Traffic noise in LCA

According to some recent studies, noise from road transport is estimated to cause human health effects of the same order of magnitude as the sum of all other emissions from the transport life cycle. Thus, ISO 14′040 implies that traffic noise effects should be considered in life cycle assessment (LCA) studies where transports might play an important role. So far, five methods for the inclusion of noise in LCA have been proposed. However, at present, none of them is implemented in any of the major life cycle inventory (LCI) databases and commonly used in LCA studies. The goal of the present paper is to define a requirement profile for a method to include traffic noise in LCA and to assess the compliance of the five existing methods with this profile. It concludes by identifying necessary cornerstones for a model for noise effects of generic road transports that meets all requirements. Requirements for a methodological framework for inclusion of traffic noise effects in LCA are derived from an analysis of how transports are included in 66 case studies published in International Journal of Life Cycle Assessment in 2006 and 2007, in the sustainability reports of ten Swiss companies, as well as on the basis of theoretical considerations. Then, the general compliance of the five existing methods for inclusion of noise in LCA with the postulated requirement profile is assessed. Six general requirements for a methodological framework for inclusion of traffic noise effects in LCA were identified. A method needs to be applicable for (1) both generic and specific transports, (2) different modes of transport, (3) different vehicles within one mode of transport, (4) transports in different geographic contexts, (5) different temporal contexts, and (6) last but not least, the method needs to be compatible with the ISO standards on LCA. One of the reviewed methods is not specific for transports at all and two are only applicable for specific transports. The other two allow generic and specific road transports to be assessed. The methods either deal with road traffic noise only or they compare noise from different sources, ignoring the fact that not only physical sound levels but also the source of sound determines the effect. Three methods only differentiate between vehicle classes (lorries and passenger cars) while one method differentiates between specific vehicles of the same class. Four of the methods consider the geographic context and three of them differentiate between day- and nighttime traffic. None of the existing methods for traffic noise integration in LCA complies with the proposed requirement profile. They either lack the genericness for a wide application or they lack the specificity needed for differentiations in LCA studies. There is no method available that allows for appropriate inter- or intramodal comparison of traffic noise effects. Thus, the benefit of the existing methods is limited. They can, in the better cases, only demonstrate the relative importance of road or rail traffic noise effects compared to the nonnoise-related effects of transportation. Currently, none of the major LCI databases includes traffic noise indicators. Thus, noise effects are usually not considered in LCA studies. We introduce a requirement profile for methods that allow the inclusion of noise in LCI. Due to the estimated significance of noise in transport LCA, this inclusion will change the overall results of many LCA studies. None of the existing methods fully complies with the requirement profile. Two of the methods can be modified and extended for inclusion in generic LCI databases. A third model allows for intermodal comparison. From an LCA perspective, all methods include weaknesses and need to be amended in order to make them widely usable. In part 2 of this paper, an in-depth analysis of the promising methods is provided, improvement potential is evaluated, and a new context-sensitive framework for the consistent LCI modeling of noise emissions from road transportation is presented. Appropriate methods for modeling rail and air traffic noise will have to be developed in the future in order to arrive at a methodological framework fully compliant with the requirement profile. Furthermore, future research is needed to identify appropriate methods for impact assessment.

