Carbon neutrality: Operations management research opportunities
Bibliographic record
Abstract
Abstract Climate change, primarily driven by greenhouse gas emissions (GHGs), is a pressing environmental and societal concern. Carbon neutrality, or net zero, involves reducing carbon dioxide emissions, the most common GHG, and then balancing residual emissions through removing or offsetting. Particularly difficult challenges have emerged for firms seeking to reduce emissions from Scope 1 (internal operations) and Scope 3 (supply chain). Incremental changes are very unlikely to meet the objective of carbon neutrality. Synthesizing a framework that draws together both the means of achieving carbon neutrality and the scope of change helps to clarify opportunities for research by operations management scholars. Companies must assess and apply promising technologies, form new strategic relationships, and adopt novel practices while taking into account costs, risks, implications for stakeholders, and, most importantly, business sustainability. Research on carbon neutrality is encouraged to move beyond isolated discussions focused on specific tactics and embrace a more, though not fully, holistic examination. Research opportunities abound in both theoretical and empirical domains, such as exploring tradeoffs between different tactics, balancing portfolios, and investigating the strategic deployment of initiatives over time. As a research community, we are critically positioned to develop integrative insights at multiple levels, from individual processes to horizontal and vertical partnerships and ultimately to large‐scale systemic realignment and change.
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 | 0.023 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".