Thesis Public Defense | VNP26 - Nguyễn Thị Thúy Ngân

Willingness to pay for renewable energy: A case study in HCMC, Vietnam Student: Nguyễn Thị Thúy Ngân, VNP 26 Supervisor: Dr. Trương Đăng Thụy Abstract: This research estimates willingness to pay for renewable energy and examines its determinants by using the Discrete Choice Experiment. The data was collected in 2020

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October 1, 2021 - 2:00 pm

End

October 1, 2021 - 3:00 pm

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Willingness to pay for renewable energy: A case study in HCMC, Vietnam

Student: Nguyễn Thị Thúy Ngân, VNP 26

Supervisor: Dr. Trương Đăng Thụy

Abstract:

This research estimates willingness to pay for renewable energy and examines its determinants by using the Discrete Choice Experiment. The data was collected in 2020 from 286 households with electricity connections in Ho Chi Minh City, Vietnam. In addition to Conditional Logit as the standard model, Mixed Logit and Latent Class are applied in this study to fix its limitations. Several outstanding findings are presented as follows. Firstly, an explicit linear relationship is found between the value of WTP and the increase in RE share. Secondly, similar to previous literature, solar energy is the most preferred RE source; however, the WTP for RE generated from biomass exceeds that from wind, although knowledge of wind energy is more popular than biomass through statistics, implying a huge potential for development of biomass energy projects. Furthermore, two different latent classes are identified, including awareness of wind and biomass energy, home ownership, the number of people at home during the day and the total times of outage. Noticeably, there is no relationship between the WTP for clean energy and income
found in this study, while almost all research found a positive correlation. The estimated results show comparative similarities in all three models, indicating the high reliability of research results.

Keywords: renewable energy resources, willingness to pay, choice experiment method, conditional logit model, mixed logit model, latent class analysis

JEL classification: O13; P28 ; Q42.

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