Life Cycle Assessment (LCA) has been introduced to Thai industries in 1997 as one of the ISO 14000 series. The concept of LCA is being gradually accepted. However, there are few formal LCA studies in Thailand so far due to a limited number of LCA experts and a lack of sufficient databases relevant to domestic conditions. The LCA activities in Thailand can be divided into 3 areas, which are (1) Workshops and seminars (2) Use of LCA studies in Ecolabelling and (3) Life Cycle Inventory (LCI) and LCA studies. The first LCI study was to develop LCI data for Thailand Electricity Grid Mixes. There are a few LCA thesis studies in some universities, but these studies used databases from commercial software programs. The study and use of LCA may increase in the future only if domestic background database will be provided by research institutes and the government, and if industry understands LCA methodology through periodical workshops and seminars. INTRODUCTION Life Cycle Assessment has been introduced to Thai industries in 1997 as one of the ISO 14000 series. The concept of LCA is being gradually accepted. However, there are few formal LCA studies in Thailand so far due to a limited number of LCA experts and a lack of sufficient databases relevant to domestic conditions. ACTIVITIES The LCA activities in Thailand can be divided into 3 areas including (1) Workshops and seminars (2) Use of LCA studies in Ecolabelling and (3) Life Cycle Inventory (LCI) and LCA studies. 1. Workshop and Seminar To introduce the LCA concept to Thai Industries, the Thailand Environment Institute (TEI), in cooperation with many organizations, organized LCA seminars/workshops in Thailand annually between 1997-2002. All seminars successfully gained attention from Thai industry and educational institutes. The Thailand LCA Forum (http://doi.eng.cmu.ac.th/Thailca) has been launched by TEI in January of 2002. 2. Use of LCA studies in Ecolabelling The Green Label project was initiated in October 1993 by the Thailand Business Council for Sustainable Development (TBCSD) in association with the Ministry of Industry. This project is supported by the Secretariat, which is formed by a partnership between the Thai Industrial Standards Institute (TISI) and TEI. The objectives of the project are to establish the product criteria and award certification to specific products that are shown to have less impact on the environment, when compared with other products serving the same function (not including foods, drinks, and pharmaceuticals). The project came about from the idea that the green label can stimulate market choice thus encouraging producers to improve the environmental quality of their products and services in response to consumer demand. Award of the Thai Green label is based on the product criteria developed by a technical subcommittee. The subcommittee consists of representatives from the scientific, business and environmental groups and others if appropriate and available. A new subcommittee is established for each selected product category. At present, there are 29 product categories that are eligible for the Thai Green Label, and up to the end of November 2001, 227 individual products have received the Green Label award. Being aware of the high cost involved and time consumed in developing product criteria through format LCA, the Thai Green Label scheme has decided that a full quantitative LCA is not applicable for setting criteria for all products, especially in developing countries. The development of award criteria for the scheme has followed different methodologies. It will take into account not only significant environmental impact during the life cycle of the products (Life Cycle Consideration: LCC), but also capability to meet proposed criteria with reasonable process modification and/or improvement. The availability of testing institutes and the ability to perform tests are considered carefully, while setting the criteria. Results from existing LCA studies have been used as a scientific tool in the Thai Green Label Scheme for the development of environmental criteria for a few product categories. 3. Life Cycle Inventory (LCI) and LCA Studies

Life cycle inventory (LCI) regionalization (i.e., the determination of input and output flows from production processes at a subcountry scale) is a priority in life cycle assessment (LCA) studies, particularly in the agri-food sector. Many regionalized LCAs fail to ensure that microlevel inventories are consistent with country-level aggregated data-or "scale consistent." They also fail to construct LCIs using international reference guidelines and trustworthy standardized data sources. This failure generates inaccuracies and biases in inventories and can compromise comparability among international LCA studies. Our study introduces scale consistency as a principle for regionalized agri-food LCIs. We present a generic procedure that defines how scale-dependent LCI flows should be regionalized, depending on data availability. We then present a list of inventory flows that require regionalization and their suggested calculation procedures (methods and models) from 2 methodological guides developed by projects Agribalyse and World Food LCA Database. As proof of concept, we apply the procedure to Portugal and assess whether the methods and models proposed for each type of inventory flow in both guides can potentially be applied consistently with the data available. For 17 inventory flows, we apply calculated scale-consistent inventory flows for Portuguese agriculture, covering 260 products that can be used in future LCA studies. Comparing results with international databases, we show that this procedure can improve country-level estimates significantly. Our study is the first step in introducing scale consistency as a guiding principle for regionalized LCIs for agri-food LCA studies. Integr Environ Assess Manag 2017;13:939-951. © 2017 SETAC.

LCA studies require a high volume of data and their quality has a direct influence on the quality of the Life Cycle Inventory (LCI) and Life Cycle Assessment (LCA) study overall. The use of LCA databases enables users to (i) reduce time, efforts, and resources for data collection and (ii) reflect supply chains they have no direct control over. On the other side, it creates the need to align own modeling of the foreground LCA study with the modeling in the database. In recent years, countries worldwide have been more and more motivated in supporting LCA studies by providing national databases that reflect their economy, energy mix, and disposal technologies. This article aims to give insights on the main needs, requirements, and challenges for the creation of an LCA database, with a special focus on national, reference databases. First, the article defines the main characteristics of LCA datasets and discusses data collection approaches. Secondly, LCA databases are defined, and the creation of LCA databases from developed datasets is addressed, including the case of national LCA databases. Finally, the existence of tools that could ease the LCA dataset and database creation process is investigated, namely the LCA Collaboration Server and the LCA Data-Machine. It is important that countries willing to create a national database are supported, for example with capacity-building workshops, by actors with a long tradition in the field, which is of mutual benefit: Countries with a long tradition in LCA will benefit from interactions with newcomers, for instance by discussing together unsolved methodological and interoperability issues; newcomers do not need to start from scratch but can benefit from gained experiences. Creating databases that provide specific data for various parts of the world supports LCA methodology and application in general, and it is not the least a chance for local LCA communities to bring in innovation into LCA, and benefit from existing experiences at the same time.

