An econometric analysis of support scheme effects on investments in renewable energy in Europe

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2 An econometric analysis of support scheme effects on investments in renewable energy in Europe 37th IAEE International Conference June 17th, 2014 New York Professor Torjus Folsland Bolkesjø Petter Thørring Eltvig Erik Nygaard (presenter)

3 BACKGROUND Renewable electricity (RES-E) in Europe heavily supported through various policy schemes Especially through feed-in tariffs (FIT) Still, quantitative analysis of European policy scheme effects is an area of surprisingly sparse research Especially analyses taking differences in policy design and market into account Jenner (2012) and Jenner et al. (2013) exceptions Develops new indicators for the strength of the FIT and regress these, and other variables, on RES-E capacity/production Quite surprisingly, they do not find clear evidence that FIT has affected wind power capacities in Europe 3

4 SCOPE OF THIS STUDY Objective Retest how FITs and other RES-E policies has affected RES-E investments Countries France, Germany, Italy, Spain and the UK (EU s largest electricity producers) Time period: Incentive schemes included: Feed in tariffs (FIT), renewable requirements (RPS) and tendering schemes Technologies PV, biomass, onshore wind 4

5 SFIT Behavior model TFB6 Return on investment (ROI) calculated for: 1. RES-E investment without FITs 2. RES-E investment with FITs 3. An investment with return equal to bank lending rate 5

6 Slide 5 TFB6 Her må du beregne en del tid Torjus Folsland Bolkesjø, 6/11/2014

7 SFIT FIT s strength ROI with FITs subtracted ROI in most profitable alternative scenario share of ROI attributable to FITs isolated 6

8 Added capacity and SFIT for PV in Germany 7

9 Added capacity and SFIT for onshore wind power in Germany 8

10 Added capacity and SFIT for bioelectricity in Germany 9

11 ECONOMETRIC MODEL SPECIFICATION Dependent variable Cumulative installed capacity Independent variables Feed in tariffs: SFIT Jenner (2012) Renewable portfolio standard (RPS): IPR Yin & Powers (2010) Tendering schemes: Dummy variable Control variables: Nuclear share, coal share, renewable share, gas share, petroleum share in el. mix. Growth in: GDP per capita, energy use per capita 10

12 DATA Comprehensive panel data set including: Installed capacities Electricity prices Costs FIT scheme characteristics RPS characteristics Tendering schemes (yes or no) Production mix Specified for each year, country and technology 11

13 RESULTS Variable SFIT IPR Tender (binary) Nuclear share Coal share Gas share Petroleum share Renewable share Energy use per capita GDP per capita N R-squared Table 1: Fixed-effects regression models for onshore wind, solar PV and biomass Onshore wind 1.458*** *** *** *** ** ** * Significant at: *p<0.10, **p<0.05, ***p<

14 RESULTS Variable Onshore wind Solar PV SFIT IPR Tender (binary) Table 1: Fixed-effects regression models for onshore wind, solar PV and biomass 1.458*** *** 1.871*** Nuclear share Coal share Gas share Petroleum share Renewable share Energy use per capita GDP per capita N R-squared *** *** ** ** * Significant at: *p<0.10, **p<0.05, ***p< *** ** *** **

15 RESULTS Variable Onshore wind Solar PV Biomass SFIT IPR Tender (binary) Table 1: Fixed-effects regression models for onshore wind, solar PV and biomass 1.458*** *** 1.871*** *** Nuclear share Coal share Gas share Petroleum share Renewable share Energy use per capita GDP per capita N R-squared *** *** ** ** * Significant at: *p<0.10, **p<0.05, ***p< *** ** *** ** * *

16 MAIN CONCLUSIONS PV and onshore wind power have, due to FiTs, had high expected ROI in the five largest power markets in Europe the last five years Significantly lower ROI for bioelectricity Feed in tariffs: Positive impact on PV (in line with Jenner (2012) and Jenner et al. (2013)) Positive impact on onshore wind (contrary to Jenner (2012) and Jenner et al. (2013)) No significant effect found for bioelectricity RPS is found to positively impact bioelectricity A marginal FIT increase of 1 -cent/kwh would increase the annual RE generation by 94 to 9886 GWh in our sample 15

17 Thank you for your attention!

18 Impacts of a 1 c /kwh increase in the FIT level ±1 eurocent change of FIT amount France Germany Italy Spain UK Technology PV Wind PV Wind PV Wind PV Wind PV Wind Δ SFIT Δ Capacity MW Capacity factor % 12,0 22,3 11,0 17,5 17,0 19,1 17,0 24,8 8,0 26,1 Δ Production GWh Cost of 1 cent increase Mill. euro 2,84 24,31 24,01 98,86-23,57 5,22 96,11 0,94 16,1 17

19 Added capacity and SFIT for PV and wind in Spain 18

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