Economic comparison of GHG mitigation strategies24

4.1  Introduction


At least four classes of greenhouse gas mitigation options are available: energy efficiency, fuel switching, introduction of carbon dioxide capture and storage (CCS) to electricity generation, and reductions in emissions of greenhouse gases other than carbon dioxide (CO2). These options vary by cost, timing, and our ability to represent them in an economic analysis. Our objective in this study is to provide a balanced analysis of these classes, across a variety of climate policy scenarios for Germany. Policy scenarios are represented as a response to varying levels of a price for greenhouse gas emissions, either applied economy-wide or targeted at energy-intensive sectors of the economy.

Our approach is to combine results from a computable general equilibrium (CGE) model for Germany and related analysis of non-CO2 greenhouse gases. The CGE framework presents a flexible tool for simulating greenhouse gas emissions that can accommodate a wide variety of assumptions about electricity technologies, CO2 prices, fuel prices, and baseline energy consumption. We use the CGE model as a core to provide analysis of the energy efficiency, fuel switching, and CCS mitigation options. Analysis of the non-CO2 greenhouse gas mitigation options is achieved using marginal abatement cost curves, expressed as a percentage reduction from baseline emissions, made available to the Stanford Energy Modeling Forum. Consistency between the two types of analysis is achieved by applying the same policy scenarios. Allowing for a reduction of emissions of non-CO2 gases adds a set of mitigation opportunities to the analysis that is not usually included in energy-economic modeling efforts.

We use the Second Generation Model (SGM; Edmonds et al., 2004; Sands, 2004), an economy-wide computable general equilibrium model, applied to Germany. Energy efficiency options are represented in the standard CGE format, where non-energy inputs substitute for energy inputs within economic production functions, or system of consumer demand equations, as the price of energy increases relative to other goods. The electric power sector provides substantial opportunities for fuel switching and the deployment of advanced electricity generating technologies in both a projected baseline and in alternative climate policy scenarios. Our methodology relies on engineering descriptions of electricity generating technologies and how their competitive position varies with a CO2 price or change in fuel price.


There are two parts to our analysis of non-CO2 greenhouse gases for Germany: first we determine baseline emissions through 2050, and then simulate the impact of a price for greenhouse gas emissions using marginal abatement cost curves targeted to specific activities that emit methane, nitrous oxide, or one of the fluorinated greenhouse gases (F-gases). The baseline includes low-cost reductions in greenhouse gas emissions that are expected even without a CO2-equivalent price. The marginal abatement cost curves determine a percentage reduction in greenhouse gas emissions, relative to the baseline, for any given CO2-equivalent price.

We exercise our modeling framework for Germany under various hypothetical policy scenarios: (1) greenhouse gas incentives are targeted to the electric power and energy-intensive industries (i.e. those covered by the EU emissions trading scheme); (2) all sectors of the economy face a common price for greenhouse gas emissions; and (3) with and without consideration of non-CO2 greenhouse gas mitigation options. Mitigation policies are represented with a set of constant-CO2-price experiments covering a range of CO2-equivalent prices high enough so that CCS technologies can at least break even.

Section IV2 provides a brief overview of historical and current greenhouse gas emissions and reduction efforts in Germany. We introduce the SGM model in section IV3 and describe how it can be used to analyze the costs of greenhouse gas mitigation under different policy and technology assumptions. We simulate the potential role of advanced electricity generation technologies including the option of carbon dioxide capture and storage (CCS). In section IV4, we discuss the environmental and economic results of the policy scenarios with a special focus on the potential contribution of each class of mitigation options. Section IV5 summarizes the results and provides some conclusions.

