Adaptive neuro fuzzy evaluation of energy and non‐energy material productivity impact on sustainable development based on circular economy and gross domestic product
使用自适应神经模糊推理系统，分析了1990-2020年OECD成员国的能源与非能源材料生产率对GDP的影响，发现金属消费是GDP变化的最相关因素，非能源材料生产率与城市垃圾的组合是预测GDP的最佳情景。
Abstract Circular economy represents a concept for turning of material and energy wastes into resources for other purposes in a closed loop system. The purpose of circular economy is to minimize the energy and material wastes. The effect of energy and non‐energy material productivity on the gross domestic product (GDP) was analyzed in this study. The main contribution of the investigation was to determine which sector of energy or non‐energy material productivity has the more relevance on the GDP. Energy productivity represent energy consumption sector while non‐energy material productivity represents the sector closely connected to the circular economy. The used database covers OECD members in period 1990–2020. According to the results one can determine the current status of the economic development and what needs to be improved to reduce the energy and material wastes. On the contrary GDP needs to be tracked in order determine which factor has the more influence on the GDP. For such a purpose adaptive neuro fuzzy inference system (ANFIS) was used. The metals consumption represents the most relevant factor for GDP variation and prediction. The results show also the combination of non‐energy material productivity and generated municipal waste is the best‐case scenario for the GDP prediction. The obtained results could represent the best practices for implementation of circular economy concept.