Climate policy beyond ideological trenches
Bibliographic record
Abstract
The climate crisis demands urgent and effective policy interventions, yet the discourse remains mired in ideological polarization. On one side, some argue that reducing consumption is the primary solution to the climate crisis, while others emphasize that technological innovation is the only viable option. We argue that a convergence of perspectives is needed and propose using the ecological footprint metric as a framework for evaluating the environmental impacts of different policies. The metric, expressed as a fraction with consumption in the numerator and efficiency in resource use in the denominator, allows for an equitable evaluation of the outcomes of policies that focus on either reducing consumption or improving efficiency. Through simulations, we analyze the ecological footprint outcomes of various scenarios—Business-As-Usual, Tech World, Consumption Reduction, and Smart Sustainability. We show that trade-offs between consumption and efficiency are hardly avoidable, and policies that address both aspects—such as those outlined in the Smart Sustainability scenario—are more likely to reverse the growing trend of global ecological footprints. While sharp and unexpected disruptions—such as major epidemics causing abrupt declines in consumption or breakthrough innovations dramatically improving efficiency—could in theory shift these dynamics, bridging ideological divides remains the most prudent approach for crafting policies that can effectively address the climate crisis and ensure a sustainable future.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.010 |
| 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".