China stands as one of the leading producers of waste electrical and electronic equipment (WEEE), facing significant challenges in managing the substantial volumes generated. Despite existing regulations, the informal treatment of WEEE persists in some areas due to inadequate recycling networks at the city level. Consequently, there is a critical need for a detailed geographical mapping of WEEE generation to address improper disposal practices effectively. This study introduces the cMAC – EEEs (city Material Cycles and Manufactured Capital – EEEs) database, providing estimates of WEEE generation across approximately 300 prefecture-level cities from 1978 to 2017. It focuses on five commonly used types of electrical and electronic equipment (refrigerators, air conditioners, washing machines, computers, TVs) originating from three key sources (urban residents, rural residents, enterprises). The findings reveal (1) significant spatial variation in WEEE generation within China, with eastern and central city clusters identified as hotspots, particularly for urban residents and enterprises, while the western region exhibits the highest growth rate in WEEE generation, notably among rural residents. (2) The growth in obsolete computers and air conditioners is prominent, especially in rural areas and among enterprises, whereas the generation of obsolete TVs, washing machines, and refrigerators is leveling off and expected to decrease in some urban areas. (3) Enterprises account for a substantial portion of WEEE generation, though uncertainties exist, necessitating further refinement. The study highlights that less developed regions lack adequate recycling facilities, with specific limitations in refrigerators and air conditioners recycling capabilities. To enhance WEEE management, it advocates for increased interregional collaboration and capacity building in less developed areas. Additionally, the regulation of WEEE from private enterprises requires improvement. At the product level, a greater focus on recycling practices for refrigerators and air conditioners is recommended.
Resource-based cities are currently facing challenges due to ecological pollution and an unbalanced industrial structure, which hinders sustainable economic growth. The focus on green development as a strategy for economic growth and environmental protection is becoming increasingly popular. This study employs a spatial econometric model to explore the effect of green development on economic resilience in Chinese resource-based cities from 2011 to 2019, revealing a positive correlation between green development and economic resilience. For each 1 unit increase in green development, economic resilience increases by an average of 0.512 units. Furthermore, the analysis of heterogeneity reveals differences in the factors influencing various resource-based cities. In addition, provincial green policies bolster economic resilience by encouraging green development. This research aids in comprehending the balance between the economy and the environment.
To assess the effectiveness of China’s emissions trading scheme (ETS) in facilitating energy structure optimization, we constructed a fuel-switching model utilizing data from 1067 generating units under the Chinese ETS framework. The model simulates the fuel-switching price in China’s thermal power sector, taking into account various allowance allocation strategies. The results show the following: 1) Thermal power plants will transition from coal to gas if the current ETS auction rate surpasses 26%. 2) Furthermore, in scenarios where the ETS operates independently, a transition will occur if the carbon allowance market is entirely auction-based and the carbon price attains 119.50 USD/tCO2. 3) In a collaborative scenario involving both the ETS and a gas feed-in tariff subsidy, a carbon price of 9.39 USD/tCO2 will effect a transition from coal to gas, provided both the auction ratio and subsidy price are maximized.
Rail transit plays a crucial role in improving urban sustainability and livability. In many Chinese cities, the planning of rail transit routes and stations is focused on facilitating new developments rather than revitalizing existing built-up areas. This approach reflects the local governments’ expectations of substantial growth to reshape the urban structure. However, existing research on transit-oriented development (TOD) rarely explores the spatial interactions between individual transit stations and investigates how they can be integrated to achieve synergistic effects and balanced development. This study proposes that rail transit systems impact urban structure through two “forces”: the provision of additional and reliable carrying capacity and the reduction of travel time between locations. Metro passenger flow is used as a proxy for these forces, and community detection techniques are employed to identify the actual and optimal spatial clusters in Wuhan, China. The results reveal that the planned sub-centers align reasonably well with the optimal spatial clusters in terms of spatial configuration. However, the actual spatial clusters tend to have longer internal travel times compared to the optimal clusters. Further exploration suggests the need for equalizing land use density within planned spatial clusters served by the metro system. Additionally, promoting concentrated, differentiated, and mixed functional arrangements in metro station areas with low passenger flows within the planned clusters could be beneficial. This paper presents a new framework for investigating urban spatial clusters influenced by a metro system.
