Drivers and effects of digitalization on energy demand in low-carbon scenarios

Figures & data

Table 1. The ten selected reports, our shorthand notation, their geographical area, focus and scenarios.

Shorthand	Title, organization or project (reference)	Area	Emissions or climate focus	Digitalization focus	Scenarios
CCC	The Sixth Carbon Budget: The UK's Path to Net Zero (Committee on Climate Change, 2020)	UK	Net-zero emissions by 2050	Enabling net-zero economy	Central scenario and four exploratory scenarios
RSOC	Digital Technology and the Planet: Harnessing Computing to Achieve Net Zero (The Royal Society, 2020)		Net-zero – challenges for digital technologies	Essential role in achieving net-zero	–
CAT	Zero Carbon Britain: Rising to the Climate Emergency (Centre for Alternative Technology, 2019)		Zero carbon UK by 2030	Smart appliances and grid balancing	Single scenario
CREDS	The Role of Energy Demand Reduction in Achieving Net-Zero in the UK, Centre for Research into Energy Demand Solutions (Barrett et al., 2021)		Net-zero emissions by 2050 – role of energy demand	One trend affecting energy demand	Four scenarios by level of ambition
CDBB	Four Futures, One Choice: Options for the Digital Built Britain of 2040, Centre for Digital Built Britain (Broo et al., 2020)	Great Britain	Built environment in 2040 (including 1.5°C target)	Explored in each scenario	Four scenarios (2X2); qualitative
NATGRID	Future Energy Scenarios (National Grid, 2020)		Net-zero emissions by 2050	Enables net-zero through smart flexible grid	Four scenarios (2X2)
INHERIT	Reaching the ‘Triple-Win’, Four Future Scenarios of a Healthier, More Equitable and Sustainable Europe in 2040, INHERIT project (Guillen-Hanson et al., 2018)	EU	Triple Win (health, equality, sustainability)	Explored in each scenario	Four scenarios (2X2); qualitative
BÖLL	A Societal Transformation Scenario for Staying below 1.5°C, Heinrich Böll Foundation (Kuhnhenn et al., 2020)	Global	Staying below 1.5°C through societal transformation	Risk of catalyzing more energy use	Single Scenario
GRUBLER	A Low Energy Demand Scenario for Meeting the 1.5°C Target and Sustainable Development Goals without Negative Emission Technologies (Grubler et al., 2018)		Staying below 1.5°C through low energy demand	Pervasive trend, essential part of net-zero transition	Single scenario
SMARTER	#SMARTer2030: ICT Solutions for 21^st Century Challenges (Accenture Strategy, 2015)		ICT solutions for emissions (and other challenges)	Central role in reducing emissions	Technological (or socio-technical) potential

Table 2. Mechanisms leading to increase (+) or decrease (-) in energy demand.

Category	Mechanism
Direct effects	Increasing number of ICT devices and increased ICT infrastructure (+)
	Energy efficiency improvements in ICT devices and infrastructure (−)
Energy efficiency and rebound effects	Application of ICT improves energy efficiency in other parts of the economy (−)
	Increase in service demands in other parts of the economy due to the use of ICT (+)
Economic growth	Adoption of ICT improves labour productivity of economy (−)
	Adoption of ICT leads to increasing income inequality (+ or −)
Sectoral change	Digitalization leads to increasing share of lower energy intensive sectors in the economy (−)
	Digitalization reduces energy intensity of new services in these sectors (−)

Note: Based on Lange et al. (2020).

Table 3. Direct effects of digitalization and ITC.

Effect	Details
Increased efficiency of devices	There is a need to insure continued improvements of devices – design, repair and upgrades (CCC) Longevity and repair – legislation needed for ‘right to repair’, alongside ensuring repairability, repair infrastructure (BÖLL); behaviour change as ‘individual action’ leads to using devices for longer (RSOC) Trend towards smaller, more efficient devices and commitment of leading operators to continue this trend (SMARTER) Efficiency improvements in digital technologies including standardization and economies of scale, rapid innovation cycles and learning effects; In addition, ‘shorter lifetime accelerates the adoption of more efficient models contributing to rise the conversion efficiencies of the appliance stock more rapidly’ (GRUBLER sup. p. 27).
Increased infrastructure	Increased energy demand for operating a growing ITC infrastructure (RSOC)
Increased number of devices	Increased number of devices (and use) – shaped by the nature of ‘competitive digitalisation’ (BÖLL) Huge increase expected, perhaps 100 billion by 2030 (SMARTER) Drop in number of ICT appliances per household (UK) – by 10% due to demand saturation, sharing and cloud computing (CREDS) Integration of various services into a single device, especially smart phones, can yield ‘up to a 100-fold potential power savings when in use’ (GRUBLER); granular technologies are smaller and have a lower unit cost, therefore lower adoption barriers.
Increased usage	A marked increase in activity (and number of devices) due to digitally enabled services leading to better quality of life; mostly in the Global South (GRUBLER) ‘Individual action’ could lead to a reduction in usage, e.g. people ‘streaming responsibly’ (RSOC).
Increased usage of data	There is a need to ensure continued improvement of data use (data centre efficiency) (CCC) Moving to the cloud and server virtualization increase efficiency of data, but there is need for more efficiency (RSOC) Increased communication leads to more data centres, energy needed for servers and cooling (NATGRID)

Table 4. Indirect effects: efficiency and rebound in transport.

