Evidence map›Paper›PMID 36293981›Full record

ArticleInternational journal of environmental research and public health2022

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.

Shaoqi Sun, Yuanli Xie, Yunmei Li, Kansheng Yuan, Lifa Hu

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Shaoqi SunCollege of Urban and Environmental Sciences, Northwest University, Xi'an 710127, China.ORCID 0000-0002-2141-4486
Yuanli XieCollege of Urban and Environmental Sciences, Northwest University, Xi'an 710127, China.
Yunmei LiCollege of Urban and Environmental Sciences, Northwest University, Xi'an 710127, China.
Kansheng YuanCollege of Urban and Environmental Sciences, Northwest University, Xi'an 710127, China.
Lifa HuCollege of Urban and Environmental Sciences, Northwest University, Xi'an 710127, China.

Funding

Projects of Shaanxi Philosophy Social Science Fund 2020ZD24
6 · The paper itself

Abstract

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.

Indexed as

CarbonUrbanizationCarbon DioxideChinaEconomic DevelopmentSpatial RegressionCarbonCarbon Dioxidecarbon emissionenergy consumptiongeographically and temporally weighted regressionnighttime light dataspatial-temporal heterogeneitytwo-stage nested Theil index

Identifiers

PMID36293981
PMCPMC9602533

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.