Nature Climate Change最新文献_第9页

Navigating the black box of fair national emissions targets 在公平的国家排放目标的黑盒子中导航

IF 27.1 1区地球科学

Nature Climate Change Pub Date : 2025-06-16 DOI: 10.1038/s41558-025-02361-7

Mark M. Dekker, Andries F. Hof, Yann du Robiou Pont, Nicole van den Berg, Vassilis Daioglou, Michel den Elzen, Rik van Heerden, Elena Hooijschuur, Isabela Schmidt Tagomori, Chantal Würschinger, Detlef P. van Vuuren

{"title":"Navigating the black box of fair national emissions targets","authors":"Mark M. Dekker, Andries F. Hof, Yann du Robiou Pont, Nicole van den Berg, Vassilis Daioglou, Michel den Elzen, Rik van Heerden, Elena Hooijschuur, Isabela Schmidt Tagomori, Chantal Würschinger, Detlef P. van Vuuren","doi":"10.1038/s41558-025-02361-7","DOIUrl":"10.1038/s41558-025-02361-7","url":null,"abstract":"Current national emissions targets fall short of the Paris Agreement goals, prompting the need for equitable ways to close this gap. Fair emissions allowances rely on effort-sharing formulas based on fairness principles, yielding diverse outcomes. These variations, shaped by normative decisions, complicate policymaking and legal assessments of climate targets. Here we provide up-to-date numbers, comprehensively accounting for three dimensions—physical and social uncertainties, global strategies and equity—and the relative impact of them on each country’s emissions allowance. In the short run, normative considerations substantially impact fair emissions allowances—directing current discussions to this debate—while global discussions on temperature targets and non-CO2 emissions take over in the long run. We identify many countries with insufficient nationally determined contributions in light of fairness and discuss implications for increased domestic mitigation and financing emissions reductions abroad—yielding a total international finance flux of $US0.5–7.4 trillion in 2030. Fair climate targets aligned with the Paris Agreement can be calculated in multiple ways, yielding diverse outcomes. Researchers unpack how equity, global strategies and political and social uncertainties shape fair share allocations, using them to assess nationally determined contributions and guide global climate finance.","PeriodicalId":18974,"journal":{"name":"Nature Climate Change","volume":"15 7","pages":"752-759"},"PeriodicalIF":27.1,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41558-025-02361-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144296158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Using deep learning to generate key variables in global mitigation scenarios 利用深度学习在全球缓解情景中生成关键变量

IF 27.1 1区地球科学

Nature Climate Change Pub Date : 2025-06-13 DOI: 10.1038/s41558-025-02352-8

Peijin Li, Rongqi Zhu, Haewon McJeon, Edward Byers, Peijie Zhou, Yang Ou

{"title":"Using deep learning to generate key variables in global mitigation scenarios","authors":"Peijin Li, Rongqi Zhu, Haewon McJeon, Edward Byers, Peijie Zhou, Yang Ou","doi":"10.1038/s41558-025-02352-8","DOIUrl":"10.1038/s41558-025-02352-8","url":null,"abstract":"Integrated assessment models (IAMs) are the dominant tools for projecting mitigation scenarios. However, IAM-based scenarios often face challenges such as modelling biases and large computational burden. Here we develop a deep learning framework to generate key variables through synthetic mitigation scenarios aligned with the Sixth Assessment Report (AR6) Scenarios Database. By analysing 1,202 scenarios from a diverse set of IAMs, we select key drivers that enable a more detailed sectoral representation. Next, we trained three generative deep learning models to produce 30,000 synthetic scenarios at low computational cost across various IPCC AR6 climate categories, replicating variable distributions and correlations while also demonstrating physical consistency in power sector variables through internal validation checks. We found that the variational autoencoder achieved the highest label transferring accuracy among three frameworks. This study illustrates the potential of deep learning to complement IAM approaches and provides a basis for handling complex mitigation scenario generation tasks. Integrated assessment model-based scenarios are commonly used to project future emission pathways but suffer from submission biases and high computational cost. Here researchers develop a deep learning framework to generate synthetic scenarios and replicate key variables across a wide range of mitigation ambitions.","PeriodicalId":18974,"journal":{"name":"Nature Climate Change","volume":"15 7","pages":"760-768"},"PeriodicalIF":27.1,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144278568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Future climate-driven fires may boost ocean productivity in the iron-limited North Atlantic 未来气候导致的火灾可能会提高北大西洋铁资源有限的海洋生产力

IF 27.1 1区地球科学

Nature Climate Change Pub Date : 2025-06-13 DOI: 10.1038/s41558-025-02356-4

Elisa Bergas-Masso, Douglas S. Hamilton, Stelios Myriokefalitakis, Sagar Rathod, María Gonçalves Ageitos, Carlos Pérez García-Pando

