Unveiling patterns in human dominated landscapes through mapping the mass of US built structures

Humanity's impact on Earth's landscapes and ecosystems is a significant concern, leading to discussions about the Anthropocene epoch. The accumulation of human-made objects, including buildings and infrastructure (collectively 'built structures'), represents a major aspect of this transformation. These structures lock in material and energy use, waste, and greenhouse gas emissions. Globally, the mass of these 'socioeconomic material stocks' now exceeds 1000 Gt, roughly equaling the dry matter of all Earth's biomass. Buildings and mobility infrastructures constitute the vast majority of these stocks, highlighting their social and ecological importance. They are essential for economic processes and provide crucial services. This research focuses on mapping and analyzing the mass of built structures in the conterminous United States to understand spatial patterns and their implications for resource efficiency and sustainability.

Existing research has explored the concept of 'manufactured capital,' 'technomass,' 'human-made mass,' 'in-use stocks,' and 'socioeconomic material stocks' as key elements of sustainability science. Studies have documented the global growth of these stocks, often correlating with GDP. Previous work has generally focused on global or large-scale estimates. This research contributes by focusing on high-resolution mapping within a specific geographical area (conterminous US), providing a detailed analysis of material distribution and intensity.

The study quantified the mass of 14 building materials across eight building types and nine mobility infrastructure types in the conterminous US. High-resolution spatial data were used to map the distribution of these materials. This involved harmonizing geospatial data with material composition data from various sources. Material composition was generalized by averaging data from multiple sources, with uncertainty estimated from the variability in published material composition data. Assumptions on material stock composition were based on minimum-standard specifications; however, local deviations might occur due to factors like ground conditions, historical construction standards, and construction practices. The methodology included accounting for the varying proportions of paved and unpaved surfaces and gravel to dirt composition of local and rural roads, stratified by climate zones. The study also addressed uncertainties arising from the definition of parking surfaces, comparing results with those of local studies. Complex interactions between information layers, potentially influencing overall uncertainty, were acknowledged. The data sources used included the National Land Cover Database, building footprint data, building height reference data, skyscraper height data, OpenStreetMap data, data on building climate zones, socio-economic variables from the US Census Bureau and US Bureau of Economic Analysis, and state and county GIS boundaries.

The high-resolution maps revealed that the mass of built structures in the conterminous US is 2.6 times greater than the mass of all plant biomass. Most inhabited areas are mass-dominated by buildings or infrastructure. Analysis of material intensity (mass of materials per inhabitant) showed substantial spatial variation. Densely populated areas like the Bronx, NY, had the lowest material intensity (90 t/capita), while sparsely populated areas like Loving County, TX, had the highest (42,691 t/capita). Spatial patterns differed significantly between buildings and mobility infrastructure. Buildings exhibited higher intensities in the northern Great Plains, while mobility infrastructure showed much higher spatial disparity, reflecting their role in serving areas beyond immediate populations. Parking surfaces constituted 11% of the total stock, or 43 t/capita, with uncertainties arising from the definition used. Results were consistent with findings from local studies. The study also revealed that a significant portion of the population (26%) lives in counties where mobility infrastructures prevail, highlighting the importance of infrastructure beyond urban areas. This equates to 18% of the urban and 62% of the rural population.

The findings highlight the uneven distribution of built mass across the US, with significant implications for resource use and environmental impact. Densely built areas show greater efficiency in material use per capita, while sparsely populated areas exhibit much higher intensities, often due to extensive infrastructure networks supporting activities beyond the immediate population. Out-migration in rural areas further intensifies this issue, with infrastructure remaining even as populations decrease. These findings emphasize the need for strategies promoting more resource-efficient settlement design and a circular economy. The study points to future research directions such as spatially explicit understanding of stock dynamics driven by demand in distant areas, similar to consumption-based environmental footprinting.

This study provides high-resolution insights into the distribution and intensity of built structures' mass in the conterminous US, revealing substantial spatial heterogeneity. Densely built areas demonstrate efficiency in material use per capita, while sparsely populated areas, especially those experiencing out-migration, exhibit far higher intensities. These findings are crucial for developing sustainable settlement designs and promoting a circular economy. Further research should investigate the influence of distant demands on localized material stocks to better comprehend and address the environmental implications of built environments.

While the study employed a robust methodology, several limitations exist. Uncertainties in material stock estimations arise from the variability in published material composition data and assumptions made about material composition for less standardized local and rural roads. The study's approach to defining and quantifying parking surfaces might introduce some uncertainty. Furthermore, the analysis does not fully capture the complexities of how built structures serve purposes beyond the immediate local population.