Designingthe Ethylene Factory for Products of CarbonDioxide Reduction: Techno-Economic and Life Cycle Assessments

The use of Life Cycle Assessment (LCA) has become a common mechanism to evaluate and report the environmental performance of services and products due to its holistic approach and for its standardised method which guaranteeing reproducibility. There is a huge ongoing effort to improve and promote the use of LCA in Europe, by means of the Single Market of Green Products Initiative, which promotes the use of the Product Environmental Footprint (PEF) and the Organisation Environmental Footprint (OEF). Although LCA has been applied in a great variety of industries, there is an even higher worldwide trend of simplification focussing on a single indicator, carbon footprint (CF), relevant to global warming, which is internationally considered as a critical environmental concern. The scope of the CF assessment could be corporate (when all production processes of a company are evaluated) or product (when one of the products is evaluated throughout its life cycle). However, sometimes product CF studies collect corporate data, since for most companies it is easier to report global annual consumptions and emissions instead of the product's specific inputs and outputs. In this framework, this study aims to apply and compare the product and corporate CF methodologies to the case study of the spirit drinks sector in Cantabria (Northern Spain). In particular, to a SME dedicated to the artisanal elaboration of premium spirit drinks such as gin and vodka.The value obtained of the Product Carbon Footprint (PCF) was 0.57 kg CO2 eq. for a bottle (70 cl) of classic gin whereas the Corporate Carbon Footprint (CCF) presented a value of 4.58×103 kg CO2 eq. for Scope 2 and 5.58×104 kg CO2 eq. for Scope 3 in the year 2017. The results indicated that significant environmental impacts were caused during the production of the glass bottle as well as the production of the electricity required in the beverage company.

The carbon footprint of a product represents the amount of greenhouse gas (GHG) emissions released during its production, transportation, and consumption and is calculated as carbon dioxide equivalent (CO2-eq). It should be integrated into different existing and future seafood awareness campaigns to create more holistic yardsticks by which consumers, retail businesses, and producers can assess the environmental impacts of seafood. This study used the life cycle assessment (LCA) method for the first time to quantify the carbon footprint of salmon fillet products processed in Vietnam for export. The carbon footprint of 1-kg salmon fillet at the factory gate ranges between 7.20 and 15.05kg CO2-eq, depending on transportation modes of head-on-gutted (HOG) salmon from Norway to Vietnam. Transportatiton by airfreight doubled carbon footprint of salmon fillet products processed in Vietnam compared to sea freight. Feed and electricity were identified as the two most respective contributing factors during the stage of cultivation, processing fresh salmon in Norway, and the stage of salmon fillet processing in Vietnam. They accounted for about 95% and 50% of the total carbon footprint in these stages of the production chain, respectively. To reduce the carbon footprint of the salmon fillet products processed in Vietnam, the company should (i) make a careful production plan to use sea freight transportation instead of airfreight and (ii) use more electricity from renewable energy sources. Furthermore, the carbon footprint of these products can be reduced by improving the cultivation process via changing feed ingredients and enhancing the feed conversion ratio (FCR).

As global warming becomes increasingly severe, environmental consciousness has become critical in the design and operation of globally integrated supply chain networks. Product Carbon Footprint is defined as the life cycle Greenhouse Gas (GHG) emissions of goods and services and it can be considered as a simplified life cycle assessment restricted to a single impact category. In order for companies to better confront GHG emission issues, calculating product carbon footprint and analysing how various parameters affect the carbon footprint over the entire life cycle of a product is necessary. This paper studies the carbon footprint across supply chains and proposes a method of production carbon footprint analysis in a supply chain based on life cycle assessment, including the following: taking product life cycle as the span of carbon footprint analysis, with all kinds of complex information system as objects and then carrying out the carbon footprint knowledge extraction according to the concept model format in physical database; building a carbon footprint analysis ontology, which is related to product life cycle in supply chain environment; calculating the quantification of carbon footprint through GHG emissions over the entire life cycle, and designing a tool for product carbon footprint in supply chain environment.

