摘要：
As the development of urbanization in China enters the middle and late stages, improving the quality of human settlements has become a social concern. Promoting urban ecological environment management and building cities that are pleasant to live, work, and visit has become a new requirement for urban space quality improvement. ‘Spatial genes’ refer to unique and relatively stable spatial combination patterns formed by long-term interaction between urban space, natural environment, and history-culture, carrying region-specific information (Jin et al. Urban Planning 43:14–21, 2019). Located in the Western Hunan Tujia and Miao Autonomous Prefecture of Hunan Province, Fenghuang Ancient Town is a national historical and cultural city with a long history and deep cultural heritage, and it is a well-known tourist-type historical and cultural city in China; its geographic features, humanistic values, and characteristic architecture all have irreplaceable research value. In this paper, Fenghuang Ancient Town is selected as the research object, based on the spatial gene theory, through the collation and analysis of the network evaluation text of social media, the relationship between tourists and spatial gene influencing factors in the urban space is dissected, and spatial genes are identified, extracted, and classified to explore the tourists’ satisfaction evaluation of Fenghuang Ancient Town. At the same time, spatial analysis and spatial reconstruction are carried out using spatial syntax from the perspectives of geography and spatial layout, to promote the spatial upgrading and transformation of the urban environment through the re-modelling of the spatial genes of Fenghuang Ancient Town, and to provide decision-making guidance for the future urban development of Fenghuang Ancient Town.
摘要：
Urban resilience evaluates systems’ capacities to prepare for, adapt to, absorb, and recover from disruptions. Evaluation frameworks incorporate metrics like recovery speed, adaptive ability, and absorptive capacity. Assessing critical infrastructure interdependencies is challenging yet vital to limit failure propagation. While static assessments, multi-layer frameworks, and software like Hazus are used, limitations persist. Machine learning often focuses on infrastructure data for recovery monitoring. A common workflow entails acquiring and organizing data, then applying supervised, unsupervised, or reinforcement learning models. Supervised learning uses labeled data while unsupervised learning detects patterns in unlabeled data. Reinforcement learning optimizes rewards through trial-and-error interactions. Machine learning assists in meeting intensifying urbanization and climate change challenges. Leveraging advances in sensors, IoT, and computing enables tasks like image labeling and semantic segmentation. The techniques facilitate resilience through real-time data analytics for informed decision-making and responsive disaster management.
Urban resilience evaluates systems’ capacities to prepare for, adapt to, absorb, and recover from disruptions. Evaluation frameworks incorporate metrics like recovery speed, adaptive ability, and absorptive capacity. Assessing critical infrastructure interdependencies is challenging yet vital to limit failure propagation. While static assessments, multi-layer frameworks, and software like Hazus are used, limitations persist. Machine learning often focuses on infrastructure data for recovery monitoring. A common workflow entails acquiring and organizing data, then applying supervised, unsupervised, or reinforcement learning models. Supervised learning uses labeled data while unsupervised learning detects patterns in unlabeled data. Reinforcement learning optimizes rewards through trial-and-error interactions. Machine learning assists in meeting intensifying urbanization and climate change challenges. Leveraging advances in sensors, IoT, and computing enables tasks like image labeling and semantic segmentation. The techniques facilitate resilience through real-time data analytics for informed decision-making and responsive disaster management.
作者机构：
[Han, Xiaojuan; Peng, Fang; Zeng, Qing] College of Architecture and Urban Planning, Hunan City University, Yiyang, Hunan, China;[Su, Tao] School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, Guangdong, China. sutao@mail.sysu.edu.cn
通讯机构：
[Su, Tao] S;School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, Guangdong, China.
