Environmental Efficiency Research Articles

Governance In Smart Cities: Identifying And Analyzing Key Indicators Of Governance Structures

Background: Smart cities are characterized by integrating Information and Communication Technology (ICT) to address urban challenges, involving human and social capital as well as smart governance. Although there is no consensus on their definition, smart cities are generally seen as innovative projects to foster urban development through technology. However, effective governance in these cities poses significant challenges, requiring a multifaceted approach that involves collaboration between public and private sectors and civil society. This study seeks to identify key indicators for constructing effective governance frameworks in smart cities. Materials and Methods: Through a qualitative analysis of 70 articles from the ScienceDirect portal (2020-2024), this study explores the criteria and indicators of governance in smart cities. The articles were reviewed and filtered using a protocol with nine guiding questions, resulting in the extraction of 311 terms, which were then grouped into 33 indicators across five themes. These themes - Governance and Participation, Policies and Regulations, Strategies and Urban Planning, Technology and Innovation, and Indicators and Assessments - form the foundation for effective governance structures in smart cities. Results: The results highlight that governance in smart cities must be dynamic and adaptable, incorporating citizen participation, transparent public policies, open data practices, and efficient use of ICT. The findings contribute to a more comprehensive understanding of how governance structures can foster sustainability, inclusivity, and efficiency in smart urban environments. By synthesizing key governance indicators, this research provides a framework to guide the development of governance models that can support the evolving needs of smart cities. Conclusion: Effective governance in smart cities requires dynamic, adaptable structures that foster collaboration between public and private sectors and civil society. This study provides a framework to promote sustainability, inclusivity, and efficiency in smart urban environments.

Improving Iron Content in Sustainable Mycoprotein Production through Seawater Fermentation

The growing global population and rising protein demand are straining freshwater resources. Fusarium venenatum (Fv) mycoprotein offers a sustainable protein alternative, with environmental efficiency and potential health benefits. However, its low iron content remains a concern, especially for vegetarians and vegans. This study introduces a sustainable approach, employing seawater as a fermentation medium for Fv production. Our analysis reveals that mycoprotein derived from SEA Fv exhibits elevated levels of sodium and calcium, with a notably high iron content (2.2 mg/100g wet weight). The sodium content, while 3.31 times higher than in non-seawater fermentation, remains within recommended daily intake parameters. No plasticizers or heavy metals were detected in the SEA Fv cell body, minimizing long-term toxicity risks from seawater use. A unique metabolite, dihydroorotic acid, was identified from an in-house library of 774 metabolites, serving as an internal biomarker for seawater-based production methods. An acute safety study condensing 600g of SEA Fv to simulate high mycoprotein digestion showed no effects on key physical behaviors or major organs, including the heart and lungs. This positions the product as a viable protein alternative with enhanced iron content, highlighting seawater-based fermentation as a sustainable method for future food production and industry progress.

TECHNOLOGY FOR THE PRODUCTION OF COMPOSITE MATERIAL-SULFUR CONCRETE PRODUCTS FROM MODIFIED SULFUR

Due to the increase in the processing of sulfur-containing hydrocarbon raw materials (oil and gas), sulfur is released as one of the large - tonnage waste. Long-term storage of large sulfur reserves has a negative impact on the environment. Composite material of sulfur modification-characterized by environmental efficiency, economic efficiency in construction. This review examines the sources of sulfur origin, properties, technology for making sulfur concrete from it and the use of sulfur-based concrete in the construction industry today. Experimental evaluation of modified sulfur concrete, other methods of preparation of mixtures were identified, and the importance of adaptation to foreign experience in the development of sulfur concrete production was shown. One of the most promising projects is the production of concrete containing sulfur binder in order to reduce the load on the environment. After all, sulfur concrete has such advantages as resistance to acid and radiation, frost resistance and the ability to recycle. The aim of the work was to study a new composition of sulfur concrete based on a bitumen composition. To conduct the study, samples of sulfur concrete with different percentage contents of the constituent components were prepared.

