ECOPT 2 : An adaptable life cycle assessment model for the environmentally constrained optimization of prospective technology transitions

Life cycle assessment (LCA) is a method to evaluate the environmental impacts of technologies from cradle to grave. However, LCAs are commonly defined in terms of the consumption of a single unit of a product and thus ignore scaling issues in large-scale deployment of technologies. Such product-level LCAs often do not consider capital manufacturing capacity and supply chain bottlenecks that may hinder the rapid, widespread uptake of emerging technologies entering the market; emerging technologies often require the expansion of existing supply chains or the development of entirely new supply chains, such as the manufacturing of novel materials. As a result, such LCA studies are limited in their ability to realistically assess impacts at the macro-scale and thus to guide large-scale decisions. In this work, we present ECOPT2, a generalized adaptable model that combines these constraints to the LCA approach using a mathematical programming approach and dynamic stock modeling. ECOPT2 combines LCA factors with transition scenarios from energy systems models to determine the environmentally optimal deployment of new technologies while accounting for material circularity constraints and barriers to uptake. We also introduce the structure of the software tool and demonstrate its features using a stylized vehicle electrification scenario. © 2022 The Authors. Journal of Industrial Ecology published by Wiley Periodicals LLC on behalf of International Society for Industrial Ecology

Lifting industrial ecology modeling to a new level of quality and transparency: a call for more transparent publications and a collaborative open source software framework

Industrial ecology (IE) is a maturing scientific discipline. The field is becoming more data and computation intensive, which requires IE researchers to develop scientific software to tackle novel research questions. We review the current state of software programming and use in our field and find challenges regarding transparency, reproducibility, reusability, and ease of collaboration. Our response to that problem is fourfold: First, we propose how existing general principles for the development of good scientific software could be implemented in IE and related fields. Second, we argue that collaborating on open source software could make IE research more productive and increase its quality, and we present guidelines for the development and distribution of such software. Third, we call for stricter requirements regarding general access to the source code used to produce research results and scientific claims published in the IE literature. Fourth, we describe a set of open source modules for standard IE modeling tasks that represent our first attempt at turning our recommendations into practice. We introduce a Python toolbox for IE that includes the life cycle assessment (LCA) framework Brightway2, the ecospold2matrix module that parses unallocated data in ecospold format, the pySUT and pymrio modules for building and analyzing multiregion input-output models and supply and use tables, and the dynamic_stock_model class for dynamic stock modeling. Widespread use of open access software can, at the same time, increase quality, transparency, and reproducibility of IE research.FWN – Publicaties zonder aanstelling Universiteit Leide

The ecoinvent database version 3 (part II): analyzing LCA results and comparison to version 2

Industrial Ecolog

What future for primary aluminium production in a decarbonizing economy?

Aluminium is an energy intensive material with an environmental footprint strongly dependent on the electricity mix consumed by the smelting process. This study models prospective environmental impacts of primary aluminium production according to different integrated assessment modeling scenarios building on Shared Socioeconomic Pathways and their climate change mitigation scenarios. Results project a global average carbon intensity ranging between 8.6 and 18.0 kg CO2 eq/kg in 2100, compared to 18.3 kg CO2 eq/kg at present, that could be further reduced under mitigation scenarios. Co-benefits with other environmental indicators are observed. Scaling aluminium production impacts to the global demand shows total emission between 1250 and 1590 Gt CO2 eq for baseline scenarios by 2050 while absolute decoupling is only achievable with stringent climate policy changing drastically the electricity mix. Achieving larger emission reductions will require circular strategies that go beyond primary material production itself and involve other stakeholders along the aluminium value chain

When do allocations and constructs respect material, energy, financial, and production balances in LCA and EEIO?

Conservation of mass and energy are essential to physical accounting, just as price and market balances are essential to economic accounting. These principles guide data collection and inventory compilation in industrial ecology. The resulting balanced surveys, however, can rarely be used directly for life cycle assessment (LCA) or environmentally extended input‐output (EEIO) analysis; some modeling is necessary to recast coproductions by multifunctional activities as monofunctional unit processes (a.k.a. Leontief production functions or technical “recipes”). This modeling is done with allocations in LCA and constructs in input‐output. In this article, we ask how these models respect or perturb the balances of the original inventory. Which allocations or constructs, applied to what type of data set, have the potential to simultaneously respect its multiple physical, financial, and market balances? Our analysis builds upon the recent harmonization of allocations and constructs and the ongoing development of multilayered supply and use inventory tables. We derive the necessary and sufficient conditions for balanced models, investigate the role of data aggregation, and clarify these models' relation to system expansion. We find that none of the modeling families in LCA and EEIO are balanced in general, but special data characteristics can allow for the respect of multiple balances. An analysis of these special cases allows for clear guidance for data compilation and methods integration