A Large Language Model-based Framework to Retrieve Life Cycle Inventory and Environmental Impact Data from Scientific Literature.

Excerpts reprinted with permission from Parvatker, A. G.; Eckelman, M. J. Comparative Evaluation of Chemical Life Cycle Inventory Generation Methods and Implications for Life Cycle Assessment Results. ACS Sustain. Chem. Eng. 2019, 7 (1). Copyright 2019 American Chemical Society. Excerpts reprinted with permission from Parvatker, A. G.; Tunceroglu, H.; Sherman, J. D.; Coish, P.; Anastas, P.; Zimmerman, J. B.; Eckelman, M. J. Cradle-to-Gate Greenhouse Gas Emissions for Twenty Anesthetic Active Pharmaceutical Ingredients Based on Process Scale-Up and Process Design Calculations. ACS Sustain. Chem. Eng. 2019, 7 (7), 6580-6591. Copyright 2019 American Chemical Society. Excerpts reprinted with permission from Parvatker, A. G.; Eckelman, M. J. Simulation-Based Estimates of Life Cycle Inventory Gate-to-Gate Process Energy Use for 151 Organic Chemical Syntheses. ACS Sustain. Chem. Eng. 2020, 8 (23), 8519-8536. Copyright 2019 American Chemical Society. Synthetic chemicals are ubiquitous and are an integral part of almost every value chain in the industrialized world. Hence, comprehensive assessment of health and environmental impacts over the chemicals life cycle is critical in the pursuit of sustainable development, using quantitative modeling tools such as life-cycle assessment (LCA). However, lack of primary production data, and time- or resource-intensive design calculations often forces LCA practitioners to rely on simplistic methods for estimating chemical process data. Use of such "shortcut" methods for calculating chemical life cycle inventory (LCI) data introduces uncertainties and inaccuracies in environmental sustainability analysis. Research in this doctoral dissertation focuses on developing and implementing novel methods to reduce the time and resources required in chemical LCI data generation. An analysis of eight alternative methods to account for missing chemical LCI data was conducted to identify their strengths and drawbacks. Out of the four methods with whichfull LCIs can be generated, the advanced process-based methods give the most accurate life cycle GHG emission results compared to plant data for the case studies of styrene and its downstream product, acrylonitrile-butadiene-styrene (ABS). Stoichiometric calculations, which are the most commonly used approach, underestimate the actual global warming results by 35-50%.1 Among the 18 impact categories of the ReCiPe LCIA method, results for the estimated LCI data were within 10% of the actual plant results for only 4-5 categories. These techniques were then used in conjunction with a novel process scale-up and design method to estimate LCIs and cradle-to-gate greenhouse gas emissions for twenty common active pharmaceutical ingredients (API) used as injectable anesthetics and 130 intermediate pharma compounds. The cradle-to-gate GHG emissions of the 20 anesthetic drugs range from 11 kg CO2 eq. for succinylcholine to 3,000 kg CO2 eq. for dexmedetomidine. The LCI methods and data generated in this work greatly expand the available environmental data on APIs and can serve as a guide for LCA practitioners in future analysis of other pharmaceutical drugs. Process energy is a key input in a chemical LCI and has significant impact on LCA results. A streamlined process simulation-based methodology was developed to estimate energy consumption in chemical manufacturing. The methodology is applied to 151 different chemical processes using Aspen Plus to estimate their gate-to-gate process energy use, representing the largest such simulation-based LCI data set to date.3 The results from process simulations were then used to develop predictive models for heating and cooling requirements in chemical production. The multiple linear regression (MLR) and artificial neural network (ANN) models with R2 of 0.63 to 0.73 with measured error of less than 50% are robust alternatives for rapid estimations and screening analysis compared to the current methods used in LCI databases. The series of methods presented in this work can be used with varying availability of chemical synthesis data for life cycle inventory modeling. This research not only introduces and demonstrates novel methods for chemical LCI data estimation but also adds a significant number of new LCI data sets to the literature.--Author's abstract

