Improving Air Quality in Communities by Using a Multicriteria Decision-Making Model Based on Big Data: A Critical Review


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 CO2 emissions, which is becoming a severe problem. Through analyzing relevant big data, people can explore the causes of high CO2 emissions and propose effective solutions. Factors contributing to high CO2 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.


Hsueh, S. L. Cheng, A. C.


Applied Ecology and Environmental Research, 2017


carbon dioxide, public policy issues, Delphi method, AHP, Fuzzy logic theory, community education