With the acceleration of global urbanization, high-quality dark sky resources are becoming increasingly scarce. As the intersection of the front line of light pollution diffusion and ecological buffer zones, the urban fringe of megacities faces dark sky protection challenges arising from complex baseline light environments and intensified human activities. Taking Guangzhou as a case study, this paper proposes a dark sky park site selection and light environment optimization framework based on multi-source data integration. By integrating high-resolution nighttime light remote sensing data from SDGSAT-1, ground-based SQM measurements, all-sky fish-eye imagery, and POI-based socio-perceptual data, a multi-dimensional site suitability evaluation model is constructed, incorporating natural baseline conditions, transportation accessibility, and socio-economic factors. The results indicate that (1) distinct “dark sky islands” are present in northeastern Guangzhou, and a total of 13 natural protected areas with potential for dark sky park development were identified, among which Conghua Lianxi Municipal Forest Park exhibits the highest overall suitability with a score of 0.89; (2) Ground-based measurements confirm that under moonless conditions the zenith night sky brightness in this area reaches up to 21.28 mag/arcsec2, meeting the Dark-Sky International (IDA) Silver Tier standard (21.00≤SQM≤21.74), with the optimal observation window occurring between 02:00 and 05:00; (3) POI-based source attribution analysis reveals that near-horizontal light pollution is primarily generated by the adjacent Shantou-Zhanjiang Expressway as a linear source, while public service facilities within the protected areas exhibit characteristics of high-intensity point light sources. Based on these findings, this study proposes an integrated optimization pathway that combines top-down and bottom-up approaches, providing quantitative evidence and a practical framework for constructing dark sky ecological networks in the urban fringe of megacities.

In the context of the global urban transition towards stock-based regeneration (a shift from outward urban expansion to the redevelopment of existing built environments), the provision of public service facilities is facing a paradigm shift from mere physical spatial implementation to sustainable long-term operation. Traditional planning pathways heavily rely on static spatial allocation policies and upfront indicator compliance, yet systematically neglect the dynamic adaptability of facilities throughout their entire life cycles. Taking the megacity of Guangzhou, China, as a longitudinal case study, this paper reveals a typical compliance versus failure paradox-a situation where facilities strictly meet technical planning standards on paper but fail to deliver intended social welfare outcomes in practice. Using early comprehensive redevelopment projects like Liede Village as examples, the public service facilities strictly met the statutory allocation standard (5.7%) during the construction phase. However, after more than a decade of operation, these facilities have exhibited severe structural supply-demand mismatches and long-term operational dilemmas. To address this issue, this study proposes a four-dimensional governance framework-Value, Actor, Space, Institution (VASI)-that transcends traditional spatial perspectives. Through an in-depth analysis of the Guangzhou case using this framework, the research confirms that the root causes of the compliance failure lie in the absence of life-cycle costing (a method of assessing the total financial cost of facility ownership over its entire lifespan), the severe structural misalignment of rights and responsibilities between construction and operation actors, and the long-term void in post-occupancy evaluation feedback mechanisms. This paper argues that the planning of public service facilities in high-density megacities must achieve a theoretical leap from rigid upfront technical allocation to adaptive whole-life-cycle systemic governance, providing theoretical references and a practical guide for global cities facing similar stock-based regeneration challenges as they move towards equitable and socio-economically sustainable urban regeneration.

Urban flooding has become a major challenge in historic districts, where intensified risks intersect with strict cultural heritage preservation requirements. This study develops an integrated framework that combines multi-dimensional urban flood risk assessment with the spatial optimization of Nature-based Solutions (NbS) and applies it to Liwan District, Guangzhou. The framework evaluates flood risk across 22 sub-districts through exposure, vulnerability, and adaptability indexes. The results show that Lingnan Subdistrict, despite its low exposure, faces the highest overall flood risk due to high vulnerability and limited adaptability. These conditions are often overlooked in conventional exposure-based assessments, thereby highlighting the importance of a multi-dimensional perspective for balancing flood resilience and heritage conservation. Moreover, the findings further indicate that expanding permeable surface coverage is an effective strategy for mitigating runoff and provide practical guidance for planners and policymakers in heritage-rich urban areas. Overall, this research bridges flood risk assessment and cultural heritage preservation and offers an integrated framework to support adaptive, spatially explicit, and climate-resilient planning in historic cities.

