Coupling analysis of passenger and train flows is an important approach in evaluating and optimizing the operation efficiency of large-scale urban rail transit (URT) systems. This study proposes a passenger–train interaction simulation approach to determine the coupling relationship between passenger and train flows. On the bases of time-varying origin–destination demand, train timetable, and network topology, the proposed approach can restore passenger behaviors in URT systems. Upstream priority, queuing process with first-in-first-serve principle, and capacity constraints are considered in the proposed simulation mechanism. This approach can also obtain each passenger’s complete travel chain, which can be used to analyze (including but not limited to) various indicators discussed in this research to effectively support train schedule optimization and capacity evaluation for urban rail managers. Lastly, the proposed model and its potential application are demonstrated via numerical experiments using real-world data from the Beijing URT system (i.e., rail network with the world’s highest passenger ridership).

Quality 4.0 is an emerging concept that has been increasingly appreciated because of the intensification of competition, continually changing customer requirements and technological evolution. It deals with aligning quality management practices with the emergent capabilities of Industry 4.0 to improve cost, time, and efficiency and increase product quality. This article aims to comprehensively review extant studies related to Quality 4.0 to uncover current research trends, distil key research topics, and identify areas for future research. Thus, 46 journal articles extracted from the Scopus database from 2017 to 2022 were collected and reviewed. A descriptive analysis was first performed according to the year-wise publication, sources of publication, and research methods. Then, the selected articles were analyzed and classified according to four research themes: Quality 4.0 concept, Quality 4.0 implementation, quality management in Quality 4.0, and Quality 4.0 model and application. By extracting the literature review findings, we identify the Quality 4.0 definitions and features, develop the quality curve theory, and highlight future research opportunities. This study supports practitioners, managers, and academicians in effectively recognizing and applying Quality 4.0 to enhance customer satisfaction, achieve innovation enterprise efficiency, and increase organizational competitiveness in the era of Industry 4.0.

Named entity recognition (NER) is essential in many natural language processing (NLP) tasks such as information extraction and document classification. A construction document usually contains critical named entities, and an effective NER method can provide a solid foundation for downstream applications to improve construction management efficiency. This study presents a NER method for Chinese construction documents based on conditional random field (CRF), including a corpus design pipeline and a CRF model. The corpus design pipeline identifies typical NER tasks in construction management, enables word-based tokenization, and controls the annotation consistency with a newly designed annotating specification. The CRF model engineers nine transformation features and seven classes of state features, covering the impacts of word position, part-of-speech (POS), and word/character states within the context. The F1-measure on a labeled construction data set is 87.9%. Furthermore, as more domain knowledge features are infused, the marginal performance improvement of including POS information will decrease, leading to a promising research direction of POS customization to improve NLP performance with limited data.

With the development of the bike-sharing system (BSS) and the introduction of green and low carbon development, the environmental impacts of BSS had received increasing attention in recent years. However, the emissions from the rebalancing of BSS, where fossil-fueled vehicles are commonly used, are usually neglected, which goes against the idea of green travel in a sharing economy. Previous studies on the bike-sharing rebalancing problem (BRP), which is considered NP-hard, have mainly focused on algorithm innovation instead of improving the solution model, thereby hindering the application of many existing models in large-scale BRP. This study then proposes a method for optimizing the CO₂ emissions from BRP and takes the BSS of Beijing as a demonstration. We initially analyze the spatial and temporal characteristics of BSS, especially the flow between districts, and find that each district can be independently rebalanced. Afterward, we develop a rebalancing optimization model based on a partitioning strategy to avoid deciding the number of bikes being loaded or unloaded at each parking node. We then employ the tabu search algorithm to solve the model. Results show that (i) due to over launch and lack of planning in rebalancing, the BSS in Beijing shows great potential for optimization, such as by reducing the number of vehicle routes, CO₂ emissions, and unmet demands; (ii) the CO₂ emissions of BSS in Beijing can be reduced by 57.5% by forming balanced parking nodes at the end of the day and decreasing the repetition of vehicle routes and the loads of vehicles; and (iii) the launch amounts of bikes in specific districts, such as Shijingshan and Mentougou, should be increased.

