Future scenarios for the cost of capital of energy technologies linked to the Shared Socioeconomic Pathways

Future scenarios for the cost of capital of energy technologies linked to the Shared Socioeconomic Pathways | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF data-descriptor Future scenarios for the cost of capital of energy technologies linked to the Shared Socioeconomic Pathways Luke Hatton, Gbemi Oluleye, Florian Egli, Katharina Wildgruber, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9348818/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Scenario modelling plays an important role in providing technical insights for stakeholders on the implications of future policy, technological and socioeconomic changes on energy and climate systems. Despite its importance to the cost competitiveness of energy technologies, the cost of capital (CoC) has received limited treatment in scenario modelling, due to challenges accessing empirical data and a limited number of estimation methodologies. Here, we present a global dataset of CoC scenarios for energy projects, covering 188 countries from 2025 to 2100, linked directly to the five Shared Socioeconomic Pathways. We estimate the CoC for five technology maturity levels, defined using the IEA’s extended Technology Readiness Level benchmarks, enabling stakeholders to explore the effects of technological development on the CoC. To provide a wide basis for modelling efforts, we also incorporate the effects of supportive policy environments on financing conditions. Uncertainty is treated through providing upper and lower bound estimates alongside a central case, based on historical ranges. By addressing a substantial data gap, this dataset will enable more accurate technical insights from energy and climate scenario modelling efforts, providing an avenue to explore how the CoC affects the cost and effectiveness of mitigation pathways. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background & Summary Limiting the global mean temperature rise above pre-industrial levels to “well below 2C” to meet the goals of the Paris Agreement will require energy transitions across every sector of the global economy 1 . Scenario modelling is a key approach used in industry, policy, and by other stakeholders to explore the implications of policy, technological and socioeconomic change on future emissions trajectories and the required scales of technology deployment & investment flows. Examples of key climate policy targets and initiatives that were informed by scenario modelling include tripling global renewable capacity by 2030 2,3 , doubling energy efficiency levels by 2030 4 and achieving net zero emissions by 2050 5 to limit warming to 1.5C or below. Some of the most sophisticated scenario modelling efforts include the use of integrated assessment models (IAMs) which link social, economic and climate system models 6 , 7 . Alignment on a standard modelling basis for scenario analysis in IAMs and other models is important to the integrated analysis of climate impacts, adaptation, mitigation and vulnerabilities, which led to the development of five Shared Socioeconomic Pathways (SSPs) by the modelling community 8 . These scenarios depict plausible global socioeconomic development trajectories that lead to differing challenges for mitigation and adaptation efforts, offering alternative narratives for development with associated uncertainties that provide a standard basis for modelling efforts. The SSPs play a central underpinning role in research collated by the Intergovernmental Panel on Climate Change 9 in its Assessment Reports - particularly Working Group III (Mitigation). Scenario modelling based on the SSPs will continue to play a key role in informing the upcoming 7th Assessment Report 10 and other future efforts, with the framework for the next generation of scenarios currently being debated and discussed. Existing work has already highlighted the importance of accurate assumptions on the cost of capital for scenario modelling 11 – 14 , including in IAMs and energy system models (ESMs). Due to the capital-intensive nature of many clean energy technologies, their economic feasibility and cost competitiveness is highly sensitive to the assumed cost of capital 12 , 15 , meaning that inaccurate or poorly justified assumptions can substantially bias results and technical insights 12 . The importance of the cost of capital to modelling results is compounded by the substantial challenges accessing empirical cost of capital data 16 , due to transactions typically taking place under project finance structures with limited publicly accessible data and stakeholders hesitant to share data on desired returns openly due to concerns over loss of competitiveness 16 , 17 . These empirical data gaps means that the use of standard assumptions is widespread (e.g., 5–10% globally 18 , with associated limitations 12 ), although modelling studies have begun to integrate country- and technology-specific costs of capital 19 , 20 , aided by the increased availability of estimation methods 16 , 21 – 23 . Existing empirical data from energy projects shows that the cost of capital can vary substantially between countries, across technologies and between time periods 24 , 25 , due to macroeconomic conditions and differences in project- and country-level risk factors 26 , 27 . Estimates show that the cost of capital could be to up to three or four times higher in low-income countries relative to high-income contexts 21 , with high costs of capital in developing countries driven largely by macroeconomic and political factors reflected in sovereign credit ratings 28 , 29 . Inaccurate modelling assumptions around the cost of capital can therefore substantially misrepresent financing conditions faced by energy project developers, highlighting the importance of ample treatment in scenario analysis and ESMs 13 . It is particularly important in developing country contexts, which face high costs of capital that are at least twice levels in advanced economies 21 , 27 and so require de-risking and other interventions to scale energy transition efforts 20 . Modelling efforts have shown that the trajectory of mitigation pathways is heavily dependent on assumed scenarios for the cost of capital 20 , 30 , with high costs of capital posing challenges to the cost and equity of the global energy transition 15 , 31 , 32 . Given the importance of cost of capital to modelled mitigation pathways, sufficient treatment of future fluctuations is key to the accuracy and utility of energy & climate scenario modelling 13 . However, uncertainty around how to depict the future evolution(s) in underlying factors driving the cost of capital for energy projects – including the risk-free rate, country risk premium, and project- or market-level risk factors – pose challenges to the development of standardised cost of capital scenarios. Figure 1 illustrates how the cost of capital even for a given country and technology can change substantially year on year, due to macroeconomic (changes in the global risk-free rate) and national factors (e.g., changes in country-level risk premiums from up- or down-grades in sovereign credit ratings). Standardised scenarios for the cost of capital would therefore be a valuable basis and resource for modellers to avoid “reinventing the wheel” for each modelling project that would also aid the ease of inter-model and inter-study comparisons, similar to the economic growth and population change basic drivers that are included in the core SSPs 8 . Across technologies, the cost of capital can also vary substantially in line with maturity, national policy support and financier experience with the technology category 33 . For example, Fig. 2 shows how the hurdle rate (a minimum rate of return for project developers, for which the cost of capital is a lower bound) for selected energy technologies varies in the UK 34 , with the hurdle rate for a tidal range project 70% higher than for solar PV or onshore wind in real terms. These estimates also include the effects of existing government revenue support schemes, which can reduce the effective hurdle rate by an estimated 1.5–3% 34 . Depicting the differences in the cost of capital across energy technologies, as well as across countries and between time periods, is therefore essential to providing more accurate insights from scenario modelling to inform policy and commercial decisions. However, there has been limited treatment to date of future changes in the cost of capital across technologies under energy and climate modelling. For example, modelling conducted by the International Energy Agency in its Global Climate and Energy Model uses a standardised range of 4–9% in real terms for clean generation technologies 35 , whilst many other IAMs use a standard discount rate across all technologies and countries 20 . Limited inclusion of cost of capital scenarios in modelling is markedly different to deployment costs, which are typically taken at a country- or regional level with yearly specificity and/or are calculated endogenously within many existing IAMs and energy system models. Standard scenarios for the cost of capital for energy projects would greatly aid energy and climate scenario analysis, by providing a strong basis for modelling to be conducted on and enabling more accurate technical insights. Drawing on the examples of the SSPs and linking directly to their underlying narratives and GDP per capita projections, here we adapt an existing peer-reviewed and stakeholder verified model 20 – 22 that estimates the cost of debt, equity and overall cost of capital for 188 countries to provide cost of capital scenarios out to 2100. We selected the SSPs due to the important role they play in informing policy and research efforts around climate change adaptation and mitigation. We summarise the narratives for each of the scenarios (taken from Riahi et al. 8 ) and translate these into high-level implications for the cost of capital for energy technologies in Table 1 . Table 1 Summary of the SSP narratives and implications on the cost of capital for clean and fossil energy technologies, with scenario narratives for the SSPs taken from Riahi et al 8 . SSP Scenario narrative Implications for the cost of capital of energy projects 1: Sustainability The world shifts gradually, but pervasively, toward a more sustainable path, emphasizing more inclusive development that respects perceived environmental boundaries. Management of the global commons slowly improves, educational and health investments accelerate the demographic transition, and the emphasis on economic growth shifts toward a broader emphasis on human well-being. Driven by an increasing commitment to achieving development goals, inequality is reduced both across and within countries. Consumption is oriented toward low material growth and lower resource and energy intensity. Country risk premiums in developing countries fall as development goals are achieved. 2: Middle of the Road The world follows a path in which social, economic, and technological trends do not shift markedly from historical patterns. Development and income growth proceeds unevenly, with some countries making relatively good progress while others fall short of expectations. Global and national institutions work toward but make slow progress in achieving sustainable development goals. Environmental systems experience degradation, although there are some improvements and overall the intensity of resource and energy use declines. Global population growth is moderate and levels off in the second half of the century. Income inequality persists or improves only slowly and challenges to reducing vulnerability to societal and environmental changes remain Country risk premiums in developing countries fall, albeit slower than SSP1, as development proceeds unevenly. 3: Regional Rivalry A resurgent nationalism, concerns about competitiveness and security, and regional conflicts push countries to increasingly focus on domestic or, at most, regional issues. Policies shift over time to become increasingly oriented toward national and regional security issues. Countries focus on achieving energy and food security goals within their own regions at the expense of broader-based development. Investments in education and technological development decline. Economic development is slow, consumption is material-intensive, and inequalities persist or worsen over time. Population growth is low in industrialized and high in developing countries. A low international priority for addressing environmental concerns leads to strong environmental degradation in some regions. Country risk premiums in developing countries fall slowly, with limited economic development and high inequalities. 4: Inequality Highly unequal investments in human capital, combined with increasing disparities in economic opportunity and political power, lead to increasing inequalities and stratification both across and within countries. Overtime, a gap widens between an internationally connected society that contributes to knowledge- and capital-intensive sectors of the global economy, and a fragmented collection of lower-income, poorly educated societies that work in a labour intensive, low-tech economy. Social cohesion degrades and conflict and unrest become increasingly common. Technology development is high in the high-tech economy and sectors. The globally connected energy sector diversifies, with investments in both carbon-intensive fuels like coal and unconventional oil, but also low-carbon energy sources. Environmental policies focus on local issues around middle- and high-income areas. Country risk premiums in developing countries fall inconsistently, with inequality between regions rising. 5: Fossil-fuelled Development This world places increasing faith in competitive markets, innovation, and participatory societies to produce rapid technological progress and development of human capital as the path to sustainable development. Global markets are increasingly integrated. There are also strong investments in health, education, and institutions to enhance human and social capital. At the same time, the push for economic and social development is coupled with the exploitation of abundant fossil fuel resources and the adoption of resource and energy intensive lifestyles around the world. All these factors lead to rapid growth of the global economy, while global population peaks and declines in the 21st century. Local environmental problems like air pollution are successfully managed. There is faith in the ability to effectively manage social and ecological systems, including by geo-engineering if necessary Country risk premiums in developing countries fall, as the integration of global markets and investments in human and social capital drive development. To reflect the differences in the cost of capital across technology categories, we develop estimates for a range of technology maturity categories, grouped into five “buckets” as demonstrated in Fig. 3 (selected on the basis of the existing spreads of energy technology costs of capital, see Methods ). As technology-specific risk premiums also depend on policy interventions and support schemes – which can range across countries and between technologies - we also include a quantification of the effects on the cost of capital. We define “Strong” policy support to include the presence of revenue stabilisation (e.g., Contracts for Difference) or other fiscal support measures, whilst “Weak” reflects full exposure to merchant risk. These dimensions for cost of capital scenarios enable modellers to integrate data directly into models with direct links to technology maturity and deployment, maximising its utility and applicability. Inclusion of the effect of policy support – which can bring the cost of debt down by 2% 34 - also allows for modellers to explore different scenarios including both the phase out and rollback of policy support over time. Estimates presented in this dataset do not depict the full range of risk factors that contribute to the energy project’s cost of capital, including those associated with market arrangements, currency dynamics and project-specific contexts (e.g., off taker risks). These factors were not included in the modelled results due to their strong dependence on factors to external to the underlying drivers contained in the SSPs, which would introduce additional assumptions and related limitations to the cost of capital scenarios developed here. We note that the approach of risk disaggregation means that other risk factors can be added easily on to the estimates presented here, enabling a wider range of use cases including accounting for project-level risks or other factors (e.g., currency risk). We extend the country coverage from 176 to include data on the 188 countries with GDP per capita projections under the SSPs, setting unrated countries to a Moody’s rating of Ca (a notch above default). Historical ranges in key underlying drivers of the cost of capital over the last ten years are integrated to give an uncertainty bound for future estimates, with data also provided at a yearly resolution for maximum utility. Given that many IAMs aggregate countries into larger regions, we also provide regional GDP-weighted benchmarks to maximise utility and the ease of integrating estimates into future modelling studies. Our scenario estimates demonstrate a substantial difference in the overall cost of capital a) between countries and b) across the five SSP scenarios, highlighting the importance of accounting for future changes in the cost of capital when conducting scenario modelling. Figure 4 shows the geographic distribution of the estimated cost of capital across SSPs for 2030, 2050 and 2100, with estimates shown for Mature technologies under Strong policy support. The median difference for a country across the five SSP scenarios in 2100 is 2.47% (ranging from zero to 6.73% across countries), largest for EMDEs where economic growth and subsequent reductions in country risk premiums differ most across SSPs. Substantial disparities across countries are also present in all scenarios out to 2050, particularly for SSP2-4 where regional inequalities and unequal economic growth deliver limited reductions in the country risk premium across developing countries and persist for some of the least developed countries out to 2100. Whilst SSP1 and SSP5 show more widespread reductions (particularly SSP1) enabled by wider economic growth, disparities between developing regions and advanced economies still persist in 2100. Regional and income-based disparities remain present across the future SSP-linked cost of capital scenarios, even under those with wider economic growth and sharp reductions in inequality. Figure 5 presents boxplots of the cost of capital on a regional basis across SSP1-5 for 2050 and 2100 for Mature technologies with Strong