Gross world product and consumption in a global warming model with endogenous technological change

This paper analyzes the macro-economic costs and effects on consumption and energy demand of limiting the global average atmospheric temperature increase to 2 °C. We use a macro-economic model in which there are two competing energy technologies (carbon and non-carbon, respectively), technological change is represented endogenously, and energy is aggregated through a CES function implying positive demand for the relatively expensive non-carbon technology. Technological change is represented through a learning curve describing decreasing energy production costs as a function of cumulative experience. We find that energy savings constitutes an important mechanism for decreasing abatement costs in the short- and medium-term, while the acquisition of additional learning experience substantially decreases abatement costs in the longer-term.     

Technological change in energy systems: Learning curves, logistic curves and input-output coefficients

Learning curves have recently been widely adopted in climate-economy models to incorporate endogenous change of energy technologies, replacing the conventional assumption of an autonomous energy efficiency improvement. However, there has been little consideration of the credibility of the learning curve. The current trend that many important energy and climate change policy analyses rely on the learning curve means that it is of great importance to critically examine the basis for learning curves. Here, we analyse the use of learning curves in energy technology, usually implemented as a simple power function. We find that the learning curve cannot separate the effects of price and technological change, cannot reflect continuous and qualitative change of both conventional and emerging energy technologies, cannot help to determine the time paths of technological investment, and misses the central role of R&D activity in driving technological change. We argue that a logistic curve of improving performance modified to include R&D activity as a driving variable can better describe the cost reductions in energy technologies. Furthermore, we demonstrate that the top-down Leontief technology can incorporate the bottom-up technologies that improve along either the learning curve or the logistic curve, through changing input-output coefficients. An application to UK wind power illustrates that the logistic curve fits the observed data better and implies greater potential for cost reduction than the learning curve does.     

Testing for the presence of some features of increasing returns to adoption factors in energy system dynamics: An analysis via the learning curve approach

The purpose of this paper is to explain the sources of energy system lock-in. It presents a comparative analysis of the respective contributions of some features of increasing returns to adoption factors, i.e. learning-by-doing, learning-by-searching and returns to scale effects in explaining the technological change dynamics in the energy system. The paper is technically based on a critical analysis of the learning curve approach. Econometric estimation of learning and scale effects inherent to seven energy technologies were performed by the use of several learning curve specifications. These specifications permit to deal with some crucial issues related to the learning curve estimation which are associated with the problem of omitted variable bias, the endogeneity effects and the choice of learning indicators. Results show that dynamic economies from learning effects coupled with static economies from scale effects are responsible for the lock-in phenomena of the energy system. They also show that the magnitude of such effects is correlated with the technology life cycle (maturity). In particular, results point out that, 1) the emerging technologies exhibit low learning rates associated with diseconomies of scale which are argued to be symptomatic of the outset of the deployment of new technologies characterized by diffusion barriers and high level of uncertainty, 2) the evolving technologies present rather high learning rates meaning that they respond quickly to capacity expansion and R&D activities development, 3) conventional mature technologies display low learning rates but increasing returns to scale implying that they are characterized by a limited additional diffusion prospects.     

Concepts of learning and experience in developing solar thermal technologies

This study presents a plausible picture of development of solar thermal technology, using the learning and experience curve concepts. The cost estimates for solar thermal energy technologies are typically made assuming a fixed production process, characterized by standard capacity factors, overhead, and labor costs. The learning curve is suggested as a generalization of the costs of potential solar energy system. The concept of experience is too ambiguous to be useful for cost estimation. There is no logical reason to believe that cost will decline purely as a function of cumulative production, and experience curves do not allow the identification of logical sources of cost reduction directly. The procedures for using learning and aggregated cost curves to estimate the costs of solar technologies are outlined. Because adequate production data often do not exist, production histories of analogous products/processes are analyzed, and learning and aggregated cost curves for these surrogates estimated. If the surrogate learning curves apply, they can be used to estimate solar thermal technology costs. The steps involved in generating these cost estimates are given. Second-generation glass-steel heliostat design concept developed by MDAC is described; a costing scenario for 25,000 units/year is detailed; surrogates for cost analysis are chosen; learning and aggregated cost curves are estimated; and the aggregate cost curve for the MDAC designs is estimated. The surrogate concept of cost estimation combines qualitative steps, which are highly subjective, with quantitative techniques, which require thorough knowledge and understanding to justify their use. As such, the results, interpretations, and inferences must be qualified by an understanding of the process by which they were developed. The method of surrogate learning curves had limitations in both the data acquisition and data analysis phases of activity. Improvements in the validity of cost data and in the task used for this type of study are necessary to enhance the reliability of unit cost predictions resulting from this technique.     