Trends toward the globalization of the manufacturing industry and the increasing demands for small-batch, short-cycle, and highly customized products result in complexities and fluctuations in both external and internal manufacturing environments, which poses great challenges to manufacturing enterprises. Fortunately, recent advances in the Industrial Internet of Things (IIoT) and the widespread use of embedded processors and sensors in factories enable collecting real-time manufacturing status data and building cyber–physical systems for smart, flexible, and resilient manufacturing systems. In this context, this paper investigates the mechanisms and methodology of self-organization and self-adaption to tackle exceptions and disturbances in discrete manufacturing processes. Specifically, a general model of smart manufacturing complex networks is constructed using scale-free networks to interconnect heterogeneous manufacturing resources represented by network vertices at multiple levels. Moreover, the capabilities of physical manufacturing resources are encapsulated into virtual manufacturing services using cloud technology, which can be added to or removed from the networks in a plug-and-play manner. Materials, information, and financial assets are passed through interactive links across the networks. Subsequently, analytical target cascading is used to formulate the processes of self-organizing optimal configuration and self-adaptive collaborative control for multilevel key manufacturing resources while particle swarm optimization is used to solve local problems on network vertices. Consequently, an industrial case based on a Chinese engine factory demonstrates the feasibility and efficiency of the proposed model and method in handling typical exceptions. The simulation results show that the proposed mechanism and method outperform the event-triggered rescheduling method, reducing manufacturing cost, manufacturing time, waiting time, and energy consumption, with reasonable computational time. This work potentially enables managers and practitioners to implement active perception, active response, self-organization, and self-adaption solutions in discrete manufacturing enterprises.

A smart society is an advanced form of society following agricultural society, industrial society, and information society, with digital data processing system as its main carrier. However, the governance of a smart society still faces many challenges. In view of this problem, first, this research constructs a smart society governance modernization strategy. Second, the innovation mode of a society governance mechanism driven by digital technology is proposed, including the precise intellectual control of a digital twin, the intelligent ubiquitous sensing of the Internet of Things, the empowerment remodeling of a blockchain and the livelihood service of artificial intelligence. Third, this study systematically explores the practice of smart society governance modernization from the aspects of basic information platform construction, evaluation system construction, application demonstration of epidemic prevention and control driven by big data, support of spatial intelligence and artificial intelligence technology for people’s livelihood, smart campus, public resources, and data governance application demonstration to provide theoretical guidance for promoting digital technology innovation in the process of the governance of a smart society.

Social infrastructure has become an important element for measuring national economic development and social benefits that are usually financed in the form of government grants, private investment, and public‒private partnerships (PPPs). However, social infrastructure PPPs have attracted considerable public debate due to their low profitability and complex operational demands. This study aims to answer the question, “are PPPs still an answer for social infrastructure?”, from the perspectives of the body of knowledge, application status, and prospects. Initially, an iterative search and review procedure and a scientometric analysis were performed to systematically screen the literature and to structure the body of knowledge of the social infrastructure PPPs literature. Furthermore, the application status and trends were analyzed to further explore the studied countries/regions, application sectors, and research topics. Results show that PPPs still have valuable application potential for delivering social infrastructure despite their mixed results across different topics. Six main research themes and the corresponding research trends were also identified, namely, financing and economics viability, risk management, performance management, contract and relationship management, governance and regulation, and facilitating and hindering factors. These findings offer practitioners and researchers a comprehensive overview and constructive guidance on social infrastructure PPPs.