Introducing menus of three weekly insect- or plant-based dinner meals slightly reduced meat consumption in Danish families: Results of a randomized intervention study

The environmental concerns associated with excess meat consumption have emphasized the need for sustainable alternatives. Edible insects offer a promising alternative due to their environmental efficiency and nutritious profile, but their widespread adoption in Western diets remains a challenge. The objective of the study was to investigate the impact of exposing families (parents and children) to insect-based or plant-based dinner menus on dietary pattern, meat intake, and protein intake over a six-week intervention period. The study was a two-arm randomized equivalence trial comparing an insect-based menu to a plant-based control. Families received either an insect or plant-based menu to replace meat in dinner meals three times a week for six weeks, aiming to replace 20% of their meat protein intake. Dietary changes were assessed through dietary registrations and daily questionnaires. Both adults and children maintained their estimated daily total protein intake, while reducing daily meat protein intake. Neither group met the 20% weekly meat replacement goal. In the insect-based menu group, adults and children reached an average 5.5% and 2.3% weekly meat replacement, respectively. In the plant-based menu group, adults and children replaced 9.0% and 4.3%, respectively. Meat attachment had an effect on meat protein intake. The menus slightly reduced meat protein intake. The weekly frequency of meat meals slightly declined, but portions remained the same. By enhancing insect and plant-based food quality and understanding consumer behavior, insect- and plant-based products have the potential to be a complementary alternative in a sustainable dietary transition without sacrificing nutrition. Clinical trial registryClinicalTrials.gov: NCT05156853; clinicaltrials. gov/study/NCT05156853.

A joint vehicular device scheduling and uncertain resource management scheme for Federated Learning in Internet of Vehicles

Federated learning (FL) offers an effective framework for the efficient process in vehicular edge computing. However, FL encompasses the process of distributing and uploading model parameters, which are inevitably transmitted in a wireless network environment. Some challenges in FL-assisted Internet of Vehicles (IoV) sceneries gradually emerging, such as data heterogeneity, concerned device resources, and unstable communication environment, which necessitate intelligent vehicle selection schemes that accelerate training efficiency. Based on these, we consider a new scenario, specifically an FL-assisted IoV system under uncertain communication conditions, and develop an interval many-objective vehicle selection and bandwidth allocation (IMoVSBA) joint optimization scheme. This scheme takes into account computation latency, energy consumption, server utilization, and data quality, while meeting multi-criteria resource optimization requirements. Among these, server utilization is a new objective designed specifically for this joint optimization problem. For the proposed problem, a novel interval many-objective evolutionary algorithm with individual comprehensive indicator to control the evolution direction (IMaOEACI) is designed. Simulation results demonstrate that this method outperforms other schemes in terms of accuracy, training cost, and server utilization, effectively improving training efficiency in wireless channel environments and reasonably utilizing bandwidth resources. It provides significant scientific value and application potential in the field of the IoVs.

Advancing energy recovery ventilators with phase change materials for cooling load reduction and heat exchange efficiency improvement

Efforts to reduce building energy emissions and achieve net-zero carbon scenarios globally have increasingly focused the building sector on improving energy efficiency. Heating, ventilation, and air conditioning (HVAC) systems play a vital role in providing comfortable indoor environments but significantly contribute to energy consumption and greenhouse gas emissions. Energy recovery ventilators are efficient active systems that prevent unnecessary indoor heat loss and gain, regardless of outdoor climate, making them excellent technologies for achieving zero energy. In a hot and humid summer climate, such as in Korea, it is crucial to enhance the efficiency of energy recovery ventilators and reduce cooling energy consumption. A Green HVAC design aimed at reducing the energy consumption of active systems to near zero was proposed by applying phase change material-based modules with high heat storage performance to energy recovery ventilators. The phase change material module is stabilized with high thermal conductivity aluminum packing and is configured as a mesh-type structure to amplify heat exchange efficiency with air. Compared to the energy recovery ventilator, the application of the module demonstrated a reduction in supply air temperature by an average of 2.6 °C in high-temperature outdoor. During the effective operating period, a continuous indoor intake temperature reduction effect was observed, and the sensible heat exchange efficiency showed a 21.32 % improvement with the module using n-octadecane. Phase change material modules that can enhance ventilation and improve energy efficiency in high-temperature environments can be proposed as a technology to achieve Green HVAC.

