Effects of land urbanization and land finance on carbon emissions: A panel data analysis for Chinese provinces

The rapid urbanization in China comes with several economic, social, and environmental issues, most of which are related to land use. This study contributes to research on the land–growth–environment nexus by investigating the effect of land urbanization and land finance on carbon emissions in China from 2004 to 2013 using the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model. Results show that land finance and land urbanization significantly affect carbon emissions. The rate of land urbanization contributes to the reduction of carbon emissions; however, it has less impact compared with other determinants. The effect of land finance and land urbanization on carbon emissions indicates that a local government’s willingness to lease land for revenue aggravates carbon emissions. Economic growth and industrial structure also influence carbon emissions. Furthermore, the land requisition system and rural land conversion market should be enhanced through the guidance provided by the 13th Five-Year Plan (2016–2020) to promote the diversification of land transfer, fully consider regional differences, and establish a distinct policy focus that can contribute to emission reduction and land use.     

基于改进STIRPAT模型的城镇化水平对工业废水排放的影响——以长江经济带为例

长江经济带城镇化的发展促进了工业的进步,而工业发展的同时也伴随着工业废水排放的增加及对水环境的破坏。通过建立城镇化与工业废水排放之间的STIRPAT模型,分析长江经济带城镇化对工业废水排放的具体影响。结果表明:长江三角洲经济区及长江经济带整体城镇化水平与工业废水排放之间存在着倒N型关系,长江上游经济区和长江中游经济区的城镇化水平与工业废水排放之间存在倒U型曲线关系;经济发展水平和人口规模对工业废水排放的影响存在地区差异;长江经济带及各区域技术水平和产业结构对工业废水排放存在显著的正向影响。据此,在推动新型城镇化建设中,应采取政策扶持、转变经济发展方式、提高公众环保意识、调整产业结构及加速技术创新等措施,以期减少工业废水排放。     

城市化对资源消耗和污染排放的影响分析

本文选取黑龙江、上海、河南、广东和甘肃等5个省份,采用1985-2009年平衡面板数据,利用STIRPAT模型,研究了不同发展水平下的城市化对资源消耗和污染排放的影响.主要结论为:人口规模、人均收入和经济结构都是影响环境的重要因素;总体上,城市化与资源消耗和污染排放都呈正相关关系,但从5个不同发展水平的省份来看,城市化...     

基于空间面板STIRPAT模型的省域碳排放研究

本文基于空间面板STIRPAT模型对1996~2010年影响全国省域人均碳排放因素进行研究,结果表明:省域碳排放具有明显的正向空间聚群效应;人均产出、对外贸易、能源强度、能源结构等指标与人均碳排放之间存在正向关系;人口密度、城市化率、产业结构等指标表现出的更多是抑制碳排放的作用。建议:强化区域联防联控的大气污染防治工作机制;推进工业化、城镇化;调整能源结构、产业结构,走可持续绿色低碳发展之路;优化对外贸易结构,推进"走出去"战略。     

基于STIRPAT模型研究南京碳排放的影响因素

在巴黎气候变化大会上,中国政府提出了“2030年左右实现碳达峰,并争取尽快实现”的新阶段目标。城市是能源资源消耗和CO2排放的集聚区域,城市化产生的碳排放是当今中国影响气候变化的重要因素。文章采用南京1997~2017年21年数据,从人口规模、财富水平、城市化水平、技术水平、产业结构、国际贸易水平、科技创新能力七个方面选取更符合南京国情的社会经济变量建立STIRPAT模型,研究影响南京市碳排放的主要因素。结果表明:人口总量和城市化率是影响南京碳排放的主要因素。     

新疆能源消费碳足迹变化、影响因素及其演进分析

经济增长对能源消费的依赖性与生态环境恶化的矛盾日趋严重。本文采用 IPCC 方法测算了新疆能源消费碳足迹,利用岭回归对扩展的 STIRPAT 模型进行拟合,分析了各因素对碳足迹影响的演进规律,结果表明：1990-2011年新疆能源消费碳足迹整体上呈上升趋势,年均增长率为5.82%,其中能源消费碳足迹的构成中以煤炭为主,石油次之,天然气最小;新疆能源消费碳足迹产值在绝对数值和增长率方面都处于较低的水平,能源利用效率还有待提高;新疆不存在环境库兹涅茨曲线;各驱动因素对碳足迹增长的贡献会随着时间推移发生变化,人均GDP 从2011年起已经成为对碳足迹影响最大的驱动力,而城镇化率对碳足迹的影响相对有限。     

Analysis of the major drivers of the ecological footprint using the STIRPAT model and the PLS method—A case study in Henan Province, China