As a green transport alternative, electric bikes (e-bikes) have experienced unprecedented global growth. Understanding their relationship with the built environment—especially path-related features, which critically influence unsheltered mode choice—is essential given their expanding fleets. Yet, few studies have quantitatively examined the role of path-based built environment on e-bike travel behavior. Using travel survey data from the Jinan metropolitan area in China, this study applied the XGBoost model to explore nonlinear associations between e-bike use and built environment features at the origin, destination, and along the path. Results indicate that: (1) e-bikes are primarily used for work commutes, followed by leisure and school trips, with average distances of 3.76 km, 2.73 km, and 2.20 km, respectively; (2) built environment factors outweigh socioeconomic and travel attributes in e-bike choice, with path-level features being most influential; (3) travel distance is the dominant factor, showing an inverted-V relationship with a threshold around 3.5–4 km; (4) route congestion exhibits an M-shaped association, while intersection density negatively correlates with e-bike use, and other route features (e.g., Green View Index) display inverted-V patterns. These findings support planning strategies for promoting e-bike use through cycling-friendly environments.

Urban festive lighting, represented by light shows, can boost nighttime economy, but the threat of high-intensity light interference to ecological spaces cannot be overlooked. With the Guangzhou International Light Festival as a case, we constructed a high-resolution ground-based observation network covering up to 16 km from the light source center, to accurately quantify the light disturbance within ecological spaces and its impact distance. The results showed that, compared with normal nights without light shows, during the light festival period, the average night sky brightness within 16 km of the Canton Tower (light show center) increased by more than 0.26 mag·arcsec-2(increase >1.6%), with a maximum increase at a single point for a single observation reaching 0.65 mag·arcsec-2(increase 4.2%). The dynamic high-frequency fluctuations of light exhibited significant nonlinear characteristics. At a distance of 6 km from the light source, both the standard deviation of zenith brightness (0.19) and the instantaneous fluctuation amplitude (3.5%) exceeded those at the source center, forming a dynamic peak zone where far-field intensity was stronger than near-field intensity. This study found the significant impact distance of light shows was 6-7 km. Based on the finding, we proposed planning and management strategies such as delineating a 6 km light-ecological buffer zone and avoiding peak bird migration periods, providing a scientific basis for coordinating the development of the nighttime economy and the protection of nocturnal ecosystems.

In recent years, the surge of delivery riders for the Internet platform economy has drawn widespread attention and sparked debate regarding their roles in urban governance. While some reported their traffic violations and disorderly conduct, 93 % of news reports observed their beneficial acts in urban governance. Whether the delivery riders can act as informal guardians who may potentially prevent crime has yet to be explored quantitatively in existing literature. To fill the gap, using mobile phone big data, this study identified and quantified the spatial distribution of delivery riders in ZG city. Then, this study employed a zero-inflated negative binomial model to analyze the relationship between their spatial distribution and street crime. Results showed that nearly 15,000 delivery riders of ZG city were identified from a large sample of mobile phone trajectory data, and they are mainly concentrated in economically active areas with busy commerce and service activities and dense populations. The number of riders' visits exhibits a significant negative association with street crime. This demonstrates that delivery riders can indeed act as informal guardians, which has not been previously reported in existing literature. This study enriches the theory of informal guardianship in the Internet platform economy era and highlights the social value of delivery riders in urban governance, offering practical insights for urban safety planning.

