Considering the emission-intensive materials consumption and their associated environmental impacts, the building industry is a priority sector for reducing its environmental impacts and meeting greenhouse gas (GHG) emissions targets globally. The embodied carbon of building structures, substructures, and enclosures is responsible for 11% of global GHG emissions and 28% of global building sector emissions. Thus, various strategies are gradually adopted to reduce the emissions from buildings, including the early adoption of lifecycle assessment (LCA) tool during the design stage (new construction) to select the less emissive materials, sustainable strategies during the retrofitting process, and demolition/ deconstruction process including the sustainable materials management at the end-of-life of buildings. Numerous efforts have been devoted to developing such tools for conducting building LCA. This study aimed to critically analyze the existing tools to understand their implications and adoption by users through pre-defined criteria such as geographic coverage, construction applications, targeted users, distinct features and specificity, adopted databases and methods, etc. Two groups of tools dedicated to whole building LCA (e.g., TallyLCA, Athena, One Click, etc.), and building materials/ elements (e.g., EC3, TallyCAT, WoodWorks Carbon Calculator, BEAM Estimator, EPIC, BEES) were selected, and their adoption in the existing literature was explored. The study found that most of the tools are regional with specific applications though some of them are adopted globally. To enhance the accuracy of the assessment, local/regional tools with databases are preferred in the scientific community. Some of the tools are based on carbon emission only, where the Environmental Product Declaration (EPD) was used as a database. The use of such tools (as standalone) may be inappropriate for whole building analysis due to limited impact indicators and also a lack of transportation of materials modeling. Though some of the tools adopted waste management (and recycling partially), most of them failed to model the end-of-life of buildings comprehensively, particularly recycling and reuse based on the circular economy (CE) principle. Therefore, a methodological framework is proposed to strengthen the existing ones for developing adaptive building LCA tools for promoting CE, particularly for whole building LCA. The outcomes of this study would facilitate the users to select the most suitable tool for their specific applications comprehensively and accurately while promoting CE adoption in the industry.

The development of timber-based structural materials has the potential to reduce the life-cycle greenhouse gas (GHG) footprint of buildings and even store carbon on a multi-decade timeframe. However, new bio-based materials, such as improved structural composite lumber, often have limited data available for conducting robust life cycle assessment (LCAs). This limitation can affect the ability to conduct early stages of evaluating these potential materials and building designs. In this study, a heuristic decision tree was developed with the goal of providing users a means of estimating life cycle inventory (LCI) parameters of their desired timber-based structural material, even if there is a scarcity of data. Three different sources of timber will also be compared in terms of GHG emissions and energy requirements, while highlighting the effect of steps taken in harvesting, sawmill, and final product formation on GHG emissions. The goal of this work is to provide a simplified decision-making tool that can be used to quickly assess the likely GHG footprint of timber-based structural materials in the absence of detailed material- and location-specific data. This in turn can guide users in designing novel biogenic-based structural materials that can minimize GHG emissions.

Decarbonization technologies are a solution to reducing greenhouse gas emissions and mitigating climate change. However, the decarbonization transition will undoubtedly be mineral-intensive with critical minerals, like cobalt, lithium, and nickel, serving as the backbone of many decarbonization technologies. Mining is the primary acquisition method for critical minerals but wields many environmental and social effects – both positive (i.e. job opportunities and boosting the local economy1) and negative (i.e. land degradation2, reduced water quality3, and community displacement2). Life cycle analysis (LCA) is a tool that can assess effects and sustainability. However, there are significant gaps and a lack of structure in how LCAs of mineral mining are completed (e.g., inappropriate data sources, inconsistent system boundaries, indicators, and functional units). Overall, there is no standardized framework for critical mineral mining LCAs, which complicates comparisons, decision-making, and policy design.

We propose a critical mineral mining LCA framework using the standard four life-cycle phases.4 We conduct an LCA of a proposed copper-nickel mine in Minnesota, the first non-ferrous mine in the state, to guide development and application of the framework.

Several aspects of this LCA demonstrate the framework. For example, we employ two functional units, one ton of mineral equivalent and one year of mining operation. We define the system boundary to encompass mining operations and reclamation. We used foreground system processing and site information from legal documents as an appropriate, open data source. We use Greenhouse Gases, Regulated Emissions, and Energy Use in Technologies (GREET) Model5, and literature for background system inventory data.

We calculated energy use, water use, and greenhouse emissions of the proposed mine. Mineral refining processes (beneficiation and hydrometallurgical) and wastewater treatment account for most of the energy and water consumption, representing 81% and 94% of the total energy and water burdens, respectively. However, refining and wastewater treatment processes only comprise 52% of the greenhouse gas (GHG) emissions. Land clearing and blasting contribute 40% of GHG emissions. Land clearing will release 6.7 billion kg of CO2 into the atmosphere, 35% of total GHGs.

