Construction industry has been criticized as the industry producing high pollution. Especially, a newly built house is often torn down and renovated the walls in the interior space at the life cycle stage of a housing project when buyers are using and maintaining, due to distinct customer demand and preference. Structural walls and beam columns are even randomly drilled for the piping requirement of air conditioner pipelines to result in public danger. It could be attributed to the regulation of interior environmental decoration without safety check in Taiwan and some interior designers, in order to strive for cases in the high competition, corresponding to customers' demands for changing interior patterns. In addition to newly built houses, the sale and transfer of second-hand houses is another thought for the renovation work of interior space environmental decoration to disturb the life of neighbors and seriously damage the environment. Accordingly, Delphi method, Analytical hierarchy process, and Utility theory are applied in this study to establish an AHP expected utility based evaluation model for management performance on interior environmental decoration, aiming to provide the safety management for interior environmental decoration and reduce the management review for energy consumption and environmental protection.

Hsueh -Cheng: Improving air quality in communities by using a multicriteria decision-making model based on big data - -mail: hsueh.sl@msa.hinet.net; tel: +86-9-32-883-292; fax: +86-7-693-9663 Abstract. Information technology has advanced rapidly and has long been used in various fields and industries. The accumulated data are valuable in practical applications related to topics, such as scientific research, commercial development, and policy-making references. Recent global climate anomalies are due to the ongoing reclamation and extensive use of natural resources in the ongoing process of human evolution and development. Demand for industrial development and economic competition among countries have caused high CO 2 emissions, which is becoming a severe problem. Through analyzing relevant big data, people can explore the causes of high CO 2 emissions and propose effective solutions. Factors contributing to high CO 2 emissions not only include the strong dependence on energy and its use in economic, industrial, and commercial development, but families and individuals also contribute to air pollution. To solve this problem, this study investigated topics on public policy issues involving big data, community education effectiveness, and low-interest loans. In addition, this study adopted the Delphi method, analytical hierarchy process, and fuzzy logic theory to establish a multicriteria decision-making model based on big data to evaluate the processes of reducing air pollution in urban areas. Because community education contributes to resolving public policy problems, the proposed MCDM model enables researchers to determine improvements in urban air quality and aids in discerning the effects of community education on the promotion of environmental protection policies. In addition, this study proposed methods involving grants and low-interest financing to enhance improvements.