A new method to estimate the lifetime of long‐life product categories

Abstract

Increased recycling and reuse rates are a central part of the objectives laid out by the COP21. Nonetheless, the practical implementation of what has been called the circular economy, as well as its true potential, are not easily established. This is because the impact and implementation time scales of any intervention depend on knowing the lifetime of products, which is frequently unknown. This is particularly true in construction, responsible for 39% of worldwide emissions, 11% of which are embodied. Most material flow analysis (MFA) models will simply assume a range of plausible life expectancies when bottom‐up data are lacking. In this work, we propose a novel method of identification using the high quality but highly aggregated trade data available and use it to establish a “mortality curve” for buildings and other long‐lasting products. This identification method is intended to provide more reliable inputs to existing MFA models. It is widely applicable because of the general availability of the underlying data. Using it on United Kingdom trade data, we identify product classes at 1 year for packaging/home scrap, 1 to around 10 years for vehicles/equipment, and around 50 years for construction. The identification approach was then validated by using classical approaches using bottom‐up data for vehicles.

Modelling transport emissions in an uncertain future: What actions make a difference?

“…In the present work, φ a (and consequently the expected vehicle lifetime) is assumed constant over time, in line with past literature (set at 2018 values T =22.5,b=6.5 determined using registration data from (DfT, 2019c)). This is a simplification, as the characteristic lifetime T of vehicles has been increasing slowly over time (Dunant et al, 2020). However, including a dynamic φ y,a greatly increases the computational burden of running a stock model (as the stock needs to be recalculated in each model run), which is impractical when using the Monte Carlo based methods required to produce Sobol indices.…”

A generalized framework for analyzing car lifetime effects on stock, flow, and carbon footprint

“…Dynamic stock‐flow analysis frameworks have been widely used to conduct a replacement life cycle assessment (LCA) of durable goods such as cars (Kagawa et al., 2011, 2013; Kim et al., 2003; Lenski et al., 2010; Nakamoto, 2020; Nakamoto et al., 2019; Pauliuk et al., 2012; Spielmann & Althaus, 2007), air conditioners (De Kleine et al., 2011; Nishijima, 2017), aircrafts (Kito, 2021), buildings (Dunant et al., 2020), and refrigerators (Bakker et al., 2014). In a comprehensive review of the LCA literature, Schaubroeck et al.…”

“…Kagawa et al (2006) examined the effect of extending car lifetime and showed that a 1-year increase in average car lifetime led to a decrease in waste landfill, including car shredder residuals, over the period from 1990 to 1995. Dynamic stock-flow analysis frameworks have been widely used to conduct a replacement life cycle assessment (LCA) of durable goods such as cars (Kagawa et al, 2011(Kagawa et al, , 2013Kim et al, 2003;Lenski et al, 2010;Nakamoto, 2020;Nakamoto et al, 2019;Pauliuk et al, 2012;Spielmann & Althaus, 2007), air conditioners (De Kleine et al, 2011;Nishijima, 2017), aircrafts (Kito, 2021), buildings (Dunant et al, 2020), and refrigerators (Bakker et al, 2014). In a comprehensive review of the LCA literature, Schaubroeck et al (2020) pointed out that a product sustainability assessment should involve life cycle thinking rather than being restricted to a fixed life stage.…”

Material efficiency for climate change mitigation

“…Dunant et al. (2020) tackle a persistent challenge in modeling technology stock turnover, namely, accurately estimating technology lifespans in the absence of detailed bottom‐up technology data in different regions. Improved lifespan modeling is important for quantifying opportunities for reuse, refurbishment, and materials‐efficient product replacement, among other strategies.…”