{"title":"The role of environmental tax in guiding global climate policies to mitigate climate changes in European region","authors":"Le Thanh Ha","doi":"10.1111/nrm.12412","DOIUrl":null,"url":null,"abstract":"The purpose of this study is to perform a practical inquiry into the influence of environmental tax laws on the execution of climate‐related financial policies. Our research will assess the effectiveness of environmental tax measures in 23 European countries from 2011 to 2020. The panel‐corrected standard error (PCSE) model and the feasible generalized least squares (FGLS) model are used in the empirical examination of the link between environmental tax laws and the implementation of climate‐related financial measures. This study is based on panel data with cross‐sectional dependence. The results of our estimation highlight the need to improve policy effectiveness by using all four ecological tax indicators. These include total environmental tax revenue, energy tax revenue, pollution and resource tax revenue, and transportation tax revenue. Furthermore, we provide actual evidence clarifying the process by which the implementation of environmental tax policies improves the efficacy of climate‐related financial policies in the short term as well as the long term. According to the findings, a third of environmental tax policy indicators have a long‐term effect on the implementation of climate‐related financial measures, with no short‐term effects seen. Our findings are critical for economists and policy‐makers who support the environmental tax as an effective tool to promote a country's climate policy implementations.","PeriodicalId":49778,"journal":{"name":"Natural Resource Modeling","volume":"3 1","pages":""},"PeriodicalIF":1.8000,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Natural Resource Modeling","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1111/nrm.12412","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
The purpose of this study is to perform a practical inquiry into the influence of environmental tax laws on the execution of climate‐related financial policies. Our research will assess the effectiveness of environmental tax measures in 23 European countries from 2011 to 2020. The panel‐corrected standard error (PCSE) model and the feasible generalized least squares (FGLS) model are used in the empirical examination of the link between environmental tax laws and the implementation of climate‐related financial measures. This study is based on panel data with cross‐sectional dependence. The results of our estimation highlight the need to improve policy effectiveness by using all four ecological tax indicators. These include total environmental tax revenue, energy tax revenue, pollution and resource tax revenue, and transportation tax revenue. Furthermore, we provide actual evidence clarifying the process by which the implementation of environmental tax policies improves the efficacy of climate‐related financial policies in the short term as well as the long term. According to the findings, a third of environmental tax policy indicators have a long‐term effect on the implementation of climate‐related financial measures, with no short‐term effects seen. Our findings are critical for economists and policy‐makers who support the environmental tax as an effective tool to promote a country's climate policy implementations.
期刊介绍:
Natural Resource Modeling is an international journal devoted to mathematical modeling of natural resource systems. It reflects the conceptual and methodological core that is common to model building throughout disciplines including such fields as forestry, fisheries, economics and ecology. This core draws upon the analytical and methodological apparatus of mathematics, statistics, and scientific computing.