Comparative analysis of visual amenity services valuation: A nationwide assessment through propensity scoring matching and hedonic regression

J Environ Manage. 2023 Jan 1;325(Pt B):116564. doi: 10.1016/j.jenvman.2022.116564. Epub 2022 Oct 28.

Abstract

Conventional hedonic valuations of environmental amenities and cultural ecosystem services (CES) expose two limitations. First, related studies are unable to fully capture the value of visual amenity services which synergistically contribute and enhance the provision of valuable CES together with recreation, educational and spiritual services. Second, studies using linear hedonic regression cannot address potential bias resulting from multicollinearity in independent variables. We found that popular choices of covariates are correlated with the main amenity variable, which can lead to an undermined estimation precision. Therefore, to address those shortcomings, we first employed a specific proxy dummy variable to assign treatment and control individuals based on the service type. Second, we adopted propensity score matching (PSM) methodology to match treatment and control observations conditional on overlapping baseline covariates in order to avoid collinearity. Then, we carried out a comparative evaluation of a nationwide visual amenity service of the ocean ecosystem in China, via our new PSM-based average treatment effects (ATE) methodology and a conventional linear hedonic regression. Two methodologies showed opposite results, with an 8.3% premium in apartment price via PSM-ATE and a negative 0.9% premium via hedonic linear regression. Via a novel evaluation method and a nationwide case study, we conclude that diversifying and enriching the current methodology should be the priority for environmental amenity and cultural ecosystem services-related valuations.

Keywords: Cultural ecosystem services; Environmental amenities; Hedonic regression; Ocean ecosystem; Propensity score matching; Visual amenity service.

MeSH terms

  • China
  • Conservation of Natural Resources*
  • Ecosystem*
  • Humans