WACC the dog: The effect of financing costs on the levelized cost of solar PV power

Renewable Energy 75 (2015) 888e898

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WACC the dog: The effect of financing costs on the levelized cost of solar PV power Janosch Ondraczek a, b, *, Nadejda Komendantova b, c, Anthony Patt b, c a

University of Hamburg, Research Unit Sustainability and Global Change (FNU), Grindelberg 5, 20144 Hamburg, Germany International Institute of Applied Systems Analysis (IIASA), Program on Risk, Policy and Vulnerability, Schlossplatz 1, 2361 Laxenburg, Austria c €tsstrasse 22, 8092 Zürich, Switzerland Swiss Federal Institute of Technology (ETH), Department of Environmental Systems Science, Universita b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 22 November 2013 Accepted 20 October 2014 Available online 18 November 2014

The adoption of solar photovoltaic (PV) technologies has expanded rapidly in recent years, leading to suggestions that this growth, which occurred mostly in high-latitude countries with often low levels of sunshine, may have come at an unnecessarily high price. However, the factors influencing the cost of solar PV, and the subsidies required to sustain its uptake, include more than just the level of sunshine. While cross-country differences in technology costs are hard to ascertain, it is possible to account for the cost of capital on a country-by-country basis. In this paper, we therefore map the cost of solar PV globally, accounting for differences in both the solar resource and the financing cost in order to calculate the levelized cost of electricity (LCOE) from solar PV systems in 143 countries. In contrast to the work of other researchers who typically treat financing costs as uniform across countries, our results suggest that the LCOE of solar PV systems in northern countries may in fact be lower than in equatorial countries, and high latitude countries may thus not have been an unwise location to subsidize the adoption of solar PV technologies in the past. Our results further suggest that efforts to expand PV installation in equatorial developing countries may benefit greatly from policies designed to make low cost finance more widely available, which underlines on-going efforts to “de-risk” low carbon investments. © 2014 Elsevier Ltd. All rights reserved.

Keywords: Solar photovoltaic power Levelized cost of electricity Cost of capital Financing cost Global LCOE model Grid parity analysis

1. Introduction The solar photovoltaic (PV) power industry has grown rapidly in recent years, and associated with that growth has been a decline in costs, which was both a cause and an effect of the rapid expansion in the global PV capacity. Over the six-year period 2006e2011, global installed capacity rose at an average annual growth rate of 56%. The year 2012 broke yet another record, with global capacity increasing by almost 29.4 GW, or 42%, in comparison to 2011 and total global installed PV capacity reaching over 100 GW [1]. At the same time, the cost of solar PV technologies declined significantly, with the cost of PV modules in 2011 alone falling by more than 50%, and the installed cost of complete roof-mounted systems dropping by more than 20% [2]. A total decline of 75% in PV prices in the last three years was possible due to the fall of factory-gate prices for crystalline-silicon PV modules below the $1.00/W mark in 2011: European PV modules are now selling for less than V0.80 per watt, Chinese ones for less than V0.65 per watt [3]. * Corresponding author. University of Hamburg, Research Unit Sustainability and Global Change (FNU), Grindelberg 5, 20144 Hamburg, Germany. Tel.: þ49 40 42838 2053, þ49 179 594 5061 (mobile); fax: þ49 40 42838 7009. E-mail address: [email protected] (J. Ondraczek). http://dx.doi.org/10.1016/j.renene.2014.10.053 0960-1481/© 2014 Elsevier Ltd. All rights reserved.

There are indications that PV has already reached parity with retail electricity prices in several geographiesdnotably southern Californiadand projections that it will attain such grid parity in many more markets over the coming decade [4]. In the context of rural and off-grid electrification in developing countries, PV is often also cheaper than the baseline (e.g. diesel generators or kerosene lighting). Yet, while the growth in solar PV has brought about marked cost reductions, substantially more is needed before PV becomes fully cost-competitive, unsubsidized, with other forms of power generation across most markets [5,6]. To date, capacity growth, technological progress and the subsequent reduction in costs were driven mainly by subsidies, e.g. embedded in feed-intariffs, production tax credits and rebates, to renewable energy sources. These amounted to $88 billion globally in 2011, a figure that is projected to rise to $240 billion by 2035. In comparison, fossil fuel consumption subsidies reached $544 billion in 2012 [5]. Analysts have suggested that the growth in solar PV has come at an unnecessarily high price, with unnecessarily high subsidies. For example it is Germany, a country not known for its abundant sunshine, that has seen the greatest PV development in recent years. Numerous studies have pointed to the possible inefficiency of the fact that predominantly mid- to high-latitude countries, like

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Fig. 1. Effects of WACC and GHI on LCOE. The blue curve shows the linear relationship of LCOE to WACC, while the red curve shows the exponential relationship to GHI. Both curves assume all other determinants of LCOE to be kept constant (ceteris paribus). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Germany, have accounted for the overwhelming share in capacity growth (e.g. Refs. [7,8]). In reaching this conclusion, these studies have considered the index of total sunshine (as measured by global horizontal irradiance, or GHI) as the primary exogenous factor driving average costs, and have treated other drivers of PV cost, such as investment and operating costs, as well as the cost of capital, as largely endogenous and thus irrelevant to their analysis [4,8,9]. However, the factors influencing the cost of PV, and the subsidies required to sustain its construction, include more than just the strength of the sun. While differences in costs of such factors as initial capital spending, operation and maintenance, and decommissioning are hard to ascertain, it is possible to account for the cost of capital, on a country-by-country basis. The cost of capital is an important input to the calculation of the levelized cost of electricity (LCOE), as it determines the rate by which both costs and electricity yields are discounted over the lifetime of a solar PV system. For our purposes, we use the (weighted average) cost of capital to determine the discount rate for every country in our sample.1 Based on previous work and data we thus assume that the country-specific interest rates can also be treated as exogenous [10], and we investigate whether they play a larger role in influencing the average cost of solar PV power than does GHI. In doing so, we do not attempt to analyze the historical diffusion of solar energy technologies, as our data are not suitable for this investigation. However, Pfeiffer and Mulder [11], using a different dataset, find a strong positive influence of economic and regulatory instruments (i.e. subsidies), among other factors, which further underlines the importance of our own work. LCOE values are highly location-specific, both because of regional cost differences and because of the varying strength of the sun, which affects energy output [12]. In this paper, we therefore map the cost of solar PV globally, accounting for both the quality of the solar resource and the cost of capital, in order to differentiate the LCOE from PV. In so doing, we address three issues that have not

