Virtual Only | Track 3 | Session 4. Smart Risk Management and Climate Technologies

With the rapid pace of global urbanization, cities—viewed as Complex Adaptive Systems—face increasingly intricate sustainability challenges. Cold-region cities, in particular, must confront additional stressors such as extreme cold, heavy snow, and permafrost, compounding common urban issues like population growth, resource strain, and environmental pollution. These climatic extremes significantly impact urban morphology, infrastructure operations, human behavior, and microclimates. Complexity science, which elucidates multifactorial interactions and system evolution, offers theoretical and methodological tools to address these issues, thereby overcoming the limitations of conventional urban planning in managing uncertainty and dynamic interdependencies. This study explores how complexity science methodologies can be applied to address spatial and infrastructural challenges in cold-region cities. Specifically, it examines how methods such as complex network analysis, multi-agent system modeling, and machine learning can support adaptive planning under cold-specific constraints such as freeze-thaw cycles, snow loads, and extreme weather events. The research identifies current gaps, assesses methodological effectiveness, and proposes pathways to better integrate complexity science to inform sustainable and resilient development strategies in cold-region cities. Methodologically, the study conducts a systematic literature review, synthesizing articles and conference papers related to the application of complexity science in cold-region urban contexts. It focuses on how complex systems thinking supports urban morphology optimization, microclimate regulation, infrastructure resilience, and the evolution of human-environment interaction patterns. The review also maps methodological trends, relevant data types, and their adaptability to cold-region challenges. The study highlights that although complexity science offers novel insights into the nonlinear, multi-scale, and multi-component coupling dynamics of cold-region cities, several significant challenges persist. These include difficulties in obtaining high-resolution, multi-source heterogeneous data; insufficient interdisciplinary integration; and limited application of theoretical models in practical planning and management. To address these challenges, the study proposes four key future research directions: (1) Developing fine-grained coupled models adapted to cold-region characteristics by integrating multi-physical processes and social behaviors into high-fidelity simulation frameworks; (2) Advancing intelligent simulation and decision-support technologies by harnessing cold-region climate big data and urban operational monitoring to improve AI-driven forecasting, simulation, and optimization, thereby enhancing responses to extreme weather and sudden snow or ice-related hazards; (3) Enhancing interdisciplinary collaboration, through the establishment of more robust cross-sectoral cooperation frameworks and knowledge-sharing platforms that bridge complexity science with urban planning, climate adaptation, and disaster risk management; (4) Formulating cold-region-specific resilience strategies by advancing adaptive planning approaches and infrastructure systems capable of withstanding extreme cold, snow disruptions, and permafrost impacts. These directions aim to lay a stronger scientific foundation and offer more precise decision-making support for the sustainable and resilient development of cold-region cities.

Residential areas constitute a major component of urban building energy consumption, and their energy use is significantly shaped by spatial morphology. In cold regions, such consumption displays pronounced seasonal variation due to prolonged heating demands and specific climatic conditions. Understanding these seasonal fluctuations and the morphological factors influencing them is essential not only for developing energy-efficient residential areas but also for informing broader urban energy conservation strategies and climate-adaptive spatial planning policies. This study focuses on Harbin, a representative city in China's cold region known for its long, harsh winters and distinct seasonal climate. A multi-source spatial morphology database was established for 15 sampled residential communities in the city’s main urban area. Based on the climatic characteristics of cold regions, three study periods were defined: winter (November–December), summer (July–August), and the transitional season (September–October). These were used to investigate how spatial morphology affects residential energy consumption across different seasons, with an emphasis on identifying key drivers of seasonal differences. First, this study identified a set of spatial morphological factors through a combination of literature review, web-based data extraction, and field surveys. Using VirVil-HTB2 and EnergyPlus, energy simulation models were developed to estimate energy intensity in each season for each residential area. Spearman correlation analysis and multiple linear regression were applied to determine the most influential spatial factors—termed Spatial Morphology Key Factors (SMKFs)—that affect energy use patterns. Second, descriptive statistical analysis was conducted to compare seasonal energy profiles and detect characteristic patterns of variation across the three periods. These analyses helped to clarify inter-seasonal trends and validate the hypothesis that cold-region urban