International Journal of Urban Management and Energy Sustainability

International Journal of Urban Management and Energy Sustainability

Assessing Urban Thermal Vulnerability Using GIS and Multi-Criteria Decision-Making for Climate Change Adaptive Planning (Case Study: Tabriz City, Iran)

Document Type : Case Study

Author
Farhad Faghihi
Department of Civil Engineering, Technical and Vocational University (TVU), Tehran, Iran
10.22034/ijumes.2026.738888
Abstract
Rapid urbanization and climate change are intensifying urban heat, making the identification of heat-vulnerable areas essential for climate-adaptive planning. Guided by the IPCC framework, which defines vulnerability through exposure, sensitivity, and adaptive capacity, this study develops and demonstrates an integrated approach for assessing and mapping urban thermal vulnerability in Tabriz, a large semi-arid city in northwestern Iran with limited green space. The methodology integrates geographic information systems (GIS), multi-criteria decision-making, and social-science measurement. Twelve indicators were organized across the three vulnerability components. Real land-use data were processed in GIS, while residents’ perceptions were assessed using a structured five-point Likert questionnaire administered to 384 respondents determined by the Cochran formula. An 18-member expert panel provided pairwise comparisons. Criterion weights were derived using the Fuzzy Analytic Hierarchy Process (Fuzzy-AHP), while exploratory and confirmatory factor analyses examined construct validity. Covariance-based structural equation modelling (SEM/LISREL) tested hypothesized relationships, and weighted overlay analysis generated a five-level Thermal Vulnerability Index (TVI) map, validated against summer land-surface temperature. The land-use analysis indicates that barren and abandoned land (35.4%), agriculture (19.1%), and residential fabric (17.2%) dominate Tabriz city, whereas green land uses comprise only 5.3% of the urban area. The demonstrative model indicates that exposure and sensitivity increase thermal vulnerability, while adaptive capacity reduces it and partially mediates the effect of exposure. Approximately 36% of the urban area falls within high or very-high vulnerability classes, primarily in dense built-up zones with limited green cover. The framework provides a transparent, reproducible, and transferable decision-support tool for prioritizing heat-adaptation interventions.

Graphical Abstract

Assessing Urban Thermal Vulnerability Using GIS and Multi-Criteria Decision-Making for Climate Change Adaptive Planning (Case Study: Tabriz City, Iran)

Highlights

·         An integrated GIS–Fuzzy-AHP–SEM framework maps urban thermal vulnerability through the IPCC exposure–sensitivity–adaptive-capacity model.

·         Real land-use GIS data for Tabriz show a structurally heat-prone city: 35.4% barren land, 32% built-up, and only about 5.3% green cover.

·         In the demonstrative model, exposure (β ≈ +0.46) and sensitivity (β ≈ +0.38) raise vulnerability while adaptive capacity lowers it (β ≈ −0.33); exposure is partly mediated by adaptive capacity.

·         The classified Thermal Vulnerability Index places about 36% of Tabriz in high or very-high classes, concentrated in dense residential, commercial and industrial–transport zones.

·         A Cochran-based questionnaire (n = 384) and a transparent, transferable decision-support tool help planners target heat adaptation where it is most needed.

 