Future land use change significantly affects the urban thermal environment, increasing potential urban heat risks. The study of the dynamic changes in land use under future urban development scenarios is still lacking, and insufficient attention has been paid to their underlying impact on urban heat risks. This study introduced an urban heat risk prediction framework to explore the influence of land use change on the distribution of the risk. The Patch-generating Land Use Simulation (PLUS) model was used to model future land use change. Indicators of hazard, exposure, and vulnerability, associated with land use, were set as inputs into the Crichton risk model. The proposed framework was demonstrated under four future urban development scenarios in a high-density city: economic development (ED), natural development (ND), ecological protection (EP), and coordinated development (CD) scenarios. The results showed that the highest growth in terms of land use, population, and economic factors may occur under the ED scenario, followed by the ND and EP scenarios. Compared to land use patterns in 2020, between 4.78 % and 9.40 % of cropland and woodland will be converted into built-up land by 2035. The heat risk index was expected to increase by 3.29 % similar to 4.53 % under the ED and EP scenarios. Meanwhile, a significant percentage (27.66 %) of urban areas were classified as high risk regions under the ED scenario, and were primarily concentrated within the urban center. In addition, high risk areas were expected to expand toward the city's fringes, near its sub-centers, indicating that regions experiencing significant growth will face increased heat risks during future land use changes. This study identified high risk areas under future development scenarios, which offers support for urban planning and the development of mitigation strategies for heat risks.

China’s rapid urbanization has presented challenges for sustainably revitalizing the historic and cultural heritage within its urban villages. Often, these efforts overlook the crucial roles of community ties and cultural values. This study focuses on 15 representative urban villages in Guangzhou (2019–2024). It tests the core idea that the physical layout of these spaces reflects underlying community structures and cultural values shaped by specific policies. Integrating this understanding into landscape planning can significantly improve revitalization outcomes. We used a mixed-methods approach: (1) Extended fieldwork to understand community networks and cultural practices; (2) Spatial analysis to measure how building density relates to land uses; (3) Sentiment analysis to reveal how people perceive cultural symbols; (4) A coordination model to link population influx with landscape suitability. Key findings reveal different patterns: Villages with strong clan networks maintained high cultural integrity and public acceptance through bodies like ancestral hall councils. Economically driven villages showed a split—open for business but culturally closed, with very low tenant participation. Successful revitalization requires balancing three elements: protecting physical landmarks in their original locations; modernizing cultural events; and reconstructing community narratives. Practically, we propose a planning framework with four approaches tailored to different village types. For instance, decaying villages should prioritize repairing key landmarks that hold community memory. Theoretically, we build a model linking social and spatial change, extending the cultural value concepts of Amos Rapoport to the context of fast-growing cities. This provides a new methodological perspective for managing urban–rural heritage in East Asia.

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.

Carbon dioxide emission is one of the major contributors to extreme climate change and has caused much attention by scholars. Improving carbon emission performance (CEP) is a key path to achieving carbon emission targets with sustainable economic development, which should become a new focus of academic attention. Landscape pattern is an important perspective to explore the mechanism of urban CEP in addition to socioeconomic factors. Relevant literature had several limitations for studying landscape factors of the whole urban area, while not focusing on the construction land that is the primary contributor to carbon dioxide emissions and cannot eliminating the impacts of non-construction land on carbon sinks, and also overlooking spatial heterogeneity in Chinese cities, which may lead to inaccurate conclusions of the mechanism of urban landscape patterns on CEP. By shifting the focus from carbon emissions to CEP, this paper specifically takes all Chinese cities as samples and investigates the influence mechanism of CEP through the lens of landscape pattern based on the fine-grained carbon emission data and land use raster data from 2005 to 2015. First, this paper combines the undesirable output slack-based measure (UN_SBM) model and the super-efficiency slack-based measure model with undesirable output (Un_Super_SBM) model to measure CEP and uses the landscape index to quantify landscape patterns. Second, the regression models were used to quantitatively reveal the impacts of urban landscape patterns on CEP. It is found "Area-weighted mean shape index" (AWMSI) and "Splitting index" (SPLIT) have negative impacts on CEP across all cities. In terms of heterogeneity, the index "Total landscape area" (TA) exhibits a significant positive effect on CEP in Northeastern cities, and SPLIT has negative impact only in Central cities. This study aims to offer effective suggestions for urban governments and decision makers to develop specific strategies by optimizing landscape patterns to improve CEP for sustainable development.

