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Global Expansion of Renewable Energy Generation: An Analysis of Policy Instruments

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Abstract

This study analyzes the degree to which renewable energy policies, in particular feed-in tariffs and renewable portfolio standards, facilitate renewable energy generation growth across a wide range of countries using an original cross-national dataset of 164 countries between 1990 and 2010. Results provide evidence that both policies are important predictors of renewable energy market growth. The dependent variable is operationalized first as the percentage of total electricity from renewable energy and second as the annual increase in total renewable energy generation in a country. Results are robust to several alternative model specifications including those that exclude hydroelectric generation in the construct of renewable energy. The degree to which feed-in tariffs are endogenous, however, is not conclusive. Besides the prominent role of these policies, results reveal that factors related to annual increases in renewable energy differ from those related to an overall transition toward greater reliance on renewable energy. This suggests that simply increasing renewable generation does not necessarily decrease reliance on fossil fuels or help countries make the shift to a clean energy economy.

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Notes

  1. Since 2010, there have been at least nine multi-country, empirical studies of RE policy effectiveness. Six of these studies included a sample of fewer than 30 countries, all of them OECD members (Polzin et al. 2015; Johnstone et al. 2010; Popp et al. 2011; Gan and Smith 2011; Jenner et al. 2012; Marques and Fuinhas 2011). The time span in these studies ranges from 1994–2003 (Gan and Smith 2011) to 1978–2003 (Johnstone et al. 2010), with an average span of 18 years. To the authors’ knowledge, three studies include countries outside the OECD. Aguirre and Ibikunle (2014) include the OECD, Brazil, Russia, India, China, and South Africa from 1990 to 2010. Dong (2012) includes 53 unspecific countries from 2005 to 2009. The largest sample used in a previous study on RPS and FIT effectiveness, to the authors’ knowledge, is the 122 country sample evaluated in Zhao et al. (2013) from 1980 to 2010.

  2. A number of studies that assess RE policy effectiveness have used capacity as a dependent variable, particularly when the research question is focused specifically on investment in RE. However, data on RE capacity are limited outside the OECD. Also, even if a country has significant installed RE capacity resources, they may not actually generate electricity, particularly if they are not well maintained or if economic and political conditions change. Since these factors are most likely to occur outside OECD countries, we favor generation as the best measure of RE market developments across a diverse range of countries.

  3. To ensure consistent coding across countries, we define the policy measures as follows. A FIT is a policy that provides a per-kWh payment to RE generators at higher than normal electricity rates or calculated to ensure cost recovery for investors for at least 10 years. An RPS is a policy that mandates or requires a utility or grid company to procure a certain percentage of its electricity from renewable sources or green certificates each year.

  4. Statistical results were entirely consistent with a 50 % threshold rather than a 25 % threshold. We used this coding system to track RPS and FIT only in these federalist countries. We did not track any subsidies or incentives at the sub-national level, partly because even in federalist countries, subsidies are often offered at the national level, and partly because data availability on sub-national subsidies and incentives would likely be better in developed and Anglophone countries, introducing an unnecessary source of bias.

  5. It would additionally be ideal to include a measure of electricity market regulatory status that defines the degree to which a country’s electricity sector is regulated or deregulated. Such data, however, are not available across such a large country sample.

  6. As a check on the quality of the Freedom House data, we also estimate models using data from the Polity Project, which classifies the regime on a spectrum from autocracy to consolidated democracy (Center for Systemic Peace 2013). We note differences in results using the two measures in a subsequent footnote.

  7. This same set of Rothstein tests are run on the alternative dependent variable used in the much of the previous literature, total renewable energy generation, and pass without any source of concern.

  8. We additionally ran models that excluded the regulatory framework variable. Both policy variables retained statistical significance and the results on the control variables were also entirely consistent with those models in which regulatory frameworks are included.

  9. When we check the robustness of this result with the Polity variable, we find that the direction of association reverses so that it is consistent with our a priori assumptions. We elect not to present results using the Polity variable instead of the freedom variable because the Polity data are missing a large number of observations.

  10. On average across the study period the following countries have low freedom scores and well over half of their electricity comes from hydroelectricity: Afghanistan, Albania, Angola, Bhutan, Bosnia and Herzegovina, Burundi, Cameroon, Central African Republic, Ethiopia, Gabon, Georgia, Ghana, Guinea, Lao PDR, Rwanda, Togo, and Tajikistan. Several other countries have low freedom scores and just under half of their electricity comes from hydroelectricity: Cote d’Ivoire, Haiti, Kenya, Nigeria, Sudan, Swaziland, Zimbabwe, and Vietnam.

  11. This same result holds for both sets of models when the policy variables are lagged by 1 year.

  12. Results on the control variables are in keeping with previous results discussed above, and can be made available upon request.

  13. We also ran these same models on the full sample of countries, not just the ever-adopters. Model results did not change.

  14. Interestingly, current or formerly Communist regimes appear more likely to have universal FIT rates.

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Authors and Affiliations

  1. School of Public and Environmental Affairs, Indiana University, 1315 E. 10th Street, Room 535, Bloomington, IN, 47401, USA

    Sanya Carley & Jennifer N. Brass

  2. School of Government and Public Policy, University of Arizona, Tucson, AZ, USA

    Elizabeth Baldwin

  3. Department of Political Science, Indiana University, Bloomington, IN, USA

    Lauren M. MacLean

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  1. Sanya Carley
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  2. Elizabeth Baldwin
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  3. Lauren M. MacLean
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  4. Jennifer N. Brass
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Correspondence to Sanya Carley.

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Carley, S., Baldwin, E., MacLean, L.M. et al. Global Expansion of Renewable Energy Generation: An Analysis of Policy Instruments. Environ Resource Econ 68, 397–440 (2017). https://doi.org/10.1007/s10640-016-0025-3

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