摘要：
Energy consumption forecasting in green buildings remains challenging due to complex climate-building interactions and temporal dependencies in energy usage patterns. Existing prediction models often fail to capture long-term dependencies and adapt to diverse climatic conditions, limiting their practical applicability. This study presents an integrated forecasting framework that combines sequence-to-sequence (Seq2Seq) architecture with reinforcement learning and transfer learning techniques. The framework employs long short-term memory (LSTM) networks enhanced with attention mechanisms to model temporal dependencies and climate variability in energy consumption data. The attention mechanism enables the model to focus on relevant temporal features while transfer learning facilitates adaptation across different climate zones. Experimental validation on two publicly available green building datasets demonstrates superior performance, achieving 96.2% accuracy, mean square error of 0.2635, and coefficient of determination ( R2 ) of 0.98. The proposed framework exhibits strong generalization capabilities across diverse climate conditions and building types. However, the framework requires substantial training data (6-12 months of high-quality sensor data) and shows reduced performance during extreme weather events, with RMSE increases of 15-20% under such conditions. These results suggest significant potential for improving energy management strategies in green buildings, contributing to enhanced energy efficiency and reduced carbon emissions in the construction sector. The framework is applicable to green buildings with reliable sensor infrastructure and adequate historical data, with performance optimized for standard operational conditions.
通讯机构：
[Wang, ZX ] H;Hunan City Univ, Sch Architecture & Urban Planning, Yiyang 413000, Peoples R China.
关键词：
Core Agglomeration "Scale-Vitality" Center;Close Collaboration Circle "Scale-Vitality" Center;Radiation and Synergy Circle "Scale-Vitality" Center
摘要：
Investigating the coupling coordination between urban scale and vitality is critical for enhancing holistic urban development quality and advancing sustainability. Taking the Changsha-Zhuzhou-Xiangtan (ChangZhuTtan) metropolitan area as a case study, this research integrates multi-source raster and vector data to: (1) analyze spatial patterns of urban scale and virtual-substantive vitality; (2) delineate a "scale-vitality" hierarchical zonal structure; (3) quantify coupling relationships across subzones; and (4) propose synergistic spatial optimization strategies. Key findings reveal that, distinct core-periphery structure characterizes urban scale and vitality, with Changsha's central districts dominating population, land use, and economic metrics, while Zhuzhou and Xiangtan exhibit moderate concentrations. Significant positive correlations exist between urban scale and dual vitality types, with scale-driven vitality enhancement being most pronounced in core agglomeration zones. Furthermore, in the metropolitan core, where both urban scale and vitality values are high, they exhibit a high-value coupling state. As they expanded outward, both metrics gradually decreased, resulting in a low-value coupling state. However, zonal comparisons (core agglomeration circle-peripheral expansion circle) reveal that the proportion of spatially coupled units progressively increases. By elucidating scale-vitality coupling in the ChangZhuTtan metropolitan area, this study provides actionable insights for spatial planning and sustainable urban transition. The methodology framework is replicable for similar metropolitan regions globally.
摘要：
INTRODUCTION: University campuses, with their abundant natural resources and sports facilities, are essential in promoting walking activities among students, faculty, and nearby communities. However, the mechanisms through which campus environments influence walking activities remain insufficiently understood. This study examines universities in Wuhan, China, using crowdsourced data and machine learning methods to analyze the nonlinear and interactive effects of campus built environments on exercise walking. METHODS: This study utilized crowdsourced exercise walking data and incorporated diverse campus characteristics to construct a multidimensional variable system. By applying the XGBoost algorithm and SHAP (SHapley Additive exPlanations), an explainable machine learning framework was established to evaluate the importance of various factors, explore the nonlinear relationships between variables and walking activity, and analyze the interaction effects among these variables. RESULTS: The findings underscore the significant impact of several key factors, including the proportion of sports land, proximity to water bodies, and Normalized Difference Vegetation Index NDVI, alongside the notable influence of six distinct campus area types. The analysis of nonlinear effects revealed distinct thresholds and patterns of influence that differ from other urban environments, with some variables exhibiting fluctuated or U-shaped effects. Additionally, strong interactions were identified among variable combinations, highlighting the synergistic impact of elements like sports facilities, green spaces, and waterfront areas when strategically integrated. CONCLUSION: This research contributes to the understanding of how campus built environments affect walking activities, offering targeted recommendations for campus planning and design. Recommendations include optimizing the spatial configuration of sports facilities, green spaces, and water bodies to maximize their synergistic impacts on walking activity. These insights can foster the development of inclusive, health-promoting, and sustainable campuses.