1 Readers should note that we use the various terms weighted average cost of capital (WACC), cost of capital, financing cost and discount rate interchangeably throughout the remainder of this paper as they essentially stand for the same concept within the context of our research.

been fully addressed in the academic literature before: First, we investigate the importance of the cost of finance as a determinant of the cost of solar power on a global level, and we find that it matters much more than is commonly assumed. This aspect, which has so far been overlooked in other global assessments, is the main contribution of our paper. Second, we investigate whether the actual pattern for the global adoption of solar PV technologies may have been economically efficient once the cost of capital is taken into account. Third, we attempt to explain the apparent mismatch between the solar resource potential and this adoption pattern with reference to the cost of finance, rather than subsidies. For both issues our results suggest that high-latitude countries, such as Germany, may not have been an unwise location to subsidize the adoption of solar PV technologies (if this adoption is an explicit policy goal, e.g. for climate change mitigation), and further suggest that efforts to expand PV installation in developing countries may benefit greatly from policies designed to make low cost finance more widely available, e.g. by “de-risking” renewable energy investments. Given that we take a global perspective, our results on a country-level are necessarily imprecise. Rather, what this paper is concerned with is the ‘big picture’, where our research adds the most value. The remainder of this paper is structured as follows. Section 2 provides the necessary background to this study by presenting the relevant literature on the cost of solar electricity generation on a global and national level, as well as introducing readers to the derisking debate. Section 3 lays out the conceptual framework and the methodology employed, as well as the data used for our analysis. Section 4 presents our main results, and Section 5 concludes with a discussion of these results and their (policy) implications. 2. Background Previous academic work calculated the global cost of solar PV electricity based on the assumption that the cost of capital is uniform across countries. For instance, in his analysis of optimal locations for global utility-scale solar PV utilization, Ummel [9] uses a single rate of 12.6% as the “capital recovery factor” (p. 4) across all countries. Likewise, Hauff et al. [8] do not report considering differences in the cost of capital in their analysis of the market

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Fig. 2. Map of annual electricity production (in kWh) of solar PV installations (in kWp, Map a), and installed PV capacity (in Wp) per capita in 2010 (Map b).

potential for, and LCOE of, solar electricity in what they term “Sunbelt” countries, i.e. those located between the latitudes of 35 N and 35 S. Breyer and Gerlach [4], who provide a global overview on the expected grid-parity of solar PV, base their analysis on a weighted average cost of capital (WACC) of 6.4%, again applying this rate uniformly across all 160 countries analyzed. Reports by various international organizations typically also treat the cost of finance as constant across countries and technologies: For example, the International Energy Agency's “Projected costs of generating electricity” uses uniform discount rates of 5% and 10% to calculate the cost of various power technologies [13], whereas a recent report by the International Renewable Energy Agency uses a uniform rate of 10% [14]. However, both reports pursue the aim of cross-technology comparisons, not cross-country comparisons, which justifies their approaches to the cost of finance, and they also explicitly mention the large importance of the cost of capital for the LCOE of various (renewable) power generation technologies. Finally, the Global Solar Opportunity Tool of the Clean Energy Solutions Center [15] also assumes a uniform discount rate of 5%, instead of a cost of capital that varies by country. We graphically illustrate the stylized relationship between horizontal irradiation (i.e. GHI), WACC and the LCOE assumed by Breyer and Gerlach [4], among others, in Fig. 1. This figure, which underlines the importance of our analysis, also presents the linear relationship between WACC and LCOE. Readers should take note that the slope of the blue line is much steeper than for the red line, i.e. the influence of WACC on LCOE is more pronounced than for GHI on LCOE. To our knowledge, only few studies take into consideration varying levels of the cost of capital in different parts of the world. These typically focus on a small sample of countries, or an individual country. A recent example of this approach are Peters et al. [16], who analyze the economics of various solar energy technologies in five countries (USA, Germany, Spain, China, and Egypt) by varying the discount rate, among other factors. Indeed, they point to the

importance of using “location-specific realistic discount rates” (p. 6425), rather than only calculating sensitivities. In this vein, they use a discount rate of 8% for Germany, the USA and Spain, 12.6% for China, and 14.1% for Egypt. Similarly, Schmidt et al. [12] look at six countries (Brazil, Egypt, India, Kenya, Nicaragua, and Thailand) in their analysis of the cost of wind and solar power in developing countries, and take into account variations in the equity rate of return across countries. Their estimates range from 13.3 to 17.6% for the six countries they look at. Likewise, Komendantova et al. [17] point to the role of varying internal rates of return in their analysis of the cost of concentrated solar power in the North African region, and Ondraczek [18] highlights the importance of system costs and discount rate assumptions in the case of Kenya. However, none of this previous work attempts to incorporate global differences in the cost of capital into the analysis of the cost of solar electricity, as was also pointed out by Schmidt [19]. The major contribution of our paper therefore lies in the attempt to fill this gap in the scientific literature by taking a global perspective in the investigation of the role of financing costs for the economics of solar PV. Frequent questions and discussions about the efficiency of locating PV development in high latitude countries derive from results similar to those shown in Fig. 2. In Fig. 2a we map the estimated productivity of new rooftop PV installations on a country-bycountry basis, which varies according to the quality of the solar resource, in terms of average kWh per year of power that 1 kW of installed peak capacity would generate. Fig. 2a uses data compiled by Breyer and Gerlach [4]. Their measure of solar resource (GHI) corrects for the lower utilization of diffuse light when cloud cover is present.2 The figure indicates that solar irradiance varies by a factor of 2.3 or more between northern European countries, such as

2 The authors' data do not, however, take account of the effects of ambient temperatures on module efficiency, which can be quite large in tropical regions.

