CLIMATE CHANGE ACT 2008 IMPACT ASSESSMENT€¦ · CLIMATE CHANGE ACT 2008 IMPACT ASSESSMENT March 2009
Overview of the Global Change Assessment Model (GCAM) · PDF fileOverview of the Global Change...
Transcript of Overview of the Global Change Assessment Model (GCAM) · PDF fileOverview of the Global Change...
Joint GCAM Community Modeling Meeting and GTSP Technical Workshop Joint Global Change Research Institute College Park, Maryland, USA
Overview of the Global Change Assessment Model (GCAM) THE JGCRI GCAM TEAM
Thursday, October 13, 2016
PNNL-SA-90713
Outline
! Brief introduction to Integrated Assessment Models
! Overview of GCAM
! Detailed information on GCAM’s ! Socioeconomics, ! Energy, ! Agriculture and land use, ! Policies, ! Emissions ! Climate
! Discussion
INTRODUCTION TO IA MODELS
3
What is an Integrated Assessment (IA) Model?
IAMs integrate representations of multiple human and natural Earth systems. ! IAMs provide insights that
would be otherwise unavailable from disciplinary research.
! IAMs capture interactions between complex and highly nonlinear systems.
! IAMs provide natural science researchers with information about human systems such as GHG emissions, land use and land cover.
! IAMs support national, international, regional, and private-sector decisions.
Human Systems
Natural Earth Systems
ENERGY
Economy Security
Settlements
Food
Health
Managed Ecosystems
TechnologyScience
TransportPopulation
Sea Ice Carbon Cycle
Earth System Models
EcosystemsOceans
Atmospheric Chemistry
Hydrology
Coastal Zones
The Global Change Assessment Model (GCAM) is a “High-Resolution” IA Model
Model Home Institution AIM
Asia Integrated Model National Institutes for
Environmental Studies, Tsukuba Japan
GCAM Global Change Assessment Model
Joint Global Change Research Institute, PNNL, College Park, MD
IGSM Integrated Global System Model
Joint Program, MIT, Cambridge, MA
IMAGE The Integrated Model to Assess the Global Environment
PBL Netherlands Environmental Assessment Agency, Bildhoven,
The Netherlands
MESSAGE Model for Energy Supply Strategy Alternatives and their General
Environmental Impact
International Institute for Applied Systems Analysis; Laxenburg,
Austria
REMIND Regionalized Model of Investments and Technological
Development
Potsdam Institute for Climate Impacts Research; Potsdam,
Germany
High-Resolution Models and Modeling Teams
General Characteristics of High-Resolution, Global IA models
! High-resolution, global IA models are: ! Global in scope, ! Include representations of energy and agricultural/land use systems ! Include all anthropogenic sources of emissions, ! Include some representation of the climate system.
! However, there is significant variation across models as to their: ! Spatial resolution ! Inclusion of gases and substances ! Energy system detail ! Representation of agriculture and land-use ! Economic assumptions ! Degree of foresight ! Sophistication of the climate model component ! Degree of integration of model components
Integrated Assessment Research and Model Development is Problem Driven
1980’s Projections of emissions and
concentrations Comparison of Emissions Trajectories Consistent With Various Atmospheric
CO2 Concentrations Developed by the IPCC (S350-S750) and by Wigley, Richels, and Edmonds (S350a-S750a)
-2
0
2
4
6
8
10
12
14
16
1990 2015 2040 2065 2090 2115 2140 2165 2190 2215 2240 2265 2290
PgC
/yr
S350
S450
S550
S650
S750
WRE 350
WRE 450
WRE 550
WRE 650
WRE 750
energy-economy-climate
Integrated Assessment Research and Model Development is Problem Driven
Energy Supply
Carbon Prices
Energy, Technology, and Mitigation
