The Energy [R]evolution scenarios were jointly commissioned by Greenpeace and the European Renewable Energy Council from the Institute of Technical Thermodynamics, part of the German Aerospace Center (DLR). The supply scenarios were calculated using the MESAP/PlaNet simulation model adopted in the previous editions of Energy[R]evolution studies published in 2007 and 2008. Detailed analyses carried out during preparation of the 2008 Energy [R]evolution study were also used as input for the 2010 edition. These studies comprise in particular the analysis of global energy demand from Ecofys Netherlands (Graus and Blomen 2008) and the study on global sustainable biomass potentials from the German Biomass Research Center (Seidenberger et al. 2008), see the “Estimates of the potential of renewable energy sources” section below. The future development pathway for car technologies is based on a special report produced in 2008 by the Institute of Vehicle Concepts, DLR for Greenpeace International (Schmid 2008).
The MESAP/PlaNet model
The simulation model PlaNet of the energy and environmental planning package MESAP (2008) has been created for long-term strategic planning on a national, regional or local level. PlaNet consists of two independent modules: a flow calculation module, balancing the flows of commodities of an energy demand and supply model, a cost calculation module for the calculation of the corresponding macroeconomical costs. Energy system analyses with PlaNet are carried out in two sequential steps: first the energy and material flows are determined; then based on the results of the flow calculation, the costs of this energy system are calculated.
The PlaNet flow calculation uses a set of linear equations, which can be solved sequentially. In a simulation model, the user specifies the activities or drivers of demand represented as quantities of a commodity, for example the population or the GDP. With the help of intensities (ratios between flows) like electricity consumption per person, the demand for energy services or the final energy demand can be determined. If a commodity is produced by more than one process, market shares for these processes have to be specified. The market shares define the output of a process. The input into this process will be calculated with process efficiency. This schematic allows the integrated calculation of energy flows from primary energy sources to demand drivers. The cost calculations are based on the results of the flow calculation. The estimates of the future development of population, GDP and energy intensities used in this study are presented in detail below (“Key drivers for energy demand” section).
A ten-region global energy system model implemented in the MESAP/PlaNet environment (MESAP 2008) is used for simulating global energy supply strategies. The ten regions correspond to the world regions as specified by the IEA’s World Energy Outlook 2009 (Africa, China, India, Latin America, Middle East, OECD Europe, OECD North America, OECD Pacific, Other Developing Asia, Transition Economies) (IEA 2009a). Model calibration for the base year 2007 is based on IEA energy statistics (IEA 2009b, c).
The scenarios
Three scenarios up to the year 2050 are outlined in this research: a Reference scenario, a basic Energy [R]evolution scenario with a target to reduce energy related CO2 emissions by 50%, from their 1990 levels, and an advanced Energy [R]evolution version which envisages a fall of more than 80% in CO2 by 2050.
The Reference scenario is based on the reference scenario in the International Energy Agency’s 2009 World Energy Outlook (IEA 2009a). This only takes existing international energy and environmental policies into account. The Reference scenario does not consider additional policies to reduce greenhouse gas emissions. As the IEA’s projection only covers a time horizon up to 2030, it has been extended by extrapolating its key macroeconomic and energy indicators forward to 2050. Long-term projections of economic developments are only indicative and are used to project future development of the global energy demand and are by no means forecasts. The Reference scenario provides a baseline for comparison with both Energy [R]evolution scenarios. Compared to the previous (2007) IEA projections (IEA 2007), WEO 2009 assumes a slightly lower average annual growth rate of world Gross Domestic Product (GDP; 3.1%, instead of 3.6% over the period 2007–2030). At the same time, it expects global final energy consumption in 2030 to be 6% lower than in the WEO 2007 report. China and India are expected to grow faster than other regions, followed by the Other Developing Asia group of countries, Africa and the Transition Economies (mainly the former Soviet Union). The OECD share of global purchasing power parity (PPP) adjusted GDP is expected decrease from 55% in 2007 to 29% by 2050.
