Journal article
Potentials and opportunities for low carbon energy transition in Vietnam: A policy analysis
Energy Policy, Vol.134
2019
Abstract
This paper intends to synthesis the status quo and projected energy situation for Vietnam, as one of the fastest growing energy economies among the Association of South East Asian Nations (ASEAN). Focus is drawn towards the existing policy landscape's ability to foster low carbon development progression as an alternative to the existing fossil fuel driven economy, while also aligning with achievement of Sustainable Development Goals (SDGs) for the nation. Effectiveness of Vietnam's policy portfolio for fostering low carbon development was informed via a systematic literature review and Multi Criteria Decision Analysis (MCDA). This was founded upon six developed criteria, informed from the literature review. The results were presented and analyzed via chronological and hierarchical policy taxonomies. Findings indicate that to support a pathway towards low carbon development, policies need to include mechanisms that favor Renewable Energy technology and also foster the mobilization of private investment or international cooperation. The research also indicates a significant connection between clean energy sector progression and achievement of relevant SDGs. Such a correlation should heighten the priority of energy sector actions, such as policy mechanisms and outcomes, not only quantitative targets.
Details
- Title
- Potentials and opportunities for low carbon energy transition in Vietnam: A policy analysis
- Authors/Creators
- C. Shem (Author/Creator) - Murdoch UniversityY. Simsek (Author/Creator) - Pontificia Universidad Católica de ChileU.F. Hutfilter (Author/Creator) - Murdoch UniversityT. Urmee (Author/Creator) - Murdoch University
- Publication Details
- Energy Policy, Vol.134
- Publisher
- Elsevier BV
- Identifiers
- 991005540705007891
- Copyright
- © 2019 Elsevier Ltd.
- Murdoch Affiliation
- School of Engineering and Information Technology
- Language
- English
- Resource Type
- Journal article
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- 4.61 Artificial Intelligence & Machine Learning
- 4.61.56 Fuzzy Decision-Making
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- Economics
- Energy & Fuels
- Environmental Sciences
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