Value of Technology
1990’s through 2000’s
$0
$100
$200
$300
$400
$500
$600
$700
$800
$900
$1,000
2020 2040 2060 2080 2100
Inde
x (y
r 20
00 =
1)
MINICAM_Level1
MINICAM_Level2
MINICAM_Level3
MINICAM_Level4 Cumulative Carbon Emissions and Cumulative
Compliance Cost across Scenarios
$0
$2
$4
$6
$8
$10
$12
$14
450 ppmv 550 ppmv 650 ppmv 750 ppmv
Cum
ulat
ive
Glo
bal C
ompl
ianc
e C
ost
(Tril
lion
U.S
. 200
0$, 5
% D
isco
unt R
ate)
0
200
400
600
800
1,000
1,200
1,400
Cum
ulat
ive
Car
bon
Em
issi
ons
(GTC
)
All Advances
Reference Technology
Cumulative Emissions
Advanced Technology
ENERGY-ECONOMY-climate 1980’s Projections of emissions
and concentrations Comparison of Emissions Trajectories Consistent With Various Atmospheric CO2 Concentrations Developed by the IPCC (S350-S750) and by Wigley,
Richels, and Edmonds (S350a-S750a)
-2
0
2
4
6
8
10
12
14
16
1990 2015 2040 2065 2090 2115 2140 2165 2190 2215 2240 2265 2290
PgC
/yr
S350
S450
S550
S650
S750
WRE 350
WRE 450
WRE 550
WRE 650
WRE 750
Energy Systems
Integrated Assessment Research and Model Development is Problem Driven
ENERGY-ECONOMY-land-climate
2000’s Mitigation and land use
-3
-2
-1
0
1
2
3
4
5
6
7
8
2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
GtCO
2/yr
LowAgProductivityGrowth
ReferenceAgProductivityGrowth
HighAgProductivityGrowth Crop production and land use
changes
1980’s Projections of emissions
and concentrations Comparison of Emissions Trajectories Consistent With Various Atmospheric CO2 Concentrations Developed by the IPCC (S350-S750) and by Wigley,
Richels, and Edmonds (S350a-S750a)
-2
0
2
4
6
8
10
12
14
16
1990 2015 2040 2065 2090 2115 2140 2165 2190 2215 2240 2265 2290
PgC
/yr
S350
S450
S550
S650
S750
WRE 350
WRE 450
WRE 550
WRE 650
WRE 750
Energy, Technology, and Mitigation
1990’s through 2000’s Land Use Change
Emissions
Integrated Assessment Research and Model Development is Problem Driven
1980’s Projections of emissions and concentrations
Comparison of Emissions Trajectories Consistent With Various Atmospheric CO2 Concentrations Developed by the IPCC (S350-S750) and by Wigley,
Richels, and Edmonds (S350a-S750a)
-2
0
2
4
6
8
10
12
14
16
1990 2015 2040 2065 2090 2115 2140 2165 2190 2215 2240 2265 2290
PgC
/yr
S350
S450
S550
S650
S750
WRE 350
WRE 450
WRE 550
WRE 650
WRE 750
Energy, Technology, and Mitigation
1990’s through 2000’s
2000’s Mitigation and land use
TODAY
????
Examples of Current Questions for the IA Community
! Type 1: Where are the NDCs taking us? How to implement and ramp up action? What’s actually realistic for different countries?
! Type 2: How will these mitigation activities link to the other societal goals (e.g., SDGs)? Will they be limited by energy-water-land constraints?
! Type 3: How can we plan investments and strategy taking into account climate impacts and a broad range of additional stressors and dynamics?
! Type 4: Where are the biggest future climate-related national security risks, domestically and internationally?
! Can you help us interpret and understand this stuff? What’s the confidence in any of this?
Incorporating climate impacts, adaptation, and
vulnerability
Increased “realism”,
particularly with regards to regional
dynamics
GCAM has a long history…
! GCAM was one of four models chosen to create the representative concentration pathways (RCPs) for the IPCC’s AR5.
! GCAM was one of six models chosen to create the shared socioeconomic pathways (SSPs).
! GCAM was one of three models used to create scenarios for Climate Change Science Program (CCSP).
! GCAM was a prominent tool for analysis in the Climate Change Technology Program (CCTP).
