Search Results (217)

The design and planning of Western-style constructions during the early Qing Dynasty in China constituted a significant multicultural encounter that fused technological advancement with aesthetic innovation. This cultural interplay is particularly evident in the imperial garden and pavilion projects commissioned by the Qing court, which served as physical and symbolic sites of cross-cultural dialogue. Influenced by the intellectual and artistic movements of the European Renaissance, Western architectural concepts gradually found their way into the spatial and visual language of Chinese architecture, especially within the royal gardens and aristocratic buildings of the time. These structures were not simply imitative but rather represented a selective adaptation of Western ideas to suit Chinese imperial tastes and principles. This article examines the architectural language that emerged from this encounter between Chinese and European cultures, analysing symbolic motifs, spatial design, ornamental aesthetics, the application of linear perspective, and the integration of foreign architectural forms. These elements collectively functioned as tools to construct a unique visual discourse that communicated both political authority and cultural hybridity. The findings underscore that this architectural phenomenon was not merely stylistic imitation, but rather a dynamic convergence of technological knowledge and artistic vision across cultural boundaries. Full article

Building information modeling (BIM) has emerged as a fundamental component of Industry 4.0 recently. BIM construction engineers (BCEs) play a pivotal role in implementing BIM, and their personal competency is crucial to the successful application and promotion of BIM technology. Existing research on evaluating BIM capabilities has mainly focused on the enterprise or project level, neglecting individual-level analysis. Therefore, this study aims to establish an individual-level competency evaluation model for BCEs. Firstly, the competency of BCEs was divided into five levels by referring to relevant standards and domestic and foreign research. Secondly, through the analysis of literature data and website data, the competency evaluation indicator system for BCEs was constructed, which includes four primary indicators and 27 secondary indicators. Thirdly, variable weight theory was used to optimize the weights determined by general methods and calculate the comprehensive weights of each indicator. Then the competency levels of BCEs were determined by the interval grey clustering method. To demonstrate the application of the proposed method, a case study from a Chinese enterprise was conducted. The main results derived from this case study are as follows: domain competencies have the greatest weight among the primary indicators; the C9-BIM model is the secondary indicator with the highest weight (${\mathsf{\omega }}_j$ = 0.0804); and the competency level of the BCE is “Level 3”. These results are consistent with the actual situation of the enterprise. The proposed model in this study provides a comprehensive tool for evaluating BCEs’ competencies from an individual perspective, and offers guideline for BCEs to enhance their competencies in pursuing sustainable professional development. Full article

This study explored the impact of network structure on conflict and project value creation. Network density and network centrality are two network structure dimensions. A survey was undertaken among professionals working in Chinese construction projects. A total of 308 surveys were analyzed using the structural equation model. The results revealed that network centrality has a negative impact on project value creation while network density has a positive impact. Network centrality has a negative impact on substantive conflicts but a positive impact on affective conflicts. The link between centrality and project value creation is weakened by substantive conflict but strengthened by affective conflict. This research gives a new direction for construction project governance and project value management. Furthermore, this research validates the constructive role of substantive conflicts, as well as the destructive impact of affective conflicts, thereby adding to the literature on conflict governance. Full article

Accurate forecasting of carbon emissions at the county level is critical to support China’s dual-carbon goals. However, most current studies are limited to national or provincial scales, employing traditional statistical methods inadequate for capturing complex nonlinear interactions and spatiotemporal dynamics at finer resolutions. To overcome these limitations, this study develops and validates a high-resolution predictive model using advanced gradient boosting algorithms—Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM)—based on socioeconomic, industrial, and environmental data from 2732 Chinese counties during 2008–2017. Key variables were selected through correlation analysis, missing values were interpolated using K-means clustering, and model parameters were systematically optimized via grid search and cross-validation. Among the algorithms tested, LightGBM achieved the best performance (R² = 0.992, RMSE = 0.297), demonstrating both robustness and efficiency. Spatial–temporal analyses revealed that while national emissions are slowing, the eastern region is approaching stabilization, whereas emissions in central and western regions are projected to continue rising through 2027. Furthermore, SHapley Additive exPlanations (SHAP) were applied to interpret the marginal and interaction effects of key variables. The results indicate that GDP, energy intensity, and nighttime lights exert the greatest influence on model predictions, while ecological indicators such as NDVI exhibit negative associations. SHAP dependence plots further reveal nonlinear relationships and regional heterogeneity among factors. The key innovation of this study lies in constructing a scalable and interpretable county-level carbon emissions model that integrates gradient boosting with SHAP-based variable attribution, overcoming limitations in spatial resolution and model transparency. Full article

