Search Results (1,185)

This study systematically examines the impacts of extreme temperatures on the digital economy development index and the underlying mechanisms based on panel data from 281 prefecture-level cities in China from 2012 to 2023. This study explicitly distinguishes the distinctive adaptive capacity of the digital economy in responding to climate risks. Through global and local spatial autocorrelation analysis, the study finds that both extreme temperatures and the digital economy exhibit significant spatial clustering. This study employs the spatial Durbin model (SDM) and effect decomposition and further incorporates the GS2SLS estimator alongside dual instrumental variables constructed from historical geographic characteristics to address endogeneity, thereby identifying the asymmetrical impacts of extreme heat and extreme cold on the digital economy with great rigor. Specifically, extreme heat fosters short-term local digital demand that is subsequently translated into long-term growth in IT human capital and infrastructure, thereby increasing the DEDI. However, its net spatial effect is inhibitory due to energy crowding out. Extreme cold, by contrast, primarily disrupts supply chains and intensifies energy consumption, with its impact largely confined to the local scope. Green technological innovation mitigates the impact of extreme heat on the digital economy through demand substitution, while, under extreme cold, it manifests as the physical protection of infrastructure. Meanwhile, an optimized industrial structure substantially reduces the economy’s dependence on supply chains, amplifying the promotional effect of extreme temperatures on the digital economy and reflecting the transformation capacity of regions under complex environmental conditions. Heterogeneity analysis demonstrates that the effects of extreme temperatures vary significantly across different urban agglomerations, economic zones, geographic regions and city types. This study not only extends the theoretical framework for the economic assessment of climate risks and spatial econometric analysis to the climate sensitivity of the digital economy but also provides empirical evidence for understanding the complex relationship between climate change and digital economy development and offers references for differentiated policies in a coordinated regional digital economy. Full article

Effective supply chain planning increasingly requires balancing cost-efficiency with environmental responsibility, particularly as organisations face growing pressure to reduce the carbon footprint of logistics operations. This study develops a mixed-integer linear programming model to optimise inventory and transportation decisions in a multi-echelon distribution network comprising a central warehouse, regional warehouses, and retailers. The model integrates a continuous-review (r,Q) replenishment policy, stochastic demand, safety stock requirements, transportation lead times, and stockout behaviour, enabling a detailed representation of operational dynamics under uncertainty and environmental concerns. Unlike most sustainable inventory models—which typically treat environmental impacts and replenishment control separately or rely on simplified service assumptions—this study provides an integrated framework that jointly embeds (r,Q) policies, stochastic demand, stockouts and distance-based CO₂ metrics within a unified optimisation structure. The model advances prior work by explicitly integrating continuous-review (r,Q) replenishment policies with distance-based CO₂ metrics under stochastic demand, a combination rarely addressed in sustainable multi-echelon inventory models. A multi-objective formulation captures the trade-off between economic performance and CO₂ emissions, allowing the identification of Pareto-efficient strategies that reconcile financial and environmental goals. Reducing emissions by over 90% requires an additional cost of only about 4%, demonstrating that substantial emission reductions can be achieved at relatively low additional cost. The findings offer practical insights for managers seeking to design more sustainable and cost-effective distribution policies, highlighting the value of integrated optimisation approaches in contemporary logistics systems. Full article

The use of renewable energy sources instead of conventional ones, together with the development of efficient electricity storage solutions, represents a central objective of the transition to sustainable and resilient energy systems. In this context, two main development directions are the integration of hydrogen in the energy chain (Power-to-Gas) and the use of batteries, each with specific advantages and disadvantages, compared to internal combustion engines. The purpose of this work was to evaluate the dynamic response time of a hydrogen fuel cell model powered by green hydrogen, under conditions of sudden and instantaneous power demand, for its integration into wind-based renewable energy systems. Experimental research was carried out on an autonomous installation designed to operate continuously for an unlimited duration, simulating the integration of hydrogen produced from wind sources. The novelty consists of the application of an instrumental method for automatic measurement of the response time of a proton exchange membrane hydrogen fuel cell, based on the automatic acquisition and processing of measured electrical signals. The response time of the fuel cell was compared with that of an internal combustion engine based on the classic Carnot cycle, using a dedicated oscilloscope. The load connection time, the current and voltage variation as a function of time were recorded simultaneously. The results show that the response time of the fuel cell is relatively short (approximately 0.3 ms), much lower than that of the internal combustion engine (0.7 s), being of the order of about 2333 times smaller. In conclusion, the hydrogen fuel cell can be effectively integrated into renewable energy systems for the role of an uninterruptible power supply, with an exceptionally fast dynamic response, suitable for applications in regulating and supporting wind-powered networks. Full article