! GCAM has participated in virtually every major climate/energy/economics assessment over the last 20 years: ! Every EMF study on climate ! Every IPCC assessment
! GCAM has been used for strategic planning by energy and other private companies.
! GCAM is now used by research institutions and governments internationally.
The Global Change Assessment Model
! GCAM is a global integrated assessment model ! GCAM links Economic, Energy, Land-use, Water, and Climate systems
! Runs in 5-year time-steps. ! Meant to analyze consequences of
policy actions and interdependencies ! GCAM is a community model ! Documentation available at:
wiki.umd.edu/gcam ! Used to evaluate impacts of
these threads: ! Socioeconomic
development ! Climate mitigation and
impacts ! Technology and
resource developments ! Energy policies
283 Land Regions
32 Energy Economy Regions
233 Water Basins
Scenario Assumption
s
Policies
Technology Characteristics
Labor Productivity
Population
Model Equations, Relationships,
and Parameters
Modeled Scenario
Concentrations and Temperature
Land Use
Agricultural Production
Energy Supplies and Demands
Prices
Emissions
Policies
Technology Characteristics
Labor Productivity
Population
What do IA Models do?
What is GCAM?
15
What is GCAM?
! What does it do? ! How does it work? ! What kinds of questions can it answer? ! What’s new in this release?
16
What is GCAM?
283 Land Regions
32 Energy Economy Regions
235 Water Basins
! GCAMisaglobalintegratedassessmentmodel! GCAMlinksEconomic,Energy,Land-use,Water,andClimatesystems
! Runsin5-year9me-steps.! Meanttoanalyzeconsequencesof
policyac9onsandinterdependencies! GCAMisanopen-source
communitymodel! Documenta9onavailableat:
wiki.umd.edu/gcam! Usedtoevaluateimpactsofthese
threads:! Socioeconomic
development! Climatetreatycompliance! Technologyandresource
developments! Energypolicies
What’s inside GCAM?
The Solver Algorithm
! Start with a vector of initial guess prices ! The main body of GCAM calculates demand disequilibrium
! This part can be run multithreaded. ! Solve:
! Broyden’s method solver. ! Numerous heuristics to improve solver convergence. ! Iteration step can be calculated either with LU decomposition or SVD (use
the latter if you can). ! Result:
! Equilibrium prices ! Quantities, market shares, land use, resource depletion, etc.
!P !
E(!P)
!E(!P) = 0
Frequently Asked Questions
! Common question: ! Does GCAM include foresight?
! Answer: ! For long-term investment
decisions, we assume that decision-makers think about the future. That is, they may consider the costs and profits over the full lifetime of an electricity generation unit before building.
! However, we are myopic in that we uses current prices when making these decisions.
! Decision-makers do not know future prices! Source: Calvin et al. (2014). The EU20-20-20 Energy Policy
as a Model for Global Climate Mitigation. Climate Policy.
Land Use Change Emissions Leakage
Frequently Asked Questions
! Common question: ! Does GCAM optimize?
! Answer: ! Not exactly. ! GCAM is a market equilibrium model, so it adjusts prices until supplies
and demands are equal. ! However, GCAM assumes that producers maximize profit and
consumers minimize cost. ! And, under certain conditions, welfare economics tells us that market
equilibria are (Pareto) optimal. ! GCAM is not intertemporally optimizing.
! Prices and production quantities: ! Energy sectors ! Transportation ! Primary energy resources ! Agricultural products
! Land use ! Crops (by type) ! Pasture ! Unmanaged
! Water demand ! Raw demand by sector ! Response to scarcity
! Greenhouse gases ! Economic cost of policies
! Economic loss ! Income transfer
Car
bon
Pric
e
What can you do with GCAM?
22
Deep Regional Dives in GCAM
What’s new in GCAM?
! Hector is now the default climate model. ! Near-term population and GDP adjusted to match IMF projections for
all included scenarios. ! Users can now configure GCAM to save only the results they plan to
use. ! Updates to electricity and liquid fuel sector costs, reflecting recent
literature. ! Various performance (i.e., speed) enhancements. ! Various minor bug fixes.