Reliability of Life Cycle Assessment (LCA) results depends on the availability and quality of Life Cycle Inventory (LCI) data. In order to provide high-quality LCI data for background systems in LCA and to make it applicable to a wider range of fields, harmonization strategies for already existing datasets and databases are required. In view of the high significance of LCI data as a basis of major fields of action within a sustainability strategy, the German Helmholtz Association (HGF), under the leadership of the Forschungszentrum Karlsruhe (FZK) has taken up this issue in its research programme. In 2002, the FZK conducted a preliminary study on ‘Quality Assurance and User-oriented Supply of a Life Cycle Inventory Data’ funded by the Federal Ministry of Education and Research (BMBF). Within the framework of this study, a long-term concept for improving the scientific fundamentals and practical use of LCI data was developed in association with external experts. The focus is on establishing a permanent German ‘Network on Life Cycle Inventory Data’ which will serve as the German information and cooperation platform for all scientific and non-scientific actors in the field of life cycle analysis. This network will integrate expertise on LCA in Germany, harmonise methodology and data, and use the comprehensive expert panel as an efficient basis for further scientific development and practical use of LCA. At the same time, this network will serve as a platform for cooperation on an international level. Current developments address methodological definitions for the initial information infrastructure. As a novel element, user needs are differentiated in parallel according to the broad application fields of LCI-data from product declaration to process design. Case studies will be used to define tailored interfaces for the database, since different data quality levels will be encountered.

PurposeToxicity impacts of chemicals have only been covered to a minor extent in LCA studies of textile products. The two main reasons for this exclusion are (1) the lack of life cycle inventory (LCI) data on use and emissions of textile-related chemicals, and (2) the lack of life cycle impact assessment (LCIA) data for calculating impacts based on the LCI data. This paper addresses the first of these two.MethodsIn order to facilitate the LCI analysis for LCA practitioners, an inventory framework was developed. The framework builds on a nomenclature for textile-related chemicals which was used to build up a generic chemical product inventory for use in LCA of textiles. In the chemical product inventory, each chemical product and its content was modelled to fit the subsequent LCIA step. This means that the content and subsequent emission data are time-integrated, including both original content and, when relevant, transformation products as well as impurities. Another key feature of the framework is the modelling of modularised process performance in terms of emissions to air and water.Results and discussionThe inventory framework follows the traditional structure of LCI databases to allow for use together with existing LCI and LCIA data. It contains LCI data sets for common textile processes (unit processes), including use and emissions of textile-related chemicals. The data sets can be used for screening LCA studies and/or, due to their modular structure, also modified. Modified data sets can be modelled from recipes of input chemicals, where the chemical product inventory provides LCA-compatible content and emission data. The data sets and the chemical product inventory can also be used as data collection templates in more detailed LCA studies.ConclusionsA parallel development of a nomenclature for and acquisition of LCI data resulted in the creation of a modularised inventory framework. The framework advances the LCA method to provide results that can guide towards reduced environmental impact from textile production, including also the toxicity impacts from textile chemicals.RecommendationsThe framework can be used for guiding stakeholders of the textile sector in macro-level decisions regarding the effectiveness of different impact reduction interventions, as well as for guiding on-site decisions in textile manufacturing.

Life cycle assessment (LCA), a quantitative method for evaluating the total environmental impact of a product, from the materials in its manufacture to its final disposal, is playing an increasingly important role in manufacturing. When the LCA method is applied to a product containing many kinds of electronic components, there is a need for life cycle inventory (LCI) data on the components. This paper provides an original calculation of the LCI data for each electronic components industry. These data show the amount of input energy and emissions into the atmosphere per yen of production yield. It is demonstrated that the magnitude of the LCI data for each industry is essentially equal to that of the other industries. Furthermore, we conclude that the LCI data for all electronic components are roughly equivalent, making it possible to calculate the LCI data of any electronic component by simply multiplying the LCI data for the industry by the price of the component. Furthermore, after comparing the materials production stages with the component manufacturing stage in the calculation, it became clear that for several component industries the materials production stage could not be omitted from the calculation.

