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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. . INDEX. Aim.

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analyzing the determinants of wind capacity additions in the eu an econometric study

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.

index
INDEX
  • Aim.
  • Background.
  • Existing literature.
  • Conceptual framework.
  • Hypotheses.
  • Results.
  • Concluding remarks.
slide3
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.
background
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.
existing literature on the determinants
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.
conceptual framework
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.
conceptual framework1
Conceptual framework
  • Techno-economic variables.
    • Maturity levels.
    • Potentials and costs.
    • Existing capital stock
    • Other
conceptual framework2
Conceptual framework
  • Policy variables.
    • Deployment targets.
    • Instruments and design elements.
    • Support levels.
    • Policy stability.
slide9
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).

slide10
What instruments are applied in Europe?

Source: Resch et al (2009)

slide12
EVOLUTION OF SUPPORT SCHEMES IN THE EU

Source: European Commission (2008)

conceptual framework3
Conceptual framework
  • Administrative and grid-connection variables.
  • Public acceptability.
  • General situation of the economy and investment climate.
  • Electricity-sector variables.
  • Other variables.
slide14
Data
  • 24 EU countries.
  • Data for dependent and explanatory variables: different sources.
the hypotheses
The hypotheses
  • The dependent variable
results
Results

Correlation matrix

results1
Results

Ramsey-RESET test.

results2
Results

Breusch-Pagan/Cook-Weisberg test.

results3
Results

Information Matrix Test.

results4
Results

Regressions

(standardised coefficients).

results5
Results
  • RESUPWIN
  • Positive sign.
  • Not statistically significant.
  • Support levels not determining factor
  • Confirmation of the results in other studies.
slide24
WIND ON-SHORE

Source: European Commission (2008).

results6
Results
  • ADPOTWIN
  • Positive sign.
  • Not statistically significant.
  • Potentials not determining factor
  • Schmalensee (2009) for the U.S.
  • Implications for effectiveness and cost-efficiency.
results7
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.
results8
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
results9
Results
  • BCI
  • Positive sign.
  • Statistically significant.
  • Support for 3) and 4).
results10
Results
  • CHANGESYS and ADAPSYS
  • Negative sign.
  • Statistically significant.
  • Support for 2).
results11
Results
  • ADWARWIN
  • Negative sign.
  • Statistically significant.
  • It confirms the relevance of administrative barriers as a main barrier to wind investments
results12
Results
  • SHANUHY
  • Negative sign.
  • Not statistically significant.
  • Complementarity.
results13
Results
  • ACCWIN
  • Negative sign.
  • Not statistically significant.
  • Indirect effect?? Correlation RESUPWIN and ACCWIN is 0.24
results14
Results
  • ELDEMPH05
  • Positive sign.
  • Not statistically significant.
  • Cost-competitiveness with other energy sources
concluding remarks
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.
concluding remarks1
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.
limitations and avenues for further research
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.
limitations and avenues for further research1
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.
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