FINEPRINT - Fine-scale modelling of material footprints and related impacts (www.fineprint.global)

Land-use activities are increasingly globalized and industrialized. While this contributes to a reduction of pressure on domestic ecosystems in some regions, spillover effects from these processes represent potential obstacles for global sustainable land-use. This contribution scrutinizes the complex global resource nexus of national land-use intensity, international trade of biomass goods, and resource footprints in land-use systems. Via a systematic account of the global human appropriation of net primary production (HANPP) and input–output modelling, we demonstrate that with growing income countries reduce their reliance on local renewable resources, while simultaneously consuming more biomass goods produced in other countries requiring higher energy and material inputs. The characteristic 'outsourcing' country appropriates 43% of its domestic net primary production, but net-imports a similar amount (64 gigajoules per capita and year) from other countries and requires energy (11 GJ/cap/yr) and material (~400 kg/cap/yr) inputs four to five times higher as the majority of the global population to sustain domestic land-use intensification. This growing societal disconnect from domestic ecological productivity enables a domestic conservation of ecosystems while satisfying growing demand. However, it does not imply a global decoupling of biomass consumption from resource and land requirements.

Human land use is the main driver of terrestrial biodiversity loss. It has been argued that producers and consumers have a shared responsibility for biodiversity loss because this land use is directly and indirectly driven by the local and global demand for products. Such responsibility sharing would be an important step for global biodiversity cooperation and conservation. Here, we use a global multiregional input-output framework to estimate consumption-based biodiversity loss, integrating with both the physical Food and Agriculture Biomass Input-Output (FABIO) dataset and a global monetary input-output table (EXIOBASE). We use an environmental justice framework for assigning biodiversity loss responsibility between producers and consumers. In this framework, we employ the Human Development Index (HDI) as a proxy of the weighting parameter for both producers and consumers. An environmental justice perspective may provide a fairer distribution of responsibility in a world where different nations have very different capabilities and see varying benefits from international trade. Environmentally just accounting increases the footprint of the Global North compared to other common approaches for sharing responsibility across all producers and consumers along international supply chains. We describe how environmental justice may inform cooperation in biodiversity protection between stakeholders along global supply chains.

A dietary shift from animal-based foods to plant-based foods in high-income nations could reduce greenhouse gas emissions from direct agricultural production and increase carbon sequestration if resulting spared land was restored to its antecedent natural vegetation. We estimate this double effect by simulating the adoption of the EAT–Lancet planetary health diet by 54 high-income nations representing 68% of global gross domestic product and 17% of population. Our results show that such dietary change could reduce annual agricultural production emissions of high-income nations’ diets by 61% while sequestering as much as 98.3 (55.6–143.7) GtCO2 equivalent, equal to approximately 14 years of current global agricultural emissions until natural vegetation matures. This amount could potentially fulfil high-income nations’ future sum of carbon dioxide removal (CDR) obligations under the principle of equal per capita CDR responsibilities. Linking land, food, climate and public health policy will be vital to harnessing the opportunities of a double climate dividend.

Technological breakthroughs and policy measures targeting energy efficiency and clean energy alone will not suffice to deliver Paris Agreement-compliant greenhouse gas emissions trajectories in the next decades. Strong cases have recently been made for acknowledging the decarbonisation potential lying in transforming linear economic models into closed-loop industrial ecosystems and in shifting lifestyle patterns towards this direction. This perspective highlights the research capacity needed to inform on the role and potential of the circular economy for climate change mitigation and to enhance the scientific capabilities to quantitatively explore their synergies and trade-offs. This begins with establishing conceptual and methodological bridges among the relevant and currently fragmented research communities, thereby allowing an interdisciplinary integration and assessment of circularity, decarbonisation, and sustainable development. Following similar calls for science in support of climate action, a transdisciplinary scientific agenda is needed to co-create the goals and scientific processes underpinning the transition pathways towards a circular, net-zero economy with representatives from policy, industry, and civil society. Here, it is argued that such integration of disciplines, methods, and communities can then lead to new and/or structurally enhanced quantitative systems models that better represent critical industrial value chains, consumption patterns, and mitigation technologies. This will be a crucial advancement towards assessing the material implications of, and the contribution of enhanced circularity performance to, mitigation pathways that are compatible with the temperature goals of the Paris Agreement and the transition to a circular economy.

