Carbon emissions affect sustainable urban development, and cities are the main carbon emission factors. To improve carbon performance from the perspective of urban density, the temporal and spatial evolution characteristics and associations, and indicative thresholds of urban density and carbon performance in the Chengdu-Chongqing urban agglomeration are analyzed by combining Slack Based Measure-Data Envelopment Analysis (SBM-DEA), Standard Deviation Ellipse Analysis, Regression Model, and Spatial Autocorrelation in this paper. The results show that: (1) there is no obvious trend in population density, road density increases faster than building density, and carbon performance shows a decreasing and then increasing trend. (2) The center of gravity of building density and population density migrates towards Chengdu-Chongqing. The road density develops in the southwest-northeast direction, and the spatial pattern of carbon performance shows the characteristic of “high in the southwest and low in the northeast”. (3) The building density, population density, road density, and carbon performance show an upward curve, an “N” curve, and an inverted “N” curve, respectively, and the order of influence is building density > road density > population density. (4) There is a positive spatial correlation between urban density and carbon performance in the Chengdu-Chongqing urban agglomeration, but it is not uniformly distributed, and there is a phenomenon of clustering in specific local areas, especially in the core city areas such as Chengdu.
- Open Access
- Article
Research on the Associations and Indicative Thresholds of Urban Density on Carbon Performance: A Case Study of Chengdu-Chongqing Urban Agglomeration
- Yangli Li 1,2,*,
- Lu Wei 2,
- Erli Zeng 3
Author Information
Received: 21 Sep 2025 | Revised: 30 Dec 2025 | Accepted: 31 Dec 2025 | Published: 05 Jan 2026
Abstract
Keywords
urban density | carbon performance | associations and indicative thresholds | Chengdu-Chongqing urban agglomeration | standard deviation ellipse | polynomial regression | bivariate spatial autocorrelation
References
- 1.
Jia, L.; Zhang, J.; Li, R.; et al. Spatial correlation investigation of carbon emission efficiency in the Yangtze River Delta of China: The role of low-carbon pilot cities. Ecol. Indic. 2025, 172, 113282.
- 2.
Bayramli, G.; Karimli, T. Driving factors of CO2 emissions in South American countries: An application of Seemingly Unrelated Regression model. Reg. Sustain. 2024, 5, 100182.
- 3.
Xu, X.; Chen, L.; Du, X.; et al. Development pathways for low carbon cities in China: A dual perspective of effectiveness and efficiency. Ecol. Indic. 2024, 169, 112848.
- 4.
Sun, Y.; Zhang, R.; Du, X.; et al. Does low-carbon city construction promote integrated economic, energy, and environmental development? An empirical study based on the low-carbon city pilot policy in China. Sustainability 2023, 15, 16241.
- 5.
Fan, F.; Dai, S.; Yang, B.; et al. Urban density, directed technological change, and carbon intensity: An empirical study based on Chinese cities. Technol. Soc. 2023, 72, 102151.
- 6.
Ma, N.; Chen, L.; Zeng, Z. Dangerous density: Urban density and the criminalization of China. Int. Rev. Econ. Financ. 2025, 99, 104025.
- 7.
Arbel, Y.; Fialkoff, C.; Kerner, A.; et al. Do population density, socio-economic ranking and Gini Index of cities influence infection rates from coronavirus? Israel as a case study. Ann. Reg. Sci. 2022, 68, 181–206.
- 8.
Wang, S.; Wang, L.; Liao, X.; et al. Impact of residential density on heavy metal mobilization in urban soils: Human activity patterns and eco-health risks in the Beijing-Tianjin-Hebei region. Ecotoxicol. Environ. Saf. 2025, 302, 118559.
- 9.
Li, Y.; Schubert, S.; Kropp, J.P.; et al. On the influence of density and morphology on the Urban Heat Island intensity. Nat. Commun. 2020, 11, 2647.
- 10.