摘要：
High-albedo ground and wall materials are promoted to mitigate heat stress in tropical climates, yet conflicting evidence driven by climatic and metric variability make their impact on Outdoor Thermal Comfort (OTC) unclear. This study employed parametric simulations to assess how ground and wall albedo affect OTC, measured via the Universal Thermal Climate Index (UTCI) in typical urban canyons. Using ENVI-met, we tested ground albedo (0.2-0.8) and wall albedo (0.05-0.90) with emissivity fixed at 0.9. Findings reveal that ground albedo had a minimal impact on the UTCI (mean amplitude 0.44 degrees C), while wall albedo reduced the UTCI by up to 2.80 degrees C, prioritizing wall material selection for heat mitigation. It was also found that the increase in ground albedo offsets the cooling potential of high-albedo walls. Furthermore, differences in the impact under shaded and unshaded areas were observed. These results question assumptions of universal high-albedo benefits, recommending case-specific simulations in urban design.
期刊：
Construction and Building Materials,2025年493:143281 ISSN：0950-0618
通讯作者：
Qiang Xie
作者机构：
[Jiajun Yi] School of Architecture and Urban Planning, Hunan City University, Yiyang, China;[Yanxiang Xiao] College of Modern Agriculture, Yiyang Vocational and Technical College, Yiyang, China;[Fen Yu] Hunan City University Design Institute Co., Ltd., Yiyang, China;[Qiang Xie] Hunan City University Testing Center Co., Ltd., Yiyang, China
通讯机构：
[Qiang Xie] H;Hunan City University Testing Center Co., Ltd., Yiyang, China
摘要：
In response to the "dual carbon" strategy, the combination of reed waste and Portland cement to synthesize lightweight composites is crucial for promoting building energy conservation, carbon sequestration, and emission reduction. However, traditional biomass-based lightweight composites exhibit poor interfacial compatibility and numerous micropores, leading to weak water resistance and mechanical strength. These issues significantly limit the potential applications of these composites in various fields. Here, we synthesized epoxidized cardanol acetate (ECA) and utilized its response characteristics in alkaline slurry environment to decompose and recombine molecular chains, thereby improved the interfacial compatibility of hollow composites and achieved dense integration of their wall layers. The research strategy mainly involved the hydrolysis of −COOCH 3 in ECA, which released carboxyl and hydroxyl groups. The hydroxyl groups formed bonds with reed fibers, while the carboxyl groups chelated with silicate crystals. This resulted in a chemical "molecular bridge," which bonded the reed fibers to the silicate cement. The hydrolyzed long-chain ECA molecules formed a polymer network within the composites, which improved the overall stability of the hollow wall structure. Compared with ordinary hollow composites, the compressive strength of hollow composites prepared by this method increased by 73 %, and the bending strength increased by 50 %, with higher mechanical strength. The softening coefficient of composites was 0.93, with excellent water resistance and good thermal insulation performance. These reed fiber/Portland cement-based hollow composites have significant potential applications in building energy efficiency, non-load-bearing infill walls, and prefabricated buildings.
In response to the "dual carbon" strategy, the combination of reed waste and Portland cement to synthesize lightweight composites is crucial for promoting building energy conservation, carbon sequestration, and emission reduction. However, traditional biomass-based lightweight composites exhibit poor interfacial compatibility and numerous micropores, leading to weak water resistance and mechanical strength. These issues significantly limit the potential applications of these composites in various fields. Here, we synthesized epoxidized cardanol acetate (ECA) and utilized its response characteristics in alkaline slurry environment to decompose and recombine molecular chains, thereby improved the interfacial compatibility of hollow composites and achieved dense integration of their wall layers. The research strategy mainly involved the hydrolysis of −COOCH 3 in ECA, which released carboxyl and hydroxyl groups. The hydroxyl groups formed bonds with reed fibers, while the carboxyl groups chelated with silicate crystals. This resulted in a chemical "molecular bridge," which bonded the reed fibers to the silicate cement. The hydrolyzed long-chain ECA molecules formed a polymer network within the composites, which improved the overall stability of the hollow wall structure. Compared with ordinary hollow composites, the compressive strength of hollow composites prepared by this method increased by 73 %, and the bending strength increased by 50 %, with higher mechanical strength. The softening coefficient of composites was 0.93, with excellent water resistance and good thermal insulation performance. These reed fiber/Portland cement-based hollow composites have significant potential applications in building energy efficiency, non-load-bearing infill walls, and prefabricated buildings.
