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Quarterly Labour Force Survey Household Dataset, July - September, 2025
No full textAbstract copyright UK Data Service and data collection copyright owner.BackgroundThe Labour Force Survey (LFS) is a unique source of information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. It is used to inform social, economic and employment policy. The LFS was first conducted biennially from 1973-1983. Between 1984 and 1991 the survey was carried out annually and consisted of a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter (data were then collected seasonally). From 1992 quarterly data were made available, with a quarterly sample size approximately equivalent to that of the previous annual data. The survey then became known as the Quarterly Labour Force Survey (QLFS). From December 1994, data gathering for Northern Ireland moved to a full quarterly cycle to match the rest of the country, so the QLFS then covered the whole of the UK (though some additional annual Northern Ireland LFS datasets are also held at the UK Data Archive). Further information on the background to the QLFS may be found in the documentation.Household datasetsUp to 2015, the LFS household datasets were produced twice a year (April-June and October-December) from the corresponding quarter's individual-level data. From January 2015 onwards, they are now produced each quarter alongside the main QLFS. The household datasets include all the usual variables found in the individual-level datasets, with the exception of those relating to income, and are intended to facilitate the analysis of the economic activity patterns of whole households. It is recommended that the existing individual-level LFS datasets continue to be used for any analysis at individual level, and that the LFS household datasets be used for analysis involving household or family-level data. From January 2011, a pseudonymised household identifier variable (HSERIALP) is also included in the main quarterly LFS dataset instead.Change to coding of missing values for household seriesFrom 1996-2013, all missing values in the household datasets were set to one '-10' category instead of the separate '-8' and '-9' categories. For that period, the ONS introduced a new imputation process for the LFS household datasets and it was necessary to code the missing values into one new combined category ('-10'), to avoid over-complication. This was also in line with the Annual Population Survey household series of the time. The change was applied to the back series during 2010 to ensure continuity for analytical purposes. From 2013 onwards, the -8 and -9 categories have been reinstated.LFS DocumentationThe documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each volume alongside the appropriate questionnaire for the year concerned. However, LFS volumes are updated periodically by ONS, so users are advised to check the ONS LFS User Guidance page before commencing analysis.Additional data derived from the QLFSThe Archive also holds further QLFS series: End User Licence (EUL) quarterly datasets; Secure Access datasets (see below); two-quarter and five-quarter longitudinal datasets; quarterly, annual and ad hoc module datasets compiled for Eurostat; and some additional annual Northern Ireland datasets.End User Licence and Secure Access QLFS Household datasetsUsers should note that there are two discrete versions of the QLFS household datasets. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. Secure Access household datasets for the QLFS are available from 2009 onwards, and include additional, detailed variables not included in the standard EUL versions. Extra variables that typically can be found in the Secure Access versions but not in the EUL versions relate to: geography; date of birth, including day; education and training; household and family characteristics; employment; unemployment and job hunting; accidents at work and work-related health problems; nationality, national identity and country of birth; occurrence of learning difficulty or disability; and benefits. For full details of variables included, see data dictionary documentation. The Secure Access version (see SN 7674) has more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements.Changes to variables in QLFS Household EUL datasetsIn order to further protect respondent confidentiality, ONS have made some changes to variables available in the EUL datasets. From July-September 2015 onwards, 4-digit industry class is available for main job only, meaning that 3-digit industry group is the most detailed level available for second and last job.Review of imputation methods for LFS Household data - changes to missing valuesA review of the imputation methods used in LFS Household and Family analysis resulted in a change from the January-March 2015 quarter onwards. It was no longer considered appropriate to impute any personal characteristic variables (e.g. religion, ethnicity, country of birth, nationality, national identity, etc.) using the LFS donor imputation method. This method is primarily focused to ensure the 'economic status' of all individuals within a household is known, allowing analysis of the combined economic status of households. This means that from 2015 larger amounts of missing values ('-8'/-9') will be present in the data for these personal characteristic variables than before. Therefore if users need to carry out any time series analysis of households/families which also includes personal characteristic variables covering this time period, then it is advised to filter off 'ioutcome=3' cases from all periods to remove this inconsistent treatment of non-responders.
