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Estimating the the regional total factor efficiency and pollutants' marginal abatement costs in China: A parametric approach

机译:估算中国区域总要素效率和污染物的边际减排成本:一种参数方法

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Applying a parameterized directional distance function approach, this paper estimates the regional total factor efficiency and pollutants' marginal abatement costs (MACS) in China covering the years 2003-2012. We find that if all provinces produced on the production frontier, China could potentially conserve energy consumption by 29.5%, and reduce emissions of carbon dioxide (CO2) and sulfur dioxide (SO2) by 28.2% and 27.4%, respectively: The provinces in southeast coastal area performed better than the rest of the country. The bulk of potentials for energy saving and pollution reduction appeared in middle Yellow River, northwest, north coast, and northeast areas. The MAC of CO2 increased steadily and continuously, while that of SO2 showed a sharp increase throughout the whole study period. The average MACS of CO2 and SO2 for the whole country were 5512 Yuan/tonne and 154,395 Yuan/tonne, respectively. The MACS of SO2 in different areas were remarkably unbalanced. Our estimates suggest that the opportunities for double dividend of economic production and pollution reduction are potentially achievable. Policymakers are required to facilitate technology transfer and encourage scientific and technological cooperation between inter-Aand intra-area. Additionally, they should also consider the regional heterogeneity when making the reduction allocations within the economic sectors and make the market prices of pollutants flexible. (C) 2016 Elsevier Ltd. All rights reserved.
机译:本文应用参数化方向距离函数方法,估算了2003-2012年中国区域总要素效率和污染物的边际减排成本(MACS)。我们发现,如果所有省份都在生产边界上生产,中国可以潜在地节省29.5%的能源消耗,并分别减少28.2%和27.4%的二氧化碳(CO2)和二氧化硫(SO2)排放量:东南部的省份沿海地区的表现要好于全国其他地区。节能减排的巨大潜力出现在黄河中部,西北,北海岸和东北地区。在整个研究期间,CO2的MAC稳定且持续增加,而SO2的MAC则显示出急剧增加。全国平均CO2和SO2的MACS分别为5512元/吨和154395元/吨。不同地区二氧化硫的MACS明显不平衡。我们的估计表明,经济生产和减少污染的双重红利的机会是可以实现的。决策者必须促进技术转让,并鼓励跨区域和跨区域之间的科​​技合作。此外,他们在经济部门内进行减排分配时还应考虑区域异质性,并使污染物的市场价格具有弹性。 (C)2016 Elsevier Ltd.保留所有权利。

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