Search Results (174)

Against the backdrop of increasingly stringent environmental regulation and increasing uncertainty in supply chain operations, this study examines how environmental penalties affect corporate supply chain resilience. Using Chinese A-share listed firms from 2009 to 2024, this paper constructs a firm-level panel dataset and employs a two-way fixed-effects model to estimate the relationship between environmental penalty intensity and supply chain resilience. Environmental penalty intensity is measured by the annual penalty amount imposed on each firm, while supply chain resilience is captured through an entropy-weighted index reflecting both resistance and recovery capacities. To alleviate endogeneity concerns, this study further uses an instrumental-variable approach based on the interaction between a firm’s one-year lagged penalty amount and city-level thermal inversion days. The results show that environmental penalties reduce corporate supply chain resilience. This negative effect is heterogeneous across firm characteristics and is partially mediated by reduced operational efficiency and crowded-out R&D investment. This conclusion remains robust after replacing the dependent variable, changing the clustering level of standard errors, and excluding observations from the COVID-19 pandemic period. Mechanism tests suggest that environmental penalties weaken supply chain resilience partly by reducing operational efficiency and crowding out R&D investment. Heterogeneity analysis indicates that the negative effect is more pronounced among young firms, non-high-tech firms, and firms located in regions with lower environmental regulation intensity. This study contributes to the literature by distinguishing environmental penalties from broader environmental regulation and by examining their implications for supply chain resilience. The findings also suggest that environmental enforcement should maintain deterrence while improving transparency, predictability, and targeted compliance guidance. Full article

The Ordos Basin contains abundant deep coalbed methane (CBM) resources, whose efficient development largely depends on the effective implementation of large-scale volumetric fracturing technologies. To comprehensively evaluate the fracability of deep coal reservoirs in this basin, this study focuses on the No. 8 coal seam of the Benxi Formation. Based on rock mechanical experiments and well-logging data, multivariate linear regression models were established to predict Young’s modulus (E) and Poisson’s ratio (μ). The Huang model was applied to determine the three principal in situ stresses of the coal seam. Furthermore, a comprehensive fracability evaluation model was constructed by integrating three key indicators, namely brittleness index (BI), horizontal stress difference (Δσ_h), and tensile strength (S_t). The entropy evaluation method was used to determine the weights of these indicators, and the fracability index (F) of deep coal reservoirs was calculated. The results show that the weights of the factors controlling fracability decrease in the following order: tensile strength (0.434), brittleness index (0.332), and horizontal stress difference (0.234). The No. 8 coal seam in the northern and southern parts of the basin, including the Daning–Jixian, Shenfu, Jiaxian, northern Yulin, and southern Yanchuan areas, exhibits relatively favorable fracability, whereas northern Liulin and southern Yulin show comparatively poor fracability. In addition, the fracability index shows a clear positive correlation with the peak gas production of vertical CBM wells. Based on this relationship, the deep coal reservoirs were classified into three categories: Class I reservoirs (F > 0.55), characterized by high fracability and high production potential; Class II reservoirs (0.50 ≤ F ≤ 0.55), characterized by moderate fracability and moderate production potential; and Class III reservoirs (F < 0.50), characterized by low fracability and low production potential. These findings provide a scientific basis for identifying fracturing sweet spots and for the classification evaluation of deep CBM resources in the Ordos Basin. Full article

The rapid growth of organic waste volumes in urban areas and increasing environmental pressures necessitate the transition toward sustainable and risk-informed municipal waste management systems. This study aims to develop a data-driven decision support framework for the risk-informed management of municipal organic waste within the context of sustainable urban development. The proposed approach integrates multi-source municipal data, advanced preprocessing techniques, entropy-based feature weighting, and an ensemble of machine learning models, including Random Forest, Gradient Boosting, and XGBoost. An integrated environmental risk index is formulated to quantify the state of the waste management system and to support predictive analytics. The results demonstrate high predictive performance and reveal that key risk drivers include demographic pressure, transport accessibility, infrastructure characteristics, and seasonal variability of waste generation. The developed framework enables the integration of predictive risk analytics into municipal decision support systems, facilitating optimized waste collection logistics, infrastructure planning, and early identification of critical conditions. The findings confirm that data-driven approaches can significantly enhance the efficiency and adaptability of urban waste management systems. The proposed framework contributes to sustainable urban development by supporting circular economy principles and enabling proactive, risk-aware governance of municipal organic waste systems. Full article

