Carbon Footprint Analysis Research Articles

Exploring environmental sustainability through dimension reduction algorithms: A cost-effective UV spectroscopic method for simultaneous determination of veterinary binary mixtures of doxycycline hydrochloride and tylosin tartrate

This study presents a cost-effective and environmentally friendly approach for the simultaneous determination of a veterinary binary mixture comprising doxycycline hydrochloride (DOX) and tylosin tartrate (TYZ) utilizing UV spectroscopy alongside dimension reduction algorithms (DRAs). Seventeen DRAs were evaluated, and their performances were compared based on four metrics: mean squared error (MSE), mean absolute error (MAE), median absolute error (MedAE), and coefficient of determination (R2). Based on the performance indices, Isomap algorithm demonstrated the highest predictive capacity among all DRAs. MSE, MAE, MedAE and R2 values of (0.38, 0.28, 0.19 and 0.999) and (0.08, 0.26, 0.22 and 0.998) were obtained for calibration and test datasets, respectively across a concentration range 4.67–30 μg mL−1 for DOX. MSE, MAE, MedAE and R2 values of (0.54, 0.34, 0.19 and 0.994) and (0.07, 0.19, 0.07 and 0.998) were obtained for calibration and test datasets, respectively across a concentration range 3.51–24 μg mL−1 for TYZ. The developed method underwent validation utilizing the accuracy profile approach. An ecological impact assessment was carried out employing six greenness evaluation tools: The Green Solvent Selection Tool (GSST), National Environmental Methods Index (NEMI), the Assessment of Green Profile (AGP), carbon footprint analysis, Analytical Greenness Calculator (AGREE), and Complementary Green Analytical Procedure Index (Complex GAPI). Additionally, we applied blueness and whiteness assessments using Blue Applicability Grade Index (BAGI) and Red-Green-Blue 12 (RGB 12) algorithms, respectively. The proposed method demonstrated higher GSST scores, a more "green" profile in NEMI, a superior AGP profile, and better environmental sustainability in Complex GAPI. The calculated carbon footprint value was 0.0002 kg CO2 equivalent per sample, The AGREE score was 0.87, BAGI was assessed at 72.5, and the whiteness assessment by the RGB12 algorithm was 89.6. Statistical comparison of the proposed method with a previously reported HPLC method for dosage form analysis revealed no significant differences at a 95% confidence level. This study highlights the novelty of combining UV spectroscopy with dimension reduction algorithms, offering significant advancements over traditional UV and chemometric methods for the analysis of these drugs. This approach not only enhances the efficiency and accuracy of determining active ingredients in pharmaceutical dosage forms but also contributes to environmental sustainability.

Comparative Analysis of Sustainability, Carbon Footprint, and AI's Role in Reducing Emissions Across AWS, Azure, and Google Cloud

Comparative Analysis of Sustainability, Carbon Footprint, and AI's Role in Reducing Emissions Across AWS, Azure, and Google Cloud

SUSTAINABLE SUPPLIER SELECTION IN INDIRECT PROCUREMENT: BEST PRACTICES AND CASE STUDIES IN ENGINEERING

This systematic review examines sustainable supplier selection in indirect procurement within the engineering sector, focusing on best practices, criteria, and impacts on organizational performance. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the review synthesizes findings from 35 articles published between 2000 and 2023. Key best practices identified include the integration of sustainability criteria into supplier evaluations, the use of lifecycle assessments (LCAs) to gauge environmental impact, and the implementation of carbon footprint analyses. Additionally, the emphasis on fair labor practices and the requirement for suppliers to obtain certifications like SA8000 enhance social sustainability and corporate social responsibility (CSR). The evolution of supplier selection criteria to encompass environmental, social, and economic factors reflects a more holistic approach, addressing complex sustainability challenges and driving long-term value and resilience in supply chains. The review also highlights the positive impacts of sustainable procurement practices, such as cost savings, risk mitigation, enhanced brand reputation, and improved operational efficiency. These findings underscore the strategic importance of sustainable procurement in achieving organizational sustainability goals and fostering sustainable development in the engineering sector.

