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Study on the spatial distribution of urban carbon emissions at the micro level based on multisource data

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Abstract

Global warming is currently an area of concern. Human activities are the leading cause of urban greenhouse gas intensification. Inversing the spatial distribution of carbon emissions at microscopic scales such as communities or controlling detailed planning plots can capture the critical emission areas of carbon emissions, thus providing scientific guidance for intracity low-carbon development planning. Using the Sino—Singapore Tianjin Eco-city as an example, this paper uses night-light images and statistical yearbooks to perform linear fitting within the Beijing—Tianjin—Hebei city-county region and then uses fine-scale data such as points of interest, road networks, and mobile signaling data to construct spatial characteristic indicators of carbon emissions distribution and assign weights to each indicator through the analytic hierarchy process. As a result, the spatial distribution of carbon emissions based on detailed control planning plots is calculated. The results show that among the selected indicators, the population distribution significantly influences carbon emissions, with a weight of 0.384. The spatial distribution of carbon emissions is relatively distinctive. The primary carbon emissions are from the Sino—Singapore Cooperation Zone due to its rapid urban construction and development. In contrast, carbon emissions from other areas are sparse, as there is mostly unused land under construction.

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References

  • Bai C, Zhou L, Xia M, Feng C (2020) Analysis of the spatial association network structure of China's transportation carbon emissions and its driving factors. J Environ Manag 253:109765

    Article  Google Scholar 

  • Bu Y, Wang E, Qiu Y, MöST D (2022) Impact assessment of population migration on energy consumption and carbon emissions in China: a spatial econometric investigation. Environ Impact Assess Rev 93:106744

    Article  Google Scholar 

  • Cai B, Liang S, Zhou J, Wang J, Cao L, Qu S, Xu M, Yang Z (2018) China high resolution emission database (CHRED) with point emission sources, gridded emission data, and supplementary socioeconomic data. Resour Conserv Recycl 129:232–239

    Article  Google Scholar 

  • Cheng Y, Xiao Y (2022) Factors of carbon emissions from Chinese urban and rural residents: a time-varying study. Appl Econ Lett 29:1696–1701

    Article  Google Scholar 

  • Chuai X, Feng J (2019) High resolution carbon emissions simulation and spatial heterogeneity analysis based on big data in Nanjing City, China. Sci Total Environ 686:828–837

    Article  CAS  Google Scholar 

  • Clarke-Sather A, Qu J, Wang Q, Zeng J, Li Y (2011) Carbon inequality at the sub-national scale: a case study of provincial-level inequality in CO2 emissions in China 1997–2007. Energy Policy 39:5420–5428

    Article  Google Scholar 

  • Demuzere M, Orru K, Heidrich O, Olazabal E, Geneletti D, Orru H, Bhave AG, Mittal N, Feliu E, Faehnle M (2014) Mitigating and adapting to climate change: multi-functional and multi-scale assessment of green urban infrastructure. J Environ Manag 146:107–115

    Article  CAS  Google Scholar 

  • Doll C, Muller J, Elvidge C (2000) Night-time imagery as a tool for global mapping of socioeconomic parameters and greenhouse gas emissions. AMBIO: A Journal of the Human. Environment 29:157–162

    Google Scholar 

  • Dong J, Li C (2022) Structure characteristics and influencing factors of China's carbon emission spatial correlation network: a study based on the dimension of urban agglomerations. Sci Total Environ 853:158613

    Article  CAS  Google Scholar 

  • Du M, Feng R, Chen Z (2022) Blue sky defense in low-carbon pilot cities: a spatial spillover perspective of carbon emission efficiency. Sci Total Environ 846:157509

    Article  CAS  Google Scholar 

  • Fang G, Gao Z, Tian L, Fu M (2022) What drives urban carbon emission efficiency? – Spatial analysis based on nighttime light data. Appl Energy 312:118772

    Article  Google Scholar 

  • Guo J, Li J (2021) Efficiency evaluation and influencing factors of energy saving and emission reduction: an empirical study of China’s three major urban agglomerations from the perspective of environmental benefits. Ecol Indic 133:108410

