Transcript GGR347/1407, Closing Lecture: – PUTTING IT ALL TOGETHER CONSTRUCTING SCENARIOS OF

GGR347/1407, Closing Lecture:
PUTTING IT ALL TOGETHER –
CONSTRUCTING SCENARIOS OF
FUTURE GLOBAL DEMAND FOR
FUELS AND ELECTRICITY
Future energy use in a given sector (transportation,
buildings, industry, food system) in a given region can
be represented as the product of:
• Population
• Average GDP (income) per person
• Average activity levels per person as functions of income
(activity levels would be: annual distance travelled, commercial
and residential building floor area used, indoor temperatures
that are maintained in summer and winter, amounts of materials
consumed)
• Proportions of different activities (i.e., proportion of total travel
by LDVs (light-duty vehicles) or air, amount of meat in the diet)
• Energy intensities per unit of activity (i.e., kWh/m2/yr for
different building services, MJ/km for different transportation
modes, MJ/kg for different material consumed)
Framework Presented Here
• Divide the world into 10 geopolitical regions
• Within each region, collect information on
population and income (easy), and on the
various activity levels and characteristics and
efficiencies of the equipment used (hard)
• Construct scenarios for changing population and
income to 2100, on changing activity levels, and
on changing energy intensities (these are not
predictions!)
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 (SPA)
Activity Drivers:
Population scenarios: – UNDP 25th and
___75th percentiles from a randomly-___generated set of scenarios
GDP scenarios: made up (for
___illustrative purposes only)
Low population scenario
3000
(a)
2500
Population (millions)
SPA
CPA
2000
SSA
LAM
WEU
1500
MENA
NAM
1000
FSU
PAO
EEU
500
0
2000
2020
2040
2060
2080
2100
2120
High population scenario
3500
(b)
3000
Population (millions)
2500
SPA
CPA
SSA
2000
LAM
WEU
1500
MENA
NAM
FSU
1000
PAO
EEU
500
0
2000
2020
2040
2060
2080
2100
2120
Looking ahead to Chapter 11: Fertility rates circa 2005.
Blue is for below-replacement fertility
Per capita annual income is assumed to initially grow
exponentially, but at a declining rate, gradually reaching
some stable “ultimate” income level. This behaviour is
captured by the logistic function. Annual per capita income
I(t) is computed
I(t)=IU / (1+((IU-Io)/Io)e –a(I - to) )
where I(t) is GDP per capita in year t, Iu is the ultimate per
capita income, a is a growth parameter, and Io is the
starting income in year starting year to (2010)
Low GDP/P scenario
60000
(a)
50000
GDP/person (2005$)
NAM
40000
WEU
PAO
EEU
30000
CPA
FSU
LAM
20000
SPA
MENA
SSA
10000
0
2000
2020
2040
2060
2080
2100
2120
High GDP/P scenario
60000
NAM
(b)
PAO
50000
WEU
GDP/person (2005$)
EEU
CPA
40000
FSU
LAM
SPA
30000
MENA
SSA
20000
10000
0
2000
2020
2040
2060
2080
2100
2120
Resulting world population and average GDP/P
12
48
10
40
8
32
6
24
4
16
GDP/capita
2
0
2000
8
0
2020
2040
2060
2080
2100
Average GDP/capita (1000s 2005$)
Population (billions)
Population
Resulting world GDP for high population combined with high
GDP/P and low population combined with low GDP/P
4
400
World GDP (trillions 2005$)
3
300
250
2
200
150
1
100
50
0
2000
Rate of growth in
world average GDP/capita
0
2020
2040
2060
2080
2100
Rate of Growth in GDP/P (%/yr)
World GDP
350
Transportation Sector
Activity Levels
The activity levels (amounts) 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 3wheelers, buses and mini-buses, by rail and by
air
• The share of different vehicle market segments
(such as compact cars, SUVs) within the LDV
market
Passenger travel today (1000s km per person per year):
Source: World Business Council for Sustainable Development
Observations on present-day per-capita travel
• Annual travel varies by more than a factor of 10, from 1600
km/person in Africa to 21,000 km/person in NAM
• Travel is overwhelming by LDVs (16,000 km/P/yr) and air
(3500 km/P/yr) in NAM and by buses and mini-vans in
Africa
• Per person travel by LDVs in WEU is only half that in NAM
• 2-wheelers (i.e., mopeds) and 3-wheelers (rickshaws) are
important (~ 25% of total) in Asia, while buses and
minivans are important (> 25%) everywhere else except
NAM and WEU
• Rail accounts for only about 5% of travel in WEU and FSU,
