Leo Schrattenholzer

Learning rates for energy technologies

Abstract Technological learning, i.e., cost reductions as technology manufacturers accumulate experience, is increasingly being incorporated in models to assess long-term energy strategies and related greenhouse gas emissions. Most of these applications use learning rates based on studies of non-energy technologies, or sparse results from a few energy studies. This report is a step towards a larger empirical basis for choosing learning rates (or learning rate distributions) of energy conversion technologies for energy models. We assemble data on experience accumulation and cost reductions for a number of energy technologies, estimate learning rates for the resulting 26 data sets, analyze their variability, and evaluate their usefulness for applications in long-term energy models.

Global bioenergy potentials through 2050

Abstract Estimates of world regional potentials of the sustainable use of biomass for energy uses through the year 2050 are presented. The estimated potentials are consistent with scenarios of agricultural production and land use developed at the International Institute for Applied Systems Analysis, Austria. They thus avoid inconsistent land use, in particular conflicts between the agricultural and bioenergy land use. As an illustration of the circumstances under which a large part of this potential could be used in practice, a global energy scenario with high economic growth and low greenhouse gas emissions, developed by IIASA and the World Energy Council is summarised. In that scenario, bioenergy supplies 15% of global primary energy by 2050. Our estimation method is transparent and reproducible. A computer program to repeat the calculation of the estimates with possibly changed assumptions is available on request.

MESSAGE–MACRO: linking an energy supply model with a macroeconomic module and solving it iteratively

MESSAGE–MACRO is the result of linking a macroeconomic model with a detailed energy supply model. The purpose of the linkage is to consistently reflect the influence of energy supply costs as calculated by the energy supply model in the optimal mix of production factors included in the macroeconomic model. In this article, we describe an automated link of two independently running models. The advantages of this setup over a single, fully integrated model are twofold: First, it is more flexible, leaving the constituent models intact for independent runs, thus making further model development an easier task. Second, the decomposed model solution benefits numerically from having the most non-linearities concentrated in the smaller of the two modules. The emphasis of the paper is on methodology, but we also include an example demonstrating the feedback mechanisms of MESSAGE–MACRO by applying it to two global economic–energy–environment scenarios. The two scenarios are a reference scenario and a scenario that limits the global atmospheric carbon concentration to 550 ppmv. The scenarios are compared in terms of GDP, energy supply and demand, and energy prices.

Experiments with a methodology to model the role of RD first results

This paper presents the results of using a stylized optimization model of the global electricity supply system to analyze the optimal research and development (RD the formulation is a straightforward expansion of conventional one-factor learning curves, in which only cumulative experience is included as a factor, which aggregates the effects of accumulated knowledge and cumulative experience, among others. The responsiveness of technological progress to the two factors is quantified using learning parameters, which are estimated using empirical data. Sensitivities of the model results to the parameters are also tested. The model results also address the effect of competition between technologies and of CO2 constraints. The results are mainly methodological; one of the most interesting is that, at least up to a point, competition between technologies—in terms of both market share and R&D support—need not lead to “lock-in” or “crowding-out”.

Long-term strategies for mitigating global warming

This special issue reviews technological options for mitigating carbon dioxide (CO2) emissions. The options analyzed include efficiency improvements, renewable energies, clean fossil and zero-carbon energy technologies, carbon sequestration and disposal, enhancement of natural carbon sinks (halting deforestation, afforestation, and other sink enhancement options), and geo-engineering measures to compensate for increases in CO2 concentrations. Reduction potentials, costs, and the relative contribution of individual options, as well as their limiting factors and possible timing of introduction and diffusion, are discussed. The study concludes with a discussion of methodological issues and of trade-offs and constraints for implementation strategies to mitigate anthropogenic sources of change in the global carbon cycle.

Learning curves and technology assessment

This paper uses the formal concept of learning curves to analyse regular behaviour of performance improvements in various energy technologies. The concept allows the estimation of a single indicator of technological progress, the learning rate, which expresses the constant percentage improvement (usually in terms of cost reductions) in a technology for each doubling of the technologies cumulative installed capacity. We present 42 energy-related learning rates, either calculated directly from available data or assembled from the literature. We elaborate briefly on eight of these to illustrate issues addressed by technology assessments to convert these raw historical learning rates into prospective learning rate distributions for use in long-term energy models. The paper includes a sensitivity analysis of policy- relevant variables with respect to learning rates, a discussion of possible extensions and limitations of the approach and an outlook on future work in the field.

Costs of reducing carbon emissions: An integrated modeling framework approach

This paper presents an approach to estimating world-regional carbon mitigation cost functions for the years 2020, 2050, and 2100. The approach explicitly includes uncertainty surrounding such carbon reduction costs. It is based on the analysis of global energy-economy-environment scenarios described for the 21st century. We use one baseline scenario and variants thereof to estimate cumulative costs of carbon mitigation as a function of cumulative carbon emission reductions. For our baseline for estimating carbon mitigation cost curves, we use the so-called IIASA F scenario. The F scenario is a high-growth, high-emissions scenario designed specifically to be used as a reference against which to evaluate alternatives. Carbon emissions and energy systems costs in the F scenario are then compared with (reduced) emissions and (higher) costs (including macroeconomic adjustment costs) of alternative scenarios taken from the IIASA scenario database. As a kind of sensitivity analysis of our approach, we also present the results of a scenario involving assumptions on particularly rapid technological progress.

Achieving a Sustainable Global Energy System

Sustainable development and global climate change have figured prominently in scientific analysis and international policymaking since the early 1990s. This book formulates technology strategies that will lead to environmentally sustainable energy systems, based on an analysis of global climate change issues using the concept of sustainable development. The authors focus on environmentally compatible, long-term technology developments within the global energy system, while also considering aspects of economic and social sustainability.

The Effect of Government Actions on Environmental Technology Innovation: Applications to the Integrated Assessment of Carbon Sequestration Technologies

This project seeks to improve the ability of integrated assessment models (IA) to incorporate changes in technology, especially environmental technologies, cost and performance over time. In this report, we present results of research that examines past experience in controlling other major power plant emissions that might serve as a reasonable guide to future rates of technological progress in carbon capture and sequestration (CCS) systems. In particular, we focus on U.S. and worldwide experience with sulfur dioxide (SO{sub 2}) and nitrogen oxide (NO{sub x}) control technologies over the past 30 years, and derive empirical learning rates for these technologies. The patterns of technology innovation are captured by our analysis of patent activities and trends of cost reduction over time. Overall, we found learning rates of 11% for the capital costs of flue gas desulfurization (FGD) system for SO{sub 2} control, and 13% for selective catalytic reduction (SCR) systems for NO{sub x} control. We explore the key factors responsible for the observed trends, especially the development of regulatory policies for SO{sub 2} and NO{sub x} control, and their implications for environmental control technology innovation.

The IIASA Set of Energy Models: its Design and Application

The set of energy models used in the Energy Systems Program at the International Institute for Applied Systems Analysis (IIASA) is described. This set of models – designed for studying the long-term, dynamic, and regional/global aspects of large-scale energy systems – serves as a means of synthesis for the energy studies at IIASA, in developing energy strategies and in evaluating their economic and environmental impacts. The critical question considered in the modeling is whether economies can afford the requisite expenditures of time and capital to achieve alternative energy strategies during the long-term transition to sustainable energy systems. The several individual models and their interrelationships were developed with these considerations in mind.