4.2 Background


Germany is one of the largest greenhouse gas emitters in the European Union, accounting for about one-fourth of European Union (EU) greenhouse gas emissions. In 2004, Germany emitted greenhouse gases of about 1009 million t CO2-equivalent (DIW, 2006). CO2 emissions accounted for the major share (87.6%) of overall greenhouse gas emissions in Germany, while non-CO2 greenhouse gases amounted to 12.4% of total greenhouse gas emissions. Compared to the base year25, greenhouse gas emissions were 17.6% lower in 2004. Within the burden sharing agreement under the Kyoto Protocol, Germany is committed to reduce greenhouse gas emissions (GHG) by 21% in 2008-2012 compared to 1990. Assuming the recent downward trend will be continued, this target is likely to be met. A long-term national target is to reduce GHG emissions by 40% by year 2020 relative to 1990.

Greenhouse gas emissions originate from many different sources. While CO2 emissions can be linked to fossil fuel use, in particular the combustion of fossil fuels and, to a lesser extent, fossil fuel use related industrial process emissions, non-CO2 emissions emanate from activities that are not necessarily related to fossil fuel use. CH4 emissions, for example, originate from non-energy activities such as cattle raising, rice fields, sanitary landfills, manure, and wastewater as well as energy related activities, such as production and distribution of natural gas, coal mining, combustion of biomass etc. Similarly, N2O emanates from fertilizer use and selected natural resources, as well as combustion processes, to a large share transport related, and industrial processes. SF6 stems from electrical switchgear and other industrial processes, and emissions of F-gases result from purely industrial processing with no link to fossil fuel use.

In Germany, nitrous oxide (N2O) and methane (CH4) account for the largest shares of non-CO2 greenhouse gases, followed by HFCs. From 1990 to 2004, N2O and CH4 emissions have been declining (Figure 4.1). For CH4, this was achieved by lowering levels of coal production, reducing sizes of livestock herds and carrying out waste-management measures such as reducing landfill storage of untreated household waste (via intensified recycling of biological waste and increased thermal treatment of unrecycled waste) and intensified collection and use of landfill gas. Modernization of gas-distribution networks and conversions from liquid to gas fuels, in smaller combustion systems, also contributed to emissions reductions (NC3, 2002).


Figure 4.1 Non-CO2 greenhouse gas emissions in Germany, 1995-2004

Note: Base year 1990 for CH4 and N2O, 1995 for PFC, HFC and SF6. Source: DIW (2006)

For N2O, the reduction is mainly due to technical measures introduced in the industrial sector to reduce adipic acid production. Those measures were part of the voluntary agreement of industries to reduce greenhouse gas emissions (NC3, 2002). The reductions in N2O emissions were achieved even though emissions reductions from fertilizer use in agriculture were counterbalanced by growth in emissions from road transport. As to the F-gases, HFCs grew by about 40% over the last decade as a result of increased use of HFCs as a substitute for CFCs. PFC compounds, on the contrary, have been considerably reduced since 1990. The reduction has been brought about mainly through reduction of emissions in the aluminum industry (NC3, 2002). SF6 emissions have undergone only slight changes in the last decade (NC3, 2002).

4.3 Methods

4.3.1  SGM-Germany

We employ the same computable general equilibrium model as in the previous two chapters, the Second Generation Model (SGM), to conduct an economic analysis of greenhouse gas mitigation options in Germany. The analysis brings together historical data on the German economy and energy system, parameters of advanced generating technologies, policies governing nuclear and renewable energy, and population projections. For a detailed description of the model, its main features, assumptions, set up and restrictions, for references, and information on data used please see chapter II3.


The electricity sector nesting differs slightly from the one introduced in chapter II3. As in chapter III, we include an additional coal technology that represents an advanced version of conventional pulverized coal power production (PCA). The same technology can be implemented with or without the option of CO2 capture and storage. We included an advanced pulverized coal version to allow oxyfuel and post-combustion CO2 capture and storage to compete with other electricity technologies.

Figure 4.2 provides the nested logit structure of electricity technologies employed in this chapter. At each nest, technologies compete on levelized cost per kWh. If the cost per kWh is equal among competing technologies in a nest, then each technology receives an equal share of new investment. A parameter at each nest determines the rate that investment shifts among technologies as levelized costs diverge. As a CO2 price is introduced, the levelized cost per kWh increases for all generating technologies that emit CO2. Technologies that are less carbon intensive receive a larger share of new investment than before the CO2 price was introduced.

