Assessing recycling, displacement, and environmental impacts using an economics‐informed material system model

Abstract

Material production drives an increasingly large fraction of CO 2 -equivalent emissions. Material efficiency strategies such as recycling serve to reduce these emissions.Current analyses of the effectiveness of such strategies do not include economically induced rebound effects, overestimating the associated environmental benefits. We present a dynamic supply chain simulation model for copper through 2040 incorporating inventory-driven price evolution, dynamic material flow analysis, and life cycle assessment alongside mine-level economic evaluation of opening, closing, and production decisions. We show that permanent increases in recycling displace ∼0.5 kilotonnes mine production per kilotonne increase in scrap supply on average, while short-lived recycling policies can lead to increased mine production. We find evidence for supply chain evolution pathways minimizing the rebound effect and maximizing displacement of primary material, where increasing refined copper and concentrate prices and decreasing demand serve to decrease mining. However, even in best-case scrap supply scenarios, CO 2 e emissions from the copper sector increase 25% by 2040 relative to 2018 due to demand growth, ore grade decline, and lower displacement among large scrap supply changes. With implementation of best available technologies across all supply chain components, we estimated 2040 CO 2 e emissions 10% below those of 2018 are possible, though still well short of 2 • C emissions targets. We find increasing mine taxes and royalties, reclamation costs, and exploration costs further increase displacement, as does the inclusion of scrap prices on major futures exchanges. These results highlight the importance of considering the economics of the entire material supply chain when implementing material efficiency strategies. This article met the requirements for a Gold-Gold JIE data openness badge described at http://jie.click/ badges.

Supply Chain Factors Contributing to Improved Material Flow Indicators but Increased Carbon Footprint

“…In light of this, it is important to conduct a careful scientific investigation in order to establish whether a circular system truly contributes to decarbonization, as opposed to merely observing the CU indicators. 49 As in the case of the CU indicators, improvements in RP were not uniformly consistent with reductions in CF. That improvements in MFIs can, at least in some cases, be inconsistent with reductions in carbon emissions is an important lesson.…”

“…We have demonstrated that, in some sectors, improvements in the CU indicators did not produce CF reductions. In light of this, it is important to conduct a careful scientific investigation in order to establish whether a circular system truly contributes to decarbonization, as opposed to merely observing the CU indicators …”

“…In light of this, it is important to conduct a careful scientific investigation in order to establish whether a circular system truly contributes to decarbonization, as opposed to merely observing the CU indicators. 49 …”

A hierarchical Bayesian regression model that reduces uncertainty in material demand predictions

“…Second, future research should extend this methodology to study the effects of covariates other than price and income. For example, researchers can study the effect of sector-specific variables (e.g., automotive sales, electricity demand), region-specific variables (e.g., urbanization), or recycling rates, which impacts demand through feedback (Ryter et al, 2021(Ryter et al, , 2022. Adding more covariates can allow interesting analyses such as studying the impact of vehicle electrification (by using EV sales as an inflow variable that impacts demand), transition to renewable energy, and increased recycling rates on metal consumption.…”

Quantifying the drivers of long‐term prices in materials supply chains

“…Given we have already argued for the importance of price feedbacks, we will focus on approaches that incorporate market clearing of both supply and demand (for common supply and demand modeling approaches, see the review by Watari et al., 2021). Broadly speaking, metals markets are studied using (i) econometric models (Fisher et al., 1972; Fu et al., 2017; Watkins & McAleer, 2004), (ii) agent‐based models (Bollinger et al., 2012; Cao et al., 2021; Riddle et al., 2015), and (iii) system dynamics models (Elshkaki, 2013; Sprecher et al., 2015a; Sverdrup et al., 2017) or a combination of the three (Ryter et al., 2022). Econometric models often assume a partial equilibrium between demand and supply which are both estimated via statistical relationships with variables such as economic growth and population (Zink et al., 2016, 2018).…”