Resource Consumption Research Articles

Exploring the Potential Synergies between Industry 5.0 and Green Lean Six Sigma for Sustainable Performance: A New Dimension of Operational Excellence

Today's companies are adopting a sustainable strategy, often in a reactive manner, to respond to customer demands, stakeholder concerns, and compliance with regulatory requirements, while seeking to proactively maintain a strong standing in the future market. This article explores the synergies between Industry 5.0 technologies and Green Lean Six Sigma (GLSS), highlighting how their integration can catalyse sustainable transformations in manufacturing. The study combines a literature review with empirical survey conducted across companies in various industrial sectors. The objective is to identify and examine potential synergies between Industry 5.0 technologies and Green Lean Six Sigma tools to improve the environmental impact of manufacturing companies. The results highlight the importance of Industry 5.0 technologies in boosting the efficiency of GLSS, thereby promoting more sustainable manufacturing practices. Key areas of synergy include sustainable process optimization, energy efficiency, sustainable personalization, data analytics for sustainability, and sustainable innovation. This article emphasizes the mutual benefits of integrating Industry 5.0 technologies with the Green Lean Six Sigma methodological framework. This combination reduces the consumption of materials and resources, improves energy efficiency, and stimulates innovation in favor of eco-responsible production. It calls for an integrated approach, considering technology, human resources, and processes, to enable manufacturing companies to achieve optimized operational performance while contributing to environmental sustainability.

Digitalization for ensuring innovative development of the enterprise

This paper examines the influence of digitalization on the innovative development of enterprises. The integration of digital technologies serves as a key driver for product, process, marketing, and organizational innovations, which collectively contribute to competitive advantages and the enhancement of business process efficiency. Core avenues of digitalization encompass automation, data analytics, the Internet of Things (IoT), artificial intelligence (AI), cloud computing, and digital platforms. These technologies equip firms with the capacity to swiftly adapt to market fluctuations, optimize productivity, and foster resilience in an increasingly dynamic economic environment. The strategic implementation of digitalization extends beyond operational enhancements, enabling the reconfiguration of traditional business models. By leveraging digital tools, enterprises are able to deliver personalized customer experiences, enhance decision-making through real-time data analytics, and achieve more efficient coordination across value chains. Additionally, the adoption of digital platforms facilitates rapid scaling and the exploration of novel revenue channels through the provision of innovative digital services. In the context of a globalized economy, where adaptability and technological sophistication are critical, digital transformation emerges as a decisive factor in sustaining long-term competitiveness. Furthermore, this paper addresses the intersection of digitalization and sustainability. A growing number of enterprises are employing digital technologies to decrease resource consumption and mitigate environmental impact, aligning their practices with the increasing emphasis on corporate social responsibility and the principles of a circular economy. As businesses continue to pursue digital transformation, their capacity to innovate, maintain competitive positioning, and contribute to sustainable development will play a pivotal role in shaping the future trajectory of the business landscape.

Aromatic Biobased Polymeric Materials Using Plant Polyphenols as Sustainable Alternative Raw Materials: A Review.

In the foreseeable future, the development of petroleum-based polymeric materials may be limited, owing to the gradual consumption of disposable resources and the increasing emphasis on environmental protection policies. Therefore, it is necessary to focus on introducing environmentally friendly renewable biobased materials as a substitute for petroleum-based feed stocks in the preparation of different types of industrially important polymers. Plant polyphenols, a kind of natural aromatic biomolecule, exist widely in some plant species. Benefiting from their special macromolecular structure, high reactivity, and broad abundance, plant polyphenols are potent candidates to replace the dwindling aromatic monomers derived from petroleum-based resources in synthesizing high-quality polymeric materials. In this review, the most related and innovative methods for elaborating novel polymeric materials from plant polyphenols are addressed. After a brief historical overview, the classification, structural characteristics, and reactivity of plant polyphenols are summarized in detail. In addition, some interesting and innovative works concerning the chemical modifications and polymerization techniques of plant polyphenols are also discussed. Importantly, the main chemical pathways to create plant polyphenol-based organic/organic-inorganic polymeric materials as well as their properties and possible applications are systematically described. We believe that this review could offer helpful references for designing multifunctional polyphenolic materials.