{"title":"Future climate-driven fires may boost ocean productivity in the iron-limited North Atlantic","authors":"Elisa Bergas-Masso, Douglas S. Hamilton, Stelios Myriokefalitakis, Sagar Rathod, María Gonçalves Ageitos, Carlos Pérez García-Pando","doi":"10.1038/s41558-025-02356-4","DOIUrl":"10.1038/s41558-025-02356-4","url":null,"abstract":"Rapid shifts in fire regimes affect the carbon cycle by releasing carbon and nutrients such as iron (Fe), potentially enhancing marine productivity and carbon export. Here we use fire emission projections and Earth system models to examine how climate-driven changes in fire emissions may alter soluble Fe (SFe) deposition and productivity. By century’s end, climate change could increase Fe emissions from fires by 1.7–1.8 times beyond projections considering only direct human influences. Model projections show rising SFe deposition in Northern Hemisphere high latitudes under increasing socio-economic activity, potentially boosting the impact of SFe deposition on productivity in the Fe-limited North Atlantic by up to 20% annually (40% in summer), assuming stable macronutrient levels. However, declining macronutrient availability may shrink Fe-limited areas, where climate-driven fires could offset productivity losses by 7–8%. In the Southern Ocean, fossil fuel emissions primarily control SFe deposition, as reductions in anthropogenic fires counterbalance climate-driven increases. Fire emissions can be an important source of nutrients such as iron, particularly for the oceans. Here the authors estimate that climate-change-driven changes in fire emissions could increase iron deposition in ocean ecosystems, enhancing productivity particularly in the North Atlantic.","PeriodicalId":18974,"journal":{"name":"Nature Climate Change","volume":"15 7","pages":"784-792"},"PeriodicalIF":27.1,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41558-025-02356-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144278821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Facebook algorithm’s active role in climate advertisement delivery Facebook算法在气候广告投放中的积极作用

IF 27.1 1区地球科学

Nature Climate Change Pub Date : 2025-06-13 DOI: 10.1038/s41558-025-02326-w

Aruna Sankaranarayanan, Piotr Sapiezynski, Una-May O’Reilly

{"title":"Facebook algorithm’s active role in climate advertisement delivery","authors":"Aruna Sankaranarayanan, Piotr Sapiezynski, Una-May O’Reilly","doi":"10.1038/s41558-025-02326-w","DOIUrl":"10.1038/s41558-025-02326-w","url":null,"abstract":"Climate advertising on social media can shape attitudes towards climate change. Delivery algorithms, as key actors in the climate communication ecosystem, determine ad audience selection and might introduce demographic bias. Here, we present a two-part study—an observational analysis (n = 253,125) and a field experiment (M = 650)—to investigate algorithmic bias in Facebook’s climate ad dissemination. Our findings provide preliminary evidence that the algorithm’s selection of ad audiences can be explained by factors such as ad content, audience location (US states), gender and age group. Moreover, the cost-effectiveness of contrarian ads is linked with the conservative political alignment of a state, while the cost-effectiveness of advocacy ads correlates with liberal political alignment, higher population and per-capita gross domestic product; ad targeting strategies further modulate these effects. The skew in the distribution of climate ads across US states, age groups and genders reinforces existing climate attitudes. Content delivery algorithms on social media exhibit biases in audience selection, which are understudied in the climate context. This study combines observational analysis and a field experiment to reveal algorithmic bias in Facebook’s climate ad data across location, gender and age groups.","PeriodicalId":18974,"journal":{"name":"Nature Climate Change","volume":"15 7","pages":"719-724"},"PeriodicalIF":27.1,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144278822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring climate futures with deep learning 用深度学习探索气候的未来

IF 27.1 1区地球科学

Nature Climate Change Pub Date : 2025-06-13 DOI: 10.1038/s41558-025-02350-w

Alaa Al Khourdajie

引用次数: 0

Recommendations for producing knowledge syntheses to inform climate change assessments 为气候变化评估提供综合知识的建议

IF 27.1 1区地球科学

Nature Climate Change Pub Date : 2025-06-10 DOI: 10.1038/s41558-025-02354-6

James D. Ford, Robbert Biesbroek, Lea Berrang Ford, Felix Creutzig, Neal Haddaway, Sherilee Harper, Jan C. Minx, Mark New, Anne J. Sietsma, Carol Zavaleta-Cortijo, Max Callaghan

{"title":"Recommendations for producing knowledge syntheses to inform climate change assessments","authors":"James D. Ford, Robbert Biesbroek, Lea Berrang Ford, Felix Creutzig, Neal Haddaway, Sherilee Harper, Jan C. Minx, Mark New, Anne J. Sietsma, Carol Zavaleta-Cortijo, Max Callaghan","doi":"10.1038/s41558-025-02354-6","DOIUrl":"10.1038/s41558-025-02354-6","url":null,"abstract":"Climate change assessments (CCAs) play a critical role in taking stock of the available science and other forms of knowledge and informing policy processes. As the underlying evidence base increases exponentially, the complexity also increases and challenges CCA author teams to capture all the relevant knowledge. Therefore, CCAs will need to transition from predominantly assessing primary research to focusing on the assessment and critical appraisal of knowledge syntheses of such work, alongside capturing knowledges held outside traditional scientific sources. To support this, a stronger knowledge synthesis culture is needed, and we propose key recommendations and offer guidance for producing robust, transparent, reproducible, inclusive and timely syntheses that can inform CCAs across scales. Climate change assessment reports are increasing in complexity as the knowledge base grows exponentially. In this Perspective, the authors advocate, and provide recommendations, for knowledge synthesis to become more common as a way to better inform such assessments.","PeriodicalId":18974,"journal":{"name":"Nature Climate Change","volume":"15 7","pages":"698-708"},"PeriodicalIF":27.1,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144252361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Conflicting selection on flowering time 开花时间选择的矛盾

IF 27.1 1区地球科学

Nature Climate Change Pub Date : 2025-06-09 DOI: 10.1038/s41558-025-02360-8

Tegan Armarego-Marriott

引用次数: 0

Lost along the way 迷失在路上

IF 27.1 1区地球科学

Nature Climate Change Pub Date : 2025-06-09 DOI: 10.1038/s41558-025-02359-1

Jasper Franke

引用次数: 0

Plant processes matter 植物过程很重要