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much more beneficial to buy certificates than subsidize renewable energies. Nevertheless, in 2009, about 340 million metric tonnes of CO2 emissions were saved by the use of RES in EU. Taking the price of 15 EUR/tonne of CO2 in 2009, savings will result in EUR 51 billion for 2009 year alone (Poser et al., 2014). In order to see correlation between change in RES share in gross final energy consumption and greenhouse gas (hereinafter GHG) abatement for Germany we will run the following regressions 14 GHGE = β 0 + β 1 RESE GHGHC = β 0 + β 1 RESHC GHGT = β 0 + β 1 REST where, GHGE = GHGE t − GHGE t−1 , i.e. yearly change in amount of GHG abatement induced by change in RES-E share in gross final electricity consumption; and RESE = RESE t − RESE t−1 , i.e. yearly change of RES-E share in gross final electricity consumption. Analogously, other two regressions should be read. Likewise, all three regressions were tested for heteroskedasticity (hettest), normality (swilk) and specification test for omitted variables (ovtest) results can be found in Appendix A in notes under the relevant table. No regressions were run for the Czech Republic due to lack of data. Figure 4 is a scatter plot for all three regressions. Y-axis is measured in tonnes of CO2 equivalent and X-axis is measured in percentage points. Regression of GHG abatement by RES-E resulted in R-squared of 0.5653, i.e. 56.53% of variability in dependent variable is explained by the independent one. Likewise, equation of the regression – a blue line – 14 Stata outputs for all regressions can be found in Appendix A. 11
Figure 4: GHG abatement by different types of RES by E by H/C by T Linear (by E) Linear (by T) Linear (by H/C) 25000 20000 15000 10000 5000 0 -2,0 -1,0 0,0 1,0 2,0 3,0 4,0 5,0 6,0 -5000 -10000 is: ̂ GHGE = −317.18 (1820.415) + 3609.046RESE (845.79) Coefficient is statistically significant even at 1% significance level. Thus, one percent increase of RES-E share in gross final electricity consumption results in abatement of 3609.046 tonnes of CO2 equivalent. Constant in this regression is statistically insignificant. Regression of GHG abatement by RES-H/C has R-squared of 0.5113. Equation of the regression line – an orange line – is: GHGHC ̂ = −290.5662 + 3132.254 RESHC (818.14) (818.3969) Constant is statistically insignificant, whereas coefficient is statistically significant even at 1% significance level. Therefore, when RES-H/C share in final energy consumption in heating and cooling is increased by 1%, GHG abatement increases by 3132.254 tonnes of 12
Page 1 and 2: MPRA Munich Personal RePEc Archive
Page 3 and 4: 1 Introduction This paper provides
Page 5 and 6: Figure 2: German price structure 0,
Page 7 and 8: Hydropower also was affected, in 20
Page 9 and 10: of nuclear power. In contrast with
Page 11: Behind the meter storages are locat
Page 15 and 16: number of the certificates will gra
Page 17 and 18: Figure 5: German landfill, sewage a
Page 19 and 20: Therefore, one cent of FIT correspo
Page 21 and 22: Figure 8 is a scatter plot for offs
Page 23 and 24: Figure 9: German biomass 400 350 30
Page 25 and 26: level of 0.7148. The equation of th
Page 27 and 28: esult in same installed capacities.
Page 29 and 30: Bibliography • Erdmann, G. (2008)
Page 31 and 32: Table 4: Stata output for German GH
Page 33 and 34: Table 10: Stata output of German re
Page 35 and 36: Table 16: Stata output of German re

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