Material Stock and Embodied Greenhouse Gas Emissions of Global and Urban Road Pavement

Abstract

Roads play a key role in movements of goods and people but require large amounts of materials emitting greenhouse gases to be produced. This study assesses the global road material stock and the emissions associated with materials’ production. Our bottom-up approach combines georeferenced paved road segments with road length statistics and archetypical geometric characteristics of roads. We estimate road material stock to be of 254 Gt. If we were to build these roads anew, raw material production would emit 8.4 GtCO2-eq. Per capita stocks range from 0.2 t/cap in Chad to 283 t/cap in Iceland, with a median of 20.6 t/cap. If the average per capita stock in Africa was to reach the current European level, 166 Gt of road materials, equivalent to the road material stock in North America and in East and South Asia, would be consumed. At the urban scale, road material stock increases with the urban area, population density, and GDP per capita, emphasizing the need for containing urban expansion. Our study highlights the challenges in estimating road material stock and serves as a basis for further research into infrastructure resource management.

TRIPI: A global dataset and codebase of the total resources in physical infrastructure encompassing road, rail, and parking

“…First, we collected data on the material composition of different elements of transportation infrastructure. We reviewed available literature on material intensities [ 3 , [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] ]. For rail infrastructure, bridges, and tunnels, intensities from the literature are used which can be found in the folder material intensities.…”

“…This is where this dataset provides a unique perspective by not only accounting for paved roads but the actual material that the road is paved with following local observations. This is combined with an extensive dataset from Rousseau et al [3] which provides material intensities for the paved road network for different global regions and different road types, we can calculate the total materials used in the global transportation infrastructure. As the surface information is not in all cases commonly found in literature, additional data was found on materials specifically for bricks, stone and paving stones which will be addressed in the next paragraphs.…”

“…The data is divided into three different levels of regional definition National, the regions of the Global Roads Inventory Project (GRIP), and the regions of the Integrated Model to Assess the Global Environment (IMAGE), and is available on the GitHub repository. This regional definition is chosen as the country level is often used in analysis, GRIP regions as these represent seven global regions and is used for inclusion of the material intensity information from Rousseau et al [3] , and finally IMAGE as this is an IAMs where attempts are being made to cover materials within the CIRCOMOD project. Some sub-national regions are also included in the database, to show the potential of regional definition of the codebase.…”

High-Resolution Mapping of Material Stocks in Belgian Road Infrastructure: Material Efficiency Patterns, Material Recycling Potentials, and Greenhouse Gas Emissions Reduction Opportunities

“…Most recently, several studies have attempted to provide a granular understanding of materials stocked in road infrastructure. − Most of them use an archetype-based approach based on design standards and assumptions to determine material intensities and quantify material stocks. However, this precludes capturing locational heterogeneities in road properties (e.g., pavement structure, material type, layer thickness, and material composition) governed by traffic, climatic, and soil conditions.…”

The Bahamas at risk: Material stocks, sea‐level rise, and the implications for development

“…Most recently, the combination of geospatial data and socioeconomic data with statistics on material stocks (MS) (Heeren & Hellweg, 2019; Tanikawa et al., 2015) has advanced our understanding of MS dynamics. A small number of MS studies have applied freely and openly available geospatial data (OpenStreetMap, OSM) on their approaches (Deetman et al., 2021; Haberl et al., 2021; He et al., 2020; Inostroza et al., 2019; Kloostra, 2021; Miatto et al., 2021; Rousseau et al., 2022; Thunshirn, 2020). Few others have investigated MS at risk from natural disasters (Gallardo et al., 2014; Kahhat et al., 2017; Tanikawa & Sugimoto, 2016; Tanikawa et al., 2014), while fewer have had small islands as the study area (Bradshaw et al., 2020; Merschroth et al., 2020; Symmes et al., 2019; Ye, 2022).…”