Taking Henan Province of China as an example, we computed and analyzed the ecological footprint (EF) in 1983–2006. The results showed that the EF in Henan Province quadrupled in the 23 years, but its ecological carrying capacity (EC) was rather low and was in a state of slow decline, indicating that Henan's ecological deficit (ED) had become a remarkable social problem. Therefore, the major drivers of the EF's change were analyzed. According to the simulations with STIRPAT model, the major drivers of Henan's EF were human population (P), GDP per capita (A₁), quadratic term of GDP per capita (A₂), percent of economy excluded in the service sector (Ta₁) and percent of urban population (Tb₁). However, these drivers themselves had strong collinearity, which might produce some uncertain impact to the final results. In order to avoid the impact of collinearity, the method of partial least squares (PLS) was used. The results showed that the major drivers of EF were P, A₁, A₂ and Tb₁. Ta₁ was excluded. Compared with the results by the STIRPAT model, which showed that P is the most dominant driver and the effect of the other drivers could almost be ignored, the results by PLS method were considered as more reasonable and acceptable because the impacts of the A (Affluence) and T (Technology) conditions to the regional EF were still too important to be ignored. In addition, the results acquired by both methods showed that the curvilinear relationship between economic development and ecological impact (EF) or the classical EKC hypothesis didn't exist in Henan Province.     

Examining the influencing factors of CO2 emissions at city level via panel quantile regression: evidence from 102 Chinese cities

Ascertaining the influencing factors of carbon dioxide emissions in Chinese cities is an important issue for policy-makers. This paper investigates the effect of several determinants on carbon emissions per capita in Chinese cities. Non-normally distributed and heterogeneous features of carbon emissions per capita in Chinese cities are considerably important. The empirical results demonstrate that GDP per capita has an increasingly positive impact on carbon emissions per capita due to the growth in household consumption. Urbanization has a slightly decreasing positive effect on carbon emissions per capita with a quantile increase resulting from continuous highway construction. Industrialization has a decreasing positive effect with carbon emission per capita quantile increases because of increasing energy efficiency and lower costs related to carbon reductions. The population has a decreasing negative effect on carbon emissions because of people’s increasing demand for environmental safety. The distributions of emissions per capita conditional on the 10th and 90th quantiles of independent variables also vary considerably. Specific policy implications are provided based on these results.     

Determinants of the global and regional CO2 emissions: What causes what and where?

Using the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model and an unbalanced panel dataset of 128 countries covering 1990–2014, this study aims to examine the key impact factors (KIFs) of the global and regional carbon dioxide (CO₂) emissions and analyse the effectiveness of non-renewable and renewable energies. Given the potential cross-sectional dependence and slope heterogeneity, a series of econometric techniques allowing for cross-sectional dependence and slope heterogeneity is applied. The overall estimations imply that the KIFs at the global level are economic growth, followed by population size, non-renewable energy, and energy intensity in order of their impacts on CO₂ emissions; conversely, the KIFs at the regional level vary across different regions and estimators. The results also suggest that renewable energy can lead to a decline in CO₂ emissions at the global level. At the regional level, only for two regions (i.e., S. & Cent. America and Europe & Eurasia) renewable energy has a significant and negative effect on CO₂ emissions, which may be affected by the share of renewable energy consumption in the primary energy mix. Finally, the results indicate varied causality relationships among the variables across regions.

Abbreviations: AMG: Augmented mean group; BP: British Petroleum; BRICS: Brazil, Russia, India, China, and South Africa; CCEMG: Common correlated effects mean group; CD: Cross-section dependence; CIPS: Cross-sectionally augmented Im, Pesaran, and Shin; CO₂: Carbon dioxide; PS: Population size; D-H: Dumitrescu-Hurlin; EI: Energy intensity; EU: European Union; EU-5: Germany, France, Italy, Spain, and the United Kingdom; Europe & Eurasia, Europe and Eurasia; GDP: Gross domestic product; IEA: International Energy Agency; KIF: Key impact factor; LM: Lagrange multiplier; Mtoe, Million tonnes oil equivalent; NRE: Non-renewable energy; RE: Renewable energy; S. & Cent. America, South and Central America; STIRPAT: Stochastic Impacts by Regression on Population, Affluence, and Technology; VECM: Vector error correction model; WDI: World Development Indicators     

中国区域碳排放空间计量研究

如何从空间视角实现经济发展与碳减排双赢,是建设“美国中国”的重要推手,也是生态文明建设的必然要求。基于STIRPAT模型,从区域层面构建碳排放驱动因素扩展STIRPAT模型,并运用空间杜宾模型实证考察各驱动因素对碳排放规模和碳排放强度的影响。结果显示：地区间碳排放存在显著的示范和带头作用,驱动因素通过直接和间接途径影响碳排放,除能源价格外,其他影响因素均表现出显著性。因此,实现碳减排需要充分考虑空间相关性、异质性和外溢性,稳步推进城镇化进程,加大技术创新步伐,优化产业结构升级和能源消费结构,适度提高能源价格,在扩大对外开放的同时加大对外商投资的甄别。