Dissecting the natural and human drivers of urban thermal resilience across climates

Abstract

In the context of current global warming, understanding urban thermal resilience (UTR) dynamics across different climatic zones is crucial. This study aims to examine the complex interactions among urban morphology, green-blue infrastructure, and climate factors affecting UTR. Moving beyond traditional methods that compare urban and rural thermal differences, our research innovatively measures UTR by evaluating urban disturbances caused by extreme thermal events. To improve accuracy and reliability, we utilize an AI-powered Monte Carlo Simulation framework. Our findings emphasize the critical role of blue-green spaces in boosting UTR, whereas urban morphology often has a suppressive impact. Additionally, atmospheric humidity is identified as a critical factor affecting UTR. The study interestingly finds varied climatic responses: dense urban areas enhance resilience in arid and cold regions but reduce it in tropical and temperate zones. These findings highlight the need for a balance between sustainable urban living and infrastructure development.

Keywords

Urban thermal resilience / Extreme heat events / Climatic heterogeneity / Green-blue factors / Urban morphology

CRediT authorship contribution statement

Renlu Qiao: Writing – review & editing, Conceptualization. Tao Wu: Writing – review & editing. Zexu Zhao: Methodology, Software. Shuo Gao: Data curation, Conceptualization. Ting Yang: Data curation. Chenyang Duan: Visualization, Validation. Shiqi Zhou: Methodology, Data curation. Xiaochang Liu: Software, Resources. Li Xia: Conceptualization. Xi Meng: Methodology, Investigation. Lei Jin: Writing – review & editing, Data curation. Zhiyu Liu: Writing – review & editing, Data curation, Conceptualization. Zhiqiang Wu: Writing – original draft, Supervision, Conceptualization.

Declaration of competing interests

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.

Acknowledgements

The study is financed by ‘Data Analysis of Thermal Environment and Low-Carbon Intelligent Optimization Design of Urban Ecological Layout’s Impact’ under National Natural Science Foundation of China (Grant No. 524B200113), ‘Basic Theory of Sustainable Urban Planning, Construction, and Governance’ under the 14th Five-Year Plan of the State Key Research and Development Program of the People’s Republic of China (Grant No. 2022YFC3800205), ‘Key Technologies for Regional Carbon Neutral Mega-City Planning and Design’ under Shanghai Science and Technology Support Program for Carbon (Grant No. 22DZ1207800), and Shanghai Intelligent Science and Technology IV Summit Discipline ‘Cross-Innovation Science and Education Integration Fund’.

References

[1] Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A., Hegewisch, K. C., 2018. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958 to 2015. Sci. Data, 5 (1) , Article 170191. doi: 10.1038/sdata.2017.191.

[2] Azhdari, A., Soltani, A., Alidadi, M., 2018. Urban morphology and landscape structure effect on land surface temperature: evidence from Shiraz, a semi-arid city. Sustain. Cities Soc. 41, 853–864. doi: 10.1016/j.scs.2018.06.034.

[3] Beck, H. E., Zimmermann, N. E., McVicar, T. R., Vergopolan, N, Berg, A, Wood, E. F., 2018. Present and future Köppen-Geiger climate classification maps at 1-km resolution. Sci. Data, 5 (1) , Article 180214. doi: 10.1038/sdata.2018.214.

[4] Chen, J., Gao, M., Cheng, S., Hou, W., Song, M., Liu, X., Liu, Y., Shan, Y., 2020a. Countylevel CO₂ emissions and sequestration in China during 1997–2017. Sci. Data 7 (1), 391. doi: 10.1038/s41597- 020- 00736- 3.

[5] Chen, Z., Yu, B., Yang, C., Zhou, Y., Yao, S., Qian, X., Wang, C., Wu, B., Wu, J., 2020b. An extended time-series (2000–2018) of global NPP-VIIRS-like nighttime light data. Harvard Dataverse, V3 doi: 10.7910/DVN/YGIVCD.

[6] Chun, B, Guhathakurta, S., 2015. Daytime and nighttime urban heat islands statistical models for Atlanta. Environ. Plann. B: Urban Anal. City Sci., 44 (2) , pp. 308-327. doi: 10.1177/0265813515624685.

