An evaluation of spatial–temporal dynamics of city size distribution & CO2 emission in China from nighttime light data: a Pathway to sustainable urban planning

Neel Chaminda Withanage , Jingwei Shen , N. P. Ravindra Deyshappriya , Prabuddh Kumar Mishra , K. Samitha Udayanga

Computational Urban Science ›› 2025, Vol. 5 ›› Issue (1)

PDF
Computational Urban Science ›› 2025, Vol. 5 ›› Issue (1) DOI: 10.1007/s43762-025-00199-5
Original Paper
research-article

An evaluation of spatial–temporal dynamics of city size distribution & CO2 emission in China from nighttime light data: a Pathway to sustainable urban planning

Author information +
History +
PDF

Abstract

Understanding the relationship between urban growth and CO2 emissions is essential for sustainable urban and environmental planning in China. Even though some studies have been conducted in this regard, there is a lack of comprehensive studies that integrate socioeconomic and nighttime light (NL) data on both spatial and temporal scales. Therefore, using NL data as a proxy for urban growth, this study offers a novel approach to assess city size distribution (CD) and CO2 emission dynamics from 2000 to 2020 at the provincial and prefecture levels. The present study was conducted in three phases: (1) assessing the association between urban growth and socioeconomic characteristics; (2) measuring CD dynamics using corrected NL data; and (3) modeling CO2 emission dynamics through panel data analysis. While the Ordinary Least Squares (OLS) method examined the relationship between socioeconomic characteristics and urban growth, the CD dynamics were measured using Catteow’s formula. A panel unit root test, panel co-integration test, and panel regression analyses were performed to explore the relationship between urban growth and CO2 emissions. Results revealed that maximum NL data have stronger correlations with population, GDP, and EPC at the provincial level than at the prefecture level, with an average R2 range from 0.6219 to 0.8985. The analysis of CD dynamics revealed an increase in urban disparity, particularly among larger cities, with the q value rising from 0.7920 to 0.8268. CO₂ emissions expanded by 250.76% from 2000 to 2020, with the highest growth seen in coastal megacities. Panel unit root and co-integration tests confirmed a long-term relationship between urban growth and CO2 emissions at both scales. Panel regression analysis showed a positive and significant impact of urban growth on CO2 emissions at the national level and across all regions and provinces. These findings highlight the importance of sustainable urban planning strategies that incorporate socioeconomic characteristics with spatial and temporal considerations to reduce CO2 emissions in China. However, further research is necessary to explore multidimensional strategies for balancing urban expansion and CO2 emissions.

Keywords

City size / CO2 emission / DMSP-OLS / Nighttime light / Panel data analysis / Zipf’s law

Cite this article

Download citation ▾
Neel Chaminda Withanage, Jingwei Shen, N. P. Ravindra Deyshappriya, Prabuddh Kumar Mishra, K. Samitha Udayanga. An evaluation of spatial–temporal dynamics of city size distribution & CO2 emission in China from nighttime light data: a Pathway to sustainable urban planning. Computational Urban Science, 2025, 5(1): DOI:10.1007/s43762-025-00199-5

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

AndersonG, GeY. The size distribution of Chinese cities. Regional Science and Urban Economics, 2005, 35: 756-776.

[2]

ArshadS, HuS, AshrafBN. Zipf’s law and city size distribution: A survey of the literature and future research agenda. Physica A: Statistical Mechanics and Its Applications, 2018, 492: 75-92.

[3]

Bee, M., Riccaboni, M., Schiavo, S. (2019) Distribution of City Size: Gibrat, Pareto, Zipf. In: D'Acci, L. (eds). Math.Urban Morp. Model Simu Sci, Eng. Tech. Birkhäuser, Cham.77–91. https://doi.org/10.1007/978-3-030-12381-9_4

[4]

ChenY. The evolution of Zipf’s law indicative of city development. Physica A, 2016, 443: 555-567.

[5]

ChenY, ZhouY. Scaling laws and indications of self-organized criticality in urban systems. Chaos, Soli. & Frac., 2008, 35(1): 85-98.

[6]

ChengF, LiuS, HouX, ZhangY, DongS, CoxixoA, LiuG. Urban land extraction using DMSP/OLS nighttime light data and OpenStreet Map datasets for cities in China at different development levels. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11: 2587-2599.

[7]

CherniJA, KentishJ. Renewable energy policy and electricity market reforms in China. Energy Policy, 2007, 35(36): 3616-3629

[8]

Chien, S.S. (2010) Prefectures and prefecture-level cities: The political economy of administrative restructuring in China local administration In Tradition and Changes of Sub-National Hierarchy; Chung, J.H., Tao-Chiu, L., Eds.; Routledge: Oxford, UK, pp. 127–143.

