Join In . . . and Drop Out? Firm Adoption of and Disengagement From Voluntary Environmental Programs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Voluntary environmental programs (VEPs) offer opportunities for companies and stakeholders to improve environmental outcomes valued by society in the absence of regulatory mandates. Research has addressed numerous antecedents for firm adoption of VEPs, enhancing knowledge of how stakeholders and firms engage on substantive issues of public importance. However, program adoption is dynamic, and stagnant participation rates may threaten program longevity when firms do not realize expected benefits. Prior literature has not sufficiently addressed the factors that compel firms to drop out. In this study I articulate three consequential drivers of firm commitment to VEPs—transparency, effort, and achievement—and empirically estimate their effects on firm disengagement from one such prominent program: CDP (formerly known as Carbon Disclosure Project). Findings indicate that firm transparency and effort represent powerful commitment mechanisms driving continued program participation. This study contributes to theory over multiple literatures related to VEP participation and offers practical guidance for both VEPs and firms.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it