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首页> 外文期刊>Journal of Cleaner Production >Multi-objective programming for energy system based on the decomposition of carbon emission driving forces: A case study of Guangdong, China
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Multi-objective programming for energy system based on the decomposition of carbon emission driving forces: A case study of Guangdong, China

机译：基于碳排放驱动力分解的能源系统多目标规划 - 以广东，中国广东为例

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摘要

Energy-related carbon emissions are increasing the rate of climate change, and controlling carbon emissions is a common challenge for the international public. Despite attempts to restrict the utilization of fossil energy and advancing technology for cleaner production, there has been little discussion on the determinants of change in carbon emissions for future scenarios and planning energy systems according to the analysis of low carbon development. In this study, a comprehensive energy optimization planning framework under a low-carbon mode is established. A framework based on the gray model (GM) and logarithmic mean Divisia index (LMDI) method are constructed to predict the emission mitigation potential and decompose the carbon emission driving factors. The decomposition results are key input prerequisites for the following energy optimization model: An interval parameter multiple-objective programming (IPMOP) optimization model, which is developed to support regional energy system administration by seeking the trade-offs among economic development, energy utilization, and environmental protection under multiple uncertainties. Furthermore, the proposed approach is applied to a case study in Guangdong, China. The results reveal that (a) the clean production effect (GDP per unit of atmospheric pollutants emission) would become the primary positive force for carbon emission increase, and the pollutant reduction effect (total atmospheric pollutants emission) would play the primary negative role; (b) the coaldominated energy structure in Guangdong is expected to be transformed to a petroleum-dominated energy structure; (c) the GDP in Guangdong would steadily increase over time, but the pace of economic growth will decelerate, and the annual average growth rate of GDP for the coming fifteen years will be [3.67%, 4.26%]. This study provides a new pathway for policymakers to identify the determinants of carbon emission increase and to generate optimal solutions on a regional scale.
机译：能源相关的碳排放正在增加气候变化的速度，控制碳排放是国际公众的共同挑战。尽管试图限制化石能源的利用率和更清洁生产的推进技术，但根据低碳发育分析，对未来情景和规划能源系统的碳排放变化的决定因素几乎没有讨论。在本研究中，建立了低碳模式下的全面的能量优化规划框架。构建基于灰色模型（GM）和对数平均Divisia指数（LMDI）方法的框架，以预测排放缓解潜力并分解碳排放驱动因子。分解结果是以下能量优化模型的关键输入先决条件：通过寻求经济发展，能源利用率和能源利用和在多个不确定性下的环境保护。此外，拟议的方法适用于中国广东的案例研究。结果表明（a）清洁生产效果（每单位大气污染物排放的GDP）将成为碳排放增加的主要积极力，污染物减少效应（总大气污染物排放）将发挥主要负面作用; （b）预计广东煤层化能量结构将转变为石油主导的能源结构; （c）广东国内生产总值随着时间的推移稳步增长，但经济增长的步伐将减速，未来十五岁的GDP年平均增长率将是[3.67％]。本研究为政策制定者提供了一种新的途径，以确定碳排放量的决定因素，并在区域规模上产生最佳解决方案。

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