Technological learning in energy–environment–economy modelling: A survey

This paper aims at providing an overview and a critical analysis of the technological learning concept and its incorporation in energy–environment–economy models. A special emphasis is put on surveying and discussing, through the so-called learning curve, both studies estimating learning rates in the energy field and studies incorporating endogenous technological learning in bottom-up and top-down models. The survey of learning rate estimations gives special attention to interpreting and explaining the sources of variability of estimated rates, which is shown to be mainly inherent in R&D expenditures, the problem of omitted variable bias, the endogeneity relationship and the role of spillovers. Large-scale models survey show that, despite some methodological and computational complexity related to the non-linearity and the non-convexity associated with the learning curve incorporation, results of the numerous modelling experiments give several new insights with regard to the analysis of the prospects of specific technological options and their cost decrease potential (bottom-up models), and with regard to the analysis of strategic considerations, especially inherent in the innovation and energy diffusion process, in particular the energy sector's endogenous responses to environment policy instruments (top-down models).     

ECLIPSE: An integrated energy-economy model for climate policy and scenario analysis

This paper describes the development of the Energy and Climate Policy and Scenario Evaluation (ECLIPSE) model—a flexible integrated assessment tool for energy and climate change policy and scenario assessment. This tool builds on earlier efforts to link top-down and bottom-up models, and combines a macroeconomic energy demand model and a consumer-budget transport demand model with the technology-rich bottom-up energy and transport system model Energy Research and Investment Strategy (ERIS), and solves the models iteratively. Compared to previous efforts, ECLIPSE includes many new features, such as a more disaggregated production function, improved calibration and parameterization and separate modeling of passenger transport demand. The separate modeling of transportation makes ECLIPSE particularly well-suited to analyzing interactions between the transport sector and the broader energy market and economy. This paper presents results illustrating some features of the integrated model, compares technology deployment results with ECLIPSE and the bottom-up ERIS model, and briefly describes illustrative baseline and greenhouse gas mitigation scenarios to highlight some of the features of the framework outlined in this paper. A number of modeling and policy insights arising from this scenario analysis are discussed.     

Incorporating technology buying behaviour into UK-based long term domestic stock energy models to provide improved policy analysis

The UK has a target for an 80% reduction in CO₂ emissions by 2050 from a 1990 base. Domestic energy use accounts for around 30% of total emissions. This paper presents a comprehensive review of existing models and modelling techniques and indicates how they might be improved by considering individual buying behaviour. Macro (top-down) and micro (bottom-up) models have been reviewed and analysed. It is found that bottom-up models can project technology diffusion due to their higher resolution. The weakness of existing bottom-up models at capturing individual green technology buying behaviour has been identified. Consequently, Markov chains, neural networks and agent-based modelling are proposed as possible methods to incorporate buying behaviour within a domestic energy forecast model. Among the three methods, agent-based models are found to be the most promising, although a successful agent approach requires large amounts of input data. A prototype agent-based model has been developed and tested, which demonstrates the feasibility of an agent approach. This model shows that an agent-based approach is promising as a means to predict the effectiveness of various policy measures.     

Endogenous technological change and emissions: the case of the German steel industry

The paper starts from the observation that innovation and technical progress are only portrayed superficially in the predominant environmental economic top-down models, and that the assumption of perfect factor substitution does not correctly mirror actual production conditions in many energy-intensive production sectors. Bottom-up models, on the other hand, neglect macroeconomic interdependencies between the modelled sector and the general economy. This paper presents a new modelling approach: in an integrated bottom-up/top-down approach based on the example of crude steel production in Germany, it is demonstrated how technological progress can be portrayed as process-related and policy-induced and how the technology choice between limitational processes can be explicitly modelled and implemented in the econometric input–output model PANTA RHEI. The new modelling approach presented permits a process-specific analysis of the impacts of changed frame conditions, the effects of which on the choice of technology, on the one hand, and the technological progress on the other, can be described endogenous to the model. These features are demonstrated in a CO₂ tax simulation. Results show that policy-induced technological change is—besides a switch in production processes—the major source of CO₂ reduction for the steel sector.     

Endogenous implementation of technology gap in energy optimization models—a systematic analysis within TIMES G5 model

Evaluation of global diffusion potential of learning technologies and their timely specific cost development across regions is always a challenging issue for the future technology policy preparation. Further the process of evaluation gains interest especially by endogenous treatment of energy technologies under uncertainty in learning rates with technology gap across the regions in global regional cluster learning approach. This work devised, implemented, and examined new methodologies on technology gaps (a practical problem), using two broad concepts of knowledge deficit and time lag approaches in global learning, applying the floor cost approach methodology. The study was executed in a multi-regional, technology-rich and long horizon bottom-up linear energy system model on The Integrated MARKAL EFOM System (TIMES) framework. Global learning selects highest learning technologies in maximum uncertainty of learning rate scenario, whereas any form of technology gap retards the global learning process and discourages the technologies deployment. Time lag notions of technology gaps prefer heavy utilization of learning technologies in developed economies for early reduction of specific cost. Technology gaps of any kind should be reduced among economies through the promotion and enactment of various policies by governments, in order to utilize the technological resources by mass deployment to combat ongoing climate change.     