Hong, S.; Hui, E.C.M.; Lin, Y. Relationships between carbon emissions and urban population size and density, based on geo-urban scaling analysis: A multi carbon source empirical study. Urban Clim. 2022, 46, 101337.
- 11.
Yang, Y.; Takase, T. Spatial characteristics of carbon dioxide emission intensity of urban road traffic and driving factors: Road network and land use. Sustain. Cities Soc. 2024, 113, 105700.
- 12.
Zheng, J.L. Urban Morphology and Residents’ Carbon Emission—A Study Based on Compact Cities Form. Econ. Manag. 2016, 30, 89–96. (In Chinese)
- 13.
Wang, X.; Xie, S.; Wei, Z.; et al. A Study on the Impact of China’s Prefabricated Building Policy on the Carbon Reduction Benefits of China’s Construction Industry Based on a Difference-in-Differences Method. Sustainability 2024, 16, 7606.
- 14.
Yuan, R.; Xu, X.; Wang, Y.; et al. Evaluating carbon-emission efficiency in China’s construction industry: An SBM-model analysis of interprovincial building heating. Sustainability 2024, 16, 2411.
- 15.
Feng, T.; Zhou, B. Impact of urban spatial structure elements on carbon emissions efficiency in growing megacities: The case of Chengdu. Sci. Rep. 2023, 13, 9939.
- 16.
Yu, Q.N.; Jia, W.X.; Pan, C.; et al. Spatial and Temporal Variation Characteristics of Urban Forms’ Impact on Regional Carbon Emissions in the Yangtze River Delta. China Popul. Resour. Environ. 2015, 25, 44–51. (In Chinese)
- 17.
China Statistical Yearbook, Statistical Yearbooks at All Levels. Available online: http://www.stats.gov.cn/ (accessed on 20 February 2025).
- 18.
National Greenhouse Gas Emission Inventories Published by the, I.P.C.C. Available online: https://www.ipcc.ch/ (accessed on 20 February 2025).
- 19.
Sichuan Statistical Yearbook. Available online: https://tjj.sc.gov.cn/ (accessed on 20 February 2025).
- 20.
Chongqing Statistical Yearbook. Available online: https://www.cq.gov.cn/ (accessed on 20 February 2025).
- 21.
Statistical Yearbook of Urban Construction. Available online: https://www.mohurd.gov.cn/ (accessed on 5 May 2023).
- 22.
Wang, R.; Wang, Q.; Yao, S. Evaluation and difference analysis of regional energy efficiency in China under the carbon neutrality targets: Insights from DEA and Theil models. J. Environ. Manag. 2021, 293, 112958.
- 23.
Han, D.; Cao, Z. Evaluation and influential factors of urban land use efficiency in Yangtze river economic belt. Land 2024, 13, 671.
- 24.
Kuang, B.; Lu, X.; Zhou, M.; et al. Provincial cultivated land use efficiency in China: Empirical analysis based on the SBM-DEA model with carbon emissions considered. Technol. Forecast. Soc. Chang. 2020, 151, 119874.
- 25.
Cong, J.H.; Liu, X.M.; Zhao, X.R. Demarcation problems and the corresponding measurement methods of the urban carbon accounting. China Popul. Resour. Environ. 2014, 24, 19–26.
- 26.
Zhang, Y.; Jiang, P.; Cui, L.; et al. Study on the spatial variation of China’s territorial ecological space based on the standard deviation ellipse. Front. Environ. Sci. 2022, 10, 982734.
- 27.
Chen, F.; Shen, S.F.; Li, Y.H.; et al. The effect of urban density on spatial Carbon performance: A case study of Shanghai. Urban Probl. 2022, 2, 96–103.
- 28.
Wang, Z.; Liu, Y.; Zhang, Y.; et al. Spatially varying relationships between land subsidence and urbanization: A case study in Wuhan, China. Remote Sens. 2022, 14, 291.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Suite 4002 Level 4, 447 Collins Street, Melbourne, Victoria 3000, Australia
General Inquiries: info@sciltp.com