A Robust Static Model of Basic Oxygen Furnace for Analyzing Emerging Steelmaking Scenarios

A robust static model, which incorporates emerging steelmaking scenarios in terms of solid charge mix with the given hot metal in basic oxygen furnace process, is developed. It employs mass and enthalpy balances to comprehend nonequilibrium conditions, considering four key empirical parameters: iron loss, post‐combustion ratio, heat loss, and undissolved lime content in slag, which are fine‐tuned using plant data through a multivariate approach, ensuring the reliability. The model is validated in a basic oxygen furnace (BOF) shop using data from over 4000 heats, achieving a strike rate of ≈77% for input lime prediction within ±1 ton and ≈80% for input oxygen prediction within ±600 Nm3. Model implementation in BOF shop provides valuable guidance to the operators, resulting in the reduction of average oxygen and lime consumption by 139 Nm3 and 652 kg heat−1, respectively. The model also enables the determination of the maximum scrap utilization of ≈16% for 0.8% silicon and ≈14% for 0.6% silicon in hot metal, respectively. The model aids in calculating the maximum tap temperature for varying hot metal silicon and iron ore addition. Overall, the model optimizes primary steelmaking, enhancing efficiency, reducing resource consumption, and offering insights into alternative iron sources like direct reduced iron.

Recycled aggregate concrete using seawater: Optimizing concrete's sustainability

Concrete is the most widely used building material worldwide and causes huge carbon emissions, high water consumption and negative impacts on the environment. In this context, in order to reduce natural resources consumption and obtaining a “greener” concrete, the use of seawater and recycled aggregates becomes a promising option.In this study, the effect of the use of seawater in the composition and curing of concrete with natural aggregates (NA) and recycled aggregates (RA) applied at different ratios (0 %, 50 % and 100 %) on its mechanical and durability properties was evaluated. By applying structural equation modelling (SEM) with the R version 4.2.2, the dependence relationships between the variables were empirically evaluated.The analysis of the results has made it possible to establish the advantages and disadvantages of the manufacture of concrete with seawater and RA. It was found there is a need to increase the water/cement ratio in concrete made with seawater, the effective water/cement ratio was increased by 0.03 with respect to the control (in order to maintain workability constant) and that the use of seawater did not influence the mechanical properties of concrete made with NA. However, in concrete made with RA, the mechanical properties improved with the presence of seawater in the mixing, 39 MPa compressive strength at 28 days was obtained for concrete made with 50 % of RA. Durability properties were negatively affected by the presence of seawater and RA, with an increase in water absorption, shrinkage and carbonation depth. The seawater curing process did not influence the mechanical and durability properties of concrete.These results indicate that seawater alone does not influence the mechanical and durability properties of concrete. However, there the combined effect of seawater and RA improves the final characteristics of concrete.

How do water, food, and energy resources impact environmental conditions in Ghana? A novel dynamic ARDL simulation approach

Rising water, food, and energy demands pose significant resource risks and environmental threats. Despite the critical nature of this issue, there is a notable absence of empirical research linking the consumption of these resources to environmental degradation in Ghana. To fill this gap, this study investigates the short-term and long-term effects of water, food, and energy resource consumption on environmental degradation in Ghana, analyzing data from 1994 to 2020. Ghana's increasing environmental degradation accentuates the urgency of this research, its vulnerability to climate change, and the imperative to sustain human life and protect the environment. Utilizing the novel dynamic ARDL simulation method, this research offers new perspectives that diverge from previous findings. The study's findings demonstrate that water resource consumption positively impacts environmental degradation in the short and long term. However, food and energy resource consumption mitigates environmental degradation in the short and long term. Therefore, the study advocates adopting resource management strategies emphasizing efficiency and sustainability. It calls for policies that improve water efficiency, boost renewable energy uptake, and encourage sustainable farming practices. These measures are intended to lessen the negative impacts of resource exploitation and aid in environmental preservation efforts.

Life Cycle Assessment of Consumption Patterns – Understanding the links between changing social practices and environmental impacts

As we are still not on track to achieve environmental sustainability through technological change, we need to further stress the importance of understanding routinized types of behaviour by private households and the environmental assessment of corresponding consumption of resources. Social practice theories allow us to understand the constitution of human activity and its embeddedness in society. In transformation research, the relevance of social practice theories has been recognized. However, there is still a lack of understanding regarding the conditions under which practices change substantially and what kinds of tools and governance approaches are suitable to foster change in social practices. We therefore propose a novel way to address the transformation of consumption patterns of private households by a) connecting social practice theories to life cycle assessment for a quantitative environmental evaluation of transformation processes and b) using narrative approaches to understand the links between several social practices and conditions for change. An illustrative study of a bottom-up neighbourhood initiative shows how specific meanings and values (expressed, for example, through meanings of “enough”) affect several social practices from different consumption categories (nutrition, daily mobility, travelling). Environmental assessments further show that changing social practices across consumption categories can substantially decrease environmental pressures from private household consumption, especially in the field of nutrition. The understanding of how various social practices are connected in a nexus through narratives embodying deeper meanings point to novel ways of fostering change and the relevance of social practices related to urban or neighbourhood initiatives as an entry point to a more sustainable way of living.

