Circularity indicator for residential buildings: Addressing the gap between embodied impacts and design aspects

https://doi.org/10.1016/j.resconrec.2020.105120Get rights and content

Abstract

In the European Union, the built environment is responsible for more than the 25% of all waste generated, highlighting the need to adopt circular practices. To indicate the level of circularity, common indicators mainly focus on: 1) the amount of virgin materials, 2) the amount of unrecoverable waste, and 3) the product lifetime. However, a holistic methodology covering the macro (material impact), meso (supply chain) and micro level (design) is still to be fully developed. In this research, two indicators - the Building Circularity Indicator (BCI) and the novel Predictive BCI (PBCI) - combine the Material Circularity Indicator with Embodied Energy (EE), Embodied CO2 (EC) analyses and Design for Disassembly (DfD) criteria. A full and simplified version are tested for different case studies in different climate zones in the EU. EE ranges between 1.49 GJ/m2 and 7.60 GJ/m2, while EC between 0.15 tCO2/m2 and 0.73 tCO2/m2. In the full version, the BCI and PBCI ranges respectively from 0.23 and 0.28 to 0.04 and 0.10 with regard to mass, EE and EC. The simplified version ranges between 0.10 and 0.62, revealing to be a more accurate indicator when data are available for only a few dozen components. To enable comparisons among different buildings, results show how different interpretations of the DfD criteria affect the BCI, highlighting the need to indicate strict boundary conditions, a minimum number of evaluated components, and precise criteria on how the DfD criteria relate to either a material, a subcomponent/component, or its relationship to its context.

Introduction

The current economic system is based on the linear sequence of “take-make-use-dispose”, relying on the exploitation of raw materials and on the irreversible disposal of waste at the End of Life (EoL). The current model is highly unsustainable: on an annual basis, it uses more than 79Gt of raw materials worldwide and more than 50% of Greenhouse Gas (GHG) emissions derive from raw materials management activities (Organisation for Economic Co-operation, 2018). In the European Union (EU), resources are exploited faster than the speed the planet is able to regenerate them (European Environmental Bureau, 2017). The Built Environment (BE) is responsible for more than 25% of all waste generated (Ellen MacArthur Foundation, 2015) and most of the Construction and Demolition Waste (CDW) are downcycled (Zhang et al., 2020). The consumption of raw materials and its collateral environmental impact highlights the need to adopt circular practices.

To indicate the level of circularity, a large number of indicators are exploited. These Circularity Indicators (CI), such as the Material Circularity Indicator (MCI) developed by the Ellen MacArthur Foundation (EMF), mainly focus on three aspects (Ellen MacArthur Foundation, 2015):

  • 1.

    the amount of used virgin materials;

  • 2.

    the amount of unrecoverable waste; and

  • 3.

    the lifetime of the products.

However, a holistic methodology covering the circular assessment on the macro (material impact), meso (supply chain) and micro (design) level still needs to be fully developed (Verberne, 2016). To overcome these gaps, this research focuses on two main research questions:

  • 1.

    How to improve the environmental assessment of the raw materials used in a Building Circularity Indicator?

  • 2.

    How to quantify the End of Life potential of materials and building components worth recovering by adopting Design for Disassembly (DfD) criteria?

To bridge the gap between embodied aspects and design aspects, in this research, the Material Circularity Indicator (Ellen MacArthur Foundation, 2015) is combined with Embodied Energy (EE), Embodied CO2 (EC) analyses (Ramesh et al., 2010) and Design for Deconstruction criteria (Akinade et al., 2017) in two indicators: the Building Circularity Indicator (BCI) and the new proposed Predictive BCI (PBCI). Both indicators are presented in a Full and Simplified version. The two indicators were tested on 8 demonstrators in different climate zones in the EU. On a macro level, the environmental impact assessment is implemented by evaluating the EE and EC, instead of only the mass of the used materials. On a micro level, the relationship between environmental impacts and design criteria, typically provided simply as DfD guidelines, is established. On a meso level, a precise methodology to facilitate the decision of which parts of a product can be really recycled or reused is provided.

This paper is structured as follows. In Section 2, a brief literature review is presented - this relates to EE and EC assessment, existing CIs, and DfD criteria. In Section 3, the new proposed methodology is introduced to further advance the BCI linking DfD criteria and EE and EC analysis. In Section 4, results for the 8 demonstrators, in terms of embodied aspects, recovering potential and BCI, are analyzed. Finally, in Section 5, concluding remarks and further improvements are pointed out.