Owing to the heterogeneity between functional units and resource scarcity, manufacturing firms have been struggling with intra-organizational coordination for productivity and innovation. Traditional organizational structures, such as linear-functional and matrix, may cause responsibility division and independent goals (Shahani, 2020), and are more difficult to be adopted by large-sized innovative manufacturing firms for quantity production. This is based on a review of several new forms of organization (i.e., network organization, multiteam system, and platform organization) compared with the traditional forms of organization (i.e., linear, matrix, and business unit organization, among others). This study proposes a three-dimensional (3D) magic-cube organizational structure, considering the product dimensions, business, and administration. Moreover, the characteristics, propositions of system operation, system dynamic model, and working model of the 3D magic-cube organization are described. Finally, the 3D model is applied in a Chinese manufacturing firm to test its effectiveness. By redesigning the post and pay system, the pilot organization establishes a project-driven and cross-functional coordination mechanism, positively affecting the firm’s financial profit, output value, labor productivity, and income of per capita. The proposed 3D model can be adopted by large- or medium-sized manufacturing firms for product development and innovation. The implications of both practice and theory are also discussed in this study.

The construction industry is a major contributor to environmental pollution. The effect of the construction industry on the environment may be mitigated using eco-friendly construction materials, such as biocomposites. Once developed, biocomposites may offer a viable alternative to the current materials in use. However, biocomposites are lagging in terms of adoption and eventual use in the construction industry. This article provides insights into the steps for biocomposites to become a product that is ready to use by the construction industry in a structural role. The development and the adoption of such a material is tackled with the use of two concepts, i.e., technology readiness level and roadmapping, and explored in a case study on the “liquid wood”. Furthermore, interviews in the construction industry are carried out to identify the industry’s take on biocomposites. A customized roadmap, which underlines a mostly nontechnical perspective concerning this material, has emerged. Additionally, the adoption and diffusion issues that the “liquid wood” may encounter are outlined and complemented with further recommendations.

The incorporation of disruptive innovations into the transportation industry will inevitably cause major upheavals in the transportation sector. However, existing research lacks systematic theories and methodologies to represent the underlying characteristics of future urban transport systems. Furthermore, emerging modes in urban mobility have not been sufficiently studied. The National Natural Science Foundation of China (NSFC) officially approved the Basic Science Center project titled “Future Urban Transport Management” in 2022. The project members include leading scientists and engineers from Beijing Jiaotong University, Beihang University, and Beijing Transport Institute. Based on a wide range of previous projects by the consortium on urban mobility and sustainable cities, this project will encompass transdisciplinary and interdisciplinary research to explore critical issues affecting future urban traffic management. It aims to develop fundamental theories and methods based on social and technological developments in the near future and explores innovative solutions to implement alongside these emerging developments in urban mobility.

Project management teams are critical in the implementation of megaprojects, but their evolution throughout the project lifecycle has not been clearly explained. This paper explores the organizational evolution of megaproject management teams through a longitudinal retrospective case study of the Hong Kong–Zhuhai–Macao Bridge (HZMB) project. The organizational evolution is examined in terms of management objectives, management content, and organizational structure. The organizational evolution of the HZMB project management team exhibits stage differentiation with the coexistence of turbulence and stability. Changes in the external environment are the driving force for organizational evolution, whereas a flexible organizational strategy is critical in promoting this evolution. Basing on the HZMB case study, this paper summarizes six critical measures that facilitate the organizational evolution of megaproject management teams. Our findings add value to megaproject management theory and provide a better understanding of the dynamics and complexity of megaproject organizational management.

Although China’s construction machinery thrives to meet the needs of construction, a number of challenges still remain to be overcome, such as lack of thorough knowledge of regional disparities and several limitations in terms of carbon emissions and economic development. Meanwhile, a low-carbon economy was proposed and implemented in China. This research aims to investigate the differences in industrial agglomeration of construction machineries and further explore the relationship between industrial agglomeration and low-carbon economy. On this basis, spatiotemporal analysis was performed to evaluate the levels of industrial agglomeration in different regions based on the situations of China’s construction machinery industry. Furthermore, this study explored the interaction between industrial agglomeration and low-carbon economy utilizing the coupling coordination analysis method. Results showed that the coupling coordination of the two subsystems was extremely unbalanced in 2006, and it maintained an increasing trend, reaching a relatively high level in 2018. Finally, suggestions, such as establishing a policy guarantee system and implementing variable policies in different regions, were proposed to provide guidelines for the government decision-making and promote the sustainable development of China’s construction machinery industry.