Carrying out this LCA using publicly available information identified challenges and gaps in completing mining LCAs that must be addressed using new, local, and timely data sources. For example, water pollutant emissions and biodiversity changes are prominent mining environmental effects. We describe an approach to improving mining LCA coverage of these important effects as part of developing a standard LCA framework for critical minerals mining. The framework will enable better comparisons, decision-making, and policy design. The standardized framework will offer a template for critical mineral mining LCAs conducted by other researchers and allow comparison of mines and LCA results.

References:

1. Hosseinpour, M., et al. Evaluation of positive and negative impacts of mining on sustainable... (2022).

2. Wilson, S. A., et al. Livelihood impacts of iron ore mining-induced... (2022).

3. Uugwanga, M. N. & Kgabi, N. A. Heavy metal pollution index of surface and groundwater… (2021).

4. International Standards Organization. ISO 14040:2006 (2006).

5. Argonne National Laboratory. (2021).

To alter the way society uses and manufactures polymers, polymer scientists must screen their sustainable polymer design ideas at early stages of research. Taking this step will enable them to evaluate tradeoffs among feedstock choice, reaction engineering strategies, and possible end-of-life fates. However, a comprehensive whole-life cycle screening tool for sustainble polymer design is notably absent from the literature. In response to this challenge, we have developed a tool to support bench scale polymer scientists in the design of polymers with sustainablity advantages.

The framework of our polymer screening tool encompasses feedstock production, solvent production, conversion, and end-of-life polymer recycling. We included 13 types of feedstocks, 23 classical solvents, 15 recycling techniques for the most common polymers, and 22 plastic resins. We extracted open-source data from the Greenhouse Gases, Regulated Emissions, and Energy Use in Technologies (GREET) model, Environmental Footprint (EF) database, journal articles, and other publicly available literature, and assembled them in the tool for ease of use. Users have the freedom to choose their own system boundary and design polymer production pathways using various feedstocks, solvents, yield, conversion processes, and end-of-life recycling methods. They can explore these choices on energy and water consumption and greenhouse gas emissions of their envisioned polymer.

To demonstrate the features and utility of the tool, we have piloted its use for two early-stage polymer research projects. We considered the extent to which bench scale scientists can make design choices using the tool and the level of information they have about their polymer.

By providing an open-source platform that leverages existing data and models, this tool seeks to foster collaboration and innovation, promoting a more sustainable future for the polymer industry.

Anticipatory Life Cycle Assessment (LCA) was developed to evaluate the environmental impacts of emerging technology1. Inclusive of uncertainty, which can be used to assess both reasonable and extreme cases, modeling advances including dynamic and thermodynamic modeling, and stochastic decision support, anticipatory LCA has the potential to inform designers early in the design process while the cost of change is low. Until now, however, the ability to apply anticipatory LCA has been limited to LCA expert practitioners using the most advanced LCA tools.

Several modeling and assessment techniques have been developed over the last 10 years which have the potential to bring much of anticipatory LCA capability to non LCA-expert audiences. Underspecification is a strategy for streamlining the life cycle inventory data gathering process, enabling designers to choose from an array of inventory instead of having to identify an exact proxy. Since it was first explored by MIT researchers over 10 years ago1, it has been applied primarily in the design of buildings, allowing architects to use LCA to guide major design choices.

The significant computational addition to anticipatory LCA enabled by underspecification is further enhanced by the use of SMAA (stochastic multi-attribute analysis)2. SMAA takes a stochastic approach to determining which impacts are the most important and provides the probability that each design has the least life cycle environmental impact. This is an improvement over single score systems, which rely on weights that represent the values of a very small group of stakeholders.

In 2020, EarthShift Global joined sustainable design researcher Tejaswini Chatty (then a PhD student at Dartmouth College) and the design firm Synapse to begin designing an LCA tool which uses underspecification and SMAA to enable anticipatory LCA for non-expert audiences. The tool is designed around the needs and behavioral patterns of product designers. It enables the use of LCA much earlier in the design process and speed up data collection and modeling3.

By combining underspecification, SMAA, and a subjective, percentage-based uncertainty assignment for primary data, this tool enables many of the principles of anticipatory LCA very early in the design cycle, encouraging environmentally-conscious innovation at minimal cost to the manufacturer.

1 Wender et al., “Anticipatory Life-Cycle Assessment for Responsible Research and Innovation.”

2 Prado and Heijungs, “Implementation of Stochastic Multi Attribute Analysis (SMAA) in Comparative Environmental Assessments.”

3 Chatty, “Enabling the Integration of Sustainable Design Methodological Frameworks and Computational Life Cycle Assessment Tools into Product Development Practice.”