Quantifying and mapping informal and formal building material stocks in Lima

Residential building material stocks and component-level circularity: The case of Singapore

From expansion to efficiency: Machine learning-based forecasting of Japan's building material stocks under demographic declines

Building construction and use are major drivers of climate change. To avoid these impacts, research is exploring the potential of narrowing material and energy cycles (“narrow” strategies) by reducing floor area per capita. Yet, integrated assessment models (IAM) have mostly overlooked material stocks and flow, service levels, and geospatial location of buildings. Thus, they have only limited capacity in modelling such floor area dynamics which are dependent on urbanization trends and changes in dwelling preferences. Current modelling of narrow strategies in IAMs, where available, is therefore focused on normative targets of overall reduction in floor area per capita without consideration of local building stock limitations. Inspired by recent advances in urban metabolism studies of representing urban form in material and energy flow modelling, we propose an integration of detailed geospatial building and material stock accounts with IAMs. Specifically, we demonstrate how the open-source building stock database EUBUCCO can inform the modelling of narrow strategies in the building sector model MESSAGEix-Buildings by adding subnational detail and urban density parameters. EUBUCCO is the first near-complete 3D model of individual buildings including building footprints, heights, usage types and material content in Europe. The database is a collection of government, volunteered, and satellite-derived data, and missing attribute values are inferred with machine learning. Material content is based on the globally harmonized material intensity database RASMI. MESSAGEix-Buildings is a framework to model the material stocks and flows and energy demand of buildings at national or larger scales in future scenarios. Embedding material flow analysis for stock turnover accounting and energy demand modelling at sectoral level, the framework is soft-linked to the MESSAGEix-GLOBIOM IAM to account for demand and supply sides interactions and greenhouse gas (GHG) emissions under different climate policies. EUBUCCO is integrated in MESSAGEix-Buildings by differentiating building and material stocks, as well as demographic trends and climate, at NUTS2 level and degree of urbanicity. This enables for improved representation of floor area and urban form-related aspects, and their regional distributions. With this geospatially informed IAM module, we can model the effect of regionally differentiated floor area reduction pathways, consider population-trend-dependent urban mining potentials, and effects of reverse urbanization such as revitalization of rural building stocks. Ultimately, this integrated module can inform prioritization of such reuse and reduce strategies for climate change mitigation in the housing sector. 

Hazardous materials in buildings cause project uncertainty concerning schedule and cost estimation, and hinder material recovery in renovation and demolition. The study aims to identify patterns and extent of polychlorinated biphenyls (PCBs) and asbestos materials in the Swedish building stock to assess their potential presence in pre-demolition audits. Statistics and machine learning pipelines were generated for four PCB and twelve asbestos components based on environmental inventories. The models succeeded in predicting most hazardous materials in residential buildings with a minimum average performance of 0.79, and 0.78 for some hazardous components in non-residential buildings. By employing the leader models to regional building registers, the probability of hazardous materials was estimated for non-inspected building stocks. The geospatial distribution of buildings prone to contamination was further predicted for Stockholm public housing to demonstrate the models’ application. The research outcomes contribute to a cost-efficient data-driven approach to evaluating comprehensive hazardous materials in existing buildings.

Population growth in cities leads to high raw material consumption and greenhouse gas emissions. In temperate climates were heating of buildings is among the major contributors to greenhouse gases, thermal insulation of buildings became a standard in recent years. Both population growth and greenhouse gas mitigation may thus have some influence on the quantity and composition of building material stock in cities. By using the case study of Vienna, this influence is evaluated by calculating the stock of major building materials (concrete, bricks, mortar, and plaster, steel, wood, glass, mineral wool, and polystyrene) between the years 1990 and 2015. The results show a growth of the material stock from 274 kt in the year 1990 to 345 kt in the year 2015, resulting in a total increase of 26%. During the same period, the population grew by 22%. On a material level, the increase of thermal insulation materials like polystyrene and mineral wool by factors of 6.5 and 2.5 respectively were much higher than for other materials, indicating energy efficiency and greenhouse gas mitigation in the building construction sector. The displacement of brickwork by concrete as the most important construction material, however, is rather a response to population growth as concrete buildings can be raised faster. A question for the future is to which extent this change from brickwork to high carbon-intensive concrete countervails the achievements in greenhouse gas reduction by thermal insulation.