Promoting the high-quality development of farmers’ specialized cooperatives and narrowing regional development gaps is critical for advancing China’s rural revitalization strategy. Based on provincial panel data covering 30 Chinese regions from 2015 to 2023, this paper constructs a five-dimensional evaluation index system including standardized operation, operational performance, service scope, driving effect, and industrial upgrading, and adopts the entropy weight method to quantify the comprehensive development level of cooperatives. By combining spatial autocorrelation, kernel density estimation, the Dagum Gini coefficient and the Geodetector model, this paper explores the spatio-temporal evolution, regional disparities and multi-factor coupled driving mechanism of cooperative development. The main findings are as follows: (1) While the total quantity of cooperatives keeps expanding nationwide, their overall development level presents an evolutionary feature of declining first and then rising; industrial upgrading gradually becomes a new growth engine, whereas operational performance and driving effect slip downward. (2) The spatial layout of cooperatives maintains a typical pyramid structure; high-value agglomeration shifts from the Yangtze River Delta to southeast coastal regions, and low-value clusters are persistently concentrated in Northeast China. (3) The overall Dagum Gini coefficient reflects widening-then-shrinking regional gaps, and intra-eastern provincial differences constitute the primary source of nationwide spatial divergence. (4) Household consumption and rural labor force stock serve as core driving factors; regional economic development, agricultural production efficiency, rural human capital and land resource allocation form a coupled driving system, and all explanatory variables show mutual enhancement effects without offsetting interactions. Targeted policy suggestions are put forward to realize balanced and high-quality development of farmers’ specialized cooperatives across China. Full article

Urban–rural income inequality is a major challenge to social stability, inclusive growth, and sustainable modernization in developing and transition economies. Using panel data from 30 Chinese provinces from 2012 to 2020, this study examines how public service level (PWS) and marketization level (ML) affect urban–rural income inequality within a unified analytical framework. Urban–rural income inequality is measured by the Theil index, and entropy-weighted composite indices are constructed for PWS and ML. A panel fixed-effects model is employed to estimate the direct effects of PWS, ML, and their coupling coordination, while also testing the mediating roles of the growth rate of household income (GRHI) and the efficiency of human capital allocation (EHCA), as well as the threshold effect of the urbanization rate (Urb). The results show that both PWS and ML significantly reduce urban–rural income inequality and that stronger coupling coordination between them further narrows the income gap. PWS mainly works by promoting GRHI, whereas ML operates by improving EHCA. Moreover, the effects of both PWS and ML become stronger only when Urb exceeds a certain threshold. These findings provide insights for promoting inclusive and balanced development. Full article

This study examines the relationship between rural land circulation and common prosperity across 30 Chinese provinces over the period 2010–2022. We construct a multidimensional common prosperity index based on economic development, income distribution, public services, and social security using the entropy weight method. A Spatial Durbin Model (SDM) is employed to capture both local effects and interregional spillovers. The results show that rural land circulation exerts a positive and statistically significant impact on common prosperity. Effect decomposition further indicates that the influence is primarily driven by local (direct) effects, while spatial spillovers also play a meaningful role, suggesting that improvements in one region can generate positive externalities for neighboring areas. Additional analysis reveals three key channels through which land circulation is associated with common prosperity: improvements in agricultural productivity, increases in farmer income, and urbanization advancement. The effects exhibit clear regional heterogeneity, being strongest in central China, moderate in western regions, and statistically insignificant in the eastern provinces, reflecting diminishing marginal returns as land markets mature. Moreover, the impact of land circulation is more pronounced in regions with higher levels of digital economy development, indicating that digitalization enhances the efficiency and inclusiveness of land market transactions. These findings are robust to alternative spatial weight matrices, variable definitions, and sample adjustments. Overall, the results highlight the importance of regionally differentiated land circulation policies and the role of market integration in promoting balanced and inclusive development. Full article