Figure 4.2 Nested logit structure of electric generating technologies in SGM-Germany

Note: PC refers to conventional and PCA to advanced pulverized coal electricity generation. “PCAccs” represents advanced pulverized coal with CO2 capture and storage, “IGCCccs” represents coal IGCC with CO2 capture and storage, and “NGCCccs” represents NGCC with CO2 capture and storage. .

4.3.2 Greenhouse gas emissions


Emissions of non-CO2 greenhouse gases are calculated differently than emissions of CO2, which emanate from the burning of fossil fuels and are considered to be proportional in a fixed ratio to the energy content of the fuel used. This implies that they are linked to fossil fuel consumption in each economic sector and are calculated on a sectoral basis for each model time step. The introduction of a climate policy affects the cost of production and also the pattern of investment. This implies a change in the relative demand of factor inputs, in particular energy, and thus mitigation of CO2 emissions. Non-CO2 emissions, however, are not limited to fuel use activities. Therefore, emissions of those gases require a different tracking procedure. Table 4.1 shows the greenhouse gases and their sources that are included in our analysis.

Table 4.1 Greenhouse gas emission sources


Source #

Emissions Source



Oil combustion


Gas combustion


Coal combustion



Coal production


Enteric fermentation


Natural gas and oil systems


Solid waste



Agricultural soil


Industrial processes




Fossil fuels




Solvent use and other product use



Ozone depleting substances substitutes








Electricity distribution



We use SGM-Germany to simulate the development of energy consumption and CO2 emissions from 1995 up to 2050, for both baseline and mitigation scenarios. Reductions in CO2 emissions are obtained by operating SGM-Germany at various CO2 price paths. Several advanced electricity generation options are available, including carbon dioxide capture and storage.


For the baseline scenario of non-CO2 greenhouse gas emissions we rely on exogenous information and projections of DIW (2006), Diekmann et al. (2005), UBA (2005), NC3 (2002), Prognos/EWI (1999) for emissions of non-CO2 greenhouse gases from different sources. In the mitigation scenarios, reductions in emissions of the non-CO2 greenhouse gases are represented by marginal abatement cost curves for a specific set of mitigation activities. We use cost curves constructed by the U.S. Environmental Protection Agency for the Stanford Energy Modeling Forum (EMF-21). EMF-21 cost curves and assumptions are documented in DeAngelo et al. (2006), Delhotal et al. (2006), and Ottinger et al. (2006). The EMF-21 cost curves were constructed for various world regions, including the United States and the European Union (EU-15). Fawcett and Sands (2006) provide an application of the EMF-21 cost curves to greenhouse gas emissions in the United States. However, the cost curves are not differentiated by country within EU-15. We used the EU-15 cost curves, expressed as a percentage reduction from baseline at various CO2 prices to represent emissions reduction opportunities in Germany. EMF-21 provided marginal abatement cost curves for the following activities involving methane and nitrous oxide: enteric fermentation (CH4), coal mining (CH4), natural gas production and distribution (CH4), solid waste management (CH4), agricultural soils (N2O), and production of adipic and nitric acid (N2O). In addition, marginal abatement cost curves were provided for three types of F-gases: hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and sulphur hexafluoride (SF6).

4.4 Results

This study is designed to provide an economic comparison across a range of greenhouse gas mitigation scenarios for Germany. The scenarios vary across the available mitigation options and coverage of the economy. We start out by presenting results for the electricity sector. We use the general equilibrium framework to conduct a baseline analysis and alternative policy scenarios in order to yield information on the future electricity mix and the role of carbon dioxide capture and storage technologies within this mix. We then present emissions projections and results on abatement costs and economic growth with and without the inclusion of greenhouse gas mitigation options.