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Ireland, and equatorial countries, such as Niger. More generally, the best solar resource is located along the equatordin the countries of the “Sunbelt”dand one might thus expect the largest PV capacities to be concentrated in Central America, Africa and South Asia. However, as Fig. 2b (using data from Ref. [6]) shows, the pattern of existing PV installations, mapped on an installed watt peak (Wp) per person basis, is very different from what the physical resource might suggest. The leading countries for total installed capacity at year-end 2012 were Germany, Italy, the USA, China, and Japan, accounting for nearly 70% of global capacity, while the leaders for solar PV per inhabitant were Germany, the Czech Republic, Spain, Belgium, and Luxembourg in 2010 [1,6]. At least two of these countries offer a notably poor solar resource, and none of them is part of the “Sunbelt”. Our research is also closely linked to the debate on de-risking renewable energy investments, and underlines its importance within the context of climate change mitigation. Three recent reports that add to, and inform, this debate are the GET FiT Plus report on “De-risking Clean Energy Business Models in a Developing Country Context”, the UNEP Finance Initiative's report on “Financing renewable energy in developing countries” and the “Derisking Renewable Energy Investment” report by the United Nations Development Programme. The first of these proposes a Global Energy Transfer Feed-in Tariff program (hence, GET FiT) that comprises public support for financial incentives, risk mitigation and technical assistance for investments in renewable energy technologies [20], and is now being piloted in places such as Uganda. The second report points towards the fact that in many instances political, regulatory and macroeconomic risks render private finance for renewable energy investments unavailable [21], whereas the UNDP report argues that, while technology costs may be decreasing, securing long-term, affordable finance remains a major challenge and a huge hurdle for many low-carbon technologies. The report illustrates the impact of higher financing costs through a number of case studies, which show that the LCOE of renewables is influenced more by a higher WACC than that of conventional power sources, such as gas, and it proposes a framework to de-risk renewable energy investments through a mix of policy instruments (see Ref. [19] as well as Ref. [22]). 3. Methodology and data Our research builds upon the literature presented in the preceding section. From this we learn that actual LCOE values are determined by a wider set of factors than just the amount of sunshine in a given country. In addition to the annual power output per watt of installed peak capacity, LCOE value computations include the system cost (including modules, inverters, grid connection, mounting structures, and installation), operation and maintenance costs, decommissioning costs, and the cost of capital. The latter is of relevance because most individuals and firms installing PV panels need to obtain appropriate financedusually a combination of equity and money borrowed from lendersdin order to pay for their investment. Even if they were not to borrow the money to finance the costs of PV that would come at an opportunity cost, equal to the interest rate or the return on equity that they could receive investing that money somewhere else. Determining the market competitiveness of energy technologies thus includes a number of factors, including their fixed and variable costs, the proportion of maximum possible load that they actually deliver given environmental constraints, and the degree to which their output is congruent with periods of peak demand [23]. Among the various measures of competitiveness, the levelized cost of electricity is the most commonly used indicator. It expresses the average cost per unit of electricity over the lifetime of a given investment, discounting all

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costs to their net present value at the time of installation, and dividing these by the anticipated total energy output [24]. In order to estimate the levelized cost of solar PV electricity in our set of 143 countries, we first develop a global LCOE model and populate it with input values for all parameters that are treated uniformly across our dataset (Section 3.1). In the second step, we calculate the average electricity yield from a prototypical solar energy system in each of these countries (Section 3.2). This serves as an input to the global LCOE model. In the concluding step, we calculate the weighted average cost of capital for each country by relying on various sources of data (Section 3.3), and the cost of capital we thus derive is used as the country-specific discount rate in our global model.

3.1. Global LCOE model and uniform input values To estimate LCOE values per country, we make use of nationally specific data for the solar resource and the cost of capital, and assume uniform values for the costs of installation, operation and maintenance, and decommissioning. In fact these latter values do vary by location; generally they are lower in those countries and regions where the PV market is more developed and hence more competitive. In Germany, for example, average installation costs were $2847 per kWp at the end of 2011 [25], while globally they were reported to be $5010 [26]. The lower costs in Germany result from the large size of the German PV market, the resulting competition between PV module suppliers, as well as a large number of firms engaged in the installation business. Seel et al. [27] point towards several additional factors that also contribute to the lower cost levels seen in Germany, including lower non-module hardware and particularly soft costs. In our calculations of global LCOE values we take the year 2011 as the base year for all costs. For our analysis we use the global average cost of installing a commercial solar PV system (50 kW), where the most developed solar energy markets (USA, Germany, Japan) have a large weight. This is our baseline scenario. To check the robustness of our results, we further conduct a sensitivity analysis assuming an investment cost that is about 40% lower than the global average. Here we use the German costs for a residential system (up to 10 kW) as our reference point. Although residential systems are typically more expensive than commercial systems, and thus not strictly comparable, we use this number for want of statistics on German installation costs for commercial systems. As such, the LCOE in the alternative scenario is likely to constitute an upper bound estimate. For our calculation of the difference to electricity rates we use the residential electricity price despite the commercial set-up. That is, we assume small businesses face similar electricity prices as households, which in our view is a valid assumption given that commercial users of electricity face similar rates as households in most countries (see Ref. [28]). In order to determine the LCOE from solar PV systems, we develop a global LCOE model. Our LCOE model, shown in Eq. (1), follows the methodology commonly used in the literature (e.g. Refs. [13,18,29]). It has to be mentioned that the LCOE methodology is not without its critics, as it also has severe methodological shortcomings, e.g. in the economic valuation of intermittent resources,3 as well as the exclusion of transmission and balancing costs. Nevertheless, the methodology is widely deemed to be more

3 Reichelstein and Sahoo [28] suggest adding a co-variation coefficient to the “traditional” LCOE calculation in order to capture the interplay of intermittent generation and power demand/prices. Applying this methodology to the case of the western USA, they find that the traditional LCOE might overestimate the “true” cost of solar PV by 10e15%, and underestimate the “true” cost of wind by 10e15%.

