Can Securities Regulation Influence Effective Corporate Response to Climate Change in Nigeria? An Analysis of Global Practices and Nigerian Securities Regulation
Bibliographic record
Abstract
Climate change and its effects are pervasive and escalating. It poses not only direct environmental risks but also economic and financial risks associated with mitigating and adapting to its effects. Within the corporate sector, its actual and potential effects on businesses is an ongoing discussion. This thesis contributes to this discussion by offering securities regulation as a response to tackling general Nigerian environmental laws’ limitations in influencing companies to effectively respond to climate change through mitigation and adaptation. To determine the efficacy of securities regulation as a response, this thesis conducts a review of the relevant key features of securities regulation, disclosure, and materiality. It also reviews existing research on elements of climate change-related disclosure that led to a positive change in corporate environmental performance and behaviour. It examines key securities and securities-related regulations in Nigeria, focusing on climate change-related disclosure. Lastly, it examines selected voluntary global disclosure mechanisms which provide investors with decision-useful information as well as other jurisdictional climate change disclosure practices in Europe, US and Canada. This thesis finds that the compulsoriness of climate-change-related disclosure and the disclosure framework mandated by securities regulators are key to influencing effective corporate response to climate change and improving corporate environmental performance and behaviour. These findings show that changes to Nigerian securities regulations’ provisions which effectively frame climate change-related disclosure and mandate specific disclosure frameworks can influence effective corporate response to climate change. They can also cause positive changes to corporate environmental performance and behaviour more broadly.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".