Volume 1 photovoltaic solar energy 1 05 – historical and future cost dynamics of photovoltaic technology

Volume 1 photovoltaic solar energy 1 05 – historical and future cost dynamics of photovoltaic technology Volume 1 photovoltaic solar energy 1 05 – historical and future cost dynamics of photovoltaic technology Volume 1 photovoltaic solar energy 1 05 – historical and future cost dynamics of photovoltaic technology Volume 1 photovoltaic solar energy 1 05 – historical and future cost dynamics of photovoltaic technology Volume 1 photovoltaic solar energy 1 05 – historical and future cost dynamics of photovoltaic technology Volume 1 photovoltaic solar energy 1 05 – historical and future cost dynamics of photovoltaic technology

1.05 Historical and Future Cost Dynamics of Photovoltaic Technology GF Nemet and D Husmann, University of Wisconsin–Madison, Madison, WI, USA © 2012 Elsevier Ltd All rights reserved 1.05.1 1.05.2 1.05.2.1 1.05.2.2 1.05.2.3 1.05.2.4 1.05.2.5 1.05.2.6 1.05.2.7 1.05.2.8 1.05.2.8.1 1.05.2.8.2 1.05.2.9 1.05.2.10 1.05.3 1.05.3.1 1.05.3.1.1 1.05.3.1.2 1.05.3.2 1.05.3.3 1.05.3.3.1 1.05.4 1.05.4.1 1.05.4.1.1 1.05.4.1.2 1.05.4.1.3 1.05.4.1.4 1.05.4.1.5 1.05.4.2 1.05.5 1.05.5.1 1.05.5.2 1.05.5.3 1.05.6 References Introduction: Observed Reductions in the Cost of Photovoltaics What Caused the 700Â Reduction in the Cost of PV? Identifying Drivers of Change R&D and Efficiency Improvements Sequential Niche Markets Expectations about Future Demand Learning by Doing Intertechnology Spillovers Materials Drivers Related to Supply and Demand Industry structure Demand shocks and rising elasticity Quality and Product Attributes Interactions between R&D and Experience in Production Using Learning Curves to Predict Costs Use of Experience Curves in Modeling and Policy Modeling Policy Problems with Using Experience Curves How Reliable Are Experience Curve Predictions? Assessing the significance of recent deviations Nonincremental Cost-Reducing Developments Identifying Breakthroughs Defining breakthrough First pass: Expert opinion Why patent analysis? Backward citation analysis Implementing backward citation analysis for PV Results: Combining Expert Opinion and Patent Analysis Modeling Nonincremental Changes in PV An Approach to Modeling Nonincremental Technological Change Results for Nonincremental Technological Change Summary of Nonincremental Modeling Future Progress and Development 47 48 49 49 49 50 51 51 51 52 52 52 53 54 54 54 55 55 55 57 58 61 61 61 62 62 62 63 65 67 67 68 69 69 70 1.05.1 Introduction: Observed Reductions in the Cost of Photovoltaics The cost of photovoltaics (PV) has declined by a factor of nearly 700 since the 1950s, which is more than that for any other energy technology in that period At present, however, PV remains a niche electricity source and in the overwhelming majority of situations does not compete economically with conventional sources, such as coal and gas, or even with other renewable sources, such as wind and biomass The extent to which the technology improves over the next few decades will determine whether PV reaches terawatt scale and makes a meaningful contribution to reducing greenhouse gas emissions or remains limited to niche applications In this chapter, we discuss the observed cost reductions in PV and the factors affecting them We discuss the use of learning curves for forecasting PV costs and the nonincremental changes to technology that complicate such models We include a discussion of an alternate forecasting methodology that incorporates R&D impacts and conclude with implications for policy and items for future progress Reductions have occurred across a wide variety of components within PV systems Foremost, the cost of PV modules have declined from about $2700 W−1 in the 1950s to around $3 W−1 in 2006 (Figure 1) [1] Although difficult to verify, claims that the marginal cost of manufacturing modules is as low as $1 W−1 are now widespread [2] Comprehensive Renewable Energy, Volume doi:10.1016/B978-0-08-087872-0.00103-7 47 48 Economics and Environment $10 000 2008$ W–1 $1000 $100 $10 $1 1950 1960 1970 1980 1990 2000 2010 2000 2010 −1 Figure Cost of PV modules, 1957–2006 (2008$ W ) [1] 1000 2008$ kWh–1 100 10 0.1 0.01 1950 1960 1970 1980 1990 −1 Figure Levelized cost of electricity generated from PV (2008$ kWh ) [1] Most assessment of PV has focused on the evolution of the core technology, the modules that convert sunlight to electricity As the prices of modules have fallen, the rest of the components that comprise a PV system have accounted for an increasing share of the overall costs A study of balance-of-system (BOS) prices in the 1990s found that the rate of technology improvement in BOS components has been quite similar to that of modules [3], which is rather surprising given the heterogeneous set of components and activities that fall under the rubric of BOS Inverters, the largest hardware component of BOS, improved more slowly than modules; it was the noninverter costs – installation, wiring, mounting systems – that improved even more quickly Dispersion in the data for BOS components is higher than that for modules Ultimately, because technology adoption decisions are based on the cost of electricity produced, the cost of fully installed systems is what matters most Recent work at Lawrence Berkeley National Laboratory has documented the cost of installed systems in the 2000s [4] Costs have fallen from over $10 W−1 in 1998 to under $8 W−1 in 2007 Figure shows the long-term decline in the cost of electricity from PV, a factor of 800 reduction 1.05.2 What Caused the 700Â Reduction in the Cost of PV? A variety of factors, including government activities, have enabled the nearly orders of magnitude reduction in the cost of PV over the past six decades Despite this achievement, the technology still remains expensive, such that widespread deployment depends on substantial future improvement No single determinant predominantly explains the improvement to date; R&D, economies of scale, learning by doing, and knowledge spillovers from other technologies have all played a role in reducing system costs Moreover, interactions among factors that enable knowledge feedbacks – for example, between demand subsidies and R&D – have also proven important But not all of the factors were important for the entire sequence of the technology’s development Certain factors dominated for periods These stages have been correlated with shifts in the geographic foci of effort, both by the private sector and by the government: in the 1970–85, R&D to improve efficiencies and manufacturing techniques; in the 1990s to early 2000s, long-term demand programs to enable economies of scale; and in the 2000s, efforts to stimulate local learning by doing to reduce installation costs in addition to continuous improvements through manufacturing scale [5] As alternatives to crystallized silicon PV emerge, efforts to improve them follow a similar cycle Historical and Future Cost Dynamics of Photovoltaic Technology 1.05.2.1 49 Identifying Drivers of Change Nemet [6] sought to understand the drivers behind technical change in PV by disaggregating historical cost reductions into observable technical factors That study spanned the period of nascent commercialization in the mid-1970s through the early 2000s During the 26-year period studied, there was a factor of 20 reduction in the cost of PV modules The results of that study point to two factors that stand out as most important: plant size accounts for 43% of the change in PV cost and efficiency accounts for 30% of the change Of the remaining factors, the declining cost of silicon accounts for 12% Yield, silicon consumption, wafer size, and polycrystalline share each have impacts of 3% or less These observed changes are summarized in Figure The following sections discuss the sources of these observed changes Table summarizes this discussion 1.05.2.2 R&D and Efficiency Improvements The doubling of the average electrical conversion efficiency of PV systems since the 1970s has been important to cost reductions, accounting for about a third of the decline in cost over time R&D, especially public sector R&D, has been central to this change (Figure 4) Data on the highest laboratory cell efficiencies over time show that of the 16 advances in efficiency since 1980 [7], only were accomplished by firms that manufacture commercial cells Most of the improvements were accomplished by universities, none of which would have learned from experience with large-scale production; government and university R&D programs produced 10 of the 16 breakthroughs in cell efficiency Almost every one of the 20 most important improvements in PV occurred during a 10-year period between the mid-1970s and the mid-1980s [8], most of them in the United States where over a billion was invested in PV R&D during that period [9] Section 1.05.4 discusses a novel methodology for identifying these types of nonincremental improvements 1.05.2.3 Sequential Niche Markets Deployment of PV has benefited from a sequence of niche markets where users of the technology were less price-sensitive and had strong preferences for characteristics such as reliability and performance that allowed product differentiation Governments have played a large role in creating or enhancing some of these niche markets In the 1960s and 1970s, the US space program and the $25 $25.30 2002$ W–1 $20 $15 43% $10 30% $5 12% $3.68 3% 3% Water size Si used 2% 2% 5% $0 1979 price Si Plant Efficiency price size Yield PolyUn2001 x-stal explained price Figure Portion of cost reduction in PV modules accounted for by each factor [1] Table Summary of effects of items on the cost of PV (1980–2001) and reasons for the change in each factor Item Share change in PV module costs attributable (%) Main drivers of change in each factor Plant size Efficiency Silicon cost Wafer size Si used Yield Polycrystalline share Other factors 43 30 12 3 2 Expected future demand and risk management R&D, some LBD for lab-to-market Spillover benefit from information technology industry Strong LBD LBD and technology spillover Strong LBD New process, LBD possible Not examined LBD, learning by doing 50 Economics and Environment 30 400 Govt R&D Efficiency (%) 300 20 Highest laboratory 200 15 10 Avg commercial 100 R&D (2003$ million) 25 0 1940 1950 1960 1970 1980 1990 2000 Figure Improvements in energy conversion efficiency of PV and US public investment in PV R&D [1] Sales (2002$ million) 40 35.8 30 22.5 19.9 20 Satellites Terrestrial 10 0.9 1974 1979 Figure Shift in market from space to terrestrial applications from 1974 to 1979 [10] Department of Defense accounted for more than half of the global market for PV (Figure 5) The high cost of electricity in space allowed PV to be competitive even at an early stage Subsequent markets – telecom repeater stations, off-grid homes, and especially consumer electronics such as toys, calculators, and watches – were, importantly, independent of government decisions, and allowed the industry to expand from the mid-1980s until the late 1990s when energy prices, and thus alternative energy, were low social priorities From the 1990s onward, households that had strong preferences for environmental protection, especially conspicuously, created larger markets 1.05.2.4 Expectations about Future Demand Increasing demand for PV has reduced costs by enabling opportunities for economies of scale in manufacturing Nemet [6] assembled empirical data to populate a simple engineering-based model identifying the most important factors affecting the cost of PV over the past three decades The study found that three factors account for almost all of the observed cost reductions: (1) a orders of magnitude increase in the size of manufacturing facilities, which provided opportunities for economies of scale (Figure 6); (2) a doubling in the electrical conversion efficiency of commercial modules; and (3) a fall in the price of the primary input Output per plant (MW yr–1) 15 10 1975 1980 Figure Size of PV manufacturing facilities [1] 1985 1990 1995 2000 Historical and Future Cost Dynamics of Photovoltaic Technology 51 material, purified silicon Because investments in larger facilities take time to pay off, economies of scale depend on expectations of future demand As a result, public programs that reduce uncertainty by setting clear long-term expectations, such as Japan’s Sunshine Program in the 1990s [11], are more effective at enabling scale economies than generous subsidies that can suddenly disappear, such as California’s incentives for wind and solar in the early 1980s Government programs with longer time horizons such as Germany’s in the 2000s and the recently launched California Solar Initiative create similar opportunities [1, 12] Japan’s program was especially innovative in that it not only took a long time horizon but also set a declining subsidy such that it fell to zero after 10 years of the program This provided not only expectations of demand but also clear expectations of future levels of subsidy Germany’s program is especially notable in that production there has become sufficient to create external economies of scale – the emergence of machine tool manufacturers that now produce equipment specifically for the PV industry [13] Similarly, production of lower purity, thus cheaper, solar-grade silicon is now