For many cities, the energy consumed for building heating and cooling is responsible for over half of the total CO2 emissions. The prevalence of low energy-efficient buildings, especially those predating the implementation of energy efficiency standards, poses a significant hurdle for cities striving to achieve climate neutrality. To make matters worse, increasing energy prices lead to numerous households being unable to afford adequate heating and cooling, jeopardising their health and well-being.

Our study uses aerial survey, thermal remote sensing, and city modelling techniques to build a 3D thermographic model, aiming to investigate building energy leaks at a city scale. First, we conducted aerial surveys at night during the cold season to avoid sun radiation and better capture building heat losses. Second, we used texture matching to attach thermal images on LoD2 building models as building textures. Thirdly, we integrate building renovation-related data as attributes of each building, such as energy consumption, building type, year of construction and renovation potential. Lastly, we will use 3DCityDB as the backend and CesiumJS as the front end to develop a 3D web client. Moreover, to tackle the energy poverty problem, we further integrate social-economic data and energy poverty indicators into our 3D WebGIS application.

Multi-stakeholders will use our modelling results to improve energy efficiency and reduce CO2 emissions. More specifically, the 3D model provides property owners with an intuitive way to detect heat loss, support their actions in building renovation, and reduce energy costs. Our modelling will support the city renewal offices for their building energy consultant services. The identified information on energy poverty households will be used by the social welfare offices for their social actions in low-income neighbourhoods.

The overall goal of our study is to support decision-makers in their action on building energy renovation and energy justice in practice.

How to cite: Xu, S., Haarbrink, R., and Santhanavanich, T.: A City-scale 3D Thermographic Model for Building Energy Efficiency and Energy Justice, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-1027, https://doi.org/10.5194/icuc12-1027, 2025.

The accelerating urbanization shifts energy demand towards cities, which account already for ~75% of global energy consumption and ~70% of greenhouse gas emissions. Implementing effective building retrofit strategies is therefore key to reduce energy consumption and to decarbonize building operation, a priority reflected in existing policies and directives. In contrast to research efforts focused primarily on environmental and economic indicators, we introduce a building retrofit planning framework that combines socio-economic and environmental data within an AI computational pipeline for assessment, evaluation and decision-making.

Our framework identifies effective building envelope retrofit solutions considering building characteristics, climate projections, and socio-economic information collected from neighborhoods and residents. Besides single-building analysis, the optimization objectives and constraints additionally capture systemic effects, enabling coordinated decision-making at neighbourhood level. Energy efficiency and savings potential are quantified through physics-based models that use publicly available geospatial databases to extract building-specific geometric information, while information about the thermal properties of materials is derived from archetype classification data. To support large-scale decision-making, datasets generated by physics-based models are used to train a surrogate model for energy prediction, facilitating efficient evaluation of multiple retrofit scenarios.

To showcase the effectiveness of our building retrofit planning framework, we conduct a city-wide case study in Rotterdam. With the case study we introduce policymakers to the retrofit planning framework allowing them to designing equitable, actionable and sustainable building retrofit strategies at multiple scales. Our ultimate goal is to promote the adoption of sustainable retrofit packages at household and neighborhood levels,  accelerating carbon footprint reduction in urban environments.

How to cite: Los, A., Andriotis, C., Moody, R., Khademi, S., Morato Dominguez, P., Koniari, A. M., van Rooy, I., and Steenbergen-Cockerton, H.: Data-driven building retrofit planning considering socio-economic and environmental factors, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-714, https://doi.org/10.5194/icuc12-714, 2025.

