确定受益于交通项目的地区的地理范围:广义综合控制

IF 1.6 4区 工程技术 Q4 TRANSPORTATION Journal of Transport and Land Use Pub Date : 2021-01-05 DOI:10.5198/JTLU.2021.1784
T. Kunimi, H. Seya
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引用次数: 2

摘要

在评估基础设施项目的利益时,必须考虑谁将从该项目中受益,以及利益位于何处。然而,目前还没有确定的方法来准确地确定后者。为了填补这一方法上的空白,本研究提出了一种基于广义综合控制方法的交通项目受益区地理范围事后识别方法。具体来说,它允许在单次运行中将多个处理单元与其反事实进行比较-将每个处理站点的土地价格变化(实际结果)与反事实结果进行比较,然后估计处理站点的个人(即单位级别)处理效果。该方法在日本大型重型铁路筑波快线项目中得到了实证应用。我们的方法能够检测1)利益关联的复杂空间形状;2)负溢出效应;3)选择(火车路线)的增加,通常不会在基于享乐主义方法的效益评估系统中考虑,但可以资本化为土地价格。
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Identification of the geographical extent of an area benefiting from a transportation project: A generalized synthetic control
In evaluating the benefits of an infrastructure project, it is essential to consider who is benefiting from the project and where benefits are located. However, there is no established way to accurately determine the latter. To fill this methodological gap, this study proposes an approach for the ex-post identification of the geographical extent of an area benefiting from a transportation project based on a generalized synthetic control method. Specifically, it allows comparing multiple treatment units with their counterfactuals in a single run—changes in land prices (actual outcome) at each treated site are compared to the counterfactual outcome, and the individual (i.e., unit-level) treatment effect on the treated site is then estimated. This approach is empirically applied to a large-scale Japanese heavy railway, the Tsukuba Express line project. Our approach enables the detection of 1) the complicated spatial shape of benefit incidence; 2) negative spillovers; and 3) the increase in options (train routes), typically not considered in a benefit evaluation system based on the hedonic approach, but which can be capitalized into land prices.
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来源期刊
CiteScore
3.40
自引率
5.30%
发文量
34
审稿时长
30 weeks
期刊介绍: The Journal of Transport and Land Usepublishes original interdisciplinary papers on the interaction of transport and land use. Domains include: engineering, planning, modeling, behavior, economics, geography, regional science, sociology, architecture and design, network science, and complex systems. Papers reporting innovative methodologies, original data, and new empirical findings are especially encouraged.
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