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Table 1 Globally uniform input variables used in global solar LCOE model. Values listed here are used uniformly across all countries. Input parameter

Value

Unit

Source(s)

Plant lifetime (T) Investment cost/kWp (It) a) Global average: b) Germany: Operating cost (Ot) Scrap value (Dt) Degradation factor (d)

25

years

[12,13,16]

5010 2847 1.5 20.0 0.5

USD USD percent percent percent

[26] [25] [16] [13] [16]

useful for the purpose of technology-comparisons than other approaches, such as a strict comparison of the capacity cost (e.g. USD per Wp), which does not take into account electricity yields, or the somewhat imprecise metric of ‘grid parity’ (which gets distorted by energy subsidies etc.), and also used broadly in policymaking.4 We calculate the LCOE for solar PV for every country in our sample using the following formula:

PT t t¼0 ðIt þ Ot þ Dt Þ=ð1 þ rn Þ LCOEn ¼ P ; where t T t t¼0 Sn ð1  dÞ =ð1 þ rn Þ

ð1Þ

LCOEn ¼ levelized cost of electricity in country n T ¼ economic lifespan of project   t ¼ year t; 0; 1; 2; & n ; where t ¼ 0 is the year of installation and start of operation It ¼ initial investment cost in period t Ot ¼ operation and maintenance cost in period t Dt ¼ decommissioning cost or scrap value in period t rn ¼ discount rate in country n Sn ¼ rated energy output in country n d ¼ annual module degradation factor Eq. (1) thus describes the LCOE model developed and used for the calculation of the cost of solar electricity in each of the 143 countries analyzed. All variables needed for the LCOE calculation can be seen in this equation. Most of the variables used in Eq. (1) are kept constant across countries, but vary over time. Thus, the initial investment (It) will occur in period t ¼ 0, while operation and maintenance costs (Ot), denoted as a fraction of the initial capital outlay, are assumed to occur in all subsequent periods (t ¼ 1 to t ¼ T). The decommissioning cost (or scrap value, Dt, denoted as a fraction of the initial capital outlay), is assumed to occur at the end of the project lifetime, i.e. in period t ¼ T only. Table 1 shows the values used for the parameters just mentioned, which are in line with recent literature. The discount rate (rn) and the rated energy output of the solar PV system (Sn) depend on the conditions in country n, but do not vary over time. Finally, we use a project lifetime (T) and an annual module degradation factor (d), i.e. the factor by which the initial module output declines over time, which are assumed to be the same across all countries and also time-independent. Once we have thus calculated the LCOE for every country in our sample, we are also able to estimate the difference to grid prices that may still need to be bridged for the widespread adoption of solar PV. In the case of commercial or residential rooftop PV

4

Readers are referred to Ondraczek [18] for further details on the LCOE model employed, a short discussion of its limitations and the required input parameters. In addition, Reichelstein and Sahoo [28] go into more detail on the “true” cost of intermittent power sources.

systems, one might approximate this implicit subsidy requirement with the difference between the LCOE and the average retail cost of electricity, as one can assume that any subsidy (in whichever form) would need to at least cover the difference between the LCOE of PV and the electricity price to make an investment privately worthwhile. We calculate this difference based on residential electricity rates published by Breyer and Gerlach [4] to give an indication of the likely magnitude of the required subsidy per country. In the following sub-sections we explain how we determine the two remaining factors, rated energy output of the solar PV system (Section 3.2) and discount rate (Section 3.3), that vary by country. 3.2. Solar electricity yield calculation per country LCOE values are highly location-specific, both because of regional cost differences and because of the varying strength of the sun, which affects energy output [12]. As GHI values can vary widely within single countries, we use national averages based on the population-weighted values derived from Breyer and Gerlach [4] for our calculation of the solar electricity yield per country (SYn). We use their population-weighted irradiation value IRn (in kWh/ m2/a), which is supplied for a total of 160 countries (see Table 2 for key descriptives, as well as Table A.1 in the supplementary material for data on all countries). This value has been calculated by the authors on the assumption of a solar PV system with an optimallyoriented, fixed tilt. Irradiation values for each location calculated are then averaged by the population distribution for each respective country, i.e. regions with low population densities (and the irradiation levels observed there) receive a lower weight than regions with high population densities when calculating the average solar irradiation for a country. The population-weighted value thus not only takes account of a country's solar resource but also of its population distribution. Further information on the solar yield calculation can be found in Breyer and Gerlach (ibid) as well as Breyer and Schmid [30]. In order to calculate SYn (in kWh/kWp/a), we apply the following formula:

SYn ¼ IRn  PR; where

ð2Þ

SYn ¼ solar yield in country n IRn ¼ population­weighted irradiation value in country n PR ¼ performance ratio The performance ratio PR denotes the overall efficiency of the solar PV system in turning the available solar energy into electricity. The PR is typically reported to range from 0.75 to 0.85 [4,16]. Conservatively, we use the lower end of this range (PR ¼ 0.75) as the parameter value for our calculation. This takes into account that smaller solar PV systems and solar PV systems in developing countries can be expected to be less efficient than those seen in the most advanced solar PV markets. SYn is the solar yield in country n for the first year of operation (t ¼ 0) of the solar energy system. In order to account for module degradation, the degradation factor d needs to be used for all subsequent years. Hence, using SYn from Eq. (2) as the input for the rated energy output in country n (i.e. Sn) in Eq. (1) enables us to calculate the annual solar electricity yield of a typical solar PV system for every country and for all time periods. 3.3. Cost of capital and discount rate per country As was already pointed out, the financing cost is an important input to the calculation of the LCOE, as it determines the rate by

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Table 2 Five highest and lowest countries for GHI, WACC, LCOE and difference between LCOE and grid price, assuming global average investment costs.