High-Performance Grape Disease Detection Method Using Multimodal Data and Parallel Activation Functions

This paper introduces a novel deep learning model for grape disease detection that integrates multimodal data and parallel heterogeneous activation functions, significantly enhancing detection accuracy and robustness. Through experiments, the model demonstrated excellent performance in grape disease detection, achieving an accuracy of 91%, a precision of 93%, a recall of 90%, a mean average precision (mAP) of 91%, and 56 frames per second (FPS), outperforming traditional deep learning models such as YOLOv3, YOLOv5, DEtection TRansformer (DETR), TinySegformer, and Tranvolution-GAN. To meet the demands of rapid on-site detection, this study also developed a lightweight model for mobile devices, successfully deployed on the iPhone 15. Techniques such as structural pruning, quantization, and depthwise separable convolution were used to significantly reduce the model’s computational complexity and resource consumption, ensuring efficient operation and real-time performance. These achievements not only advance the development of smart agricultural technologies but also provide new technical solutions and practical tools for disease detection.

Evaluation of geotechnical, mineralogical and environmental properties of clayey soil stabilized with different industrial by-products: A comparative study

The utilisation of soil stabilization techniques employing binders like lime or cement enables the use of soils that are classified as disposable, thereby reducing the need for landfills and the consumption of natural resources. However, the production of lime and cement results in significant CO2 emissions. Therefore, the application of industrial by-products (IBP) for stabilizing expansive soils presents an opportunity to minimise the use of traditional binders. This study investigates the geotechnical, mechanical, mineralogical, and environmental properties of four IBP (biomass bottom ash, biomass fly ash, steel slag, and mixed recycled aggregate) combined with a silica-based nanomaterial for road layer applications. The technical feasibility of the use of IBP was demonstrated, especially in combination with nanomaterials, allowing a 66 % reduction in the percentage of added lime, obtaining significant improvements with minimal plasticity and swelling index and bearing capacity CBR values higher than 25 %, applying BBA, BFA and SS. An environmental assessment of leachate analysis was performance, showing an immobilization of heavy metals that makes the application of IBP feasible. In addition, a life cycle analysis study showed the environmental benefits of applying IBP, such as a 50 % reduction in CO2 emissions due to the reduction of lime, resulting from the use of these materials, thus promoting more sustainable economic models.

Energy-efficient hierarchical cluster-based routing strategies for Internet of Nano-Things: Algorithms design and experimental evaluations

Nanodevices (NDs), which are only a few nanometers (nm) in size, need to communicate with each other to perform complex operations. In nanonetworks, this communication typically involves multiple hops, requiring efficient routing protocols. Existing protocols are not well suited for nanonetworks due to their high resource consumption and setup overhead. In this paper, we propose three novel routing protocols for nanodevices. Non-Back Flooding Routing (NBFR) and Layer-Based Flooding Routing (LBFR) aim to reduce unnecessary packet transmission by utilizing distance and layer information based on received signal power. On the other hand, Tree-Based Forwarding Routing (TBFR) is a unicast-based approach that aims to transmit the packet to the destination using the shortest and most reliable path possible through a tree structure. The performance of these proposed methods is compared to well-known methods in terms of packet transmission, energy consumption, end-to-end delay, and setup overhead. TBFR achieved a packet transmission success of 92.95% in topology with the highest density of nanorouters (NRs), while it reached up to 99.57% for fewer nanorouters. Moreover, its end-to-end delay values are much lower than those of multi-path routing protocols. It also consumed one-fifth of the energy compared to its most challenging multi-path competitor, NBFR, regarding packet transmission success. However, for dense nanosensor (NS) topologies, NBFR and LBFR achieved higher packet transmission rates of 87.04% and 86.66%, respectively. Furthermore, in addition to achieving low end-to-end delays, the energy consumption of NBFR is very close to that of TBFR. In summary, the tests show that TBFR is more suitable for communication among nanorouters due to the requirement of building the tree structure, which results in a slightly higher setup overhead. In contrast, NBFR and LBFR are more suitable for communication between nanosensors because of their simplicity and low setup overhead. But, it should be noted that NBFR requires a larger header than the other alternatives.

