Energy and the New Reality, Volume 2:

C-Free Energy Supply

Chapter 12: Integrated Scenarios

L. D. Danny Harveyharvey@geog.utoronto.ca

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Publisher: Earthscan, UKHomepage: www.earthscan.co.uk/?tabid=101808

Overview of this chapter

• Summary of characteristics of C-free energy sources• Review of energy demand scenarios from Volume 1• Construction of C-free energy scenarios• Material and energy flows associated with the supply

scenarios• CO2 emissions and climate response• Climate-carbon cycle feedbacks

We focus on stabilization of atmospheric CO2 at 450 ppmv because

• With the heating effect of other GHGs, this is the radiative equivalent of a doubling of the CO2 concentration of 280 ppmv

• We are currently (mid 2010) at 390 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

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 by 2-3oC warming (10-20% worldwide, 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)

Comparison of fossil fuel and renewable energy sources

of electricity

From Table 12.1: Projected future capital costs of various electricity sources

R en ew a b le N on-ren ew a b le P V $ 1 0 0 0 -2 5 0 0 /k W N u c lea r $4 000-60 00/k W C S T P $ 2 0 0 0 -3 0 0 0 /k W N G C C $6 00-90 0/k W O n sh o re w in d $1 000-14 00/k W w ith C ca p tu re $ 9 0 0- 1 3 0 0 /k W O ffsh o re w in d $ 1 4 0 0 -2 0 0 0 /k W C o a l $ 1 2 0 0 -1 5 0 0 /k W G eo th e rm a l (H D R ) $ 2 5 0 0 /k W w ith C ca p tu re $ 1 5 0 0 -2 6 0 0 /k W L arg e h y d ro $ 1 5 0 0 -3 0 0 0 /k W S m a ll h y d ro $ 9 0 0 -1 3 0 0 /k W B io m ass $ 1 0 0 0 -2 0 0 0 /k W

From Table 12.1: Projected future costs of electricity from various electricity sources

R enew a b le N on-ren ew a b le P V 4- 1 5 c en ts /k W h N u c lea r 9 -1 5 c en ts /k W h C S T P 5 -8 N G C C 4 .5 -1 2 O n sh o re w in d 4- 1 0 w ith C ca p tu re 5 .4 -1 4 O ffsh o re w in d 5- 1 2 C o a l 3 .3 -5 .2 G eo th e rm a l (H D R ) 4 w ith C ca p tu re 4 .1 -7 .3 L a rg e h y d ro 1 .2 -2 .4 S m a ll h y d ro 1 .1 -1 .5 B io m ass 4 -8

From Table 12.2: Land area required to hypothetically meet the entire 2005 world electricity demand over a period of 100 years using various electricity sources

R en ew a b le N o n -ren ew a b le B io m ass 4 -1 2 m illio n km 2 p lan tation s H y d ro , B raz il 1 -5 3 m illion k m 2 , floo ded H y d ro , C anad a 0 .4 -5 m illion k m 2 , floo ded S o lar 0 .2 -0 .3 m illio n k m 2 , g rou nd

a rray s

C oa l • 3 00 0 -1 00 00 k m 2 p ow erp lan ts • u p to 2 .7 m illio n k m 2 su rface m in in g ov e r 1 00 y rs , o r • u p to 1 m illion k m 2 s lu m pin g from un d ergrou n d m in ing and /or • u p to 5 00 ,0 00 k m 2 fo res t p lan ta tio n s to p rod u ce tim b er su pp orts fo r u n de rg ro u nd m ines

W in d 5 ,00 0 -6 ,00 0 k m 2 , fo u nd ation s + access road s

N u c lea r 2 6 ,0 0 0 k m 2 p ow erp lan ts and 1 30 ,0 00 k m 2 m in in g , m illin g and w aste repo sito rie s @ 0 .2 % o re g rade

N a tu ra l g as

L a rg e a rea a sso c ia ted w ith d rilling a n d ex traction

From Table 12.3: Comparison of EROEI for various electricity sources.

