About the Journal
In Memory of Professor Jerzy Radecki
Editorial Office
Editorial Board
Scope
Impact Factor
Indexed in...
Journal Impact Factor contributing items
Contact
Subscription
Current issue
Online first
Notes to Authors
Frequently Asked Questions
Editorial policies
PJOES Publication Policy Overview
PJOES Peer Review Policy
PJOES Research Ethics and Malpractice Policy
PJOES Plagiarism Policy
PJOES Conflict of Interest Policy
PJOES Open Access Policy
PJOES Instructions for Reviewers
Generative AI and AI-Assisted Technologies Policy
Archive
ORIGINAL RESEARCH
Research on the Spatial Heterogeneity
of Carbon Intensity at the Provincial Level
in China and Its Driving Factors
1
College of Civil Engineering, Jiangxi Science and Technology Normal University,
No.605 Fenglin Avenue, 330013, Nanchang, China
2
Disaster Prevention and Mitigation Engineering Technology Research Base of Think Tank, Jiangxi Science
and Technology Normal University, No.605 Fenglin Avenue, 330013, Nanchang, China
Submission date: 2024-03-02
Final revision date: 2024-04-24
Acceptance date: 2024-06-12
Online publication date: 2024-09-13
Publication date: 2025-05-09
Corresponding author
Pol. J. Environ. Stud. 2025;34(4):4049-4062
KEYWORDS
TOPICS
ABSTRACT
To effectively and expeditiously address emission reduction, a comprehensive understanding
of the current status of carbon intensity and the spatial interactions of carbon intensity in China is
necessary. This paper utilizes GIS technology, the Moran Index, and combines traditional Markov
chain, spatial Markov chain, and social network analysis (SNA) methods to investigate various
features of carbon intensity at the provincial level in China. The study yields the following findings:
(1) The center of China’s carbon intensity has shifted towards the northwest, whereas the center of
economic development has moved towards the south. This indicates a significant spatial divergence
in China’s low-carbon development level. (2) The distribution pattern of carbon emission intensity
in China is dominated by the proximity of high carbon emission intensity provinces to other high
carbon emission intensity provinces and low carbon emission intensity provinces to other low carbon
emission intensity provinces. (3) Carbon emission intensity exhibits significant spatial spillover effects,
with positive spillover effects being more pronounced in regions with low carbon emission intensity.
(4) The trend toward the development of China’s overall carbon intensity is positive, but the spatial
connectivity network of carbon intensity demonstrates a tendency to be entrenched, and leading
provinces in low-carbon development have yet to fully realize their positive driving role.
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 (45)
1.
LABARAN Y., MATHUR V., MUHAMMAD S., MUHAMMAD S. Carbon footprint management: A review of construction industry. Cleaner Engineering and Technology, 9, 100531, 2022. https://doi.org/10.1016/j.clet... https://doi.org/10.1016/j.clet....
2.
LIU J., MURSHED M., CHEN F., SHAHBAZ M., KIRIKKALELI D., KHAN Z. An empirical analysis of the household consumption-induced carbon emissions in China. Sustainable Production and Consumption, 26, 943, 2021. https://doi.org/10.1016/j.spc.... https://doi.org/10.1016/j.spc.....
3.
ZHOU K., YANG J., YANG T., DING T. Spatial and temporal evolution characteristics and spillover effects of China's regional carbon emissions. Journal of Environmental Management, 325, 116423, 2022. https://doi.org/10.1016/j.jenv... https://doi.org/10.1016/j.jenv... PMid:36244288.
4.
LI W., WANG W., GAO H., ZHU B., GONG W., LI Y., QIN Y. Evaluation of regional meta frontier total factor carbon-emission performance in China's construction industry: analysis based on modified non-radial directional distance function. Journal of Cleaner Production, 256, 120425, 2020. https://doi.org/10.1016/j.jcle... https://doi.org/10.1016/j.jcle....
5.
HUANG Y., ZHOU Y. How does vertical fiscal imbalance affect environmental pollution in China? Environmental Science and Pollution Research, 27 (25), 31969, 2020. https://doi.org/10.1007/s11356... https://doi.org/10.1007/s11356... PMid:32504439.
6.
SUN Y., HAO S., LONG X. A study on the measurement and influencing factors of carbon emissions in China's construction sector. Building and Environment, 229, 109912, 2023. https://doi.org/10.1016/j.buil... https://doi.org/10.1016/j.buil....
7.
ZHAO Q., GAO W., SU Y., WANG T. Carbon emissions trajectory and driving force from the construction industry with a city-scale: A case study of Hangzhou. Sustainable Cities and Society, 88, 104283, 2022. https://doi.org/10.1016/j.scs.... https://doi.org/10.1016/j.scs.....
8.
JIANG B., HUANG B., ZHANG H. Research on the factors influencing carbon emissions from the construction industry in Jiangsu Province based on the LMDI model. Environmental Science and Technology, 44 (10), 338, 2021.
9.
ZHENG Y., BEN Y., WANG K., JIANG X. Evaluation of carbon emission efficiency and driving factors for logistics enterprises. Journal of Transportation Systems Engineering and Information, 23 (2), 11, 2023.
10.
LIU C., QU J., GE Y., TANG J., GAO X., LIU L. Carbon emission prediction in China's transportation industry based on LSTM model. Chinese Journal of Environmental Science, 43 (5), 2574, 2023.
11.
ZUO J., ZHONG Y., YANG Y., FU C., HE X., BAO B., QIAN F. Analysis of carbon emission, carbon displacement and heterogeneity of Guangdong power industry. Energy Reports, 8 (6), 438, 2022. https://doi.org/10.1016/j.egyr... https://doi.org/10.1016/j.egyr....
12.
WANG H., LI B., KHAN M. Prediction of Shanghai electric power carbon emissions based on improved STIRPAT model. Sustainability, 14 (20), 13068, 2022. https://doi.org/10.3390/su1420... https://doi.org/10.3390/su1420....
13.
LV H., SHI B., LI N., KANG R. Intelligent manufacturing and carbon emissions reduction: Evidence from the use of industrial robots in China. International Journal of Environmental Research and Public Health, 19 (23), 15538, 2022. https://doi.org/10.3390/ijerph... https://doi.org/10.3390/ijerph... PMid:36497614 PMCid:PMC9737826.
14.
LIN B., TENG Y. Decoupling of economic and carbon emission linkages: Evidence from manufacturing industry chains. Journal of Environmental Management, 322, 116081, 2022. https://doi.org/10.1016/j.jenv... https://doi.org/10.1016/j.jenv....
15.
ZHANG Z., ZHANG D. Spatial correlation and carbon balance zoning of carbon emissions in the Beijing-Tianjin-Hebei region based on social network analysis. Chinese Journal of Environmental Science, 43 (04), 2057, 2023.
16.
LI Y., ZHANG S., ZHANG Y. Study on the spatiotemporal evolution and emission reduction of provincial carbon emissions in China from the perspective of green finance. Soft Science, 37 (12), 39, 2023.
17.
LI Y., ZHANG S. Spatiotemporal heterogeneity of the temporal and spatial evolution of urban carbon emission intensity in China and its influencing factors. Environmental Science in China, 43 (6), 3244, 2023.
18.
GAO H., LI T., YU J., SUN Y., XIE S. Spatial correlation network structure of carbon emission efficiency in China's construction industry and its formation mechanism. Sustainability, 15 (6), 5108, 2023. https://doi.org/10.3390/su1506... https://doi.org/10.3390/su1506....
19.
WANG Y., DUAN F., MA X., HE L. Carbon emissions efficiency in China: key facts from regional and industrial sector. Journal of Cleaner Production, 206, 850, 2019. https://doi.org/10.1016/j.jcle... https://doi.org/10.1016/j.jcle....
20.
XU H., LI Y., ZHENG Y., XU X. Analysis of spatial associations in the energy-carbon emission efficiency of the transportation industry and its influencing factors: Evidence from China. Environmental Impact Assessment Review, 197, 106905, 2022. https://doi.org/10.1016/j.eiar... https://doi.org/10.1016/j.eiar....
21.
SONG H., GU L., LI Y., ZHANG X., SONG Y. Research on carbon emission efficiency space relations and network structure of the Yellow River basin city cluster. International Journal of Environmental Research and Public Health, 19 (19), 12235, 2022. https://doi.org/10.3390/ijerph... https://doi.org/10.3390/ijerph... PMid:36231537 PMCid:PMC9566447.
22.
TANG Y., YANG Z., YAO J., LI X., CHEN X. Carbon emission efficiency and spatially linked network structure of China's logistics industry. Frontiers in Environmental Science, 10, 2057, 2022. https://doi.org/10.3389/fenvs.... https://doi.org/10.3389/fenvs.....
23.
WANG S., WANG J., FANG C., FENG K. Inequalities in carbon intensity in China: a multi-scalar and multi-mechanism analysis. Applied Energy, 254, 113720, 2019. https://doi.org/10.1016/j.apen... https://doi.org/10.1016/j.apen....
24.
WANG S., FANG C., MA H., WANG Y., QIN J. Spatial differences and multi-mechanism of carbon footprint based on GWR model in provincial China. Journal of Geographical Sciences, 24 (4), 612, 2014. https://doi.org/10.1007/s11442... https://doi.org/10.1007/s11442....
25.
WU H. Provincial carbon emission reduction in China: Spatiotemporal patterns, evolution mechanisms, and policy recommendations - based on the theory and methods of spatial econometrics. Management World, 2015 (11), 3, 2015 [in Chinese].
26.
HUANG H.Z., JIA J.S., CHEN D.L., LIU S.T. Evolution of spatial network structure for land-use carbon emissions and carbon balance zoning in Jiangxi Province: A social network analysis perspective. Ecological Indicators, 158, 111508, 2024. https://doi.org/10.1016/j.ecol....