Metal mining plays a significant role in the Brazilian economy since its foundation as an overseas colony. The rapid increase in ore extraction brings along pressures on the country’s water resources, as mining is a particularly water-intensive activity. However, site-specific data on water input and management are scarce. We propose a methodology for estimating water input in mining at a high geographical resolution. We focus on the three key metals mined in Brazil: iron, aluminum (i.e. bauxite ore), and copper, and derive water input coefficients for all mines from governmental and corporate sources as well as from the literature. We estimate that overall, the sum of the water inputs estimated for Brazilian bauxite, copper, and iron ore mining decreased by 15% from an average of 506.5±62.4 hm³ in 2014 to an average of 408.4±67.2 hm³ in 2017. The regions where most water was appropriated were Northern (Pará state) and Southeast (Minas Gerais) for iron, Northern (Pará) for aluminum, and Northern (Pará) and Central West (Goiás) for copper. We show that there are still significant consistency and data availability gaps, and that further work is still necessary to improve site-specific reporting and open access to data collected by public institutions.

Informed environmental-economic policy decisions require a solid understanding of the economy's biophysical basis. Global physical input-output tables (gPIOTs) collate a vast array of information on the world economy's physical structure and its interdependence with the environment, which can help to monitor progress toward a sustainable circular economy. However, building gPIOTs requires dealing with mismatched and incomplete primary data with high uncertainties, which makes it a time-consuming and labor-intensive endeavor. We address this challenge by introducing the PIOLab: A virtual laboratory for building gPIOTs. This represents the newest branch of the industrial ecology virtual laboratory (IELab) concept, a cloud-computing platform and collabora-tive research environment through which participants can pool resources to assemble individual input-output tables that target specific research questions. To overcome the lack of primary data, the PIOLab builds extensively upon secondary data derived from a variety of models commonly used in industrial ecology. We use the case of global iron-steel supply chains to describe the architecture of the PIOLab and highlight its analytical capabilities. A major strength of the gPIOT is its ability to provide mass-balanced indicators on both apparent/direct and embodied/indirect flows, for regions and dis-aggregated economic sectors. We present the first gPIOTs for 10 years (2008-2017), covering 32 regions, 30 processes, and 39 types of iron/steel flows. Diagnostic tests of the data reconciliation show a good level of adherence between raw data and the values realized in the gPIOT. We conclude with elaborating on how the PIOLab will be extended to cover other materials and energy flows. This article met the requirements for a Gold-Gold JIE data openness badge described at http://jie.click/badges.

Deforestation of the Amazon rainforest is a threat to global climate, biodiversity, and many other ecosystem services. In order to address this threat, an understanding of the drivers of deforestation processes is required. Spillover effects and factors that differ across locations and over time play important roles in these processes. They are largely disregarded in applied research and thus in the design of evidence-based policies. In this study, we model connectivity between regions and consider heterogeneous effects to gain more accurate quantitative insights into the inherent complexity of deforestation. We investigate the impacts of agriculture in Mato Grosso, Brazil, for the period 2006–2017 considering spatial spillovers and varying impacts over time and space. Spillovers between municipalities that emanate from croplands in the Amazon appear as the major driver of deforestation, with no direct effects from agriculture in recent years. This suggests a moderate success of the Soy Moratorium and Cattle Agreements, but highlights their inability to address indirect effects. We find that the neglect of the spatial dimension and the assumption of homogeneous impacts lead to distorted inference. Researchers need to be aware of the complex and dynamic processes behind deforestation, in order to facilitate effective policy design.

