Life Cycle Assessment (LCA) seeks to quantify the environmental impacts of product systems and services from “cradle-to-grave”, or from raw material extraction through the end-of-life. The ideal outcome of this exercise is the identification of actions that can be taken by firms and policymakers to reduce global environmental damage. LCA is quite young relative to the classical academic disciplines, and faces significant challenges in establishing its relevance for decision-making. Mainstream LCA practice seeks to account for environmental damage using a class of frameworks termed Attributional LCA (ALCA). This typically involves the use of normative, technology-focused rules to allocate inputs, outputs and emissions over product systems that interact with each other. The application of such rules can sever cause-effect relationships that strongly influence the environmental consequences of changes to industrial systems. This thesis develops and demonstrates new methodologies pertaining to Consequential LCA (CLCA), which has not been standardized and fully adopted in mainstream practice. In CLCA, I seek to assess the net environmental outcomes of decisions, rather than attribute environmental impacts using a set of normative rules. This leads to an inevitable focus on social dynamics and causal inference, which are scarcely addressed in the LCA field.
The first chapter is an extensive literature review on the history and current state of methods for characterizing the environmental consequences of actions in LCA. I first discuss the major existing differences between ALCA and CLCA in the literature. Then, I provide a detailed review of methods that have been proposed to evolve the structure of CLCA models towards a robust representation of cause-effect relationships. I recommend the use of an iterative framework between structural CLCA models and causal inference analysis, a class of methods largely absent from the LCA literature. The remainder of my dissertation applies this iterative framework and focuses on the integration of LCA with the modelling and quantification of social mechanisms. In Chapter 2, I build a CLCA model of automotive material substitution including parameterized market forces that drive the environmental impacts of changes in scrap generation and recycling activity. I show that market forces contribute significantly to uncertainty in modelling the greenhouse gas consequences of automotive material substitution using local and global sensitivity analysis. I also find that in 16% of trials of a Monte Carlo simulation, substituting aluminum for steel in a fleet of vehicles does not constitute a net decrease in greenhouse gas emissions. This finding contrasts with previous studies on the topic, and is influenced by the incorporation of market forces into the model. Chapter 3 explores the environmental consequences of recycling as an example of these market forces in greater depth. I generalize this concept as a question of the cause-effect relationship between recycling and production of materials from primary resources. For the first time in the industrial ecology literature, I propose the use of difference-in-differences (DID), a quasi-experimental statistical method that classifies observational data into treatment and control groups, to test hypotheses about this key relationship. I simulate the application of the DID estimator to the question of whether or not increases in the use of recycled aluminum in the automotive industry would lead to an equivalent reduction in the use of primary aluminum. Finally, in Chapter 4, I exploit the fact that water is used, recycled, and reused in localized units to create treatment and control groups of recycled water users. I design an empirical DID study that explores the question of whether or not increases in wastewater recycling lead to equivalent reductions in potable water usage. I find that in a large urban water district in California, the wastewater recycling program has displaced over 25 million cubic feet of potable water production with a displacement rate of 93.4%. Chapter 4 is the first empirical application of quasi-experimental methods to quantifying the relationship between recycling and primary production, and the first attempt to test hypotheses regarding the potable water savings achieved from wastewater recycling.