{"title":"气候雄心、背景情景还是模型?全球情景中能源相关指标差异的归因","authors":"Alaa Al Khourdajie , Jim Skea , Richard Green","doi":"10.1016/j.egycc.2024.100126","DOIUrl":null,"url":null,"abstract":"<div><p>We attribute variations in key energy sector indicators across global climate mitigation scenarios to climate ambition, assumptions in background socioeconomic scenarios, differences between models and an unattributed portion that depends on the interaction between these. The scenarios assessed have been generated by Integrated Assessment Models (IAMs) as part of a model intercomparison project exploring the Shared Socio-economic Pathways (SSPs) used by the climate science community. Climate ambition plays the most significant role in explaining many energy-related indicators, particularly those relevant to overall energy supply, the use of fossil fuels, final energy carriers and emissions. The role of socioeconomic background scenarios is more prominent for indicators influenced by population and GDP growth, such as those relating to final energy demand and nuclear energy. Variations across some indicators, including hydro, solar and wind generation, are largely attributable to inter-model differences. Our Shapley–Owen decomposition gives an unexplained residual not due to the average effects of the other factors, highlighting some indicators (such as the use of carbon capture and storage (CCS) for fossil fuels, or adopting hydrogen as an energy carrier) with outlier results for particular ambition-scenario-model combinations. This suggests guidance to policymakers on these indicators is the least robust.</p></div>","PeriodicalId":72914,"journal":{"name":"Energy and climate change","volume":"5 ","pages":"Article 100126"},"PeriodicalIF":5.8000,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666278724000023/pdfft?md5=a0bcdbed86f34f78b1460e17e378772c&pid=1-s2.0-S2666278724000023-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Climate ambition, background scenario or the model? 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The role of socioeconomic background scenarios is more prominent for indicators influenced by population and GDP growth, such as those relating to final energy demand and nuclear energy. Variations across some indicators, including hydro, solar and wind generation, are largely attributable to inter-model differences. Our Shapley–Owen decomposition gives an unexplained residual not due to the average effects of the other factors, highlighting some indicators (such as the use of carbon capture and storage (CCS) for fossil fuels, or adopting hydrogen as an energy carrier) with outlier results for particular ambition-scenario-model combinations. This suggests guidance to policymakers on these indicators is the least robust.</p></div>\",\"PeriodicalId\":72914,\"journal\":{\"name\":\"Energy and climate change\",\"volume\":\"5 \",\"pages\":\"Article 100126\"},\"PeriodicalIF\":5.8000,\"publicationDate\":\"2024-01-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2666278724000023/pdfft?md5=a0bcdbed86f34f78b1460e17e378772c&pid=1-s2.0-S2666278724000023-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Energy and climate change\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2666278724000023\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Energy and climate change","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666278724000023","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0
摘要
我们将全球气候减缓情景下能源行业关键指标的变化归因于气候目标、背景社会经济情景假设、模型之间的差异以及取决于这些因素之间相互作用的未归因部分。所评估的情景由综合评估模型(IAMs)生成,是探索气候科学界使用的共享社会经济路径(SSPs)的模型相互比较项目的一部分。气候雄心在解释许多与能源相关的指标,尤其是与总体能源供应、化石燃料使用、最终能源载体和排放相关的指标方面发挥着最重要的作用。对于受人口和 GDP 增长影响的指标,如与最终能源需求和核能有关的指标,社会经济背景情景的作用更为突出。包括水力发电、太阳能发电和风力发电在内的一些指标之间的差异主要归因于模型之间的差异。我们的 Shapley-Owen 分解给出了一个未解释的残差,它不是由于其他因素的平均效应造成的,突出了一些指标(如化石燃料碳捕集与封存(CCS)的使用,或采用氢作为能源载体)在特定目标-情景-模型组合下的离群结果。这表明在这些指标上对政策制定者的指导是最不可靠的。
Climate ambition, background scenario or the model? Attribution of the variance of energy-related indicators in global scenarios
We attribute variations in key energy sector indicators across global climate mitigation scenarios to climate ambition, assumptions in background socioeconomic scenarios, differences between models and an unattributed portion that depends on the interaction between these. The scenarios assessed have been generated by Integrated Assessment Models (IAMs) as part of a model intercomparison project exploring the Shared Socio-economic Pathways (SSPs) used by the climate science community. Climate ambition plays the most significant role in explaining many energy-related indicators, particularly those relevant to overall energy supply, the use of fossil fuels, final energy carriers and emissions. The role of socioeconomic background scenarios is more prominent for indicators influenced by population and GDP growth, such as those relating to final energy demand and nuclear energy. Variations across some indicators, including hydro, solar and wind generation, are largely attributable to inter-model differences. Our Shapley–Owen decomposition gives an unexplained residual not due to the average effects of the other factors, highlighting some indicators (such as the use of carbon capture and storage (CCS) for fossil fuels, or adopting hydrogen as an energy carrier) with outlier results for particular ambition-scenario-model combinations. This suggests guidance to policymakers on these indicators is the least robust.