Lacking systematic supply-use information of agricultural biomass and food products within China makes the existing provincial environmental pressure assessments (e.g., water consumption) either not detailed enough (e.g., by the input-output table-based approach) or not comprehensive enough (e.g., by the process-based approach). This study develops a symmetric inter-provincial multi-regional input-output (MRIO) model that hybridizes the physical food and agricultural biomass system with the monetary supply chain of China. First, we construct the inter-provincial supply, use, and input-output tables in physical units of 84 agriculture, food and forestry products. These physical supply/use/MRIO tables systematically capture agri-food product flows, at an unprecedented level of product detail, along domestic supply chains within China. Then we integrate the physical MRIO table of agri-food products into the monetary all-sector MRIO table to construct a symmetric hybrid MRIO table of China. The application of our hybrid MRIO model to the case of provincial blue water footprint assessments reveals that the hybrid model enhances both the monetary MRIO table-based approach and the process-based approach from different aspects. For instance, the hybrid MRIO model can reduce the uncertainty of monetary MRIO modeling due to the aggregation of products with different environmental properties into homogeneous sectors. Lastly, uncertainty analysis is implemented to quantify the main sources of uncertainties, and understand the reliability of our new hybrid MRIO model for future sustainable development design.

In a recent study, Chaves et al. find international consumption and trade to be major drivers of ‘malaria risk’ via deforestation. Their analysis is based on a counterfactual ‘malaria risk’ footprint, defined as the number of malaria cases in absence of two malaria interventions, which is constructed using linear regression. In this letter, I argue that their study hinges on an obscured weighting scheme and suffers from methodological flaws, such as disregard for sources of bias. When addressed properly, these issues nullify results, overturning the significance and reversing the direction of the claimed relationship. Nonetheless, I see great potential in the mixed methods approach and conclude with recommendations for future studies.

Mining activities induce profound changes to societies and the environment they inhabit. With global extraction of metal ores doubling over the past two decades, pressures related to mining have dramatically increased. In this paper, we explore where growing global metal extraction has particularly taken effect. Using fine-grain data, we investigate the spatial and temporal distribution of mining of nine metal ores (bauxite, copper, gold, iron, lead, manganese, nickel, silver and zinc) across approximately 3,000 sites of extraction worldwide between 2000 and 2019. To approach the related environmental implications, we intersect mining sites with terrestrial biomes, protected areas, and watersheds categorised by water availability. We find that 79% of global metal ore extraction in 2019 originated from five of the six most species-rich biomes, with mining volumes doubling since 2000 in tropical moist forest ecosystems. We also find that half of global metal ore extraction took place at 20 km or less from protected territories. Further, 90% of all considered extraction sites correspond to below-average relative water availability, with particularly copper and gold mining occurring in areas with significant water scarcity. Our study has far-reaching implications for future global and local policy and resource management responses to mitigate the negative effects of the expected expansion of metal mining.

To feed future populations on ever-scarcer natural resources, policy initiatives aim to decrease resource footprints of food consumption. While adopting healthier diets has shown great potential to reduce footprints, current political initiatives primarily address strategies to reduce food waste, with the target of halving food waste at retail and consumption levels by 2030. Using Germany as a case study, we compare the resource-saving potential of this political target with three scenarios of nutritionally viable, plant-based dietary patterns and investigate interactions and trade-offs. By using the Food and Agriculture Biomass Input-Output model, we capture biomass, cropland, and blue water footprints of global supply chains. The results show that dietary changes are particularly effective in reducing biomass and cropland footprints, showing a decrease of up to 61% and 48%, respectively, whereas halving food waste decreases biomass and cropland footprints by 11% and 15%, respectively. For blue water savings, halving food waste is more effective: water use decreases by 14% compared to an increase of 6% for dietary change with the highest water consumption. Subsequently, a combination of the scenarios shows the highest total reduction potential. However, our findings reveal that despite reduced footprints, a dietary shift can lead to an increased amount of food waste due to the rising consumption of products associated with higher food waste shares. Therefore, policy strategies addressing both targets might be contradicting. We conclude that international and national policies can be most effective in achieving higher resource efficiency by exploiting the reduction potentials of all available strategies while simultaneously considering strategy interactions.