THE MACRO-ECONOMY IN GCAM
2016
Core GCAM structure: The Macro-economy
26
WATER
► Energy ► Agriculture ► Domestic
Water Demand
► Surface ► Ground ► Natural Lakes
Water Supply ► Water prices
Water Markets
Land Characteristics
Climate Inputs
The Macro-economy in the present release version
! 𝐺𝐷𝑃↓𝑟,𝑡+1 = 𝑃𝑂𝑃↓𝑟,𝑡+1 (1+𝐺𝑅𝑂↓𝑟,𝑡 )↑𝑡𝑆𝑡𝑒𝑝 ( 𝐺𝐷𝑃↓𝑟,𝑡 /𝑃𝑂𝑃↓𝑟,𝑡 ) 𝑃↓𝑟,𝑡+1↑𝛼
! r=region, t=time period ! POP=population, ! GRO=rate of growth of per capita income ! P=aggregate price of energy service ! α=GDP feedback elasticity
! Note that the GDP feedback elasticity is set to zero in default mode.
GCAM 4.3 is moving to SSP2 population and GDP
! Middle of the Road scenario from Shared Socioeconomic Pathways:
! O’Neill et al. Climatic Change (2014) 122: 387.
! Near-term GDP growth is adjusted to reflect economic stagnation in several countries.
! Current release does not include other SSP assumptions beyond population and GDP.
Population and GDP 2… 2… 2… 2… 2… 2… 2… 2… 2… 2…
USA EU-12 EU-15 EuropeanFreeTradeAssociation Australia_NZCanada Japan China India Africa_EasternAfrica_Northern Africa_Southern Africa_Western SouthAfrica IndonesiaPakistan SouthKorea Taiwan CentralAsia SouthAsiaSoutheastAsia Argentina Brazil Colombia MexicoCentralAmericaandCaribbean SouthAmerica_Northern SouthAmerica_Southern Russia Europe_EasternEurope_Non_EU MiddleEast
ENERGY
30
The Global Change Assessment Model
The Energy System: Structure
Oil Production
Bioenergy Conversion
Electric Power
Generation
Liquids Refining
Hydrogen
N. Gas Production
Coal Production
Bioenergy Production
Gas Processing
Nuclear
Hydro
Solar
Liquids Market
Bioenergy Market
Natural Gas Market
Hydrogen Market
Electricity Market
Wind
Geothermal
Buildings Sector
Industrial Sector
Transport Sector
Coal Market
Uranium
Resource Production Energy Transformation End-Use Final Energy Carriers
The Energy System: Resources
! Resources serve as inputs to conversion technologies to produce energy carriers such as electricity, liquid fuels, and hydrogen. ! For example, several types of solar technologies – CSP, central PV,
rooftop PV – draw from the solar resource to produce electricity. ! Exhaustible Resources in GCAM
! Coal ! Natural Gas ! Oil (conventional and unconventional) ! Uranium
! Renewable Resources in GCAM ! Solar ! Wind (onshore and offshore combined into one) ! Geothermal ! Bioenergy (several forms)
The Energy System: Resources: Conventional Oil
! Oil, Gas, and Coal Resources derived from Rogner 1997 (per the GCAM wiki), but please refer to that source for original data.
! Note: there is an additional 90 ZJ of unconventional oil in GCAM 4
0
2
4
6
8
10
12
14
0 5 10 15 20 25
2010$/G
J
ZetaJoules
GlobalConven5onalOilSupply
0
1
2
3
4
5
6
7
USA
Africa_Eastern
Africa_Northern
Africa_Southe
rn
Africa_Western
Australia_N
ZBrazil
Canada
Cent.A
mericaandCar.
CentralA
sia
China
EU-12
EU-15
Europe
_Eastern
Europe
_Non
_EU
Europe
anFreeTradeAss.
India
Indo
nesia
Japan
Mexico
MiddleEast
Pakistan
Russia
SouthAfrica
SouthAm
erica_Northern
SouthAm
erica_Southe
rn
SouthAsia
SouthKo
rea
Southe
astA
sia
Taiwan
ArgenT
na
Colombia
ZetaJoules
RegionalConven5onalOilSupply
The Energy System: Resources: Natural Gas
! Oil, Gas, and Coal Resources derived from Rogner 1997; please refer to that source for original data.