policy support (visualisations for all combinations are included in the Supplementary Material). Regional and income-based disparities remain substantial even by 2100, particularly for countries and regions rated as high risk under current sovereign credit ratings in 2025 and for SSPs 2–4. In the former, the median cost of capital for low-income countries is around double the median in high-income countries in 2100, whilst for SSP3 this rises to triple the lowest levels in high-income countries. Even in SSP1 - which sees the largest reduction in economic inequality between countries rated as advanced economies and EMDEs in 2025 – a substantial cost of capital differential remains between country groupings. Africa faces the highest median costs of capital across in all SSPs, reflecting disparities in economic development and the high Africa risk premium inherent in current sovereign credit ratings that has been discussed in other work 36 , 37 . Median costs of capital in Africa reach a low of 10.5% in 2100 (SSP1) and high of 13% (SSP4), still over double levels in high income countries in 2100 across all SSPs. Regional disparities in the costs of capital are largely a consequence of the differences in country risk premiums in EMDEs and advanced economies, noted by other work, that do not fully close over any of the SSP scenarios. Figure 6 presents the GDP-weighted mean cost of capital for developing countries and advanced economies across technology maturity ranges with Strong policy support, based on their categorisation in 2025 (see Supplementary Data for the full list). Disparities between EMDEs and advanced economies decrease most notably in SSP1, SSP2 and SSP5, with substantial differences remaining in SSP4 and SSP3 particularly under high inequalities and/or regional rivalries. For SSP3, there is an increase in the GDP-weighted mean cost of capital in countries categorised as EMDEs due to the pace of economic growth in GDP per capita terms in LMIC and LICs exceeding UMICs starting in 2035, due to the slowdown in economic and population growth in industrialised countries. The GDP-weighted mean cost of capital across EMDEs rises as LMICs and LICs — which have higher costs of capital — account for a growing share of total EMDEs output relative to UMICs, even as all individual countries within those income groups see their cost of capital and country default spread decline under increases in GDP per capita under SSP3. Technology maturity can also have a substantial impact on the cost of capital, as we show through Fig. 6 . Between Mature technologies and First of a Kind (FOAK), there is a premium of approximately 4% which reflects the additional risks associated with deploying early-stage projects for which there has been no deployment at a commercial scale. Across the other technology maturity categories (Early Commercial, Scaling and Commercial) the overall cost of capital falls as maturity rises, with a substantial gap between advanced economies and EMDEs that does not close even in SSPs with widespread growth (SSP1/5). Whilst technology maturity is modelled here in discrete bins which are related to the IEA’s TRL scale, we note that the combination of technology maturity and developer/financier experience with deploying technologies make it a continuous scale in practice. Scenario analysis should always include ample treatment of uncertainty, which we include directly within the modelled cost of capital scenarios by identifying the major uncertainties in the underlying input parameters for the methodology (risk-free rate and equity risk premium, see Methods and Technical Validation ). These parameters are held constant at 10-year averages for the central scenario estimates but the impact that they have on the overall cost of capital is included through a lower bound and upper bound, which takes the historical maximum and minimum of the relevant variables (risk-free rate and equity risk premium). Figure 7 shows the range of cost of capital above and below the central (i.e., average) scenario results, for Mature projects in EMDEs and advanced economies with Strong policy support. Notably, we hold the risk-free rate constant in all scenarios, rather than developing individual scenarios for how global base rates are likely change due to the complexity and uncertainty over their evolution, though we note that the risk disaggregation approach allows for easy modification of assumptions for individual variables such as the risk-free rate. Methods The methodology used to develop this dataset is based on a growing body of literature on cost of capital estimation methodologies, developed by IRENA 22 , used in Calcaterra et al 20 and Hatton et al 21 and benchmarked against empirical data in Wildgruber et al. 23 . Here, we extend the estimation methodology here to include future scenario projections out to 2100 using GDP per capita scenarios taken from the five IPCC’s Shared Socioeconomic Pathways 8 at a national level for the 188 countries with estimates available. The cost of debt and cost of equity are estimated by disaggregating contributing risk factors at a country level into four key aspects: the risk-free rate, the country risk premium, and a technology risk premium, alongside either a lenders margin (debt) or equity risk premium (equity). Relevant uncertainties are quantified for both the cost of debt and cost of equity, based on historical ranges over the last ten years. Estimates for the overall debt share are then developed and combined with current data on corporate tax rates in individual countries, which we hold constant across the whole time period due to the lack of a standard basis to project or develop scenarios for future changes in national corporate tax rates. Capital Structure and Overall Cost of Capital The overall cost of capital (WACC) for a given country (c), year (t) and technology (x) is evaluated from the cost of debt ( \(\:{C}_{D}^{c,t,x}\) ) and cost of equity ( \(\:{C}_{e}^{c,t,x}\) ) using Equation [1]. Interest repayments are tax deductible in the majority of jurisdictions, so the “tax shield” was modelled using corporate tax rates ( \(\:{\tau\:}^{c}\) ) taken from the Tax Foundation 38 that were held constant from 2025 levels. Assumptions are developed for the debt share ( \(\:{R}_{D}^{c}\) ) at a country level, which was assumed to vary inversely in a linear fashion with country ratings-based default spreads (see below) between 40–80% using the same approach in Hatton et al 21 . These boundaries were based on reporting of empirical financing terms for energy projects taken from the Diacore 39 and AURES-II projects 24 , with the debt share then calculated using Equation [2]. \(\:{WACC}^{c,t,x}={R}_{D}^{c}{C}_{D}^{c,t,x}(1-{\tau\:}^{c})+(1-{R}_{D}^{c}){C}_{E}^{c,t,x}\) [1] \(\:{R}_{D}^{c,t}=0.8-0.4\:\left(\frac{{r}_{cds}^{c,t}}{{r}_{cds,\text{max}in\:t}^{c,t}}\right)\) [2] Data reported here is all in nominal terms to provide maximum usability. All estimates are also evaluated at commercial rates, assuming investment from international banks or international financial institutions, given that they currently play a key role in financing energy and infrastructure projects globally 40 . The limitations of these assumptions and how the cost of other financiers can be depicted using the dataset and methodology presented here are discussed in the Technical Validation section. Evolution of country default spreads and risk premiums Country risk factors typically contribute to the largest portion of the cost of capital in EMDEs 21 , 28 . Due to the sovereign ceiling effect 41 , country risk is derived by investors from sovereign credit ratings produced by the “Big Three” credit rating agencies (Moody’s, Fitch and S&P) and is accounted for here through the “ratings-based country default spread” derived directly from the sovereign credit rating. The country default spread represents the additional yield (spread) that investors demand due to macroeconomic and political risk factors (e.g., corruption, macroeconomic instability, political uncertainty). For equity investors, the country risk premium demanded is 1.35 times higher than the country default spread, reflecting the additional risk that they take on as equity investors which is derived from an equity-to-bond ratio 42 . Assessing how sovereign credit ratings are likely to change under the evolution of GDP capita in each of SSP scenarios is therefore central to understanding future changes in the cost of debt, equity, and the overall cost of capital. Methodologies used to develop sovereign credit ratings vary subtly between the “Big Three” but a strong body of existing literature has shown the disproportionate effect that GDP per capita has on affecting credit ratings from all three 29 , 43 . Here, we evaluate the country default spread by analysing the influence that GDP per capita has on historical country default spreads over the last 25 years, which is then combined with GDP per capita projections under the SSPs to determine estimates of how country default spreads and risk premiums will change (with the assumption that all other conditions are held constant). \(\:{CDS}_{c,t}={\beta\:}_{0}\:\text{}+{\beta\:}_{1}\:\text{l}\text{o}\text{g}\left({\text{G}\text{D}\text{P}\text{p}c}_{c,t-1}\right)+{\beta\:}_{2}\:\bullet\:{\text{I}\text{n}\text{f}\text{l}\text{a}\text{t}\text{i}\text{o}\text{n}}_{c,\:\:t-1}+{\beta\:}_{3}\:\bullet\:{\text{D}\text{e}\text{f}\text{i}\text{c}\text{i}\text{t}}_{c,\:\:t-1}+{\beta\:}_{4}\bullet\:\:{\text{D}\text{e}\text{b}\text{t}}_{c,\:\:t-1}{+\:\beta\:}_{5}\bullet\:\:{\text{G}\text{o}\text{v}.\:\text{P}\text{r}\text{i}\text{m}\text{a}\text{r}\text{y}\:\text{B}\text{a}\text{l}\text{a}\text{n}\text{c}\text{e}}_{c,\:\:t-1}\:{+\alpha\:}_{c}\:+{\gamma\:\:}_{t}+{\epsilon\:}_{c,t}\:\) [3] Using the last 25 years of reported data on country default spreads and country risk premiums from Damodaran 42 , here we use a linear panel regression incorporating fixed effects to extract the relationship with GDP per capita as shown in Equation [3],. Drawing on existing literature, we also use inflation, current account deficit, debt to GDP, debt service ratio and government primary balance as variables in the regression, given that they are parameters highlighted as directly informing the Big Three’s credit rating decisions, including country ( \(\:{\alpha\:}_{c}\) ) and time ( \(\:{\gamma\:\:}_{t}\) ) fixed effects and where \(\:{\epsilon\:}_{c,t}\:\) is the error term for each country (c) and year (t). We also choose to assess the impacts of including the previous year’s CDS for each country to account for the “stickiness” of sovereign credit ratings and other institutional factors not directly included in our regression, in line with other work 19 . Table 2 Results of the linear regression applied to the country default spreads reported by Damodaran between 2000–2025 Variable (1) (2) (3) (4) (5) (6) (7) Simple With FE Full: Main Lagged + Full Log GDP 0.1543*** -1.9729*** -1.8158* -1.9033* -1.1254* -1.1403* -0.34181 (0.0197) (0.8095) (0.7378) (0.7880) (0.5744) (0.5807) (0.2016) Inflation 0.0517*** 0.0516*** 0.0429** 0.0450** 0.0203*** (0.0085) (0.0086) (0.0086) (0.0097) (0.0043) Public Deficit 0.0118 0.0245* -0.0717 -0.0069 (0.0170) (0.0127) (0.0763) (0.0406) Public Debt 0.0302*** 0.0269*** 0.0105*** (0.0053) (0.0063) (0.0025) Public P.B 0.01006 0.0295 (0.0710) (0.0385) Lagged CDS 0.6727*** (0.0500) Country FE No Yes Yes Yes Yes Yes Yes Year FE No Yes Yes Yes Yes Yes Yes Lagged CDS No No No No No No Yes Observations 2250 2250 2250 2250 2250 2250 2225 N entities 118 118 118 118 118 118 118 N time periods 25 25 25 25 25 25 25 R² -0.36595 0.79803 0.81228 0.81255 0.84000 0.84031 0.90114 Within R² -0.00217 0.24650 -0.08887 -0.11918 0.30411 0.28866 0.60119 RMSE 3.49519 1.314398 1.29570 1.29479 1.19621 1.179508 0.94122 Clustered (Country) standard errors in parentheses Signif. Codes: ***: p < 0.001, **: p < 0.01, *: p < 0.05, .: p < 0.1 The results of the regression are shown in Table 2 . Confirming results from other work, we show that the log of GDP per capita has a statistically significant effect on the country default spread (i.e., on the sovereign credit rating), justifying the use of the methodology here based on GDP per capita scenario projections. The coefficient from the regression \(\:{\beta\:\:}_{1}\) for log GDP per capita is -1.1403 (see Table 2 for the full results of the linear regression), with inflation and public debt levels also showing strong statistically significant effects on the country default spread. Assessment of the effect of including the lagged CDS formulation shows that it increases the fit of the regression, from an R 2 of 0.84 to 0.9 between (6) and (7). Despite this fact, we chose not to include it in the main formulation as (i) it strengthens the path dependency on the current CDS levels, which could bias scenarios under national macroeconomic contexts in 2025 (ii) similarly, for unrated countries set to Ca in the modelling it locks in much higher CDS values across a longer period and (iii) it also weakens the relationship between GDP per capita and the CDS by 70%, providing more limited national differences across the SSPs at a national scale for use in scenario analysis. Our coefficient for log GDP is lower than Waidelich et al 19 due to the inclusions of these factors, as well as not including the lagged CDS into the main formulation. GDP per capita scenarios under the five SSPs at a national scale were used with the regression relationships to evaluate future country risk premia and default spreads, using Equation [4]. This is derived directly from Equation [3] under the assumption that all other parameters included in the regression are held constant at 2025 levels, due to these factors not been included into the SSP narratives due to their specificity. \(\:{r}_{cds}^{c,t}={r}_{cds}^{c,2025}+\:{\beta\:\:}_{1}\bullet\:\:\text{log}\left(\frac{{GDPpc}_{2025+t}}{{GDPpc}_{2025}}\right)\:\:\:\:\) [4] Cost of debt Equation [4] was used to evaluate the cost of debt ( \(\:{C}_{D}^{c,t})\) for a given country (c), year (t) and technology category (x), through risk disaggregation. The risk-free rate ( \(\:{r}_{risk-free}\) ) was set to the average yield on a 10-year U.S. Treasury Bond between 2015 and 2025 (2.5%) – which is typically used as a risk-free rate proxy in financial analysis - with corresponding uncertainty bounds based on the minimum and maximum monthly average values period (an upper bound of 4.8% in October 2023 and lower bound of 0.6% from July 2020). As discussed above, the country default spread ( \(\:{r}_{cds}^{c}\) ) was projected forward based on the computed evolution of GDP per capita across the five SSPs scenarios and historical relationships extracted between GDP per capita and country default spreads. The lenders margin ( \(\:{r}_{debt}^{c}\) ) was calculated using a direct linear interpolation between 1.5–2.5% in relation to the country risk premium (i.e., if the country risk premium is halfway between 0% and the yearly maximum, the lenders margin is 2.0%). The 1.5–2.5% range was based on AEW’s tracking of the terms of project finance infrastructure loans 44 , which finds that the average debt margin is 1.9% for Europe and 2.3% in the rest of the world. It was extended to account for the fact that the reported values are averages, with lower risk countries & projects in Europe likely to have a smaller margins and vice versa for high-risk countries elsewhere. As the underlying loan database is likely skewed toward high-income countries, the upper end of this range is also likely conservative for high-risk countries. \(\:{C}_{D}^{c,t}={r}_{risk-free}^{t}+{r}_{cds}^{c,t}+{r}_{debt}^{c,t}+{r}_{tech}^{c,t,x}+{r}_{policy-deficit}^{c,t}\) [5] Technology risk premiums ( \(\:{r}_{tech}^{c,t})\) have been evaluated by existing work, including for specific generation technologies such as wind and solar 21–2320–23 . Given the dependency on deployment rates, national policies and regulatory environments, this work chooses not to compute technology-specific rates. Instead, we produce estimates for five “buckets” of technology maturity (illustrated above in Fig. 3 ) broadly corresponding to the risk categories used by CEPA in their study of hurdle rates for electricity generation and storage technologies 34 . Namely, the categories are First-Of-A-Kind (FOAK, 1), Early Commercial (2), Scaling (3), Commercial (4) and Mature (5), which we map these onto the IEA’s extended Technology Readiness Levels scale (1: TRL8, 2: TRL9, 3: TRL9-10, 4: TRL10, 5: TRL11) as shown in Fig. 3 . As with existing work 21 – 23 , relative technology premium values are used to reflect the share of the absolute technology risk premium that is included within the lenders margin (100% in the case of the Mature category). The relative technology premium ( \(\:{r}_{tech}^{c,t}\) ) across categories 2–5 were taken range from 0–4.8%, inversely with maturity and with linear steps of 1.2%, based on hurdle rates reported by CEPA ranging between 7.6% to 12.9% 34 . We note that the hurdle rates are given in CEPA’s study in real, pre-tax terms, but that under the small long-term inflation target in the UK (2%) and the same corporate tax rate across technologies this only has a minor impact on the relative premium (+ 2% higher on a percentage basis for nominal as for real), which is ignored here as it is taken as negligible. The first maturity category (12.9% for tidal range and novel batteries) was estimated by adding 1.5% to the second maturity category, which we retain as the same premia between 1 and 2 for FOAK technology deployments. Finally, as in Fig. 3 we also include a policy maturity premium corresponding to the lack or rollback of policy support to aid modelling efforts, which we assume here to be 2% based on reporting by CEPA for the additional cost of debt for projects exposed to merchant risk without policy support 34 . “Strong” policy maturity is therefore defined as the presence of revenue stabilisation or other supportive policy environments, whilst “Weak” reflects full exposure to merchant risk with no supportive policy frameworks and a more limited regulatory environment. Cost of equity Equation [4] was used to evaluate the cost of equity ( \(\:{C}_{E}^{c,t})\) for a given country (c), year (t) and technology category (x), through the same risk disaggregation approach used for the cost of debt. The risk-free rate ( \(\:{r}_{risk-free}\) ) was also set to the average yield on a 10-year U.S. Treasury Bond between 2015 and 2025 (2.5%), with corresponding uncertainty bounds. Equity investors also take additional risks compared to other investors, with larger potential losses given that they are the last to be paid in the event of project failure or company bankruptcy. The additional premium required to account for the risks associated with equity investments ( \(\:{r}_{equity}\) ) was set as the 10-year average global equity risk