Coupled grey and green infrastructure (CGGI) is increasingly recognized as a viable approach to sustainable urban stormwater management. This study evaluates CGGI and grey infrastructure (GREI)-only schemes with various degree of centralization of the layout (DCL) in addressing urban flood and drainage issues in a historical and cultural district (HCD) which typically consists of high impervious surfaces, dense urban structures, and fragile heritage buildings. Yongqing Fang Community in Guangzhou, China, was selected as a case study in which the performance of the Grey-only and CGGI schemes are evaluated and compared. The results obtained indicated that the CGGI scheme was more advantageous in terms of cost-effectiveness and scalability, yielding potential savings of $30,500 to $163,400. Moreover, the fully decentralized layout of the two schemes could result in cost savings of 29.0% and 29.6%, respectively, over the fully centralized layout. However, CGGI shows marginally lower adaptability in response to extreme rainfall events compared to that of GREI-only solutions. Technical resilience (Tech-R) of GREI-only scored higher by 0.1% to 0.8%, 0.5% to 3.5%, and 0.7% to 4.8% for 10-year, 50-year, and 100-year rainfall scenarios, respectively. Nonetheless, CGGI schemes demonstrated superior adaptability in structural failure scenarios, and reduced surface overflow by 22.6%, 19.0%, and 18.4% compared to GREI for the same scenarios. In both the CGGI and GREI-only schemes, decentralized layouts are likely to outperform centralized layouts in both extreme rainfall events and in failure scenarios. These findings underscore the importance of decentralized layout of the drainage infrastructure which could enhance the hydrological performance of integrated drainage infrastructures, offering insights for due considerations in designing multi-objective infrastructures for urban flood mitigation in HCDs.

In this study, a multi-stage planning framework was constructed by using SWMM simulation modeling and NSGA-II and applied to optimize the layout of integrated grey–green infrastructure (IGGI) under land use change and climate change scenarios. The land use change scenarios were determined based on the master plan of the study area, with imperviousness of 50.7% and 62.0% for stage 1 and stage 2, respectively. Rainfall trends for stage 1 and stage 2 were determined using Earth-E3 from the CMIP6 model. The rainfall in stage 2 increased by 14.9% from stage 1. Based on these two change scenarios, the spatial configuration of IGGI layouts with different degrees of centralization of the layout (DCL) under the two phases was optimized, with the lowest life cycle cost (LCC) as the optimization objective. The results showed that the layout with DCL = 0 had better performance in terms of LCC. The LCC of the layout with DCL = 0 was only 66.9% of that of the layout with DCL = 90.9%. In terms of Tech-R, stage 2 had better performance than stage 1. Furthermore, the average technological resilience (Tech-R) index of stage 2 was 0.8–3.4% higher than that of stage 1. Based on the LCC and Tech-R indices of all of the layouts, TOPSIS was used to compare the performance of the layouts under the two stages, and it was determined that the layout with DCL = 0 had the best economic and performance benefits. The results of this study will be useful in exploring the spatial configuration of urban drainage systems under land use change and climate change for sustainable stormwater management.

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.

The indiscriminate evolution of urban configurations aggravates flood vulnerabilities, threatening sustainable urban expansion. Present methodologies fall short in supplying urban planners with flood mitigative strategies centered on urban configuration facets. Leveraging the power of the XGBoost algorithm, this study posits an advanced optimization schema, adroitly balancing the dual objectives of mitigating urban flooding and enhancing economic growth, with minimal disruption to established urban layouts. Shenzhen serves as the investigative ground, where the model displays exceptional accuracy, resilience, and interpretability in predicting Pluvial Flooding Susceptibility (PFS) and Economic Contribution (EC). Model interpretation divulges the profound influence of three-dimensional urban configuration elements, primarily the Building Congestion Degree, on PFS and EC. Pareto solution exploration for multi-objective optimization unveils the ideal urban configuration interval. To minimize PFS while maximizing EC, the research suggests pertinent measures: augmenting vegetation density, regulating the impervious coverage ratio within 50–70%, limiting two- and three-dimensional building density thresholds, and moderately escalating urban drainage network density. Additionally, it encourages a comprehensive appreciation of function-oriented land usage and intrinsic site topographical characteristics to reconcile varied urban development goals during planning. By fusing data-derived insights with multi-objective optimization, this research anticipates influencing urban planning models, thus enhancing decision-making related to urban configuration and fostering flood-resilient, sustainable, and economically prosperous urban habitats.

