Causal Inference Strategies for Quantifying Displaced Production from Recycling
Joseph Palazzo
University of California, Santa Barbara
Joe holds a Master's degree in physics from Rensselaer Polytechnic Institute and spent over three years working as a semiconductor engineering professional before becoming an Industrial Ecologist. His current research focuses on how economic and social phenomena translate into environmental impacts. For example, the primary benefit of recycling is its ability to displace primary material production. However, the quantification of this potential displacement is a complex research problem affected by social dynamics. Recently, Joe has used previously untapped causal inference methods to quantify this interaction and applied his methods to water reuse. These new conceptual models are expected to have an impact on industrial ecology as a whole, as there are many questions affecting environmental impacts that stand to benefit from the full spectrum of causal inference approaches. Joe also doubles as an accomplished percussionist, having collaborated and performed with a broad array of music professionals over the past fifteen years.
Abstract
The displacement of primary production and raw material harvesting are key potential environmental benefits of recycling. Displacement is also a critical effect in consequential life cycle assessment (CLCA). Structural market... [ view full abstract ]
The displacement of primary production and raw material harvesting are key potential environmental benefits of recycling. Displacement is also a critical effect in consequential life cycle assessment (CLCA). Structural market models for quantifying displacement are useful, but are only one potential method of identifying the causal link between recycling and primary production. This paper frames the displacement problem as a general causal inference exercise and identifies alternative strategies to structural market models. Specific alternatives are single-equation instrumental variables (SEIV) regression and quasi-experimental inference via the difference-in-differences (DID) estimator.
Both methods are applied to the question of whether or not wastewater reclamation displaces water harvesting from other sources. The SEIV approach is applied to a 10-year national panel of urban water statistics from Australia, and the DID approach is demonstrated using customer-level consumption data in California. Preliminary evidence suggests that increases in per-capita water recycling are correlated with increases in per-capita primary water production in Australia, implying that displacement did not occur on the national level. The point estimate is counter to the desired effect, actually suggesting that areas adding recycled water capacity relaxed water conservation practices compared to others during the 2005-2015 time frame. On the other hand, two urban customers converting from potable to recycled water in California achieve high displacement rates, with DID estimates ranging from 94.4 to 127%. In this case, recycled water was packaged with a greater conservation awareness program, and the evidence suggests that total water consumption decreased after conversions.
This study highlights an important opportunity to improve the integration of causal inference into CLCA and industrial ecology in general. We have demonstrated that a key consequential effect can be estimated using frameworks that deviate from much of the CLCA literature. Framing consequential questions as examinations of cause-and-effect relationships, and not exclusively price elasticities, strengthens the robustness of CLCA for decision-making. Finally, observing displacement rates greater than zero and less than one for the same material strongly suggests that displacement not only depends on what we recycle, but how we recycle it.
Authors
-
Joseph Palazzo
(University of California, Santa Barbara)
-
Roland Geyer
(University of California, Santa Barbara)
-
Richard Startz
(University of California, Santa Barbara)
Topic Areas
• Life cycle sustainability assessment , • Advances in methods (e.g., life cycle assessment, social impact assessment, resilience a , • Sustainable urban systems
Session
MS-18 » Computational methods to support decision-making (14:00 - Monday, 26th June, Room I)
Presentation Files
The presenter has not uploaded any presentation files.
