Avoiding a Global Carbon Crisis: Learning from the Financial Crisis
Bibliographic record
Abstract
Abstract The global financial crisis originated in the subprime mortgage market in the United States in 2008 and its effects spread to all of the world's major financial markets. Only governmental programs and subsidies have prevented an outright crash of the world economy. What can the financial crisis teach us about the impending carbon crisis? What is needed to move the global economy toward sustainability, using renewable energy regimes, and low carbon consumption and production? This paper provides a nontechnological response to these questions by highlighting six key sociocultural lessons and suggesting two key recommendations in how to overcome the current carbon lock‐in. Firms should establish proactive climate strategies. Policymakers can facilitate this by developing farsighted governance mechanisms and setting the right incentives and boundary conditions. We conclude that a mix of both is required to prevent the global carbon crisis from becoming a reality. © 2013 Wiley Periodicals, Inc .
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".