and almost 10% in EEU and PAO (Japan and S Korea)
2- and 3-wheelers, and buses and mini-buses,
each have an energy today of about 0.5-0.6
MJ/pkm. This is less than the most fuel efficient
cars envisaged (~ 1 MJ/km) if they have only one
person in them. Thus, if a significant fraction of
travel currently by 2- and 3 wheelers or by buses
and mini-buses shifts to single-occupancy LDVs
rather than to various forms of rail travel (subways,
trams, trains) as Asian countries becomes richer,
transportation energy intensity in Asia will increase
even with massive improvements in LDV fuel
economy
Future Scenario:
Total annual distance travelled per capita is assumed to
increase with increasing income according to the logistic
function. Thus, per capita travel increases in each region
as
D(I)=DU/(1+((DU-Do)/Do)e
–a(I - Io)
)
where Do and Io are the average per capita travel and
incomes in the starting year (2005 for this sector) in a
given region, respectively, and DU is the ultimate or
asymptotic per capita travel. Note that this is the same
equation as used to create a variation of income itself
over time, except that income rather than time is the
independent variable
Transportation scenarios adopted here
(not a prediction, but a “what if” exercise):
Estimated travel today and allowed to occur with
arbitrarily high income (“asymptotic”)
Resulting growth in travel for the high population & GDP/P
and the low population and GDP/P scenarios (Figure 10.11a):
450
People
100
300
Freight
50
0
2000
150
2020
2040
2060
Year
2080
0
2100
Movement of freight (trilllion
tkm/yr
Movement of people (trillion
pkm/yr)
150
Assumptions concerning the share of different travel modes achieved
as income becomes arbitrarily large for the Base case and “Green”
scenarios. In the Base scenarios, final LDV share is either assumed to
be the same as today or to increase from today’s shares to a share of
30% or 50%.
Note that the assumptions in the “Green”
scenarios, in which LDV shares fall and rail
shares rise in NAM and some other regions,
imply significant investments in urban rapid
transit (subway, LRT) infrastructure and
inter-city high-speed rail, and a significant
focus in rapidly modernizing cities on
compact, mixed-use, transit-friendly urban
forms
The share of “compact” cars as a fraction of the total LDV stock
varies greatly between regions, ranging from
•
•
•
•
20% in NAM to
35% in PAO and FSU
40% in WEU, and
75% in EEU
The share of SUVs + Pickup trucks is 48% of all LDVs in NAM
(where almost everyone owns a car) and surprisingly large in
many other regions (40% in MENA and CPA, 45% in LAM, 100%
in SSA), where people are either too poor to own a car or rich
enough to buy a gas-guzzler. Only in PAO, WEU and EEU do
we find comparative wealth and low (<6% of total) ownership of
gas-guzzlers. This seems to be related to distribution of income.
In my Base scenario, shares of different types of LDVs
remain fixed at their current values. In the “Green”
scenario, the share of Compact cars in NAM gradually
increases from 20% to 35% and that of mid-sized cars
increases from 32% to 54% (going back to the same
shares as occurred in NAM in the early 1980s). Shares of
SUVs and pickup trucks drop to near zero in all regions.
Available data, and data massaging:
• Estimates of (i) total annual per capita travel, (ii) split among LDVs,
rail, air and other modes, (iii) average vehicle occupancies, and (iv)
urban vs highway driving (for LDVs) from WBCSD
• Estimates of shares of different LDV segments (compact, mid-size,
SUV, pickup truck) in various regions compiled from various sources
• Estimates of electric vs diesel share of rail travel from somewhere
• Estimates of amount of freight transported (tkm/yr) globally by
different modes from WBCSD
• Estimates of the energy intensity of different modes of passenger
and freight transport in different countries from WBCSD and other
sources
• Data on data energy use by different fuels by country from the
International Energy Agency (IEA)
The various data sources have to be adjusted such that total
transportation energy use of different kinds calculated from population,
activity levels, and energy intensities, summed over all transportation
energy uses, equals the IEA totals for each region.