    Article  Google Scholar 

  • He Y, Wei Z, Liu G, Zhou P (2020) Spatial network analysis of carbon emissions from the electricity sector in China. J Clean Prod 262:121193

    Article  Google Scholar 

  • Huang C, Zhuang Q, Meng X, Zhu P, Han J, Huang L (2022a) A fine spatial resolution modeling of urban carbon emissions: a case study of Shanghai, China. Sci Rep 12:9255

    Article  CAS  Google Scholar 

  • Huang X, Chen S, Xiong D, Xu C, Yang Z (2022b) Analysis and prediction of influence factors of green computing on carbon cycle process in Smart City. Comput Intell Neurosci 2022:14

    Article  Google Scholar 

  • Jia T, Yang S, Li X, Yan P, Yu X, Luo X, Chen K (2020) Computation of carbon emissions of residential buildings in Wuhan and its spatiotemporal analysis. Journal of Geo-information Science 22:1063–1072

    Google Scholar 

  • Li S, Zhou C, Wang S, Hu J (2018) Dose urban landscape pattern affect CO2 emission efficiency? Empirical evidence from megacities in China. J Clean Prod 203:164–178

    Article  Google Scholar 

  • Li Y, Li T, Lu S (2021) Forecast of urban traffic carbon emission and analysis of influencing factors. Energy Efficiency 14:84

    Article  Google Scholar 

  • Liu K, Ni Z, Ren M, Zhang X (2022a) Spatial differences and influential factors of urban carbon emissions in China under the target of carbon neutrality. Int J Environ Res Public Health 19:6427

    Article  CAS  Google Scholar 

  • Liu X, Jin X, Luo X, Zhou Y (2022b) Multi-scale variations and impact factors of carbon emission intensity in China. Sci Total Environ 857:159403

    Article  Google Scholar 

  • Liu Z, Guan D, Moore S, Lee H, Su J, Zhang Q (2015a) Steps to China's carbon peak. Nature 522:279–281

    Article  CAS  Google Scholar 

  • Liu Z, Guan D, Wei W, Davis SJ, Ciais P, Bai J, Peng S, Zhang Q, Hubacek K, Marland G, Andres RJ, Crawford-Brown D, Lin J, Zhao H, Hong C, Boden TA, Feng K, Peters GP, Xi F et al (2015b) Reduced carbon emission estimates from fossil fuel combustion and cement production in China. Nature 524:335–338

    Article  CAS  Google Scholar 

  • Lu J, Zhao J, Jiang H (2017) Spatial distribution characteristics and influencing factors of urban residents' travel carbon emissions in Guangzhou. Int J Appl Logist 7:41–51

    Article  Google Scholar 

  • Lu W (2018) The impacts of information and communication technology, energy consumption, financial development, and economic growth on carbon dioxide emissions in 12 Asian countries. Mitig Adapt Strateg Glob Chang 23:1351–1365

    Article  Google Scholar 

  • Naseer S, Song H, Aslam M, Abdul D, Tanveer A (2021) Assessment of green economic efficiency in China using analytical hierarchical process (AHP). Soft Comput 26:2489–2499

    Article  Google Scholar 

  • Patino-Aroca M, Parra A, Borge R (2022) On-road vehicle emission inventory and its spatial and temporal distribution in the city of Guayaquil, Ecuador. Sci Total Environ 848:157664

    Article  CAS  Google Scholar 

  • Qi H, Shen X, Long F, Liu M, Gao X (2022) Spatial-temporal characteristics and influencing factors of county-level carbon emissions in Zhejiang province, China. Environ Sci Pollut Res Int 30:10136–10148

    Article  Google Scholar 

  • Ren X, Sun Z (2021) Analysis of influencing factors of land use carbon emission based on STIRPAT model: a case study of Duolun County, Inner Mongolia. Int J Environ Res 2:12

    Google Scholar 

  • Shi K, Chen Y, Yu B, Xu T, Chen Z, Liu R, Li L, Wu J (2016) Modeling spatiotemporal CO2 (carbon dioxide) emission dynamics in China from DMSP-OLS nighttime stable light data using panel data analysis. Appl Energy 168:523–533

    Article  CAS  Google Scholar 

  • Shu Y, Lam N (2011) Spatial disaggregation of carbon dioxide emissions from road traffic based on multiple linear regression model. Atmos Environ 45:634–640

