Search Results (74)

Urban vegetation monitoring is essential for sustainable city planning but is often constrained by the high cost and limited frequency of field-based inventories. This study evaluates the use of the Normalized Difference Vegetation Index (NDVI), derived from Sino-Brazilian CBERS-4A satellite imagery, as a spatially explicit and low-cost proxy for urban tree census data. CBERS-4A provides medium-resolution multispectral data freely accessible across South America, yet remains underutilized in urban environmental applications. Focusing on Aracaju, a metropolitan region in northeastern Brazil, we compared NDVI-based classification results with official municipal tree census data from 2022. The analysis revealed a strong spatial correlation, supporting the use of NDVI as a reliable indicator of canopy presence at the urban block scale. In addition to mapping vegetation distribution, the NDVI results identified areas with insufficient canopy coverage, directly informing urban greening priorities. By validating remote sensing data against field inventories, this study demonstrates how CBERS-4A imagery and vegetation indices can support municipal tree management and serve as scalable tools for environmental planning and policy. Full article

China’s UHV power grid, a core “new infrastructure” initiative, is vital for its next-generation power systems. This study quantifies UHV project carbon emissions using a carbon source inventory system, identifies key drivers via Random forest regression (RFR) and SHAP interpretable ML models, and validates findings with a 1000 kV UHV AC project in southwest China. Results highlight material production (97% emissions) and construction phases (3%) as primary carbon sources. The proposed solutions are as follows: ① green materials (low-carbon concrete) and modular construction; ② digital tools for optimized project management. These strategies enable emission reductions while supporting China’s carbon neutrality goals. Full article

This systematic literature review explores how conflict management strategies (CMS) impact supply chain performance (SCP), focusing on the mediating roles of supply chain operational processes (SCOP) and customer-centric green supply chain management (CCGSCM) within Jordan’s food manufacturing sector. Framed within smart city initiatives and sustainable development goals (SDGs 9, 11, and 12), this study addresses critical gaps identified in the literature, particularly the lack of integrated examination of CMS impacts in emerging markets like Jordan. Utilizing thematic analysis, this review consolidates key findings across relevant studies from 2010 to 2025 sourced from top-tier databases. The results reveal that collaboration emerges as the most effective CMS strategy, enhancing stakeholder interactions, operational coordination, and resilience. SCOP significantly mediate CMS–SCP relationships, with logistics and inventory management notably vital in mitigating disruptions. Additionally, CCGSCM is highlighted as pivotal for sustainability and operational efficiency in post-COVID market conditions. The findings offer valuable insights for practitioners and policymakers, providing strategic recommendations for integrating technology-driven and relationship-focused CMS tailored to Jordan’s unique socio-economic context, thereby aligning operational practices with global sustainability goals (SDGs 9, 11, and 12). Full article

Geosystem services are increasingly recognized as critical for the sustainable development of rapidly growing cities in the Global South, because of their association with improved public health, reduction in environmental pollution, microclimate effects, and the ecological goods and services that provide benefits to local people. However, maintaining urban green spaces is a particular issue in cities in the Global South, such as São Paulo (Brazil) and Johannesburg (South Africa), where rapid inward migration and poor urban planning result in low environmental quality and the deterioration of geosystem services. This study explores the geosystem (including environmental and ecosystem) services provided in protected green spaces in these two cities, using the specific examples of Parque Estadual da Cantareira (São Paulo) and Melville Koppies (Johannesburg). This study uses an inventory-based approach to list and critically explore the availability and properties of different geosystem services found in these sites, and their wider implications for environmental planning and sustainable urban development. The results show that, although superficially similar, these sites have very different geosystem services, and that a simple inventorizing approach for geodiversity and geosystem service provision as used in many previous studies is highly problematic and over-simplifies site-scale geological and environmental properties, and how these are used and valued by local people. A more integrated approach dealing with the interplay of geosystem, environmental, and ecosystem services can provide a much firmer basis for urban planning and management in the Global South, suitable for achieving the Sustainable Development Goals. Full article

