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- MakaleAçık Erişim2026Scopus SJRQ1Assessing interrelationships among circular economy implementation strategies
Ahmadi, Hadi Badri; Gul, Muhammet; Gupta, Himanshu; Yucesan, Melih; Liou, James J.H.
Strategies for implementing a circular economy (CE) are designed to enhance resource efficiency, minimize the consumption of raw materials, and decrease waste. Although many articles have investigated CE implementation strategies, research focusing on the interactions and interrelationships among these strategies remains scarce. To address this issue, this study develops an evaluation framework, with the aim of assessing the interactions among strategies for CE implementation in the context of the manufacturing sector of an emerging economy using Decomposed Fuzzy Decision-Making Trial and Evaluation Laboratory (DF-DEMATEL) methodology. The findings suggest that “Investment in green technology” is the most critical strategy and should receive the greatest attention for successfully implementing CE. This research helps industrial decision-makers and specialists to invest in the most critical strategies within their supply chains and move towards sustainable development.
- MakaleAçık Erişim2025Scopus SJRQ4EVALUATION OF IMPLEMENTATION STRATEGIES IN THE CONTEXT OF ZERO-WASTE CITY AND CIRCULAR ECONOMY CONCEPT
Derse, Onur; Polat, Elifcan Göçmen
In recent years, there is a growing interest for waste systems in many countries around the world due to inevitable consequences of major environmental concerns. Urbanization, consumption styles, rapid developments of technologies have increased the consumption inevitably brings about the waste generation, which significantly poses risks to the environment. Although most papers have focused on waste management problems, there is a great gap of evaluation of implementation strategies for the zero-waste city and circular economy concepts. The goal “zero-waste” is strategically achieved with enriched engineering methods including all stakeholders to overcome the challenging waste issues. In this context, this study presents a systematic approach of implementing the policies and strategies focused on the most essential dimensions analysis associated with zero-waste and circular economy based on the PESTEL (political, economic, social, technological, environmental, and legal). In the study, Best-Worst Method (BWM) is first provided to evaluate a range of zero-waste implementation strategies which are essential for planners and policymakers. In addition, computational results are compared with the Stepwise Weight Assessment Ratio Analysis (SWARA), and the Fuzzy Analytical Hierarchy Process (F-AHP) methods to validate the results, and the results of the methods were found to be consistent. Environmental, economic and technological dimensions were obtained as the most influential strategies. According to the results of BWM and Fuzzy AHP methods, the four most important sub-strategies are S16 (Implementing a closed loop chain), S13 (Promoting sustainability), S5 (Investing in waste management infrastructure) and S12 (Green energy technologies). It is seen that these strategies are mostly environmental. According to the SWARA method, the most important sub-strategies under the economic dimension are S5, S4 (Providing economic incentives), and S6 (Development of green finance initiatives). The findings should also encourage the practitioners and researchers, as the detailed evaluation of strategies in terms of the zero-waste and circular economy provides a database to transform current cities into zero waste cities.
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İncele - MakaleAçık Erişim2022Scopus SJRQ1DEEP LEARNING BASED A COMPREHENSIVE ANALYSIS FOR WASTE PREDICTION
Utku, Anıl; Kaya, Sema Kayapinar
In its simplest definition, waste can be defined as any substance that is used, not needed and causes harm to the environment. Waste management covers control activities such as prevention of the formation of waste, reuse, separation according to its characteristics and type, storage, transportation, recycling and disposal. The main purpose of waste management is to leave a livable world to future generations, to create a sustainable environment, to protect natural resources, to save energy and costs, to reduce the rate of pollution and the amount of hazardous waste. In today's world where urbanization and industrialization rates are increasing, waste management is gaining importance. The aim of this study is to utilize waste data from Istanbul, Turkey's largest and fastest growing city, to estimate waste amount using a constructed Long Short-Term Memory (LSTM) based deep learning model. The developed LSTM-based model has been compared in practice with k-Nearest Neighbors (kNN), random forest (RF), Support Vector Machine (SVM), multi-layered perceptron (MLP) and Gated Recurrent Unit (GRU). As a result of the comparative and comprehensive analyzes, the experimental results showed that the developed LSTM-based deep learning method is more successful in the waste prediction problem than the other compared models.
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