Empirical challenges in the use of learning curves for assessing the economic prospects of renewable energy technologies

In bottom-up energy models endogenous technical change is introduced by implementing technology learning rates, which specify the quantitative relationship between the cumulative experiences of the technology on the one hand and cost reductions on the other. The main purpose of this paper is to critically analyze the choice of modeling and estimation strategies in learning curve analyses of power generation costs. We identify and discuss a number of theoretical and econometric issues involved in the estimation of learning curves. These include the presence of omitted variable bias and simultaneity, but also methodological problems related to the operationalization of theoretical concepts (i.e., learning-by-doing) and the associated use of data. We illustrate the importance of these issues by employing panel data for wind power installations in four western European countries, which are used to compare the results from different learning curve model specifications. The results illustrate that the estimates of learning rates may differ significantly across different model specifications and econometric approaches. The paper ends by outlining a number of recommendations for energy model analysts, who need to select appropriate energy technology learning rates from the empirical literature, or who choose to perform the empirical work themselves.     

How to do structural validity of a system dynamics type simulation model: The case of an energy policy model

System dynamics based simulation models are becoming increasingly popular in the analysis of important energy policy issues including global warming, deregulation, conservation and efficiency. The usefulness of these models is predicated on their ability to link observable patterns of behavior of a system to micro-level structures. This paper argues that the structural validity of the simulation model—right behavior for the right reasons—is a stringent measure to build confidence in a simulation model regardless of how well the model passes behavior validity tests. That leads to an outline of formal structural validity procedures available but less explored in system dynamics modeling ‘repertoire’. An illustration of a set of six tests for structural validity of a system dynamics model for energy policy analysis follows. Then using Theil inequality statistics, the behavior validity of the model is also tested. Finally, some conclusions on the increased appeal for simulation models for energy policy analysis and design are presented.     

A harmonized calculation model for transforming EU bottom-up energy efficiency indicators into empirical estimates of policy impacts

This study is an impact analysis of European Union (EU) energy efficiency policy that employs both top-down energy consumption data and bottom-up energy efficiency statistics or indicators. As such, it may be considered a contribution to the effort called for in the EU's 2006 Energy Services Directive (ESD) to develop a harmonized calculation model. Although this study does not estimate the realized savings from individual policy measures, it does provide estimates of realized energy savings for energy efficiency policy measures in aggregate. Using fixed effects panel models, the annual cumulative savings in 2011 of combined household and manufacturing sector electricity and natural gas usage attributed to EU energy efficiency policies since 2000 is estimated to be 1136 PJ; the savings attributed to energy efficiency policies since 2006 is estimated to be 807 PJ, or the equivalent of 5.6% of 2011 EU energy consumption. As well as its contribution to energy efficiency policy analysis, this study adds to the development of methods that can improve the quality of information provided by standardized energy efficiency and sustainable resource indexes.     

Modeling of end-use energy consumption in the residential sector: A review of modeling techniques

There is a growing interest in reducing energy consumption and the associated greenhouse gas emissions in every sector of the economy. The residential sector is a substantial consumer of energy in every country, and therefore a focus for energy consumption efforts. Since the energy consumption characteristics of the residential sector are complex and inter-related, comprehensive models are needed to assess the technoeconomic impacts of adopting energy efficiency and renewable energy technologies suitable for residential applications.The aim of this paper is to provide an up-to-date review of the various modeling techniques used for modeling residential sector energy consumption. Two distinct approaches are identified: top-down and bottom-up. The top-down approach treats the residential sector as an energy sink and is not concerned with individual end-uses. It utilizes historic aggregate energy values and regresses the energy consumption of the housing stock as a function of top-level variables such as macroeconomic indicators (e.g. gross domestic product, unemployment, and inflation), energy price, and general climate. The bottom-up approach extrapolates the estimated energy consumption of a representative set of individual houses to regional and national levels, and consists of two distinct methodologies: the statistical method and the engineering method.Each technique relies on different levels of input information, different calculation or simulation techniques, and provides results with different applicability. A critical review of each technique, focusing on the strengths, shortcomings and purposes, is provided along with a review of models reported in the literature.     