The Energy [R]evolution scenario has a key target of 50% renewables of the final energy consumption by 2050. A second objective is the global phasing out of nuclear energy. To achieve these goals, the scenario is characterised by significant efforts to fully exploit the large potential for energy efficiency. At the same time, all cost-effective sustainable renewable energyFootnote 1 sources are used for heat and electricity generation, as well as the production of bio fuels. The general framework parameters for population and GDP growth remain unchanged from the Reference scenario.
The Advanced Energy [R]evolution scenario takes a much more radical approach and aims to reduce global energy related CO2 emissions by more than 80% in 2050, based on 1990 levels, to increase the likelihood of limiting warming to less than a +2° increase of the global average temperature. In order to achieve this even more ambitious reduction of CO2 emissions, the advanced scenario assumes much shorter lifetimes for coal-fired power plants—20 years instead of 40 years. The shorter lifetime for coal power plants enables a larger deployment of renewable energy sources and the annual growth rates of renewable energy sources, especially solar photovoltaic, wind and concentrating solar power plants, have therefore been increased.
The advanced scenario also uses the general framework parameters of population and economic growth, as well as most of the energy efficiency roadmap from the basic Energy [R]evolution (E[R]) scenario. In the transport sector, however, the advanced E[R] scenario has a final energy demand 15% to 20% lower in 2050 compared to the basic E[R] scenario. This is due to a combination of increased use of public transport and a faster uptake of efficient combustion vehicles and—after 2025—a larger share of electric vehicles. Within the heating sector, there is a faster expansion of combined heat and power generation (CHP) in the industry sector, more electricity for process heat and a faster growth of solar and geothermal heating systems. Combined with a larger share of electric drives in the transport sector, this results in a higher overall demand for power. Even so, the overall global electricity demand in the advanced Energy [R]evolution scenario is still lower than in the Reference scenario. In the advanced scenario, the latest market development projections of the renewable industry have been calculated for all sectors. More electric and hydrogen vehicles, combined with the faster implementation of smart grids and expanding super grids (about 10 years ahead of the basic E[R] scenario) allows a higher share of fluctuating renewable power generation (photovoltaic and wind). The threshold of a 40% proportion of renewables in global primary energy supply is therefore passed just after 2030 (also 10 years ahead of the basic E[R] scenario). By contrast, the quantity of biomass used for energy purposes and large hydro power remain approximately the same in both Energy [R]evolution scenarios, for sustainability reasons.
Both the basic and the advanced Energy [R]evolution scenarios have been developed in a backcasting process.Footnote 2 The CO2 emission target has been defined on the basis of the IPCC 4th assessment report, category 1 scenario (IPCC 2007) to restrict the increase in global mean temperatures under +2°C with a required CO2 reduction of −85% to −50% by 2050. Therefore, the main target is to reduce global CO2 emissions to 10 Gt/a by 2050 in the basic Energy [R]evolution scenario and 3.7 Gt/a in the advanced Energy [R]evolution scenario, thus limiting global average temperature increase well below 2°C and preventing dangerous anthropogenic interference with the climate system (Hansen et al. 2008, see also the United Nations Framework Convention on Climate Change, Article 2, UNFCCC 1992). As the authors do not consider nuclear energy as an option that supports the transition towards a sustainable energy supply system, a second constraint is the phasing out of nuclear power plants until 2050.
Energy demand projections
In order to estimate the global and regional energy efficiency potential, the Dutch institute Ecofys developed energy demand scenarios for the Greenpeace Energy [R]evolution analysis in 2008 (Graus and Blomen 2008). These scenarios cover energy demand over the period 2005 to 2050 for ten world regions. Two low energy demand scenarios for energy efficiency improvements have been defined. The first is based on the best technical energy efficiency potentials and is called “Technical”. The second energy efficiency scenario is based on more moderate energy savings, taking into account implementation constraints in terms of costs and other barriers and is called “Revolution”. The technical potential is defined as the energy use that can be reduced by implementing established technical measures, in comparison to the level of energy use in a reference scenario, where current trends continue and no large changes take place in the production and consumption structure of the economy. The technical potential scenario assumes that measures can be implemented after 2010 and that equipment or installations are replaced at the end of their lifetime by state-of-the-art equipment. However, the Revolution scenario assumes that only a fraction of the technical energy efficiency potential can be implemented. This approach takes into account barriers for implementing technical measures for energy efficiency improvements, such as costs. Energy demand in both the basic and the advanced Energy [R]evolution scenarios is based on this second, more conservative “Revolution” scenario. The main results of the “Revolution” scenario are summarised below.
