Machine learning and spatial regression approaches to estimating willingness to pay for ecosystem services in Jakarta and Taoyuan

Shafira Wuryandani, Yu-Pin Lin (), Pei-Chen Lin, Dirk Schmeller () and Gerard Ros
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Shafira Wuryandani: NTU - National Taiwan University
Yu-Pin Lin: NTU - National Taiwan University
Pei-Chen Lin: NTU - National Taiwan University
Dirk Schmeller: CRBE - Centre de Recherche sur la Biodiversité et l'Environnement - IRD - Institut de Recherche pour le Développement - CNRS - Centre National de la Recherche Scientifique - Toulouse INP - Institut National Polytechnique (Toulouse) - Comue de Toulouse - Communauté d'universités et établissements de Toulouse - EPE UT - Université de Toulouse - Comue de Toulouse - Communauté d'universités et établissements de Toulouse
Gerard Ros: WUR - Wageningen University and Research [Wageningen]

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Abstract: Ecosystem services (ES) are essential to environmental sustainability and human well-being. Among them, hydrological ecosystem services (HES) play a critical role in flood mitigation, climate regulation, and water security. This study examines the socioeconomic drivers influencing individuals' willingness to pay (WTP) for HES in two contrasting urban settings: Jakarta in Indonesia and Taoyuan in Taiwan. We applied and compared three data-driven models (i.e., Logistic Regression, Geographically Weighted Logistic Regression, and Extreme Gradient Boosting) to assess both spatial and non-linear determinants of WTP. A total of 1006 respondents were surveyed using structured face-to-face interviews. Respondents were selected via purposive sampling to ensure representation of relevant sociodemographic and regional characteristics. Results show that WTP, expressed in 2024 USD per person per year, is lower in Jakarta (5.52 USD) compared to in Taoyuan (9.99 USD). Demographic and socio-economic variables, particularly gender and education, are key predictors of WTP, followed by support for environmental initiatives. The influence of these factors varies spatially, suggesting that effective ES protection policies should be tailored to local population characteristics. By integrating spatially explicit and data-driven approaches, Payment for Ecosystem Services (PES) policies can more effectively promote community participation and support sustainable ecosystem preservation, particularly for urban ES valuation across East and Southeast Asia.

Keywords: Ecosystem services; Payment for ecosystem services; Socioeconomic characteristics; Willingness to pay; Contingent valuation (search for similar items in EconPapers)
Date: 2026-04
New Economics Papers: this item is included in nep-dcm and nep-sea
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Published in Ecosystem Services, 2026, 78, pp.101819. ⟨10.1016/j.ecoser.2026.101819⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05475310

DOI: 10.1016/j.ecoser.2026.101819

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