Occupation data for 2021 and 2022 data filesThe ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.Main Topics:The LFS household datasets cover:characteristics of the household: number of people of working age; number of people over working age; number of children aged 0 to 4; number of children aged 5 to 15; number of dependent children (i.e. those in full-time education) aged 16 to 18economic activity in the household: number of people in employment; number of people in full-time employment; number of people in part-time employment; unemployed; economically inactive; students; sick or disabled; economically inactive but would like to work and are not seeking work because they do not believe there is work available ('discouraged workers'); care of dependants</ul
Gender Equitable Interactions Online: Supporting Gender Equity in Work-based Videoconferencing, 2022-2025
No full textThe GEiO project builds on and extends previous work that has demonstrated the negative consequences of gender inequalities in working life, as well as the growing demand for organisations to operate within socially and legally acceptable frameworks concerning human rights.
The project expands this research base by focusing on the rapid increase in virtual work during the Covid-19 pandemic, a trend that is likely to persist in the future for many organisations. A key objective was to generate new transnational evidence on the, until now, underexplored ways in which digital videoconferencing innovations can serve both to reinforce and to challenge gender inequity at work.
Three studies were conducted across four nations (Germany, Iceland, Spain and UK) to explore how gender is relevant to videoconferencing at work. Three different and complementary methods were used. The first study employed conversational analysis to make sense of video-recordings of online work meetings held in international corporations in each of the participating countries. The second study used Q methodology to explore shared understandings of digital working. Participants were recruited from the international corporations used in study one and other organisations known to use videconferencing. The third study used a Story Completion Task to capture social perceptions of videoconference meetings. Participants for this study were recruited from the personal and professional networks of each of the national research teams.
Broadly, our findings reveal that gender continues to infuence how people speak, listen, lead, and respond in digital spaces. Stereoptypical expectations about how men and women should communicate persist. However, as well as reinforcing traditional power imbalances, online meetings can also become spaces for more equitable collaboration especially if training and awareness building is provided.The GEiO project investigates the role of gender in online work meetings across four nations (Germany, Iceland, Spain and the UK). Research exploring how gender becomes relevant to videoconferencing at work remains in its infancy. Existing investigations suggest that with the exponential rise in digitally mediated working patterns and new reliance on videoconferencing platforms, organisations are unready to address gender inequality online.
A key aim of this project is to build new transnational evidence on the currently unexplored ways in which digital videoconferencing innovations maintain or can be used to resist gender inequity at work. We will be working with international corporations in each of the partner countries. Participating corporations will support data collection and provide feedback on the research process and findings. Three different and complementary methods will be used to explore gendered processes in this context. The first study will use video-recordings of online work meetings to analyse conversational patterns. The second study will explore shared understandings of digital working through rank orderings of relevant statements by participants that will be both statistically and thematically analysed. A Story Completion task will be used in the third study. This method can be used to capture social perceptions of videoconference meetings through storytelling. By approaching the data both by study and by country, this methodological design will enable cross-national comparisons at different levels of analysis. The research findings will provide a firm basis for knowledge exchange with private sector organisations to develop evidence-based training on digital gender equity. An accredited micro credential training course on gender equitable interactions online will be produced. It will be translated into the languages used in each of the partner locations to support good organisational practice.</p
An Ethnographic, Multi-Method Interview Dataset in Collaboration With a Community-Based Organisation in a Devolved Nation in the UK, 2021-2023
No full textThis is a PhD qualitative dataset. The thesis explored experiences of, and community-responses to, food insecurity.
Purpose of Data Collection: The thesis explored experiences of, and community-responses to, food insecurity.
Aims of Project: To generatively understand how community-based food hubs respond to food insecurity; to explore how experiences of food insecurity (may) impact on understandings and practices of sustainability and ethical consumption.
Content Note: The transcripts discuss content that readers may find upsetting. Please use your own discretion when working with the dataset. Themes such as trauma, hunger, substance misuse, violence, suicide, and abuse are discussed.
Participant Sample: A combination of snowball and targeted sampling was undertaken to gain various perspectives from people who occupied different roles, such as consumer, volunteer, or staff.
Data Collection Methods: Data was ethnographically generated in an urban city in a devolved nation in the UK. Informed consent was reached through consent forms, informal chats, and ongoing communications pre- and post-interviews for a year after the interviews to ensure withdrawal, if required, was possible.
Ethical Approval was granted by the University of [university name] Ethics Committee (Reference 400200066). In additional to the University’s procedural ethics requirements, I also developed a collaborative ethics document with participants.Purpose of Data Collection: The thesis explored experiences of, and community-responses to, food insecurity.
Aims of Project: To generatively understand how community-based food hubs respond to food insecurity; to explore how experiences of food insecurity (may) impact on understandings and practices of sustainability and ethical consumption.