Estimation and mapping of the material stocks of buildings of Europe: a novel nighttime lights-based approach

Urban material stock (UMS) represents elegant thinking by perceiving cities as a repository of construction materials that can be reused in the future, rather than a burdensome generator of construction and demolition waste. Many studies have attempted to quantify UMS but they often fall short in accuracy, primarily owing to the lack of proper quantification methods or good data available at a micro level. This research aims to develop a simple but satisfactory model for UMS quantification by focusing on individual buildings. Generally, it is a “bottom‐up” approach that uses building features to proximate the material stocks of individual buildings. The research benefits from a set of valuable, “post‐mortem” ground truth data related to 71 buildings that have been demolished in Hong Kong. By comparing a series of machine learning‐based models, a multiple linear regression model with six building features, namely building type, building year, height, perimeter, total floor area, and total floor number, is found to yield a satisfactory estimate of building material stocks with a mean absolute percentage error of 9.1%, root‐mean‐square error of 474.13, and R‐square of 0.93. The major contribution of this research is to predict a building's material stock based on several easy‐to‐obtain building features. The methodology of machine learning regression is novel. The model provides a useful reference for quantifying UMS in other regions. Future explorations are recommended to calibrate the model when data in these regions is available.

This research begins by summarising the principles of Material Flow Analysis (MFA) and its different dimensions from a methodological perspective. Afterwards, it applies a bottom-up method and temporal-spatial views analysis to urban construction material flow and stock with Geographical Information System (GIS) data. Due to a lack of availability of spatial data, GIS-based material flow and stock research are static (with data from only one year), limiting the extent to which scholars and policy makers can understand changes to the spatial structure of the urban environment and material metabolism that would have implications for sustainable urban policy. Therefore, a 4d-GIS method is developed in order to refine the spatial data and time series, addressing this gap and elucidating the urban stock variation over time. This study then introduces several cases of material flow and stock over space and time, showing that a 4d-GIS method is a powerful tool for revealing the evolution of construction material in buildings. It is also beneficial for estimating the lifetime of different cohorts which refers to buildings constructed in the same period, and for understanding the waste potential of demolition. This has the benefit of revealing the impact of socio-economic development on the material flow and stock and it draws out the challenges of speeding up societal metabolism towards sustainable development and provides an early warning for future waste management.

Towards net zero carbon buildings: Accounting the building embodied carbon and life cycle-based policy design for Greater Bay Area, China

SummaryThis article describes research conducted for the Japanese government in the wake of the magnitude 9.0 earthquake and tsunami that struck eastern Japan on March 11, 2011. In this study, material stock analysis (MSA) is used to examine the losses of building and infrastructure materials after this disaster. Estimates of the magnitude of material stock that has lost its social function as a result of a disaster can indicate the quantities required for reconstruction, help garner a better understanding of the volumes of waste flows generated by that disaster, and also help in the course of policy deliberations in the recovery of disaster‐stricken areas. Calculations of the lost building and road materials in the five prefectures most affected were undertaken. Analysis in this study is based on the use of geographical information systems (GIS) databases and statistics; it aims to (1) describe in spatial terms what construction materials were lost, (2) estimate the amount of infrastructure material needed to rehabilitate disaster areas, and (3) indicate the amount of lost material stock that should be taken into consideration during government policy deliberations. Our analysis concludes that the material stock losses of buildings and road infrastructure are 31.8 and 2.1 million tonnes, respectively. This research approach and the use of spatial MSA can be useful for urban planners and may also convey more appropriate information about disposal based on the work of municipalities in disaster‐afflicted areas.