Impacts of life cycle inventory databases on life cycle assessments: A review by means of a drivetrain case study

Digitalising food manufacturing

Life Cycle Inventory: An In-Depth Look at the Modeling, Data, and Available Tools

The Biofuels Industrial Strategy established by the government of South Africa in 2007 has emphasised the need to understand the potential benefits and consequences of developing biofuels in the country. Life cycle assessment (LCA) is a preferred tool to measure energy and environmental performances of biofuel production; however, LCA relies heavily on data and developing a life cycle inventory (LCI) plays a crucial role in any LCA studies. The lack of availability and difficulty in obtaining data from reliable sources can provide a great challenge during the process of developing biofuels LCI. This study develops a LCI database for the production of ethanol using sugarcane molasses in South Africa. The inventory includes all the input and output data associated with various sub-system of ethanol production. Relevant data are collected through national statistics and various literatures. The LCI has been developed as a first step towards the LCA studies which can assist in measuring the performance of South African ethanol development in the future.

LCA (Life Cycle Assessment) is a well known methodology to assess the impact on the environment over the life cycle of a product, process, or activity. This methodology is based on the LCI (Life Cycle Inventory) database, a data set of all resources (material and energy) that are consumed or emitted in order to produce 1 unit of the product. Because electricity is a basic infrastructure, a Thailand electricity grid LCI database is needed to assess the environmental impact not only for the product used in Thailand, but also for any product that is exported to other countries. A complete LCI database for the electricity grid in Thailand is not yet available, and the LCI database developed in this work applies from the fuel acquisition stage to the production stage. The analysis shows the unique characteristics of the Thailand electricity grid. An LCI database for each type of fuel and for each electricity generation system was developed. The characteristics of each type of fuel and electricity generation system are indicated in terms of Life Cycle GHG (Greenhouse Gas) emissions to reflect their global warming potential. Data on the Life Cycle GHG emission per kWh of electricity produced are also provided. The first Thailand LCI database for the fuels used in the electricity generation system was developed using data obtained from the EGAT (Electricity Generating Authority of Thailand), IPPs (Independent Power Producers), and PTT (Petroleum Authority of Thailand) during the Thai fiscal year 2005 (from October 2004 to September 2005). The database was used to analyze the current situation and the characteristics of the electricity generation system in Thailand and to compare it with the systems used in other developed countries.

The body of life cycle assessment (LCA) literature is vast and has grown over the last decade at a dauntingly rapid rate. Many LCAs have been published on the same or very similar technologies or products, in some cases leading to hundreds of publications. One result is the impression among decision makers that LCAs are inconclusive, owing to perceived and real variability in published estimates of life cycle impacts. Despite the extensive available literature and policy need formore conclusive assessments, only modest attempts have been made to synthesize previous research. A significant challenge to doing so are differences in characteristics of the considered technologies and inconsistencies in methodological choices (e.g., system boundaries, coproduct allocation, and impact assessment methods) among the studies that hamper easy comparisons and related decision support. An emerging trend is meta-analysis of a set of results from LCAs, which has the potential to clarify the impacts of a particular technology, process, product, or material and produce more robust and policy-relevant results. Meta-analysis in this context is defined here as an analysis of a set of published LCA results to estimate a single or multiple impacts for a single technology or a technology category, either in a statisticalmore » sense (e.g., following the practice in the biomedical sciences) or by quantitative adjustment of the underlying studies to make them more methodologically consistent. One example of the latter approach was published in Science by Farrell and colleagues (2006) clarifying the net energy and greenhouse gas (GHG) emissions of ethanol, in which adjustments included the addition of coproduct credit, the addition and subtraction of processes within the system boundary, and a reconciliation of differences in the definition of net energy metrics. Such adjustments therefore provide an even playing field on which all studies can be considered and at the same time specify the conditions of the playing field itself. Understanding the conditions under which a meta-analysis was conducted is important for proper interpretation of both the magnitude and variability in results. This special supplemental issue of the Journal of Industrial Ecology includes 12 high-quality metaanalyses and critical reviews of LCAs that advance understanding of the life cycle environmental impacts of different technologies, processes, products, and materials. Also published are three contributions on methodology and related discussions of the role of meta-analysis in LCA. The goal of this special supplemental issue is to contribute to the state of the science in LCA beyond the core practice of producing independent studies on specific products or technologies by highlighting the ability of meta-analysis of LCAs to advance understanding in areas of extensive existing literature. The inspiration for the issue came from a series of meta-analyses of life cycle GHG emissions from electricity generation technologies based on research from the LCA Harmonization Project of the National Renewable Energy Laboratory (NREL), a laboratory of the U.S. Department of Energy, which also provided financial support for this special supplemental issue. (See the editorial from this special supplemental issue [Lifset 2012], which introduces this supplemental issue and discusses the origins, funding, peer review, and other aspects.) The first article on reporting considerations for meta-analyses/critical reviews for LCA is from Heath and Mann (2012), who describe the methods used and experience gained in NREL's LCA Harmonization Project, which produced six of the studies in this special supplemental issue. Their harmonization approach adapts key features of systematic review to identify and screen published LCAs followed by a meta-analytical procedure to adjust published estimates to ones based on a consistent set of methods and assumptions to allow interstudy comparisons and conclusions to be made. In a second study on methods, Zumsteg and colleagues (2012) propose a checklist for a standardized technique to assist in conducting and reporting systematic reviews of LCAs, including meta-analysis, that is based on a framework used in evidence-based medicine. Widespread use of such a checklist would facilitate planning successful reviews, improve the ability to identify systematic reviews in literature searches, ease the ability to update content in future reviews, and allow more transparency of methods to ease peer review and more appropriately generalize findings. Finally, Zamagni and colleagues (2012) propose an approach, inspired by a meta-analysis, for categorizing main methodological topics, reconciling diverging methodological developments, and identifying future research directions in LCA. Their procedure involves the carrying out of a literature review on articles selected according to predefined criteria.« less

