Fuel poverty is the requirement to spend 10% or more of household income to maintain an adequate level or warmth. The energy efficiency of a house can be measured using the Standard Assessment Procedure (SAP). The procedure calculates a number between 1 and 100, low numbers generally indicates a house that has low levels of insulation and an inefficient heating system where as numbers closer to 100 indicate a very energy efficient house. SAP is the Government's recommended system for energy rating of dwellings. SAP is being used as a proxy for fuel poverty in households of people claiming income based benefits, given the link between income poverty and fuel poverty.

The number of households in the corresponding geographical area (modelled). Household numbers have been applied at sub-regional areas of geography and fixed to ensure that fuel poverty and household numbers at the English Region level match.

Data is Powered by LG Inform Plus and automatically checked for new data on the 3rd of each month.

This shows fuel poor households as a proportion of all households in the geographical area (modelled) using the Low Income Low Energy Efficiency (LILEE) measure. Since 2021 (2019 data) the LILEE indicator considers a household to be fuel poor if: it is living in a property with an energy efficiency rating of band D, E, F or G as determined by the most up-to-date Fuel Poverty Energy Efficiency Rating (FPEER) methodologyits disposable income (income after housing costs (AHC) and energy needs) would be below the poverty line. The Government is interested in the amount of energy people need to consume to have a warm, well-lit home, with hot water for everyday use, and the running of appliances. Therefore, fuel poverty is measured based on required energy bills rather than actual spending. This ensures that those households who have low energy bills simply because they actively limit their use of energy at home, Fuel poverty statistics are based on data from the English Housing Survey (EHS). Estimates of fuel poverty at the regional level are taken from the main fuel poverty statistics. Estimates at the sub-regional level should only be used to look at general trends and identify areas of particularly high or low fuel poverty. They should not be used to identify trends over time.Data is Powered by LG Inform Plus and automatically checked for new data on the 4th of each month.

This dataset is a Scottish Fuel Poverty Index created in the summer of 2023 by EDINA@University of Edinburgh as part of their student internship programme. The user guide provides descriptions of each data variable used in creating the index. The basic rationale was to replicate for Scotland work that had been conducted previously but only in respect to England and Wales. The two indices are not strictly directly comparable due to data availability and spatial granularity but provide standalone snapshots of relative fuel poverty across Great Britain. The Scottish Index is fully open source and for purposes of transparency and repeatability this guide provides an open methodology and is accompanied by the underlying data. Data are provided in good faith 'as is' and is the sole product of student effort as part of mentoring activities conducted by EDINA at the University. Each variable that was used in the Index was normalised relative to the individual values for that variable - which means the values presented in the underlying FPI data table do not represent the actual numbers for each local authority - merely the percentage relative to the other local authorities in Scotland. A separate file 'Fuel-poverty-index-raw-data-with-calc.csv' is available which contains the raw percentages used for the index along with a table containing the calculations used to obtain the final score and the main FPI data table. Fuel Poverty Index Excel: This file contains each Scottish local authority's ability to pay score, demand score and final score which were all obtained from the several different variables. The raw data for these variables can be found in the Raw Data file and an explanation for each variable can be found in the User Guide document. The scores are between 1 to 100 and are normalised relative to each other. This means the final scores do not represent the actual physical values for each area. Fuel Poverty Index csv: This file contains the normalised processed data that makes up the Scottish fuel poverty index with variables being in range of 1 to 100. Some variables have been weighted depending on how important they are to the index. The final scores rating each Scottish local authority from 1 to 100 are also included. Raw data: This file contains the raw unprocessed data that the index was created from for all Scottish local authorities. User Guide: This file contains the documentation of the process to create the index as well as descriptions of what each column in the Fuel Poverty Index csv file contain. This file also provides some examples of the visualisation created from the index Fuel Poverty Index Shapefile: This folder contains the .shp shape file comprising all the data from Fuel Poverty Index csv, in addition to also having the geospatial polygons associated with each local authority boundary. For the best viewing, the British National Grid EPSG 27700 coordinate system should be used.

