Urban heat island (UHI) effects pose significant challenges to sustainable urban development, necessitating innovative modeling techniques to optimize urban morphology for thermal resilience. This study integrates the Pix2Pix and CycleGAN architectures to generate high-fidelity urban morphology models aligned with local climate zones (LCZs), enhancing their applicability to urban climate studies. This research focuses on eight major Chinese coastal cities, leveraging a robust dataset of 4712 samples to train the generative models. Quantitative evaluations demonstrated that the integration of CycleGAN with Pix2Pix substantially improved structural fidelity and realism in urban morphology synthesis, achieving a peak Structural Similarity Index Measure (SSIM) of 0.918 and a coefficient of determination (R²) of 0.987. The total adversarial loss in Pix2Pix training stabilized at 0.19 after 811 iterations, ensuring high convergence in urban structure generation. Additionally, CycleGAN-enhanced outputs exhibited a 35% reduction in relative error compared to Pix2Pix-generated images, significantly improving edge preservation and urban feature accuracy. By incorporating LCZ data, the proposed framework successfully bridges urban morphology modeling with climate-responsive urban planning, enabling adaptive design strategies for mitigating UHI effects. This study integrates Pix2Pix and CycleGAN architectures to enhance the realism and structural fidelity of urban morphology generation, while incorporating the LCZ classification framework to produce urban forms that align with specific climatological conditions. Compared to the model trained by Pix2Pix coupled with LCZ alone, the approach offers urban planners a more precise tool for designing climate-responsive cities, optimizing urban layouts to mitigate heat island effects, improve energy efficiency, and enhance resilience.

Urban stormwater management systems are increasingly strained by rapid urbanization and climate change, yet existing planning approaches often lack holistic optimization frameworks that account for both green and grey infrastructure (GREI) under uncertain future conditions. This study introduces a multi–objective optimization framework for Grey–Green Infrastructure (GGI), which integrates green infrastructure (GI) with GREI to enhance urban flood resilience, cost efficiency, and adaptability. The framework addresses life cycle cost (LCC), technological resilience (Tech-R), and operational resilience (Oper-R), offering a comprehensive approach to navigating the complexities of urban stormwater management. Key findings reveal that: (1) GGI systems optimized for resilience achieve a 33% improvement in Oper-R, with only a marginal increase in LCC of less than 9%, highlighting their robustness under GREI failure scenarios; (2) the integration of bioretention cells (BCs) and porous pavements (PPs) into GGI increases Tech-R by 7.1%, enhancing soil water retention and permeability, particularly in densely urbanized contexts; and (3) decentralized GGI systems exhibit superior adaptability to extreme weather events, with Design D reducing LCC to USD 53.9 M while maintaining no overflow under a 5–year rainfall event. The framework was validated in Zhujiang New Town, Guangzhou, where optimized GGI designs reduced average pipe diameters and manhole depths by 0.2–0.3 m compared to GREI–only systems, demonstrating both cost and resilience advantages. These findings provide decision–makers with a robust tool for evaluating trade–offs in stormwater infrastructure planning, advancing sustainable urban water management.

Nature-based solutions (NBS) encompass a diverse range of ecosystem-based strategies aimed at addressing urban sustainability challenges. Among these, skyscraper greenery emerges as a specialized application of NBS, integrating vertical vegetation systems into high-rise architecture to enhance carbon sequestration, mitigate urban heat islands, and improve air quality. By extending NBS principles into the vertical dimension of cities, this approach offers a scalable solution for climate adaptation in high-density urban environments. This study provides a comprehensive bibliometric analysis of skyscraper greenery research from 2003 to 2023, employing advanced tools such as CiteSpace and Bibliometrix to assess publication trends, elucidate key research themes, and identify prevailing knowledge gaps. The findings underscore the environmental benefits of skyscraper greenery, including its role in alleviating the urban heat island effect, improving air quality, and enhancing urban biodiversity. Additionally, economic advantages, such as reductions in energy consumption and operational costs, further highlight its multifaceted utility. Carbon sequestration within skyscraper greenery primarily occurs through vegetation’s photosynthetic processes, which are influenced by plant species, substrate composition, and system design. Thermal performance, ecosystem services, and biodiversity emerge as pivotal themes driving research in this domain. However, the field faces persistent challenges, including inconsistent methodologies for measuring carbon sequestration, a lack of technical standards, and limited public awareness. Future studies must prioritize the standardization of carbon measurement protocols, optimization of plant and substrate selection, and integration of skyscraper greenery within comprehensive urban sustainability frameworks. Addressing socio-economic barriers and enhancing policy incentives will be essential for widespread adoption. This review emphasizes the transformative potential of skyscraper greenery as a multifunctional strategy for climate mitigation, advancing resilient, low-carbon, and sustainable urban environments.

