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

Abstract

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.

Mapping and Influencing the Mechanism of CO2 Emissions from Building Operations Integrated Multi-Source Remote Sensing Data

“…The Logarithmic Mean Divisia index (LMDI) model is an extension of index decomposition analysis (IDA) and has been widely used in exploring driving factors in energy consumption, CO 2 emissions, and other social and ecological subjects [67,68]. The LMDI was applied to decompose the factors of BCE growth (∆BCE) in this study.…”

Research on the Factors Influencing CO2 Emission Reduction in High-Energy-Consumption Industries under Carbon Peak

“…The LMDI model is a widely used method for decomposing energy consumption, allowing for the analysis of trends in energy consumption changes by breaking it down into various contributing factors. Specifically, the LMDI model can decompose energy consumption based on its structural, intensity, and combinatorial effects [20][21][22]. In the context of this study, the decomposition of CO 2 emissions is categorized into four primary factors: carbon emissions, energy intensity, energy structure, and economic scale.…”

Plant-level real-time monitoring data reveal substantial abatement potential of air pollution and CO2 in China’s cement sector

“…The first two types of uncertainty could be controlled at least at the 2014-2018 levels, assuming few associated future technical improvements; in contrast, the long-term projection for activity levels (represented by cement production) otherwise suffers from high uncertainties, which not only largely depend on the socioeconomic development but are also sensitive to a series of technical parameters in the estimation (Table S11). Thus, we considered the socioeconomic uncertainties in the projected economic growth and population values and then cement production value, by using the five shared socioeconomic pathways (SSPs); 69 we assumed normal distributions for the technical parameters of cement intensities and lifetime of cement products with CVs of 30% 75 and 20%, 51 respectively; we assumed uniform distributions for CEMS observations on the acceptable uncertainty ranges (Note S3); and we captured the uncertainties arising from the use of theoretical flue-gas rates and CO 2 emissions factor in a similar way to the uncertainty analysis for ex post estimates. Figure 4 illustrates the associated results and reveals that our projections are relatively stable, with projected measure-specific contributions to future mitigation generally similar across the five SSPs (bars in bright colors), as well as 2 SDs of ±36.4 and 95% CIs of (À32.9%, 41.4%).…”