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
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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.
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BFBNIB, NMLJ, NUK, PNG, SAZU, UL, UM, UPUK
Consumption-based carbon accounts (CBCAs) track how final demand in a region causes carbon emissions elsewhere due to supply chains in the global economic network, taking into account international ...trade. Despite the importance of CBCAs as an approach for understanding and quantifying responsibilities in climate mitigation efforts, very little is known of their uncertainties. Here we use five global multiregional input-output (MRIO) databases to empirically calibrate a stochastic multivariate model of the global economy and its GHG emissions in order to identify the main drivers of uncertainty in global CBCAs. We find that the uncertainty of country CBCAs varies between 2 and 16% and that the uncertainty of emissions does not decrease significantly with their size. We find that the bias of ignoring correlations in the data (that is, independent sampling) is significant, with uncertainties being systematically underestimated. We find that both CBCAs and source MRIO tables exhibit strong correlations between the sector-level data of different countries. Finally, we find that the largest contributors to global CBCA uncertainty are the electricity sector data globally and Chinese national data in particular. We anticipate that this work will provide practitioners an approach to understand CBCA uncertainties and researchers compiling MRIOs a guide to prioritize uncertainty reduction efforts.
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IJS, KILJ, NUK, PNG, UL, UM
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
Energy-related CO2 emissions in China have been extensively investigated. However, the mechanisms of how energy-related emissions are driven by inter-sectoral linkages remains unexplored. In this ...paper, a subsystem input-output model was developed to investigate the temporal and sectoral changes of emissions in China from 1997 to 2012. We decomposed total emissions into internal, spillover, feedback, and direct components. Our results show that the equipment manufacturing, construction and services sectors are the main sources of emissions during the whole period, which have a larger spillover component, primarily through indirect upstream emissions in the heavy-manufacturing, transportation, and power sectors. The emissions from the power and transportation sectors are dominated by direct rather than the spillover emissions. The shares of the feedback and internal components in the heavy manufacturing sectors were significantly higher than those of other sectors. Our results suggest that further addressing carbon emissions along the supply chain of equipment manufacturing, construction and services sectors, and improving technologies in the heavy manufacturing and power sectors holds important future opportunities for curbing the rapid growth of carbon emissions in China.
•A historical investigation of the evolution of China's energy-related CO2 emissions on a sectoral level.•The output-induced and the demand-induced CO2 emissions are compared.•Using an input-output model to explore inter-sectoral linkages by decomposing total emissions into internal, spillover, feedback and direct components.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
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.
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BFBNIB, GIS, IJS, INZLJ, KISLJ, NMLJ, NUK, PNG, UL, UM, UPUK, ZRSKP
Structural and index decomposition analyses allow identifying the main drivers of observed changes over time of energy and environmental impacts. These decomposition analyses have become very popular ...in recent decades and, many alternative methods to implement them have become available. Several of the most popular methods have been developed earlier in index number theory, a context in which each particular method is defined by adhering to a set of properties. The goal of the present paper is to review the main results of index number theory and discuss its connection to decomposition analyses. By doing so, we can present a decision tree that allows users to choose a decomposition method that meets desired properties. We report as hands-on example an empirical case study of the carbon footprint of the Netherlands in the period 2004-2005.
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Household greenhouse-gas footprints (HGFs) are an important source of global emissions but can vary widely between urban and rural areas. These differences are important during the ongoing rapid, ...global, urbanization process. We provide a global overview of HGFs considering this urban-rural divide. We include 16 global regions, representing 80% of HGFs and analyze the drivers of urban and rural HGFs between 2005 and 2015. We do this by linking multi-regional input-output (MRIO) tables with household consumption surveys (HCSs) from 43 regions. Urban HGFs from high-income regions continue to dominate, at 75% of total HGFs over 2010–2015. However, we find a significant increase of rural HGFs (at 1% yr−1), reflecting a convergent trend between urban and rural HGFs. High-income regions were responsible for the majority of urban HGFs (USA: 27.8% and EU: 18.7% in 2015), primarily from transport and services, while rural HGFs were predominately driven in emerging regions (China: 24% and India: 21.8% in 2015) mainly driven by food and housing. We find that improving emission intensities do not offset the increase in HGFs from increasing consumption and population during the period. A broad transition of expenditure from food to housing in rural areas and to transport in urban areas highlights the importance of reducing the emission intensities of food, housing, and transportation. Counterintuitively, urbanization increased HGFs in emerging regions, resulting in a >1% increase in China, Indonesia, India and Mexico over the period, due to large migrations of people moving from rural to urban areas.
