[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126172-en":3,"doc-seo-126172-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126172,3985741905716,"Rowan","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Reconstructing Global Daily CO2 Emissions via Machine Learning","High temporal resolution CO2 emission data help reveal drivers behind short-term changes. Existing emission products are mostly available at annual resolution, limiting analysis of daily dynamics. This study reconstructs a global daily CO2 emissions dataset back to 1970 using machine learning, trained on national-scale patterns linking daily variations to predictors from 2019 onward. Daily variability dominates smoothed seasonality and increases with extreme temperatures, elevating emissions when ambient temperature exceeds a climate tipping threshold.","Reconstructing Global Daily CO2 Emissions via Machine Learning  \nTao Li 1, Lixing Wang 1, Zihan Qiu2, Philippe Ciais3, Taochun Sun 1, Matthew W. Jones4, Robbie M. Andrew5, Glen P. Peters5, Piyu ke 1, Xiaoting Huang 1 Robert B. Jackson6 and Zhu Liu 1*  \n1Department of Earth System Science, Tsinghua University, Beijing, 100084, China 2Tsinghua University Institute for Interdisciplinary Information Sciences (IIIS) FIT Building, Tsinghua University, Beijing 100084, China  \n3Laboratoire des Sciences du Climat et de l'Environnement, IPSL, CEA/CNRS/UVSQ, UniversitéParis-Saclay, Pairs, 91191, France  \n4Tyndall Centre for Climate Change Research, School of Environmental Sciences, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, UK  \n5CICERO Center for International Climate Research, Oslo 0349, Norway  \n6Earth System Science Department, Stanford University, 473 Via Ortega, Stanford, CA 94305, USA.  \n*Email: [zhuliu@tsinghua.edu.cn](zhuliu@tsinghua.edu.cn)  \nAbstract  \nHigh temporal resolution CO2 emission data are valuable for understanding the drivers of emission changes. Current emission datasets, however, are generally only available with an annual resolution. Here, we extended a global daily CO2 emissions dataset backwards in time to 1970 using a machine learning algorithm, which was trained to predict historical daily emissions on national scales based on relationships between daily emission variations and predictors established for the period since 2019. Variation in daily CO2 emissions far exceeded the smoothed seasonal variations. For example, the range of daily CO2 emissions of China and India increased from 1.2 and 0.2 Mt/day in 1970 to 10.8 and 4.2 Mt/day in 2022, reaching approximately 31% of the year average daily emissions of China and 46% of India in 2022, respectively. The relationship between daily CO2 emission and the ambient temperature is well described by a linearplus-plateau function, in which we identified the emission-climate tipping temperature (Tc) is 16.9℃ for global average (19.5℃ for China, 15.0℃ for U.S., and 18.2℃ for Japan), demostrating increased emissions associated with higher ambient temperature. The long-term time series spanning over fifty years of global daily CO2 emissions reveals an increasing trend in emissions due to extreme temperature events, driven by the rising frequency of these occurrences. This work adds to evidence that, due to climate change, greater efforts may be needed to reduce CO2 emissions.  \nMain  \nDaily emission data are required to study the impact of short-term temperature fluctuations, such as heat waves lasting several days to weeks, on CO2 emissions. Hightemporal resolution estimates of CO2 emissions provide a more detailed picture and offer insights into driving factors that annual data may overlook. Utilizing highresolution CO2 emission estimates, the effects of extreme temperature events, COVID- 19, as well as blackouts on CO2 emissions can be quantified, enhancing the understanding of emission change drivers 1. High temporal resolution CO2 emissions inventory can be used in atmospheric inversion models to quantify the impact of extreme temperature events on forest carbon sinks2. These assessments are vital for formulating policies aimed at achieving net-zero greenhouse gas emissions through forest carbon sinks.  \nFossil CO2 emissions arise predominantly from the combustion of fossil fuels linked to activities such as electricity generation, mobility, industrial production, and similar. With various sources of activity data and satellite measurements, some data can be obtained at daily or even hourly or sub-hourly frequency, suggesting the possibility of presenting CO2 emissions with high temporal resolution. For example, attempts have been made to estimate the CO2 emissions decline due to COVID-19 based on forced confinement policies3,4 and data on mobility changes5. Extending this further, if the high resolution data is also collected and processed i","cbCaij3w8xj61KPf","https://ap.wps.com/l/cbCaij3w8xj61KPf","pdf",2588082,5,1,27,"English","en",105,"# Abstract\n# Motivation and background\n## Need for daily CO2 emissions\n## Limits of existing high-resolution datasets\n# Downscaling and scaling approaches","[{\"question\":\"Why is high temporal resolution CO2 data important?\",\"answer\":\"It enables analysis of the effects of short-term temperature fluctuations, such as multi-day heat waves, on CO2 emissions and helps uncover drivers annual datasets can miss.\"},{\"question\":\"How does the study reconstruct daily CO2 emissions back to 1970?\",\"answer\":\"It extends a global daily CO2 emissions dataset to 1970 using a machine learning model trained to predict historical daily national emissions based on relationships and predictors from the post-2019 period.\"},{\"question\":\"What role does ambient temperature play in daily CO2 emissions?\",\"answer\":\"The study links daily CO2 emissions to ambient temperature using a linear-plus-plateau relationship and identifies an emission-climate tipping temperature, above which emissions increase with higher temperatures.\"}]","Reconstructing Global Daily CO2 Emissions via Machine Learning | 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is high temporal resolution CO2 data important?","Question",{"text":77,"@type":78},"It enables analysis of the effects of short-term temperature fluctuations, such as multi-day heat waves, on CO2 emissions and helps uncover drivers annual datasets can miss.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How does the study reconstruct daily CO2 emissions back to 1970?",{"text":82,"@type":78},"It extends a global daily CO2 emissions dataset to 1970 using a machine learning model trained to predict historical daily national emissions based on relationships and predictors from the post-2019 period.",{"name":84,"@type":75,"acceptedAnswer":85},"What role does ambient temperature play in daily CO2 emissions?",{"text":86,"@type":78},"The study links daily CO2 emissions to ambient temperature using a linear-plus-plateau relationship and identifies an emission-climate tipping temperature, above which emissions increase with higher 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