profitable because plants can be built at large scale We also have begun to observe some economies of scale in unit size, where large installations show much lower per-watt costs [4] Increasing installation size is likely to become more important as economies of scale reduce module-manufacturing costs, leaving installation costs as an increasingly large share of system costs The main drivers of the change in plant size over the period were growth in expected future demand and the ability to manage investment risk Whether experience plays a role in enabling the shift to large facilities depends on new manufacturing problems at larger scales and how experience may help in overcoming these problems Examples from three PV firms indicate that limited manufacturing experience did not preclude rapid increases in production Mitsubishi Electric expanded from essentially zero produc­ tion in 1997 to 12 MW and as of 2000 planned to expand to 600 MW by 2012 [14] While the firm had decades of experience in research and satellite PV applications, its cumulative production was minimal It began substantial manufacturing activity only with the opening of its Iida plant and its entry into the Japanese residential PV market in 1998 Similarly, Q-Cells, a German firm, began producing cells only in 2001 with a 12 MW line and increased production to 50 MW in only years [15] Sharp considered construction of a 500 MW yr−1 plant in 2006, which would amount to a 10-fold expansion in the firm’s capacity in only years By 2011, it had increased capacity to 2.8 GW yr−1 Note that by mid-2011, six firms had manufacturing capacities above GW yr−1 In the rapid expansions of the past 10 years, the ability to raise capital and to take on the risk of large investments that enable construction of large manufacturing facilities appears to have played more important roles than learning by experience in enabling cost reductions These results support the claim that “sometimes much of what is attributed to experience is due to scale” [16] 1.05.2.5 Learning by Doing Learning from experience in production has played a role, albeit not a dominant one, in reducing module costs These changes occurred in several different processes Experience in manufacturing led to lower defect rates and the utilization of the entire wafer area, which increased yields Experience was probably important in enabling the growth of larger crystals and the formation of longer conductors from cell edges to electrical junctions; savings accrued from the ensuing larger wafer sizes Less silicon was consumed as experience helped improve sawing techniques so that less crystal was lost as sawdust and thinner cells could be produced The development of wire saws, a spillover technology from the radial tire industry, is less clearly related to experience Learning by doing helped the gradual shift to polycrystalline ingots Casting of rectangular multicrystalline ingots was a new technology that partially derives from experience with the Czochralski process for growing individual crystals While learning by doing and experience play more important roles in these factors, together they account for only 10% of the overall change in module cost [6] Learning by doing plays a much more important role in reducing installation costs [17] An important aspect of this learning is that it is a local phenomenon, whereas module production is truly global [18] As installation costs become a large portion of costs, the extent of this learning by doing will be important Whether or not the benefits of this learning are appropriable will determine whether governments need to play a role in promoting learning investments More generally, the global aspect of module manufacturing suggests that interfirm and international technology spillovers are likely to be more important in module produc­ tion than in installation Finally, learning by doing may be important in the translation of laboratory breakthroughs to commercial products, as observed in Figure 1.05.2.6 Intertechnology Spillovers Like many technologies, PV has benefited from the adoption of innovations that originated in other industries These include the use of excess purified silicon from the chip-making industry, the use of wire saws from radial tires to slice multiple silicon wafers, electronic connectors to ease installation, screen-printing techniques from lithography, as well as an array of manufacturing techniques taken from microprocessors (for crystallized silicon) and liquid crystal displays (LCDs; for thin film) 1.05.2.7 Materials Reductions in the cost of purified silicon were a spillover benefit from manufacturing improvements in the microprocessor industry Until the 2000s, the PV industry accounted for less than 15% of the world market for purified silicon [19] Through that time, the PV industry did not purify its own silicon but instead purchased silicon from producers whose main customers were in the much larger microprocessor industry, where purity standards were higher Therefore, experience in the PV industry was irrelevant to silicon cost reductions More recently, input costs, especially of purified silicon, increased in the mid-2000s in line with other commodity prices 52 Economics and Environment [20] This change has had the beneficial effect of creating strong incentives to reduce wafer thickness and find ways to conserve materials Global recession in 2008–09 has seen commodity prices drop However, the benefits of lower silicon utilization remain Moreover, the shift to PV-specific silicon production has increased the potential for scale economies at lower levels of purity If lifetimes and efficiencies can be maintained at these lower levels of purity, there is strong potential for materials costs to fall beyond the short-term business cycle effects 1.05.2.8 Drivers Related to Supply and Demand Changes in demand and in market structure have affected prices as well, even if they not directly affect the technical characteristics of the technology 1.05.2.8.1 Industry structure Changes in industry concentration have affected market power and have led to a changing relationship between prices and costs over time Market share data indicate a decline in industry concentration during this period This change typically produces an increase in competitiveness, a decrease in market power, and lower profit margins For example, there were only two US firms shipping terrestrial PV from 1970 to 1975 [21, 22] In 1978, about 20 firms were selling modules and the top firms made up 77% of the industry [23] By 1983, there were dozens of firms in the industry, with the largest three firms accounting for only 50% of the megawatts sold [24] One way to quantify this change is to use the Herfindahl–Hirschman index (HHI), which provides a way of measuring industry concentration [25, 26] The HHI is calculated by summing the squares of the market shares of all firms in an industry The maximum possible HHI is 10 000 The data show a trend to a less concentrated US market during the 1970s (Figure 7) Concentration in the global market remained stable in the 1990s, the period for which comprehensive worldwide data are available The increase in international trade in PV over the last three decades indicates that the relevant scale of analysis shifted from a national market in the earlier years to an international market today 1.05.2.8.2 Demand shocks and rising elasticity Demand dynamics have shifted prices in opposite directions Foremost, the surge in subsidy programs for PV in the 2000s resembles a demand shock, as observed in other sectors The industry has had a difficult time adjusting quickly The very high levels of demand in the 2005–07 period are in part to blame for the high prices during that period This led to a reversal in the multidecade downward cost trajectory that can be observed in Figure Some of this cost increase was due to higher materials costs [20] and the rest likely due to higher willingness to pay as aggressive subsidy programs brought new consumers to the market for PV In contrast, historically, the shift from satellites to terrestrial applications affected prices because of a difference in the demand elasticity of the two types of customers Price data from that period provide some supporting evidence In 1974–79, the price per watt of PV modules for satellites was 2.5 times higher than that for terrestrial modules [10] The impact of this price difference on average PV prices is calculated by taking into account the change in market share mentioned below In this period, the combination of these price and market shifts accounts for $22 of the $28 price decline not explained by the model Satellite customers, with their hundreds of millions of dollars of related investments, almost certainly had a higher willingness to pay for PV panels than early terrestrial applications such as telecom repeater sites or buoys for marine navigation The difference in quality must account for some of the price difference But the difference in willingness to pay may also have led to higher differences between cost and price for satellite than for terrestrial applications Another historical explanation for the change in cost is that changes in production methods occurred due to an increase in the number of customers and the types of products they demanded There was a shift away from a near-monopsony market in the early 1970s Originally a single customer, the US space program, accounted for almost all sales Conversely, in 1976, the US government 5000 US market HHI 4000 3000 Highly concentrated 2000 1000 Moderately concentrated Unconcentrated 1970 1975 Figure Concentration in the PV industry (HHI) [1] Global market 1980 1985 1990 1995 2000 2005 Price of PV electricity (log scale) Historical and Future Cost Dynamics of Photovoltaic Technology Space, communications, navigation, pipelines 53 Rural off-grid Consumer Resid off-grid products Green on-grid Resid on-grid Current price of PV electricity Utility scale Market size (log scale) Figure Illustrative demand curve for PV electricity Reproduced from Nemet GF (2009) Interim monitoring of cost dynamics for publicly supported energy technologies Energy Policy 37(3): 825–835 [28] accounted for only one-third of terrestrial PV purchases [27] With the rise of the terrestrial industry, a larger set of customers emerged over the course of the decade When this change in the structure of demand occurred, one result was the shift away from producing customized modules, such as the 20 kW panels on Skylab, to producing increasingly standard products at much higher volumes Figure provides a schematic example of what a demand curve for PV electricity might look like considering both historical data and projections of the future opportunity The x-axis shows the size of each market segment The y-axis shows that the price customers are willing to pay for PV electricity in each segment Note the use of log–log axes For example, in the upper left region, the cost of electricity is very high in off-grid and remote locations, although markets are relatively small At the other end of the curve, markets for wholesale electricity are very large, but require PV to compete with large central station sources of electricity 1.05.2.9 Quality and Product Attributes Changes in quality and product attributes also affected costs During the 1970s, the market for PV modules shifted toward terrestrial applications Whereas in 1974, 94% of PV systems were manufactured for applications in space, by 1979, the market share for space had fallen to 36% The shift led to a reduction in the quality of modules, which rendered certain characteristics nonessential, allowing manufacturers to switch to less costly processes First, space and weight constraints on rockets required high-efficiency panels to maximize watts delivered per square meter The relaxation of this requirement for terrestrial applications enabled manufacturers to employ two important cost-saving processes [10] Cheaper materials were now tolerable Modules could use the entire area of the silicon wafer – even the portions near the edges, which tend to suffer from defects and high electrical resistivity Also, the final assembly process could use a chemical polish to enhance light transmission through the glass cover, rather than the more expensive ground optical finish that was required for satellites Second, reliability targets fell Satellite programs, such as Vanguard and Skylab, needed satellite PV modules that would operate reliably without maintenance, perhaps for 20 years Terrestrial applications, on the other hand, could still be useful with much shorter lifetimes The rapid growth in the terrestrial market was the main driver of this change There are three assumptions that are commonly made when applying the experience curve model using prices rather than costs: that margins are constant over time, that margins are close to zero with only minor perturbations, and that margins are often negative due to forward pricing Yet changes in demand and industry structure are important in that they erode support for these three assumptions Indeed, earlier work pointed out that firms’ recognition of the value of market domination, particularly during incipient commercialization, leads to unstable pricing behavior [29] An implication of the variation in the price–cost margin is that