residential areas exhibit marked seasonal fluctuations in energy consumption. Finally, a composite index—the Seasonal Carbon Variation Index (SCVI)—was proposed to quantitatively capture the extent of seasonal variation. Taking SCVI as the dependent variable and SMKFs as independent variables, further statistical modeling was performed to reveal how spatial form regulates seasonal changes in energy consumption and carbon output. The results show that: (1) spatial factors such as floor area ratio, average building height, and green coverage rate are significantly correlated with energy use across seasons; (2) residential energy consumption differs significantly among summer, transitional, and winter periods, confirming the seasonal nature of energy demand in cold-region urban areas; and (3) SMKFs exert a strong influence on SCVI, underscoring their regulatory role in shaping seasonal energy dynamics and their potential leverage in climate-sensitive planning interventions. In conclusion, the study reveals distinct seasonal patterns in energy consumption for cold-region residential areas and clarifies the pathways through which urban morphology affects these patterns. The findings offer evidence-based guidance for energy-efficient planning and renewal of urban residential areas in cold climates. By identifying key morphological drivers and establishing a quantitative indicator (SCVI) of seasonal energy variation, this research provides a scientific foundation for sustainable urban development in cold regions and contributes to the optimization of spatial planning strategies to respond to climate-sensitive energy demands in a rapidly urbanizing world.

Rapidly urbanizing cities in the Global South face escalating compound climate risks, which include extreme heat, air pollution, and stress on energy systems. In these high-exposure and capacity-constrained contexts, the reconfiguration of core infrastructure, especially urban transport systems, has become essential to climate adaptation strategies. Although public transit electrification is traditionally regarded as a mitigation measure, it increasingly has the potential to function as adaptive infrastructure that provides co-benefits across environmental, spatial, and public health domains. This study investigates the extent to which the full electrification of Shenzhen’s public bus fleet, which was completed in 2017, has served as a multi-dimensional adaptation intervention. It examines how the transition, which involved institutional coordination, infrastructure restructuring, and spatial targeting, has contributed to reducing vulnerability to heat and air pollution in climate-sensitive urban districts. The central research question is how public bus electrification has enhanced urban adaptive capacity through measurable co-benefits, rather than solely through carbon mitigation. This study adopts a two-stage empirical framework to assess the adaptive value of Shenzhen’s public bus electrification. In the first stage, a Difference-in-Differences (DID) model estimates the causal effect of the policy on transport-related CO₂ emissions from 2014 to 2022. It covers the period from 2014 to 2022, which captures both the pre-policy baseline and post-implementation effects, with the full electrification of Shenzhen’s public bus fleet completed in 2017. Treatment is defined as the period following the completion of fleet electrification, and district-level fixed effects are included to control for unobserved heterogeneity. In the second stage, a GIS-based spatial overlay analysis is conducted to examine how co-benefits are distributed across the city. This involves mapping emission reductions against population density, urban heat exposure zones, and land use patterns at the subdistrict level. Data sources include official emissions inventories, transport operation records, remote sensing–derived land surface temperature data, and statistical yearbooks. This methodological design directly operationalizes the co-benefits framework and links mitigation outcomes to spatially targeted adaptation potential. The DID results indicate a statistically significant reduction in transport-related carbon emissions that can be attributed to the electrification program. These reductions are most pronounced in central districts where ridership is dense and heat exposure levels are high. Spatial analysis shows that emission reduction hotspots correspond to locations where climate vulnerability is also high. This suggests that public bus electrification may provide geographically concentrated adaptation benefits. In addition, the transition has produced co-benefits that include improved air quality, lower exposure to particulates during periods of extreme heat, and reduced peak electricity loads during hot seasons. Institutional analysis shows that the rapid implementation of the program was made possible through centralized procurement, strong coordination between energy and transport agencies, and targeted fiscal incentives. This study proposes a reconceptualization of bus electrification as a form of adaptive infrastructure that addresses both mitigation and adaptation goals. By combining quasi-experimental econometric methods with spatial evaluation techniques, it presents a framework that can be applied to assess adaptation-oriented infrastructure in other resource-constrained urban environments. The case of Shenzhen provides practical and transferable insights for Global South cities that are seeking to align sustainable mobility policies with broader climate resilience objectives.