OPTIMIZING HVAC EFFICIENCY AND RELIABILITY: A REVIEW OF MANAGEMENT STRATEGIES FOR COMMERCIAL AND INDUSTRIAL BUILDINGS

This systematic review examines sustainable supplier selection in indirect procurement within the engineering sector, focusing on best practices, criteria, and impacts on organizational performance. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the review synthesizes findings from 35 articles published between 2000 and 2023. Key best practices identified include the integration of sustainability criteria into supplier evaluations, the use of lifecycle assessments (LCAs) to gauge environmental impact, and the implementation of carbon footprint analyses. Additionally, the emphasis on fair labor practices and the requirement for suppliers to obtain certifications like SA8000 enhance social sustainability and corporate social responsibility (CSR). The evolution of supplier selection criteria to encompass environmental, social, and economic factors reflects a more holistic approach, addressing complex sustainability challenges and driving long-term value and resilience in supply chains. The review also highlights the positive impacts of sustainable procurement practices, such as cost savings, risk mitigation, enhanced brand reputation, and improved operational efficiency. These findings underscore the strategic importance of sustainable procurement in achieving organizational sustainability goals and fostering sustainable development in the engineering sector.

A comprehensive energy, water and carbon footprint analysis of petroleum fuel production in Iran: an allocation approach with uncertainty quantification

A comprehensive energy, water and carbon footprint analysis of petroleum fuel production in Iran: an allocation approach with uncertainty quantification

The carbon footprint of French hospital ophthalmology consultations.

France carbon footprint, with 604 million tons of CO2 equivalent (CO2eq) annual emissions, far exceeds the worldwide scientific specifications to not exceed 2 °C of warming in 2100. The healthcare sector is one of the biggest contributors to greenhouse gas emissions. The aim of this study is to quantify and evaluate the carbon footprint of the consultation activity of an ophthalmology department at a single institution in France. The perimeter of the investigation included consultations and excluded the surgeries and the hospitalisation. We calculated the carbon footprint of a single day of practice in our scope of investigation. We included consumption of energy, patient travel, staff travel, pharmaceutics and medical devices purchases, computer hardware, biomedical equipment/exam materials, and waste. We used the official French public database of emission factors: Base Empreinte® of the Environmental and Energy Management Agency of France. The carbon footprint of a single day of our department was estimated at 1 688.65 kgCO2eq. It represents an average of 9.28 kgCO2eq per patient. Energy consumption contributed for 114.80 kgCO2eq (6.8%), travel for 1324.76 kgCO2eq (78.4%), pharmaceutics and medical devices for 208.33 kgCO2eq (12.3%), equipment for 14.38 kgCO2eq (0.9%) and waste for 26.38 kgCO2eq (1.6%). This study highlights the importance of patient travels, and possibly pharmaceutics, in the carbon footprint of hospital ophthalmology practice in France. More studies are needed to establish it at the national or international scale, as well as more carbon footprint analyses on products, especially those of high prices, to increase the accuracy of these studies.

Carbon footprint analysis of wastewater treatment processes coupled with sludge in situ reduction

The goal of this study was to assess the impacts or benefits of sludge in situ reduction (SIR) within wastewater treatment processes with relation to global warming potential in wastewater treatment plants, with a comprehensive consideration of wastewater and sludge treatment. The anaerobic side-stream reactor (ASSR) and the sludge process reduction activated sludge (SPRAS), two typical SIR technologies, were used to compare the carbon footprint analysis results with the conventional anaerobic - anoxic - oxic (AAO) process. Compared to the AAO, the ASSR with a typical sludge reduction efficiency (SRE) of 30 % increased greenhouse gas (GHG) emissions by 1.1 - 1.7 %, while the SPRAS with a SRE of 74 % reduced GHG emissions by 12.3 - 17.6 %. Electricity consumption (0.025 - 0.027 kg CO2-eq/m3), CO2 emissions (0.016 - 0.059 kg CO2-eq/m3), and N2O emissions (0.009 - 0.023 kg CO2-eq/m3) for the removal of secondary substrates released from sludge decay in the SIR processes were the major contributor to the increased GHG emissions from the wastewater treatment system. By lowering sludge production and the organic matter content in the sludge, the SIR processes significantly decreased the carbon footprints associated with sludge treatment and disposal. The threshold SREs of the ASSR for GHG reduction were 27.7 % and 34.6 % for the advanced dewatering - sanitary landfill and conventional dewatering - drying-incinerating routes, respectively. Overall, the SPRAS process could be considered as a cost-effective and sustainable low-carbon SIR technology for wastewater treatment.

