Search Results (35)

This study evaluates the viability of a specific hybrid renewable energy system (HRES) installation designed for a remote community as a case study in Cuba. The system integrates solar, wind, and biomass resources to address localised challenges of energy insecurity and environmental degradation. Rather than offering a generalised evaluation of HRES technologies, this work focuses on the performance, impacts, and viability of this particular configuration within its unique geographical, social, and technical context. Using life cycle assessment (LCA) and input–output modelling, the research assesses environmental and socioeconomic impacts. The proposed HRES reduces greenhouse gas emissions by 60% (from 1.14 to 0.47 kg CO₂eq/kWh) and fossil energy consumption by 50% compared to diesel-based systems. Socioeconomic analysis reveals that the system generates 40.3 full-time equivalent (FTE) jobs, with significant employment opportunities in operation and maintenance. However, initial investments primarily benefit foreign suppliers due to Cuba’s reliance on imported components. The study highlights the potential for local economic gains through workforce training and domestic manufacturing of renewable energy technologies. These findings underscore the importance of integrating multiple renewable sources to enhance energy resilience and sustainability in Cuba. Policymakers should prioritise strategies to incentivise local production and capacity building to maximise long-term benefits. Future research should explore scalability across diverse regions and investigate policy frameworks to support widespread adoption of HRES. This study provides valuable insights for advancing sustainable energy solutions in Cuba and similar contexts globally. Full article

Life Cycle Impact Assessments (LCIAs) examine the environmental impacts of products using life cycle inventories (LCIs) of quantified inputs and outputs of a product through its life cycle. Currently, estimated impacts from mining are dominated by long-term metal release from tailings due to inaccurate assumptions regarding metal release and transport within and from mine materials. A conceptual model approach is proposed to support the development of a new database of LCI data, applying mechanistic processes required for the release and transport of metals through tailings and categorizing model inputs into ‘bins’. The binning approach argues for accuracy over precision, noting that precise metal release rates are likely impossible with the often-limited data available. Three case studies show the range of forecasted metal release rates, where even after decades of monitoring within the tailings and underlying aquifer, metal release rates span several orders of magnitude (<100 mg/L to >100,000 mg/L sulfate at the Faro Mine). The proposed tool may be useful for the development of a new database of LCI data, as well as to analyze mine’s regional considerations during designs for risk evaluation, management and control prior to development, when data is also scarce. Full article

This study reviews life cycle assessments (LCAs) of reprocessed single-use devices (rSUDs) in healthcare to quantify their greenhouse gas (GHG) emission reductions compared to original equipment manufacturer (OEM) SUDs (single-use devices). rSUDs offer notable reductions in solid waste generation, but, until recently, a reduction in greenhouse gases and other emissions from the reprocessing process was only hypothesized. Emerging LCAs in this space can help validate the assumptions of better environmental performance from greater circularity in the medical device industry. Four LCAs analyzing eight devices found consistent and significant GHG reductions ranging from 23% to 60% with rSUD use. Primary data from rSUD manufacturers were utilized in all studies, with SimaPro v9.3.0.2 and Ecoinvent v3.8 being the predominant LCA software and database. Raw material extraction and production dominated SUD emissions, while electricity use and packaging materials were key contributors for rSUDs. Sensitivity analyses highlighted the influence of electricity sources, collection rates, and reprocessing yields on rSUD environmental performance. A comparison with economic input–output-based models revealed an alignment at the time between price differentials and LCA-derived GHG differences, though this may not always hold true. This review demonstrates the substantial environmental benefits of rSUDs, supporting their role as a readily achievable step towards more sustainable and circular healthcare systems. Full article

Supply chain management (SCM) involves complexities and uncertainties in the flow of goods and services from raw materials to end users. Inaccurate estimation of raw materials, labor, or equipment can lead to financial losses and environmental impacts. This study explores the application of system dynamics modeling (SDM) in manufacturing hemp-reinforced polymer composites (HRPC) to optimize resource usage. Using SDM software STELLA^® (Version 3.7.3), selected for its affordability and features, the research demonstrates how system dynamics (SD) can enhance sustainability by minimizing materials, labor, and equipment, reducing energy consumption. A literature review identified a gap in existing research, as we found no prior studies simulating HRPC manufacturing using SDM. The study concludes that SDM is an effective tool for optimizing resource use and improving manufacturing efficiency. By simulating multiple supply chain scenarios in a risk-free environment, the model helps reduce resource consumption and enhance sustainability. Additionally, outputs from the STELLA^® model can be used as inputs for life cycle assessment (LCA) to quantitatively measure environmental impacts. Full article

