A co-financing model for the mitigation of highway runoff pollution

Ramísio, Paulo J.; Arezes, Diogo; Pinto, Lígia

doi:10.1016/j.jenvman.2021.112432

Cited by 9 publications

(6 citation statements)

References 31 publications

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“…Pollution control measures such as the construction of urban sewage discharge systems can effectively control environmental pollution (Zhou, 2014). However, they require high investment and continuous maintenance, and the sources of pollution control funds need to be further explored (Ramísio et al, 2021). Some scholars believe that the active role of financial institutions in pollution management and environmental protection should be given full play, and innovative financing models should be used to broaden the sources of pollution management funds (Zhang & Vigne, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

Digital finance and sustainable development: Evidence from environmental inequality in China

et al. 2022

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Achieving environmental equity is important for ensuring resource utilization efficiency and realizing sustainable development. Using panel data of 286 cities in China from 2011 to 2018, this paper analyzes whether financial support in the form of digital finance can mitigate environmental inequality. The findings indicate that (1) digital finance has a significant mitigating effect on environmental inequality. Digital finance development has a stronger industrial pollution emission reduction effect on high‐polluting regions than on low‐polluting regions and can reduce the environmental pollution gap between regions. The results of robustness tests such as an endogeneity test support this finding. (2) Digital finance development can have a stronger mitigating effect on environmental inequality in regions with a stronger local government governance capacity and environmental preferences. Digital finance can be a more significant inhibitor of environmental inequality in areas where industries transfer out due to their stronger needs for industrial transformation and finance. (3) Residential income and green technology innovation are important factors affecting industrial pollution emissions and reductions. Digital finance development can narrow the income gap between regions and promote the convergence of green technology innovation capacity, in turn alleviating environmental inequality. In the future, while continuing to promote the development of digital finance, it is necessary to improve the governance capacity of local governments, to increase the efficiency of resource utilization in places where industrial transfer is undertaken and to strengthen the role of financial support in industrial pollution control.

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Section: Literature Reviewmentioning

confidence: 99%

Digital finance and sustainable development: Evidence from environmental inequality in China

et al. 2022

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“…The results showed that the process of road construction increases the landscape fragmentation and decreases its contagion which affects animal migration. Ramísio et al [10] estimated the highway runoff pollution based on a case study on 279-km Portuguese Highway. This study also used the willingness to pay (WTP) method to estimate the willingness of the public to afford the constructed wetlands cost.…”

Section: Evaluation Of Ecological Externalitiesmentioning

confidence: 99%

“…The best sites for highway development usually tend to be ideal for agriculture because they are flat and stable [17]. The removal of vegetation leads to changes in ecology and soil and runoff pollution [10]. Sdoukopoulos et al [36] found that area taken by transport infrastructures is one of the most commonly used indicators of the evaluation of the externalities of transport infrastructures.…”

Section: ) Evaluation Indicators In the Ecological Dimensionmentioning

confidence: 99%

“…It is a pity that this indicator generally lacks statistics of relevant data. To governance ecological externalities, the government develops some natural landscapes [42], highway forest belts [22], noise barriers [25], and wetlands [10] along highway infrastructures. On the contrary, these related infrastructure produces positive external effects on the environment.…”

Section: ) Evaluation Indicators In the Ecological Dimensionmentioning

confidence: 99%

“…Considering the unbalanced economic development, the transportation authority vigorously invests in Buildings 2022, 12, 328 2 of 22 the construction of highway infrastructure for the undeveloped areas to leverage the advantages of convenient transportation on economic development [7]. In terms of environmental pollution and ecological damage, policies for highway toll [4], motor vehicle emission limits [8], schemes for the shelter forest along the highway [9], and the financing model considering wetlands costs to mitigate highway runoff pollution [10] have been formulated. However, the practical results demonstrate that the effect of the governance or internalization of these externalities is not ideal.…”

Section: Introductionmentioning

confidence: 99%

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A Three-Dimensional Evaluation Model of the Externalities of Highway Infrastructures to Capture the Temporal and Spatial Distance to Optimal—A Case Study of China

Zhu

Zhang

et al. 2022

Buildings

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Various externalities caused by highway infrastructures, such as promoting economic development, traffic congestion, and air pollution, are becoming more and more important. Currently, there is no multi-dimensional quantitative evaluation of the externalities of highway infrastructures, hindering the sustainable planning and development of highway infrastructures. Therefore, this study aims to develop a three-dimensional evaluation model of the externalities of highway infrastructures. To achieve the above objective, this study: (1) developed a three-dimensional evaluation index system through a comprehensive literature review and interviews with experts; (2) weighted the evaluation indexes using the entropy weight method; (3) developed the comprehensive evaluation model using the grey correlation analysis method; (4) validated the developed model by using statistical data of Jiangsu province, China. The analysis results showed that the developed model is feasible and effective in evaluating the externalities of highway infrastructures as the analysis results are consistent with reality. In addition, the model can capture the value of externality-related information, the distance to the optimal state of the externalities of highway infrastructures, and the temporal and spatial trends of the externalities of highway infrastructures for a region. The results of this study for the first time set a basis for investigating the influential mechanism of the multi-dimensional externalities of highway infrastructures. Moreover, the results provide theoretical support for the scientific formulation of relevant policies and decision-making for the government.

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