Contingent Valuation Machine Learning (CVML): A Novel Method for Estimating Citizens’ Willingness to Pay for a Safer and Cleaner Environment

Abstract

This paper introduces an advanced method that integrates contingent valuation and machine learning (CVML) to estimate residents’ demand for reducing or mitigating environmental pollution 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 two interconnected modules: Module I, an unsupervised learning algorithm, and Module II, a supervised learning algorithm. Module I is responsible for grouping the data into groups based on common characteristics, thereby grouping the corresponding dependent variable, whereas Module II is in charge of demonstrating the ability to predict and the capacity to appropriately assign new samples to their respective categories based on input attributes. Taking a survey on the topic of air pollution in Hanoi in 2019 as an example, we found that CVML can predict households’ willingness to pay for polluted air mitigation at a high degree of accuracy (i.e., 98%). We found that CVML can help users reduce costs or save resources because it makes use of secondary data that are available on many open data sources. These findings suggest that CVML is a sound and practical method that could be widely applied in a wide range of fields, particularly in environmental economics and sustainability science. In practice, CVML could be used to support decision-makers in improving the financial resources to maintain and/or further support many environmental programs in years to come.

Keywords

• willingness to pay
• CVML method
• low cost
• high performance
• improved environment