北京地区碳排放脱钩特征、驱动因素分析及趋势预测

发布时间：2018-05-09 10:34

本文选题：碳排放 + 脱钩弹性　；参考：《北方工业大学》2014年硕士论文

【摘要】：由二氧化碳排放增加导致的全球气候变暖问题严重影响了人类生存和发展。2013年初,由于大量排放的污染物无法消散,肆虐的雾霾天气再一次给中国的环境问题敲响了警钟,而北京的情况尤为严重。在这样的大背景下,研究北京的碳排放与经济发展的关系以及真正驱动北京二氧化碳排放的原因将有助于衡量影响北京地区能源碳排放的关键指标,从而有针对性的提出减排政策,为建设“世界城市”提供有力的理论依据。本文首先利用Tapio和IGT脱钩模型研究了北京地区经济发展与碳排放之间脱钩特征,研究发现：除了1993年、1997年和2011年以外,其他年份北京地区经济发展与能源碳排放之间的脱钩关系都属于相对脱钩；能源消费结构的优化,清洁能源使用量的不断增加,节能减排力度的加强以及以第三产业为主的产业结构形式都是北京地区能够呈现大部分时间相对脱钩状态的主要原因；Tapio方程和IGT方程在研究脱钩状态上各有利弊,Tapio方程测量精度更高,而IGT方程可以计算脱钩的临界值。为了进一步研究影响北京地区碳排放的脱钩特征,本文运用LMDI分解方法对北京地区的能源碳排放驱动因素进行了分解,分解为能源结构、产业结构、人均产出、人均收入、生产能源强度、生活能源强度、总人口、交通能源强度、交通工具平均运输线路长度、交通工具规模等10个因素,在此基础上,运用STIRPAT模型定量分析了这些因素对碳排放的影响弹性大小。结果发现：北京地区能源强度是能源碳排放最大的负向驱动因素,能源结构和产业结构因素对减排做出了很大的贡献,而经济发展规模与人口规模是拉动北京地区碳排放增长的主要因素,交通运输业对北京地区的能源碳排放影响不容忽视。从影响因素弹性大小来看城市化率是对北京碳排放影响最大的因素。人口、城市化率、人均GDP和煤炭消费比例及北京市机动车保有量对碳排放有促进作用,而对碳排放有抑制作用的因素是能源消耗强度和第三产业占GDP比重。最后,本文通过GM-PLS组合预测模型和GM(1,1)单一预测模型的比较,研究得出GM-PLS组合模型的预测精度要高于GM(1,1)单独预测的精度,进而利用GM-PLS组合预测模型对北京地区2012-2015年的碳排放强度进行了预测。从预测结果发现,北京的节能减排目标效果很好。
[Abstract]:Global warming caused by the increase of carbon dioxide emissions has seriously affected the survival and development of human beings. In early 2013, the rampant haze weather once again sounded the alarm for China's environmental problems because a large number of pollutants could not dissipate. The situation in Beijing is especially serious. Against this background, studying the relationship between Beijing's carbon emissions and economic development and the causes that really drive Beijing's carbon dioxide emissions will help measure the key indicators that affect energy carbon emissions in Beijing. Therefore, the emission reduction policy is put forward, which provides a powerful theoretical basis for the construction of "world city". In this paper, the characteristics of decoupling between economic development and carbon emissions in Beijing are studied by using Tapio and IGT decoupling models. In other years, the decoupling relationship between economic development and energy and carbon emissions in Beijing was relatively decoupled; the optimization of energy consumption structure and the increasing use of clean energy, The enhancement of energy saving and emission reduction and the industrial structure based on the tertiary industry are the main reasons for the relative decoupling in Beijing for most of the time. Tapio equation and IGT equation have both advantages and disadvantages in the study of decoupling state. The Tapio equation is more accurate. IGT equation can calculate the critical value of decoupling. In order to further study the decoupling characteristics of carbon emissions in Beijing area, this paper uses LMDI decomposition method to decompose the driving factors of energy carbon emissions in Beijing area, which can be divided into energy structure, industrial structure, per capita output, per capita income, energy structure, industrial structure, per capita output and per capita income. On the basis of 10 factors, such as energy intensity of production, energy intensity of daily life, total population, energy intensity of traffic, average length of transportation line and scale of transportation, The effect of these factors on carbon emissions is quantitatively analyzed by STIRPAT model. The results show that the energy intensity in Beijing is the largest negative driver of energy carbon emissions, and the energy structure and industrial structure contribute greatly to the emission reduction. The scale of economic development and population scale are the main factors to promote the growth of carbon emissions in Beijing, and the impact of transportation on energy carbon emissions in Beijing can not be ignored. Urbanization is the most important factor affecting carbon emissions in Beijing. Population, urbanization rate, per capita GDP and coal consumption ratio and motor vehicle ownership in Beijing promote carbon emissions, while energy consumption intensity and tertiary industry account for the proportion of GDP. Finally, through the comparison between the GM-PLS combined prediction model and the single prediction model, it is found that the prediction accuracy of the GM-PLS combination model is higher than that of the GM1 / 1) single prediction model. Furthermore, the carbon emission intensity of Beijing region from 2012 to 2015 is predicted by GM-PLS combined forecasting model. From the forecast results, Beijing's energy conservation and emission reduction targets are very effective.
【学位授予单位】：北方工业大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：X321;F127

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