Land Surface Temperature (LST) is widely used for urban heat monitoring; however, growing evidence suggests that LST often diverges from near-surface air temperature (Ta). Existing research on the LST-Ta difference is limited by a lack of high-resolution Ta data, insufficient investigation across varying urban settings, and inadequate consideration of dynamic meteorological factors. To address these gaps, this study integrates Weather Research and Forecasting model coupled with the Urban Canopy Model (WRF-UCM) simulations with satellite observations to quantify spatiotemporal LST-Ta differences across Local Climate Zones (LCZs). The main findings are: (1) During daytime, built-up zones show positive temperature differences (LST > Ta), whereas natural zones display negative differences (LST < Ta). At night, negative differences prevail. (2) Dynamic meteorological factors, including relative humidity, wind speed, and wind direction, are identified as dominant drivers of the LST-Ta difference. (3) Static geographical elements, including NDVI, proximity to water, and surface albedo, play a secondary yet non-negligible role. (4) Local spatial configurations can drive divergent LST-Ta difference patterns even within the same LCZ type. These insights bridge the gap between satellite observations and near-surface thermal conditions, providing a transferable framework for precise and timely heat risk assessment during heatwaves.

The widespread adoption of electric vehicles (EVs) is essential for mitigating climate change. However, deployment of charging stations (CSs) has not kept pace with rapid EV growth, largely because of insufficient strategic planning. This study investigates CSs in Wuhan, China, using long-term, high-resolution charging records to generate typical 15-min utilization rate curves. First, we identify four distinct utilization patterns using unsupervised clustering and validate their robustness through a static classification model. This step confirms that the identified temporal patterns are not random artifacts but are intrinsic to specific built environments and long-term operational attributes. Subsequently, by constructing a novel dynamic spatial network using real-time traffic data, this study systematically quantifies the competition, agglomeration, and spillover effects within the CS network. Results show that station usage is embedded in a complex spatial network, revealing distinctive spatiotemporal mechanisms across the identified patterns. Based on these findings, we propose pattern-specific spatial optimization strategies. Following a "pattern identification-pattern validation-mechanism analysis-spatial optimization" framework, this study provides both theoretical insights and practical guidance for CS planning.

Amid China’s ongoing urban transformation, the rational spatial distribution of hospitals as critical public service infrastructure is central to promoting equity and improving healthcare accessibility. However, empirical evidence on how accessibility contributes to predicting hospital choice at the individual level—particularly through nonlinear distance effects—remains limited. Adopting a behavior-oriented perspective, this study examines hospital choice within a constrained setting, specifically focusing on inpatient decisions for respiratory conditions between two tertiary (Class 3 A) hospitals in Wuhan that are geographically proximate and comparable in institutional attributes. Using inpatient records and a behavior-calibrated interpretation of spatial accessibility, we integrate explainable machine learning with geospatial and environmental data to identify distinct distance-response regimes governing patient decision-making, rather than assuming uniform distance decay. The results indicate that spatial distance shows the strongest association with hospital selection but operates through threshold-based behavioral switching, with marked transitions occurring at specific distance intervals. Embedding these behavior-derived distance thresholds into a Two-Step Floating Catchment Area (2SFCA) framework, the study refines conventional accessibility assessment by explicitly accounting for both population demand and hospital resource availability. Spatial analysis shows that greater medical resource abundance significantly attenuates distance sensitivity, highlighting the context-dependent nature of spatial friction in healthcare utilization. By linking individual choice behavior with accessibility modeling, this study advances a behavior-calibrated framework for assessing healthcare accessibility. The findings provide applied insights for optimizing medical facility planning and resource allocation, particularly in rapidly urbanizing contexts where conventional distance-based assumptions may misrepresent actual patterns of healthcare use.

With the new phase of urbanization in China, enhancing urban spatial quality has become a key task in urban development. As an important indicator of residents’ willingness to live, housing prices provide valuable feedback from their perspective for improving spatial quality. Taking Wuhan as a case study, this paper constructs an indicator system with 12 explanatory variables, including a subjective evaluation of buildings generated using deep learning techniques. Using OLS and GWR models, the study analyzes the factors influencing housing prices and their spatiotemporal dynamics in Wuhan’s core urban areas from 2016 to 2024, encompassing the full cycle of housing price fluctuations from an upward to a downward trend. The findings reveal that, as housing prices return to more rational levels, the impact of location factors diminishes, while the influence of community quality factors—such as property fees, green space ratio, and building quality—significantly increases. Factors such as proximity to hospitals also exhibit a certain degree of spatiotemporal complexity. This trend highlights residents’ growing attention to housing quality and living environments, marking a fundamental shift in the behavior of homebuyers. The results of this study provide crucial insights into the evolution of residential preferences and the spatiotemporal dynamics of the housing market. They offer significant theoretical and practical references for understanding residents’ housing needs from their perspective, thereby promoting the healthy development of the real estate market and improving urban spatial quality.

