Material-intensity database of residential buildings: A case-study of Sweden in the international context

Gontia, Paul; Nägeli, Claudio; Rosado, Leonardo; Kalmykova, Yuliya; Österbring, Magnus

doi:10.1016/j.resconrec.2017.11.022

Cited by 69 publications

(78 citation statements)

References 16 publications

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“…The relatively higher stock can be explained by the high number of SF residential areas in the EU 25, which have a higher stock per capita than MF building areas. Moreover, the difference can also be explained by the brick and concrete structure of SF buildings, which are predominant in the EU 25 (Wiedenhofer et al., ), contrasted to the mainly wooden structure SF in Sweden (Gontia et al., ). In contrast, other studies showed larger residential stock per capita.…”

Section: Discussionmentioning

confidence: 99%

“…The material-intensity coefficients for the three components considered are shown in Table 1. For residential buildings, the coefficients were collected from an earlier study (Gontia, Nägeli, Rosado, Kalmykova, & Österbring, 2018). Both single-family (SF) and multifamily (MF) residential buildings were included and further separated into four age classes (Table 1).…”

Section: Materials-intensity Coefficientsmentioning

confidence: 99%

“…a Concrete cement is part of mineral-binding material category. An extended list of the construction materials and their classification into categories can be found at Gontia et al (2018). Note that the six material categories shown in the mentioned article are complemented here with asphalt material category.…”

Section: Spatial Datamentioning

confidence: 99%

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Spatial analysis of urban material stock with clustering algorithms: A Northern European case study

Gontia

Thuvander

Ebrahimi

et al. 2019

J of Industrial Ecology

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A large share of construction material stock (MS) accumulates in urban built environments. To attain a more sustainable use of resources, knowledge about the spatial distribution of urban MS is needed. In this article, an innovative spatial analysis approach to urban MS is proposed. Within this scope, MS indicators are defined at neighborhood level and clustered with k‐mean algorithms. The MS is estimated bottom‐up with (a) material‐intensity coefficients and (b) spatial data for three built environment components: buildings, road transportation, and pipes, using seven material categories. The city of Gothenburg, Sweden is used as a case study. Moreover, being the first case study in Northern Europe, the results are explored through various aspects (material composition, age distribution, material density), and, finally, contrasted on a per capita basis with other studies worldwide. The stock is estimated at circa 84 million metric tons. Buildings account for 73% of the stock, road transport 26%, and pipes 1%. Mineral‐binding materials take the largest share of the stock, followed by aggregates, brick, asphalt, steel, and wood. Per capita, the MS is estimated at 153 metric tons; 62 metric tons are residential, which, in an international context, is a medium estimate. Denser neighborhoods with a mix of nonresidential and residential buildings have a lower proportion of MS in roads and pipes than low‐density single‐family residential neighborhoods. Furthermore, single‐family residential neighborhoods cluster in mixed‐age classes and show the largest content of wood. Multifamily buildings cluster in three distinct age classes, and each represent a specific material composition of brick, mineral binding, and steel. Future work should focus on megacities and contrasting multiple urban areas and, methodologically, should concentrate on algorithms, MS indicators, and spatial divisions of urban stock.

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Section: Discussionmentioning

confidence: 99%

Section: Materials-intensity Coefficientsmentioning

confidence: 99%

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Spatial analysis of urban material stock with clustering algorithms: A Northern European case study

Gontia

Thuvander

Ebrahimi

et al. 2019

J of Industrial Ecology

Self Cite

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“…In most cases, the quantity of MS would be underestimated or unable to be estimated because the MI obtained through these means is often fragmented. Conducting field work is recognized as the best way to acquire the MI data, but these kinds of primary data are only available in some countries (Chen & Graedel, ; Gontia, Nägeli, Rosado, Kalmykova, & Österbring, ; Kleemann, Lederer et al., ; Schiller et al., ). Gontia et al.…”

Section: Introductionmentioning

confidence: 99%

“…Gontia et al. () suggested the construction of a mass and material intensity database could be used to solve the data demanding problem of the bottom‐up approach, but building a database requires a long period of time, which might not be able to satisfy the global pressing demand for circular economy transformation. There is an urgent need to develop an accurate and less data demanding MS evaluation method.…”

Section: Introductionmentioning

confidence: 99%

A hybrid material flow analysis for quantifying multilevel anthropogenic resources

Cheng

Hsu

Hung

et al. 2019

J of Industrial Ecology

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This study develops a hybrid material flow analysis (HMFA) method to evaluate the annual additional quantity of material stock, known as net additions to stock (NAS) at both micro‐ and macro‐levels through analyzing the fixed capital formation (FCF) and total supply in input‐output tables (IOTs). HMFA turns NAS from a balance indicator in the top‐down approach to an indicator with meaningful value in terms of urban ore evaluation. To verify the validity of HMFA, this study compares the developed HMFA with a top‐down approach and a bottom‐up approach through assessing the NAS of Taiwan and Germany. The quantity of NAS estimated by HMFA is considered as a more conservative upper bound than that by the top‐down approach, while underestimation often occurs with a bottom‐up approach. HMFA has been proven as an efficient and rational evaluation method which overcomes a key limitation in assessing micro‐level material stock by a top‐down approach, and solves the data demanding problem of the bottom‐up approach for quantifying material stock.

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Construction and Demolition Waste

Migliore

Talamo

Paganin

2019

Springer Tracts in Civil Engineering

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Material-intensity database of residential buildings: A case-study of Sweden in the international context

Cited by 69 publications

References 16 publications

Spatial analysis of urban material stock with clustering algorithms: A Northern European case study

Spatial analysis of urban material stock with clustering algorithms: A Northern European case study

A hybrid material flow analysis for quantifying multilevel anthropogenic resources

Construction and Demolition Waste

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