ER100/200 & PubPol 184/284
Energy and Society
Lecture 2: Energy and Society
Professor Kammen
’
1. How Energy Use Shapes Society & the Environment
2. Units, forecasts, and ‘back of the envelope
Last update: August 30, 2015
ER100 Lecture 2: page 1
ER100 Lecture 2: page 2
Readings Reminder …
Rubin, EE, Rates of Technology Adoption, Pages 669 – 677.
Lovins, Amory (1976) “Energy Strategy: The Road Not Taken”, Foreign Affairs, 55(1):
65–96.
ER200 & Pub Pol 284:
A nice commentary on the Lovins paper from The New York Times:
http://www.nytimes.com/2008/10/07/science/07tier.html?_r=1&8dpc&oref=slogin
Supplemental:
Toolkit 1 (a review and refresher) – optional/reference for those who have done
these sorts of problems before.
A bit more than back of the envelope, applied to scaling-up technologies:
http://www.gigatonthrowdown.org/
ER100 Lecture 2: page 3
Getting Comfortable with Energy Units
1 barrel (bbl) of crude oil = 42 gallons = 6.12 x 109 joules, so …?
1 MToe = million tons of oil, equivalent = 1013 joules,
so …?
A useful unit calculator http://www.iea.org/statistics/resources/unitconverter/
You will also find the unit conversions in the Reader
We now will begin to use energy unit analysis to analyze,
both the technical and policy aspects of energy conversion
and use.
ER100 Lecture 2: page 4
Energy Units and Scales
(Source: IPCC Energy Primer)
zettajoule (ZJ)
Quick recap: exponentials to common basis are additive!
103 x 106 = 10(3+6) = 109 or 1000 MJ = 1 GJ
ER100 Lecture 2: page 5
To make sense of the world,
make consistent comparisons
6
ER100 Lecture 2: page 6
Energy Stocks & Flows for the Earth
(the whole story, but only an engineer could love it like this …
ER100 Lecture 2: page 7
ER100 - Lecture #3 - Page 7
Energy Orders of Magnitude
(EJ = 1018 J)
5,500,000 EJ Annual solar influx
1,000,000 EJ Fossil occurrences
50,000 EJ Fossil reserves
440 EJ World energy use 2000 (14 TW)
100 EJ USA primary energy supply
50 EJ OECD transport energy use
20 EJ Saudi Arabia oil production
4 EJ Italy oil reserves
1 EJ NY city or Singapore
energy use
Stocks; flows (yr-1)
ER100 Lecture 2: page 8
Dung Patty Preparation in India
ER100 Lecture 2: page 9
Back of the envelope calculation:
• How much gasoline is used in the U.S. for all
light duty vehicles (passenger cars, SUVs and
light trucks) in 1 year?
• You may need to give the answer in gallons,
barrels, and quadrillion Btus per year, etc …
ER100 Lecture 2: page 10
Back of the envelope calculations:
General approach
• First make a model, balancing accuracy against
the time needed to create the calculation
– Back of the envelope, spreadsheet, or code
• A simple model
miles driven
gallon
x
– Gas used per week (gallons) =
week
miles
– If a car gets 20 mpg in normal use, for a 1000 mile trip, the simple
estimate is that the car will use 50 gallons.
ER100 Lecture 2: page 11
BOE calculations: Unit analysis
• You can always multiply some number by 1
without changing its value.
• Example: Calculate the average load over 1
year (kW) for an electricity end-use that
consumes 10,000 kWh per year
10, 000 kWh
1 year
x
= 1.14 kW
year
8760 hours
In that equation, the hours and the years
cancel, yielding kW.
ER100 Lecture 2: page 12
Back of the envelope calculation:
Answer:
• The average rated fuel economy is 25 miles per gallon for
light duty vehicles, but 21 mpg “on the road.”
• A typical car is driven about 12,000 miles per year, and
there are about 100 M households, each household owns
just under 2 cars/light trucks, for a total of about 200 M
vehicles.
12 ,000 miles/yr
200 million vehicles x
gallon
x
vehicle
21 miles
= 114 B gals/year
Barrel
42 gallons
= 2.7 B barrels/year
2.7 billion barrels
1 year
x
year
365 days
= 7.4 M barrels/day
114 billion gallons x
114 billion gallons 125,000 Btus Quadrillion
x
x
year
gallon
1015 Btus
ER100 Lecture 2: page 13
= 14 quadrillion Btus
How about a more complex model?
