Making the energy transition in rural east Africa: Is leapfrogging an alternative?

There is renewed optimism about the potential for leapfrogging in the rural energy sector of East Africa. By adopting highly efficient and renewable technologies many believe the region can rapidly bypass the conventional path of energy development and skip directly into the use of more efficient and environmentally friendly technologies. This study explores the potential for energy leapfrogging by examining three technological approaches targeted at rural households in East Africa: conventional grid expansion, renewable energy technologies supplying electricity, and improved cookstoves. The study identifies economic, social, political, and cultural factors limiting the ability of rural people to rapidly switch into using and/or supplying these technologies. The potential for leapfrogging may be overstated by planners and experts who focus on the technical and economic viability of the technologies while insufficiently considering the social conditions and economic realities of daily life in the region. Moreover, energy leapfrogging itself is considered a misconception. Energy transitions in rural areas are incremental processes—not leaps—dependent upon household and regional accumulations of technological capabilities. These capabilities have technical, organizational, and institutional components and are manifest in individuals' capacity to adapt to new technologies, their ability to take economic risks, and in their desire to modify their behavior. In designing technology dissemination or energy supply projects, planners must thoroughly account for the capabilities existing in rural areas.     

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.     

Modelling creative destruction: Technological diffusion and industrial structure change to 2050

Future disruptive, pervasive technologies will have important consequences for industrial structure, economic growth and the environment. Drawing on theories of technological diffusion, industrial evolution and long-term technological change this paper explores the effect of the development and diffusion of two future pervasive technologies on five industrial sectors in three regions during the 21st century in terms of their effect on economic structural change. Through semi-structured interviews with over 100 experts in the two technologies, the paper quantifies the effects of future biotechnologies and nanotechnologies on the industrial structure of the EU, USA and China in 2020 and 2050. The paper finds that as a result of the development and diffusion of future biotechnologies and nanotechnologies, some industries grow whilst others decline and some new ones emerge. The evidence suggests that the effect is different across countries and time; whereas the experts commonly believe that effect of the technologies on the industrial structure of the EU and US is likely to be similar, the effect in China is considered to be less by 2020 but the same as in the EU and US by 2050. This finding has important implications for the location of production, economic growth and energy demand in the future.     

Multigeneration diffusion model for economic assessment of new technology

In the era of 21st century, development of emerging information technology is the essence of the advancement. This kind of new technology, however, often requires a great deal of amount of initial investment for both procedures of R&D and commercialization. As cost invested in developing the specified technology is increasing, investors are paying more attention to cost to benefit analysis (CBA). One of the basic elements of CBA for new technological development is the diffusion pattern of demand of such technology. Typically, it would be hard to presume the diffusion pattern of demand when the new product or the technology is under development. In this case, a simulation study is necessary. Many studies of technology evaluation have adopted a single generation model to simulate the diffusion pattern of demand. This approach, however, considers the diffusion of the new technology itself, not taking into account newer generation, which can replace the one just invented. In the real market situation, one must consider the competition and substitution phenomena between old and new technologies. In this paper, we show how multigeneration technology diffusion model can be applied for more accurate CBA for information technology. Additionally, Monte Carlo simulation is performed to find influential factors on the CBA of a cybernetic building system (CBS).     

新旧动能转换背景下制造业产业升级评价研究

现阶段,我国经济发展进入新常态。经济发展模式开始由要素驱动、投资驱动向创新驱动转变, 用增长促发展,用发展促增长。新旧动能转换与产业升级是经济发展进入新常态的必然选择,新旧动能转换是产业转型升级的核心动力,产业升级是新旧动能转换的外在表现。基于2006-2017年山东省17个地市的面板数据,本文从需求、供给和环境三方面研究发现：山东省及其各区域正处于改造旧动能、培育新动能的关键时期,消费需求、人力资本和技术创新对产业升级有积极的推动作用。进一步对山东省的东、中、西部三个区域研究发现,产业升级具有区域差异性。     

