In 2019, CO2 emissions in Germany dropped by 40 Mt compared to the previous year, of which a major share was realized in the power sector. Many factors, such as carbon and fuel prices as well as the capacity expansion of renewable energy sources, influence Germany’s CO2 emissions. This ex-post regression analysis of the German power market from 2016 to 2019 investigates the contribution of each factor to the CO2 emissions reduction. The results suggest that short-term market developments for gas prices are responsible for a major share of 2019’s CO2 emissions reduction when compared to 2018 levels. The sustainable transition of the power system, in form of adequate CO2 prices and additional renewable energy capacities, becomes the dominating factor when analyzing the CO2 emissions reductions over the longer time span from 2016 to 2019. Exogenous factors, such as increasing gas prices, could raise Germany’s carbon emissions again. This reversion of carbon emissions reductions can be avoided if the overall-society compromise on the coal phase-out is realized sooner than later.
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The costs for carbon emissions per MWh are calculated by multiplying the carbon price with the related emission factor and dividing this product by the power plant efficiency.
Natural gas and coal, especially lignite, are segmented markets with different price developments and dynamics for various regional markets, such as continental Europe, UK, Japan, the US.
The following time series from Thomson Reuters have been used: EEXEUAS (daily CO2-price at EEX), EEXNNCG (daily gas price at NCG) and LMCYSPT (daily coal prices at ICE for ARA).
The tests’ null hypotheses of homoskedastic and uncorrelated error terms, respectively, are rejected at the 0.1% significance level for all five regressions.
Note that gas prices are denoted in EUR/MWh and coal prices in EUR/t, which is roughly equal 0.14 EUR/MWh (assuming an energy content of roughly 7 MWh per 1 t of coal, based on API2). Hence the coefficient of -20 has to be multiplied by ca. 7 to obtain the effect of a 1 EUR/MWh increase, which puts the effects of gas and coal prices in the same order of magnitude.
These numbers are calculated as the sums of coefficients in the first three lines in columns 2)–5) in Table 3 (columns 2)–4) with a positive instead of negative sign).
For generation from nuclear power plants, not detailed in this analysis, the adjusted R2-value is below 50%, which supports the notion of declining explanatory power for base-load power plants.
For both year-to-year analyses, there is a static effect, which captures categorical changes between years, not captured by any other variable. These effects correspond to the year dummy variables of the regression.