• If a 1000 mile trip does not have same highway/city split
as normal use, we must rely on a more complicated
model, Such as:
City miles
gallon
Highway miles
gallon
x
+
x
week
City miles
week
Highway miles
– Gas/week (gals) =
– assuming 15 mpg city, 25 mpg highway, and 95:5 split
between highway and city driving, yields 41.3 gallons
• Assumptions about how many miles driven are buried in
the first model.
• There are always buried assumptions …
• How does regenerative braking, or an EV change things?
ER100 Lecture 2: page 14
Use your simple model
to test new cases…
•Engine downsized ~15%
•Idle-off and regenerative braking
•Efficiency increased ~50%
•Batter state of charge kept in narrow range
•Engine downsized ~33%
•Larger battery and grid charging
•Energy for short trips is from grid
•Deeper discharge of batteries
Plug in hybrid with cellulosic ethanol in the tank: 100+ miles per gallon
Breakthrough: stationary and mobile energy sources now linked
ER100 Lecture 2: page 15
August 15, 2003: 8:15 PM What about this vehicle ….
August 16, 2005—Speeding from the scene of the crime, a Chinese boy tows a floating plastic bag of stolen
natural gas last week.
ER100 Lecture 2: page 16
1942...
ER100 Lecture 2: page 17
2002...
BOE calculations: some advice
• Once you’ve made a model, plug in the numbers
you know, and make assumptions for those you
don’t know.
• Don’t get hung up on a particular number! Set
up the calculation and get data later.
• Don’t be afraid to approximate to speed things
up (e.g., to divide 4000 by 35, divide 3500 by
35 instead to get 100).
• Keep evolving your ‘model’ to check out
interesting cases, like the fuel vs. EV slide…
ER100 Lecture 2: page 18
BOE calculations … more to do
• Bound the problem
– Plug in high and low estimates for key parameters
– Combine all high estimates in one scenario, and all low estimates in
another
– Test sensitivity of each variable to arbitrary changes in inputs
• Create a data sheet, and get comfortable with the key
units and conversions
(don’t memorize, organize!)
• Understand commonly used units (Quads, TWh, joules,
Mbtus, MMBtus, kW vs kWh, tons)
ER100 Lecture 2: page 19
AB 32 Emissions Reductions
% Change from 1990 levels
50%
CEC Data
Business as Usual
40%
AB 32 Scenario
30%
20%
10%
0%
-10%
1990
ER100 Lecture 2: page 20
1995
2000
2005
2010
2015
2020
The Cascade of Commitment:
IPCC Science, CA and US targets
3.0
Business as usual (EIA)
2.5
Historic U. S.
emissions
Intensity Target:
President Bush (2004) and
China (current)
U.S. GHG Emissions (GT C eq.)
2.0
1.5
Kyoto protocol
1.0
0.5
The Obama climate target
The California target
0.0
1990
EU Copenhagen plan
IPCC Assessment: Climate Stabilization Zone
2000
2010
2020
2030
2040
Kammen, “September 27, 2006 – A day to remember”, San Francisco Chronicle, September 27,
ER100 Lecture 2: page 21
2050
Check the Units, carbon emissions are often expressed at gC/MJ,
and at present Global emissions are 16 gC/MJ, and global energy
use is 420 EJ so:
=16gC / MJ
18 ù
é
é16gC ù
10 J é1MJ ùé1ton ùé 1GT ù
=ê
[ 420EJ ]ê úê 6 úê 6 úê 9 ú
ú
ë MJ û
ë EJ ûë10 J ûë10 g ûë10 tonsû
18
6700x10 GT(C)
=
6
6
9
10 x10 x10
21
21
= [7x10 ]/[10 ]GT(C) = 7GT(C)
ER100 Lecture 2: page 22
Check the Units,
carbon emissions are
often expressed at
gC/MJ, and at present
Global emissions are 16
gC/MJ, and global
energy use is 420 EJ so:
ER100 Lecture 2: page 23
Major U.S. Public R&D programs
red=defense, black=space, orange=health, blue=energy
Nemet, G. F. (2007). Policy and innovation in low-carbon energy technologies. PhD Dissertation, University of California.
