IEBUILDING

General Scope and Connection with Climate Mitigation

Introduction

The simple MS Excel model served as input to a discussion on how macro-economic representation of Circular Economy strategies relevant to building construction could be improved based on available Industrial Ecology data (such as material intensities per unit of floor space) and tools (such as dynamic stock modelling).

The reason for focusing on buildings only, in this example model, is that buildings comprise an important part of in-use material stocks (so their production is responsible for a large share of greenhouse gas emissions) and their long lifetimes cause a typical lag between material demand (for construction) and waste-generation (after demolishing). This is an important dynamic when studying the potential for a Circular Economy (or a circular construction sector) that is typically missing in macro-economic modelling. This model explores which steps and what data would be required for economic models to better capture these explicit material-related stock dynamics, with buildings as an example.

The model provides a hypothetical example-setup - in an Input-Output Table data structure - that starts with the assumption that it is possible to dis-aggregate the economic data on the construction sector into building-related construction & other construction activities (mostly related to infrastructure). This should be possible based on NACE rev. 2 classification (building-related construction activities are tracked under code F41), and would allow to connect to a wealth of knowledge on building-related material demand in the research field of Industrial Ecology that often relate building material use to the physical floor space (in square meters) of the buildings. So, the model exemplifies an attempt to bridge the level of detail typically found in economic models (left side, typically in monetary terms or $/yr) and the higher level of detail typically found in Industrial Ecology models (right side, typically expressed in physical units, such as kg or square meter of floor space), as shown in the figure below.

The MS Excel model links the monetary and the physical data by assuming that sectoral activity related to building construction leads to a given total amount of added building floor space by assuming a fixed floor space price. The total amount of floor space is then further dis-aggregated into residential and commercial floor space as informed by (Deetman et al., 2020). This allows to implement a different lifetime dynamic & different material-composition (adjusted from Marinova et al., 2020) to the two different building classes (residential & commercial). The implications for material use are subsequently calculated by applying material intensities in terms of kg per square meter (kg/m²) for 3 different commercial building types and 3 different residential building types, each with 4 possible construction types. These assumed building practices can be changed over time to represent the effects of material substitution.

Model Scope

Though the model provides a hypothetical case study, the example setup has a global geographic coverage (with a distinction between 2 regions, being Europe and the 'Rest of the World'). The dynamic changes are tracked over a 10 year period. Drivers of growth are exogenous through hypothetical population change, leading to (simplified) changes in economic activity for 10 economic sectors. Implications on material demand are only elaborated for the construction activities related to buildings, and cover 3 materials being Steel, Concrete and Wood.

Model Development

• Status: Finished
• Environment: MS Excel
• Documentation: In-file & on this site
• Source code: download

Circular Economy Features

The simple discussion model allows to assess the effects of building construction on total material use by representing the demand for construction materials (kg/yr), the size of the in-use stocks (kg) as well as tracking the volume of demolition waste flows by material (kg/yr). The total waste flows are complemented with simple assumptions on recycling practices, which allow to assess the potential effects of higher recycling rates in the construction sector (Recycle (R8)). Adjusting the model drivers or the floor space price allows to assess the effects of rethinking the ever growing need for floor space by assuming smaller houses or lower per capita floor space demand (Rethink (R1)).

The model is mostly suited however, to show the effect of reducing material demand through construction material substitution, for example the substitution of typical construction practices based on steel & concrete by wood-based alternatives (Reduce (R2)).

CE strategies and connection with climate change mitigation.

The model allows to assess the reduction potential in terms of reduced material use in the construction sector. Including the consequences for the anticipated volume of waste materials from demolition & deconstruction activities, thus allowing to assess the circularity gap of the construction sector. However, the implications in terms of Greenhouse Gas emissions are not explicitly covered in the simple discussion model, but (if required) could be explored in the same way as done in the example by (Zhang et al., 2021).

Synergies and trade-off between the R word in the context of the stylized model

While the simple discussion model can be used to assess the complementary potential of different circular construction practices (e.g. through simultaneous implementation of material substitution & building lifetime extension strategies) the model does not allow to assess economic trade-offs such as rebound effects as it does not deal with costs.

Insights for Analytical Framework

The simple discussion model was designed to facilitate discussion and explore the potential to link macro-economic models to detailed data on physical stocks & flows of materials, as common to the industrial ecology research field. The example model, focused on the material use in buildings, showed that through dis-aggregation of the construction sector to distinguish specific building-related activity, it is possible to connect to floor space related data and statistics to further explore the material used in the construction of buildings. In the example model, this is done by using a parallel, exogenous, accounting of different building types, starting with the dis-aggregation of residential & commercial buildings, and then a further dis-aggregation of building & construction types based on literature, which allows to assess the potential of various circular construction practices on the use of various materials in a dynamic context.

This exploration demonstrates the importance of capturing building stock dynamics in macro-economic modelling as it shows the implications of continued expansion of building stocks on material demand, and it shows that the availability of recyclable demolition scrap is stongly subject to the inertia of stocks with long lifetimes, as is the case for buildings. This has implications for recycling rates as well as potential effects to future markets for secondary materials, both may be relevant to capture in macro-economic models dealing with long term implications of a circular economy.

While these are insights that will be further incorporated into the available models and explored throughout the CIRCOMOD scenarios, the discussion model also led to a more fundamental observation to do with the question ‘What drives demand for construction?’. The model elaborates two distinct assumptions regarding this question. Through the first line of though (inspired by industrial ecology literature), the given population change leads to a changing requirement for total in-use floor space that needs to be subsequently provided through additional construction activity. This could be referred to as a mostly stock-driven model. The second line of thought (rooted in a more macro-economic thinking) assumes that the given change in the population affects the sectoral economic activity, including the activity in the construction sector, which leads to a change in new building construction that subsequently leads to a build-up of a corresponding total stock (in terms of building floor space & the associated weight of the materials). The latter is referred to as a mostly inflow-driven model. While the discussion model does not deal with a preference for either of these two lines of thought, it does show how these different assumptions on drivers lead to distinctly different results, especially with regard to the speed at which the building stock size, the material demand and scrap generation respond to changes in the population. Keeping an awareness of such fundamental differences in assumptions on key model drivers could be important in explaining and understanding the outcomes of different models throughout the CIRCOMOD project.

Sources

Deetman et al. 2020 - "Modelling global material stocks and flows for residential and service sector buildings towards 2050", Journal of Cleaner Production, Volume 245, 1 February 2020, 118658, https://doi.org/10.1016/j.jclepro.2019.118658

Marinova et al. 2020 - "Global construction materials database and stock analysis of residential buildings between 1970-2050", Journal of Cleaner Production, Volume 245, 1 February 2020, 119146 https://doi.org/10.1016/j.jclepro.2019.119146

Zhang et al. 2021 - "Global greenhouse gas emissions from residential and commercial building materials and mitigation strategies to 2060" Nature Communications Volume 12, 6126 https://doi.org/10.1038/s41467-021-26212-z