Impacts of Digitalised Daily Life on Climate Change, 2023-2024

Vrain, Emilie (2025). Impacts of Digitalised Daily Life on Climate Change, 2023-2024. [Data Collection]. Colchester, Essex: UK Data Service. 10.5255/UKDA-SN-858037

iDODDLE is a four year project running from 2021-2025. iDODDLE’s aim is to help develop a new thematic and inter-disciplinary science of digitalised daily life in support of action on climate change.
The aims of iDODDLE are:
1) To understand the ways in which digitalised daily life impacts climate change. Examples of these ways include substituting physical for digital, accessing services instead of owning goods, and integrating households into supply networks.
2) To determine the conditions under which digitalised daily life has beneficial or adverse impacts on climate change. Examples of these conditions include access to infrastructure, trust in institutions, and technophile lifestyles.
3) To develop an evidence-based programme of action for ensuring digitalised daily life helps tackle climate change. Examples of this evidence base include quantitative systems analysis of energy and material flows at national and global scales.
iDODDLE’s research activities are organised into three themes – on people (micro-level), on system conditions (macro-level), and on action (policy and practice).

Data description (abstract)

This dataset stems from a longitudinal mixed-methods study exploring how domestic automation technologies—specifically automated vacuum cleaners (AVCs)—affect time-use patterns, household behaviours, and energy demand. Motivated by growing interest in the sustainability implications of digitalisation, the study investigates how automation reshapes daily routines, including the timing, frequency, and layering of domestic tasks, with downstream consequences for energy consumption.

Data were collected across three phases: before, during, and one year following the introduction of AVCs in participating UK households. The study design combined detailed time-use diaries, quantitative surveys, appliance-level energy monitoring, and in-depth qualitative interviews. This allowed for an integrated understanding of both behavioural adaptations and energy impacts over time.

Key topics covered include domestic routines, automation use and non-use, perceived convenience and cleanliness norms, time allocation across tasks, multitasking behaviours, and energy consumption related to cleaning and other activities.

Results highlight the contingent and variable impacts of automation: while some households experienced energy efficiency gains and reductions in manual cleaning effort, others saw increased cleaning frequency, prolonged device use, and additive energy demand. These insights contribute to broader debates on digitalisation, rebound effects, and sustainable consumption.

This dataset supports future research on household technology adoption, behavioural change, energy use, and the social and temporal dynamics of digital innovation.

Data creators:

Creator Name	Affiliation	ORCID (as URL)
Vrain Emilie	Environmental Change Institute, University of Oxford	https://orcid.org/my-orcid?orcid=0000-0001-8226-4621

Sponsors:

European Research Council

Grant reference:

101003083

Topic classification:

Society and culture

Keywords:

DIGITAL TECHNOLOGY, AUTOMATION, HOUSEHOLDS, HUMAN BEHAVIOUR, TIME, ENERGY CONSUMPTION

Project title:

iDODDLE: The Impacts of Digitalised Daily Life on Climate Change

Alternative title:

iDODDLE Living Lab Automation

Grant holders:

Charlie Wilson

Project dates:

From	To
1 October 2022	30 September 2025

Date published:

15 Sep 2025 11:49

Last modified:

15 Sep 2025 11:49

Coverage and Methodology

Collection period:

Date from:	Date to:
1 June 2023	30 November 2024

Geographical area:

Oxfordshire, UK

Country:

United Kingdom

Spatial unit:

No Spatial Unit

Data collection method:

This data collection was conducted as part of a longitudinal, mixed-methods study exploring the time-use and energy implications of domestic automation, specifically through the adoption of automated vacuum cleaners (AVCs). Ten households were selected from a wider Oxford-based living lab infrastructure (2022–2025), with the aim of capturing diverse automation experiences across household compositions, daily routines, and prior digital technology use. Only households without prior AVC use were eligible to minimise familiarity bias.
Participants engaged in a three-phase protocol: a 30-day pre-trial baseline period, a 30-day AVC trial with app tracking and weekly tasks, and a follow-up interview conducted 12–18 months later. Data were collected through floor cleaning diaries, smart plugs, app logs, weekly qualitative tasks, online surveys, and semi-structured interviews. A combination of continuous and discrete data collection methods was used to document changes in cleaning routines, automation use, time allocation, and energy use under real-world conditions.

Observation unit:

Individual, Household

Kind of data:

Numeric, Text

Type of data:

Cohort and longitudinal studies, Experimental data , Qualitative and mixed methods data

Resource language:

English

Access and Administration

Data sourcing, processing and preparation:

This data collection was generated as part of a bespoke, longitudinal experimental trial study, with all data collected directly from participants in real-world home settings. Data quality was ensured through several steps: weekly check-ins with households enabled timely clarification or correction of missing entries; app usage and smart plug logs provided objective verification of self-reported behaviours; and photos and screenshots submitted by participants added contextual detail. Audio and written responses were manually transcribed and anonymised. Interview transcripts and qualitative data were thematically coded, and quantitative data were cleaned, standardised to weekly intervals, and checked for internal consistency across sources. To preserve confidentiality, all identifying information was removed during data processing.

Rights owners: