The Policy Role of Corporate Carbon Management: Co‐regulating Ecological Effectiveness
Bibliographic record
Abstract
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.021 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".