With the increasing frequency of extreme climate events and the global push for carbon neutrality, renewable energy development has become a crucial strategy for enhancing regional climate adaptation. Solar energy, as an important clean energy source, is central to promoting green transformation and achieving emission reduction targets. However, solar development faces significant land use challenges. The low energy density and high land requirements of photovoltaic systems can intensify land resource competition, leading to conflicts and negative environmental impacts if not carefully planned. Furthermore, climate change and fragile ecosystems threaten the stability of centralized, long-distance energy systems, which has accelerated a shift toward decentralized, local energy supply. In this context, scientifically assessing the spatial suitability and environmental sensitivity of photovoltaic projects, while coordinating energy production, land use, and ecological protection, has become an urgent challenge for spatial planning and management. This study proposes a dual assessment framework based on sensitivity and suitability, with Baishui County in the Loess Plateau as the study area. The study integrates GIS and Multi-Criteria Decision Analysis, using a three-step spatial assessment process: sensitivity assessment, suitability assessment, and overlay analysis. First, sensitivity assessment is conducted using landscape characteristic areas as basic units, considering landscape features, ecological and cultural values, and environmental risks. Clustering and spatial overlay analysis define sensitivity levels, determine the maximum allowable development scale for each area, and establish constraints on photovoltaic intensity. Second, suitability assessment uses grids as units to systematically evaluate power generation efficiency, operational costs, facility resilience, and socio-ecological impacts. Boolean logic is applied to screen constraints, and both the Analytic Hierarchy Process and Weighted Linear Combination are applied to calculate comprehensive suitability scores and classify areas into grades. Finally, the results of sensitivity and suitability assessments are combined, with moderate sensitivity areas serving as thresholds to set the maximum allowable development scale for each grid, ensuring spatial coordination between suitability and safety. The research findings show clear spatial differences in photovoltaic development potential in Baishui County. Regions with open, flat terrain, sparse farmland, and degraded ecological functions, such as Leiya and Xigu towns in the southeast, have low land use efficiency and low ecological sensitivity to photovoltaic development. These areas are suitable for coordinating ecological restoration with large-scale contiguous photovoltaic layouts. Areas with fragmented terrain but locally flat topography and scattered farmland, represented by Shiguan and Beiyuan towns in the north, are suitable for medium-scale distributed photovoltaic systems that form a mosaic pattern with agricultural land. Open and flat areas with dense residential settlements and contiguous farmland, such as Yaohe, Lingao, and Dukang towns in the southwest, as well as fragmented gully areas in the central Luohe tributary region, have limited land resources and are more suitable for small-scale embedded photovoltaic projects that integrate with agricultural and residential needs. River corridors, nature reserves and other high ecological value areas provide critical ecosystem services and have extremely high sensitivity; photovoltaic development should be strictly restricted in these areas, including the Luohe and Baishui river areas as well as Fangshan Forest Park. The results indicate that the dual assessment framework proposed in this study can effectively identify suitable photovoltaic sites and scale boundaries under various geographical and environmental conditions, achieving a dynamic balance between technical and economic feasibility and socio-ecological risks. The framework provides a scientific basis and spatial reference for differentiated photovoltaic layout strategies and multifunctional land use patterns in Baishui County, while offering theoretical innovation and practical guidance for integrating renewable energy spatial planning with ecological protection in similar regions. It contributes to improving regional climate adaptation capacity and achieving sustainable development goals.

