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

  • Aim.

  • Background.

  • Existing literature.

  • Conceptual framework.

  • Hypotheses.

  • Results.

  • Concluding remarks.


AIM EU.

  • 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 EU.

  • 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 EU.

  • 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 EU.

  • 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 EU.

  • Techno-economic variables.

    • Maturity levels.

    • Potentials and costs.

    • Existing capital stock

    • Other


Conceptual framework2
Conceptual framework EU.

  • Policy variables.

    • Deployment targets.

    • Instruments and design elements.

    • Support levels.

    • Policy stability.


WIND ON-SHORE EU.

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).


What instruments are applied in Europe? EU.

Source: Resch et al (2009)



EVOLUTION OF SUPPORT SCHEMES IN THE EU EU.

Source: European Commission (2008)


Conceptual framework3
Conceptual framework EU.

  • Administrative and grid-connection variables.

  • Public acceptability.

  • General situation of the economy and investment climate.

  • Electricity-sector variables.

  • Other variables.


Data EU.

  • 24 EU countries.

  • Data for dependent and explanatory variables: different sources.


The hypotheses
The hypotheses EU.

  • The dependent variable




Results
Results EU.

Correlation matrix


Results1
Results EU.

Ramsey-RESET test.


Results2
Results EU.

Breusch-Pagan/Cook-Weisberg test.


Results3
Results EU.

Information Matrix Test.


Results4
Results EU.

Regressions

(standardised coefficients).


Results5
Results EU.

  • RESUPWIN

  • Positive sign.

  • Not statistically significant.

  • Support levels not determining factor

  • Confirmation of the results in other studies.


WIND ON-SHORE EU.

Source: European Commission (2008).


Results6
Results EU.

  • ADPOTWIN

  • Positive sign.

  • Not statistically significant.

  • Potentials not determining factor

  • Schmalensee (2009) for the U.S.

  • Implications for effectiveness and cost-efficiency.


Results7
Results EU.

  • 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 EU.

  • 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 EU.

  • BCI

  • Positive sign.

  • Statistically significant.

  • Support for 3) and 4).


Results10
Results EU.

  • CHANGESYS and ADAPSYS

  • Negative sign.

  • Statistically significant.

  • Support for 2).


Results11
Results EU.

  • ADWARWIN

  • Negative sign.

  • Statistically significant.

  • It confirms the relevance of administrative barriers as a main barrier to wind investments


Results12
Results EU.

  • SHANUHY

  • Negative sign.

  • Not statistically significant.

  • Complementarity.


Results13
Results EU.

  • ACCWIN

  • Negative sign.

  • Not statistically significant.

  • Indirect effect?? Correlation RESUPWIN and ACCWIN is 0.24


Results14
Results EU.

  • ELDEMPH05

  • Positive sign.

  • Not statistically significant.

  • Cost-competitiveness with other energy sources


Concluding remarks
Concluding remarks EU.

  • 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 EU.

  • 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 EU.

  • 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 EU.

  • 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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