This study explores the water consumption and greenhouse gas (GHG) emissions in the Yarkand River Basin, focusing on their dynamic interactions across industrial sectors. Utilizing environmental input–output analysis (IOA), the CROPWAT model, and life cycle assessment (LCA), we quantified the historical evolution of physical and virtual water cycles in relation to the water–carbon nexus. Our findings reveal that the planting industry, particularly the production of export-oriented, water-intensive crops like cotton, significantly contributes to both blue and green water consumption, exacerbating regional water scarcity. The persistent external market demand drives this over-extraction, further strained by the basin’s limited water retention capabilities. Although advancements have been made in reducing the per-unit water footprint of crops, total water consumption continues to rise due to agricultural expansion, intensifying pressure on blue water resources. Additionally, agricultural GHG emissions have surged, driven by increased electricity consumption, heavy fertilizer use, and escalating soil N₂O emissions. In light of these challenges, our research underscores the critical need for integrated resource management strategies that align with sustainable development goals. By promoting efficient water allocation within the agricultural sector and diversifying crop structures downstream, we can enhance ecosystem resilience and reduce environmental degradation. Furthermore, the advancement of value-added agricultural processing and the implementation of innovative water conservation technologies are essential for fostering economic sustainability. These strategies not only mitigate the environmental impacts associated with agricultural practices but also strengthen the region’s adaptive capacity in the face of climate change and fluctuating market demands. Our findings contribute to the broader discourse on sustainable agricultural practices, emphasizing the interconnectedness of water management, climate resilience, and economic viability in arid regions. Full article

The construction sector is one of the principal contributors to carbon dioxide emissions (CDEs) and has a vital role to play in responding to the issue of long-term environmental sustainability. This research proposes a process-based hybrid life-cycle assessment (LCA) method depending on a process-based LCA and an input–output LCA. The process-based hybrid LCA model provides a supplementary method to quickly estimate carbon emissions that are not considered in the system boundary due to the limitation of inventory data. The proposed hybrid method was applied to a carbon emissions assessment in a slurry shield tunnel. The results suggest that 93.88% of emissions are from materials. Of the materials contribution, 55.9% comes from steel and 34.55% arises from concrete. It has also been found that emissions during the tunneling stage are negatively correlated with the efficiency of tunnel construction. Recommendations for carbon emissions reductions in tunnel construction are provided for promoting sustainable transportation and management. Full article

In supply chain management (SCM), goods and services flow from the raw materials stage to the end user with complexities and uncertainty at each stage. Computer modeling and simulation is a particularly useful method to examine supply chain operational issues because it can solve operational complexities that are challenging and time consuming to analyze. Manufacturing companies fear losing valuable time and assets during the manufacturing process; the inaccurate estimation of raw materials, human capital, or physical infrastructure not only leads to monetary loss for the manufacturing unit, but also has a detrimental effect on the environment. The purpose of this paper is to demonstrate that system dynamics modeling (SDM) in sustainable supply chain management (SSCM) can be applied to apparel manufacturing to optimize materials, labor, and equipment usage. Utilizing system dynamics (SD), the manufacturing unit can improve sustainability by reducing materials, labor, and equipment usage, which in turn reduces energy use. In our literature review, we did not identify any study addressing supply chain simulation of the manufacturing of shirts using SDM. We chose shirt manufacturing to demonstrate the model because of its relatively simple manufacturing process. In our study, we conclude that SDM simulation is an efficient way to optimize materials, labor, and equipment in apparel manufacturing. This leads to a more sustainable manufacturing process, as the model simulates different manufacturing supply chain scenarios in a risk-free environment, thereby minimizing waste and resources. Further, the outputs from the STELLA^® model can be used as inputs into a subsequent life cycle assessment (LCA) model to determine the quantitative environmental impacts. Full article