! Note: The highest cost grade of natural gas is not shown here. We have ~200 ZJ more natural gas available in the model (hydrates).
0
2
4
6
8
10
12
14
0 5 10 15 20 25 30 35 40
2010$/G
J
ZetaJoules
GlobalNaturalGasSupply
0
1
2
3
4
5
6
USA
Africa_Eastern
Africa_Northern
Africa_Southe
rn
Africa_Western
Australia_N
ZBrazil
Canada
Cent.A
mericaandCar.
CentralA
sia
China
EU-12
EU-15
Europe
_Eastern
Europe
_Non
_EU
Europe
anFreeTradeAss.
India
Indo
nesia
Japan
Mexico
MiddleEast
Pakistan
Russia
SouthAfrica
SouthAm
erica_Northern
SouthAm
erica_Southe
rn
SouthAsia
SouthKo
rea
Southe
astA
sia
Taiwan
ArgenS
na
Colombia
ZetaJoules
RegionalNaturalGasSupply
The Energy System: Resources: Coal
! Typically use around 30 ZJ to 2100 – so very flat part of curve ! Total Resources in GCAM extend to over 250 ZJ at higher
costs
0
10
20
30
40
50
60
70
80
USA
Africa_Eastern
Africa_Northern
Africa_Southe
rn
Africa_Western
Australia_N
ZBrazil
Canada
Cent.A
mericaandCar.
CentralA
sia
China
EU-12
EU-15
Europe
_Eastern
Europe
_Non
_EU
Europe
anFreeTradeAss.
India
Indo
nesia
Japan
Mexico
MiddleEast
Pakistan
Russia
SouthAfrica
SouthAm
erica_Northern
SouthAm
erica_Southe
rn
SouthAsia
SouthKo
rea
Southe
astA
sia
Taiwan
ArgenT
na
Colombia
ZetaJoules
RegionalCoalSupply
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
0 10 20 30 40 50
2010$/G
J
ZetaJoules
GlobalCoalSupply-Truncated
The Energy System: Renewable Resources
! In GCAM the capacity factors of wind turbines are based on detailed global supply curves [1, 2]. Similarly, the capacity factors of photovoltaic panels in GCAM are based on rooftop PV supply curves developed for the United States [3].
! [1] Y. Zhou and S. J. Smith, “Spatial and temporal patterns of global onshore wind speed distribution,” Environmental Research Letters, vol. 8, no. 3, p. 034029, 2013.
! [2] Y. Zhou, P. Luckow, S. J. Smith, and L. Clarke, “Evaluation of global onshore wind energy potential and generation costs,” Environmental science & technology, vol. 46, no. 14, pp. 7857–7864, 2012.
! [3] P. Denholm and R. Margolis, “Supply curves for rooftop solar pv-generated electricity for the united states,” 2008.
Bioenergy Production
Bioenergy Production
Purpose Grown Bioenergy
Crop & Forestry Residues
Municipal Solid Waste
Purpose Grown Bioenergy: • Production depends on land allocation and regional yield from Ag model • Land allocation depends on the profit rate of biomass AND all competing land uses • Includes 1st and 2nd generation crops
Crop & Forestry Residues: • Potential production depends on crop production in ag model • Fraction harvested depends on the price of bioenergy; higher prices lead to more production • Some amount of residue must remain on the field for erosion control
Municipal Solid Waste: • Potential production depends population and income • Fraction used for bioenergy depends on the price of bioenergy; higher prices lead to more production
Note: We also model traditional bioenergy. However, it is not added to the bioenergy resource pool and is instead consumed directly by the buildings sector. Similarly, we model 1st generation bioenergy (corn, sugar, oil crops), but it is converted directly to ethanol or diesel and not added to the bioenergy resource pool.
