Are the intensity of energy use, land agglomeration, CO2emissions, and economic progress dynamically interlinked across development levels?

被引:83
作者
Ahmad, Munir [1 ]
Li, Heng [2 ]
Anser, Muhammad Khalid [3 ]
Rehman, Abdul [4 ]
Fareed, Zeeshan [5 ]
Yan, Qingyou [6 ]
Jabeen, Gul [6 ]
机构
[1] Zhejiang Univ, Sch Econ, Hangzhou, Zhejiang, Peoples R China
[2] Hong Kong Polytech Univ, Dept Bldg & Real Estate, Hong Kong, Peoples R China
[3] Xian Univ Architecture & Technol, Sch Publ Adm, Econ, Xian, Shaanxi, Peoples R China
[4] Henan Agr Univ, Coll Econ & Management, Zhengzhou, Henan, Peoples R China
[5] Huzhou Univ, Sch Business, Huzhou, Peoples R China
[6] North China Elect Power Univ, Sch Econ & Management, Beijing 102206, Peoples R China
关键词
Intensity of energy use; land agglomeration; carbon dioxide emissions; economic progress; Chinese economy; ENVIRONMENTAL KUZNETS CURVE; PANEL-DATA ANALYSIS; CARBON EMISSIONS; CO2; EMISSIONS; DECOMPOSITION ANALYSIS; ERROR-CORRECTION; PER-CAPITA; GROWTH; CONSUMPTION; CHINA;
D O I
10.1177/0958305X20949471
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Although urban agglomerations have introduced substantial contributions to the economies around the globe, it has also led to the serious environmental challenges. However, this situation may vary across the development levels. The existing knowledge offers a gap in terms of both theoretical and empirical grounds. The Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) is previously not known to incorporate land agglomeration and the intensity of energy use. Besides, the investigation of linkages among the variables of interest across the development levels within a country is not known to be considered by the existing knowledge. This study systematically investigates the heterogeneous dynamic causality among the intensity of energy use, land agglomeration, carbon dioxide emissions (CO2), and economic progress across the development levels in the Chinese economy, considering 29 provinces for the period 2000 to 2018. To this end, a long-term co-integration association is tested and found existent among the variables of interest. A dynamiccommon correlated effects mean group approachis applied for impact analysis. The key findings include: The impacts of economic progress and land agglomeration on CO(2)are found positive and significant in the country panel and western zone of China (WZC). It turned to be neutral in the case of the central zone of China (CZC) and significantly negative in the eastern zone of China (EZC). To this end, economic progress presented a 'development ladder-based CO(2)mitigation effect,' while the land agglomeration exposed the 'land agglomeration ladder-based CO(2)mitigation effect'. Further, the causalities extracted are: first, economic progress is found in positive bilateral linkages with the intensity of energy use and land agglomeration for all the panels. Second, a positive and unilateral causal bridge is found operating from land agglomeration to the intensity of energy use and from the intensity of energy use to CO2. Third, a unilateral linkage of mixed nature is exposed to exist from land agglomeration to CO2, with positive causal links for country panel and WZC, negative causal links for EZC, while a neutral linkage is found for CZC. Fourth, a bidirectional link with mixed causalities appeared in the country panel and WZC. Economic progress increased CO(2)in WZC. Next, a negative bilateral link is observed between the two variables in EZC. Additionally, this link remained neutral in CZC. Based on empirics, it is revealed that the development level matters in determining the links among the variables of interest.
引用
收藏
页码:690 / 721
页数:32
相关论文
共 78 条
[71]   Driving Factors of SO2 Emissions in 13 Cities, Jiangsu, China [J].
Wang, Yizhong ;
Wang, Qunwei ;
Hang, Ye .
CUE 2015 - APPLIED ENERGY SYMPOSIUM AND SUMMIT 2015: LOW CARBON CITIES AND URBAN ENERGY SYSTEMS, 2016, 88 :182-186
[72]   How do urbanization and consumption patterns affect carbon emissions in China? A decomposition analysis [J].
Wang, Zhen ;
Cui, Can ;
Peng, Sha .
JOURNAL OF CLEANER PRODUCTION, 2019, 211 :1201-1208
[73]   Testing for error correction in panel data [J].
Westerlund, Joakim .
OXFORD BULLETIN OF ECONOMICS AND STATISTICS, 2007, 69 (06) :709-748
[74]   Impacts of energy consumption, energy structure, and treatment technology on SO2 emissions: A multi-scale LMDI decomposition analysis in China [J].
Yang, Xue ;
Wang, Shaojian ;
Zhang, Wenzhong ;
Li, Jiaming ;
Zou, Yafeng .
APPLIED ENERGY, 2016, 184 :714-726
[75]   Techno-economic comparative analysis of Biomass Integrated Gasification Combined Cycles with and without CO2 capture [J].
Zang, Guiyan ;
Jia, Junxi ;
Tejasvi, Sharma ;
Ratner, Albert ;
Lora, Electo Silva .
INTERNATIONAL JOURNAL OF GREENHOUSE GAS CONTROL, 2018, 78 :73-84
[76]   Effects of land urbanization and land finance on carbon emissions: A panel data analysis for Chinese provinces [J].
Zhang, Wenjing ;
Xu, Hengzhou .
LAND USE POLICY, 2017, 63 :493-500
[77]   Decoupling and decomposition analysis of carbon emissions from economic output in Chinese Guangdong Province: A sector perspective [J].
Zhao, Ming-Ming ;
Li, Rongrong .
ENERGY & ENVIRONMENT, 2018, 29 (04) :543-555
[78]   Impacts of shifting China's final energy consumption to electricity on CO2 emission reduction [J].
Zhao, Weigang ;
Cao, Yunfei ;
Miao, Bo ;
Wang, Ke ;
Wei, Yi-Ming .
ENERGY ECONOMICS, 2018, 71 :359-369