Can Corporate Sustainability Drive Economic Value Added? Evidence from Larger European Firms
Bibliographic record
Abstract
This study analyses the association between firms’ sustainability and economic performance in Europe, considering the channels of margin and turnover. The sample is composed of firms listed in the STOXX Europe 600 Index from 2012 to 2020. The sustainability performance is captured by the combined and individual ESG scores from Refinitiv, and dynamically tested with proxies of economic performance, including economic value added, return on firms’ assets and its components, margin and turnover. The methodological approach comprises different panel data specifications and tackles the potentially unobserved, time-invariant heterogeneity, endogeneity concerns, and reverse causality biases. Our findings point to a strong positive association between firms’ sustainability and economic performance in Europe, although the individual ESG forces are not at play with the same intensity. The environmental pillar is the one that is systematically associated with better economic performance across all estimations. The influence of sustainability performance on economic performance is also channeled by both profit margin and turnover. We find that a 1% improvement in the ESG score yields an increase in the economic value added of 0.08%, EVA over revenues. In general, our findings point to a shift from the conventional business model perspective to the incorporation of a core sustainability proposition and agenda that brings advantages and drives economic performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".