Future energy intensities:
• For LDVs, the key dataset used is a comprehensive
analysis Argonne National Laboratory in the US of the
potential (low) fuel and electricity requirements of advanced
vehicles of different sizes and with different drive trains
(conventional, HEV, PHEV, BEV, fuel cell) using different
fuels (diesel, gasoline, hydrogen), for urban and highway
driving
• For other modes of transportation, potentials from the
literature (and discussed in the transportation lectures) are
assumed to be eventually reached
• For freight transportation, it is assumed that an increasing
share of total transport on land has to occur by truck as the
global economy grows and the world (hopefully)
decarbonizes, because relatively less of the transport will
be of goods (such as wheat, iron ore, coal) that can be
transported by rail.
2011 Argonne National Lab study, fuel and electricity
energy intensity for compact cars
Energy Intensity (MJ/km )
4.0
3.5
Fuel
3.0
Electricity
2.5
2.0
1.5
1.0
0.5
0.0
Conventional
Today
HEV 2045
PHEV20
2045
PHEV40
2045
BEV 2045
Impact of vehicle choice from the
2011 Argonne National Lab study
6
Gasoline Conventional today
Energy Intensity (MJ/km)
Gasoline HEV Future
5
H2 fuel cell HEV future
4
3
2
1
0
Compact
Mid Size
Small SUV
Mid Size
SUV
Pickup truck
Summary of Efficiency Potentials
• LDVs, urban driving: PHEVs running on fuel, factor of 3
lower energy intensities
• Combine with a shift of 60% of driving to C-free grid
electricity: fuel demand would be reduced by a factor of
7-8.
• Combined with a small downsizing of LDVs, and we’re
talking factor of 10 reduction in fuel demand.
• LDVs in highway driving – more like a factor of 4
• Trucks for freight – factor of 2-3 per tkm
• Air transport – factor of 2 reduction per pkm
• For each km of LDV driving shifting from fuel to
electricity, the electrical energy required is about 1/3 the
fuel energy displaced (minimizing the impact on
electricity demand)
Construction of Scenarios
• Account for growth in population and income and changes in
annual passenger travel, freight transport (varies with global
GDP), splits among different modes of travel, and shares of
different vehicle classes within the LDV portion
• Assume a fast (by 2025) or slow (by 2035) transition to the
energy intensity reductions shown above for new vehicle
sales
• Account for the gradual turnover of existing vehicle fleets
• Also assume a gradual transition away from conventional
LDVs to some combination of PHEVs, BEVs (battery-electric
vehicles), and FCVs (fuel cell vehicles)
• Except for a 100% BEV scenario, there will be some residual
fuel demand – so two scenarios for meeting it are considered
– a biomass-intensive scenario, and a hydrogen-intensive
scenario
Fast+Green scenario
• Fast (by 2025) achievement, in new vehicles, of all the
energy intensity reductions considered here
• Annual distance travelled per person drops by about
25% in PAO, NAM and WEU and is capped at 60008000 km elsewhere
• The share of total travel by LDVs drops from 0.8 to 0.7
in NAM, drops to 0.5 in WEU and EEU, drops to 0.4 in
PAO, FSU and LAM, and is capped at 0.25 elsewhere
• The share of total travel by air drops to or is capped at
0.1 everywhere
• The ratio of global tonne-km/yr of freight to world GDP
drops by a factor of 2.0 as world GDP increases by a
factor of 2.7 (so there is much more local production)
Scenario for changing market share of different
LDV drive-trains
1.0
Fraction of Vehicle Stock
0.9
0.8
0.7
Conventional
0.6
HEV
0.5
PHEV20
PHEV40
0.4
BEV
0.3
0.2
0.1
0.0
2005
2025
2045
2065
Year
2085
Changing Market Share for Different Fuels for LDVs
1.0
0.9
0.8
Market Share
0.7
Fossil fuels