[7] Chun, B, Guldmann, J. M., 2014. Spatial statistical analysis and simulation of the urban heat island in high-density central cities. Landsc. Urban Plan., 125 , pp. 76-88. doi: 10.1016/j.landurbplan.2014.01.016.

[8] Clinton, N, Gong, P., 2013. MODIS detected surface urban heat islands and sinks: global locations and controls. Remote Sens. Environ., 134 , pp. 294-304. doi: 10.1016/j.rse.2013.03.008.

[9] Ebi, K. L., Bowen, K., 2016. Extreme events as sources of health vulnerability: drought as an example. Weather Clim. Extremes, 11 , pp. 95-102. doi: 10.1016/j.wace.2015.10.001.

[10] Feyisa, G. L., Dons, K, Meilby, H., 2014. Efficiency of parks in mitigating urban heat island effect: an example from Addis Ababa. Landsc. Urban Plan., 123 , pp. 87-95. doi: 10.1016/j.landurbplan.2013.12.008.

[11] Gago, E. J., Roldan, J, Pacheco-Torres, R, Ordóñez, J., 2013. The city and urban heat islands: a review of strategies to mitigate adverse effects. Renew. Sustain. Energy Rev., 25 , pp. 749-758. doi: 10.1016/j.rser.2013.05.057.

[12] Gao, Y, Zhao, J, Yu, K., 2022. 216 , Article 109037. doi: 10.1016/j.buildenv.2022.109037.

[13] Giridharan, R, Ganesan, S, Lau, S. S. Y., 2004. Daytime urban heat island effect in high-rise and high-density residential developments in Hong Kong. Energy Build., 36 (6) , pp. 525-534. doi: 10.1016/j.enbuild.2003.12.016.

[14] Gunawardena, K. R., Wells, M. J., Kershaw, T., 2017. Utilising green and bluespace to mitigate urban heat island intensity. Sci. Total Environ., 584–585 , pp. 1040-1055. doi: 10.1016/j.scitotenv.2017.01.158.

[15] Guo, A, Yang, J, Xiao, X, Xia, J, Jin, C, Li, X., 2020. Influences of urban spatial form on urban heat island effects at the community level in China. Sustain. Cities Soc., 53 , Article 101972. doi: 10.1016/j.scs.2019.101972.

[16] Harper, K. L., Lamarche, C, Hartley, A, Peylin, P, Ottlé, C, Bastrikov, V, San Martín, R, Bohnenstengel, S. I., Kirches, G, Boettcher, M, Shevchuk, R, Brockmann, C, Defourny, P., 2023. A 29-year time series of annual 300-metre resolution plant functional type maps for climate models. Earth Syst. Sci. Data, 15 (3) , pp. 1465-1499. doi: 10.5194/essd-15-1465-2023.

[17] Heitman, J. L., Horton, R, Sauer, T. J., Ren, T. S., Xiao, X., 2010. Latent heat in soil heat flux measurements. Agric. For. Meteorol., 150 (7) , pp. 1147-1153. doi: 10.1016/j.agrformet.2010.04.017.

[18] Henderson-Sellers, A, Gornitz, V., 1984. Possible climatic impacts of land cover transformations, with particular emphasis on tropical deforestation. Clim. Change, 6 (3) , pp. 231-257. doi: 10.1007/BF00142475.

[19] Hoelscher, M-T, Nehls, T, Jänicke, B, Wessolek, G., 2016. Quantifying cooling effects of facade greening: shading, transpiration and insulation. Energy Build., 114 , pp. 283-290. doi: 10.1016/j.enbuild.2015.06.047.

[20] Hussain, M, Butt, A. R., Uzma, F, Ahmed, R, Irshad, S, Rehman, A, Yousaf, B., 2019. A comprehensive review of climate change impacts, adaptation, and mitigation on environmental and natural calamities in Pakistan. Environ. Monit. Assess., 192 (1) , p. 48. doi: 10.1007/s10661-019-7956-4.