Climate change and sustainable development have become critical global issues. The foodservice industry and its supply chain are among the sectors with high carbon emissions. As such, many foodservice businesses are actively implementing carbon reduction initiatives. This study established professional competence indicators for carbon-reducing culinary practices from a sustainability perspective. Using the Modified Delphi Method, 14 food service experts were invited to participate in a three-round survey questionnaire to develop a competency framework applicable to kitchen operations. The findings identified three major dimensions of carbon-reducing culinary professional competence: knowledge competence (19 indicators), attitude competence (13 indicators), and technical competence (13 indicators), totaling 45 indicators. Attitude competence was deemed the most critical, followed by knowledge competence. Regarding the attitude dimension, the most important indicators included selecting suppliers aligning with carbon reduction and sustainability goals, commitment to sustainability initiatives, green procurement, energy management, and water conservation practices. Understanding inventory preservation management regulations and principles was the most essential indicator in the knowledge dimension. In the technical dimension, using seasonal ingredients and accurately managing inventory levels were the key factors. The findings provide a reference for government agencies, the foodservice industry, and higher education institutions in promoting sustainable culinary development. Strengthening chefs’ awareness and competencies in carbon reduction is essential in advancing sustainability within the food supply chain. Full article

Urban greenery is a key component of green infrastructure, contributing to environmental sustainability and urban well-being. Between 2019 and 2020, a comprehensive inventory of ornamental flora was conducted in Assemini (Sardinia, Italy), documenting 198 vascular plant taxa, including 155 exotic, 41 native, and 2 cryptogenic species from 65 families. Among the exotic species, most were neophytes (63%), and 14% were archaeophytes. In terms of life forms, scapose phanerophytes, with a tree-like growth habit, dominated (45%), while Mediterranean and American chorotypes were the most represented, each accounting for 21%. A total of 7356 plants were recorded, comprising trees (61.3%), shrubs (32.3%), and climbers (5.7%), belonging to 90 shrub, 89 tree, and 19 climber taxa. The highest number of plants was found in “Green Areas” and “Schools”, which also exhibited the greatest biodiversity, with 136 different taxa each. The most planted species were Quercus ilex, Nerium oleander, and Olea europaea. The survey also identified 21 allergenic, 36 toxic, and 35 mechanically harmful species, primarily located in “Green Areas” and “Schools”. Biodiversity analysis using the Shannon Index revealed significant diversity, with Fabaceae, Apocynaceae, and Fagaceae emerging as the most represented families. These findings highlight the importance of plant inventories in urban green space management for sustainable planning. Well-maintained green spaces can enhance ecological resilience, improve public health, and promote social cohesion in future urban developments. Full article

Retailing strategy can be considered as the most critical factor for the success of industries. Managing deteriorating products in retail demands a strategic approach aimed at mitigating losses while maximizing profitability. This entails a proactive stance towards identifying products nearing expiration, becoming obsolete or showing signs of deterioration. Offering discounts or promotions can stimulate consumer interest and clear out inventory. The promotion of products within the context of retail management involves a multifaceted approach aimed at increasing awareness, generating interest, and ultimately driving sales. Sustainability helps retailers to develop social as well as economic consistency. Every country and their respective governments are currently working towards sustainable development. New technologies in this direction have been introduced. The present paper introduces a retailing model considering green technology as it is becoming popular to lower environmental risks. The items considered in this study are perishable in nature. As product prices and the promotion of products highly influence demand, a demand pattern dependent on price and promotion is therefore considered. This paper presents a sustainable retail-based inventory model that considers preservation technology to lower the rate of deterioration and increase product shelf life. As carbon emissions is currently the biggest threat to the environment, enforcing a penalty may lower its emissions. Carbon emissions costs due to storage, transportation, and preservation are considered herein. This model studies the effect of various cost parameters on the model. A numerical analysis is performed to validate the result. The results of this study show that the implementation of preservation technology not only increases cycle time but also significantly reduces total cost, hence increasing profit. Sensitivity analysis is performed to show the behaviors of different cost parameters on total cost and decision variables. Mathematica 11 and Maple 18 software are used for graphical representation. Full article

The construction sector is a significant contributor to global greenhouse gas (GHG) emissions, necessitating urgent decarbonization efforts to align with international climate goals such as the Paris Agreement and the European Green Deal. This study explores a comprehensive framework for construction companies to map and reduce their GHG emissions through a structured four-step approach: defining emission scopes, conducting GHG inventories, setting reduction targets, and planning actionable reductions. Four key pathways are proposed: electricity decarbonization through renewable energy adoption and energy efficiency measures; direct emissions reduction via fleet electrification and infrastructure optimization; recycling and resource efficiency improvements through waste diversion and material reuse; and supply chain emissions reduction by enforcing sustainability standards and responsible sourcing practices. The analysis highlights the importance of integrating technological, organizational, and policy-driven solutions, such as rooftop photovoltaic systems, virtual power purchase agreements, waste management strategies, and supplier codes of conduct aligned with global sustainability benchmarks. The study concludes that construction companies can achieve significant emission reductions by adopting a structured, multi-pathway approach; emphasizing progress over perfection; and aligning their strategies with national and international climate targets. This research provides actionable insights for the construction sector to transition toward a net-zero future by 2050. Full article

