Sustainable Policymaking: Balancing Profitability & Sustainable Development in Businesses
Bibliographic record
Abstract
Despite multiple decades' worth of credible data confirming the extent of sustainability problems, our society has subsequently shown very limited progress with finding viable solutions on such critical subjects. Indeed, there is growing evidence of a general misunderstanding regarding the challenges related to the climate change issue. More specifically, we point to evidence outlining sustainable development (SD) and climate change as complex meta-problems. This explains why current global environmental policies are inefficient in addressing the causes of SD and climate change and why they fail to induce sustainable practices in consumers and organizations. In this paper, we argue that the primary obstacles businesses face in adopting proper sustainable practices are found in their neoclassical business worldview and in the increased competition levels resulting from internationalization and market deregulation. To counter these obstacles, we suggest solutions that have the potential to bring true SD. First, we believe the use of specific economic tools such as sovereign funds, green investments and ethical financial indexes, can have significant effects on neoclassical businesses in stewarding them towards sustainable practices. Finally, we call for increased interdisciplinary interactions between the scientific community, policymakers and business leaders in order to better manage meta-problems related to climate 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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.025 | 0.016 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.011 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".