[21] Ibrahim, N. A., Wan Alwi, S. R., Manan, Z. A., Mustaffa, A. A., Kidam, K., 2022. Risk matrix approach of extreme temperature and precipitation for renewable energy systems in Malaysia. Energy, 254 , Article 124471. doi: 10.1016/j.energy.2022.124471.

[22] Imam Syafii, N, Ichinose, M, Kumakura, E, Jusuf, S. K., Chigusa, K, Wong, N. H., 2017. Thermal environment assessment around bodies of water in urban canyons: a scale model study. Sustain. Cities Soc., 34 , pp. 79-89. doi: 10.1016/j.scs.2017.06.012.

[23] Javanroodi, K, Mahdavinejad, M, Nik, V. M., 2018. Impacts of urban morphology on reducing cooling load and increasing ventilation potential in hot-arid climate. Appl. Energy, 231 , pp. 714-746. doi: 10.1016/j.apenergy.2018.09.116.

[24] Ji, L, Shu, C, Laouadi, A, Lacasse, M, Wang, L., 2023. Quantifying improvement of building and zone level thermal resilience by cooling retrofits against summertime heat events. Build. Environ., 229 , Article 109914. doi: 10.1016/j.buildenv.2022.109914.

[25] Jie, R, Wentao, Y, Yuting, H, Zihao, L., 2024. Defining urban network resilience: a review. Front. Urban Rural Plan., 2 (1) , p. 14. doi: 10.1007/s44243-024-00039-w.

[26] Kalkstein, L. S., Davis, R. E., 1989. Weather and human mortality: an evaluation of demographic and interregional responses in the United States. Ann. Assoc. Am. Geogr., 79 (1) , pp. 44-64. doi: 10.1111/j.1467-8306.1989.tb00249.x.

[27] Javanroodi, K., Mahdavinejad, M., Nik, V.M., 2018. Impacts of urban morphology on reducing cooling load and increasing ventilation potential in hot-arid climate. Appl. Energy 231, 714–746. doi: 10.1016/j.apenergy.2018.09.116.

[28] Ke, G, Meng, Q, Finley, T, Wang, T, Chen, W, Ma, W, Ye, Q, Liu, T. Y. 2017. LightGBM: a highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, pp.3147-3155.

[29] Lelieveld, J, Hadjinicolaou, P, Kostopoulou, E, Chenoweth, J, El Maayar, M, Giannakopoulos, C, Hannides, C, Lange, M. A., Tanarhte, M, Tyrlis, E, Xoplaki, E., 2012. Climate change and impacts in the Eastern Mediterranean and the Middle East. Clim. Change, 114 (3) , pp. 667-687. doi: 10.1007/s10584-012-0418-4.

[30] Leuzinger, S., Vogt, R., Körner, C., 2010. Tree surface temperature in an urban environment. Agric. For. Meteorol. 150 (1), 56–62. doi: 10.1016/j.agrformet.2009.08.006.

[31] Li, C, Song, Y, Kaza, N, Burghardt, R., 2019. Explaining spatial variations in residential energy usage intensity in Chicago: the role of urban form and geomorphometry. J. Plan. Educ. Res., 43 (2) , pp. 317-331. doi: 10.1177/0739456×19873382.

[32] Li, D, Bou-Zeid, E., 2013. Synergistic interactions between urban heat islands and heat waves: the impact in cities is larger than the sum of its parts. J. Appl. Meteorol. Climatol., 52 (9) , pp. 2051-2064. doi: 10.1175/JAMC-D-13-02.1.

[33] Liao, W., Hong, T., Heo, Y., 2021. The effect of spatial heterogeneity in urban morphology on surface urban heat islands. Energy Build. 244, 111027. doi: 10.1016/j.enbuild. 2021.111027.

[34] Liu, Y, Li, Q, Yang, L, Mu, K, Zhang, M, Liu, J., 2020. Urban heat island effects of various urban morphologies under regional climate conditions. Sci. Total Environ., 743 , Article 140589. doi: 10.1016/j.scitotenv.2020.140589.