Biomass in biomass-intensive
Biomass in H2-intensive
H2 in H2-intensive
0.6
0.5
0.4
0.3
0.2
0.1
0.0
2000
2020
2040
2060
Year
2080
2100
Global fossil fuel use by LDVs, Low GDP scenario,
Fast Transition
Improvement in fuel economy
Change in vehicle drive train
Change in vehicle fuels
Change in segment shares
Reduction in pkm travelled
Modal shift away from LDVs
Increase in passenger loading
Less aggressive driving
Final result
LDV On-Site Fossi lFuel Use (EJ/yr)
100
80
60
40
20
0
2005
2015
2025
2035
2045
2055
Year
2065
2075
2085
2095
Reductions in global biofuel use by LDVs, Low GDP scenario
LDV On-Site Bio-Fuel Use (EJ/yr)
80
60
Improvement in fuel economy
Change in vehicle drive train
Change in segment shares
Reduction in pkm travelled
Modal shift away from LDVs
Increase in passenger loading
Less aggressive driving
Extra final result
Fixed fuel share
40
20
0
2005
2015
2025
2035
2045
2055
Year
2065
2075
2085
2095
Note the
• Primal importance of vehicle efficiency and drive-train
changes (to PHEVs, BEVs or FCVs) in reducing fuel
requirements (prior to any switch in fuels), and
• The key importance of a handful of relatively small
behavioural changes in reducing the eventual biomass
requirements (factor of 2 difference by mid-century,
when the shift to biomass could begin in ernest, if that is
the eventual path)
• Thus – technology is very important in the short-term,
but behaviour (which takes longer to change) is
important in the longer term for the eventual demand for
biomass (or hydrogen, or rare earths for batteries)
The Brazilian cerrado, potential land for soybean and sugarcane
cultivation and home to > 900 species of birds and 300 species of
mammals, many threatened with extinction
Source: (C) by Luiz Claudio Marigo/naturepl.com
Transportation biofuel use, biomass intensive scenario, low
population and GDP/P growth
Global Results for Transportation
Energy Use
(in each of ten regions, apply the income scenarios
to generate annual travel/person, account for
changing energy intensities and proportions of
travel by different modes, and multiply by
population to get total energy use)
Total transportation fossil fuel demand
140
(a)
120
High slow
High fast
Low slow
Fossil Fuel Demand (EJ/yr)
100
Low fast
High fast green
Low fast green
80
60
40
20
0
2000
2020
2040
2060
2080
2100
Note that even the highest growth curve in the
preceding figure assumes the achievement (by
2035) of levels of vehicle fuel efficiency far in
excess of anything contemplated at present.
Transportation oil demand still rises by 50% from
2005, and by 25% for the lowest scenario. As this
may exceed oil supply (depending on when oil
supply peaks), even more stringent scenarios –
that add behavioural changes to the technological
improvements. This gives the dotted lines, labelled
“Green”
Recall: Figure 2.21 from Chapter 2: geologicallyconstrained assessment of future oil supply
Total transportation biofuel demand for the
biomass-intensive scenario variants
Building Sector Energy Use
Overview of global on-site building energy use in 2010
40
Residential buildings
35
Global Energy Use in 2010 (EJ)
Commercial buildings
30
25
20
15
10
5
0
Electricity
Source: Harvey et al. (2014)
Fossil fuels
District heat
Biofuels
Building Sector Floor Area in
2010, and Floor Area Scenario
Residential per capita floor area in 2010
Note that there can be > factor of 3 disagreements between different estimates
80
Based on UV2012
Based on ETP2012
Based on a mix of sources
Scaled in relation to EEU based on GDP
(a)
Per capita floor area (m2/person)
70
60
50
40
30
20
10
0
PAO
NAM
Source: Harvey et al. (2014)
WEU
EEU
FSU
LAM
SSA
MEA
CPA
SPA
Commercial floor area per person.