premium computed by Damodaran between 2015–2025 based on financial market data (5.11%) 42 . The country risk premium ( \(\:{r}_{country}^{c}\) ) was evaluated by adjusting the projected country default spread under the five SSP scenarios to account for the additional country risk that equity investors take (i.e., multiplication by a factor of 1.35), as outlined above. Relative technology risk premiums ( \(\:{r}_{tech})\) were computed using the same approach outlined above for the cost of debt, in line with other work 21 – 23 . The absolute technology risk premium for Mature technologies was set to 1.5% based on reported spreads for renewables in developed markets (as in the cost of debt, a large portion of the technology premium is included in the lenders margin). The policy maturity premium was assumed to be 3% based against on reporting from CEPA on the additional equity return for projects exposed to merchant risk without policy support 34 . \(\:{C}_{E}^{c,t}={r}_{risk-free}^{t}+{r}_{country}^{c,t}+{r}_{equity}+{r}_{tech}^{c,t,x}+{r}_{policy-maturity}^{c,t}\) [6] Yearly fluctuations in the cost of capital Variables associated with the SSPs are produced at a 5-year resolution at a national level in their most recent update, taken from the OECD ENV-Growth model. To represent fluctuations within the 5-year periods, linear interpolations were applied to develop yearly estimates of the country risk premiums and country default spreads and linked variables (debt share, lenders margin). All other contributions to the cost of debt and cost of equity were taken as constant across the full period, with their impacts on the overall cost of capital included through the uncertainty analysis in the lower and upper bounds of the uncertainty analysis which uses the historical upper and lower bounds of (1) the risk-free rate and (2) equity risk premium over the last 10 years (see Technical Validation). Data Records All data is provided in Excel worksheets saved in CSV files for maximum usability, which are publicly accessible at https://zenodo.org/records/19449137 (DOI: 10.5281/zenodo.19449137 ). Data is provided in both a wide format (with separate rows for each technology category and country) and in a long format (with indexer columns for the year, country and technology category, with a value column for the cost of capital) to improve the ease of usability across the possible range of data analysis tools. Country codes in ISO3 formats are included in all sheets, as well as GDP (PPP) values to provide potential weights for aggregation for users (e.g., for energy system models where many countries are aggregated in single regions). Reported values are given in units of percentage points as nominal, post-tax terms (in line with other estimation approaches in the literature). Technical Validation Overview The methodology used here builds on existing approaches for cost of capital estimation 20 – 22 , which have been verified using expert surveys, stakeholder engagement and comparison to empirical data extracted from the literature. For example, the historical application of the model used here was benchmarked in Hatton et al 21 against empirical data from the IEA’s Cost of Capital Observatory 27 and other cost of capital data collated in the GNESTE database for key energy technologies 45 , whilst the model it was based on was verified with a wide number of stakeholders by IRENA 22 . The modelling approach was also tested against project-level data in Wildgruber et al. 23 Scenario projections for any technoeconomic parameter should not eschew ample treatment through uncertainty and sensitivity analysis, which could include emerging approaches such as robust decision making (RDM) 46 . To aid the analysis of uncertainty in the SSP-linked cost of capital scenarios developed by this work, we also provide an initial quantification of the uncertainty in the cost of capital derived from a) the risk-free rate and b) the equity risk premium. These parameters were selected as they are modelled in this work using historical averages, with uncertainties developed based on historical minimum and maximum for each parameter (see Methods above). The other parameters - country default spread and technology risk premium - are either derived directly from the GDP per capita scenario projections based on historical data (country default spread) or an initial assumption that we recommend is changed based on technology- and market-specific conditions (technology category risk premium) accordingly with modelling requirements. Debt shares and corporate tax rates for energy projects are also another source of uncertainty in the cost of capital estimates presented here, with existing data showing that the former can range substantially both a) between and b) within countries for the same technology and the same year. For example, other project- and market-level contextual factors such as construction risks, national regulation and technology-specific characteristics could increase or decrease the debt share for a given country, in addition to macroeconomic factors. Corporate tax rates may also be affected by market- or technology-specific fiscal incentives, which are not modelled here due to the lack of a unified dataset covering all countries modelled here. To address these uncertainties, the cost of debt and cost of equity are provided individually in the dataset for each year, SSP, country and technology category to enable users to define their own assumptions in line with the desired application. Central estimates of the cost of capital are also provided based on assumed debt shares and national corporate tax rates, as a base case for other users. Limitations The dataset developed here addresses a key data gap for modellers and other stakeholders, by developing cost of capital scenarios linked directly to the SSPs out to 2100 which include strong granularity on the technological maturity and the level of policy coherence and support. However, as with any forward-looking scenarios and estimates, there are limitations that must be considered when using the data in energy systems modelling or integrated assessment models (IAMs), especially given the sensitivity of models to the cost of capital 12 , 13 . Two broad limitations are inherent in the methodological approach: (1) the assumed additivity of contributing risk factors and 2) that project-level risk factors do not account for a substantial portion of risks relative to country- or technology- factors. These assumptions are necessary to generate sufficient data for scenario analysis but may introduce inaccuracies compared to how investors price their capital, which should be borne in mind when considering technical insights. Additional limitations are discussed in the following paragraphs, which can largely be addressed through tailoring the risk disaggregation approach to relevant application contexts e.g., through the addition or subtraction of additional factors such as currency risk. As in Hatton et al. 2025 21 and IRENA 2023 22 , estimates have been constructed from the perspective of an international investor providing their capital for investment through either debt or equity at commercial rates. The extent of domestic capital market development and exchange rate dynamics may affect whether the cost of capital from domestic commercial investors would be provided at a similar rate, whilst public investors (both domestic and international) are able to provide capital at concessional rates below commercial rates. In some of the least developed countries – where finance provided by international public financiers such as the World Bank or other MDBs plays a substantial role in infrastructure investments – a large proportion of projects may be able to access costs of capital substantially below the estimates provided here 30 . Exact terms will depend on how funding through concessional programs and institutions such as the World Bank’s International Development Association evolve in the future, which is far beyond the scope of the scenario analysis conducted here. Domestic public support, either through direct provision or sector-specific fiscal incentives, may also lead to lower costs of capital than are computed here, for example through concessional funding from national development banks or lower corporate tax rates provided for energy developers. Project- and technology-specific risk factors not included in the scope of the scenario analysis and may drive discrepancies between the estimates presented here and the future evolution of financing terms. For example, project-level data on renewable financing terms 24 , 39 shows that there are notable differences in the technology risk premium and overall cost of capital within a same country caused by differences in maturity, resource availability, construction risks and supporting policy and regulatory structures. Previous work has shown solar PV to have been regarded as lower risk than onshore wind in terms of risk premia 16 – though recent empirical analysis has shown the opposite with structurally lower WACCs for onshore wind than solar 23 , deviating from Steffen 2020 16 - whilst offshore wind has been shown to have an additional premium to onshore due to risks associated with scale, operation, and construction requirements 25 . Project- and financier-level contexts may also affect the cost of capital 47 , 48 e.g., supplier specific risks, differences in the risk appetite of investors and the overall competitiveness of the given project. These risks are not included in the data due to their specificity but could be quantified and added onto the estimates presented here, if required, under the risk disaggregation approach. Cost of capital estimates presented here also do not account for currency risk, which stem from depreciation of the currency of revenue against the financing currency. For many IAMs and energy system models, costs and prices are often dollar denominated to avoid complexity, with limited treatment of future inflation and currency exchange rate movements. We present the estimates here in nominal terms to avoid making assumptions over future inflation rates, which may be incompatible or different to assumptions made by modellers and other stakeholders. Relevant currency risks could be added onto the cost of debt or equity presented here, if required for a given application. The authors note that there has been limited treatment of exchange rate movements, currency depreciation and the implications for modelled mitigation pathways, despite the growing problem it presents in many developing countries. Future research should explore the implications of currency depreciation, its implications for currency risks and the impacts it has on the overall cost of capital. Declarations Usage Notes Readers can download the full dataset from the Zenodo repository found at: https://zenodo.org/records/19449137 (DOI:10.5281/zenodo.19449137 ) . Data availability Data has been stored in a Zenodo repository, organised into both long and wide CSV formats, which is accessible to the public at https://zenodo.org/records/19449137 (DOI:10.5281/zenodo.19449137 ) . Subsets of the dataset for given technology and policy combinations for the central estimates are provided in long format to aid ease of use. Data used for the regression can be found at Damodaran (https://pages.stern.nyu.edu/~adamodar/), World Bank (https://databank.worldbank.org/source/world-development-indicators) and the IMF World Economic Outlook database (https://www.imf.org/en/publications/weo/weo-database/2025/april). Code Availability The scripts for data processing and visualisation can be accessed in the GitHub repository, which can be found here: https://github.com/LukeHatton21/wacc-ssp-scenarios Funding L.H was funded under EPSRC EP/W524323/1 This work was partially funded by the Climate Compatible Growth (CCG) programme. CCG contributed to funding the time of some of the co-authors for the production of this material and publishing fees. CCG is funded by the Foreign, Commonwealth and Development Office (FCDO) from the UK government; however, the views expressed herein do not necessarily reflect the UK government’s official policies.The article processing charge was paid from the Imperial College London Open Access Fund Author statement Luke Hatton: Conceptualization, Methodology, Formal analysis, Data curation, Visualisation, Writing – Original draft preparation. Gbemi Oluleye: Supervision, Writing – Review & Editing. Adam Hawkes: Supervision,Writing – Review & Editing. Florian Egli : Writing – Review & Editing. Katharina Wildgruber: Writing – Review & Editing. Paul Waidelich: Writing – Review & Editing. Data statement and Availability The data and code for modelling and visualisation can be found at https://github.com/LukeHatton21/wacc-ssp-scenarios. Declaration of Competing Interest The authors report no direct competing interests. L.H currently works for the International Energy Agency as a consultant, including supporting their Cost of Capital Observatory, which was cited in the text. P.W. works for an economic consulting firm that, among other things, advises on cost of capital matters in the energy sector. References IPCC. Climate Change 2022: Mitigation of Climate Change . doi: 10.1017/9781009157926 (2022). BNEF. Tripling Global Renewables by 2030: Hard, Fast and Achievable . https://assets.bbhub.io/professional/sites/24/BNEF_2023-11-21_triplingrenewables_Final.pdf (2023). IEA. COP28 Tripling Renewable Capacity Pledge: Tracking Countries’ Ambitions and Identifying Policies to Bridge the Gap . https://www.iea.org/reports/cop28-tripling-renewable-capacity-pledge (2024). Tripling Renewable Power and Doubling Energy Efficiency by 2030 Crucial Steps towards 1.5°C . (International Renewable Energy Agency, Abu Dhabi, 2023). IEA. World Energy Outlook 2024 . https://www.iea.org/reports/world-energy-outlook-2024 (2024). Weyant, J. Some Contributions of Integrated Assessment Models of Global Climate Change. Review of Environmental Economics and Policy 11, 115–137 (2017). Van Beek, L., Hajer, M., Pelzer, P., Van Vuuren, D. & Cassen, C. Anticipating futures through models: the rise of Integrated Assessment Modelling in the climate science-policy interface since 1970. Global Environmental Change 65, 102191 (2020). Riahi, K. et al. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global Environmental Change 42, 153–168 (2017). IPCC. Climate Change 2023: Synthesis Report . doi: 10.59327/IPCC/AR6-9789291691647 (2023). Intergovernmental Panel on Climate Change (IPCC). 2025 — IPCC. (2025). Agutu, C., Egli, F., Williams, N. J., Schmidt, T. S. & Steffen, B. Accounting for finance in electrification models for sub-Saharan Africa. Nat Energy 7, 631–641 (2022). Egli, F., Steffen, B. & Schmidt, T. S. Bias in energy system models with uniform cost of capital assumption. Nat Commun 10, 4588 (2019). Lonergan, K. E. et al. Improving the representation of cost of capital in energy system models. Joule 7, 469–483 (2023). Dato, P. et al. Computation of weighted average cost of capital (WACC) in the power sector for African countries and the implications for country-specific electricity technology cost. Applied Energy 397, 126333 (2025). Hatton, L., Oluleye, G., Jansen, M., Staffell, I. & Hawkes, A. High costs of capital hinder the equity, speed, and abatement potential of solar and wind deployment globally. Preprint at https://doi.org/10.21203/rs.3.rs-6620411/v1 (2025). Steffen, B. Estimating the cost of capital for renewable energy projects. Energy Economics 88, 104783 (2020). Dobrotkova, Z., Surana, K. & Audinet, P. The price of solar energy: Comparing competitive auctions for utility-scale solar PV in developing countries. Energy Policy 118, 133–148 (2018). Bachner, G., Mayer, J. & Steininger, K. W. Costs or benefits? Assessing the economy-wide effects of the electricity sector’s low carbon transition – The role of capital costs, divergent risk perceptions and premiums. Energy Strategy Reviews 26, 100373 (2019). Waidelich, P. et al. The impact of financing conditions on global deep decarbonization. Preprint at https://doi.org/10.21203/rs.3.rs-7921923/v1 (2025). Calcaterra, M. et al. Reducing the cost of capital to finance the energy transition in developing countries. Nat Energy https://doi.org/10.1038/s41560-024-01606-7 (2024) doi:10.1038/s41560-024-01606-7. Hatton, L., Staffell, I., Jansen, M., Oluleye, G. & Hawkes, A. Historical and future projected costs of capital for ten energy technologies across 176 countries. Sci Data 12, 1912 (2025). IRENA. The Cost of Financing for Renewable Power . https://www.irena.org/Publications/2023/May/The-cost-of-financing-for-renewable-power (2023). Wildgruber, K., Thompson, C. & Egli, F. An empirically validated open-access approach for calculating the cost of capital of renewables. Energy Strategy Reviews 64, 102103 (2026). Agustin Roth et al. Renewable Energy Financing Conditions in Europe: Survey and Impact Analysis . http://aures2project.eu/wp-content/uploads/2021/06/AURES_II_D5_2_financing_conditions.pdf (2021). Steffen, B., Egli, F., Gumber, A., Ðukan, M. & Waidelich, P. A global dataset of the cost of capital for renewable energy projects. Sci Data 12, 1624 (2025). IEA. The Cost of Capital in Clean Energy Transitions . https://www.iea.org/articles/the-cost-of-capital-in-clean-energy-transitions . International Energy Agency. Cost of Capital Observatory. https://www.iea.org/reports/cost-of-capital-observatory (2025). IEA. Reducing the Cost of Capital: Strategies to Unlock Clean Energy Investment in Emerging and Developing Economies . https://www.iea.org/reports/reducing-the-cost-of-capital (2024). Sachs, J., Sachs, L., Vega, A. M. & Willis, B. Lowering the Cost of Capital for Climate and SDG Finance in Emerging Market and Developing Economies . https://ccsi.columbia.edu/content/lowering-cost-of-capital-climate-sdgs-emdes (2025). Briera, T. & Lefèvre, J. Reducing the cost of capital through international climate finance to accelerate the renewable energy transition in developing countries. Energy Policy 188, 114104 (2024). Ameli, N. et al. Higher cost of finance exacerbates a climate investment trap in developing economies. Nat Commun 12, 4046 (2021). Bhattacharya, A., Songwe, V., Soubeyran, E. & Stern, N. Raising Ambition and Accelerating Delivery of Climate Finance: Third Report of the Independent High-Level Expert Group on Climate Finance . https://www.lse.ac.uk/granthaminstitute/wp-content/uploads/2024/11/Raising-ambition-and-accelerating-delivery-of-climate-finance_Third-IHLEG-report.pdf (2024). Egli, F. Renewable energy investment risk: An investigation of changes over time and the underlying drivers. Energy Policy 140, 111428 (2020). Cambridge Economic Policy Associates (CEPA) LLP. Hurdle Rate Estimates for Electricity Sector Technologies . https://assets.publishing.service.gov.uk/media/68cd1d818c44a661b4995d9f/cepa-desnz-hurdle-rates-electricity.pdf (2025). IEA. Global Energy and Climate Model Documentation 2025 . https://iea.blob.core.windows.net/assets/9155992e-962c-45f4-97c1-78fd43a94178/GlobalEnergyandClimateModelDocumentation2025.pdf (2025). Gbohoui, W., Ouedraogo, R. & Some, Y. M. Sub-Saharan Africa’s Risk Perception Premium: In the Search of Missing Factors . https://www.imf.org/-/media/Files/Publications/WP/2023/English/wpiea2023130-print-pdf.ashx (2023). Sweerts, B., Longa, F. D. & Van Der Zwaan, B. Financial de-risking to unlock Africa’s renewable energy potential. Renewable and Sustainable Energy Reviews 102, 75–82 (2019). Enache, C. Corporate Tax Rates Around the World, 2024. Tax Foundation https://taxfoundation.org/data/all/global/corporate-tax-rates-by-country-2024/ . DiaCore, Fraunhofer ISI, ECOFYS. The Impact of Risks in Renewable Energy Investments and the Role of Smart Policies . https://www.isi.fraunhofer.de/content/dam/isi/dokumente/ccx/dia-core/D3-4_diacore_2016_impact_of_risk_in_res_investments.pdf (2016). International Energy Agency. World Energy Investment 2025 . https://www.iea.org/reports/world-energy-investment-2025 (2025). Almeida, H., Cunha, I., Ferreira, M. A. & Restrepo, F. The Real Effects of Credit Ratings: The Sovereign Ceiling Channel. The Journal of Finance 72, 249–290 (2017). Damodaran, A. Country Default Spreads and Risk Premiums. NYU https://pages.stern.nyu.edu/~adamodar/New_Home_Page/datafile/ctryprem.html (2025). Elkhoury, M. Credit Rating Agencies and Their Potential Impact on Developing Countries . https://repositorio.cedes.org/bitstream/123456789/2591/1/gdsddf20081_en.pdf#page=170 (2009). AEW. AEW Research Monthly Report: Private Infra Lenders Target Solid Margins at Modest Risk . https://www.aew.com/site-assets/images/July-2024-Research-Report-ENG.pdf (2024). Hatton, L. et al. The global and national energy systems techno-economic (GNESTE) database: Cost and performance data for electricity generation and storage technologies. Data in Brief 110669 (2024) doi: 10.1016/j.dib.2024.110669 . Weaver, C. P. et al. Improving the contribution of climate model information to decision making: the value and demands of robust decision frameworks. WIREs Climate Change 4, 39–60 (2013). Đukan, M. & Kitzing, L. The impact of auctions on financing conditions and cost of capital for wind energy projects. Energy Policy 152, 112197 (2021). Jadidi, H., Firouzi, A., Rastegar, M. A., Zandi, M. & Eicker, U. Risk mitigation in project finance for utility-scale solar PV projects. Energy Economics 143, 108221 (2025). Additional Declarations Competing interest reported. The authors report no direct competing interests. L.H currently works for the International Energy Agency as a consultant, including supporting their Cost of Capital Observatory, which was cited in the text. P.W. works for an economic consulting firm that, among other things, advises on cost of capital matters in the energy sector. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9348818","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"data-descriptor","associatedPublications":[],"authors":[{"id":626709000,"identity":"0ad2ea04-c0e1-4317-817f-0ed73ae51aef","order_by":0,"name":"Luke Hatton","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDklEQVRIie3RMUvEMBTA8VcKuaXXrhEUv0KgcCgEP0sfhU49HVwcbnhQ6FRxrQh+BsfbTC10qrjqZnF1OW7pIpqeFQ4h6uiQ/5QEfryEANhs/zAfvM9FEGRKDQsGLm1OuIGwgagIYKdsopE4fyRCpWI8+5VMq/Wql8eg2tXd01Ke+JOKoF8AXpKJ+DFXUXLqZOc39bxNDnMPySkawCvzxYQmNWZwr0leCwZIMCXAazMJ+4HkkD5r8i5Y0JHz9jOZbaYUkIImSjCO5A5TjBdzvdlBmyRY8kbot8SadFTvNjw0PT+YFOHjmZR4+5C9rOfLI7F/EVfd60Lulcowxt3ejD8Cyvwr3/siNpvNZtvuA8x9XWc1WfQbAAAAAElFTkSuQmCC","orcid":"","institution":"Imperial College London","correspondingAuthor":true,"prefix":"","firstName":"Luke","middleName":"","lastName":"Hatton","suffix":""},{"id":626709007,"identity":"ded629c5-d807-4a38-a7c1-809eea2983a3","order_by":1,"name":"Gbemi Oluleye","email":"","orcid":"","institution":"Imperial College London","correspondingAuthor":false,"prefix":"","firstName":"Gbemi","middleName":"","lastName":"Oluleye","suffix":""},{"id":626709009,"identity":"d3becec4-b35d-4b11-ae0e-a50f3de01bb7","order_by":2,"name":"Florian Egli","email":"","orcid":"","institution":"Technical University of Munich","correspondingAuthor":false,"prefix":"","firstName":"Florian","middleName":"","lastName":"Egli","suffix":""},{"id":626709012,"identity":"b0ec1379-23fb-44b6-aa05-3ae25eba5370","order_by":3,"name":"Katharina Wildgruber","email":"","orcid":"","institution":"Technical University of Munich","correspondingAuthor":false,"prefix":"","firstName":"Katharina","middleName":"","lastName":"Wildgruber","suffix":""},{"id":626709016,"identity":"d865b69e-7d6f-4f20-8419-d1c83c622036","order_by":4,"name":"Paul Waidelich","email":"","orcid":"","institution":"ETH Zurich","correspondingAuthor":false,"prefix":"","firstName":"Paul","middleName":"","lastName":"Waidelich","suffix":""},{"id":626709017,"identity":"86b809d9-4cc2-4487-bda2-d4fef2dfe2c9","order_by":5,"name":"Adam Hawkes","email":"","orcid":"","institution":"Imperial College London","correspondingAuthor":false,"prefix":"","firstName":"Adam","middleName":"","lastName":"Hawkes","suffix":""}],"badges":[],"createdAt":"2026-04-07 19:09:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9348818/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9348818/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108009464,"identity":"86ce3c78-16a5-459d-9745-70cc2f48ba8c","added_by":"auto","created_at":"2026-04-28 13:10:15","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":107544,"visible":true,"origin":"","legend":"\u003cp\u003eHistorical fluctuations in the global risk-free rate (a, taken as the 10-year U.S. Treasury yield) and changes in the country risk premium portion of the cost of capital for solar projects (b and c, with Argentina and Viet Nam shown as indicative examples for where country risk premiums have risen and fallen respectively). 10-year Treasury yield data is taken from the Board of Governors of the Federal Reserve System, whilst breakdowns for the cost of capital for solar in Argentina and Viet Nam were taken from Hatton et al. \u003csup\u003e21\u003c/sup\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9348818/v1/d3d1f01148a7bc53b65efb20.jpg"},{"id":108009499,"identity":"3d90efd4-02d1-440f-a2e2-0d61f6f52476","added_by":"auto","created_at":"2026-04-28 13:10:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":56970,"visible":true,"origin":"","legend":"\u003cp\u003eRange of minimum returns required by developers (“hurdle rate”) across technology categories, estimated by CEPA in their modelling study for the UK Government in 2025\u003csup\u003e34\u003c/sup\u003e. Hurdle rates include the effects of relevant revenue stabilisation or other government support mechanisms in the UK, with technological maturity within the UK market rather than globally also reflected in the final numbers. Hurdle rates differ slightly from the cost of capital, as they are a measure of the minimum return that companies expect from undertaking energy projects whereas the cost of capital is the minimum return demanded by investors. In order for a company to produce value from a project, the hurdle rate must be higher than the project or company cost of capital.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9348818/v1/f388dd69f1f7eed114d1e99d.png"},{"id":108009487,"identity":"bd3d1214-bed8-4b2d-a259-46a2d78a6ed0","added_by":"auto","created_at":"2026-04-28 13:10:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":54740,"visible":true,"origin":"","legend":"\u003cp\u003eAn illustration of the intersections between technological maturity and policy maturity that has been covered in this dataset for each scenario. The five technology categories were informed by a similar categorisation based on project and technology risk made by CEPA for the UK Government, datapoints from which are included here for selected technologies. Risk-free rates, country and equity risk and the assumed debt share here use illustrative values.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9348818/v1/94d127be555bcbff6f541c0c.png"},{"id":108009887,"identity":"bd0bdf88-44e1-4d30-9af7-8e31a5e1e878","added_by":"auto","created_at":"2026-04-28 13:11:48","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1390574,"visible":true,"origin":"","legend":"\u003cp\u003eGeographic distribution of the cost of capital for Mature energy technologies (TRL11) with Strong policy support by Shared Socioeconomic Pathway, for 2030, 2050 and 2100. Data gaps correspond to countries that either do not have GDP per capita projections under the SSP scenarios (e.g., Venezuela, Syria) and/or are not rated due to non sovereignty. Countries without a rating and corresponding country risk premium in the Damodaran database were set to Ca in Moody’s rating (one notch above default). Full plots for all technology maturity and policy coherence combinations are included in the Supplementary Material. Weak policy support increases the cost of capital between 2 – 3 % depending on the debt share and corporate tax rating.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9348818/v1/5aa2a7274f9795fb5b8f4bc8.jpeg"},{"id":108009393,"identity":"47b15ce1-c35c-4c8c-aac5-39276e5460f7","added_by":"auto","created_at":"2026-04-28 13:10:12","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":579809,"visible":true,"origin":"","legend":"\u003cp\u003eRanges of the cost of capital for Mature energy technologies (TRL11) under Strong policy support schemes, with comparisons across country groupings by income and region, for 2050 and 2100 and the five modelled SSP scenarios. Boxplots include the range of values falling between the 1\u003csup\u003est\u003c/sup\u003e and 3\u003csup\u003erd\u003c/sup\u003e quantile, with the median highlighted in black. Values that fall outside of the 1.5 times interquartile range are deemed outliers and are not shown in the figure. Full plots for all technology maturity and policy coherence combinations are included in the Supplementary Material alongside the country groupings used. Weak policy support increases the cost of capital between 2 – 3 % depending on the debt share and corporate tax rating.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9348818/v1/d34f4f86f460c6c330926ca6.jpeg"},{"id":108009901,"identity":"a45c98fb-2fec-4ea1-a289-bc5d9da6b957","added_by":"auto","created_at":"2026-04-28 13:11:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":76639,"visible":true,"origin":"","legend":"\u003cp\u003eGDP-weighted mean costs of capital for energy projects in developing countries and advanced economies, under the five SSP scenarios and across the range of technology maturity categories. Advanced economies are shown as solid lines whilst EMDEs are shown through dashed lines, with colours denoting the relevant SSP scenario, based on the World Bank’s current classifications. In SSP3 there is a plateau (advanced economies) and subsequent increase (EMDEs) in GDP-weighted mean cost of capital in EMDEs as population and economic growth in industrialising countries slows from 2040 onwards and in some advanced economies falls under aging populations with limited immigration towards the end of the century. For SSP2 and 4, slower economic growth in EMDEs means that the effects of national reductions in the cost of capital even out across income groups, causing a plateau in the GDP-weighted average cost of capital.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-9348818/v1/3f26fb4893cdf17090514dc2.png"},{"id":108009391,"identity":"bec60cd4-00a8-44c2-8f53-e20a13ad10e0","added_by":"auto","created_at":"2026-04-28 13:10:12","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":76728,"visible":true,"origin":"","legend":"\u003cp\u003eMean cost of capital for Mature energy technology projects across each of the SSPs, for EMDEs (dashed line) and advanced economies (solid line). The range is calculated based on uncertainties in the risk-free rate, lenders margin and equity risk premium, which in the central scenario are set as the 10-year average (see Methods and Technical Validation).\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-9348818/v1/daadaa03f70ae6727983919a.png"},{"id":108011539,"identity":"120b2df3-5f35-4ad2-b68c-69159530c816","added_by":"auto","created_at":"2026-04-28 13:14:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2800846,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9348818/v1/4b531996-6b2f-486c-8d78-00c4aa31cca9.pdf"},{"id":108009602,"identity":"4cb635d9-c2fa-449b-98ba-33e0c5b73cf7","added_by":"auto","created_at":"2026-04-28 13:10:31","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":4771765,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-9348818/v1/850f4c3079fce69b91290616.docx"}],"financialInterests":"Competing interest reported. The authors report no direct competing interests. L.H currently works for the International Energy Agency as a consultant, including supporting their Cost of Capital Observatory, which was cited in the text. P.W. works for an economic consulting firm that, among other things, advises on cost of capital matters in the energy sector.","formattedTitle":"Future scenarios for the cost of capital of energy technologies linked to the Shared Socioeconomic Pathways","fulltext":[{"header":"Background \u0026 Summary","content":"\u003cp\u003eLimiting the global mean temperature rise above pre-industrial levels to “well below 2C” to meet the goals of the Paris Agreement will require energy transitions across every sector of the global economy\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Scenario modelling is a key approach used in industry, policy, and by other stakeholders to explore the implications of policy, technological and socioeconomic change on future emissions trajectories and the required scales of technology deployment \u0026amp; investment flows. Examples of key climate policy targets and initiatives that were informed by scenario modelling include tripling global renewable capacity by 2030\u003csup\u003e2,3\u003c/sup\u003e, doubling energy efficiency levels by 2030\u003csup\u003e4\u003c/sup\u003e and achieving net zero emissions by 2050\u003csup\u003e5\u003c/sup\u003e to limit warming to 1.5C or below.\u003c/p\u003e \u003cp\u003eSome of the most sophisticated scenario modelling efforts include the use of integrated assessment models (IAMs) which link social, economic and climate system models\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Alignment on a standard modelling basis for scenario analysis in IAMs and other models is important to the integrated analysis of climate impacts, adaptation, mitigation and vulnerabilities, which led to the development of five Shared Socioeconomic Pathways (SSPs) by the modelling community\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. These scenarios depict plausible global socioeconomic development trajectories that lead to differing challenges for mitigation and adaptation efforts, offering alternative narratives for development with associated uncertainties that provide a standard basis for modelling efforts. The SSPs play a central underpinning role in research collated by the Intergovernmental Panel on Climate Change\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e in its Assessment Reports - particularly Working Group III (Mitigation). Scenario modelling based on the SSPs will continue to play a key role in informing the upcoming 7th Assessment Report\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e and other future efforts, with the framework for the next generation of scenarios currently being debated and discussed.\u003c/p\u003e \u003cp\u003eExisting work has already highlighted the importance of accurate assumptions on the cost of capital for scenario modelling\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, including in IAMs and energy system models (ESMs). Due to the capital-intensive nature of many clean energy technologies, their economic feasibility and cost competitiveness is highly sensitive to the assumed cost of capital\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, meaning that inaccurate or poorly justified assumptions can substantially bias results and technical insights\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. The importance of the cost of capital to modelling results is compounded by the substantial challenges accessing empirical cost of capital data\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, due to transactions typically taking place under project finance structures with limited publicly accessible data and stakeholders hesitant to share data on desired returns openly due to concerns over loss of competitiveness\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. These empirical data gaps means that the use of standard assumptions is widespread (e.g., 5–10% globally\u003csup\u003e18\u003c/sup\u003e, with associated limitations\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e), although modelling studies have begun to integrate country- and technology-specific costs of capital\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, aided by the increased availability of estimation methods\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eExisting empirical data from energy projects shows that the cost of capital can vary substantially between countries, across technologies and between time periods\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, due to macroeconomic conditions and differences in project- and country-level risk factors\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Estimates show that the cost of capital could be to up to three or four times higher in low-income countries relative to high-income contexts\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, with high costs of capital in developing countries driven largely by macroeconomic and political factors reflected in sovereign credit ratings\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Inaccurate modelling assumptions around the cost of capital can therefore substantially misrepresent financing conditions faced by energy project developers, highlighting the importance of ample treatment in scenario analysis and ESMs\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. It is particularly important in developing country contexts, which face high costs of capital that are at least twice levels in advanced economies\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e and so require de-risking and other interventions to scale energy transition efforts\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eModelling efforts have shown that the trajectory of mitigation pathways is heavily dependent on assumed scenarios for the cost of capital\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, with high costs of capital posing challenges to the cost and equity of the global energy transition\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Given the importance of cost of capital to modelled mitigation pathways, sufficient treatment of future fluctuations is key to the accuracy and utility of energy \u0026amp; climate scenario modelling\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. However, uncertainty around how to depict the future evolution(s) in underlying factors driving the cost of capital for energy projects – including the risk-free rate, country risk premium, and project- or market-level risk factors – pose challenges to the development of standardised cost of capital scenarios. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates how the cost of capital even for a given country and technology can change substantially year on year, due to macroeconomic (changes in the global risk-free rate) and national factors (e.g., changes in country-level risk premiums from up- or down-grades in sovereign credit ratings). Standardised scenarios for the cost of capital would therefore be a valuable basis and resource for modellers to avoid “reinventing the wheel” for each modelling project that would also aid the ease of inter-model and inter-study comparisons, similar to the economic growth and population change basic drivers that are included in the core SSPs\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAcross technologies, the cost of capital can also vary substantially in line with maturity, national policy support and financier experience with the technology category\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. For example, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows how the hurdle rate (a minimum rate of return for project developers, for which the cost of capital is a lower bound) for selected energy technologies varies in the UK\u003csup\u003e34\u003c/sup\u003e, with the hurdle rate for a tidal range project 70% higher than for solar PV or onshore wind in real terms. These estimates also include the effects of existing government revenue support schemes, which can reduce the effective hurdle rate by an estimated 1.5–3%\u003csup\u003e34\u003c/sup\u003e. Depicting the differences in the cost of capital across energy technologies, as well as across countries and between time periods, is therefore essential to providing more accurate insights from scenario modelling to inform policy and commercial decisions. However, there has been limited treatment to date of future changes in the cost of capital across technologies under energy and climate modelling. For example, modelling conducted by the International Energy Agency in its Global Climate and Energy Model uses a standardised range of 4–9% in real terms for clean generation technologies\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, whilst many other IAMs use a standard discount rate across all technologies and countries\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Limited inclusion of cost of capital scenarios in modelling is markedly different to deployment costs, which are typically taken at a country- or regional level with yearly specificity and/or are calculated endogenously within many existing IAMs and energy system models.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eStandard scenarios for the cost of capital for energy projects would greatly aid energy and climate scenario analysis, by providing a strong basis for modelling to be conducted on and enabling more accurate technical insights. Drawing on the examples of the SSPs and linking directly to their underlying narratives and GDP per capita projections, here we adapt an existing peer-reviewed and stakeholder verified model\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e that estimates the cost of debt, equity and overall cost of capital for 188 countries to provide cost of capital scenarios out to 2100. We selected the SSPs due to the important role they play in informing policy and research efforts around climate change adaptation and mitigation. We summarise the narratives for each of the scenarios (taken from Riahi et al.\u003csup\u003e8\u003c/sup\u003e) and translate these into high-level implications for the cost of capital for energy technologies in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab1\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of the SSP narratives and implications on the cost of capital for clean and fossil energy technologies, with scenario narratives for the SSPs taken from Riahi et al\u003csup\u003e8\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003eSSP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eScenario narrative\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eImplications for the cost of capital of energy projects\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1: Sustainability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eThe world shifts gradually, but pervasively, toward a more sustainable path, emphasizing more inclusive development that respects perceived environmental boundaries. Management of the global commons slowly improves, educational and health investments accelerate the demographic transition, and the emphasis on economic growth shifts toward a broader emphasis on human well-being. Driven by an increasing commitment to achieving development goals, inequality is reduced both across and within countries. Consumption is oriented toward low material growth and lower resource and energy intensity.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCountry risk premiums in developing countries fall as development goals are achieved.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2: Middle of the Road\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eThe world follows a path in which social, economic, and technological trends do not shift markedly from historical patterns. Development and income growth proceeds unevenly, with some countries making relatively good progress while others fall short of expectations. Global and national institutions work toward but make slow progress in achieving sustainable development goals. Environmental systems experience degradation, although there are some improvements and overall the intensity of resource and energy use declines. Global population growth is moderate and levels off in the second half of the century. Income inequality persists or improves only slowly and challenges to reducing vulnerability to societal and environmental changes remain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCountry risk premiums in developing countries fall, albeit slower than SSP1, as development proceeds unevenly.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e3: Regional Rivalry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eA resurgent nationalism, concerns about competitiveness and security, and regional conflicts push countries to increasingly focus on domestic or, at most, regional issues. Policies shift over time to become increasingly oriented toward national and regional security issues. Countries focus on achieving energy and food security goals within their own regions at the expense of broader-based development. Investments in education and technological development decline. Economic development is slow, consumption is material-intensive, and inequalities persist or worsen over time. Population growth is low in industrialized and high in developing countries. A low international priority for addressing environmental concerns leads to strong environmental degradation in some regions.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCountry risk premiums in developing countries fall slowly, with limited economic development and high inequalities.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e4: Inequality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eHighly unequal investments in human capital, combined with increasing disparities in economic opportunity and political power, lead to increasing inequalities and stratification both across and within countries. Overtime, a gap widens between an internationally connected society that contributes to knowledge- and capital-intensive sectors of the global economy, and a fragmented collection of lower-income, poorly educated societies that work in a labour intensive, low-tech economy. Social cohesion degrades and conflict and unrest become increasingly common. Technology development is high in the high-tech economy and sectors. The globally connected energy sector diversifies, with investments in both carbon-intensive fuels like coal and unconventional oil, but also low-carbon energy sources. Environmental policies focus on local issues around middle- and high-income areas.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCountry risk premiums in developing countries fall inconsistently, with inequality between regions rising.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e5: Fossil-fuelled Development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eThis world places increasing faith in competitive markets, innovation, and participatory societies to produce rapid technological progress and development of human capital as the path to sustainable development. Global markets are increasingly integrated. There are also strong investments in health, education, and institutions to enhance human and social capital. At the same time, the push for economic and social development is coupled with the exploitation of abundant fossil fuel resources and the adoption of resource and energy intensive lifestyles around the world. All these factors lead to rapid growth of the global economy, while global population peaks and declines in the 21st century. Local environmental problems like air pollution are successfully managed. There is faith in the ability to effectively manage social and ecological systems, including by geo-engineering if necessary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCountry risk premiums in developing countries fall, as the integration of global markets and investments in human and social capital drive development.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eTo reflect the differences in the cost of capital across technology categories, we develop estimates for a range of technology maturity categories, grouped into five “buckets” as demonstrated in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e (selected on the basis of the existing spreads of energy technology costs of capital, see \u003cb\u003eMethods\u003c/b\u003e). As technology-specific risk premiums also depend on policy interventions and support schemes – which can range across countries and between technologies - we also include a quantification of the effects on the cost of capital. We define “Strong” policy support to include the presence of revenue stabilisation (e.g., Contracts for Difference) or other fiscal support measures, whilst “Weak” reflects full exposure to merchant risk. These dimensions for cost of capital scenarios enable modellers to integrate data directly into models with direct links to technology maturity and deployment, maximising its utility and applicability. Inclusion of the effect of policy support – which can bring the cost of debt down by 2%\u003csup\u003e34\u003c/sup\u003e - also allows for modellers to explore different scenarios including both the phase out and rollback of policy support over time.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEstimates presented in this dataset do not depict the full range of risk factors that contribute to the energy project’s cost of capital, including those associated with market arrangements, currency dynamics and project-specific contexts (e.g., off taker risks). These factors were not included in the modelled results due to their strong dependence on factors to external to the underlying drivers contained in the SSPs, which would introduce additional assumptions and related limitations to the cost of capital scenarios developed here. We note that the approach of risk disaggregation means that other risk factors can be added easily on to the estimates presented here, enabling a wider range of use cases including accounting for project-level risks or other factors (e.g., currency risk). We extend the country coverage from 176 to include data on the 188 countries with GDP per capita projections under the SSPs, setting unrated countries to a Moody’s rating of Ca (a notch above default). Historical ranges in key underlying drivers of the cost of capital over the last ten years are integrated to give an uncertainty bound for future estimates, with data also provided at a yearly resolution for maximum utility. Given that many IAMs aggregate countries into larger regions, we also provide regional GDP-weighted benchmarks to maximise utility and the ease of integrating estimates into future modelling studies.\u003c/p\u003e \u003cp\u003eOur scenario estimates demonstrate a substantial difference in the overall cost of capital a) between countries and b) across the five SSP scenarios, highlighting the importance of accounting for future changes in the cost of capital when conducting scenario modelling. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e shows the geographic distribution of the estimated cost of capital across SSPs for 2030, 2050 and 2100, with estimates shown for Mature technologies under Strong policy support. The median difference for a country across the five SSP scenarios in 2100 is 2.47% (ranging from zero to 6.73% across countries), largest for EMDEs where economic growth and subsequent reductions in country risk premiums differ most across SSPs. Substantial disparities across countries are also present in all scenarios out to 2050, particularly for SSP2-4 where regional inequalities and unequal economic growth deliver limited reductions in the country risk premium across developing countries and persist for some of the least developed countries out to 2100. Whilst SSP1 and SSP5 show more widespread reductions (particularly SSP1) enabled by wider economic growth, disparities between developing regions and advanced economies still persist in 2100.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRegional and income-based disparities remain present across the future SSP-linked cost of capital scenarios, even under those with wider economic growth and sharp reductions in inequality. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e presents boxplots of the cost of capital on a regional basis across SSP1-5 for 2050 and 2100 for Mature technologies with Strong policy support (visualisations for all combinations are included in the Supplementary Material). Regional and income-based disparities remain substantial even by 2100, particularly for countries and regions rated as high risk under current sovereign credit ratings in 2025 and for SSPs 2–4. In the former, the median cost of capital for low-income countries is around double the median in high-income countries in 2100, whilst for SSP3 this rises to triple the lowest levels in high-income countries. Even in SSP1 - which sees the largest reduction in economic inequality between countries rated as advanced economies and EMDEs in 2025 – a substantial cost of capital differential remains between country groupings. Africa faces the highest median costs of capital across in all SSPs, reflecting disparities in economic development and the high Africa risk premium inherent in current sovereign credit ratings that has been discussed in other work\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Median costs of capital in Africa reach a low of 10.5% in 2100 (SSP1) and high of 13% (SSP4), still over double levels in high income countries in 2100 across all SSPs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRegional disparities in the costs of capital are largely a consequence of the differences in country risk premiums in EMDEs and advanced economies, noted by other work, that do not fully close over any of the SSP scenarios. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e presents the GDP-weighted mean cost of capital for developing countries and advanced economies across technology maturity ranges with Strong policy support, based on their categorisation in 2025 (see Supplementary Data for the full list). Disparities between EMDEs and advanced economies decrease most notably in SSP1, SSP2 and SSP5, with substantial differences remaining in SSP4 and SSP3 particularly under high inequalities and/or regional rivalries. For SSP3, there is an increase in the GDP-weighted mean cost of capital in countries categorised as EMDEs due to the pace of economic growth in GDP per capita terms in LMIC and LICs exceeding UMICs starting in 2035, due to the slowdown in economic and population growth in industrialised countries. The GDP-weighted mean cost of capital across EMDEs rises as LMICs and LICs — which have higher costs of capital — account for a growing share of total EMDEs output relative to UMICs, even as all individual countries within those income groups see their cost of capital and country default spread decline under increases in GDP per capita under SSP3.\u003c/p\u003e \u003cp\u003eTechnology maturity can also have a substantial impact on the cost of capital, as we show through Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. Between Mature technologies and First of a Kind (FOAK), there is a premium of approximately 4% which reflects the additional risks associated with deploying early-stage projects for which there has been no deployment at a commercial scale. Across the other technology maturity categories (Early Commercial, Scaling and Commercial) the overall cost of capital falls as maturity rises, with a substantial gap between advanced economies and EMDEs that does not close even in SSPs with widespread growth (SSP1/5). Whilst technology maturity is modelled here in discrete bins which are related to the IEA’s TRL scale, we note that the combination of technology maturity and developer/financier experience with deploying technologies make it a continuous scale in practice.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eScenario analysis should always include ample treatment of uncertainty, which we include directly within the modelled cost of capital scenarios by identifying the major uncertainties in the underlying input parameters for the methodology (risk-free rate and equity risk premium, see \u003cb\u003eMethods\u003c/b\u003e and \u003cb\u003eTechnical Validation\u003c/b\u003e). These parameters are held constant at 10-year averages for the central scenario estimates but the impact that they have on the overall cost of capital is included through a lower bound and upper bound, which takes the historical maximum and minimum of the relevant variables (risk-free rate and equity risk premium). Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e shows the range of cost of capital above and below the central (i.e., average) scenario results, for Mature projects in EMDEs and advanced economies with Strong policy support. Notably, we hold the risk-free rate constant in all scenarios, rather than developing individual scenarios for how global base rates are likely change due to the complexity and uncertainty over their evolution, though we note that the risk disaggregation approach allows for easy modification of assumptions for individual variables such as the risk-free rate.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe methodology used to develop this dataset is based on a growing body of literature on cost of capital estimation methodologies, developed by IRENA\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, used in Calcaterra et al\u003csup\u003e20\u003c/sup\u003e and Hatton et al\u003csup\u003e21\u003c/sup\u003e and benchmarked against empirical data in Wildgruber et al.\u003csup\u003e23\u003c/sup\u003e. Here, we extend the estimation methodology here to include future scenario projections out to 2100 using GDP per capita scenarios taken from the five IPCC’s Shared Socioeconomic Pathways\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e at a national level for the 188 countries with estimates available. The cost of debt and cost of equity are estimated by disaggregating contributing risk factors at a country level into four key aspects: the risk-free rate, the country risk premium, and a technology risk premium, alongside either a lenders margin (debt) or equity risk premium (equity). Relevant uncertainties are quantified for both the cost of debt and cost of equity, based on historical ranges over the last ten years. Estimates for the overall debt share are then developed and combined with current data on corporate tax rates in individual countries, which we hold constant across the whole time period due to the lack of a standard basis to project or develop scenarios for future changes in national corporate tax rates.\u003c/p\u003e\u003cp\u003eCapital Structure and Overall Cost of Capital\u003c/p\u003e\u003cp\u003eThe overall cost of capital (WACC) for a given country (c), year (t) and technology (x) is evaluated from the cost of debt (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{D}^{c,t,x}\\)\u003c/span\u003e\u003c/span\u003e) and cost of equity (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{e}^{c,t,x}\\)\u003c/span\u003e\u003c/span\u003e) using Equation [1]. Interest repayments are tax deductible in the majority of jurisdictions, so the “tax shield” was modelled using corporate tax rates (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\tau\\:}^{c}\\)\u003c/span\u003e\u003c/span\u003e) taken from the Tax Foundation\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e that were held constant from 2025 levels. Assumptions are developed for the debt share (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}_{D}^{c}\\)\u003c/span\u003e\u003c/span\u003e) at a country level, which was assumed to vary inversely in a linear fashion with country ratings-based default spreads (see below) between 40–80% using the same approach in Hatton et al\u003csup\u003e21\u003c/sup\u003e. These boundaries were based on reporting of empirical financing terms for energy projects taken from the Diacore\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e and AURES-II projects\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, with the debt share then calculated using Equation [2].\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{WACC}^{c,t,x}={R}_{D}^{c}{C}_{D}^{c,t,x}(1-{\\tau\\:}^{c})+(1-{R}_{D}^{c}){C}_{E}^{c,t,x}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e\u003cem\u003e[1]\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}_{D}^{c,t}=0.8-0.4\\:\\left(\\frac{{r}_{cds}^{c,t}}{{r}_{cds,\\text{max}in\\:t}^{c,t}}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cem\u003e[2]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003eData reported here is all in nominal terms to provide maximum usability. All estimates are also evaluated at commercial rates, assuming investment from international banks or international financial institutions, given that they currently play a key role in financing energy and infrastructure projects globally\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. The limitations of these assumptions and how the cost of other financiers can be depicted using the dataset and methodology presented here are discussed in the Technical Validation section.\u003c/p\u003e\u003cp\u003eEvolution of country default spreads and risk premiums\u003c/p\u003e\u003cp\u003eCountry risk factors typically contribute to the largest portion of the cost of capital in EMDEs\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Due to the sovereign ceiling effect\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, country risk is derived by investors from sovereign credit ratings produced by the “Big Three” credit rating agencies (Moody’s, Fitch and S\u0026amp;P) and is accounted for here through the “ratings-based country default spread” derived directly from the sovereign credit rating. The country default spread represents the additional yield (spread) that investors demand due to macroeconomic and political risk factors (e.g., corruption, macroeconomic instability, political uncertainty). For equity investors, the country risk premium demanded is 1.35 times higher than the country default spread, reflecting the additional risk that they take on as equity investors which is derived from an equity-to-bond ratio\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Assessing how sovereign credit ratings are likely to change under the evolution of GDP capita in each of SSP scenarios is therefore central to understanding future changes in the cost of debt, equity, and the overall cost of capital.\u003c/p\u003e\u003cp\u003eMethodologies used to develop sovereign credit ratings vary subtly between the “Big Three” but a strong body of existing literature has shown the disproportionate effect that GDP per capita has on affecting credit ratings from all three\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Here, we evaluate the country default spread by analysing the influence that GDP per capita has on historical country default spreads over the last 25 years, which is then combined with GDP per capita projections under the SSPs to determine estimates of how country default spreads and risk premiums will change (with the assumption that all other conditions are held constant).\u003c/p\u003e\u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{CDS}_{c,t}={\\beta\\:}_{0}\\:\\text{}+{\\beta\\:}_{1}\\:\\text{l}\\text{o}\\text{g}\\left({\\text{G}\\text{D}\\text{P}\\text{p}c}_{c,t-1}\\right)+{\\beta\\:}_{2}\\:\\bullet\\:{\\text{I}\\text{n}\\text{f}\\text{l}\\text{a}\\text{t}\\text{i}\\text{o}\\text{n}}_{c,\\:\\:t-1}+{\\beta\\:}_{3}\\:\\bullet\\:{\\text{D}\\text{e}\\text{f}\\text{i}\\text{c}\\text{i}\\text{t}}_{c,\\:\\:t-1}+{\\beta\\:}_{4}\\bullet\\:\\:{\\text{D}\\text{e}\\text{b}\\text{t}}_{c,\\:\\:t-1}{+\\:\\beta\\:}_{5}\\bullet\\:\\:{\\text{G}\\text{o}\\text{v}.\\:\\text{P}\\text{r}\\text{i}\\text{m}\\text{a}\\text{r}\\text{y}\\:\\text{B}\\text{a}\\text{l}\\text{a}\\text{n}\\text{c}\\text{e}}_{c,\\:\\:t-1}\\:{+\\alpha\\:}_{c}\\:+{\\gamma\\:\\:}_{t}+{\\epsilon\\:}_{c,t}\\:\\)\u003c/span\u003e \u003c/span\u003e \u003cem\u003e[3]\u003c/em\u003e \u003c/p\u003e\u003cp\u003eUsing the last 25 years of reported data on country default spreads and country risk premiums from Damodaran\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, here we use a linear panel regression incorporating fixed effects to extract the relationship with GDP per capita as shown in Equation [3],. Drawing on existing literature, we also use inflation, current account deficit, debt to GDP, debt service ratio and government primary balance as variables in the regression, given that they are parameters highlighted as directly informing the Big Three’s credit rating decisions, including country (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{c}\\)\u003c/span\u003e\u003c/span\u003e) and time (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\gamma\\:\\:}_{t}\\)\u003c/span\u003e\u003c/span\u003e) fixed effects and where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{c,t}\\:\\)\u003c/span\u003e\u003c/span\u003e is the error term for each country (c) and year (t). We also choose to assess the impacts of including the previous year’s CDS for each country to account for the “stickiness” of sovereign credit ratings and other institutional factors not directly included in our regression, in line with other work\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab2\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of the linear regression applied to the country default spreads reported by Damodaran between 2000–2025\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\"\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003eSimple\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eWith FE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\"\u003e \u003cp\u003eFull:\u003c/p\u003e \u003cp\u003eMain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\"\u003e \u003cp\u003eLagged + Full\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eLog GDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.1543***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e-1.9729***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e-1.8158*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e-1.9033*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e-1.1254*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e-1.1403*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e-0.34181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e(0.0197)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e(0.8095)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e(0.7378)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e(0.7880)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e(0.5744)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e(0.5807)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e(0.2016)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eInflation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.0517***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.0516***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.0429**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.0450**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.0203***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e(0.0085)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e(0.0086)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e(0.0086)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e(0.0097)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e(0.0043)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePublic Deficit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.0118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.0245*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e-0.0717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e-0.0069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e(0.0170)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e(0.0127)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e(0.0763)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e(0.0406)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePublic Debt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.0302***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.0269***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.0105***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e(0.0053)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e(0.0063)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e(0.0025)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePublic P.B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.01006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.0295\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e(0.0710)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e(0.0385)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eLagged CDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.6727***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e(0.0500)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCountry FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eLagged CDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e2250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e2250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e2225\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eN entities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eN time periods\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eR²\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e-0.36595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.79803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.81228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.81255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.84000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.84031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.90114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eWithin R²\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e-0.00217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.24650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e-0.08887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e-0.11918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.30411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.28866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.60119\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eRMSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e3.49519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.314398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.29570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.29479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e1.19621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e1.179508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e0.94122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\"\u003e \u003cp\u003eClustered (Country) standard errors in parentheses\u003c/p\u003e \u003cp\u003eSignif. Codes: ***: p \u0026lt; 0.001, **: p \u0026lt; 0.01, *: p \u0026lt; 0.05, .: p \u0026lt; 0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003eThe results of the regression are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Confirming results from other work, we show that the log of GDP per capita has a statistically significant effect on the country default spread (i.e., on the sovereign credit rating), justifying the use of the methodology here based on GDP per capita scenario projections. The coefficient from the regression \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:\\:}_{1}\\)\u003c/span\u003e\u003c/span\u003e for log GDP per capita is -1.1403 (see Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e for the full results of the linear regression), with inflation and public debt levels also showing strong statistically significant effects on the country default spread. Assessment of the effect of including the lagged CDS formulation shows that it increases the fit of the regression, from an R\u003csup\u003e2\u003c/sup\u003e of 0.84 to 0.9 between (6) and (7). Despite this fact, we chose not to include it in the main formulation as (i) it strengthens the path dependency on the current CDS levels, which could bias scenarios under national macroeconomic contexts in 2025 (ii) similarly, for unrated countries set to Ca in the modelling it locks in much higher CDS values across a longer period and (iii) it also weakens the relationship between GDP per capita and the CDS by 70%, providing more limited national differences across the SSPs at a national scale for use in scenario analysis.\u003c/p\u003e\u003cp\u003eOur coefficient for log GDP is lower than Waidelich et al\u003csup\u003e19\u003c/sup\u003e due to the inclusions of these factors, as well as not including the lagged CDS into the main formulation.\u003c/p\u003e\u003cp\u003eGDP per capita scenarios under the five SSPs at a national scale were used with the regression relationships to evaluate future country risk premia and default spreads, using Equation [4]. This is derived directly from Equation [3] under the assumption that all other parameters included in the regression are held constant at 2025 levels, due to these factors not been included into the SSP narratives due to their specificity.\u003c/p\u003e\u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{r}_{cds}^{c,t}={r}_{cds}^{c,2025}+\\:{\\beta\\:\\:}_{1}\\bullet\\:\\:\\text{log}\\left(\\frac{{GDPpc}_{2025+t}}{{GDPpc}_{2025}}\\right)\\:\\:\\:\\:\\)\u003c/span\u003e \u003c/span\u003e \u003cem\u003e[4]\u003c/em\u003e \u003c/p\u003e\u003cp\u003eCost of debt\u003c/p\u003e\u003cp\u003eEquation [4] was used to evaluate the cost of debt (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{D}^{c,t})\\)\u003c/span\u003e\u003c/span\u003e for a given country (c), year (t) and technology category (x), through risk disaggregation. The risk-free rate (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{r}_{risk-free}\\)\u003c/span\u003e\u003c/span\u003e) was set to the average yield on a 10-year U.S. Treasury Bond between 2015 and 2025 (2.5%) – which is typically used as a risk-free rate proxy in financial analysis - with corresponding uncertainty bounds based on the minimum and maximum monthly average values period (an upper bound of 4.8% in October 2023 and lower bound of 0.6% from July 2020). As discussed above, the country default spread (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{r}_{cds}^{c}\\)\u003c/span\u003e\u003c/span\u003e) was projected forward based on the computed evolution of GDP per capita across the five SSPs scenarios and historical relationships extracted between GDP per capita and country default spreads. The lenders margin (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{r}_{debt}^{c}\\)\u003c/span\u003e\u003c/span\u003e) was calculated using a direct linear interpolation between 1.5–2.5% in relation to the country risk premium (i.e., if the country risk premium is halfway between 0% and the yearly maximum, the lenders margin is 2.0%). The 1.5–2.5% range was based on AEW’s tracking of the terms of project finance infrastructure loans\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, which finds that the average debt margin is 1.9% for Europe and 2.3% in the rest of the world. It was extended to account for the fact that the reported values are averages, with lower risk countries \u0026amp; projects in Europe likely to have a smaller margins and vice versa for high-risk countries elsewhere. As the underlying loan database is likely skewed toward high-income countries, the upper end of this range is also likely conservative for high-risk countries.