R en ew ab le N on-ren ew ab le W in d in G e rm a n y 4 0 -8 0 N u c lea r 1 6 -1 8 @ 0 .2 % o re g ra d e

3 -5 @ 0 .1 % o re g rad e C S T P in su n n y re g io n s P a ra b o l ic tro u g h P a ra b o l ic d ish C e n tra l to w er

4 0 1 4 8

C o a l

5 .0 a t 3 2 % p o w erp lan t e ffic ie n c y 6 .7 a t 4 2 % po w e rp la n t e ffic ie n c y

P V in ce n tra l E u ro p e to d a y fu tu re

8 -2 5 > 2 5

N G C C

2 .2 a t 5 4 % p o w erp lan t e ffic ie n c y

B io m ass co g en e ra tio n 3 6 -4 8

Notes for the preceding slide:

• For fossil fuels and nuclear, the EROEI is based on all the energy inputs except the fuel itself

• This includes energy for mining, processing and transporting the fuel used at the power plant, and for end-of-life decommissioning of the powerplant

• The low EROEI for NGCC arises because the energy required to explore, drill, build pipelines and transmit natural gas is about 25% of the energy value of the fuel used. Thus, for a plant at 54% efficiency, the EROEI is 0.54 over (0.25 x 1.0) = 2.2

From Table 12.4: Cost of equipment, fuel and heat from various sources.

E q u ip m ent C os t ($ /k W )

F u e l C ost ($ /G J)

E ffic ien cy C os t o f h ea t ($ /G J)

S o la r D H W 1 00 0 0 0 .6 1 0 .3 1 50 0 0 0 .4 1 5 .3 B iom ass 7 5 3 0 .9 0 3 .8 1 50 6 0 .9 5 7 .5 N atu ra l g as 5 0 6 0 .9 0 6 .5 1 50 1 8 0 .9 5 1 9 .5 E lectr ic ity a t 1 0 cen ts/k W h

5 0 2 8 1 .0 0 2 8 .3

From Table 12.4: Cost of various fuels that could be used for transportation, as produced from various sources.

Fue l p ro duced and source

C ost o f Inpu t

C onversion E fficiency (% )

Fue l cos t ($ /G J)

L and a rea (1000 km 2 )

pe r E J/y r E thano l from ligno -cellu losic b iom ass

$3-6 /G J

46-54

6 -10

90-11 0

E thano l from sug arcane

$2 /G J

32-45

4 -6

26 -36

H 2 from w ind e lec tr ic ity

5-10 cen ts /kW h

75

19-37

0 .11

H 2 from C ST

energy

5-10 cen ts /kW h

100

14-28

2 .8

H 2 from PV

10-2 0

cen ts /kW h

75

38-74

4 .9

H 2 from b iom ass

$3-6 /G J

50-60

5 -12

80-100

By comparison, gasoline at $1/litre is equivalent to $18/GJ.Note that H2 in fuel cell vehicles can be used twice as efficiently as gasoline or ethanol in an advanced vehicle with an internal combustion engine

From the preceding slide, note that

• Hydrogen produced from renewable electricity will be very expensive compared to hydrogen made from biomass

• But hydrogen made from biomass will take up a lot of land compared to hydrogen made from wind-based electricity

• Ethanol from sugarcane will likely require 3x less land than ethanol from ligno-cellulosic biomass or hydrogen from biomass

Recall: Options to get relatively steady electricity output from wind with large

capacity factors (70% or more) are:

• To place widely dispersed windfarms in the best wind regions (which tend to be 750-3000 km from major demand centres) and oversize them by a factor of 2-3 relative to the transmission link

• To use hydro-power as (in effect) temporary storage to levelize or control the combined wind+hydro power output

• To use compressed air energy storage (CAES), initially with natural gas, later with gasified biomass or with storage of heat produced during compression (advanced adiabatic CAES)

Figure 3.44 Oversizing Concept

Figure 3.48 Comparison of electricity costs from local and distant oversized wind farms vs wind speed

0

5

10

15

20

25

30

4 5 6 7 8 9 10 11 12

Mean Wind Speed (m/s)

Ele

ctri

city

Co

st (

cen

ts/k

Wh

)

Distant, base cost, no over-sizing

Wind farm base capital cost $1000/kW, Transmission cost $460/kWFull-load transmission loss of 5%

Distant, 1.5 x base cost, 3-fold over-sizing

Distant, 1.5 x base cost, no over-sizing

Local Wind Farm

Figure 3.36 Transmission corridors transmitting 10 GW of electric power

800 kV AC

600 kV HVDC

800 kV UHVDC

425 m

150m

100m

Figure 12.1a Wind electricity generation potential based on winds at a height of 100m, kWh/m2 based on total wind farm area (with a turbine spacing of 7D x 7D, where D=80 m is the rotor diameter)

