{"title":"超过RCP8.5:使用准代表性浓度路径的边际缓解","authors":"J. Isaac Miller , William A. Brock","doi":"10.1016/j.jeconom.2021.06.007","DOIUrl":null,"url":null,"abstract":"<div><p><span>Assessments of decreases in economic damages from climate change mitigation typically rely on climate output from computationally expensive pre-computed runs of general circulation models under a handful of scenarios with discretely varying targets, such as the four representative concentration pathways for CO</span><sub>2</sub><span><span> and other anthropogenically emitted gases. Although such analyses are valuable in informing scientists and policymakers about massive multilateral mitigation goals, we add to the literature by considering potential outcomes from more modest policy changes that may not be represented by any well-known concentration pathway. Specifically, we construct computationally efficient Quasi-representative Concentration Pathways (QCPs) to leverage concentration pathways of existing peer-reviewed scenarios. Computational efficiency allows for bootstrapping to assess uncertainty. We illustrate our methodology by considering the impact on the relative risk of mortality from heat stress in London from the United Kingdom’s </span>net zero emissions goal. More than half of our interval estimate for the business-as-usual scenario covers an annual risk at least that of a COVID-19-like mortality event by 2100. Success of the UK’s policy alone would do little to mitigate the risk.</span></p></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"239 1","pages":"Article 105152"},"PeriodicalIF":9.9000,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Beyond RCP8.5: Marginal mitigation using quasi-representative concentration pathways\",\"authors\":\"J. Isaac Miller , William A. Brock\",\"doi\":\"10.1016/j.jeconom.2021.06.007\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p><span>Assessments of decreases in economic damages from climate change mitigation typically rely on climate output from computationally expensive pre-computed runs of general circulation models under a handful of scenarios with discretely varying targets, such as the four representative concentration pathways for CO</span><sub>2</sub><span><span> and other anthropogenically emitted gases. Although such analyses are valuable in informing scientists and policymakers about massive multilateral mitigation goals, we add to the literature by considering potential outcomes from more modest policy changes that may not be represented by any well-known concentration pathway. Specifically, we construct computationally efficient Quasi-representative Concentration Pathways (QCPs) to leverage concentration pathways of existing peer-reviewed scenarios. Computational efficiency allows for bootstrapping to assess uncertainty. We illustrate our methodology by considering the impact on the relative risk of mortality from heat stress in London from the United Kingdom’s </span>net zero emissions goal. More than half of our interval estimate for the business-as-usual scenario covers an annual risk at least that of a COVID-19-like mortality event by 2100. Success of the UK’s policy alone would do little to mitigate the risk.</span></p></div>\",\"PeriodicalId\":15629,\"journal\":{\"name\":\"Journal of Econometrics\",\"volume\":\"239 1\",\"pages\":\"Article 105152\"},\"PeriodicalIF\":9.9000,\"publicationDate\":\"2024-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Econometrics\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0304407621001792\",\"RegionNum\":3,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Econometrics","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0304407621001792","RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
Beyond RCP8.5: Marginal mitigation using quasi-representative concentration pathways
Assessments of decreases in economic damages from climate change mitigation typically rely on climate output from computationally expensive pre-computed runs of general circulation models under a handful of scenarios with discretely varying targets, such as the four representative concentration pathways for CO2 and other anthropogenically emitted gases. Although such analyses are valuable in informing scientists and policymakers about massive multilateral mitigation goals, we add to the literature by considering potential outcomes from more modest policy changes that may not be represented by any well-known concentration pathway. Specifically, we construct computationally efficient Quasi-representative Concentration Pathways (QCPs) to leverage concentration pathways of existing peer-reviewed scenarios. Computational efficiency allows for bootstrapping to assess uncertainty. We illustrate our methodology by considering the impact on the relative risk of mortality from heat stress in London from the United Kingdom’s net zero emissions goal. More than half of our interval estimate for the business-as-usual scenario covers an annual risk at least that of a COVID-19-like mortality event by 2100. Success of the UK’s policy alone would do little to mitigate the risk.
期刊介绍:
The Journal of Econometrics serves as an outlet for important, high quality, new research in both theoretical and applied econometrics. The scope of the Journal includes papers dealing with identification, estimation, testing, decision, and prediction issues encountered in economic research. Classical Bayesian statistics, and machine learning methods, are decidedly within the range of the Journal''s interests. The Annals of Econometrics is a supplement to the Journal of Econometrics.