Modelling global material stocks and flows for residential and service sector buildings towards 2050

Abstract

Residential buildings and service sector buildings have an important contribution to climate change, directly via energy use in these buildings and indirectly through construction activities and the production and disposal of buildings materials. In this paper, we introduce a model that looks at total global building stock for 26 regions between 1970 and 2050 and calculates the floor space and building materials both in new buildings and in demolished buildings. For residential buildings, we build upon the work of Marinova et al. (2019, this issue), who used a building material database to come up with scenarios for materials in the residential building stock. This paper adds two things. First, we introduce a new regression-based model for service building floor space, recognizing 4 different types of servicerelated buildings. Secondly, we use a dynamic stock model, based on lifetime distributions found in literature, to calculate the construction (inflow) and demolition (outflow) of building floor space for both residential and service-related purposes. We combine this with data from the building material database to come up with scenarios for the annual demand for construction materials worldwide as well as an estimation of the availability of waste materials after building demolition towards 2050. The model can thus be used to assess the potential for closing the material cycles in the construction sector, while distinguishing urban and rural areas explicitly. The results show that demand for construction materials will continue to increase in most regions, even in developed countries. Global demand for steel and cement for the building sector is estimated to be 769 Mt/yr and 11.9 Gt/yr respectively, by the end of the modelling period. This represents a respective growth of 31% and 14% compared to today. Drivers behind this are an expected growth of global residential building stock of about 50%, and a growth of about 150% in the building stock for services. Our model projects that by 2050, only 55% of construction-related demand for copper, wood and steel could potentially be covered by recycled building materials. For other materials the availability of scrap may be higher, reaching up to 71% of new demand in the case of aluminium. This means that in most regions urban mining cannot cover the growing demand for construction materials.

Matching Demolition and Construction Material Flows, an Urban Mining Case Study

“…Continuing on the earlier work by [10,11], Heeren and Fishman summarized 301 building material data points across 21 countries [12]. Using the currently available building data, Marinova et al (2020) and Deetman et al (2020) were able to estimate global building material stocks and flows for residential and service sector buildings [13,14]. Müller, (2006), established the cyclical behavior of demolition and construction materials flows, with the availability of demolition waste lagging behind the construction material demand in size and time [15].…”

A Life Cycle Thinking Framework to Mitigate the Environmental Impact of Building Materials

“…Data source of building material intensity: Heeren and Fishman (2019), 3 Huang et al (2018) 4 Marinova et al (2020); 5 data source of annual constructed building area: Deetman et al (2020). 6 can help assess the environmental impacts associated with all the stages of the life of building materials (Figure 3). Material, energy usage, and emissions inventory data from the various stages of building materials need to be captured, either from case investigations or through building material databases such as Ecointent, 25 ELCD (European Life Cycle Database), 26 and US LCI (US Life Cycle Inventory).…”

Documentation of part IV of the RECC model framework: Open Dynamic Material Systems Model for the Resource Efficiency-Climate Change Nexus (ODYM-RECC), v2.2

“…Future metal and material demand has been projected and studied from the perspective of different macro-level scenarios (Deetman et al, 2019(Deetman et al, , 2018Elshkaki et al, 2018Elshkaki et al, , 2016Elshkaki and Graedel, 2013;Hatayama et al, 2010;Schipper et al, 2018;van der Voet et al, 2018;Watari et al, 2019), but those assessments are not linked to resource efficiency, but represent a very important starting point for our work, as we can link our scenarios and data to these studies.…”

“…In the simplest case, this would just mean that the regression model equations are replaced by formatted IAM model output. Such approach has already been lined out for the five materials Cu, Co, Li, Nd, and and Ta, where IMAGE model output for the use phase was converted to material in-use stocks, inflows, and outflows of the use phase (Deetman et al, 2019(Deetman et al, , 2018. Also, the implementation of the material cycle consequences of electricity generation installation from MESSAGE is already under way.…”