Collaboration or compliance? Unpacking ESG performance and carbon penalties
Bibliographic Data
| ID | 6455175 |
|---|---|
| Authors | Manjeevan Seera (0000-0002-2797-3668), Ravichandran K Subramaniam (0000-0001-9165-0196), Shyamala Dhoraisingam Samuel (0000-0001-5202-5355) |
| Year | 2025 |
| Volume | 9 |
| Pages | 100758-100758 |
| Publication date | 2025-05-29 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Sustainable Futures (JOURNAL) |
| Journal identifiers | ISSN: 2666-1888 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.sftr.2025.100758 |
| OpenAlex | W4410856695 |
| Language | EN |
| Citations received | 2 |
| References cited | 22 |
This study addresses a key research gap by investigating how collaboration with government agencies influences ESG (Environmental, Social, and Governance) performance and carbon penalty outcomes among Fortune 500 firms, using machine learning techniques. Applying logistic regression and decision tree models to data from 2017 to 2021, we find that Scope 3 emissions dominate corporate carbon footprints and that active government collaboration is associated with fewer EPA fines and higher environmental scores. The machine learning approach allows for capturing complex, non-linear interactions that traditional statistical methods might miss. Policy implications suggest that regulators should design targeted collaboration programmes to improve environmental compliance, while firms should enhance supply chain management to address indirect emissions risks
Business · Carbon fibers · Compliance (psychology · Unpacking · Climate Change Policy and Economics · Computer Science · Energy, Environment, and Transportation Policies · Environmental Sustainability in Business · Psychology · Social Psychology · Accounting
| Unique citing works | 2 |
|---|---|
| Citations per year | 2 |
| Citation span | 2025 - 2025 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 2 |