For the 2010 update of the Energy [R]evolution scenario, including the advanced version, the Graus and Blomen (2008) analysis has been reconfigured using the latest IEA statistics from World Energy Outlook 2009 (IEA 2009a). The WEO 2009 edition has a lower global final energy demand in 2030 in comparison to the 2007 edition; 438 EJ in comparison to 478 EJ (including non-energy use). The difference is mainly caused by lower GDP growth rates due to the recent financial and economic crisis, leading to a 14% lower global GDP in 2030 in comparison to the 2007 edition. In addition, an increased share of electric vehicles in the advanced scenario results in a lower final energy demand required to meet the same level of transport activity.
Key drivers for energy demand
Population development
One important underlying factor in energy scenario building is future population development. Population growth affects the size and composition of energy demand, directly and through its impact on economic growth and development. World Energy Outlook 2009 uses the United Nations Development Programme (UNPD 2009) projections for population development. For this study, the most recent population projections from UNDP up to 2050 in the medium variant are applied. Based on UNDP’s 2009 assessment, the world’s population is expected to grow by 0.86% per year on average over the period 2007 to 2050, from 6.7 billion people in 2007 to more than 9.1 billion by 2050. Population growth will slow over the projection period, from an average 1.2% per year during between 2007 and 2010 to 0.4% per year between 2040 and 2050. The population of the developing regions will continue to grow most rapidly. The Transition Economies will face a continuous decline, followed after a short while by the OECD Pacific countries. OECD Europe and OECD North America are expected to maintain their population, with a peak in around 2020/2030 and a slight decline afterwards. The share of the population living in today’s non-OECD countries will increase from the current 82% to 85% in 2050. China’s contribution to world population will drop from 20% today to 16% in 2050. Africa will remain the region with the highest growth rate, leading to a share of 22% of world population in 2050. Satisfying the energy needs of a growing population in the developing regions of the world in an environmentally friendly manner is a key challenge for achieving a global sustainable energy supply.
Economic growth
Economic productivity is a key driver for energy demand. Since 1971, each 1% increase in global GDP has been accompanied by a 0.6% increase in primary energy (Graus and Blomen 2008) consumption. The decoupling of energy demand and GDP growth is therefore a prerequisite for reducing demand in the future, if a continuing growth of GDP is to be achieved. Most global energy/economic/environmental models constructed in the past have relied on market exchange rates to place countries in a common currency for estimation and calibration. This approach has been the subject of considerable discussion in recent years, and the alternative of PPP (Nordhaus 2005) exchange rates has been proposed. Purchasing power parities compare the costs in different currencies of a fixed basket of traded and non-traded goods and services and yield a widely based measure of the standard of living. This is important in analysing the main drivers of energy demand or for comparing energy intensities among countries. Although PPP assessments are still relatively imprecise compared to statistics based on national income and product trade and national price indexes, they are considered to provide a better basis for global scenario development. In this study, we relied on PPP adjusted GDP estimates from the World Energy Outlook 2009 (IEA 2009a). However, as WEO 2009 only covers the time period up to 2030, the projections for 2030–2050 are based on our own estimates.
Energy-intensity decrease
An increase in economic activity and a growing population does not necessarily have to result in an equivalent increase in energy demand. There is still a large potential for exploiting energy efficiency measures. The energy intensity of an economy in this study is defined as final energy use per unit of gross domestic product. Under the Reference scenario, we assume that energy intensity will be reduced by 1.25% on average per year, leading to a reduction in final energy demand per unit of GDP of about 15% between 2007 and 2020. This value compares well with the reduction of the energy intensity of EU-25 between 1990 and 2004 (see below). In comparison, the total energy consumption in the EU-25 grew at an annual rate of just over 0.8% over the period from 1990 to 2004, while GDP grew at an average annual rate of 2.1% during the same period (EEA 2010). As a result, total energy intensity in the EU-25 fell at an average rate of −1.2% per year (a total decrease of −16% between 1990 and 2004). Despite this relative decoupling, total energy consumption has increased by 12.0% overall in the period 1990–2004. Energy intensity declined over 1990–2000 (and continuously during 1996–2000) but has remained broadly stable since then. For the entire simulation period (2007 to 2050), an average annual decrease of the energy intensity of 1.25% results in a total reduction of 56% in these 53 years. Although the current energy intensity is very different from region to region, our study implicitly assumes that all regions will be able to reduce energy intensity to Japan’s level of 2007 within the next 30 years.
