Quantifying the Sustainable Urban Economy Through Integrated Metrics, Models, and Sensitivity Analysis

Authors

DOI:

https://doi.org/10.25034/ijcua.2026.v10n2-1

Keywords:

sustainable urban economy, urban sustainability assessment, eco-efficiency, spatial equity, composite index, sensitivity analysis

Abstract

Urban sustainability assessment requires metrics that jointly represent ecological limits, economic performance, and distributive equity. This structured, “method-centered” review evaluates eight established quantitative approaches, including ecological footprint, composite sustainability indices, location quotient, green GDP, data envelopment analysis, Shannon diversity, Gini inequality, and net present value and then develops a Sustainable Urban Economy Integrated Index (SUEI). Twenty-five verified peer-reviewed studies published from 2021 to 2026, together with foundational sources, were coded by method, scale, dimension, data requirements, and analytical contribution. An author-developed capability rubric compares the methods across eight criteria, and four fully disclosed weighting scenarios test rank sensitivity. Because the rubric records author appraisals of analytical capability rather than observed city outcomes, all derived statistics are interpreted descriptively. Composite indices and Gini assessment tie for the highest equal-weight capability mean (4.13/5), yet method rankings vary materially across normative priorities (tie-corrected Kendall’s W = 0.525). The revised SUEI uses directionally consistent, benchmark-based normalization, separate ecological, economic, and social subindices, geometric aggregation, and explicit safeguards against indicator duplication. Illustrative cases from Freiburg, Curitiba, Singapore, and Copenhagen clarify scale and interpretation without being treated as comparable empirical observations; their analytical units differ by roughly three orders of magnitude, which is itself evidence of why boundary choice governs interpretation. The synthesis shows that no single method is sufficient: credible assessment requires triangulating physical pressure, efficiency or productivity, equity, and, where relevant, investment evidence. The framework provides a transparent basis for future harmonized city-panel applications, sensitivity testing, and spatial validation.

Downloads

Download data is not yet available.

References

Beatley, T. (2000). Green urbanism: Learning from European cities. Island Press.

Buehler, R., & Pucher, J. (2011). Sustainable transport in Freiburg: Lessons from Germany’s environmental capital. International Journal of Sustainable Transportation, 5(1), 43–70. https://doi.org/10.1080/15568311003650531

Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444. https://doi.org/10.1016/0377-2217(78)90138-8

City of Copenhagen. (2025). Status på København 2025 [Status of Copenhagen 2025]. Københavns Kommune.

City of Freiburg. (2021). Statistisches Jahrbuch 2020 [Statistical yearbook 2020]. Amt für Bürgerservice und Informationsmanagement, Stadt Freiburg im Breisgau. https://www.freiburg.de/pb/site/Freiburg/get/params_E-624194162/1631036/Statistisches_Jahrbuch_2020.pdf

City of Freiburg. (2024). Quartier Vauban [Information panels]. Stadt Freiburg im Breisgau. https://www.freiburg.de/pb/site/Freiburg/get/params_E-1604864046/647919/Infotafeln_Vauban_en.pdf

Department of Statistics Singapore. (2025). Land area and dwelling units, annual. https://data.gov.sg/datasets/d_0b2c034da121ef8efc71949af1694b4d/view

Foroozesh, F., Monavari, S. M., Salmanmahiny, A., Robati, M., & Rahimi, R. (2022). Assessment of sustainable urban development based on a hybrid decision-making approach: Group fuzzy BWM, AHP, and TOPSIS–GIS. Sustainable Cities and Society, 76, 103402. https://doi.org/10.1016/j.scs.2021.103402

Furlan, M., Lima, P. A. B., Paião Junior, G. D., Mariano, E. B., & Pires, S. M. M. (2025). Proposing a composite index and maturity model for urban sustainability in the Brazilian context: A machine learning and data envelopment analysis approach. Sustainable Development, 33(1), 251–269. https://doi.org/10.1002/sd.3120

Guan, J., Wang, R., Van Berkel, D., & Liang, Z. (2023). How spatial patterns affect urban green space equity at different equity levels: A Bayesian quantile regression approach. Landscape and Urban Planning, 233, 104709. https://doi.org/10.1016/j.landurbplan.2023.104709

Iannillo, A., & Fasolino, I. (2021). Land-use mix and urban sustainability: Benefits and indicators analysis. Sustainability, 13(23), 13460. https://doi.org/10.3390/su132313460

Instituto Brasileiro de Geografia e Estatística [IBGE]. (2023). Curitiba, Paraná: Panorama [Census 2022 results]. https://www.ibge.gov.br/en/cities-and-states/pr/curitiba.html

Jiang, L., Chen, Y., Zha, H., Zhang, B., & Cui, Y. (2022). Quantifying the impact of urban sprawl on green total factor productivity in China: Based on satellite observation data and spatial econometric models. Land, 11(12), 2120. https://doi.org/10.3390/land11122120

Khodakarami, L., Pourmanafi, S., Mokhtari, Z., Soffianian, A. R., & Lotfi, A. (2023). Urban sustainability assessment at the neighborhood scale: Integrating spatial modellings and multi-criteria decision making approaches. Sustainable Cities and Society, 97, 104725. https://doi.org/10.1016/j.scs.2023.104725

Kovács, Z., Farkas, J. Z., Szigeti, C., & Harangozó, G. (2022). Assessing the sustainability of urbanization at the sub-national level: The ecological footprint and biocapacity accounts of the Budapest Metropolitan Region, Hungary. Sustainable Cities and Society, 84, 104022. https://doi.org/10.1016/j.scs.2022.104022