0 10 15 20 40

Figure 12.1b Electricity generation potential (kWh/m2) from concentrating solar thermal power assuming a collector:ground area

ratio of 0.25 and 15% overall sunlight-to-electricity efficiency

2 0 4 0 8 0 9 0 1 0 0 1 2 0

The following map shows the minimum of the computed cost of PV and wind electricity,

assuming

• annual electricity production from concentrating solar thermal power (CSTP) and wind as shown in the previous slides

• capital cost for wind power of $1500, annual O&M equal to 2% of the capital cost, and 5% financing over 20 years

• capital cost for CSTP of $3000/kW, annual O&M equal to 5% of the capital cost, and 5% financing over 20 years

Figure 12.1c Minimum of CSTP and wind electricity cost (cents/kWh) (excluding transmission cost)

5 6 7 8 10

C-free energy sources for transportation:

• Plug-in hybrid electric vehicles- renewable electricity from the grid would

cover 60-75% of the distances driven - would also facilitate a greater proportion of non-transportation

electricity use being supplied by intermittent renewables, as the vehicle batteries would serve as short-term energy storage, thereby compensating for short term (second to hours) variation in electricity supply

• Hydrogen or biofuels for long-range driving in vehicles with high efficiency

• Investments in high-quality rail-based urban transit infrastructure combined with transit-supportive urban form

Biomass energy

• Given the priority need for land for food production, the future global potential will depend strongly on human diet (diets with high meat require much more land, thereby crowding out bioenergy crops)

• The most effective use is bioenergy for combined heat and electricity (cogeneration)

• Biofuels from temperate food crops (ethanol from corn or wheat, biodiesel from oily crops) make absolutely no sense. Ethanol from sugarcane is possibly justifiable, although long-term sustainability has not been proven

• Biofuels from ligno-cellulosic crops might be an ecologically viable method for meeting a small portion of transportation energy demand (that which remains after reducing demand through better urban form and mass transit and through use of renewable electricity in plug-in hybrid electric vehicles)

Summary of Volume 1:Construction of Energy

Demand Scenario

To derive future demand for energy, the world is divided into 10 geopolitical regions. Energy use in each sector (buildings, transportation, industry, agriculture) is computed as follows

Energy Demand = Population (P) x ($ of GDP/P) x (Activity Level/$ of GDP) x (MJ/Activity) (Energy Intensity)

The ‘activities’ are things such as building floor space used, distance travelled per year by various modes of transportation, and consumption of industrial output

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)

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

Figure 10.5a from Volume 1: Low population scenario

0

500

1000

1500

2000

2500

3000

2000 2020 2040 2060 2080 2100 2120

Year

Po

pu

lati

on

(m

illi

on

s)

SAPA

CPA

SSA

LAM

WEU

MENA

NAM

FSU

PAO

EEU

Figure 10.5b from Volume 1: High population scenario

0

500

1000

1500

2000

2500

3000

3500

2000 2020 2040 2060 2080 2100 2120

Year

Po

pu

lati

on

(m

illi

on

s)

SAPA

CPA

SSA

LAM

WEU

MENA

NAM

FSU

PAO

EEU

GDP per person:

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

Figure 10.6a from Volume 1: Low GDP/P scenario

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

2000 2020 2040 2060 2080 2100 2120

Year

GD

P/p

erso

n (

2005

$)

NAM

WEU

PAO

EEU

CPA

FSU

LAM

SAPA

MENA

SSA

Figure 10.6b from Volume 1: High GDP/P scenario

0

10000

20000

30000

40000

50000

2000 2020 2040 2060 2080 2100 2120

Year

GD

P/p

erso

n (

2005

$)

NAM

PAO

WEU

EEU

CPA

FSU

LAM

SAPA

MENA

SSA

Figure 12.2a Resulting world population and average GDP/P

0

2

4

6

8

10

12

2000 2020 2040 2060 2080 2100Year

Po

pu

lati

on

(b

illi

on

s)

0

8

16

24

32

40

48

Per

Cap

ita

GD

P (

1000

s 20

05$)

Population

GDP/capita

Figure 12.2b Resulting world GDP for high population combined with high GDP/P and low population combined with low GDP/P

0

50

100

150

200

250

300

350

400

2000 2020 2040 2060 2080 2100

Year

Wo

rld

GD

P (

tril

lio

ns

20

05

$)

0

1

2

3

4

Ra

te o

f G

row

th i

n G

DP

/P (

%/y

r)

World GDP

Rate of growth inworld average GDP/capita

Activity drivers:

• Residential and commercial floor area per capita in each region as a logistic function of mean regional GDP/P

• Average annual distance travelled per capita in each region as a logistic function of mean regional GDP/P

• Proportion of travel by different modes as a logistic function of regional GDP/P with various imposed caps

• In the absence of structural shifts in the economy, the global movement of freight increases in proportion to the size of the world economy

• In the absence of structural shifts in the economy, industrial output increases in proportion to the size of world economy

Resulting growth in global floor area for the high population & GDP/P and the low population & GDP/P scenarios (Figure 10.9 from Volume 1):

0

50

100

150

200

250

300

350

400

450

2000 2020 2040 2060 2080 2100

Year

Flo

or

Are

a (b

illi

on

s m

2 )

Residential floor area

Commercial floor area

Resulting growth in travel for the high population & GDP/P and the low population and GDP/P scenarios (Figure 10.11a from Volume 1):

0

50

100

150

2000 2020 2040 2060 2080 2100

Year

Mo

vem

ent

of

peo

ple

(tr

illi

on

p

km/y

r)

0

150

300

450

Mo

vem

ent

of

frei

gh

t (t

rill

lio

n

tkm

/yr

People

Freight

Volume 1 considered the potential reductions in energy intensity in

• New and existing buildings• All forms of transportation• The major industries (such as iron and steel,

aluminium, copper, cement, glass, pulp and paper, and plastics)

• Agriculture and the food system• Municipal services

Reduction of Energy Intensity in Buildings

For new buildings, it was concluded that energy use can be reduced to 25-50% of that for recent buildings in all parts of the world

Comprehensive renovations can achieve savings of 33-50% of current energy use (and savings of up to 90% in heating energy use)

A stock turnover model was used to compute the change in total building energy use over time in 10 different regions as standards for new and renovated buildings are gradually improved

Reduction of Energy Intensity of Transportation

Future energy intensity for passenger transportation is computed as:

Current fossil fuel energy intensity (MJ/person-km) x (Fossil fuel energy intensity factor + electricity energy intensity factor)

As in all other sectors, fuels and electricity demand are tracked separately.

Considerations in computing the future energy intensity of cars & light trucks:

• Advanced but non-hybrid gasoline vehicles: 36% reduction in fuel use compared to comparable present-day vehicles

• 10% savings due to downsizing (20% in US, 0% elsewhere)

• Plug-in hybrid vehicles are assumed to by powered 25% from fuels, 75% from electricity

• Energy/km using electricity is 1/3 that using gasoline in an advanced vehicle

• Energy/km using hydrogen as the fuel in a PHEV is 40% that of the advanced (but non-hybrid) gasoline vehicle

Figure 10.11b from Volume 1: Transport intensity with slow or fast implementation of strict fuel economy standards. Electricity and fuel

energy intensity factors are added and multiplied by the energy intensity today (MJ/person-km) to get future energy intensity.

0.0

0.2

0.4

0.6

0.8

1.0

2000 2020 2040 2060 2080 2100

Year

Inte

nsi

ty F

acto

r

Freight fuel, slow

Freight fuel, fast

LDV fuel, slow

LDV fuel, fast

LDV electricity, fast

LDV electricity, slow

However, these efficiency improvements and the shift to PHEVs are not likely to be enough to avert shortages in transportation fuels, given the near

certainty that oil supply will peak during the next 10 years. Aggressive shifting to public transport and

restrictions on air travel (perhaps through high prices) will also be needed

Figure 10.13 from Volume 1: Transportation energy demand for the low population & GDP/P scenario for cases of slow and fast transition to radically more fuel-efficient transportation equipment and for the ‘Fast+Green’ scenario

0

20

40

60

80

100

120

140

2000 2020 2040 2060 2080 2100

Tra

nsp

ort

atio

n F

uel

Dem

and

(E

J/yr

)

Year

Slow

Fast

Fast and Green

Figure 2.21 from Volume 1: geologically-constrained assessment of future oil supply

Source: Campbell and Siobhan (2009, An Atlas of Oil and Gas Depletion, Jeremy Mills Publishing, UK)

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 average energy requirement to make steel by a factor of 4-6 and aluminium by a factor of 5-7

• Factor of two potential reduction in world average cement energy use

• Pulp and paper industry can become a net exporter of energy

Structural Shifts in the Economy

• As wealth (GDP/P) increases, proportionately more money is spent on services and less on industry, and within the industry sector, there is a shift from heavy industry to light industry