Market-oriented ecological restoration is vital for advancing ecological civilization and promoting harmonious human–nature relationships. However, the precise implementation pathway remains unclear. Few studies specifically address challenges that arise during ecological restoration implementation. Ensuring the smooth and effective implementation and landing of ecological restoration projects harmonizes ecological and economic objectives at the regional scale and fosters sustainable development in the region. Based on the policies of market-oriented ecological restoration collected from various Chinese provinces, and through multi-level institutional analysis, the policy measures are categorized into three phases: early, middle, and late. For each phase, we summarize the challenges encountered in implementing market-oriented ecological restoration projects. Finally, by the method of constructing theoretical models, we propose sustainable countermeasures based on multiple theoretical models. The results show (1) China’s ecological restoration sector is experiencing rapid growth, and market-oriented policies in China, multiple Chinese provinces, and municipalities have enacted successive market-oriented ecological restoration policies, and the outlook for ecological restoration marketization in China remains highly promising. (2) The implementation process of current market-oriented ecological restoration projects confronts and encounters several challenges. These include the absence of project screening and evaluation mechanisms, limited investment and financing channels, ill-defined approval processes, ambiguous delineation of departmental responsibilities, insufficient industry incentives, and the absence of effective operational and management mechanisms. (3) To address the identified challenges, taking forest ecological restoration as an example, theoretical models should be developed encompassing six critical dimensions: the aspects of the mechanism, mode, approval process, management system, industrial chain, and platform. This aims to provide sustainable pathways for the effective implementation of market-oriented forest ecological restoration projects. Full article

Under the background of globalization and the latest technological changes, many enterprises ensure corporate competitiveness and sustainable development by deploying production globalization and transforming production modes. This paper proposes a task-based enterprise model to study how enterprises’ production mode transformation toward intelligent manufacturing affects corporate offshoring production. Intelligent manufacturing forms relative push–pull forces on corporate offshoring production through reshoring effects and offshoring effects on the extensive margin of task sets while promoting corporate offshoring production through productivity effects on the intensive margin. Empirically, this paper constructs a staggered quasi-natural experiment using China’s Intelligent Manufacturing Pilot Demonstration Projects (IMPDP), adopts the heterogeneity-robust nonlinear Difference-in-Differences (DID) method, and confirms that intelligent manufacturing has significant positive causal effects on Chinese manufacturing enterprises’ offshoring production. The reshoring effect of intelligent manufacturing is stronger than the offshoring effect, but its powerful productivity effect masks the reshoring effect in overall empirical results. The positive effects of intelligent manufacturing are more significant in non-state-owned enterprises (non-SOEs) and capital-intensive enterprises. Further considering host country selection for corporate offshoring, this study finds that intelligent manufacturing simultaneously promotes corporate offshoring production to both developed and developing countries, but enterprises prefer Belt and Road Initiative countries. Additionally, intelligent manufacturing also promotes corporate offshore trade activities while causing the reshoring of offshore R&D activities. Overall, the transition of production modes toward intelligent manufacturing in Chinese manufacturing enterprises generally leads to a further expansion of corporate offshoring production. Full article

