{"title":"Mitigation efforts to reduce carbon dioxide emissions and meet the Paris Agreement have been offset by economic growth.","authors":"Jitong Jiang, Skylar Shi, Adrian E Raftery","doi":"10.1038/s43247-025-02743-x","DOIUrl":null,"url":null,"abstract":"<p><p>Projecting future climate change is important for implementing the 2015 Paris Agreement, which aims to limit greenhouse gas emissions to a level that would keep the global average temperature increase to 2100 below 2 °C. The Intergovernmental Panel on Climate Change uses emissions scenarios for projecting climate change, but since 2017, an alternative fully statistical Bayesian probabilistic approach has been developed. Both approaches rely on an equation that expresses emissions as the product of population, Gross Domestic Product (GDP) per capita, and carbon intensity, namely carbon emissions per unit of GDP. Here, we use data on these quantities for 2015-2024 to probabilistically assess the changes in climate change prospects associated with post-Paris emissions. These show that carbon intensity declined (i.e., improved) substantially over that period, but that overall carbon emissions rose, due to the rapid rise in world GDP, which more than canceled out the progress made. We found that the projected temperature increase to 2100 declined only slightly, from 2.6° C to 2.4 °C. Meanwhile, the chance of staying below 2 °C remained low, at 17%. However, the chance of the most catastrophic climate change, above 3 °C, has gone down substantially, from 26% to 9%.</p>","PeriodicalId":10530,"journal":{"name":"Communications Earth & Environment","volume":"6 1","pages":"823"},"PeriodicalIF":8.9000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12534177/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Communications Earth & Environment","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1038/s43247-025-02743-x","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/10/17 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Projecting future climate change is important for implementing the 2015 Paris Agreement, which aims to limit greenhouse gas emissions to a level that would keep the global average temperature increase to 2100 below 2 °C. The Intergovernmental Panel on Climate Change uses emissions scenarios for projecting climate change, but since 2017, an alternative fully statistical Bayesian probabilistic approach has been developed. Both approaches rely on an equation that expresses emissions as the product of population, Gross Domestic Product (GDP) per capita, and carbon intensity, namely carbon emissions per unit of GDP. Here, we use data on these quantities for 2015-2024 to probabilistically assess the changes in climate change prospects associated with post-Paris emissions. These show that carbon intensity declined (i.e., improved) substantially over that period, but that overall carbon emissions rose, due to the rapid rise in world GDP, which more than canceled out the progress made. We found that the projected temperature increase to 2100 declined only slightly, from 2.6° C to 2.4 °C. Meanwhile, the chance of staying below 2 °C remained low, at 17%. However, the chance of the most catastrophic climate change, above 3 °C, has gone down substantially, from 26% to 9%.
期刊介绍:
Communications Earth & Environment is an open access journal from Nature Portfolio publishing high-quality research, reviews and commentary in all areas of the Earth, environmental and planetary sciences. Research papers published by the journal represent significant advances that bring new insight to a specialized area in Earth science, planetary science or environmental science.
Communications Earth & Environment has a 2-year impact factor of 7.9 (2022 Journal Citation Reports®). Articles published in the journal in 2022 were downloaded 1,412,858 times. Median time from submission to the first editorial decision is 8 days.