Department of Industrial Engineering, Shiraz University of Technology, P. O. Box: 71555-313, Shiraz, Iran.
Program Sustainability Lead at KiwiRail, Auckland 1060, New Zealand.
J Environ Manage. 2022 Feb 15;304:114097. doi: 10.1016/j.jenvman.2021.114097. Epub 2021 Dec 8.
To avoid the calamitous consequences of an even warmer world, the efforts are focused on overarching and immediate solutions to reduce the greenhouse gases. The emissions trading scheme is deemed worldwide as an ecological and robust management mechanism to curb carbon emissions. The challenge is how to design such a scheme to attain the basic purpose of installing a uniform system and equilibrium efficiency achievement. For the first time, existing idea of group method of data handling (GMDH) type neural network (NN) is developed to predict capital stock, labor force, CO emission, energy consumption, and gross domestic product (GDP) based on past information for the top-26 emitting countries. Then, this study deals with the allocation of emission quotas by proposing a two-step optimization mechanism that takes full advantages of the context-dependent data envelopment analysis (DEA) and equilibrium efficient frontier DEA (EEFDEA) models. In the first step, under the premise of constant total carbon emissions, a pragmatic efficiency-oriented carbon quota trading system is established to attain equilibrium state. In the second step, under the measured total emission mitigation target, the carbon quotas allocation mechanism is formulated to translate the top emitters' mitigation target into national purposes from two features of efficiency and fairness as well as to specify the comprehensive targets for the top emitters to maintain their equilibrium state. Two of the main findings are: 1) The top emitters should decrease the total CO2 emission by at least 37% by 2023. 2) In light of the CO2 emission mitigation allocation, the countries with larger potentials are China, Japan, and the US yet to receive the larger portions of 20%, 9% and 22%, respectively. Finally, the allocation method that takes regional heterogeneity into account is more logical since it alleviates pressure on the emitters to decrease carbon emissions and establishes a baseline for distributing CO emission quotas across the emitters to enhance adaptation to nations' present circumstances.
为避免全球变暖带来灾难性后果,人们致力于寻找全面且立竿见影的解决方案,以减少温室气体排放。排放交易计划被认为是全球范围内遏制碳排放的一种生态且强有力的管理机制。面临的挑战是如何设计这样一个方案,以实现建立统一系统和实现均衡效率的基本目标。本文首次开发了基于数据处理分组方法(GMDH)类型神经网络(NN)的现有理念,用于根据过去的信息预测资本存量、劳动力、二氧化碳排放、能源消耗和国内生产总值(GDP),涉及的国家为前 26 大排放国。然后,通过提出充分利用基于上下文的数据包络分析(DEA)和均衡有效前沿 DEA(EEFDEA)模型的两步优化机制,研究了排放配额的分配问题。在第一步中,在总碳排放量不变的前提下,建立了一个注重实效的碳配额交易系统,以达到均衡状态。在第二步中,在衡量的总减排目标下,制定了碳配额分配机制,将前排放国的减排目标从效率和公平两个特征转化为国家目标,并为前排放国制定了综合目标,以保持其均衡状态。得到的两个主要结论是:1)到 2023 年,前排放国应至少减少 37%的总二氧化碳排放。2)从二氧化碳减排分配来看,中国、日本和美国具有较大的减排潜力,分别应获得 20%、9%和 22%的更大份额。最后,考虑区域异质性的分配方法更符合逻辑,因为它减轻了排放国减少碳排放的压力,并为在排放国之间分配二氧化碳排放配额建立了一个基准,以增强各国适应本国国情的能力。