Our policy analysis consists of a CO2 policy scenario that includes a stepwise CO2 price increase from 10 € per ton of CO2-eq in 2005, to 20 € per ton of CO2 in 2010 and continues to increase to 50 € per ton of CO2-eq in 2025; we also conduct five constant-price scenarios at 10, 20, 30, 40 and 50 € per ton of CO2-eq starting in 2005. For the latter four scenarios, the CO2-equivalent price is introduced in 2005 at 10 € per ton of CO2-eq and increased to 20, 30, 40 and 50 € respectively by 2010 (compare Table 4.2). In the first set of results, referred to as partial coverage, CO2 incentives are targeted to the electric power and energy-intensive industries (i.e. those covered by the EU emissions trading scheme). Specifically, the sectors covered by the CO2 price are: coke production, electricity production, pulp and paper production, chemicals, non-metallic minerals, and primary metals production. In the second set of results, the CO2 prices are applied to all sectors of the economy. New fossil technologies are introduced to the model beginning in 2015, while technologies with CCS and advanced wind are introduced after 2015.


Table 4.2 Greenhouse gas price scenarios. All scenarios reach a maximum CO2-equivalent price in 2025 and the price remains constant thereafter. These prices can be applied to either the entire economy (full coverage) or sectors covered by the EU emissions trading program (partial coverage).

CO 2 price scenario







stepwise CO2-eq price







10 € per t CO2-eq







20 € per t CO2-eq







30 € per t CO2-eq







40 € per t CO2-eq







50 € per t CO2-eq







4.4.1  Electricity sector results

In this section, we draw on our detailed representation of advanced electric generating technologies in the general equilibrium model, SGM-Germany, and simulate the future electricity mix with these technologies including the option of CO2 capture and storage technologies in a base case and under different assumptions about a CO2 price.

Figure 4.3 shows the baseline electricity generation mix by technology in SGM-Germany up to the year 2050. Total generation rises gradually over time. The share of nuclear power is exogenously reduced to zero by 2030, reflecting the German nuclear phase out. Wind power subsidized by the renewable energy law rises steadily and accounts for a share of 12% of total electricity generation by 2030 and stays at this level thereafter. New electricity generating technologies are introduced to the model beginning in 2015. Advanced wind power that is assumed to not benefit from the renewable energy law and is assumed to compete in the market accounts for a small share of electricity generation, but its cost per kWh is still high relative to other generating technologies. The shares of advanced fossil fuel based technologies, i.e. NGCC, IGCC and advanced pulverized coal (PCA), grow rapidly to replace all nuclear power and much of conventional coal based power generation. All generating plants are modeled with a lifetime of 35 years.


CO2 capture and storage (CCS) for fossil fuel based technologies is introduced after 2015. CCS does not gain a market share in the baseline; its share increases with the CO2 price and as old generating capital is retired. SGM-Germany operates a capital vintage approach where capital stock is grouped into five-year vintages. New capital has flexibility to adjust to a new set of energy and CO2 prices but old capital does not. Therefore, the full impact of a CO2 price is delayed until all old capital retires.

Figure 4.3 Baseline electricity generation in TWh

The climate policy scenario consists of a stepwise CO2 price increase (compare Table 4.2). As shown in Figure 4.4, total electricity generation is lower in the climate policy scenario than in the baseline. The impact of CO2 price on electricity demand is relatively small, because electricity prices are already high in Germany so that the additional costs effect is small. The shares of advanced wind and natural gas based production increase in the climate policy case, while the shares of both conventional and advanced pulverized coal decrease. By 2050, the CO2 price has increased to 50 € per ton and is well beyond the breakeven price for CCS with IGCC, so a large share of IGCC capacity includes CCS by then. The CO2 price, however, remains below the breakeven price for CCS with PCA and also NGCC over the entire time horizon so substantially less PCA and NGCC capacity includes CCS by 2050. CCS in this scenario applies to new generating plants only, and is phased in as old plants retire. With higher CO2 prices, energy technologies that are less carbon-intensive (renewable technologies, CO2 capture and storage for fossil fuel based technologies) increase their share of electricity generation. At lower levels of CO2 prices (20 to 50 € per t CO2), CO2 capture and storage technologies as well as advanced wind still come into place, but with a reduced share of generation.