Li, S.-E., & Cheng, K.-M. (2022). Do large cities have a productivity advantage in China? From the perspective of green total factor productivity growth. Journal of Cleaner Production, 379, 134801. https://doi.org/10.1016/j.jclepro.2022.134801

Liu, B., Yang, Z., Xue, B., Zhao, D., Sun, X., & Wang, W. (2022). Formalizing an integrated metric system measuring performance of urban sustainability: Evidence from China. Sustainable Cities and Society, 79, 103702. https://doi.org/10.1016/j.scs.2022.103702

Liu, Y., Qu, Y., Cang, Y., & Ding, X. (2022). Ecological security assessment for megacities in the Yangtze River basin: Applying improved emergy-ecological footprint and DEA-SBM model. Ecological Indicators, 134, 108481. https://doi.org/10.1016/j.ecolind.2021.108481

Long, L.-J. (2021). Eco-efficiency and effectiveness evaluation toward sustainable urban development in China: A super-efficiency SBM–DEA with undesirable outputs. Environment, Development and Sustainability, 23(10), 14982–14997. https://doi.org/10.1007/s10668-021-01282-7

Lu, Y., Chen, R., Chen, B., & Wu, J. (2024). Inclusive green environment for all? An investigation of spatial access equity of urban green space and associated socioeconomic drivers in China. Landscape and Urban Planning, 241, 104926. https://doi.org/10.1016/j.landurbplan.2023.104926

Malah, A., & Bahi, H. (2022). Integrated multivariate data analysis for urban sustainability assessment: A case study of Casablanca city. Sustainable Cities and Society, 86, 104100. https://doi.org/10.1016/j.scs.2022.104100

Marjanović, I., Milanović Zbiljić, S., Stanković, J. J., & Marković, M. (2026). Towards urban sustainability: Composite index of smart city performance. Sustainability, 18(1), 372. https://doi.org/10.3390/su18010372

Medeiros, R. M., Duarte, F., Bojic, I., Xu, Y., Santi, P., & Ratti, C. (2024). Merging transport network companies and taxis in Curitiba’s BRT system. Public Transport, 16(1), 269–293. https://doi.org/10.1007/s12469-023-00342-7

Saraswat, A., Pipralia, S., & Kumar, A. (2025). Advances in urban sustainability assessment: A systematic literature review of methods, frameworks, and future directions. Frontiers of Urban and Rural Planning, 3(1), Article 15. https://doi.org/10.1007/s44243-025-00063-4

Wackernagel, M., & Rees, W. (1996). Our ecological footprint: Reducing human impact on the Earth. New Society Publishers.

Wang, C., & Jiang, J. (2025). The impact of public eco-concern on urban eco-efficiency: A dual perspective analysis of net effect and configuration. Journal of Environmental Management, 384, 125535. https://doi.org/10.1016/j.jenvman.2025.125535

Wang, K.-L., Pang, S.-Q., Zhang, F.-Q., Miao, Z., & Sun, H.-P. (2022). The impact assessment of smart city policy on urban green total-factor productivity: Evidence from China. Environmental Impact Assessment Review, 94, 106756. https://doi.org/10.1016/j.eiar.2022.106756

Wilbers, G.-J., de Bruin, K., Seifert-Dähnn, I., Lekkerkerk, W., Li, H., & Budding-Polo Ballinas, M. (2022). Investing in urban blue–green infrastructure: Assessing the costs and benefits of stormwater management in a peri-urban catchment in Oslo, Norway. Sustainability, 14(3), 1934. https://doi.org/10.3390/su14031934

Wu, J., & Bai, Z. (2022). Spatial and temporal changes of the ecological footprint of China’s resource-based cities in the process of urbanization. Resources Policy, 75, 102491. https://doi.org/10.1016/j.resourpol.2021.102491

Wu, L., & Kim, S. K. (2021). Does socioeconomic development lead to more equal distribution of green space? Evidence from Chinese cities. Science of the Total Environment, 757, 143780. https://doi.org/10.1016/j.scitotenv.2020.143780

Yao, F., Xue, L., & Liang, J. (2022). Research on coupling coordination and influencing factors between urban low-carbon economy efficiency and digital finance: Evidence from 100 cities in China’s Yangtze River economic belt. PLOS ONE, 17(7), Article e0271455. https://doi.org/10.1371/journal.pone.0271455

Zhao, X., Nakonieczny, J., Jabeen, F., Shahzad, U., & Jia, W. (2022). Does green innovation induce green total factor productivity? Novel findings from Chinese city-level data. Technological Forecasting and Social Change, 185, 122021. https://doi.org/10.1016/j.techfore.2022.122021

Zhu, Y., Xu, Y., & Luo, Y. (2023). The green GDP accounting system based on the BP neural network: An environmental pollution perspective. Frontiers in Environmental Science, 11, Article 1277717. https://doi.org/10.3389/fenvs.2023.1277717

Zou, J., Ding, R., Zhu, Y., Peng, L., & Jiang, S. (2024). Urban eco-efficiency of China: Spatial evolution, network characteristics, and influencing factors. Ecological Indicators, 167, 112641. https://doi.org/10.1016/j.ecolind.2024.112641

Downloads

Published

2026-09-29

How to Cite

Athari, S. A. ., Azoury, N., & Kirikkaleli, D. (2026). Quantifying the Sustainable Urban Economy Through Integrated Metrics, Models, and Sensitivity Analysis. Journal of Contemporary Urban Affairs, 10(2), 296-318. https://doi.org/10.25034/ijcua.2026.v10n2-1