Climate Change and Corporate Regulation in Angola: Reforming the Regulatory Framework for Climate Change Mitigation
Bibliographic record
Abstract
Angola, like many African countries, continuously suffers the deleterious impacts of climate change despite its minimal global carbon contributions. Despite this, it has not taken active steps to institute a climate change regulatory framework or established any strong regulatory regime to aid the regulation of corporate activities and participation in mitigation projects in the country. This article examines the regulatory landscape of climate change and corporations in Angola and assesses the country’s ability to tackle the challenges posed by climate change. It examines the country's position in the international climate change arena and scrutinizes the contribution of corporations to Angola's carbon emissions. The absence of legislation on climate change in Angola creates a void which allows corporations in the country to avoid any responsibility for mitigation projects and activities. This article, therefore, analyses the strengths and weaknesses of the alternative regulatory frameworks such as judicial, market, and surrogate regulation that can operate to fill this void and the extent to which they curtail corporate excesses in climate change and incentivise participation in mitigation activities. In scrutinising the deficiencies of Angola’s climate change regulatory framework, the article adopts the dilute interventionism model which employs both prescriptive and facilitative measures to regulate corporations and mitigate the impact of climate change. It highlights the structure of the legislative framework, regulator, and technical expertise necessary for the successful implementation of the said model. Additionally, the article argues in favour of adopting a veto firewall protection to maintain the independence of the proposed sole independent regulator to be responsible for regulating the climate change activities of corporations in Angola.
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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 | 0.020 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.001 | 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".