These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
123 related articles for article (PubMed ID: 39029176)
1. Analysis on the spatial correlation network and driving factors of carbon emissions in China's logistics industry. Kang X; Chen L; Wang Y; Liu W J Environ Manage; 2024 Aug; 366():121916. PubMed ID: 39029176 [TBL] [Abstract][Full Text] [Related]
2. [Evolution and Influencing Factors of Spatial Correlation Network of Construction Carbon Emission in China from the Perspective of Whole Life Cycle]. Ren XS; Li ZR Huan Jing Ke Xue; 2024 Mar; 45(3):1243-1253. PubMed ID: 38471841 [TBL] [Abstract][Full Text] [Related]
3. Spatial correlation network structure of China's building carbon emissions and its driving factors: A social network analysis method. Huo T; Cao R; Xia N; Hu X; Cai W; Liu B J Environ Manage; 2022 Oct; 320():115808. PubMed ID: 35947905 [TBL] [Abstract][Full Text] [Related]
4. Panel data analysis of energy conservation and emission reduction on high-quality development of logistics industry in Yangtze River Delta of China. Fan L; Liu H; Shao Z; Li C Environ Sci Pollut Res Int; 2022 Nov; 29(52):78361-78380. PubMed ID: 35689767 [TBL] [Abstract][Full Text] [Related]
5. A Study on The Driving Factors and Spatial Spillover of Carbon Emission Intensity in The Yangtze River Economic Belt under Double Control Action. Ding X; Cai Z; Xiao Q; Gao S Int J Environ Res Public Health; 2019 Nov; 16(22):. PubMed ID: 31766158 [TBL] [Abstract][Full Text] [Related]
6. Analysis of the spatial relevance and influencing factors of carbon emissions in the logistics industry from China. Guo X; Wang D Environ Sci Pollut Res Int; 2022 Jan; 29(2):2672-2684. PubMed ID: 34374021 [TBL] [Abstract][Full Text] [Related]
7. Spatial-temporal evolution and influencing factors of total factor productivity in China's logistics industry under low-carbon constraints. Li M; Wang J Environ Sci Pollut Res Int; 2022 Jan; 29(1):883-900. PubMed ID: 34345991 [TBL] [Abstract][Full Text] [Related]
8. Impacts of logistics agglomeration on carbon emissions in China: a spatial econometric analysis. Liu J; Hu Q; Wang J; Li X Environ Sci Pollut Res Int; 2023 Aug; 30(37):87087-87101. PubMed ID: 37418183 [TBL] [Abstract][Full Text] [Related]
9. [Spatiotemporal Differentiation of Carbon Emissions from Logistics Industry at Provincial Scale in China Under the Background of High-quality Economic Development]. Zhang LY; Xu YN; Weng DW; Wang S; Hu XS; Qiu RZ Huan Jing Ke Xue; 2024 Sep; 45(9):5086-5096. PubMed ID: 39323127 [TBL] [Abstract][Full Text] [Related]
10. Identifying spatial relations of industrial carbon emissions among provinces of China: evidence from unsupervised clustering algorithms. Liu S; Yang C; Liu L Environ Sci Pollut Res Int; 2022 Nov; 29(51):77958-77972. PubMed ID: 35687286 [TBL] [Abstract][Full Text] [Related]
11. The Evolution of the Spatial Association Effect of Carbon Emissions in Transportation: A Social Network Perspective. Ma F; Wang Y; Yuen KF; Wang W; Li X; Liang Y Int J Environ Res Public Health; 2019 Jun; 16(12):. PubMed ID: 31216689 [TBL] [Abstract][Full Text] [Related]
12. Analysis of logistics capacity, influencing factors and spatial spillover effect in Yangtze River Economic Belt. Fanghu L; Yinnan H; Biao W PLoS One; 2024; 19(5):e0303200. PubMed ID: 38776351 [TBL] [Abstract][Full Text] [Related]
13. The impact of industrial structure adjustment on the spatial industrial linkage of carbon emission: From the perspective of climate change mitigation. Zheng Y; Tang J; Huang F J Environ Manage; 2023 Nov; 345():118620. PubMed ID: 37544026 [TBL] [Abstract][Full Text] [Related]
14. Carbon emissions in the logistics industry: driving factors and decoupling effects. Ding H; Wu X; Guo Y; Liu C Environ Sci Pollut Res Int; 2024 Apr; 31(17):25721-25735. PubMed ID: 38483717 [TBL] [Abstract][Full Text] [Related]
15. Structural decomposition analysis of embodied carbon in trade in the middle reaches of the Yangtze River. Chen Z; Ni W; Xia L; Zhong Z Environ Sci Pollut Res Int; 2019 Jan; 26(1):816-832. PubMed ID: 30415365 [TBL] [Abstract][Full Text] [Related]
16. Spatiotemporal pattern of regional carbon emissions and its influencing factors in the Yangtze River Delta urban agglomeration of China. Lv T; Hu H; Zhang X; Xie H; Fu S; Wang L Environ Monit Assess; 2022 Jun; 194(7):515. PubMed ID: 35731371 [TBL] [Abstract][Full Text] [Related]
17. Carbon peak forecast and low carbon policy choice of transportation industry in China: scenario prediction based on STIRPAT model. Li C; Zhang Z; Wang L Environ Sci Pollut Res Int; 2023 May; 30(22):63250-63271. PubMed ID: 36961638 [TBL] [Abstract][Full Text] [Related]
18. Research on Determining the Critical Influencing Factors of Carbon Emission Integrating GRA with an Improved STIRPAT Model: Taking the Yangtze River Delta as an Example. Guo F; Zhang L; Wang Z; Ji S Int J Environ Res Public Health; 2022 Jul; 19(14):. PubMed ID: 35886642 [TBL] [Abstract][Full Text] [Related]
19. Dynamic contribution and structural changes of carbon emissions in China's energy chemical industry with high-emission subsectors heterogeneity. Zhang H; Li S; Xiong B; Li L Environ Sci Pollut Res Int; 2023 Apr; 30(19):54600-54615. PubMed ID: 36881232 [TBL] [Abstract][Full Text] [Related]
20. Spatial network and influencing factors of green water use efficiency in the YREB: considering carbon emissions and pollution indicators. Zhang R; Zhang L; Wang Z Environ Sci Pollut Res Int; 2024 Mar; 31(11):17324-17338. PubMed ID: 38337118 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]