In recent years, rapid urbanisation and climate change have exacerbated land-use transformation, leading to higher ecological vulnerability, and exploring the spatial and temporal evolution of carbon emissions under land-use change is of great significance for optimising land-use structure and sustainable regional development. Focusing on Xinjiang, a critical ecological barrier in northwestern China, this study systematically investigates the evolutionary characteristics of land use patterns, and their carbon emission effects across Xinjiang from 1980 to 2023. Key findings reveal: (1) Grassland (34.6%) and unused land (48.2%) dominate Xinjiang's land cover, yet significant structural shifts occurred over four decades. Water bodies contracted by 1.51×10⁴ km² (-30.31%), while forested areas diminished by 1.02×10⁴ km² (-27.17%). Conversely, cultivated land and built-up areas surged by 3.38×10⁴ km² (59.98%) and 0.69×10⁴ km² (205.5%), respectively. Notably, bidirectional conversions between grassland and unused land totaled 24.52×10⁴ km², triggering systemic ecological degradation in oasis zones. (2) The Tianshan North Slope exhibited a "dual-increase-dual-decline" transition pattern. Built-up areas expanded by 260.86% (net increase: 0.30×10⁴ km²), paralleled by a 45.27% growth in cultivated land (0.93×10⁴ km²). This contrasted sharply with precipitous declines in water bodies (-42.07%) and forests (-42.35%), collectively eroding ecosystem services. (3) Regional carbon emissions exhibited phased growth, multiplying 6.95-fold during 2000–2023 (annualized growth: 9.4%), peaking at 19.47% from 2010–2015. Although carbon sink intensity maintained modest annual growth (1.2–3.8%), the carbon exchange rate (0.32→0.18) and carbon sink potential index (0.85→0.61) declined persistently, reducing ecological carbon offset capacity by 42.6% against energy-derived emissions. The proposed “land use-carbon metabolism” analytical framework provides decision-making support for optimizing territorial spatial configurations and enhancing ecological network resilience. We emphasize institutionalizing synergistic mechanisms among production-living-ecological (PLE) spaces to achieve dynamic equilibrium between oasis expansion and ecological carrying capacity, offering theoretical and practical guidance for arid zone sustainability.

Understanding carbon storage dynamics is essential for climate-resilient urban planning, especially in arid and semi-arid regions vulnerable to extreme conditions. The focus of this study is to assess the changes in carbon storage patterns on the northern slope of the Tianshan Mountains from 1980 to 2023 through the InVEST model, factoring in four key carbon pools: aboveground biomass, belowground biomass, soil organic carbon, and organic matter in decay. Combining land use data with climate-adjusted carbon density measures—which are refined using models based on mean annual temperature (MAT) and precipitation (MAP)—the study outlines how carbon is distributed in response to changing environmental factors. Findings reveal a modest overall increase in carbon storage (+0.8%), driven primarily by cropland expansion (+45.26%), while forest (-35.7%) and shrubland (-64.25%) areas declined significantly. Urban areas showed the highest relative carbon increase (+260.77%) due to land conversion, though with limited absolute contribution. Wetlands demonstrated partial recovery (+21.25%), whereas unutilized land decreased, resulting in carbon losses (-5.17%). The findings include spatial maps that show the changing trends of carbon distribution and changes in land utilization to support positive planning and efficient policy formulation. The observed loss of forest and shrubland not only reduces regional carbon stocks but also increases vulnerability to urban heat island effects, soil erosion, and climate-related hazards in expanding urban zones. These results provide important insights into how changes in land use impact regional carbon dynamics and climate adaptation opportunities. The framework can inform urban planning strategies that aim to enhance carbon sequestration, reduce surface temperatures, and improve ecological resilience under intensifying climate extremes. By identifying priority areas for conservation, restoration, or sustainable development, this approach supports integrated land management policies aligned with climate mitigation and adaptation goals.