Keywords
Climate-change
Fuzzy AHP
Geographic Information Systems
thermal vulnerability
urban heat
·         Abdulateef, M. F., & Al-Alwan, H. A. S. (2022). The effectiveness of urban green infrastructure in reducing surface urban heat island: Baghdad city as a case study. Ain Shams Engineering Journal, 13(1), 101526. https://doi.org/10.1016/j.asej.2021.06.012
·         Amindin, A., Pouyan, S., & Pourghasemi, H. R. (2021). Spatial and temporal analysis of temperature and land surface changes using Landsat satellite images. Environmental Science and Pollution Research, 28(30), 41439–41450. https://doi.org/10.1007/s11356-021-13693-0
·         Azhdari, A., Soltani, A., & Alidadi, M. (2018). Urban morphology and landscape structure effect on land surface temperature: Evidence from Shiraz, a semi-arid city. Sustainable Cities and Society, 41, 853–864. https://doi.org/10.1016/j.scs.2018.06.034
·         Azizi, P., Soltani, A., Bagheri, F., Sharifi, S., & Mikaeili, M. (2022). An integrated modelling approach to urban growth and land use/cover change. Land, 11(10), 1715. https://doi.org/10.3390/land11101715
·         Barat, A., Kumar, S., Kumar, P., & Parth Sarthi, P. (2018). Characteristics of surface urban heat island (SUHI) over the Gangetic Plain of Bihar, India. Asia-Pacific Journal of Atmospheric Sciences, 54(2), 205–214. https://doi.org/10.1007/s13143-018-0004-4
·         Bokaie, M., Zarkesh, M. K., Arasteh, P. D., & Hosseini, A. (2016). Assessment of urban heat island based on the relationship between land surface temperature and land use/land cover in Tehran. Sustainable Cities and Society, 23, 94–104. https://doi.org/10.1016/j.scs.2016.03.009
·         Bose, A., & Chowdhury, I. R. (2020). Monitoring and modeling of spatio-temporal urban expansion and land-use/land-cover change using a Markov chain model: A case study in Siliguri Metropolitan area, West Bengal, India. Modeling Earth Systems and Environment, 6(4), 2235–2249. https://doi.org/10.1007/s40808-020-00842-6
·         Chen, Z., Gan, K. K., Zhou, T., Du, Q., & Zeng, M. (2022). Using structural equation modeling to examine pathways between environmental characteristics and perceived restorativeness of public rooftop gardens. Frontiers in Public Health, 10, 801453. https://doi.org/10.3389/fpubh.2022.801453
·         Deilami, K., Kamruzzaman, M., & Liu, Y. (2018). Urban heat island effect: A systematic review of spatio-temporal factors, data, methods, and mitigation measures. International Journal of Applied Earth Observation and Geoinformation, 67, 30–42. https://doi.org/10.1016/j.jag.2017.12.009
·         Ermida, S. L., Soares, P., Mantas, V., Göttsche, F.-M., & Trigo, I. F. (2020). Google Earth Engine open-source code for land surface temperature estimation from the Landsat series. Remote Sensing, 12(9), 1471. https://doi.org/10.3390/rs12091471
·         García, D. H. (2021). Modelling of the urban heat island on local climatic zones of a city using Sentinel 3 images: Urban determining factors. Urban Climate, 37, 100840. https://doi.org/10.1016/j.uclim.2021.100840
·         Ghosh, A., Chatterjee, U., Pal, S. C., Islam, A. R. M. T., Alam, E., & Islam, M. K. (2023). Flood hazard mapping using GIS-based statistical model in vulnerable riparian regions of a sub-tropical environment. Geocarto International, 38(1), 2285355. https://doi.org/10.1080/10106049.2023.2285355
·         Gupta, R., Sharma, M., Singh, G., & Joshi, R. K. (2023). Characterizing urban growth and land surface temperature in the western Himalayan cities of India using remote sensing and spatial metrics. Frontiers in Environmental Science, 11, 1122935. https://doi.org/10.3389/fenvs.2023.1122935
·         Inostroza, L., Palme, M., & de la Barrera, F. (2016). A heat vulnerability index: Spatial patterns of exposure, sensitivity and adaptive capacity for Santiago de Chile. PLOS ONE, 11(9), e0162464. https://doi.org/10.1371/journal.pone.0162464
·         Kabir, K. A., & Guha Thakurta, P. K. (2024). Synergizing AHP and TOPSIS MCDM techniques with GIS for inclusive land suitability evaluation in sustainable development. AIP Conference Proceedings, 3164(1), 040004. https://doi.org/10.1063/5.0214686
·         Khelili, M., Anoune, N., & Haoues, C. (2022). Assessment of land suitability for urban growth using multi-criteria decision analysis by integrating GIS and the AHP method in Setif, Algeria. International Journal of Innovative Studies in Sociology and Humanities, 7(11), 8–19. https://doi.org/10.20431/2456-4931.071102
·         Kılıç, D., Yağcı, C., & İşcan, F. (2022). A GIS-based multi-criteria decision analysis approach using AHP for rural settlement site selection and eco-village design in Erzincan, Turkey. Socio-Economic Planning Sciences, 86, 101478. https://doi.org/10.1016/j.seps.2022.101478
·         Kiavarz, M., Hosseinbeigi, S. B., Mijani, N., Shahsavary, M. S., & Firozjaei, M. K. (2022). Predicting spatial and temporal changes in surface urban heat islands using multi-temporal satellite imagery: A case study of Tehran metropolis. Urban Climate, 45, 101258. https://doi.org/10.1016/j.uclim.2022.101258
