Preference Construction Processes for Renewable Energies: Assessing the Influence of Sustainability Information and Decision Support Methods
Keywords
renewable energy sourcespreference construction
multiple criteria decision analysis
life cycle assessment
sustainability assessment
Environmental effects of industries and plants
TD194-195
Renewable energy sources
TJ807-830
Environmental sciences
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Sustainability information and decision support can be two important driving forces for making sustainable transitions in society. However, not enough knowledge is available on the effectiveness of these two factors. Here, we conducted an experimental study to support the hypotheses that acquisition of sustainability information and use of decision support methods consistently construct preferences for renewable power generation technologies that use solar power, wind power, small-scale hydroelectric power, geothermal power, wood biomass, or biogas as energy sources. The sustainability information was prepared using a renewable energy-focused input-output model of Japan and contained life cycle greenhouse gas emissions, electricity generation costs, and job creation. We measured rank-ordered preferences in the following four steps in experimental workshops conducted for municipal officials: provision of (1) energy-source names; (2) sustainability information; (3) additional explanation of public value; and (4) knowledge and techniques about multi-attribute value functions. The degree of changes in preference orders was evaluated using Spearman’s rank correlation coefficient. The consistency of rank-ordered preferences among participants was determined by using the maximum eigenvalue for the coefficient matrix. The results show: (1) the individual preferences evolved drastically in response to the sustainability information and the decision support method; and (2) the rank-ordered preferences were more consistent during the preference construction processes. These results indicate that provision of sustainability information, coupled with decision support methods, is effective for decision making regarding renewable energies.Date
2016-11-01Type
ArticleIdentifier
oai:doaj.org/article:c07dd6743751484382b1661d58386ac42071-1050
10.3390/su8111114
https://doaj.org/article/c07dd6743751484382b1661d58386ac4