Convenience orientation, environmental concerns and resource conservation behaviours

There is a concern that convenience orientation, especially lifestyles that prefer convenience to sustainability, could lead to increased resource consumption, posing a challenge to sustainable consumption. This study aims to provide empirical evidence showing that convenience orientation may not necessarily harm the environment but helps conserve resources. We designed our survey to collect data on convenience orientation and environmental concerns from 361 households living in the capital city of Pakistan. Using the structural equation modelling approach, we examine how convenience orientation, environmental concerns and socioeconomic importance affect food, water, and energy conservation behaviours. Our results reveal a positive association between convenience orientation, environmental concerns and conservation behaviours while socioeconomic importance negatively associated with the food, water and energy conservation behaviours. This finding is contradictory to concerns that convenience orientation might undermine sustainability efforts.

RT-PPS: Real-time privacy-preserving scheme for cloud-hosted IoT data

The Internet of Things (IoT) is a rapidly growing network of devices that can communicate with each other and with cloud-based services. These devices generate vast amounts of data that can be used to provide valuable insights into user behavior, environmental conditions, and other important factors. However, as this data is collected and processed by cloud-hosted services, there is a growing concern about privacy and security. Without adequate protection, sensitive information could be exposed to hackers or other malicious actors, putting both individuals and organizations at risk. To address this challenge, real-time privacy-preserving techniques can be used to protect IoT data without compromising its value. This paper introduces an efficient Real-time privacy-preserving scheme (RT-PPS) for cloud-hosted IoT data. RT-PPS employs multi-authority attribute-based encryption on a hybrid cloud environment to keep data secure and private, while still allowing it to be processed and analyzed by cloud-hosted services. RT-PPS has efficient response time and resource consumption, which gives it the ability to handle a huge number of concurrent users at the same time without notable delay. The proposed RT-PPS has been validated through extensive experimental evaluation on a variety of configurations. Moreover, the proposed scheme has been computationally compared with the state-of-the-artwork. RT-PPS has shown excellent performance, effectiveness, and efficiency. The RT-PPS encryption time for a 1 GB dataset while considering 1024 slices is approximately 1000 ms. Also, the RT-PPS decryption time for a 1 GB ciphertext while considering 1024 slices are approximately 235 ms. Finally, RT-PPS is proven secure against any polynomial-time attacks and their variations that have at most a negligible advantage in the introduced security model. Moreover compared to most of the state-of-the-artwork, RT-PPS reduced the ciphertext size and lowered the computations in the encryption, key generation, decryption, and ciphertext update while assuring their security. By implementing RT-PPS, organizations can take advantage of the benefits of IoT data while protecting the privacy of their users and maintaining the security of their systems.

The value of the systemic immune-inflammation index in assessing disease severity in autoimmune encephalitis

Background Autoimmune encephalitis (AE) is a group of autoimmune diseases targeting the central nervous system, characterized by severe clinical symptoms and substantial consumption of medical resources. Neuroinflammation plays a crucial role in disease progression, and detecting inflammatory responses can provide insights into disease status and disease severity. The systemic immune-inflammation index (SII), a novel marker of inflammatory status, has been rarely studied in AE. Methods Retrospective analysis of data from AE patients admitted to the First Affiliated Hospital of Zhengzhou University between January 2019 and September 2023 was conducted. Univariate analysis and logistic regression were used to assess the association between SII and patient severity. Nomograms for predicting AE severity were established, and receiver operating characteristic (ROC) curves, concordance index (C-index), calibration curves, and decision curve analysis were employed to evaluate predictive accuracy. Additionally, the Clinical Assessment Scale in Autoimmune Encephalitis (CASE) score was used to assess patient severity. Results This study enrolled 157 patients, of whom 57 were classified as severe according to the CASE score. SII, cerebrospinal fluid (CSF) cell counts, disturbance of consciousness, and behavioural abnormalities independently associated with the occurrence of severe cases. The C-index of the nomograms was 0.87, indicating strong association with disease severity, as supported by the calibration. Additionally, SII levels were highest within seven days of onset and decreased after one month. In subgroup analyses of different antibodies, SII also associations with severe cases in NMDAR encephalitis. Conclusions Higher SII levels are associated with an increased likelihood of developing severe AE, peaking within 7 days of disease onset and decreasing thereafter, potentially offering a prognostic marker to assess disease progression early in its course.