This study assesses the performance impact of lean construction practices in Engineering, Procurement, and Construction (EPC) projects in China. While lean methods have demonstrated substantial benefits in conventional construction, their implementation in the EPC context—characterized by higher complexity and integration—remains underexplored, particularly within the Chinese infrastructure sector. This research develops a structured framework that classifies lean practices into five functional categories: planning and scheduling (PS), process and workflow optimization (PWO), quality and safety enhancement (QSE), resource and maintenance (RM), and visualization and communication (VC). This study evaluates the influence of these practices on four key performance indicators: efficiency and resource management, quality and safety, stakeholder satisfaction, and organizational and market impact. Data were collected from 456 EPC stakeholders via a structured questionnaire and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that PS, PWO, and QSE exert the strongest positive effects on performance, while RM and VC contribute in more context-specific ways. The results highlight the value of lean practices for improving operational performance, stakeholder engagement, and sustainability in complex project delivery systems and underscore the need for policy support and digital integration to enhance lean adoption in Chinese EPC sector. Full article

The fragmented nature of Chinese households’ forestland hinders the realization of economies of scale in forestry production. Understanding the role of forestry service outsourcing organizations in mitigating this fragmentation provides a critical foundation for the exploration of pathways to scaled forestry management. Based on tracking data from 500 households across 10 counties in Fujian Province between 2013 and 2018, this study examines an unbalanced panel containing six periods and 2780 valid observations. It constructs a panel Logit model to examine the influence of forestry service outsourcing organizations on the likelihood of forestland transfer by households, and it employs a panel Tobit model to analyze the relationship between these organizations and the scale of forestland transferred. To capture potential heterogeneity, the analysis incorporates households’ part-time status and the forestland terrain conditions. The results indicate that the duration of establishment of county-level forestry project teams and forestry companies in households’ regions significantly reduces the tendency of households to lease out their forestland, especially for those in plain and hilly regions and part-time forestry producers. Furthermore, the longer the establishment history of township-level forestry project teams, the more inclined households are to retain their family forestland management rights. Our study demonstrates that, when specialized forestry service outsourcing organizations emerge in the market, households are less likely to lease out their forestland, thereby retaining management rights, avoiding the risk of forestland loss, and reducing forestland abandonment. As forestry service outsourcing organizations continue to develop and expand—with improvements in service levels and production efficiency—forestry production is gradually transitioning toward a new stage of service-oriented scale operations. Full article

Rapid urbanization in China is intensifying travel demand while making transport the nation’s third-largest source of carbon emissions. Anticipating continued growth in private-car fleets, this study integrates vehicle-stock forecasting with multi-scenario emission modeling to identify effective decarbonization pathways for Chinese cities. First, Kendall rank and grey relational analyses are combined to screen the key drivers of car ownership, creating a concise input set for prediction. A Lévy-flight-enhanced Sparrow Search Algorithm (LSSA) is then used to optimize the smoothing factor of the Generalized Regression Neural Network (GRNN), producing the Levy flight-improved Sparrow Search Algorithm optimized Generalized Regression Neural Network (LSSA-GRNN) model for annual fleet projections. Second, a three-tier scenario framework—Baseline, Moderate Low-Carbon, and Enhanced Low-Carbon—is constructed in the Long-range Energy Alternatives Planning System (LEAP) platform. Using Ningbo as a case study, the LSSA-GRNN outperforms both the benchmark Sparrow Search Algorithm optimized Generalized Regression Neural Network (SSA-GRNN) and the conventional GRNN across all accuracy metrics. Results indicate that Ningbo’s car fleet will keep expanding to 2030, albeit at a slowing rate. Relative to 2022 levels, the Enhanced Low-Carbon scenario delivers the largest emission reduction, driven primarily by accelerated electrification, whereas public transport optimization exhibits a slower cumulative effect. The methodological framework offers a transferable tool for cities seeking to link fleet dynamics with emission scenarios and to design robust low-carbon transport policies. Full article

