Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/74863
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dc.contributor.authorRoengchai Tansuchaten_US
dc.contributor.authorChia Lin Changen_US
dc.date.accessioned2022-10-16T06:51:39Z-
dc.date.available2022-10-16T06:51:39Z-
dc.date.issued2022-11-01en_US
dc.identifier.issn23524847en_US
dc.identifier.other2-s2.0-85131424736en_US
dc.identifier.other10.1016/j.egyr.2022.05.187en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85131424736&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/74863-
dc.description.abstractNowadays, many scientific researchers confirm that carbon emissions cause global warming. Accurate carbon emission pricing is a direct economic measure of greenhouse gas emissions’ actual cost or price. However, Thailand currently does not have a carbon emission price from the formal mandatory carbon market. This paper aims to calculate Thailand's latent carbon emission price based on the KLEM production function and Nonlinear Autoregressive Distributed Lag Model (NARDL). Used are the seasonally adjusted data on the volume of carbon emissions, energy resources, and energy prices from January 2009 to December 2020. The best fit model for NARDL (3,4,0,4,2,0,4,2,0), while that for the ARDL (2,0,4,0,0). In all cases considered, the average estimated monthly carbon emission prices lie in the range of 42.21–78.61. The calculated latent carbon emission price may differ depending on the proportion of the energy source and the energy price in each country.en_US
dc.subjectEnergyen_US
dc.titleLatent carbon emission pricing model for Thailand: A nonlinear autoregressive distributed lag modelen_US
dc.typeJournalen_US
article.title.sourcetitleEnergy Reportsen_US
article.volume8en_US
article.stream.affiliationsAsia Universityen_US
article.stream.affiliationsNational Chung Hsing Universityen_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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