Climate change is expected to intensify the frequency and severity of droughts across Europe, jeopardizing agricultural production and food systems. As drought severity can range widely within one country, supply chain risk modeling must capture subnational production and export specialization to accurately link drought scenarios and observations with the consumers at risk. This study does that by developing a subnational (NUTS-2) version of the national Food and Agriculture Biomass Input–Output (FABIO) model. We assess how food commodities are exposed to the 2018 Northern Europe drought, tracing drought-exposed crop production (here measured by the EUMETSAT ASCAT instruments aboard the ESA MetOp satellites) through interregional trade to final demand. We show drought events can aƯect distant regions, as trade redistributes risk among regions. National models can significantly mask regional diƯerences, showing artificially homogeneous drought exposure. These findings demonstrate the importance of high-resolution trade models for identifying vulnerable supply chains and informing climate adaptation strategies in European food systems.

We develop a deep learning approach to estimate employment by sector at fine spatial resolution using features of the built environment. A two-stage model first predicts total employment per tile (R^2 = 0.92) and then allocates employment across sectors, achieving strong accuracy. We show that commonly used gridded GDP methods substantially mischaracterize local sectoral composition, whereas our approach closely recovers observed patterns and generalizes to new countries without retraining. These results indicate that open geospatial data can be used to infer the spatial structure of economic activity, providing a new tool for economic measurement.

An important prerequisite for accurately characterizing economic exposure from climate change at the national scale is a spatial inventory of economic activity and value creation. Current options for such inventories are limited, being either spatially precise but economically bounded sector-specific or owner-specific datasets, or gridded gross domestic product (GDP) products with coarse spatial resolution and inadequate sectoral resolution. To address these limitations, we develop a map of national GDP with high spatial and sectoral resolution. We stress this with meter-scale flood hazard maps to characterize GDP at risk from flooding. We further couple this to a macroeconomic input–output analysis to use the new sectoral resolution to estimate the scope of indirect economic exposure to flood at a national scale.

Carbon pricing is a core climate policy in many countries. However, the distribution of impacts is highly unequal across income brackets, but also across household types and regions. The complex interplay between household characteristics and location specific factors such as building stock and transport infrastructure considerably hampers our understanding of the inequality impacts of carbon taxes and the development of remedial measures. In this paper, we simulate the impacts of carbon taxes and compensation on the purchasing power of more than 38 million German households living in over 11 000 municipalities. We find that the strength of impacts varies more within income groups (horizontal inequality) than across income groups (vertical inequality), based on demographic, socio-economic and geographic factors. Without compensation, a carbon tax of €50 per ton doubles the number of households at risk of becoming energy poor, the majority of them low-income families in remotely located small and medium cities. A lump sum payment of €100 per capita and year reduces inequality impacts and additional energy poverty risk substantially.

Carbon inequality is the gap in carbon footprints between the rich and the poor, reflecting an uneven distribution of wealth and mitigation responsibility. Whilst much is known about the level of inequality surrounding re-sponsibility for greenhouse gas (GHG) emissions, little is known about the evolution in carbon inequality and how the carbon footprints of socio-economic groups have developed over time. Inequality can be reduced either by improving the living standards of the poor or by reducing the overconsumption of the rich, but the choice has very different implications for climate change mitigation. Here, we investigate the carbon footprints of income quintile groups for major 43 economies from 2005 to 2015. We find that most developed economies had declining carbon footprints but expanding carbon inequality, whereas most developing economies had rising footprints but divergent trends in carbon inequality. The top income group in developing economies grew fastest, with its carbon footprint surpassing the top group in developed economies in 2014. Developments are driven by a reduction in GHG intensity in all regions, which is partly offset by income growth in developed countries but more than offset by the rapid growth in selected emerging economies. The top income group in developed economies has achieved the least progress in climate change mitigation, in terms of decline rate, showing resistance of the rich. It shows mitigation efforts could raise carbon inequality. We highlight the necessity of raising the living standard of the poor and consistent mitigation effort is the core of achieving two targets.

Chinese cities are core in the national carbon mitigation and largely affect global decarbonisation initiatives, yet disparities between cities challenge country-wide progress. Low-carbon transition should preferably lead to a convergence of both equity and mitigation targets among cities. Inter-city supply chains that link the production and consumption of cities are a factor in shaping inequality and mitigation but less considered aggregately. Here, we modelled supply chains of 309 Chinese cities for 2012 to quantify carbon footprint inequality, as well as explored a leverage opportunity to achieve an inclusive low-carbon transition. We revealed significant carbon inequalities: the 10 richest cities in China have per capita carbon footprints comparable to the US level, while half of the Chinese cities sit below the global average. Inter-city supply chains in China, which are associated with 80% of carbon emissions, imply substantial carbon leakage risks and also contribute to socioeconomic disparities. However, the significant carbon inequality implies a leveraging opportunity that substantial mitigation can be achieved by 32 super-emitting cities. If the super-emitting cities adopt their differentiated mitigation pathway based on affluence, industrial structure, and role of supply chains, up to 1.4 Gt carbon quota can be created, raising 30% of the projected carbon quota to carbon peak. The additional carbon quota allows the average living standard of the other 60% of Chinese people to reach an upper-middle-income level, highlighting collaborative mechanism at the city level has a great potential to lead to a convergence of both equity and mitigation targets.