Urban microclimate faces serious challenges due to increased urbanization and frequent heatwave events. Many studies focused on investigating the holistic quantitative relationships between urban morphology factors and heat island intensity at the city scale, but less effort has been devoted to exploring the relationships on a block scale. Additionally, there is a lack of fast prediction methods for urban microclimate for local climate zones (LCZ) planning and design. To address these challenges, this study proposes a Long Short-Term Memory Networks (LSTM) model to predict the effects of urban morphology factors on the air temperature under local climate zones. The effects of the spatial morphology features on the air temperature were characterized and quantified employing a post-interpretation method. The Pearl River New Town (PRNT), the downtown area of Guangzhou, China, was considered as the research area for the model implementation. The results showed that air temperature prediction accuracy is the best when using the historical three-time step data, with R2 of 0.975. LCZ A has the highest prediction accuracy, with an R2 of 0.990. LCZ 5 has the lowest accuracy, with an R2 of 0.881. Moreover, the effect of urban morphology factors on air temperature was found to be greater than the effect of land cover type. In this regard, the sky view factor (SVF) has the highest impact, followed by the aspect ratio (AR) and the pervious surface fraction (PSF). Nevertheless, the warming effect in built type was stronger than that in land cover. During the heatwave period, the maximum and minimum temperature changes were recorded in LCZ 4 and LCZ A, respectively, with values of 9.7 °C and 8.6 °C. It was shown that low-rise areas are more resilient than high-rise areas during heatwave periods. This is because low-rise areas generally exhibit a smaller increase in air temperature. These findings provide a better understanding of the relationship between urban microclimate and urban form, and a method of rapidly predicting the microclimate of a neighborhood block. It provides guidance and support, with great significance for climate-friendly urban planning.

Concurrent meteorological extremes (CMEs) represent a class of pernicious climatic events characterized by the coexistence of two extreme weather phenomena. Specifically, the juxtaposition of Urban Extreme Rainfall (UER) and Urban Extreme Heat (UEH) can precipitate disproportionately deleterious impacts on both ecological systems and human well-being. In this investigation, we embarked on a meticulous risk appraisal of CMEs within China’s Greater Bay Area (GBA), harnessing the predictive capabilities of three shared socioeconomic pathways (SSPs) namely, SSP1-2.6, SSP3-7.0, and SSP5-8.5, in conjunction with the EC-Earth3-Veg-LR model from the CMIP6 suite. The findings evidence a pronounced augmentation in CME occurrences, most notably under the SSP1-2.6 trajectory. Intriguingly, the SSP5-8.5 pathway, typified by elevated levels of greenhouse gas effluents, prognosticated the most intense CMEs, albeit with a temperate surge upon occurrence. Additionally, an ascendant trend in the ratio of CMEs to the aggregate of UER and UEH portends an escalating susceptibility to these combined events in ensuing decades. A sensitivity analysis accentuated the pivotal interplay between UER and UEH as a catalyst for the proliferation of CMEs, modulated by alterations in their respective marginal distributions. Such revelations accentuate the imperative of assimilating intricate interdependencies among climatic anomalies into evaluative paradigms for devising efficacious climate change countermeasures. The risk assessment paradigm proffered herein furnishes a formidable instrument for gauging the calamitous potential of CMEs in a dynamically shifting climate, thereby refining the precision of prospective risk estimations.