Life cycle inventory data for materials grouped according to environmental and material properties

Food systems are key drivers of environmental impacts, which may be assessed using life cycle assessment (LCA). Canadian agri-food LCA research has been limited by the unavailability of common life cycle inventory (LCI) data resources characterizing processes in Canadian-specific supply chains. We address this issue through identification and evaluation of publicly available Canadian agri-food LCI data for possible inclusion in the forthcoming Canadian Agri-food Life-Cycle Data Centre (CALDC). A data-reporting template was developed based on comparison of the ILCD and ecoSpold2 data-reporting formats, which contains all fields required for minimum compliance with both formats. Canadian agri-food-related practitioners at academic, government and industry organizations were then identified via web and literature searches, along with associated, publicly available resources containing Canadian agri-food LCI data tables. These data sets were classified by process type and geography, and then screened on the basis of their reported metadata against the identified reporting template to determine which data sets are currently reported in sufficient detail to warrant inclusion in the CALDC. This screening focused, in particular on metadata, and the identification of potential areas of improvement in metadata reporting. The identified data-reporting template contained 65 mandatory fields for LCI data, 58 of which related to metadata. Identification of available Canadian agri-food LCI data sets indicated a high degree of coverage in the field crop sector, and the need for significant further development with respect to data in the livestock, poultry, dairy, and agricultural support sectors. Screening of the data sets against the reporting template indicated that none of the identified data sets are currently reported in sufficient detail to warrant inclusion in the CALDC. In order to enable their inclusion, metadata reporting needs to improve in several areas—in particular, with respect to reporting of the percentage of supply represented by the data sets, and information regarding critical review of the data sets. Comparison of ILCD and ecoSpold2 reporting formats yielded a data-reporting template, the use of which ensures compliance with both formats. Provision of this template will help facilitate interoperability between LCI databases and LCA software across all sectors. Application to publicly available Canadian agri-food sector data indicated that the scope of available LCI data sets in Canada must increase in coverage, quantity, and metadata reporting completeness in order to adequately support agri-food LCA modelling activities and associated sustainability initiatives.

The applicability of non-local LCI data for LCA

Decision tree-based approach to extrapolate life cycle inventory data of manufacturing processes

PurposeThe European Commission’s Integrated Product Policy Communication, 2003, defined Life Cycle Assessment (LCA) as the ‘best framework for assessing the potential environmental impacts of products’. Since then, the use of LCA and life cycle approaches has been developing in a wide range of European policies, and its use has also significantly grown in business. Increasing the availability of quality-assured Life Cycle Inventory (LCI) data is the current challenge to ensure the development of LCA in various areas.MethodsOne solution to increase availability is to use LCI data from multiple database sources but under the condition that such LCI data are fully interoperable.Results and discussionThis paper presents original solutions and recent achievements towards increased availability, quality and interoperability of life cycle inventory data, developed through European Commission-led activities and based on wide stakeholder consultation and international dialogue. An overview of related activities, such as the International Reference Life Cycle Data System (ILCD), the European Reference Life Cycle Database (ELCD) and the ILCD Entry-Level quality requirements are presented. The focus is then on the Life Cycle Data Network (LCDN).ConclusionsA non-centralised data network of LCI datasets complying with minimum quality requirements that was politically launched in February 2014, already includes several database nodes from different worldwide sources and has the potential to contribute to the needs of the international community.

Despite being renewable, photovoltaic energy is not burden‐free, since energy and materials are necessary to manufacture, maintain, dismantle, and recycle photovoltaic systems. Over its life cycle, the assessed carbon footprint of silicon‐based photovoltaic energy published in the literature often ranges from 40 to 110 gCO2eq/kWh. However, most of these estimations rely on life cycle inventory (LCI) data that represent the early‐stage performance of the photovoltaic industry. Indeed, collecting LCI data is time‐consuming and practitioners often reuse existing outdated data, which becomes problematic as the photovoltaic industry has been rapidly and significantly evolving. This analysis relies on the parametrization of existing LCI data to better account for the progress already accomplished by the photovoltaic industry. A Life Cycle Assessment (LCA) model, called PARASOL_LCA, is thus developed. The results of the analysis highlight that the use of outdated LCI data leads to an overestimation of environmental impacts of photovoltaic energy by a factor of 2 or even more for the best current available technologies. The analysis also shows that PARASOL_LCA, with its numerous parameters, can also serve to assess the environmental performance of prospective photovoltaic technologies and to identify impact reduction levers through sensitivity analysis.