ER100 Lecture 2: page 24
IPAT
• Often useful to think of environmental
impact as the product of three factors:
Impact = Population    Affluence    Technology 
 pollution 

  pollution 
$
  people  




y
person

y
$





• Population may increase (in poor countries)
• Affluence should increase in poor countries
• Can improved technology offset rising
population and affluence?
ER100 Lecture 2: page 25
The IPAT Identity: Why so handy?
The controversial
"Ehrlich" identity is often
used to decompose
growth in resource use,
efficiency of resource
use, and emissions.
Impact = Population * Affluence * Technology
One version of this might be :
é$ GDPù é Energy[J] ù
ú· ê
Energy Use [J] = Population · ê
ú
êë person úû ë $ GDP û
ER100 Lecture 2: page 26
The IPAT Identity In Use:
$ GDP Energy[J]
Energy Use [J] = Population *
*
person
$ GDP
Or,
Energy Carbon
Carbon Emissions = Population *
*
person Energy
If all are exponential, then we have a very simple formulation:
P = P1P2 …Pn
P = (p1er1t) (p2er2t)... (pnernt) = (p1p2...pn)e(r1+r2 + …rn)t
P = Pert
ER100 Lecture 2: page 27
ER100 Lecture 2: page 28
Source: Scientific American Special Issue on energy 1970
ER100 Lecture 2: page 29
ER100 Lecture 2: page 30
White’s Law
“Culture advances as the quantity and quality of energy used
increases. This relationship can be captured formally as an
equation.”
C=kxExT
Leslie White, 1973
C = culture
E = energy
T = technology
k = scaling (efficiency) constant
ER100 Lecture 2: page 31
The Chase Manhattan Bank stated, in its
1976 Energy Report, that
“there is no documented evidence that
indicates the long-lasting, consistent
relationship between energy use and GDP
will change in the future. There is no
sound, proven basis for believing a billion
dollars of GDP can be generated with less
energy in the future. “
32
ER100 Lecture 2: page 32
US energy use/$ GDP already cut 40%, to
Soft
Energy
Path: 1976
very Amory
nearly Lovins’
the 1976
“Soft
Energy
Pat1976h”
250
200
primary energy
consumption
(quadrillion BTU/year)
"hard path" projected by
industry and government
around 1975
150
actual total
consumption
reported by USEIA
"soft path" proposed by
Lovins in 1976
100
coal
coal
oil and gas
50
soft technologies
(which do not include big
hydro or nuclear)
oil and gas
nuclear
renewables
0
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
2025
US
energy
use/$
GDP already
cutPath:
40%,1976
to
Update:
Amory
Lovins’
Soft Energy
250
very nearly
primary the 1976 “Soft Energy
energy”
Pat1976h
consumptio
n
(quadrillion
BTU/year)
200
150
"hard path" projected by
industry and government
~1975
USEIA Annual
Energy Outlook
Reference
Case,
2004 and 2006
actual total consumption
reported by USEIA
100
c
ccoal
50
oil
"soft path"
oil and gas
proposed by
Lovins, Foreign
Affairs, Fall 1976
soft
0
1975
1980
1985
1990
1995
nucl
renew
2000
2005
2010
2015
2020
2025
Average
Berkeleyite
Average
Dane
ER100 Lecture 2: page 35
Energy Star
Home
When is Correlation no longer informative?
ER100 Lecture 2: page 36
When is Correlation no longer informative?
ER100 Lecture 2: page 37
U.S. Energy & Economy
500
GNP
Energy
Carbon
400
Indexed
(1950=100)
300
200
Carbon
100
0
1950
Source: EIA, BEA, PCAST
ER100 Lecture 2: page 38
GDP
37% improvement
1960
1970
1980
1990
2000
U.S. Efficiency Improvements
Savings >$170 billion annually, since 1990
500
GDP
400
Indexed
(1950=100)
300
200
Carbon
100
0
1950
Source: EIA, BEA, PCAST
ER100 Lecture 2: page 39
37% improvement
1960
1970
1980
1990
2000
ER100 Lecture 2: page 40
ER100 Lecture 2: page 41
Two Views
• Pessimists (“Mathusian” or “Cassandra”)
– Developed economies unsustainable; developing
cannot follow in their path; technology is not
keeping pace with resource depletion,
environmental impact
• Optimists (“Cornucopian” or “Dr. Pangloss”)
– No barriers to growth; substitutes will be
developed for scarce resources; economic
development and technology produce net
improvement in environmental quality
ER100 Lecture 2: page 42
The ER100 Bet:
Simon offered to bet $1000 that
the price of any five commodities
would decrease from 1980 to
1990. Ehrlich et al. selected Cu,
Cr, Ni, Sn, W. Simon won.