Under the advanced Energy [R]evolution scenario, it is assumed that active policy and technical support for energy efficiency measures will lead to an even higher reduction in energy intensity of almost 73% between 2007 and 2050. The advanced Energy [R]evolution scenario follows the same efficiency pathway, apart from in the transport sector, where a further reduction of 17% due to less vehicle use and lifestyle changes has been assumed. The increased share of electric vehicles in this scenario, with greater efficiency of electric drives, leads to a further decrease in final energy use. The energy intensity in an economy tends to decrease over time as a result of a number of factors, e.g.
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Autonomous energy efficiency improvement. These energy efficiency improvements occur because due to technological developments each new generation of capital goods is likely to be more energy efficient than the one before. This is mainly caused by (temporary) increases in energy prices from which economic actors try to save on energy, e.g. by investing in energy efficiency measures or changing their behaviour.
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Policy-led energy efficiency means economic actors change their behaviour and invest in more energy efficient technologies.
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Structural changes in the economy can reduce the energy over GDP ratio, e.g. a shift in the economy away from energy-intensive industrial activities to services related activities.
The energy-intensity decrease in the reference scenario results from a mix of these three factors and differs per region and per sector. For the period 2005–2030, the energy-intensity decrease is taken from the WEO 2009. For the period 2030–2040 and the period 2040–2050, the development is based on the energy intensity per region and sector in the period 2020–2030 in WEO. However, we made a correction for the change in GDP growth rate per period to avoid a situation where the energy intensity decrease in the Reference scenario is larger than the economic growth rate. For the period 2030–2040 and 2040–2050, the energy intensity decrease is calculated by the following two formulae:
$$ \begin{array}{*{20}{c}} {{\hbox{E}}{{\hbox{I}}_{{2030 - 2040}}} = {\hbox{E}}{{\hbox{I}}_{{2020 - 2030}}} \times \left( {{\hbox{GD}}{{\hbox{P}}_{{2030 - 2040}}}/{\hbox{GD}}{{\hbox{P}}_{{2020 - 2030}}}} \right)} \\{{\hbox{E}}{{\hbox{I}}_{{2040 - 2050}}} = {\hbox{E}}{{\hbox{I}}_{{2020 - 2030}}} \times \left( {{\hbox{GD}}{{\hbox{P}}_{{2040 - 2050}}}/{\hbox{GD}}{{\hbox{P}}_{{2020 - 2030}}}} \right)} \\\end{array} $$
where:
- EI2020–2030
:
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Energy intensity decrease 2020–2030 in WEO (%/year)
- GDP2020–2030
:
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GDP growth rate 2020–2030 in WEO (%/year)
- EI2030–2040
:
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Energy intensity decrease in period 2030–2040 (%/year)
- GDP2030–2040
:
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GDP growth rate in period 2030–2040 (%/year)
- EI2040–2050
:
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Energy intensity decrease in period 2040–2050 (%/year)
- GDP2040–2050
:
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GDP growth rate in period 2040–2050 (%/year)
Technical potential for energy efficiency improvement
After defining energy intensities of the Reference scenario, technical potentials for energy efficiency improvement are estimated. In this step, a list is drawn up of energy savings options taken into account per sector. After that, the technical energy savings potential is estimated per measure. The technical potentials are based on:
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Current best practice technologies
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Emerging technologies that are currently under development
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Continuous innovation in the field of energy efficiency, leading to new technologies in the future
The key assumptions for calculating technical potential are:
This study aims at calculating energy efficiency improvement by developing indicators for energy-intensity per sector and where possible by subsector.