Given that group technology can reduce the changeover time of equipment, broaden the productivity, and enhance the flexibility of manufacturing, especially cellular manufacturing, group scheduling problems (GSPs) have elicited considerable attention in the academic and industry practical literature. There are two issues to be solved in GSPs: One is how to allocate groups into the production cells in view of major setup times between groups and the other is how to schedule jobs in each group. Although a number of studies on GSPs have been published, few integrated reviews have been conducted so far on considered problems with different constraints and their optimization methods. To this end, this study hopes to shorten the gap by reviewing the development of research and analyzing these problems. All literature is classified according to the number of objective functions, number of machines, and optimization algorithms. The classical mathematical models of single-machine, permutation, and distributed flowshop GSPs based on adjacent and position-based modeling methods, respectively, are also formulated. Last but not least, outlooks are given for outspread problems and problem algorithms for future research in the fields of group scheduling.

Predicting demand for bike share systems (BSSs) is critical for both the management of an existing BSS and the planning for a new BSS. While researchers have mainly focused on improving prediction accuracy and analysing demand-influencing factors, there are few studies examining the inherent randomness of stations’ observed demands and to what degree the demands at individual stations are predictable. Using Divvy bike-share one-year data from Chicago, USA, we measured demand entropy and quantified the station-level predictability. Additionally, to verify that these predictability measures could represent the performance of prediction models, we implemented two commonly used demand prediction models to compare the empirical prediction accuracy with the calculated entropy and predictability. Furthermore, we explored how city- and system-specific temporally-constant features would impact entropy and predictability to inform estimating these measures when historical demand data are unavailable. Our results show that entropy and predictability of demands across stations are polarized as some stations exhibit high uncertainty (a low predictability of 0.65) and others have almost no check-out demand uncertainty (a high predictability of around 1.0). We also validated that the entropy and predictability are a priori model-free indicators for prediction error, given a sequence of bike usage demands. Lastly, we identified that key factors contributing to station-level entropy and predictability include per capita income, spatial eccentricity, and the number of parking lots near the station. Findings from this study provide more fundamental understanding of BSS demand prediction, which can help decision makers and system operators anticipate diverse station-level prediction errors from their prediction models both for existing stations and for new ones.

Given the aging society, an increase in social demand, information- and communication technology-driven culture, and government policy support emerges to enable the development of the socialized care services system for the aged (SCSSA). The development of the SCSSA would be a significant step toward addressing China’s aging population. However, the construction of the SCSSA challenges the theories and methods of traditional elderly care service system construction. Specifically, the implementation path for such elderly care service policies is unclear, the necessary technological support is insufficient, and the mechanism for integrating intelligent information technology remains underexplored. Thus, this paper focuses on the needs of the elderly, grounded in the context of the changing elderly care service policies in China, and proposes a research paradigm that integrates system construction and support measure embedding. We then construct the original SCSSA, which includes “material + spirit + medical treatment + healthcare” and propose a method of optimization and iteration. Finally, we build the research framework of systematic support measures from the perspectives of policy reconstruction, institutional embeddedness, and technical support. Our work provides theoretical support and practical guidance for the construction and dynamic optimization of the SCSSA, thus making a significant contribution that will help China effectively cope with its aging society.

