Forecasting the Electronification of Payments with Learning Curves: The Case of Finland

This paper examines the electronification of noncash payments in Finland and the extent to which noncash payment means are used as substitutes for cash. We model the processes of cash substitution and electronification of payments as 'S'-shaped learning curves and generate forecasts by extrapolating these curves. The 'S'-shaped learning curves fit the data well. Our results indicate that in Finland the cash substitution process as a whole is approaching the saturation point. Although the electronification process is clearly ongoing as regards larger-value bill payments, for small-value point-of-sale payments we seem to have reached saturation. Electronification of payments, having progressed swiftly and extensively in Finland, is already beginning to slow down. We conclude the paper with a discussion of the reasons for this turn of events and of the different factors that affect the speed of diffusion of new means of payment.payments; electronification; learning curves

How well does Learning-by-doing Explain Cost Reductions in a Carbon-free Energy Technology?

The incorporation of experience curves has enhanced the treatment of technological change in models used to evaluate the cost of climate and energy policies. However, the set of activities that experience curves are assumed to capture is much broader than the set that can be characterized by learning-by-doing, the primary connection between experience curves and economic theory. How accurately do experience curves describe observed technological change? This study examines the case of photovoltaics (PV), a potentially important climate stabilization technology with robust technology dynamics. Empirical data are assembled to populate a simple engineering-based model identifying the most important factors affecting the cost of PV over the past three decades. The results indicate that learning from experience only weakly explains change in the most important cost-reducing factors— plant size, module efficiency, and the cost of silicon. They point to other explanatory variables to include in future models. Future work might also evaluate the potential for efficiency gains from policies that rely less on ‘riding down the learning curve’ and more on creating incentives for firms to make investments in the types of cost-reducing activities quantified in this study.Learning-by-doing, Experience Curves, Learning Curves, Climate Policy

Alchemical and structural distribution based representation for improved QML

We introduce a representation of any atom in any chemical environment for the generation of efficient quantum machine learning (QML) models of common electronic ground-state properties. The representation is based on scaled distribution functions explicitly accounting for elemental and structural degrees of freedom. Resulting QML models afford very favorable learning curves for properties of out-of-sample systems including organic molecules, non-covalently bonded protein side-chains, (H$_2$O)$_{40}$-clusters, as well as diverse crystals. The elemental components help to lower the learning curves, and, through interpolation across the periodic table, even enable "alchemical extrapolation" to covalent bonding between elements not part of training, as evinced for single, double, and triple bonds among main-group elements

Learning Curves For Energy Technology: A Critical Assessment

In this paper, which forms a chapter in the forthcoming Book “Delivering a Low Carbon Electricity System: Technologies, Economics and Policy”, Jamasb and Kohler revisit the literature on learning curves and their application to energy technology and climate change policy analysis and modeling. The academic literature and policy documents have in recent years embraced the learning curves and applied the concept to technology analysis and forecasting cost reductions. We argue that learning curves have often been used or assumed uncritically in technology analysis and draw parallels between the use of learning rates in energy technological progress and climate change modeling to that of discount rates in social cost benefit analysis. The paper discusses that care needs to be taken in applying learning curves, originally developed as an empirical tool to assess the effect of learning by doing in manufacturing, to analysis innovation and technical change. Finally, we suggest some potential extensions of learning curves, e.g. by incorporating R&D and diffusion effects into learning models, and other areas where learning curves may potentially be a useful tool in energy technology policy and analysis

Guidelines for application of learning/cost improvement curves

The differences between the terms learning curve and improvement curve are noted, as well as the differences between the Wright system and the Crawford system. Learning curve computational techniques were reviewed along with a method to arrive at a composite learning curve for a system given detail curves either by the functional techniques classification or simply categorized by subsystem. Techniques are discussed for determination of the theoretical first unit (TFU) cost using several of the currently accepted methods. Sometimes TFU cost is referred to as simply number one cost. A tabular presentation of the various learning curve slope values is given. A discussion of the various trends in the application of learning/improvement curves and an outlook for the future are presented