The strategic placement of electric vehicle charging stations (EVCS) is fundamental to the widespread adoption of electric vehicles and plays a crucial role in mitigating climate change. As an emerging public transportation infrastructure, balancing fairness and efficiency in EVCS deployment is essential. To address the challenge of optimizing both fairness and efficiency in EVCS planning, this study introduces a two-step optimization (2SO) model that incorporates dynamic spatiotemporal demand. The model's effectiveness is validated through an empirical analysis in Wuhan, China, using millions of charging records and dynamic population distribution data. Results show that the optimization enhances overall fairness by providing more equitable access to charging services across different regions. In terms of efficiency, the optimization significantly improves charging equipment utilization and maximizes resource allocation. Specifically, prioritizing charging station allocation during working hours better accommodates dynamic charging demand and facilitates global optimization. This study validates the proposed optimization method, providing scientific guidance for urban charging station deployment, supporting city managers in making informed decisions, and promoting the widespread adoption of electric vehicles to advance sustainable urban transportation. Additionally, it supports energy conservation, emission reduction, and climate change mitigation, laying a strong foundation for achieving sustainable development goals.

With the rapid expansion of urban rail transit networks, an accessibility-usage mismatch has been revealed, where taxi trips have pick-up and drop-off points within metro catchment areas, yet commuters choose taxis (i.e., metro-replaceable trips). Existing literature fails to reveal the underlying reasons, fully account for influencing factors, or identify weather-sensitive metro stations. To address these, this study develops a comprehensive framework, including metro-replaceable trip identification, feature extraction, SHapley Additive exPlanations (SHAP) approach for identifying driving factors, and clustering algorithms for recognizing commuting patterns and weather-sensitive metro stations. The main findings are: (1) Weather conditions are the driving factors. (2) Rain prompts a shift from the metro to taxis, while in cold or windy weather, commuters prefer the metro. (3) Thresholds to alleviate the mismatch include travel distances over 7500 (morning) and 6000 m (evening); MetroTaxi time ratio below 2.6 (morning); Taxi-Metro price ratio over 6.5 (evening); and departure times outside 7:40-8:40 and 17:45-18:55. (4) For commercial metro stations, density is more crucial, while diversity matters more for residential ones. (5) Weather-sensitive stations are located in densely populated residential or commercial areas, with entrances along Wuhan's primary roads. These findings offer valuable insights to promote green commuting and improve metro competitiveness.

The regeneration of plants endemic to remote mountain areas is thought to be relatively unimpacted by human disturbances but rather dominated by abiotic factors, such as geography, climate, and soil. However, because human disturbances are accelerating the extinction of montane plants and the loss of montane forest, this balance may be shifting. Yet, the relative effects of abiotic factors and human disturbances to montane plant regeneration are still largely unclear. Here, we investigated the geographic pattern of regeneration (ratio of seedling and ratio of sprout) and assessed the impacts of abiotic and anthropogenic factors for an endangered montane tree species (Davidia involucrata) across its distribution range in China. We found that the ratio of seedling increased from south to north, whereas the ratio of sprout exhibited an opposite pattern, indicating that under climate warming this species may adopt sprout regeneration as a potential strategy to buffer population contraction at the southern edge. Moreover, while climatic factors were the main drivers of regeneration, anthropogenic factors were also important. Of note, the proportion of pasture land area had a significant positive effect on sprouting, with more sprout regeneration at grazed sites and a higher ratio of sprout at sites with a greater intensity of human disturbance. Our findings suggested that, in addition to climate change, human disturbance is also an important driving factor of the regeneration of plants native to remote mountain areas, and we emphasized that researchers and policymakers should take it into account when protecting endangered plants and managing forest biodiversity.

