Editing IPCC:AR6/WGIII/Chapter-16 (section)

=== Cross-Chapter Box 11 | Digitalisation: Efficiency Potentials and Governance Considerations ===

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'''Authors:''' Felix Creutzig (Germany), Elena Verdolini (Italy), Paolo Bertoldi (Italy), Luisa F. Cabeza (Spain), María Josefina Figueroa Meza (Venezuela/Denmark), Kirsten Halsnæs (Denmark), Joni Jupesta (Indonesia/Japan), Şiir Kilkiş (Turkey), Michael König (Germany), Eric Masanet (the United States of America), Nikola Milojevic-Dupont (France), Joyashree Roy (India/Thailand), Ayyoob Sharifi (Iran/Japan)

'''Digital technologies impact positively and negatively on GHG emissions through: their own carbon footprint; technology application for mitigation; and induced larger social change. Digital technologies also raise broader sustainability concerns due to their use of rare materials and associated waste, and their potential negative impact on inequalities and''' '''labour demand.'''

'''Direct impacts emerge because digital technologies consume large amounts of energy, but also have the potential to steeply increase energy efficiency in all end-use sectors through material input savings and increased coordination (''' medium evidence ''',''' medium agreement ''')''' ( [[#Horner--2016|Horner et al. 2016]] ; [[#Huang--2016|Huang et al. 2016]] ; [[#IEA--2017b|IEA 2017b]] ; [[#Jones--2018|Jones 2018]] ). Global energy demand from digital appliances reached 7.14 EJ in 2018 (Chapter 9, Box 9.5), implying higher related carbon emissions. However, a small smartphone offers services previously requiring many different devices ( [[#Grubler--2018|Grubler et al. 2018]] ). Demand for data services is increasing rapidly; quantitative estimates of the growth of associated energy demand range from slow and marginal to rapid and sizeable, depending the efficiency trends of digital technologies ( [[#Avgerinou--2017|Avgerinou et al. 2017]] ; [[#Vranken--2017|Vranken 2017]] ; [[#Stoll--2019|Stoll et al. 2019]] ; [[#Masanet--2020|Masanet et al. 2020]] ) ( [[IPCC:Wg3:Chapter:Chapter-5#5.3.4.1|Section 5.3.4.1]] ). Renewable energy can serve as a low-carbon energy provider for the operation of a data centre, which in turn can provide waste heat for other purposes. Digital technologies can markedly increase the energy efficiency of mobility and residential and public buildings, especially in the context of systems integration ( [[#IEA--2020a|IEA 2020a]] ). Reduction in energy demand and associated GHG emissions from buildings and industry, while maintaining service levels is estimated at 5 to 10%, with larger savings possible. Approaches include building energy management systems (BEMS), home energy management system (HEMS), demand response and smart charging (Cross-Chapter Box 11, Table 1). Data centres can also play a role in energy system management, for example, by increasing renewable energy generation through predictive control ( [[#Dabbagh--2019|Dabbagh et al. 2019]] ), and by helping to drive the market for battery storage and fuel cells ( [[#Riekstin--2014|Riekstin et al. 2014]] ). Temporal and spatial scheduling of electricity demand can provide about 10 GW in demand response in the European electricity system in 2030 ( [[#Wahlroos--2017|Wahlroos et al. 2017]] , 2018; [[#Koronen--2020|Koronen et al. 2020]] ; [[#Laine--2020|Laine et al. 2020]] ).

'''Cross-Chapter Box 11, Table 1 | Selected sector approaches for reducing GHG emissions that are supported by new digital technologies.''' '''Contributions of digitalisation include a) supporting role (+), b) necessary role in mix of tools (++), c) necessary unique contribution (+++), but digitalisation may also increase emissions (−).''' (Chapters 5, 8, 9 and 11).

{| class="wikitable"
|-
! '''Sector'''

! '''Approach'''

! '''Quantitative evidence'''

! '''Contribution of digitalisation'''

! '''Systems perspective and broader societal impacts'''

! '''References'''

|-
| '''Residential energy use'''

| Nudges (feedback, information, etc.)

| 2–4% reduction in global household energy use possible

| + In combination with monetary incentives, non-digital information

| New appliances increase consumption

| [[#Zangheri--2019|Zangheri et al. (2019)]] ; [[#Buckley--2020|Buckley (2020)]] ; [[#Nawaz--2020|Nawaz et al. (2020)]] ; [[#Khanna--2021|Khanna et al. (2021)]]

|-
| '''Smart mobility'''

| Shared mobility and digital feedback (ecodriving)

| Reduction for shared cycling and shared pooled mobility; increase for ride hailing/ ride sourcing; reduction for ecodriving

| '''−''' or ++ Apps together with big data and machine learning algorithm key precondition for new shared mobility

| Ride hailing increases GHG emissions, especially due to deadheading

| [[#Zeng--2017|Zeng et al. (2017)]] ; OECD and ITF (2020)

|-
| '''Smart cities'''

| Using digital devices and big data to make urban transport and building use more efficient

| Precise data about roadway use can reduce material intensity and associated GHG emissions by 90%

| ++ Big data analysis necessary for optimisation

| Efficiency gains are often compensated by more driving and other rebound effects; privacy concerns linked with digital devices in homes

| [[#Milojevic-Dupont--2021|Milojevic-Dupont and Creutzig (2021)]] (Chapter 10, Box 10.1)

|-
| '''Agriculture'''

| Precision agriculture through sensors and satellites providing information on soil moisture, temperature, crop growth and livestock feed levels

| Very high potential for variable-rate nitrogen application, moderate potential for variable-rate irrigation

| + ICTs provide information and technologies which enables farmers to increase yields, optimise crop management, reduce fertilisers and pesticides, feed and water; increases efficiency of labour-intensive tasks

| The digital divide is growing fast, especially between modern and subsistence farming;

Privacy and data may erode trust in technologies

| [[#Deichmann--2016|Deichmann et al. (2016)]] ; [[#Chlingaryan--2018|Chlingaryan et al. (2018)]] ; [[#Soto%20Embodas--2019|Soto Embodas et al. (2019)]] ; [[#Townsend--2019|Townsend et al. (2019)]]

|-
| '''Industry'''

| Industrial internet of things (IIoT)

| Process, activity and functional optimisation increases energy and carbon efficiency

| ++ Increased efficiency

++ 1.3 GtCO 2 -eq estimated abatement potential in manufacturing

+ Promote sustainable business models

| Optimisation in value chains can reduce wasted resources

| [[#GeSI--2012|GeSI (2012)]] ; [[#Wang--2016|Wang et al. (2016)]] ; [[#Parida--2019|Parida et al. (2019)]] ; [[#Rolnick--2021|Rolnick et al. (2021)]]

|-
| '''Load management and battery storage optimisation'''

| Big data analysis for optimising demand management and using flexible load of appliances with batteries

| Reduces capacity intended for peak demand, shifts demand to align with intermittent renewable energy availability

| + Accelerated experimentation in material science with artificial intelligence

++ / +++ Forecast and control algorithms for storage and dispatch management

| Facilitate integration of renewable energy sources

Improve utilisation of generation assets

System-wide rebound effects possible

| [[#Akorede--2010|Akorede et al. (2010)]] ; [[#Aghaei--2013|Aghaei and Alizadeh (2013)]] ; [[#de%20Sisternes--2016|de Sisternes et al. (2016)]] ; [[#Voyant--2017|Voyant et al. (2017)]] ; [[#Gür--2018|Gür (2018)]] ; [[#Hirsch--2018|Hirsch et al. (2018)]] ; [[#Sivaram--2018a|Sivaram (2018a)]] ; [[#Vázquez-Canteli--2019|Vázquez-Canteli and Nagy (2019)]] (Chapter 6, [[IPCC:Wg3:Chapter:Chapter-6#6.4|Section 6.4]] )