The efficient management of the green power grid supply chain is of great significance in addressing global energy transformation and achieving sustainable development goals. However, traditional methods struggle to effectively cope with the complexity and dynamics of demand forecasting and the multi-objective optimization problems in material allocation. In response to this challenge, this paper proposes a machine-learning-based demand forecasting and allocation optimization method, aiming to improve the management efficiency of the supply chain and reduce environmental impacts. First, based on the whole-process data of power grid materials, a multi-model fusion strategy is adopted for demand forecasting. By combining machine learning models such as long short-term memory (LSTM), extreme gradient boosting (XGBoost), and random forest, the prediction accuracy and the generalization ability of the model are significantly improved. Moreover, a “distributed collaborative optimization algorithm” is proposed. By decomposing the power grid regions, this paper optimizes transportation routes and inventory management, and comprehensively reduces transportation, inventory, and environmental protection costs while taking into account the real-time requirements in a complex supply chain environment. Finally, an empirical analysis is carried out in combination with the optimized allocation plan, verifying the practical effectiveness of the proposed method. The results indicate that the optimized scheme significantly outperforms the traditional method in terms of total cost, transportation efficiency, and carbon emissions. Specifically, the optimized scheme achieves a 13% reduction in transportation costs, a 10% decrease in inventory costs, and a 25% cut in environmental protection expenses. Additionally, it decreases transportation-related carbon emissions by approximately 250 tons. The demand forecasting and allocation optimization method based on machine learning has obvious economic and environmental advantages in the green power grid material supply chain. By effectively integrating various algorithms, this paper enhances the accuracy and stability of material management while substantially reducing operating costs and carbon emissions. This is in line with the sustainable goals of green power grid development. The paper provides an optimized framework with practical value for managing the green supply chain in the power grid industry. Full article

Additive manufacturing and digital warehouses are transforming the way industries manage and maintain their spare parts inventory. Considering digital warehouses and on-demand manufacturing for spare parts during the project phase is a strategic decision that involves trade-offs depending on the operational needs and pricing structure. This paper aims to explore the spare part evaluation process considering both physical and digital warehouse inventories. A case asset is purposefully selected and four spare part management concepts are studied using a simulation modeling approach. The results highlight that the relevant digital warehouse scenario, used in this case, managed to completely reduce all emissions related to global spare parts supply; however, this was at the expense of reducing availability by 15.1%. However, the hybrid warehouse scenario managed to increase availability by 11.5% while completely reducing all emissions related to global spare parts supply. Depending on the demand rate, the digital warehousing may not be sufficient alone to keep the production availability at the highest levels; however, it is effective in reducing the stock amount, simplifying the inventory management, and making the supply process more green and resilient. A generic estimation model for spare parts engineers is provided to determine the optimal specifications of their spare parts supply and inventory while considering digital warehouses and on-demand manufacturing. Full article

A challenge for urban ecology is to reduce biotic homogenisation by promoting plant diversity from local to city scales. As ecological and social components constantly interact in cities, an urban landscape characterisation reflecting socio-spatial functioning seems essential. However, spatially explicit description of cities at a relevant scale for urban planning are uncommon in ecological studies. Here, we explored a new approach based on the city-block scale, common in urban geography and planning, to directly link urban uses and patterns of herbaceous plant communities. We characterised all city blocks of a medium-sized French city (Blois). We inventoried grassland and meadows in 129 city blocks (10% of the whole city) for seven public and private urban uses (collective housing, individual housing, industrial, public service, park, land reserve, and road verge). We measured alpha diversity, community composition, regional originality of urban uses, and beta diversity between them. Urban land reserved for future development and parks harbour unique community composition within the city. Collective and individual housings have the same average alpha diversity, but the variability in community composition was higher for individual housing blocks. School and industrial city blocks have important alpha diversity and regional originality. Road verges have the highest alpha diversity but low regional originality and many common urban and regional species. Large green spaces with original communities should be protected during urban densification. The verticalization of residential housing could be an efficient means of internal urban densification if the lowest level of management intensity is promoted to maintain diversified vegetation. Some little-studied uses (schools, industrial city blocks) present opportunities to impede urban homogenisation. Full article