The digital transformation of the built asset industry is moving toward closer integration of physical and digital assets and resources. Within the framework of Cyber-Physical Systems (CPSs) and Digital Twins (DTs), an increasing number of studies focus on the technical aspects of CPS and DT. However, a unified framework describing the dimensions and characteristics essential for integrating lifecycle information remains elusive. To leverage these concepts effectively, it is necessary to develop new frameworks to classify and put into relationship various components that comprise the lifecycle information integration of physical and digital assets and resources. This paper addresses these gaps by proposing a taxonomy of Built Asset Lifecycle Information Couples, which outlines the dimensions and characteristics crucial for the lifecycle information integration of built assets and resources. The proposed taxonomy contributes to the efforts aimed at organizing the knowledge domain of lifecycle information management in the built environment.
With the burgeoning emphasis on sustainable construction practices in China, the demand for green building assessment has significantly escalated. The overall evaluation process comprises two key components: The acquisition of evaluation data and the evaluation of green scores, both of which entail considerable time and effort. Previous research predominantly concentrated on automating the latter process, often neglecting the exploration of automating the former in accordance with the Chinese green building assessment system. Furthermore, there is a pressing requirement for more streamlined management of structured standard knowledge to facilitate broader dissemination. In response to these challenges, this paper presents a conceptual framework that integrates building information modeling, ontology, and web map services to augment the efficiency of the overall evaluation process and the management of standard knowledge. More specifically, in accordance with the Assessment Standard for Green Building (GB/T 50378-2019) in China, this study innovatively employs visual programming software, Dynamo in Autodesk Revit, and the application programming interface of web map services to expedite the acquisition of essential architectural data and geographic information for green building assessment. Subsequently, ontology technology is harnessed to visualize the management of standard knowledge related to green building assessment and to enable the derivation of green scores through logical reasoning. Ultimately, a residential building is employed as a case study to validate the theoretical and technical feasibility of the developed automated evaluation conceptual framework for green buildings. The research findings hold valuable utility in providing a self-assessment method for applicants in the field.
In the face of sudden pandemics, it becomes crucial for project managers to quickly adapt and make informed decisions that anticipate the consequences of their actions. This highlights the need for proactive management strategies to enhance epidemic response efforts. However, current research mainly emphasizes the negative impacts of pandemics, often neglecting the development of adaptable management approaches for construction sites. This study aims to fill this research void by developing strategies tailored to managing pandemics at construction sites. Using agent-based modeling, the study simulates the movement patterns of workers and the consequent spread of an epidemic under different risk scenarios and management tactics. The results indicate that measures such as wearing masks, managing group activities, and enforcing entry controls can significantly reduce epidemic spread on construction sites, with entry controls showing the greatest effectiveness.
The ongoing stability of supply chains faces significant challenges from trade protectionism, anti-globalization trends, and the COVID-19 pandemic. To remain resilient in this dynamic market environment, supply chains must evolve through iterative upgrades and transition to a higher level of sustainability, a process termed “step development.” The current literature, however, offers limited insights into achieving such step development in supply chain sustainability and its critical supporting elements. This study, grounded in theory, involved interviews with representatives from eight diverse Chinese enterprises. We introduce a model delineating supporting factors and a roadmap for Sustainable Step Development of Supply Chains (SSDSC). Our findings highlight three pivotal categories of support for SSDSC: institutional, technological, and social factors. Additionally, we observed that external influences in these categories positively modulate their internal counterparts. The study identifies industrial technology, digital intelligence technology, corporate responsibility, and stakeholder needs as key elements in this process. We conclude by offering theoretical and practical recommendations to foster SSDSC.