·         Lenzholzer, S., Carsjens, G. J., Brown, R. D., Tavares, S., Vanos, J., Kim, Y. J., & Lee, K. (2020). Awareness of urban climate adaptation strategies, An international overview. Urban Climate, 34, 100705. https://doi.org/10.1016/j.uclim.2020.100705
·         Macnee, R. G. D., & Tokai, A. (2016). Heat wave vulnerability and exposure mapping for Osaka City, Japan. Environment Systems and Decisions, 36(4), 368–376. https://doi.org/10.1007/s10669-016-9607-4
·         Merga, B. B., Tabor, K. W., & Melka, G. A. (2024). Analysis of the cooling effect of urban green spaces in mitigating micro-climate change using geospatial techniques. Sustainable Environment, 10(1), 2350806. https://doi.org/10.1080/27658511.2024.2350806
·         Naserikia, M., & Hart, M. A. (2022). Background climate modulates the impact of land cover on urban surface temperature. Scientific Reports, 12, 15433. https://doi.org/10.1038/s41598-022-19431-x
·         Norton, B. A., Coutts, A. M., Livesley, S. J., Harris, R. J., Hunter, A. M., & Williams, N. S. G. (2015). Planning for cooler cities: A framework to prioritise green infrastructure to mitigate high temperatures in urban landscapes. Landscape and Urban Planning, 134, 127–138. https://doi.org/10.1016/j.landurbplan.2014.10.018
·         Peng, Y., Peng, Z., Feng, T., Zhong, C., & Wang, W. (2021). Assessing comfort in urban public spaces: A structural equation model involving environmental attitude and perception. International Journal of Environmental Research and Public Health, 18(3), 1287. https://doi.org/10.3390/ijerph18031287
·         Ranagalage, M., Estoque, R. C., & Handayani, H. H. (2018). Relation between urban volume and land surface temperature: A comparative study of planned and traditional cities in Japan. Sustainability, 10(7), 2366. https://doi.org/10.3390/su10072366
·         Rathi, S. K., Chakraborty, S., Mishra, S. K., Dutta, A., & Nanda, L. (2022). A heat vulnerability index: Spatial patterns of exposure, sensitivity and adaptive capacity for urbanites of four cities of India. International Journal of Environmental Research and Public Health, 19(1), 283. https://doi.org/10.3390/ijerph19010283
·         Rincón, D., Khan, U. T., & Armenakis, C. (2018). Flood risk mapping using GIS and multi-criteria analysis: A greater Toronto area case study. Geosciences, 8(8), 275. https://doi.org/10.3390/geosciences8080275
·         Sahraei, R., Kanani-Sadat, Y., Homayouni, S., Safari, A., Oubennaceur, K., & Chokmani, K. (2023). A novel hybrid GIS-based multi-criteria decision-making approach for flood susceptibility analysis in large ungauged watersheds. Journal of Flood Risk Management, 16(4), e12879. https://doi.org/10.1111/jfr3.12879
·         Spyrou, G., Ioannou, B., Souliotis, M., Savvides, A. L., & Fokaides, P. A. (2023). The adaptability of cities to climate change: Evidence from cities’ redesign towards mitigating the urban heat island effect. Sustainability, 15(7), 6133. https://doi.org/10.3390/su15076133
·         Talukdar, S., Singha, P., Mahato, S., Praveen, B., & Rahman, A. (2020). Dynamics of ecosystem services (ESs) in response to land use land cover (LU/LC) changes in the lower Gangetic plain of India. Ecological Indicators, 112, 106121. https://doi.org/10.1016/j.ecolind.2020.106121
·         Tien Bui, D., Khosravi, K., Shahabi, H., Daggupati, P., Adamowski, J. F., Melesse, A. M., ... & Lee, S. (2019). Flood spatial modeling in northern Iran using remote sensing and GIS. Remote Sensing, 11(13), 1589. https://doi.org/10.3390/rs11131589
·         Truong, P., Le, N., Hoang, T., Nguyen, T., Nguyen, T., Kieu, T., Nguyen, T., Izuru, S., Le, V., Raghavan, V., Nguyen, V., & Tran, T. (2023). Climate change vulnerability assessment using GIS and Fuzzy AHP on an indicator-based approach. International Journal of Geoinformatics, 19(2), 39–53. https://doi.org/10.52939/ijg.v19i2.2565
·         Wang, Y., Huang, J., Chen, C., Shen, J., & Sheng, S. (2021). The cooling intensity dependent on landscape complexity of green infrastructure in the metropolitan area. Journal of Environmental Engineering and Landscape Management, 29(3), 318–336. https://doi.org/10.3846/jeelm.2021.15573
·         Wu, C., Shui, W., Huang, Z., Wang, C., Wu, Y., Wu, Y., Xue, C., Huang, Y., Zhang, Y., & Zheng, D. (2022). Urban heat vulnerability: A dynamic assessment using multi-source data in a coastal metropolis of Southeast China. Frontiers in Public Health, 10, 989963. https://doi.org/10.3389/fpubh.2022.989963
·         Zhang, Y., Murray, A. T., & Turner, B. L. (2017). Optimizing green space locations to reduce daytime and nighttime urban heat island effects in Phoenix, Arizona. Landscape and Urban Planning, 165, 162–171. https://doi.org/10.1016/j.landurbplan.2017.04.009

  • Receive Date 08 February 2026
  • Revise Date 22 April 2026
  • Accept Date 28 May 2026