As global warming becomes increasingly severe, environmental consciousness has become critical in the design and operation of globally integrated supply chain networks. Product Carbon Footprint is defined as the life cycle Greenhouse Gas (GHG) emissions of goods and services and it can be considered as a simplified life cycle assessment restricted to a single impact category. In order for companies to better confront GHG emission issues, calculating product carbon footprint and analysing how various parameters affect the carbon footprint over the entire life cycle of a product is necessary. This paper studies the carbon footprint across supply chains and proposes a method of production carbon footprint analysis in a supply chain based on life cycle assessment, including the following: taking product life cycle as the span of carbon footprint analysis, with all kinds of complex information system as objects and then carrying out the carbon footprint knowledge extraction according to the concept model format in physical database; building a carbon footprint analysis ontology, which is related to product life cycle in supply chain environment; calculating the quantification of carbon footprint through GHG emissions over the entire life cycle, and designing a tool for product carbon footprint in supply chain environment.

Comparisons of emerging carbon capture and utilization (CCU) technologies with equivalent incumbent technologies are necessary to support technology developers and to help policy-makers design appropriate long-term incentives to mitigate climate change through the deployment of CCU. In particular, early-stage CCU technologies must prove their economic viability and environmental reduction potential compared to already-deployed technologies. These comparisons can be misleading, as emerging technologies typically experience a drastic increase in performance and decrease in cost and greenhouse gas emissions as they develop from research to mass-market deployment due to various forms of learning. These changes complicate the interpretation of early techno-economic assessments (TEAs) and life cycle assessments (LCAs) of emerging CCU technologies. The effects of learning over time or cumulative production themselves can be quantitatively described using technology learning curves (TLCs). While learning curve approaches have been developed for various technologies, a harmonized methodology for using TLCs in TEA and LCA for CCU in particular is required. To address this, we describe a methodology that incorporates TLCs into TEA and LCA to forecast the environmental and economic performance of emerging CCU technologies. This methodology is based on both an evaluation of the state of the art of learning curve assessment and a literature review of TLC approaches developed in various manufacturing and energy generation sectors. Additionally, we demonstrate how to implement this methodology using a case study on a CO2 mineralization pathway. Finally, commentary is provided on how researchers, technology developers, and LCA and TEA practitioners can advance the use of TLCs to allow for consistent, high-resolution modeling of technological learning for CCU going forward and enable holistic assessments and fairer comparisons with other climate technologies.

Examining the carbon footprint of rice production and consumption in Hubei, China: A life cycle assessment and uncertainty analysis approach

Under the background of carbon peaking and carbon neutrality goals, the Ministry of Ecology and Environment, together with 15 national ministries and commissions, has formulated the Implementation Plan on Establishing a Carbon Footprint Management System, and it is urgent for traditional Chinese medicine(TCM) pharmaceutical enterprises to carry out research on carbon footprint accounting methods of related products. Based on the life cycle assessment(LCA) theory, taking mulberry leaf extract produced by a certain enterprise as an example, this study analyzed the carbon footprint of TCM extracts during the life cycle. The results show that for every 1 kg of product produced, the carbon emissions from the stages of raw material acquisition, transportation, and extract production are-20.569, 1.205, and 173.577 kgCO_2eq(CO_2 equivalent), respectively. The carbon footprint of the product is 154.213 kgCO_2eq·kg~(-1). In addition, the carbon emission is the highest in the production stage, in which the consumption of ethanol solvents makes the greatest contribution to the carbon footprint, accounting for 25.71%, more than one-fourth of the total carbon footprint. The second contribution was from the treatment process of TCM residues, accounting for 19.67%, closely followed by wastewater treatment(17.71%), the consumption of hot steam(17.43%), and drinking water(16.90%). The consumption of electric power and packaging materials has a smaller carbon emission of 2.58%. In particular, the carbon emission caused by the consumption of packaging materials is only 0.04%, which is negligible. The results of the study are expected to provide a reference for TCM enterprises to carry out research on the carbon footprint of products, offer ideas for collaborative innovation in reducing pollution and carbon emissions throughout the entire industry chain of TCM, and develop new quality productivity of modern TCM industry based on green and low-carbon manufacturing.