Enriching NPK Mineral Fertilizer with Plant-Stimulating Peptides Increases Soilless Tomato Production, Grower Profit, and Environmental Sustainability.

The need to increase agricultural production to feed a steadily growing population may clash with the more environmentally friendly but less efficient production methods required. Therefore, it is important to try to reduce the use of chemical inputs without compromising production. In this scenario, natural biostimulants have become one of the most sought-after and researched technologies. In the present study, the results of a greenhouse experiment on hydroponic tomatoes (Solanum lycopersicum L.) are presented, which involved comparing the use of ordinary NPK fertilizer (Cerbero®) with the use of NPK fertilizers enriched with 0.5% protein hydrolysate of plant origin (Cerbero Green®) at both standard (100%) and reduced (70%) fertilization rates. The results highlight how the use of Cerbero Green® fertilizers improves the production performance of tomatoes. More specifically, they show that the use of Cerbero Green® leads to higher marketable yields, especially under reducing fertilizer use, ensuring a positive net change in profit for the grower. In addition, carbon footprint analysis has revealed that the use of Cerbero Green® reduces the environmental impact of hydroponic tomato growing practices by up to 8%. The observed higher yield of hydroponically grown tomatoes even with reduced fertilization rates underlines once again the key role of natural biostimulants in increasing both the economic and environmental sustainability of horticultural production.

Techno‐economic and carbon footprint analyses of steam Rankine cycle

AbstractSteam Rankine cycle (SRC), which is mainly utilised in power generation sector, faces external irreversibility in its daily operation causing inefficiency in the system. To address this issue, reheat Rankine cycle (RHRC) and regenerative Rankine cycle (RRC) have been widely studied and implemented in power plants to improve thermal efficiency and reduce external irreversibility of Rankine cycle. This study investigates the implementation of different RRC configurations in a combined heat and power plant, including RRC with modified thermal deaerator, RRC with open feed water heater (OFWH) and closed feed water heater (CFWH). A base case simulation model was first constructed using commercial simulation software Aspen HYSYS for the basic SRC system based on actual plant data. Various scenarios were then evaluated for their profitability and sustainability through techno‐economic analysis (TEA) and carbon footprint analysis (CFA). From both analyses, the scenario of RRC with CFWH showed the greatest long‐term potential, generating the highest annual profit of $ 771 691 and carbon footprint reduction of 14.63%, while RRC with modified thermal deaerator showed the greatest potential in the short run with the highest return of investment (ROI) of 201.51% and shortest payback period (PBP) of 0.50 year.

Assessment of Corporate Carbon Footprint and Energy Analysis of Transformer Industry

Transformers are primarily key components in power transmission and distribution systems. In the electrical industry, transformers are becoming increasingly important to increase energy efficiency and reduce environmental impact. In the process from the production to the use of transformers, various strategies and technologies are adopted to reduce the carbon footprint. To achieve decarbonization targets and a future with sustainable energy, ongoing efforts to reduce the carbon footprint of transformers need to continue. Therefore, this study aims to calculate the carbon footprint (CF) of Beta Energy in the Adana Province of Türkiye. A comprehensive inventory is being created to determine and monitor the greenhouse gas emissions of Beta Energy, a transformer manufacturer. This inventory includes direct and indirect greenhouse gas emissions from all of the company’s activities. The findings show that in 2023, the total CF of Beta Energy is equal to 1,799,482.72 tons of CO2-eq and considering the total of 6044 transformers sold in 2023, results in 297 tons of CO2-eq/transformer per product. The results show that the transformer manufacturing industry has a high carbon footprint because it is an energy-intensive process. The areas where the most carbon emissions occur in transformer production are revealed by CF hot spot analysis in this study. To minimize both current and future greenhouse gas emissions during transformer production, the measures to be taken during the R&D, production, transportation, and service stages are revealed. This study aims to establish a foundation for Beta Energy’s efforts to reduce greenhouse gas emissions by managing them effectively.