Low-carbon regulation and market competition present new opportunities and challenges for supply chain firms, emphasizing the significance of carbon reduction and channel encroachment in enhancing competitiveness. This study formulates various game models to evaluate manufacturers’ encroachment strategies (with or without encroachment) under different conditions of low-carbon investment by retailers. It investigates the operational decisions and carbon abatement strategies of firms under various scenarios. The findings reveal that encroachment elevates unit abatement levels but decreases wholesale prices and retailer profits when unit encroachment costs are below certain thresholds. In contrast, the manufacturer consistently benefits from channel encroachment. Retailer-initiated low-carbon investments can motivate manufacturers to reduce emissions. A lower carbon price potentially offers financial advantages to retailer engaging in such investments. Additionally, the likelihood of reduced environmental damage postchannel encroachment, compared to preprofessional encroachment, increases when the retailer invests in low-carbon initiatives. The retailer’s profit is inversely related to the carbon price, and a higher carbon price can strengthen the incentive effect of low-carbon investment on the manufacturer’s abatement endeavors.
Using the Hotelling model and evolutionary game theory, this paper studies the optimal production strategy of duopoly auto manufacturers and explores the impacts of two government policies (manufacturer and consumer subsidies) on strategies related to the production of electric vehicles (EVs) or fuel vehicles (FVs). The study finds that consumers’ environmental preferences have direct effects on manufacturers’ market shares and profits, which in turn, affect the manufacturers’ production strategy selection. Specifically, when consumer environmental preference is sufficiently high, both auto manufacturers will eventually choose to produce EVs; when it is moderate, only one with a cost advantage will choose to produce EVs. Finally, when it is low, neither auto manufacturer will produce EVs. The findings further reveal that the more significant the difference in EV production costs is, the more inclined auto manufacturers are to choose a different final stable strategy. Regardless of whether the government subsidizes manufacturers or consumers, the policy only works if subsidies reach a certain threshold. The study also identifies the conditions under which government subsidies are considered more cost-effective.
Maintenance of aero-engine fleets is crucial for the efficiency, safety, and reliability of the aviation industry. With the increasing demand for air transportation, maintaining high-performing aero-engines has become significant. Collaborative maintenance, specifically targeting aero-engine fleets, involves the coordination of multiple tasks and resources to enhance management efficiency and reduce costs. Digital Twin (DT) technology provides essential technical support for the intelligent operation and maintenance of aero-engine fleets. DT maps physical object properties to the virtual world, creating high-fidelity, dynamic models. However, DT-enhanced collaborative maintenance faces various challenges, including the construction of complex system-layer DT models, management of massive integrated DT data, and the development of fusion mechanisms and decision-making methods for DT data and models. Overcoming these challenges will allow the aviation industry to optimize aero-engine fleet maintenance, ensuring safety, efficiency, and cost-effectiveness while meeting the growing demand for air transportation.
Reaching consensus within larger social network groups has emerged as a pivotal concern in the digital age of connectivity. This article redefines group consensus as the emergence of collective intelligence resulting from self-organizing actions and interactions of individuals within a social network group. In our exploration of extant research on group consensus, we illuminate two frequently underestimated, yet noteworthy facets: Dynamism and emergence. In contrast to the conventional perspective of consensus as a mere outcome, we perceive it as an ongoing, dynamic process. This process encompasses self-organized communication and interaction among group members, collectively guiding the group towards cognitive convergence and viewpoint integration. Consequently, it is imperative to redirect our focus from the outcomes of group interactions to an examination of the relationships and processes underpinning consensus formation, thus elucidating the mechanisms responsible for the generation of group consensus. The amalgamation of cognitive contexts and accurate simplification of real-world scenarios for simulation and experimental analysis offers a pragmatic operational approach. This study contributes novel theoretical underpinnings and quantitative insights for establishing and sustaining group consensus within the realm of engineering management practices. Concurrently, it holds substantial importance for advancing the broader research landscape pertaining to social consensus.
