我国区域碳强度的影响因素及灵敏度分析

发布时间：2018-04-18 18:43

本文选题：碳强度 + 面板数据　；参考：《中国矿业大学》2017年硕士论文

【摘要】：本文通过通径分析明确了能源消费结构、产业结构、城市化率、技术进步水平、经济水平五个影响因素相互间的关系及它们与碳强度之间的直间接关系,并进行了影响机理分析。接着综合采用STIRPAT扩展模型和面板数据模型从国家层面和八大综合经济区域层面对我国碳强度的影响因素进行了分析研究。在此基础上,以岭回归和替代弹性等相关方法得到了各变量变动对碳强度影响的灵敏度矩阵,并进行了灵敏度分析,同时也计算了相关条件下对实现“十三五”规划碳强度目标的贡献潜力。本文的主要结论如下:(1)通过通径分析研究发现:从整体来看,能源消费结构和产业结构对碳强度的总影响为正,对碳强度的增加是促进的,城市化率,技术进步水平和经济水平比对碳强度的总影响为负,对碳强度的增加是抑制的。(2)通过影响机理分析,每个因素都通过其他因素对碳强度有间接的影响,只是各自的间接影响程度强弱不同。其中产业结构和经济水平通过其他因素对碳强度的总间接影响都是正向增强,直接影响却是负向减弱;而能源消费结构、城市化率和技术进步水平通过其他因素对碳强度的总间接影响确都是负向减弱,直接影响却是正向增强。能源消费结构对碳强度的增加主要是通过直接影响来促进的,总间接影响有一定的抑制作用;产业结构对碳强度的增加主要是通过总间接影响来促进的,而直接影响是抑制的;城市化率对碳强度的增加更多的是通过总间接影响来产生抑制作用;技术进步水平对碳强度的增加更多的是通过总间接影响来产生抑制作用;经济水平对碳强度的增加主要是通过直接影响来抑制的,而总间接影响是促进的。(3)在全国和八大区域模型中产业结构对碳强度均产生显著正影响,能源消费结构对碳强度也普遍表现为显著正影响(除了南部沿海模型表现为负相关以外);技术进步对碳强度普遍产生显著负影响;经济水平对碳强度也普遍体现显著负影响;而城市化率则表现为除了大西南区域显著负相关以外,其他模型区域均不显著。经济增长和技术进步对碳强度的降低有促进作用,产业结构和能源消费结构对碳强度的降低有抑制作用。在全国总体模型中,产业结构对我国碳强度的影响程度是最强的,技术进步和经济水平对我国碳强度的影响程度次之,能源消费结构对我国碳强度的影响程度最弱。在区域模型的研究中,各因素对碳强度的影响程度在区域之间是存在显著差异的。降低煤炭消费占比,降低第二产业占比,增加科技研发投入和提高人均GDP水平都是符合低碳发展需要的,他们都是实现我国碳强度目标的重要路径。(4)煤炭消费占比降低1个百分点,若分别完全由第二次产业占比、RD机构投入比重比重、人均GDP水平来替代,则碳强度相应降低0.1766、0.1962、0.2063个百分点。根据“十三五”规划相关目标及政策预测,通过灵敏度矩阵可知,当降低煤炭消费比例5%,降低第二产业占比5.5%,提高RD机构投入比重0.4%,提高人均GDP水平10.1%时,到2020年可以使碳强度降低4.46个百分点,对实现“十三五”规划碳强度目标(碳强度降低18%)的贡献潜力为24.78%。
[Abstract]:Through path analysis the energy consumption structure, industrial structure, city rate, the level of technological progress, direct and indirect relationship between the economic level of the five factors and their interactions with carbon intensity, and the influence mechanism analysis. Then used STIRPAT model and panel data model are studied in the face of the influence factors of China's carbon intensity from the national level and the eight integrated economic region. On this basis, Yiling regression and elasticity of substitution and other related method to get the sensitivity matrix of the variable effects on carbon intensity, and the sensitivity analysis, also calculated the potential contribution to the realization of "13th Five-Year plan" carbon intensity the target related conditions. The main conclusions of this paper are as follows: (1) through path analysis found: on the whole, the energy consumption structure and industrial structure of carbon intensity The total effect is positive, the carbon intensity is to promote the city rate, total effect of technological progress and economic level on carbon intensity is negative, the carbon intensity is inhibited. (2) the influence mechanism analysis, each factor through other factors on carbon intensity have indirect effect only indirectly, their influence degree is different. The industrial structure and the level of the economy through other factors total indirect effect on carbon intensity is positive enhancement, direct effect is negative and decreased; the energy consumption structure, city rate and the level of technological progress through other factors the total indirect effect on carbon intensity that is negative to weaken, direct influence is positive. Enhance the increase in energy consumption structure on carbon intensity is mainly promoted through direct effects, the total indirect effect has certain inhibitory effect; industrial structure on carbon intensity increase If the total indirect effect to promote, and the direct effect is suppressed; increase the rate of City carbon intensity is more through the total indirect effect to produce inhibition; increase the level of technological progress on carbon intensity is more through the total indirect effect to produce inhibition; increase the economic level of the main carbon intensity is inhibited by the direct effects and indirect effects is to promote the total. (3) in the country and eight regions in the model of industrial structure on carbon intensity has a significant positive impact on the carbon intensity of energy consumption structure also generally showed significant positive effects (in addition to the performance of the southern coastal model is negatively related to outside technology); progress generally have a significant negative impact on the economic level of carbon intensity; carbon intensity is generally reflected a significant negative impact; while the city rate showed a significant negative correlation in the southwest region, other regional model Were not significant. The economic growth and technological progress have stimulative effect to reduce carbon intensity, industrial structure and energy consumption structure to reduce the carbon intensity of inhibition in the whole country. In the model, the influence degree of the industrial structure of China's carbon intensity is the strongest, technological progress and economic level of China's carbon intensity the influence of the degree of influence of the weakest energy consumption structure of China's carbon intensity in the study area. In the model, the impact of various factors on carbon intensity have a significant difference in area between. To reduce coal consumption accounted for second, reducing industrial proportion, increase R & D investment and improve the level of GDP per capita it meets the needs of low-carbon development, they are an important path to achieve the target of carbon intensity in China. (4) coal consumption accounted for 1 percentage points lower, respectively, if entirely by the second industries accounted for the proportion of investment institutions, RD The proportion of alternative to the level of GDP per capita, the carbon intensity decreased by 0.1766,0.1962,0.2063 percentage points. According to the "13th Five-Year" planning objectives and policies predicted by the sensitivity matrix shows when reduce the proportion of coal consumption reduced 5%, second industries accounted for 5.5%, the proportion of investment increased 0.4% RD, increased by 10.1% per capita GDP, 2020 the carbon intensity decreased by 4.46 percentage points, to achieve the "13th Five-Year plan" carbon intensity targets (18% reduction in carbon intensity) potential contribution to 24.78%.

【学位授予单位】：中国矿业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：X321

【参考文献】