Renewable energy sources for power production (RES) are an essential element of international climate change mitigation measures. Their contribution regarding emission reduction, however, cannot be directly measured. Several methods have been employed in the literature to calculate the emission reduction of RES, however, validating them is not possible. Hence, the question presents itself as to the relative advantages and disadvantages of the respective methods. To address this question, this paper examines the existing methodological approaches, namely (1) the displacement estimations, (2) an econometric approach and (3) optimization model-based dispatch calculations. In a first step, the respective approaches are discussed and quantitatively compared against each other. Subsequently, all methods are implemented for Germany for the years 2016 and 2017 and the specific emissions displaced by RES are calculated. The results indicate that all methods calculate CO2 reductions for wind onshore between 500–900 kgCO2 per MWh and for solar between 400–700 kgCO2 per MWh, indicating that each can provide valuable insights. For Germany, employing a dispatch model entails advantages since most drivers of energy system-related carbon emissions can be incorporated and the method can be applied to all RES technologies. In particular, the inclusion of cross-border electricity flows and the measurement of dynamic effects, two important aspects with possibly substantial effects on carbon emissions, can be incorporated.
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The range is the result of the different assumptions made concerning the displacement of RES of exports and hydro. For the lower value it is assumed that no additional carbon emissions are avoided by exports and hydro while for the upper value the displacement factor of coal is assumed.
The significance of the effect of renewable energy generation is obtained by applying the Wald test for joint significance because the effect is composed of several coefficients. Newey-West robust standard errors are estimated to account for heteroskedasticity and autocorrelation.