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.

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.

The call for a decent life for all within planetary limits poses a dual challenge: provide all people with the essential resources needed to live well and, collectively, not exceed the source and sink capacity of the biosphere to sustain human societies. We examine the corridor of possible distributions of household energy and carbon footprints that satisfy both minimum energy use for a decent life and available energy supply compatible with the 1.5 °C target in 2050. We estimated household energy and carbon footprints for expenditure deciles for 28 European countries in 2015 by combining data from national household budget surveys with the environmentally-extended multi-regional input–output model EXIOBASE. We found a top-to-bottom decile ratio (90:10) of 7.2 for expenditure, 3.1 for net energy and 2.6 for carbon. The lower inequality of energy and carbon footprints is largely attributable to inefficient energy and heating technologies in the lower deciles (mostly Eastern Europe). Adopting best technology across Europe would save 11 EJ of net energy annually, but increase environmental footprint inequality. With such inequality, both targets can only be met through the use of CCS, large efficiency improvements, and an extremely low minimum final energy use of 28 GJ per adult equivalent. Assuming a more realistic minimum energy use of about 55 GJ ae⁻¹ and no CCS deployment, the 1.5 °C target can only be achieved at near full equality. We conclude that achieving both stated goals is an immense and widely underestimated challenge, the successful management of which requires far greater room for maneuver in monetary and fiscal terms than is reflected in the current European political discourse.

The environmental and social consequences of clearing tropical forests for palm oil and soybean monoculture have been analyzed in a number of studies and are widely recognized. Some initiatives and studies have examined portions of the supply chain from the perspective of individual companies and stages in the supply chain. We complement this work by providing a consistent, detailed, global trade-linked analysis of the four major vegetable oils, connecting land use for production and its biodiversity impact, through global supply chains, to final consumers. To this end, we develop a global model by fully integrating FAO's physical supply-utilization accounts into the environmentally extended multiregional input-output model EXIOBASE. Global supply chains are linked with the life-cycle impact assessment model LC-Impact to assess biodiversity impact of land use via global maps of oil crop cultivation. For the period 2000-2010, we find significant substitution of domestically produced oils with relatively low biodiversity impacts with Indonesian palm oil and Brazilian soybean oil for the major consuming countries, China, Europe and the US. Whereas soybean oil remains the vegetable oil with the largest impact on biodiversity at a global scale, biodiversity footprints of palm oil have grown substantially larger in the period 2000-2010, driven by demand from Europe and China. Our results suggest that demand-side policies focused on specific oils, such as palm oil, might lead to switching oils and unintended shifts of environmental impacts.

The lack of subnational trade data has dampened the development of reliable regional and multiregional models for regional policy development. So, most researchers and vendors of regional and interregional economic models continue to rely on location quotients, supply‐demand pool techniques, or minor modifications of them, despite knowing that they under‐estimate interregional trade. In this piece, we analyse the relative viability of estimates of intraregional trade—so called “regional purchase coefficients” (RPCs). We do so for manufacturing sectors in 28 EU countries using the World Input‐Output Database. We introduce an RPC‐estimating technique using a quasi‐binomial regression approach for goods‐producing industries; we apply standard supply/demand ratios as RPCs for service‐based industries. We then apply the estimates to an aggregate EU input‐output (I‐O) table and measure how closely the results approximate the I‐O tables (direct requirements matrices) for each of the 28 EU nations. We compare these findings to those obtained by other conventional approaches. We also evaluate their ability to replicate the country Leontief inverses and output multipliers. We find quasi‐binomial regression approaches superior across the board.

Large databases mapping commodity flows measured in various units such as currency, tons or caloric values are the backbone of many recent environmental-economic studies. Their construction typically requires combining large amounts of partial information in a series of successive steps. These include the estimation of unobserved flows, transformations between units, handling aggregation re-classification and, finally, reconciling estimates with mass, financial and/or energy balances. This paper proposes a maximum entropy model that allows for the simultaneous estimation of unobserved commodity flows as well as corresponding prices such that data constraints in various units of measurement, levels of aggregation and possibly mismatching classifications are simultaneously satisfied. Its capability is assessed through a Monte-Carlo analysis and its performance compared with a simple step-wise approach. Our results suggest that the simultaneous approach performs significantly better in a vast majority of cases.