Knowledge-Based Recommendation System for Plate Waste Reduction in Latvian Schools

Food waste indicates ineffective and irresponsible consumption of resources, particularly during the food consumption stage. The aim of our research study is to optimize the catering management process at Latvian schools by reducing the amount of plate waste. The experts developed a set of recommendations aimed at improving the catering management process at schools. The recommendations developed were supported by measurable parameters, which must be monitored by school staff. The capability-driven development approach was applied to model the recommendation system. A plate waste predictive module and a large language model classifier were integrated into the system to support sustainable decision-making. The large language model classifier was trained to filter questions and recommendations. Three training methods were compared: training from scratch and finetuning by using datasets DBPedia and News Category Dataset. As a result, we present the list of recommendations based on the literature review, and the prototype of the knowledge-based recommendation system was developed to audit the school catering management process and promote sustainable school management and decision-making. The recommendation system aims to reduce plate waste due to deficiencies in the implementation of the catering process and to promote responsible food consumption at schools.

Natural resource extraction - Sustainable development relationship and energy productivity moderation in resource-rich countries: A panel Bayesian regression analysis

Social development is essential for improving quality of life, fostering stability, driving economic growth, achieving sustainable goals, enhancing human capital, encouraging civic engagement, building resilience, and fostering innovation, thereby ensuring inclusive, equitable, and sustainable progress. The Social Progress Index (SPI), also known as social development, contains all these features. This study aims to examine the impact of natural resource extraction on social development quadratically using data from 2011 to 2022. The focus of the study is on resource-rich countries. In this study, productive capacity in the energy sector is used as a social development determinant and a natural resource consumption moderator, ensuring sustainability in social development. Analyzing the quadratic relationship allows us to model and understand nonlinear relationships between the predictor and the outcome variable, capturing the effects that change direction or strength at different levels of the predictor. In this study, Bayesian regression is incorporated as an econometric approach. It provides more flexible modelling, incorporates prior knowledge, and offers a full distribution of parameter estimates, leading to better uncertainty quantification and more robust predictions. These estimates confirms a U-shaped natural resource extraction and social development relationship. Energy productive capacities in resource-rich countries not only enhance social development but also ensure the sustainable extraction of natural resources leading to sustainable social development. Population density and biodiversity are incorporated as control variables of the model. The impact of both is positive on social development.

Exploring the Circular Economy in the Fashion Industry: Transforming Waste into Valuable Resources

The fashion industry, a major global economic sector, faces significant environmental challenges due to high resource consumption and waste generation. This paper evaluates the effectiveness of circular economy practices as a solution to these issues. Traditional linear models of production and disposal are insufficient for sustainable development, necessitating a shift towards circular economy principles that focus on resource efficiency, waste reduction, and recycling. Through detailed case studies of Patagonia and H&M, this study highlights how circular economy practices, such as the use of renewable materials and comprehensive recycling systems, contribute to environmental sustainability and meet consumer expectations. Despite these advancements, barriers such as technological limitations, high initial investments, and the need for stronger policy support remain. Recommendations include increasing investment in recycling technologies, enhancing consumer education, and fostering industry-wide cooperation. Future research should expand to include a broader range of case studies to better understand global applications of circular economy practices.

Water–carbon–economy multivariate spatial–temporal collaborative relationships and nonlinear projections in urban agglomerations

A complex dynamic coupling process exists among water resource consumption, carbon emissions, and economic growth; however, their intricate collaborative mechanisms remain unclear. This study proposes a new barycenter formula-based multivariate spatial–temporal collaborative relationship model for identifying the complex relationships and interactions among the water footprint (WF), carbon footprint (CF), and gross domestic product (GDP) in the three largest urban agglomerations in the Yangtze River Economic Belt (YREB) of China. A nonlinear grey Bernoulli model is then advanced for illustrating the future evolution in WF–CF–GDP and their multivariate collaborative relationship. Results reveal that WF and CF of all urban agglomerations increase continuously, with annual growth rates of 0.59% and 5.63%, respectively. WF, CF, and GDP are significantly positively correlated, and they exhibit similar spatial clustering characteristics and centroid migration directions along the main course of the Yangtze River. Moreover, their multivariate temporal collaborative relationship (0.79) significantly surpasses their spatial (0.63), while the spatial–temporal (0.77) is intermediate. WF has the greatest impact on the binary spatial–temporal collaborative relationship between CF and GDP. In contrast, GDP has the least impact on the relationship between CF and WF. The projected WF, CF, and GDP indicate that the YREB will face more severe water and carbon pressures as its economy grows. Their multivariate spatial–temporal collaborative relationships are projected to decrease by 10.26% in 2050. These findings contribute to promoting collaborative and sustainable development of inter-regional socio-economic and ecological environment.

