{"title":"利用机器学习了解不同国家和不同时期对环境增税的反对意见","authors":"Johannes Brehm and Henri Gruhl","doi":"10.1088/1748-9326/ad5d0a","DOIUrl":null,"url":null,"abstract":"Taxes targeting fuel, road usage, or carbon emissions for environmental protection often face public opposition. Can widely accessible machine learning methods aid in predicting and understanding opposition to environmental taxes? This study uses the random forest algorithm to predict opposition to increased environmental taxes based on 41 theoretically relevant respondent characteristics. Drawing on nationally representative surveys, we predict individual tax opposition across 28 countries in 2010 and 2020 (N = 70 710). Personal values and environmental evaluations tend to be more influential than demographics in predicting tax opposition, with key variables differing between countries and over time. A lack of commitment to pro-environmental behavior is the most important predictor in emerging economies. Conversely, concerns about environmental issues and prioritization of jobs and prices are influential in high-income countries, gaining prominence over the previous decade. Policymakers can leverage these insights to tailor communication of environmental tax increases in different contexts, emphasizing, for instance, job creation.","PeriodicalId":11747,"journal":{"name":"Environmental Research Letters","volume":"35 1","pages":""},"PeriodicalIF":5.8000,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Leveraging machine learning to understand opposition to environmental tax increases across countries and over time\",\"authors\":\"Johannes Brehm and Henri Gruhl\",\"doi\":\"10.1088/1748-9326/ad5d0a\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Taxes targeting fuel, road usage, or carbon emissions for environmental protection often face public opposition. Can widely accessible machine learning methods aid in predicting and understanding opposition to environmental taxes? This study uses the random forest algorithm to predict opposition to increased environmental taxes based on 41 theoretically relevant respondent characteristics. Drawing on nationally representative surveys, we predict individual tax opposition across 28 countries in 2010 and 2020 (N = 70 710). Personal values and environmental evaluations tend to be more influential than demographics in predicting tax opposition, with key variables differing between countries and over time. A lack of commitment to pro-environmental behavior is the most important predictor in emerging economies. Conversely, concerns about environmental issues and prioritization of jobs and prices are influential in high-income countries, gaining prominence over the previous decade. Policymakers can leverage these insights to tailor communication of environmental tax increases in different contexts, emphasizing, for instance, job creation.\",\"PeriodicalId\":11747,\"journal\":{\"name\":\"Environmental Research Letters\",\"volume\":\"35 1\",\"pages\":\"\"},\"PeriodicalIF\":5.8000,\"publicationDate\":\"2024-07-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Environmental Research Letters\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://doi.org/10.1088/1748-9326/ad5d0a\",\"RegionNum\":2,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Research Letters","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1088/1748-9326/ad5d0a","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Leveraging machine learning to understand opposition to environmental tax increases across countries and over time
Taxes targeting fuel, road usage, or carbon emissions for environmental protection often face public opposition. Can widely accessible machine learning methods aid in predicting and understanding opposition to environmental taxes? This study uses the random forest algorithm to predict opposition to increased environmental taxes based on 41 theoretically relevant respondent characteristics. Drawing on nationally representative surveys, we predict individual tax opposition across 28 countries in 2010 and 2020 (N = 70 710). Personal values and environmental evaluations tend to be more influential than demographics in predicting tax opposition, with key variables differing between countries and over time. A lack of commitment to pro-environmental behavior is the most important predictor in emerging economies. Conversely, concerns about environmental issues and prioritization of jobs and prices are influential in high-income countries, gaining prominence over the previous decade. Policymakers can leverage these insights to tailor communication of environmental tax increases in different contexts, emphasizing, for instance, job creation.
期刊介绍:
Environmental Research Letters (ERL) is a high-impact, open-access journal intended to be the meeting place of the research and policy communities concerned with environmental change and management.
The journal''s coverage reflects the increasingly interdisciplinary nature of environmental science, recognizing the wide-ranging contributions to the development of methods, tools and evaluation strategies relevant to the field. Submissions from across all components of the Earth system, i.e. land, atmosphere, cryosphere, biosphere and hydrosphere, and exchanges between these components are welcome.