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•Differences between urban and rural HGFs are investigated.•The ongoing consumption transition led to an emission pattern shift.•Rural emissions are slowly converging with high urban emissions.•High income regions drive 75% of household emissions.•Urbanization contributed to an increase of HGFs of above 1% yr−1.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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.
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CEKLJ, NUK, ODKLJ, UL, UM, UPUK
In order to respond to climate change, China has committed to reduce national carbon intensity by 40–45% in 2020 and 60–65% in 2030, relative to 2005. Given that energy-intensive industries represent ...~80% of total CO2 emissions in China and that China is a large and diverse country, this paper aims to investigate the potential contribution of regional convergence in energy-intensive industries to CO2 emissions reduction and to meeting China's emissions goals. To the best of our knowledge this matter has never been explored before. Using panel data from 2001 to 2015, we build three scenarios of future carbon intensities: business as usual (BAU), frontier (based on the directional distance function, in which all regions reach the efficiency frontier) and best available technology (BAT, in which all regions adopt the lowest-emitting technology). The frontier and BAT scenarios representa weak and a strong form of regional convergence, respectively, and the BAU assumes that it develops following historical patterns. We then use the Kaya identity to estimate CO2 emissions up to 2030 under the three scenarios. Our results are as follows: (1)Under BAU, the CO2 emissions of energy-intensive industries increase from 7382.8Mt in 2015 to 8127.6Mt in 2030. Under the frontier scenario the emissions in 2030 are 44.23% lower than under business as usual, while under the BAT scenario this value becomes 84.81%. Electricity and ferrousmetals are the sectors that most contribute to the reduction potential. (2)Even under BAU the carbon intensityof energy-intensive industries as a whole and all of its constituent sub-sectors except for electricity will decrease by more than the nationally-mandated averages. (3)Regional convergence could help the energy-intensive industries peak its CO2 emissions before 2030, while under BAU the absolute emissions of the energy-intensive industries keep increasing.
•The impact of regional convergence in energy-intensive industries on CO2 emissions was addressed.•We explored whether the energy-intensive industries can achieve the 2020 and 2030 goals.•In the BAU scenario the energy-intensive industries can achieve the carbon intensity targets.•With regional convergence the energy-intensive industries can reach the CO2 peak before 2030.•The regional convergence offers a large potential for emissions reduction of the electricity sector.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Summary
Global multiregion input‐output (MRIO) tables have been developed to capture international spillover effects due to demand in one country and production in other countries. International ...spillovers have been growing and have become so dominant, especially in environmental analysis, that their inclusion is essential when analyzing impacts of consumption. MRIO tables give full coverage of the world economy, but do not always respect the official data of a given country. When international spillovers also cause increased production in the country of demand, we see what are known as “feedback effects.” As coupled models are being developed that make use of an official foreground national input‐output table (IOT) alongside an existing global MRIO, we are left in the situation where a coupled model does not use the official foreground information when modeling international feedback loops. The question thus arises: How large are these feedback loops for different environmental impacts? We look specifically at the amount of domestic production that is embodied in imports back into that region. We find that for emissions, the feedbacks are small, usually <2% of the total import footprint, though up to 6%+ for some countries in some years for some stressors. Our findings suggest that using Leontief multipliers from available MRIOs may be an acceptable method for modeling imports into national IOTs for environmentally extended MRIO analysis.
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BFBNIB, DOBA, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, SIK, UILJ, UKNU, UL, UM, UPUK
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