[9]

Deyshappriya, N.P.R. (2015) Stock market-growth nexues:An application of dynamic panel data and co-integration analysis for developed and emerging markets,Unpublised Master Thesis.University of Nagoya, Japan.

[10]

DickeyDA, FullerWA. Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 1979, 74: 427-431

[11]

DingT, HuangY, HeW, ZhuangD. Spatial–temporal heterogeneity and driving factors of carbon emissions in China. Environmental Science and Pollution Research International, 2021, 28: 35830-35843.

[12]

DubenC, KrauseM. Population, light, and the size distribution of cities. Journal of Regional Science, 2020, 61: 189-211

[13]

ElvidgeC, BaughKE, KihnEA, KroehlHW, DavisER. Mapping city lights with nighttime data from the DMSP operational linescan system. Photogrammetric Engineering and Remote Sensing, 1997, 63(6): 727-734

[14]

ElvidgeC, ZiskinD, BaughKE, TuttleBT, GhoshT, PackDW, ErwinEH, ZhizhinM. A fifteen year record of global natural gas flaring derived from satellite data. Energies, 2009, 2(3): 595-622.

[15]

FanC. The vertical and horizontal expansions of China’s city system. Urban Geo, 1999, 20(6): 493-515.

[16]

FangL, LiP, SongS. China’s development policies and city size distribution: An analysis based on Zipf’s law. Urban Studies, 2017, 54(12): 2818-2834.

[17]

FloridaR, Rodríguez-PoseA, StorperM. Cities in a post-COVID world. Urban Stu., 2021, 58(3): 623-649.

[18]

Gao, C., Ge, H. (2020) Spatiotemporal characteristics of China’s carbon emissions and driving forces: A Five-Year Plan perspective from 2001 to 2015. J. Cleaner Prod. 248(119280). https://doi.org/10.1016/j.jclepro.2019.119280

[19]

GaoY, ZhangM. Investigating the link between urbanization and carbon emissions using night-time light data: Evidence from Chinese cities. Environmental Science and Pollution Research, 2022, 29: 5320-5334.

[20]

GiesenK, SudekumJ. Zipf’s law for cities in the regions and the country. Journal of Economic Geography, 2011, 11(4): 667-686.

[21]

GuanD, LiuZ, GengY, LindnerS, HubacekK. The structural path of China's regional emissions: Transfers and spillovers. Envir. Sci. Tech., 2014, 48(15): 8828-8835.

[22]

HeC, MaQ, LiuZ, ZhangQ. Modeling the spatiotemporal dynamics of electric power consumption in mainland China using saturation corrected DMSP/OLS nighttime stable light data. International Journal of Digital Earth, 2013, 6: 1-22.

[23]

HuangQ, HeC, GaoB, YangY, LiuZ, ZhaoY, DouY. Detecting the 20 year city-size dynamics in China with a rank clock approach and DMSP/OLS nighttime data. Landscape and Urban Planning, 2015, 137: 138-148.

[24]

JayasingheCB, WithanageNC, MishraPK, AbdelrahmanK, FnaisMS. Evaluating urban heat islands dynamics and environmental criticality in growing city of a tropical country using remote sensing indices: The example of Matara city. Sri Lanka. Sustainability., 2024, 16(23): 1-30.

[25]

LeeS, CaoC. Soumi NPP VIIRS day/night band stray light characterization and correction using calibration view data. Remote Sensing, 2016, 8. 138

[26]

LevinA, LinC, JamesC. Unit root tests in panel data: Asymptotic and fifinite sample properties. Journal of Economics, 2002, 108: 1-24

[27]

LiX, ZhouY, EomJ, AsrarGR. Spatiotemporal patterns of urbanization in China: A comparison of nightlight and population data. Remote Sens. Envir., 2021, 258112396.

[28]

LiaW, SunaB, ZhaocJ, ZhangT. Economic performance of spatial structure in Chinese prefecture regions: Evidence from night-time satellite imagery. Habitat International, 2018, 76: 29-39.

[29]

LiuS, ShenJ, LiuG, WuY, ShiK. Exploring the effect of urban spatial development pattern on carbon dioxide emissions in China: A socioeconomic density distribution approach based on remotely sensed nighttime light data. Computers, Environment and Urban Systems, 2022, 96(101847): 1-14.

