[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2534":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"view_count":15,"doi":25,"paper":26,"created_at":44},2534,"Aerosol pollution and economic growth in Thailand with a machine learning analysis of the environmental Kuznets Curve","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44292-026-00092-8","Abstract Aerosol pollution is a chronic air pollution problem that occurs every year in Thailand. It is mostly caused by human activities and climate conditions, and has severe negative impacts on both human health and the economy. This study investigates the relationship between aerosol pollution and various economic and environmental factors using the Environmental Kuznets Curve (EKC) framework. Additionally, it forecasts aerosol pollution through 2050 by applying four machine learning approaches and comparing their accuracy and predictive performance. The results indicate that the Random Forest (RF) model provided the most accurate estimation across the train, validation, and test periods. The study found an inverted U-shape relationship between aerosol pollution and economic growth, consistent with an EKC pattern. This suggests that as Thailand’s economy expands, aerosol pollution is projected to decline at an average annual rate of − 0.0569% to − 0.0636% from 2000 to 2050, except for the Southern region (R4), which shows an increasing rate. Furthermore, environmental features, specifically regional characteristics (R), Normalized Difference Vegetation Index (NDVI), rainfall, and wind speed, strongly influence aerosol pollution levels. Among economic features, population density was found to positively influence aerosol pollution. The study’s findings indicate that population density and GPP per capita remain primary drivers of aerosol pollution in high-activity regions. Consequently, although not directly analyzed in our models, we propose introducing green finance instruments as a strategic mechanism to decouple economic activity from environmental degradation.","摘要 气溶胶污染是泰国每年都会发生的长期性空气污染问题。其主要由人类活动和气候条件引起，并对人类健康和经济造成严重负面影响。本研究采用环境库兹涅茨曲线（EKC）框架，探讨气溶胶污染与多种经济及环境因素之间的关系。此外，本研究应用四种机器学习方法，对2050年前的气溶胶污染进行预测，并比较其准确性与预测性能。结果表明，随机森林（RF）模型在训练期、验证期和测试期均提供了最准确的估计。研究发现，气溶胶污染与经济增长之间呈倒U型关系，符合EKC模式。这表明，随着泰国经济扩张，2000年至2050年间气溶胶污染预计将以年均−0.0569%至−0.0636%的速度下降，但南部地区（R4）除外，其呈上升趋势。此外，环境特征，特别是区域特征（R）、归一化植被指数（NDVI）、降雨量和风速，对气溶胶污染水平具有强烈影响。在经济特征中，人口密度被发现对气溶胶污染具有正向影响。研究结果表明，在高活动区域，人口密度和人均GPP仍是气溶胶污染的主要驱动因素。因此，尽管我们的模型未直接分析，我们建议引入绿色金融工具作为一种战略机制，以将经济活动与环境退化脱钩。",null,"Discover Atmosphere","2026-09-13T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"研究泰国气溶胶污染与经济关系，属环境经济与大气科学范畴，与三农、农业信息化、智慧农业无直接关联，不建议进入每日精选。",[19],{"name":10,"url":6},[21,22,23,24],"绿色金融","气溶胶污染","环境库兹涅茨曲线","机器学习预测","10.1007\u002Fs44292-026-00092-8",{"doi":25,"openalex_id":27,"authors":28,"venue":10,"cited_by_count":15,"oa_url":35,"card":36,"direction":42,"ingested_from":43},"W7212433043",[29,32],{"name":30,"orcid":31},"Thanakhom Srisaringkarn","https:\u002F\u002Forcid.org\u002F0009-0008-2284-2563",{"name":33,"orcid":34},"Kentaka Aruga","https:\u002F\u002Forcid.org\u002F0000-0003-3033-272X","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44292-026-00092-8.pdf",{"tldr":37,"method":38,"finding":39,"direction":40,"opportunity":41},"用机器学习验证泰国气溶胶污染与经济增长呈环境库兹涅茨曲线倒U型关系并预测至2050年。","基于EKC框架，比较随机森林等四种机器学习模型，结合NDVI、降雨、风速等环境经","气溶胶污染与经济增长呈倒U型，2000-2050年预计年均下降0.0569%-0.0636%，但南部","农业绿色发展与碳","可探索绿色金融工具在农业高活动区解耦经济增长与气溶胶污染的作用机制与政策效果。","农业遥感与作物表型","openalex","2026-09-15T23:30:21.061546Z"]