Synergistic effects of multi-walled carbon nanotubes and Mn0.4Cu0.6Fe2O4 on mercury removal with high efficiency and sulfur resistance

Although ferrite-based adsorbents are the potential mercury removal materials for the high thermal stability, they usually suffer from a low efficiency in flue gas environment, especially under SO2 condition. In the present paper, the multi-walled carbon nanotubes (MWCNTs) are utilized to improve the adsorption capacity of the Mn0.4Cu0.6Fe2O4 adsorbents as well as inhibit the influence of flue gas composition. The influences of temperature, adsorbent type and the flue gas composition on Hg0 removal efficiency are evaluated by experiments. The physical adsorption property of MWCNTs provides a platform for Hg0 oxidation by Mn0.4Cu0.6Fe2O4. The synergistic effect between MWCNTs and Mn0.4Cu0.6Fe2O4 enhances the mercury removal efficiency as well we the sulfur resistance. The results find that the adsorbent of Mn0.4Cu0.6Fe2O4 containing 14 % MWCNTs has a high mercury removal efficiency of 95.6 % at 120 °C even under 1000 ppm SO2 concentration. The kinetic behaviors of adsorbent adsorption are analyzed by theoretical models. The mechanisms of porous carbon-containing modifier to improve the mercury removal performance of Mn0.4Cu0.6Fe2O4 are explored carefully. The present ferrite-based adsorbent exhibits promising prospects for the practical industrial applications of the low temperature mercury removal from coal-fired flue gas.

Meta-Reinforcement Learning in Nonstationary and Nonparametric Environments.

Recent state-of-the-art artificial agents lack the ability to adapt rapidly to new tasks, as they are trained exclusively for specific objectives and require massive amounts of interaction to learn new skills. Meta-reinforcement learning (meta-RL) addresses this challenge by leveraging knowledge learned from training tasks to perform well in previously unseen tasks. However, current meta-RL approaches limit themselves to narrow parametric and stationary task distributions, ignoring qualitative differences and nonstationary changes between tasks that occur in the real world. In this article, we introduce a Task-Inference-based meta-RL algorithm using explicitly parameterized Gaussian variational autoencoders (VAEs) and gated Recurrent units (TIGR), designed for nonparametric and nonstationary environments. We employ a generative model involving a VAE to capture the multimodality of the tasks. We decouple the policy training from the task-inference learning and efficiently train the inference mechanism on the basis of an unsupervised reconstruction objective. We establish a zero-shot adaptation procedure to enable the agent to adapt to nonstationary task changes. We provide a benchmark with qualitatively distinct tasks based on the half-cheetah environment and demonstrate the superior performance of TIGR compared with state-of-the-art meta-RL approaches in terms of sample efficiency (three to ten times faster), asymptotic performance, and applicability in nonparametric and nonstationary environments with zero-shot adaptation. Videos can be viewed at https://videoviewsite.wixsite.com/tigr.

