# Glossary This glossary aims to clarify the terminology used in the method section of the present documentation, as well as the articles related to _mescal_. ## LCA | Term | Synonyms | Meaning | |---------------------------|---------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | LCI dataset | Activity dataset, process dataset | _An ecoinvent activity dataset represents a unit process of a human activity and its exchanges with the environment and with other human activities. There are several activity types in the ecoinvent v3 database; transforming activities, treatment activities, market activity, import and export activities, production and supply mixes, etc._ (ecoinvent glossary) | | LCI database | | Set of LCI datasets interconnected by intermediary flows. For example, ecoinvent is an LCI database (Wernet et al., 2016). | | Intermediary flow | Economic flow, technosphere flow, intermediate exchange | _An exchange between two activities that stays within the technosphere and is not emitted to or taken from the environment._ (ecoinvent glossary) | | Elementary flow | Elementary exchange | _Exchange with the natural, social or economic environment. Examples: Unprocessed inputs from nature, emissions to air, water and soil, physical impacts, working hours under specified conditions._ (ecoinvent glossary) | | Technosphere | | _Represents all human activities. An exchange of a certain activity can be between the activity and the environment (elementary exchange, for example CO2 emission to air) or between two activities (intermediate exchange, for example wastewater to be released from one activity to another—treatment of wastewater)._ (ecoinvent glossary) | | Ecosphere | Biosphere | Represents the environment: it is the sink for all emissions resulting from human activities, while being the source of all raw materials utilized in the technosphere. | | Foreground system | | _The foreground system is defined as those processes of the system that are specific to it_ (Benini et al., 2014). In the present work, we contextualize the previous definition as follows: the foreground system is defined as the set of energy technologies and resources in the ESM, i.e., processes of the energy system of the location of interest. | | Background system | | _The background system comprises those processes that are not under the direct control or decisive influence of the producer of the good_ (Benini et al., 2014). In the present work, we contextualize the previous definition as follows: the background system represents other processes, i.e., which are not part of the energy system and/or which are outside of the ESM geographical scope. | | Market-type dataset | Market activity | _A market activity transfers a product or service (intermediate exchange) from one or more transforming activities that produce it, to the transforming activities that consume it. [...] A market activity therefore provides the average consumption mix of a product for a given region, and the marginal consumption mix in the case of system models that use marginal suppliers._ (ecoinvent glossary) | | Inventory spatialization | | _Attribution of a geographic location to an EF, which is inherited from the process it stems from. This geographic location is necessary when using regionalized CFs. Different types of geographic information may be used (specific geographical coordinates, administrative region, archetypes, etc.) and eventually match the native spatial resolution of the impact method._ (Patouillard et al., 2018) | | Inventory regionalization | | _Improvement of the geographic representativeness of inventory data (type and quantity of economic flows and EFs) to be more representative of specific geographic areas in the product life cycle._ (Patouillard et al., 2018) | | Impact regionalization | | _Used to calculate regionalized CFs to assess spatialized EFs representative of specific geographic areas. LCIA method developers determine the optimal spatial scale, called the native resolution, for a given impact category by considering the spatial variability of the LCIA model parameters and most influential mechanisms. The native resolution may be defined using geographic differentiation or archetypes._ (Patouillard et al., 2018) | | Impact category | | _Class representing environmental issue of concern (ISO 14040). E.g. Climate change, Acidification, Ecotoxicity etc._ (Benini et al., 2014) | | Characterization factor | | _Factor derived from a characterisation model which is applied to convert an assigned life cycle inventory analysis result to the common unit of the impact category indicator (ISO 14040)_. (Benini et al., 2014) | | LCA indicator | LCIA indicator, impact category indicator, LCA metric | _Quantifiable representation of an impact category (ISO 14040). E.g. kg CO2-equivalents for climate change._ (Benini et al., 2014) | | LCA impact score | LCA score, LCIA score | _One LCIA score is one LCIA indicator for one dataset, for example IPCC2013 Global Warming Potential (GWP) 100a for “mango production, BR.” If you consider IPCC2013 GWP100a and EF3.0 land use (soil quality index) for “mango production, BR,” this counts as two indicators for one dataset, i.e., two LCIA scores._ (ecoinvent glossary) | ## ESM | Term | Synonyms | Meaning | |----------------------------|-----------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Technology | | Energy conversion technology, e.g., coal power plant, onshore wind turbine, etc. | | Resource | | Energy resource (imported or extracted), e.g., coal, uranium, wood, electricity, etc. | | Vector | Flow, carrier | Inputs and outputs of energy conversion technologies, e.g., electricity, natural gas, hydrogen, etc | | Ex-post analysis | | _These take output from the energy optimization to perform the LCA for specific technologies, specific sectors or a global level, but do so to assess the environmental impact and lack the interaction with other parts of the energy system since there is no feedback to the results._ (Blanco et al., 2020) | | Soft-linking | | _Soft-linked models involve separate sub-models, mainly combined by applying post-calculation, thus exchanging data, allowing flexibility and easier adjustments. They operate independently but share information through sequential or iterative processes._ (Schnidrig et al., 2024) | | Hard-linking | | _Hard-linked models integrate sub-models into a single, unified framework. This setup ensures dynamic interactions and consistent data treatment, suitable for detailed simulations of complex systems, thus allowing the integration of the features of both models within a common framework._ (Schnidrig et al., 2024) | | Snapshot model | Static model | _Snapshot models are used to evaluate the energy system configuration and operation over a timespan. “Energy system configuration” refers to the key characteristics of a national energy system, i.e. mix of technologies for electricity and heat supply, building stock, among others. The configuration of the energy system remains unchanged over the considered time span._ (Codina Girones et al., 2015) | | Pathway model | Evolution model, transition model | _Evolution energy models analyse the evolution of a national energy system over a time horizon. The time horizon extends from the initial year to the end year and is broken down into a series of multiple-year or single-year periods. Each period is in turn subdivided into time-slices. Time-slices represent time intervals with similar conditions (i.e. weekends in winter, Monday mornings in summer, etc), with the purpose of better capturing seasonal, weekly or daily variations in energy supply and demand._ (Codina Girones et al, 2015) | | Perfect-foresight approach | | _Perfect-foresight approach is based on the assumption that the decision-makers have complete knowledge on the whole transition. Thus, they have full information on cost trends, consumption variation, decay of performance of certain technologies, future decommissioning of power plants, future improvement of the efficiencies of certain technologies, etc. This approach is realized through the formulation of a unique optimization problem analyzing all the time-periods simultaneously. These models can also be classified as intertemporal models._ (Prina et al., 2020) | | Myopic approach | | _The myopic approach is instead characterized by the time-horizon divided into a sequence of optimization problems where the output of the prior serves as input for the following. For this reason, these models can also be called recursive. The decider has not thus a complete information on the whole horizon, leading to decisions in a certain step sub-optimal with regards of what happens in the following steps. This last approach is more realistic as in reality the decisions are taken without a complete information about the future changes. However, the decider could be misguided in the first time-steps due to its limited knowledge and may not be capable to repair the early wrong decisions in the following steps._ (Prina et al., 2020) | ## References Benini, L., Sala, S., Manfredi, S., & Góralczyk, M. (2014). Indicators and targets for the reduction of the environmental impact of EU consumption: Overall environmental impact (resource) indicators-Deliverable 3. European Commission. Blanco, H., Codina, V., Laurent, A., Nijs, W., Maréchal, F., & Faaij, A. (2020). Life cycle assessment integration into energy system models: An application for Power-to-Methane in the EU. Applied Energy, 259, 114160. https://doi.org/10.1016/j.apenergy.2019.114160 Codina Gironès, V., Moret, S., Maréchal, F., & Favrat, D. (2015). Strategic energy planning for large-scale energy systems: A modelling framework to aid decision-making. Energy, 90, 173–186. https://doi.org/10.1016/j.energy.2015.06.008 ecoinvent. (n.d.). Ecoinvent glossary. Retrieved March 25, 2025, from https://support.ecoinvent.org/glossary ISO. (2006). ISO 14040—International organization for standardization. Environmental management: Life cycle assessment-principles and framework. Patouillard, L., Bulle, C., Querleu, C., Maxime, D., Osset, P., & Margni, M. (2018). Critical review and practical recommendations to integrate the spatial dimension into life cycle assessment. Journal of Cleaner Production, 177, 398–412. https://doi.org/10.1016/j.jclepro.2017.12.192 Prina, M. G., Manzolini, G., Moser, D., Nastasi, B., & Sparber, W. (2020). Classification and challenges of bottom-up energy system models—A review. 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