城镇化及房地产投资对中国碳排放的影响机制及效应研究

doi:10.13249/j.cnki.sgs.2019.04.014

摘要/Abstract

摘要：

以城镇化及房地产投资对碳排放的影响机理为研究基础,首先基于变形Kaya恒等式和LMDI分解方法对1997~2015年中国30个省份的碳排放变化进行因素分解,重点考察了城镇化和房地产投资对碳排放的影响,并采用空间面板数据模型从直接影响和空间溢出效应两方面进行实证检验。研究结果表明：① 1997~2015年中国碳排放量一直保持增长趋势,房地产投资碳排放系数是最主要碳排放促减因素,城镇房地产投资强度、城镇化水平和地区总人口变化对碳排放具有促增作用,且效果逐年增大。② 各省碳排放量在空间上存在显著差异,总体上呈现东高西低的分布特征。碳排放量较少的省区空间集聚程度有所增强,地区间差异在不断缩小。③ 城镇化水平对碳排放的直接影响显著为负,但其空间溢出效应显著为正;城镇房地产投资强度对碳排放的直接影响影响具有促增效应,其空间溢出效应并不显著;两者的交互作用具的直接效应和空间溢出效应显著为负;经济发展水平对本地区碳排放的直接效应和空间溢出效应均显著为正;政府投资对碳排放的直接影响显著为负,但空间溢出效应并不显著;产业结构对本地区的碳排放没有显著的影响,但是其空间溢出效应显著为负;对外开放程度对本地区的碳排放具有显著的促减作用,但是对相邻地区的碳排放具有促增效应;随着城镇化水平和经济发展水平的提高,碳排放水平分别呈现出显著的U型和倒U型曲线关系。

关键词: 城镇化, 房地产投资, 碳排放, LMDI, 空间面板数据模型

Abstract:

Urbanization and real estate investment are important influencing factors of carbon emission in a region. Therefore, it is necessary to systematically analyze and test the internal impact mechanism of urbanization and real estate investment on carbon emission. Combining the deformation of Kaya identity and LMDI decomposition, this article decomposes the carbon emission changes of 30 provinces in China (due to data limitation, the data of Hong Kong, Macao, Taiwan and Tibet are not included in the study area) from 1997 to 2015. The spatial evolution characteristics of carbon emissions in Chinese provinces are described by Moran’s I index and LISA spatial agglomeration map. This article further uses the spatial panel data model to empirically test the effects of urbanization and real estate investment on carbon emission from both direct impact and spatial spillover effects. The results are as follow: First, from the time trend, China’s carbon emission has maintained a growth trend from 1997 to 2015. From the perspective of the decomposition factors of carbon emission, the carbon emission coefficient of real estate investment is the main driving factor to curb carbon emissions, while the intensity of urban real estate investment, the level of urbanization and the change of total population in the region have positive effects on carbon emission, and the effects are increasing year by year. Second, from the spatial distribution characteristics of carbon emission, there are significant differences for the carbon emission in the provincial-level, and the distribution characteristics are generally expressed as higher carbon emission in the east and lower in the west. The spatial concentration of provinces with less carbon emission has increased, and the regional differences among them have been shrinking. Third, from the regression results of the spatial panel data model, the direct impact of urbanization on carbon emission is significantly negative, but the spatial spillover effect is significantly positive. The direct effect of the intensity of urban real estate investment on carbon emission is positive, but its spillover effect is not significant. The direct effect and spatial spillover effect of the interaction between urbanization and the intensity of urban real estate investment are significantly negative. The direct effect and spatial spillover effect of economic development on carbon emission are significantly positive. The direct impact of government investment on carbon emission is significantly negative, but the spillover effect is not significant. Industrial structure has no significant direct effect on the carbon emission, but its spatial spillover effect is negatively negative. The degree of opening to the outside world has a significant negative direct effect on the carbon emission, but it has a positive effect on the carbon emission in the adjacent areas. With the improvement of the level of urbanization and economic development, there is a significant U-shaped and inverted U-shaped curve relationship between the two and carbon emission, respectively. This article further proposes countermeasures and suggestions to improve regional carbon emission reduction from the perspective of urbanization and real estate policies.

Key words: urbanization, real estate investment, carbon emissions, LMDI, spatial panel data model

中图分类号:

F205

范建双, 周琳. 城镇化及房地产投资对中国碳排放的影响机制及效应研究[J]. 地理科学, 2019, 39(4): 644-653.

Jianshuang Fan, Lin Zhou. The Mechanism and Effect of Urbanization and Real Estate Investment on Carbon Emissions in China[J]. SCIENTIA GEOGRAPHICA SINICA, 2019, 39(4): 644-653.

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变量名称	混合估计模型	空间固定效应模型	时间固定效应模型	空间和时间固定效应模型
LM spatial lag	23.158^***	20.744^***	2.201	1.873^***
LM spatial error	2.558	11.396^***	0.166	2.646^***
R-LM spatial lag	45.160^***	14.844^***	4.317^**	1.624^***
R-LM spatial error	24.560^***	5.497^**	2.282	2.398^***

变量名称	固定效应模型	固定效应模型（误差修正）	随机效应模型
Wald spatial lag	60.032^***	54.029^***	57.787^***
Wald spatial error	56.403^***	56.403^***	55.130^***
Hausman	- 12.855 -

变量名称	空间和时间固定效应模型
变量名称	直接影响	空间溢出效应	总效应
lnUP	-0.229^**（-2.404）	1.312^**（2.619）	1.083^*（1.991）
（lnUP）²	-0.011^**（-2.616）	0.869^*（2.611）	0.858^**（2.476）
lnRU	0.240^*（1.794）	0.325（1.189）	0.565^**（2.128）
lnUP×lnRU	-0.157^**（-2.123）	-0.315^**（-2.627）	-0.471^**（-2.606）
lny	0.543^***（3.409）	0.675^*（1.704）	1.218^***（2.924）
（lny）²	0.110^***（2.816）	-0.333^***（-4.076）	-0.223^*（-2.873）
lnGI	-0.369^***（-3.615）	-0.242（-0.991）	-0.611^**（-2.559）
lnIS	0.181（0.641）	-0.908^**（-2.418）	-0.726（-1.005）
lnTrade	-0.035^***（-3.186）	0.084^***（2.996）	0.049（1.639）