Spatio-temporal dynamic evolution of carbon emission intensity and the effectiveness of carbon emission reduction at county level based on nighttime light data

Liu, Qingfang; Song, Jinping; Dai, Teqi; An, Shi; Xu, Jianhui; Wang, Enru

doi:10.1016/j.jclepro.2022.132301

Cited by 43 publications

(21 citation statements)

References 43 publications

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“…Specifically, the Open‐Data Inventory for Anthropogenic Carbon Dioxide has released the raster dataset of global carbon emissions, with the simulation accuracy of 80% (Kobashi et al, 2020; Oda et al, 2018). Moreover, nighttime light data are widely used in the related methods and models (Chen et al, 2021; Fang et al, 2022; Liu, Song, et al, 2022). The Operational Linescan System (OLS) of the Defense Meteorological Satellite Program (DMSP) began to detect nighttime light point information data in 1992 and formed the global DMSP‐OLS nighttime light data from 1992 to 2013 (Bennett & Smith, 2017; Elvidge et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Land use data can improve the accuracy of carbon emission spatial inversion model

Wang,

Yang,

Bai

et al. 2024

Land Degrad Dev

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Investigating the spatial distribution and correlation characteristics of carbon emissions would be conducive to the policy formulation for precise carbon emission spatial reduction. Firstly, a new carbon emission spatial inversion model was developed, incorporating nighttime light data and land use data. After verifying the validity and accuracy of the inversion results, the continuous carbon emission spatial data in the Beijing‐Tianjin‐Hebei Urban Agglomeration (BTHUA) were acquired from 2000 to 2019. Then, the spatial distribution and correlation characteristics were further analyzed in the BTHUA. Finally, policy recommendations were proposed for carbon emission reduction and urban sustainable development. The results showed that the built model can improve the accuracy of the carbon emission spatial inversion data. The carbon emissions were low in the northwest and high in the southeast of the BTHUA, with a noticeable expansion of the high carbon emission contiguous areas around Beijing, Tianjin, Shijiazhuang, and other prefecture‐level cities, which was consistent with the socioeconomic development pattern. The center of gravity of carbon emissions moved to the southeast, showing a relatively stable distribution. The spatial correlation degree of carbon emissions among cities gradually increased, with Beijing and Tianjin playing a prominent role. As a scientific tool, the spatial inversion model helps to produce more accurate spatial data. The results and conclusions can provide useful and scientific references for spatial analysis and regulation strategies of regional carbon emission reduction.

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Section: Introductionmentioning

confidence: 99%

Land use data can improve the accuracy of carbon emission spatial inversion model

Wang,

Yang,

Bai

et al. 2024

Land Degrad Dev

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show abstract

“…Zhao [29] used the spatial panel data model to study the regional differences between 30 provinces in China from 1991 to 2010 and found a significant spatial agglomeration on CEI. Liu [30] used kernel density estimation, a spatial Markov chain, and a spatial variogram model to study the temporal and spatial dynamic evolution characteristics of CEI in 41 counties of Qinghai Province, and found that the CEI of most counties showed a downward trend.…”

Section: Introductionmentioning

confidence: 99%

Spatial Differences and Influencing Factors of Carbon Emission Intensity in China’s Urban Agglomerations toward the Carbon Neutrality Target

Wang,

Hui,

Liu

2024

Atmosphere

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It is of great scientific value to study the spatial differences and influencing factors of carbon emission intensity (CEI) in urban agglomerations (UAs), and it also has reference significance for China in formulating energy-saving and emission-reduction policies to achieve the target of carbon neutrality. Taking 165 prefecture-level cities in 19 UAs in China from 2007 to 2019 as the research object, this study investigated the spatial differences of CEI in UAs using exploratory spatial data analysis and explored the influencing factors of CEI via Geodetector. The results showed the following: (1) The CEI of the UAs showed a downward trend. (2) The CEI of the UAs has typical spatial agglomeration characteristics, where the North comprises mainly high-high and low-high types, whereas the South is primarily high-low and low-low types. (3) The influencing factors of CEI have undergone a transformation from industrial structure to population urbanization.

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“…In addition, some scholars have found that China’s carbon emissions have obvious spatial aggregation characteristics and spillover effects at the provincial level [ 25 , 26 ]. On the basis of this analysis theory, Moran’s I index, Markov chain, and spatial Durbin model were used to analyze the temporal and spatial evolution characteristics and driving factors of carbon emissions in China [ 27 , 28 , 29 , 30 ]. An urban agglomeration is the main area where human activities and production gather, so it is also the hot spot of carbon emission research.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Dynamic Evolution and Spatial-Temporal Heterogeneity of Carbon Emissions at County Level along “The Belt and Road”—A Case Study of Northwest China

Sun

Xie

et al. 2022

IJERPH

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Northwest region is the main energy supply and consumption area in China. Scientifically estimating carbon emissions (CE) at the county level and analyzing the spatial-temporal characteristics and influencing factors of CE in a long time series are of great significance for formulating targeted CE reduction plans. In this paper, Landscan data are used to assist NPP-VIIRS-like data to simulate the CE from 2001 to 2019. Spatial-temporal heterogeneity of CE was analyzed by using a two-stage nested Theil index and geographically and temporally weighted regression model (GTWR). The CE in northwest China at the county increases yearly while the growth rate slows down from 2001 to 2019. The spatial pattern forms a circle expansion centered on the high-value areas represented by the provincial capital, which is also obvious at the border between Shaanxi and Ningxia. Axial expansion along the Hexi Corridor is conspicuous. The spatial pattern of CE conforms to the Pareto principle; the spatial correlation of CE in northwest counties is increasing year by year, and the high-high agglomeration areas are expanding continuously. It is an obvious high carbon spillover effect. Restricted by the ecological environment, the southwest of Qinghai and the Qinling-Daba Mountain area are stable low-low agglomeration areas. The spatial pattern of CE in northwest China shows remarkable spatial heterogeneity. The difference within regions is greater than that between regions. The “convergence within groups and divergence between groups” changing trend is obvious. According to the five-year socioeconomic indicators, the economic scale (GDP), population scale (POP), and urbanization level (UR) are the main influencing factors. The direction and intensity of the effect have changed in time and space. The same factor shows different action intensities in different regions.

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Spatio-temporal dynamic evolution of carbon emission intensity and the effectiveness of carbon emission reduction at county level based on nighttime light data

Cited by 43 publications

References 43 publications

Land use data can improve the accuracy of carbon emission spatial inversion model

Land use data can improve the accuracy of carbon emission spatial inversion model

Spatial Differences and Influencing Factors of Carbon Emission Intensity in China’s Urban Agglomerations toward the Carbon Neutrality Target

Analysis of Dynamic Evolution and Spatial-Temporal Heterogeneity of Carbon Emissions at County Level along “The Belt and Road”—A Case Study of Northwest China

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