This shows fuel poor households as a proportion of all households in the geographical area (modelled) using the Low Income Low Energy Efficiency (LILEE) measure. Since 2021 (2019 data) the LILEE indicator considers a household to be fuel poor if: it is living in a property with an energy efficiency rating of band D, E, F or G as determined by the most up-to-date Fuel Poverty Energy Efficiency Rating (FPEER) methodologyits disposable income (income after housing costs (AHC) and energy needs) would be below the poverty line. The Government is interested in the amount of energy people need to consume to have a warm, well-lit home, with hot water for everyday use, and the running of appliances. Therefore, fuel poverty is measured based on required energy bills rather than actual spending. This ensures that those households who have low energy bills simply because they actively limit their use of energy at home, Fuel poverty statistics are based on data from the English Housing Survey (EHS). Estimates of fuel poverty at the regional level are taken from the main fuel poverty statistics. Estimates at the sub-regional level should only be used to look at general trends and identify areas of particularly high or low fuel poverty. They should not be used to identify trends over time.Data is Powered by LG Inform Plus and automatically checked for new data on the 3rd of each month.

The number of households in the corresponding geographical area (modelled). Household numbers have been applied at sub-regional areas of geography and fixed to ensure that fuel poverty and household numbers at the English Region level match.

Data is Powered by LG Inform Plus and automatically checked for new data on the 3rd of each month.

Percentage of households in fuel poverty as measured by the Department for Energy Security and Net Zero. Statistics by tenure taken from the English Housing Survey. This dataset is one of the Greater London Authority's measures of Economic Fairness. Click here to find out more.

This shows fuel poor households as a proportion of all households in the geographical area (modelled) using the Low Income Low Energy Efficiency (LILEE) measure. Since 2021 (2019 data) the LILEE indicator considers a household to be fuel poor if: it is living in a property with an energy efficiency rating of band D, E, F or G as determined by the most up-to-date Fuel Poverty Energy Efficiency Rating (FPEER) methodologyits disposable income (income after housing costs (AHC) and energy needs) would be below the poverty line. The Government is interested in the amount of energy people need to consume to have a warm, well-lit home, with hot water for everyday use, and the running of appliances. Therefore, fuel poverty is measured based on required energy bills rather than actual spending. This ensures that those households who have low energy bills simply because they actively limit their use of energy at home, Fuel poverty statistics are based on data from the English Housing Survey (EHS). Estimates of fuel poverty at the regional level are taken from the main fuel poverty statistics. Estimates at the sub-regional level should only be used to look at general trends and identify areas of particularly high or low fuel poverty. They should not be used to identify trends over time.Data is Powered by LG Inform Plus and automatically checked for new data on the 3rd of each month.

Household budget survey and EU SILC datasets for Greece. Within these datasets we calculated a series of energy poverty and transport poverty indicators, as well as vulnerability indicators for households and transport for the Social Climate Plan. The dataset consists of the Household Budget Survey and the EU SILC for Greece. It is used to generate indicators for energy and transport poverty, as well as energy and transport vulnerability for the Social Climate Plan, under the EUropean Union's Social Climate Fund regulation.

Energy poverty is becoming a main challenge of the European welfare systems and beyond, abounding on the inequalities derived from living conditions and social determinants, with a direct and negative impact on health and wellbeing, mainly in urban contexts. Health problems attributable to energy poverty include respiratory diseases, heart attacks, stroke and mental disorders (stress, anxiety, depression), but also other acute health issues, such as hypothermia, injuries or influenza. The complex nature of this recently identified phenomenon requires a comprehensive analysis of the problem and its solution from a multidimensional approach, which should involve environmental, political, social, regulatory and psychological issues, thus involving other Social Determinants of Health and health inequalities. Urban policies and initiatives might respond very efficiently to energy poverty and their effects on the citizens wellbeing and health, by providing evidence-based interventions covering different angles of the challenges, including complementary actions covering individual (behavioural), but also social-political actions (regulations, urban planning) that include health in all policies.

In this context, the main objective of WELLBASED is to deliver a comprehensive urban programme to contribute to significantly reducing energy poverty and its effects on the citizens health and wellbeing. The programme will be implemented and evaluated in 6 different pilot cities. The design of the urban programme will be built on evidence-based approaches,

representing not only different urban realities but also a diverse range of welfare and healthcare models.