Informed environmental-economic policy decisions require a solid understanding of the economy's biophysical basis. Global physical input-output tables (gPIOTs) collate a vast array of information on the world economy's physical structure and its interdependence with the environment. However, building gPIOTs requires dealing with mismatched and incomplete primary data with high uncertainties, which makes it a time-consuming and labor-intensive endeavor. We address this challenge by introducing the PIOLab: A virtual laboratory for building gPIOTs. It represents the newest branch of the Industrial Ecology virtual laboratory (IELab) concept, a cloud-computing platform and collaborative research environment through which participants can use each other's resources to assemble individual input-output tables targeting specific research questions. To overcome the lack of primary data, the PIOLab builds extensively upon secondary data derived from a variety of models commonly used in Industrial Ecology. We use the case of global iron-steel supply chains to describe the architecture of the PIOLab and highlight its analytical capabilities. A major strength of the gPIOT is its ability to provide mass-balanced indicators on both apparent/direct and embodied/indirect flows, for regions and disaggregated economic sectors. We present the first gPIOTs for 10 years (2008-2017), covering 32 regions, 30 processes and 39 types of iron/steel flows. Diagnostic tests of the data reconciliation show a good level of adherence between raw data and the values realized in the gPIOT. We conclude with elaborating on how the PIOLab will be extended to cover other materials and energy flows.

The area used for mineral extraction is a key indicator for understanding and mitigating the environmental impacts caused by the extractive sector. To date, worldwide data products on mineral extraction do not report the area used by mining activities. In this paper, we contribute to filling this gap by presenting a new data set of mining extents derived by visual interpretation of satellite images. We delineated mining areas within a 10 km buffer from the approximate geographical coordinates of more than six thousand active mining sites across the globe. The result is a global-scale data set consisting of 21,060 polygons that add up to 57,277 km 2. The polygons cover all mining above-ground features that could be identified from the satellite images, including open cuts, tailings dams, waste rock dumps, water ponds, and processing infrastructure. The data set is available for download from https://doi.

Ecologically unequal exchange theory posits asymmetric net flows of biophysical resources from poorer to richer countries. To date, empirical evidence to support this theoretical notion as a systemic aspect of the global economy is largely lacking. Through environmentally-extended multi-regional input-output modelling, we provide empirical evidence for ecologically unequal exchange as a persistent feature of the global economy from 1990 to 2015. We identify the regions of origin and final consumption for four resource groups: materials, energy, land, and labor. By comparing the monetary exchange value of resources embodied in trade, we find significant international disparities in how resource provision is compensated. Value added per ton of raw material embodied in exports is 11 times higher in high-income countries than in those with the lowest income, and 28 times higher per unit of embodied labor. With the exception of embodied land for China and India, all other world regions serve as net exporters of all types of embodied resources to high-income countries across the 1990-2015 time period. On aggregate, ecologically unequal exchange allows high-income countries to simultaneously appropriate resources and to generate a monetary surplus through international trade. This has far-reaching implications for global sustainability and for the economic growth prospects of nations.

Global supply chains shift environmental and social impacts of consumption to remote locations. This opacity challenges many sustainability goals. To help businesses and governments realize more sustainable supply chains, new approaches are using spatial data and machine-learning techniques to connect Earth observation data to conventional economic tools.