Cities in the Middle East face intensified heat stress and thermal discomfort due to rapid urbanization and global warming. Many Gulf cities, characterized by a hot arid climate (Köppen-Geiger: BWh), experience prolonged and extremely hot, dry summers exacerbated by the urban heat island effect. This poses challenges of extremely high cooling energy demand, increased outdoor thermal discomfort, and adverse impacts on well-being, energy use, economic growth, and the environment. Therefore, this research involves developing a microscale model for a dense mid-rise urban area in Doha, Qatar, characterized by limited vegetation and natural surfaces. The study begins by assessing the current outdoor thermal environment and thermal comfort for a typical mid-summer day (5^th July). It then simulates future climate scenarios using projections from IPCC high (A2) emission scenarios for 2041–2069 and 2070–2099. The microclimate simulation software ENVI-met computes the outdoor thermal conditions, while its outputs are integrated into EnergyPlus to calculate localized building energy consumption. Subsequently, the research designs, models, and evaluates various heat mitigation scenarios based on four main strategies: (1) green infrastructure, including extensive and intensive green roofs and walls, increased tree canopy cover, and the introduction of a small urban park; (2) cool materials for roofs (α=0.8) and pavements (α=0.5); (3) urban morphology modifications, varying building heights (15, 25, 35 m) and form; and (4) shading structures installation in pedestrian areas. Later, combinations of the best-performing heat mitigation strategies are simulated to maximize the cooling effects. These strategies' cooling benefits are analyzed under current and future climate scenarios to identify optimal solutions for mitigating urban heat stress, reducing buildings’ energy consumption, and enhancing thermal comfort in hot arid urban environments.

Acknowledgments:

Research reported in this work was supported by the Qatar Research Development and Innovation Council (Grant: ARG01-0503-230061).

How to cite: Abedrabboh, O., Siddique, A., Moosakutty, S. P., Alfarra, M. R., and Fountoukis, C.: Integrated modelling of microclimate adaptation: Evaluating heat mitigation strategies for Doha, Qatar, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-209, https://doi.org/10.5194/icuc12-209, 2025.

To understand the impact of cities on the global climate, the level of representation of cities across time and space in climate models is crucial. Global spatiotemporally-varying anthropogenic heat emission (AHE) datasets are essential in urban climate modeling, urban climate-change investigations, and coupled building-energy model development. This work aims to develop an open-source tool for users to generate present and CMIP-consistent projections of AHE datasets at 1-km resolution. To obtain CMIP6-consistent projections of AHE maps at 5-year intervals from 2020, a workflow is constructed based on the following components: (1) top-down AH model, (2) an integrated assessment model (IAM), (3) 1-km scenario-based projections of population, (4) monthly statistics of daily temperature projections. The workflow begins by generating regional-level energy consumptions from the Global Change Analysis Model (GCAM), one of the IAM's used to develop the Shared Socioeconomic Pathways (SSP) in the Coupled Model Intercomparison Project. The modeled projections of energy consumption components are then allocated to countries based on their energy intensity, which relies on GDP data from GCAM and World Bank. They are then utilized as inputs in the AH4GUC model, the base model for mapping the AHE based on the top-down approach. Combining existing global projections of population, heating-degree/cooling-degree days projections, global road network datasets, and satellite datasets, 1-km projections of AHE are achieved.

How to cite: Varquez, A. C. G., Sekiya, M., Khanh, D. N., Inagaki, A., Kanda, M., Ihara, T., and Itsubo, N.: Integrated assessment model-driven 1-km anthropogenic heat emission generation tool, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-630, https://doi.org/10.5194/icuc12-630, 2025.

How to cite: Lam, Y. F. and Chang, M. H. J.: Effect of New Urban Development on Heat-energy-carbon Emission in Hong Kong, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-606, https://doi.org/10.5194/icuc12-606, 2025.

As urbanization accelerates globally, cities play a crucial role in shaping the environmental impacts of the built environment, particularly in terms of carbon emissions. This study provides a comprehensive analysis of the impact of urban development, with a focus on urban infrastructure, on carbon emission intensity (CEI) across Chinese cities, using both global and local regression models. The global regression model identifies per capita road area, economic growth, and industrial structure as key factors in reducing CEI. The Geographically Weighted Regression (GWR) model further reveals significant spatial heterogeneity in these impacts. Economic growth consistently shows a negative relationship with CEI across all cities, with the strongest effects in the northernmost regions. In contrast, per capita emissions have a consistently positive association with CEI, especially in northeastern cities. Interestingly, other infrastructure-related factors exhibit bidirectional effects depending on the region: for example, per capita road area increases CEI in western cities but reduces it in northeastern regions, while per capita green area raises CEI in eastern cities but decreases it in the west. These findings highlight the need for region-specific policy interventions to effectively manage urban growth and reduce carbon emissions within the built environment, thereby contributing to China's low-carbon economy goals and supporting global efforts to address climate change.

How to cite: Chen, K.: Spatial Variations in the Impact of Urban Infrastructure on City-Level Carbon Emission Intensity in China: A Geographically Weighted Regression Approach, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-126, https://doi.org/10.5194/icuc12-126, 2025.