Economics of producing hydrogen as transportation fuel using offshore wind energy systems

Over the past few years, hydrogen has been recognized as a suitable substitute for present vehicular fuels. This paper covers the economic analysis of one of the most promising hydrogen production methods—using wind energy for producing hydrogen through electrolysis of seawater—with a concentration on the Indian transport sector. The analysis provides insights about several questions such as the advantages of offshore plants over coastal installations, economics of large wind-machine clusters, and comparison of cost of producing hydrogen with competing gasoline. Robustness of results has been checked by developing several scenarios such as fast/slow learning rates for wind systems for determining future trends. Results of this analysis show that use of hydrogen for transportation is not likely to be attractive before 2012, and that too with considerable learning in wind, electrolyzer and hydrogen storage technology.     

Introducing technology learning for energy technologies in a national CGE model through soft links to global and national energy models

This paper describes a method to model the influence by global policy scenarios, particularly spillover of technology learning, on the energy service demand of the non-energy sectors of the national economy. It is exemplified by Norway. Spillover is obtained from the technology-rich global Energy Technology Perspective model operated by the International Energy Agency. It is provided to a national hybrid model where a national bottom-up Markal model carries forward spillover into a national top-down CGE¹ model at a disaggregated demand category level. Spillover of technology learning from the global energy technology market will reduce national generation costs of energy carriers. This may in turn increase demand in the non-energy sectors of the economy because of the rebound effect. The influence of spillover on the Norwegian economy is most pronounced for the production level of industrial chemicals and for the demand for electricity for residential energy services. The influence is modest, however, because all existing electricity generating capacity is hydroelectric and thus compatible with the low emission policy scenario. In countries where most of the existing generating capacity must be replaced by nascent energy technologies or carbon captured and storage the influence on demand is expected to be more significant.     

Technology development and energy productivity in China

Understanding the range of impacts of technological innovation and diffusion in a large carbon-intensive country like China is important for understanding the future trajectory of global carbon emissions. In this paper, we utilize a uniquely rich data set of Chinese firm characteristics and technological innovation activities to identify the key determinants of rising energy productivity within China's industrial sector. We find rising relative energy prices, research and development expenditures, and ownership reform in the enterprise sector, as well as shifts in China's industrial structure, are the principal drivers of China's declining energy intensity and use over the period 1997–1999. We also find large differences in factor-biases between technology produced internally and technology imported from abroad. Technology tends to reflect the resource scarcities of the country supplying the technology. Whether internal or imported, however, we find technology development to exhibit an energy-saving bias. We also find that the firm's in-house technology development activities are important for creating the absorptive capacity required for the successful diffusion of imported technology.     

Optimal policy of energy innovation in developing countries: Development of solar PV in Iran

The purpose of this study is to apply managerial economics and methods of decision analysis to study the optimal pattern of innovation activities for development of new energy technologies in developing countries. For this purpose, a model of energy research and development (R&D) planning is developed and it is then linked to a bottom-up energy-systems model. The set of interlinked models provide a comprehensive analytical tool for assessment of energy technologies and innovation planning taking into account the specific conditions of developing countries. An energy-system model is used as a tool for the assessment and prioritization of new energy technologies. Based on the results of the technology assessment model, the optimal R&D resources allocation for new energy technologies is estimated with the help of the R&D planning model. The R&D planning model is based on maximization of the total net present value of resulting R&D benefits taking into account the dynamics of technological progress, knowledge and experience spillovers from advanced economies, technology adoption and R&D constraints. Application of the set of interlinked models is explained through the analysis of the development of solar PV in Iranian electricity supply system and then some important policy insights are concluded.     

Technological learning and renewable energy costs: implications for US renewable energy policy

This paper analyzes the relationship between current renewable energy technology costs and cumulative production, research, development and demonstration expenditures, and other institutional influences. Combining the theoretical framework of ‘learning by doing’ and developments in ‘learning by searching’ with the fields of organizational learning and institutional economics offers a complete methodological framework to examine the underlying capital cost trajectory when developing electricity cost estimates used in energy policy planning models. Sensitivities of the learning rates for global wind and solar photovoltaic technologies to changes in the model parameters are tested. The implications of the results indicate that institutional policy instruments play an important role for these technologies to achieve cost reductions and further market adoption.     

