# Calculation Methods for Construction Material Stocks: A Systematic Review
## 3 Results
### Content Analysis
#### 3.2.2 Material Stock Calculation Methods
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This review realized that more studies employed the bottom-up method to estimate construction material stock. For example, [PERSON] and [PERSON] [17] aimed to spatially quantify the material stock and their embodied environmental requirements of the City of Melbourne. The study modeled each building on expert opinion based on land use, height, and age to find its bill of quantities. The embodied energy, water, and greenhouse gas emissions were calculated using a comprehensive hybrid analysis technique. The results showed that rebuilding Melbourne would require 904 kt of material per km\({}^{2}\), 10 PJ/km\({}^{2}\), 17.7 million m\({}^{3}\) of embodied water/km\({}^{2}\), and would emit 605 ktCO\({}_{2}\)e/km\({}^{2}\). In another study, [PERSON] et al. [8] evaluated the current building material stock and future demolition waste for buildings in Jakarta and Bandung, focusing primarily on unplanned houses. The paper further aimed to find the embodied energy and CO\({}_{2}\) emission. The study used the actual on-site measurement of 297 houses in Jakarta and 247 houses in Bandung to estimate the material inventory and used the input-output analysis method to find CO\({}_{2}\) emission. The result showed a material intensity of 2.67 ton/m\({}^{2}\) in Jakarta and 2.54 ton/m\({}^{2}\) in Bandung. Based upon the reuse and reduce rates, the waste material was found between 2.1-123.9 million tonnes, embodied energy between 192.1-247.8 Peta Joule, and CO\({}_{2}\) emissions from 19.2-24.3 million tons. The approach of measuring on-site was a difficult task, as only some buildings could be investigated. However, since the design of the slums is similar, so the approach of sampling would have less sample errors. The actual measurement approach is the most accurate one because there could be discrepancies in the design and actual construction. In another study, [PERSON] et al. [26] estimated the material stock and flows for domestic buildings in Germany. The study categorized residential buildings into different categories based upon the type of construction and the year constructed. Thereafter, the total material stocks were estimated using the material intensity of each building type. It was shown that there were 8.5 million tons of inflow and 3.0 million tons of outflow between 1991 and 2010. Contrarily, [PERSON] et al. [19] attempted to calculate the material stock and flows of buildings using the bottom-up approach at the component level, not the material level in Singapore. The calculation of the components and material ratio in a building was done based on the historical data and designs. Results showed that the city had 125.7 million tons of non-metallic minerals, 6.52 million tons of steel, 6.45 million windows, 8.61 million doors, 1.97 million toilet accessories, 15.33 million lighting fixtures, 0.99 million kitchen accessories, and 52.54 million m\({}^{2}\) of tiles. The per capita material stock was 27.4 tons of non-metallic minerals and 1.4 tons of steel. The average inflow had been 1.94 million tons of concrete and 0.1 million tons of steel, while outflow was 0.31 million tons of concrete and 0.02 million tons of steel. The material stock's component level could be used to estimate the waste reuse as it would be more accurate than the material level. GIS had been extensively utilized in estimating the construction material stock. For instance, [PERSON] and [PERSON] [37] endeavored to estimate the spatial distribution of construction materials in two regions, namely an approximately 8 km\({}^{2}\) urban area of Salford in Manchester, UK, and an 11 km\({}^{2}\) of Wakayama City center, Japan. They attempted to assess the demolition curve of buildings based on characteristics of an area and clarify material accumulation from the viewpoint of recyclability. To this end, they applied four-dimensional Geographical Information Systems (4d-GIS) data at an urban scale. The results showed that the buildings' lifespans for the two selected regions were less than the average national.