[30]

LiuK, NiZ, RenM, ZhangX. Spatial differences and influential factors of urban carbon emissions in China under the target of carbon neutrality. International Journal of Environmental Research and Public Health, 2022, 1911. 6427

[31]

MiZ, ZhangY, GuanD, ShanY, LiuZ, CongR, MengJ, WeiYM. Consumption-based emission accounting for Chinese cities. App. Energy., 2016, 184: 1073-1081.

[32]

MpakairiKS, MuvengwiJ. Night-time lights and their influence on summer night land surface temperature in two urban cities of Zimbabwe: A geospatial perspective. Urban Climate, 2019, 29. 100468

[33]

National Bureau of Statistics. ChinaStatistical Yearbook 2000–2020, 2023China Statistical Press.

[34]

NwaeremaP, AjiereS. Regional mapping of land surface temperature (LST), land surface emissivity (LSE) and normalized difference vegetation index (NDVI) of South-South coastal settlements of Rivers State in Nigeria. World News Nat. Sci., 2020, 28: 76-86

[35]

Oda, T., Maksyutov, S. (2015) ODIAC Fossil Fuel CO2 Emissions (ODIAC: 2020), Center for global environmental research, national institute for environmental studies. https://doi.org/10.17595/20170411.001

[36]

OdaT, MaksyutovS. A very high-resolution (1 km x 1 km) global fossil fuel CO2 emission inventory derived using a point source database and satellite observations of nighttime lights. Atmos. Che. Physics., 2011, 11: 543-556.

[37]

OuJ, LiuX, LiX, ChenY. Quantifying the relationship between urban forms and carbon emissions using panel data analysis. Landscape Eco., 2013, 28: 1889-907.

[38]

PedroniP. Critical values for co integration tests in heterogeneous panels with multiple regressors. Oxford Bulletin of Economics and Statistics, 1999, 61(s1): 653-70.

[39]

Pengfei, Xu., Guangyao, Zhou., Qiuhao, Zhao., Yiqing, Lu., Jingling, Chen. (2024) Spatial-temporal dynamics and influencing factors of city level carbon emission of mainland China. Eco. Indi. 167(112672). https://doi.org/10.1016/j.ecolind.2024.112672 

[40]

ProvilleJ, Zavala-AraizaD, WagnerG. Night-time lights: A global, long term look at links to socio-economic trends. PLoS One, 2017, 123e0174610

[41]

Raghunath, A. (2017) Chapter 20 - Complex Survey Design: Regression Analysis, Editor (s): Raghunath Arnab, Survey Sampling Theory and Applications, Academic Press, 2017;673–689. https://doi.org/10.1016/B978-0-12-811848-1.00020-0

[42]

ShiK, ChenY, YuB, XuT, ChenZ, LiuR, LiL, WuJ. Modeling spatio temporal CO2 (carbon dioxide) emission dynamics in China from DMSP-OLS nighttime stable light data using panel data analysis. Applied Energy, 2016, 168: 523-533.

[43]

ShiK, ChenY, LiL, HuangC. Spatio temporal variations of urban CO2 emissions in China: A multi scale perspective. Applied Energy, 2018, 211: 218-229.

[44]

ShiK, YuB, ZhouY, ChenY, YangC, ChenZ, et al.. Spatio temporal variations of CO2 emissions and their impact factors in China: A comparative analysis between the provincial and prefectural levels. Applied Energy, 2019, 233–234: 170-181.

[45]

ShiK, ShenJ, WuY, TangX. Identifying and quantifying urban polycentric development in China from DMSP-OLS data and urban land data sets. IEEE Geoscience and Remote Sensing Letters, 2020, 99: 1-5.

[46]

ShiK, WuY, LiuS. Does China’s city-size distribution present a flat distribution trend? A socioeconomic and spatial size analysis from DMSP-OLS nighttime light data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 5171-5179.

[47]

SunB, HanS, LiW. Effects of the polycentric spatial structures of Chinese city regions on CO2 concentrations. Trans Rese., 2020, 82(102333): 1-12.

[48]

SunX, YuanO, XuZ, YinY, LiuQ, WuL. Did Zipf’s Law hold for Chinese cities and why? Evidence from multi-source data. Land Use Pol., 2021, 106(105460): 1-12.

[49]

SuttonP, RobertsD, ElvidgeC, BaughK. Cencus from heaven: An estimate of the global human population using nighttime satellite imagery. Int. J. Remote sens., 2001, 22: 3061-3076101080/01431160010007015

[50]

SuttonPC, ElvidgeC, GhoshT. Estimation of gross domestic product at sub-national scales using nighttime satellite imagery. Int. J. Ecolo. Econ. Stat., 2001, 39(3): 701-717

[51]

United Nations, Department of Economic and Social Affairs, Population Division. 2019. World Urbanization Prospects :2018 Highlights (ST/ESA/SER.A/421).