The large-scale online supermarket is a newly emerging online retailing mode which brings great convenience to people. Online supermarkets are characterized by having large amounts of daily orders with potentially multiple items, diverse delivery times, and a high order-split rate. Multiple shipments for one order caused by order splitting result in high cost and disturbance and a large number of discarded consumable packages at online retailers and customers, causing severe damage to the environment. Accordingly, research on split-order consolidation fulfilment is critical for the advancement of the practice and theory in the context of highly complex online retailing. This paper first analyzes the characteristics and the challenges associated with the split-order consolidation problem that online supermarket is confronting and summarizes the new operational process of split-order consolidation fulfilment. Then, a time–space network optimization model is built, and its corresponding solution algorithm is presented to solve the questions of where and when to consolidate the split orders. Finally, the computation results of the numerical experiments are provided to verify the effectiveness of the algorithm, and a sensitivity analysis of the relevant parameters is performed. This work highlights the effect of order consolidation processes and fulfilment methods on the order fulfilment decision-making for online supermarkets. The purpose of this article is to help pave the way for more effective online supermarket management and order implementation.

Under the ambitious goal of carbon neutralization, photovoltaic (PV)-driven electrolytic hydrogen (PVEH) production is emerging as a promising approach to reduce carbon emission. Considering the intermittence and variability of PV power generation, the deployment of battery energy storage can smoothen the power output. However, the investment cost of battery energy storage is pertinent to non-negligible expenses. Thus, the installation of energy-storage equipment in a PVEH system is a complex trade-off problem. The primary goals of this study are to compare the engineering economics of PVEH systems with and without energy storage, and to explore time nodes when the cost of the former scenario can compete with the latter by factoring the technology learning curve. The levelized cost of hydrogen (LCOH) is a widely used economic indicator. Represented by seven areas in seven regions of China, results show that the LCOH with and without energy storage is approximately 22.23 and 20.59 yuan/kg in 2020, respectively. In addition, as technology costs drop, the LCOH of a PVEH system with energy storage will be less than that without energy storage in 2030.

Recently, firms have begun to handle the design, manufacturing, and maintenance of capital goods through a consolidated mechanism called the integrated product-service system. This new paradigm enables firms to deliver high-reliability products while lowering the ownership cost. Hence, holistic optimization models must be proposed for jointly allocating reliability, maintenance, and spare parts inventory across the entire value chain. In the existing literature, these decisions are often made fragmentally, thus resulting in local optimality. This study reviews the extant works pertaining to reliability-redundancy allocation, preventative maintenance, and spare parts logistics models. We discuss the challenges and opportunities of consolidating these decisions under an integrated reliability-maintenance-inventory framework for attaining superior system availability. Specific interest is focused on the new product introduction phase in which firms face a variety of uncertainties, including installed base, usage, reliability, and trade policy. The goal is to call for tackling the integrated reliability-maintenance-inventory allocation model under a nonstationary operating condition. Finally, we place the integrated allocation model in the semiconductor equipment industry and show how the firm deploys reliability initiatives and after-sale support logistics to ensure the fleet uptime for its global customers.

Indoor environment has significant impacts on human health as people spend 90% of their time indoors. The COVID-19 pandemic and the increased public health awareness have further elevated the urgency for cultivating and maintaining a healthy indoor environment. The advancement in emerging digital twin technologies including building information modeling (BIM), Internet of Things (IoT), data analytics, and smart control have led to new opportunities for building design and operation. Despite the numerous studies on developing methods for creating digital twins and enabling new functionalities and services in smart building management, very few have focused on the health of indoor environment. There is a critical need for understanding and envisaging how digital twin paradigms can be geared towards healthy indoor environment. Therefore, this study reviews the techniques for developing digital twins and discusses how the techniques can be customized to contribute to public health. Specifically, the current applications of BIM, IoT sensing, data analytics, and smart building control technologies for building digital twins are reviewed, and the knowledge gaps and limitations are discussed to guide future research for improving environmental and occupant health. Moreover, this paper elaborates a vision for future research on integrated digital twins for a healthy indoor environment with special considerations of the above four emerging techniques and issues. This review contributes to the body of knowledge by advocating for the consideration of health in digital twin modeling and smart building services and presenting the research roadmap for digital twin-enabled healthy indoor environment.