The main energy consuming sectors are the industry and transport sectors, as well as “other sectors”, (residential sectors, services and agriculture; Graus and Blomen 2008) the subsector energy use and the selection of the measures per sector are discussed and shown in detail. Options are selected, which are expected to result in a substantial reduction of energy demand before 2050.
In the Reference scenario, total global energy demand is expected to increase from 305 EJ in 2007 to 352 EJ in 2050. The growth in the transport sector is projected to be the largest, with energy demand expected to grow from 82 EJ in 2007 to 158 EJ by 2050 (see Table 1). Demand from “other sectors” is expected to grow the least, from 124 EJ in 2007 to 198 EJ by 2050. Under the (basic) Energy [R]evolution scenario, however, growth in overall final energy demand can be limited to an increase of 12% up to 2050 in comparison to the 2007 level (341 EJ in 2050), whilst taking into account implementation constraints in terms of costs and other barriers. The increase of the energy demand in the transport sector is very small, while in the industry and other sectors, final energy demand increases by ca. 17% (resp. 15%) between 2007 and 2050.
Table 1 Change in global final energy demand by 2050 in comparison to 2005 level
Figure 1 shows the potential for energy efficiency measurements for the industry, transport and other sectors in 2050 for the different world regions, i.e. the difference between the energy demand in these sectors in 2050 in the Reference scenario and the respective demand in the basic E[R] scenario, normalised to the 2005 level of total energy demand in each world region. Furthermore, the remaining total energy demand in 2050 in the basic E[R] scenario (relative to 2005 levels) is shown.
The technical savings potential up to 2050 from all the measures described in (Graus and Blomen 2008) is summarised in Table 2. Since it was not clear what assumptions the IEA WEO Reference scenario was based on, they have assumed an efficiency improvement of 1% per year. Electricity use in the “other” sector was assumed to decline at the same rate as residential use (lighting, appliances, cold appliances, computers/servers and air conditioning). They have assumed a minimum energy efficiency improvement of 1.2% in the Technical scenario and 1.1% in their Revolution scenario, including autonomous improvements. For services and agriculture, they have assumed the same percentage savings potential as for the household sector all aggregated in “other sectors”. The new Reference scenario based on WEO 2009 data now includes a lower level of energy demand in the residential sector. Therefore the savings used in the new Energy [R]evolution scenarios are lower than the figures shown in Table 2. The resulting final energy demand reduction for the Energy [R]evolution scenarios compared to the Reference scenario is shown in Table 3 for each world region.
Table 2 Technical savings potential by 2050 for different types of energy use n the buildings sector
Table 3 Reduction of final energy demand in other sectors between 2005 and 2050
Estimates of the potential of renewable energy sources
Worldwide renewable energy resources exceed by several times current energy demand. The availability of renewable energy sources however differ between world regions (UBA 2009). The supply with energy from renewable sources in the both the basic and the advanced Energy [R]evolution scenarios is constrained by estimates of renewable energy potentials by world region and technology (REN21 (2008); Hoogwijk and Graus (2008) and UBA (2009)). Assessments of the global technical potential vary significantly up to 15,857 EJ/a (UBA 2009). This meta study performed by the DLR, Wuppertal Institute and Ecofys, commissioned by the German Federal Environment Agency analysed ten major studies of global and regional potentials by organisations such as the United Nations Development Programme and a range of academic institutions. Each of the major renewable energy sources was assessed, with special attention paid to the effect of environmental constraints on their overall potential. The study provides data for the years 2020, 2030 and 2050. The potential for energy supply from biomass in each world region was addressed separately from the results in the UBA (2009) study (see below).
