Building Climate Capability: Green HRM and Emissions-Linked Rewards in Bangladesh
Bibliographic record
Abstract
Corporations are increasingly adopting climate objectives; yet, many find it challenging to integrate these goals into daily operations and achieve quantifiable carbon reductions. This paper hypothesizes and examines how Green Human Resource Management (GHRM) and emissions-linked incentives (ELR) together develop climate capabilities, which are characterized as the routines, skills, and governance practices that facilitate Scope 1–3 abatement. Utilizing strategic human capital and dynamic capabilities, we assert that (i) GHRM directly improves employees’ climate knowledge, motivation, and opportunities for action; (ii) ELR reinforces the instrumental pathway by linking rewards to validated emissions performance; and (iii) climate capabilities mediate the effects on pro-environmental behavior (PEB) and emissions performance. We estimate a structural equation model using a multi-firm, multi-respondent survey including managers and staff, alongside organization-level emissions KPIs. Results demonstrate that GHRM (β≈0.41, p<.001) and ELR (β≈0.27, p<.01) are both predictors of climate capacities, exhibiting a positive interaction (β≈0.12, p<.05). Capabilities subsequently predicted PEB (β≈0.48, p<.001) and enhancements in emissions intensity (β≈0.22, p<.05). We examine the consequences of implementation: integrate climate responsibilities into roles, certify training, accurately assess abatement, and incentivize teams based on confirmed reductions.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".