Disruptive technologies provide a new paradigm for supply chain risk management and bring opportunities and challenges for the improvement of supply chain resilience (SCRes). This study summarizes the application cases of some disruptive technologies in the SCRes and analyzes the benefits and damages brought by disruptive technologies to the SCRes. The results show that disruptive technologies can provide the supply chain with flexibility, visibility, agility, and other capabilities at various stages of risk management. Hence, technology advancements greatly increase the level of the SCRes. Although disruptive technologies undermine the construction of SCRes, these damages can be eliminated through technology iteration or other disruptive technologies. Furthermore, disruptive technologies will provide better stability for the SCRes. The study also makes several suggestions for the use of disruptive technologies in the construction of the SCRes.

With fierce competition in the global high-speed railway (HSR) market, international reputation has become essential for enterprises to venture into overseas markets. However, few studies have been performed on the international reputation of HSR enterprises. This study aims to reveal the formation mechanism of HSR enterprises’ international reputation by developing a theoretical framework. The researchers identified five factors and proposed a hypothetical path model based on a comprehensive literature review. After the pilot study, questionnaires were distributed to practitioners in the international HSR industry for data collection. The path model was validated based on partial least-squares structural equation modeling. Eight of the nine paths are statistically supported. Researchers have structured a theoretical framework for the international reputation of HSR enterprises from two perspectives: Being good and being known. Subsequently, a strategic framework was developed to provide targeted promotion strategies for HSR enterprises. The findings of this study contribute to existing international reputation theories using the theoretical model and provide beneficial guidance for HSR enterprises to improve their international reputation through a strategic framework.

In this study, we investigate a forest-based solution representation for split delivery vehicle routing problems (SDVRPs), which have several practical applications and are among the most difficult vehicle routing problems. The new solution representation fully reflects the nature of split delivery, and can help reduce the search space when used in heuristic algorithms. Based on the forest structure, we devise three neighborhood search operators. To highlight the effectiveness of this solution representation, we integrate these operators into a standard tabu search framework. We conduct extensive experiments on three main SDVRPs addressed in the literature: The basic SDVRP, the multidepot SDVRP, and the SDVRP with time windows. The experimental results show that the new forest-based solution representation is particularly effective in designing and implementing neighborhood operators, and that our new approach outperforms state-of-the-art algorithms on standard datasets.

Over the last two decades, many modeling and optimization techniques have been developed for earth observation satellite (EOS) scheduling problems, but few of them show good generality to be engineered in real-world applications. This study proposes a general modeling and optimization technique for common and real-world EOS scheduling cases; it includes a decoupled framework, a general modeling method, and an easy-to-use algorithm library. In this technique, a framework that decouples the modeling, constraints, and optimization of EOS scheduling problems is built. With this framework, the EOS scheduling problems are appropriately modeled in a general manner, where the executable opportunity, another format of the well-known visible time window per EOS operation, is viewed as a selectable resource to be optimized. On this basis, 10 types of optimization algorithms, such as Tabu search and genetic algorithm, and a parallel competitive memetic algorithm, are developed. For simplified EOS scheduling problems, the proposed technique shows better performance in applicability and effectiveness than the state-of-the-art algorithms. In addition, a complicatedly constrained real-world benchmark exampled by a four-EOS Chinese commercial constellation is provided, and the technique is qualified and outperforms the in-use scheduling system by more than 50%.

Logistical activities have a significant global environmental impact, necessitating the adoption of green logistics practices to mitigate environmental effects. The COVID-19 pandemic has further emphasized the urgency to address the environmental crisis. Operations research provides a means to balance environmental concerns and costs, thereby enhancing the management of logistical activities. This paper presents a comprehensive review of studies integrating operations research into green logistics. A systematic search was conducted in the Web of Science Core Collection database, covering papers published until June 3, 2023. Six keywords (green logistics OR sustainable logistics OR cleaner logistics OR green transportation OR sustainable transportation OR cleaner transportation) were used to identify relevant papers. The reviewed studies were categorized into five main research directions: Green waste logistics, the impact of costs on green logistics, the green routing problem, green transport network design, and emerging challenges in green logistics. The review concludes by outlining suggestions for further research that combines green logistics and operations research, with particular emphasis on investigating the long-term effects of the pandemic on this field.

