Search Results (539)

Rapid urbanization within ecologically fragile mountainous regions exacerbates tensions between development needs and land use sustainability, yet few studies have systematically quantified long-term land use/land cover (LULC) dynamics in large-scale mountainous urban agglomerations. Focusing on the Chengdu–Chongqing Urban Agglomeration (CCUA) in Southwest China—an archetypal mountainous megaregion undergoing accelerated development—this study analyzed LULC evolution from 1985 to 2019 using multi-period data, identified dominant driving factors through logistic regression, and projected future LULC patterns under various scenarios via the Future Land Use Simulation (FLUS) model. The outcomes indicate that (1) over the past decades, construction land expanded by over 4000 km², an increase of about 318%, while cultivated land decreased by nearly 8600 km², a reduction of 6.86%; (2) the dominant transformation type was the conversion of cultivated land to forest, followed by its conversion to construction land; (3) elevation, slope, and average annual temperature emerged as significant predictors of LULC change, highlighting the critical influence of topographical and climatic conditions; and (4) natural development scenarios (NDS) and ecology and cultivated protection scenarios (ECPS) represent suitable development pathways. These findings contribute to evidence-based spatial governance and provide policy guidance for ecological protection in the CCUA and other similarly vulnerable areas. Full article

Sea–rail intermodal transport offers high efficiency and environmental benefits, yet its development in China remains limited. Existing studies have mainly assessed the macro-level benefits of sea–rail intermodal transport policies, but rigorous evidence on whether incentive policies work and how their effects differ across policy types remains scarce, which limits evidence-based policy design and efficient allocation between subsidies and capacity expansion. To address this gap, a dual-policy identification framework was established that combines a multi-period difference-in-differences model with event study analysis and used station–month data from China to assess the independent effects, underlying mechanisms, and spatiotemporal heterogeneity of railway freight price subsidies and freight train expansion on container throughput. The results indicate that both policies significantly increased container throughput. Railway freight price subsidies exhibited stronger and more persistent effects with a certain lag, whereas freight train expansion produced rapid but short-lived responses. The impacts of both policies were more pronounced in short-distance transport, but weakened or even turned negative over longer distances. Moreover, the number of participating entities served as a key mediating pathway, while information sharing positively moderates policy impacts. This study makes theoretical contributions to the identification of heterogeneity, mechanism analysis, and spatiotemporal characterization of SRIT incentive policy effects, while offering refined and actionable guidance for SRIT policy optimization. Full article

The rapid expansion of transportation infrastructure on Hainan Island has intensified ecological pressures such as landscape fragmentation and decreased connectivity, threatening the environmental integrity of Hainan Tropical Rainforest National Park. As China’s only tropical island national park, it is important to maintain biodiversity and ecological resilience. Therefore, this study attempts to examine the park and its 5 km buffer zone to assess how transport expansion from 2003 to 2023 has altered land use patterns and landscape connectivity. Through the analysis of multi-period land use data, the land use changes are tracked by using ArcGIS and Fragstats 4.3 software, and the landscape dynamics are quantified. We linked these patterns to ecological processes via a resistance-surface model, which is further refined by spatial structural indices to better reflect ecological realism. Ecological sources were subsequently identified through morphological analysis and ecosystem service evaluation, and circuit theory was applied to delineate potential corridors and construct an ecological security network. The results indicate that (1) transportation development has significantly increased landscape fragmentation and ecological resistance, particularly along major highways; (2) while core forest areas inside the park remain relatively intact, the buffer zones show accelerating degradation; and (3) Although there are many ecological conflict points between the transportation network and the ecological corridor, the construction of animal channels in combination with bridges, tunnels and culverts can effectively improve ecological connectivity and protect the integrity of animal habitat. These findings highlight the vulnerability of ecological integrity as the network expands. The proposed modeling framework provides a more realistic assessment of infrastructure impact and offers a scientific basis for coordinating ecological protection and transport planning in tropical island national parks. Full article

As a new type of production factor, releasing data dividends is of great significance in improving corporate energy efficiency. Based on the data of listed enterprises in China from 2011 to 2022, the establishment of government open data platforms in each prefecture-level city is taken as a policy shock event, and the impact of government open data on corporate energy efficiency is empirically examined through a multi-period DID model. The results show that government open data improves enterprise energy efficiency by approximately 2.5% (relative to the mean), and capacity utilization and biased technological progress are the main pathways of action. In addition, the application of big data technology can better fulfill the role of data factors in improving enterprise energy efficiency. Heterogeneity analysis finds that government open data has a stronger effect on enterprise energy efficiency improvement in areas with high manufacturing concentration, environmental tax rate leveling, and high Internet penetration. The study suggests that enterprises should apply big data technology and build a mechanism for integrating data assets and energy management so as to fulfill the important role of data elements in the green development of enterprises. Full article