The Life Cycle Assessment (LCA) is an impact research methodology that focuses on the life cycle of a product (by extension, services), and is standardized by the ISO 14000 Series. This methodology has been applied in so many areas related to sustainable development, in order to evaluate the environmental, economic and social aspects of the processes of production and distribution of products and service goods. Despite this wide range of applications, the technique still presents weaknesses, especially in the question of the evaluation and expression of the uncertainties present in the various phases of the studies and inherent to the stochastic or subjective variations of the data sources and the generation of models, sometimes reducing the consistency and accuracy of the proposed results. In the present study, we will evaluate a methodology to deal with the best expression of such uncertainties in LCA studies, focusing on the Life Cycle Inventory (LCI) phase. The hypothesis explored is that the application of the Monte Carlo Simulation and Fuzzy Set Theory to the estimation and analysis of stochastic uncertainties in LCA allows a better expression of the level of uncertainty in terms of the Guide to Expression of Uncertainty in Measurements [11], in situations where the original life cycle inventory does not specify the initial uncertainties. The iron ore transport was selected as a process unit by means of an off-road- truck (OHT) with a load capacity of 220 tons and a power of 1700 kW, acting on the route between the mine and the primary crushing of a mining company, in the city of Congonhas (MG). Monte Carlo simulations and Fuzzy Set Theory applications were performed using spreadsheets (MS Excel). The LCA study was conducted in OpenLCA 1.6 (open source) software from data inventories of ELCD database 3.2, also freely accessible. The results obtained were statistically compared using Hypothesis Test and Variance Analysis to identify the effect of the techniques on the results of the Life Cycle Impact Assessment (LCIA) and a Sensitivity Analysis was performed to test the effect of the treatment and function of the distribution of probabilities in the expression of the parameters associated with the items of the original life cycle inventory. Research indicates that inventories with treated data may have their uncertainty expressed to a lesser degree than that expressed in the original inventory, with no change in the final values of the Life Cycle Impact Assessment (LCIA). The treatment of life cycle inventory data through Monte Carlo Simulation and Fuzzy Set Theory resulted in the possibility of expressing the LCI results with a degree of uncertainty lower than that used to express the uncertainty under the standards. Data treatment through Monte Carlo simulation with normal probability distribution showed the lowest values of uncertainty expression with significant difference in relation to the original inventory, at a significance level of 1%.