Uncertainty in the learning rates of energy technologies: An experiment in a global multi-regional energy system model

The diffusion of promising energy technologies in the market depends on their future energy production–cost development. When analyzing these technologies in an integrated assessment model using endogenous technological learning, the uncertainty in the assumed learning rates (LRs) plays a crucial role in the production–cost development and model outcomes. This study examines the uncertainty in LRs of some energy technologies under endogenous global learning implementation and presents a floor-cost modeling procedure to systematically regulate the uncertainty in LRs of energy technologies. The article narrates the difficulties of data assimilation, as compatible with mixed integer programming segmentations, and comprehensively presents the causes of uncertainty in LRs. This work is executed using a multi-regional and long-horizon energy system model based on “TIMES” framework. All regions receive an economic advantage to learn in a common domain, and resource-ample regions obtain a marginal advantage for better exploitation of the learning technologies, due to a lower supply-side fuel-cost development. The lowest learning investment associated with the maximum LR mobilizes more deployment of the learning technologies. The uncertainty in LRs has an impact on the diffusion of energy technologies tested, and therefore this study scrutinizes the role of policy support for some of the technologies investigated.     

Alternative policy impacts on US GHG emissions and energy security: A hybrid modeling approach

This study addresses the possible impacts of energy and climate policies, namely corporate average fleet efficiency (CAFE) standard, renewable fuel standard (RFS) and clean energy standard (CES), and an economy wide equivalent carbon tax on GHG emissions in the US to the year 2045. Bottom–up and top–down modeling approaches find widespread use in energy economic modeling and policy analysis, in which they differ mainly with respect to the emphasis placed on technology of the energy system and/or the comprehensiveness of endogenous market adjustments. For this study, we use a hybrid energy modeling approach, MARKAL–Macro, that combines the characteristics of two divergent approaches, in order to investigate and quantify the cost of climate policies for the US and an equivalent carbon tax. The approach incorporates Macro-economic feedbacks through a single sector neoclassical growth model while maintaining sectoral and technological detail of the bottom–up optimization framework with endogenous aggregated energy demand. Our analysis is done for two important objectives of the US energy policy: GHG reduction and increased energy security. Our results suggest that the emission tax achieves results quite similar to the CES policy but very different results in the transportation sector. The CAFE standard and RFS are more expensive than a carbon tax for emission reductions. However, the CAFE standard and RFS are much more efficient at achieving crude oil import reductions. The GDP losses are 2.0% and 1.2% relative to the base case for the policy case and carbon tax. That difference may be perceived as being small given the increased energy security gained from the CAFE and RFS policy measures and the uncertainty inherent in this type of analysis.     

Government regulation as an impetus for innovation: Evidence from energy performance regulation in the Dutch residential building sector

The recent implementation of energy performance policy as a way to tackle energy consumption in the building sector in Europe draws attention to the effect it has on the development and diffusion of energy-saving innovations. According to innovation system literature, government regulation through norms and standards is one of the factors stimulating innovation. This paper concentrates on the role of stricter government regulation as an incentive to innovation in the Dutch residential building sector. Innovation in this sector is predominantly a process of applying incremental modifications to comply with new and stricter government regulations and standards. Energy performance policy in its current shape will therefore not contribute to the diffusion of really new innovation in energy techniques for residential buildings in the Netherlands. If diffusion of really new innovation is an explicit aim of energy performance policy then the European wide introduction of this scheme needs reconsideration.     

Assessing innovation in emerging energy technologies: Socio-technical dynamics of carbon capture and storage (CCS) and enhanced geothermal systems (EGS) in the USA

This study applies a socio-technical systems perspective to explore innovation dynamics of two emerging energy technologies with potential to reduce greenhouse gas emissions from electrical power generation in the United States: carbon capture and storage (CCS) and enhanced geothermal systems (EGS). The goal of the study is to inform sustainability science theory and energy policy deliberations by examining how social and political dynamics are shaping the struggle for resources by these two emerging, not-yet-widely commercializable socio-technical systems. This characterization of socio-technical dynamics of CCS and EGS innovation includes examining the perceived technical, environmental, and financial risks and benefits of each system, as well as the discourses and actor networks through which the competition for resources – particularly public resources – is being waged. CCS and EGS were selected for the study because they vary considerably with respect to their social, technical, and environmental implications and risks, are unproven at scale and uncertain with respect to cost, feasibility, and life-cycle environmental impacts. By assessing the two technologies in parallel, the study highlights important social and political dimensions of energy technology innovation in order to inform theory and suggest new approaches to policy analysis.     

The synthesis of bottom-up and top-down approaches to climate policy modeling: Electric power technologies and the cost of limiting US CO2 emissions

In the US, the bulk of CO₂ abatement induced by carbon taxes comes from electric power. This paper incorporates technology detail into the electricity sector of a computable general equilibrium model of the US economy to characterize electric power's technological margins of adjustment to carbon taxes and to elucidate their general equilibrium effects. Compared to the top-down production function representation of the electricity sector, the technology-rich hybrid specification produces less abatement at a higher welfare cost, suggesting that bottom-up models do not necessarily generate lower costs of abatement than top-down models. This result is shown to be sensitive to the elasticity with which technologies’ generating capacities adjust to relative prices.