Simon subsequently offered to bet
that any set of environmental
measures relating to human
welfare would get improve.
Ehrlich et al. selected CO2, N2O,
O3, temperature, SO2 in Asia,
tropical forest, per-capita grain
and fish, species, AIDS, sperm
counts, rich-poor gap.
Simon declined.
ER100 Lecture 2: page 43
Only 4 of 47 elements increased in price over the last century
100.00
Price/Price in 2000
10.00
1.00
Ta
0.10
Tl
Sr
Cs
0.01
10
ER100 Lecture 2: page 44
20
30
40
50
60
70
80
90
100
Caution and a Method: Know the Trend:
Environmental Indicators vs. Income
“Kuznets Curves”
ER100 Lecture 2: page 45
Disruption By
Natural
Baseline
Index
Land Use
(106 km2)
Water
Use
(km3/y)
135
15
5
cultivated 2/3
sustainable
fuelwood
Industrial
Energy
0.15
Other
Activity
Human
Natural
1.5
(2/3 hydro)
cities,
transport
0.15
800
?
500
0.2
process,
cooling, evap
all other
(of usable)
forest
clearing
0.2
6.3
fuelwood
fossil-fuels
cement,
urbanizatn
0.04/y
preindustrial
atmosphere
100
40
280
10
0.35
160
210
wetlands,
termites,
ocean
ruminants,
paddies,
burning
100
65
natural gas,
coal mines
landfills,
sewage
ice-free land
50,000
total runoff
(2/3 unusable)
CO2
Emission
(GtC/y)
CO2
Added
(GtC)
Tradit’nl
Agriculture Energy
Disturbance:
150
NPP
(net primary
productivity)
harvested
2,000
irrigation
1
0.5
594
CH4
Emission
(MtC/y)
ER100 Lecture 2: page 46
?
2.3
Disruption By
Index
Natural
Tradit’nl
Baseline Agriculture Energy
Industrial
Energy
Other
Activity
Human
Natural
Nitrogen
Fixation
(MtN/y)
200
biological
fixation
60
fertilizer
30
fossil-fuel
combustn
1
industrial
processes
0.5
N2O
Emission
(MtN/y)
9
oceans,
soils
4.4
soils,
ruminants
?
?
1.3
industrial
processes
0.4
Sulfur
100
Emission decay, sea
(MtS/y)
spray
0.8
burning
0.3
burning
60
coal, oil
burning
10
smelting
0.7
React HC
800
Emission
vegetation
(Mt/yr)
30
burning
4
burning
ER100 Lecture 2: page 47
1
30
20
combustn, Industrial
refining
processes
0.1
Disruption By
Index
PM
Emission
(Mt/yr)
Natural
Tradit’nl
Baseline Agriculture Energy
500
sea spray,
volcanoes,
dust
Industrial
Energy
Other
Activity
Human
Natural
15
burning
40
fossil-fuel
combustn
50
industrial
processes
0.3
100
metals
production
13
40
burning,
wheat
handling
Lead
Emission
(ktPb/y)
25
volcanoes,
dust
0.4
burning
0.2
burning
230
gasoline
additives
Mercury
Emission
(ktHg/y)
25
outgassing
0.7
burning,
biocides
0.2
burning
3
oil, coal
burning
13
mining,
mobilizatn
0.7
Oil
Emission
(Mt/y)
0.5
natural
seeps
3
tankers,
platforms
2
lube-oil,
waste
10
Radiation
(Mrem)
800
1
150
reactors,
coal burning
medical,
fallout
ER100 Lecture 2: page 48
radon,
cosmic rays
?
0.2
The Paper of the Year, 2009 …. A recycled idea
ER100 Lecture 2: page 49
Rockstrom, et al, 2009
ER100 Lecture 2: page 50
Rockstrom, et al, 2009