industry structure affects the learning rate In the case of an industry that becomes more competitive over time, such as PV, a price-based experience curve overestimates the rate of technical progress One solution for addressing this problem would be for future work to obtain real cost data where possible An alternative would be to use an approach in which costs can be derived from prices and market shares using the assumptions in Cournot competition that a firm’s profit margins decrease as the number of firms in the market increases and that a single firm’s profit margins increase with that firm’s market share [30] However, comparisons of competing technologies are best made on the basis of prices, not costs, since prices reflect what a consumer faces in deciding whether to adopt a technology and which to adopt A more general approach would be to incorporate market dynamics into predictions of technological change: industry concentration, market power, and changes in elasticity of demand affect prices The HHI analysis described above shows that concentration is not stable over time, especially if international trade is taken into account The assumptions of perfect competition and prices equal marginal cost are too strong in the early stages of the product life cycle when the technology is improving rapidly, industry structure is unstable, and new types of customers are entering the market 54 Economics and Environment 1.05.2.10 Interactions between R&D and Experience in Production While there appear to be periods when one factor was dominant in supporting innovation, it is also the case that the interaction of various factors was important In particular, Japan in the 1990s had both strong demand-side policies and support for R&D [31] In this case, it was not so much efficiency breakthroughs and alternative cell designs that drove improvements, but rather support from the Japanese government, via the Ministry of International Trade and Industry (MITI) This enabled coordination of expectations and sharing of best practices, which enabled manufacturing improvements The influence of the US federal govern­ ment may have played a similar role in enabling the 1970s breakthroughs, not just through providing resources from R&D but by creating a sense of commitment that convinced many to work on the technical and market challenges associated with commercia­ lizing this nascent technology [32] 1.05.3 Using Learning Curves to Predict Costs The potential for future cost reductions, combined with the magnitude of the potential impact of very inexpensive PV, has created a strong demand for tools that enable prediction of future costs The experience curve has emerged as the most important of these tools The following section surveys the use of experience curves in policy and modeling, caveats in their application, assessment of their reliability, and the implications for policy makers [33] While this section focuses on the historically dominant design in the industry, crystalline silicon PV, more recent work has looked at other approaches, such as thin films [34] and organics (discussed below) Also, note here that we use the terminology experience curve (defined above), rather than the more specific concept of the learning curve, which, strictly defined, focuses on labor costs and within-firm knowledge accumulation Characterizations of technological change have identified patterns in the ways that technologies are invented, improve, and diffuse into society [35] Studies have described the complex nature of an innovation process in which uncertainty is inherent [36], knowledge flows across sectors are important [37], and lags can be long [38] Perhaps because of characteristics such as these, theoretical work on innovation provides only a limited set of methods with which to predict changes in technology The learning curve model offers an exception Experience curves have been assembled for a wide range of technologies While there is broad variation in the observed rates of ‘learning’, studies provide evidence that costs almost always decline as cumulative production increases [16, 39–42] The roots of these microlevel observations can be traced back to early economic theories about the importance of the relationship between specialization and trade, which were based in part on individuals developing expertise over time [41] The notion of the experience curve varies from the more specific formulation behind the learning curve in that it aggregates from individuals to entire industries and from labor costs to all manufacturing costs In the literature on experience curves, the technological ‘learning’ refers to a broad set of improvements in the cost and performance of technologies, not strictly to the more precise notion of learning by doing [43] An experience curve for PV modules is shown in Figure 9, that is, a double-logarithmic graph of PV module price as a function of cumulative capacity 1.05.3.1 Use of Experience Curves in Modeling and Policy PV module prices (2006$ W–1) Experience curves have become a widely used tool both for models of future energy supply and to inform the design of public policies related to PV For example, they provide a method for evaluating the cost-effectiveness of public policies to support new technologies [44] and for weighing public technology investment against environmental damage costs [45] Energy supply models now also use learning curves to implement endogenous improvements in technology Prior to the 1990s, technological change was typically included as an exogenous increase in energy conversion efficiency or ignored [46] Studies in the 1990s began to use the learning curve to treat technology dynamically [47, 48], and since then, it has become a powerful and widely used model for 100 1976 1980 1990 10 2000 2006 1 10 100 1000 Cumulative capacity (MW) 10 000 Figure Experience curve for PV modules, 1976–2006 Reproduced from Nemet GF (2009) Interim monitoring of cost dynamics for publicly supported energy technologies Energy Policy 37(3): 825–835 [28] Historical and Future Cost Dynamics of Photovoltaic Technology 55 projecting technological change Recent work, however, has cautioned that uncertainties in key parameters may be significant [49], making application of the learning curve to evaluate public policies inappropriate in some cases [50] 1.05.3.1.1 Modeling The rate and direction of future technological change in energy technologies are important sources of uncertainty in models that assess the costs of stabilizing the climate [51] Treatment of technology dynamics in integrated assessment models has become increasingly sophisticated [52] as models have incorporated lessons from the economics of innovation and as increased processing power and improved algorithms have enabled optimization of phenomena such as increasing returns, which in the past had made computation unwieldy [53] Yet the representation of technological change in large energy-economic models remains highly stylized relative to the state of the art of understanding about the economics of innovation [54] Perhaps one reason for the lag between the research frontier for the economics of innovation and the modeling of it has to with incompatibilities in the methodological approaches of the two fields On the one hand, research on the economics of innovation has tended to emphasize uncertainty [55], cumulativeness [38], and nonergodicity [56] The outcomes of this line of inquiry, which dates back to Schumpeter [35], and even Marx [57], have often been characterized by richness of description, a case study approach, and, arguably, more progress with rigorous empirical observation than with strong theoretical claims On the other hand, optimization and simulation models require compact quantitative estimation of parameters, with uncertainties that not become infinite once propagated through the model One of the few concepts that have bridged the epistemological gap between the economics of innovation and the integrated assessment of climate change is the experience curve Experience curves provide a way to project future costs conditional on the cumulative quantity of capacity produced The resulting cost predictions are less deterministic than those generated by temporal-based rates of technological change, but they are also not simply scenarios, internally consistent descriptions of one possible future state of technology; they are conditional predictions 1.05.3.1.2 Policy Large programs and deviations from trends in cost reductions are challenging policy makers to make decisions about whether, when, and how much to stimulate the development of energy technologies that have high external benefits The net benefits of subsidies and other incentives programs depend heavily on the extent to which technologies improve over time Experience curves provide a way for policy makers to incorporate technology dynamics into decisions that involve the future costs of technologies They are now used widely to inform decisions that involve billions, and even trillions, of dollars in public funding The general notion that learning from experience leads to cost reductions and performance improvements is well supported by a large array of empirical studies across a variety of technologies But the appropriateness of using experience curves to guide policy is less uniformly acknowledged Despite caveats in previous work, the cost projections that result from experience curves are typically used without characterizing uncertainty in those estimates As a result, experience curves are now employed widely to inform decisions that involve billions of dollars in public funds They have been used both directly – as graphical exhibits to inform debates – and indirectly – as inputs to energy-economic models that simulate the cost of achieving environmental goals Much of the early work to translate the insights from experience curve studies to energy policy decisions is included in a study for the International Energy Agency [49] Other studies have used the tool directly to make claims about policy implications [44, 45, 58] As mentioned above, energy-economic models that minimize the cost of energy supply now also include experience curve relationships to include technology dynamics Model comparison studies have found that models’ estimates of the social costs of policy are sensitive to how technological change is characterized [51] Working Group III of the Intergovernmental Panel on Climate Change (IPCC) used results from a variety of energy-economic models to estimate the magnitude of economically available greenhouse gas emissions in its Fourth Assessment Report [59] The results of this assessment are widely used to inform national climate change policies, as well as the architecture for the next international climate policy regime In the 17 models they review, some form of experience curve is used to characterize technological change in at least 10 of those models See Table 11.15 of IPCC [59] Another influential report in 2006, the Stern Review on the Economics of Climate Change [60], relied heavily on experience curves to model technological change This report has been central to the formation of climate policy in the United Kingdom and has played a role in debates in the United States as well, both at the federal level and in California The International Energy Agency relies on experience curves in its assessment of the least cost method for meeting greenhouse gas reduction targets and energy demand for 2050 [61] Note that the ‘learning investments’ that result from the analyses in this report are estimated in a range of $5–8 trillion Debates about subsidies and production requirements for ethanol also use historical experience curves as a justifica­ tion for public support of the production of biofuels [62] At the state level, experience curves have provided one of the most influential justifications for a $3 billion subsidy program for PV [12] Experience curves have also been used in economic models of the cost of meeting California’s ambitious greenhouse gas reduction targets [63] Finally, in decisions by the 24 states that have passed renewable portfolio standards, debates include discussions of how mandatory renewables deployment will bring down its cost [64, 65] 1.05.3.2 Problems with Using Experience Curves Demand among policy makers for rigorous, transparent, and reliable tools with which to predict future costs continues to be high Yet an array of studies, which are cited below, warn about the limitations of experience curves 56 Economics and Environment The learning curve model operationalizes the explanatory variable experience using a cumulative measure of production or use Change in cost typically represents the dependent variable and provides a measure of learning and technological improvement Learning curve studies have experimented with a variety of functional forms to describe the relationship between cumulative capacity and cost [66] The log-linear function is most common perhaps for its simplicity and generally high goodness-of-fit to observed data The central parameter in this learning curve model is the exponent, defining the slope of a power function, which appears as a linear function when plotted on a log–log scale This parameter is known as the learning coefficient (b) and can be used to calculate the