Highest ranked countries: 1. 2. 3. 4. 5. Lowest ranked countries: 139. 140. 141. 142. 143. Median value: a

Population-weighted GHIa

Weighted average cost of capital

Levelized cost of electricity

Delta between LCOE and grid price

[kWh/m2/a]

[%]

[USct/kWh]

[USct/kWh]

Niger (2382) Namibia (2352) Djibouti (2318) Grenada (2317) Botswana (2302)

Japan (3.7) Ireland (3.8) Switzerland (3.9) UK (4.1) Netherlands (4.3)

Spain (29.7) Puerto Rico (31.0) Japan (32.2) Israel (32.9) Malta (33.4)

Spain (6.7) Italy (8.1) Japan (10.2) Cyprus (12.1) Malta (12.4)

Belgium (1203) Finland (1181) UK (1128) Norway (1103) Ireland (1055) 1939

Sao Tome & Principe (21.6) Brazil (28.4) Madagascar (29.0) Congo DR (32.4) Zimbabwe (254.9) 12.8

Kyrgyz Rep. (81.3) Madagascar (99.4) Brazil (108.1) Congo DR (124.6) Zimbabwe (772.5) 53.8

Kyrgyz Rep. (79.3) Georgia (79.8) Brazil (87.1) Congo DR (120.6) Zimbabwe (771.5) 38.6

Source: [4].

which both costs and electricity yields are discounted. For our purposes, we use the weighted average cost of capital to determine the discount rate for every country in our sample, which is in line with the literature (e.g. Refs. [13,18,22]). In those cases where project developers borrow money to finance construction, the cost of capital shows up as a real cost, while in cases where they make use of their own equity, the prevailing interest rate is an opportunity cost [16]. Interest rates are especially important for renewable technologies such as solar PV, given high upfront costs and the absence of fuel costs. Interest rates are typically country-specific, and respond to macro-economic drivers including balances of accounts, balances of payments, monetary and fiscal policy decisions, perceptions of inherent systemic risk, as well as the overall maturity of financial markets [23]. From the perspective of an energy planner considering PV policy, the cost of equity and debt capital is largely an exogenous factor, as changes in spending within the energy sector make virtually no difference in the broader economic factors driving the interest rate. In the United States in 2010, for example, total investment in residential PV panels was $1.6 billion [31,32], compared to an accounts balance deficit of $470 billion [33], a difference of over two orders of magnitude. The cost of capital varies widely across countries for various reasons. In an early investigation, Painuly [23] gives a number of reasons why the WACC may differ between countries. He identifies such elements as scarcity of capital, governmental policies, lack of access to (cheap) capital, risk perceptions of financial institutions, macro-economic parameters such as the inflation rate and demand for credit. According to Oxera [34], the WACC is influenced by the exposure to systemic risk inherent in the market and perceptions of this risk by investors, an observation that is also made in the literature on investment risks and de-risking (cf. Section 2). The WACC is also influenced by the relative supply and demand of finance. Especially in many developing countries, where the finance industry is less competitive, this results in higher interest rates. To measure the WACC, we use in our research the real equity rate of return and the nominal prime lending rate. In calculating and using country-specific costs of capital, we implicitly assume that the cost of capital of the country where the project is implemented (rather than some other country) is to be used for the calculation of the country-specific LCOE. This approach is based on three assumptions. First, investors and lendersdindependent of their origindwill always take many factors into consideration when pricing their equity or loans, such as the macroeconomic environment, investment and political risks, and the opportunity cost of investing in an alternative project in the same country. As countries differ in all these areas, differing interest rates result (see

below). Second, small-scale projects, e.g. those on the commercial or residential level that we consider here, will almost always require local financing, as they are too small and unsophisticated to directly access international financial markets. Third, given the extent of deployment of renewable energies needed to make a meaningful contribution to climate change mitigation will require funding from both within and outside countries, rather than just a few demonstration projects funded by the public sector or development assistance from abroad. For our calculation of the specific discount rate per country, we follow the methodologies of Schmidt et al. [12] and Peters et al. [16], and base or WACC calculation on the “Guidelines on the Assessment of Investment Analysis” developed by the Clean Development Mechanism's Executive Board [35]. These suggest that, in the absence of project-specific data, the discount rate to be used for the analysis of CDM projects should be a WACC representative for the country. In line with the CDM Executive Board's approach we therefore use the following formula to calculate the WACC for every country in our sample (adapted from Ref. [4]):

WACCn ¼

En Dn  kE þ  kD ; where n n En þ Dn En þ Dn

ð3Þ

WACCn ¼ weighted average cost of capital in country n En ¼ amount of equity used in financing project in country n kE ¼ equity rate of return in country n n Dn ¼ amount of debt used in financing project in country n kD ¼ debt interest rate in country n n We use the country-specific WACCn from Eq. (3) as the input for the discount rate in country n (i.e. rn) in Eq. (1), enabling us to calculate the LCOE of solar PV for every country. For our calculation of WACCn, we use the following parameter values. For Non-Annex 1 countries, i.e. those without a binding obligation to reduce greenhouse gas emissions under the Kyoto protocol, the share of equity and debt in the project is assumed to be 50:50, which is in line with the recommendations of the CDM Executive Board [35]. Thus, En equals 0.5 and Dn equals 0.5 for these countries. For Annex 1 countries, the share of equity and debt in the project is assumed to be 30:70, taking into account that commercial banks are generally accepting higher leverage in stable economies with secure property rights [36]. Thus, En is set to 0.3 and Dn equals 0.7 for Annex 1 countries. For the cost of equity, kEn , there is no single source of global data, so we use two datasets reporting the real rate of return on equity