Unsustainable environmental degradation and extreme economic inequality are two of humanity's most pressing challenges. They are intimately linked. Climate-altering greenhouse gas (GHG) emissions are disproportionately driven by consumption among wealthy and socially privileged groups, yet poorer and socially marginalized peoples face disproportionate climate harms. Here we use the Eora MRIO database and Consumer Expenditure Surveys to quantify GHG emissions related to goods and services consumed by United States households between 1996 and 2019 -including construction of a synthetic dataset to estimate top 1% and top 0.1% household emissions. Top 1% households are of particular interest because their emissions have been largely missed or simplistically and inaccurately estimated in past analysis, yet they exert disproportionate political power in shaping U.S. climate policy. Results suggest significant GHG inequality across economic class and racial lines. In 2019, we estimate the U.S. top 0.1% had emissions (955 t CO2e) 57x higher than bottom decile U.S. households and 597x higher than an average low-income country household. White non-Hispanic household emissions were 1.3x higher than Black households. If climate policy does not account for such extreme emissions disparities, it will limit effectiveness, erode public support, and disproportionately harm economic and socially marginalized groups.

Demand for food products, often from international trade, has brought agricultural land use into direct competition with biodiversity. Where these potential conflicts occur and which consumers are responsible is poorly understood. By combining conservation priority (CP) maps with agricultural trade data, we estimate current potential conservation risk hotspots driven by 197 countries across 48 agricultural products. Globally, a third of agricultural production occurs in sites of high CP (CP > 0.75, max = 1.0). While cattle, maize, rice, and soybean pose the greatest threat to very high-CP sites, other low-conservation risk products (e.g., sugar beet, pearl millet, and sunflower) currently are less likely to be grown in sites of agriculture–conservation conflict. Our analysis suggests that a commodity can cause dissimilar conservation threats in different production regions. Accordingly, some of the conservation risks posed by different countries depend on their demand and sourcing patterns of agricultural commodities. Our spatial analyses identify potential hotspots of competition between agriculture and high-conservation value sites (i.e., 0.5° resolution, or ~367 to 3,077km 2 , grid cells containing both agriculture and high-biodiversity priority habitat), thereby providing additional information that could help prioritize conservation activities and safeguard biodiversity in individual countries and globally. A web-based GIS tool at https://agriculture.spatialfootprint.com/biodiversity/ systematically visualizes the results of our analyses.

Civil infrastructure will be essential to face the interlinked existential threats of climate change and rising resource demands while ensuring a livable Anthropocene for all. However, conventional infrastructure planning largely neglects the ...

Income inequality poses a significant challenge for many countries, including Mexico. By 2018, according to CONEVAL, 52.4 million Mexicans were living in poverty, equalling 41.9% of the population. Mexico’s status as the 15th largest economy worldwide makes it a compelling case for analysing income distribution and its impacts on social class structure, particularly since Mexico was the 11th largest GHG emitter. This study focuses on exploring the dynamics of income and carbon inequality, assessing the differences between deciles, geographic domains such as urban and rural ones and 32 States. We do this by using Mexico’s 2018 National Household Income and Expenditure Survey coupled with an environmentally extended multi-regional input-output model to estimate decile’s consumption-based carbon footprints. We find that per capita GHG emissions by the 1% ultra-rich were 12 and 8.5 times bigger than the 10% low- and middle-income deciles, respectively. As such only a quarter of the Mexican population is within the Paris Agreement carbon budget (less than 2.2 tCO2e per capita). Reducing poverty and inequalities seems imperative for a country that is and will continue to be largely affected by climate change. Still, it should not come at the expense of increasing the carbon footprint per capita.