The strain on the global food sector to reconcile environmental sustainability with operational efficiency has been intensifying. In a growing economy, this study investigates the revolutionary potential of integrated digital ecosystems that include blockchain, big data analytics, and IoT-enabled real-time monitoring on the performance of Green Supply Chain Management (GSCM). The research, that relies on the Technology–Organization–Environment (TOE) framework, utilizes a rigorous mixed-methods approach which utilizes Fuzzy-Set Qualitative Comparative Analysis (fsQCA) and Structural Equation Modeling (SEM) on data from food-processing firms in Pakistan. Green innovation is an important moderating catalyst, and SEM results confirm that digital integration significantly enhances waste reduction and energy efficiency, explaining 62% of performance variance. A further configurational analysis indicates causal equifinality and reveals 3 distinct paths to superior sustainability, from “Innovation-Driven Institutionalization” to “Government-Supported Scaling.” It demonstrates that various combinations of external support and internal readiness may ultimately contribute to success. The findings are supported by post-implementation evaluations, which show a 29% decrease in energy consumption and a 55% reduction in cold-chain losses. These findings offer novel insights for practitioners and policymakers, validating that environmental stewardship and commercial profitability are mutually reinforcing objectives in the digital age. Full article

Corporate carbon reduction is essential for sustainable development, yet little is known about whether equity linkages within supply chains facilitate firms’ low-carbon transition. Using data on Chinese A-share listed firms from 2008 to 2022, this study examines the effect of supply chain shareholding, defined as equity ownership by suppliers and customers in a focal firm, on corporate carbon emission intensity. We find that supply chain shareholding significantly reduces corporate carbon emission intensity, and this result remains robust after a series of robustness and endogeneity tests. Mechanism analyses show that supply chain shareholding lowers carbon emission intensity by strengthening corporate green governance, promoting green innovation, and facilitating cleaner production. Further analyses indicate that this effect is more pronounced under stricter air quality requirements, in regions with stronger environmental regulation, and among heavily polluting industries. These findings highlight the role of supply chain governance in corporate carbon reduction and suggest that equity linkages within supply chains can support firms’ low-carbon transition. Full article

Carbon emission regulations and customers’ green preferences require ports and shipping companies to develop green services, but green investments entail significant costs. Vertical alliance cooperation between ports and shipping companies through sharing costs can address this issue. Most studies use non-cooperative game to analyze the competitive relationship between ports and shipping companies. Although such research can capture price competition, they struggle to address the distribution of cooperative benefits within an alliance. They also fail to simultaneously reflect the coexistence of competition and cooperation. So, we constructed a non-cooperative–cooperative biform game to analyze green investment under vertical alliance. In the non-cooperative stage, the model captures vertical price competition between ports and shipping companies, as well as horizontal competition among supply chains. In the cooperative stage, the Shapley value is used to allocate the coalition profits from green investment cooperation. The results indicate that alliance cooperation can promote the green development of shipping. Moderate green competition can promote the green development of shipping. Route substitution competition will increase service prices and green investment level and reduce the cost-sharing ratio for shipping companies. Port congestion prompts ports to increase green investment level. These findings offer references for the green collaborative development of ports and shipping companies across different countries, thereby enriching the research framework for global sustainable development in shipping. Full article

Background: The growing emphasis on sustainability has increased interest in understanding how environmentally oriented supply chain practices translate into organizational outcomes. However, empirical research examining how circular economy principles, technological integration, and sustainable supply chain management (SSCM) practices jointly influence green supply chain performance remains limited, particularly in developing economies. Methods: A quantitative research design was employed using survey data collected from 333 professionals in the Lebanese consumer goods industry through structured Likert-scale questionnaires. The proposed conceptual model was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to evaluate the measurement model and test the relationships among circular economy practices, technological integration, SSCM practices, green supply chain performance, and organizational performance. Results: The findings indicate that technological integration, circular economy practices, and SSCM practices collectively enhance green supply chain performance. The results further show that improved green supply chain performance supports stronger organizational outcomes. Conclusions: This study contributes to sustainable supply chain literature by integrating circular economy principles, technological capabilities, and SSCM practices within a unified framework. It highlights the strategic role of green supply chain performance in linking sustainability initiatives to organizational outcomes and provides insights for managers seeking to implement integrated sustainability strategies. Full article