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003c/colgroup\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{D}^{c,t}={r}_{risk-free}^{t}+{r}_{cds}^{c,t}+{r}_{debt}^{c,t}+{r}_{tech}^{c,t,x}+{r}_{policy-deficit}^{c,t}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cem\u003e[5]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003eTechnology risk premiums (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{r}_{tech}^{c,t})\\)\u003c/span\u003e\u003c/span\u003e have been evaluated by existing work, including for specific generation technologies such as wind and solar\u003csup\u003e21–2320–23\u003c/sup\u003e. Given the dependency on deployment rates, national policies and regulatory environments, this work chooses not to compute technology-specific rates. Instead, we produce estimates for five “buckets” of technology maturity (illustrated above in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) broadly corresponding to the risk categories used by CEPA in their study of hurdle rates for electricity generation and storage technologies\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Namely, the categories are First-Of-A-Kind (FOAK, 1), Early Commercial (2), Scaling (3), Commercial (4) and Mature (5), which we map these onto the IEA’s extended Technology Readiness Levels scale (1: TRL8, 2: TRL9, 3: TRL9-10, 4: TRL10, 5: TRL11) as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. As with existing work\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, relative technology premium values are used to reflect the share of the absolute technology risk premium that is included within the lenders margin (100% in the case of the Mature category). The relative technology premium (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{r}_{tech}^{c,t}\\)\u003c/span\u003e\u003c/span\u003e) across categories 2–5 were taken range from 0–4.8%, inversely with maturity and with linear steps of 1.2%, based on hurdle rates reported by CEPA ranging between 7.6% to 12.9%\u003csup\u003e34\u003c/sup\u003e. We note that the hurdle rates are given in CEPA’s study in real, pre-tax terms, but that under the small long-term inflation target in the UK (2%) and the same corporate tax rate across technologies this only has a minor impact on the relative premium (+ 2% higher on a percentage basis for nominal as for real), which is ignored here as it is taken as negligible. The first maturity category (12.9% for tidal range and novel batteries) was estimated by adding 1.5% to the second maturity category, which we retain as the same premia between 1 and 2 for FOAK technology deployments. Finally, as in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e we also include a policy maturity premium corresponding to the lack or rollback of policy support to aid modelling efforts, which we assume here to be 2% based on reporting by CEPA for the additional cost of debt for projects exposed to merchant risk without policy support\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. “Strong” policy maturity is therefore defined as the presence of revenue stabilisation or other supportive policy environments, whilst “Weak” reflects full exposure to merchant risk with no supportive policy frameworks and a more limited regulatory environment.\u003c/p\u003e\u003cp\u003eCost of equity\u003c/p\u003e\u003cp\u003eEquation [4] was used to evaluate the cost of equity (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{E}^{c,t})\\)\u003c/span\u003e\u003c/span\u003e for a given country (c), year (t) and technology category (x), through the same risk disaggregation approach used for the cost of debt. The risk-free rate (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{r}_{risk-free}\\)\u003c/span\u003e\u003c/span\u003e) was also set to the average yield on a 10-year U.S. Treasury Bond between 2015 and 2025 (2.5%), with corresponding uncertainty bounds. Equity investors also take additional risks compared to other investors, with larger potential losses given that they are the last to be paid in the event of project failure or company bankruptcy. The additional premium required to account for the risks associated with equity investments (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{r}_{equity}\\)\u003c/span\u003e\u003c/span\u003e) was set as the 10-year average global equity risk premium computed by Damodaran between 2015–2025 based on financial market data (5.11%)\u003csup\u003e42\u003c/sup\u003e. The country risk premium (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{r}_{country}^{c}\\)\u003c/span\u003e\u003c/span\u003e) was evaluated by adjusting the projected country default spread under the five SSP scenarios to account for the additional country risk that equity investors take (i.e., multiplication by a factor of 1.35), as outlined above. Relative technology risk premiums (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{r}_{tech})\\)\u003c/span\u003e\u003c/span\u003e were computed using the same approach outlined above for the cost of debt, in line with other work\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The absolute technology risk premium for Mature technologies was set to 1.5% based on reported spreads for renewables in developed markets (as in the cost of debt, a large portion of the technology premium is included in the lenders margin). The policy maturity premium was assumed to be 3% based against on reporting from CEPA on the additional equity return for projects exposed to merchant risk without policy support\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{E}^{c,t}={r}_{risk-free}^{t}+{r}_{country}^{c,t}+{r}_{equity}+{r}_{tech}^{c,t,x}+{r}_{policy-maturity}^{c,t}\\)\u003c/span\u003e \u003c/span\u003e \u003cem\u003e[6]\u003c/em\u003e \u003c/p\u003e\u003cp\u003eYearly fluctuations in the cost of capital\u003c/p\u003e\u003cp\u003eVariables associated with the SSPs are produced at a 5-year resolution at a national level in their most recent update, taken from the OECD ENV-Growth model. To represent fluctuations within the 5-year periods, linear interpolations were applied to develop yearly estimates of the country risk premiums and country default spreads and linked variables (debt share, lenders margin). All other contributions to the cost of debt and cost of equity were taken as constant across the full period, with their impacts on the overall cost of capital included through the uncertainty analysis in the lower and upper bounds of the uncertainty analysis which uses the historical upper and lower bounds of (1) the risk-free rate and (2) equity risk premium over the last 10 years (see Technical Validation).\u003c/p\u003e\u003cp\u003eData Records\u003c/p\u003e\u003cp\u003eAll data is provided in Excel worksheets saved in CSV files for maximum usability, which are publicly accessible at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://zenodo.org/records/19449137\u003c/span\u003e\u003cspan class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5281/zenodo.19449137\u003c/span\u003e\u003cspan class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Data is provided in both a wide format (with separate rows for each technology category and country) and in a long format (with indexer columns for the year, country and technology category, with a value column for the cost of capital) to improve the ease of usability across the possible range of data analysis tools. Country codes in ISO3 formats are included in all sheets, as well as GDP (PPP) values to provide potential weights for aggregation for users (e.g., for energy system models where many countries are aggregated in single regions). Reported values are given in units of percentage points as nominal, post-tax terms (in line with other estimation approaches in the literature).\u003c/p\u003e"},{"header":"Technical Validation","content":"\u003cp\u003eOverview\u003c/p\u003e\u003cp\u003eThe methodology used here builds on existing approaches for cost of capital estimation\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, which have been verified using expert surveys, stakeholder engagement and comparison to empirical data extracted from the literature. For example, the historical application of the model used here was benchmarked in Hatton et al\u003csup\u003e21\u003c/sup\u003e against empirical data from the IEA’s Cost of Capital Observatory\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e and other cost of capital data collated in the GNESTE database for key energy technologies\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, whilst the model it was based on was verified with a wide number of stakeholders by IRENA\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The modelling approach was also tested against project-level data in Wildgruber et al.\u003csup\u003e23\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eScenario projections for any technoeconomic parameter should not eschew ample treatment through uncertainty and sensitivity analysis, which could include emerging approaches such as robust decision making (RDM)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. To aid the analysis of uncertainty in the SSP-linked cost of capital scenarios developed by this work, we also provide an initial quantification of the uncertainty in the cost of capital derived from a) the risk-free rate and b) the equity risk premium. These parameters were selected as they are modelled in this work using historical averages, with uncertainties developed based on historical minimum and maximum for each parameter (see Methods above). The other parameters - country default spread and technology risk premium - are either derived directly from the GDP per capita scenario projections based on historical data (country default spread) or an initial assumption that we recommend is changed based on technology- and market-specific conditions (technology category risk premium) accordingly with modelling requirements.\u003c/p\u003e\u003cp\u003eDebt shares and corporate tax rates for energy projects are also another source of uncertainty in the cost of capital estimates presented here, with existing data showing that the former can range substantially both a) between and b) within countries for the same technology and the same year. For example, other project- and market-level contextual factors such as construction risks, national regulation and technology-specific characteristics could increase or decrease the debt share for a given country, in addition to macroeconomic factors. Corporate tax rates may also be affected by market- or technology-specific fiscal incentives, which are not modelled here due to the lack of a unified dataset covering all countries modelled here. To address these uncertainties, the cost of debt and cost of equity are provided individually in the dataset for each year, SSP, country and technology category to enable users to define their own assumptions in line with the desired application. Central estimates of the cost of capital are also provided based on assumed debt shares and national corporate tax rates, as a base case for other users.\u003c/p\u003e\u003cp\u003eLimitations\u003c/p\u003e\u003cp\u003eThe dataset developed here addresses a key data gap for modellers and other stakeholders, by developing cost of capital scenarios linked directly to the SSPs out to 2100 which include strong granularity on the technological maturity and the level of policy coherence and support. However, as with any forward-looking scenarios and estimates, there are limitations that must be considered when using the data in energy systems modelling or integrated assessment models (IAMs), especially given the sensitivity of models to the cost of capital\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Two broad limitations are inherent in the methodological approach: (1) the assumed additivity of contributing risk factors and 2) that project-level risk factors do not account for a substantial portion of risks relative to country- or technology- factors. These assumptions are necessary to generate sufficient data for scenario analysis but may introduce inaccuracies compared to how investors price their capital, which should be borne in mind when considering technical insights. Additional limitations are discussed in the following paragraphs, which can largely be addressed through tailoring the risk disaggregation approach to relevant application contexts e.g., through the addition or subtraction of additional factors such as currency risk.\u003c/p\u003e\u003cp\u003eAs in Hatton et al. 2025\u003csup\u003e21\u003c/sup\u003e and IRENA 2023\u003csup\u003e22\u003c/sup\u003e, estimates have been constructed from the perspective of an international investor providing their capital for investment through either debt or equity at commercial rates. The extent of domestic capital market development and exchange rate dynamics may affect whether the cost of capital from domestic commercial investors would be provided at a similar rate, whilst public investors (both domestic and international) are able to provide capital at concessional rates below commercial rates. In some of the least developed countries – where finance provided by international public financiers such as the World Bank or other MDBs plays a substantial role in infrastructure investments – a large proportion of projects may be able to access costs of capital substantially below the estimates provided here\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Exact terms will depend on how funding through concessional programs and institutions such as the World Bank’s International Development Association evolve in the future, which is far beyond the scope of the scenario analysis conducted here. Domestic public support, either through direct provision or sector-specific fiscal incentives, may also lead to lower costs of capital than are computed here, for example through concessional funding from national development banks or lower corporate tax rates provided for energy developers.\u003c/p\u003e\u003cp\u003eProject- and technology-specific risk factors not included in the scope of the scenario analysis and may drive discrepancies between the estimates presented here and the future evolution of financing terms. For example, project-level data on renewable financing terms\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e shows that there are notable differences in the technology risk premium and overall cost of capital within a same country caused by differences in maturity, resource availability, construction risks and supporting policy and regulatory structures. Previous work has shown solar PV to have been regarded as lower risk than onshore wind in terms of risk premia\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e – though recent empirical analysis has shown the opposite with structurally lower WACCs for onshore wind than solar\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, deviating from Steffen 2020\u003csup\u003e16\u003c/sup\u003e - whilst offshore wind has been shown to have an additional premium to onshore due to risks associated with scale, operation, and construction requirements\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Project- and financier-level contexts may also affect the cost of capital\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e e.g., supplier specific risks, differences in the risk appetite of investors and the overall competitiveness of the given project. These risks are not included in the data due to their specificity but could be quantified and added onto the estimates presented here, if required, under the risk disaggregation approach.\u003c/p\u003e\u003cp\u003eCost of capital estimates presented here also do not account for currency risk, which stem from depreciation of the currency of revenue against the financing currency. For many IAMs and energy system models, costs and prices are often dollar denominated to avoid complexity, with limited treatment of future inflation and currency exchange rate movements. We present the estimates here in nominal terms to avoid making assumptions over future inflation rates, which may be incompatible or different to assumptions made by modellers and other stakeholders. Relevant currency risks could be added onto the cost of debt or equity presented here, if required for a given application. The authors note that there has been limited treatment of exchange rate movements, currency depreciation and the implications for modelled mitigation pathways, despite the growing problem it presents in many developing countries. Future research should explore the implications of currency depreciation, its implications for currency risks and the impacts it has on the overall cost of capital.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eUsage Notes\u003c/p\u003e\n\u003cp\u003eReaders can download the full dataset from the Zenodo repository found at: https://zenodo.org/records/19449137 (DOI:10.5281/zenodo.19449137\u003cstrong\u003e)\u003c/strong\u003e. \u003c/p\u003e\n\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eData has been stored in a Zenodo repository, organised into both long and wide CSV formats, which is accessible to the public at https://zenodo.org/records/19449137 (DOI:10.5281/zenodo.19449137\u003cstrong\u003e)\u003c/strong\u003e. Subsets of the dataset for given technology and policy combinations for the central estimates are provided in long format to aid ease of use.\u003c/p\u003e\n\u003cp\u003eData used for the regression can be found at Damodaran (https://pages.stern.nyu.edu/~adamodar/), World Bank (https://databank.worldbank.org/source/world-development-indicators) and the IMF World Economic Outlook database (https://www.imf.org/en/publications/weo/weo-database/2025/april). \u003c/p\u003e\n\u003cp\u003eCode Availability\u003c/p\u003e\n\u003cp\u003eThe scripts for data processing and visualisation can be accessed in the GitHub repository, which can be found here: https://github.com/LukeHatton21/wacc-ssp-scenarios \u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eL.H was funded under EPSRC EP/W524323/1 This work was partially funded by the Climate Compatible Growth (CCG) programme. CCG contributed to funding the time of some of the co-authors for the production of this material and publishing fees. CCG is funded by the Foreign, Commonwealth and Development Office (FCDO) from the UK government; however, the views expressed herein do not necessarily reflect the UK government’s official policies.The article processing charge was paid from the Imperial College London Open Access Fund\u003c/p\u003e\n\u003cp\u003eAuthor statement\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLuke Hatton:\u003c/strong\u003e Conceptualization, Methodology, Formal analysis, Data curation, Visualisation, Writing – Original draft preparation. \u003cstrong\u003eGbemi Oluleye:\u003c/strong\u003e Supervision, Writing – Review \u0026amp; Editing.