Seed mineral nutrition is essential for early seedling establishment, and varies under different environmental conditions. However, the intraspecific variation of multi-elements in seeds and the relative effects of climate and soil on seed elements remain unclear, even though understanding these factors is crucial for predicting plant reproductive responses to global changes. Here, we sampled seeds from Euptelea pleiospermum across 18 populations in China. We quantified the inter-population variation of 12 elements in the seeds and analysed their relationship with soil characteristics and climatic variables. We also explored the relationship of N and P concentrations between seeds and leaves. Results showed that seed elements were highly variable across different populations, with macroelements exhibiting lower variability than most of the microelements. Along the latitudinal gradient, the concentrations of K, Ca, Fe and Al in seeds increased, while the concentrations of C and Mn decreased. The stoichiometry of seed elements did not significantly correlate with latitude. Seed element concentrations were associated with both soil and climatic variables, and the influence of soil conditions on intraspecific variations is comparable to or even greater than climatic factors. However, seed stoichiometry was less related to environmental factors. Seeds had higher P but lower N than leaves, with no correlation between seed elements and leaf elements. Our findings suggest that mountain tree species respond to different local environments by adjusting seed element concentrations while maintaining relatively stable seed stoichiometry. We emphasize that, in addition to climate change, soil conditions should be considered when predicting the influence of environmental changes on the elemental composition of plant reproductive organs. (sic)(sic) (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic) (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(Euptelea pleiospermum)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)18(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)12(sic)(sic)(sic)(sic)(sic)(C,N,P,K,Ca,Mg,Fe,Mn,B,Zn,Cu,Al)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)N,P(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic):(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)K,Ca,Fe(sic)Al(sic)(sic)(sic)(sic), (sic)C(sic)Mn(sic)(sic)(sic)(sic);(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic);(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)P(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)N(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)N,P(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).

Amid the global shift towards sustainable development, this study addresses the burgeoning electric vehicle (EV) market and its infrastructure challenges, particularly the lag in public charging facility development. Focusing on Wuhan, it utilizes big data to analyze EV charging behavior’s spatiotemporal aspects and the urban environment’s influence on charging efficiency. Employing a random forest regression and multiscale geographically weighted regression (MGWR), the research elucidates the nonlinear interaction between urban infrastructure and charging station usage. Key findings include (1) a direct correlation between EV charging patterns and urban temporal factors, with notable price elasticity; (2) the predominant influence of commuting distance, supplemented by the availability of fast-charging options; and (3) a strategic proposal for increasing slow-charging facilities at key urban locations to balance operational costs and user demand. The study combines spatial analysis and charging behavior to recommend enhancements in public EV charging infrastructure layouts.

Spatial co-location patterns reflect the inherent correlations among geographical elements. Mining co-location patterns of POIs can provide valuable insights for urban planning and resource management. Generally, co-location mining comprises two steps: proximity relationship determination (geospatial analysis) and frequent pattern recursion (logical reasoning). Previous methods often separate these two steps: serializing proximity relationships to enumerate frequent sequences. However, this approach suffers from limited flexibility and intuitiveness: as continuous spatial contexts are segmented into numerous small parts, it fails to adequately represent geographic correlations and hinders the effective visualization of logical reasoning. Facing these challenges, this study proposes a novel graph-based spatial co-location mining method (GSCM), which leverages graphs to integrate geospatial analysis and logical reasoning. Initially, to establish adjacency relationships, GSCM constructs the adaptive neighborhood graph, which dynamically adjusts proximity thresholds to accommodate geographic heterogeneity. Subsequently, the Apriori logical recursive process is realized on the graph structure. By leveraging graph searching, pruning, and growing, the potential growth directions of co-location patterns are identified, enhancing both the efficiency and intuition of frequent pattern recursion. Through experiments conducted on large-scale POI datasets from Wuhan, GSCM is compared with existing baseline methods, verifying its potential to uncover co-location patterns in complex spatial contexts.

The routine monitoring of eutrophication is an important measure for observing the variation in water quality and protecting the ecological health of lakes. However, in situ information reflects eutrophication levels within a limited distance and period. In this study, we retrieved the trophic level index (TLI) based on Landsat 8 remote sensing images and using a machine learning (ML) method in Liangzi Lake in Hubei Province, China. The results showed that random forest (RF) outperformed other ML algorithms in estimating the TLI, evaluated by its higher fitness through the Monte Carlo method (median values of R2, RMSE, and MAE are 0.54, 0.047, and 0.037, respectively). In general, 8% of the areas of Liangzi Lake presented an increasing eutrophication level from 2014 to 2022, and 20.1% of the areas reached a mild eutrophication level in 2022. In addition, we found that temperature and anthropogenic activities may impact the eutrophication conditions of the lake. This work uses remote sensing imagery and a ML method to monitor the dynamics of the lake’s eutrophication status, thereby providing a valuable reference for pollution control measures and enhancing the efficiency of water resource management.

