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Dynamic Policy Impacts on a Technological-Change System of Renewable Energy: An Empirical Analysis

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Abstract

Recently, harmonization of renewable energy policies has drawn growing attention as an important issue. To find the most effective combination, this paper identifies simultaneous interactions in an endogenous technological-change system and analyzes empirically the static and dynamic impacts of renewable energy policies in solar PV and wind power. The empirical results indicate that policy outcomes create a virtuous cycle in the technological change system through market oppurntunity, learning-by-searching, and learning-by-doing. According to the results, the static impact of technology-push and tariff incentive policies are effective on invention, while renwables obligation and CO2 tax appear to encourage cost reduction in the technologies. When the dynamic impacts of policy are considered, tariff incentives appears to outperform a renewables obligation policy with very small margin.We also found that the dynamic impact of CO2 tax varies with the level of technological development maturity.

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Notes

  1. Investment covers almost all the levelized cost of renewable energy, approximately 87 %, on average, for solar PV and 75 %, on average, for wind power (IEA-NEA-OCED 2010). Accordingly, in this paper, the terminology ‘innovation’ has very narrow scope (cost reduction). However, in general, ‘innovation’ has a broader meaning like the commercialization of a new product. It may cover invention, new product development and commercialization. In this paper, we follow the definition of Söderholm and Klaassen (2007) on ‘innovation’.

  2. As anonymous reviewers pointed out, model set-up for the policy in this paper still has some limitations. One of the important limitations is that policy is treated as ‘exogenous’ although with this system approach one could expect that both the stringency and the mix of policy could change as the technology matures or market environment changes. Recently, Ek and Söderholm (2010) addressed this issue.

  3. Some previous studies mention various factors to account for international knowledge spillover such as geographic differentiation (Kneller 2005), trades between two countries (Coe and Hepman 1995; Coe et al. 2009), and internal factor of spillover’s recipients (absorptive capacity; for example, Bosetti et al. 2008; Cohen and Levinthal 1989; Griffith et al. 2003). Among them, this paper focuses on absorptive capacity [Eqs. (4), (5)] rather than other factors. First of all, we believe “knowledge” transfer may depend more on knowledge related capacity (absorptive capacity, for example) than on physical factors such as geographical differentiation. Kneller (2005) finds that absorptive capacity, rather than physical distance, plays a greater role in determining the amount of knowledge transfers on the international level. Second, it is quite difficult to access geographical and trades data between every two countries.

  4. Australia, Austria, Canada, Denmark, France, Germany, Italy, Japan, Republic of Korea, the Netherlands, Norway, Spain, Sweden, Switzerland, the United Kingdom, and the United States.

  5. Canada, Denmark, Finland, Germany, Italy, Japan, the Netherlands, Norway, Spain, Sweden, Switzerland, the United Kingdom, and the United States. The Annex II group is obligated to reduce \(\hbox {CO}_{2}\) emissions and to offer technology transfer and loan support to developing countries if clean technologies can be viably introduced and diffused, either by the country’s vested interest or because of global regulations. These developed countries are suitable for this study because they have advanced technology and well-constructed system for renewable energy.

  6. The government support for R&D in time t may affect contemporaneous invention but also can incorporate time lags creation of new invention based on government R&D expenditure may take some times and the time lags depend on certain characteristics of technologies such as a technical standard, technological level, R&D efficiency, and path-dependence (i.e., technical inertia). Accordingly referring to a study (Peters et al. 2012), in this paper we considered public R&D variable, which incorporates several time lags from \(t-1\) to \(t-3\), as well as t. As estimated results, public R&D has a significant effect on invention in time \(t-1\) of solar PV, while in time t of wind power technology.

  7. For example, the United Kingdom has levied taxes with constant rates on fossil fuel from 2001 to 2005, and Germany has set small, increased, tariff incentives, 0.24€cents/kWh, from 1991 through 2000 (IEA 2004).

  8. These specifications are based on economic reasoning. For example, we assume that science and research related variable such as ‘public R&D \((TP_{i,n,t})\)’ and ‘national scientific resource \((SR_{n,t})\)’ influence ‘invention’ (creation of new knowledge) ‘directly’ and only influence ‘cost and diffusion’ ‘indirectly’ by affecting invention. We also assume that ‘raw material prices \((RMP_{i,t})\)’ and ‘GDP’ affect ‘cost’ ‘directly’ and only influence ‘diffusion and invention’ ‘indirectly’ because renewable system costs are very sensitive to raw material price (such as silicon price) and the cost for installation of renewable system may directly depend on infrastructure development and production ability which is measured by each country’s gross domestic products (GDP). In addition, price of fossil fuels \((FFP_{n,t})\) and size of electricity market measured by electricity generation \((TEG_{n,t})\) are assumed to have ‘direct’ positive impact on ‘diffusion’ of renewable energy because fossil fuels can be the substitute goods for renewable energy technologies and diffusion level of renewables directly depends on size of electricity market.