Self-adaptive radiation recuperator with double phase transition temperature switches based on VO2 intelligent films

Developing renewable energy for heating and cooling to achieve carbon neutrality and environmental protection is of great significance. The combination of photothermal (PT) and radiative cooling (RC) technology to continuously obtain energy throughout the day is gradually attracting interest, in which vanadium dioxide (VO2) intelligent phase transition film serves as a bridge between the daytime PT mode and the nighttime RC mode. The high phase transition temperature of VO2 poses a challenge to environmental adaptability and dynamic switching in practical applications. In this work, a novel self-adaptive radiation recuperator with double phase transition temperature (37 °C and 68 °C) switches is proposed to exchange energy from the sun and outer space by radiation. Compared with VO2 and W-doped VO2 single-layer structure, this method has a higher self-adaptability to easily achieve the phase transition temperature of mode switching. The absorptivity of the radiation recuperator is 0.75 in the solar band (0.3–2.5 μm), and the emissivity switching capability is present from 0.3 to 0.86 in the atmospheric window band (8–14 μm). The transition mode generated by the presence of double phase transition temperature improves temperature self-adaptability and improves energy collection efficiency in dynamic environments.

Improving thermal environment and ventilation efficiency in high-temperature excavation tunnels via an innovative heat insulation and cooling baffle

An increasing number of tunnels inevitably encounter high-temperature environments induced by elevated geotemperatures. The energy demand for ventilation and cooling in excavation tunnels increases significantly as the temperature of the surrounding rock increases, thereby hindering the sustainable exploitation of deep resources. This paper presents an optimization method for airflow organization in excavation tunnels that integrates thermal insulation and cooling (TIC) baffles. The proposed approach significantly improves the cooling effect of the auxiliary ventilation system while reducing energy consumption. This method offers the advantages of simplicity, convenience, and cost-effectiveness. A multifield coupling model using COMSOL software was developed and validated to analyze the system, and the application scenario was explored. Several ventilation scenarios were examined, highlighting that TIC baffles effectively reduce the heat release from the surrounding rock and lower the airflow temperature in personnel areas in the excavation tunnel. By implementing TIC baffles, the average airflow temperature in the excavation tunnel decreases by 1.5 °C to 1.8 °C, resulting in a saving of approximately 5.11 kW in cooling energy. Increasing the circulating water flow in the heat-exchange pipe of the TIC baffles or reducing the initial circulating water temperature can enhance the cooling capacity of the TIC baffles and lower the tunnel airflow temperature to a certain extent. The distance between the TIC baffles and surrounding rock significantly affects the airflow temperature between the left and right TIC baffles. An excessive length of TIC baffles (L > 7 m) leads to heat accumulation and high-temperature airflow in localized areas. In This study, a method for optimizing the thermal environment and saving energy in high-temperature excavation tunnels is proposed.

Sustainable practices in steel manufacturing: Substitution elasticities between waste, raw materials, and energy for greenhouse gas emission reduction

This study investigates the potential of waste utilization to mitigate greenhouse gas (GHG) emissions within the steel manufacturing industry. Utilizing a translog production function, we analyze the substitution elasticities among scrap steel, iron ore, and energy, exploring their roles in carbon reduction. Our approach incorporates the Resource Efficiency and Climate Change Mitigation model to simulate the impact of various waste utilization strategies on GHG emissions. The findings highlight that scrap steel and iron ore exhibit substitution dynamics. Iron ore and energy exhibit complementary relationship, and there is also a complementary relationship between scrap steel and energy but with lower elasticity, indicating that increasing scrap steel usage can significantly reduce emissions while decreasing iron ore demand. Specifically, enhancing the input efficiency of scrap steel in secondary steel production can reduce GHG emissions by over 8.3 tons for each additional ton produced—surpassing the impact of merely increasing the volume of reused material, which offers a reduction of more than 2.2 tons per ton. The strategies centered on reduction and recycling demonstrate even lower GHG reduction efficiency. The findings advocate for governmental intervention to promote and support waste reuse initiatives, highlighting the potential environmental benefits and efficiency gains from such policies.