In the context of a rapidly evolving data-driven economy and increasingly stringent carbon reduction policies, the impact of supply chain digitalization (SCD) on corporate carbon emission intensity (CEI) has become an important research topic. Using panel data on Chinese A-share listed firms from the Shanghai and Shenzhen stock exchanges over the period 2013–2023, this study employs Python-based text analysis of corporate annual reports to explore the effect of SCD on corporate CEI. The results show that SCD significantly reduces corporate CEI. Mechanism analysis further indicates that this effect operates through three channels: alleviating financing constraints, promoting green innovation, and reducing supply chain disruption risk. Heterogeneity analysis reveals that the mitigating effect of SCD on corporate CEI is more pronounced among non-state-owned firms, large-scale firms, firms in non-high-tech industries, firms in highly environmentally sensitive industries, and firms located in regions with more developed digital infrastructure. Further analysis shows that SCD contributes to improvements in both firms’ sustainability and financial performance. Overall, this study provides important policy implications for both governments and firms. Full article

Conventional spatial frequency domain imaging (SFDI) based optical property inversion is inefficient, while deep learning methods suffer from heavy reliance on large-scale real datasets. To address this contradiction, a simulation-driven approach for subsurface fruit bruise discrimination was proposed. An SFDI simulation environment was built with Blender to generate 800 paired datasets of diffuse reflectance images and optical transport coefficients, overcoming the high cost and long cycle of real dataset acquisition. We designed the CBAM-GAN-U-Net model and adopted surface profile correction in the prediction method to eliminate curved surface-induced non-planar distortion, with the whole method validated on liquid phantoms, green apples and crown pears. This prediction method achieved high accuracy in predicting the reduced scattering coefficient μ_s′, with NMAE of 0.021 ± 0.007 (phantoms), 0.039 ± 0.012 (severely bruised green apples) and 0.044 ± 0.015 (severely bruised crown pears), outperforming U-Net and GANPOP. Based on the predicted μ_s′, a discrimination strategy combining coefficient of variation, mean ratio and receiver operating characteristic (ROC) curve analysis was adopted, attaining 100% accuracy for non-bruised/bruised fruit discrimination, with misclassification rates of 6% (green apples) and 8% (crown pears) for mild/severe bruise differentiation. This method enables accurate subsurface fruit bruise detection, providing a reliable technical solution for the fruit and vegetable industry and helping reduce postharvest supply chain losses. Full article

The maritime sector plays a critical role in global logistics systems, acting as a key link within international supply chains. Companies in this sector generate significant employment across the logistics and global value chain. However, it is noticeable that this sector still lacks innovative ideas related to the growth of the level of sustainable development. A wider adoption of green innovations could significantly improve environmental performance and reduce the ecological impact of maritime activities. A key factor that can stimulate the development of innovations in the maritime sector is green finance solutions. Dedicated financing for the greening of the maritime sector can catalyze innovation implementation processes both on ships and in ports. This article analyzes the opportunities for investments in greening activities using specific green finance instruments. This article presents the current situation of the maritime sector in terms of innovation and opportunities for project financing and increasing the value of companies, as well as key technological solutions that increase the level of sustainability in this sector. One of the key challenges is the limited intervention of governments and international organizations in accelerating maritime decarbonization. Maritime sector companies are slow to make progress towards sustainability—there is a lack of fundamental innovation and voluntary efforts to decarbonize. This has led to a situation where a large part of the innovations created are unprofitable today. The authors of this article suggested key investment directions—digitalization and robotization solutions, modernization of old ships and greening solutions for port companies. These actions would provide a short-term breakthrough, but it is necessary to consistently invest in new types of innovations based on scientific research. Full article