Figure 4.4 Electricity generation mix with a stepwise CO2 price increase

4.4.2 Results for greenhouse gas emissions

Baseline projections for CO2 (from SGM) and the non-CO2 greenhouse gases (from German data sources) are shown in Figure 4.5. Baseline emissions of CO2 resulting from fossil fuel use decline in accordance with past data until 2005 and slowly rise again thereafter. Emissions of non-CO2 gases show a future pattern consistent with past trends (compare section IV2). CH4 emissions continue to fall rapidly until 2010 and then gradually decline; N2O emissions fall until year 2000 and then level off; emissions of the F-gases increase gradually until 2020 and remain constant thereafter. Projections for the non-CO2 gases are not available after 2030; therefore, baseline levels of non-CO2 gases are held constant after 2030. Emissions of non-CO2 greenhouse gases are weighted at their 100-year global warming potential. All results are expressed as annual emissions in metric tons of CO2-equivalent, through the year 2050.

Figure 4.5 Greenhouse gas emissions pathway, baseline


Figure 4.6 and Figure 4.7 show simulated greenhouse gas emissions at CO2–eq price scenarios of 20 € and 50 € respectively, targeted to those sectors that are covered under the EU emissions trading scheme. CO2 prices follow the time paths shown in Table 5.1. Reductions in CO2 emissions are derived from simulations with SGM Germany and include mitigation activities in form of fuel switching, output adjustment, efficiency improvement and inclusion of CCS in response to the CO2 prices. By 2020, a 50 € price yields a 10% reduction of CO2 emissions, which doubles to more than 20% by 2040.

Reductions in greenhouse gas emissions other than CO2, however, are less sensitive to a CO2–eq price policy. Much of the mitigation potential is exhausted in the baseline with early reduction. Marginal abatement cost curves are applied to the remaining baseline emissions, by greenhouse gas (CH4, N2O, HFCs, PFCs, SF6) and by activity within CH4 and N2O, to simulate a climate policy. The marginal abatement cost curves are used as look-up tables to derive a percentage reduction in CO2-eq emissions for any given price of CO2.26 The cost curves typically allow inexpensive emissions reductions up to a turning point, with further reductions very expensive. Most reductions in non-CO2 greenhouse gas emissions beyond the baseline occur at CO2-eq prices below 20 €.

Figure 4.6 Greenhouse gas emissions pathway, 20 € per ton CO2-eq


Figure 4.7 Greenhouse gas emissions pathway, 50 € per ton CO2-eq

4.4.3 Economic comparison

For any selected year, we can express emissions reduction potential in the form of marginal abatement cost curves. This is done in Figure 4.8 and Figure 4.9 for two different time periods (2020 and 2040) with four separate components: efficiency improvements outside of electricity generation; efficiency improvements and fuel switching within electricity generation; CCS within electricity generation; and reductions in emissions of non-CO2 greenhouse gases. This provides a graphical view of the relative sizes of reduction potential across major classes of greenhouse gas mitigation options, and how that varies across CO2 prices.

For each of the four components, we derive its contribution to the overall marginal abatement cost curve by conducting a set of CO2-eq price scenarios and determining the reduction in emissions relative to the baseline. The component of non-CO2 greenhouse gas emissions reductions is calculated based on exogenous information as described in the previous section. For the CCS component, we operate SGM-Germany with and without the option of CCS and allocate the difference in electricity sector emissions to CCS-related emissions reductions. The fuel switching component is derived by comparing electricity-sector emissions for each CO2 price scenario with a calculation assuming fuel shares remain at baseline levels, adjusting for output changes in electricity generation. Residual emissions reductions are then considered to be due to efficiency improvement in the economy. The outer lines in Figure 4.8 and Figure 4.9 encompass all these options and provide economy-wide marginal abatement cost curves.


