Corporate GHG Emissions and Financial Performance: A Cross-Country Panel Analysis of Sectoral Heterogeneity in Advanced and Emerging Economies
Bibliographic record
Abstract
As the urgency to address climate change intensifies, understanding the financial implications of corporate greenhouse gas (GHG) emission reduction has become critical. This study examines the relationship between emission reductions and corporate financial performance (CFP) in 468 companies across advanced and emerging market economies (EMEs) from 2010 to 2022. Using a standardized emissions score to mitigate inconsistencies in greenhouse gas (GHG) reporting, we analyze how sectoral and regional dynamics influence financial outcomes using a panel fixed-effects model. The results are mixed: emission reductions are positively associated with CFP in advanced economies and low-emitting sectors. However, companies in high-emitting industries experience a negative relationship between emission reductions and CFP. The findings underscore the need for policies and corporate strategies calibrated by sector and country development status, as the emissions–profitability relationship varies across contexts.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".