What drives urban carbon emission efficiency? – Spatial analysis based on nighttime light data

Fang, Guochang; Gao, Zhengye; Li, Tian; Fu, Min

doi:10.1016/j.apenergy.2022.118772

Cited by 161 publications

(32 citation statements)

References 53 publications

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“…Considering spatial correlation, the improved non-radial directional distance function and spatial econometric model were deployed to evaluate the TFCEE of 285 cities in China, and the synergistic effect of digital finance and green technology innovation on TFCEE was examined (Zhang and Liu, 2022b). Against a background of low-carbon development, the carbon emissions and CEE of 282 Chinese cities from 2004 to 2018 were estimated based on nighttime lighting data, and the impact of industrial agglomeration and synergy on TFCEE was empirically verified with the spatial Durbin error model (Fang et al, 2022). Nevertheless, certain scholars argue that the existing system of input-output variables does not conform to the actual production process, and the inconsistency between the input value and value-added measures will make the efficiency deviate from the actual value.…”

Section: Measurement Of Ceementioning

confidence: 99%

Exploring the impact of renewable energy investment and energy resource endowment on carbon emission efficiency: A spatial econometric analysis based on 60 BRI countries

Yuan

Wang

et al. 2022

Preprint

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With climate change caused by massive greenhouse gas emissions emerging as an issue of global concern, it is urgent to improve carbon emission efficiency (CEE) for countries along the Belt and Road (BRI). The super-efficiency slacks-based measure (SBM) model is adopted to evaluate the CEE of 60 BRI countries from 2010 to 2019, while the Global Malmquist-Luenberger (GML) index, Theil index, and coefficient of variation (CV) are coupled to quantify the spatial and temporal variation and dynamic evolution of CEE. Subsequently, the spatial Durbin model (SDM) provides an empirical investigation regarding the impact of renewable energy investment and energy resource endowment on CEE given the spatial spillover effect. It is found that (1) From a static perspective, the CEE of BRI countries is generally poor and unevenly distributed in terms of temporal and spatial dimensions. The regional variation of CEE in Southeast Asia is narrowing, while it is widening in other regions. (2) Referring to the dynamic level, the GML index is featured U-shaped fluctuations, and both technical efficiency and technological progress contribute to the improvement of CEE in BRI countries. (3) The CEE of BRI countries exhibits significant spatial dependence, with high-high aggregation as well as low-low aggregation. (4) There is a significant positive influence of renewable energy investment on CEE in the country and neighboring countries, while energy resource endowment presents a remarkable adverse correlation. Consequently, it is suggested that the inter-regional cooperation among BRI countries should be strengthened to reinforce renewable energy investment, exert the technology and knowledge spillover effect sufficiently, and break the resource curse in the environmental field.

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Section: Measurement Of Ceementioning

confidence: 99%

Exploring the impact of renewable energy investment and energy resource endowment on carbon emission efficiency: A spatial econometric analysis based on 60 BRI countries

Yuan

Wang

et al. 2022

Preprint

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“…Promoting comprehensive carbon emission performance (namely, increasing resources utilization rate alongside economic development to reduce CO 2 emissions) could contribute to the carbon peak and carbon neutrality, and it is also the inevitable choice for China’s low-carbon development. Numerous studies have investigated how to promote carbon emission performance from the perspective of environmental policies [ 4 , 5 , 6 ], city characteristics [ 7 ], urbanization [ 8 , 9 ], FDI [ 10 , 11 ], and innovation [ 12 , 13 ]. The importance of finance in reducing carbon emissions and promoting efficiency has also been a widely discussed topic in recent years.…”

Section: Introductionmentioning

confidence: 99%

Can Digital Finance Promote Comprehensive Carbon Emission Performance? Evidence from Chinese Cities

Shao

Cheng

Wang

et al. 2022

IJERPH

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Improving urban comprehensive carbon emission performance (CCEP) is the inevitable choice for China’s low-carbon development. With the continuous integration of digital technology and financial elements, the development of urban digital finance has also been significantly improved. To further explore the impact of urban digital finance on urban low-carbon development, using the data of 281 cities in China from 2011 to 2019, this paper firstly evaluates the urban CCEP, and further empirically investigates how digital finance influences CCEP. The empirical results show that: (1) Digital finance significantly improves the urban CCEP, and after conducting robustness tests and addressing the endogeneity issue, the above conclusion is robust. (2) For the sub-indicators, there is a U-shaped relationship between the coverage breadth of digital finance and CCEP. Moreover, the improvement of usage depth and digital support services could promote CCEP. (3) The channel tests indicate that digital finance improves the CCEP mainly by promoting green technology innovation and the development of urban tertiary industry. Meantime, digital finance has a stronger impact on improving CCEP in cities with more developed traditional finance, and the positive effect is significant in non-old industrial base cities and a two-control zone. Finally, this paper puts forward relevant policy suggestions.

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“…Therefore, it can be concluded that Luojia-01 data have changed the study emphasis of NTL data from focusing on urban agglomerations and metropolitan areas to focusing on a single city [40,41]. However, although NTL data could relatively represent the urban internal structure, there is a light spill phenomenon [42,43], which means that the areas with nighttime light are larger than actual urban areas [44,45]. Therefore, studies that aim to improve the extraction accuracy of NTL data have been conducted [46].…”

Section: Introductionmentioning

confidence: 99%

Using POI Data and Baidu Migration Big Data to Modify Nighttime Light Data to Identify Urban and Rural Area

Chen

Deng

2022

IEEE Access

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The spatial difference between urban and rural areas is the direct result of urban-rural relations. Accurate identification of urban-rural area is helpful to judge the urban-rural mechanism and promote the integration development of urban-rural area. Previous studies only used single nighttime light (NTL) data to identify urban and rural areas, which is likely to have an impact on the identification results due to the large brightness difference of lights. Therefore, based on NTL data and combine with data level fusion algorithm, this study separately fuses point of interest (POI) data that representing the quantity distribution of urban infrastructure and Baidu migration big (BM)data that representing the change relationship of regional population mobility to identify urban and rural areas by using deep learning method. The results show that the highest accuracy of urban-rural spatial identification with single NTL data is 84.32% and kappa is 0.6952, while the highest accuracy identified by data fusion is 95.02% and kappa is 0.8259. It can be seen that the differences caused by light brightness are effectively corrected after data fusion, which greatly improves the accuracy of urban and rural spatial identification. By comparing the results of NTL data modified by different big data, this study analyzes and identifies the accuracy of urban and rural area by using deep learning method, which not only enriches the study of data fusion in urban area, but also provides a basis for analyzing regional urban-rural relations and urban-rural development. Therefore, this study is believed to have important practical value for the coordinated development of urban and rural areas.

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What drives urban carbon emission efficiency? – Spatial analysis based on nighttime light data

Cited by 161 publications

References 53 publications

Exploring the impact of renewable energy investment and energy resource endowment on carbon emission efficiency: A spatial econometric analysis based on 60 BRI countries

Exploring the impact of renewable energy investment and energy resource endowment on carbon emission efficiency: A spatial econometric analysis based on 60 BRI countries

Can Digital Finance Promote Comprehensive Carbon Emission Performance? Evidence from Chinese Cities

Using POI Data and Baidu Migration Big Data to Modify Nighttime Light Data to Identify Urban and Rural Area

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