Wind Power in Europe: A Simultaneous Innovation–Diffusion Model

The purpose of this paper is to provide a quantitative analysis of innovation and diffusion in the European wind power sector. We derive a simultaneous model of wind power innovation and diffusion, which combines a rational choice model of technological diffusion and a learning curve model of dynamic cost reductions. These models are estimated using pooled annual time series data for four European countries (Denmark, Germany, Spain and the United Kingdom) over the time period 1986–2000. The empirical results indicate that reductions in investment costs have been important determinants of increased diffusion of wind power, and these cost reductions can in turn be explained by learning activities and public R&D support. Feed-in tariffs also play an important role in the innovation and diffusion processes. The higher is the feed-in price the higher is, ceteris paribus, the rate of diffusion, and we present some preliminary empirical support for the notion that the impact on diffusion of a marginal increase in the feed-in tariff will differ depending on the support system used. High feed-in tariffs, though, also have a negative effect on cost reductions as they induce wind generators to choose high-cost sites and provide fewer incentives for cost cuts. This illustrates the importance of designing an efficient wind energy support system, which not only promotes diffusion but also provides continuous incentives for cost-reducing innovations.      

ENERGY EFFICIENCY AND ECONOMIC GROWTH

Recent contributions by Brookes (1990), Saunders (1992), and Inhaber and Saunders (1994) argue that cost-effective improvements in energy efficiency may, in the long run, lead energy use to grow more rapidly than it would in a world of fixed technologies. Since efficiency improvements may be viewed as a form of technological change that both reduces the effective cost of energy services and stimulates economic activity, energy demand may, under some circumstances, rise even as energy productivity improves. This paper examines this hypothesis using a simple model that distinguishes the roles of energy and energy services in production activities. In this model, improved energy efficiency can-not give rise to increased energy use unless: (i) energy costs dominate the total cost of energy services and (ii) expenditures on energy services constitute a large share of economic activity. Since neither of these assumptions is empirically plausible, the paper concludes that energy efficiency improvements will yield long-run reductions in energy use under the assumptions of the model.     

新常态下内需拉动经济增长是否存在有效边界*

新常态下,中国如何维持经济中高速增长既是重点,也是难点。由于外需拉动型经济增长方式逐渐难以为继,扩大内需的重要性日益突出。然而,扩大内需真的是有效国策吗？投资和消费拉动经济增长,是否存在有效边界？基于此,本文通过构建面板门限模型,利用2012-2013年中国275个市级数据,对内需拉动经济增长是否存在有效边界问题进行了实证检验。研究结果显示：投资拉动经济增长确实存在有效边界,当投资率较低时,增加投资能够有效拉动经济增长,当投资率已经较高时,增加投资对经济增长的拉动作用将不再显著;消费拉动经济增长也存在有效边界,当投资效率较低时,增加消费会抑制经济增长,当投资效率足够高时,增加消费才能够有效拉动经济增长。\     

Coal gasification: An economic evaluation

Political interest in developing the capability to produce gaseous fuel from coal in the United States has been cyclical in nature, depending primarily upon the security of the international market for oil and public attitudes toward nuclear power. Interest in coal gasification technology by private investors, however, depends primarily on the economic and technological considerations analyzed in this paper. A cost forecasting model is developed with the capability to take into account future economic and technological uncertainties associated with producing high BTU gas (a substitute for natural gas) from coal. The cost forecasting model incorporates probabilistic information on key economic and technological parameters subject to future uncertainty and simulates, by Monte Carlo methods, the costs which private investors would incur over the life of a commercial size coal gasification plant. The results suggest it is highly unlikely that the coal gasification process could produce high BTU gas more cheaply than the price at which natural gas is likely to be available.The cost forecasting model is also modified to compare the cost per kilowatt–hour of generated electricity when fueling a 1,000 Mw power plant with oil versus high BTU gas from coal. Again, based upon the costs to private investors, the simulation results indicate a very low probability that high BTU gas from coal would prove the least costly fuel for generating electricity.The implied economic infeasibility for private investment in coal gasification does not necessarily provide a basis for public policy to abandon the technology. Public policy recommendations must consider social costs as well as private costs. Possibly the greatest social cost associated with abandoning coal gasification is the risk of a significant energy supply interruption. A diversified national energy policy including coal gasification may in fact be less costly if relevant social costs are included in the calculations. Results from the cost forecasting model indicate the size and type of public subsidies that may be necessary to support a diversified energy industry which would include coal gasification.     