This study presents a novel integrated methodology for optimizing forest fire emergency rescue vehicle scheduling through the synergistic combination of a multi-criteria demand urgency grading framework and mechanistic fire spread propagation modeling, enhancing spatiotemporal resource allocation efficiency under evolving wildfire scenarios. The research focuses on three core aspects: First, a multi-dimensional demand urgency evaluation system is established, incorporating fire threat, response efficiency, and path factors. Subjective and objective weights are determined through fuzzy analytic hierarchy process and entropy method, respectively, while grey relational analysis TOPSIS method is employed for prioritizing affected areas. The model’s validity is verified using wildfire data from the Greater Khingan Mountains. Second, a multi-objective vehicle scheduling model is developed, combining total rescue time, cost, and urgency ranking index via weighted sum method. A fire spread model is innovatively introduced to dynamically adjust urgency classification, with genetic algorithm (GA) and Genetic Simulated Annealing Algorithm (GASA) designed for solution optimization. Finally, empirical analysis of 13 fire cases in the Greater Khingan Mountains (2020) demonstrates that GASA outperforms GA, achieving 17% reduction in rescue time, 1% cost savings, 22% shorter travel distance, and 0.7% improvement in urgency ranking. Incorporating the fire spread model enhances the urgency ranking index by 10.78%, where the improvement is defined as the percentage increase in the achieved objective function value f3 compared to the solution obtained without dynamic fire propagation information. By integrating dynamic urgency assessment with intelligent algorithms, this research constructs a spatiotemporal-aware emergency scheduling framework aligned with forest fire evolution patterns, providing theoretical foundations and practical strategies to enhance rescue efficiency and resource allocation, with significant implications for disaster management. Full article

With the advancement of carbon peaking and carbon neutrality goals, the increasing penetration of renewable energy sources such as wind and photovoltaic power poses severe challenges to the power supply reliability of AC/DC hybrid distribution networks due to their fluctuating, intermittent, and stochastic outputs. This paper proposes a reliability assessment method for AC/DC hybrid distribution networks under large-scale renewable energy integration based on clustering of typical operating scenarios. The net load duration curve is adopted as the feature variable to characterize typical operating scenarios. An improved t-distributed Stochastic Neighbor Embedding (t-SNE) nonlinear dimensionality reduction method with Kullback–Leibler (KL) divergence elbow correction is proposed for effective reduction of high-dimensional time-series data. An adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN) parameter optimization method based on the k-nearest-neighbor curve and a secondary K-means clustering method based on entropy-weighted multi-objective optimization are further developed, forming a hybrid t-SNE-DBSCAN–K-means clustering algorithm. The power supply reliability is then assessed based on the clustered typical operating scenarios. A typical AC/DC hybrid distribution network is used as the test system. Results show that the DB index of the proposed clustering method improves by at least 22% compared with conventional methods, the maximum relative error between the typical-day-based and full time-series simulation results is less than 6%, and the computational efficiency improves by about 8.8 times, achieving a good balance between accuracy and efficiency. Full article

The coupling coordination among digital village development, agricultural modernization, and agricultural carbon emission efficiency is critical for achieving green and high-quality agricultural development. Using panel data of 30 Chinese provinces (excluding Hong Kong, Macao, Taiwan, and Tibet) from 2011 to 2024, this study measures agricultural carbon emission efficiency via the super-efficiency SBM model, evaluates the levels of digital village development and agricultural modernization using the entropy method, constructs a coupling coordination degree model to analyze the spatiotemporal evolution characteristics of the three systems, and employs the Geographically and Temporally Weighted Regression (GTWR) model to reveal the spatiotemporally heterogeneous effects of governmental, market, and social factors on the coupling coordination degree. The results show that: (1) The three systems exhibit unbalanced development. The digital village development index increased from 0.430 to 0.539; agricultural modernization grew slowly from 0.308 to 0.411; and agricultural carbon emission efficiency surged from 0.146 to 0.655. (2) The coupling coordination degree of the three systems rose continuously from 0.382 to 0.661, transitioning from near disorder to primary coordination. Spatially, the eastern and northeastern regions led while the western region lagged, though Xinjiang reached good coordination (0.786) in 2024. (3) The GTWR model reveals that the marketization index (ranging from −0.0362 to 0.0559), agricultural land transfer rate (ranging from −0.1630 to 1.7952), fiscal support for agriculture (ranging from −0.0003 to 0.0232), and agricultural socialized services (ranging from 0.0540 to 1.0460) have positive effects with significant spatial heterogeneity. Rural infrastructure exhibits a “positive in the south, negative in the north” pattern (ranging from −0.0019 to 0.0012), while the overall social consumption level (ranging from −0.9680 to 0.6548) exerts a negative inhibiting effect. These findings provide a theoretical basis for understanding the spatial heterogeneity of the coupling coordination among the three systems and emphasize that differentiated, regionally tailored strategies are key to promoting green and high-quality agricultural development. Full article