Sustainable biomass potential
Bio energy is an important storable renewable energy source. However, the use of bio energy is controversial and a sustainable fuel supply chain is crucial. The limited availability of sustainable bio energy requires very efficient use especially for heating and cooling in cogeneration power plants, where overall efficiency is far higher than biomass use in the transport system (in combustion engines). As a response to the controversial discussion on the availability of biomass resources, a study on the global potential for sustainable biomass was commissioned as part of the Energy [R]evolution 2008 project (Seidenberger et al. 2008). The German Biomass Research Centre, the former Institute for Energy and Environment, compiled research into worldwide energy crop potentials in different scenarios till 2050. Additionally, scientific literature on the status quo of worldwide potential studies and the state of the art of remote sensing for investigation of biomass potentials was compiled by Seidenberger et al. (2008). As the results of the Seidenberger et al. (2008) study are not publicly available, the key results of this study are summarised in the following paragraphs:
Global potentials of biomass residues
Residues are products from forestry, agricultural waste and by-products from food production as well as waste from wood products and animals. Residues can be dry matter, e.g. wood chips as well as wet matter, e.g. animal waste. The share of each residue is a fraction of the total amount of residual biomass can vary in different regions and is mainly dependent on the population, living standards and the methods and intensity of the agricultural and forestry production in the particular region. Several studies analysing long-term residue potential in a more or less detailed way are available. A direct comparison of the studies is difficult, since the baseline assumptions are different.
Following Seidenberger et al. (2008), we used results from Dessus et al. (1993) for 2020, as it is the only study with region-specific residue potentials for 2020. For 2050, biomass residuals potential is based on Smeets et al. (2007) as the authors have defined sustainability criteria in their assessment. Moreover, Smeets et al. (2007) offer a relatively high level of transparency and traceability from the methodological point of view. Nevertheless, it must be pointed out that the calculated potentials seem to be conservative and were partly converted from the original aggregation necessary for our scenario analysis, which is listed below in Table 4. Because of the lack of data, the Asian region is most problematic.
Table 4 Residue potentials by region, based on Dessus et al. (1993) and Smeets et al. (2007)
Global potentials of energy crops
Besides the utilisation of biomass from residues, the production of energy crops in agricultural production systems is of controversial. Therefore, the technical potentials of energy crops were calculated assuming that the demand for food takes priority. In a first step, different scenarios for the demand of arable land and grassland for food production were calculated for each of 133 countries.
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BAU scenario: Agricultural conditions existing at present time also apply for the future.
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Basic scenario: No forest clearing; reduced use of fallow areas for agriculture
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Sub 1 scenario: Basic scenario + ecological area expanded, followed by reduced yield level
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Sub 2 scenario: Basic scenario + food consumption is reduced for industrialised countries
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Sub 3 scenario: combination of Sub 1 and 2 scenarios
The needs and surpluses of agricultural areas are balanced between the countries of the groups EU-27, other European countries, North America, Central America, South America, Oceania, Asia and Africa to estimate the area available for the cultivation of energy crops in each world region. In a next step, the surpluses of agricultural area in each world region are classified as arable land and grassland. On grassland hay and grass silage are produced, on arable land fodder silageFootnote 3 and short rotation coppiceFootnote 4 (SRC) are cultivated. Silage of green fodder and grass are assumed for biogas production, wood from SRC and hay from grasslands are assumed for the production of heat, electricity and synthetic fuels (BtLFootnote 5 or ethanol from lignocelluloses).Footnote 6 Country specific yield developments are taken into consideration.
As a result, the global biomass potential from energy crops in 2050 was estimated to range from 6 EJ in the Sub 1 scenario to 97 EJ in the BAU scenario (see Fig. 2). In comparison to the BAU scenario, potentials decrease clearly in the Basic and Sub 1 scenario, and the lowest potentials exist in the Sub 1 scenario. The considerable higher demand of agricultural area in the Sub 1 scenario compared to the Basic and the BAU scenario is due to an ecological orientated agriculture with less fertilizer and less pesticide and therefore lower specific yields. In the Sub 2 scenario, considerable higher energy crop potentials can be released by changing the human food pattern, reducing meat consumption and consequently, the area necessary for fodder production. Also in the Sub 3 scenario, considerable potentials can be realized, in most cases even higher than in the BAU scenario. The most important country for the differences between the scenarios in 2050 is Brazil. In the BAU scenario, big agricultural areas are released by deforestation in Brazil, whereas in the Basic and Sub 1 scenario, this deforestation does not occur anymore. Consequently, no additional agricultural area for energy crops is available in Brazil in these two scenarios. In contrast high potentials are available in the Sub 2 scenario as a consequence of the reduced meat consumption of the Brazilian. Because of high population and low quantity of agricultural area, no area surpluses for energy crop production are available in Central America, Asia and Africa. However, the EU, North America and Australia have relatively stable potentials.
