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Taiwan's government has promoted investment in an offshore wind power farm, and local fishermen have protested. A social impact assessment (SIA) has examined the impact of the proposed offshore wind power farm on all stakeholders. The main objective of the present study was to develop an indicator system for measuring the social sustainability of offshore wind power farms; this study also reports on the particular case of Taiwan's offshore wind power project. This study began by defining 35 social sustainability indicators and selecting 23 representative indicators by using rough set theory. Subsequently, 14 key indicators were constructed using the social construction of technology (SCOT) method. Finally, we developed a social impact index for evaluating the social sustainability of offshore wind power farms by using the analytic network process and Dempster-Shafer theory. Our social impact index yields a total score of 0.149 for Taiwan's pilot offshore wind power project; this result indicates that the pilot project is socially sustainable. A substantial contradiction exists between the fishermen's protest and the results of the social impact assessment. The findings can assist the government in building a coordination platform for the investors and the fishermen. Government regulation is necessary to set boundaries for fishing areas that protect both the fishermen's and investors' rights.

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This paper addresses the issue of measuring transport sustainability for decision-makers. The Pareto rule states that 80% of important information is concentrated in 20% of content. Simplifying useful information for decision-makers is becoming increasingly more important in a complicated society. This paper integrates data envelopment analysis (DEA) and rough set theory (RST) methods for measuring transport sustainability to obtain effective and clear decision-support information for decision-makers, including transport sustainability performance and its related knowledge base. The current work first compiles and summarizes transport sustainability indicators into five generalized efficiency indicators, termed 'cost efficiency', 'cost effectiveness', 'service effectiveness', 'service reduction', and 'service impact'. Then, applies the layered DEA to classify the decision making units (DMUs: time series data of indicator systems) according to five generalized efficiency indicators. The RST application sets up the related knowledge base, including decision rules and core indicators. An empirical study demonstrates Taiwan's transportation sector during the 1993-2007 period. The empirical study results show core indicators of 'cost efficiency', 'service reduction', and 'service impact'. Certain important decision rules indicate that if the 'cost efficiency' or 'service reduction' indicator performs well, the transport system will move toward more sustainable development. Simplified and important information provided to decision-makers introduces them to strategic tools for improving transport sustainability.