Carbon emission efficiency and spatio-temporal dynamic evolution of the cities in Beijing-Tianjin-Hebei Region, China

Under the influence of complex urbanization, improving the carbon emission efficiency (CEE) plays an important role in the construction of low-carbon cities in China. Based on the panel data of 283 prefectural-level cities in China from 2005 to 2017, this study evaluated the CEE by the US-SBM model, and explored the spatial agglomeration evolution characteristics of CEE from static and dynamic perspectives by integrating ESDA and Spatial Markov Chains. Then, the spatial heterogeneity of the impacts of multi-dimensional urbanization on CEE were analyzed by using the Geographically and Temporally Weighted Regression (GTWR). The results show that: (1) with the evolution of time, the CEE has a trend of gradual improvement, but the average is 0.4693; (2) from the perspective of spatial static agglomeration, the “hot spots” of CEE mainly concentrated in Shandong Peninsula, Pearl River Delta, and Chengdu-Chongqing urban agglomeration; The dynamic evolution of CEE gradually forms the phenomenon of “club convergence”; (3) urbanization of different dimensions shows spatial heterogeneity to CEE. The impact of economic urbanization in northern cities on CEE shows an inverted “U” shape, and the negative impact of spatial urbanization on CEE appears in the northwest and resource-based cities around Bohai Sea. Population and social urbanization have a positive promoting effect on CEE after 2010. These findings may help China to improve the level of CEE at the city level and provide a reference for low-carbon decision-making.

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How technological progress affects the carbon emission efficiency? Evidence from national panel quantile regression

Journal of Cleaner Production ◽

10.1016/j.jclepro.2021.127133 ◽

2021 ◽

pp. 127133

Author(s):

Zihan Xie ◽

Rong Wu ◽

Shaojian Wang

Keyword(s):

Quantile Regression ◽

Technological Progress ◽

Carbon Emission ◽

Emission Efficiency ◽

Panel Quantile Regression

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Spatio-temporal evolution characteristics and influencing factors of carbon emission reduction potential in China

Environmental Science and Pollution Research ◽

10.1007/s11356-021-14913-3 ◽

2021 ◽

Author(s):

Zhangwen Li ◽

Caijiang Zhang ◽

Yu Zhou

Keyword(s):

Influencing Factors ◽

Emission Reduction ◽

Carbon Emission ◽

Reduction Potential ◽

Temporal Evolution ◽

Carbon Emission Reduction ◽

Evolution Characteristics ◽

Emission Reduction Potential ◽

Spatio Temporal

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Study on Estimation and Improvement of Agricultural Carbon Emission Efficiency in China

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/450/1/012097 ◽

2020 ◽

Vol 450 ◽

pp. 012097

Author(s):

Haoyue Wu ◽

Luxi Luo ◽

Wenkuan Chen

Keyword(s):

Carbon Emission ◽

Emission Efficiency

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Research on the Spatio-Temporal Dynamic Evolution Characteristics and Influencing Factors of Electrical Power Consumption in Three Urban Agglomerations of Yangtze River Economic Belt, China Based on DMSP/OLS Night Light Data

Remote Sensing ◽

10.3390/rs13061150 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1150

Author(s):

Yang Zhong ◽

Aiwen Lin ◽

Chiwei Xiao ◽

Zhigao Zhou

Keyword(s):

Influencing Factors ◽

Yangtze River ◽

Electrical Power ◽

Random Forest Algorithm ◽

Temporal Dynamic ◽

Urban Agglomerations ◽

Evolution Characteristics ◽

Spatio Temporal ◽

Electrical Power Consumption ◽

Gwr Model

In this paper, based on electrical power consumption (EPC) data extracted from DMSP/OLS night light data, we select three national-level urban agglomerations in China’s Yangtze River Economic Belt(YREB), includes Yangtze River Delta urban agglomerations(YRDUA), urban agglomeration in the middle reaches of the Yangtze River(UAMRYR), and Chengdu-Chongqing urban agglomeration(CCUA) as the research objects. In addition, the coefficient of variation (CV), kernel density analysis, cold hot spot analysis, trend analysis, standard deviation ellipse and Moran’s I Index were used to analyze the Spatio-temporal Dynamic Evolution Characteristics of EPC in the three urban agglomerations of the YREB. In addition, we also use geographically weighted regression (GWR) model and random forest algorithm to analyze the influencing factors of EPC in the three major urban agglomerations in YREB. The results of this study show that from 1992 to 2013, the CV of the EPC in the three urban agglomerations of YREB has been declining at the overall level. At the same time, the highest EPC value is in YRDUA, followed by UAMRYR and CCUA. In addition, with the increase of time, the high-value areas of EPC hot spots are basically distributed in YRDUA. The standard deviation ellipses of the EPC of the three urban agglomerations of YREB clearly show the characteristics of “east-west” spatial distribution. With the increase of time, the correlations and the agglomeration of the EPC in the three urban agglomerations of the YREB were both become more and more obvious. In terms of influencing factor analysis, by using GWR model, we found that the five influencing factors we selected basically have a positive impact on the EPC of the YREB. By using the Random forest algorithm, we found that the three main influencing factors of EPC in the three major urban agglomerations in the YREB are the proportion of secondary industry in GDP, Per capita disposable income of urban residents, and Urbanization rate.

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