Does stringent climate policy decouple economic growth from greenhouse gas emissions?
Bibliographic record
Abstract
Abstract A foundational question in environmental sociology is whether economic growth can be sufficiently decoupled from greenhouse gas emissions. Scholars working in different analytical perspectives assert that such a decoupling is largely contingent on more stringent climate policy that mandates or incentivizes the reduction of carbon-intensive production. However, there is limited research on whether policy has such a moderating influence. Here, we extend the literature by testing whether more stringent climate policy moderates the effect of economic growth on greenhouse gas emissions using panel data from 1990 to 2022 for forty-nine countries. Building on the extended two-way fixed effects estimator, we advance an approach for estimating country-specific and average short-run and long-run effects with dynamic models that we show outperform other macro panel estimators using Monte Carlo experiments. Using this approach, we find that, on average, strong climate policy stringency decouples economic growth from emissions in the short run and the long run and that the decoupling effect is largest in higher-income nations. However, we also find that greater policy stringency is associated with increases in emissions in lower-income and middle-income nations. We then build a hypothetical three-nation World that consists of a lower-income, middle-income, and higher-income nation and develop a suite of scenarios that differ based on their rate of economic growth and climate policy stringency. The results suggest that steady-state and degrowth scenarios offer the most sustainable futures in terms of lower emissions and that degrowth is the most equitable in terms of reducing emissions. We conclude by arguing that these findings have significant implications for policymaking and for key theoretical debates in sociology regarding economic growth and the environment.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".