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An inquiry into inter-provincial carbon emission difference in China: Aiming to differentiated KPIs for provincial low carbon development

机译:中国省际碳排放差异的调查:旨在针对省级低碳发展的差异化关键绩效指标

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摘要

Reasonable formulation of carbon emission reduction strategies at sub-national scale is an important technique to realize the national target. However, a set of binding unified key performance indicators (KPIs) is usually not conducive to equitable regional development. Our study employs a hybrid carbon emission estimation method and a multi-index joint representation approach to explore the inter-provincial energy-related carbon emission difference in the year of 2012 in China. Stepwise regression method and hierarchical clustering model were used to classify 30 provinces into economically developed low-carbon region, industrially optimized low-carbon region, resource-abundant high-carbon region, and economically developing high-carbon region. Different regions should take differentiated measures and KPIs related to the local government's efforts to promote low carbon roadmap according to local conditions: The lack of natural resource provides the impetus to improve energy structure in the economically developed low-carbon region. The industrially optimized low-carbon region should place great importance on economic growth and per capita GDP improvement. The low-carbon transformation strategy of the resource-abundant high-carbon regions should focus on carbon emission reduction performance and carbon intensity decline. For economically developing high-carbon regions, decision makers should conduct a sound SWOT analysis of regional low carbon development. (C) 2015 Elsevier Ltd. All rights reserved.
机译:合理制定亚国家规模的碳减排战略是实现国家目标的重要技术。但是,一组具有约束力的统一关键绩效指标(KPI)通常不利于公平的地区发展。我们的研究采用混合碳排放估算方法和多指标联合表示方法,探讨了中国2012年跨省能源相关的碳排放差异。采用逐步回归方法和层次聚类模型,将30个省区划为经济发达的低碳地区,产业优化的低碳地区,资源丰富的高碳地区和经济发展的高碳地区。不同地区应采取与当地政府根据当地情况推动低碳路线图的努力有关的差异性措施和关键绩效指标:缺乏自然资源为经济发达的低碳地区改善能源结构提供了动力。经过工业优化的低碳地区应高度重视经济增长和人均GDP的提高。资源丰富的高碳地区的低碳转型策略应着重于碳减排性能和碳强度下降。对于经济发展中的高碳地区,决策者应该对地区低碳发展进行合理的SWOT分析。 (C)2015 Elsevier Ltd.保留所有权利。

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