Last updated on 16 Mar 2017 15:51 (cf. Authors)
For many years, the focus for emission inventory work in Germany has been on the past. The main goal was to provide complete time series of emission data for all source categories. This report deals with these efforts in great detail.
While decent data on historic emissions are key to the political process and to decisions on abatement technology promotion, future emission paths do have the power to shed a new light on these discussions. The main idea behind emission projections is not so much about exact numbers, but about the comparison of scenarios. Scenarios are made up by a distinct number of actions and measures, translated into future values for activity rates and emission factors. The questions answered by emission projection is therefore not "How many kilo-tonnes of sulfur oxide will be emitted in 2020?" but "How does scenario X/provision Y compare to the reference emission path? Does it significantly help our goal to further reduce emissions?"
The German projections have seen a major overhaul in 2010. A new emission projection database (called "emission scenarios") has been created. Multiple scenarios are taken into account, sketching development of activity data and emission factors up to 2050. The new system features integrated assessment for both greenhouse gases (GHG) and air pollutants. In particular, existing projections for GHG can be applied to air pollution contexts. The databases also allows for the flexible combination of distinct scenarios for specific source categories to add up to a complete inventory. Today, the projection database is fully operational and used as the common basis for reporting on emission projections.
The main German emission inventory uses a database called "Central Emission System" (CES). The CES holds very detailed information on activity data (AD) and emission factors (EF) for GHG, air pollutants, heavy metals, and persistent organic pollutants. To give some number: there are about 2,000 AD, 20,000 EF, and 20,000 emission time series' in the CES. All these data together represents the German inventory for historic emissions.
For projection purposes, this structure is too detailed. Projecting future trajectories for such a high number of time series has proven to be very difficult, if not impossible. Therefore, the new projection system applies some aggregation techniques. The number of AD time series' is boiled down to 153, for example. This is mainly achieved by discarding source category distinction: for the most important sectors "Energy" and "Industrial Processes" the system splits data into 13 instead of hundreds of sub-categories. The aggregation is automatically fed by the CES and can be easily updated upon changes in the main database.
By default, all data from the CES for historic years is collected and put into the projection database. In the "Reference" scenario (REF, see below) most time series are set to extrapolate the last given value, i.e. all future emissions will remain at the same level as the last historic value. Other scenarios are free to change any of the extrapolated values of the REF to reflect their "storyline". The figure below illustrates this workflow.
Germany's current projections are summarized and published in the report "Luftqualität 2020/2030: Weiterentwicklung von Prognosen für Luftschadstoffe unter Berücksichtigung von Klimastrategien" which roughly translates to "Air quality 2020/2030: Developing scenarios for air pollution taking into account climate change strategies". This reports combines and updates a long list of work done in the field of emission projections earlier and also includes a variety of additional, detailed information beyond the scope of this IIR chapter, such as projected gridded data and local, city-level analysis.
For national emission projections, the report draws from both climate projections activity data and sector-specific reports on air pollution emission factor development in the future. It establishes a "patchwork" of scenarios combining all these inputs (see page 48 and 49 of the report). While this saved the authors from doing all the projection work themselves, it also has some disadvantages. For example, the question for underlying parameters such as oil prices or economic development becomes unanswerable, simply because the assumptions are likely to be different for each and every bit of input data. However, each combination of scenarios is subsequently assigned an identifier and then compared to the baseline up to the year 2030.
The following table lists the most important scenario combinations and gives their identifiers. While in German, it gives an easy overview on the five sectors as columns and the pieces of the "scenario puzzle" used for each of them under each of the five major national scenarios, shown here as rows.
The five sectors in question are (from left to right):
- stationary combustion
- road transport and mobile machinery
- industry and other source categories
- solvent use
For all sectors, emission scenarios were developed in the greatest possible consistency with the energy and greenhouse gas emission scenarios of the "Policy Scenarios for Climate Protection VI" project. The two policy scenarios used in the latter project are the Current Policy Scenario (APS), which reflects energy and climate measures that have already been decided, and the Energiewende scenario (EWS), which assumes additional climate protection measures. Please refer to the document itself (page 41 and section 3.8) for all the details.
The diagrams below show the current German projection submission. The values represent the APS and APS+ scenarios as described above. The charts also indicate linear trends and include an early estimate for the 2016 emission value. Emission reduction targets are shown as defined by the revised Gothenburg Protocol (for 2020) and the revised NEC Directive (2030). Click any diagram to enlarge.
Next section: 13.1 Point Sources