Dynamic Simulation Approaches to Consequential Life Cycle Assessment to Evaluate Recycling and Substitution in Metal and Paper-Derived Products

Life cycle assessment is a tool for modeling the environmental impact of products, processes and services. Consequential life cycle assessment (cLCA) focuses on how changes, resulting from a stakeholder decision, impact the market of study as well as other markets. Current modeling approaches are either too restricted in scope or too conceptually challenging, limiting their applicability to a small subset of systems. The aims of this project are to develop the general methodology and develop the qualitative aspect of cLCA with a focus on enhanced stakeholder interaction. The methodology will be applied to the paper/paperboard and aluminum systems with a focus on how the presence of separate markets for primary and recycled material, as well as substitute materials, influences policy.

This research will provide a general framework to expand the number of cases to which cLCA can be effectively applied. Building upon the research team's previous work evaluating materials markets and land use impacts, this will be accomplish by: 1) quantifying the economic and response characteristics of supply through a combination of empirical analysis, econometrics, and material flow modeling 2) quantifying the economic and response characteristics of demand through quantitative description of the utility function and decision processes of key sectors, and 3) demonstrating a simulation of supply-demand interaction coupled with parametric life cycle inventories. This project will develop a combined environmental, physical, and economic model in an endogenous feedback simulation platform. The recycling policy arena consists of a diverse set of stakeholders and this work will investigate how effective use of communication tools, such as material flow diagrams, can make policy models both more transparent and more interactive. The authors hypothesize that strong intellectual contributions can be forged in melding methodological developments across the set of fields that model consequences of change in complex materials systems, including materials availability assessment and LCA. Policy measures such as subsidies and taxes are often used to promote materials recycling or substitution to materials with perceived improved environmental performance. The problem faced by decision makers is how to direct policy to have the greatest positive impact given limited legislative resources. Current analysis tools evaluate the direct impact of recycling locally, i.e. for a specific material, product and region. The methods described herein will inform industry and policy makers seeking to evaluate the impact globally, i.e. maximize (and optimize) the benefits of recycling over many materials, products, and regions. For example, this would be useful for policy makers at the federal or state level wanting to understand the net impact of a policy and the new baseline following change. While cLCA is currently used to inform the US policy for biofuels, it may be extendible to inform legislation towards improved materials sustainability. The cLCA approach has the potential to answer questions such as: What would future scenarios look like with and without proposed policies in materials recycling and substitution? Which products should be recycled, landfilled, and used to produce energy? What are the specific implications of policy for other parts of the economy? In the case of the paper system, stakeholders have expressed a strong need for a tool that is supplemental to the ones they currently use to assess environmental impact of recycled paper (current tools are based on attributional LCA). Excitement has been particularly strong for the possibility to communicate with a broad set of stakeholders from the initial stages of model development and all the way to the results. It is therefore expect that our final model will find immediate use as decision-support tool for environmental policy in the pulp and paper industry.