WELLBASED’s overarching objective will be realised by a series of Specific Objectives (SO) as follows:

SO1- To design a comprehensive urban programme to reduce energy poverty and its effects on health and wellbeing based on existing evidence, adaptable and transferrable to different European realities.

SO2- To foster the implementation of urban planning that considers health as a horizontal challenge.

SO3- To evaluate the short and mid-term effects of the programme on specific health conditions and wellbeing indicators.

SO4-To analyse the social and gender determinants linked to the intervention and its effects.

SO5-To determine the cost-effectiveness of the proposed programme in the different cities and compare its outcomes in the relevant dimension.

SO6- To develop policy recommendations to reduce energy poverty in cities and keeping sustainable high levels of urban health and quality of life.

SO7-To establish a systematic data collection framework and data platform on urban health which will enable better analysis and informed decisions on urban health issues.

SO8-To analyse the possibility to exploit the open data generated by the project to generate new business models for local SMEs.

SO9- To roll out a campaign to sign the " WELLBASED Manifesto". Cities determined to combat energy poverty and improve the health and well-being of their citizens are invited to demonstrate their commitment by signing the manifesto.

The project will run for 48 months, starting in March 1st 2021 and finishing in February 2025.

Finally, in order to fulfil one of the final objectives of the project, the data collected and analysed during the course of the study will be made available to the scientific community, following a process of aggregation and anonymisation of the data in order to comply with the European Union's guidelines on the protection of personal data. Local references, personal data and data that could indirectly lead to guessing the origin of the data have been eliminated.

If you would like information about the mechanism for accessing this data, please contact: a.vangrieken@erasmusmc.nl

This dataset provides data about household energy expenditure and energy poverty in Lithuania. The dataset contains detailed data about 5031 households and is based on the Lithuanian Survey on Income and Living Conditions (2019) micro dataset provided by Statistics Lithuania. It includes additional data derived from original survey data and energy poverty calculation results at household level.

Duomenų rinkinyje pateikiami duomenys apie namų ūkių energijos išlaidas ir energijos nepriteklių Lietuvoje 2019 metais. Duomenų rinkinys apima 5131 namų ūkį. Rinkinio pagrindas - Pajamų ir gyvenimo sąlygų statistinio tyrimo duomenys, skelbiami Lietuvos Statistikos departamento. Duomenų rinkinys apima ir papildomus duomenis gautus remiantis originalios apklausos duomenimis bei energijos nepritekliaus skaičiavimų rezultatus namų ūkio lygmenyje.

This dataset provides numbers of Canadian households in conditions of energy poverty at the 2021 Census sub-division level in Canada, cross-tabulated by demographic, income, and tenure/housing characteristics. It was developed by Abhilash Kantamneni and team at Efficiency Canada, using custom calculations of Statistics Canada household income and demographic data.

