甘肃省工业碳排放时空特征及其影响因素分析
首发时间:2014-05-27
摘要:随着我国社会经济的快速发展,能源消耗和环境恶化等问题日趋严重,对人类的可持续发展带来严峻的挑战;特别是工业生产所排放的碳带来的温室效应成为当今社会所关注的焦点,所以目前对工业碳排放研究已成为一个热点研究问题。为此,本文分析了2000-2011年甘肃省工业碳排放总量、工业碳排放强度在时间和空间范围的变化,建立了甘肃省碳排放量影响因素灰色关联度分析模型,定量分析了11年间人口数量、经济规模、能耗结构等因素对甘肃省工业碳排放增量的影响。分析结果表明:从时间角度看,甘肃省11年间工业碳排放量呈上升趋势,而碳排放强度呈下降趋势;从空间角度看,嘉峪关等五市属于碳排放高强度区;从影响因素分析,煤炭和石油是工业碳排放的主体,关联度最高。对此提出了相应的低碳节能减排政策建议。
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Factor analysis of spatial and temporal characteristics of carbon and the influence of industry of Gansu Province
Abstract:With the rapid development of China's social economy, the problem of energy consumption and environmental deterioration is becoming more and more serious, bring the severe challenge to human sustainable development. Especially the greenhouse effect of carbon emissions of industrial production has become the attention focus of today's society, so the research on industrial carbon emissions has become a hot research issue. Therefore, this paper analyzes the changes of Gansu province from 2000 to 2011, total industrial emissions industrial carbon emissions intensity, established the impact of carbon emissions in Gansu province factors grey relational degree analysis model, quantitative analysis of the effects of 2000 factors, 2011 years the population number of economies of scale, energy consumption structure of Gansu Province industrial carbon emissions the. Through the analysis on the factors of Gansu province from 2000 to 2011 carbon emissions characteristics and influence in Gansu Province found that: Analysised from the Angle of time the total emission of industrial carbon showed increasing trend, but the carbon emission intensity of industrial decline. From the space perspective, jiayuguan and other five city belongs to high carbon emissions intensity area. From influence factors analysis of coal and oil is the main part of the industrial carbon emissions, the highest correlation. Finally put forward low carbon energy-saving emission reduction policies and corresponding suggestions.
Keywords: Industrial Carbon Emissions Spatial-Temporal Feature Influencing Factor Gansu Province
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