progress ratio (PR) and learning ratio (LR) as shown below C0 and Ct are unit costs at time t = and time t, respectively, and Q0 and Qt represent cumulative outputs at time t = and time t, respectively � Ct ¼ C0 Qt Q0 �– b PR ¼ – b LR ¼ ð1 – PRÞ Wene [67] has developed a cybernetic theory that predicts an LR of 20% Several studies have criticized the learning curve model, especially in its more general form as the experience curve Dutton and Thomas [16] surveyed 108 learning curve studies and showed a wide variation in learning rates, leading them to question the explanatory power of experience Figure 10 combines their learning rate data with those in a more recent survey of learning rates by McDonald and Schrattenholzer [68] The learning rate for PV, 0.23, lies near the mode of the distribution Argote and Epple [69] explored this variation further and proposed four alternative hypotheses for the observed technical improvements: economies of scale, knowledge spillovers, and two opposing factors, organizational forgetting and employee turnover Despite such critiques, the application of the learning curve model has persisted, without major modifications, as a basis for predicting technical change, informing public policy, and guiding firm strategy Below, the advantages and limitations of using the more general version of the learning curve for such applications are outlined The experience curve provides an appealing model for several reasons: Availability of the two empirical time series required to build an experience curve – cost and production data – facilitates testing of the model As a result, a rather large body of empirical studies has emerged to support the model Compare the simplicity of obtaining cost and production data with the difficulty of quantifying related concepts such as knowledge stocks [70] and inventive output [71] Still, data quality and uncertainty are infrequently explicitly addressed and as shown below can have a large impact on results Earlier studies of the origin of technical improvements, such as in the aircraft industry [39] and shipbuilding [40], provide narratives consistent with the theory that firms learn from past experience Studies cite the generally high goodness-of-fit of power functions to empirical data over several years, or even decades, as validation of the model The dynamic aspect of the model – the rate of improvement adjusts to changes in the growth of production – makes the model superior to forecasts that treat change purely as a function of time The reduction of the complex process of innovation to a single parameter, the learning rate, facilitates its inclusion in large optimization and simulation models The combination of a rich body of empirical literature and the more recent applications of learning curves in predictive models has revealed weaknesses that echo earlier critiques Frequency 15 10 –1 –0.5 PV Learning rate 0.5 Figure 10 Frequency distribution of learning rates calculated in 156 learning curve studies The learning rate for PV, 0.23, lies slightly above the mode of the distribution Reproduced from Nemet GF (2009) Interim monitoring of cost dynamics for publicly supported energy technologies Energy Policy 37(3): 825–835 [28] 58 Economics and Environment Three sources of uncertainty complicate experience curve predictions First, there is the typical dispersion in learning rates caused by imperfect correlations between cumulative capacity and cost Sark [87] explores the effects of this ‘r-squared’ variation to calculate an error around the learning rate Inconsistencies in the chosen system boundaries, for example, geographic scope, may introduce some of this variation The second source has to with whether historically observed learning rates can be expected to continue in the future Even in his seminal work on learning by doing, Arrow [43] argued that that learning is subject to “sharply diminishing returns” Looking at studies within single manufacturing facilities, Hall and Howell [74] and Baloff [75] find that learning rates become essentially flat after a relatively short amount of time – approximately years in these studies As a result, some have suggested that a cubic or logistic function offers a more realistic functional form than a power function [76] The third source of uncertainty derives from the choice of historical time period used to calculate learning rates The timing issue captures variation in the source data, as well as changes in the slope over time Studies have characterized the effects of uncertainty A prominent study showed that calculating the error in the PR could be used to develop a range of learning rates to use for sensitivity analysis in policy modeling [88] Nemet [28] assessed these sources of uncertainty using a simple and transparent model of the costs of subsidizing technologies until they are competitive with alternatives Those calculations include (1) the learning rate, (2) the year at which the cost of a subsidized technology approaches a target level, and (3) the discounted cost of government subsidies needed to achieve that level The first result from that study is that there is a wide dispersion in learning rates depending on what time period was used That study estimated the learning rate for PV in each of the 253 time periods of 10 years or greater between 1976 and 2006 Figure 11 plots these learning rates by the year at which each time series ends For example, the values shown for 1995 include all 11 time series that end in 1995 This set of values indicates the range of learning rates that would have been available to an analyst using experience curves to project costs in 1995 The data begin in 1985 because that is the first year for which 10 years of historical data (1976–85) are available The data reveal two features about the trend in calculated learning rates: (1) there is a negative time trend, the mean of the learning rate values has decreased over time by approximately 0.005 per year, and (2) the dispersion in learning rate values around the annual mean has increased over time The dispersion includes an oscillation with maxima in 1995 and 2006 The second result from that study was estimation of the break-even year using the dispersion in learning rates described above The target cost for PV modules used in this example is Pa = $1 W−1 [89] A 73% subsidy on actual 2006 prices is needed for consumers’ costs to equal this target Figure 12(b) shows distributions of the estimated years at which the price of PV will equal that of this competing technology Descriptive statistics for these distributions are shown in Table for all time series and for all series that end in 2006 The median crossover year for all series, ta = 2034, occurs 14 years earlier than the estimates using only data through 2006, ta = 2048 Note that the dispersion has also increased with the more recent data set The third result from that study was to calculate the learning investment required to subsidize PV to the crossover point The median cost to subsidize PV is $62 billion when using all time series and $163 billion when using only the time series that end in 2006 Note that a difference in median learning rate of 40% leads to a difference in median program costs of between a factor of and The dispersion in costs has also become large; the range from the 5th percentile to the 95th percentile spans an order of magnitude Furthermore, notice that costs around the 95th percentile become very large, rising to the tens of trillions Slow learning has nonlinear effects on cost and leads to very expensive subsidy programs – even when these future costs are discounted to present values Figure 12 summarizes these results Figure 12(a) shows the distribution of learning rates for all 253 periods (black columns) The white columns show the distribution of rates using only those series that end in 2006 The latter is the data set one would expect a contemporary planner to use The median of the distribution of learning rates from all 253 time series (LR = 0.21) is substantially higher than the median of the series ending in 2006 (LR = 0.15), although this difference is not significant 1.05.3.3.1 Assessing the significance of recent deviations One immediate policy implication of these results is that the possibility of very expensive subsidy programs makes early identification of such a scenario important The price escalation in the mid-2000s prompts the question, “do recently observed costs represent a 0.3 Learning rate 0.25 0.2 0.15 0.1 0.05 1975 1980 1995 1985 1990 End year of learning interval 2000 2005 Figure 11 Learning rates for PV (1976–2006) calculated for all periods ≥10 years (n = 253) Reproduced from Nemet GF (2009) Interim monitoring of cost dynamics for publicly supported energy technologies Energy Policy 37(3): 825–835 [28] Historical and Future Cost Dynamics of Photovoltaic Technology 59 (a) All series from 1976 to 2006 (n = 253) Series ending in 2006 (n = 22) Frequency 15 10 (b) 0.05 0.1 25 0.3 0.35 0.4 All series from 1976 to 2006 (n = 253) Series ending in 2006 (n = 22) 20 Frequency 0.15 0.2 0.25 Learning rate 15 10 2010 2020 2030 2040 2050 2060 Break-even year 2070 2080 2090 (c) 30 All series from 1976 to 2006 (n = 253) Frequency 25 Series ending in 2006 (n = 22) 20 15 10 0 100 200 300 400 Cost to subsidize to breakeven ($ billion) >500 Figure 12 (a) Calculated learning rates for PV; (b) year at which the price of PV equals that of competing technology; (c) present value of cost to subsidize PV until it equals the cost of competing technology Reproduced from Nemet GF (2009) Interim monitoring of cost dynamics for publicly supported energy technologies Energy Policy 37(3): 825–835 [28] significant deviation from the historical trend or does historical variation explain them?” Nemet [28] introduces two methods for addressing this question First, recent costs are compared to the confidence interval for the power function resulting from the dispersion in past observations Second, these costs are compared to the set of all possible experience curve forecasts made over time The first method uses straightforward statistics to examine whether recent variation fits within the confidence interval for observations around the power function This variation is caused by the imperfect fit of the power function to the experience curve data [87] Here a confidence interval is constructed for the data through 2003 This range is compared to the most recent years of data, 2004, 2005, and 2006, to determine whether they fit within the range defined by projecting the experience curve for years The data from 1976 to 2003 have r2 = 0.98 and LR = 0.22 The variation around the experience curve power function using least squares yields a 95% confidence interval around the LR of 0.22 Ỉ 0.01 Projecting the experience curve to the capacity reached in 2006 (E2006) yields a 95% confidence interval of expected costs in 2006 of $1.58–$2.51 The actual value for 2006, $3.74, lies outside this range The second approach assumes the perspective of a policy analyst making ex ante forecasts each year, incorporating new data as they become available This approach assesses whether recent observations could have been projected by the set of all possible historical forecasts To illustrate, Figure 13 shows the predictions, over time, of the price of PV for the cumulative capacity that was reached in 2006, E2006 The first result is that none of the 231 possible projections for 2006 would have predicted a level at or above the actual 2006 price Next, this method is used to project prices for the cumulative capacities reached in all years from 1986 to 2006 In Figure 14, the range in gray represents the full range of forecasts for the capacity that was reached each year For example, the gray range for 2006 includes all of the 231 data points portrayed in Figure 13 Actual prices each year are shown as a line with 60 Economics and Environment Descriptive statistics for distributions of experience curve results for PV Table Cost to breakeven ($ billion) Learning rate Break-even year 0.25 0.21 0.14 0.03 2028 2034 2049 38 62 175 229 For all time series ending in 2006 (n = 22) 5th percentile 0.21 Median 0.15 95th percentile 0.08 σ 0.04 2034 2048 2082 15 59 163 2172 713 For all time series (n = 253) 5th percentile Median 95th percentile σ Reproduced from Nemet GF (2006) Beyond the learning curve: Factors influencing cost reductions in photovoltaics Energy Policy 34(17): 3218–3232 [6]; Nemet GF (2009) Interim monitoring of cost dynamics for publicly supported energy technologies Energy Policy 37(3): 825–835 [28] $ W–1 1975 1980 1985 1990 1995 End year of learning interval 2000 2005 Figure 13 Trend in predictions of PV prices for the capacity levels reached in 2006 The dashed line shows the actual value in 2006 Reproduced from Nemet GF (2009) Interim monitoring of cost dynamics for publicly supported energy technologies Energy Policy 37(3): 825–835 [28] 15 $ W–1 10 Actual prices 1975 1980 1985 1990 1995 2000 2005 Figure 14 Price projections for the cumulative capacities reached in all years from 1986 to 2006 The gray region shows the range of all forecasts for the price of PV at the cumulative capacity reached each year Actual prices are shown as a line with white circles [28] white circles The second result is that, other than two individual occurrences, the only