Economic development, carbon emissions and climate policies
Bibliographic record
Abstract
If economic activity is considered the primary driver of climate change through emissions of carbon dioxide, then supporting economic growth and fighting emissions would appear to be at odds. However, the process of economic development may itself foster complementarity between GDP growth and emissions reductions. Such complementary in the relationship between economic development and emissions reduction might reflect changes in the industrial composition of economic activity, technological advancements or environmental consciousness. This view is in line with the Environmental Kuznets Curve (EKC) hypothesis: that per- capita income growth is associated with increases in carbon emissions up to a certain threshold of economic development, but beyond that threshold, higher per-capita incomes are associated with lower emissions per capita. The EKC hypothesis, suggests that economic development is actually a pathway to environmental improvements. We test the EKC hypothesis for 191 countries over 1989-2022, enabling us to study the overall validity of the EKC hypothesis at global level. Moreover, by interacting GDP per capita with an index measuring the stringency of climate policies, we shed light on whether and how climate policies mediate the impact of GDP on emissions. We find that emissions respond to increasing per-capita income levels nonlinearly, with a turning point at about $25,000 on average. Importantly, we show that climate policies shape the relationship between income and emissions by making the EKC lower and flatter, thus favouring a decoupling between emissions and economic activity. Our results have important policy implications, as they identify economic development as a pathway to environmental improvements. We also show that environmental policies are an essential ingredient to achieve decoupling of emissions and economic output over the longer term.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".