Labour productivity and economic impacts of carbon mitigation: a modelling study and benefit–cost analysis
Bibliographic record
Abstract
BACKGROUND: Despite the emerging carbon neutrality pledges from different countries, it is still unclear how much these pledges would cost and how the costs would compare with the economic benefits. Comparisons at the country level are important for tightening country-specific emissions trajectories to keep the temperature limit targets outlined in the Paris Agreement within reach. We aimed to systematically estimate avoided heat-related labour productivity losses against the costs of climate change mitigation at country and regional levels. METHODS: In this modelling study, to address the above-mentioned research gaps, we first selected two representative climate change scenarios (Representative Concentration Pathway 6.0 [RCP6.0] scenario, a higher warming scenario representing limited mitigation pledges before the Paris Agreement with around 3°C warming by the end of this century; and RCP2.6 scenario, a lower warming scenario assuming global temperature rise is limited to 2°C) and estimated heat-related labour productivity loss using the exposure-response function at country and regional levels. By representing the direct heat-related labour productivity losses in a multiregional global computable general equilibrium model, we then did a benefit-cost analysis to quantify the economic benefits of avoided heat-related labour productivity losses as well as the estimated reduction in gross domestic product (GDP) related to carbon reduction. FINDINGS: By 2100, the overall economic losses due to heat-related labour productivity loss could range from about 1·5% of global GDP under the RCP6.0 scenario to about 0·1% of global GDP under the RCP2.6 scenario. The productivity losses will be highly concentrated in low-latitude regions, especially in southeast Asia, India, and the Middle East, implying the necessity of additional adaptation measures. By 2100, about 51·8% of global climate change mitigation costs could be offset by economic benefits from reduced labour productivity losses. Cumulatively, about 17·0% of climate change mitigation costs could be offset by the economic benefits between 2020 and 2100, when using a 2% social discounting rate. The costs and benefits of climate change mitigation will be distributed highly unevenly across regions due to their varying climate zones and economic structures. Regions with benefits from reduced productivity losses higher than mitigation costs are mainly low-latitude and tropical regions with lower income and lower emissions, such as southeast Asia, Brazil, and Mexico. More than half the climate change mitigation costs could be offset by the economic benefits by 2100 for the world's largest emitters, including the USA, China, the EU, and India. Low benefit-cost ratios are expected in economies that rely on fossil fuels, such as Canada, Russia, and the Middle East. INTERPRETATION: Although pledging carbon neutrality implies radical changes to most economies, substantial health and economic gains can be achieved by reduced heat-related labour productivity loss, even without accounting for other benefits. The benefit-cost analysis in this study shows the potential for choosing more stringent climate change mitigation pathways in some regions. Regions with low benefit-cost ratios need to restructure their economies to reduce mitigation costs as well as losses from declined fossil fuel exports. FUNDING: National Natural Science Foundation of China, Tsinghua-Toyota Joint Research Fund, the Wellcome Trust, Tsinghua University-China Three Gorges Corporation Joint Research Center for Climate Governance Mechanism and Green Low-carbon Transformation Strategy, the National Research Foundation, Prime Minister's Office, Singapore (Campus for Research Excellence and Technological Enterprise [CREATE] programme), and the Global Energy Interconnection Development and Coorperation Organization.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".