These geospatial data resources and the linked mapping tool below reflect currently available data on three categories of potentially qualifying Low-Income communities: Census tracts that meet the CDFI's New Market Tax Credit Program's threshold for Low Income, thereby are able to apply to Category 1. Census tracts that meet the White House's Climate and Economic Justice Screening Tool's threshold for disadvantage in the 'Energy' category, thereby are able to apply for Additional Selection Criteria Geography. Counties that meet the USDA's threshold for Persistent Poverty, thereby are able to apply for Additional Selection Criteria Geography. Note that Category 2 - Indian Lands are not shown on this map. Note that Persistent Poverty is not calculated for US Territories. Note that CEJST Energy disadvantage is not calculated for US Territories besides Puerto Rico. The excel tool provides the land area percentage of each 2023 census tract meeting each of the above categories. To examine geographic eligibility for a specific address or latitude and longitude, visit the program's mapping tool. Additional information on this tax credit program can be found on the DOE Landing Page for the 48e program at https://www.energy.gov/diversity/low-income-communities-bonus-credit-program or the IRS Landing Page at https://www.irs.gov/credits-deductions/low-income-communities-bonus-credit. Maps last updated: September 1st, 2024 Next map update expected: December 7th, 2024 Disclaimer: The spatial data and mapping tool is intended for geolocation purposes. It should not be relied upon by taxpayers to determine eligibility for the Low-Income Communities Bonus Credit Program. Source Acknowledgements: The New Market Tax Credit (NMTC) Tract layer using data from the 2016-2020 ACS is from the CDFI Information Mapping System (CIMS) and is created by the U.S. Department of Treasury Community Development Financial Institutions Fund. To learn more, visit CDFI Information Mapping System (CIMS) | Community Development Financial Institutions Fund (cdfifund.gov). https://www.cdfifund.gov/mapping-system. Tracts are displayed that meet the threshold for the New Market Tax Credit Program. The 'Energy' Category Tract layer from the Climate and Economic Justice Screening Tool (CEJST) is created by the Council on Environmental Quality (CEQ) within the Executive Office of the President. To learn more, visit https://screeningtool.geoplatform.gov/en/. Tracts are displayed that meet the threshold for the 'Energy' Category of burden. I.e., census tracts that are at or above the 90th percentile for (energy burden OR PM2.5 in the air) AND are at or above the 65th percentile for low income. The Persistent Poverty County layer is created by joining the U.S. Department of Agriculture, Economic Research Service's Poverty Area Official Measures dataset, with relevant county TIGER/Line Shapefiles from the US Census Bureau. To learn more, visit https://www.ers.usda.gov/data-products/poverty-area-measures/. Counties are displayed that meet the thresholds for Persistent Poverty according to 'Official' USDA updates. i.e. areas with a poverty rate of 20.0 percent or more for 4 consecutive time periods, about 10 years apart, spanning approximately 30 years (baseline time period plus 3 evaluation time periods). Until Dec 7th, 2024 both the USDA estimates using 2007-2011 and 2017-2021 ACS 5-year data. On Dec 8th, 2024, only the USDA estimates using 2017-2021 data will be accepted for program eligibility.

Ethical clearance reference number: refer to the uploaded document Ethics Certificate.pdf.

General (0)

0 - Built diagrams and figures.pdf: diagrams and figures used for the thesis

Analysis of country data (1)

0 - Country selection.xlsx: In this analysis the sub-Saharan country (Niger) is selected based on the kWh per capita data obtained from sources such as the United Nations and the World Bank. Other data used from these sources includes household size and electricity access. Some household data was projected using linear regression. Sample sizes VS error margins were also analyzed for the selection of a smaller area within the country.

Smart metering experiment (2)

The figures (PNG, JPG, PDF) include:

    - The experiment components and assembly
    - The use of device (meter and modem) softwar tools to program and analyse data
    - Phasor and meter detail
    - Extracted reports and graphs from the MDMS

The datasets (CSV, XLSX) include:

    - Energy load profile and register data recorded by the smart meter and collected by both meter configuration and MDM applications.
    - Data collected also includes events, alarm and QoS data.

Data applicability to SEAP (3)

3 - Energy data and SEAP.pdf: as part of the Smart Metering VS SEAP framework analysis, a comparison between SEAP's data requirements, the applicable energy data to those requirements, the benefits, and the calculation of indicators where applicable. 3 - SEAP indicators.xlsx: as part of the Smart Metering VS SEAP framework analysis, the applicable calculation of indicators for SEAP's data requirements.

Load prediction by machine learning (4)

The coding (IPYNB, PY, HTML, ZIP) shows the preparation and exploration of the energy data to train the machine learning model. The datasets (CSV, XLSX), sequentially named, are part of the process of extracting, transforming and loading the data into a machine learning algorithm, identifying the best regression model based on metrics, and predicting the data.

HRES analysis and optimization (5)

The figures (PNG, JPG, PDF) include:

    - Household load, based on the energy data from the smart metering experiment and the machine learning exercise
    - Pre-defined/synthetic load, provided by the software when no external data (household load) is available, and
    - The HRES designed
    - Application-generated reports with the results of the analysis, for both best case HRES and fully renewable scenarios.

