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Publisher: Earthscan, UK Homepage: www.earthscan.co.uk/?tabid=101807

Energy and the New Reality, Volume 1: Energy Efficiency and the Demand for Energy Services Chapter 10: Energy Demand Scenarios L. D. Danny Harvey harvey@geog.utoronto.ca.

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  1. Energy and the New Reality, Volume 1:Energy Efficiency and the Demand for Energy ServicesChapter 10: Energy Demand Scenarios L. D. Danny Harveyharvey@geog.utoronto.ca This material is intended for use in lectures, presentations and as handouts to students, and is provided in Powerpoint format so as to allow customization for the individual needs of course instructors. Permission of the author and publisher is required for any other usage. Please see www.earthscan.co.uk for contact details. Publisher: Earthscan, UKHomepage: www.earthscan.co.uk/?tabid=101807

  2. Outline Review of driving factors of CO2 emissions Review of findings concerning potential reductions in physical energy intensity Translation of reductions in physical energy intensity into reductions in economic energy intensity Scenarios of future demand for fuels and electricity Estimation of a range of feasible rates of reduction in economic primary energy intensity over the next 40-100 years

  3. We focus on atmospheric CO2stabilizing or peaking at 450 ppmv • With the heating effect of other GHGs, this is the radiative equivalent of a doubling or more of the pre-industrial CO2 concentration of 280 ppmv • By 2008 we had reached 385 ppmv • Aerosols temporarily (because they last only days in the atmosphere and so require a continuous emission source) offset ¼ to ½ of the heating effect of increasing GHGs • Doubled CO2 (or its equivalent) will likely eventually warm the climate by 1.5-4.5oC in the global average, more over continents and much more in polar regions

  4. Impacts with a CO2 doubling: • Loss of coral reefs worldwide with 1-2oC global mean warming (we’re already at 0.8oC and have seen major impacts) (near certainty with 2oC warming) • 15-30% of species committed to extinction with 2oC warming by 2050 (highly likely) • Destabilization of Greenland and West Antarctic ice caps with sustained 1-4oC warming (very likely at 4oC warming) • Significant losses in food production with 2-3oC global mean warming (10-20% worldwide loss, more in certain regions) • Severe water stress in regions dependent on glaciers and winter snowpack for summer water supplies • Potential increase in the severity of hurricanes • Acidification of the oceans (this is certain)

  5. Stabilization at 450 ppmv CO2 requires that anthropogenic (industrial + land use) emissions go to zero before the end of this century, as shown earlier in Figure 1.9a: (C emissions under BAU and permitted for stabilization at various CO2 concentrations)

  6. Figure 1.9b: Primary power (rate of use of primary energy) from fossil fuels under BAU and permitted with stabilization at 450 ppmv CO2 BAU Total

  7. Drivers of CO2 emission in the Kaya identity (introduced in Chapter 1): Emission = P x GDP($/yr)/P x MJ/$ x kgC/MJ where P = population GDP=Gross Domestic Product ($/yr) MJ (megajoules) refers to primary energy used MJ/$ = energy intensity, and kgC/MJ = C intensity

  8. Here, in Volume 1, we focus on the determinants of primary energy or power demand, while in Volume 2 we focus on reducing the C-intensity of the energy supply through the buildup of C-free energy sources

  9. Components of the Kaya identity giving primary energy demand only: Energy Demand = P x GDP($/yr)/P x MJ/$

  10. Scenarios incorporating alternative assumptions for growth of population, GDP/P and rate of decrease of energy intensity

  11. Figure 10.1a Population & GDP/P scenarios

  12. Figure 10.1b World GDP Scenarios (from multiplication of the previous P and GDP/P scenarios)

  13. Figure 10.2a Scenarios for energy intensity and rate of growth of C-free power

  14. Figure 10.2b Resulting primary power for either low P and GDP/P growth (blue) or high P and GDP/P growth (red) and either 1%/yr decline in energy intensity (solid) or 2%/yr decline (dashed)

  15. Figure 10.3a CO2 emissions for 4 scenarios

  16. Figure 10.3b CO2 emissions

  17. Figure 10.3 shows that • The relative importance of changing the product of Population x GDP/P or of changing the rate of reduction in energy intensity depends on the order in which these two terms are changed (that which is changed first appears to be more important) • Both Pop x GDP/P and rate of reduction in energy intensity are important to the long term reduction of CO2 (but population and economic growth are downplayed or ignored in most other discussions)

  18. Figure 10.4a Primary power demand in 2050 vs rate of decrease in energy intensity for high population & GDP/P and low population & GDP/P scenarios

  19. Figure 10.4b Carbon-free power required in 2050 in limiting atmospheric CO2 to 450 ppmv

  20. Economic growth depends on the rate of change of the product of population and GDP/P. The required C-free power supply at any time in the future, for a given CO2 concentration limit, is the difference between the total primary power demand and the permitted fossil fuel power supply. Figure 10.4 shows that, at high rates of improvement in energy intensity, the impact of the alternative economic growth scenario on the required C-free power supply in 2050 is quite large (and larger than the relative impact on total power demand).