Effect	Details
Reduced travel	Video conferencing and ICT-enabled working from home leads to reduced travel – mentioned in many scenarios. ICT impacts through reduced need to travel for other purposes – health, learning, commerce also reduces travel (SMARTER); virtual substitution reduces travel for various purposes, mostly in the Global North (GRUBLER). Reduced travel leads to reduced level of car ownership, fewer cars manufactured (CREDS).
Improved traffic management and integrated travel	AI can improve significantly improve congestion prediction and smart parking similarly can greatly reduce congestion in busy city areas (RSOC). Similarly, SMARTER find that ICT can reduce travel time, distance and congestion through smart traffic control and fast parking platforms. Apps and other ICT platforms enable ride sharing and car sharing, leading to reduced car km travelled and even a drop in demand for cars, leading to 15% reduction in car production (relative to the baseline) (SMARTER). GRUBLER similarly show an increase in vehicle usage and occupancy halving the number of cars globally by 2050. Integrated urban transport networks include digitalized timetables and ticketing (CREDS)
Digitalization enabled behaviour change	‘Business and operating models that offer ‘mobility as a service’ can leverage digital technologies to support more fundamental changes to how individuals access transport services, reducing the number of personal vehicles on the roads’. (RSOC p. 27) ICT enables significant rise in public transport use through more information, intelligent sensors, transparency and increased personal choice (SMARTER).
Increased efficiency of vehicles and driving	ICT enables more efficient vehicles and smart driving technologies that save energy (SMARTER).
Autonomous vehicles enable change	Ride hailing services provided by autonomous vehicles reduce vehicle ownership and travelled distance as they act as taxis or public transport. GRUBLER also suggest AVs can be part of a shared vehicle fleet, increasing occupancy and reducing car numbers. CDBB similarly suggest that artificial intelligence and AVS enable a smaller vehicle fleet. However, in scenarios where AVs are privately owned, they can increase average miles travelled (NATGRID).
Freight and delivery improvements	‘digital technology can help consolidate demand and facilitate collaboration between delivery and logistics providers’ (RSOC p. 28); Digitalized technologies improve freight logistics (CREDS); Smart logistics for fleets and routes, digital warehouses and operational agility could save 723 billion litres of fuel by 2030 (SMARTER). Dematerialization moderates growth in freight transport (GRUBLER).

Table 5. Indirect effects: efficiency and rebound in home energy use.

Effect	Details
Smart energy systems enable improvements	‘Smart meters could contribute a 25% emissions saving from UK homes by 2035 (compared with 2015 levels), by enabling a flexible, decentralized and decarbonized energy system’ (RSOC p. 26). Energy management and automation could reduce up to 40% of households’ CO2 consumption. (SMARTER) Smart appliances, smart meters enable demand management and load shifting (NATGRID). INHERIT (p. 27) tell us that ‘the behaviour of humans within the house, as well as indoor air quality are carefully monitored and integrated into the smart home. Sensor-based environments adjust to the needs of the inhabitants (light, air quality, heat)’.
Enabled behaviour change	Smart home technologies and data analytics, alongside dashboards and apps allow people to understand their energy usage and emissions, and encourage changes to individual behaviour change, saving energy. (RSOC) Smart meters show how much energy households use, motivating cutbacks in consumption (SMARTER). Further, visualizations help consumers understand their consumption. All in all, this amounts to 8% energy reduction in households. Smart technology integration and ‘digital lighting’ gives consumers more control over energy use, even when away from home (CREDS). Not all scenarios show engagement with smart meters! (NATGRIDS) Cost reflective charges and smart tariffs enable load shifting (CCC)
Increased working from home	Increased working from home increases home energy use (CREDS). CDBB (p. 41) focus on social housing, where ‘homes are designed to enable customization and upgrades, with ICT-connected study and workspaces designed into units to facilitate remote working and learning’
Demand management	‘As many as 8.1 m homes actively manage heating demand with residential thermal storage and load shifting by 2050’. (NATGRID p. 8)

Table 6. Digitalization effects on economic growth.

Effect	Details
Rapid innovation cycles	ICT enables better forecasting and monitoring (of emissions); this could ‘shorten innovation cycles to bring new energy technologies to market faster’ (CCC p. 404).
Data as economic driver	‘Achieving this transition requires business models and technology approaches that can create economic value from the use of data, supported by action at different levels to ensure everyone in society can access these and participate in new forms of economic activity’. (RSOC p. 90). Examples for ‘creating value’ are given, e.g. mobility as a service, distributed energy sales.
Digital skills as economic driver	Digitalization has ‘opportunities for individuals to reskill and upskill as the nature of their job changes due to digitalisation’ (RSOC p. 12) – leading to increased productivity.
Asset-light business models driving growth	‘Innovative new business models are disrupting existing businesses, delivering exponential growth with asset-light business structures. … As digital density increases through rapid smartphone penetration, new business models unimaginable a decade ago have the potential to transform our lives and to drive strong growth opportunities across the different sectors’. (SMARTER p. 27)
Competition driving growth	Competitive digitalization is a catalyst for more growth – and more energy use – if the trend of digital futures shaped by big companies continues (BÖLL)
Productivity driving growth	People can work wherever they are (without travelling to the office) leads to a higher quality of life, and hence productivity: ‘Increase in worker productivity due to a better working atmosphere and living quality’ (SMARTER p. 131).
Job gains or losses, working hours	Some studies suggest 30% of UK jobs are vulnerable to automation, which could have an effect of inequality. However, full time work week has not changed in the past 20 years despite automation – there are various factors involved, including the need for increasing labour productivity. Working less could feasibly be supported by increased automation (CREDS p. 26–7). Other studies highlight the potential for job creation with careful management (e.g. SMARTER).
Automation, AI	Many businesses invest in automation (and other technological solutions) as it becomes cheaper than human labour; this is especially the case with a shrinking workforce due to an ageing population (CDBB). Furthermore, there is ‘Increasing digitalization of many professions to support a smaller workforce in maintaining productivity’ (CDBB p. 48).

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