Scenario development in China's electricity sector

The continuing growth of China's electricity sector will affect global environmental and economic sustainability due to its impacts on greenhouse gas emissions and global resource depletion. In 2005, the generation of electricity in China resulted in the emissions of 2290 million metric tonnes of carbon dioxide (approximately 53% of the nation's total) and required 779 million metric tonnes of coal (approximately 50% of China's total coal consumption). These figures are expected to increase with China's economic growth. In order to gauge the range in which fuel consumption and CO₂ emissions could grow a scenario-based conceptual model has been developed by the authors (published in (vol.) of this journal). The application and analysis of this shows that under a business as usual (BAU) scenario, electricity generation could contribute upwards of 56% of China's energy related greenhouse gas emissions by 2020. Meanwhile, consumption of coal will also increase, growing to nearly 60% of total national demand by 2020. However, variations in a number of key drivers could produce significant deviation from the BAU scenario. With accelerated economic output, even with greater technological advances and greater potential to bring natural gas on stream, carbon dioxide emissions would rise 10% above the BAU. Alternatively, in a scenario where China's economy grows at a tempered pace, less investment would be available for advanced technologies, developing natural gas infrastructure, or nuclear energy. In this scenario, reduced economic growth and electricity demand would thereby be countered by reduced efficiency and a higher contribution of coal.     

Using solar PV feed-in tariff policy history to inform a sustainable flexible pricing regime to enhance the diffusion of energy storage and electric vehicles

The aim of this paper is to analyses residential solar PV feed-in tariffs (FiT) policy history to inform the development of a sustainable flexible pricing regime to enhance the diffusion of energy storage, electric vehicles, solar PV installations and other distributed resources focusing on the case of ‘solar rich’ Australia. Solar PV has reached price parity at the retail level where the electricity price charged includes both transmission and distribution costs, in addition to the wholesale price. So the economic rationale for paying a FiT premium above market rates to achieve dynamic efficiency is no longer warranted. However, there is justification pay a premium to encourage dynamic innovation in energy storage. Socially, FiTs can be a problem because they can transfer wealth from poorer to richer households. Additionally, new investment in distribution and transmission, driven by peak demand spikes from air conditioners can act as a further transfer. Environmentally, FiTs can also fall short of their full potential to cut emissions if they lack ‘time of use’ price signals that reflect movements in the wholesale price. We suggest a sustainable flexible price regime that can be designed to addresses all three areas of concern: social, environmental and economic. The resultant transmission and distribution investment deferment would meet both environmental and economic objectives. We argue that the time has come to design a sustainable flexible price regime for solar PV that focusses upon allocative efficiency as an explicit goal and to introduce support for other distributed resources including energy storage to encourage dynamic efficiency.     

An evolutionary model of energy transitions with interactive innovation-selection dynamics

We develop a stylized application of a new evolutionary model to study an energy transition in electricity production. The framework describes a population of boundedly rational electricity producers who decide each period on the allocation of profits among different energy technologies. They tend to invest in below-average cost energy technologies, while also devoting a small fraction of profits to alternative technological options and research on recombinant innovation. Energy technologies are characterized by costs falling with cumulative investments. Without the latter, new technologies have no chance to become cost competitive. We study the conditions under which a new energy technology emerges and technologies coexist. In addition, we determine which investment heuristics are optimal in the sense of minimizing the total cost of electricity production. This is motivated by the idea that, while diversity contributes to system adaptability (innovation) and resilience to unforeseen contingencies (keeping options open), a high cost will discourage investments in it.     