The aging population has posed a challenge to China’s carbon neutrality pledge. To study the household carbon footprint in an aging society, this paper has combined the age-specific consumption pattern and environmental input-output life cycle assessment (EIO-LCA) to calculate the carbon footprint of household consumption across age groups, and then identified the key pathways of carbon emissions via structural path analysis (SPA). Results indicate that the elderly contribute 11.65% to total consumption-based carbon emissions. The working group (ages 15–64) has the highest average carbon footprint (0.85 tCO₂e), while the elderly group (ages 65 and above) has the lowest average carbon footprint (0.82 tCO₂e). Urban households of all ages have a higher carbon footprint than rural households. Housing and food are the dominant sources of the elderly carbon footprint. Notably, the production and distribution of electric power and heat power sector associated with housing energy consumption plays a leading role in the carbon emissions pathways of elderly consumption. Measuring the carbon footprint of older people can support policy designs and decision making in key sectors along the supply chain, and further encourage low-carbon lifestyles among China’s elderly. Additionally, the findings of this study have broad applications, especially for developing countries undergoing demographic transitions. Full article

This paper aims to review one of the least used, but no less important, approaches in the assessment of the environmental implications of electricity generation: the Economic Input-Output Life Cycle Assessment (EIO-LCA). This methodology is a top-down approach intertwined with the environmental satellite accounts provided by the national statistical office. Through the use of economic input-output (IO) tables and industrial sector-level environmental and energy data, the EIO-LCA analysis allows for broad impact coverage of all sectors directly and indirectly involved with electricity generation. In this study, a brief overview of this methodology and the corresponding assumptions is presented, as well as an updated review of the different applications of the EIO-LCA approach in electricity generation, suggesting a possible classification of the many studies developed in this context. The different ways of overcoming the problem of disaggregation in the electricity sector are also addressed, namely by considering different IO table formats (i.e., symmetric or rectangular tables). This is a particularly relevant feature of our review, as the way in which electricity generation is modeled can result in different calculations of the costs and benefits of environmental policies. In this context, this paper further contributes to the literature by explaining and providing examples of distinct approaches to modeling the electricity sector in IO models on a detailed level. Full article

The Internet of Things (IoT) is demonstrating its huge innovation potential, but at the same time, its spread can induce one of highest environmental impacts caused by the IoT industry. This concern has motivated the rise of a new research area aimed at devising green IoT deployments. Our work falls in this research area by contributing to addressing the problem of assessing the environmental impact of IoT deployments. Specifically, we propose a methodology based on an analytical model to assess the environmental impact of an outdoor IoT deployment powered by solar energy harvesting. The model inputs the specification of the IoT devices that constitute the deployment in terms of the battery, solar panel and electronic components, and it outputs the energy required for the entire life-cycle of the deployment and the waste generated by its disposal. Given an existing IoT deployment, the models also determine a functionally equivalent baseline green solution, which is an ideal configuration with a lower environmental impact than the original solution. We validated the proposed methodology by means of the analysis of a case study conducted over an existing IoT deployment developed within the European project RESCATAME. In particular, by means of the model, we evaluate the impact of the RESCATAME system and assess its impact with respect to its baseline. In a scenario with a 30-year lifespan, the model estimates for the system more than 3 times the energy required by its baseline green solution and a waste for a volume 15 times greater. We also show how the impact of the baseline increases when assuming deployments in locations at increasing latitudes. Finally, the article presents an implementation of the proposed methodology as a web service that is publicly available. Full article

This study quantitatively analyses the impacts that recent unprecedent events have had on the Saudi economy and environment using the Global Trade Analysis Project (GTAP) model. These events include: the global outbreak of COVID-19 and the associated disruption to the global supply chain, the alarming rate of climate change, and various political conflicts. These events have affected global food and energy prices. The results of this study revealed a decline in Saudi GDP, household income, purchase ability, and welfare. A trade deficit was indicated in the Saudi trade balance because of higher food prices and a reduction in two of the main Saudi exports (oil and petroleum products). A decrease in the output of most Saudi industries was shown, despite the increase in exports for most sectors. This was because of the reduction in Saudi households’ domestic consumption. Regarding the environmental impact, the Input–Output Life Cycle Assessment (IO-LCA) approach was used to estimate the total CO₂ emissions of the Saudi economy. In total, approximately 740.6 million metric tons of CO₂ emissions were estimated. By using a recently published specific carbon intensity for Saudi oil, total Saudi CO₂ emissions were 24.59% less than the non-specific measure. Full article