Chapter 11 - Life cycle assessment of biomethane technology

Life cycle inventory (LCI) data gathering is one of the most time-consuming and data-intensive tasks in life cycle assessment (LCA) studies. Until now, it has mainly been described at a theoretical level and is associated with non-specific guidelines in LCA reference documents, thus leaving LCA practitioners with limited guidance prone to inefficient, not transparent, and sometimes inconsistent steps taken in the LCI phase. To tackle this problem, a stepwise data collection guidance is developed based on a critical review of existing guidelines and LCA case studies published in peer-reviewed scientific journals. The guidance encompasses three steps: (i) planning of data collection, including the outlining of LCI blocks, which represent unit processes in an aggregation level that is technology-wise appropriate; (ii) data gathering using the LCI blocks; and (iii) LCI blocks finalization. A customizable, generic LCI template for data collection is provided to facilitate the conduct of steps (i)-(iii). A real-life data collection case of lithium-ion batteries manufacturing is used to demonstrate the guidance's applicability. LCI block datasets were produced using the customized LCI template and the proposed guidance. Cooperation with the case data provider thus enabled to fill the data collection template, which was then reviewed iteratively. Although this case evidences the guidance's operability and its benefits in facilitating and strengthening LCA practice, several constraints undermining reproducibility and transparency could be identified, in particular issues related to data confidentiality. Despite these limitations, which require further efforts from practitioners and more transparency from stakeholders involved in the commission of LCA studies, the authors call for applying the proposed stepwise guidance to enhance future LCA practice.

In life cycle assessment (LCA), performing Monte Carlo simulation (MCS) using fully dependent sampling typically involves repeated inversion of a technology matrix for a sufficiently large number of times. As the dimension of technology matrices for life cycle inventory (LCI) databases grows, MCS using fully dependent sampling is becoming a computational challenge. In our previous work, we pre-calculated the distribution functions of the entire LCI flows in the ecoinvent ver. 3.1 database to help reduce the computation time of running fully dependent sampling by individual LCA practitioners. However, it remains as a question whether the additional errors due to the use of pre-calculated uncertainty values are large enough to alter the conclusion of a comparative study, and, if so, what is the odds of such cases. In this study, we empirically tested the probability of altering the conclusion of a comparative LCA due to the use of pre-calculated uncertainty values. We sampled 10,000 random pairs of elementary flows of ecoinvent LCIs (ai and bi) and ran MCSs (1) using pre-calculated uncertainty values and (2) using fully dependent sampling. We analyzed the distribution of the differences between ai and bi (i.e., ai−bi) of each run, and quantified the probability of reversing (e.g., ai > bi became ai < bi) or moderating the conclusion (e.g., ai > bi became ai ≈ bi). In order to better replicate the situation under a comparative LCA setting, we also sampled 10,000 random pairs of elementary flows from the processes that produce electricity, and repeated the same procedure. The results show that no LCIs derived using pre-calculated uncertainty values constitute large enough differences from those using fully dependent sampling to reverse the conclusion. However, in 5.3% of the cases, the conclusion from one approach is moderated under the other approach or vice versa. When elementary flow pairs are sampled only from the electricity-producing processes, the probability of moderating the conclusions increases to 10.5%, while that of reversing the conclusions remains nil. As the number of unit processes in LCI databases increases, running full MCSs in a PC-environment will continue to be a challenge, which may lead some LCA practitioners to avoid uncertainty analysis altogether. Our results indicate that pre-calculated distributions for LCIs can be used as a proxy for comparative LCA studies in the absence of adequate computational resources for full MCS. Depending on the goal and scope of the study, LCA practitioners should consider using pre-calculated distributions if the benefits of doing so outweighs the associated risks of altering the conclusion.

Advances in Life Cycle Assessment (LCA) toward greater automation and methodological integration have intensified challenges in standardizing heterogeneous raw Life Cycle Inventory (LCI) data, which rarely aligns with LCI database nomenclature. Rule-based mapping approaches struggle with linguistic variations, typographical errors, unit inconsistencies, and location granularity mismatches. Furthermore, they fail to adapt automatically when data or terminology change. FAULDIER (Framework for lArge langUage modeL assisteD lIfe cyclE inventoRy) is proposed as a framework to bridge heterogeneities between raw LCI data and LCI database requirements. It aims to automate data transformation by resolving naming inconsistencies, classifying flow types, and harmonizing locations and units. By using LLMs, FAULDIER supports handling multilingual inputs, correcting typographical errors, resolving location granularity mismatches, and choosing proxies for missing processes. In a test scenario using the open LCI database FORWAST and a use case characterized by non-standardized multilingual entries, unit inconsistencies, and typographical errors, FAULDIER achieved approximately 57% process and elementary flow mapping accuracy (single-expert validated), with unit conversion error rates below 1%. Current limitations include LCI database constraints, LLM token limitations, performance variability of open-weight LLMs, mapping ability, and reproducibility across runs. Within these limitations, FAULDIER indicates the feasibility of LLM-assisted LCI construction for LCA modeling, particularly for non-standardized raw LCI data. Future work could focus on developing confidence metrics for mapped LCI data, optimizing LLM query efficiency, and expanding testing across additional LCI databases, use cases, and LLMs.