time the actual prices have consistently fallen outside the range of all possible learning rate-derived price forecasts was in 2004–06 The outcome of this analysis concurs with that of the confidence interval analysis: the recent deviations in PV fall outside the range of historical precedent While further analysis is certainly needed to characterize the sources and persistence of these deviations, these methods may be useful as a preliminary screen to identify near-term deviations that merit further investigation An important aspect to account for – given the prominent role described earlier in this chapter – is the role of niche markets In Historical and Future Cost Dynamics of Photovoltaic Technology 61 particular, empirical analysis of the level of willingness to pay in niche markets and the size of these markets will add insight into the extent to which they reduce the cost of subsidy programs These results indicate (1) a need for policy makers to more explicitly consider uncertainty in cost projections and (2) the importance of the development of better tools to identify the significance of near-term deviations from projections These results suggest that projected subsidy costs are highly sensitive to timing of the data used: both ‘when’ the forecast was made and the ‘duration’ of the historical data set used The high dispersion in costs – and especially the skewness of the distribution toward high values – emphasizes the importance of interim monitoring of technological improvement These results should not be surprising given the results above show that learning derived from experience is only one of several explanations for the cost reductions in PV Its role in enabling changes in the two most important factors identified in this study – plant size and module efficiency – is small compared to those of expected future demand, risk management, R&D, and knowledge spillovers This weak relationship suggests careful consideration of the conditions under which one should rely on experience curves to predict technical change The results have two normative conclusions for policy makers First, if policy makers are to rely on future cost projections derived from experience curves, they need to be explicit about the reliability of predictions Policy decisions should be made acknowledging the observed variation in the rates of technological improvement over time Given the current state of knowledge about what actually causes variation in learning rates, policy makers would well to consider learning as a stochastic process – that is, that some aspects of the process remain unpredictable [90] In this respect, learning is similar to the outcomes of R&D investments; they are inherently uncertain despite improvements in understanding about R&D productivity [91] Important further work on this topic involves assessment of whether this uncertainty is likely to diminish over time as more observations are obtained, as suggested by the central limit theorem It is unclear whether the results in this study so far support such a notion In Figure 11, it appears that the dispersion in learning rates has increased over time Even if such a convergence were to occur, a practical issue for policy makers would be whether it will occur quickly enough to inform decision making and whether the cumulative capacity required for convergence is small relative to the size of the world energy demand Second, devising ex ante methods to identify the significance of near-term deviations in technology cost and performance trends is essential How should policy makers respond to situations such as those in Figure 14 in which recent prices appear to be deviating from the experience curve path? Are these short-term deviations driven by supply bottlenecks, or are they representations of the lower limits on cost? Deviations make policy difficult; policy makers need to be vigilant against encountering the extremely expensive outcomes found above For example, debate over subsidies amounting to several billion dollars in the 2007 Independence and Security Act in the US Energy Information Administration (EIA) suggests that programs involving hundreds of billions will be subject to scrutiny [92] The social value that these technologies have the potential to deliver is substantial, but may take many years to realize As a result, policy makers may also need to defend technology support against competing social priorities when deviations are actually short-term aberrations How can near-term data be used to assess confidence in longer-term projec­ tions? The methods developed in this section offer some avenues for analysis, but ultimately better tools will be required The inclusion of experience curves in models that optimize and simulate the costs of climate policy has enhanced their realism Given the vast set of results showing that energy technologies improve over time, incorporating experience curves represents a substantial improvement over omitting them and implicitly assuming a learning rate of zero But the results summarized here indicate that a broader set of influences than experience alone contributed to the rapid cost reductions in the past; one implication is that experience curves overestimate the technical improvements that should be expected to accrue from deployment alone As a result, these findings support the efforts by modelers to explore ways of incorporating explanatory variables other than cumulative capacity, especially when nonincremental changes occur The following sections describe some attempts to deepen our under­ standing of the impact of other factors on PV, both historically and prospectively 1.05.4 Nonincremental Cost-Reducing Developments The irregular occurrence of nonincremental technological improvement accounts for much of the concern about using experience curve cost projections to inform policy Projecting the future costs of PV depends on a much better characterization of these changes – including when they are likely to occur, how big an impact they will have on costs, and conversely the consequences on costs of a sustained period without such changes To achieve greater understanding of nonincremental changes in PV, in a recent master’s thesis, Husmann [93] studied historical silicon PV breakthroughs under the expectation that past breakthroughs can illustrate the causes and effects of nonincremental changes on a still-developing technology This section describes a novel methodology for identifying nonincremental changes (‘breakthroughs’) and provides some preliminary results Ultimately, the identification and evaluation of specific technical improvements will be useful for informing the type of modeling described in Section 1.05.5 1.05.4.1 Identifying Breakthroughs Nonincremental changes can be identified using a combination of expert opinion and patent analysis 1.05.4.1.1 Defining breakthrough There is no widely accepted definition of breakthrough, even among researchers who study innovations in products and technology [94] Among PV researchers, important achievements in the field are called many things; technical developments [8], major results 62 Economics and Environment [95], and advances [7] are just a few of the names used in the PV literature For the sake of simplicity, we will use breakthrough, since it is a word recognized in both research and colloquial literature We define breakthrough as a technical achievement that represents a new combination of technologies A breakthrough must either open up a new area of technology for exploration and use, or be a specific improvement that has spread to become industry standard Although innovation researchers often require both technical and economic achievements in order to identify their breakthroughs, the truth is that technical achievement will not necessarily result in economic achievement [96] Our goal was to first identify technological breakthroughs and then determine their economic value However, we recognize that our methods for identifying breakthroughs contain a (sometimes strong) bias for economic success That bias will be discussed later in this chapter 1.05.4.1.2 First pass: Expert opinion Silicon solar cells have been around since the early 1950s [97], which means that some researchers today have been studying crystalline silicon PV for their entire lives Their extensive knowledge and experience represents a great resource, if it can only be tapped and distilled Fortunately, that potential has been recognized, which has led to a profusion of recent papers describing the history of PV and, importantly, listing the most important PV breakthroughs in the authors’ opinions Although none of the papers was written with the specific goal of identifying breakthroughs, they all mentioned various discoveries and technologies that have played important roles in crystalline silicon PV history We read through six of the papers that seemed most relevant to crystalline silicon PV [8, 95, 96, 98–100] and compiled a list of breakthroughs mentioned by the experts In the end, we had a list of 79 breakthroughs spanning from 1951 to 2000 We left out more recent developments, since identification of true breakthroughs requires several years of postbreakthrough observation An initial problem was that the original list of breakthroughs was too long Not only would it have been prohibitively timeconsuming to research all 79, but it also implied that a breakthrough took place every months, which was not a reasonable assumption Though the list was thorough, if we had tried to shorten it, we would have run into problems with the implicit biases found in expert opinion Not only are experts more familiar with breakthroughs in their own areas of research – and thus more likely to list them as breakthroughs – but they can also suffer from success bias, in which experts tend to associate commercial success with technical value [101] The combination of the six papers helped to reduce possible bias in any one researcher, but if we had asked an expert to pick just a few breakthroughs from our list of 79, it would have immediately suffered from that researcher’s bias – and there were not enough researchers with enough time to look through the list Instead, we chose to use another method of breakthrough identification to reduce the list of breakthroughs 1.05.4.1.3 Why patent analysis? Our second method of breakthrough identification is patent citation analysis We decided to look at patents, because citation frequency of a patent can serve as a proxy for a possible breakthrough Each patent is meant to represent a novel, useful, and nonobvious invention [102] Not every invention is a breakthrough, but some may be – and our goal is to identify the patented inventions that are breakthroughs It is also true that not all inventions are patented [103], but if they become common knowledge, then they may be incorporated into other patents Thus, the breakthroughs that are incorporated into other patents become discoverable The only problem is if the breakthrough is a trade secret, because by definition it cannot be identified by someone who does not know the secret Aside from trade secrets, it is likely that all the breakthroughs we are looking for can be found in at least one patent Previous patent studies have shown that the semiconductor industry produces a large number of patents Since key semiconductor breakthroughs have been patented, it makes sense that many key PV breakthroughs have also been patented Even though trade secrets are an acknowl­ edged intellectual property (IP) protection tool used by the industry, important patents appear to outweigh important secrets We are also focusing on patents because they can help us avoid expert bias – the tendency of experts to consider their own areas of expertise as important – and success bias – the tendency to equate commercial success with technical value Any form of patent analysis precludes the possibility of expert bias, because patent citations are typically created by several experts: the inventors and their attorneys suggest the citations, while the patent examiner selects the citations from the list of suggestions and his or her own knowledge of prior art [104] This process, combined with the fact that almost every patent represents a unique combination of inventors, attorneys, and examiners, ensures that no one expert has undue influence over the patents being analyzed – especially for such a large group of patents as the one we analyzed Patent analysis can also be constructed to limit success bias One way to this is to differentiate between backward and forward citations Backward citations represent the prior art that the patent is either building off of or surpassing It cannot be influenced by commercial success, because it is created before an invention is even patented Forward citations represent the number of times that the patent serves as prior art to another patent Not only can forward citations be heavily influenced by the commercial success of the patent, but they can also be influenced by the prior commercial success of the inventor Because of the success bias present in forward citations, we have chosen a method of patent analysis that relies primarily on backward citations in identifying breakthroughs 1.05.4.1.4 Backward citation analysis A full explanation and derivation of backward citation