Note the huge discrepancies between the various estimates for China (CPA)
25
(b)
Based on UV2012
Based on ETP2012
Based on scaled McNeil
Other estimates
Per capita floor area (m2/person)
20
15
10
5
0
PAO
NAM
Source: Harvey et al. (2014)
WEU
EEU
FSU
LAM
SSA
MEA
CPA
SPA
Plot of commercial floor area/person vs GDP/P with the
2 Chinese floor area estimates (snapshot in time)
Source: Harvey et al. (2014)
Plot of the ratio of commercial to residential floor area vs
GDP/P with the 2 Chinese commercial floor area estimates
0.5
0.4
Commercial/Residential
China, High
0.3
0.2
China, Low
0.1
0.0
0
Source: Harvey et al. (2014)
10
20
30
GDP/person (1000US$)
40
50
Chosen starting per capita floor areas (for 2010)
70
Residential
60
Commercial
Per capita floor area (m2)
50
40
30
20
10
0
PAO
NAM
Source: Harvey et al. (2014)
WEU
EEU
FSU
LAM
SSA
MEA
CPA
SPA
Increase in commercial floor space per employee as
average per capita GDP increases (variation over time)
Source: McNeil et al (2008, Fig. 11)
Increase in residential floor area per capita with increasing
after-tax income in different countries (variation over time)
Source: Schipper et al (2001)
Floor area per capita is assumed to increase with
increasing income according to the logistic function.
Thus, floor area per capita increases as
A(I)=AU/(1+((AU-A2010)/A2010)e –a(I - I2010) )
where A2005 and I2005 are the per capita areas and
incomes in 2005, respectively, and AU is the ultimate
or asymptotic per capita area. Note that this is the
same equation as used to create a variation of income
itself over time, except that income rather than time is
the independent variable
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”)
Resulting growth in global floor area for the high population & GDP/P
and the low population and GDP/P scenarios (Figure 10.9):
450
400
Residential floor area
Floor Area (billions m2)
350
300
250
200
Commercial floor area
150
100
50
0
2000
2020
2040
2060
Year
2080
2100
Starting (2010) energy intensities
by end use and region
Residential energy intensity in 2010 excluding cooking and
domestic hot water
200
(a)
Miscellaneous loads
Lighting
Energy Intensity (kWh/m2yr)
150
Space Cooling
Space Heating
100
50
0
PAO
NAM
Source: Harvey et al. (2014)
WEU
EEU
FSU
LAM
SSA
MENA
CPA
SPA
Residential energy uses per person
Note the dominant and large energy use for cooking and hot
water in SSA – related to inefficient use of biomass
40
(b)
Cooking
Miscellaneous loads
Lighting
Space Cooling
Energy Use (GJ/person-yr)
30
Water heating
Space Heating
20
10
0
PAO
NAM
WEU
EEU
FSU
LAM
SSA
MENA
CPA
SPA
Commercial building energy intensity in 2010
400
Largest cooling energy intensity is in PAO, NAM and LAM
Miscellaneous loads
Cooking
Lighting
Energy Intensity (kWh/m2yr)
300
Ventilation
Space Cooling
Water heating
Space Heating
200
100
0
PAO
NAM
Source: Harvey et al. (2014)
WEU
EEU
FSU
LAM
SSA
MENA
CPA
SPA
The preceding slides give estimates of present
average energy intensities for different end uses in
each region – but that is not enough. We need to
know the deviation of energy intensity from the
average as a function of building age. Why?
Because the calculations will divide the 2010
building stock into “cohorts” built at different times,
and it will be assumed that renovations or
demolitions will begin with the oldest cohort. The
change in energy use from renovation or
demolition+replacement depends on the energy
intensity of a renovated building before and after
renovation. So – we have to specify the starting
energy intensity of each cohort that is renovated or
replaced.