Promoting sustainable and balanced economic growth remains a key challenge for developing countries. This study empirically investigates the impact of China’s Pilot Free Trade Zone (PFTZ) on regional economic growth from 2010 to 2023, offering important insights into how targeted policy instruments can contribute to sustainable economic growth. Employing a multiperiod difference-in-differences model and a capital–technology–marketization framework, this study finds that PFTZ implementation has a significant and direct influence on promoting provincial economic growth. The growth effects are primarily driven by improved capital flows and enhanced technological innovation. Notably, these positive effects are more pronounced in central and western Chinese provinces and regions with lagging economic development, indicating that PFTZs can serve as effective tools for reducing regional disparities. These findings provide new empirical evidence regarding the regional heterogeneity of PFTZ policy impacts and offer valuable insights into the design, timing, and spatial targeting of PFTZ initiatives in developing countries seeking to support inclusive and sustainable development across the country. Full article

Conventional rail transit train running gear fault diagnosis mainly depends on routine maintenance inspections and manual judgment. However, these approaches lack robustness under complex operational environments and elevated noise levels, rendering them inadequate for real-time performance and the rigorous accuracy standards demanded by modern rail transit systems. Furthermore, many existing deep learning–based methods suffer from inherent limitations in feature extraction or incur prohibitive computational costs when processing multivariate time series data. This study represents one of the early efforts to introduce the TimesNet time series modeling framework into the domain of fault diagnosis for rail transit train running gear. By utilizing an innovative multi-period decomposition strategy and a mechanism for reshaping one-dimensional data into two-dimensional tensors, the framework enables advanced temporal-spatial representation of time series data. Algorithm validation is performed on both the high-speed train running gear bearing fault dataset and the multi-mode fault diagnosis datasets of gearbox under variable working conditions. The TimesNet model exhibits outstanding diagnostic performance on both datasets, achieving a diagnostic accuracy of 91.7% on the high-speed train bearing fault dataset. Embedded deployment experiments demonstrate that single-sample inference is completed within 70.3 ± 5.8 ms, thereby satisfying the real-time monitoring requirement (<100 ms) with a 100% success rate over 50 consecutive tests. The two-dimensional reshaping approach inherent to TimesNet markedly enhances the capacity of the model to capture intrinsic periodic structures within multivariate time series data, presenting a novel paradigm for the intelligent fault diagnosis of complex mechanical systems in train running gears. The integrated human–machine interaction system includes a comprehensive closed-loop process encompassing detection, diagnosis, and decision-making, thereby laying a robust foundation for the continued development of train running gear predictive maintenance technologies. Full article

Urban carbon reduction is instrumental in enabling cities to realize their developmental sustainability objectives. However, regional disparities in economic development pose significant challenges to low-carbon transitions. This study utilizes panel data from 282 cities in China spanning 2006–2021, considering the energy saving and emission reduction (ESER) fiscal policy as an external shock. Using a multi-period difference-in-differences approach, we assess how ESER impacts urban carbon emissions. Our findings indicate that ESER significantly reduces municipal carbon emissions by an average of 23.3% compared to non-pilot cities. Mechanism analyses suggest that this effect operates through reduced energy consumption, improved industrial structure, and enhanced green innovation. ESER’s impact exhibits heterogeneity across cities with different levels of economic development, population size, innovation capacity, and industrial composition. Moreover, we find evidence of spatial spillover effects, as ESER benefits extend to neighboring regions. These results confirm the effectiveness of ESER in promoting low-carbon development and offer practical implications for enhancing environmental governance through green fiscal instruments. Full article

As a crucial modeling tool for stochastic financial markets, the Lévy risk model effectively characterizes the evolution of risks during enterprise operations. Through dynamic evaluation and quantitative analysis of risk indicators under specific dividend- distribution strategies, this model can provide theoretical foundations for optimizing corporate capital allocation. Addressing the inadequate adaptability of traditional single-period threshold strategies in time-varying market environments, this paper proposes a dividend strategy based on multiperiod dynamic threshold adjustments. By implementing periodic modifications of threshold parameters, this strategy enhances the risk model’s dynamic responsiveness to market fluctuations and temporal variations. Within the framework of the spectrally negative Lévy risk model, this paper constructs a stochastic control model for multiperiod threshold dividend strategies. We derive the integro-differential equations for the expected present value of aggregate dividend payments before ruin and the Gerber–Shiu function, respectively. Combining the methodologies of the discounted increment density, the operator introduced by Dickson and Hipp, and the inverse Laplace transforms, we derive the explicit solutions to these integro-differential equations. Finally, numerical simulations of the related results are conducted using given examples, thereby demonstrating the feasibility of the analytical method proposed in this paper. Full article