    Article  CAS  Google Scholar 

  • Shu Y, Lam N, Reams M (2010) A new method for estimating carbon dioxide emissions from transportation at fine spatial scales. Environ Res Lett 5:044008

    Article  Google Scholar 

  • Wang L, Zhang N, Deng H, Wang P, Yang F, John J, Q. & Zhou, X. (2022a) Monitoring urban carbon emissions from energy consumption over China with DMSP/OLS nighttime light observations. Theor Appl Climatol 149:983–992

    Article  Google Scholar 

  • Wang Q, Chiu Y, Chiu C (2015) Driving factors behind carbon dioxide emissions in China: a modified production-theoretical decomposition analysis. Energy Econ 51:252–260

    Article  Google Scholar 

  • Wang Q, Huang J, Zhou H, Sun J, Yao M (2022b) Carbon emission inversion model from provincial to municipal scale based on nighttime light remote sensing and improved STIRPAT. Sustainability 14:6813

    Article  CAS  Google Scholar 

  • Xiao H, He X, Kuang Y, Wu B (2021) Carbon emission evaluation in Jinan Western New District based on multi-source Data Fusion. J Resour Ecol 12:346–357

    Google Scholar 

  • Xu H, Li Y, Zheng Y, Xu X (2022) Analysis of spatial associations in the energy–carbon emission efficiency of the transportation industry and its influencing factors: evidence from China. Environ Impact Assess Rev 97:106905

    Article  Google Scholar 

  • Xu P (2023) The impact of heterogeneous environmental regulations on regional spatial differences in net carbon emissions. Environ Sci Pollut Res Int 30(1):1413–1427

    Article  Google Scholar 

  • Yang Y, Li H (2022) Monitoring spatiotemporal characteristics of land-use carbon emissions and their driving mechanisms in the Yellow River Delta: a grid-scale analysis. Environ Res Lett 214:114151

    Article  CAS  Google Scholar 

  • Yu B, Shi K, Hu Y, Huang C, Chen Z, Wu J (2015) Poverty evaluation using NPP-VIIRS nighttime light composite data at the county level in China. IEEE J Sel Top Appl Earth Obs Remote Sens 8:1217–1229

    Article  Google Scholar 

  • Zhao N, Ghosh T, Samson E (2012a) Mapping spatio-temporal changes of Chinese electric power consumption using night-time imagery. Int J Remote Sens 33:6304–6320

    Article  Google Scholar 

  • Zhao Y, Nielsen C, McElroy M (2012b) China's CO2 emissions estimated from the bottom up: recent trends, spatial distributions, and quantification of uncertainties. Atmos Environ 59:214–223

    Article  CAS  Google Scholar 

  • Zhu J, Dou Z, Yan X, Yu L, Lu Y (2023) Exploring the influencing factors of carbon neutralization in Chinese manufacturing enterprises. Environ Sci Pollut Res Int 30(2):2918–2944

    Article  Google Scholar 

Download references

Funding

This work was supported by the National Key Research and Development Plan (2022YFF0606402, 2022YFF0606404) and the National Natural Science Foundation of China (41701438).

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Authors and Affiliations

  1. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China

    Xiaojing Yao, Wei Zheng, Dacheng Wang, Shenshen Li & Tianhe Chi

  2. College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing, 100083, China

    Wei Zheng

Authors
  1. Xiaojing Yao
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  2. Wei Zheng
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  3. Dacheng Wang
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  4. Shenshen Li
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  5. Tianhe Chi
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Contributions

X.J. Yao and W. Zheng designed the study, performed the experiments, and wrote the manuscript; D.C. Wang and S.S Li performed the experiments and analyzed the data; T.H. Chi provided suggestions for the manuscript.

Corresponding author

Correspondence to Dacheng Wang.

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Yao, X., Zheng, W., Wang, D. et al. Study on the spatial distribution of urban carbon emissions at the micro level based on multisource data. Environ Sci Pollut Res 30, 102231–102243 (2023). https://doi.org/10.1007/s11356-023-29536-z

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  • DOI: https://doi.org/10.1007/s11356-023-29536-z

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