With the annual increase in carbon emissions and the warming of the global temperature, it is imperative to accelerate the construction of a green, low-carbon, circular economic system. The photocatalytic reduction of CO₂ can convert the emitted CO₂ into valuable carbonaceous products, which is of great significance for alleviating the global CO₂ emission problem. In this study, the literature on the “photocatalytic reduction of CO₂” from two Chinese and foreign databases was used as the analysis sample. From the perspective of net present value, nitride-based catalysts were selected as the research object. An in-depth analysis of the costs and economic benefits of the nitride-based photocatalytic reduction of CO₂ was carried out, considering four factors: catalyst efficiency, light conditions, discount rate, and depreciation period. The analysis results show that with a project duration of 10 years and a discount rate of 10%, the net present values of all the catalysts are negative, indicating that from an economic perspective, investment projects using general catalysts to reduce CO₂ are not feasible under current conditions. However, it is worth noting that when the light conditions are changed and sunlight is used as the light source, the net present values corresponding to the Ta₃N₅/Bi and NiOx/Ta₃N₅ catalysts have turned positive, showing a certain economic feasibility. When the yield is increased to 2.64 times and 6.15 times of the original values, the net present values corresponding to the T-CN/ZIS (refers to ZnIn₂S₄ (ZIS) nanosheets grown in situ on tubular g-C₃N₄ microtubes (T-CNs)) catalyst and the Ta₃N₅ cuboid catalyst turn positive, and only the net present value of the g-C₃N₄/Bi₂O₂[BO₂(OH)] catalyst remains negative. Full article

The High-Intensity Heavy-Ion Accelerator Facility (HIAF) is a significant national science and technology infrastructure project, constructed by the Institute of Modern Physics, Chinese Academy of Sciences (IMP, CAS). It is designed to provide intense proton, heavy ion beams, and target-produced radioactive ion beams for nuclear physics and related research. Large-aperture, high-precision, room-temperature, and superconducting dipole magnets are extensively used to achieve high-intensity beams. However, for large-scale magnets (particularly superconducting magnets), the traditional Hall probe mapping measurement platform encounters several limitations: a long preparation time, high cost, low testing efficiency, and positional inaccuracies caused by repeated magnet disassembly. This paper presents a new magnetic field mapping measurement system incorporating ultrasonic motors operable in strong magnetic fields (≥7 T), enabling portable, highly efficient, and high-precision magnetic field measurements. After system integration and commissioning, the prototype dipole magnet for the high-precision spectrometer ring (SRing) was measured. The measurement system demonstrated superior accuracy and efficiency compared with traditional Hall probe mapping systems. On this basis, the magnetic field distribution and integral excitation curve of all 11 warm-iron superconducting dipole magnets and 3 anti-irradiation dipole magnets in the HIAF fragment separator (HFRS) were measured. Each magnet took less than 1 day to measure, and all magnetic field measurement results met the physical specifications. Full article

Flow velocity is a critical factor in determining the suitability of fish habitats. Understanding the preference patterns of the four major Chinese carps (FMCCs) for different flow velocities is crucial for their habitat conservation and restoration. In this study, the preference of individual fish species, approximately 15 cm in length, for flow velocity was investigated at flow velocity gradients of 0.0, 0.4, 0.8, 1.2, 1.6, and 2.0 times their body length. Additionally, a deep learning algorithm based on convolutional neural networks (CNNs) was employed for fish target detection. The results showed that, at this length, black carp (Mylopharyngodon piceus) preferred fast currents when the inlet flow velocity was between 0.4 and 1.6 times their body length, while grass carp (Ctenopharyngodon idella), silver carp (Hypophthalmichthys molitrix), and bighead carp (Hypophthalmichthys nobilis) preferred fast currents when the inlet flow velocity of the test flume was between 0.4 and 2.0 times their body length. However, this preference for fast currents decreased as the overall flow velocity increased to a specific threshold, eventually leading to their avoidance. The highest preference for fast currents among the four species was observed at inlet flow velocities of 1.2, 0.4, 0.8, and 0.8 times their body length, respectively. The findings of this study provide important insights into habitat conservation and restoration for the FMCCs in projects focused on the construction of navigation channels and water conservancy. Full article

