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Background: Climate change threatens to undermine the past 50 years of gains in public health. In response, the National Health Service (NHS) in England has been working since 2008 to quantify and reduce its carbon footprint. This Article presents the latest update to its greenhouse gas accounting, identifying interventions for mitigation efforts and describing an approach applicable to other health systems across the world., Methods: A hybrid model was used to quantify emissions within Scopes 1, 2, and 3 of the Greenhouse Gas Protocol, as well as patient and visitor travel emissions, from 1990 to 2019. This approach complements the broad coverage of top-down economic modelling with the high accuracy of bottom-up data wherever available. Available data were backcasted or forecasted to cover all years. To enable the identification of measures to reduce carbon emissions, results were disaggregated by organisation type., Findings: In 2019, the health service's emissions totalled 25 megatonnes of carbon dioxide equivalent, a reduction of 26% since 1990, and a decrease of 64% in the emissions per inpatient finished admission episode. Of the 2019 footprint, 62% came from the supply chain, 24% from the direct delivery of care, 10% from staff commute and patient and visitor travel, and 4% from private health and care services commissioned by the NHS., Interpretation: This work represents the longest and most comprehensive accounting of national health-care emissions globally, and underscores the importance of incorporating bottom-up data to improve the accuracy of top-down modelling and enabling detailed monitoring of progress as health systems act to reduce emissions., Funding: Wellcome Trust., (Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Environmentally extended multi-regional input-output (EE-MRIO) models provide us with a wealth of data relating to consumption-based environmental impacts at a national level. The results can identify the categories of consumption and sectors of production that contribute most to environmental impact allowing policy makers to prioritise intervention into particular areas. However, these data are not readily accessible to policy makers and civil society, making it difficult to extract and communicate the important messages it contains. The web-based tool - EUREAPA - was created as a usable, task-oriented interface to improve access to environmental and economic data held within a complex EE-MRIO model and make it more relevant to policy makers and civil society. The project team of scientists and IT specialists used an iterative, agile and participatory approach to engage potential end-users in the specification and testing of the tool. The engagement process identified two principal functions that were essential for the EUREAPA tool: viewing data and creating scenarios. The viewing data function allows users to analyse the wealth of data held within the model and present results from a range of perspectives. This helps to understand the causes of environmental pressure and identify priorities for policy intervention. The scenario function helps to communicate how changes in consumption and production might affect the future environmental impact of citizens of the EU, and facilitates long-term planning. Through this dialogue process the project has been able to ensure EUREAPA is relevant, user-friendly and fit-for-purpose. It is intended that EUREAPA will be adopted by policy makers and civil society as an important policy planning and assessment aid in the complex field of sustainable consumption and production., (© 2013 Elsevier B.V. All rights reserved.)

To estimate household emissions from a consumption-perspective, national accounts are typically disaggregated to a sub-national level using household expenditure data. While limitations around using expenditure data are frequently discussed, differences in emission estimates generated from seemingly comparable expenditure microdata are not well-known. We compare UK neighbourhood greenhouse gas emission estimates derived from three such microdatasets: the Output Area Classification, the Living Costs and Food Survey, and a dataset produced by the credit reference agency TransUnion. Findings indicate moderate similarity between emission estimates from all datasets, even at detailed product and spatial levels; importantly, similarity increases for higher-emission products. Nevertheless, levels of similarity vary by products and geographies, highlighting the impact microdata selection can have on emission estimates. We focus our discussion on how uncertainty from microdata selection can be reduced in other UK and international contexts by selecting data based on the data generation process, the level of disaggregation needed, physical unit availability and research implications. [ABSTRACT FROM AUTHOR]

Financial transactions are increasingly used by consumer apps and financial service providers to estimate consumption-based carbon emissions. This approach promises a low-resource, ultra-fast, and highly scalable approach to measuring emissions at different levels of potential policy intervention--spanning the national, subnational, local, and individual level. Despite this potential, there is a lack of research exploring the validity of this approach to carbon profiling. Here we address this oversight in three ways. First, we provide a step-by-step description of our approach toward estimating carbon footprints from micro-level transaction data generated by more than 100,000 customers of a large retail bank in theUnitedKingdom. Second, we quantitatively compare emission estimates obtained from transaction data with those calculated from a more standard data source used in carbon profiling, the largest household expenditure survey in the United Kingdom. Third, we offer a detailed qualitative comparison of the advantages and disadvantages of transactions versus alternative data sources (such as survey data), across key dimensions including data availability, data quality, and data detail. We find that financial transactions offer a credible alternative to survey-based sources and, if made more widely accessible, could provide important advantages for profiling emissions. These include objective, micro-level data on consumption behaviors, larger sample sizes, and longitudinal, frequent data capture. [ABSTRACT FROM AUTHOR]

A major challenge for cities taking action on climate change is assessing and managing the contribution of urban consumption which triggers greenhouse gas (GHG) emissions outside city boundaries. Using a novel method of creating city‐level input–output tables, we present the first consistent, large‐scale, and global assessment of three‐scope GHG inventories for 79 members of the C40 Cities Climate Leadership Group. These inventories cover the emissions from sources located within city boundaries (Scope 1), emissions occurring as a consequence of the use of grid‐supplied electricity, heat, steam, and/or cooling (Scope 2), and all other GHG emissions that occur outside the city boundary as a result of activities taking place within the city (Scope 3). We find that, by only accounting for territorial emissions, without Scope 3, the 79 C40 cities under‐report 4% of global annual GHG emissions from six key infrastructure‐related transboundary sources (73%) and from service‐related sectors (27%). In contrast, when only accounting for consumption‐based emissions, the C40 cities would miss the mitigation target on 41% of their territorial emissions. We argue that cities should complement their GHG inventories, adding full Scope 3 to Scopes 1 and 2, and develop low‐carbon consumption strategies in addition to current infrastructure‐focused action on climate change. [ABSTRACT FROM AUTHOR]

Recent empirical assessments revealed that footprint indicators calculated with various multi-regional input–output (MRIO) databases deliver deviating results. In this paper, we propose a new method, called structural production layer decomposition (SPLD), which complements existing structural decomposition approaches. SPLD enables differentiating between effects stemming from specific parts in the technology matrix, e.g. trade blocks vs. domestic blocks, while still allowing to link the various effects to the total region footprint. Using the carbon footprint of the EU-28 in 2011 as an example, we analyse the differences between EXIOBASE, Eora, GTAP and WIOD. Identical environmental data are used across all MRIO databases. In all model comparisons, variations in domestic blocks have a more significant impact on the carbon footprint than variations in trade blocks. The results provide a wealth of information for MRIO developers and are relevant for policy makers designing climate policy measures targeted to specific stages along product supply chains. [ABSTRACT FROM PUBLISHER]

Community-level initiatives will play a key role in meeting greenhouse gas reduction targets. This paper examines the experience gained in applying a targeted social marketing approach to foster local-scale community pro-environmental behavioural change in the City of York, UK. This involved determining the neighbourhood carbon footprint, identifying residents that had access to appropriate infrastructure and were receptive to green issues. Six community teams were recruited from the selected neighbourhoods and provided with information, advice and mentoring on how to reduce their carbon footprint over a six-month period. A statistically significant reduction in carbon emissions was achieved. Each participant achieved a mean reduction in their carbon footprint of 2.0 tonnes of CO2e/year. The largest reductions were achieved in the areas of shopping and home energy. In addition, it helped to foster community spirit. Based on the experience gained from implementing this approach, a cost-effective model of community engagement is proposed. [ABSTRACT FROM PUBLISHER]