SummaryThe construction industry is an important contributor to urban economic development and consumes large volumes of building material that are stocked in cities over long periods. Those stocked spaces store valuable materials that may be available for recovery in the future. Thus quantifying the urban building stock is important for managing construction materials across the building life cycle. This article develops a new approach to urban building material stock analysis (MSA) using land‐use heuristics. Our objective is to characterize buildings to understand materials stocked in place by: (1) developing, validating, and testing a new method for characterizing building stock by land‐use type and (2) quantifying building stock and determining material fractions. We conduct a spatial MSA to quantify materials within a 2.6‐square‐kilometer section of Philadelphia from 2004 to 2012. Data were collected for buildings classified by land‐use type from many sources to create maps of material stock and spatial material intensity. In the spatial MSA, the land‐use type that returned the largest footprint (by percentage) and greatest (number) of buildings were civic/institutional (42%; 147) and residential (23%; 275), respectively. The model was validated for total floor space and the absolute overall error (n = 46; 20%) in 2004 and (n = 47; 24%) in 2012. Typically, commercial and residential land‐use types returned the lowest overall error and weighted error. We present a promising alternative method for characterizing buildings in urban MSA that leverages multiple tools (geographical information systems [GIS], design codes, and building models) and test the method in historic Philadelphia.

Since the beginning of the 21st century, driven by industrialization and its corresponding economic development, China has been experiencing a period of rapid urbanization. The continued expansion of residential space contributes to material stocks of residential buildings, accounting for a large proportion of the total material stocks. Based on a 4D-GIS model, we studied spatiotemporal distribution characteristics and driving factors of residential building material stock in the central urban area of Xi’an from 1992 to 2021. The study innovatively combined this with the spatial development rule, development speed, and expansion direction of cities to analyze the relationship between stock growth and urban development. We found that residential development in central Xi’an is still undergoing a relatively rapid developmental stage. The spatial growth of residential building stock has a distinct agglomeration pattern, showing the characteristics of multi-center agglomeration, and the hot spots of stock growth are concentrated on and expanding to the edges of central cities. The growth of residential building stock has a distinct direction, primarily in the northeast–southwest direction, consistent with the pattern of urban expansion. We also found that social, economic, and transport-related factors are the main drivers of growth of residential building material stock. This study can help policymakers, urban planners, and environmental planners consider the rational development and utilization of land resources and building materials, and it lays a research foundation for the recycling of construction waste in the future.

Building material stock studies are essential for advancing the circular economy in construction. However, existing models often lack both accuracy and scalability. While machine learning has demonstrated significant potential to enhance predictive accuracy, its adoption has been hindered by a shortage of high‐quality training data. In this study, we introduce a novel methodology leveraging a large language model to extract previously untapped building material data from building energy performance certificates with a focus on exterior walls. This approach enabled us to create a dataset of over 20,000 buildings—significantly larger than those used in previous studies. Leveraging this dataset, we developed a machine learning model to predict material composition based on building characteristics such as construction year, use, and location. Furthermore, we integrated knowledge of construction history to estimate the material stock of walls in terms of volume, mass, and associated CO2 emissions for each building in the dataset. Our analysis revealed significant regional variations in material use patterns, emphasizing the critical role of location—a parameter often overlooked in existing building material stock models. These findings provide valuable insights for improving building stock modeling and highlight the importance of regionally tailored policies in advancing the circular economy in the construction sector.

Carbon emissions from accumulated stock of building materials in China

The presence of hazardous materials hinders the circular economy in construction and demolition waste management. However, traditional environmental investigations are costly and time-consuming, and thus lead to limited adoption. To deal with these challenges, the study investigated the possibility of employing registered records as input data to achieve in situ hazardous building materials management at a large scale. Through characterizing the eligible building groups in question, the risk of unexpected cost and delay due to acute abatement could be mitigated. Merging the national building registers and the environmental inventory from renovated and demolished buildings in the City of Gothenburg, a training dataset was created for data validation and statistical operations. Four types of inventories were evaluated to identify the building groups with adequate data size and data quality. The observations’ representativeness was described by plotting the distribution of building features between the Gothenburg dataset and the training dataset. Evaluating the missing data and the positive detection rates affirmed that reports and protocols could locate hazardous materials in the building stock. The asbestos and polychlorinated biphenyl (PCB)-containing materials with high positive detection rates were highlighted and discussed. Moreover, the potential inventory types and building groups for future machine learning prediction were delineated through the cross-validation matrix. The novel study contributes to the method development for assessing the risk of residual hazardous materials in buildings.