793 Environmental Impacts of Excess Perioperative Testing: A Carbon Footprint Analysis of the Group and Save and Characterisation of Transfusion Rates in Common Surgical Procedures

793 Environmental Impacts of Excess Perioperative Testing: A Carbon Footprint Analysis of the Group and Save and Characterisation of Transfusion Rates in Common Surgical Procedures

Artificial Intelligence and Digital Tools for Assisting Low-Carbon Architectural Design: Merging the Use of Machine Learning, Large Language Models, and Building Information Modeling for Life Cycle Assessment Tool Development

The construction sector is a significant contributor to global carbon emissions and a major consumer of non-renewable resources. Architectural design decisions play a critical role in a building’s carbon footprint, making it essential to incorporate environmental analyses at various design stages. Integrating artificial intelligence (AI) and building information modeling (BIM) can support designers in achieving low-carbon architectural design. The proposed solution involves the development of a Life Cycle Assessment (LCA) tool. This study presents a novel approach to optimizing the environmental impact of architectural projects. It combines machine learning (ML), large language models (LLMs), and building information modeling (BIM) technologies. The first case studies present specific examples of tools developed for this purpose. The first case study details a machine learning-assisted tool used for estimating carbon footprints during the design phase and shows numerical carbon footprint optimization results. The second case study explores the use of LLMs, specifically ChatGPT, as virtual assistants to suggest optimizations in architectural design and shows tests on the suggestions made by the LLM. The third case study discusses integrating BIM in the form of an IFC file, carbon footprint analysis, and AI into a comprehensive 3D application, emphasizing the importance of AI in enhancing decision-making processes in architectural design.

Spatiotemporal carbon footprint analysis of bottled water production by ultrafiltration and reverse osmosis

The sustainable engineering of water treatment processes is critical for reducing the environmental impact of the bottled water industry, a sector experiencing growth parallel to rising standards of living. This study focuses on the environmental footprint of two membrane processes—ultrafiltration (UF) and reverse osmosis (RO)—used in the production of bottled drinking water. By employing life cycle assessment (LCA), we compare the carbon footprints of mineral water production via UF from high-quality sources against purified water production using RO technology. Initial findings indicate minor differences in the carbon footprints for one 550-mL bottled water produced by each method. However, the incorporation of green manufacturing practices reveals a significant reduction in the carbon footprint. Specifically, our analysis shows that with the deep decarbonization of the power grid and freight electrification, the carbon footprint of mineral water can be reduced by 36.04 %. Additionally, through the adoption of renewable energy and the recycling of plastic packaging after consumption, the carbon footprint of mineral water could be lowered to 0.0295 kg CO2-eq per 550-mL bottled water, demonstrating that mineral water offers low-carbon potential. This study further explores the roles of production location, transportation, and the adoption of various decarbonization strategies in optimizing the environmental footprint of bottled water. Our findings reveal the quantitative decarbonization potential of membrane processes, coupled with sustainable practices, for bottled drinking water production, supporting the industry's shift towards greater environmental sustainability.

