Evaluating the Drivers of Willingness to Pay for Stormwater Fees Using Machine Learning Analysis of Citizen Perceptions and Attitudes
Bibliographic Data
| ID | 13113012 |
|---|---|
| Authors | Brian Bidolli (Technische Universität Berlin, corresponding author), Hamid Mostofi (0000-0002-3301-499X, SRH University Berlin) |
| Year | 2026 |
| Volume | 10 |
| Issue | 1 |
| Pages | 27-27 |
| Publication date | 2026-01-02 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Urban Science (JOURNAL) |
| Journal identifiers | ISSN: 2413-8851 • E-ISSN: 2413-8851 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/urbansci10010027 |
| OpenAlex | W7118035802 |
| Language | EN |
| Citations received | 1 |
| References cited | 50 |
Urban stormwater management presents significant challenges for municipalities seeking to balance environmental resilience with financial considerations and social equity. This study investigates the factors shaping residents’ willingness to pay (WTP) for a proposed stormwater management fee in Norwalk, Connecticut, within the context of local sustainability plans. A survey of 457 residents assessed demographics, personal beliefs, perceptions of benefits, risks, and WTP. Since participation was voluntary and open, an exact response rate could not be calculated, and the resulting respondent profile differed from city benchmarks. The results were analyzed using descriptive and inferential statistics alongside a Random Forest machine learning model assessing two payment scenarios, achieving classification accuracies above the majority-class baseline (approximately 60–68%). Across both scenarios, expectations of tangible and locally visible outcomes, including infrastructure upgrades and climate resilience improvements, were the strongest determinants of WTP. When respondents evaluated a specific fee amount rather than a general modest fee, concerns about affordability and program effectiveness became more influential and revealed the conditional nature of financial support. The findings illustrate the value of machine learning for analyzing public attitudes toward environmental finance and highlight how policy framing, transparency, and communication shape acceptance of sustainability measures. These insights provide a data-driven foundation for future research on public engagement and equity in local environmental policy and stormwater plan development
Baseline (sea · Context (archaeology · Descriptive statistics · Green infrastructure · Resilience (materials science · Respondent · Stormwater · Sustainability · Willingness to pay · Economic and Environmental Valuation · Sustainable Building Design and Assessment · Urban Stormwater Management Solutions
Applied Predictive Modeling
Ensemble Methods in Machine Learning
All Frames Are Not Created Equal
An introduction to recursive partitioning
Impediments and Solutions to Sustainable, Watershed-Scale Urban Stormwater Management
Random Forests for Classification in Ecology
Factors influencing the acceptability of energy policies
Value Orientations, Gender, and Environmental Concern
Predictive Analytics in Information Systems Research1
Random Forests
The theory of planned behavior
Making sense of Cronbach's alpha
Stormwater utility fees and household affordability of urban water services
The Psychological Distance of Climate Change
Internet, Phone, Mail, and Mixed‐Mode Surveys
Can machine learning predict environmental attitudes and beliefs
Actors working the institutions in sustainability transitions
Innovations in Public Participation and Environmental Decision Making
New Environmental Theories
A Pure Theory of Local Expenditures
The Role of Topic Interest in Survey Participation Decisions
Climate Change 2022 - Impacts, Adaptation and Vulnerability
Collective Action and the Evolution of Social Norms
The Politicization of Climate Change and Polarization in the American Public's Views of Global Warming, 2001-2010
| Citations per year | 1 |
|---|---|
| Citation span | 2026 - 2026 (1) |
| Highly cited | No |