Example: commercial buildings in the US
160
80
140
70
Space heating
120
60
Space cooling
Ventilation
100
50
Lighting
80
40
60
30
40
20
20
10
0
1900
1920
1940
1960
Vintage
1980
2000
0
2020
Other Energy Intensities (kWh/m2yr)
Space Heating Energy Intensity (kWh/m2yr)
Note declining space heating energy requirements in more recently built
buildings, but greater lighting, ventilation and cooling requirements
Procedure: Assume
• That the entire building stock that existed in
2010 is renovated or replaced by 2050
• That buildings are renovated or replaced starting
with the oldest buildings
• That new buildings also built if the floor area is
expanding
• That the energy intensity of new or replacement
buildings gradually decreases over time
compared to new buildings built in 2010
• That the energy intensity achieved after
renovation also decreases gradually over time
In developed countries, the energy intensity for heating has
fallen over time, so buildings built in 2010 have lower
heating energy intensity than the stock average. This is
also true for overall energy use in commercial buildings,
although – as we have seen – lighting, ventilation and
cooling energy use in new buildings in the US was
increasing over time until recently. However, in developing
countries, the most recent buildings have several times the
energy requirements of the existing stock average, and
renovations often lead to increases rather than decreases
in energy use. This is accounted for.
Another factor is that in many parts of the world, indoor
temperatures in winter are uncomfortably cold (i.e., 8-10 C
in rural China), so improved insulation will at first lead
mainly to warmer conditions rather then reduced energy
use. This is also accounted for.
Heating energy requirements of residential buildings built at different
times in the past in various countries, in comparison with the Passive
House standard
Heating Energy Intensity (kWh/m2/yr)
500
Sweden
UK
Germany
Bulgaria
Slovenia
Portugal
Italy
Canada
Australia
400
300
200
100
Passive House Standard
0
1905
1915
1925
1935
1945
1955
1965
Mid-Decade Year
Source: Harvey (2013a)
1975
1985
1995
2005
2015
Trends in energy use of new commercial buildings in California, complying with
various versions of the ASHRAE-90.1 building code
1.2
75% reduction:
Representative of
the improvement
needed everywhere
for a global zeroCO2 emission
scenario
Relative Energy Use
1.0
0.8
0.6
?
0.4
0.2
0.0
Stock
average
1999
2004
2007
Year of Construction
2010
2014
2020
My assumptions for advanced standards
in new buildings:
• Space Heating: 0-30 kWh/m2/yr in residential buildings,
_____________0-20 kWh/m2/yr in commercial buildings,
____________________depending on the climate
• Space Cooling: 0-25 kWh/m2/yr in residential buildings
• _____________0-30 kWh/m2/yr in commercial buildings
• Lighting:
• Ventilation:
• Plug loads:
• Hot water:
• Cooking:
These amount to overall reductions compared to recent
new buildings of factors of 2-4, and – in OECD countries –
a larger reduction relative to the 2010 stock average.
The different scenarios considered:
•
•
•
•
•
•
Business-as-usual: current standards for new and renovated buildings
persist to 2100, and current rates of renovation and replacement are
maintained (1-2%/yr)
Accelerated turnover: same as BAU but all buildings are renovated by
2055.
Slow-Delayed: Advanced standards are achieved by 2055, and
renovation ramps up between 2025-2035 to that required for renovation
of the entire building stock by 2055
Slow-Early: Advanced standards are achieved by 2055, and
renovation ramps up between 2015-2025 to that required for renovation
of the entire building stock by 2055
Fast-Delayed: Advanced standards are achieved by 2025, and
renovation ramps up between 2025-2035 to that required for renovation
of the entire building stock by 2055
Fast-Early: Advanced standards are achieved by 2025, and
renovation ramps up between 2015-2025 to that required for renovation
of the entire building stock by 2055
Note: The EU requires member states to reach a standard of “nearly zero”
energy use by 2020, and the Architecture 2030 initiative is targeting 75%
reduction in gross energy use (i.e, before considering solar PV offsets) of
all new buildings by 2030.