The increasing use of renewable energy sources in national electricity networks is challenging because of intermittence, i.e., the fact that the availability of the fuel (e.g., solar irradiance, wind) is volatile. This is a new challenge for the energy sector that has led to much research about energy storage. In the manufacturing sector, dealing with the volatility of demand is not a new problem and is addressed by the application of aggregate production planning techniques. Solving an aggregate production planning problem is about finding the best trade-off because capacity and inventory utilization. This paper explores the application of this technique to energy management problems and explains how it can be used as a complementary solution to energy storage, showing how industrial entities can play an active role in greening the electricity sector, solely through a different planning of their inventory levels. Full article

This research employs numerical simulations and scenario analysis to assess a supply chain model’s economic and environmental performance operating under stochastic market demand, with inventory levels managed by a periodic review (R, s, S) inventory system. The inventory model in this research is designed to determine the minimal inventory levels required to achieve predefined fill rates across various operational constraints. The supply chain’s inventory model simulates optimal responses to normally distributed market demand within 365-day periods characterized by mean and two levels of demand variability through two fill rate levels, two workweek schedules, 15 review periods, and 16 lead times. By conducting an extensive analysis of the 192000 simulation experiments of the supply chain under periodic review (R, s, S) inventory system, complex influences between system variables and economic outcomes of supply chain operation measured by ordering, transportation, holding, penalty, and total costs along with greenhouse gas emissions arising from inventory-related transportation according to the ISO 14083 standard are analyzed. The insights from this research have significant practical implications, providing valuable guidance for supply chain managers, researchers, and freight companies offering guidance for improving economic and environmental performance. Full article

City manufacturing has once again become one of the priority areas for the sustainable development of smart cities thanks to the use of a wide range of green technologies and, first of all, additive technologies. Shortening the supply chain between producers and consumers has significant effects on economic, social, and environmental dimensions. Zoning of city multifloor manufacturing (CMFM) in areas with a compact population in large cities in the form of clusters with their own city logistics nodes (CLNs) creates favorable conditions for promptly meeting the needs of citizens for goods of everyday demand and for passenger and freight transportation. City multifloor manufacturing clusters (CMFMCs) have been already studied quite a lot for their possible uses; nevertheless, an identified research gap is related to supply chain design efficiency concerning CMFMCs. Thus, the main objective of this study was to explore the possibilities of lean supply chain management (LSCM) as the integrated application of lean manufacturing (LM) approaches and I4.0 technologies for customer-centric value stream management based on eliminating all types of waste, reducing the use of natural and energy resources, and continuous improvement of processes related to logistics activities. This paper presents a decision support model for LSCM in CMFMCs, which is a mathematical deterministic model. This model justifies the minimization of the number of road transport transfers within the urban area and the amount of stock that is stored in CMFMC buildings and in CLNs, and also regulating supplier lead time. The model was verified and validated using appropriately selected test data based on the case study, which was designed as a typical CMFM manufacturing system with various parameters of CMFMCs and urban freight transport frameworks. The feasibility of using the proposed model for value stream mapping (VSM) and managing logistics processes and inventories in clusters is discussed. The findings can help decisionmakers and researchers improve the planning and management of logistics processes and inventory in clusters, even in the face of unexpected disruptions. Full article

Single-tree segmentation on multispectral UAV images shows significant potential for effective forest management such as automating forest inventories or detecting damage and diseases when using an additional classifier. We propose an automated workflow for segmentation on high-resolution data and provide our trained models in a Toolbox for ArcGIS Pro on our GitHub repository for other researchers. The database used for this study consists of multispectral UAV data (RGB, NIR and red edge bands) of a forest area in Germany consisting of a mix of tree species consisting of five deciduous trees and three conifer tree species in the matured closed canopy stage at approximately 90 years. Information of NIR and Red Edge bands are evaluated for tree segmentation using different vegetation indices (VIs) in comparison to only using RGB information. We trained Faster R-CNN, Mask R-CNN, TensorMask and SAM in several experiments and evaluated model performance on different data combinations. All models with the exception of SAM show good performance on our test data with the Faster R-CNN model trained on the red and green bands and the Normalized Difference Red Edge Index (NDRE) achieving best results with an F1-Score of 83.5% and an Intersection over Union of 65.3% on highly detailed labels. All models are provided in our TreeSeg toolbox and allow the user to apply the pre-trained models on new data. Full article