• As the energy intensity (MJ/$) of services is less than that of industry, and that of heavy industry is less than that of light industry, this shift leads to an overall reduction in energy use

• In the scenarios presented in Volume 1, it is assumed that 50% of the economic value-added of industry and freight transport that would otherwise occur by 2100 is shifted to the services sector (represented by an increase in energy use by commercial buildings)

Overall Result:

• Global primary energy demand in 2100 is less than half global primary energy demand today in the low population in GDP scenario while the global economy is three times larger, and comparable to current primary energy demand today in the high population and GDP/P scenario while the global economy is 7 times larger

• This causes the average energy intensity to decrease by almost a factor of six

• For the period 2005-2060, world average energy intensity falls at an average compounded rate of about 2.7%/yr

The next two slides show the overall global energy demand (separately for fuels and for electricity) for the Low Scenario (low growth of population and of GDP/P) and for the High Scenario (high growth of population and of GDP/P), considering Slow and Fast implementation of energy efficiency measures and taking into account structural shifts in the economy (less industrial production, more services) as wealth increases.

These demand scenarios are what has to be satisfied eventually entirely by C-free energy sources

Figure 12.3a Global demand for fuels and electricity for the Low Scenario

0

100

200

300

400

500

2000 2020 2040 2060 2080 2100

Sec

on

dar

y E

ner

gy

Use

(E

J/yr

)

Year

Fuels, slowFuels, slow+shiftFuels, fast+shiftElectricity, slowElectricity, fastElectricity, fast+shift

Figure 12.3b Global demand for fuels and electricity for the High Scenario

0

100

200

300

400

500

2000 2020 2040 2060 2080 2100

Seco

nd

ary

En

erg

y U

se (

EJ/y

r)

Year

Fuels, slowFuels, fastFuels, fast+shiftElectricity, slowElectricity, fastElectricity, fast+shift

Construction of C-Free Energy Supply Scenarios

Approach

• Consider two variants of each scenario: a biomass-intensive variant and a hydrogen-intensive variant

• Work out the biomass and hydrogen fuel supplies needed for each variant (the total required fuel differs between the variants because biomass and hydrogen would be used with different efficiencies)

• Prescribe various ultimate amounts of C-free power supply such that, if they were achieved by 2100, there would be no remaining fossil fuel use

• Use a logistic function to generate a variation in the power supply from each energy source over time

Approach (continued)

• Compute required material flows and energy investments to build up the C-free energy supplies, and check for feasibility

• Fossil fuel use in each end-use sector is given by the total demand for fuel in that sector minus the C-free fuel supply for that sector

• Electricity demand is equal to the electricity end-use demand (from Volume 1) plus additional electricity needed to produce hydrogen by electrolysis of water

• Fossil fuel use to generate electricity is given by the total electricity demand minus the total C-free energy supply, divided by the efficiency in generating electricity from fossil fuels

Logistic function:

P(t) = PU /(1+((PU-Po)/Po)e -a(t-to) )

where Po is the power supply in year to, PU is the ‘ultimate’ power supply that is asymptotically approached with time constant a, and P(t) is the power supply in year t.

Supplemental Figure: Generic logistic growth curve

0

5

10

0 100 200 300 400 500

Time (arbitrary units)

Po

wer

Su

pp

ly (

arb

itra

ry u

nit

s)

0.00

0.01

0.02

0.03

0.04

0.05

0.06

Gro

wth

in

P S

up

ply

((a

rbit

rary

u

nit

)/(t

ime)

)

P supply

Rate of increase is P supply

P(t)

dP/dt

Table 12.10: Parameters chosen here to generate scenarios for the supply of electricity from various C-free energy sources

P V C S T P W in d B io m a ss G eo th er m a l H y d ro C ap ac ity in 2 0 0 5 (G W ) 5 .2 0 5 9 .1 4 8 .9 8 6 6 .8 P u (G W ) lo w d e m an d 3 0 0 0 6 0 0 0 6 0 0 0 2 0 0 1 0 0 1 2 0 0 P u (G W ) h ig h d e m a n d 4 0 0 0 1 2 0 0 0 1 2 0 0 0 2 0 0 1 0 0 1 2 0 0 a , s lo w d e p lo y m en t 0 .0 8 0 .0 8 0 .0 6 0 .0 6 0 .0 6 0 .0 1 a , ra p id d ep lo y m en t 0 .1 5 0 .1 5 0 .1 0 .1 0 .1 0 .0 2 C ap ac ity fac to r 0 .1 5 0 .6 0 .2 0 .6 0 .9 0 .4