Learning pathways for energy supply technologies: Bridging between innovation studies and learning rates

Understanding and supporting learning for different emerging low carbon energy supply technology fields is a key issue for policymakers, investors and researchers. A range of contrasting analytical approaches are available: energy system modelling using learning rates provides abstracted, quantitative and output oriented accounts, while innovation studies research offers contextualised, qualitative and process oriented accounts. Drawing on research literature and expert consultation on learning for several different emerging energy supply technologies, this paper introduces a ‘learning pathways’ matrix to help bridge between the rich contextualisation of innovation studies and the systematic comparability of learning rates. The learning pathways matrix characterises technology fields by their relative orientation to radical or incremental innovation, and to concentrated or distributed organisation. A number of archetypal learning pathways are outlined to help learning rates analyses draw on innovation studies research, so as to better acknowledge the different niche origins and learning dynamics of emerging energy supply technologies. Finally, a future research agenda is outlined, based on socio-technical learning scenarios for accelerated energy innovation.     

Energy in America's future

Energy in America's Future, the book reported on in this article, assembled facts and performed analyses required for making informed energy choices. It reached broad conclusions on supply and demand prospects and on the environmental and other social aspects of policy choices. Although near-term constraints are severely binding, the nation's natural resource position is favorable for long-run supply expansion. Future technologies are also promising in cost terms. Although costs are bound to rise, a ceiling on long-run supply costs looks to be possible at real levels surprisingly close to current prices. Energy consumption in relationship to overall national output (GNP) will be growing at a slower rate than in recent decades. Consumers will use relatively less energy as a result of higher energy prices, conservation incentives, and new energy-using technology. Even so, overall energy use will grow. Major energy demand policy issues concern not just the likely rate of growth of consumption, but also the possible problems connected with optimizing energy efficiency at the expense of economic efficiency (i.e., output in relation to all inputs, particularly those of labor and capital). Environmental impacts and concerns about questions of human health and safety will continue to affect public acceptance of particular energy technologies. The public will need to be adequately informed on the comparative risks of energy supply technologies, and the technical and institutional means available for reducing these risks. The price system must be permitted to function to the maximum feasible extent. In addition, the task for political leadership is to forge a public consensus in support of achievable energy goals. This has been difficult because of conflicting public perceptions of the facts and clashes in social values among different groups. The future prospects for achieving a national energy consensus should be enhanced by the outlook for slower energy demand growth and favorable supply prospects along with potential improvements in the environmental side effects associated with new and improved supply technologies. As knowledge of these changing circumstances becomes more broadly disseminated, the ability to achieve broad public support of energy goals should grow stronger.     

Energy sector in transition—technologies and regulatory policies in flux

A transition of the energy sector towards a renewable-based system is one of the challenges that results from the debate on the global climate problems and diminishing fossil fuels. This will demand access to innovations in energy production technologies and usage. However, policies directly focussing on innovation and diffusion of new technologies are losing ground. Policy discussions have been dominated by the liberalisation of energy markets and the introduction of new economic instruments all over Europe. Whether this delegation of responsibility for the transition to new economic instruments provides the expected innovations is questionable.In Europe, Denmark has served as an example country being one of the front runners in the development of energy technologies based on renewable sources, especially wind turbines. But also in Denmark policy preferences have changed from the support for technological innovation and implementation to support for delegating responsibility to a market for green energy and CO₂ certificate trading.The different policy regimes marking this change are analysed concerning their ability to support technological innovation. Important elements for the construction and legitimacy of policy regimes comes from academic disciplines involved in policy design. The analysis highlights the basic arguments and the type of knowledge involved and indicates some of the limitations in the measures' ability to solve the problems delegated to them. Experience thus far provides little if any evidence that simple, market-based models can facilitate the need for future energy technology innovations in a satisfactory way, which leads to a need for more complex and heterogeneous sets of measures.     