To address peak-period congestion in high-density air route networks and the high cost and limited precision of traditional global control methods, this study proposes a congestion mitigation method based on pinning control theory. First, a comprehensive evaluation index system for critical waypoints is constructed from complex-network structural characteristics, traffic flow characteristics, and congestion-state information. Pearson correlation analysis is used to examine redundancy among candidate indicators, and the entropy-weighted TOPSIS method is then employed to evaluate waypoint importance and identify critical pinning nodes. Second, a GA-PID pinning control optimization model is established to realize closed-loop optimization of network congestion by dynamically regulating a small number of critical nodes. Finally, simulation experiments are conducted using actual operational trajectory data from the Yangtze River Delta airspace. The results show that the proposed method reduces the network congestion coefficient from 176 to 137, representing a decrease of 22.16%, and increases airspace resource utilization from 70.76% to 84.41%, representing an improvement of 19.29%. Compared with the baseline GA method, the proposed method achieves better optimization performance and requires adjustments at only 13 waypoints, whereas the baseline GA method requires adjustments at 25 waypoints, demonstrating lower control costs and higher regulation efficiency. Full article

This study evaluates the efficiency of fiscal support policies for village-level collective economies in S Province, a frontier region of China, over the analytical period of 2018–2023, which includes the policy implementation years (2019–2022) plus one pre-policy and one post-policy year. Integrating theories of collaborative governance, resource alertness, and inclusive rural development, we construct an efficiency measurement framework to assess policy performance across 13 regions. Static efficiency is measured using DEA-BCC and super-efficiency SE-DEA models, while dynamic total factor productivity (TFP) is analyzed via the DEA–Malmquist index. The entropy-weighted method is employed to ensure robust indicator weighting. The findings reveal the following: (1) The average super-efficiency is 0.855, indicating relatively high expenditure efficiency but significant regional disparities and room for improvement. (2) The TFP declined by an average of 9.7% over the analytical period (2018–2023), primarily due to technological regression, despite stable technical efficiency. Based on the TFP performance, regions are categorized into high-, middle-, and low-efficiency tiers. Accordingly, we propose policy recommendations including efficiency-driven funding allocation, long-term support mechanisms combining technological innovation and management empowerment, regionally differentiated strategies, and strengthened multi-stakeholder collaboration. This study provides empirical evidence for optimizing fiscal policies to promote the sustainable development of rural collective economies and advance inclusive rural development in frontier regions. Full article

As a core component of power system reform, the transmission and distribution price reform plays a critical role in optimizing the grid regulation model and promoting efficient allocation of power resources by establishing an independent pricing mechanism based on “permitted cost plus reasonable return”. This study evaluates the provincial transmission and distribution price reform effect in China. First, an evaluation index system is constructed from four dimensions, namely, economic efficiency, security guarantee, market mechanism and social welfare. Second, a comprehensive evaluation model is developed using a multi-attribute decision-making model consist of the Best–Worst Method (BWM), entropy weight method (EWM) and cloud model. Of these, the BWM and EWM are employed to determine the indicator weights, and the cloud model is utilized to rank the transmission and distribution price reform effect. Third, an empirical assessment and analysis are conducted on three typical provinces in China. Empirical analysis reveals significant regional heterogeneity in reform effectiveness. Based on the comprehensive cloud expectation (Ex) values, Province B (eastern coastal) ranks first with an Ex of 82.10 (on a 0–100 scale), falling into the “good” grade; Province C (northern) ranks second with an Ex of 81.05, also “good”; and Province A (central-western) ranks third with an Ex of 78.70, likewise “good”. Province B’s leading position is attributed to synergistic outcomes in cost control, market vitality, and social welfare. The study can provide references for the sustainable development of electric power companies and the electricity industry. Full article

