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

=== 16.2.4 Representation of the Innovation Process in Modelled Decarbonisation Pathways ===

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A variety of models are used to generate climate mitigation pathways, compatible with 2°C and well below 2°C targets. These include integrated assessment models (IAMs), energy system models, computable general equilibrium models, and agent based models. They range from global (Chapter 3) to national models and include both top-down and bottom-up approaches (Chapter 4). Innovation in energy technologies, which comprises the development and diffusion of low-, zero- and negative-carbon energy options, but also investments to increase energy efficiency, is a key driver of emissions reductions in model-based scenarios.

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==== 16.2.4.1 Technology Cost Development ====

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Assumptions on energy technology cost developments is one of the factors that determine the speed and magnitude of the deployment in climate-energy-economy models. The modelling is informed by the empirical literature that estimates the rates of cost reduction for energy technologies. A first strand of literature relies on the extrapolation of historical data, assuming that costs decrease either as a power law of cumulative production, exponentially with time ( [[#Nagy--2013|Nagy et al. 2013]] ) or as a function of technical performance metrics ( [[#Koh--2008|Koh and Magee 2008]] ). Another approach relies on expert estimates of how future costs will evolve, including expert elicitations ( [[#Verdolini--2018|Verdolini et al. 2018]] ).

In these models, technology costs may evolve exogenously or endogenously ( [[#Mercure--2016|Mercure et al. 2016]] ; [[#Krey--2019|Krey et al. 2019]] ). In the first case, technology costs are assumed to vary over time at some predefined rate, generally extrapolated from past observed patterns or based on expert estimates. This formulation of cost dynamics generally underestimates future costs ( [[#Meng--2021|Meng et al. 2021]] ) as, among other things, it does not capture any policy-induced carbon-saving technological change or any spillover arising from the accumulation of national and international knowledge (Sections 16.2.2 and 16.2.3) or positive macroeconomic effects of a transition ( [[#Karkatsoulis--2016|Karkatsoulis et al. 2016]] ). The influence of cost and diffusion assumptions may be evaluated through sensitivity analysis. In the second case, costs are a function of a choice variable within the model. For instance, technology costs decrease as a function of either cumulative installed capacity (learning by doing) ( [[#Seebregts--1998|Seebregts et al. 1998]] ; [[#Kypreos--2003|Kypreos and Bahn 2003]] ) or R&amp;D investments or spillovers from other sectors and countries.

One factor in this ‘learning by researching’ is applied to a wide range of energy technologies but also to model improvements in the efficiency of energy use ( [[#Goulder--1999|Goulder and Schneider 1999]] ; [[#Popp--2004|Popp 2004]] ). More complex formulations include two-factor learning processes ( [[#Criqui--2015|Criqui et al. 2015]] ; [[#Emmerling--2016|Emmerling et al. 2016]] ; [[#Paroussos--2020|Paroussos et al. 2020]] ) ( [[#16.2.2.1|Section 16.2.2.1]] ), multifactor learning curves ( [[#Kahouli--2011|Kahouli 2011]] ; [[#Yu--2011|Yu et al. 2011]] ), or other drivers of cost reduction such as economies of scale and markets ( [[#Elia--2021|Elia et al. 2021]] ). The application of two-factor learning curves to model energy technology costs is often constrained by the lack of information on public and/or private energy R&amp;D investments in many fast-developing and developing countries ( [[#Verdolini--2018|Verdolini et al. 2018]] ). The approach used to model energy technology cost reductions varies across technologies, even within the same model, depending on the availability of data and/or the level of maturity. Less mature technologies generally depend highly on learning by research, whereas learning by doing dominates in more mature technologies ( [[#Jamasb--2007|Jamasb 2007]] ).

In addition to learning, knowledge spillover effects are also integrated in climate-energy-economy models to reflect the fact that innovation in a given country depends also on knowledge generated elsewhere ( [[#Emmerling--2016|Emmerling et al. 2016]] ; [[#Fragkiadakis--2020|Fragkiadakis et al. 2020]] ). Models with a more detailed representation of sectors ( [[#Paroussos--2020|Paroussos et al. 2020]] ) can use spillover matrices to include bilateral spillovers and compute learning rates that depend on the human capital stock and the regional and/or sectoral absorption rates ( [[#Fragkiadakis--2020|Fragkiadakis et al. 2020]] ). Accounting for knowledge spillovers in the EU for PV, wind turbines, electric vehicles, biofuels, industry materials, batteries and advanced heating and cooking appliances can lead to the following results in a decarbonisation scenario over the period 2020–2050 as compared to the reference scenario: an increase of 1.0–1.4% in GDP, 2.1–2.3% in investment, and 0.2–0.4% in employment by clean energy technologies ( [[#Paroussos--2017|Paroussos et al. 2017]] ). When comparing two possible EU transition strategies – being a first-mover with strong unilateral emission reduction strategy until 2030 versus postponing action for the period after 2030 – endogenous technical progress in the green technologies sector can alleviate most of the negative effects of pioneering low-carbon transformation associated with loss of competitiveness and carbon leakage ( [[#Karkatsoulis--2016|Karkatsoulis et al. 2016]] ).

