Abstract
This paper analyses the effect of government policies and other determinants on private finance investment in renewable energy. A unique dataset of financial transactions for renewable energy projects is constructed using the Bloomberg New Energy Finance database. The dataset covers 87 countries, six renewable energy sectors (wind, solar, biomass, small hydropower, marine and geothermal) and the 2000–2011 time-span. In a first set of models undertaken at the level of the financial deal we find that, in contrast to quota-based schemes, price-based support schemes are positively correlated with private finance contributions. This result holds for complementary analyses undertaken at the level of the project. However, for those projects in which public finance complements private finance (co-financed projects) neither quota-based measures nor price-based support schemes have a significant effect on private finance flows.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
Another notable feature is the importance of production and grid externalities due to the intermittent nature of some renewable resources (see e.g. Benatia et al. 2013).
From the Cleantech company (www.cleantech.com).
Note that the definition used by BNEF might differ from other common uses of the term “asset finance”.
Our full dataset contains 18,927 projects associated with 19,626 financial deals. They are located in one of 151 countries and span the 1977–2013 time period. The restriction of the sample is due to the availability of policy data.
Demonstration projects are part of the pre-commercial stage of technology development, they are usually large single events (projects) intended to ‘demonstrate’ the mitigation potential of a technology (not necessarily its commercialization potential). On the other hand, technology deployment and diffusion are generally programmes directed to households and firms to encourage adoption of new technologies (the commercialization stage).
We explore additional sources of data: (i) the World Bank’s indicator of “ease of doing business” (World Bank 2013; ii) IMF’s “number of other depository corporations” such as commercial banks, credit unions, etc.; (iii) “number of branches” (by the IMF) that includes all units of each type of reporting institution that provide financial services to customer; and (iv) “interest rate spread” by the IMF. Unfortunately, due to many missing values and limited country and time coverage we are not able to incorporate most of these variables in the analysis.
Defined as companies that have been set up, usually to commercialise intellectual property or technology, but are either at a very early stage or have not yet risen funding from an incubator, venture capital, private equity company or corporate venturer. They may be spin-outs from a university, company or other organisation, or they may have just been founded by an entrepreneur to exploit a market need (BNEF 2012).
Two organisation types—charity/non-profit association and defunct—are excluded from the classification. They concern a very small share of organisations—0.51 % of debt providers and 1.74 % of equity providers. Their volumes of finance cannot be attributed to a private or public classification.
Financial risk refers to the risk of default on borrowed funds. This is different from risk of an investment (or “project risk”) that does not depend on the way it is financed.
For example, there are very few observations for number of ODCs and branches, reducing the estimation sample by 75 %. The ease of doing business rank is highly correlated with credit depth of information and interest rate spread, causing collinearity in the model. Finally, concerning interest rate spread there are no data for the key countries, such as the United States, the United Kingdom, and other European countries which are not covered after year 2003.
We only include projects for which all the deals were for “new build” and whose status is “completed”, excluding projects with deal status abandoned, planned and refinancing deals.
Assignment of policy variables to projects could be problematic because we aggregate different deals that may have occurred at different dates. However, 77 % of the projects are financed by deals closed in the same year and 20 % more are financed by deals closed over a 2 or 3 year time span; thus, taking into account the financing year of the project does not present major concerns to our assumptions on the policy framework.
According to BNEF metadata, exposure to new energy is itself measured by the percentage of funds raised by the organisation to be spent in “new energy” activities.
Public finance may originate from other public entities than governments. The variables can thus be considered as proxies that measure public spending and public budget.
The sets of explanatory variables are different in the two equations.
Tobit is formally based on the introduction of a latent variable for the dependent variable and on likelihood maximization.
Our sample is composed of 2,446 projects that fall into three different subsamples depending on the source of finance: 1,933 projects that receive solely private financing (79 %), 424 co-financed projects (17.4 %), and 89 projects solely financed by public investors (3.6 %).
Estimations are carried out using aML software (Lillard and Constantijn 2003).
The Inverse Hyperbolic Sine transformation is defined for positive values by:
$$\begin{aligned} \hbox {ihs}(\hbox {y})=\hbox {ln}(\hbox {y}+\surd (1+\hbox {y}\wedge {2}))\approx \hbox {ln}(2\hbox {y})=\hbox {ln}(2)+\hbox {ln}(\hbox {y}) \end{aligned}$$Equivalence is already quite accurate for y = 2 and can be considered as very accurate for y = 3 since values differ by less than 1 % after this threshold.
