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eISSN: 2083-5906
ISSN: 1230-1485
ISSN: 1230-1485
ORIGINAL RESEARCH
Research on the Calculation Method of Carbon
Emissions Integrating Nighttime Lighting Data and
the Coefficient of Urban Industrial Structure Level
1
Geomathematics Key Laboratory of Sichuan Province, Chengdu University of Technology, Chengdu 610059, China
2
School of Mathematical Sciences, Chengdu University of Technology, Chengdu 610059, China
3
Chengdu College of Arts and Sciences, Chengdu 610401, China
Submission date: 2025-03-31
Final revision date: 2025-06-18
Acceptance date: 2025-09-05
Online publication date: 2025-12-03
Corresponding author
Xiaoyu Zhang
School of Mathematical Sciences, Chengdu University of Technology, Chengdu 610059, China
School of Mathematical Sciences, Chengdu University of Technology, Chengdu 610059, China
KEYWORDS
TOPICS
ABSTRACT
Amid the global challenge of carbon emissions (CE), developing a method for accurately measuring
city-level carbon emissions is crucial for crafting effective carbon reduction strategies. Therefore,
the integration of urban nighttime light data (NTL) and urban industrial structure (IS) characteristics
contributes to the characterization of urban carbon emissions. It uses the ISC to reflect urban industrial
structure variances across 21 cities and counties in Sichuan Province, establishing a correlation between
NTL-ISC and urban carbon emissions through a PSO-SVM model. This approach is evaluated against
the conventional NTL method, aiming for precise urban CE quantification. Additionally, the impact of
the tertiary sector, population density, and urbanization on CEI and CEC is analyzed. Findings indicate:
(1) A comparison between the traditional linear regression method, solely based on NTL (adjusted
R2 = 0.86), and the machine learning method incorporating NTL-ISC (adjusted R2 = 0.89), demonstrates
the efficacy of the proposed carbon emission measurement methodology. (2) Population density exhibits
divergent contribution rates to cities with medium and low IS, with the tertiary industry’s impact
inversely related to population density. The urbanization rate significantly affects Panzhihua. This study
enhances and broadens the methodologies for measuring urban carbon emissions using NTL, offering
support for cities of varied industrial structures in devising tailored carbon reduction policies.
CONFLICT OF INTEREST
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
REFERENCES (36)
1.
SUFYANULLAH K., AHMAD K.A., ALI M.A.S. Does emission of carbon dioxide is impacted by urbanization? An empirical study of urbanization, energy consumption, economic growth and carbon emissions-Using ARDL bound testing approach. Energy Policy. 164, 112908, 2022. https://doi.org/10.1016/j.enpo....
2.
SHAN Y., FANG S., CAI B., ZHOU Y., LI D., FENG K., HUBACEK K. Chinese cities exhibit varying degrees of decoupling of economic growth and CO2 emissions between 2005 and 2015. One Earth. 4 (1), 124, 2021. https://doi.org/10.1016/j.onee....
3.
HUO T., LI X., CAI W., ZUO J., JIA F., WEI H. Exploring the impact of urbanization on urban building carbon emissions in China: Evidence from a provincial panel data model. Sustainable Cities and Society. 56, 102068, 2020. https://doi.org/10.1016/j.scs.....
4.
WU S., HU S., FRAZIER A.E., HU Z. China's urban and rural residential carbon emissions: past and future scenarios. Resources, Conservation and Recycling. 190, 106802, 2023. https://doi.org/10.1016/j.resc....
5.
VERMA P., KUMARI T., RAGHUBANSHI A.S. Energy emissions, consumption and impact of urban households: A review. Renewable and Sustainable Energy Reviews. 147, 111210, 2021. https://doi.org/10.1016/j.rser....
6.
PU Y., WANG Y., WANG P. Driving effects of urbanization on city-level carbon dioxide emissions: From multiple perspectives of urbanization. International Journal of Urban Sciences. 26 (1), 108, 2022. https://doi.org/10.1080/122659....
7.
