{"title":"Exploring the drivers of ecological footprint: Impacts of road transportation infrastructure, transport tax, and environment technologies","authors":"Junwook Chi","doi":"10.1080/15568318.2024.2423726","DOIUrl":null,"url":null,"abstract":"<div><div>This study explores the dynamic effects of road transportation infrastructure, transport taxes, economic growth, trade openness, and environmental technologies on the ecological footprint using three quantile regression techniques: Simultaneous, Powell, and Method of Moments Quantile Regression (MMQR). These variables are selected for their significant influence on both economic outcomes and environmental sustainability, reflecting the interconnectedness of transportation systems, government policies, and technological advancements in shaping ecological impacts. Based on panel data from 19 European countries between 1995 and 2020, the results reveal that investments in road infrastructure and higher transport taxes contribute to reducing ecological footprint. At the middle and high quantiles, advancements in environment-related technologies further decrease the ecological footprint, whereas trade openness tends to worsen ecological quality. Importantly, the relationship between GDP and the ecological footprint follows an inverted U-shape, confirming the Environmental Kuznets Curve (EKC) hypothesis across all quantiles, which suggests that economic growth can support environmental sustainability in the long run. These findings are validated by robustness checks using fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS), and canonical cointegration regression (CCR) models. Key policy implications include the need for targeted investments in road infrastructure and transport tax strategies, tailored to national contexts, along with technology-driven strategies to curb environmental degradation. Lastly, the panel causality test reveals unidirectional causality from economic growth, road transportation infrastructure, and environment-related technologies to ecological footprint, highlighting their significance as short-term contributors to environmental improvement.</div></div>","PeriodicalId":47824,"journal":{"name":"International Journal of Sustainable Transportation","volume":"18 11","pages":"Pages 920-934"},"PeriodicalIF":3.1000,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Sustainable Transportation","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/org/science/article/pii/S1556831824000479","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL STUDIES","Score":null,"Total":0}
引用次数: 0
Abstract
This study explores the dynamic effects of road transportation infrastructure, transport taxes, economic growth, trade openness, and environmental technologies on the ecological footprint using three quantile regression techniques: Simultaneous, Powell, and Method of Moments Quantile Regression (MMQR). These variables are selected for their significant influence on both economic outcomes and environmental sustainability, reflecting the interconnectedness of transportation systems, government policies, and technological advancements in shaping ecological impacts. Based on panel data from 19 European countries between 1995 and 2020, the results reveal that investments in road infrastructure and higher transport taxes contribute to reducing ecological footprint. At the middle and high quantiles, advancements in environment-related technologies further decrease the ecological footprint, whereas trade openness tends to worsen ecological quality. Importantly, the relationship between GDP and the ecological footprint follows an inverted U-shape, confirming the Environmental Kuznets Curve (EKC) hypothesis across all quantiles, which suggests that economic growth can support environmental sustainability in the long run. These findings are validated by robustness checks using fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS), and canonical cointegration regression (CCR) models. Key policy implications include the need for targeted investments in road infrastructure and transport tax strategies, tailored to national contexts, along with technology-driven strategies to curb environmental degradation. Lastly, the panel causality test reveals unidirectional causality from economic growth, road transportation infrastructure, and environment-related technologies to ecological footprint, highlighting their significance as short-term contributors to environmental improvement.
期刊介绍：
The International Journal of Sustainable Transportation provides a discussion forum for the exchange of new and innovative ideas on sustainable transportation research in the context of environmental, economical, social, and engineering aspects, as well as current and future interactions of transportation systems and other urban subsystems. The scope includes the examination of overall sustainability of any transportation system, including its infrastructure, vehicle, operation, and maintenance; the integration of social science disciplines, engineering, and information technology with transportation; the understanding of the comparative aspects of different transportation systems from a global perspective; qualitative and quantitative transportation studies; and case studies, surveys, and expository papers in an international or local context. Equal emphasis is placed on the problems of sustainable transportation that are associated with passenger and freight transportation modes in both industrialized and non-industrialized areas. All submitted manuscripts are subject to initial evaluation by the Editors and, if found suitable for further consideration, to peer review by independent, anonymous expert reviewers. All peer review is single-blind. Submissions are made online via ScholarOne Manuscripts.
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