The relationship between economic affluence, quality of life, and environmental implications of production and consumption activities is a recurring issue in sustainability discussions. A number of studies examined selected relationships, but the general implications for future development options to achieve environmentally and socially sustainable development of countries at different levels of per capita resource footprints, quality of life, and income have not yet been investigated in detail. In this study, we use a global dataset with 173 countries to assess the overall relationship between resource footprints, quality of life, and economic development over the period of 1990–2015. We select the material footprint and carbon footprint and contrast them with the Human Development Index, the Happiness Index, and GDP per capita. Regression analyses show that the relationship between various resource footprints and quality of life generally follows a logarithmic path of development, while resource footprints and GDP per capita are linearly connected. From the empirical results, we derive a generalized path of development and cluster countries along this path. Within this comprehensive framework, we discuss options to change the path to respect planetary and social boundaries through a combination of resource efficiency increases, substitution of industries and sufficiency of consumption. We conclude that decoupling and green growth will not realize sustainable development if planetary boundaries have already been transgressed.

In their recent study, Chaves et al. (1) investigate the role of international trade and consumption as a driver of malaria risk via deforestation. They base their analysis on a malaria risk footprint, which they define as a counterfactual. The measure is constructed using a linear regression model, which is used to capture causal linkages. Chaves et al. (1) find international trade as a major driver of malaria risk and suggest policy measures accordingly. In this letter, we argue that this interdisciplinary study suffers from a number of problems with far-reaching implications for its results and hence, the conclusions that can be drawn concerning malaria prevention. We outline severe methodological flaws and demonstrate how findings hinge on strong and intransparent assumptions. Nonetheless, we see great potential in the mixed methods approach and conclude with recommendations for future studies.

Metal mining has significant impacts on the land it uses. With increasing demand for metals, these impacts will continue to intensify. One way to look at land use and related environmental impacts is the concept of ecosystem services (ES), defined as the benefits people derive from services provided by ecosystems. This paper estimates the costs of the reduction of ES due to metal mining`s global land use by analysing four key metal ores – bauxite (aluminium), copper, gold and iron, and by doing so, provides also novel information from which biomes those metals are extracted. The overall ES cost caused by metal mining is estimated at about USD 5.4 billion/year (2016), with about two thirds in forested areas. If added to prices, it would lead to increases of between 0.8 % and 7.9 % for the four commodities studied. The authors do not understand ES valuation as a market-based, stand-alone tool to lower the land impact of metal mining. Other policy tools would have to play a leading role, such as zoning regulations, environmental minimum standards or closure legislation. However, it would be a useful support for such policy tools in all stages of mining where land use aspects play a role.

The relationship between economic affluence, quality of life and environmental implications of production and consumption activities is a recurring issue in sustainability discussions. A number of studies examined selected relationships, but the general implications for future development directions of countries at different development stages are hardly addressed. In this paper, we use a global dataset with 173 countries to assess the overall relationship between resource footprints, quality of life and economic development over the period of 1990-2015. We select the Material Footprint and Carbon Footprint and contrast them with the Human Development Index, the Happiness Index and GDP per capita. Regression analyses show that the relationship between various resource footprints and quality of life generally follows a logarithmic path of development, while resource footprints and GDP per capita are linearly connected. From the empirical results, we derive a generalised path of development and cluster countries along this path. Within this comprehensive framework, we discuss options to change the path to respect planetary and social boundaries through a combination of resource efficiency increases, substitution of industries and sufficiency of consumption. We conclude that decoupling and green growth will not realise sustainable development, if planetary boundaries have already been transgressed. Supplementary Information available at epub.wu.ac.at/id/eprint/7334.

Deforestation of the Amazon rainforest is a threat to global climate, biodiversity, and many other ecosystem services. In order to address this threat, an understanding of the drivers of deforestation processes is required. Indirect impacts and determinants that eventually differ across locations and over time are important factors in these processes. These are largely disregarded in applied research and thus in the design of evidence-based policies. In this study, we employ a flexible modelling framework to gain more accurate quantitative insights into the complexities of deforestation phenomena. We investigate the impacts of agriculture in Mato Grosso, Brazil, for the period 2006--2017 and explicitly consider spatial spillovers and varying impacts over time and space. Spillover effects from croplands in the Amazon appear as the major driver of deforestation, with no direct effects from agriculture in later years. This suggests moderate success of the Soy Moratorium and Cattle Agreements, but highlights their inability to address indirect effects. We find that neglect of spatial dynamics and the assumption of homogeneous impacts leads to distorted inference. Researchers need to be aware of the complex and dynamic processes behind deforestation, in order to facilitate effective policy design.