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investments instead. In the absence of project-specific information on the expected rate of return on equity, the CDM Executive Board recommends default values that are to be used for investment analyses for all 153 Non-Annex 1 countries. We use the values provided for investments in the energy industry (group 1) for our analysis of the 122 Non-Annex 1 countries in our sample. For the 40 Annex 1 countries in the sample of Breyer and Gerlach [4] we cannot use the same source. Therefore, we base our analysis for these countries on data compiled by Dimson et al. [37]. In line with the methodology recommended by the CDM Executive Board for Non-Annex 1 countries, we also use the arithmetic mean of the real rate of return on equity, in this case for the period 1900e2010. The authors report the rate of return on equity for 19 of the 40 Annex 1 countries in our sample; for 5 of the remaining 21 Annex 1 countries we are able to use neighboring countries with a comparable country credit rating as proxies (e.g. China for Hong Kong, and Kazakhstan for Russia). This brings the total number of countries with data on real rates of return on equity to 177, 143 of which are also contained in the solar yield dataset of Breyer and Gerlach [4]. For the cost of debt, kDn , we use data from the International Monetary Fund (IMF) on the commercial (or prime) lending rates in 176 countries [38]. To our knowledge, the IMF's data constitutes the best available, and most comprehensive, dataset for the largest number of countries. The (commercial) lending interest rate is the rate charged by banks on loans to prime customers, and using this nominal rate is in line with the recommendations of the CDM Executive Board, as no project-specific data is available. We use average lending rates for the period 2006e2011, except in the case of countries where this data is not available. Here we use data from the last available year. 13 countries for which we have both solar data and data on the cost of equity are missing from the IMF dataset. We replace these missing values with data from neighboring countries (e.g. Vietnam for Cambodia, and Jordan for the Palestinian Territories). This brings the total number of countries with data on the cost of debt to 188, 143 of which are also contained in the solar yield dataset of Breyer and Gerlach [4]. A full table containing data on all 143 countries analyzed by us can be found in the supplementary material (Table A.1). Using the lending interest rates, as recommended by the CDM Executive Board in cases where no project-specific data is available, has some limitations that should be acknowledged. First, the lending interest rates collected by the IMF are for loans to prime customers, which are likely to be different from the interest rates that banks would offer to a renewable energy project. Typically, a project finance or consumer loan is prone to have a higher interest rate than the reported prime lending rate, due to the higher risk associated with such a loan compared to lending to a prime customer. However, no similar dataset exists for project finance or consumer loans. On the other hand, some of the rates reported by the IMF appear rather high compared to the rates reported for solar PV projects in the literature (e.g. Ref. [4]). For example, the calculated WACC for Germany is 9.2% based on the above methodology, whereas anecdotal evidence suggests that it is actually in the range of 4e6%, due to lowinterest loans of the German development bank KfW and low opportunity costs of many private investors (see, e.g., [39]). Second, the prime lending rate does not reflect different perceptions regarding the technology risk associated with solar PV and, more generally, the familiarity of lenders with project finance and renewable energies. Therefore, lenders will, as a matter of principle, regard loans to solar PV projects very differently from those to their best customers, which most likely results in higher interest rates. However, we assume that this effect will be decidedly more pronounced in emerging solar energy markets, whereas banks in established solar PV markets will already be familiar with

the technology. This suggests that the interest rates for solar PV projects are going to be relatively higher in emerging solar PV markets, and relatively lower in established markets. As such, our results constitute lower bound estimates in the case of emerging markets. Lastly, readers should note that the interest rate is only one financing term, and the loan tenor (i.e. duration of the loan) and other terms are also very important. As loan maturities and other financing terms are generally more restrictive in lessdeveloped financial markets (and in cases where banks are less familiar with a new technology or industry), this suggests that our estimated cost of capital in emerging solar markets is generally underestimated compared to developed PV markets. There are many reasons why financing costs may differ between countries despite globally integrated financial markets and globally operating multinational banks. However, it is beyond the scope of this paper to investigate this aspect further, so readers are recommended to turn to the relevant literature (e.g. Refs. [40,41]). As Table 2 indicates, we do indeed observe a very heterogeneous set of financing costs across our sample. The table shows the five countries with the highest and lowest WACC, respectively, as well as the median WACC of our sample. Readers should note that countries with particularly high capital costs are all located near the equator, where solar potential generally is highest, while financing costs tend to be much lower in developed, i.e. OECD countries. The factor between the country with the lowest WACC (Japan) and the highest WACC (Zimbabwe) is nearly 70, or 8.8 if the extreme outlier Zimbabwe is excluded. This divergence in country WACCs by far exceeds the difference in countries' solar resources of maximum 2.3, as mentioned before, which further suggests that differences in WACC between countries may have a much larger effect on LCOE than differences in their GHI. 4. Results In order to shed more light on the importance of financing costs for the economics of solar PV, we have calculated the LCOE for solar PV power for the 143 countries in our sample, as well as the average difference between this LCOE and the retail price of electricity. Our results suggest that the most attractive countries for continued investment are those with well-developed financial sectors, rather than those with the strongest sunshine. Hence, instead of being highly inefficient, the current pattern of PV investment may be a sensible one for bringing this technology into the mainstream. Indeed, we find a high correlation between the country-level WACC and LCOE for solar PV (0.82), which implies that the LCOE variation we observe is to a large extent determined by the cost of capital. On the other hand, we find no strong correlation between the level of sunshine and the country-level LCOE and GHI and the cost of capital. Fig. 3 shows a global map of calculated LCOE values, with alternative number scales corresponding to colors, reflecting the two sets of assumptions for endogenous investment costs mentioned in the previous section. It is important to note that, even though the choice of investment cost changes the resulting LCOE for individual countries, it does not change the general picture. Hence, the LCOE values we calculate in the (low) ‘German cost’ scenario indicate the potential cost of electricity, were each country to develop its PV market. Full results are listed in the supplementary material (Table A.2). Readers should also note that our results on a country-level are necessarily imprecise, given that we take a global perspective. Hence, our aim to investigate the global interplay of solar irradiation and financing costs necessitates a “broadbrush” approach on other variables, as explained in Section 3. As can be expected, our results therefore differ from those of other research efforts, such as the studies highlighted in Section 2.

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Fig. 3. Global map of LCOE based on population-weighted national average GHI and country-specific WACC. The left hand numerical scale reflects an assumption of countries achieving 2011 global average installation costs. The right hand numerical scale reflects an assumption of countries achieving late-2011 installation costs found in Germany. The rank ordering of countries remains constant across the two assumed sets of costs.

