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DTSTART:20200101T000000
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DTSTART;TZID=Asia/Krasnoyarsk:20210922T000000
DTEND;TZID=Asia/Krasnoyarsk:20210922T000000
DTSTAMP:20260622T091758Z
CREATED:20260622T091758Z
LAST-MODIFIED:20260622T091758Z
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SUMMARY:Thesis Public Defense | VNP26 - Nguyễn Thị Thúy Ngân
DESCRIPTION:Willingness to pay for renewable energy: A case study in HCMC\, Vietnam \nStudent: Nguyễn Thị Thúy Ngân\, VNP 26 \nSupervisor: Dr. Trương Đăng Thụy \nAbstract: \nThis 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\nfound 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. \nKeywords: renewable energy resources\, willingness to pay\, choice experiment method\, conditional logit model\, mixed logit model\, latent class analysis \nJEL classification: O13; P28 ; Q42.
URL:https://vi.vnp.edu.vn/event/thesis-public-defense-vnp26-nguyen-thi-thuy-ngan/
CATEGORIES:THESIS PUBLIC DEFENSE
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DTSTART;TZID=Asia/Krasnoyarsk:20210922T000000
DTEND;TZID=Asia/Krasnoyarsk:20210922T000000
DTSTAMP:20260622T091800Z
CREATED:20260622T091800Z
LAST-MODIFIED:20260622T091800Z
UID:23083-1632268800-1632268800@vi.vnp.edu.vn
SUMMARY:Thesis Public Defense | VNP26 - Nguyễn Phú Quốc
DESCRIPTION:Artificial intelligence and unemployment: An international evidence \nStudent: Nguyễn Phú Quốc\, VNP 26 \nSupervisor: Dr. Võ Hồng Đức \nAbstract: \nThe term artificial intelligence (AI) was first defined in 1956 by John McCarthy as “a machine that behaves in ways that would be called intelligent if a human were so behaving” or “the science and engineering of creating intelligent machines”. The initial study and its modest achievements of AI with mere pre-programmed performances remained far beyond human intellectual abilities\, though the discipline functioned as a very foundation for further development in the field. \nNowadays\, modern information technologies and the advent of cognitive machines powered by AI have powerfully transformed people’s life and work. While AI can amplify productivity in some industries\, there are instances where they can take over human work and revolutionize occupations to some degree. This paper scrutinizes the possible influence of AI on unemployment using a broad database of AI-related patents in 40 developed and emerging markets from 2000 to 2019. The study employs a panel smooth transition regression (PSTR) model to analyze the relationship between AI and unemployment under various inflation levels. The study contributes to the existing literature with several findings. First\, findings from our analysis confirm the non-linear relationship between AI\nand unemployment depending on the threshold of inflation. In general\, AI increases unemployment until a certain threshold of inflation is attained\, then the disclosed ascendancy reduces its effect afterwards. Second\, the smooth mechanism employed in this analysis can capture individual estimates varying among countries over time. Finally\, this pioneering research extends the literature coverage to provide evidence on both developed and emerging economies. \nKeywords: Artificial intelligence (AI)\, patents\, unemployment rate\, panel smooth threshold regression (PSTR)
URL:https://vi.vnp.edu.vn/event/thesis-public-defense-vnp26-nguyen-phu-quoc/
CATEGORIES:THESIS PUBLIC DEFENSE
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