¿Libre de la Maleza Estatista? Assessing Neoliberal Promises and Water Markets in Chile

AbstractNeoliberal approaches to water governance, pioneered in Chile in the 1980s, are reappearing today on the centerstage of the water policy debate. While advocates claim that strong property rights, limits on government authority, and water markets can enhance environmental sustainability, efficiency, neutrality, and equity in the distribution of water rights, limited empirical evidence exists on whether neoliberal policies have delivered on these key promises. In this paper, we combine hydrological analysis with a nationwide data set on government water rights allocations between 1981 and 2021 to determine when and where water has been allocated beyond sustainable limits. We then integrate water market transaction and agricultural data to assess how allocations and scarcity conditions relate to spatial and temporal patterns in irrigation, crop distribution, and water market activity. Our results indicate that 30% of catchments are overallocated, and that continued government allocations of water rights during scarcity exacerbate already‐high inequalities in the distribution of water. We find no evidence that scarcity or water markets induced improvements in numerous efficiency metrics. Overall, our results support growing claims that the neoliberal water model fails to fulfill its key promises, notably to the detriment of nature and marginalized rural communities.

Development of Energy Security Definition and Indexes for Sustainable Future: A Systematic Literature Review

Abstract In recent years, energy security has been gaining increasing attention worldwide, leading to a rapid growth in research and literature on the subject. As a result, many definitions regarding energy security have been proposed, reflecting the diverse perspectives and considerations involved in ensuring a secure and sustainable energy supply. There has also been a growing interest in how to measure, which has in turn led to the energy security indicators and indices development. Thus, this paper explores the definition of energy security, as well as its various dimensions and the indices employed to measure it. As a result, the scope of energy security has broadened to encompass contemporary concerns such as the environment, governance, social aspects, and technological efficiency, which were not traditionally given much consideration in the past. As a result, multiple themes have been considered, leading to a more holistic understanding and approach to energy security. This evolution has significant implications for the construction of the energy security index especially in Indonesia, as a more comprehensive set of indicators is now required to offer a comprehensive assessment of energy security within a country and provide valuable insights for energy policymakers.

Eco-friendly construction mortars for heavy metals immobilization – Effect of partial PC replacement by lignite-based fly ash and prolonged high humidity curing on physical and chemical parameters

The study presented in this manuscript concerns the safe immobilization of heavy metals (HMs) present in fly ash (FA) from lignite combustion in Portland cement- (PC-) based blended mortars with an emphasis on the influence of prolonged high humidity curing on material properties. The performed experiments covered both the aspect of the FA-PC replacement ratio as well as the changes in structural, hygric, chemical, and mechanical parameters of the prepared composites after 28, 90, and 180 days of curing. It was shown, that the used FA shows a high pozzolanic activity, which strongly contributed to the mechanical parameters of the developed blended mortars, namely the strength activity index in the range of 0.94–1.06 for 180-day samples. The blended mortars were cautiously studied concerning the HM immobilization efficiency. The immobilization efficiency was analyzed through the HM leaching test, showing that HMs present in the leachates manifested concentrations well below the prescribed limits. In addition, the immobilization effectiveness was found to be higher with a longer curing time. As FA-PC replacement has recently become a highly researched topic, this study adds a significant point of view in terms of the safety and environmental efficiency of the designed composites. Furthermore, the proposed safe handling of hazardous HM-containing FA through their implementation in construction mortars while ensuring high HM-immobilization effectiveness represents the next step in the ongoing research into the decarbonization of the construction industry and minimization of the depletion of natural resources.

Eco-efficiency of coffee production and consumption in the UK at the product and sectoral levels

