基准情境与干扰情境下中国典型城市碳排放趋势预测
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摘要
从城市群协调发展的角度,通过可计算的一般均衡模型与地理加权回归模型,对未来30年中国城市群碳排放影响因素的时空分布趋势进行了预测,并结合不同的政策干扰方案,模拟了分布趋势的变动情况。结果显示:在基准情景下,随着贸易中心的转移,外贸结构的空间分布在2035-2050年将发生一次显著的位移,外贸结构对碳排放量的敏感程度由以珠三角、长三角为主的南方城市群向京津冀等北方城市群过渡;产业结构政策对北方城市碳排放量的影响要显著高于南方城市,碳排放空间分布趋势在2035-2050年间将由北方城市向川渝等南方城市群转移;人民币汇率调整下外贸结构的转变对南方城市碳排放的影响程度更大,并且在一定程度上将限制基准情景下位移的发生。总体而言,技术水平对京津冀及东北地区城市群碳排放影响因素的空间分布趋势影响更大一些,适度的技术投入将有助于北方地区城市碳排放水平降低,但过多的技术投入会产生边际效应递减的现象。
This essay firstly analyzes the spatial difference of carbon emission by GWR Model from a coordinated growth perspective based on the idea of integrated management.Then it forecasts the spatial tendency of carbon emission by General Equilibrium Model and GWR Model,and simulates the variation of spatial distribution trend in different policies. The results show that: With the transfer of the trade center,the spatial distribution of foreign trade structure occurred a significant shift in 2035-2050; The sensitivity of foreign trade structure to carbon emissions begins to shift to northern urban agglomerations;The influencd of industrial structure policy on carbon emissions in northern cities is significantly higher than that in southern China. The spatial distribution trend of carbon emissions shifted from the northern region to the southern urban agglomerations during the period of 2035-2050; The change of foreign trade structure under RMB exchange rate adjustment has a greater influence on carbon dioxide emissions in the southern region. The policy limits the occurrence of shifts in the benchmark scenario.In general, the technical level has a greater influence on the spatial distribution trend of the factors affecting carbon emissions in Beijing-Tianjin-Hebei and Northeast China urban agglomerations. Appropriate technical input will help to reduce carbon emissions in the northern region,but excesive technical input will produce a diminishing marginal effect.
This essay firstly analyzes the spatial difference of carbon emission by GWR Model from a coordinated growth perspective based on the idea of integrated management.Then it forecasts the spatial tendency of carbon emission by General Equilibrium Model and GWR Model,and simulates the variation of spatial distribution trend in different policies. The results show that: With the transfer of the trade center,the spatial distribution of foreign trade structure occurred a significant shift in 2035-2050; The sensitivity of foreign trade structure to carbon emissions begins to shift to northern urban agglomerations;The influencd of industrial structure policy on carbon emissions in northern cities is significantly higher than that in southern China. The spatial distribution trend of carbon emissions shifted from the northern region to the southern urban agglomerations during the period of 2035-2050; The change of foreign trade structure under RMB exchange rate adjustment has a greater influence on carbon dioxide emissions in the southern region. The policy limits the occurrence of shifts in the benchmark scenario.In general, the technical level has a greater influence on the spatial distribution trend of the factors affecting carbon emissions in Beijing-Tianjin-Hebei and Northeast China urban agglomerations. Appropriate technical input will help to reduce carbon emissions in the northern region,but excesive technical input will produce a diminishing marginal effect.
引文
[1]曹洪刚等.中国碳排放的区域差异实证研究———基于2000-2011年省级面板数据的研究[J].工业技术经济,2015(11):84-96
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[6]郭文,孙涛.人口结构变动对中国能源消费碳排放的影响———基于城镇化和居民消费视角[J].数理统计与管理,2017(2):295-312
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[9]胡艳兴,潘竟虎,王怡睿.基于ESDA—地理加权回归的1997-2012年中国省域能源消费碳排放时空演变特征[J].环境科学学报,2015(6):1896-1906
[10]王秋贤,高志强,宁吉才.基于公平的中国省域碳排放差异模型构建探讨———以中国2010年碳排放为例[J].资源科学,2014(5):998-1004
[11] Fotheringham A S,Brunsdon C,and Charlton M. Geographically weighted regression[M]. New York:Wiley,2002
[12] Fotheringham A. S,Brunsdon C,and Charlton M. Geographically weighted regression:the analysis of spatially varying relationships[J]. International Union of Crystallography,2002(6):C125-C126
[13]邱立新,徐海涛.中国城市群碳排放时空演变及影响因素分析[J].软科学,2018(1):109-113
[2] Jones C M,Kammen D M. Quantifying carbon footprint reduction opportunities for U. S. households and communities[J]. Environmental Science&Technology,2011(9):4088-4095
[3] Jones C M,Kammen D M. Spatial distribution of U. S. household carbon footprints reveals suburbanization under-mines greenhouse gas benefits of urban population density[J]. Environmental Science&Technology,2014(2):895-902
[4]刘莉娜等.中国居民生活碳排放的区域差异及影响因素分析[J].自然资源学报,2016(8):1364-1377
[5]岳婷,龙如银.中国省域生活能源碳排放空间计量分析[J].北京理工大学学报(社会科学版),2014(2):40-46
[6]郭文,孙涛.人口结构变动对中国能源消费碳排放的影响———基于城镇化和居民消费视角[J].数理统计与管理,2017(2):295-312
[7]颜艳梅等.中国碳排放强度影响因素对区域差异的作用分析[J].环境科学学报,2016(9):3436-3444
[8]王雅楠,赵涛.基于地理加权回归模型中国碳排放空间差异研究[J].中国人口·资源与环境,2016(2):27-34
[9]胡艳兴,潘竟虎,王怡睿.基于ESDA—地理加权回归的1997-2012年中国省域能源消费碳排放时空演变特征[J].环境科学学报,2015(6):1896-1906
[10]王秋贤,高志强,宁吉才.基于公平的中国省域碳排放差异模型构建探讨———以中国2010年碳排放为例[J].资源科学,2014(5):998-1004
[11] Fotheringham A S,Brunsdon C,and Charlton M. Geographically weighted regression[M]. New York:Wiley,2002
[12] Fotheringham A. S,Brunsdon C,and Charlton M. Geographically weighted regression:the analysis of spatially varying relationships[J]. International Union of Crystallography,2002(6):C125-C126
[13]邱立新,徐海涛.中国城市群碳排放时空演变及影响因素分析[J].软科学,2018(1):109-113