The Basic and Sub 3 scenario are of particular importance, since the Basic scenario would be the “minimum solution” for future agriculture. The Sub 3 scenario demonstrates the development of an ecological orientated agriculture. But such a development is only realistic, if the eating behaviour changes. Otherwise, the higher demand of food crops from an increasing world population cannot be compensated. The results of the calculation show that the availability of biomass resources is driven by different factors (as evident in the boundary conditions set in the different scenarios above), which do not only affect the global food situation but also the conservation of natural forests and other biospheres. So, the assessment of future biomass potentials is the starting point for the discussion about the integration of bio energy into a renewable energy system.
Global total potential for biomass for energy purposes
The total global biomass potential (energy crops and residues) in 2020 ranges from 66 EJ (Sub scenario 1) to 110 EJ (Sub scenario 2). For 2050, scenario results range from 94 EJ (Sub scenario 1) to 184 EJ (BAU scenario). Those numbers are conservative calculations and have an estimated uncertainty, especially for in 2050, of a factor of two. Reasons for this uncertainty are due to the unknown consequences of climate change on agricultural production, possible changes of the worldwide political and economical dynamics, a higher yield increase as a consequence of a change in agricultural techniques and/or the faster development in plant breeding. The global potential for biomass residues is estimated to be 88 EJ in 2050 (see Table 4). With a biomass consumption of 88.7 EJ in 2050, the Energy [R]evolution scenario complies with the most stringent requirements towards sustainable biomass use.
Economic boundary conditions
To implement the Energy [R]evolution pathways, an assessment of costs and benefits for society is essential. For this study, we focused on the costs of the power sector, calculating power generation costs as well as necessary investments and fuel costs for each scenario. The main assumptions for the cost calculation are presented in the following: Assumptions for heat and transport prices require a much more detailed approach for each region, thus were not included in the economic assessment.
Fuel price projections
The recent dramatic fluctuations in global oil prices have resulted in slightly higher forward price projections for fossil fuels. Under the 2004 “high oil and gas price” scenario from the European Commission, for example, an oil price of just $34 per barrel was assumed in 2030. More recent projections of oil prices by 2030 in the World Energy Outlook (IEA 2009a) range from $2008 80/bbl in the lower prices sensitivity case and up to $2008 150/bbl in the higher prices sensitivity case. The Reference scenario in WEO 2009 assumes an oil price of $2008 115/bbl. Since the first Energy [R]evolution study was published in 2007, however, the actual price of oil has moved over $100/bbl for the first time, and in July 2008 reached a record high of more than $140/bbl. Although oil prices fell back to $100/bbl in September 2008 and around $80/bbl in April 2010, the projections in the IEA reference scenario might still be considered too conservative. Taking into account the growing global demand for oil, we have assumed a price development path for fossil fuels based on the IEA WEO 2009 higher prices sensitivity case extrapolated forward to 2050 (see Table 5). As the supply of natural gas is limited by the availability of pipeline infrastructure, there is no world market price. In most regions of the world, the gas price is directly tied to the price of oil. As a consequence, gas prices used in this study are assumed to increase to $24–29/GJ by 2050. Additional price projections for biomass considered that biomass from energy crops are mainly available in the industrialised countries, especially in Europe as calculated by Seidenberger et al. (2008). Thus, biomass prices in Europe are assumed to be much higher than prices for residual biomass in the other regions.
Table 5 Fossil fuel and biomass price assumptions for the three scenarios (in US$ 2008)
Cost of CO2 emissions
Assuming that a CO2 emission trading system will be established across all world regions in the longer term, the cost of CO2 allowances needs to be included in the calculation of electricity generation costs. Projections of emissions costs are even more uncertain than energy prices, however, and available studies span a broad range of future estimates. As in the previous Energy [R]evolution study, we assume CO2 costs of $10/tCO2 in 2015, rising to $50/tCO2 by 2050. Additional CO2 costs are applied in Kyoto Protocol Non-Annex B (developing) countries only after 2020. The cost projections for CO2 are relatively conservative due to the fact that a global emission trading system requires a strong and ambitious mandatory framework to reduce global energy related CO2 emissions. However, the UNFCCC conference in Copenhagen in December 2009 failed to agree on such legally binding targets, and a global emission trading scheme will require several more years of negotiations.