作者机构：
[Ou, Guangyu; Zeng, Qing] Hunan City Univ, Coll Architecture & Urban Planning, Yiyang 413000, Peoples R China.;[Zeng, Qing] Key Lab Key Technol Digital Urban Rural Spatial Pl, Yiyang 413000, Peoples R China.;[Zeng, Qing] Key Lab Urban Planning Informat Technol Hunan Prov, Yiyang 413000, Peoples R China.
通讯机构：
[Ou, GY ] H;Hunan City Univ, Coll Architecture & Urban Planning, Yiyang 413000, Peoples R China.
关键词：
hot summer and cold winter areas;BIM;campus living room;daylighting;optimization design
摘要：
In the context of green building development, the lighting design of campus living rooms in hot summer and cold winter areas faces the dual challenges of glare control in summer and insufficient daylight in winter. Based on BIM technology, this study uses Revit 2016 modeling and the HYBPA 2024 performance analysis platform to simulate and optimize the daylighting performance of the campus activity center of Hunan City College in multiple rounds of iterations. It is found that the traditional single large-area external window design leads to uneven lighting in 70% of the area, and the average value of the lighting coefficient is only 2.1%, which is lower than the national standard requirement of 3.3%. Through the introduction of the hybrid system of "side lighting + top light guide", combined with adjustable inner louver shading, the optimized average value of the lighting coefficient is increased to 4.8%, the uniformity of indoor illuminance is increased from 0.35 to 0.68, the proportion of annual standard sunshine hours (>= 300 lx) reaches 68.7%, and the energy consumption of the artificial lighting is reduced by 27.3%. Dynamic simulation shows that the uncomfortable glare index at noon on the summer solstice is reduced from 30.2 to 22.7, which meets the visual comfort requirements. The study confirms that the BIM-driven "static-dynamic" simulation coupling method can effectively address climate adaptability issues. However, it has limitations such as insufficient integration with international healthy building standards, insufficient accuracy of meteorological data, and simplification of indoor dynamic shading factors. Future research can focus on improving meteorological data accuracy, incorporating indoor dynamic factors, and exploring intelligent daylighting systems to deepen and expand the method, promote the integration of cross-standard evaluation systems, and provide a technical pathway for healthy lighting environment design in summer-hot and winter-cold regions.
通讯机构：
[Zeng, Q ] H;Hunan City Univ, Coll Architecture & Urban Planning, Yiyang, Peoples R China.
摘要：
Global awareness of sustainable development has heightened interest in green buildings as a key strategy for reducing energy consumption and carbon emissions. Accurate prediction of energy consumption plays a vital role in developing effective energy management and conservation strategies. This study addresses these challenges by proposing an advanced deep learning framework that integrates Time-Dependent Variational Autoencoder (TD-VAE) with Adaptive Gated Self-Attention GRU (AGSA-GRU). The framework incorporates self-attention mechanisms and Multi-Task Learning (MTL) strategies to capture long-term dependencies and complex patterns in energy consumption time series data, while simultaneously optimizing prediction accuracy and anomaly detection. Experiments on two public green building energy consumption datasets validate the effectiveness of our proposed approach. Our method achieves a prediction accuracy of 93.2%, significantly outperforming traditional deep learning methods and existing techniques. ROC curve analysis demonstrates our model's robustness, achieving an Area Under the Curve (AUC) of 0.91 while maintaining a low false positive rate (FPR) and high true positive rate (TPR). This study presents an efficient solution for green building energy consumption prediction, contributing significantly to energy conservation, emission reduction, and sustainable development in the construction industry.
通讯机构：
[Yang, J ] K;[Yang, J; Chen, Y ] H;Hunan City Univ, Coll Architecture & Urban Planning, Yiyang 413000, Hunan, Peoples R China.;Key Lab Key Technol Digital Urban Rural Spatial Pl, Yiyang 413000, Hunan, Peoples R China.;Key Lab Urban Planning Informat Technol Hunan Prov, Yiyang 413000, Hunan, Peoples R China.