Travel mode serves as the link for communication among people, between people and objects, and between people and places. The human mobility is closely related to travel mode. Scientifically predicting human mobility can help alleviate traffic congestion and provide flexible travel choices. However, in current predictions, only the travel demand or human mobility is taken into account, while the influence of residents on the travel mode choice is neglected. Therefore, taking Wuhan City as an example, this paper proposes a new method for predicting human mobility by employing graph neural network techniques and travel mode choice behavior. The prediction model established in this study utilizes network analysis to measure the accessibility time and road network distance of traffic travel mode. The graph neural network method is employed to capture the dynamic temporal and spatial relationships underlying human mobility. Furthermore, the performance of the prediction model is evaluated. The results indicate that at a fine spatial scale, the new method can more accurately reveal the spatial patterns of changes in human mobility, significantly improving the accuracy of predicting human mobility.

Given the background of ecological fragility in western China, the northward migration of the livestock industry, and the “carbon peak” in China, it is practically significant to discuss the evolution of carbon dioxide equivalent emission intensity (CEI) in major livestock (pigs, cattle and sheep) rearing in the Shaanxi–Gansu–Ningxia (SGN) region. This discussion aims to protect the ecology of western China, achieve sustainable and healthy development of the livestock industry, and realize the national goal of “double carbon”. In this study, we utilized statistical data from 2010 to 2021 for pigs, cattle, and sheep at the municipal level in the SGN region. We applied the methodology provided by the IPCC to comprehensively measure the carbon dioxide equivalent emissions (CEs), explore spatial and temporal trends, and analyze the driving forces behind spatial variations in the intensity with the assistance of GeoDetector. The following conclusions were drawn: Firstly, the total CEs generally exhibit fluctuating and increasing patterns. Moreover, the total CEs in different cities (states) within the region show obvious variations, with a tendency to shift toward the north. Secondly, the CEI demonstrates a clear downward trend. However, the CEI in different cities (states) exhibits increasing spatial heterogeneity. Furthermore, the western part of the region is evolving toward high-value areas, while the eastern part is evolving toward low-value areas. Lastly, the results of the GeoDetector indicate that the core driving factors are the pig, cattle, and sheep rearing structure; the urban population proportion; and the per capita gross national product. In summary, the total amount of CEs demonstrates a fluctuating increase, while the intensity shows a clear downward trend. Therefore, it is recommended to reduce CEs from livestock rearing in this region by optimizing the rearing structure of pigs, cattle, and sheep, promoting low-carbon consumption, and moderately importing livestock products.

The aging population has brought increased attention to the urgent need to address social isolation and health risks among the elderly. While previous research has established the positive effects of parks in promoting social interaction and health among older adults, further investigation is required to understand the complex relationships between perceptions of the park environment, social interaction, and elderly health. In this study, structural equation modeling (SEM) was employed to examine these relationships, using nine parks in Wuhan as a case study. The findings indicate that social interaction serves as a complete mediator between perceptions of the park environment and elderly health (path coefficients: park environment on social interaction = 0.45, social interaction on health = 0.46, and indirect effect = 0.182). Furthermore, the results of the multi-group SEM analysis revealed that the mediating effect was moderated by the pattern of social interaction (the difference test: the friend companionship group vs. the family companionship group (Z = 1.965 > 1.96)). Notably, family companionship had a significantly stronger positive impact on the health of older adults compared to friend companionship. These findings contribute to our understanding of the mechanisms through which urban parks support the physical and mental well-being of the elderly and provide a scientific foundation for optimizing urban park environments.