The assessment of the carbon and water footprints of agricultural products is important for fruit producers because it enables improvements in environmental management along the production chain as well as the opening of new markets. This study analyses the carbon and water footprints of green coconut produced in seven farms located at the main producing States in Brazil (Ceara, Alagoas, Sergipe and Bahia), investigating opportunities for reducing these footprints. The carbon footprint was calculated based on ISO 14067 and the water footprint, on ISO 14046. Primary data were collected from orchards with dwarf coconut trees, located in the states of Ceara (CE1, CE2, CE3 and CE4 farms), Alagoas (AL farm), Sergipe (SE farm) and Bahia (BA farm). The impact categories considered and their assessment models were as follows: (i) for the carbon footprint, climate change impact was assessed (ILCD midpoint); (ii) for the water footprint, water scarcity (AWARE), human toxicity, cancer, non-cancer, and freshwater ecotoxicity and marine and freshwater eutrophication (ILCD midpoint) were assessed. Sensitivity analysis was performed for variations in emissions from land use change (LUC) and water scarcity characterization factors. Uncertainty analysis was applied to identify best performing farms and their practices. The farms that resulted in lower footprints (AL and CE4) caused less carbon losses in LUC and used less nitrogen fertilizers and irrigation water. LUC emissions answered for one third of coconut carbon footprint when orchards were installed in areas with Caatinga vegetation. However, if coconut orchards replaced annual crops, carbon footprint may reduce up to 61%. Regarding water scarcity, in the case of applying monthly AWARE factors, the impact increased as much as 95% in relation to impacts calculated using annual factors. The use of regionalized annual or monthly AWARE factors increased impact up to 97% in relation to when annual and monthly AWARE were used. The analysis of alternatives for footprint reduction showed that both footprints can be reduced in all regions with changes in orchard lifespan, irrigation and fertilization. Increasing the useful life of the orchard results in a reduction of up to 38% in footprints, adjusting irrigation, up to 49%, and the amount of fertilizer, up to 70% of the carbon footprint and up to 82% of water footprint profile. Regionalized factors were more accurate for identifying critical watersheds for coconut production.

Carbon footprint of sugar production in Mexico

The mitigation of climate change requires research, development, and deployment of new technologies that are not only economically viable but also environmentally benign. Systematic and continuous technology assessment from early technology maturity onwards allows assessment practitioners to identify economic and environmental characteristics. With this information, decision-makers can focus time and resources on the most promising technologies. A broad toolset for technology assessment exists—stretching from the well-established life cycle assessment (LCA) methodology to more loosely defined techno-economic analysis (TEA) methods and the increasingly popular principles of technology maturity assessment such as the concept of technology readiness levels (TRL). However, current technology assessment practice faces various challenges at early stages, resulting in a potential mismatch of study results and stakeholders' needs and an escalation of assessment effort. In this practice review, we outline current challenges in the interplay of LCA, TEA, and TRL and present best practices for assessing early-stage climate change mitigation technologies in the field of carbon capture and utilization (CCU). The findings help practitioners systematically identify the TRL of a technology and adapt technology assessment methodologies accordingly. We highlight the methodological challenges for practitioners when adapting the goal and scope, identifying benchmark technologies, creating a comprehensive inventory, comparing early stage to commercial stage, ensuring clarity of recommendations for decision-making under high uncertainty, and streamlining conventional LCA and TEA assessment approaches and provide actionable recommendations. Overall, this work contributes to identifying promising technologies faster and more systematically, accelerating the development of new technologies for climate change mitigation and beyond.

Greenhouse gas emissions are significant contributors to global warming, and steel enterprises need to find more efficient and environmentally friendly solutions to reduce CO2 emissions while maintaining high process efficiency and low production costs. Carbon capture and utilization (CCU) is a promising approach which can convert captured CO2 into valuable chemicals, reducing dependence on fossil fuels and mitigating climate change. This study uses life cycle assessment (LCA) to compare the environmental impacts of BF-BOF steel plants with and without CCU. When evaluating seven scenarios, including three carbon capture and two carbon utilization technologies, against a baseline, the results demonstrate significant environmental benefits from implementing CCU technologies. Although the activated carbon TSA route for CO2-based methanol production showed good environmental performance, its toxicity risks highlight the advantages of combining TSA with steel slag carbonation as a better non-toxic solution.