A percolation model of eco-innovation diffusion: The relationship between diffusion, learning economies and subsidies

An obstacle to the widespread adoption of environmentally friendly energy technologies such as stationary and mobile fuel cells is their high upfront costs. While much lower prices seem to be attainable in the future due to learning curve cost reductions that increase rapidly with the scale of diffusion of the technology, there is a chicken and egg problem, even when some consumers may be willing to pay more for green technologies. Drawing on recent percolation models of diffusion, we develop a network model of new technology diffusion that combines contagion among consumers with heterogeneity of agent characteristics. Agents adopt when the price falls below their random reservation price drawn from a lognormal distribution, but only when one of their neighbors has already adopted. Combining with a learning curve for the price as a function of the cumulative number of adopters, this may lead to delayed adoption for a certain range of initial conditions. Using agent-based simulations we explore when a limited subsidy policy can trigger diffusion that would otherwise not happen. The introduction of a subsidy policy seems to be highly effective for a given high initial price level only for learning economies in a certain range. Outside this range, the diffusion of a new technology either never takes off despite the subsidies, or the subsidies are unnecessary. Perhaps not coincidentally, this range seems to correspond to the values observed for many successful innovations.     

Modeling technological change in energy demand forecasting: A generalized approach

Conventional economic modeling of energy demand has characterized technological choice as an investment decision driven primarily by the relationship between capital costs and operating costs. Yet the implementation of this approach has tended to yield unrealistically high estimates of the implicit discount rate governing investment decisions, particularly those involving energy efficient technologies. This result arises from incomplete specification of the process of technological choice and the diffusion of innovations. General models of diffusion include conventional costs as one set of factors among many others that influence the spread of new technologies. These more general models have been widely applied to the adoption of other new or improved products, and their use in energy demand forecasting would lead to more accurate and reliable projections. Modification of the forecasts would have policy implications. In particular, the cost of a strategy to reduce greenhouse gas emissions by encouraging more rapid diffusion of energy efficient technologies is likely to be considerably smaller than would be suggested by the conventional economic models.     

Aggregate labor market fluctuations under news shocks

The standard RBC model fails to replicate the relationship between aggregate hours worked and average productivity. We propose a DSGE model that incorporates habit formation preferences, capital adjustment costs, and news shocks to solve the puzzle implied in the standard RBC model with only technological shocks. The aggregate labor supply curve is shifted due to the wealth effect caused by the variation of consumption under a news shock. Moreover, capital adjustment costs help amplify the variation of consumption, and thus the movement of the aggregate labor supply curve under the news shock. Also, the aggregate demand curve will be shifted, as it operates in the standard RBC model after the realization of the news shock. As a result of the joint movement of the aggregate labor supply curve and aggregate labor demand curve under the news shock, the model achieves a relationship quite close to the empirically observed relationship.     

能量转换视角下人工智能关键核心技术产业化路径解析

人工智能是驱动新一轮科技革命和产业变革的中坚力量，探究人工智能关键核心技术产业化路径对于我国数字经济发展、数字化转型和创新型国家建设具有重要意义。基于能量转换视角，探讨人工智能技术产业化路径形成机理，识别人工智能关键核心技术产业化路径并对其进行解析。研究发现：①人工智能技术产业化过程存在能量转换，技术创新能量、催化孕育能量、商业转化能量和业态塑造能量共同构成能量转换的核心环节，决定技术产业化路径具体过程；②人工智能关键核心技术包括机器学习、计算机视觉、自然语言处理等八大技术领域，不同属性核心技术构成相应技术集群，形成以识别、交互和执行为主题的技术产业化路径；③技术集群属性是影响技术产业化路径的关键因素。从能量视角对人工智能关键核心技术进行有效识别和归纳，为我国科学推进人工智能技术产业化进程提供相应思路启示与政策响应。     

