Metrics on resource productivity currently used by governments suggest that some developed countries have increased the use of natural resources at a slower rate than economic growth (relative decoupling) or have even managed to use fewer resources over time (absolute decoupling). Using the material footprint (MF), a consumption-based indicator of resource use, we find the contrary: Achievements in decoupling in advanced economies are smaller than reported or even nonexistent. We present a time series analysis of the MF of 186 countries and identify material flows associated with global production and consumption networks in unprecedented specificity. By calculating raw material equivalents of international trade, we demonstrate that countries' use of nondomestic resources is, on average, about threefold larger than the physical quantity of traded goods. As wealth grows, countries tend to reduce their domestic portion of materials extraction through international trade, whereas the overall mass of material consumption generally increases. With every 10% increase in gross domestic product, the average national MF increases by 6%. Our findings call into question the sole use of current resource productivity indicators in policy making and suggest the necessity of an additional focus on consumptionbased accounting for natural resource use.raw material consumption | multiregion input-output analysis | sustainable resource management

Human activities are causing Earth's sixth major extinction event-an accelerating decline of the world's stocks of biological diversity at rates 100 to 1,000 times pre-human levels. Historically, low-impact intrusion into species habitats arose from local demands for food, fuel and living space. However, in today's increasingly globalized economy, international trade chains accelerate habitat degradation far removed from the place of consumption. Although adverse effects of economic prosperity and economic inequality have been confirmed, the importance of international trade as a driver of threats to species is poorly understood. Here we show that a significant number of species are threatened as a result of international trade along complex routes, and that, in particular, consumers in developed countries cause threats to species through their demand of commodities that are ultimately produced in developing countries. We linked 25,000 Animalia species threat records from the International Union for Conservation of Nature Red List to more than 15,000 commodities produced in 187 countries and evaluated more than 5 billion supply chains in terms of their biodiversity impacts. Excluding invasive species, we found that 30% of global species threats are due to international trade. In many developed countries, the consumption of imported coffee, tea, sugar, textiles, fish and other manufactured items causes a biodiversity footprint that is larger abroad than at home. Our results emphasize the importance of examining biodiversity loss as a global systemic phenomenon, instead of looking at the degrading or polluting producers in isolation. We anticipate that our findings will facilitate better regulation, sustainable supply-chain certification and consumer product labelling.

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We have developed a new series of environmentally extended multi-region input-output (MRIO) tables with applications in carbon, water, and ecological footprinting, and Life-Cycle Assessment, as well as trend and key driver analyses. Such applications have recently been at the forefront of global policy debates, such as about assigning responsibility for emissions embodied in internationally traded products. The new time series was constructed using advanced parallelized supercomputing resources, and significantly advances the previous state of art because of four innovations. First, it is available as a continuous 20-year time series of MRIO tables. Second, it distinguishes 187 individual countries comprising more than 15,000 industry sectors, and hence offers unsurpassed detail. Third, it provides information just 1-3 years delayed therefore significantly improving timeliness. Fourth, it presents MRIO elements with accompanying standard deviations in order to allow users to understand the reliability of data. These advances will lead to material improvements in the capability of applications that rely on input-output tables. The timeliness of information means that analyses are more relevant to current policy questions. The continuity of the time series enables the robust identification of key trends and drivers of global environmental change. The high country and sector detail drastically improves the resolution of Life-Cycle Assessments. Finally, the availability of information on uncertainty allows policy-makers to quantitatively judge the level of confidence that can be placed in the results of analyses.

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While it is understood that cities generate the majority of carbon emissions, for most cities, towns, and rural areas around the world no carbon footprint (CF) has been estimated. The Gridded Global Model of City Footprints (GGMCF) presented here downscales national CFs into a 250 m gridded model using data on population, purchasing power, and existing subnational CF studies from the US, China, EU, and Japan. Studies have shown that CFs are highly concentrated by income, with the top decile of earners driving 30%-45% of emissions. Even allowing for significant modeling uncertainties, we find that emissions are similarly concentrated in a small number of cities. The highest emitting 100 urban areas (defined as contiguous population clusters) account for 18% of the global carbon footprint. While many of the cities with the highest footprints are in countries with high carbon footprints, nearly one quarter of the top cities (41 of the top 200) are in countries with relatively low emissions. In these cities population and affluence combine to drive footprints at a scale similar to those of cities in high-income countries. We conclude that concerted action by a limited number of local governments can have a disproportionate impact on global emissions.

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