The datasets (XLSX) include the 12-month input load for the simulation, and the input/output analysis and calculations. 5 - Gorou_Niger_20220529_v3.homer: software (Homer Pro) file with the simulated HRES

· Conferences (6)

6 – IEEE_MISTA_2022_paper_51.pdf: paper (research in progress) presented at the IEEE MISTA 2022 conference, occurred in March-2022, and published in the respective proceeding, 6 - IEEE_MISTA_2022_proceeding.pdf. 6 - ITAS_2023.pdf: paper (final research) recently presented at the ITAS 2023 conference in Doha, Qatar, in March-2023. 6 - Smart Energy Seminar 2023.pptx: PowerPoint slide version of the paper, recently presented at the Smart Energy Seminar held at CPUT, in March-2023.

Abstract copyright UK Data Service and data collection copyright owner.

The English Housing Survey (EHS) is a continuous national survey commissioned by the Ministry of Housing, Community and Local Government (MHCLG) that collects information about people's housing circumstances and the condition and energy efficiency of housing in England. The EHS brings together two previous survey series into a single fieldwork operation: the English House Condition Survey (EHCS) (available from the UK Data Archive under GN 33158) and the Survey of English Housing (SEH) (available under GN 33277). The EHS covers all housing tenures. The information obtained through the survey provides an accurate picture of people living in the dwelling, and their views on housing and their neighbourhoods. The survey is also used to inform the development and monitoring of the Ministry's housing policies. Results from the survey are also used by a wide range of other users including other government departments, local authorities, housing associations, landlords, academics, construction industry professionals, consultants, and the general public.

The EHS has a complex multi-stage methodology consisting of two main elements; an initial interview survey of around 12,000 households and a follow-up physical inspection. Some further elements are also periodically included in or derived from the EHS: for 2008 and 2009, a desk-based market valuation was conducted of a sub-sample of 8,000 dwellings (including vacant ones), but this was not carried out from 2010 onwards. A periodic follow-up survey of private landlords and agents (the Private Landlords Survey (PLS)) is conducted using information from the EHS interview survey. Fuel Poverty datasets are also available from 2003, created by the Department for Energy and Climate Change (DECC).

The EHS interview survey sample formed part of the Integrated Household Survey (IHS) (available from the Archive under GN 33420) from April 2008 to April 2011. During this period the core questions from the IHS formed part of the EHS questionnaire.

End User Licence and Special Licence Versions:
From 2014 data onwards, the End User Licence (EUL) versions of the EHS will only include derived variables. In addition the number of variables on the new EUL datasets has been reduced and disclosure control increased on certain remaining variables. New Special Licence versions of the EHS will be deposited later in the year, which will be of a similar nature to previous EHS EUL datasets and will include derived and raw datasets.

Further information about the EHS and the latest news, reports and tables can be found on the GOV.UK English Housing Survey web pages.

English Housing Survey, 2022: Housing Stock Data contains data from the households who have taken part in both the interview and physical surveys as well as physical survey data on a random sample of vacant dwellings identified by the interviewer. The data from the interview survey only are available under English Housing Survey, 2022-2023: Household Data.

The EHS Housing survey consists of two components.

Interview survey on the participating household - An interview is first conducted with the householder. The interview topics include: household characteristics, satisfaction with the home and the area, disability and adaptations to the home, ownership and rental details and income details. All interviewees are guaranteed confidentiality and all data are anonymised.

Physical survey on the housing Stock - A visual inspection of both the interior and exterior of the dwelling is carried out by a qualified surveyor to assess the condition and energy efficiency of the dwelling. Topics covered include whether the dwelling meets the Decent Homes Standard; cost to make the dwelling decent; existence of damp and Category 1 Hazards as measured by the Housing Health and Safety Rating System (HHSRS); Energy Efficiency Rating. The physical survey is carried out on the dwelling of a sub-sample of the participants of the interview survey. The sub-sample consists of the dwelling of participants living in private or social rented properties and a sub-sample of those in owner occupied properties. A proportion of the dwellings found to be vacant during the interview survey are also included in the physical survey.

