The Impact of Sustainability Accounting on Environmental Performance and Productivity: A Panel Data Analysis
Bibliographic record
Abstract
Environmental accounting is a crucial tool for sustainable development as it enables the analysis, study, and measurement of natural resources' control, valuation, and management from an accounting perspective.This study aims to explore the potential of sustainable accounting as a tool for promoting the Sustainable Development Goals (SDGs).The hypotheses propose that the adoption of environmental accounting enhances a company's environmental performance, directly increases firm productivity, and indirectly increases productivity through improved environmental performance.To test these hypotheses, panel data from 2011 to 2020 is used, and the relationship among environmental accounting adoption, environmental performance, and productivity is estimated using Ordinary Least Squares (OLS), Fixed Effects (FE), and Random Effects (RE) models.The results show that the environmental accounting adoption dummy is significantly positive in all models (OLS, FE, and RE), indicating that firms that have adopted environmental accounting demonstrate higher environmental performance.The FE model is found to be the most reliable based on the results of the F-test, Breusch-Pagan test, and Durbin-Wu-Hausman test.The coefficient estimates in the FE model suggest that the effect of environmental accounting adoption is about one-third and one-half of that estimated in the OLS and RE models, respectively.Additionally, the findings suggest that firms with higher environmental performance, larger size, higher consumer relevance, and lower debt ratios demonstrate higher productivity.These results indicate that sustainability accounting has the potential to significantly contribute to the achievement of the SDGs.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".