[35] Lundberg, S. M., Erion, G, Chen, H, DeGrave, A, Prutkin, J. M., Nair, B, Katz, R, Himmelfarb, J, Bansal, N, S-Lee, I., 2020. From local explanations to global understanding with explainable AI for trees. Nat. Mach. Intell., 2 (1) , pp. 56-67. doi: 10.1038/s42256-019-0138-9.

[36] Lundberg, S. M, Lee, S-.I. 2017. A unified approach to interpreting model predictions. Proceedings of the 31st International Conference on Neural Information Processing Systems, Curran Associates Inc., Long Beach, California, USA, pp.4768-4777.

[37] Metropolis, N, Ulam, S., 1949. The Monte Carlo method. J. Am. Stat. Assoc., 44 (247) , pp. 335-341. doi: 10.1080/01621459.1949.10483310.

[38] O’Brien, W, Bennet, I., 2016. Simulation-based evaluation of high-rise residential building thermal resilience. ASHRAE Trans., 122, 455-468.

[39] Peng, H., Nishiyama, Y., Sezaki, K., 2022. Assessing environmental benefits from shared micromobility systems using machine learning algorithms and Monte Carlo simulation. Sustain. Cities Soc. 87, 104207. doi: 10.1016/j.scs.2022.104207.

[40] Peng, S, Piao, S, Ciais, P, Friedlingstein, P, Ottle, C, F-Bréon, M, Nan, H, Zhou, L, Myneni, R. B., 2012. Surface urban heat island across 419 global big cities. Environ. Sci. Technol., 46 (2) , pp. 696-703. doi: 10.1021/es2030438.

[41] Pesaresi, M, Politis, P., 2022. GHS-BUILT-H R2022A-GHS Building Height, Derived from AW3D30, SRTM30, and Sentinel2 Composite. European Commission, Joint Research Centre (JRC)

[42] Qiao, R, Liu, T., 2020. Impact of building greening on building energy consumption: a quantitative computational approach. J. Clean. Prod., 246 , Article 119020. doi: 10.1016/j.jclepro.2019.119020.

[43] Qiao, R., Liu, X., Gao, S., Liang, D., GesangYangji, G., Xia, L., Zhou, S., Ao, X., Jiang, Q., Wu, Z., 2024a. Industrialization, urbanization, and innovation: nonlinear drivers of carbon emissions in Chinese cities. Appl. Energy 358, 122598. doi: 10.1016/j. apenergy.2023.122598.

[44] Qiao, R., Wu, Z., Jiang, Q., Liu, X., Gao, S., Xia, L., Yang, T., 2024b. The nonlinear influence of land conveyance on urban carbon emissions: an interpretable ensemble learning-based approach. Land Use Policy 140, 107117. doi: 10.1016/j.landusepol. 2024.107117.

[45] Qiao, R., Gao, S., Liu, X., Xia, L., Zhang, G., Meng, X., Liu, Z., Wang, M., Zhou, S., Wu, Z., 2024c. Understanding the global subnational migration patterns driven by hydrological intrusion exposure. Nat. Commun. 15 (1), 6285. doi: 10.1038/ s41467- 024- 49609- y.

[46] Qiao, R, Zhao, Z, Wu, T, Zhou, S, Xiang, A. O., Yang, T, Liu, X, Liu, Z, Wu, Z., 2025. Unveiling the nonlinear drivers of urban land resources on carbon emissions: the mediating role of industrial upgrading and technological innovation. Resour. Conserv. Recycl., 212 , p. 108000. doi: 10.1016/j.resconrec.2024.108000.

[47] Raftery, A. E., Zimmer, A, Frierson, D. M. W., Startz, R, Liu, P., 2017. 2 °C warming by 2100 unlikely. Nat. Clim. Chang., 7 (9) , pp. 637-641. doi: 10.1038/nclimate3352.

[48] Ramamurthy, P, Bou-Zeid, E., 2014. Contribution of impervious surfaces to urban evaporation. Water Resour. Res., 50 (4) , pp. 2889-2902. doi: 10.1002/2013WR013909.