With the ongoing changes in global economic and social environments, project organization resilience has emerged as a core competency in addressing environmental uncertainty, thereby becoming a prominent research area within construction management. This resilience is intricately linked to data resources; however, construction enterprises face systemic challenges in governance—fragmented data standards, siloed storage, and underutilized analytics—which limit their ability to translate data into crisis-responsive actions. Consequently, it is of paramount importance to investigate how data governance capabilities influence project organization resilience. This study initially identifies five dimensions of data governance capabilities within construction enterprises, spanning the three stages of planning, flow, and application: top-level design, data standard management, data collection, data storage, and data application. These dimensions are derived through a combination of literature review and expert interviews. Subsequently, this study establishes a theoretical model titled “Data Governance Capability—Project Organization Resilience”, exploring the relationships among these data governance capability dimensions and their correlations with project organization resilience. Data were collected through 142 valid questionnaires from practitioners in the Chinese construction industry and analyzed by the Partial Least Squares Structural Equation Modeling (PLS-SEM) method. The results indicate a significant relationship between data governance capabilities and project organization resilience, contributing to the research on the antecedents of the latter. This study offers a scale for construction enterprises to systematically assess data governance capabilities and provides guidance on enhancing organization resilience by progressively nurturing these capabilities. Full article

Accelerated urbanization in China poses significant challenges for developing urban planning strategies that are responsive to diverse climatic conditions. This demands a sophisticated understanding of the complex interactions between 3D urban forms and local climate dynamics. This study employed the Conditional Generative Adversarial Network (cGAN) of the Pix2Pix algorithm as a predictive model to simulate 3D urban morphologies aligned with Local Climate Zone (LCZ) classifications. The research framework comprises four key components: (1) acquisition of LCZ maps and urban form samples from selected Chinese megacities for training, utilizing datasets such as the World Cover database, RiverMap’s building outlines, and integrated satellite data from Landsat 8, Sentinel-1, and Sentinel-2; (2) evaluation of the Pix2Pix algorithm’s performance in simulating urban environments; (3) generation of 3D urban models to demonstrate the model’s capability for automated urban morphology construction, with specific potential for examining urban heat island effects; (4) examination of the model’s adaptability in urban planning contexts in projecting urban morphological transformations. By integrating urban morphological inputs from eight representative Chinese metropolises, the model’s efficacy was assessed both qualitatively and quantitatively, achieving an RMSE of 0.187, an R² of 0.78, and a PSNR of 14.592. In a generalized test of urban morphology prediction through LCZ classification, exemplified by the case of Zhuhai, results indicated the model’s effectiveness in categorizing LCZ types. In conclusion, the integration of urban morphological data from eight representative Chinese metropolises further confirmed the model’s potential in climate-adaptive urban planning. The findings of this study underscore the potential of generative algorithms based on LCZ types in accurately forecasting urban morphological development, thereby making significant contributions to sustainable and climate-responsive urban planning. Full article

The photovoltaic industry plays a critical role in promoting global sustainability. Enhancing the reliability of photovoltaic structures is essential for achieving sustainable development. This study involved the analysis of a photovoltaic power generation project in Hubei Province to compare differences in the structural loads of photovoltaic supports as outlined in Chinese, American, and European codes. Additionally, the ABAQUS numerical simulation was used to investigate the mechanical characteristics of photovoltaic support joint connections and analyze the causes of structural deformation. Innovative joint connections were proposed to optimize the structural performance of photovoltaic supports. The results showed that photovoltaic supports designed using Chinese codes exhibit lower reliability compared to those designed using American and European codes. Specifically, at least three bolts should be installed at the purlin hanger to connect the purlin and the beam. Z-shaped and Π-shaped purlin hangers are recommended for connecting beams and purlins, as they reduce joint deformation while preventing excessive stress in L-shaped purlin hangers. C-shaped steel is suggested for braces, offering both ease of construction and structural reliability. The proposed connection design minimizes additional steel consumption while enhancing overall performance. Full article