In the context of the rapid growth of the digital economy and the continued implementation of China’s “dual carbon” strategy, clarifying the interactive relationships among the digital economy, low-carbon logistics, and the ecological environment is crucial for promoting sustainable regional development and green transformation. Based on the theoretical mechanisms underlying the coordinated development of these three systems, this study constructs a comprehensive evaluation index system for the Digital Economy–Low-Carbon Logistics–Ecological Environment (DLE) system. The entropy weighting method, a modified coupling coordination model, kernel density estimation, spatial autocorrelation analysis, and the barrier model are integrated to investigate the spatiotemporal evolution and driving mechanisms of coupling coordination among the three systems. The results indicate that (1) the development levels of the digital economy, low-carbon logistics, and the ecological environment have generally increased, although their evolutionary trajectories differ across stages. The digital economy shows the most rapid improvement, low-carbon logistics maintains steady progress, and the ecological environment exhibits gradual optimization. (2) From a temporal perspective, the overall coupling coordination of the national DLE system has shown a fluctuating upward trend, with the coordination type gradually evolving from a near-coordination stage to an initial coordination stage, though it remains at a low-to-medium coordination level overall. (3) From a spatial perspective, the coupling coordination degree presents a stable gradient pattern, with higher levels in eastern China, intermediate levels in central China, and lower levels in western China. Medium- and high-coordination areas are gradually extending from coastal regions to inland areas, while regional disparities remain evident. (4) The spatial autocorrelation results reveal significant positive spatial clustering at the provincial level. Both high-value and low-value clusters show a certain degree of stability, indicating clear spatial spillover effects. (5) An analysis of constraining factors reveals that insufficient scale of digital economic development and innovation application capabilities, constraints on ecological and environmental resource carrying capacity and governance, as well as low operational efficiency and delayed transformation of low-carbon logistics, are the primary types of obstacles hindering the coordinated improvement of the three systems. These findings provide empirical evidence and policy implications for leveraging the digital economy to facilitate low-carbon logistics transformation and enhance coordinated regional sustainability. Full article

As data-intensive applications continue to scale reconfigurable array processors (RAPs), electrical networks-on-chip (NoCs) are increasingly constrained by energy-delay bottlenecks due to RC-delay constraints. Hybrid optoelectronic NoCs (HONoCs) suffer from a fundamental medium-selection dilemma: optical circuit switching incurs microsecond-scale setup overheads for long flows, whereas static distance thresholds fail to capture the spatiotemporal heterogeneity of traffic, causing wavelength waste for bursty flows and congestion diffusion under non-stationary loads. This paper presents an adaptive switching framework that is aware of spatiotemporal locality. We introduce the Temporal-Spatial Locality Index (TSLI) to classify flows into Electrophilic (EF), Photophilic (PF), and Hybrid-sensitive (HF) categories, and propose Cross-layer Congestion Entropy (CCE) to unify electrical and optical resource states. Based on these metrics, an Adaptive Medium Selection State Machine (AMSSM) dynamically switches among Electro-Dominant (EDM), Electro-Optical Synergistic (EOSM), and Optical-Dominant (ODM) modes, while a Weighted Multi-dimensional Medium Matching (WMMM) algorithm performs fine-grained channel selection. A Predictive Optical Path Provisioning (POPP) mechanism further amortizes setup latencies via trend-aware pre-establishment. Evaluation on an 8 × 8 mesh HONoCs demonstrates 22% higher saturation throughput, 38% lower energy-delay product (EDP), and 57% reduction in average latency under non-stationary traffic, compared to static thresholds. The proposed mechanisms provide a theoretical foundation and engineering paradigm for efficient on-chip interconnects. Full article

Improving productivity under resource and environmental constraints is a key challenge for sustainability-oriented transformation in manufacturing. Using panel data from 30 Chinese provinces during the period 2010–2024, this study examines how regional digital innovation capability is associated with manufacturing total factor productivity at the provincial level. A multidimensional digital innovation index is constructed using the entropy-weighting method, while manufacturing total factor productivity (TFP) is measured using the DEA–Malmquist index. In this study, conventional manufacturing TFP is treated as a productivity-oriented proxy within a sustainability-oriented transformation context, rather than as a direct measure of environmental performance. The empirical framework applies a two-way fixed-effects model and is complemented by supplementary instrumental-variable estimation, mediation analysis, and threshold regression to examine transmission channels and nonlinear effects. The results indicate that digital innovation capability is positively associated with manufacturing TFP, with stronger associations observed in regions that have more developed digital and innovation foundations. Decomposition results show that the gains are mainly related to technological progress rather than short-term efficiency improvements, suggesting that digitalization is reflected primarily through innovation-led upgrading. Mechanism tests further show that improvements in R&D efficiency, data element allocation, and human capital structure play important mediating roles. A significant threshold effect is also observed: When the share of digital-skilled labor exceeds a critical level, the productivity return from digital innovation increases markedly. These findings underscore the role of digital innovation and digital maturity in supporting manufacturing productivity upgrading within a sustainability-oriented transformation context and imply that policy should prioritize coordinated investment in digital infrastructure, data governance, and digital skills development. Full article