[49] Salvador, C, Nieto, R, Vicente-Serrano, S. M., García-Herrera, R, Gimeno, L, Vicedo-Cabrera, A. M., 2023. Vicedo-Cabrera. Public health implications of drought in a climate change context: a critical review. Annu. Rev. Public Health, 44 (1) , pp. 213-232. doi: 10.1146/annurev-publhealth-071421-051636.

[50] Seyam, S., 2019. The impact of greenery systems on building energy: systematic review. J. Build. Eng., 26 , Article 100887. doi: 10.1016/j.jobe.2019.100887.

[51] Shahriari, B, Swersky, K, Wang, Z, Adams, R. P, Nd Freitas., 2016. Taking the human out of the loop: a review of Bayesian optimization. Proc. IEEE, 104 (1) , pp. 148-175. doi: 10.1109/JPROC.2015.2494218.

[52] Shreevastava, A, Bhalachandran, S, McGrath, G. S., Huber, M, Rao, P. S. C., 2019. Paradoxical impact of sprawling intra-Urban Heat Islets: reducing mean surface temperatures while enhancing local extremes. Sci. Rep., 9 (1) , p. 19681. doi: 10.1038/s41598-019-56091-w.

[53] Slee, B., Parkinson, T., Hyde, R., 2014. Quantifying useful thermal mass: how much thermal mass do you need? Archit. Sci. Rev. 57 (4), 271–285. doi: 10.1080/00038628.2014.951312.

[54] Stone, B., Hess Jeremy, J., Frumkin, H., 2010. Urban form and extreme heat events: are sprawling cities more vulnerable to climate change than compact cities? Environ. Health Perspect. 118 (10), 1425–1428. doi: 10.1289/ehp.0901879.

[55] Sun, Y, Gao, C, Li, J, Li, W, Ma, R., 2018. Examining urban thermal environment dynamics and relations to biophysical composition and configuration and socio-economic factors: a case study of the Shanghai metropolitan region. Sustain. Cities Soc., 40 , pp. 284-295. doi: 10.1016/j.scs.2017.12.004.

[56] Tan, P. Y., Wong, N. H., Tan, C. L., Jusuf, S. K., Chang, M. F., Chiam, Z. Q., 2018. A method to partition the relative effects of evaporative cooling and shading on air temperature within vegetation canopy. J. Urban Ecol., 4 (1) , p. juy012. doi: 10.1093/jue/juy012.

[57] Umezawa, T, Matsueda, H, Oda, T, Higuchi, K, Sawa, Y, Machida, T, Niwa, Y, Maksyutov, S., 2020. Statistical characterization of urban CO₂ emission signals observed by commercial airliner measurements. Sci. Rep., 10 (1) , p. 7963. doi: 10.1038/s41598-020-64769-9.

[58] Venter, Z. S., Chakraborty, T, Lee, X., 2021. Crowdsourced air temperatures contrast satellite measures of the urban heat island and its mechanisms. Sci. Adv., 7 (22) , p. eabb9569. doi: 10.1126/sciadv.abb9569.

[59] Wan, Z., 2008. New refinements and validation of the MODIS Land-Surface Temperature/Emissivity products. Remote Sens. Environ., 112 (1) , pp. 59-74. doi: 10.1016/j.rse.2006.06.026.

[60] Wang, C, Liu, Z, Du, H, Zhan, W., 2023. Regulation of urban morphology on thermal environment across global cities. Sustain. Cities Soc., 97 , Article 104749. doi: 10.1016/j.scs.2023.104749.

[61] Wang, S, Zhou, C, Li, G, Feng, K., 2016. CO₂, economic growth, and energy consumption in China’s provinces: investigating the spatiotemporal and econometric characteristics of China’s CO₂ emissions. Ecol. Indic., 69 , pp. 184-195. doi: 10.1016/j.ecolind.2016.04.022.

[62] Weber, S, Sadoff, N, Zell, E, de Sherbinin, A., 2015. Policy-relevant indicators for mapping the vulnerability of urban populations to extreme heat events: a case study of Philadelphia. Appl. Geogr., 63 , pp. 231-243. doi: 10.1016/j.apgeog.2015.07.006.

