Analyzing the determinants of wind capacity additions in the EU. An econometric study.

1. Analyzing the determinants of wind capacity additions in the EU. An econometric study. Pablo del Río González Consejo Superior de Investigaciones Científicas Miguel Angel Tarancón Universidad de Castilla-La Mancha IAEE International Conference. Stockholm, June 21st 2011.

2. INDEX • Aim. • Background. • Existing literature. • Conceptual framework. • Hypotheses. • Results. • Concluding remarks.

3. AIM • Aim: • The aim of this paper is to identify the sources of differences in wind on-shore electricity generation capacity additions in the EU Member States. • An econometric model is developed in which capacity additions are explained according to several variables.

4. Background • Capacity additions in renewable electricity are crucial in order to decarbonise the energy system. • 20-20-20-10. • National Renewable Energy Action Plans indicating how MS plan to reach those targets. • Thus, an analysis of the main drivers and barriers to those capacity additions can shed light on the most appropriate policies to encourage them.

5. Existing literature on the determinants • Case studies (dozens). • Mostly focused on the policy variable. • A few econometric studies on the topic (four). • Focus on the US.

6. Conceptual framework • Techno-economic variables. • Policy variables. • Administrative and grid-connection variables. • Public acceptability. • General situation of the economy and investment climate. • Electricity variables. • Other variables.

7. Conceptual framework • Techno-economic variables. • Maturity levels. • Potentials and costs. • Existing capital stock • Other

8. Conceptual framework • Policy variables. • Deployment targets. • Instruments and design elements. • Support levels. • Policy stability.

9. WIND ON-SHORE Price ranges (average to maximum support) for direct support of wind onshore in EU27 (average tariffs are indicative) compared to long-term marginal generation costs (minimum to average costs). Support schemes are normalised to 15 years. Source: Ragwitz et al (2007).

10. What instruments are applied in Europe? Source: Resch et al (2009)

11. EVOLUTION OF SUPPORT SCHEMES IN THE EU

12. EVOLUTION OF SUPPORT SCHEMES IN THE EU Source: European Commission (2008)

13. Conceptual framework • Administrative and grid-connection variables. • Public acceptability. • General situation of the economy and investment climate. • Electricity-sector variables. • Other variables.

14. Data • 24 EU countries. • Data for dependent and explanatory variables: different sources.

15. The hypotheses • The dependent variable

16. The hypotheses

17. The hypotheses

18. Results Correlation matrix

19. Results Ramsey-RESET test.

20. Results Breusch-Pagan/Cook-Weisberg test.

21. Results Information Matrix Test.

22. Results Regressions (standardised coefficients).

23. Results • RESUPWIN • Positive sign. • Not statistically significant. • Support levels not determining factor • Confirmation of the results in other studies.

24. WIND ON-SHORE Source: European Commission (2008).

25. Results • ADPOTWIN • Positive sign. • Not statistically significant. • Potentials not determining factor • Schmalensee (2009) for the U.S. • Implications for effectiveness and cost-efficiency.

26. Results • TYPSUPWIN • Positive sign. • Not statistically significant. • FITs have not led to greater capacity additions. • Type of support scheme is not as relevant as expected. • Key variable: risks.

27. Results • Four major aspects lead to large investors’ RISKS, some are related to the instrument, others are not: • The type of instrument. • General investment risks in a country. • Constantly changing RES-E support schemes • The design details of the instrument

28. Results • BCI • Positive sign. • Statistically significant. • Support for 3) and 4).

29. Results • CHANGESYS and ADAPSYS • Negative sign. • Statistically significant. • Support for 2).

30. Results • ADWARWIN • Negative sign. • Statistically significant. • It confirms the relevance of administrative barriers as a main barrier to wind investments

31. Results • SHANUHY • Negative sign. • Not statistically significant. • Complementarity.

32. Results • ACCWIN • Negative sign. • Not statistically significant. • Indirect effect?? Correlation RESUPWIN and ACCWIN is 0.24

33. Results • ELDEMPH05 • Positive sign. • Not statistically significant. • Cost-competitiveness with other energy sources

34. Concluding remarks • The statistical significance and economic relevance of the explanatory variables coincide. • Relevance of risks and stability of regulation. • Security and stability vs. flexibility. • Do the right thing from the start!! Avoid major changes and retroactivity. • Reduce administrative barriers.

35. Concluding remarks • Increasing support levels: • -unlikely to trigger capacity additions? Threshold effects? • -while leading to windfall profits. • Potentials: Are capacity additions taking place in the EU where better wind resources are available? • Type of support scheme.

36. Limitations and avenues for further research • More sophisticated econometrics?? Any suggestion? • Small sample size. • Cross-section data, i.e. time-varying explanatory variables are not included. • A standard OLS model may lead to biased and inconsistent parameters due to the omission of time-variant covariates.

37. Limitations and avenues for further research • Panel-data models: the real-world of data availability. • Analyse the impact of design elements with the help of econometric models.

38. pablo.delrio@cchs.csic.es