ZHENG Y., YANG H., HUANG J., CUI Q., ZHAN J. Industrial agglomeration measured by plants' distance and CO2 emissions: Evidence from 268 Chinese prefecture-level cities. Technological Forecasting and Social Change. 176, 121469, 2022. https://doi.org/10.1016/j.tech....
8.
WANG Z., LI X., MAO Y., LI L., WANG X., LIN Q. Dynamic simulation of land use change and assessment of carbon storage based on climate change scenarios at the city level: A case study of Bortala, China. Ecological Indicators. 134, 108499, 2022. https://doi.org/10.1016/j.ecol....
9.
ZHAO C., CAO X., CHEN X., CUI X. A consistent and corrected nighttime light dataset (CCNL 1992-2013) from DMSP-OLS data. Scientific Data. 9 (1), 424, 2022. https://doi.org/10.1038/s41597....
10.
DU X., SHEN L., WONG S.W., MENG C., YANG Z. Night-time light data based decoupling relationship analysis between economic growth and carbon emission in 289 Chinese cities. Sustainable Cities and Society. 73, 103119, 2021. https://doi.org/10.1016/j.scs.....
11.
ORTAKAVAK Z., ÇABUK S.N., CETIN M., SENYEL KURKCUOGLU M.A., CABUK A. Determination of the nighttime light imagery for urban city population using DMSP-OLS methods in Istanbul. Environmental Monitoring and Assessment. 192 (12), 790, 2020. https://doi.org/10.1007/s10661....
12.
FARUOLO M., FALCONIERI A., GENZANO N., LACAVA T., MARCHESE F., PERGOLA N. A daytime multisensor satellite system for global gas flaring monitoring. IEEE Transactions on Geoscience and Remote Sensing. 60, 1, 2022. https://doi.org/10.1109/TGRS.2....
13.
WANG L., ZHANG N., DENG H., WANG P., YANG F., QU J.J., ZHOU X. Monitoring urban carbon emissions from energy consumption over China with DMSP/OLS nighttime light observations. Theoretical and Applied Climatology. 149 (3), 983, 2022. https://doi.org/10.1007/s00704....
14.
ZHENG Y., FAN M., CAI Y., FU M., YANG K., WEI C. Spatio-temporal pattern evolution of carbon emissions at the city-county-town scale in Fujian Province based on DMSP/OLS and NPP/VIIRS nighttime light data. Journal of Cleaner Production. 442, 140958, 2024. https://doi.org/10.1016/j.jcle....
15.
CHEN J., GAO M., CHENG S., HOU W., SONG M., LIU X., LIU Y., SHAN Y. County-level CO2 emissions and sequestration in China during 1997-2017. Scientific Data. 7 (1), 391, 2020. https://doi.org/10.1038/s41597....
16.
WEI G., HE B.J., SUN P., LIU Y., LI R., OUYANG X., LI S. Evolutionary trends of urban expansion and its sustainable development: Evidence from 80 representative cities in the Belt and Road Initiative region. Cities. 138, 104353, 2023. https://doi.org/10.1016/j.citi....
17.
RICKWOOD P., GLAZEBROOK G., SEARLE G. Urban structure and energy - a review. Urban Policy and Research. 26 (1), 57, 2008. https://doi.org/10.1080/081111....
18.
SHI K., SHEN J., WU Y., LIU S., LI L. Carbon dioxide (CO2) emissions from the service industry, traffic, and secondary industry as revealed by the remotely sensed nighttime light data. International Journal of Digital Earth. 14 (11), 1514, 2021. https://doi.org/10.1080/175389....
19.
LIU F. The impact of China's low-carbon city pilot policy on carbon emissions: based on the multi-period DID model. Environmental Science and Pollution Research. 30 (34), 81745, 2023. https://doi.org/10.1007/s11356....
20.
ZENG S., JING., TAN K., LIU X. Can low-carbon city construction reduce carbon intensity? Empirical evidence from low-carbon city pilot policy in China. Journal of Environmental Management. 332, 117363, 2023. https://doi.org/10.1016/j.jenv....
21.
