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To meet climate targets, consumption‐based household emissions need to fall rapidly. An important but still poorly understood question is whether generational change could contribute to decreases in emissions. It is sometimes assumed that younger generations such as Millennials and Generation Z are more concerned about climate change and have greener lifestyles than previous generations of the Silent Generation, Baby boomers, and Generation X, but carbon footprinting analysis typically focuses on age groups rather than comparing generations over time. This paper provides a first assessment of the change in consumption‐based carbon footprints of the Silent, Baby boomer, Generation X, and Millennial generations within the United Kingdom between 2001 and 2020 and a comparison of the footprints of different generational groups. The analysis is based on environmentally extended input–output analysis, using the Living Costs and Food Survey and emission data from the UK multi‐regional input–output database. We find some evidence for the hypothesis that younger generations have smaller footprints than older generations as Generation X and Millennial households have smaller carbon footprints compared to the generation before them at a similar life stage. We find that factors such as decarbonization, household occupancy, total expenditure, and changing consumption patterns contribute to the UK's changing carbon consumption emissions between 2001 and 2020, and the importance of these factors varies for different generational groups. However, future research that uses a longer time series is required to assess generational differences in carbon footprints over the whole lifespan of several generations. [ABSTRACT FROM AUTHOR]

The transition to Net Zero marks a radical reshaping of many aspects of everyday life in the effort to reduce human‐caused climate change. It also has the potential to influence positively a number of social challenges: improving public health, reducing the effects of poverty, increasing well‐being and bringing neighbourhoods together. However, these positive outcomes are by no means a given. Households on low incomes are less resilient than ever following austerity, COVID‐19 and the cost‐of‐living crisis. In this paper, we report on research undertaken in seven low‐income neighbourhoods in Leeds and Newcastle in the UK, in which we ran a series of workshops to understand perspectives and concerns on this issue. We found that people's perceived ability to engage in Net Zero was shaped by the neighbourhood they live in (due to its geographical location, local services and infrastructure), their housing (the building and its tenure) and household (the people they live with) as well as by their inability to access funds. It is clear from our data that people have big concerns about their ability to participate in the substantial changes they can see ahead. Our research suggests that ensuring a successful Net Zero transition for low‐income neighbourhoods will require a place‐based and people‐centred approach. We conclude by offering three principles for tailoring research and policy to specific geographic and socio‐economic needs, including (1) recognising patterns of difference and their spatial and social roots, (2) bringing whole life experiences into narratives of the future and (3) prioritising social inclusion in climate policy. [ABSTRACT FROM AUTHOR]

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]