PurposeThe aim of the paper is to assess the role and effectiveness of a proposed novel strategy for Life Cycle Inventory (LCI) data collection in the food sector and associated supply chains. The study represents one of the first of its type and provides answers to some of the key questions regarding the data collection process developed, managed and implemented by a multinational food company across the supply chain.MethodsAn integrated LCI data collection process for confectionery products was developed and implemented by Nestlé, a multinational food company. Some of the key features includes (1) management and implementation by a multinational food company; (2) types of roles to manage, provide and facilitate data exchange; (3) procedures to identify key products, suppliers and customers; (4) LCI questionnaire and cover letter and (5) data quality management based on the pedigree matrix. Overall, the combined features in an integrated framework provide a new way of thinking about the collection of LCI data from the perspective of a multinational food company.Results and discussionThe integrated LCI collection framework spanned across 5 months and resulted in 87 new LCI datasets for confectionery products from raw material, primary resource use, emission and waste release data collected from suppliers across 19 countries. The data collected was found to be of medium to high quality compared with secondary data. However, for retailers and waste service companies, only partially completed questionnaires were returned. Some of the key challenges encountered during the collection and creation of data included lack of experience, identifying key actors, communication and technical language, commercial compromise, confidentiality protection and complexity of multi-tiered supplier systems. A range of recommendations are proposed to reconcile these challenges which include standardisation of environmental data from suppliers, concise and targeted LCI questionnaires and visualising complexity through drawings.ConclusionsThe integrated LCI data collection process and strategy has demonstrated the potential role of a multinational company to quickly engage and act as a strong enabler to unlock latent data for various aspects of the confectionery supply chain. Overall, it is recommended that the research findings serve as the foundations to transition towards a standardised procedure which can practically guide other multinational companies to considerably increase the availability of LCI data.

The Finnish metals industry and the environment

The demand for life cycle assessments (LCA) is growing rapidly, which leads to an increasing demand of life cycle inventory (LCI) data. While the LCA community has made significant progress in developing LCI databases for diverse applications, challenges still need to be addressed. This perspective summarizes the current data gaps, transparency, and uncertainty aspects of existing LCI databases. Additionally, we survey and discuss novel techniques for LCI data generation, dissemination, and validation. We propose key future directions for LCI development efforts to address these challenges, including leveraging scientific and technical advances such as the Internet of Things (IoT), machine learning, and blockchain/cloud platforms. Adopting these advanced technologies can significantly improve the quality and accessibility of LCI data, thereby facilitating more accurate and reliable LCA studies.

Circular utilization of urban tree waste contributes to the mitigation of climate change and eutrophication

Life cycle assessment (LCA) software packages have proliferated and evolved as LCA has developed and grown. There are now a multitude of LCA software packages that must be critically evaluated by users. Prior to conducting a comparative LCA study on different concrete materials, it is necessary to examine a variety of software packages for this specific purpose. The paper evaluates five LCA tools in the context of the LCA of seven concrete mix designs (conventional concrete, concrete with fly ash, slag, silica fume or limestone as cement replacement, recycled aggregate concrete, and photocatalytic concrete). Three key evaluation criteria required to assess the quality of analysis are adequate flexibility, sophistication and complexity of analysis, and usefulness of outputs. The quality of life cycle inventory (LCI) data included in each software package is also assessed for its reliability, completeness, and correlation to the scope of LCA of concrete products in Canada. A questionnaire is developed for evaluating LCA software packages and is applied to five LCA tools. The result is the selection of a software package for the specific context of LCA of concrete materials in Canada, which will be used to complete a full LCA study. The software package with the highest score is software package C (SP-C), with 44 out of a possible 48 points. Its main advantage is that it allows for the user to have a high level of control over the system being modeled and the calculation methods used. This comparative study highlights the importance of selecting a software package that is appropriate for a specific research project. The ability to accurately model the chosen functional unit and system boundary is an important selection criterion. This study demonstrates a method to enable a critical and rigorous comparison without excessive and redundant duplication of efforts.