LI L., LEI Y., WU S., HE C., CHEN J., YAN D. Impacts of city size change and industrial structure change on CO2 emissions in Chinese cities. Journal of Cleaner Production. 195, 831, 2018. https://doi.org/10.1016/j.jcle....
22.
ZHU B., SHAN H. Impacts of industrial structures reconstructing on carbon emission and energy consumption: A case of Beijing. Journal of Cleaner Production. 245, 118916, 2020. https://doi.org/10.1016/j.jcle....
23.
YU B. Urban spatial structure and total-factor energy efficiency in Chinese provinces. Ecological Indicators. 126, 107662, 2021. https://doi.org/10.1016/j.ecol....
24.
WU J., LIU C., GUO H., LI P., SUN W. Examining intra-city carbon budget and carbon balance zoning based on firm-level big data: A case study of Nanjing, China. Ecological Indicators. 166, 112304, 2024. https://doi.org/10.1016/j.ecol....
25.
CHEN X., MA W., VALDMANIS V. Can labor productivity growth reduce carbon emission? Evidence from OECD countries and China. Management of Environmental Quality: An International Journal. 33 (3), 644, 2021. https://doi.org/10.1108/MEQ-10....
26.
PAN X., GUO S., XU H., TIAN M., PAN X., CHU J. China's carbon intensity factor decomposition and carbon emission decoupling analysis. Energy. 239, 122175, 2022. https://doi.org/10.1016/j.ener....
27.
ZHE W., XIGANG X., FENG Y. An abnormal phenomenon in entropy weight method in the dynamic evaluation of water quality index. Ecological Indicators. 131, 108137, 2021. https://doi.org/10.1016/j.ecol....
28.
YANG S., YANG X., GAO X., ZHANG J. Spatial and temporal distribution characteristics of carbon emissions and their drivers in shrinking cities in China: Empirical evidence based on the NPP/VIIRS nighttime lighting index. Journal of Environmental Management. 322, 116082, 2022. https://doi.org/10.1016/j.jenv....
29.
YANG D., LUAN W., QIAO L., PRATAMA M. Modeling and spatio-temporal analysis of city-level carbon emissions based on nighttime light satellite imagery. Applied Energy. 268, 114696, 2020. https://doi.org/10.1016/j.apen....
30.
EL AMOURY S., SMILI Y., FAKHRI Y. Design of an Optimal Convolutional Neural Network Architecture for MRI Brain Tumor Classification by Exploiting Particle Swarm Optimization. Journal of Imaging. 11 (2), 31, 2025. https://doi.org/10.3390/jimagi....
31.
Data and statistics. Available online: https://www.iea.org/data-and-s... (accessed on April 1, 2025).
32.
ZHOU Y., CHEN M., TANG Z., ZHAO Y. City-level carbon emissions accounting and differentiation integrated nighttime light and city attributes. Resources, Conservation and Recycling. 182, 106337, 2020. https://doi.org/10.1016/j.resc....
33.
JECHOW A., HÖLKER F. Evidence that reduced air and road traffic decreased artificial night-time skyglow during COVID-19 lockdown in Berlin, Germany. Remote Sensing. 12 (20), 3412, 2020. https://doi.org/10.3390/rs1220....
34.
XU G., XIU T., LI X., LIANG X., JIAO L. Lockdown induced night-time light dynamics during the COVID-19 epidemic in global megacities. International Journal of Applied Earth Observation and Geoinformation. 102, 102421, 2021. https://doi.org/10.1016/j.jag.....
35.
YIN R., HE G., JIANG W., PENG Y., ZHANG Z., LI M., GONG C. Night-time light imagery reveals China's city activity during the COVID-19 pandemic period in early 2020. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 14, 5111, 2021. https://doi.org/10.1109/JSTARS....
36.
PAVLAČKA D., VYVLEČKA P., BARVÍŘ R., RYPL O., BURIAN J. Influence of COVID-19 on night-time lights in Czechia. Journal of Maps. 19 (1), 2235381, 2023. https://doi.org/10.1080/174456....
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