How do mandatory climate-related disclosures affect energy and agriculture markets?
Bibliographic record
Abstract
With the rise of mandating climate-related disclosures (CRD), this paper investigates how energy and agriculture markets are exposed to climate disclosure risk. Using the multivariable simultaneous quantile regression and data from 1 January 2017 to 29 February 2024, we examine daily and monthly responses of energy and agriculture markets to climate disclosure risk, energy risk, market sentiment, geopolitical risk, and economic policy risk. The sample covers the global market, Australia, Canada, European Union (EU), Hong Kong, Japan, New Zealand, Singapore, the United Kingdom (UK), and the United States (US). The results show that climate disclosure risk creates both positive and negative shocks on the energy and agriculture markets and the impacts are asymmetric across quantiles in different economies. The higher climate disclosure risk, the greater impacts of crude oil future on the energy sector in North America (Canada and the US) and Europe (EU and the UK), but no greater effects in Asia Pacific (Australia, New Zealand, and Singapore). The agriculture sector can hedge against economic policy and geopolitical risks, but it is highly exposed to climate disclosure and energy risks. This study timely contributes to the modest literature on the asymmetric effects of climate disclosure risk on the energy and agriculture markets at the global and national levels. Our findings offer practical implications for policy makers and investment practitioners in understanding financial effects of mandating CRD to diversify risks depending upon market conditions and policy uncertainty.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".