For instance, while our results suggest that the LCOE for solar PV in a developing country such as Kenya ranged from 28.9 to 50.9 USct/ kWh in 2011 (depending on the assumed investment cost), other researchers report values of 21.0 and 27.1 USct/kWh, respectively, based on a more in-depth analysis (cf. [12,18]). Given that both papers assume an investment cost closer to the German investment cost used in our alternative case, and given that Ondraczek [18] assumes a distinctly lower cost of capital for the purposes of his research, these papers nevertheless broadly confirm our own results. Bearing this caveat in mind, many of the countries where PV development has been quite rapid, including the United States, Spain, Italy, and China, appear as places where the LCOE is relatively low. Of all the countries in our sample, Spain has the lowest LCOE. A number of other medium to high income countries appear as good prospects for future PV development, including Mexico, Chile, South Africa, Thailand, Malaysia, Japan, Australia, and New Zealand, which all exhibit relatively low LCOE values. The Congo (DR) and Zimbabwe have by far the highest LCOE values, despite a good solar resource, and a large number of countries in South America, parts of Africa as well as the Former Soviet Union likewise have high LCOE values. One country that stands out is Germany, with calculated LCOE values appearing quite high. Driving this result is a WACC valued9.2%dthat is high by European standards. However, actual German PV costs are substantially lower than those that we have calculated. Due to long experience with its feed-in tariff, investments in German PV are seen as so secure that project developers are able to attract investment finance at low interest rates, thus leading to lower WACC values than those generated by our global LCOE model (see previous section). In our opinion, Germany is a special case in this regard as the difference between the calculated WACC and observed financing costs is likely to be more extreme here than in any other country due to the special circumstances of the German market. Absolute LCOE values may be of concern, but of greater importance to policymakers could be the magnitude of the difference between the cost of solar PV and the electricity tariff that needs to be bridged in one way or another to stimulate new investment. We thus map the difference between the LCOE of solar PV and countrylevel residential electricity prices in Fig. 4. Again, all values are listed in the supplementary material (Table A.2). In line with the results in Fig. 3, our results indicate that the differences between the solar PV LCOE and grid prices are typically lower in more developed countries and regions, such as North America, parts of Europe and Japan. As the (implicit) subsidies required for PV are a

function of both the LCOE and the level of electricity prices, they may be especially high in those countries that artificially lower the cost of power from fossil fuel sources, a practice not uncommon in developing countries in order to make electricity more affordable to poor and middle class households [12]. Given either scenario of installation costs, there is little correspondence between the most attractive countries for the adoption of solar PV and a country's proximity to the equator. Given a scenario of German installation costs (Fig. 4b), there are ten countries for which we calculate a negative subsidy requirement, meaning that PV could already be less expensive than electricity from the grid. Eight of these are in Europe, the other two being Japan and Grenada. Table 2 presents the five highest and lowest values for GHI, WACC, LCOE, and the difference between the LCOE and retail electricity prices. The table illustrates the wide variation between countries on all four measures: Whereas the solar irradiance varies by a factor of 2.3, the weighted average cost of capital varies by a factor of 8.8 between northern countries and equatorial countries (excluding Zimbabwe) and the LCOE varies by a factor of 4.2 (assuming global average installation costs), again excluding Zimbabwe. Taking into account actual grid prices and the calculated LCOE values also results in a very wide divergence in the differences between these, with a factor of 18.1 between the lowest and highest country (based on global investment costs). In order investigate the robustness of our above results we have further undertaken a number of sensitivity tests for the main financing assumptions. First, we change the assumed share of debt in Non-Annex 1 countries from the 50% recommended by the CDM Executive Board to a more conservative 30% or even 0% (meaning that the project is funded entirely through equity), and to a more aggressive 70% gearing level (similar to Annex 1 countries). This leads to changes in the average WACC of up to 20% (depending on the scenario), and to changes in the average LCOE of up to 16% (again depending on the scenario), across our sample of 122 NonAnnex 1 countries (although results for individual countries change a lot more, of course).5 This suggests that our overall results are sufficiently robust independent of the assumed debt portion.

5 At first sight counter-intuitively, we find that a reduction in the debt share leads to lower rather than higher average WACC and LCOE figures, while an increase in the debt share raises the average WACC and LCOE. This “negative leverage effect” stems from the fact that the reported cost of debt is higher than the cost of equity for many Non-Annex 1 countries (see Table A.1 in the supplementary material). Where this is the case, project investors candin realitydbe expected to use more equity rather than debt, in order to lower their overall cost of capital.

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Fig. 4. Global maps of difference between LCOE and retail electricity prices, assuming global average investment costs (Map a) and German investment costs (Map b). Calculated values reflect local values for GHI, WACC and residential electricity rates.

Second, two other variables were identified by Ondraczek [18], among others, as being particularly important for the model's sensitivity: the plant location (which determines the level of solar irradiance) and the total investment cost. We deal with the former explicitly by using country-specific GHI values and with the latter by using either German or global investment costs in our LCOE calculations (see Sections 3.1 and 3.2, respectively). In contrast, varying other input assumptions, such as the project lifetime and operating costs, will not change the LCOE modeling results substantially (see Ref. [18], for a detailed discussion). 5. Discussion Continued growth in the solar PV market and subsequent cost decreases through economies of scale and technological progress will expand the number of countries where the technology is costcompetitive. It may also stimulate improvements in complementary technologies, such as smart grids or electricity storage. Our results suggest that from a global economic perspective, it may make sense for the current geographic pattern of growth to continue, with growth largely concentrated in countries with low interest rates, rather than those with particularly sunny skies. In the long term, there may be important spillover benefits for renewable energy associated with more general efforts to improve access to investment capital in many developing countries, such as through improved efficiency in financial markets. Our results lead us to two important conclusions. First, contrary to what many researchers and critics of solar PV subsidies assume, it may have been reasonable from the point of view of costs and benefits to adopt PV first in northern countries. Although there was no global assessment of, and subsequent global decision on, the

“right” places for the initial adoption of solar PV, the actual adoption pattern that emerged through deliberate policy measures (such as Germany's generous solar subsidies) appears justified given the large variation in LCOEs across countries (especially at a time when technology costs were higher than they are today). This variation is caused less by the solar radiation than by the lower WACC, particularly in OECD countries. Our results suggest that the lower WACC in these countries influenced the LCOE more than the higher solar radiation in southern countries, while investment costs were generally lower in northern countries, with more efficient markets for solar energy technologies, too. Second, from the perspective of resource and global efficiency, it would make sense to exploit solar energy in “Sunbelt” countries rather than in the “North” only if similar investment and capital costs were available in both world regions, which our data suggest they are not. These results shed light on the merits of past policy initiatives on the national level, and they also carry implications for future policy decisions. With respect to the past, they indicate that development of PV, at a time when it required substantial subsidies, may not have been misplaced geographically. This conclusion contradicts the results provided by other scientific research. For example, Ummel [9] suggests that solar resources should be exploited first in places with high solar irradiance such as the American southwest, the Tibetan Plateau, the Sahel, and in the Middle East, in order to provide 2000 TWh of solar power, or 7% of total global consumption, by 2020. Other researchers, such as Breyer and Gerlach [4] identified the major relationship between solar irradiation and the cost of solar electricity. While appropriate in principle, we have found in this paper that the WACC influences the cost of solar PV more strongly than solar irradiation doesdan aspect that is often overlooked in global assessments.