To address climate change, reducing food loss along the production and supply chain is a global priority. Addressing this challenge requires a shift in agrifood systems toward greater sustainability, in which new technologies and novel foods appear as promising strategies. Among emerging novel foods, 3D-printed foods are an interesting new food technology for food loss reduction, resource optimization, and by-product valorization. However, to reach market success, it needs consumer acceptance, a topic far unexplored, particularly in the Italian context. To fill the literature gap, this article investigates Italian consumers’ intention toward 3D-printed savory snacks using an extended Theory of Planned Behavior, based upon the relevant literature. Survey data were collected, and partial least squares structural equation modeling was performed to test research hypotheses. Results show that attitude and subjective norms are the strongest predictors of purchase intention. In addition, perceived usefulness is shown to be a powerful construct, positively impacting attitude and subjective norms, while self-identity as a green consumer reinforces perceptions of the benefits of 3D-printed foods. Sensory appeal impacts consumer attitude. These insights have practical policy and micro-level applications, suggesting tailored strategies, educational campaigns, and supportive policies and marketing campaigns for fostering acceptance of 3D printing in the agrifood sector. Full article

This study rounds into both the historical context and future projections of sustainable supply chain research practices. It emphasizes the necessity for the advanced analyses of research articles by combining traditional analysis with modern topic modeling and forecasting techniques. This study is organized around four primary research questions. A dataset of n = 8955 indexed article keywords and abstracts for the period of 2000–2025 was analyzed in the Python (version 3.12.) environment using n-grams, top keywords by year, k-means clustering combined with dimensionality reduction, and co-occurrence networks. Time-series forecasting models were also used to project the short-term development of clusters. The dataset retrieval was performed with search string and subject-area filters to focus the analysis on managerial and economic perspectives of sustainable supply chains. The analysis identified four keyword clusters: (1) CSR and Stakeholder Engagement, (2) Circular Economy and Sustainable Production, (3) Decision-making, Resilience and Emerging Technologies, and (4) Green Supply Chain Management. These clusters were then examined to assess current research practices from a managerial and economics perspective and their near-term evolution, with results validated through the additional clustering of abstract-level topics. This study confirms a paradigm change toward the integration of circularity, digitalization, and resilience, with technology-enabled growth. Social sustainability remains underrepresented, revealing a critical gap in current research. This study contributes methodologically by updating and extending current research practices and theoretically by revealing sustainability problems trends in supply chains. Full article

Decarbonizing downstream steel logistics remains underexplored in sustainable supply chain management. This study proposes a machine learning-enhanced tri-objective optimization framework for the ship stowage planning problem (SSPP). The framework handles heterogeneous finished steel products, including coils, plates, ingots, tubes, and sections. The model simultaneously maximizes deadweight utilization and minimizes a novel Adaptive Weighted Moment Balance (AWMB) index. It also minimizes voyage carbon emissions through a trim-and-heel resistance penalty. A spatial-to-sequential discretization strategy transforms the NP-hard placement problem into a tractable permutation optimization. A deep neural network (DNN) surrogate achieves a 3.57-fold speedup with only 1.52% hypervolume degradation. An improved NSGA-III algorithm with adaptive operators ensures Pareto front exploration. Embedded step-wise moment verification guarantees dynamic stability throughout loading and unloading. Validated on real data from a Chinese steel enterprise, the framework achieves 99.88% deadweight utilization, reduces transverse and longitudinal imbalance by 48.27% and 90.54%, and cuts CO₂ emissions by 95.5% per voyage. SOLAS constraints, load line limits, and CII/FuelEU targets are addressed through embedded stability and capacity constraints. Multi-route and weather-dependent validation remains necessary before fleet-scale deployment. Full article

Small and medium-sized enterprises (SMEs) are critical actors in promoting environmentally sustainable supply chains, particularly in emerging economies where their collective environmental footprint is substantial. Despite growing attention to green supply chain management (GSCM), research has predominantly focused on large firms, leaving the motivational drivers shaping GSCM implementation in SMEs underexplored. Addressing this gap, the present study develops and empirically tests a motivation-based framework to examine how four organizational motives, cost, market, ethical, and legitimacy, drive the depth of GSCM implementation in SMEs. In addition, environmental uncertainty is conceptualized as a key contextual contingency moderating the effectiveness of these motives. Drawing on survey data from Vietnamese SMEs, the findings reveal that all four motives positively influence implementation depth, with ethical motives exerting the strongest effect. Furthermore, environmental uncertainty significantly amplifies these relationships. By integrating multiple theoretical perspectives and emphasizing the contingent role of environmental uncertainty, this study advances GSCM research by providing a nuanced, context-sensitive understanding of how SMEs operationalize sustainability practices in dynamic and resource-constrained environments. Full article