Explore the vast range of titles available.

Dietary choices drive both health and environmental outcomes. Information on diets come from many sources, with nationally recommended diets (NRDs) by governmental or similar advisory bodies the most authoritative. Little or no attention is placed on the environmental impacts within NRDs. Here we quantify the impact of nation-specific NRDs, compared with an average diet in 37 nations, representing 64% of global population. We focus on greenhouse gases (GHGs), eutrophication, and land use because these have impacts reaching or exceeding planetary boundaries. We show that compared with average diets, NRDs in high-income nations are associated with reductions in GHG, eutrophication, and land use from 13.0 to 24.8%, 9.8 to 21.3%, and 5.7 to 17.6%, respectively. In upper-middle–income nations, NRDs are associated with slight decrease in impacts of 0.8–12.2%, 7.7–19.4%, and 7.2–18.6%. In poorer middle-income nations, impacts increase by 12.4–17.0%, 24.5–31.9%, and 8.8–14.8%. The reduced environmental impact in high-income countries is driven by reductions in calories (∼54% of effect) and a change in composition (∼46%). The increased environmental impacts of NRDs in low- and middle-income nations are associated with increased intake in animal products. Uniform adoption of NRDs across these nations would result in reductions of 0.19–0.53 Gt CO₂ eq·a−1, 4.32–10.6 Gt PO 4 3 − eq·a−1, and 1.5–2.8 million km², while providing the health cobenefits of adopting an NRD. As a small number of dietary guidelines are beginning to incorporate more general environmental concerns, we anticipate that this work will provide a standardized baseline for future work to optimize recommended diets further.

Environmentally extended input–output analysis (EEIOA) can be applied to assess the economic and environmental implications of a transition towards a circular economy. In spite of the existence of several such applications, a systematic assessment of the opportunities and limitations of EEIOA to quantify the impacts of circularity strategies is currently missing. This article brings the current state of EEIOA-based studies for assessing circularity interventions up to date and is organised around four categories: residual waste management, closing supply chains, product lifetime extension, and resource efficiency. Our findings show that residual waste management can be modelled by increasing the amount of waste flows absorbed by the waste treatment sector. Closing supply chains can be modelled by adjusting input and output coefficients to reuse and recycling activities and specifying such actions in the EEIOA model if they are not explicitly presented. Product lifetime extension can be modelled by combining an adapted final demand with adjusted input coefficients in production. The impacts of resource efficiency can be modelled by lowering input coefficients for a given output. The major limitation we found was that most EEIOA studies are performed using monetary units, while circularity policies are usually defined in physical units. This problem affects all categories of circularity interventions, but is particularly relevant for residual waste management, due to the disconnect between the monetary and physical value of waste flows. For future research, we therefore suggest the incorporation of physical and hybrid tables in the assessment of circularity interventions when using EEIOA.

Empirical estimates of source statistical economic data such as trade flows, greenhouse gas emissions, or employment figures are always subject to uncertainty (stemming from measurement errors or confidentiality) but information concerning that uncertainty is often missing. This article uses concepts from Bayesian inference and the maximum entropy principle to estimate the prior probability distribution, uncertainty, and correlations of source data when such information is not explicitly provided. In the absence of additional information, an isolated datum is described by a truncated Gaussian distribution, and if an uncertainty estimate is missing, its prior equals the best guess. When the sum of a set of disaggregate data is constrained to match an aggregate datum, it is possible to determine the prior correlations among disaggregate data. If aggregate uncertainty is missing, all prior correlations are positive. If aggregate uncertainty is available, prior correlations can be either all positive, all negative, or a mix of both. An empirical example is presented, which reports relative uncertainties and correlation priors for the County Business Patterns database. In this example, relative uncertainties range from 1% to 80% and 20% of data pairs exhibit correlations below −0.9 or above 0.9. Supplementary materials for this article are available online.

Thermoelectric generation requires large amounts of water for cooling. Recent warm periods have led to curtailments in generation, highlighting concerns about security of supply. Here we assess EU-wide climate impacts for 1,326 individual thermoelectric plants and 818 water basins in 2020 and 2030. We show that, despite policy goals and a decrease in electricity-related water withdrawal, the number of regions experiencing some reduction in power availability due to water stress rises from 47 basins to 54 basins between 2014 and 2030, with further plants planned for construction in stressed basins. We examine the reasons for these pressures by including water demand for other uses. The majority of vulnerable basins lie in the Mediterranean region, with further basins in France, Germany and Poland. We investigate four adaptations, finding that increased future seawater cooling eases some pressures. This highlights the need for an integrated, basin-level approach in energy and water policy. Climate change affects the availability of water for cooling thermoelectric power plants, causing curtailments in generation. This study models how future changes in water availability due to climate and water usage impacts power generation across the EU, and assesses different adaptation strategies.

