Non-linearity in Marginal LCA: Application of a Spatial Optimization Model

Abstract

Typical applications of LCA assume that the magnitude of life-cycle impact grows proportionally to the volume of demand, while in reality the additional impact due to marginal increase in demand may differ from the average impact. In the literature, the calculation of marginal life-cycle impacts often involves the use of optimization models, where typically the total economic costs are minimized. However, modeling spatially explicit marginal responses of a system involving multiple producers and consumers has not been discussed in LCA literature. In this paper, we demonstrate a spatial optimization technique for modeling marginal responses of a multi-producer, multi-consumer system. Our model determines the optimal production-by-location mix and associated environmental stressor at minimum systems cost. We demonstrate the model using a preliminary case study on blue water consumption by potato. We collected state-by-state data on potato yield, cost of potato production, and water use for irrigation, as well as interstate transportation fuel costs. We also estimated the marginal increase in demand for potato following USDA's recommended diet. The results show that the cradle-to-gate blue water consumption of potatoes based on 2016 demand was 96 m3/ton potato, which changes non-linearly along with the growth of potato demands. In order to meet the USDA's recommended diet, the additional demand on potato (530,000 ton per year) would result in a 29% lower blue water consumption per ton of potato (68 m3/ton) as compared to the average result of the current production system. In addition, we tested the model to analyze the marginal impacts under two scenarios: (1) high fuel tax and (2) high water price. The preliminary results indicate that water pricing is more effective than a fuel tax increase in reducing the marginal blue water consumption of potato based on our scenarios of the recommended diet demand. The results demonstrate that our model can be used to understand the non-linear behavior of marginal effect over demand crease, and for testing alternative policy scenarios involving a system with multiple producers and consumers across regions.

Multifeedstock and Multiproduct Process Design Using Neural Network Surrogate Flexibility Constraints

“…As illustrated in Figure , the variability in LCA results is the largest for designs in the middle of the profit-PSA GWP trade-off curve, as a mix of different technologies and feedstocks are used. The Pareto curve also helps to illustrate the marginal LCA results (i.e., the nonlinear response of emission when different amounts of product are generated) . It is challenging to quantify the LCA uncertainty and obtain such insights from the conventional linear LCA framework that is primarily based on matrix multiplication.…”

“…The Pareto curve also helps to illustrate the marginal LCA results (i.e., the nonlinear response of emission when different amounts of product are generated). 77 It is challenging to quantify the LCA uncertainty and obtain such insights from the conventional linear LCA framework that is primarily based on matrix multiplication. Traditionally, the Pedigree method is utilized to rate the LCA data reliability, completeness, temporal, and geometric correlation, and technological correlation from 1 to 5.…”

A more basic modeling framework for life cycle methods to cover non-linear, dynamic, and integrated effects–looking beyond linear inverse modeling

“…The world is after all dynamic and not static. Qin et al (2021) exemplify this dynamic nature in a non-linear manner in the context of LCA for the dynamic selection of potato suppliers based on external conditions (change in demand, costs, maximum capacity etc.) influencing water consumption.…”

Commentary: Non-linearity in LCA – What are we talking about?

“…As a matter of fact, we contributed to the development of non-linear LCA. One of us (Suh) co-authored a recent paper on "Non-linearity in marginal LCA" (Qin et al, 2021) and the other (Heijungs) published another recent paper with an explicit section on "Nonlinear LCA" (Heijungs, 2020). Further, this journal (of which we are associate editor and chief editor) has an ongoing research topic "Non-linearity in Life Cycle Assessment", which has so-far featured several interesting articles that discuss approaches for developing non-linear LCA (e.g., Li et al, 2020;Pizzol et al, 2021).…”