Introduction: The COVID-19 pandemic has produced a profound impact on travel behavior. Yet, few studies explored how individuals resist such impact to maintain their daily life from the perspective of people's responses. A comprehensive theoretical framework to elucidate individual travel behavior changes and a quantitative analysis of the pivotal factors driving these changes was lacking. Methods: This study constructed a framework for individuals' travel behavior in response to COVID-19 and utilized anonymized mobile phone trajectory data before and during COVID-19 in Guangzhou to explore the changes of individuals' travel behavior on workdays. Gradient boosting decision trees was used to quantify the degree of the factors driving travel changes. Results: During the pandemic workdays, non-essential trips experienced a reduction of 51.4%, while essential trips decreased by 30.94%; Subway trips decreased by 1.63%, whereas motorized trips increased by 8.75%. The most significant decrease was observed in travel duration (50.92%), followed by travel frequency (41.28%), and travel distance (39.19%). The three travel indices displayed significant social disparities and spatial disparities. However, the relative relationships among these three travel indices did not vary with groups and spaces. Risk perception and intervention measures, emerged as primary drivers of individuals' travel changes, contributing to 27%-35% of the total impact, while individual socio-economic characteristics and built environment accounting for less than 10%. Implications: The results indicated that individuals curtailed both their essential and non-essential trips and adjusted their travel mode in response to the epidemic. These changes in travel purpose and mode led to differing reduction rates in travel frequency, duration, and distance. These findings contribute to individual travel changes prediction and fine-grained epidemic modelling during other public health emergencies like COVID-19.

The pivotal role of quantitative risk assessment in managing urban stormwater is underscored by the high spatial heterogeneity and complex non-stationarity complex of urban flooding. Conventional methods, reliant on measurable data, often fall short in accurately mapping spatial variations and gauging the full impacts of urban flooding. Addressing this gap, this study proposed a robust convolutional neural networks (CNN)-based tool, specifically designed for urban flooding risk and economic losses estimation under extreme design rainfall scenarios. Furthermore, the study validates the transferability of CNN models trained in data-abundant regions to similar but data-scarce regions, using Guangzhou as a case study for urban flooding damage prediction. The findings reveal that the most affected areas, particularly the old, densely built-up urban areas in the south-central Guangzhou, are susceptible to significant economic losses during extreme rainfall events. Notably, under the most severe scenario (Scenario 5), estimated economic losses amount to approximately $6359.91 million, with industrial and residential sectors bearing the brunt, accounting for 28.29 % and 39.94 % of the total losses, respectively. These insights are crucial for prioritizing mitigation efforts and formulating effective evacuation strategies in high-risk areas, ultimately aiding in the reduction of economic losses.

Nature-based solutions (NbSs) are considered to form an innovative stormwater management approach that has living resolutions grounded in natural processes and structures. NbSs offer many other environmental benefits over traditional grey infrastructure, including reduced air pollution and climate change mitigation. This review predominantly centers on the hydrological aspect of NbSs and furnishes a condensed summary of the collective understanding about NbSs as an alternatives for stormwater management. In this study, which employed the CIMO (Context, Intervention, Mechanism, Outcome) framework, a corpus of 187 NbS-related publications (2000–2023) extracted from the Web of Science database were used, and we expounded upon the origins, objectives, and significance of NbSs in urban runoff and climate change, and the operational mechanisms of NbSs (including green roofs, permeable pavements, bioretention systems, and constructed wetlands), which are widely used in urban stormwater management, were also discussed. Additionally, the efficacy of NbSs in improving stormwater quality and quantity is discussed in depth in this study. In particular, the critical role of NbSs in reducing nutrients such as TSS, TN, TP, and COD and heavy metal pollutants such as Fe, Cu, Pb, and Zn is emphasized. Finally, the main barriers encountered in the promotion and application of NbSs in different countries and regions, including financial, technological and physical, regulatory, and public awareness, are listed, and future directions for improving and strategizing NbS implementation are proposed. This review gathered knowledge from diverse sources to provide an overview of NbSs, enhancing the comprehension of their mechanisms and applications. It underscores specific areas requiring future research attention.