Estimating Social Costs of Power Substation in Taiwan: a Comparative Analysis between Econometrics and Machine Learning

Project Details


The purpose of this study has two goals: (1) to estimating the social costs of the establishment of power substation. (2) to comparing the results obtained between the traditional econometric methods and the machine learning methods. By adopting the hedonic pricing method, our research will collect the transaction data from the housing market on the cities of Taipei, New Taipei, Taoyuan, Taichung, Tainan and Kaohsiung. Furthermore, our work will use the geographical information system (GIS) to construct the distance variable for the measurement between the power substation and the housing location. In addition, the research work will use the machine learning method, in particular, the support vector machine beside the traditional ordinary least squares estimation. The research will examine and compare the results obtained from the two estimation methods. Due to the few studies related to estimate the social costs of the establishment of power substation in Taiwan, our work has not only play the role for the academic research but also provide the quantify results for the design the suitable environmental and energy policy.
Effective start/end date1/08/2031/07/21

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):

  • SDG 8 - Decent Work and Economic Growth
  • SDG 11 - Sustainable Cities and Communities
  • SDG 17 - Partnerships for the Goals


  • social costs
  • power substation
  • hedonic pricing method
  • machine learning
  • support vector machine


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