analysis is presented by Dahlin and Behrens [101], but here we give a short overview While forward citations represent a patent’s commercial and technological impact, backward citations represent its Historical and Future Cost Dynamics of Photovoltaic Technology 63 technological roots If each backward citation represents a piece of technology that was either used to create the patent or served as a source of inspiration, then the collection of all the backward citations in one patent can represent the specific combination of technologies that the new patent represents And if each patent can be technologically represented by its backward citations, then a breakthrough technology can be identified by its backward citations alone – if the right definition of breakthrough can be found This method creates a mathematical definition of radicalness – which we usurp as our definition of breakthrough – that can be applied to a list of patents and used to identify specific patents The three components of the definition are (1) novelty – the patent differs from previous inventions; (2) uniqueness – the patent differs from current inventions; and (3) influence – the patent needs to have affected future inventions Although the third component can suffer from success bias, technological success will play a bigger role than commercial success; that is, if a particular invention works well enough to be somehow adopted by other patents, the backward citation analysis will notice this even if the authors of the other patents were not aware of the original patent We considered ignoring influence, but that resulted in the identification of both successful breakthroughs and very unique failures with no obvious way to differentiate between the two In order to determine if a patent fulfills these three components, the backward citation analysis compares a patent’s collection of backward citations to another patent’s collection If the two collections have any backward citations in common, then they are assumed to have similar technological roots The similarity between patents i and j can be presented mathematically as overlap score osij, in which osij equals the number of backward citations shared by patents i and j divided by the total number of backward citations held by patents i and j If this overlap score is computed for all the patents being analyzed, it can reveal the similarity between any one patent i and all of its peers j for any year t, where t can be any year before or after the patent was published Thus, the average annual overlap score, osti/nt, is the sum of all of the overlap scores for a patent and its peers during a particular year divided by the number of peers: osti/nt = (∑jostij)/nt This average annual overlap score is a measure of the similarity between a patent and all the analyzed patents in the chosen year This average annual overlap score is what determines whether a patent is novel, unique, or influential For a patent to be novel, it cannot be similar to patents that have been published before it: for t < ti, osti/nt must be small For a patent to be unique, it cannot be similar to patents that were published at the same time it was published: for t = ti, osti/nt must be small And for a patent to be influential, it must be similar to patents that were published after it: for t > ti, osti/nt must be big A breakthrough will satisfy all three requirements, while an incremental invention will not However, these are all relative measures that will depend on the total number of patents being analyzed and the variation between patents It is important to recognize that a breakthrough identified when looking at only PV patents may not appear to be a breakthrough when compared to patents covering, say, the World Wide Web or the steam engine Backward citation analysis can also be prone to biases of its own If a patent does not have any backward citations, it shares no backward citations with any other patents and fails the influence component This can happen occasionally if an inventor is trying to be strategic, but it is discouraged by patent examiners and therefore uncommon Another problem can result if two patents had the same inventor (e.g., patents 4510674 and 4510675), because that inventor is much more likely to list the exact same backward citations, making the two patents appear very similar However, this can be mitigated if a large number of patents are used in the analysis, because the greater number of patents tends to reduce the impact of any one overlap score Lastly, the backward citation analysis tends to recognize early breakthroughs more easily than later breakthroughs, because early breakthroughs necessarily have a smaller overlap score for the few years of patents listed before them, while later patents have a smaller overlap score for the few years of patents listed after them This is a problem specific to our use of backward citation analysis (not backward citation analysis in general) and will be addressed at length later in this chapter 1.05.4.1.5 Implementing backward citation analysis for PV In order to accurately identify crystalline silicon PV breakthroughs using backward citation analysis, we needed a list of every crystalline silicon PV patent In all practicality, compiling such a list is impossible If patents are given cryptic description – such as labeling a PV patent as a semiconductor improvement – they can evade common search terms Furthermore, not every PV patent is relevant, since many relate to amorphous silicon, cadmium telluride, or some other form of PV The only way to know if a patent is relevant to crystalline silicon PV is if a PV expert reads through the entire patent, which would require hundreds of manhours Instead, we relied on a source that identified the crystalline silicon PV patents for us: NREL’s (National Renewable Energy Laboratory) publication of U.S Photovoltaic Patents from 1951 to 1993 This is a list of 1651 PV patents that was compiled by searching for patents in the subclasses ‘Photoelectric’, ‘Testing’, and ‘Applications’ under ‘Batteries, Thermoelectric and Photoelectric’ or containing the word ‘photovoltaic(s)’ or ‘solar cell(s)’ [105] Since the search probably relied on specific patent classes [106], chances are that it missed most ‘solar cell’ patents outside of those classes; one omission (patent 4661200) has already been identified Despite the likelihood of other missed PV patents, the benefits of using NREL [105] outweigh the costs, because the patents are listed by category It appears that at least one PV expert (Thomas Basso of NREL) read through the patents and was able to identify the single-crystal silicon cells, amorphous silicon cells, III–V cells, and so on [106], thus providing the lists of crystalline silicon PV patents that we require Out of 17 categories of patents, we used 6: single-crystal silicon cells, polycrystalline and ribbon silicon cells, cell components, cell enhancement techniques, materials production and processes, and flat-plate collectors Our goal was to look 64 Economics and Environment at the patents that have affected module design as well as silicon-specific developments, because even generic module changes have had an important effect on the steady improvement of crystalline silicon PV The 1993 cutoff date was imposed on our analysis by NREL [105], since the list was never updated after that year This necessarily implies that our analysis will be historical, since the breakthroughs that we select for study are likely either no longer in use or considered ‘common knowledge’ in the PV field While going through the list of patents in NREL [105], we selectively removed or changed some of the patents to be analyzed Design patents, which can be identified by the ‘D’ in front of the patent number, were considered irrelevant They cover ornamental appearances and thus not represent a technological advance Defensive publications or technical disclosures, which can be identified by the ‘T’ in front of the patent number, were not considered patents and were not included And reissued patents, which can be identified by the ‘RE’ in front of the patent number, were changed to the original patent number This prevented a reissued patent from being compared to its original patent, since the two would necessarily have a high overlap score After changing or removing all of the design, defensive, and reissued patents, we ended up with a list of 1651 patents that were ready to be analyzed, not including repeated patents The NREL source included the same patent on multiple lists if it counted for multiple categories As we mentioned earlier, the composition of the list makes a difference in the breakthrough identification If each list were analyzed separately, it would guarantee that we would find breakthroughs for each category of module design, because we can systematically take the top 10% of the results from each category On the other hand, one complete list of all the patents could discover if one category somehow contained more breakthroughs – perhaps through more intensive study or greater innovation – but on the other hand, smaller lists by category could result in greater differentiation in scores, making it easier to separate out breakthroughs Furthermore, it would guarantee that we would have results from every category of module design In the end, we tried to balance these concerns by analyzing the categories both separately and combined in one list To conduct such a computation-intensive analysis, we used a very basic unpublished software program written by Dr Harlan Husmann The inputs to the program were lists of patents, the years when each patent was published, and the list of backward citations for each patent The program calculated the average annual overlap score for each patent each year The default level of the patent then calculates two scores for each patent: the ‘before’ score and the ‘after’ score The ‘before’ score is the sum of each average annual overlap score from the first year to the year when the patent was published – that is, it represents the novelty and uniqueness of each patent (lower ‘before’ scores represent higher novelty and uniqueness) The ‘after’ score is the sum of each average annual overlap score from the year after the patent was published to the last year – that is, it represents the influence of each patent (higher ‘after’ scores represent higher influence) The advanced level does the same, but also lists the patents that overlap each other These two scores are where our analysis diverges from the backward citation analysis described above That analysis only looks at the years before the patent is published and the years after publication, which is how they avoid biasing their results in favor of early patents We cannot this, because PV patents can lie around for years before being incorporated in the field’s body of knowledge Once the ‘before’ and ‘after’ scores are calculated, we can eliminate the incremental patents by taking the ratio of ‘after’ scores to ‘before’ scores The higher the ratio, the less likely the patent is to be incremental However, this does not remove the failed patents, because if a failure has no overlaps before publication and a tiny overlap after publication, its ratio is still infinity Therefore, to remove the failures, we then go through the high ratios and select for the high ratios that also have high ‘after’ scores There are no specific numbers that count as high ratios and ‘after’ scores, because the backward citation analysis is relative Instead, our goal was to look at approximately the top 10% of high-scoring patents, since that would reduce the 1651 patents to a more manageable group of about 160 patents that could actually be read in a reasonable amount of time Therefore, we went through the results for each list, first selecting the top ∼50% of high ratios, then reducing it to the top ∼10% with high ratios and high ‘after’ scores Then we read through the abstracts of the top 10%, checking that each patent is actually relevant to crystalline silicon PV and not some other type of PV This entire process is summarized in Table Although we analyzed a total of 1651 patents, the sum for the category lists is 1986 patents because many of the patents qualified for multiple categories The ratio cutoff and ‘after’ cutoff values emphasize the relativity of the overlap scores For example, a ratio cutoff of resulted in the selection of 55% of the patents in the ‘all patents’ list, while a ratio cutoff of resulted in a 55% selection in the ‘cell enhancements’ list While the main goal of each ratio cutoff choice was in trying to end up with ∼50% of the patents in the first round of selections and ∼10% in the second round, the cutoff values were heavily influenced by our need to pick values that did not result in too high percentages of qualifying patents and to keep both values relatively high (i.e., a ratio of at least and an ‘after’ value of at least 0.01) Once the cutoff values are applied and the ∼10% point is reached, we checked that each patent was relevant – that it applied to silicon PV cells and was not purely decorative or similarly incon­ sequential After removing the irrelevant and repeated patents in the multiple lists, we had a list of 113 patents culled from the multiple categories The list of 103 relevant patents selected from