Content Note: The transcripts discuss content that readers may find upsetting. Please use your own discretion when working with the dataset. Themes such as trauma, hunger, substance misuse, violence, suicide, and abuse are discussed.
Participant Sample: A combination of snowball and targeted sampling was undertaken to gain various perspectives from people who occupied different roles, such as consumer, volunteer, or staff.
Data Collection Methods: Data was ethnographically generated in an urban city in a devolved nation in the UK. Informed consent was reached through consent forms, informal chats, and ongoing communications pre- and post-interviews for a year after the interviews to ensure withdrawal, if required, was possible.
Ethical Approval was granted by the University of [university name] Ethics Committee (Reference 400200066). In additional to the University’s procedural ethics requirements, I also developed a collaborative ethics document with participants.</p
Attitudes Towards Brexit Panel, 2021-2022
No full textAbstract copyright UK Data Service and data collection copyright owner.This study contains the panel survey data on public attitudes towards Brexit in the United Kingdom, conducted between 2021-2022. It includes 3-wave panel data on how attitudes towards Brexit developed in the aftermath of the 2016 Brexit referendum, including questions on identification as "Leavers" and "Remainers", party identification, ideology, personality traits, political discussion and political tolerance. The 3 waves were conducted in March 2021, June 2021 and May 2022 by YouGov.Main Topics:PoliticsBrexitParty identificationPolitical attitudes</p
Scotland's Census 2022: Safeguarded Individual Microdata Sample at Region Level
No full textAbstract copyright UK Data Service and data collection copyright owner.The 2021 UK Census was the 23rd official census of the United Kingdom. The UK Census is generally conducted once every 10 years, and the 2021 censuses of England, Wales, and Northern Ireland took place on 21 March 2021. In Scotland, the decision was made to move the census to March 2022 because of the impact of the coronavirus pandemic (see SNs 9461 and 9462). The censuses were administered by the Office for National Statistics (ONS), the Northern Ireland Statistics and Research Agency (NISRA) and National Records of Scotland (NRS), respectively. Census 2021 was the first census with a digital-first design, encouraging participants to respond online rather than on a paper questionnaire. Support was given to people who could not respond online, including paper questionnaires, telephone contact centres, field force support, and an extended collection period.Topics covered in the 2021 UK Census included:demography and migrationethnic group, national identity, language and religionlabour market and travel to workhousingeducationhealth, disability, and unpaid careWelsh and other languagesUK armed forces veteranssexual orientation and gender identity.The Scotland's Census 2022: Safeguarded Individual Microdata Sample at Regional Level dataset consists of a random sample of 5% of person records from the 2022 Census. It includes records for 274,067 persons. These data cover Scotland only. The lowest level of geography is country (Scotland). The dataset contains 80 variables and a low level of detail. Further information can be found on the Scotland's Census website.
Census Microdata
Microdata are small samples of individual records from a single census from which identifying information have been removed. They contain a range of individual and household characteristics and can be used to carry out analysis not possible from standard census outputs, such as:
creating tables using bespoke variable combinationsinvestigating specific combinations of variables or categories in a high level of detailconducting non-tabular statistical analyses on record-level data.
The microdata samples are designed to protect the confidentiality of individuals and households. This is done by applying access controls and removing information that might directly identify a person, such as names, addresses and date of birth. Record swapping is applied to the census data used to create the microdata samples. This is a statistical disclosure control (SDC) method, which makes very small changes to the data to prevent the identification of individuals. The microdata samples use further SDC methods, such as collapsing variables and restricting detail. The samples also include records that have been edited to prevent inconsistent data and contain imputed persons, households, and data values. To protect confidentiality, imputation flags are not included in any 2022 Census microdata sample.Main Topics:The Scotland's Census 2022: Safeguarded Individual Microdata Sample at Regional Level dataset covers: communal establishments, demography, education, ethnicity, identity, language, religion, health, disability, unpaid care, housing, internal migration, international migration, labour market, students, travel to work, and UK armed forces veterans.</p
It’s All in the Details: Comparing and Explaining Variation in the ‘Policy Specificity’ of Sub-City Climate Activities, 2019-2022
No full textBetween late 2019 and early 2022, London boroughs developed CAPs that outlined how each borough sought to meet its climate targets; 25 boroughs aimed to be net zero or carbon neutral regarding their own estate and operations by 2030, while the others varied in their deadline years, from 2025 (Tower Hamlets) to 2040 (Havering). All these CAPs are publicly available.