Climate change and rapid urbanization are pressing environmental and social concerns, with approximately 56% of the global population living in urban areas. This number is expected to rise to 68% by 2050, leading to the expansion of cities and encroachment upon natural areas, including wetlands, causing their degradation and fragmentation. To mitigate these challenges, green and blue infrastructures (GBIs), such as constructed wetlands, have been proposed to emulate and replace the functions of natural wetlands. This study evaluates the potential of eight constructed wetlands near Beijing, China, focusing on their ecosystem services (ESs), cost savings related to human health, growing/maintenance expenses, and disservices using an emergy-based assessment procedure. The results indicate that all constructed wetlands effectively purify wastewater, reducing nutrient concentrations (e.g., total nitrogen, total phosphorus, and total suspended solids). Among the studied wetlands, the integrated vertical subsurface flow constructed wetland (CW-4) demonstrates the highest wastewater purification capability (1.63E+14 sej/m²/yr) compared to other types (6.78E+13 and 2.08E+13 sej/m²/yr). Additionally, constructed wetlands contribute to flood mitigation, groundwater recharge, wildlife habitat protection, and carbon sequestration, resembling the functions of natural wetlands. However, the implementation of constructed wetlands in cities is not without challenges, including greenhouse gas emissions, green waste management, mosquito issues, and disturbances in the surrounding urban areas, negatively impacting residents. The ternary phase diagram reveals that all constructed wetlands provide more benefits than costs and impacts. CW-4 shows the highest benefit‒cost ratio, reaching 50%, while free water surface constructed wetland (CW-3) exhibits the lowest benefits (approximately 38%), higher impacts (approximately 25%), and lower costs (approximately 37%) compared to other wetlands. The study advocates the use of an emergy approach as a reliable method to assess the quality of constructed wetlands, providing valuable insights for policymakers in selecting suitable constructed wetlands for effective urban ecological management.

Roadside green swales have emerged as popular stormwater management infrastructure in urban areas, serving to mitigate stormwater pollution and reduce urban surface water discharge. However, there is a limited understanding of the various types, structures, and functions of swales, as well as the potential challenges they may face in the future. In recent years, China has witnessed a surge in the adoption of roadside green swales, especially as part of the prestigious Sponge City Program (SCP). These green swales play a crucial role in controlling stormwater pollution and conserving urban water resources by effectively removing runoff pollutants, including suspended solids, nitrogen, and phosphorus. This review critically examines recent research findings, identifies key knowledge gaps, and presents future recommendations for designing green swales for effective stormwater management, with a particular emphasis on ongoing major Chinese infrastructure projects. Despite the growing global interest in bioswales and their significance in urban development, China’s current classification of such features lacks a clear definition or specific consideration of bioswales. Furthermore, policymakers have often underestimated the adverse environmental effects of road networks, as reflected in existing laws and planning documents. This review argues that the construction and maintenance of roadside green swales should be primarily based on three critical factors: Well-thought-out road planning, suitable construction conditions, and sustainable long-term funding. The integration of quantitative environmental standards into road planning is essential to effectively address the challenge of pollution from rainfall runoff. To combat pollution associated with roads, a comprehensive assessment of potential pollution loadings should be carried out, guiding the appropriate design and construction of green swales, with a particular focus on addressing the phenomenon of first flush. One of the major challenges faced in sustaining funds for ongoing maintenance after swale construction. To address this issue, the implementation of a green finance platform is proposed. Such a platform would help ensure the availability of funds for continuous maintenance, thus maximizing the long-term effectiveness of green swales in stormwater management. Ultimately, the findings of this review aim to assist municipal governments in enhancing and implementing future urban road designs and SCP developments, incorporating effective green swale strategies.