  9. We compared estimation results of 3 different estimation methods, i.e., 2SLS, Seemingly Unrelated Regression (SUR) method, and 3SLS (Tables 8, 9, 10, 11, 12, 13 in appendix). SUR considers correlations between equations, but doesn’t consider endogeneity (Greene 2011). The results show that there are not big differences in coefficients and statistical significance level between the 3 methods. In addition, we employed tests of significance of correlation (or covariance) among error terms of three equations. For both renewable technology, null hypothesis that covariance matrix is diagonal (zero covariance between equations) is rejected with statistics 40.52 (for PV solar) and 20.22 (Wind Power) of \(\chi ^{2}\) distribution in likelihood ratio (LR) tests, which means that there is correlation among simultaneous equations. Therefore, as mentioned above, it is more appropriate to impose 3SLS techniques to estimate not individual equation but the whole of equations simultaneously.

  10. Criterion on statistical significance in this paper is 10 % significance level. If p value of a coefficient does not exceed 10 % then we cannot reject hypothesis that the coefficient is 0, which means that this coefficient is statistically significant (it is not “zero”). We may have stricter criteria such as 5 or 1 %. We also indicate significances of the coefficients not only for 10 % significance level (*) but also for 5 and 1 % significance level (**, ***) in the tables in the paper.

  11. This paper calculates the rate (for market opportunity) as \((2^{a_1}-1)\) based on Klaassen et al. (2005)’s study to show the constant increases in invention for each diffusion (cumulative capacity) doubling and to compare this paper’s results with those of previous studies. We also use the same formula for learning-by-doing rates ((\(2^{b_1 }-1)\) in innovation model) and for learning-by-searching ((\(2^{b_2 }-1)\) in innovation model).

  12. This paper calculates the rate by introduction of a certain policy as \((e^{a_5}-1)\).

  13. As we described in Sect. 2.2, in the case of the renewable obligation, virtual competitive market for renewables is created. And more intensive competition in the renewable obligation may bring up more transactions and related costs between renewable producers, electric utilities and regulator. The transaction costs include information and searching costs (finding who has the lowest cost in renewable producers, for example), cost of proving (proving that I am the cheapest option for renewable, for example), bargaining costs between chosen renewable producers and electric utilities (or regulator). For more details, see Tirole (1988), p. 21–27.

  14. This paper calculates the rate as \(((1-0.1)^{c_1 }-1)\).

  15. The result is an isolation of dynamic (long-run) impact of single policy’s introduction or change.

  16. To determine whether the public R&D reflects an overinvestment on innovation of renewables technologies or not, we calculated public R&D efficiency with respect to cost reduction (the ratio of total cost reduction, which reflects amount of diffusion increased by public R&D, to the input of the public R&D budget) based on our dynamic impact results of public R&D using cross-country average data in the analysis periods for each technology. The calculation shows the ratio of solar PV and wind power as 227.87 and 228.89, respectively, implying that public R&D do not reflect an overinvestment and shows substantial efficiency for innovation (cost reduction).

  17. An example would be the encouragement of foreign direct investment (FDI) and international cooperation for technology development.

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Authors and Affiliations

  1. Korea Information Society Development Institute (KISDI), 18, Jeongtong-ro, Deoksan-myeon, Jincheon-gun, Chungcheongbuk-do, 365-841, Republic of Korea

    Kyunam Kim

  2. Department of Energy Resources Engineering, College of Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea

    Eunnyeong Heo

  3. Technology Management, Economics and Policy Program, College of Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea

    Yeonbae Kim

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  1. Kyunam Kim
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Correspondence to Yeonbae Kim.

Appendix: Results Comparison for Different Estimation Methods

Appendix: Results Comparison for Different Estimation Methods

See Tables 8, 9, 10, 11 and 12.

Table 8 Estimation results of the invention model, solar PV
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Table 9 Estimation results of the innovation model, solar PV
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Table 10 Estimation results of the diffusion model, Solar PV
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Table 11 Estimation results of the invention model, wind power
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Table 12 Estimation results of the innovation model, wind power
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Table 13 Estimation results of the diffusion model, wind power
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Kim, K., Heo, E. & Kim, Y. Dynamic Policy Impacts on a Technological-Change System of Renewable Energy: An Empirical Analysis. Environ Resource Econ 66, 205–236 (2017). https://doi.org/10.1007/s10640-015-9946-5

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