|}

'''However, system-wide effects may endanger energy and GHG emission savings (''' high evidence ''',''' high agreement ''').''' Economic growth resulting from higher energy and labour productivities can increase energy demand ( [[#Lange--2020|Lange et al. 2020]] ) and associated GHG emissions. Importantly, digitalisation can also benefit carbon-intensive technologies ( [[#Victor--2018|Victor 2018]] ). Impacts on GHG emissions are varied in smart and shared mobility systems, as ride hailing increases GHG emissions due to deadheading, whereas shared pooled mobility and shared cycling reduce GHG emissions, as occupancy levels and/or weight per person km transported improve ( [[IPCC:Wg3:Chapter:Chapter-5#5.3|Section 5.3]] ). Energy and GHG emission impacts from the ubiquitous deployment of smart sensors and service optimisation applications in smart cities are insufficiently assessed in the literature ( [[#Milojevic-Dupont--2021|Milojevic-Dupont and Creutzig 2021]] ). Systemic effects have wider boundaries of analysis, including broader environmental impacts (e.g., demand for rare materials, disposal of digital devices). These need to be integrated holistically within policy design ( [[#Kunkel--2020|Kunkel and Matthess 2020]] ), but they are difficult to quantify and investigate ( [[#Bieser--2018|Bieser and Hilty 2018]] ). Policies and adequate infrastructures and choice architectures can help manage and contain the negative repercussions of systemic effects (Sections 5.4, 5.6 and 9.9).

'''Broader societal impacts of digitalisation can also influence climate mitigation because of induced demand for consumption goods, impacts on firms’ competitiveness, changes the demand for skills and labour, worsening of inequality – including reduced access to services due to the digital divide – and governance aspects (''' low evidence ''',''' medium agreement ''')''' (Sections 4.4, 5.3 and 5.6). Digital technologies expand production possibilities in sectors other than ICTs through robotics, smart manufacturing, and 3D printing, and have major implications on consumption patterns ( [[#Matthess--2020|Matthess and Kunkel 2020]] ). Initial evidence suggests that robots displace routine jobs and certain skills, change the demand for high-skilled and low-skilled workers, and suppress wages ( [[#Acemoglu--2019|Acemoglu and Restrepo 2019]] ). Digitalisation can thus reduce consumers’ liquidity and consumption ( [[#Mian--2020|Mian et al. 2020]] ) and contribute to global inequality, including across the gender dimension, raising fairness concerns ( [[#Kerras--2020|Kerras et al. 2020]] ; [[#Vassilakopoulou--2021|Vassilakopoulou and Hustad 2021]] ). Digital technologies can lead to additional concentration in economic power (e.g., [[#Rikap--2020|Rikap 2020]] ) and lower competition; however, open source digital technologies can counter this tendency (e.g., [[#Rotz--2019|Rotz et al. 2019]] ). Digital technologies play a role in mobilising citizens for climate and sustainability actions ( [[#Segerberg--2017|Segerberg 2017]] ; [[#Westerhoff--2018|Westerhoff et al. 2018]] ).

'''Whether the digital revolution will be an enabler or a barrier for decarbonisation will ultimately depend on the governance of both digital decarbonisation pathways and digitalisation in general (''' medium evidence ''',''' high agreement ''').''' The understanding of the disruptive potential of the wide range of digital technologies is limited due to their ground-breaking nature, which makes it hard to extrapolate from previous history/experience. Municipal and national entities can make use of digital technologies to manage and govern energy use and GHG emissions in their jurisdiction ( [[#Bibri--2019a|Bibri 2019a]] ,b) and break down solution strategies to specific infrastructures, building, and places, relying on remote sensing and mapping data, and contextual machine learning about their use ( [[#Milojevic-Dupont--2021|Milojevic-Dupont and Creutzig 2021]] ). Mobility apps can provide mobility-as-a-service access to cities, ensuring due preference to active and healthy modes ( [[IPCC:Wg3:Chapter:Chapter-9#9.9|Section 9.9]] for the example of the Finnish city of Lahti). Trusted data governance can promote the implementation of local climate solutions, supported by available big data on infrastructures and environmental quality ( [[#Hansen--2017|Hansen and Porter 2017]] ; [[#Hughes--2020|Hughes et al. 2020]] ). Governance decisions, such as taxing data, prohibiting surveillance technologies, or releasing data that enable accountability, can change digitalisation pathways, and thus underlying GHG emission ( [[#Hughes--2020|Hughes et al. 2020]] ).