27.
ZHU C.P., SU Y.X., FAN R.G., QIN M., FU H.F. Exploring provincial carbon-pollutant emission efficiency in China: An integrated approach with social network analysis and spatial econometrics. Ecological Indicators, 159, 111662, 2024. https://doi.org/10.1016/j.ecol....
28.
GLUECKLER J., DOREIAN P. Social network analysis and economic geography positional, evolutionary and multi-level approaches. Journal of Economic Geography, 16 (6), 1123, 2016.
29.
SHEN W.R., LIANG H.W., DONG L., REN J.Z., WANG G.J. Synergistic GTWR reduction effects in Chinese urban agglomerations: Perspectives from social network analysis. Science of The Total Environment, 798, 149352, 2021. https://doi.org/10.1016/j.scit....
30.
DONG J., LI C.B. Structure characteristics and influencing factors of China's carbon emission spatial correlation network: A study based on the dimension of urban agglomerations. The Science of the Total Environment, 853, 158613, 2022. https://doi.org/10.1016/j.scit....
31.
SONG J.Z., FENG Q., WANG X.P., FU H.L., JIANG W., CHEN B.Y. Spatial association and effect evaluation of CO2 emission in the Chengdu-Chongqing urban agglomeration: Quantitative evidence from social network analysis. Sustainability, 11 (1), 1, 2019. https://doi.org/10.3390/su1101....
32.
ZHAO G., ZHAO G., CHEN L., SUN H. Spatiotemporal evolution and leapfrogging mechanism of carbon intensity in China. Chinese Journal of Population, Resources, and Environment, 27 (10), 84, 2017 [in Chinese].
33.
HUO T., CAO R., XIA N., HU X., CAI W., LIU B. Spatial correlation network structure of China's building carbon emissions and its driving factors: A social network analysis method. Journal of Environmental Management, 320, 115808, 2022. https://doi.org/10.1016/j.jenv....
34.
LI W., SUN W., LI G., CUI P., WU W., JIN B. Temporal and spatial heterogeneity of carbon intensity in China's construction industry. Resources, Conservation & Recycling, 126, 162, 2017. https://doi.org/10.1016/j.resc....
35.
DAS M., GHOSH S. Measuring Moran's I in a cost-efficient manner to describe a land-cover change pattern in large-scale remote sensing imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10 (6), 2631, 2017. https://doi.org/10.1109/JSTARS....
36.
CHEN H., LU X., GAO T., CHANG Y. Identifying hotspots of metal contamination in campus dust of Xi'an, China. International Journal of Environmental Research & Public Health, 13 (6), 555, 2016. https://doi.org/10.3390/ijerph....
37.
QIN Q., YAN H., LIU J., CHEN X., YE B. China's agricultural GHG emission efficiency: regional disparity and spatial dynamic evolution. Environmental Geochemistry And Health, 44 (9), 2863, 2020. https://doi.org/10.1007/s10653....
38.
ZHU L., WANG Y., SHANG P., QI L., YANG G., WANG Y. Improvement path, the improvement potential and the dynamic evolution of regional energy efficiency in China: based on an improved nonradial multidirectional efficiency analysis. Energy Policy, 133, 110883, 2019. https://doi.org/10.1016/j.enpo....
39.
YU Z., CHEN L., TONG H., CHEN L., ZHANG T., LI L., YUAN L., XIAO J., WU R., BAI L., SHI S. Spatial correlations of land-use carbon emissions in the Yangtze River Delta region: A perspective from social network analysis. Ecological Indicators, 142, 109147, 2022. https://doi.org/10.1016/j.ecol....
40.
ANSELN L., GRIFFITH D.A. Do spatial effects really matter in regression analysis. Papers of the Regional Science Association, 165, 11, 1988. https://doi.org/10.1111/j.1435....
41.
GONG S., XIAO K. Quantitative analysis of the shifting economic center in China over the past two millennia - A proxy analysis based on population and urban data. Geographical Science, 41 (9), 1587, 2021 [In Chinese].
42.
LIU J., LI X., SHI D. Analysis of the shift and driving factors of China's carbon emission center. Finance and Trade Economics, 2013 (12), 112, 2013 [In Chinese].
43.
SHAO S., XU L., YANG L. The spatial correlation network of carbon emissions in China: structural features and formation mechanisms. Systems Engineering Theory and Practice, 43 (4), 958, 2023 [In Chinese].
44.
MENG D., LU Y. Gravity model-based analysis of economic interaction intensity and direction in Jiangsu Province. Progress in Geography, 28 (5), 697, 2009 [In Chinese].
45.
SUN M., FEI B. Population agglomeration and carbon emissions: An empirical study from the perspective of spatial spillover effects. Population Journal, 44 (5), 72, 2022 [in Chinese].
Share
RELATED ARTICLE
| eISSN: | 2083-5906 |
| ISSN: | 1230-1485 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
