[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116874-en":3,"doc-seo-116874-105":30,"detail-sidebar-cat-0-en-105":90},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},116874,13056712833777,"Logic","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Editorial - Fuzzy decisions and machine learning methods in climate change","Editorial addresses how fuzzy decisions and machine learning methods can support climate-change research and policy, emphasizing methodology choice. Key drivers include energy efficiency, reliable and accurately measured data, and strong system quality for data collection, automation, and measurement. It argues that effective legal regulations set energy-efficiency standards and targets. Effectiveness depends on prioritizing improvement actions within limited budgets, while avoiding uncontrollable enterprise costs.","TYPE Editorial  \nPUBLISHED 28 June 2023  \nDOI 10.3389/fenvs.2023.1235845  \nOPEN ACCESS  \nEDITED BY  \nXiaolei Sun,  \nChinese Academy of Sciences (CAS), China  \nREVIEWED BY  \nYasir Ahmed Solangi, Jiangsu University, China Nikita Moiseev,  \nPlekhanov Russian University of Economics, Russia  \n*CORRESPONDENCE  \nAlexey Mikhaylov,  [alexeyfa@ya. ru](alexeyfa@ya. ru)  \nRECEIVED 06 June 2023  \nACCEPTED 23 June 2023  \nPUBLISHED 28 June 2023  \nCITATION  \nYüksel S, Dinçer H and Mikhaylov A (2023), Editorial: Fuzzy decisions and machine learning methods in climate change.  \nFront. Environ. Sci. 11:1235845 .  \ndoi: 10.3389/fenvs.2023.1235845  \nCOPYRIGHT  \n© 2023 Yüksel, Dinçer and Mikhaylov. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nEditorial: Fuzzy decisions and machine learning methods in climate change  \nSerhat Yüksel 1, Hasan Dinçer 1 and Alexey Mikhaylov 2*  \n1The School of Business, Istanbul Medipol University, Istanbul, Türkiye, 2Institute of China and Modern Asia of the Russian Academy of Sciences, Moscow, Russia  \nKEYWORDS  \nrenewable energy, economic growth, emission, emerging economies, model  \nEditorial on the Research Topic  \nFuzzy decisions and machine learning methods in climate change  \n1 Different factors for choosing the methodology  \nDifferent factors can affect Fuzzy decisions and machine learning methods in climate change. Energy efficiency ensures that energy resources are used more effectively, which means energy savings. Less energy consumption reduces energy costs and ensures that energy sources can be used for a longer period. System quality is very important for ensuring Fuzzy decisions and machine learning methods in climate change. Accurate and reliable data is needed for Fuzzy decisions and machine learning methods in climate change. Energy consumption, energy costs and other performance indicators must be accurately measured and recorded. Quality systems reliably perform data collection, automation, and measurement, ensuring the precision and accuracy of data. To ensure Fuzzy Decisions and Machine Learning Methods in Climate Change, effective legal regulations should also be provided. Energy performance regulations help set energy efficiency standards and targets. These standards and targets encourage government and organizations to achieve a certain level of energy efficiency.  \n2 Effectiveness of fuzzy decisions and machine learning methods  \nThere are many variables that have an important role on the effectiveness of Fuzzy decisions and machine learning methods in climate change. In this context, to improve this performance, businesses need to make the necessary improvements for the development of these factors. However, these improvements also lead to an increase in the costs of the enterprises. In other words, if businesses carry out these improvement practices unplanned, this causes the costs to reach an uncontrollable level. Therefore, among these actions, the more important ones need to be determined. In this way, businesses will be able to use their limited budgets for more priority issues. This will also help increase productivity so that Fuzzy decisions and machine learning methods in climate change should be improved without having high amount costs. Additionally, energy efficiency can be improved by comparing the  \nFrontiers in Environmental Science 01 [frontiersin.org](frontiersin.org)  \npractices of countries that are successful in Fuzzy decisions and machine learning methods in climate change and Energy Efficiency. Fuzzy decisions and machine learning methods in climate change are especially suita","cbCaipN3Gi74ALzb","https://ap.wps.com/l/cbCaipN3Gi74ALzb","pdf",533444,1,2,"English","en",105,"# Different factors for choosing the methodology\n## Effectiveness of fuzzy decisions and machine learning methods\n## The role of developing countries","[{\"question\":\"What factors influence the choice of methodology for fuzzy decisions and machine learning in climate change?\",\"answer\":\"Energy efficiency, system quality, and the availability of accurate and reliable data strongly shape methodology selection.\"},{\"question\":\"How can the effectiveness of fuzzy decisions and machine learning methods be improved without excessive costs?\",\"answer\":\"Businesses should prioritize the most important improvement actions and allocate limited budgets to higher-priority issues.\"},{\"question\":\"Why are developing countries highlighted in this editorial?\",\"answer\":\"Rapid population growth and pursuit of economic growth increase energy demand, making energy performance management crucial for mitigating extraordinary demand and related costs.\"}]","Editorial - 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