The UNFCCC requires the annual reporting of greenhouse gas emissions. These inventories focus on emissions within a territory, and do not capture the effect of emissions embodied in imports. Consumption based carbon accounting (CBCA) has been proposed as a complementary method to capture these emissions, and a number of global models have been developed to operationalise CBCA. However, discrepancies in country-level CBCA results occur, which can cause concern for the practical use of CBCA. Despite these quantitative difference in results, do they provide robust results when changes over time are investigated? Here we present results of all the major global models and normalise the model results by looking at changes over time relative to a common base year value. We give an analysis of the variability across the models, both before and after normalisation in order to give insights into variance at national and regional level. A dataset of harmonised results (based on means) and measures of dispersion is presented, providing a baseline dataset for CBCA validation and analysis.

When working with economic accounts it may occur that multiple estimates of a single datum exist, with different degrees of uncertainty or data quality. This paper addresses the problem of defining a method that can reconcile conflicting estimates, given best guess and uncertainty values. We proceeded from first principles, using two different routes. First, under an entropy-based approach, the data reconciliation problem is addressed as a particular case of a wider data balancing problem, and an alternative setting is found in which the multiple estimates are replaced by a single one. Afterwards, under an axiomatic approach, a set of properties is defined, which characterizes the ideal data reconciliation method. Under both approaches, the conclusion is that the formula for the reconciliation of best guesses is a weighted arithmetic average, with the inverse of uncertainties as weights, and that the formula for the reconciliation of uncertainties is a harmonic average.

This paper addresses the problem of balancing statistical economic data, when data structure is arbitrary and both uncertainty estimates and a ranking of data quality are available. Using a Bayesian approach, the prior configuration is described as a multivariate random vector and the balanced posterior is obtained by application of relative entropy minimization. The paper shows that conventional data balancing methods, such as generalized least squares, weighted least squares and biproportional methods are particular cases of the general method described here. As a consequence, it is possible to determine the underlying assumptions and range of application of each traditional method. In particular, the popular biproportional method is found to assume that all source data has the same relative uncertainty. Finally, this paper proposes a simple linear iterative method that generalizes the biproportional method to the data balancing problem with arbitrary data structure, uncertainty estimates and multiple data quality levels.

Climate change policy and the reduction of greenhouse gas emissions are currently discussed at all scales, ranging from the Kyoto Protocol to the increasingly frequent advertisement of ''carbon neutrality'' in consumer products. However, the only policy option usually considered is the reduction of direct emissions. Another potential policy tool, currently neglected, is the reduction of indirect emissions, i.e., the emissions embodied in goods and services, or the payments thereof. This book addresses the accounting of indirect carbon emissions (as embodied in international trade) within the framework of input-output analysis and derives an indicator of environmental responsibility as the average of consumer and producer responsibility. A global multi-regional input-output model is built, using databases on international trade and greenhouse gas emissions, from which embodied carbon emissions and carbon responsibilities are obtained. Carbon Responsibility and Embodied Emissions consists of a theoretical part, concerning the choice of environmental indicators, and an applied part, reporting an environmental multi-regional input-output model. It will be of particular interest to postgraduate students and researchers in Ecological Economics, Environmental Input-Output Analysis, and Industrial Ecology. João F. D. Rodrigues is currently a Researcher at the Center for Innovation, Technology and Policy Research (IN+), Instituto Superior Técnico (IST), Lisbon, Portugal. Alexandra P.S. Marques is currently a MIT Portugal Program PhD student at IST in the area of Sustainable Energy Systems. Tiago M. D. Domingos is an Assistant Professor at the Environment and Energy Scientific Area, DEM, IST, and a Researcher at IN+. 1. Introduction 2. Accounting indirect emissions 3. Carbon indicators 4. Carbon responsibility 5. Multi-regional IO model 6. Carbon responsibility of world regions 7. Discussion

In this chapter we derive the indicator of carbon responsibility, following the work reported in Rodrigues et al. (2006). First, we shall propose a number of properties that we consider that a good indicator of indirect carbon emissions should possess, and afterwards we will derive the mathematical formulation of this indicator, which we call carbon responsibility.