The cross-London London Councils association published a spreadsheet collating each goal from every CAP divided across nine climate policy programmes developed by the boroughs (based on information as of 15th June 2022).
An additional policy area – ‘air quality’ – overlaps with climate policy but is distinct, and so we do not include air quality within our analysis; however, we coded these policies and include them within our supplementary file. Bexley (Conservative-led), Hackney, and Waltham Forest (both Labour-led) did not produce Climate Plans in time to be included in this analysis, resulting in a final dataset of 29 boroughs. We counted the number of policies for each programme and borough, thus obtaining the 'policy density' scores.
The final dataset contained 2,545 policies, of which 2,508 pertained to climate activities (37 were for air quality more broadly) and are subsequently analysed.The science behind climate change has been established, and now the mitigation of climate change has become a political puzzle. We need to act quickly to mitigate the worst impacts of climate change, and so this project is designed to find and then share effective policy solutions that can be used across society.
Until very recently, attempted solutions for climate change were 'top down': for example, the United Nations organised annual conferences, and those countries responsible for producing the most greenhouse gases dominated these negotiations. However, this approach for dealing with climate change has failed to generate effective change quickly enough, and academics are looking for new governance solutions for this most pressing and significant of issues.</p
UK National Survey on Attitudes to AI Companions: Aggregate Data, 2024
No full textTo ascertain how the British public feel about AI companions, we conducted a UK-wide demographically representative national survey (implemented by professional company Walnut Unlimited - a human understanding agency, part of the Unlimited Group), across 10-12 December 2024, 2073 respondents aged 18 years or over, online omnibus). This was a part of a Responsible AI award, to create soft governance of autonomous systems that interact with human emotions and/or emulate empathy.
The survey asks 22 closed-ended, multiple-choice questions on AI companions. The first set of questions (Q.1-2) glean participants’ familiarity with, and usage of, companion apps. The second set of questions (Q.3-4) explore the acceptability of design features of AI companions. The third set of questions (Q.5-7) explore the broad benefits and concerns from using AI companions. The fourth set of questions (Q.8-13) explore views on children and companion apps. The fifth set of questions (Q.14-15) explore views on older adults and companion apps. The sixth set of questions (Q.16-18) explore views on mental health issues and companion apps. The seventh set of questions (Q.19-21) explore views on desired governance of companion apps to consider the practicalities of what societies should do about AI companions, if anything. The final question (Q.22) is an evaluative question on whether participants feel AI companions are generally a positive or negative addition to society.Funded by the Responsible AI UK Impact Accelerator, project AEGIS sees Bangor University's Emotional AI Lab partnering with Japan’s National Institute of Informatics, the Institute of Electrical and Electronics Engineers (IEEE), Monash University (Indonesia) and engaging the Information Commissioner's Office (UK).
The goal of AEGIS is to host a series of workshops, assemble a diverse expert working group and develop a ‘technical standard’ to address use of emulated empathy in general-purpose artificial intelligence systems for human-AI partnerships.
Provisionally titled Recommended Practice for Ethical Considerations of Emulated Empathy in Partner-based General-Purpose Artificial Intelligence Systems, this IEEE standard will define ethical considerations, detail good practices, and augment and complement international human rights and regional law.
Use cases encompass general-purpose artificial intelligence products marketed as ‘empathic partners’, ‘personal AI’, ‘co-pilots’, ‘assistants’, and related phrasing for ‘human-AI partnering’. Current and nascent domains of use include work, therapy, education, life coaching, legal problems, fitness, entertainment, and more.
These systems raise ethical questions that are global in nature, yet benefitting from diverse ethical approaches, especially where systems feed into the design of human-centered technologies. Some ethical questions are familiar (e.g. transparency, accountability, bias and fairness), but others are specific and unique, including psychological interactions and dependencies, child appropriateness, fiduciary issues, animism, and manipulation through partnerships with general-purpose artificial intelligence systems.