  21. For example, at 2.7%/yr reduction in energy intensity, the primary power demand in 2050 is 14.4 TW for the high economic growth scenario and 10.2 TW for the low scenario (a reduction by about 30%), whereas the required C-free power supply drops from about 8.4 TW for the high scenario to 4.2 TW for the low scenario – a reduction by 50%. Furthermore, the latter is only modestly greater than the current global C-free power supply of 3.3 TW

  22. The energy intensity that appears in the Kaya identity is an economic energy intensity (MJ per $ of economic activity)The energy intensities that we have examined throughout this course have been physical energy intensities: Physical vs Economic Energy Intensities • MJ per passenger-km of travel • MJ per tonne-km of freight movement • kWh per year per m2 of building floor area • GJ per tonne of steel, aluminium or other industrial products produced

  23. Thus, we have to decompose the Kaya identity further as: Energy Demand = Population (P) x ($ of GDP/P) x (Activity/$ of GDP) x (MJ/Activity)As GDP/P increases, there is a tendency for a shift to activities that require less energy per unit of activity (such as from construction of infrastructure to provision of services). Thus, if each individual activity is becoming less energy intensive (through improvements in efficiency), the economic energy intensity will decrease for two reasons: increasing efficiency and a shift of activities toward less energy-intensive activities

  24. To illustrate, suppose that I spend $500 on activity 1 and $500 on activity 2, where activity 1 requires (among other things) 1 kg of material for every $ spent @ 20 MJ/kg, and activity 2 requires 0.1 kg of material for every $ spent @ 40 MJ/kgThen, the total energy use is $500 x 1 kg/$ x 20 MJ/kg + $500 x 0.1kg/$ x 40 MJ/kgwhere the kg/$ are the “activities” considered here (the activity is the production of the required amount of material)The total energy use is 12000 MJ or 12 MJ/$

  25. If there is a 50% reduction in the physical energy intensity of both activities but no change in the proportion of activities, the resulting economic energy intensity is 6 MJ/$However, if I spend $250 of an extra $1000 on activity 1 and $750 on activity 2, the resulting economic energy intensity for that additional income is only 4 MJ/$Thus, if my income doubles (from $1000 to $2000) over the same time period that energy intensity drops in half, the average energy intensity of my expenditures decreases from 12 MJ/$ to 5 MJ/$

  26. In sum, as incomes increase over time, there will be a tendency for a reduction in economic energy intensity (MJ/$) that will modestly enhance the reductions that can be expected from improved energy efficiency. This will be true even with no increase in the cost of energy.However, energy costs are likely to increase sharply over the coming decades, and congestion effects will make it difficult for inefficient modes of transport (i.e., using private vehicles) to increase as fast with increasing income as some analysts expect. These two factors will further accelerate the shift toward less energy-intensive activities as world GDP increases

  27. Summary of the potential for reductions in physical energy intensity

  28. Electricity generation from fossil fuels • Pulverized-coal, 35% average efficiency today, 50% projected • IGCC (Integrated gasification combined cycle, using coal) – 50% projected • Natural gas combined cycle, 60% best today • Fuel cell turbine hybrid, 70% projected • Cogeneration, marginal efficiency of electricity generation 80-90% with adequate use of waste heat

  29. For new buildings, energy use can typically be reduced by 75% compared to current practice, through a combination of • high-performance envelopes, • utilization of passive heating and ventilation, and passive/low-energy cooling techniques, • advanced systems (especially displacement ventilation and chilled-ceiling cooling in commercial buildings), • advanced lighting systems involving daylighting, • use of the most efficient equipment, properly sized and commissioned • enlightened and co-operative occupant behaviour (especially acceptance of adaptive thermal comfort systems, daylighting and passive ventilation)