Lifecycle battery carbon footprint analysis for battery sustainability with energy digitalization and artificial intelligence

As an indispensable component and intermediate bridge, electrochemical battery as an indispensable component is essential for power supply reliability, stability, grid-friendly interaction, sustainability with e-transportation and building electrification. However, the lifecycle carbon intensity of electrochemical batteries is uncertain throughout lifecycle battery-related activities. In this study, a generic methodology is proposed to accurately quantify the lifecycle carbon intensity of electrochemical batteries. A cross-scale multi-stage analytic platform with inter-disciplinary and trans-disciplinary is formulated, involving battery materials (anode, cathode, electrolyte), charging/discharging behaviours, cascade battery utilization, recycling, and reproduction. A case study on a zero-energy district in subtropical Guangzhou indicates that lifetime EV battery carbon intensity is +556 kg CO2,eq/kWh for the scenario with pure fossil fuel-based grid reliance, while the minimum carbon intensity of EVs at −860 kg CO2,eq/kWh can be achieved for the solar-wind supported scenario. The grid mandatory EVs charging will slightly increase the battery carbon intensity to −617.2 kg CO2,eq/kWh, and the exclusion of embodied carbon on both solar PV and wind turbines will increase the battery carbon intensity to −583.8 kg CO2,eq/kWh. The proposed approach and formulated platform can enable synthetical and comprehensive analysis on battery sustainability, throughout integrated cross-disciplinary approaches for 2060 carbon neutrality in China.

New procedure for designing an innovative biomass-solar-wind polygeneration system for sustainable production of power, freshwater, and ammonia using 6E analyses, carbon footprint, water footprint, and multi-objective optimization

The correct management of renewable polygeneration systems is a highly effective approach that can not only help expand renewable energy systems but also ensure sustainable production. In the present work, a new procedure for designing renewable polygeneration systems is introduced. In this procedure, the management of polygeneration systems is discussed from various aspects, including climatic change, techno-economic and environmental conditions, layout design, ecological sustainability, and the sociological approach to polygeneration systems in a practical form. As a case study, an innovative biomass-solar-wind polygeneration system to produce power, ammonia, and freshwater for Ahvaz in Iran was designed and evaluated according to the developed procedure. The integration of multi-effect distillation and humidification-dehumidification desalination has been employed for freshwater production. For ammonia production, syngas produced in the gasification unit and nitrogen obtained from the air separation unit were utilized. Power production has been achieved using a combination of the hydrogen-ammonia Brayton cycle, the supercritical carbon dioxide cycle, and the organic Rankine cycle. The designed system was evaluated using energy, exergy, exergoeconomic, exergoenvironmental, emergoeconomic, emergoenvironmental, carbon footprint, and water footprint analyses. The layout design of the proposed system and the sociological study of the system from a systemic approach have enhanced the visibility of its application. Additionally, the investigation of biomass fuel variations and combinations according to climate, along with optimization and dynamic analyses, are key measures that allow for a more comprehensive evaluation of the system from various management perspectives. The results show that the proposed system is capable of producing 523.34 m3/day freshwater, 10.1 MW of power, and storing 17.75 ton/day of ammonia in optimal state. The energy and exergy efficiency of the polygeneration system in the optimal state is 29.97% and 13.12%. The total cost rate and total environmental impact rate of the entire system in this case are equal to 0.48 $/s and 19.79 mPts/s, respectively. Meanwhile, the defined renewable system has an ecological sustainability index of 1.76.

Dynamic response, durability, and carbon footprint analysis of the marl clay treated with sodium lignosulfonate as a sustainable-environmentally friendly approach

In the current study, the marl clay was improved by different contents of sodium lignosulfonate (NLS) and cured at different times and then, all samples were subjected to Bender Element (BE), Unconfined Compressive Strength (UCS), and Brazilian Tensile Strength (BTS) tests considering two different dry condition (DC) and wet condition (WC). The durability of the samples was further controlled by a special technique, namely the soaking test. The carbon footprint analysis was undertaken for a low-volume trench project to address the sustainability benefits associated with replacing cement and lime as traditional stabilizers with NLS. The results show that the reuse of NLS as a non-traditional alternative for stabilizing marl soil can play an influential role in improving dynamic parameters as well as sustainable development. It has been observed that the CO2 emission decreases up to 5.6 and 4.4 times compared to lime and cement, respectively. Additionally, the use of NLS enhances the UCS by 249%, BTS by 208%, and small strain shear modulus by 117%. Furthermore, reducing the adverse effects of the WC on soil properties, among others, was the main finding of utilizing the NLS in marl stabilization with curing time. NLS-treated marl samples were able to preserve the integrity of their particles even after being soaked in water for a period of 3 weeks. In contrast, the particles of the untreated sample started to disintegrate within a few seconds of initiating the soaking test. Finally, possible equations correlating the dynamic and static moduli were reported in this study.