Coffee is one of the most consumed products globally, and yet its eco-efficiency is still unknown. To address this knowledge gap, this paper examines for the first time the life cycle eco-efficiency of different coffee drinks to identify the most sustainable options and opportunities for improvements in the coffee industry. Nine types of coffee are considered: espresso, latte, cappuccino, Americano, drip-filter, coffee pods, spray-dried and freeze-dried instant, and instant coffee mix. Taking a cradle-to-grave approach, the following life stages are included: coffee cultivation, production and consumption, and end-of-life valorisation of spent coffee grounds. The methodology follows the eco-efficiency ISO 14045 standard to measure the environmental efficiency of a system relative to the economic value it generates. The eco-efficiency is estimated at both the product (one serving) and sectoral (annual consumption) levels based on life cycle assessment (LCA), life cycle costing (LCC) and economic value added (VA). The results show that latte has the highest environmental impacts and LCC but it also has the highest VA at both the product and sectoral levels. Spray-dried instant coffee, on the other hand, has the lowest impacts but also the lowest VA, which makes it the least eco-efficient option. Espresso emerges as the most eco-efficient drink because it has moderate environmental impacts and high VA. Latte and coffee pods are the second most eco-efficient drinks, followed by cappuccino, Americano, and instant coffee mix. Valorising spent coffee grounds could reduce the environmental impacts, LCC and increase the VA at the sectoral level. For example, the climate change impact would be reduced by 80.5–132.1 kt CO2 eq./yr and primary energy demand by 0.9–2.3 PJ/yr while also increasing the VA by £50.2–95 M/yr. When compared to some other food sectors, coffee is 6.4 times more eco-efficient than cheddar cheese and 5.4 times than ice cream and biscuits, respectively. The findings of this study can be used to guide businesses, consumers and policy makers in identifying more eco-efficient coffee products and improving the environmental and economic sustainability of the coffee industry.

Improved ACO algorithm fused with improved Q-Learning algorithm for Bessel curve global path planning of search and rescue robots

Addressing issues with traditional ant colony and reinforcement learning algorithms, such as low search efficiency and the tendency to produce insufficiently smooth paths that easily fall into local optima, this paper designs an improved ant colony optimization algorithm fusion with improved Q-Learning (IAC-IQL) algorithm for Bessel curve global path planning of search and rescue (SAR) robots. First, the heuristic function model in the ant colony algorithm is improved, the elite ant search strategy and the adaptive pheromone volatility factor strategy are introduced, and the initial path is searched in realize the motion environment with the help of the improved ant colony algorithm, and the initialized pheromone matrix is constructed. Second, the improved ant colony algorithm and Q-Learning (QL) algorithm are fused by utilizing the similarity between the pheromone matrix in the improved ant colony algorithm and the Q-matrix in the QL algorithm. A heuristic learning evaluation model is designed to dynamically adjust the learning factor and provide guidance for the search path. Additionally, a dynamic adaptive greedy strategy is introduced to balance the exploration and exploitation of the robot in the environment. Finally, the paths are smoothed using third-order Bessel curves to eliminate the problem of excessive steering angles. Through three sets of comparative simulation experiments conducted in Pycharm platform, the effectiveness, superiority, and practicality of the IAC-IQL algorithm were verified. The experimental results demonstrated that the IAC-IQL algorithm integrates the strong search capability of ant colony algorithm and the self-learning characteristics of QL algorithm. SAR robots equipped with the improved IAC-IQL algorithm exhibit significantly enhanced iterative search efficiency in grid simulation environment and image sampling simulation environment. The global path optimization indicators demonstrate high efficiency, and the paths are smoother.

TS-based Mobility-aware Multi-hierarchical Caching Model with Vehicle Clustering and Content Popularity Prediction

This research is concerned with the fusion of artificial intelligence (AI) and machine learning within multi-hierarchical caching systems, specifically targeting vehicular and edge caching domains. This study introduces an innovative architecture harmonizing Thompson sampling learning-based caching policies with advanced vehicle clustering and content-popularity prediction methods (TS-MMCM). Simulations show substantial performance improvements and a big impact of the proposed approach on system efficiency in dynamic network environments. The proposal demonstrates a notable gain in cache hit rates and decreased latency levels, highlighting the potential of AI to improve caching techniques in dynamic network environments.