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==== 16.2.4.2 Technology Deployment and Diffusion ====

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To simulate possible paths of energy technology diffusion for different decarbonisation targets, models rely on assumptions about the cost of a given technology relative to the costs of other technologies, and its ability to supply the energy demand under the relevant energy system and physical constraints. These assumptions include, for example, considerations regarding renewable intermittency, inertia on technology lifetime (for instance, under less stringent temperature scenarios, early retirement of fossil plants does not take place), distribution, capacity and market growth constraints, as well as the presence of policies. These factors change the relative price of technologies. Furthermore, technological diffusion in one country is also influenced by technology advancements in other regions ( [[#Kriegler--2015|Kriegler et al. 2015]] ).

Technology diffusion may also be strongly influenced, either positively or negatively, by a number of non-cost, non-technological barriers or enablers regarding behaviours, society and institutions ( [[#Knobloch--2016|Knobloch and Mercure 2016]] ). These include network or infrastructure externalities, the co-evolution of technology clusters over time (‘path dependence’), the risk-aversion of users, personal preferences and perceptions and lack of adequate institutional framework which may negatively influence the speed of (low-carbon) technological innovation and diffusion, heterogeneous agents with different preferences or expectations, multi-objectives and/or competitiveness advantages and uncertainty around the presence and the level of environmental policies and institutional and administrative barriers ( [[#Marangoni--2014|Marangoni and Tavoni 2014]] ; [[#Baker--2015|Baker et al. 2015]] ; [[#Iyer--2015|Iyer et al. 2015]] ; [[#Napp--2017|Napp et al. 2017]] ; [[#Biresselioglu--2020|Biresselioglu et al. 2020]] ; [[#van%20Sluisveld--2020|van Sluisveld et al. 2020]] ). These types of barriers to technology diffusion are currently not explicitly detailed in most of the climate-energy-economy models. Rather, they are accounted for in models through scenario narratives, such as the ones in the ''Shared Socioeconomic Pathways'' ( [[#Riahi--2017|Riahi et al. 2017]] ), in which assumptions about technology adoption are spanned over a plausible range of values. Complementary methods are increasingly used to explore their importance in future scenarios ( [[#Turnheim--2015|Turnheim et al. 2015]] ; [[#Geels--2016|Geels et al. 2016]] ; [[#Doukas--2018|Doukas et al. 2018]] ; [[#Gambhir--2019|Gambhir et al. 2019]] ; [[#Trutnevyte--2019|Trutnevyte et al. 2019]] ). It takes a very complex modelling framework to include all aspects affecting technology cost reductions and technology diffusion, such as heterogeneous agents ( [[#Lamperti--2020|Lamperti et al. 2020]] ), regional labour costs ( [[#Skelton--2020|Skelton et al. 2020]] ), materials cost and trade and perfect foresight multi-objective optimisation (Aleluia Reis et al. 2021). So far, no model can account for all these interactions simultaneously.

Another key aspect of decarbonisation regards issues of acceptability and social inclusion in decision-making. Participatory processes involving stakeholders can be implemented using several methods to incorporate qualitative elements in model-based scenarios on future change ( [[#van%20Vliet--2010|van Vliet et al. 2010]] ; [[#Nikas--2017|Nikas et al. 2017]] , 2018; [[#Doukas--2020|Doukas and Nikas 2020]] ; [[#van%20der%20Voorn--2020|van der Voorn et al. 2020]] ).

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==== 16.2.4.3 Implications for the Modelling of Technical Change in Decarbonisation Pathways ====

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Although the debate is still ongoing, preliminary conclusions indicate that integrated assessment models tend to underestimate innovation on energy supply but overestimate the contributions by energy efficiency ( [[#IPCC--2018b|IPCC 2018b]] ). Scenarios emerging from cost-optimal climate-energy-economy models are too pessimistic, especially in the case of rapidly changing technologies such as wind and batteries in the past decade. Conversely, they tend to be too optimistic regarding the timing of action, or the availability of a given technology and its speed of diffusion ( [[#Shiraki--2020|Shiraki and Sugiyama 2020]] ). Furthermore, some technological and economic transformations may emerge as technically feasible from IAMs, but are not realistic if taking into account political economy, international politics, human behaviours, and cultural factors ( [[#Bosetti--2021|Bosetti 2021]] ).

There is a range of projected energy technology supply costs included in the IPCC’s Sixth Assessment Report (AR6) Scenario Database (Box 16.1). Variations of costs over time and across scenarios are within ranges comparable to those observed in recent years. Conversely, model results show that limiting warming to 2°C or 1.5°C will require faster diffusion of installed capacity of low-carbon energy options and a rapid phase-out of fossil-based options. This points to the importance of focusing on overcoming real-life barriers to technology deployment.

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