[63] Weng, Q, Yang, S., 2004. Managing the adverse thermal effects of urban development in a densely populated Chinese city. J. Environ. Manage., 70 (2) , pp. 145-156. doi: 10.1016/j.jenvman.2003.11.006.

[64] Wong, N. H., Tan, C. L., Kolokotsa, D. D., Takebayashi, H., 2021. Greenery as a mitigation and adaptation strategy to urban heat. Nat. Rev. Earth Environ., 2 (3) , pp. 166-181. doi: 10.1038/s43017-020-00129-5.

[65] Wu, Z, Qiao, R, Zhao, S, Liu, X, Gao, S, Liu, Z, Ao, X, Zhou, S, Wang, Z, Jiang, Q., 2022. Nonlinear forces in urban thermal environment using Bayesian optimization-based ensemble learning. Sci. Total Environ., 838 , Article 156348. doi: 10.1016/j.scitotenv.2022.156348.

[66] Xi, Z, Li, C, Zhou, L, Yang, H, Burghardt, R., 2023. Built environment influences on urban climate resilience: evidence from extreme heat events in Macau. Sci. Total Environ., 859 , Article 160270. doi: 10.1016/j.scitotenv.2022.160270.

[67] Yan, C., Guo, Q., Li, H., Li, L., Qiu, G.Y., 2020a. Quantifying the cooling effect of urban vegetation by mobile traverse method: a local-scale urban heat island study in a subtropical megacity. Build. Environ. 169, 106541. doi: 10.1016/j.buildenv.2019.106541.

[68] Yan, H., Liu, Q., Zhao, W., Pang, C., Dong, M., Zhang, H., Gao, J., Wang, H., Hu, B., Yang, L., Wang, L., 2020b. The coupled effect of temperature, humidity, and air movement on human thermal response in hot–humid and hot–arid climates in summer in China. Build. Environ. 177, 106898. doi: 10.1016/j.buildenv.2020.106898.

[69] Yang, D, Luan, W, Qiao, L, Pratama, M., 2020. Modeling and spatio-temporal analysis of city-level carbon emissions based on nighttime light satellite imagery. Appl. Energy, 268 , Article 114696. doi: 10.1016/j.apenergy.2020.114696.

[70] Zhang, K, Cao, C, Chu, H, Zhao, L, Zhao, J, Lee, X., 2023. Increased heat risk in wet climate induced by urban humid heat. Nature, 617 (2023), pp. 738-742. doi: 10.1038/s41586-023-05911-1.

[71] Zhang, L., Nikolopoulou, M., Guo, S., Song, D., 2022a. Impact of LCZs spatial pattern on urban heat island: a case study in Wuhan, China. Build. Environ. 226, 109785. doi: 10.1016/j.buildenv.2022.109785.

[72] Zhang, T., Zhou, Y., Zhu, Z., Li, X., Asrar, G.R., 2022b. A global seamless 1 km resolution daily land surface temperature dataset (2003–2020). Earth Syst. Sci. Data 14 (2), 651–664. doi: 10.5194/essd- 14- 651- 2022.

[73] Zhang, X, Estoque, R. C., Murayama, Y., 2017. An urban heat island study in Nanchang City, China based on land surface temperature and social-ecological variables. Sustain. Cities Soc., 32 , pp. 557-568. doi: 10.1016/j.scs.2017.05.005.

[74] Zhao, L, Lee, X, Smith, R. B., Oleson, K., 2014. Strong contributions of local background climate to urban heat islands. Nature, 511 (2014), pp. 216-219. doi: 10.1038/nature13462.

[75] Zhou, S, Wang, Y, Jia, W, Wang, M, Wu, Y, Qiao, R, Wu, Z., 2023. Automatic responsive-generation of 3D urban morphology coupled with local climate zones using generative adversarial network. Build. Environ., 245 , Article 110855. doi: 10.1016/j.buildenv.2023.110855.

[76] Zuo, J, Pullen, S, Palmer, J, Bennetts, H, Chileshe, N, Ma, T., 2015. Impacts of heat waves and corresponding measures: a review. J. Clean. Prod., 92 , pp. 1-12. doi: 10.1016/j.jclepro.2014.12.078.