This research explores the nonlinear interactions among multidimensional proximities, including geographical, cognitive, organizational, institutional, social, and technological aspects, and their impact on innovation within networks of over three million technology firms in China. Utilizing an innovative combination of web-based hyperlink and textual data analysis, supplemented by patent information, we delve into how these proximity dimensions influence corporate innovation capabilities. Our methodology integrates text-based deep learning techniques and employs the XGBoost model along with the SHapley Additive exPlanations (SHAP) algorithm and partial dependence plots to uncover the nuanced effects of proximity on innovation. The findings reveal that while geographical distance often correlates with larger cognitive and organizational proximities, underdeveloped regions exhibit stronger technological, institutional, and social proximities compared to their developed counterparts. The study further identifies social structure and technological differences as pivotal factors impacting collaborative innovation, with both positive and negative effects fluctuating alongside changes in proximity dimensions. Notably, we uncover that geographical proximity has a pronounced boundary effect on innovation, highlighting the critical role of spatial considerations in the digital age of innovation networks. This research contributes to the understanding of urban innovation dynamics and offers valuable insights for policymakers and urban planners aiming to foster innovation ecosystems.

Land use/land cover (LULC) structure optimization can effectively increase carbon storage/carbon sequestration (CS) and help realize carbon neutrality goals1. Studying the spatial distributions of LULC and CS under climate change conditions is highly important for realizing sustainable development goals. This study is based on different climate change models, and the coordinated development of economic, water, carbon and ecological sustainability was considered to establish a comprehensive multiscale, multiscenario and multiobjective LULC optimization model. Then, different climate change scenarios were optimized, and regional CS values were predicted. The LULC simulation model provided satisfactory simulation results at different scales. Notably, the average accuracy exceeded 0.92. The optimized land expansion results exhibited heterogeneity. Forestland change accounted for the largest proportion of the total LULC change. After optimization, the CS values under the different scenarios were similar. The northwestern part of the study area served as the main carbon sink area. The aim of this study was to respond to future complex climate change by rationally planning the LULC structure, thus achieving the sustainable development of urban agglomerations.

Spring leaf phenology influences plant fitness and is highly sensitive to environmental changes. The spring phenological escape hypothesis posits that deciduous understory plants generally leaf out earlier than canopy trees to access a period of high light before the canopy closes. However, plants in different forest layers may respond differently to climate warming, which could lead to phenological mismatch between the understory and canopy species. Therefore, exploring the phenological sensitivities of plants from different forest layers is crucial for anticipating the effects of climate-driven phenological shifts. However, these types of studies are still scarce. Here, we conducted a twig experiment in climate chambers for eight tree species and seven shrub species growing in subtropical deciduous forests in the Shennongjia Mountains, central China. Specifically, we set up three treatments (control, warming, and shading) for five weeks to test the responses of the timing of bud burst and leaf unfolding, and the duration of bud development to different temperatures and light intensities. For shrubs, bud burst and leaf unfolding occurred earlier and the duration of bud development was shorter than these of trees. For both shrubs and trees, warming significantly advanced the timing of leaf unfolding and shortened the duration of bud development. Shading significantly delayed the timing of bud burst for both shrubs and trees. Shading also delayed the timing of leaf unfolding and lengthened the duration of bud development for trees, but did not significantly influence the two phenological metrics for shrubs. Tree phenology, as measured by all three metrics, was more sensitive to temperature than was shrub phenology. The timing of bud burst of shrubs was slightly more sensitive to light than that of trees. Our results indicate that shrubs leaf out earlier than trees in the same forest, which allows understory species to take advantage of the high-light resources before canopy closure. However, in our study, trees were more responsive to warming temperatures, suggesting that spring warming could advance canopy closure relative to shrub leaf phenology, potentially resulting in phenological mismatch between understory shrubs and canopy trees.

Unraveling the effects of urban morphology on CO2 emissions is essential for shaping sustainable and low-carbon urbanization practices. However, few studies have developed spatially tailored mitigation strategies based on fine-grained analysis of 3D urban morphology. This study extracts 3D urban morphology metrics from buildings and streets at a 1 km grid in central Wuhan. Notably, the inter-building obstruction and street topology are taken into account in this field for the first time. Then, Random Forest and interpretive algorithms are used to unravel the effects of urban morphology on CO2 emissions. Ultimately, Geographic Random Forest is adopted to develop spatially tailored mitigation strategies. The main results are: (1) Urban morphology contributes more to CO2 emissions than traditional socioeconomic explanations like population density and land use. (2) Sky view factor significantly influences CO2 emissions, second only to population density. (3) Vertically high-density development leads to higher emissions. (4) Optimal parameters for carbon reduction are observed with the building shape coefficient at 0.68, mean neighbor distance at 85, and Severance at 1.28. (5) Four distinct classes are classified based on local dominant influencing factors, and tailored low-carbon strategies are proposed. This methodological framework can also be applied to global cities undergoing rapid urbanization.