the list of ‘all patents’ had 35 patents in common with the list of 113 patents, so our approach resulted in a total of 181 patents that qualified as breakthroughs under the backward citation analysis As mentioned earlier, our backward citation analysis is prone to its own biases – most importantly, the fact that the overlap score favors early breakthroughs over later breakthroughs This can be seen in Figure 15, which shows, respectively, the percent and number of silicon cell patents selected by the backward citation analysis versus the year in which the patents were issued It is possible that the first few years may have high percentages because the technology was new and each patent truly represented a breakthrough However, the last year certainly had no selected patents because the ‘after’ values were zero, and it is more than likely Historical and Future Cost Dynamics of Photovoltaic Technology Table 65 Summary of the patent selection process in backward citation analysis Patent list from U.S Photovoltaic Patents Number of patents in the list Ratio cutoff Percent making the cutoff ‘After’ cutoff Percent making the cutoff Number of remaining relevant patents All patents Silicon cells Polycrystalline silicon and ribbon silicon cells Cell components Cell enhancements Material processes Flat-plate collectors 1651 251 99 55 32 43 0.02 0.01 0.07 16 11 103 26 10 420 75 713 428 2 56 55 59 32 0.03 0.06 0.03 0.03 10 12 10 32 19 34 100 90 80 Percent 70 60 50 40 30 20 10 1950 1960 1970 1980 1990 Year Figure 15 Percent of silicon cell patents selected by the backward citation analysis in the year in which the patents were issued [93] that the last few years of patent selections also suffered from artificially low ‘after’ values For the purposes of our study, this means that the range of years we are studying is slightly less than the range of years presented in the NREL document 1.05.4.2 Results: Combining Expert Opinion and Patent Analysis An ideal breakthrough selection process would combine an expert’s proficiency born of years of practice with a computer’s strict impartiality – and that is what we aim to by combining the expert’s list of breakthroughs with the patent list of breakthroughs These two lists are not particularly well matched: the expert‘s list contains the names of breakthroughs, while the patent list contains the patent numbers of breakthroughs Furthermore, the expert‘s list can be unclear in its meanings, especially if different researchers use different names for the same breakthrough or the same name for different breakthroughs Likewise, an inventor often describes a new process or invention using names and terms that not become standard in the literature – not to mention the fact that a patent number alone contains no indication of what technology the patent represents In order to combine these two very different lists, we have become interpreters of sorts by comparing experts’ descriptions of breakthroughs and inventors’ summaries of their inventions There was no strict methodology – we simply read through each patent on the list, looking for processes or technologies that matched any of the breakthroughs described in the experts’ papers In general, we relied more heavily on the patents identified in the various categories of module design, because they had already been examined and sorted by experienced PV researcher Tom Basso [106] While reading through patents, we looked for keywords (e.g., ‘boron’ in the case of boron diffusion), and while reading through research papers, we looked for references to previous research that could help elucidate the author’s meaning behind a breakthrough name For the most part, matching patents to breakthroughs was an iterative process that could always be improved with more time and expertise However, we felt confident with our results after we had identified 39 patents that were connected to 23 break­ throughs (see Table 4) The lists of patents and breakthroughs were not a one-to-one match Most of the experts’ breakthroughs had no patents that appeared related to them, whereas a few breakthroughs had multiple connected patents – up to six patents in one case This is not surprising; although we call breakthroughs nonincremental, the truth is that most breakthroughs were developed over time, even if that interval of time was shorter than most breakthroughs Furthermore, patents tend to emphasize incremental changes, since each change can earn the inventor intellectual property and money We accommodate this reality by underscoring the fact that the 23 breakthroughs from the experts’ list are the final breakthroughs that we select – not the patents This way, our final list of 66 Economics and Environment Table List of selected breakthroughs and the patents that connect to them Breakthrough Patent (year) Breakthrough description Boron diffusion P–N junctions created out of boron rather than lithium increase cell efficiency from 4% to 6% Many thin contact fingers reduce the losses experienced by the current as it travels through the cell Antireflective coatings 2794846 (1957) 3015590 (1962) 2862160 (1958) 2919299 (1959) 3040416 (1962) 3046324 (1962) 3450568 (1969) 3493437 (1970) 3533850 (1970) Better, thinner top junctions 3811954 (1974) Tunneling metal–insulator–semiconductor contact 3928073 (1975) Contacts (grid and fingers) Laminating cells to glass with polyvinyl butyral (PVB) 4104084 (1978) 3990100 (1976) 4086102 (1978) 4171997 (1979) 4009054 (1977) Unexposed silicone rubber 4057439 (1977) Aluminum-based pastes 4086102 (1978) Screen printing 4105471 (1978) Hydrogen plasma passivation 4113514 (1978) 4321420 (1982) 4322253 (1982) 4557037 (1985) 4154625 (1979) Oxide surface passivation Pulse annealing Metallization Quasi-square wafers Reduced metallization resistance 4235644 (1980) 4348546 (1982) 4361718 (1982) 4356141 (1982) Metal–insulator N–P (MINP) cell 4395583 (1983) 4694115 (1987) 4404422 (1983) Ribbon on sacrificial growth plate 4478880 (1984) Ethylene–vinyl acetate (EVA) laminate 4499658 (1985) Plasma deposition of SiN passivation 4640001 (1987) Low-contact resistance with anti-reflective films 4643913 (1987) Reactive-ion etching Passivated emitter solar cell (PESC) 4664748 (1987) 4667058 (1987) 4589191 (1986) Microgrooving 4626613 (1986) Transparent layer of antireflective material on top of the cell reduces reflection and increases the amount of sunlight that is absorbed by the cell Shallower P–N junctions increase the cell response to shorter wavelengths and thus the amount of sunlight the cell could convert A thin insulator placed between the semiconductor layer and the metal contacts reduces recombination losses and allows more electrons to reach the contacts Silicon dioxide attached to broken silicon bonds on the surface of the cell increases cell efficiency Replacing the layer of exposed silicone with glass allows PV modules to weather the elements better Replacing the layer of exposed silicone with glass allows PV modules to weather the elements better Al-based pastes used at the rear of the cell helped remove impurities and give the modules better weatherization By printing contacts on the top and bottom of the cells, PV modules can be made more easily, reducing costs Hydrogen atoms attach to broken silicon bonds throughout the cell and increase cell efficiency Pulse annealing creates larger silicon crystals, improving the quality of the silicon at a lower cost Metallization uses thin films of metal to create contacts, reducing the amount of metal used and thus the cost Square wafers use space and silicon more efficiently than circular wafers Reduced resistance between the metal contacts and silicon decreases losses and allows more electrons to reach the contacts Combining metal–insulator–semiconductor contacts with shallow N– P junctions, increasing efficiency Thin ribbons of silicon are continuously produced with less waste silicon, reducing costs EVA doesn’t yellow with exposure to sunlight as quickly as PVB, allowing the PV module to last longer Thin films of silicon can be made by heating silicon nitride until the nitrogen escapes, reducing the cost and amount of silicon used Silver pastes lowered the contact resistance, reducing recombination losses and increasing efficiency Highly reactive ions texture the surface of the cell, reducing reflective losses Contacts are made through slits in the top oxide layer, increasing contact passivation and thus efficiency Selective surface etching of microgrooves reduces reflectivity, reduces resistance losses, and is easier to work with than etched pyramids Historical and Future Cost Dynamics of Photovoltaic Technology 67 breakthroughs is not beholden to the single point of time represented by a patent; it is associated more with idea behind the patent Although we cannot prove that this is true in our backward citation analysis, we assume it is true for the purposes of our analysis One example that is illustrative of the usefulness of backward citation analysis is the case of patent number 4165241, which we had identified because its name included the words ‘printed contact’ The backward citation analysis had not identified the patent as a possible breakthrough because its ratio value was 0.1429 – far below the cutoff This was suspect because one of its inventors, John Yerkes, was a recognized PV researcher and the patent summary seemed perfect for the ‘screen-printing’ breakthrough However, after further investigation, we discovered that this patent was a forward citation for patent 4105471 by the same inventor The backward citation analysis had not only identified this patent as a breakthrough, but identified the patent times – in the list of ‘all patents’ and two different categories of module design This example does not mean that backward citation analysis will always lead to a breakthrough or the origin of a breakthrough, but it does give us confidence that it has some proficiency in tracing research developments in patents These results show the promise of a rigorous, transparent, and replicable methodology for identifying nonincremental cost reductions in PV The identification of a specific list of the most important technical improvement provides an avenue for determining the factors that enabled these breakthroughs to occur – as well as to assess the impact they have had Ultimately, this much more specific characterization of technological change will be helpful for structuring and populating models that predict future changes, such as the one described next 1.05.5 Modeling Nonincremental Changes in PV Given the importance of this array of nonincremental changes, how can modeling take them into account? The previous section shows a set of nonincremental technical advances within crystalline silicon PV The transition to new technological generations in PV is likely to have greater impacts, limiting the reliability of learning curve projections in describing them PV based on crystalline silicon has remained the overwhelmingly dominant technology for three decades, despite the advantages of thin-film technologies that use less raw material and are more amenable to mass manufacturing techniques Policy related to PV in the longer term must address subsequent generations of PV technology, such as those based purely on organic materials In a recent study, Nemet and Baker [107] combined an expert elicitation and a bottom-up manufacturing cost model to compare the effects of R&D and demand subsidies They modeled the effects of these policy instruments on the future costs of a low-carbon energy technology that is not currently commercially available, namely, organic PV That study found that production-related effects on technological advance – learning by doing and economies of scale – are not as critical to the long-term potential for cost reduction in organic PV as the investment in and success of R&D One example of a new technological generation is purely organic PV It is particularly intriguing because of characteristics that distinguish it from the current generation of PV, which consists of cells made from crystallized silicon Purely organic PV use a thin film of organic semiconductor material for photon conversion Because they not require a glass substrate, organic PV cells can be manufactured on highly flexible material, leaving open the possibility of a much wider range of applications These manufacturing techniques are more amenable to automation and high throughput because they involve chemical rather than mechanical production processes That they also require only a thin layer of light-absorbing PV material, rather than a crystal structure, means that the amount of input materials needed is very low The combination of highly automated ‘reel-to-reel’ manufacturing processes and small materials consumption gives organic PV its most appealing distinguishing characteristics – the potential for very low manufacturing costs [108] However, organic PV are not currently manufactured on a commercial scale Moreover, the current models have very low efficiency, with the highest being around 5% in laboratory conditions [109]; this compares to about 15% efficiency for silicon-based solar cells Finally, organic materials are susceptible to degradation in sunlight, leading to concerns about the lifetimes of these cells In this case of a new generation of technology like organic