By comparison, total world electrical power capacity in 2005 was about 4300 GW

(see text for the parameters chosen for the transition from fossil fuels to either biofuels or hydrogen in each end-use sector)

The following slides show the results for the Low Energy Demand Scenario

with Fast build-up of C-free energy supplies

Figure 12.14a: C-free electrical capacity (GW)

0

4000

8000

12000

16000

2005 2015 2025 2035 2045 2055 2065 2075 2085 2095

Year

C-f

ree

Ele

ctri

c C

apac

ity

(GW

)

Nuclear

Geothermal

Biomass

Wind

Solar thermal

Solar PV

Hydro

Figure 12.14b: C-free electricity generation (TWh/yr) (1 TWh = 1000 GWh)

0

10000

20000

30000

40000

50000

2005 2015 2025 2035 2045 2055 2065 2075 2085 2095

Year

C-f

ree

Ele

ctri

city

Gen

erat

ion

(T

Wh

/yr) Nuclear

GeothermalBiomassWindSolar thermalSolar PVHydro

Figure 12.14c: C-free electricity generation (TWh/yr) for direct use and to produce H2 by electrolysis

0

10000

20000

30000

40000

50000

2005 2015 2025 2035 2045 2055 2065 2075 2085 2095

Year

Ele

ctr

ic G

ener

atio

n (

TW

h/y

r)

For H2 production

Direct Use

Figure 12.14d: C-free and fossil fuel supply (EJ primary energy/yr)

0

100

200

300

400

500

600

2005 2015 2025 2035 2045 2055 2065 2075 2085 2095

Year

Fu

el U

se (

EJ

/yr)

Extra Biomass

Basic Biomass

Coal

Oil

Natural Gas

Some key parameters are

• The required rate of installation of new C-free power supplies (GW/yr)

• The required rate of construction of factories to produce C-free power systems (a given factory has a certain GW output per year, so the rate of construction of factories has units of GW/yr2)

• The required annual use of materials (steel, aluminum, copper, cement, plastics)

• The energy used in a given year constructing new C-free power systems as a percentage of the total energy supplied in that year by all systems installed up to that point in time

Figure 12.6a: Rate of installation of wind power (GW/yr)

Global addition in 2009: 36.7 GW

0

100

200

300

2007 2027 2047 2067 2087

Year

Rat

e o

f In

stal

lati

on

(G

W/y

r)

Replacement capacity

New capacity

Figure 12.6b: Rate of installation of concentrating solar thermal power (GW/yr)

0

100

200

2007 2027 2047 2067 2087Year

Rat

e o

f In

stal

lati

on

(G

W/y

r)

Replacement capacityNew capacity

Figure 12.6c: Rate of installation of PV power (GW/yr)

Global addition in 2008: 5.6 GW

0

50

100

150

2007 2027 2047 2067 2087Year

Rat

e o

f In

stal

lati

on

(G

W/y

r)

Replacement capacityNew capacity

Figure 12.8a: Annual steel requirement for the various C-free energy sources and for HVDC transmission lines

0

10

20

30

40

50

60

70

2000 2020 2040 2060 2080 2100

Year

Mat

eria

l Flo

w (

Mt/

yr) CSTP

Floating turbinesOnshore turbinesHVDC Transmission

Steel, Global Production in 2005: 1320 Mt

Figure 12.8b: Annual aluminium requirement for the various C-free energy sources and for HVDC transmission lines

0.0

0.1

0.2

0.3

0.4

2000 2020 2040 2060 2080 2100

Year

Mat

eria

l Flo

w (

Mt/

yr)

0

1

2

3

4

Mat

eria

l Flo

w (

Mt/

yr)

Onshore turbinesCSTPFloating turbinesPVHVDC Transmission

Aluminum, Global Production in 2005: 38 Mt

Use right scale

Figure 12.8c: Annual copper requirement for the various C-free energy sources and for HVDC transmission lines

0.0

0.1

0.2

0.3

0.4

0.5

0.6

2000 2020 2040 2060 2080 2100

Year

Mat

eria

l Flo

w (

Mt/

yr)

CSTPFloating turbinesOnshore turbines

Copper, Global Production in 2005: 56 Mt

Figure 12.8d: Annual concrete requirement for the various C-free energy sources and for HVDC transmission lines