Projections of future investment costs for power generation
Fossil fuel power plants
Although the fossil fuel power technologies in use today for coal, gas, lignite and oil are established and at an advanced stage of market development, further cost reduction potentials for conventional power technologies are assumed. The potential for cost reductions is limited, however, and will be achieved mainly through an increase in efficiency. Table 6 summarises our assumptions on the technical and economic parameters of future fossil-fuelled power plant technologies. In spite of growing raw material prices, we assume that further technical innovation will result in a moderate reduction of future investment costs as well as improved power plant efficiencies. These improvements are, however, outweighed by the expected increase in fossil fuel prices, resulting in a significant rise in electricity generation costs.
Table 6 Development of efficiency and investment costs for selected fossil power plant technologies
Renewable technologies
In contrast to fossil fuel power technologies, renewable energy technologies still have considerable cost reduction potentials. Table 7 summarises the assumptions for cost trends for renewable power technologies as derived from the respective extrapolated learning curves. It should be emphasised that the expected cost reduction is basically not a function of time, but of cumulative capacity, so dynamic market development is required. Most of the technologies will be able to reduce their specific investment costs between 30% and 70% of current levels by 2020 and between 20% and 60% once they have achieved full maturity (after 2040). This would continue the historical developments, where solar photovoltaic modules decreased the costs over 50% between 1990 and 2001 (EC European Commission 2005), while specific costs for wind turbine went down from US $2,700/kW to US $1,500/kW between 1982 and 2009 (Nielson et al. 2010) Reduced investment costs for renewable energy technologies lead directly to reduce heat and electricity generation costs. Electricity generation costs today are around $0.8 to $0.26 cents/kWh for the most important technologies, with the exception of photovoltaic. In the long term, costs are expected to converge at around $0.5 to $0.12 cents/kWh (including photovoltaic). These estimates depend on site-specific conditions such as the local wind regime or solar irradiation, the availability of biomass at reasonable prices or the credit granted for heat supply in the case of combined heat and power generation.
Table 7 Projected cost development for renewable power generation technologies, market volumes and investments
Estimation of job effects
Greenpeace engaged the Australian-based Institute for Sustainable Futures to model the employment effects of our 2009 sustainable future energy scenario compared to business as usual. The results, published in 2009 as “Working for the climate—Renewable Energy & The Green Job [R]evolution”, form the basis for the calculations in the 2010 Energy [R]evolution scenarios. The model calculates indicative numbers for jobs that would either be created or lost under both the Energy [R]evolution and Reference scenarios. This requires a series of assumptions summarised below.
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Start with the amount of electrical capacity that would be installed each year and the amount of electricity generated per year under the Reference (business as usual) and the two Energy [R]evolution scenarios.
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Use “employment factors” for each technology, which are the number of jobs per unit of electrical capacity (fossil as well as renewable), separated into manufacturing, construction, operation and maintenance and fuel supply.
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Take into account the “local manufacturing” and “domestic fuel production” for each region, in order to allocate the level of local jobs, and also to allocate imports to other regions.
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Multiply the electrical capacity and generation figures by the employment factors for each of the energy technologies.
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For non-OECD regions, apply a “regional job multiplier”, which adjusts the OECD employment factors for different levels of labour-intensity in different parts of the world. Regional factors are used for coal mining, so no regional adjustment is needed in this case.
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For the 2020 and 2030 calculations, reduce the employment factors by a “decline factor” for each technology; this reflects how employment falls as technology efficiencies improve.
The model used a range of inputs, including data from the International Energy Agency, US Energy Information Association, European Renewable Energy Council, European Wind Energy Association, US National Renewable Energy Laboratory, Renewable Energy Policy Project, census data from the United States, Australia and Canada and the International Labour Organisation. These calculations only take into account direct employment, for example the construction team needed to build a new wind farm. They do not cover indirect employment, for example, the extra services provided in a town to accommodate construction teams.