Improved Copper Circularity as a Result of Increased Material Efficiency in the U.S. Housing Stock

Abstract

Material efficiency (ME) can support rapid climate change mitigation and circular economy. Here, we comprehensively assess the circularity of ME strategies for copper use in the U.S. housing services (including residential buildings and major household appliances) by integrating use-phase material and energy demand. Although the ME strategies of more intensive floor space use and extended lifetime of appliances and buildings reduce the primary copper demand, employing these strategies increases the commonly neglected use-phase share of total copper requirements during the century from 23–28 to 22–42%. Use-phase copper requirements for home improvements have remained larger than the demand gap (copper demand minus scrap availability) for much of the century, limiting copper circularity in the U.S. housing services. Further, use-phase energy consumption can negate the benefits of ME strategies. For instance, the lifetime extension of lower-efficiency refrigerators increases the copper use and net environmental impact by increased electricity use despite reductions from less production. This suggests a need for more attention to the use phase when assessing circularity, especially for products that are material and energy intensive during use. To avoid burden shifting, policymakers should consider the entire life cycle of products supporting services when pursuing circular economy goals.

Exploring the opportunities in circular design as an affordable housing solution in Nigeria

“…Embedding dematerialization in the housing design can result in value retention, facilitate early systems decisions, and achieve beyond-system optimization [21]. In the study carried out by Wang et al [22], the in-use phase of building has been found to consume more copper in the area of thermal, and electrical conductivity, and the production of clean energy. The adoption of dematerialization and lifetime extension in copper application in residential building renovations has been proven to lessen the environmental impact and support sustainable consumption which has achieved a 21 % reduction in the US [22].…”

“…In the study carried out by Wang et al [22], the in-use phase of building has been found to consume more copper in the area of thermal, and electrical conductivity, and the production of clean energy. The adoption of dematerialization and lifetime extension in copper application in residential building renovations has been proven to lessen the environmental impact and support sustainable consumption which has achieved a 21 % reduction in the US [22]. Due to the resource-intensiveness of the Nigerian building sector cum the efforts to meet up with the building and infrastructure deficits in the country, the adoption of lean design and dematerialisation in housing delivery can assist in achieving resource efficiency.…”

Material Cycles, Industry and Service Provisioning: A Review of Low Energy and Material Demand Modelling and Scenarios

“…A common approach is to study the technical potential of reducing turnover across the entire material cycle, by combining possible technical changes in all process steps, including manufacturing (lightweighting and less scrap), longer and more intensive use, and better re-use and recycling (Ciacci et al, 2020;Kalt et al, 2022;Pauliuk et al, 2021;Song et al, 2023;Wang et al, 2022;Watari et al, 2022;Zhang et al, 2018). In those studies, material cycles and stocks are linked to energy use and GHG emissions, enabling thermodynamically consistent modelling of GHG mitigation potentials from materials-oriented strategies.…”

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

“…Within the category of inflow‐driven models, regression models are commonly used by researchers to analyze the demand for key materials, such as copper (Ciacci et al., 2020; Dong et al 2019. ; Fisher et al., 1972; Kuipers et al., 2018; Van der Voet et al., 2019), aluminum (Elshkaki et al., 2020), and steel (Dhar et al., 2020). Most regression models involve estimating key parameters like income elasticity (i.e., percentage change in material demand resulting from a 1% change in economic growth) and price elasticity (i.e., percentage change in demand resulting from a 1% price changes) from historic data (Crompton, 2015; Fernandez, 2018; Pei & Tilton, 1999).…”

“…Stock dynamic approaches are used to estimate demand for metals (Deetman et al., 2018; Gerst, 2009; Glöser et al., 2013; Watari et al., 2020) usually in specific smaller‐scale case studies like that for South Africa (Kapur & Graedel, 2006), Switzerland (Bader et al., 2011), China (Dong et al., 2019; Zhang et al., 2015), and the United States (He & Small, 2022; Wang et al., 2022). A comprehensive stock dynamics model of the world material stocks requires sufficient data about how much material is used in most major applications, in all regions.…”