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.     

A review of experience curve analyses for energy demand technologies

Transitioning towards a sustainable energy system requires the large-scale introduction of novel energy demand and supply technologies. Such novel technologies are often expensive at the point of their market introduction but eventually become cheaper due to technological learning. In order to quantify potentials for price and cost decline, the experience curve approach has been extensively applied to renewable and non-renewable energy supply technologies. However, its application to energy demand technologies is far less frequent. Here, we provide the first comprehensive review of experience curve analyses for energy demand technologies. We find a widespread trend towards declining prices and costs at an average learning rate of 18 ± 9%. This finding is consistent with the results for energy supply technologies and for manufacturing in general. Learning rates for individual energy demand technologies are symmetrically distributed around the arithmetic mean of the data sample. Absolute variation of learning rates within individual technology clusters of 7 ± 4%-points and between technology clusters of 7 ± 5%-points both contribute to the overall variability of learning rates. Our results show that technological learning is as important for energy demand technologies as it is for energy supply technologies. Applying the experience curve approach to forecast technology costs involves, however, unresolved uncertainties, as we demonstrate in a case study for the micro-cogeneration technology.     

培育经济发展新动能的技术、产业、政策机理研究

伴随着国际、国内经济形势发生深刻变化，我国经济过去主要依靠投资、劳动力、自然资源投入的粗放型发展模式已经难以为继，急需培育促进经济实现高质量发展的新动能。现有成果主要围绕技术基础、产业载体及政策制度等培育经济发展新动能的丰富内涵和多样化模式展开，针对内在机理的探讨较少。从新兴技术对产业结构优化起基础支撑作用、新兴产业载体释放经济新动能、市场与政府相结合的政策制度助力经济高质量发展三大维度论述基本原理，系统梳理新兴技术聚合迸发新动能、传统产业升级培育新动能以及政策制度创新生成新动能3种主要模式。最后，提出以推动技术引领发展、促进产业调整升级和出台政策“靶向支撑”为核心的“创新链-产业链-政策链”对策建议。     

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.     

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.     

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.     

技术创新中路径依赖的成因及破解分析

技术创新作为一种重要的竞争策略,已经日益受到企业的重视。然而,由于路径依赖现象的存在,新技术的推广活动在市场上面临着巨大的阻力和挑战。本文通过对路径依赖产生的原因进行分析,认为正是由于转换成本和网络效应的存在导致了路径依赖的出现。转换成本的存在虽然导致了路径依赖或锁定现象的出现,但这种锁定是有限度的,在很多情况下网络效应起到了更为重要的决定作用。在网络效应的影响下,市场容易表现出正反馈的特点：如果一种技术在市场上建立起来的安装基础具有相对优势,就有可能使市场选择向有利于自己的方向发展,从而限制其它技术的市场份额。因此,一项技术创新能否取代旧技术,关键在于用户规模能否达到所需的临界容量。根据路径依赖的形成特点,本文提出了几种破解路径依赖的有效途径：大幅度提高技术水平,采取技术兼容策略和尽快达到临界容量。     

RENAISSANCE OR REQUIEM: IS NUCLEAR ENERGY COST EFFECTIVE IN A POST‐FUKUSHIMA WORLD?

In the aftermath of Fukushima, decisions to slow or stop the future use of nuclear power have not been based on rational economic analysis. We find that there are cost‐effective technologies that would greatly mitigate future natural disasters. Even if the U.S. nuclear industry adopted new safety technologies and paid the full cost of insurance and borrowing, it is more efficient to continue to use existing nuclear plants than to replace them with new fossil fuel plants. However, new nuclear plant costs can exceed fossil fuel alternatives if the price of carbon emissions is below $118/ton. (JEL Q40, Q48, Q54)