Labour productivity and economic impacts of carbon mitigation: a modelling study and benefit–cost analysis
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».