This archive contains supplementary figures from mapping potential conflicts between global agriculture and terrestrial conservation in 2010 and 2070. Please see the captions of these figures below. Supplementary Figure 3. Distribution of regional land use by agricultural commodity and conservation priority (CP) index intervals (48 x 3 sub-figures). Supplementary Figure 4. Distribution of regional land use (left) and global land use (right) of 48 agricultural commodities (48 x 3 sub-figures). The x-axis is the land use as a proportion of the total global production area. Supplementary Figure 5. Comparison of the distribution of regional land use for 48 agricultural commodities between 2010 and 2070 scenarios (48 x 7 x 2 sub-figures). Supplementary Figure 6. Comparison of land use and conservation conflict between national and global levels. The y-axis refers to the land use as a proportion of the total global production area. There are 48 agricultural commodities for 197 countries (197 x 48 x 3 sub-figures). Supplementary Figure 7. Comparison of the land use distribution of top producers for 48 agricultural commodities between 2010 and 2070 scenarios (48 x 10 x 2 sub-figures). Supplementary Figure 8. Spatial distribution maps of production areas for 48 agricultural commodities (48 x 3 maps). Supplementary Figure 9. Conflict between conservation priority sites and (non-)domestic land use for 42 agricultural commodities associated with consumption in 197 countries (197 x 42 x 3 sub-figures). Supplementary Figure 10. Land use maps of 42 agricultural commodities linked to consumption in 197 countries (197 x 42 x 3 maps). Supplementary Figure 11. Conservation priority index and export rate of selected agricultural commodity for primary production cells (land use of each agricultural commodity > 10% of cell area). a) Current status in 2010. b) Shifts of conservation priority in 2070 (RCP 8.5). Triangles point-up and point-down to indicate increased and decreased CP, respectively. Density plots on top and right show cell densities corresponding to export rate and CP index, respectively. Supplementary Figure 12. Maps of global conservation priority index in 2010 and 2070. Supplementary Figure 13. a, Performance curves for prioritization per scenario 2010, 2070-RCP2.6, and 2070-RCP8.5). Curves show the average fraction of species' range covered in each scenario weighted by each species weight, divided by the total weight of species (y-axis), by a given fraction of the landscape (x-axis). b, Histograms and boxplots of all CP map pixels. Each jittered point in boxplots represents a map pixel.

Current policies to reduce greenhouse gas (GHG) emissions and increase adaptation and mitigation funding are insufficient to limit global temperature rise to 1.5°C. It is clear that further action is needed to avoid the worst impacts of climate change and achieve a just climate future. Here, we offer a new perspective on emissions responsibility and climate finance by conducting an environmentally extended input output analysis that links 30 years (1990–2019) of United States (U.S.) household-level income data to the emissions generated in creating that income. To do this we draw on over 2.8 billion inter-sectoral transfers from the Eora MRIO database to calculate both supplier- and producer-based GHG emissions intensities and connect these with detailed income and demographic data for over 5 million U.S. individuals in the IPUMS Current Population Survey. We find significant and growing emissions inequality that cuts across economic and racial lines. In 2019, fully 40% of total U.S. emissions were associated with income flows to the highest earning 10% of households. Among the highest earning 1% of households (whose income is linked to 15–17% of national emissions) investment holdings account for 38–43% of their emissions. Even when allowing for a considerable range of investment strategies, passive income accruing to this group is a major factor shaping the U.S. emissions distribution. Results suggest an alternative income or shareholder-based carbon tax, focused on investments, may have equity advantages over traditional consumer-facing cap-and-trade or carbon tax options and be a useful policy tool to encourage decarbonization while raising revenue for climate finance.

Food production, particularly of fed animals, is a leading cause of environmental degradation globally.1,2 Understanding where and how much environmental pressure different fed animal products exert is critical to designing effective food policies that promote sustainability.3 Here, we assess and compare the environmental footprint of farming industrial broiler chickens and farmed salmonids (salmon, marine trout, and Arctic char) to identify opportunities to reduce environmental pressures. We map cumulative environmental pressures (greenhouse gas emissions, nutrient pollution, freshwater use, and spatial disturbance), with particular focus on dynamics across the land and sea. We found that farming broiler chickens disturbs 9 times more area than farming salmon (∼924,000 vs. ∼103,500 km2) but yields 55 times greater production. The footprints of both sectors are extensive, but 95% of cumulative pressures are concentrated into <5% of total area. Surprisingly, the location of these pressures is similar (85.5% spatial overlap between chicken and salmon pressures), primarily due to shared feed ingredients. Environmental pressures from feed ingredients account for >78% and >69% of cumulative pressures of broiler chicken and farmed salmon production, respectively, and could represent a key leverage point to reduce environmental footprints. The environmental efficiency (cumulative pressures per tonne of production) also differs geographically, with areas of high efficiency revealing further potential to promote sustainability. The propagation of environmental pressures across the land and sea underscores the importance of integrating food policies across realms and sectors to advance food system sustainability.

