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| Main Authors: | , |
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| Formato: | Recurso digital |
| Idioma: | inglês |
| Publicado em: |
Zenodo
2026
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| Assuntos: | |
| Acesso em linha: | https://doi.org/10.5281/zenodo.19603620 |
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Sumário:
- <p class="MsoNormal"><span>This repository provides the processed dataset, replication procedures, survey instruments, and analytical codes used in the study "</span><strong><span>Willingness to Pay for Improved Solid Waste Management in Coastal Communities: Policy Evidence from a Hybrid Contingent Valuation, Econometric, and Machine Learning Framework"</span></strong><em><span>.</span></em><span> It supports full transparency, reproducibility, and validation of results derived from contingent valuation, econometric modeling, and machine learning approaches.</span></p> <p class="MsoNormal"><span>The dataset is based on a household survey conducted in coastal communities surrounding the Lagonoy Gulf, Philippines, and includes variables on socioeconomic characteristics, solid waste management (SWM) practices, community perceptions, and willingness to pay (WTP) for improved waste services.</span></p> <p class="MsoNormal"><span>The repository contains the following materials:</span></p> <p><span>1. Survey Instruments</span><span>, including the structured questionnaire (English and Filipino versions), enumerator instructions, and KII/FGD guide questions for qualitative and policy insights.</span></p> <p><span>2. Variable Documentation</span><span>, consisting of the survey-to-variable mapping table, coding scheme for all variables, and Likert scale constructs with aggregation methods.</span></p> <p><span>3. Dataset Structure</span><span>, covering household-level variables such as demographics (age, sex, education, occupation, income, household size), SWM practices (segregation, collection, disposal), perception indices (waste generation, handling, collection, processing, disposal, and community view), and WTP variables (decision, bid amount, and willingness level).</span></p> <p><span>4. Analytical Codes</span><span>, including STATA scripts (.do files) for data cleaning, descriptive statistics, ordinal logistic regression (WTP level), binary logistic regression (WTP decision), multiple linear regression (WTP bid amount), and non-parametric Turnbull estimation; and R scripts (.R files) for Random Forest classification and regression, as well as model evaluation (accuracy, AUC, R², and variable importance).</span></p> <p><span>5. Supporting Appendices</span><span>, which include summary tables of results, comparative analysis of parametric, non-parametric, and machine learning models, policy recommendations linked to empirical findings, and a description of the study area and its socioeconomic and environmental context.</span></p> <p class="MsoNormal"><span>This replication package enables researchers, policymakers, and practitioners to reproduce the study’s findings, validate model specifications, and apply similar approaches in environmental valuation and solid waste management research.</span></p>