Although we generated these sets of marginal abatement cost curves with a number of constant CO2-eq price scenarios, they correspond to the marginal abatement cost curves that would result for a national emissions trading system with a given target. This means that for any given reduction target the curves reveal the implied marginal costs (CO2 price) and the set of mitigation options employed.

As can be seen for the year 2020 in Figure 4.8 and even more pronounced for the year 2040 in Figure 4.9, the efficiency and fuel switching components increase gradually along with time and with the CO2 price and they have large potential at high CO2 prices. The efficiency component captures shifts in consumption by both producers and consumers: they substitute other goods for energy in consumption and production as the CO2 price rises. Substitution elasticities in production and consumer demand elasticities are the key parameters that govern the price response. The label 'fuel switching' in the figures relates to emissions reductions in the electricity sector resulting from fuel switching as well as efficiency improvement in electricity generation, with fuel switching taking on the substantially larger share of emissions reductions (compare Figure 4.4). The mix of electricity generating technologies changes in response to a CO2 price. As the CO2 price increases, the relative cost per kWh of generating electricity changes across the generating technologies. Technologies that use carbon-intensive fuels, such as pulverized coal, receive a lower share of investment in new capital than before. Another elasticity parameter determines the rate that investment shares change in response to changes in the relative cost of generating electricity.

Figure 4.8 Simulated emissions reductions over a range of CO2 prices, Germany 2020


Figure 4.9 Simulated emissions reductions over a range of CO2 prices, Germany 2040

In addition, CCS is introduced as a mitigation option in the electricity sector. CCS is not available at low CO2 prices, but can be a significant contributor to emissions reduction at CO2 prices above 30 € per ton. For each electricity generating technology that can use CCS, one can calculate a break-even CO2 price where the cost per kWh of generating electricity is the same with or without CCS. At this CO2 price, we assume that half of any new investment in that generating technology uses CCS. We have not included a retrofit option for CCS; we assume that all CCS is installed on new generating plants. Therefore, the rate of CCS installation is limited by the rate that capital stock turns over in the electricity generating sector. This can be seen by comparing the contribution of CCS to CO2 mitigation over time at relatively high CO2 prices. Figure 4.9 shows the higher mitigation potential of CCS in 2040 compared to 2020. A similar, but not quite as pronounced, case can be made for energy efficiency and fuel switching. Over time, both of these options experience an increasing economic potential and can, by 2040 and at a high 50 € per ton of CO2 price, contribute to emissions reductions at almost equal shares with CCS.

The non-CO2 greenhouse gases reach most of their full mitigation potential at low CO2 prices. This is a consequence of using exogenous marginal abatement cost curves and simplifying assumptions on the requirements for new capital. The non-CO2 mitigation options are considered to be primarily “end-of-pipe” processes that can be put in place by adding new equipment to existing capital, and need not wait for existing capital stocks to turn over.


To summarize, the analysis shows that all four mitigation options (efficiency increase, fuel switching, CCS, and GHG mitigation) respond to a CO2 price policy with varying degrees of sensitivity. Initially, non CO2-GHG mitigation and energy efficiency improvement on the producer and consumer side play the dominant role in achieving emissions reductions in response to a CO2 price. An increase in energy efficiency is stimulated already at low levels of CO2 prices and depends on the development of energy prices as well as relative prices of goods and inputs. As time moves on and new technologies become competitively available at a higher CO2 price an increasing share is taken up by fuel switching, mainly driven by changes in the electricity generation mix as outlined above. Similarly, the introduction of CCS technologies in the electricity sector after 2015 plays a major role. At a CO2 price of 50 € per ton of CO2 (year 2025) CCS is economically competitive and takes on an increasing share as capital stocks turn over.