City-level CO2 emissions inventories are foundational for supporting the EU's decarbonization goals. Inventories are essential for priority setting and for estimating impacts from the decarbonization transition. Here we present a new CO2 emissions inventory for all 116 572 municipal and local-government units in Europe, containing 108 000 cities at the smallest scale used. The inventory spatially disaggregates the national reported emissions, using nine spatialization methods to distribute the 167 line items detailed in the National Inventory Reports (NIRs) using the UNFCCC (United Nations Framework Convention on Climate Change) Common Reporting Framework (CRF). The novel contribution of this model is that results are provided per administrative jurisdiction at multiple administrative levels, following the region boundaries defined OpenStreetMap, using a new spatialization approach. All data from this study are available on Zenodo https://doi.org/10.5281/zenodo.5482480 (Moran, 2021) and via an interactive map at https://openghgmap.net (last access: 7 February 2022​​​​​​​).

Projections of greenhouse gas (GHG) emissions are critical to better understanding and anticipating future climate change under different socio-economic conditions and mitigation strategies. The climate projections and scenarios assessed by the Intergovernmental Panel on Climate Change, following the Shared Socioeconomic Pathway (SSP)-Representative Concentration Pathway (RCP) framework, have provided a rich understanding of the constraints and opportunities for policy action. However, the current emissions scenarios lack an explicit treatment of urban emissions within the global context. Given the pace and scale of urbanization, with global urban populations expected to increase from about 4.4 billion today to about 7 billion by 2050, there is an urgent need to fill this knowledge gap. Here, we estimate the share of global GHG emissions emanating from urban areas from 1990 to 2100 based on the SSP-RCP framework. The urban GHG emissions are presented in five regional aggregates and are based on a combination of the urban population share, 2015 urban per capita CO2eq emissions, SSP-based national CO2eq emissions, and recent analysis of urban per capita CO2eq trends. We find that urban areas account for the majority of global GHG emissions in 2015 (61.8%). Moreover, the urban share of global GHG emissions progressively increases into the future, exceeding 80% in some scenarios by the end of the century. The combined urban areas in Asia and Developing Pacific, and Developed Countries account for 65.0% to 73.3% of cumulative urban emissions between 2020 and 2100 across the scenarios. Given these dominant roles, we describe the implications to potential urban mitigation in each of the scenario narratives in order to meet the goal of climate neutrality within this century.

Feeding humanity puts enormous environmental pressure on our planet. These pressures are unequally distributed, yet we have piecemeal knowledge of how they accumulate across marine, freshwater and terrestrial systems. Here we present global geospatial analyses detailing greenhouse gas emissions, freshwater use, habitat disturbance and nutrient pollution generated by 99% of total reported production of aquatic and terrestrial foods in 2017. We further rescale and combine these four pressures to map the estimated cumulative pressure, or ‘footprint’, of food production. On land, we find five countries contribute nearly half of food’s cumulative footprint. Aquatic systems produce only 1.1% of food but 9.9% of the global footprint. Which pressures drive these footprints vary substantially by food and country. Importantly, the cumulative pressure per unit of food production (efficiency) varies spatially for each food type such that rankings of foods by efficiency differ sharply among countries. These disparities provide the foundation for efforts to steer consumption towards lower-impact foods and ultimately the system-wide restructuring essential for sustainably feeding humanity. Producing sufficient food to support the planet’s growing population places enormous strain on critical ecosystems. Quantifying and mapping the individual and cumulative pressures from greenhouse gases, freshwater use, habitat disturbance and nutrient pollution provides crucial insight into producing lower-impact, more sustainable foods.

Cities are pivotal hubs of socioeconomic activities, and consumption in cities contributes to global environmental pressures. Compiling city-level multi-regional input-output (MRIO) tables is challenging due to the scarcity of city-level data. Here we propose an entropy-based framework to construct city-level MRIO tables. We demonstrate the new construction method and present an analysis of the carbon footprint of cities in China's Hebei province. A sensitivity analysis is conducted by introducing a weight reflecting the heterogeneity between city and province data, as an important source of uncertainty is the degree to which cities and provinces have an identical ratio of intermediate demand to total demand. We compare consumption-based emissions generated from the new MRIO to results of the MRIO based on individual city input-output tables. The findings reveal a large discrepancy in consumption-based emissions between the two MRIO tables but this is due to conflicting benchmark data used in the two tables.

City-level CO2 emissions inventories are foundational for supporting the EU’s decarbonization goals. Inventories are essential for priority setting and for estimating impacts from the decarbonization transition. Here we present a new CO2 emissions inventory for all 116,572 municipal and local government units in Europe, containing 108,000 cities at the smallest scale used. The inventory spatially disaggregates the national reported emissions, using 9 spatialization methods to distribute the 167 line items detailed in the National Inventory Reports (NIRs) using the UNFCCC Common Reporting Framework (CRF). The novel contribution of this model is that results are provided per administrative jurisdiction at multiple administrative levels, following the region boundaries defined OpenStreetMap, using a new spatialization approach. Project website: openghgmap.net