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夏楚瑜,李艳,叶艳妹,史舟,刘婧鸣.基于净生产力生态足迹模型的工业碳排放效应、影响因素与情景模拟.生态学报,2017,37(11):3862~3871 本文二维码信息
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基于净生产力生态足迹模型的工业碳排放效应、影响因素与情景模拟
Analysis of the industrial carbon emission effect based on the Net Primary Productivity Model, its influencing factors and scene simulation
投稿时间:2016-03-31  修订日期:2016-12-16
DOI: 10.5846/stxb201603310591
关键词净生产力  生态足迹  影响因素  弹性系数  工业碳排放  情景模拟
Key Wordsnet primary productivity model  ecological footprint  influencing factors  elastic coefficient  industrial carbon emission  scene simulation
基金项目国家重点研发计划重点专项(2016YFD0201200);浙江省教育厅重点项目(Z201121260)
作者单位E-mail
夏楚瑜 浙江大学土地科学与不动产研究所, 杭州 310058  
李艳 浙江大学土地科学与不动产研究所, 杭州 310058 liyan522@zju.edu.cn 
叶艳妹 浙江大学土地科学与不动产研究所, 杭州 310058  
史舟 浙江大学农业遥感与信息技术应用研究所, 杭州 310058  
刘婧鸣 中国地质大学(武汉)公共管理学院, 武汉 430074  
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摘要:
以不同类型城市东营和滨州为例,采用基于净生产力的生态足迹模型测度2005-2014年两市工业碳排放效应,利用弹性系数模型对工业碳排放生态足迹及其影响因素进行对比,通过情景模拟分析了基准和低碳情景下两市的可持续低碳发展潜力。研究结果显示:(1)东营碳排放总量和碳排放强度明显高于滨州,两市的碳排放生态足迹总体上都处于上升趋势,年均增长率分别为12.79%和6.16%,这与两市工业化发展阶段不同有关;(2)2005-2008、2008-2011和2011-2014,东营工业碳排放生态足迹当量主导影响因素组合变化为"耕地面积-土地城镇化率-能源结构系数"转化为"耕地面积-人口规模-能源结构系数"到"耕地面积-人口规模-第二产业比重";滨州2005-2014年的主导因素组合一直为"人口规模-土地城镇化率-能源结构系数";(3)通过情景模拟分析2020年东营、滨州的低碳发展潜力:基准和低碳情景下,滨州生态赤字分别为东营的10倍和2.6倍;就"减排"潜力而言,滨州远远高于东营,但实现低碳情景是工业GDP增长从现阶段20.6%骤降到6.5%为代价,对产业结构调整升级要求很高。对东营而言,低碳情景的实现不仅要将能源利用效率提高一倍,更要保证大量重要"碳汇"资源的恢复与重建。
Abstract:
At present, there are serious environmental problems caused by global warming, primarily resulting from the interaction of industrial fossil fuel emissions and Land-Use and Land-Cover Change (LUCC). According to IPCC reports, the industrial sector is the most important source of fossil fuel consumption, which accounts for 78.75% of the carbon emission to the atmosphere. In China, the annual growth rate of carbon emissions from fossil fuel combustion has been 5.2% since 1978, and the future growth trend is difficult to reverse. Moreover, a lack of ecological security in the implementation of the cultivated land balance policy, the principle of "occupying one up one", can easily lead local governments to incorrectly understand infinite cultivated land reserve resources. Thus, to explore how natural resources, industrial development, and restricted cultivated land protection policies affect the ecological pressure of industrial carbon emission in different cities, the present study uses Dongying and Binzhou as examples. Both cities, located in the Yellow River delta, were used to research the ecological pressure of industrial carbon emission based on the Net Primary Productivity Model (NPPM); the elastic coefficient model was applied to analyze changes of influencing factors from 2005 to 2014; and finally, the potential of low-carbon sustainable development using the scene simulation method was measured. Net Primary Productivity (NPP) is employed as a common indicator of biological productivity and the Net Primary Productivity Model (NPPM) can illustrate the interaction between carbon emissions and land carbon sequestration. The following conclusions were reached: (1) carbon emissions and carbon emission intensities of Dongying were significantly higher than that of Binzhou, and the ecological footprint of carbon emissions annually increased by 12.79 and 6.16%, respectively. This is related to the difference of industrial development between the two cities. (2) After analyzing the results of the elastic coefficient model, we found the combination of critical factors of the industrial carbon emission ecological footprint of Dongying changed from a "cultivated land-land urbanization rate-energy structure coefficient" to "cultivated land-population size-energy structure coefficient" to "cultivated land-population size-the proportion of the second industry" from 2005-2008 to 2008-2011 and 2011-2014; that of Binzhou remained a "population size-land urbanization rate-energy structure coefficient" from 2005 to 2014. (3) Through the situational simulation analysis until 2020, we found that under the baseline scenario, the carbon emission ecological deficit of Binzhou was approximately ten times than that of Dongying; under the low carbon scenario, that of Binzhou was only 2.6 times that of Dongying. Regarding the emission reduction potential (the distance between the carbon emission ecological deficit under the baseline scenario and low carbon scenario), the potential of Binzhou was significantly higher than that of Dongying. However, the low carbon scenario of Binzhou is at the expense of a serious slowdown from 20.6 to 6.5% in industry GDP, which needs to forcibly eliminate high energy-consuming enterprises, and economic growth mainly relies on the completion of the third industry. Therefore, there is a very high demand for the readjustment of the industrial structure. Regarding Dongying, the low carbon scenario needs to improve the energy use efficiency by double, and ensure the restoration of large numbers of "carbon sink" resources.
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