Estimation results of the project-level analysis do not differ when using IHS or a log transformation—we refer to a translated log transformation: log (1 + x).
The remaining countries represent 3.8 % in terms of private finance volume, and 4.9 % in terms of number of transactions.
These findings are robust to exclusion of potential outliers. To test this, the first and the last percentile of observations are excluded from the sample (and also the first 5 % and the last 5 %) and the results are qualitatively the same for FITs and tax relief (not reported here).
In an alternative specification, we also test inclusion of a variable representing the growth in a country’s electricity consumption as a measure of changing market opportunities. We do not report the results because the variable is never statistically significant.
For an accurate interpretation of the results, the coefficient of dummy variable has been corrected following the work of Halvorsen and Palmquist (1980).
Results for the other control variables hold the sign and significance of D2 and are not reported for brevity. Estimation done on a sample of 5,267 observations obtaining \(\hbox {R}^{2 }= 0.7750\).
On a similar specification (not reported), we study the effect of policies by each of the income groups, we follow the classification of the World Bank, finding consistent results with those presented in Table 4.
The degree of intermittency (capacity ratio) might play a role as well.
We run these regressions for wind and solar sectors. There are 1,581 transactions financing wind projects, and 670 transactions financing solar projects. The remaining four renewable energy sectors could not be studied in this exercise either due to a low number of observations (geothermal; marine) or the lack of information in our dataset on the subsectors (small hydropower; biomass and waste).
The statistical insignificance of this triple interaction might be due to few observations with active REQ and high CMDI index (index = 6).
This corresponds to the second equation in (3).
While the magnitude of the coefficient is lower compared with the substitution effect of private on public (Table 9 cont.), recall that the estimation procedure is different (tobit vs. linear) and so the coefficients are not fully comparable.
For the remaining three linear models the interpretation is standard.
This statement holds in general. Whether or not an ambitious policy is viewed as being ‘credible’ will depend on the type of instrument and its design characteristics—as discussed below.
References
Akerlof GA (1970) The market for ’Lemons’: quality uncertainty and the market mechanism. Q J Econ 84(3):488–500
Amemiya T (1974) Multivariate regression and simultaneous equation models when the dependent variables are truncated normal. Econometrica 42(6):999–1012
Amemiya T (1979) The estimation of a simultaneous-equation Tobit model. Int Econ Rev 20(1):169–181
Barradale MJ (2008) Impact of policy uncertainty on renewable energy investment: wind power and PTC, USAEE working Paper 08-003
Beck T et al (2000) A new database on financial development and structure. World Bank Econ Rev 14:597–605
Benatia D, Johnstone N, Haščič I (2013) Effectiveness of policies and strategies to increase the capacity utilisation of intermittent renewable power plants, OECD Environment Working Papers no. 57, OECD Publishing. doi:10.1787/5k46j0trlrnn-en
BNEF (2012) Projects and asset finance handbook: a guide for researchers. November 2012 edition
BNEF (2013) Bloomberg New Energy Finance database. (August 2013 extraction)
Brunnschweiler CN (2010) Finance for renewable energy: an empirical analysis of developing and transition economies. Environ Dev Econ 15:241–274
Burbidge JB, Magee L (1988) Alternative transformations to handle extreme values of the dependent variable. J Am Stat Assoc 83(401):123–127
Cárdenas Rodríguez M, Haščič I, Johnstone N, Silva J, Ferey A (2014) Inducing private finance for renewable energy projects: evidence from micro-data, OECD Environment Working Papers no. 67, OECD Publishing. doi:10.1787/5jxvg0k6thr1-en
Corfee-Morlot J, Marchal V, Kauffmann C, Kennedy C, Stewart F, Kaminker C, Ang G (2012) Toward a green investment policy framework: the case of low-carbon, climate-resilient infrastructure. Environment Working Papers, no. 48, OECD Publishing, Paris. doi:10.1787/5k8zth7s6s6d-en