Background: We consider a variant of the traveling salesperson problem motivated by the case of a company delivering furniture. The problem is both dynamic, due to random arrivals of delivery requests, and multiperiod, due to flexibility in delivering items within a time window of a few days. A sequence of daily routes must be selected over time, and both volume and route duration constraints are relevant. Moreover, customers are irregularly distributed in clusters with high or low density. When receiving a request from a low-density cluster, we may consider the possibility of waiting for further requests from the same cluster, which involves a tradeoff between total traveled distance and service quality. Methods: We designed alternative decision policies based on approximate dynamic programming principles. We compared policy and cost function approximations, tuning their parameters by simulation-based optimization. Results: We compared the decision policies by realistic out-of-sample simulations. A simple trigger-based decision policy was able to achieve a good compromise among possibly conflicting objectives, without resorting to full-fledged multiobjective models. Conclusions: The insights into the relative strengths and weaknesses of the tested policies pave the way to practical extensions. Due to its computational efficiency, the trigger policy may be improved by base-policy rollout and integrated within a multi-vehicle routing architecture. Full article

The supply chain is a critical tool for enterprises to withstand risks and ensure sustainable development. Integrating green and environmentally friendly practices into the supply chain has become an increasingly prominent trend. This study examines the impact of green supply chain management (GSCM) on supply chain resilience, using the green supply chain pilot projects implemented in China as a quasi-natural experiment, employing a multi-period difference-in-difference (DID) model. Based on panel data from manufacturing enterprises listed on the A-share market in China from 2014 to 2022, the findings reveal three key insights. First, GSCM significantly improves the resilience of enterprise supply chains. Second, GSCM has both signaling and cost effects, as it can reduce corporate financing costs and enhance market value, lower market transaction costs, and improve productivity. These are potential channels through which GSCM exerts a positive influence. Third, the positive impact of GSCM on supply chain resilience is more pronounced in enterprises with third-party environmental certifications and higher institutional shareholder ratios. Additionally, this study also extends to demonstrate that GSCM directly and positively influences corporate environmental performance. These findings provide policy recommendations for enhancing green supply chain development and offer managerial insights to help enterprises proactively embrace green transformation. Full article

As consumer awareness grows and regulations regarding the quality and safety of perishable goods become stricter, careful management of environmental conditions throughout the supply chain is becoming essential. Among these factors, storage temperature plays a crucial role in preserving the physicochemical characteristics of products. Therefore, an effective approach to ensure quality and safety up to the final customer is to continuously monitor the temperature within warehouses, using specific location-mapping techniques and stocking optimization methods. This study proposes a dynamic optimization model for the storage location assignment problem, integrating both temperature and humidity constraints into the placement of stock-keeping units. The model operates under a multi-period, multi-product framework and leverages real-time sensor data to account for spatial temperature stratification and environmental variability within the warehouse, contributing to the reduction in the energy consumption. Two alternative optimization strategies are explored: one focused on minimizing thermal and humidity stress, and another targeting the reduction in average storage cycle time. A detailed what-if analysis is conducted across three scenarios, varying warehouse fill rates and incoming load volumes, in order to prove the effectiveness of the proposed model in a real-data context. The results show that the approach minimizing environmental stress consistently outperforms traditional methods in quality-related metrics, maintaining superior objective function values. Full article