Results in terms of total energy use for
heating, ventilation and space cooling
Base case – no expanded use of heat pumps
Residential Buildings
Commercial Buildings
Observations on the preceding results:
• Even with energy standards fixed at current levels,
global residential space heating energy use rises to a
peak by 2030 and then slowly declines
• With stringent standards by 2055, global residential
space heating energy use would be only 25-40% the
2010 amount in 2100
• With full implementation of strict standards by 2025,
global residential space heating energy use peaks
around 2020 at a level about 10% above the 2010 level
• Global residential ventilation+cooling energy use
increases dramatically, by a factor of 6 (low pop & GDP,
high efficiency) to 23 (high pop & GDP/P, low efficiency)
Observations (continued)
• Global commercial building space heating energy use
also peaks and then declines with frozen standards, but
is 3-6 times less than the 2010 level in 2100 for the
efficiency scenarios
• Global commercial ventilation+cooling energy use rises
to 6-12 times the 2010 level by 2100 for the frozen
standards scenarios (i.e., a lot!)
• For the stringent efficiency scenarios, commercial
ventilation+cooling energy use in 2100 is only 50%
above the 2010 level for the low population &GDP/P
scenario, and 3 times the 2010 level for the high
population and GDP/P scenario
How could we go to zero CO2 emissions?
• For electricity end uses – decarbonize the electricity supply. This will
be more likely to be feasible the lower that total electricity demand
can be kept.
• For fuel demand (used for space heating, hot water, and some
cooking), there are several options:
- use advanced biomass energy (such as pellet boilers) where
practical logistically (in rural areas and small communities). More
feasible in buildings with very low heat requirements (given that
biomass is bulky)
- use district heating, with the district heat supplied centrally with
biomass, or using solar thermal energy that is stored from summer
to winter underground, or drawing on deep geothermal heat. More
low-grade geothermal heat will be useable the lower the
temperature at which heat needs to be supplied.
- switch to hydrogen produced from water using C-free energy
sources
- use electric heat pumps for space and water heating, either airsource (in mild climates), ground-source, or exhaust-air heat pumps
Although using electric heat pumps for space heating adds
to electricity demand, the impact can be kept small to zero
because
• Total heat demand can be first reduced by factors of 2-10
• For a heat pump with a COP of 4 (achievable in low-energy
buildings using ground-source heat pumps), only 1 unit of electricity
is needed for every 4 units of heat demand
• Some buildings that are currently heated via electric-resistance
heating (which is like having a COP=1) could be converted to
electric heat pumps, creating reductions in existing electricity use to
offset the increase the would arise from converting some nonelectric space heating to heat pumps
All of the above will likely not be sufficient to completely eliminate fossil
fuel demand. To offset the small remaining fossil fuel use, direct
capture of CO2 from the atmosphere using renewable energy, and its
disposal in deep geological formations, might be feasible
Saving potential: 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)
The specific measures that are most appropriate
for cooling vary with the climate
• Hot summer, cold winter – can use earth pipes to precool ventilation
air in the summer (as the ground will be relative cold)
• Hot-dry climate: thermal mass with night ventilation (there is a large
day-night temperature swing in arid regions) plus external insulation
to keep daytime heat out. Evaporative cooling if water supply is not
too scarce.
• Hot-humid summer: low thermal mass (so that the building can cool
down during cool periods), open design to facilitate lots of airflow,
solar-powered desiccant cooling and dehumidification
• Mild summers: can rely on natural ventilation (perhaps driven by
solar chimneys or wind catchers ) and minimization of solar and
internal heat gains. Openings are needed, made possible in highrise office towers through the use of a double-skin façade.
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
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
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, reduce land requirements (freeing up
land for production of biomass for energy), and
permit a shift to organic systems (with a further
10-20% reduction in energy use per unit of food
produced)
Follow-up in GGR348/1408:
• Assess the potential and cost of alternative sources of Cfree energy
• Take the scenarios of global demand for fuels and
electricity and develop scenarios of renewable energy
supply that separately entirely satisfy these two
demands by 2080-2120
• Work out the required rates of installation of C-free
power and the required rates of building factories to
produce PV panels and wind turbines
• Work out the required material flows and the net energy
gain during the period when C-free power supply is
rapidly expanded
• Work out the CO2 emissions during the next century and
use this to drive a simple climate-carbon cycle model in
order to assess a range of changes in global mean
temperature (depending on the climate sensitivity and
climate-carbon cycle feedbacks)