  30. Comprehensive renovations can often achieve 50-75% energy savings in existing buildings through • External or internal insulation in residential buildings • Curtain-wall replacement in commercial buildings • Revamping of antiquated HVAC systems • Lighting upgrades • Fixing defective control algorithms • Solar renovations – glazed balconies, transpired wall solar collectors

  31. Transportation: cars & light trucks • Advanced but non-hybrid gasoline vehicle: 36% reduction in fuel use • 10% savings due to downsizing (20% in US, 0% elsewhere) • Plug-in hybrid with 25% of driving from fuels, 75% from electricity • On-site energy/km using electricity is 1/3 that using gasoline in an advanced vehicle • On-site energy using hydrogen is 40% that of the advanced (but non-hybrid) gasoline vehicle

  32. Energy Savings Potential in Industry • Biggest savings are through recycling • In combination with improvements in the efficiency of producing primary and secondary metals, 90% recycling reduces the energy requirement to make steel by a factor of 4.5 and aluminium by a factor of 7 • Factor of two potential reduction in world average cement energy use • Pulp and paper industry can become a net exporter of energy

  33. Energy Savings Potential in the Food System • 25% for direct energy use on farms • 20-50% reduction in energy required to make a given quantity of fertilizer, 50% reduction in fertilizer requirements in most industrialized countries • Low-meat diets – give a direct savings in energy inputs and permit a shift to organic systems (with 10-20% reduction in energy use per unit of food produced)

  34. GRAND SYNTHESIS – CONSTRUCTING SCENARIOS OF FUTURE GLOBAL DEMAND FOR FUELS AND ELECTRICITY

  35. The Kaya identity is applied separately to 10 different geopolitical regions, so as to take into account • differences in present population and GDP/P • differences in projected population growth and in potential growth of GDP/P • differences in per capita residential and commercial building floor area today • differences in annual per capita travel and in the types of travel today • differences in the potential limits on the growth of floor area and in travel (due to differing congestion effects) • differences in the initial use of appliances and consumer electronics today and thus in the potential for future increases • differences in the initial energy intensities of buildings and of transportation equipment

  36. The 10 geopolitical regions are: • Pacific Asia OECD (PAO) • North America (NAM) • Western Europe (WEU) • Eastern Europe (EEU) • Former Soviet Union (FSU) • Latin America (LAM) • Sub-Saharan Africa (SSA) • Middle East and North Africa (MENA) • Centrally planned Asia (CPA) • South and Pacific Asia (SAPA)

  37. For population, the UNDP high and low projections (with slight modifications) are used to 2050, then extended to 2100 using the logistic function:P(t)=PU/(1+((PU-Po)/Po)e-a(t-2050))where PU is an arbitrarily chosen final population, Po is the population in 2050 and a is growth rate factor that is fixed in time but can differ from region to region

  38. Figure 10.5a Low population scenario

  39. Figure 10.5b High population scenario

  40. Looking ahead to Chapter 11: Fertility rates circa 2005. Blue is for below-replacement fertility

  41. The logistic function is also used to generate scenarios of GDP/P in each region, given chosen asymptotic GDP/P values and growth rate tendencies. These scenarios, like the population scenarios, are not predictions. Rather, they are intended to show the eventual climate consequences of alternative possible future developments

  42. Figure 10.6a Low GDP/P scenario

  43. Figure 10.6b High GDP/P scenario

  44. Figure 10.7a Resulting world population and average GDP/P

  45. Figure 10.7b Resulting world GDP for high population combined with high GDP/P and low population combined with low GDP/P

  46. Having developed scenarios of population and GDP/P in each region, the next step is to prescribe a series of activity drivers: • Residential and commercial floor area per capita in each region as a function of mean regional per capita income • Average annual distance travelled per capita in each region as a function of mean regional per capita income • Global movement of freight – for now this will be assumed to grow with world GDP • Industrial output – for now this will be assumed to grow with world GDP

  47. Supplemental Figure: Increase in commercial floor space per employee as average per capita GDP increases Source: McNeil et al (2008, Fig. 11)

  48. Supplemental figure: Increase in residential floor area per capita with increasing after-tax income in different countries Source: Schipper et al (2001)

  49. Floor-area scenarios adopted here (not a prediction, but a “what-if” exercise): Estimated floor areas today and allowed here to occur with arbitrarily high income (“asymptotic”)

  50. The activity drivers that need to be considered for passenger transportation energy use are • Total distance travelled per person per year • The proportion of travel by light-duty vehicles (LDVs – cars and light trucks), 2- and 3-wheelers, buses and mini-buses, by rail and by air

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