Input-output analysis is one of the central methodological pillars of industrial ecology. However, the literature that discusses different structures of environmental extensions (EEs), i.e. the scope of physical flows and their attribution to sectors in the monetary input-output table (MIOT), remains fragmented. This paper investigates the conceptual and empirical implications of applying two different but frequently used designs of environmental extensions, using the case of energy accounting, where one represents energy supply while the other energy use in the economy. We derive both extensions from an official energy supply-use dataset and apply them to the same single-region input-output (SRIO) model of Austria, thereby isolating the effect that stems from the decision for the extension design. We also crosscheck the SRIO results with energy footprints from the global multi-regional input-output (GMRIO) dataset EXIOBASE. Our results show that the ranking of footprints of final demand categories (e.g., household and export) is sensitive to the extension design and that product-level results can vary by several orders of magnitude. The GMRIO-based comparison further reveals that for a few countries the supply-extension result can be twice the size of the use-extension footprint (e.g. Australia and Norway). We propose a graph approach to provide a generalized framework to disclosing the design of EEs. We discuss the conceptual differences between the two extension designs by applying analogies to hybrid life-cycle assessment and conclude that our findings are relevant for monitoring of energy efficiency and emission reduction targets and corporate footprint accounting.

This work investigates whether mining activities relate to the economic performance of mining regions and their surrounding areas. We exploit a panel of 32 Mexican, 24 Peruvian and 16 Chilean regions over the period 2008 - 2015 and, in doing so, relate mine-specific data on extraction intensity to regional economic impacts. The study employs a Spatial Durbin Model (SDM) with heteroskedastic errors to provide a flexible econometric framework to measure the impact of natural resource extraction. The results suggest that mining intensity does not significantly affect regional economic growth in both short-run and medium-run growth models.

Land resources are important for China’s rapid economic development, especially for food and construction. China’s land resources are under tremendous pressures, and therefore land use is increasingly displaced to other parts of the world. This study analyses the evolution and driving forces of China’s land consumption from 1995 to 2015. The main results show that China’s land footprint increased from 8.8% of the global land resources under human use in 1995 to 15.7% in 2015. China’s domestic land resources are mainly used for serving domestic consumption. Moreover, China needs to import virtual land from foreign countries to satisfy 30.8% of its land demand. Among the three land use types of cropland, grassland and forests, grassland had the largest fraction in China’s land footprint from 1995 to 2000, while forest has become the largest one since 2000. China’s virtual land trade experienced a sharp increase in net imports from 9.4E+04 km2 in 1995 to 3.4E+06 km2 in 2015. Observing China’s virtual land network by a cluster analysis, this study concludes that China keeps tight relationships with Australia, Japan, Brazil and Korea for its cropland consumption, and Canada, USA, Mexico, Australia, Korea and Japan are relevant for its grassland consumption. In addition, decomposition analysis results show that affluence is the major driving factor for China’s land consumption, while changes in land use intensity could mitigate some of the related effects. Lastly, policy recommendations are proposed so that China can move toward sustainable land management.