关键词：
Generalized additive models;Measurement and verification;Energy efficiency measures;Building energy performance;Monte Carlo simulation
摘要：
Accurate measurement and verification (M&V) of energy efficiency measures (EEM) in commercial buildings is a key requirement to improve energy performance and meet sustainability goals. Research suggests a new method to M&V EEM using generalized additive models (GAM) to provide a way to measure how EEMs perform across different commercial buildings (i.e., offices, mixed-use developments, and healthcare). Comparisons suggest GAM is a preferred method of predicting energy savings from previous years and provides good estimates on a new dataset (comparable to previous years). The CV(RMSE) value is acceptably low. Lighting upgrades and HVAC improvements are areas of best practice for energy savings, and all sectors studied achieved significant energy savings with reasonable return times on investment compared to all other studies conducted to date (examples include offices and healthcare). We also focus on and show climate-related factors affecting energy consumption and had some success differentiating results based primarily on temperature/RH relative humidity-triggered variables and indicated the primary "thresholds" that appeared to alter energy demand behavior. Particular high humidity and temperatures carry serious energy penalties, and future climate change calls for climate-responsive energy policies. Furthermore, Monte Carlo simulations were used to measure uncertainty and backlog of data readings, not all prompted by climate factors alone, to confirm our results were sound.
摘要：
Outdoor thermal comfort (OTC) in traditional villages is a concern for the health of the residents and the development of rural tourism. However, previous studies on outdoor thermal comfort have given limited attention to traditional villages. Based on a field study conducted during summer in a traditional village in Fenghuang, China, which is a hot-humid environment, this study assesses outdoor thermal comfort variations between residents and tourists by considering three aspects: thermal benchmarks, influencing factors, and thermal adaptations. Results show that: (1) Residents are more acclimatized to the local hot-humid environment than tourists. The neutral PET (NPET) of residents (24.8 °C) was higher than that of tourists (20.1 °C). The neutral PET range (NPETR) of residents (21.0–28.7 °C) was higher compared to that of tourists (16.5–23.6 °C). The 80 % thermal acceptability range for residents and tourists was below 30.9 °C and 25.4 °C, respectively. (2) Factors influencing the thermal perceptions of residents and tourists vary with weather conditions. Solar radiation is the dominant influence on sunny days, while temperature is the dominant influence on cloudy days. Non-meteorological factors (individual and psychological factors) have a greater impact on tourists than on residents in both weather conditions. (3) When dissatisfied with the environment, tourists prefer improved temperatures and wind, while residents prefer improved humidity. Tourists manage thermal discomfort with drinking water and reducing activity intensity, while residents prefer moving to shade and reducing activity intensity. Accordingly, some suggestions were proposed, that could offer valuable references for the environmental optimization of traditional villages.
Outdoor thermal comfort (OTC) in traditional villages is a concern for the health of the residents and the development of rural tourism. However, previous studies on outdoor thermal comfort have given limited attention to traditional villages. Based on a field study conducted during summer in a traditional village in Fenghuang, China, which is a hot-humid environment, this study assesses outdoor thermal comfort variations between residents and tourists by considering three aspects: thermal benchmarks, influencing factors, and thermal adaptations. Results show that: (1) Residents are more acclimatized to the local hot-humid environment than tourists. The neutral PET (NPET) of residents (24.8 °C) was higher than that of tourists (20.1 °C). The neutral PET range (NPETR) of residents (21.0–28.7 °C) was higher compared to that of tourists (16.5–23.6 °C). The 80 % thermal acceptability range for residents and tourists was below 30.9 °C and 25.4 °C, respectively. (2) Factors influencing the thermal perceptions of residents and tourists vary with weather conditions. Solar radiation is the dominant influence on sunny days, while temperature is the dominant influence on cloudy days. Non-meteorological factors (individual and psychological factors) have a greater impact on tourists than on residents in both weather conditions. (3) When dissatisfied with the environment, tourists prefer improved temperatures and wind, while residents prefer improved humidity. Tourists manage thermal discomfort with drinking water and reducing activity intensity, while residents prefer moving to shade and reducing activity intensity. Accordingly, some suggestions were proposed, that could offer valuable references for the environmental optimization of traditional villages.