Status of Solar-Energy Adoption in GCC, Yemen, Iraq, and Jordan: Challenges and Carbon-Footprint Analysis

Status of Solar-Energy Adoption in GCC, Yemen, Iraq, and Jordan: Challenges and Carbon-Footprint Analysis

A carbon footprint analysis of the redevelopment of former residential areas

The negative impacts of greenhouse gas emissions, particularly CO2 emissions, have garnered widespread attention. Various old residential areas in China, built more than 20 years ago, have been undergoing urgent transformation to improve citizens’ quality of life, which may significantly reduce carbon emissions. This study developed a carbon emissions accounting framework for residential areas to account the emissions impact of residential transformation. The framework addressed the inventory classifications of landscape greening, residential buildings, water resources, solid waste, infrastructure and transportation. The consumption-based calculation model and bottom-up data collection methods were used. The results show that the transformation of two old residential areas achieved considerable carbon reductions of 17.16% and 33.37% in South and North China, respectively. The macro guidance of local policies, specifically those regarding local urban infrastructure construction, significantly aided the carbon emissions reductions. Based on the calculation results, a list of recommended technologies was proposed to reduce carbon emissions, and four measures (adding greening, improving the garbage recovery rate, separating rain and sewage and transforming envelope structures for energy savings in northern China) were recommended to promote low carbon development in residential areas.

Effect of alternative dosing strategies on sustainable healthcare: A carbon footprint analysis of nivolumab and pembrolizumab treatment in the Netherlands.

e13564 Background: Hospitals contribute significantly to greenhouse gas (GHG) emissions and face a moral obligation to prioritize reduction of GHG emissions. Drugs constitute an important component of the GHG emissions of hospitals. Alternative dosing strategies (ADS) have been implemented to improve the cost-effectiveness of pembrolizumab and nivolumab (Table). However, the impact of these ADS on GHG emissions remains unknown. Therefore, this study aimed to analyse the effect of ADS implementation on the carbon footprint of treatment with pembrolizumab and nivolumab. Methods: Life Cycle Assessment (LCA) methodology was used to quantify the environmental impact. The impact category climate change was used to quantify the carbon emissions (CO2e). The GHG Protocol was used to categorize the emissions into scopes 1, 2 and 3. Life Cycle Inventory and Impact data from an academic hospital in Rotterdam were used in the LCA to calculate the CO2e for pembrolizumab and nivolumab, their different dosing intervals, and the impact of ADS. Results: In 2022, the carbon emissions related to nivolumab and pembrolizumab treatment were 445 tons CO2e, with an average of 94 kg CO2e per dosage. Pharmaceutical production was the main driver of treatment-related GHG emissions (93% of total emissions on average). Applying ADS, resulted in carbon emission reductions of 21.4-26.2% and 9.3-11.2% for pembrolizumab and nivolumab respectively (table 1). Conclusions: This study shows the substantial environmental impact of cancer treatments with pembrolizumab and nivolumab and calls for further implementation of ADS for pembrolizumab and nivolumab and other anti-PD-(L)1 monoclonal antibodies and more sustainable pharmaceutical production processes. These findings help to create environmental awareness and contribute to the promotion and understanding of sustainable low-carbon healthcare practices. Overview of registered doses of pembrolizumab and nivolumab, alternative dosing strategies and potential reductions in GHG emissions, based on real world data in The Erasmus University Medical Center in Rotterdam, the Netherlands. [Table: see text]

Optimization, Characterization, and Carbon Footprint Analysis of Alkali Activated Waste Tuff and Fly Ash Mixtures for Deep Mixed Columns

Optimization, Characterization, and Carbon Footprint Analysis of Alkali Activated Waste Tuff and Fly Ash Mixtures for Deep Mixed Columns