Evaluating new CO2 reduction technologies in Japan up to 2030

A mathematical model was developed for evaluating CO₂-reduction technologies in power generation, residential, commercial and road transport sectors in Japan. The existing and new power generation technologies evaluated included 34 centralized and 8 dispersed power generation technologies in the residential and commercial energy demand sectors. To take into account the varieties of useful energy and of its demand duration patterns among entities in the demand sectors, the hourly mean power and heating and cooling demand–supply balances in one residential and four commercial representative entities were considered for each month. The road transport sector addressed five types of automotive use. The useful-energy demands are exogenously given; the model calculates the technology installations that satisfy the demands to minimize the total systems cost for each year up to 2030. The availability of the new technologies, i.e., the first years they are installable, is derived from research and development (R&D) process analyses on the basis of surveys to experts. As a result of the model calculation, dispersed molten carbonate and solid oxide fuel cells and onboard gasoline reforming-type fuel cell vehicle (FCV) technologies are expected to have the largest economic values, approximately 60–120 billion constant 1998 yen [460–920 million U.S. dollars (USD)] among the evaluated new CO₂-reduction technologies. One of the implications from our calculations is that extending electric power corporations' commercial coverage to dispersed power generation, in addition to centralized power generation, is desirable to help lower overall costs in society, as well as to secure industry profits.     

Climate policies and learning by doing: Impacts and timing of technology subsidies

We study the role of technology subsidies in climate policies, using a simple dynamic equilibrium model with learning by doing. The optimal subsidy rate of a carbon-free technology is high when the technology is first adopted, but falls significantly over the next decades. However, the efficiency costs of uniform instead of optimal subsidies, may be low if there are adjustment costs for a new technology. Finally, supporting existing energy technologies only, may lead to technology lock-in, and the impacts of lock-in increase with the learning potential of new technologies as well as the possibilities for early entry.     

Impacts of CO2-Taxes in an Economy with Niche Markets and Learning-by-Doing

In this paper, we analyse the impact of carbon taxes on emission levels, when niche markets exist for new carbon-free technologies, and when these technologies experience ``learning-by-doing' effects. For this purpose, a general equilibrium model has been developed, DEMETER, that specifies two energy technologies: one based on fossil fuels and one on a composite of carbon-free technologies. Initially, the carbon-free technology has relatively high production costs, but niche markets ensure positive demand. Learning-by-doing decreases production costs, which increases the market share, which in turn accelerates learning-by-doing, and so forth. This mechanism allows a relatively modest carbon tax, of about 50 US$/tC, to almost stabilise carbon emissions at their 2000 levels throughout the entire 21st century. Sensitivity analysis shows that the required carbon tax for emission stabilisation crucially depends on the elasticity of substitution between the fossil-fuel and carbon-free technology.(part of the research was performed when the author was Science Fellow at CISAC, Center for International Security and Cooperation, Stanford University, Stanford, California, USA)     

Environmental Policy and the Direction of Technical Change

Should governments direct research and development (R&D) away from “dirty” technologies towards “clean” ones? How important is this compared to carbon pricing? We address these questions with the introduction of two model features to the literature on directed technological change and the environment. We introduce decreasing returns to R&D, and allow future carbon taxes to influence current R&D decisions. Our results suggest that governments should prioritize clean R&D. Dealing with major environmental problems requires an R&D shift towards clean technology. However, in the case where most researchers are working with clean technology, both productivity spillovers and the risks of future replacement increase. Consequently, the gap between the private and social values of an innovation is greatest for clean technologies.     

Population density,urbanisation and agricultural mechanisation in modern Ghana

Farmers' technological choices take place within farming systems that are shaped by population pressure, connectivity to urban markets and agro-ecological conditions. The relationship between these drivers and agricultural technology use is ambiguous. On the one hand, population growth can increase the supply of labour, driving down wage rates and reducing the incentives for mechanisation. On the other hand, rural–urban movements of people can reduce rural labour supply while simultaneously driving up the demand for food and hence the demand for mechanisation. Past theories of agricultural mechanisation have explained the low adoption of agricultural machinery in land-abundant cereal production systems of SSA in terms of these drivers. However, recent empirical observations find extensive adoption of mechanised ploughing technology by small, medium and large-scale farmers in Ghana. Examining the Ghanaian experience can thus shed new light on theories of mechanisation. A large household survey dataset covering eight districts is combined with geo-spatial data on population, urban proximity and agro-ecological factors to consider whether the existing theories are able to explain farm-level adoption decisions in this context. The analysis finds that a farmer's decision to use agricultural machinery is associated with lower population density and proximity to urban centres. In northern Ghana, these drivers of technological change are as important as farm household characteristics in understanding cross-sectional patterns of machinery adoption.     