4.4.4 From partial to full coverage of economy

In the second set of results, referred to as full coverage, CO2 incentives are applied to all sectors of the economy. We apply the same CO2 price scenarios as above, i.e. a price of 10, 20, 30, 40 and 50 € per ton of CO2-eq. The CO2 price is introduced in 2005 at 10 € per ton of CO2-eq and increased to 20, 30, 40 and 50 € respectively by 2010. .

As now all sectors of the economy are exposed to the CO2 price scheme, the resulting aggregate CO2–eq emissions reductions are much higher than in the partial coverage case. Figure 4.10 shows the distribution of emissions reductions across different mitigation classes for the two cases in a scenario with a stepwise CO2 price increase. The deviation from baseline increases over time as old capital is retired. The largest difference between the full and the partial coverage case can be seen in emissions reductions that result from energy efficiency improvement. This is because in the partial coverage case carbon incentives are only targeted to a limited set of sectors, i.e. electricity and energy intensive industries. These sectors, however, are mainly responsible for emissions reductions through fuel switching and introduction of CCS and only to a lower extent through efficiency improvement.


The remaining sectors, such as non-energy intensive manufacturing, services, transport, agriculture, and, in particular, the household sector, adjust their behavior through either efficiency improvement or output changes. In the full coverage case they face a direct CO2 price and directly contribute to emissions reductions. In addition, they experience indirect price increases, for example in electricity prices or refined petroleum price and adjust their behavior accordingly. In the partial coverage case they are not directly covered by the CO2 price scheme and only the indirect price effect applies. Therefore the impact on efficiency improvement and emissions reduction is much smaller than in a full coverage case.

Figure 4.10 Decomposition of emissions reduction with a stepwise increasing CO2 price fully and partially covering the economy

The energy efficiency component includes both changes in the ways that producers use energy but also shifts in output across production sectors as consumers adjust purchases to a new CO2 price. Output of the energy-intensive industries and electricity generation decreases more than output of other sectors of the economy, as energy costs are a larger share of the cost of production. Even the very large “services and other industries” sector, which has a low energy cost share, shows a small decrease in output, reflecting the GDP loss of the CO2 price increase.


Quantity changes of gross output by sector aggregate in relation to the baseline are shown in Figure 4.11 for the 50 € per t CO2 scenario applied to all sectors of the economy (full coverage) and in Figure 4.12 for the 50 € per t CO2 scenario applied to parts of the economy (partial coverage). In forming sector aggregates, base year prices are used as weights. Most of the economy’s output is contained in the services, other industries, and agriculture aggregate, which has a decrease in output of 0.67% in the full coverage case and 0.53% in the partial case where only parts of the economy are covered by the CO2 price and lower overall emissions reduction is achieved. This turns out to be approximately the same as the percentage loss in real GDP27. Other sectors are much smaller in terms of output, but are more sensitive to the CO2 price. Electricity production has the largest and almost identical percentage reduction in output in both the full and partial coverage case. The reduction in output across energy-intensive industries is less than 2% in both cases, yet it is slightly lower in the partial coverage case as only energy-intensive manufacturing sectors are covered by the CO2 price scheme. The most pronounced difference between the full and the partial coverage case can be seen in the energy transformation (most oil refining) and transportation sector. Output losses for transportation sector are substantially lower in the partial coverage case, when not affected by a CO2 price. Consequently, a reduced loss in output from energy transformation can be observed as demand for petroleum products by the transport sector spurs production.

Figure 4.11 Change in sectoral output, stepwise CO2 price applied to all sectors of the economy (full coverage), compared to baseline

Figure 4.12 Change in sectoral output, stepwise CO2 price increase applied to parts of the economy (partial coverage) compared to baseline

4.5 Conclusions


This study builds on previous analysis by Schumacher and Sands (2006), where the primary extensions here are the inclusion of non-CO2 greenhouse gases and a broader set of climate policies. The non-CO2 greenhouse gas mitigation options are generally considered to be end-of-pipe options that can be deployed relatively quickly on both new and existing capital equipment. The rate that other greenhouse gas mitigation options can deploy is generally limited by the rate that existing capital stocks retire. The climate policy scenarios in this study are designed to provide insights on the European Union emissions trading system, where carbon incentives are targeted at specific energy sectors.