County economies are the cornerstone of China’s economic and social development but face challenges such as a singular industrial structure and the outflow of production factors. As an important policy tool for rural revitalization, the impact mechanism of National-Level Modern Agricultural Industrial Parks (NMAIPs) on county economies remains inadequately explored. This study aims to quantify the dynamic economic effects of the NMAIP policy through rigorous empirical analysis and elucidate the core pathways driving county economic growth. Based on panel data from 44 counties in six central Chinese provinces from 2014 to 2024, this study employs a Multi-Period Difference-in-Differences (DID) model and finds a significant one-year lag effect of the NMAIP policy: in the year following park establishment, county GDP increased by an average of 8.5%, and this positive effect persisted until the fourth year but showed a trend of marginal diminution. Pathway analysis reveals that agricultural scale expansion (measured by gross output value of agriculture, forestry, animal husbandry, and fishery) and production efficiency improvement (measured by the ratio of output value to agricultural expenditure) are the core driving mechanisms, accounting for 48% and 35% of the total effect, respectively. In contrast, the mediating roles of industrial integration (comprehensive index) and industrial structure upgrading (share of agricultural services) were not statistically significant in the short run. The policy lag primarily arises from the conversion cycle of infrastructure investment to economic output, while pathway differences are closely related to the maturity of the county’s agricultural industrial chain and resource allocation efficiency. This study provides robust empirical evidence for optimizing the timing and pathways of the NMAIP policy design: policy effect evaluations require a 1–2 year “window period”; resources should be prioritized for projects that can rapidly enhance scale and efficiency (e.g., scaled planting, technology-driven efficiency gains), laying a solid agricultural foundation before gradually fostering industrial integration. This aligns with the spirit of “avoiding industrial hollowing-out” proposed in the 2024 Central “Thousand Villages Project” and provides the Chinese experience for the policy evaluation and path selection of global agricultural parks. Full article

Urban sustainability has become the most important urban development issue globally. Facing the problem of spatial structure optimization during urbanization, how to effectively use public data access to promote urban polycentric development has become a new area of concern for urban planners and policy makers. To quantify how government open-data platforms shape polycentric urban spatial structure across Chinese cities, this study takes the launch of government data platforms as a quasi-natural experiment, constructs the multi-period differences-in-differences model, uses data of 271 Chinese prefectural-level cities from 2010 to 2021, and examines the impact and mechanism of public data access on urban spatial structure. We find that public data access promotes urban polycentric development, especially in large cities, those in urban agglomerations, and resource-abundant cities. The effect follows an inverted ‘N’ trend, which reflects the evolving role of PDA across different urban development stages, highlighting the need for adaptive policies to optimize its benefits. Mechanisms include information process radicalization and industrial structure upgrading, moderated positively by government intervention and regional competition. These insights can inform policies for optimizing urban spatial patterns and advancing sustainable urban development. Full article

This paper investigates last-mile delivery and explores hybrid distributed computational models for routing and scheduling delivery services and assigning delivery-points to deliverymen over multiple time periods. The objective of these models is to minimize the number of deliverymen hired for providing delivery services over multiple periods while satisfying predetermined time limits. This paper describes the development of multiple traveling deliverymen approaches, multi-period optimization models, and a multi-period distributed algorithm, to optimize routing and scheduling for last-mile deliveries. This paper utilizes a computer-aided modeling system to facilitate the proposed distributed approach, which offers an optimization model for large numbers of delivery-points and helps in performing limited computation as required to minimize the memory usage and provide efficiently solvable models within acceptable durations of execution. To illustrate the solvability of the proposed approach and scalability to large instances, 26 case problems are presented for last-mile delivery services. The key results include optimized routing and scheduling, a minimum number of deliverymen, and a significant reduction in computational effort and time. Full article

With the widespread implementation of bridge structural health monitoring (SHM) systems, monitored bridge networks have gradually formed. Understanding vehicle loads and considering spatio-temporal correlations within bridge networks is critical for structural condition assessment and maintenance decision making. This study aims to predict traffic flows by investigating traffic flow correlations within a bridge network using multi-bridge data, thereby supporting bridge network-level SHM. A transformer-based traffic flow prediction model considering spatio-temporal correlations of bridge networks (ST-TransNet) is proposed. It integrates external factors (processed via fully connected networks) and multi-period traffic flows of input bridges (captured by self-attention encoders) to generate traffic flow predictions through a self-attention decoder. Validated using weigh-in-motion data from an 8-bridge network, the proposed ST-TransNet reduces prediction root mean square error (RMSE) to 12.76 vehicles/10 min, outperforming a series of baselines—SVR, CNN, BiLSTM, CNN&BiLSTM, ST-ResNet, transformer, and STGCN—with significant relative reductions of 40.5%, 36.9%, 36.6%, 37.3%, 35.6%, 31.1%, and 22.8%, respectively. Ablation studies confirm the contribution of each component of the external factors and multi-period traffic flows, particularly the recent traffic flow data. The proposed ST-TransNet effectively captures underlying the spatio-temporal correlations of traffic flow within bridge networks, offering valuable insights for enhancing bridge assessment and maintenance. Full article