摘要：
BACKGROUND: The community environment is an important factor affecting people's residential relocation; however, existing literature has primarily focused on the objective aspects of the community environment, with less emphasis on residents' perception of it. METHOD: To address this research gap, we selected 74 typical communities and collected 1,568 questionnaires across Guangzhou. We employed factor analysis to capture participants' community environmental perception and used binary logistic regression to analyze the association between independent and dependent variables. RESULTS: The results show that: (1) There is a significant association between age, household registration, and participants' residential relocation intention; (2) Community environmental perception can be summarized into three aspects: environmental disorder perception, community attachment, and satisfaction, all of which are significantly associated with participants' residential relocation intention; and (3) There is a positive association between perception of a disorderly environment and residents' intention to relocation, and a negative association between community attachment and satisfaction and residents' intention to relocation. CONCLUSION: This research is highly significant for enhancing our understanding of factors influencing people's residential relocation intention and for guiding community construction.
摘要：
This study developed explainable machine learning (ML) models to enhance outdoor thermal comfort (OTC) prediction in traditional villages, aiming to improve resident health and rural tourism. A village-specific OTC dataset was established through field experiments, and eight ML algorithms were utilized to predict thermal sensation (TSV), comfort (TCV), and acceptance (TAV) votes, with performance compared to empirical and mechanism models. Feature engineering, data resampling, and hyperparameter optimization were applied to optimize ML models, while the Shapley Additive exPlanations (SHAP) framework with dataset segmentation addressed explainability challenges. Results demonstrated that: (1) ML models, particularly XGBoost (XGB), outperformed empirical and mechanism models in TSV predictions. (2) Feature engineering and Bayesian optimization achieved maximum accuracy improvement of 13.6% and 4.8%, respectively, while SMOTE resampling enhanced the prediction of minority classes. (3) Meteorological impacts on TSV, TCV, and TAV exhibited seasonal variations, with air temperature (Ta) dominating in summer and global radiation (G) prevailing in winter. Non-meteorological factors, including individual and psychological characteristics, showed divergent adaptation between tourists and residents. These findings offer valuable insights for optimizing village layouts and tourism schedules while advancing data-driven OTC management frameworks. This study bridges ML applications with traditional village sustainability, offering a foundation for intelligent built-environment science.
This study developed explainable machine learning (ML) models to enhance outdoor thermal comfort (OTC) prediction in traditional villages, aiming to improve resident health and rural tourism. A village-specific OTC dataset was established through field experiments, and eight ML algorithms were utilized to predict thermal sensation (TSV), comfort (TCV), and acceptance (TAV) votes, with performance compared to empirical and mechanism models. Feature engineering, data resampling, and hyperparameter optimization were applied to optimize ML models, while the Shapley Additive exPlanations (SHAP) framework with dataset segmentation addressed explainability challenges. Results demonstrated that: (1) ML models, particularly XGBoost (XGB), outperformed empirical and mechanism models in TSV predictions. (2) Feature engineering and Bayesian optimization achieved maximum accuracy improvement of 13.6% and 4.8%, respectively, while SMOTE resampling enhanced the prediction of minority classes. (3) Meteorological impacts on TSV, TCV, and TAV exhibited seasonal variations, with air temperature (Ta) dominating in summer and global radiation (G) prevailing in winter. Non-meteorological factors, including individual and psychological characteristics, showed divergent adaptation between tourists and residents. These findings offer valuable insights for optimizing village layouts and tourism schedules while advancing data-driven OTC management frameworks. This study bridges ML applications with traditional village sustainability, offering a foundation for intelligent built-environment science.
作者机构：
[Long, Tianxiang] Hunan City Univ, Coll Architecture & Urban Planning, Yiyang 413000, Peoples R China.;[Long, Tianxiang] Key Lab Urban Planning Informat Technol, Yiyang 413000, Peoples R China.;[Long, Tianxiang] Key Lab Digital Urban & Rural Spatial Planning, Yiyang 413000, Peoples R China.;[Liu, Yuxin] Tongji Univ, Sch Econ & Management, Shanghai 200092, Peoples R China.;[Zhong, Qikang] Cent South Univ, Sch Architecture & Art, Changsha 410083, Peoples R China.