One of the first things to notice about methane and nitrous oxide is that much of the mitigation potential, relative to the Kyoto reference year of 1990, is already in the baseline emissions scenario. This leaves a relatively small amount of additional reductions available for our policy scenarios. Even so, the contribution to potential greenhouse gas mitigation from the non-CO2 greenhouse gases is still significant. One of the limitations of this study is that we did not have Germany-specific marginal abatement cost curves available. We used instead cost curves for the European Union constructed by the U.S. Environmental Protection Agency for the Stanford Energy Modeling Forum.

This study also included two types of carbon dioxide mitigation scenarios: one with the CO2 price applied to all sectors of the economy, and another with the CO2 price applied only to electricity generation and energy-intensive industries. The partial-coverage scenario is intended to better represent the emissions trading program in the European Union. One of the major differences between the full- and partial-coverage scenarios is that the transportation sector is no longer covered, and economic output from this sector does not fall as much in the partial-coverage scenario. Economic output, as well as carbon dioxide emissions, in the electricity sector and energy-intensive industries, changes very little between the two scenarios. In the partial-coverage case, about two thirds of carbon dioxide emission reductions come from the electricity sector because of fuel switch and introduction of CCS.


This study is one step toward providing more realistic scenarios of greenhouse gas mitigation options in Germany. Future efforts could involve a more refined decomposition of the energy efficiency component into production efficiency and output shift components. Currently, the energy efficiency component includes both changes in the ways that producers use energy but also shifts in output across production sectors as consumers adjust purchases to a new CO2 price. Decomposing the energy efficiency component would allow to distinguish 'pure' efficiency effects from output effects. Output effects may imply a shift in production and emissions activities to other countries or regions (often referred to as leakage effect). Furthermore, this research could be extended to include an endogenous representation of mitigation options in non-CO2 greenhouse gas emissions. This would include to have available Germany specific abatement options and costs, and to include them directly, as a function of economic activity, in the analysis. Another possible extension is an analysis of the potential for biofuels, which become more cost-effective with higher oil prices and CO2 prices.

4.6 References

Footnotes and Endnotes

24  This work will be presented at the 10th annual conference “Assessing the foundations of global economic analysis” of the Global Trade Analysis Project (GTAP) at Purdue University and at the International Energy Workshop (IEW) of the International Energy Agency (IEA), the International Institute for Applied Systems Analysis (IIASA) and the Energy Modeling Forum (EMF) in Stanford in June 2007.

25  The base year is 1990 for CO2, CH4 and N2O emissions and 1995 for emissions of F-gases (HFC, PFC, SF6).

26  The U.S. Environmental Protection Agency provided marginal abatement cost curves to the Stanford Energy Modeling Forum as discrete points defining a piecewise-linear supply curve. We fit a smooth curve to these points using an exponential functional form.

27  Gross Domestic Product (GDP) is measured in SGM as a Laspeyres quantity index with fixed base-year weights. GDP growth depends primarily on population growth and exogenous rates of technical change. The aggregate economy grows steadily in our baseline at 1-1.4% (in terms of changes in real GDP) per year between 2000 and 2035. Annual growth then picks up in 2035 as the working-age population stabilizes and is no longer falling over time.

© Die inhaltliche Zusammenstellung und Aufmachung dieser Publikation sowie die elektronische Verarbeitung sind urheberrechtlich geschützt. Jede Verwertung, die nicht ausdrücklich vom Urheberrechtsgesetz zugelassen ist, bedarf der vorherigen Zustimmung. Das gilt insbesondere für die Vervielfältigung, die Bearbeitung und Einspeicherung und Verarbeitung in elektronische Systeme.
DiML DTD Version 4.0Zertifizierter Dokumentenserver
der Humboldt-Universität zu Berlin
HTML generated: