Prioritization of the Potential Sectors for CO2 Emission Reduction based on International Policies: A Case of Turkey - Gundogdu, Fatma Kutlu; Ilbahar, Esra; Karasan, Ali; Kaya, Ihsan; Ozkaya, Bestami (IEEE, 2022)
Day by day, with the increment in the world's temperature, the ways of reducing greenhouse gas emissions (GHGE) have been started to be investigated more to slow down this process. To create a sustainable action plan and a road map, the governments and the international agencies have been started to take steps. Based on this aim, United Nations (UN) determined the most effective factors on GHGE with respect to their possible reduction amounts to take an action. On the other hand, the world bank identified related indicators of GHGE for the governments to create their individual agendas to work for a sustainable and affordable environment and city plans. This work proposes a methodology consisting of spherical fuzzy TOPSIS (SF-TOPSIS) and fuzzy inference system (FIS) for prioritizing the pre-determined sectors with respect to CO2 emission reduction based on the climate change indicators. The SF-TOPSIS technique is used to obtain input data of the FIS by considering the distance to ideal solutions of the evaluated sectors for Turkey. Through the application, it is obtained that Transport, Energy, and Industry sectors are determined as the most effective against the CO2 reduction based on the current ecosystem of Turkey. Since Turkey is a developing country and one of the G20 countries, its current focus areas are mainly increasing productivity considering high-level technologies, the supplement of inadequate and sufficient energy for both the industry and the householders, and investments in infrastructure for a better and faster transformation. Considering these aspects, the country's primary investments areas are on industry, energy, and transportation to reach a better place considering the annual gross domestic product. Therefore, the obtained results are quite applicable and meaningful, and this study can be a good starting point for further actions.
Keywords: climate change indicators; decision-making; fuzzy logic; spherical fuzzy sets; Computer Science; Operations Research & Management Science