Leading for Sustainability: Exploring the Foundations and Outcomes of Environmental Leadership
Bibliographic record
Abstract
Climate change and environmental sustainability are widespread challenges, driven in part by organizational activities and corporate practices (e.g., Steg, 2023). Recognizing their role in addressing these issues, many organizations have integrated sustainability initiatives into their practices, policies and procedures. The success of these initiatives depends on employees’ actions, who can shape organizational initiatives and inspire pro-environmental behaviors. As a result, research has examined factors influencing employees’ pro-environmental behaviors, such as personality traits, values, and organizational norms. However, a gap remains in understanding what drives environmental leadership among workplace leaders. This gap is important because leaders can affect the factors and conditions that influence climate change and sustainability through their vision, values, decisions, behaviors, resource allocation, and their role in advancing other related initiatives (see Karamally & Robertson, 2023). In this study, we present findings from a survey of 206 leaders, examining the personal, social and organizational correlates of environmental leadership. The results show that leaders’ engagement in environmental leadership behaviors was positively associated with parenthood, openness to experience, environmental passion, and engagement in pro-environmental citizenship behaviors. These results highlight several areas for future research, including the role of parenthood in leadership, and the influence of role models in shaping environmental leadership. Additionally, understanding how different leadership styles may influence climate change and sustainability is important, as different approaches may lead to different 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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".