Section snippets

Literature review

To assess the level of circularity in the BE, the first necessary step is to “take a picture” of an existing building in order to understand the in-use materials, expressed in mass, and their environmental impact, such as EE and EC. So-called “Material Passports” have been largely adopted in the construction industry as a compulsory record of information when constructing new buildings or performing renovation interventions. Innovative online platforms have been developed over the past few

Methodology

This research follows a multiple case study (Yin, 2018), done purposefully (Stake, 1995) by selecting eight relevant information-rich demonstrators all around Europe to provide an analytical generalization of the findings (Johansson, 2007) for similar buildings. Quantitative and qualitative data have been used as data sources. A concurrent mixed-method was used, giving more emphasis to quantitative rather than qualitative data (Johnson and Onwuegbuzie, 2004). Primary data have been collected

Embodied energy and carbon

Table 3 summarizes the results of the first reclamation audits, in terms of mass (t/m2), Embodied Energy (GJ/m2) and Carbon (tCO2/m2) per square meter, for each demonstrator. The values for EE and EC has been calculated thanks to the ICE database (Hammond et al., 2011). Each material has been classified into the six layers of Brand (1995) in Fig. 4a, 4 c and 4 e while Fig. 4b, 4 d and 4 f group the results per EoL strategy. The Embodied Energy per square meter, relating to the Operational

Conclusion

The increase of interest in Circular Economy shifts the attention from Embodied Energy analyses to the use of Circularity Indicators for environmental assessment. Despite the level of attention the Circular Economy is experiencing nowadays, a rigorous connection among Embodied Energy, a common approach for environmental assessment of the built environment, Circularity Indicators and design criteria is still missing.

In this work, two main research questions were addressed, i.e. 1) ”How to

CRediT authorship contribution statement

Dario Cottafava: Writing - review & editing. Michiel Ritzen: Supervision, Writing - review & editing, Validation, Project administration, Funding acquisition.

Declaration of Competing Interest

No potential conflicts of interests were reported by the authors.

Acknowledgement

The authors would like to thank John van Oorschot, Peter op ’t Veld, Ana Tisov, Zuzana Prochazkova, Patrick Daly, Cecilia Mazzoli, Kalle Kuusk, Domen Ivanšek and Dimitra Papadaki for their contribution during the data collection process and for the management of the Drive 0 project.

References (51)

  • R. Geraedts

    Flex 4.0, a practical instrument to assess the adaptive capacity of buildings

    Energy Procedia

    (2016)
  • F. Heisel et al.

    Calculation and evaluation of circularity indicators for the built environment using the case studies of umar and madaster

    J. Clean. Prod.

    (2020)
  • I.I. Issa et al.

    Leading product-related environmental performance indicators: a selection guide and database

    J. Clean. Prod.

    (2015)
  • M. Niero et al.

    Coupling material circularity indicators and life cycle based indicators: a proposal to advance the assessment of circular economy strategies at the product level

    Resour. Conserv. Recycl.

    (2019)
  • A. Parchomenko et al.

    Measuring the circular economy-a multiple correspondence analysis of 63 metrics

    J. Clean. Prod.

    (2019)
  • J.Y. Park et al.

    Establishing and testing the ǣreuse potentialǥ indicator for managing wastes as resources

    J. Environ. Manage.

    (2014)
  • T. Ramesh et al.

    Life cycle energy analysis of buildings: an overview

    Energy Build.

    (2010)
  • M. Saidani et al.

    A taxonomy of circular economy indicators

    J Clean Prod

    (2019)
  • D. Stankovic et al.

    Reconditioning and reconstruction: a second wind for serbian kindergartens

    Procedia Eng.

    (2015)
  • C. Zhang et al.

    Upgrading construction and demolition waste management from downcycling to recycling in the netherlands

    J. Clean. Prod.

    (2020)
  • M. Bengtsson et al.

    Weighting in lca–approaches and applications

    Environ. Prog.

    (2000)
  • S. Brand

    How Buildings Learn: What Happens After They’re Built

    (1995)
  • R. Castro et al.

    How to design buildings with life cycle assessment by accounting for the material flows in refurbishment

    IOP Conference Series: Earth and Environmental Science

    (2019)
  • N. Ciarimboli et al.

    Design for Disassembly in the Built Environment: a Guide to Cloosed-Loop Design and Building

    (2007)
  • Crowther, P., 1999. Design for disassembly to recover embodied...
  • Cited by (79)

    View all citing articles on Scopus

    This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 841850 (Drive 0 - www.drive0.eu) and the second author has received funding from the Dutch Organisation for scientific research (NWO) grant number HBOPD.2018.02.025.

    View full text