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Indeed, both the LCOE of PV and the difference to the grid price appear to be more sensitive to differences in the cost of capital than they are to differences in the quality of the solar resource. The existing studies on individual countries, such as Kenya, suggest that the sensitivity to the cost of capital or the discount rate is around 0.6. This means that every 10% change in the discount rate leads to a change of around 6% in the LCOE [18]. However, as the overall variation between countries is larger for the cost of capital than for the solar irradiance, the former effect outweighs the latter one. Granted, the money spent to raise capital to finance PV construction does not vanish; rather, it flows into the hands of financial institutions and investors. In countries where the cost of capital is higher, there would be a greater transfer of wealth from public funds or electricity rate payersdwhoever is bearing the cost of the subsidydto these actors, many of which operate globally. Our results and conclusion come with one important caveat: As we point out in earlier sections, our work relies on data that aredby all meansdfar from perfect. For example, we find that a reduction in the debt share in Non-Annex 1 countries leads to lower rather than higher average WACC and LCOE figures, while an increase in the debt share raises the average WACC and LCOE. This “negative leverage effect” results from the fact that the reported cost of debt we use in our calculations is actually higher than the cost of equity for many Non-Annex 1 countries. While we are unable to ascertain whether in some countries this may really be the case, it certainly runs counter to finance theory; and project investors candin realitydbe expected to use in this case more equity rather than debt, in order to lower their overall cost of capital. However, we suspect that the true reason for the apparent inversion between the cost of equity and debt lies in the inadequacy of the equity cost data provided by the Clean Development Mechanism's Executive Board that we use for estimating the cost of equity (something that Schmidt [19] has also pointed out). With respect to the future, our results indicate that the expansion of PV into less well-developed markets may be especially sensitive to efforts to make low cost capital more readily available. Previous results, assuming uniformly low costs of capital, have indeed suggested that PV has already attained or is near attaining grid parity in many developing countries [4,10]. Since our results show that grid parity is not yet widespread, they suggest that policies to make affordable, long-term capital for PV investment more widely available could take the place of PV subsidies as a means of stimulating technology adoption. Moreover, developing efficient solar markets in countries with a good solar-endowment might lower overall investment costs in these countries to levels seen in the most advanced solar PV markets. While these steps would further improve the economics of solar energy specifically in those countries that from a resourcesperspective are best placed to move towards the widespread adoption of carbon-free solar PV, they may be difficult to attain in the short term. In this regard, our research complements the work of others who focus on smaller sets of countries, by highlighting the global variation in the cost of finance for renewable energy investments, such as solar PV, and its important role in determining the cost of this potentially important mitigation option. While our research focuses on solar PV, where the observations we make are more clear-cut than for other technologies, our findings are probably transferable to other climate-mitigation technologies, too. Most importantly, we expect that what we find for solar PV will also hold (more or less, at least) for other renewable energy technologies, such as wind, that also exhibit high upfront costs and are thus adversely impacted by high financing costs. In this sense, our results underline the importance of de-risking investments into climate change mitigation technologies, particularly in those regions of the world where there is huge untapped resource potential. However, as this de-risking itself entails costs,

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policymakers are well-advised to critically analyze technology potentials first, and establish whether the underlying economics of these mitigation technologies, in the context of a given country, justify a policy intervention. Thus, it needs to be determined if it is only the cost of finance, and the level of risk, that renders a specific technology, such as solar PV, uneconomic, or whether even allowing for a lower risk (and hence cost of capital) other options exist that help meet a certain policy goal (e.g. reduced CO2 emissions or increased electricity generation) at lower overall cost. In the case of solar PV, many studiesdsome of which we refer to in Section 2dsuggest that this technology may indeed be increasingly becoming such a preferred technology option, in more and more countries and for more and more applications. What our results suggest, therefore, is the importance of devising policy instruments that effectively reduce perceived investment risks and make longterm finance more broadly available. While the specific nature of these policy measures will vary from country to country, the approaches developed by others within the debates on de-risking and renewable energy finance are certainly underpinned by our own results. Lastly, our work is a first attempt at quantifying the overall importance of global variations in the cost of finance for the economics of solar PV, and by extension other renewable energy technologies. As we have repeatedly pointed out, we have had to make several important assumptions in order to map the cost of solar PV globally, and we have attempted to be as explicit about the limitations of our methodology and data. Future research will hopefully be able to build upon our work, by using more accurate data for many of the parameters that we have used for our global model. Particularly, we would like to mention again the limitations of data for investment and operating costs, which to date exist only for a small number of countries and applications, and which we thus had to extrapolate. Likewise, accurate global data for all components of the cost of finance (debt and equity shares, cost of debt, cost of equity, loan terms etc.) is notoriously hard to come by, whereas in our experience the physical resource availability (i.e. solar irradiation) is reasonably well-understood. The timely collection of data on financing costs for low-carbon investments by an international organization, such as the IMF, IRENA or the UNEP Finance Initiative, might therefore greatly facilitate further research in this field and enable an even more substantive de-risking debate, while improving overall transparency. Acknowledgments We thank our anonymous reviewer, participants of the 20th Annual Conference of the European Association of Environmental and Resource Economists (Toulouse, 2013) and the 2nd International Symposium on Energy and Finance Issues (Paris, 2014) for their helpful comments. We are obliged to Jana Stoever for her critical review of an earlier version of our manuscript. Financial support by the European Research Council (Consolidator Grant 313533) and the National Member Organization of Germany to IIASA is gratefully acknowledged. The usual caveats apply. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.renene.2014.10.053. References [1] REN21. Renewables 2013. Global status report. Paris: REN21 Secretariat; 2013. Available from: http://www.ren21.net/ren21activities/globalstatusreport. aspx.

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