American Chemical Society
es0c02387_si_002.xlsx (859.59 kB)

Log Mean Divisia Index Decomposition Analysis of the Demand for Building Materials: Application to Concrete, Dwellings, and the U.K.

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posted on 2021-02-20, 18:13 authored by He He, Rupert J. Myers
Dwellings are material intensive products. To date, material use in dwellings has been investigated mainly using economic (exogenous) or dwelling (endogenous) drivers, with few studies comprehensively combining both. For the first time, we identify a comprehensive set of such drivers of demand for building materials and analyze them using the logarithmic mean divisia index (LMDI) method. We combine the LMDI method, the concept of dynamic material flow analysis, and physical and monetary flows to decompose the demand for building materials into the following six effects: material intensity, floor area shape, dwelling type, dwelling intensity, economic output, and population. We analyze these six effects on demand for concrete in new dwellings in the U.K. from 1951 to 2014, classified into six dwelling types and four subregions. Of these six effects, the material intensity effect is the most important, overall contributing to increasing concrete demand by +79 Mt from 1950 to 2014, while the dwelling intensity effect plays an opposite role, overall reducing concrete demand from 1950 to 2014 by −56 Mt. The economic output effect is also significant (+38 Mt from 1950 to 2014). A comparative analysis of the six effects in the four U.K. nations reveals that most of the effects arise from England, while the other nations have minor effects due to their smaller populations. Our results show that changes to the demand for concrete in the U.K. fluctuate and have mainly remained between ±30 Mt year–2 from 1950 to 2014, and thus the inflows of concrete into the in-use stock of dwellings have experienced neither entirely increasing or decreasing trends during this period. This study contributes to understanding changes in resource demand due to social, economic, and technological factors and thus improves the capability to reliably and quantitatively model the use of materials in the built environment.