Harvested biomass is linked to final consumption by networks of processes and actors that convert and distribute food and non-food goods. Achieving a sustainable resource metabolism of the economy is an overarching challenge which manifests itself in a number of the UN Sustainable Development Goals. Modelling the physical dimensions of biomass conversion and distribution networks is essential to understanding the characteristics, drivers and dynamics of the socio-economic biomass metabolism. In this paper, we present the Food and Agriculture Biomass Input--Output model (FABIO), a set of multi-regional supply, use and input--output tables in physical units, that document the complex flows of agricultural and food products in the global economy. The model assembles FAOSTAT statistics reporting crop production, trade, and utilisation in physical units, supplemented by data on technical and metabolic conversion efficiencies, into a consistent, balanced, input--output framework. FABIO covers 191 countries and 130 agriculture, food and forestry products from 1986 to 2013. The physical supply-use tables offered by FABIO provide a comprehensive, transparent and flexible structure for organising data representing flows of materials within metabolic networks. They allow tracing biomass flows and embodied environmental pressures along global supply chains at an unprecedented level of product and country detail and can help to answer a range of questions regarding environment, agriculture, and trade. Here we apply FABIO to the case of cropland footprints and show the evolution of consumption-based cropland demand in China, the EU, and the US for plant-based and livestock-based food and non-food products.

A rapidly growing share of global agricultural areas is devoted to the production of biomass for non-food purposes. The expanding non-food bioeconomy can have far-reaching social and ecological implications; yet, the non-food sector has attained little attention in land footprint studies. This paper provides the first assessment of the global cropland footprint of non-food products of the European Union (EU), a globally important region regarding its expanding bio-based economy. We apply a novel hybrid land flow accounting model, combining the biophysical trade model LANDFLOW with the multi-regional input–output model EXIOBASE. The developed hybrid approach improves the level of product and country detail, while comprehensively covering all global supply chains from agricultural production to final consumption, including highly processed products, such as many non-food products. The results highlight the EU's role as a major processing and the biggest consuming region of cropland-based non-food products, while at the same time relying heavily on imports. Two thirds of the cropland required to satisfy the EU's non-food biomass consumption are located in other world regions, particularly in China, the US and Indonesia, giving rise to potential impacts on distant ecosystems. With almost 39% in 2010, oilseeds used to produce for example biofuels, detergents and polymers represented the dominant share of the EU's non-food cropland demand. Traditional non-food biomass uses, such as fibre crops for textiles and animal hides and skins for leather products, also contributed notably (22%). Our findings suggest that if the EU Bioeconomy Strategy is to support global sustainable development, a detailed monitoring of land use displacement and spillover effects is decisive for targeted and effective EU policy making.

In various international policy processes such as the UN Sustainable Development Goals, an urgent demand for robust consumption‐based indicators of material flows, or material footprints (MFs), has emerged over the past years. Yet, MFs for national economies diverge when calculated with different Global Multiregional Input–Output (GMRIO) databases, constituting a significant barrier to a broad policy uptake of these indicators. The objective of this paper is to quantify the impact of data deviations between GMRIO databases on the resulting MF. We use two methods, structural decomposition analysis and structural production layer decomposition, and apply them for a pairwise assessment of three GMRIO databases, EXIOBASE, Eora, and the OECD Inter‐Country Input–Output (ICIO) database, using an identical set of material extensions. Although all three GMRIO databases accord for the directionality of footprint results, that is, whether a countries’ final demand depends on net imports of raw materials from abroad or is a net exporter, they sometimes show significant differences in level and composition of material flows. Decomposing the effects from the Leontief matrices (economic structures), we observe that a few sectors at the very first stages of the supply chain, that is, raw material extraction and basic processing, explain 60% of the total deviations stemming from the technology matrices. We conclude that further development of methods to align results from GMRIOs, in particular for material‐intensive sectors and supply chains, should be an important research priority. This will be vital to strengthen the uptake of demand‐based material flow indicators in the resource policy context.

A new approach to allocate environmental responsibility, the ‘value added-based responsibility’ allocation, is presented in this article. This metric allocates total environmental pressures occurring along an international supply chain to the participating sectors and countries according to the share of value added they generate within that specific supply chain. We show that – due to their position in global value chains – certain sectors (e.g. services) and countries (e.g. Germany) receive significantly greater responsibility compared to other allocation approaches. This adds a new perspective to the discussions concerning a fair distribution of mitigation costs among nations, companies and consumers.