The Role of Environmental Accounting in Mitigating Climate Change: ESG Disclosures and Effective Reporting—A Systematic Literature Review
Bibliographic record
Abstract
Climate change poses an existential threat, spurring businesses and financial markets to integrate environmental accounting and ESG (Environmental, Social, and Governance) disclosures into decision-making. This study aims to examine how environmental accounting practices and ESG reporting contribute to climate change mitigation in organizations. It seeks to highlight the significance of these tools in enhancing transparency and accountability, thereby driving more sustainable corporate behavior. By synthesizing the recent literature, the study contributes a comprehensive overview of best practices and challenges at the intersection of accounting and climate action, addressing a noted gap in consolidated knowledge. We conducted a systematic literature review (SLR) following PRISMA guidelines. A broad search (2010–2024) across Scopus, Web of Science, and Google Scholar identified 73 records, which were rigorously screened and distilled to 47 relevant peer-reviewed studies. These studies span global contexts and include both conceptual and empirical work, providing a robust dataset for analysis. Environmental accounting was found to play a pivotal role in measuring and managing corporate carbon footprints, effectively translating climate impacts into quantifiable metrics. Firms that implement rigorous carbon accounting and internalize environmental costs tend to set more precise emission reduction targets and justify mitigation investments through a cost–benefit analysis. ESG disclosure frameworks emerged as critical external tools: a high-quality climate disclosure is linked with greater stakeholder trust and even financial benefits such as lower capital costs. Leading companies aligning reports with standards like TCFD or GRI often enjoy enhanced credibility and investor confidence. However, the review also uncovered challenges, like the lack of standardized reporting, risks of greenwashing, and disparities in adoption across regions, that impede the full effectiveness of these practices. The findings underscore that while environmental accounting and ESG reporting are powerful means to drive corporate climate action, their impact depends on improving consistency, rigor, and integration. Harmonizing global reporting standards and mandating disclosures are identified as key steps to improve data comparability. Strengthening the credibility of ESG disclosures and embedding environmental metrics into core decision-making are essential to leverage accounting as a tool for climate change mitigation. The study recommends that policymakers accelerate moves toward mandatory, standardized ESG reporting and urges organizations to proactively enhance their environmental accounting systems that will support global climate objectives and further research on actual emission outcomes.
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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 | 0.025 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.021 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".