Silver-Haired, Carbon-Heavy? Director Age and Corporate Environmental Outcomes
Bibliographic record
Abstract
Corporate boards play a pivotal role in shaping firms’ environmental strategies, yet the influence of board demographics, particularly director age, on sustainability outcomes remains insufficiently understood. This study investigates how the age profile of board members affects corporate environmental performance, including greenhouse gas emissions. Analyzing a comprehensive panel of 1843US publicly listed firms (17,218 firm-year observations) from 1996 to 2018, primarily through panel regressions with firm and year fixed effects, we find consistent evidence that firms with older boards tend to exhibit poorer environmental performance and higher direct, indirect and value chain greenhouse gas emissions. We argue that this relationship is driven by age-related differences in risk tolerance, time horizons, and sensitivity to environmental concerns. Additionally, the study explores moderating factors such as poor governance oversight (board co-option), pressure for profitability from institutional ownership, CEO social and environmental consciousness (CEO gender), and managerial ability, revealing that these governance dynamics significantly influence the strength of the director age–environmental performance link. The results, robust to endogeneity concerns, underscore the importance of considering age diversity and board refreshment in corporate governance to foster more effective environmental stewardship. These insights offer valuable implications for board members, corporate leaders, and policymakers aiming to advance sustainable business practices, but also open up opportunities for further exploration in alternative institutional contexts.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".