The increasing challenges of urbanization and climate change have driven the need for innovative stormwater management solutions. Rain gardens, as a nature-based solution (NBS), have emerged as a critical component in urban water management, particularly in enhancing hydrological regulation, water quality, and ecosystem services. This bibliometric review examines the application of rain gardens in urban environments, focusing on their roles in stormwater management, pollutant removal, and ecological enhancement. Data from 728 academic papers published between 2000 and 2023 were analyzed using the Web of Science (WoS) Core Collection, employing bibliometric tools such as the “Bibliometrix” R package and CiteSpace. The analysis highlights the increasing global interest in rain gardens, particularly since 2015, with China and the United States leading research efforts. Key findings reveal that rain gardens significantly reduce runoff, improve water quality, and contribute to urban biodiversity. In addition, their integration into public spaces offers landscape esthetics and social benefits, enhancing the quality of life in urban areas. However, challenges remain in optimizing their design for diverse climates and long-term performance. The study underscores the need for further research on plant–soil interactions, pollutant removal mechanisms, and the broader ecological and social contributions of rain gardens. This review provides insights into the evolution of rain garden research and identifies future directions for advancing sustainable urban stormwater management.

This investigation delves into the waterfront plant landscapes of traditional villages in Western Hunan, China, aiming to bolster sustainable ecological resource management, amplify ecological culture, and ameliorate environmental standards. Furthermore, it endeavors to furnish a theoretical scaffold for the meticulous construction and assessment of these landscapes. This study has illustrated the waterfront botanical landscapes of 32 traditional hamlets within the Xiangxi region, integrating prior research on the waterfront botanical regression model based on the Scenic Beauty Estimation (SBE) method. It established and investigated fifteen landscape factors pivotal to the aesthetic valorization of these village waterfronts. The study concocted a beauty quality evaluation model, unearthing a significant correlation (p < 0.01) across evaluations by students majoring in landscape architecture, expert landscape architects, and laypersons, thus underscoring a consensus in aesthetic judgments. A noteworthy correlation between the beauty value (Z-value) and the entropy weight value was elucidated through the equation EWM = −0.106 + 0.425ZSBE, showcasing the landscape quality’s variance among the studied villages. The formulated evaluation model accentuates the significance of seasonal variation, scale affinity, and a rich hierarchical structure.

Traditional villages, as repositories of cultural heritage and natural landscapes, have gained increasing prominence in the tourism industry. However, balancing authenticity preservation with visitor satisfaction remains a critical challenge. This study employs a combined Analytic Hierarchy Process (AHP)–Scenic Beauty Estimation (SBE) approach under the theoretical framework of 4E theory (Entertainment, Education, Aesthetics, and Escapism) to comprehensively evaluate visitor satisfaction in traditional village tourism landscapes. Eight nationally designated tourism-oriented traditional villages in Anhua County, China were selected as case studies. Findings from the AHP analysis reveal that aesthetic and escapism experiences are the most influential dimensions in shaping visitor satisfaction, while entertainment and educational experiences, though secondary, remain integral to a well-rounded tourism framework. The SBE evaluation corroborates these results, highlighting that seasonal characteristics, stress relief, and cultural landscape diversity significantly enhance visitor experiences. Conversely, deficiencies were observed in social interactivity, satisfaction with educational experiences, and fulfillment of aesthetic needs, indicating areas for improvement. A strong positive correlation between AHP and SBE scores (Pearson correlation coefficient = 0.867, p < 0.01) underscores the alignment between expert-driven evaluations and visitor perceptions. These insights suggest that integrating expert-based hierarchical analysis with empirical visitor assessments provides a more robust and multidimensional framework for sustainable tourism management. Recommendations include enhancing social interactivity, optimizing educational components, enriching aesthetic experiences, and ensuring the preservation of vernacular landscapes to foster sustainable, experience-driven rural tourism development.

The Yuan River Basin in Hunan Province, known for its rich history and cultural heritage, is home to traditional village landscapes characterized by distinct cultural genes and regional features. This study focuses on 21 traditional villages in Xupu County, Huaihua, and develops four types of traditional village landscape cultural genome atlases at three hierarchical levels: individual villages, multiple villages, and a specific research area. These atlases include genetic linkage maps, spatial sequence maps, distribution pattern maps, and geographical pattern maps. Using Yangquepo in Zhumucun Village as a case study, we constructed a genetic linkage map for the landscape cultural genome of a single traditional village, based on the arrangement structure of courtyard layouts. Spatial sequence maps were derived from the overall spatial configuration, while distribution pattern maps and geographical pattern maps for the 21 villages were developed using kernel density analysis in ArcGIS 10.8 software. The results reveal that traditional village landscapes in the region possess a diverse cultural genome, marked by distinct regional characteristics and spatial organization patterns.

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.