PV, policy can impact future cost in multiple ways First, technology-push policies, such as direct government-sponsored R&D, can increase the likelihood of achieving technical break­ throughs Nemet and Baker assume that government R&D has an impact on two technical characteristics of organic solar cells: (1) their electrical conversion efficiency and (2) their lifetime Second, demand-pull policies, such as demand subsidies, increase demand for organic PV and thus create opportunities for cost reductions through economies of scale and learning by doing That model focuses on these two avenues of technical change Note, however, another potential impact: particularly at later stages of technology development, demand-pull policies may stimulate private sector R&D through the promise of a larger, less risky market 1.05.5.1 An Approach to Modeling Nonincremental Technological Change Nemet and Baker [106] developed the following methodology, taking the perspective that the combination of expert elicitation with a bottom-up manufacturing cost model provides a promising avenue for more robustly understanding future technology costs Adoption subsidies are modeled as having an impact on cost by enabling economies of scale through increasing demand R&D investment outcomes are modeled using the results of the expert elicitation They use this schema to evaluate the uncertain impact of combinations of R&D investments and subsidies on the cost of electricity over time The central question in that study was how R&D investment policies interact with demand subsidy policies to impact the cost of electricity from PV 68 Economics and Environment This model considers the effects of two demand-pull instruments: demand subsidies and carbon prices The authors model subsidies as a decision variable and treat carbon prices as an exogenous sensitivity (The reason for the focus on subsidies as the primary demand-pull decision variable in this model is that they can be designed to exclusively support organic PV, whereas carbon prices enhance demand for low-carbon technologies in general.) In order to assess the effectiveness with which technology policy can induce technical change in organic PV, they determined how the specific policies – investment in R&D and demand subsidies – affect technology improvements 1.05.5.2 Results for Nonincremental Technological Change They simulate efforts by the government to fund R&D and subsidize demand at three levels of policy intensity each, calculate the costs of PV electricity in 2040 and 2050 under the nine combinations of government technology programs (low R&D is $15 million yr−1 for 10 years, high R&D is $80 million yr−1 for 10 years; low subsidy is 20¢ kWh−1 for years, high subsidy is 25¢ kWh−1 declining to 5¢ kWh−1 over 20 years) While both subsidies and successful R&D programs reduce costs, the effect of successful R&D on cost in 2050 is an order of magnitude larger than the effect of subsidies Subsidies are relatively more effective in 2040 than in 2050, but the effect of successful R&D is still much larger, despite the fact that in their model only half of the benefits of R&D arrive by then Even the highest subsidy levels not achieve cost-effective organic PV without successful R&D The cost of PV without successful R&D never falls below 16¢ kWh−1, far from the target level of 4¢ kWh−1 Note also the counterintuitive result that, under successful R&D programs, the high- and low-subsidy programs produce costs in 2050 that are slightly higher than without the subsidy program This result occurs because the subsidy programs shift a substantial amount of PV production to earlier years; without subsidies, almost all of the demand for PV electricity in 2050 is met by production between 2040 and 2050 Consequently, without subsidies, the scale of manufacturing plants in 2050 reaches a larger, more efficient scale and the cost in 2050 is lower The curves in Figure 16 show the path of cost reductions over time and the relationships among the policy combinations The three subsidy curves in Figures 16(a)–16(c) are much more similar to each other than the three R&D curves in Figures 16(d)–16(f) Interestingly, the relative effectiveness of successful R&D and subsidies does not change under varying assumptions about storage and carbon prices; under all four scenarios, R&D success has a greater effect on cost reductions than subsidies in 2050 High carbon prices enhance the relative impact of subsidies and free storage increases the relative impact of R&D success, but in both cases the effects are small Furthermore, sensitivity analysis shows that the two main claims are robust to uncertainty in the data used to populate the model First, the analysis supports the claim that the base case set of assumptions represents an upper bound $1 (b) $1 (c) $1 10c 10c 10c 1c 1c (a) 1c 20 No subsidy Low subsidy High subsidy 4c goal 30 40 50 20 30 40 (e) (d) 20 50 $1 $1 10c 10c 10c 1c 1c 1c 20 30 40 50 40 50 30 40 50 (f) $1 No R&D Low R&D High R&D 4c goal 30 20 30 40 50 20 Figure 16 Impact of subsidies and R&D on cost per kWh of PV electricity: (a) no R&D; (b) low R&D; (c) high R&D; (d) no subsidy; (e) low subsidy; and (f) high subsidy Low and high R&D cases are conditional on program goals for efficiency and lifetime being reached Costs are on a log scale [106] Reproduced from Nemet GF (2006) Beyond the learning curve: Factors influencing cost reductions in photovoltaics Energy Policy 34(17): 3218–3232 [6] Historical and Future Cost Dynamics of Photovoltaic Technology 69 on the effectiveness of a subsidy program Second, all alternative scenarios support the finding that the cost-reducing effect of successful R&D is larger than the effects of subsidies Another result that is consistent across scenarios is that subsidizing a large demand for PV before the benefits of R&D arrive can be expensive Under the base case assumptions, no carbon tax and no free storage, the net present social cost of subsidies is $5 billion for the low-subsidy program and $80 billion for the high-subsidy program These values are in line with recent estimates of the cost of subsidizing the current generation of PV [110] Note that they are considerably higher than the R&D amounts we have considered, which have a net present value of $0.1 billion and $0.7 billion Also, the cost of each subsidy program increases as demand for solar electricity increases For example, in the presence of a $1000 carbon tax, the cost of the low-subsidy program rises to $30 billion and that of the high-subsidy program rises to $3 trillion Given the wide range of subsidy program costs, it may be useful in future work to use this model to optimize the timing and level of subsidies – especially given various assumptions about carbon prices and storage technology The outcomes here are similar to those discussed above on sensitivity of learning curve predictions to expensive outlier cases Despite the possibility of expensive outcomes, a case can still be made for subsidies A subsidy with no R&D is ‘less risky’, since it avoids the worst case of a very high electricity cost, and the expected cost is lower as well This result implies that if (1) the goal were simply to achieve as low an electricity cost as possible and (2) the two programs had equal costs, the ‘no R&D/high-subsidy program’ would be strictly preferred by all risk averters Note, however, that neither of these conditions necessarily holds These results imply that the value of subsidies is that they provide a hedge against the possibility that breakthroughs in technical change fail to take place In a choice under uncertainty framework, subsidies provide a benefit in reducing risk 1.05.5.3 Summary of Nonincremental Modeling The Nemet and Baker study found that (1) successful R&D enables PV to achieve a cost target of 4¢ kWh−1, (2) the cost of PV does not reach the target when only subsidies – and not R&D – are implemented, and (3) production-related effects on technological advance – learning by doing and economies of scale – are not as critical to the long-term potential for cost reduction in organic PV as the investment in and success of R&D Those results were insensitive to the intensity of either type of program, the level of a carbon price, the availability of storage technology, and uncertainty in the main parameters used in the model The central policy implication of those results is that governments must find a way to engender this R&D, whether it is funded by the government itself or by the private sector in response to changing demand conditions In fact, one might argue that the key question policy makers face, with regard to PV development, is how to encourage this R&D, rather than how to support economies of scale and learning by doing To be sure, that study found that a case could still be made for subsidies through analysis of risk Because of the possibility of R&D failure, the benefits of subsidies stochastically dominate those of R&D In the event of R&D failure, subsidies make the costs of PV much lower than they would otherwise be, albeit not at levels close to the target The importance of subsidies as a hedge against inherently uncertain R&D programs depends on the value that society places on the availability of a low-carbon energy source that is moderately inexpensive – that is, unlikely to be competitive with all other technologies but perhaps inexpensive enough to be deployed at a large-enough scale to diversify energy supply Much work needs to be done to develop this and other methods for modeling discontinuous evolution of PV technology and the impacts on costs Given the importance of historical nonincremental changes described in the previous section, as well as the possibility of even more significant changes in the future, the need for better models is great 1.05.6 Future Progress and Development The insights gained from assessing cost development in PV technology have some direct policy implications; they also clarify needs for modeling As a result of the assessment of the sources of historical cost reductions as well as modeling future costs, we now have several design criteria for public policies intended to reduce the costs of PV We need an array of supporting policy instruments: for example, R&D, demand subsidies, and encouragement of intersectoral spillovers Timing matters; making good decisions about when to switch from a focus on R&D to a more capital-intensive investment in wide-scale deployment is crucial Multiyear demand subsidies are important because the benefits of demand subsidies come from expectations about markets in the long term Intermittent demand supports may actually be worse than no support R&D support also needs a long-term commitment; budgets or grants that span multiple years are important; supporting policies, such as Japan’s Sunshine Program or US Project Independence, that demonstrated commitment by making this area of work a serious national priority were also successful Niche markets have been crucial, especially when government support was lacking The success of new technological generations may require renewed R&D support even while markets for the existing technology are expanding; new problems need to be worked and postdeployment experience needs to feed back into subsequent R&D decisions These insights imply a strong need for better tools with which to understand technological change in PV Much is at stake, in terms of both the public’s financial resources used to fund these programs and the environmental impacts these programs are designed to mediate These decisions are too important – and mistakes too expensive – to rely on simple heuristics that mask large 70 Economics and Environment uncertainties and that are easily ignored Promising developments exist An important analytical improvement has certainly been the inclusion of explicit treatment of learning uncertainty in modeling [111–113] Estimating technology costs through the summation of bottom-up characterization of technology dynamics in individual components provides an appealing alternative in that sources of uncertainty can be identified more precisely [114] The combination of such bottom-up models with experience curves and expert opinion provides a method that is more robust to bias within any single method [83] As discussed above, an alternative use of bottom-up methods is to integrate them with expert elicitation into a single model that represents both incremental and nonincremental technical change [107] This integration will help account for the introduction of new technolo­ gical generations, which seems especially likely in the case of PV Improving the accuracy and precision of models such as these is an important research endeavor Identifying the specific nonincremental changes that occurred, as described by Husman [115]., provides a methodology for connecting them to causes and impacts; 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A23–A28 Osaka, Japan, 18 May 2003 72 [96] [97] [98] [99] [10 0] [10 1] [10 2] [10 3] [10 4] [10 5] [10 6] [10 7] [10 8] [10 9] [11 0] [11 1] [11 2] [11 3] [11 4] [11 5] Economics and Environment Podolny JM and. .. collectors 16 51 2 51 99 55 32 43 0.02 0. 01 0.07 16 11 10 3 26 10 420 75 713 428 2 56 55 59 32 0.03 0.06 0.03 0.03 10 12 10 32 19 34 10 0 90 80 Percent 70 60 50 40 30 20 10 19 50 19 60 19 70 19 80 19 90 Year