OBJECTIVES OF ETUDE Building high-performance buildings and supporting the massification of energy renovation are priorities to combat climate change and energy poverty. This implies the commitment of all the players in the sector, as well as to provide security and confidence in the energy performance actually achieved and on the ability of professionals to build healthy, comfortable and sustainable buildings. Experience feedback addressing simultaneously the issues of sanitary quality, environmental comfort and energy consumption are rare. Yet the achievement of ambitious levels of energy performance can conflict with the quality of the indoor environment by altering the comfort of the occupant or the quality of indoor air. The PROFEEL programme’s Health and Energy Renovation (QSE) project aims to shed light on the impact of energy renovation works on the overall performance of renovations. Based on the experimental monitoring of 29 residential buildings that have been renovated or renovated for 1 to 3 years, the study assessed their overall performance using protocols using reference methods. DESCRIPTION OF THE DATA GAME The dataset made available covers 37 housing units and provides information on the characteristics of buildings and their systems, renovation work carried out, events leading to mould occurrence, detection of active fungal activity (Moularat et al, 2008) and pollutant concentration measurements. These measurements were carried out before and after the energy renovation work for part of the dataset (Panel 1) or were measured only after the energy renovation work (Panel 2). Of the 37 dwellings, 28 are on panel 1 and 9 of panel 2. The pollutants measured are: — Volatile organic compounds: 1-methoxy-2-propanol, 1,2,4-trimethylbenzene, 2-butoxyethanol, 2-ethylhexanol (or 2-ethyl-1-hexanol), alpha-pinene, benzene, ethylbenzene, hexane, limonene, m-xylenes, o-xylene, p-xylenes, beta-pinene, styrene, toluene; — Aldehydes: acetaldehyde, formaldehyde, hexaldehyde; — NO2; — PM2.5; — Radon. DESCRIPTION OF FILES SET TO DISPOSITION Four files are downloadable. They make it possible to contextualise the dataset, promote its understanding and facilitate its handling: — DonneesQSE_V1.csv: this file contains the dataset. The ‘NA’ modality for ‘Not available’ means an empty data; — Dico_DonneesQSE_V0: this file contains the thesaurus of the dataset; — Metadata_FR_21032022_QSE_V1.xlsx: this file shall contain all meta data enabling the dataset to be described; — QSE_MAD_dones_en_V1.doc: this file contains the description of the study in French. To quote this publication, please use the following DOI: 10.5281/zenodo.6948633 Thanks The project team would like to thank everyone who took part in the study: — To PEOPLEVOX in charge of the development of the online data collection platform; — Partners responsible for recruiting buildings and/or carrying out surveys: Atmo Grand-Est, AUE Corse, CSTB Champs sur Marne, ISPIRA, MEDIECO, Nobatek/INEF 4, QUALITAIR Corsica, TIPEE and GREEN SOLUCE; — To sampling laboratories: Laboratory POLLEM of CSTB Grenoble in charge of analyses of aldehydes and VOC samples, Laboratoire Central de la Préfecture de Police (LCPP) in charge of particle sampling analysis, at the Paris Department of Environmental Health (SPSE) of the city of Paris in charge of the analysis of NO2 samples and the DOSIRAD laboratory for radon analysis; — To study participants. This study is funded by the Profeel programme through the 2018 call for schemes for energy saving certificates (EEC), PROFEEL: innovative solutions for the energy renovation of buildings (programmeprofeel.fr). Reference S. Moularat, E. Robine, O. Ramalho M. A. Oturan, 2008. Detection of fungal development in closed spaces through the determination of specific chemical targets. Chemosphere 72(2), pp. 224-232.

This layer shows census tracts that meet the following definitions: Census tracts with median household incomes at or below 80 percent of the statewide median income or with median household incomes at or below the threshold designated as low income by the Department of Housing and Community Development’s list of state income limits adopted under Healthy and Safety Code section 50093 and/or Census tracts receiving the highest 25 percent of overall scores in CalEnviroScreen 4.0 or Census tracts lacking overall scores in CalEnviroScreen 4.0 due to data gaps, but receiving the highest 5 percent of CalEnviroScreen 4.0 cumulative population burden scores or Census tracts identified in the 2017 DAC designation as disadvantaged, regardless of their scores in CalEnviroScreen 4.0 or Lands under the control of federally recognized Tribes.