Version 1
: Received: 14 July 2023 / Approved: 14 July 2023 / Online: 14 July 2023 (10:29:02 CEST)
Version 2
: Received: 5 August 2023 / Approved: 7 August 2023 / Online: 7 August 2023 (11:49:31 CEST)
Khuc, V.Q.; Tran, D.T. Contingent Valuation Machine Learning (CVML): A Novel Method for Estimating Citizens’ Willingness to Pay for a Safer and Cleaner Environment. Urban Sci.2023, 7, 84.
Khuc, V.Q.; Tran, D.T. Contingent Valuation Machine Learning (CVML): A Novel Method for Estimating Citizens’ Willingness to Pay for a Safer and Cleaner Environment. Urban Sci. 2023, 7, 84.
Khuc, V.Q.; Tran, D.T. Contingent Valuation Machine Learning (CVML): A Novel Method for Estimating Citizens’ Willingness to Pay for a Safer and Cleaner Environment. Urban Sci.2023, 7, 84.
Khuc, V.Q.; Tran, D.T. Contingent Valuation Machine Learning (CVML): A Novel Method for Estimating Citizens’ Willingness to Pay for a Safer and Cleaner Environment. Urban Sci. 2023, 7, 84.
Abstract
This paper introduces an advanced method that integrates contingent valuation and machine learning (CVML) to estimate residents’ demand for reducing or 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 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 category based on input attributes. Take 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 is 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 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.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Received:
7 August 2023
Commenter:
Quy Khuc
Commenter's Conflict of Interests:
Author
Comment:
We meticulously revised the abstract and conclusion sections as suggested. For the introduction part, we added paragraphs that briefly review/describe the development of models for estimating willingness-to- pay value, and the machine learning method and its advantages. For the method part, we supplemented the examples of the content of the questionnaire with open-ended and payment card questions. Particularly, we improved the paper by supplementing the discussion section that mainly focuses on the main attributes and conditions of CVML. These changes make the paper more clear and/or strong. Next, we carefully revised the remaining sections by improving Figure 1, Figure 2, and Figure 3, adding Table 3, and Table 4, clarifying some Equations, and supplementing some key references. Finally, we carefully checked and corrected all typos throughout the paper.
Commenter: Quy Khuc
Commenter's Conflict of Interests: Author