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

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

<div id="h3-13-siblings" class="h3-siblings"></div>

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]] ).

<div id="16.2.4.2" class="h3-container"></div>

<span id="technology-deployment-and-diffusion"></span>