The project augments the Emotional AI Lab's UK-Japan social science work by conducting a UK national demographically representative survey, and considering results in light of studies on AI ethics. It also sees global value in drawing a range of ethical frames of reference by which to account for human-AI partnerships, not least Japan and ethically aligned regions, given their long-standing interests in human-technology partnerships.</p
1970 British Cohort Study: Age 51, Sweep 11 Geographical Identifiers, 2021 Census Boundaries, 2021-2024: Secure Access
No full textAbstract copyright UK Data Service and data collection copyright owner.The 1970 British Cohort Study (BCS70) is a longitudinal birth cohort study, following a nationally representative sample of over 17,000 people born in England, Scotland and Wales in a single week of 1970. Cohort members have been surveyed throughout their childhood and adult lives, mapping their individual trajectories and creating a unique resource for researchers. It is one of very few longitudinal studies following people of this generation anywhere in the world.Since 1970, cohort members have been surveyed at ages 5, 10, 16, 26, 30, 34, 38, 42, 46, and 51. Featuring a range of objective measures and rich self-reported data, BCS70 covers an incredible amount of ground and can be used in research on many topics. Evidence from BCS70 has illuminated important issues for our society across five decades. Key findings include how reading for pleasure matters for children's cognitive development, why grammar schools have not reduced social inequalities, and how childhood experiences can impact on mental health in mid-life. Every day researchers from across the scientific community are using this important study to make new connections and discoveries.BCS70 is run by the Centre for Longitudinal Studies (CLS), a research centre in the UCL Institute of Education, which is part of University College London. The content of BCS70 studies, including questions, topics and variables can be explored via the CLOSER Discovery website.How to access genetic and/or bio-medical sample data from a range of longitudinal surveys:For information on how to access biomedical data from BCS70 that are not held at the UKDS, see the CLS Genetic data and biological samples webpage.Polygenic IndicesPolygenic indices are available under Special Licence SN 9439. Derived summary scores have been created that combine the estimated effects of many different genes on a specific trait or characteristic, such as a person's risk of Alzheimer's disease, asthma, substance abuse, or mental health disorders, for example. These polygenic scores can be combined with existing survey data to offer a more nuanced understanding of how cohort members' outcomes may be shaped.Secure Access datasetsSecure Access versions of BCS70 have more restrictive access conditions than versions available under the standard Safeguarded Licence.SN 9392 - 1970 British Cohort Study: Age 51, Sweep 11 Geographical Identifiers, 2021 Census Boundaries, 2021-2024: Secure Access includes detailed geographical variables from the BCS70 Age 51 Sweep 11 that can be linked to the main End User Licence data, available under SN 9347 - 1970 British Cohort Study: Age 51, Sweep 11, 2021-2024. The Age 51, Sweep 11 2011 Census Boundaries are available under SN 9391.International Data Access Network (IDAN)These data are now available to researchers based outside the UK. Selected UKDS SecureLab/controlled datasets from the Institute for Social and Economic Research (ISER) and the Centre for Longitudinal Studies (CLS) have been made available under the International Data Access Network (IDAN) scheme, via a Safe Room access point at one of the UKDS IDAN partners. Prospective users should read the UKDS SecureLab application guide for non-ONS data for researchers outside of the UK via Safe Room Remote Desktop Access. Further details about the IDAN scheme can be found on the UKDS International Data Access Network webpage and on the IDAN website.Main Topics:The 1970 British Cohort Study: Age 51, Sweep 11 Geographical Identifiers, 2021 Census Boundaries, 2021-2024: Secure Access includes the following variables:
CountryDecember 2020 RegionMay 2023 WardJanuary 2003 Census Area Statistics WardApril 2023 Local AuthorityJuly 2024 Westminster Parliamentary ConstituencyIdex of Multiple Deprivation (IMD) Overall Rank England 2019, Scotland 2020, Wales 2019, Northern Ireland 2017IMD Overall Rank Decile2021 Output Area2021 Lower Super Output Area2021 Middle Super Output Area</ul
Mapping History - What Historical Maps Can Tell Us About Urban Development: Digitisation Codes, 1800-1960
No full textThis project systematically processed high-resolution and manuscript historical maps to unlock a dormant body of information about the historical development of cities and regions during periods of structural economic transformation.
The work was organised across six interlinked work packages, combining empirical and theoretical analysis in the UK, France, and Canada. Outputs included peer-reviewed publications and robust algorithms for extracting spatial data from historical sources, contributing valuable tools and insights to the fields of urban economics and economic history.
This data package contains three segmentation codes designed to extract features and segment historical maps.Little is known about the patterns of city development during the structural transformation of economies. This project will systematically process high-resolution and manuscript historical maps to make a dormant body of information about our cities' and regions' past accessible.