0

50

100

150

200

250

2000 2020 2040 2060 2080 2100

Year

Mat

eria

l Flo

w (

Mt/

yr)

CSTPOnshore turbinesHVDC Transmission

Concrete, Global Production in 2005: 26,000 Mt

Figure 12.10: Annual electricity-equivalent energy requirement for the expansion or maintenance of various C-free systems as a percentage of the

total energy output from the corresponding system for the LFf scenario

0

10

20

30

40

2000 2020 2040 2060 2080 2100

Year

En

erg

y In

pu

t as

a %

of

To

tal S

yste

m O

utp

ut CSTP

Floating turbinesOnshore turbinesPV Power

Figure 12.11: Biomass plantation area required in the biomass-intensive slow supply scenario for various demand scenarios

0

1

2

3

4

2000 2020 2040 2060 2080 2100

Year

Are

a o

f N

ew P

lan

tati

on

s (G

ha) Series5

HFs

HSs

LFs

LSs

World Pastureland Area today

World Cropland Area today

Scenario

Figure 12.12: Rate of construction of factories to produce C-free power systems (GW/yr/yr)

0

2

4

6

8

2000 2020 2040 2060 2080 2100

Year

Rat

e o

f F

act

ory

Co

ns

tru

ctio

n (

GW

/yr/

yr) CSTP

Wind power

PV Power

Figure 12.13a: Total fossil fuel CO2 emission for various energy demand scenarios combined with the slow buildup of C-free energy sources

0

2

4

6

8

10

12

14

2000 2020 2040 2060 2080 2100

Year

CO

2 E

mis

sio

n (

GtC

/yr)

HSf

HFf

LSf

LFf

LFGf

Figure 12.13b: Total fossil fuel CO2 emission for various energy demand scenarios combined with the fast buildup of C-free energy sources

0

2

4

6

8

10

12

14

2000 2020 2040 2060 2080 2100

Year

CO

2 E

mis

sio

n (

GtC

/yr)

HSs

HFs

LSs

LFs

Figure 12.14 CO2 emissions for the business-as-usual scenario where ultimate natural gas, oil and coal uses are 3, 2 and 3 times the cumulative

use to 2005, respectively

0

2

4

6

8

10

1900 1950 2000 2050 2100

Year

Em

issi

on

(G

tC/y

r)

Total

Coal

Oil

Natural Gas

Figure 12.15 Fossil fuel CO2 emissions for base case fossil fuel supplies (‘Low’, same as in Fig. 12.14) and for the case where the remaining natural gas, oil and

coal is twice as large (‘Medium’) and for the case where the remaining natural gas and oil are as in the ‘Medium’ case and the remaining coal is three times as large as for ‘Low’ (‘High’) (that is, remaining coal for ‘High’ is 6 times cumulative use to

2005). Also given are emissions for the two extreme climate-policy scenarios.

0

2

4

6

8

10

12

14

1900 1950 2000 2050 2100 2150 2200

Year

Em

issi

on

(G

tC/y

r)

High

Medium

Low

Businesss-as-Usual Scenarios:Climate Scenarios:

HSs

LFGf

Figure 12.16 Cumulative fossil fuel CO2 emission, 1800-2300, for the various scenarios (most BAU scenarios have ultimate

emissions of about 5000 GtC)

0

200

400

600

800

1000

1200

1400

1600

1800

BAUHigh

BAUMedium

BAULow

HSs HFs LSs LFs HSf HFf LSf LFf LFGf

Scenario

Ult

imat

e C

um

ula

tive

Em

issi

on

(G

tC)

Climate Response

Figure 12.17: Matching observed global mean warming over the past 150 years using a simple climate-carbon cycle model with climate sensitivities ranging from 1.0-4.5oC and increasingly large offsetting effects by pollutant

aerosolsG

loba

l Mea

n W

arm

ing

( C

)O

-0.4

0.0

0.4

0.8

1.2

1.6

1850 1890 1930 1970 2010

Year

Series1

T2x = 4.5 C , 50% aerosol offset

T2x = 3.0 C , 50% aerosol offset

T2x = 2.0 C , 20% aerosol offset

T2x = 1.5 C , 15% aerosol offset

T2x = 1.0 C , no aerosol offset

M odel Sim ulations

O bservations

Δ

Δ

Δ

Δ

Δ

Figure 12.18b: Variation of global mean temperature for various CO2 emission scenarios, assuming a midpoint climate sensitivity

of 3oC out of an uncertainty range of 1.5-4.5oC

0

1

2

3

4

5

6

7

1900 2000 2100 2200 2300 2400 2500

Year

Glo

bal

Mea

n W

arm

ing

(oC

)