The Dynamics Of Localized Technological Changes. The Interaction Between Factor Costs Inducement,Demand Pull And Schumpeterian Rivalry 1

Localized technological change is the endogenous outcome of the interplay between substitution costs. Switching costs and learning processes. New technologies are introduced when market pressures induce firms to change the levels of their inputs and their techniques. The dynamics ol localized technological change is the result us the interaction between three processes: it) the Schumpcterian competition process as analyzed by the replicator dynamics and failure inducement mechanisms. b) factor substitution stemming from changes in factors markets. and c) post-Keynesian demand pull pressures resulting from productivity growth. In such conditions out-of-equilibrium exchanges and localized technological changes drive a recursive process that is path-dependent in two senses, first it is highly sensitive to the initial conditions of the system. and second it is shaped by the interactions of agents.     

The emergence of hybrid-electric cars: Innovation path creation through co-evolution of supply and demand

Hybrid-electric vehicles have experienced a significant rate of growth in the last 10 years. This is remarkable, since the automotive sector is typically averse to the more radical technological change of engines. The internal combustion engine has been around for more than 100 years after all.In this paper we describe and explain the emergence of electric engines in the automobile market after 1990. We explicate the role of techno-economic mechanisms alongside social and regulatory mechanisms (including the social meaning of an engine). The co-evolutionary analysis is novel in the integrated conception of actor perspectives, feedback effects and competition between products. We find three sources of lock-in through path dependency: from demand, supply as well as the regulatory side. We conclude that automotive engines were significantly locked into a trajectory of internal combustion technology due to techno-economic mechanisms, which produced inertia despite social pressures. The creation of an alternative path, on the other hand, initially stalled. Various stakeholders were unsuccessful in marketing their electric or hybrid-electric vehicles in the 1990s, such as Peugeot/Citroen with various electric models, or Audi with their Duo in 1997. However, after 2000 we find that sustaining efforts of California's Air Resources Board and Toyota were triggering creation of a new innovation path of hybrid-electric engines.     

Determinants of technological change in the service industries

The Service Industries provide numerous examples of how technological change may be impeded. Either the economic state of the industry or the state of scientific and engineering knowledge can limit inventive activity and productivity advance. Consumer ignorance, lack of producer incentives, complex production interdependencies, and market fragmentation are only a few factors limiting the role of demand in stimulating the discovery and diffusion of new services and production techniques. On the cost side, technical uncertainty, time horizons, and discount rates affect decisions to engage in R & D. In addition, strategic market considerations, such as potential entry, reaction speeds of rivals, and numbers of competitors affect the benefits from early introduction of new technologies. These institutional and economic considerations need to be taken into account for technological forecasting related to the Service Industries.     

Coping with Technology Divergence Policies and Strategies for India’s Industrial Development

Recent studies on innovation have demonstrated the relationship between technology and growth. However, as most of them are centered on the experience of the highly industrialized nations, a different approach to technology policy must be taken. As late industrializers, developing countries lag in adopting foreign technologies. Institutional factors and economic policy also influence the diffusion process. With decentralized decision making, the coexistence of diverse technologies in a given industrial branch is inevitable. Consequently, social costs tend to be high because of duplication of efforts, reduced learning opportunities, and adoption of inefficient technologies. This article examines the coexistence of diverse technologies leading to technology fragmentation in India’s steel industry. Recent innovative behavior in the Japanese and Korean steel industry indicates that the effects of fragmentation can be contained through a policy of “system integration.” This is an institutional process by which industry-specific applications of scientific knowledge are fused with basic research itself. This demands a forward-looking policy that rejuvenates older industries, such as steel, in socially acceptable ways and organically creates new knowledge for national development and social welfare.