Utilizing Machine Learning for Intelligent Data Management in Event-Driven Microservices Architectures

The following research paper is a case study of three specific machine learning algorithms, LSTM networks, GBMs and RL for managing data in event-driven microservices architectures. This work assesses the performance of these algorithms using factors including anomaly detection, resource consumption prediction, and service coordination concerning such challenges as anomalies. LSTM networks was used in assessment of the anomalous patterns with accuracy reaching 92%, and false positive rate of 5%. The ability of the GBMs was evaluated for its capacity to accurately predict resource requirements, and in turn, minimize resource over-commitment and under-commitment that occurred, achieving 89% accuracy, with a percentage variation of 18% and 14% accordingly. The RL algorithms proved their potential to enhance the decision-making process governing the orchestration of services and failure recovery with the decision-makers achieving 22% increase in decision making accuracy and failure recovery time was reduced to 4. 5 minutes. These algorithms are discussed separately in the next sections with reference to their applications in intelligent data management in business event processing system. These findings are useful to improve the application of these machine learning techniques to increase the performance, utilization of resources and reliability of the system.

Integration of NLP and machine learning methods for smart contract security: a comparison with traditional approaches

In modern blockchain systems, smart contracts are one of the most critical components for ensuring the automated execution of agreements without the need for intermediaries. However, smart contracts written in languages like Solidity may contain vulnerabilities that can be exploited by malicious actors to steal funds or manipulate assets. Given the increasing number of attacks on smart contracts, the development of effective methods for detecting such vulnerabilities is crucial. Traditional approaches to detecting vulnerabilities in smart contracts include symbolic execution, fuzzing, formal verification, and pattern matching. These methods have their advantages but face several challenges, such as high resource consumption, limitations in detecting new types of vulnerabilities, and difficulties in scaling to large contracts. As a result, there is a need to introduce new approaches, such as natural language processing (NLP) and machine learning, which can address these challenges more effectively. In this study, an NLP-based method was explored, using Word2Vec to convert smart contract code into vector representations, allowing for better analysis of the semantic relationships between elements of the code. These vector representations are then fed into a bidirectional recurrent neural network with GRU blocks and an attention mechanism. This approach allows the model to focus on the most important parts of the code and improve the accuracy of vulnerability detection. The comparative analysis showed that NLP-based methods significantly outperform traditional approaches in all key metrics. In particular, the GRU model with an attention mechanism demonstrated high results in accuracy, recall, and F-measure, making it effective for detecting complex vulnerabilities such as reentrancy. Furthermore, the NLP-based approach is capable of adapting to new types of attacks thanks to training on large datasets. Thus, the integration of NLP and machine learning represents a promising direction for enhancing the security of smart contracts. Future research can focus on improving these approaches, particularly through the implementation of advanced models such as transformers.

Life cycle assessment and energy return of investment of nutritionally-enhanced snacks supplemented with Spanish quinoa

The trend in the consumption of unconventional, more nutritious foods is leading to the globalization and decentralization of their production, giving rise to the adaptation and innovation of traditional products to make them healthier and more sustainable. This article focuses on quinoa and aims to estimate the environmental impacts of its production adapted to the Spanish conditions and of potential derived snacks enriched with this pseudo cereal by applying conventional and nutritional life cycle assessment (LCA) methodologies. Besides, an exhaustive study of the energy flows by measuring the cumulative energy demand and the calculation of the energy return of investment (EROI) is carried out to assess the most impactful aspect of the processing industries. The application of cradle to gate LCA revealed that polluting emissions of Spanish quinoa are rather similar to those of the Andean grain, with an impact on climate change of 1.03 kg CO2 eq./kg. However, high resource footprints were obtained, for instance a water deprivation potential of 60 m3/kg due to the scarcity in this country. Besides, the consideration of a nutrient profile model as functional unit led to the conclusion that quinoa-based snacks are generally more environmentally sustainable than their conventional counterparts in terms of climate change, resources consumption or water degradation. EROI scores were relatively low for all options, with only between 1.77 and 4.35 % of the energy invested returned, which evidences the unsustainable agricultural practices and low efficiency of processing units. Based on this research, producers can reorient production systems in support of nature, and consumers are able to guide their choices towards improved eating patterns.