\u003cstrong\u003eAdam Hawkes: \u003c/strong\u003eSupervision,Writing – Review \u0026amp; Editing. \u003cstrong\u003eFlorian Egli\u003c/strong\u003e: Writing – Review \u0026amp; Editing. \u003cstrong\u003eKatharina Wildgruber: \u003c/strong\u003eWriting – Review \u0026amp; Editing. \u003cstrong\u003ePaul Waidelich:\u003c/strong\u003e Writing – Review \u0026amp; Editing. \u003c/p\u003e\n\u003cp\u003eData statement and Availability\u003c/p\u003e\n\u003cp\u003eThe data and code for modelling and visualisation can be found at https://github.com/LukeHatton21/wacc-ssp-scenarios. \u003c/p\u003e\n\u003cp\u003eDeclaration of Competing Interest\u003c/p\u003e\n\u003cp\u003eThe authors report no direct competing interests. L.H currently works for the International Energy Agency as a consultant, including supporting their Cost of Capital Observatory, which was cited in the text. P.W. works for an economic consulting firm that, among other things, advises on cost of capital matters in the energy sector. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eIPCC. \u003cem\u003eClimate Change 2022: Mitigation of Climate Change\u003c/em\u003e. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1017/9781009157926\u003c/span\u003e\u003cspan address=\"10.1017/9781009157926\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBNEF. \u003cem\u003eTripling Global Renewables by 2030: Hard, Fast and Achievable\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://assets.bbhub.io/professional/sites/24/BNEF_2023-11-21_triplingrenewables_Final.pdf\u003c/span\u003e\u003cspan address=\"https://assets.bbhub.io/professional/sites/24/BNEF_2023-11-21_triplingrenewables_Final.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIEA. \u003cem\u003eCOP28 Tripling Renewable Capacity Pledge: Tracking Countries\u0026rsquo; Ambitions and Identifying Policies to Bridge the Gap\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.iea.org/reports/cop28-tripling-renewable-capacity-pledge\u003c/span\u003e\u003cspan address=\"https://www.iea.org/reports/cop28-tripling-renewable-capacity-pledge\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cem\u003eTripling Renewable Power and Doubling Energy Efficiency by 2030 Crucial Steps towards 1.5\u0026deg;C\u003c/em\u003e. (International Renewable Energy Agency, Abu Dhabi, 2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIEA. \u003cem\u003eWorld Energy Outlook 2024\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.iea.org/reports/world-energy-outlook-2024\u003c/span\u003e\u003cspan address=\"https://www.iea.org/reports/world-energy-outlook-2024\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeyant, J. Some Contributions of Integrated Assessment Models of Global Climate Change. \u003cem\u003eReview of Environmental Economics and Policy\u003c/em\u003e 11, 115\u0026ndash;137 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Beek, L., Hajer, M., Pelzer, P., Van Vuuren, D. \u0026amp; Cassen, C. Anticipating futures through models: the rise of Integrated Assessment Modelling in the climate science-policy interface since 1970. \u003cem\u003eGlobal Environmental Change\u003c/em\u003e 65, 102191 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRiahi, K. \u003cem\u003eet al.\u003c/em\u003e The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. \u003cem\u003eGlobal Environmental Change\u003c/em\u003e 42, 153\u0026ndash;168 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIPCC. \u003cem\u003eClimate Change 2023: Synthesis Report\u003c/em\u003e. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.59327/IPCC/AR6-9789291691647\u003c/span\u003e\u003cspan address=\"10.59327/IPCC/AR6-9789291691647\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIntergovernmental Panel on Climate Change (IPCC). 2025 \u0026mdash; IPCC. (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgutu, C., Egli, F., Williams, N. J., Schmidt, T. S. \u0026amp; Steffen, B. Accounting for finance in electrification models for sub-Saharan Africa. \u003cem\u003eNat Energy\u003c/em\u003e 7, 631\u0026ndash;641 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEgli, F., Steffen, B. \u0026amp; Schmidt, T. S. Bias in energy system models with uniform cost of capital assumption. \u003cem\u003eNat Commun\u003c/em\u003e 10, 4588 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLonergan, K. E. \u003cem\u003eet al.\u003c/em\u003e Improving the representation of cost of capital in energy system models. \u003cem\u003eJoule\u003c/em\u003e 7, 469\u0026ndash;483 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDato, P. \u003cem\u003eet al.\u003c/em\u003e Computation of weighted average cost of capital (WACC) in the power sector for African countries and the implications for country-specific electricity technology cost. \u003cem\u003eApplied Energy\u003c/em\u003e 397, 126333 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHatton, L., Oluleye, G., Jansen, M., Staffell, I. \u0026amp; Hawkes, A. High costs of capital hinder the equity, speed, and abatement potential of solar and wind deployment globally. Preprint at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21203/rs.3.rs-6620411/v1\u003c/span\u003e\u003cspan address=\"10.21203/rs.3.rs-6620411/v1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteffen, B. Estimating the cost of capital for renewable energy projects. \u003cem\u003eEnergy Economics\u003c/em\u003e 88, 104783 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDobrotkova, Z., Surana, K. \u0026amp; Audinet, P. The price of solar energy: Comparing competitive auctions for utility-scale solar PV in developing countries. \u003cem\u003eEnergy Policy\u003c/em\u003e 118, 133\u0026ndash;148 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBachner, G., Mayer, J. \u0026amp; Steininger, K. W. Costs or benefits? Assessing the economy-wide effects of the electricity sector\u0026rsquo;s low carbon transition \u0026ndash; The role of capital costs, divergent risk perceptions and premiums. \u003cem\u003eEnergy Strategy Reviews\u003c/em\u003e 26, 100373 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWaidelich, P. \u003cem\u003eet al.\u003c/em\u003e The impact of financing conditions on global deep decarbonization. Preprint at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21203/rs.3.rs-7921923/v1\u003c/span\u003e\u003cspan address=\"10.21203/rs.3.rs-7921923/v1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCalcaterra, M. \u003cem\u003eet al.\u003c/em\u003e Reducing the cost of capital to finance the energy transition in developing countries. \u003cem\u003eNat Energy\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41560-024-01606-7\u003c/span\u003e\u003cspan address=\"10.1038/s41560-024-01606-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024) doi:10.1038/s41560-024-01606-7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHatton, L., Staffell, I., Jansen, M., Oluleye, G. \u0026amp; Hawkes, A. Historical and future projected costs of capital for ten energy technologies across 176 countries. \u003cem\u003eSci Data\u003c/em\u003e 12, 1912 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIRENA. \u003cem\u003eThe Cost of Financing for Renewable Power\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.irena.org/Publications/2023/May/The-cost-of-financing-for-renewable-power\u003c/span\u003e\u003cspan address=\"https://www.irena.org/Publications/2023/May/The-cost-of-financing-for-renewable-power\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWildgruber, K., Thompson, C. \u0026amp; Egli, F. An empirically validated open-access approach for calculating the cost of capital of renewables. \u003cem\u003eEnergy Strategy Reviews\u003c/em\u003e 64, 102103 (2026).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgustin Roth \u003cem\u003eet al. Renewable Energy Financing Conditions in Europe: Survey and Impact Analysis\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://aures2project.eu/wp-content/uploads/2021/06/AURES_II_D5_2_financing_conditions.pdf\u003c/span\u003e\u003cspan address=\"http://aures2project.eu/wp-content/uploads/2021/06/AURES_II_D5_2_financing_conditions.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteffen, B., Egli, F., Gumber, A., \u0026ETH;ukan, M. \u0026amp; Waidelich, P. A global dataset of the cost of capital for renewable energy projects. \u003cem\u003eSci Data\u003c/em\u003e 12, 1624 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIEA. \u003cem\u003eThe Cost of Capital in Clean Energy Transitions\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.iea.org/articles/the-cost-of-capital-in-clean-energy-transitions\u003c/span\u003e\u003cspan address=\"https://www.iea.org/articles/the-cost-of-capital-in-clean-energy-transitions\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInternational Energy Agency. Cost of Capital Observatory. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.iea.org/reports/cost-of-capital-observatory\u003c/span\u003e\u003cspan address=\"https://www.iea.org/reports/cost-of-capital-observatory\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIEA. \u003cem\u003eReducing the Cost of Capital: Strategies to Unlock Clean Energy Investment in Emerging and Developing Economies\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.iea.org/reports/reducing-the-cost-of-capital\u003c/span\u003e\u003cspan address=\"https://www.iea.org/reports/reducing-the-cost-of-capital\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSachs, J., Sachs, L., Vega, A. M. \u0026amp; Willis, B. \u003cem\u003eLowering the Cost of Capital for Climate and SDG Finance in Emerging Market and Developing Economies\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ccsi.columbia.edu/content/lowering-cost-of-capital-climate-sdgs-emdes\u003c/span\u003e\u003cspan address=\"https://ccsi.columbia.edu/content/lowering-cost-of-capital-climate-sdgs-emdes\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBriera, T. \u0026amp; Lef\u0026egrave;vre, J. Reducing the cost of capital through international climate finance to accelerate the renewable energy transition in developing countries. \u003cem\u003eEnergy Policy\u003c/em\u003e 188, 114104 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmeli, N. \u003cem\u003eet al.\u003c/em\u003e Higher cost of finance exacerbates a climate investment trap in developing economies. \u003cem\u003eNat Commun\u003c/em\u003e 12, 4046 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhattacharya, A., Songwe, V., Soubeyran, E. \u0026amp; Stern, N. \u003cem\u003eRaising Ambition and Accelerating Delivery of Climate Finance: Third Report of the Independent High-Level Expert Group on Climate Finance\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.lse.ac.uk/granthaminstitute/wp-content/uploads/2024/11/Raising-ambition-and-accelerating-delivery-of-climate-finance_Third-IHLEG-report.pdf\u003c/span\u003e\u003cspan address=\"https://www.lse.ac.uk/granthaminstitute/wp-content/uploads/2024/11/Raising-ambition-and-accelerating-delivery-of-climate-finance_Third-IHLEG-report.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEgli, F. Renewable energy investment risk: An investigation of changes over time and the underlying drivers. \u003cem\u003eEnergy Policy\u003c/em\u003e 140, 111428 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCambridge Economic Policy Associates (CEPA) LLP. \u003cem\u003eHurdle Rate Estimates for Electricity Sector Technologies\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://assets.publishing.service.gov.uk/media/68cd1d818c44a661b4995d9f/cepa-desnz-hurdle-rates-electricity.pdf\u003c/span\u003e\u003cspan address=\"https://assets.publishing.service.gov.uk/media/68cd1d818c44a661b4995d9f/cepa-desnz-hurdle-rates-electricity.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIEA. \u003cem\u003eGlobal Energy and Climate Model Documentation 2025\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://iea.blob.core.windows.net/assets/9155992e-962c-45f4-97c1-78fd43a94178/GlobalEnergyandClimateModelDocumentation2025.pdf\u003c/span\u003e\u003cspan address=\"https://iea.blob.core.windows.net/assets/9155992e-962c-45f4-97c1-78fd43a94178/GlobalEnergyandClimateModelDocumentation2025.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGbohoui, W., Ouedraogo, R. \u0026amp; Some, Y. M. \u003cem\u003eSub-Saharan Africa\u0026rsquo;s Risk Perception Premium: In the Search of Missing Factors\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.imf.org/-/media/Files/Publications/WP/2023/English/wpiea2023130-print-pdf.ashx\u003c/span\u003e\u003cspan address=\"https://www.imf.org/-/media/Files/Publications/WP/2023/English/wpiea2023130-print-pdf.ashx\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSweerts, B., Longa, F. D. \u0026amp; Van Der Zwaan, B. Financial de-risking to unlock Africa\u0026rsquo;s renewable energy potential. \u003cem\u003eRenewable and Sustainable Energy Reviews\u003c/em\u003e 102, 75\u0026ndash;82 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEnache, C. Corporate Tax Rates Around the World, 2024. \u003cem\u003eTax Foundation\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://taxfoundation.org/data/all/global/corporate-tax-rates-by-country-2024/\u003c/span\u003e\u003cspan address=\"https://taxfoundation.org/data/all/global/corporate-tax-rates-by-country-2024/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiaCore, Fraunhofer ISI, ECOFYS. \u003cem\u003eThe Impact of Risks in Renewable Energy Investments and the Role of Smart Policies\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.isi.fraunhofer.de/content/dam/isi/dokumente/ccx/dia-core/D3-4_diacore_2016_impact_of_risk_in_res_investments.pdf\u003c/span\u003e\u003cspan address=\"https://www.isi.fraunhofer.de/content/dam/isi/dokumente/ccx/dia-core/D3-4_diacore_2016_impact_of_risk_in_res_investments.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInternational Energy Agency. \u003cem\u003eWorld Energy Investment 2025\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.iea.org/reports/world-energy-investment-2025\u003c/span\u003e\u003cspan address=\"https://www.iea.org/reports/world-energy-investment-2025\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlmeida, H., Cunha, I., Ferreira, M. A. \u0026amp; Restrepo, F. The Real Effects of Credit Ratings: The Sovereign Ceiling Channel. \u003cem\u003eThe Journal of Finance\u003c/em\u003e 72, 249\u0026ndash;290 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDamodaran, A. Country Default Spreads and Risk Premiums. \u003cem\u003eNYU\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pages.stern.nyu.edu/~adamodar/New_Home_Page/datafile/ctryprem.html\u003c/span\u003e\u003cspan address=\"https://pages.stern.nyu.edu/~adamodar/New_Home_Page/datafile/ctryprem.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElkhoury, M. \u003cem\u003eCredit Rating Agencies and Their Potential Impact on Developing Countries\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://repositorio.cedes.org/bitstream/123456789/2591/1/gdsddf20081_en.pdf#page=170\u003c/span\u003e\u003cspan address=\"https://repositorio.cedes.org/bitstream/123456789/2591/1/gdsddf20081_en.pdf#page=170\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAEW. \u003cem\u003eAEW Research Monthly Report: Private Infra Lenders Target Solid Margins at Modest Risk\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.aew.com/site-assets/images/July-2024-Research-Report-ENG.pdf\u003c/span\u003e\u003cspan address=\"https://www.aew.com/site-assets/images/July-2024-Research-Report-ENG.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHatton, L. \u003cem\u003eet al.\u003c/em\u003e The global and national energy systems techno-economic (GNESTE) database: Cost and performance data for electricity generation and storage technologies. \u003cem\u003eData in Brief\u003c/em\u003e 110669 (2024) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.dib.2024.110669\u003c/span\u003e\u003cspan address=\"10.1016/j.dib.2024.110669\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeaver, C. P. \u003cem\u003eet al.\u003c/em\u003e Improving the contribution of climate model information to decision making: the value and demands of robust decision frameworks. \u003cem\u003eWIREs Climate Change\u003c/em\u003e 4, 39\u0026ndash;60 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eĐukan, M. \u0026amp; Kitzing, L. The impact of auctions on financing conditions and cost of capital for wind energy projects. \u003cem\u003eEnergy Policy\u003c/em\u003e 152, 112197 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJadidi, H., Firouzi, A., Rastegar, M. A., Zandi, M. \u0026amp; Eicker, U. Risk mitigation in project finance for utility-scale solar PV projects. \u003cem\u003eEnergy Economics\u003c/em\u003e 143, 108221 (2025).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9348818/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9348818/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eScenario modelling plays an important role in providing technical insights for stakeholders on the implications of future policy, technological and socioeconomic changes on energy and climate systems. Despite its importance to the cost competitiveness of energy technologies, the cost of capital (CoC) has received limited treatment in scenario modelling, due to challenges accessing empirical data and a limited number of estimation methodologies. Here, we present a global dataset of CoC scenarios for energy projects, covering 188 countries from 2025 to 2100, linked directly to the five Shared Socioeconomic Pathways. We estimate the CoC for five technology maturity levels, defined using the IEA\u0026rsquo;s extended Technology Readiness Level benchmarks, enabling stakeholders to explore the effects of technological development on the CoC. To provide a wide basis for modelling efforts, we also incorporate the effects of supportive policy environments on financing conditions. Uncertainty is treated through providing upper and lower bound estimates alongside a central case, based on historical ranges. By addressing a substantial data gap, this dataset will enable more accurate technical insights from energy and climate scenario modelling efforts, providing an avenue to explore how the CoC affects the cost and effectiveness of mitigation pathways.\u003c/p\u003e","manuscriptTitle":"Future scenarios for the cost of capital of energy technologies linked to the Shared Socioeconomic Pathways","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-28 12:56:20","doi":"10.21203/rs.3.rs-9348818/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"509c8113-013e-4e3b-ab2d-c0cffa4492bd","owner":[],"postedDate":"April 28th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"133986043635214050305864684648781658704","date":"2026-05-17T15:00:38+00:00","index":24,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-28T12:56:20+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-28 12:56:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9348818","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9348818","identity":"rs-9348818","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}