[52]

WanG, ZhuD, WangC, ZhangX. The size distribution of cities in China: Evolution of urban system and deviations from Zipf’s law. Ecolo Indi., 2020, 111(106003): 1-10.

[53]

WangS, FangC, WangY, HuangY, MaH. Quantifying the relationship between urban development intensity and carbon dioxide emissions using a panel data analysis. Ecological Indicators, 2015, 49: 121-131.

[54]

WangS, FangC, WangY. Spatiotemporal variations and driving factors of CO₂ emissions in China at the city level. Journal of Cleaner Production, 2017, 161: 1240-1250.

[55]

WangS, WangJ, FangC, et al.. Estimating the impacts of urban form on CO2 emission efficiency in the Pearl River Delta. China. Cities, 2019, 85: 117-129.

[56]

WangS, HongJ, LiH. Spatial and temporal heterogeneity of factors influencing carbon emissions from energy consumption in Chinese cities. World Reg Stu., 2024, 33(8): 102-116.

[57]

WeiW, DuH, MaL, LiuC, ZhouJ. Spatiotemporal dynamics of CO2 emissions using nighttime light data: A comparative analysis between the Yellow and Yangtze River Basins in China. Envir. Deve. Sust., 2024, 26(1): 1081-1102

[58]

Wei, J., Hu, R., Li, Y., Shen, Y. (2024) Regional disparities, dynamic evolution, and spatial spillover effects of urban-rural carbon emission inequality in China. Fron. Eco. and Evol. 12(1309500). https://doi.org/10.3389/fevo.2024.1309500

[59]

WithanageNC, JingweiS. Evaluating the spatial-temporal dynamics of urbanization in prefecture cities of China using SNPP-VIIRS nighttime light remote sensing data. Gazi Univ. J. Science: Engi. Innv., 2024, 11(2): 346-371.

[60]

WithanageNC, ShiK, ShenJ. Extracting and evaluating urban entities in China from 2000 to 2020 based on SNPP-VIIRS-like data. Remote Sensing, 2023, 15. 4632

[61]

Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. MIT press.

[62]

WuY, JiangM, ChangZ, LiY, ShiK. Does China’s Urban Development Satisfy Zipf’s Law?A Multiscale Perspective from the NPP-VIIRS Nighttime Light Data. Inte. J. Envir. Rese. Pub. Health., 2020, 17(4): 1-26.

[63]

WuJ, LiuJ, FanW, LuY. Urban spatial structure and economic performance: Evidence from prefecture-level cities in China. Cities, 2020, 96102421.

[64]

WuY, ShiK, YuB, LiC. Analysis of the impact of urban sprawl on haze pollution based on the NPP-VIIRS nighttime light remote sensing data. Geospatial Information Science, 2021, 46: 777-789.

[65]

YangW, LiT, CaoX. Examining the impacts of socio-economic factors, urban form and transportation development on CO2 emissions from transportation in China: A panel data analysis of China’s provinces. Habitat International, 2015, 49: 212-220.

[66]

ZhangY, ChengZ. Energy consumption, carbon emissions, and economic growth in China: Evidence from panel cointegration tests. Sustainability., 2020, 1251788.

[67]

ZhangY, OdehIO, HanC. Bi-temporal characterization of land surface temperature in relation to impervious surface area, NDVI and NDBI, using a sub-pixel image analysis. International Journal of Applied Earth Observation and Geoinformation, 2009, 11: 256-264.

[68]

Zhang, Y., Chen, N., Wang, S., Wen, M., Chen, Z. (2022) Will carbon trading reduce spatial inequality? A spatial analysis of 200 cities in China. J. Envir. Man. 316(116402). https://doi.org/10.1016/j.jenvman.2022.116402

[69]

ZhengX, WangJ, XuX, YuR, ZhangS. Carbon Kuznets curve in China: Nighttime light analysis in prefecture-level cities. Heliyon, 2024.

[70]

ZhouY, SmithSJ, ZhaoK, ImhoffM, ThomsonA, Bond-LambertyB, ElvidgeCD. A global map of urban extent from nightlights. Envir. Rese. Lett., 2015, 105. 054011

[71]

ZipfGK. Human behavior and the principle of least effort. American Journal of Sociology, 1949, 110306

RIGHTS & PERMISSIONS

The Author(s)

AI Summary AI Mindmap
PDF

95

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/