Contingent valuation machine learning (CVML): A novel method for estimating citizens’ willingness- to- pay for safer and cleaner environment

Quy Khuc () and Duc-Trung Tran

No r35bz, OSF Preprints from Center for Open Science

Abstract: This paper introduces an advanced method that integrates contingent valuation and machine learning (CVML) to estimate residents’ demand for mitigating environmental pollutions and climate change. To be precise, CVML is an innovative hybrid machine-learning model, and it can leverage a limited amount of survey data for prediction and data enrichment purposes. The model comprises of two interconnected modules: Module I, an unsupervised learning algorithm, and Module II, a supervised learning algorithm. Module I is responsible for clustering the data (x^sur) into groups based on common characteristics, thereby grouping the corresponding dependent variable (y^sur) values as well. Take a survey on the topic of air pollution in Hanoi in 2019 as an example, we find that CVML can predict households’ willingness– to– pay for polluted air mitigation at a high degree of accuracy (i.e., over 90%). This finding suggests that CVML is a powerful and practical method that would be potentially widely applied in fields of environmental economics and sustainability science in years to come.

Date: 2023-05-17
New Economics Papers: this item is included in nep-big, nep-cmp, nep-dcm, nep-ene and nep-env
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:r35bz

DOI: 10.31219/osf.io/r35bz

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