread0.245 · 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