High BAU

Low BAU

HSs

LFf

LFGf

LFGf + 1 GtC/yr CCS

3oC climate sensitivity

Figure 12.19 Variation of global mean temperature for the two extreme policy scenarios (HSs and LFGf) and for climate

sensitivities of 1.5oC, 3.0oC and 4.5oC

0

1

2

3

4

5

6

1950 2000 2050 2100 2150 2200 2250

Year

Glo

bal

Mea

n W

arm

ing

(oC

)

HSs

Scenario 5, T2x = 4.5 C

HSs

Scenario 5 T2x = 3.0 C

HSs

Scenario 5 T2x = 1.5 C

Climate-Carbon Cycle Feedbacks

As the climate warms in response to human emissions of GHGs, various natural emissions of GHGs will increase, thereby adding to the increase in atmospheric CO2 beyond that due to human emissions, leading to further warming and further increases in natural emissions.

These feedbacks are not included in the preceding simulation results, but there is growing evidence that they could become quite serious in the future and, indeed, have already started

The major potential climate-carbon cycle feedbacks of concern are

• Dieback of midlatitude forests due to rapid (and, in some place, adverse) changes in climate

• Conversion of the Amazon rainforest to savanna if the climate becomes more El Niño-like

• Release of CO2 and especially of CH4 from thawing Arctic soils (Siberian yedoma soils in particular)

• Release of CH4 from frozen water-CH4 compounds called clathrates that are found in permafrost regions and on continental shelves worldwide (could become significant by the time we reach 4-5oC global mean warming)

Supplemental figure. Variation in the terrestrial biosphere sink (becoming a source after 2070) as simulated by various climate-terrestrial biosphere models. By comparison, global fossil fuel

emission in 2005 was about 8.0 GtC

Source: Fischlin et al (2007, IPCC AR4, WGII)

Methane (CH4) is of particular concern because

• It is 26 times stronger than CO2 on a molecule-per-molecule basis

• It induces formation of tropospheric O3, which add to the GHG heating

• Oxidation of some of it in the stratosphere leads to the formation of water vapour in the stratosphere, where it is particular effective as a GHG

Recent work in Siberia indicates

• Potential release of 300-500 GtC from yedoma soils, much of it as methane

• The process begins slowly as the local climate warms but accelerates accelerates and becomes irreversible due to the release of heat by the decomposition process itself

• Emissions reach 2-3 GtC/yr by the time local warming reaches about 9oC and continue for 100 years or more according to simulations using permafrost models

Supplemental figure: Methane escaping from thawing yedoma soils (and lite on fire) in Siberia

Source: Walker (2007), Nature 446, 727-728

Figure 12.20: Variation in global mean temperature for energy scenario HSs and a 3oC climate sensitivity, assuming the release of C from yedoma soils

ramps up from 0.3 GtC/yr at 3.3oC global mean warming to 3 GtC/yr at 4.3oC global mean warming, with 25% of the emission as CH4 and 75% as CO2. C release is assumed to suddenly end when the total emission reaches 300 Gt

0

1

2

3

4

5

6

1950 2000 2050 2100 2150 2200 2250

Year

Glo

bal

Mea

n W

arm

ing

(oC

)

With yedoma feedback

No yedoma feedback

Figure 12.21: Radiative forcing due to various factors in the yedoma-feedback scenario

0

2

4

6

8

2000 2050 2100 2150 2200 2250

Year

Rad

iati

ve F

orc

ing

(W

/m2 )

Extra stratospheric water vapourExtra tropospheric O3Extra CH4Extra CO2Total GHG, no feedback case

Conclusions

• There is little hope of eliminating fossil fuel emissions by the end of this century under the high population and GDP/P-growth scenario

• Eliminating emissions by the end of this century under the low population and GDP/P-growth scenario does not require impossibly large material or energy flows or rates of deployment of C-free energy supplies, but would require

• A massive improvement in energy efficiency (leading to factors of 3-4 reductions in energy intensity by 2050), and

• A concerted global effort to rapidly deploy PV, concentrating solar thermal, wind and other C-free energy sources

• Even with all this, it might now be impossible to avoid catastrophic climatic change, particularly if strong positive climate-carbon cycle feedbacks come into play

• It is, nevertheless, worth trying!