Co-benefits of efficient and climate friendly cooling in China
Notice bibliographique
Résumé
The cooling sector plays a pivotal role in the global economy but significantly contributes to global warming. In 2022, cooling-related emissions accounted for 13% of global greenhouse gas (GHG) emissions. China, in particular, played a substantial role in cooling accounting for 10% of its national emissions and consuming 15% of its total electricity. This substantial environmental impact stems largely from the sector's reliance on refrigerants with high Global Warming Potential (GWP) and energy-intensive equipment. The refrigeration and air conditioning sector widely adopted hydrofluorocarbons (HFCs) as replacements for ozone-depleting substances regulated under the Montreal Protocol. However, as potent GHG, HFCs significantly contribute to global warming and are now subject to a global phase-down under the Kigali Amendment to the Montreal Protocol. Improving the energy efficiency of cooling equipment alongside the phasedown of HFCs could potentially double the mitigation benefits of the Kigali Amendment. With the growing demand for cooling in China, it is essential to explore mitigation strategies that simultaneously reduce HFC emissions and enhance energy efficiency. This study evaluates the co-benefits of efficient and climate-friendly cooling solutions in China.This study adopts a bottom-up approach to integrate the Refrigeration and Air Conditioning - Demand, Emission, and Cost (RAC-DEC) model with Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS) models. The research focuses on four key cooling subsectors: room air conditioning, mobile air conditioning, commercial air conditioning, and cold chain. The analysis is conducted under three scenarios: Business-as-Usual (BAU), reflecting current policies and practices; Kigali Amendment with enhanced energy efficiency of cooling equipment (KAE); and Accelerated Transformational Energy Efficiency (ATE). This study projects medium- and long-term trends in refrigerant and energy consumption, driven by key demand drivers for each subsector. It then quantifies both direct refrigerant emissions following the IPCC inventory methodology and indirect emissions from energy consumption. Finally, it evaluates the combined emission reduction potential under the alternative KAE and ATE scenarios.The preliminary results indicate that among China's cooling sector, the commercial refrigeration sector offers the highest potential for emission reduction, accounting for approximately 40% of the total cumulative emission reductions from 2023 to 2060. By 2060, China’s cooling sector could achieve cumulative emission reductions of approximately 11.5 Gt CO₂-eq in the KAE scenario and 16.5 Gt CO₂-eq in the ATE scenario. In the KAE scenario, emissions are expected to decline by 48% from 2022 to 2050. In contrast, the ATE scenario predicts a 70% reduction in annual emissions, dropping from 714–721 Mt CO₂-eq in 2022 to 217–218 Mt CO₂-eq by 2050. These significant reductions are primarily driven by the accelerated phase-out of HFC refrigerants, enhanced energy efficiency, and the deep decarbonization of the power system.This study underscores the critical role of the cooling sector in contributing to global climate goals, including the COP28 Global Cooling Pledge and the Kigali Amendment. By providing a methodological framework, our findings offer essential scientific support for policymakers in China and beyond, facilitating coordinated efforts to actively reduce fluorinated GHGs and enhance energy efficiency within the cooling sector.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 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 ».