Criscuolo C, Menon C (2014) Environmental policies and risk finance in the green sector: cross-country evidence. OECD Science, Technology and Industry working papers, 2014/01, OECD Publishing. doi:10.1787/5jz6wn918j37-en
Della Croce R, Kaminker C, Stewart F (2011) The role of pension funds in financing green growth initiatives. OECD working papers on finance, insurance and private pensions, No 10, OECD Publishing. doi:10.1787/5kg58j1lwdjd-en
Halvorsen R, Palmquist R (1980) The interpretation of dummy variables in semilogarithmic equations. Am Econ Rev 70(3):474–475
IEA/IRENA (2013) Global renewable energy policies and measures database, international energy agency and international renewable energy agency. (Last accessed in February 2013 http://www.iea.org/policiesandmeasures/renewableenergy)
Johnstone N, Haščič I, Popp D (2010) Renewable energy policies and technological innovation: evidence based on patent counts. Environ Resour Econ 45(1):133–155
Kaminker C, Stewart F (2012) The role of institutional investors in financing clean energy. OECD Working Papers on Finance, Insurance and Private Pensions, No. 23, OECD Publishing. doi:10.1787/5k9312v21l6f-en
Koopmans TC, Rubin H, Leipnik RB (1950) Measuring the equation systems of dynamic economics. In: Koopmans TC (ed) Statistical inference in dynamic economic models, cowles commission monograph 10, Wiley, New York
Lillard Lee A, Constantijn WA Panis (2003) aML Multilevel Multiprocess Statistical Software, Version 2.0. EconWare, Los Angeles, California
Nelson F, Olson L (1978) Specification and estimation of a simultaneous-equation model with limited dependent variables. Int Econ Rev 19(3):695–709
Nicolli F, Vona F (2012) The evolution of renewable energy policy in OECD countries: aggregate indicators and determinants, FEEM working paper no. 51
OECD (2011) Invention and transfer of environmental technologies. OECD Studies on Environmental Innovation, OECD Publishing. doi:10.1787/9789264115620-en
OECD-EPAU (2013) Renewable Energy Policy Dataset, version February 2013. Compiled by the OECD Environment Directorate’s Empirical Policy Analysis Unit (Johnstone, N., Haščič, I., Cárdenas Rodríguez M., Duclert, T.) in collaboration with an ad hoc research consortium (Arnaud de la Tour, Gireesh Shrimali, Morgan Hervé-Mignucci, Thilo Grau, Emerson Reiter, Wenjuan Dong, Inês Azevedo, Nathaniel Horner, Joëlle Noailly, Roger Smeets, Kiran Sahdev, Sven Witthöft, Yunyeong Yang, Timon Dubbeling)
OECD (2015) Policy Guidance for investment in clean energy infrastructure: expanding access to clean energy for green growth and development, OECD Publishing. doi:10.1787/9789264212664-en
Pence KM (2006) The role of wealth transformations: an application to estimating the effect of tax incentives on saving. Contrib Econ Anal Policy 5(1):1–26
Pfeiffer B, Mulder P (2013) Explaining the diffusion of renewable energy technology in developing countries. Energy Econ 40:285–296
Popp D, Haščič I, Medhi N (2011) Technology and the diffusion of renewable energy. Energy Econ 33(4):648–662
Sargan JD (1964) Three-stage least-squares and full maximum likelihood estimates. Econometrica 32(1):77–81
Stigler GJ (1967) Imperfections in the capital market. J Polit Econ 75(3):287–292
Stiglitz J, Weiss A (1981) Credit rationing in markets with imperfect information. Am Econ Rev 71(3):393–410
Tobin J (1958) Estimation of relationships for limited dependent variables. Econometrica 26(1):24–36
World Bank (2013) The doing business project. World Bank, Washington DC
Zellner A, Theil H (1962) Three-stage least squares: simultaneous estimation of simultaneous equations. Econometrica 30(1):54–78
Author information
Authors and Affiliations
Corresponding author
Additional information
Disclaimer The opinions expressed in this paper are those of the authors and do not necessarily reflect the official views of the OECD or of the governments of its member countries.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Cárdenas Rodríguez, M., Haščič, I., Johnstone, N. et al. Renewable Energy Policies and Private Sector Investment: Evidence from Financial Microdata. Environ Resource Econ 62, 163–188 (2015). https://doi.org/10.1007/s10640-014-9820-x
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10640-014-9820-x