'''Closing the digital gap in developing countries and rural communities enables an opportunity for leapfrogging (''' medium evidence ''',''' medium agreement ''').''' Communication technologies (such as mobile phones) enable the participation of rural communities, especially in developing countries, and promote technological leapfrogging, for example, decentralised renewable energies and smart farming ( [[#Ugur--2017|Ugur and Mitra 2017]] ; [[#Foster--2020|Foster and Azmeh 2020]] ; [[#Arfanuzzaman--2021|Arfanuzzaman 2021]] ). Digital technologies have sector-specific potentials and barriers, and may benefit certain regions/areas/socio-economic groups more than others. For example, integrated mobility services benefit cities more than rural and peripheral areas ( [[#OECD--2017|OECD 2017]] ).

'''Appropriate mechanisms also need to be designed to govern digitalisation as a megatrend (''' medium evidence ''',''' high agreement ''')''' . Digitalisation is expected to be a fast process, but this transformation takes place against entrenched individual behaviours, existing infrastructure, the legacy of time frames, vested interest and slow institutional processes, and requires trust from consumers, producers and institutions. A core question relates to who controls and manages data created by everyday operations (calls, shopping, weather data, service use, and so on). Regulations that limit or ban the expropriation and exploitation of behavioural data, sourced via smartphones, represent crucial aspects in digitalisation pathways, alongside the possibility to create climate movements and political pressure from the civil society. Governance mechanisms need to be developed to ensure that digital technologies such as AI take over ethical choices ( [[#Craglia--2018|Craglia et al. 2018]] ; [[#Rahwan--2019|Rahwan et al. 2019]] ). Appropriate governance is necessary for digitalisation to effectively work in tandem with established mitigation technologies and choice architectures. Consideration of system-wide effects and overall management is essential to avoid runaway effects. Overall governance of digitalisation remains a challenge, and will have large-scale repercussions on energy demand and GHG emissions.

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==== 16.2.2.4 Explaining Past and Projecting Future Technology Cost Changes ====

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Researchers and policymakers alike are interested in using observed empirical patterns of learning to project future reductions in costs of technologies. Studies cutting across a wide range of industrial sectors (not just energy) have tried to relate cost reductions to different functional forms, including cost reductions as a function of time (Moore’s law) and cost reductions as a function of production or deployment (Wright’s law, also known as Henderson’s law), finding that those two forms perform better than alternatives combining different factors, with costs as a function of production (Wright’s law) performing marginally better ( [[#Nagy--2013|Nagy et al. 2013]] ). A comparison of expert elicitation and model-based forecasts of the future cost of technologies for the energy transition indicates that model-based forecast medians were closer to the average realised values in 2019 ( [[#Meng--2021|Meng et al. 2021]] ).

Recent studies attempt to separate the influence of learning by doing (which is a basis of Wright’s law) versus other factors in explaining cost reductions, specifically in energy technologies. Some studies explain cost reductions with two factors: cumulative deployment (as proxy for experience); and R&amp;D investment – see the ‘two factor’ learning curve ( [[#Klaassen--2005|Klaassen et al. 2005]] ). However, reliable information on public energy R&amp;D investments for developing countries is not systematically collected. Available data for OECD countries cannot be precisely assigned to specific industrial sectors or sub-technologies ( [[#Verdolini--2018|Verdolini et al. 2018]] ). Some learning-curve studies take into account that historical variation in technology costs could be explained by variation in key materials and fuel costs – for example, steel costs for wind turbines ( [[#Qiu--2012|Qiu and Anadon 2012]] ), silicon costs ( [[#Nemet--2006|Nemet 2006]] ; [[#Kavlak--2018|Kavlak et al. 2018]] ) as well as coal and coal plant construction costs ( [[#McNerney--2011|McNerney et al. 2011]] ). Economies of scale played a significant role in the PV cost reductions since the early 2000s ( [[#Yu--2011|Yu et al. 2011]] ) (Box 16.4), which can also become the case in organic PV technologies ( [[#Gambhir--2016|Gambhir et al. 2016]] ; [[#Kavlak--2018|Kavlak et al. 2018]] ).

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