The proposed research will advance our understanding of long-run urban growth through the development of three innovative methodologies, which will overcome practical limitations of historical data sources: 1) A technique to extract land use patterns from historical colour maps applied to France (1750-1950); 2) A recognition algorithm to detect, tag and geo-locate points of interest in historical high-quality maps of the 70 largest urban centre in England and Wales; 3) An algorithm to geo-locate address information from Micro-censuses and trade registers.
We have identified four main research questions that will be developed in the following separate research projects. In Project 1, the main question is: what are the long-term empirical patterns of urban development, most notably the persistence of the spatial organisation of economic activity and the role of building infrastructure in shaping such persistence? In Project 2, the main question is: How do environmental disamenities and their unequal distribution within cities affect the spatial organisation of consumption amenities and production? In Project 3, the main question is: Do cities grow towards their bad parts, their neighbourhoods with the lowest environmental amenities? In Project 4, the main question is: How does vertical growth and advances in building technologies affect the spatial organisation of cities?
To address these research questions, we will organise our workflow in six inter-connected work packages (WP):
WP1--Classification of land use in France (1750-2015): The objective of WP1 will be to recover land use information at a fine scale from digitised maps using state-of-the-art machine learning techniques;
WP2--Digitisation of micro-features embedded in Ordnance Survey (OS) city maps of England and Wales (1870-1960);
WP3--Geo-localization of residents and production units in England and Wales (1851-1911);
WP4--Dynamic model of city growth with persistent building stock: WP4 builds a general equilibrium model of spatial economic activity that embeds the durability of housing and infrastructure and exploits the three hundred years of population settlement data produced in WP1;
WP5--Pollution and the long-run development of cities: WP5 builds on WP2,3 and proposes to study the joint dynamics of residential sorting and the location of production within cities to understand how a major environmental disamenity-industrial pollution-affects the spatial organisation of cities in the longer-run;
WP6--Horizontal and vertical urban growth in Montreal and Toronto: WP6 will bridge between the previous working packages WP1, WP2, WP4 and WP5, and study--empirically and theoretically--horizontal and vertical urban growth.
The project will be jointly led by three teams. The French team will be composed of Gobillon (PI), Combes (CoI) and Duranton (TM) who have contributed to the development of major theoretical approaches in urban economics. The Canadian team will be led by Heblich (PI), who is a lead researcher in urban economics/economic history, and Fortin (Co-I), a lead in GIS analysis. The UK team will be led by Zylberberg (PI), who is an economist specialist in data extraction form historical sources and remote sensing. Shaw-Taylor and Schürer, advisory board, will help design the analysis of the population micro-censuses between 1851 and 1911 (WP3). The collaboration partner, Redding (TM), involved in the design of WP3 and the implementation of WP6, is one of the World lead researchers in urban economics.
Outputs will include articles in top economic journals, and detailed algorithms to extract relevant spatial information from manuscript maps.</p
Healthy Ageing and Climate Change, 2023-2024
No full textThrough a community-based participatory approach, this project identified the barriers and opportunities to inclusive climate resilient AFCCs in consultation with older people, policymakers and practitioners, businesses, social enterprises and entrepreneurs. We co-designed solutions with these key stakeholder groups to achieve ‘actionable’ interventions that support healthy ageing for older people in response to the climate crisis.
Qualitative datasets were collected across the study including a stakeholder event and dialogue workshops with older people. The findings identified a range of key findings for how we can support ageing in place in the context of a changing climate which were distilled into a recommendations document.Age friendly cities and communities (AFCCs) encourage active ageing by optimising opportunities for health, participation, and security to enhance quality of life in old age. They can provide the resources, amenities and services to support healthy ageing-in-place, enabling older people to age well at home and in their communities. However, climate change related extreme weather events (e.g., flooding, heatwaves and storms) pose new challenges for the health and wellbeing of older people. Little research has explored how such extreme weather events will impact healthy ageing-in-place and AFCCs. There is an urgent need to deliver place-based supports that harness the contribution older people can make to climate action while reducing vulnerability, and building climate resilience through individual, social and community level interventions.
The project will seek to answer the research question: How can we build inclusive climate resilient age-friendly cities and communities (AFCCs) in the UK? The project objectives are to: (i) identify the barriers, challenges and opportunities to build inclusive AFCCs that are resilient to climate impacts and mobilises older people in climate action; (ii) co-design actionable solutions with older adults, policy makers and practitioners, and businesses and entrepreneurs to support healthy ageing across AFCCs; (iii) enhance capacity of key stakeholder groups (e.g., older people groups, community groups, policymakers, statutory and civic organisations and academia) to support climate-ready AFCCs.</p