OPTIMIZING PROCESSOR WORKLOADS AND SYSTEM EFFICIENCY THROUGH GAME-THEORETIC MODELS IN DISTRIBUTED SYSTEMS

The primary goal of this research is to examine how different strategic behaviors adopted by processors affect the workload management and overall efficiency of the system. Specifically, the study focuses on the attainment of a pure strategy Nash Equilibrium and explores its implications on system performance. In this context, Nash Equilibrium is considered as a state where no player has anything to gain by changing only their own strategy unilaterally, suggesting a stable, yet not necessarily optimal, configuration under strategic interactions. The paper rigorously develops a formal mathematical model and employs extensive simulations to validate the theoretical findings, thus ensuring the reliability of the proposed model. Additionally, adaptive algorithms for dynamic task allocation are proposed, aimed at enhancing system flexibility and efficiency in real-time processing environments. Key results from this study highlight that while Nash Equilibrium fosters stability within the system, the adoption of optimal cooperative strategies significantly improves operational efficiency and minimizes transaction costs. These findings are illustrated through detailed 3D plots and tabulated results, which provide a detailed examination of how strategic decisions influence system performance under varying conditions, such as fluctuating system loads and migration costs. The analysis also examines the balance between individual processor job satisfaction and overall system performance, highlighting the effect of rigid task reallocation frameworks. Through this study, the paper not only improves our understanding of strategic interactions within computational systems but also provides key ideas that could guide the development of more efficient computational frameworks for various applications.

Integrating machine learning with dynamic multi-objective optimization for real-time decision-making

Real-time decision-making in dynamic multi-objective optimization problems (DMOPs) is challenging due to constantly changing objectives and constraints. This paper integrates machine learning with Non-dominated Sorting Genetic Algorithm II (NSGA-II) to solve DMOPs and make real-time decisions. Learning-based methods have gained popularity for predicting solutions in new environments and capturing changing patterns in optimal solutions. However, existing approaches often struggle with training difficulty and reduced prediction accuracy due to irrelevant or redundant variables. Therefore, we introduce a new interdependent prediction (IDP) technique to identify correlations between variables and prediction targets and select significant variables for a predictive model. In this way, a better initial population is predicted. The IDP strategy is integrated within the dynamic NSGA-II, introducing a new algorithm called IDP-DNSGA-II. This integration facilitates rapid convergence, finding optimal or near-optimal solutions. The proposed method is evaluated against standard benchmarks, demonstrating superior performance in convergence speed and solution diversity with the changes in the problem environment. The IDP-DNSGA-II is validated through real-world optimization challenges in sustainable automobile production distribution in order-to-delivery systems to enhance environmental sustainability and operational efficiency. This study identifies the minimum frequency of change required in real-world problems to adequately track the optimal decision in real-time.

Sustainable public procurement for supply chain resilience and competitive advantage

Public procurement plays a crucial role in national economies, as it represents a significant share of public spending. Governments can thus use this considerable purchasing power to promote environmental sustainability, social responsibility, and economic efficiency, while encouraging the supplier market to innovate and adopt more sustainable practices to remain competitive, and to generate more resilient supply chains. Given the interdisciplinary nature of the subject of sustainable public procurement (SPP), the documentation is abundant and diverse, hence the need for an updated literature review especially in the aftermath of the COVID-19 crisis period. This article consists of a systematic literature review focusing on articles with an empirical approach. The results reveal, among other things, 8 main research themes subdivided into 19 sub-themes, with a dominance of literature on the obstacles and incentives related to the implementation of SPP, while those concerning e-procurement and the analysis of the social life cycle remain relatively limited. The study also underscores the role of local economies and SMEs in public procurement strategies. It highlights a trend towards geographical diversification, indicating the global significance of SPP, with noticeable differences in adoption between developed and developing countries. The review calls for future research to focus on developing practical frameworks and tools for integrating sustainability more comprehensively in public procurement. The findings illuminate the pivotal role of SPP as a mechanism steering societies towards a sustainable, resilient, and equitable future, particularly in a post-pandemic world.