The purpose of this paper is to evaluate the monetary GHG reduction benefits and health co-benefits for the industrial sector under the imposition of a carbon charge in Taiwan. The evaluation proceeds from 2023–2030 for different rates of carbon charge for the GHGs by a model of “Taiwan Economic Input Output Life Cycle Assessment and Environmental Value” constructed in this study. It is innovative in the literature to simulate the benefits of GHG reductions and health co-benefits of air pollutions for the industrial sector under the imposition of a carbon charge comprehensively. The results consistently show benefits whether the charge is imposed on the scope 1 and scope 2 GHG emissions or on the scope 1 emissions only. The health co-benefits are on average about 5 times those of GHG reductions benefits in 2023–2030. The average total benefits with the summation of GHG reduction benefits and health co-benefits are 821.9 million US dollars and 975.1 US million US dollars per year, respectively. However, both the GHG reduction benefits and health co-benefits are consistently increasing at a decreasing rate in 2023–2030. The increased multiple for the rate of the carbon charge is higher than the increased multiple of the total benefits and this result shows that the increase of the carbon charge becomes less effective. Full article

Cement is one of the most produced materials globally. Population growth and urbanization cause an increased demand for the cement needed for expanding infrastructures. As a result of this circumstance, the cement industry must find the optimum compromise between increasing cement production and reducing the negative environmental impact of that production. Since cement production uses a lot of energy, resources and raw materials, it is essential to assess its environmental impact and determine methods for the sector to move forward in sustainable ways. This paper uses an integrated life cycle assessment (LCA) and a system dynamics (SDs) model to predict the long-term environmental impact and future dynamics of cement production in South Africa. The first step used the LCA midpoint method to investigate the environmental impact of 1 kg of Portland cement produced in South Africa. In the cement production process, carbon dioxide (CO₂), nitrogen oxides (NO_x), sulphur dioxide (SO₂), methane (CH₄) and particulate matter (PM) were the major gases emitted. Therefore, the LCA concentrated on the impact of these pollutants on global warming potential (GWP), ozone formation, human health, fine particulate matter formation and terrestrial acidification. The system dynamics model is used to predict the dynamics of cement production in South Africa. The LCA translates its results into input variables into a system dynamics model to predict the long-term environmental impact of cement production in South Africa. From our projections, the pollutant outputs of cement production in South Africa will each approximately double by the year 2040 with the associated long-term impact of an increase in global warming. These results are an important guide for South Africa’s future cement production and environmental impact because it is essential that regulations for cement production are maintained to achieve long-term environmental impact goals. The proposed LCA–SD model methodology used here enables us to predict the future dynamics of cement production and its long-term environmental impact, which is the primary research objective. Using these results, a number of policy changes are suggested for reducing emissions, such as introducing more eco-blended cement productions, carbon budgets and carbon tax. Full article

useeior is an open-source R package that builds USEEIO models, a family of environmentally-extended input–output models of US goods and services used for life cycle assessment, environmental footprint estimation, and related applications. USEEIO models have gained a wide user base since their initial release in 2017, but users were often challenged to prepare required input data and undergo a complicated model building approach. To address these challenges, useeior was created. In useeior, economic and environmental data are conveniently retrievable for immediate use. Users can build models simply from given or user-specified model configuration and optional hybridization specifications. The assembly of economic and environmental data and matrix calculations are automatically performed. Users can export model results to desired formats. useeior is a core component of the USEEIO modeling framework. It improves transparency, efficiency, and flexibility in building USEEIO models, and was used to deliver the recent USEEIO model. Full article

Undoubtedly, most industrial buildings have a huge Life Cycle Cost (LCC) throughout their lifespan, and most of these costs occur in structural operation and maintenance costs, environmental impact costs, etc. Hence, it is necessary to think about a fast way to determine the LCC values. Therefore, this article presents an LCC deep learning prediction model to assess structural and envelope-type alternatives for industrial building, and to make a decision for the most suitable structure. The input and output criteria of the prediction model were collected from previous studies. The deep learning network model was developed using a Deep Belief Network (DBN) with Restricted Boltzmann Machine (RBM) hidden layers. Seven investigation cases were studied to validate the prediction model of a 312-item dataset over a period of 30 years, after the training phase of the network to take the suitable hidden layers of the RBM and hidden neurons in each hidden layer that achieved the minimal errors of the model. Another case was studied in the model to compare design structure alternatives, consisting of three main structure frames—a reinforced concrete frame, a precast/pre-stressed concrete frame, and a steel frame—over their life cycle, and make a decision. Precast/pre-stressed concrete frames were the best decision until the end of the life cycle cost, as it is possible to reuse the removed sections in a new industrial building. Full article