通讯机构：
[Zhong, QK ] C;Cent South Univ, Sch Architecture & Art, Changsha 410083, Peoples R China.
关键词：
population-ecology-energy-digital economy;coupling coordination development;spatial-temporal evolution;spatial autocorrelation;driving factors;Yangtze River Basin
摘要：
Against the backdrop of globalization and ecological civilization, this study aims to analyze the patterns of system coupling coordination development in the Yangtze River Basin under the interacting influences of population growth, ecological conservation, energy utilization, and digital economic development. Using a multisource model, this paper explores the state of coordinated development, spatial–temporal evolution characteristics, and influencing factors in the Yangtze River Basin from 2011 to 2020. The results indicate the following: (1) The overall degree of coupling coordination in the Yangtze River Basin shows better performances in the eastern coastal areas compared to the central and western regions. Over time, the spatial autocorrelation of coupling and coordination increases, exhibiting a significant spatial clustering trend. (2) The Moran’s I index increased from 0.327 to 0.370, with high–high clusters primarily distributed in economically developed coastal provinces, while low–low clusters were observed in remote provinces in the central and western regions, revealing regional development imbalance issues. (3) The driving force analysis shows that green coverage and GDP are the core factors influencing the spatial differentiation of coupling coordinated development. Factors such as the urbanization rate, nighttime light index, and energy consumption had significant impacts in certain years but are generally considered minor factors. The results of this study not only contribute to understanding the dynamic mechanisms of regional coupling and development but also provide a scientific basis for formulating regional coordinated development policies, promoting the achievement of win–win goals of economic growth and ecological civilization in the Yangtze River Basin and similar regions.
作者机构：
[Luo, Qiao; Yu, Hongbing] Hunan City Univ, Coll Architecture & Urban Planning, Yiyang 413000, Peoples R China.;[Luo, Qiao; Yu, Hongbing] Hunan City Univ, Coll Architecture & Urban Planning, Lab Key Technol Digital Urban Rural Spatial Planni, Yiyang 413000, Peoples R China.;[Li, Yong] Chinese Acad Sci, Inst Atmospher Phys, State Key Lab Atmospher Boundary Layer Phys & Atmo, Beijing 100029, Peoples R China.;[Jiang, Shufang; Cao, Xueyou] Forestry Bur Yiyang City, Yiyang 410004, Peoples R China.
通讯机构：
[Li, Y ] C;Chinese Acad Sci, Inst Atmospher Phys, State Key Lab Atmospher Boundary Layer Phys & Atmo, Beijing 100029, Peoples R China.
关键词：
South Dongting Lake Nature Reserve;blue space;green space;precipitation;air temperature
摘要：
In recent years, the water level of the Dongting Lake (DTL) has been continuously low, and the wetland area and landscape pattern have changed significantly. Considering the obvious spatial heterogeneity of water regime changes in different waters of the DTL, this paper takes two core areas of the South Dongting Lake Nature Reserve (SDLNR) as study areas and analyzes the spatial distribution characteristics of the wetland blue–green landscape patterns by using remote sensing image data and hydrological and meteorological data. The multi-scale correlation between runoff, precipitation, temperature, and evapotranspiration in the SDLNR was studied via cross-wavelet transform analysis. The results show the following: (1) The change in the blue–green spatial patterns in different regions in different periods is inconsistent, and this inconsistency is related to the topography, climate, and human activities in each region; (2) there are seasonal fluctuations in precipitation, air temperature, and evapotranspiration in the SDLNR. Among them, the annual mean temperature shows a rising trend and passes the significance test with 95% confidence, while the annual mean precipitation and annual mean evapotranspiration show no significant change trend; and (3) our Pearson correlation analysis and cross-wavelet change results show that precipitation and temperature are strongly correlated with runoff, with a resonance period of 8–16 months, while the correlation between evapotranspiration and runoff is not significant. We recommend that policymakers establish an effective early warning system and make plans to store water through micro-terrain transformation in possible climate change treatments and strategies.
