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.

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.

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.

The enhancement of energy efficiency stands as the principal avenue for attaining energy conservation and emissions reduction objectives within the realm of road transportation. Nevertheless, it is imperative to acknowledge that these objectives may, in part or in entirety, be offset by the phenomenon known as the energy rebound effect (ERE). To quantify the long-term EREs and short-term EREs specific to China’s road transportation, this study employed panel cointegration and panel error correction models, accounting for asymmetric price effects. The findings reveal the following: The long-term EREs observed in road passenger transportation and road freight transportation range from 13% to 25% and 14% to 48%, respectively; in contrast, the short-term EREs in road passenger transportation and road freight transportation span from 36% to 41% and 3.9% to 32%, respectively. It is noteworthy that the EREs associated with road passenger transportation and road freight transportation represent a partial rebound effect, falling short of reaching the magnitude of a counterproductive backfire effect. This leads to the inference that the upsurge in energy consumption within the road transportation sector cannot be solely attributed to advancements in energy efficiency. Instead, various factors, including income levels, the scale of commodity trade, and industrial structure, exert more substantial facilitating influences. Furthermore, the escalation of fuel prices fails to dampen the demand for energy services, whether in the domain of road passenger transportation or road freight transportation. In light of these conclusions, recommendations are proffered for the formulation of energy efficiency policies pertinent to road transportation.

Air pollution poses a significant threat to human health, particularly in urban areas with high levels of industrial activities. In China, the government plays a crucial role in managing air quality through the Air Pollution Prevention and Control Action Plan. The government provides direct financial support and guides the investment direction of social funds to improve air quality. While government investment has led to improvements in air quality across China, concerns remain regarding the efficiency of such large-scale investments. To address this concern, we conducted a study using a three-stage data envelopment analysis (DEA)-Malmquist model to assess the efficiency of government investment in improving air quality in China. Our analysis revealed regional disparities and annual dynamic changes. Specifically, we focused on the Beijing–Tianjin–Hebei areas as a case study, as the investment primarily targeted industrial activities in urban areas with the goal of improving living conditions for urban residents. The results demonstrate significant differences in investment efficiency between regions. Beijing exhibits relatively high investment efficiency, while cities in Hebei Province require improvement. We identified scale inefficiency, which refers to the ratio of air pollutant reduction to financial investment, as the main factor contributing to regional disparities. However, we found that increasing the total investment scale can help mitigate this effect. Furthermore, our study observed positive but fluctuating annual changes in investment efficiency within this city cluster from 2014 to 2018. Investment-combined technical efficiency, which represents the investment strategy, is the main obstacle to improving yearly investment efficiency. Therefore, in addition to promoting investment strategies at the individual city level, it is crucial to enhance coordination and cooperation among cities to improve the investment efficiency of the entire city cluster. Evaluating the efficiency of government investment and understanding its influencing factors can guide future investment measures and directions. This knowledge can also support policymaking for other projects involving substantial investments.

Decomposition analysis has been widely used to assess the determinants of energy and CO₂ emissions in academic research and policy studies. Both the methodology and application of decomposition analysis have been largely improved in the past decades. After more than 50 years’ developments, decomposition studies have become increasingly sophisticated and diversified, and tend to converge internally and integrate with other analytical approaches externally. A good understanding of the literature and state of the art is critical to identify knowledge gaps and formulate future research agenda. To this end, this study presents a literature survey for decomposition analysis applied to energy and emission issues, with a focus on the period of 2016–2021. A review for three individual decomposition techniques is first conducted, followed by a synthesis of emerging trends and features for the decomposition analysis literature as a whole. The findings are expected to direct future research in decomposition analysis.

The advancement of renewable energy (RE) represents a pivotal strategy in mitigating climate change and advancing energy transition efforts. A current of research pertains to strategies for fostering RE growth. Among the frequently proposed approaches, employing optimization models to facilitate decision-making stands out prominently. Drawing from an extensive dataset comprising 32806 literature entries encompassing the optimization of renewable energy systems (RES) from 1990 to 2023 within the Web of Science database, this study reviews the decision-making optimization problems, models, and solution methods thereof throughout the renewable energy development and utilization chain (REDUC) process. This review also endeavors to structure and assess the contextual landscape of RES optimization modeling research. As evidenced by the literature review, optimization modeling effectively resolves decision-making predicaments spanning RE investment, construction, operation and maintenance, and scheduling. Predominantly, a hybrid model that combines prediction, optimization, simulation, and assessment methodologies emerges as the favored approach for optimizing RES-related decisions. The primary framework prevalent in extant research solutions entails the dissection and linearization of established models, in combination with hybrid analytical strategies and artificial intelligence algorithms. Noteworthy advancements within modeling encompass domains such as uncertainty, multienergy carrier considerations, and the refinement of spatiotemporal resolution. In the realm of algorithmic solutions for RES optimization models, a pronounced focus is anticipated on the convergence of analytical techniques with artificial intelligence-driven optimization. Furthermore, this study serves to facilitate a comprehensive understanding of research trajectories and existing gaps, expediting the identification of pertinent optimization models conducive to enhancing the efficiency of REDUC development endeavors.

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.

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%.

In recent decades, healthcare providers have faced mounting pressure to effectively manage highly perishable and limited medical resources. This article offers a comprehensive review of supply chain management pertaining to such resources, which include transplantable organs and healthcare products. The review encompasses 93 publications from 1990 to 2022, illustrating a discernible upward trajectory in annual publications. The surveyed literature is categorized into three levels: Strategic, tactical, and operational. Key problem attributes and methodologies are analyzed through the assessment of pertinent publications for each problem level. Furthermore, research on service innovation, decision analytics, and supply chain resilience elucidates potential areas for future research.

Deep Learning (DL) has revolutionized the field of Artificial Intelligence (AI) in various domains such as computer vision (CV) and natural language processing. However, DL models have limitations including the need for large labeled datasets, lack of interpretability and explainability, potential bias and fairness issues, and limitations in common sense reasoning and contextual understanding. On the other side, DL has shown significant potential in construction for safety and quality inspection tasks using CV models. However, current CV approaches may lack spatial context and measurement capabilities, and struggle with complex safety and quality requirements. The integration of Neuro-Symbolic Computing (NSC), an emerging field that combines DL and symbolic reasoning, has been proposed as a potential solution to address these limitations. NSC has the potential to enable more robust, interpretable, and accurate AI systems in construction by harnessing the strengths of DL and symbolic reasoning. The combination of symbolism and connectionism in NSC can lead to more efficient data usage, improved generalization ability, and enhanced interpretability. Further research and experimentation are needed to effectively integrate NSC with large models and advance CV technologies for precise reporting of safety and quality inspection results in construction.