Nature-Based Solutions (NBSs) offer promising pathways to enhance ecological resilience and address urban water challenges, particularly in heritage cities where conventional gray infrastructure often fails to balance environmental needs with cultural preservation. This study proposes a strategic framework for the integration of NBSs into historic urban landscapes by employing Internal–External (IE) matrix modeling and an impact–uncertainty assessment, grounded in a structured evaluation of key internal strengths and weaknesses, as well as external opportunities and threats. The Internal Factor Evaluation (IFE) score of 2.900 indicates a favorable internal environment, characterized by the multifunctionality of NBS and their ability to reconnect urban populations with nature. Meanwhile, the External Factor Evaluation (EFE) score of 2.797 highlights moderate support from policy and public awareness but identifies barriers such as funding shortages and interdisciplinary coordination. Based on these findings, two strategies are developed: an SO (Strength–Opportunity) strategy, promoting community-centered and policy-driven NBS design, and a WO (Weakness–Opportunity) strategy, targeting resource optimization through legal support and cross-sectoral collaboration. This study breaks new ground by transforming theoretical NBS concepts into actionable, culturally sensitive planning tools that enable decision-makers to navigate the unique challenges of implementing adaptive stormwater and environmental management in historically constrained urban environments.

Urban flooding presents acute challenges in heritage cities, where dense populations and valuable cultural assets coexist. While Nature-Based Solutions (NbSs) have been widely studied, their implementation in heritage cities remains underexplored due to spatial constraints and cultural sensitivities. This study develops a quantitative evaluative framework integrating the Spatial Multi-Criteria Evaluation (SMCE) and NbSs to address urban flooding in Quanzhou, a UNESCO World Heritage site. In GIS-based spatial analysis, the framework prioritizes high-risk zones by synthesizing hydrological and socio-economic factors. The analysis reveals that the Surface Runoff Coefficient (SRC) contributes 30% to urban flooding exposure, with high building congestion and elevated PM2.5 levels exacerbating risks by 17% and 16.8%, respectively. Vulnerability mapping underscores the critical role of cultural heritage, accounting for 71.1% of the vulnerability index, and highlights priority townships such as Linjiang, Kaiyuan, and Lizhong, with integrated exposure and vulnerability rates of 11.8%, 10.3%, and 9.5%, respectively. This study proposes four NbS models tailored to heritage urban landscapes, with Solution I—direct surface infiltration—identified as the most applicable, covering 170.9 ha, followed by Solution II—subterranean stormwater infiltration—at 52.3 ha. Despite limited spatial feasibility (1.3–33.5% of township areas), the framework demonstrates significant potential for integrating NbSs with existing grey infrastructure, contributing to flood risk mitigation and broader sustainability goals. The findings provide actionable insights for urban planners and policymakers, offering a replicable methodology for the deployment of NbSs in heritage-rich urban contexts worldwide. By bridging flood risk management with cultural preservation, this work advances the discourse on resilient and sustainable urban planning.

Accelerated urbanization in China poses significant challenges for developing urban planning strategies that are responsive to diverse climatic conditions. This demands a sophisticated understanding of the complex interactions between 3D urban forms and local climate dynamics. This study employed the Conditional Generative Adversarial Network (cGAN) of the Pix2Pix algorithm as a predictive model to simulate 3D urban morphologies aligned with Local Climate Zone (LCZ) classifications. The research framework comprises four key components: (1) acquisition of LCZ maps and urban form samples from selected Chinese megacities for training, utilizing datasets such as the World Cover database, RiverMap’s building outlines, and integrated satellite data from Landsat 8, Sentinel-1, and Sentinel-2; (2) evaluation of the Pix2Pix algorithm’s performance in simulating urban environments; (3) generation of 3D urban models to demonstrate the model’s capability for automated urban morphology construction, with specific potential for examining urban heat island effects; (4) examination of the model’s adaptability in urban planning contexts in projecting urban morphological transformations. By integrating urban morphological inputs from eight representative Chinese metropolises, the model’s efficacy was assessed both qualitatively and quantitatively, achieving an RMSE of 0.187, an R² of 0.78, and a PSNR of 14.592. In a generalized test of urban morphology prediction through LCZ classification, exemplified by the case of Zhuhai, results indicated the model’s effectiveness in categorizing LCZ types. In conclusion, the integration of urban morphological data from eight representative Chinese metropolises further confirmed the model’s potential in climate-adaptive urban planning. The findings of this study underscore the potential of generative algorithms based on LCZ types in accurately forecasting urban morphological development, thereby making significant contributions to sustainable and climate-responsive urban planning.