Environmental impacts of CO2-based chemical production: A systematic literature review and meta-analysis

When assessing agricultural products using life cycle assessment (LCA), the farmers play a key role as they have first-hand information to understanding the activities involved in the assessed systems. However, the compilation of these data can be tiresome and complicated. To engage farmers in the LCA, a web tool (eFoodPrint Env®) was designed to facilitate their tasks as much as possible, seeking the trade-off between comprehensiveness and time consumption without affecting the quality. The model relies on primary data for the specific parcel and growing season; it starts with the ancillary materials extraction and ends with the transport of products to the corresponding cooperative. The model excludes the infrastructure except in the cases of protected crops including greenhouses. To build the inventory, the web tool guides the user through a questionnaire divided in cultivation, machinery, fertilization, plant treatment, and transport. Carbon footprint is computed with global warming potentials of the International Panel of Climate Change following the norm PAS2050. The calculations behind the web tool have the following modules: (1) farming input and output flows; (2) database and default data; (3) greenhouse infrastructure; (4) impact assessment; (5) uncertainty analysis, and (6) results module. The web tool is already in use and can be applied to most of agricultural facilities. Examples of estates of corn, nectarine, grape, and tomato are herein showed. The application displays the results distributed in the different stages considered in each product system, and the scores include error bars derived from the uncertainty analysis. Corn production showed the highest carbon footprint per kilogram of product, with a high contribution due to fertilizer production and application. The carbon footprint of tomato production in low-tunnel greenhouse showed nearly 30 % of impact related only to the greenhouse structure. Regarding uncertainty, the worst value is also associated to the corn production for which the most uncertain activities have more influence (fertilizer and transport). The design of the tool has the objective of meeting the requirements of data quality and comprehensiveness with the reality of the farms. The tool is generic enough to be applied to different cropping systems, enabling the generation of simple reports with the results of the analysis. The modular structures of both data entry and model calculation allow the identification of potential sources of uncertainty and hotspots within the studied life cycle stages.

An accurate and objective evaluation of the carbon footprint of rice production is crucial for mitigating greenhouse gas (GHG) emissions from global food production. Sensitivity and uncertainty analysis of the carbon footprint evaluation model can help improve the efficiency and credibility of the evaluation. In this study, we combined a farm-scaled model consisting of widely used carbon footprint evaluation methods with a typical East Asian rice production system comprising two fertilization strategies. Furthermore, we used Morris and Sobol' global sensitivity analysis methods to evaluate the sensitivity and uncertainty of the carbon footprint model. Results showed that the carbon footprint evaluation model exhibits a certain nonlinearity, and it is the most sensitive to model parameters related to CH4 emission estimation, including EFc (baseline emission factor for continuously flooded fields without organic amendments), SFw (scaling factor to account for the differences in water regime during the cultivation period), and t (cultivation period of rice), but is not sensitive to activity data and its emission factors. The main sensitivity parameters of the model obtained using the two global sensitivity methods were essentially identical. Uncertainty analysis showed that the carbon footprint of organic rice production was 1271.7 ± 388.5kg CO2eq t-1 year-1 (95% confidence interval was 663.9-2175.8 kg CO2eq t-1 year-1), which was significantly higher than that of conventional rice production (926.0 ± 213.6kg CO2eq t-1 year-1, 95% confidence interval 582.5-1429.7kg CO2eq t-1 year-1) (p<0.0001). The carbon footprint for organic rice had a wider range and greater uncertainty, mainly due to the greater impact of CH4 emissions (79.8% for organic rice versus 53.8% for conventional rice). EFc , t, Y, and SFw contributed the most to the uncertainty of carbon footprint of the two rice production modes, wherein their correlation coefficients were between 0.34 and 0.55 (p<0.01). The analytical framework presented in this study provides insights into future on-farm advice related to GHG mitigation of rice production.

Carbon Capture and Utilization (CCU) is an emerging field proposed for emissions mitigation and even negative emissions. These potential benefits need to be assessed by the holistic method of Life Cycle Assessment (LCA) that accounts for multiple environmental impact categories over the entire life cycle of products or services. However, even though LCA is a standardized method, current LCA practice differs widely in methodological choices. The resulting LCA studies show large variability which limits their value for decision support. Applying LCA to CCU technologies leads to further specific methodological issues, e.g., due to the double role of CO2 as emission and feedstock. In this work, we therefore present a comprehensive guideline for LCA of CCU technologies. The guideline has been development in a collaborative process involving over 40 experts and builds upon existing LCA standards and guidelines. The presented guidelines should improve comparability of LCA studies through clear methodological guidance and predefined assumptions on feedstock and utilities. Transparency is increased through interpretation and reporting guidance. Improved comparability should help to strengthen knowledge-based decision-making. Consequently, research funds and time can be allocated more efficiently for the development of technologies for climate change mitigation and negative emissions.