GCAM AG/LU Model
The Energy System: Structure
Oil Production
Bioenergy Conversion
Electric Power
Generation
Liquids Refining
Hydrogen
N. Gas Production
Coal Production
Bioenergy Production
Gas Processing
Nuclear
Hydro
Solar
Liquids Market
Bioenergy Market
Natural Gas Market
Hydrogen Market
Electricity Market
Wind
Geothermal
Buildings Sector
Industrial Sector
Transport Sector
Coal Market
Uranium
Resource Production Energy Transformation End-Use Final Energy Carriers
The Energy System: Energy Transformation and Conversion
! Final energy sectors in GCAM consume several fuels: ! Electricity
! Liquid Fuels
! Coal
! Bioenergy
! Gas
! Hydrogen
! Corresponding to each of these is a conversion sector that takes as inputs various resources. ! For example, liquid fuels are produced from bioenergy, conventional and unconventional
oil, coal, and natural gas.
! Conversion sectors can utilize a number of technologies, even for a single input fuel. ! Bioenergy-to-liquids, for example, can be produced through several different
technologies, some with CCS options.
The Energy System: Bioenergy Pathways
Oil Production
Bioenergy Conversion
Electric Power
Generation
Liquids Refining
Hydrogen
N. Gas Production
Coal Production
Bioenergy Production
Gas Processing
Nuclear
Hydro
Solar
Liquids Market
Bioenergy Market
Natural Gas Market
Hydrogen Market
Electricity Market
Wind
Geothermal
Buildings Sector
Industrial Sector
Transport Sector
Coal Market
Uranium
The Energy System: Electric Generation
Oil Production
Bioenergy Conversion
Electric Power
Generation
Liquids Refining
Hydrogen
N. Gas Production
Coal Production
Bioenergy Production
Gas Processing
Nuclear
Hydro
Solar
Liquids Market
Bioenergy Market
Natural Gas Market
Hydrogen Market
Electricity Market
Wind
Geothermal
Buildings Sector
Industrial Sector
Transport Sector
Coal Market
Uranium
Resource Production Energy Transformation End-Use Final Energy Carriers
The Energy System: Electricity Generation
Bioenergy
Electric Power
Generation
Refined Liquids
Coal
Natural Gas
Nuclear
Hydro
Solar
Wind
Geothermal
The Energy System: Electric Power Plants
! We model several fuels and technologies for generating electric power.
! For example, the current GCAM core has 4 different technology
options for coal power plants: ! Pulverized coal steam plants ! Pulverized coal steam plants with CO2 Capture and Storage (CCS) ! IGCC ! IGCC with CO2 Capture and Storage (CCS)
! Each power plant has a different efficiency, non-energy cost, and emissions factor. ! Which technology is deployed depends on the trade-offs between
emissions and other costs. For example, IGCC with CCS will only deploy with a higher value on CO2 – as in a climate policy scenario.
The Energy System: Technology Competition
! Economic competition among technologies takes place at many sectors and levels.
! Assumes a distribution of realized costs due to heterogeneous conditions.
! Market share based on probability that a technology has the least cost for an application. ! Avoids a “winner take all”
result. ! “Logit” specification.
45
A Probabilistic Approach
`
Median CostTechnology 1
Median CostTechnology 2
Median CostTechnology 3
Market Price
The Energy System: Technology Competition
Source: Clarke and Edmonds (1993), McFadden (1974)
∑=
jjj
iii c
cs σ
σ
αα
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
baseyear σ=0 σ=-1 σ=-3 σ=-6 σ=-12 σ=-24 σ=-100
tech2
tech1
Change in technology shares when tech 1’s cost increases by 20%
The Energy System: Vintaging of Capital
! We assume that capital stock in certain sectors (for example, electric power generation and oil refining sectors) is long-lived.
! This means that a power plant or refinery built in one model period *may* still be in operation many time periods later.
! However, we do not assume that existing capital is always in operation. Once the variable cost exceeds the market price, we begin to shut down existing units. This often occurs when a carbon price is applied.
Example Results: Electric Generation by Fuel
! Base Year 2010 calibrated to IEA data. ! Capital Stock vintaging and retirements ! Investments in new capital based on relative costs and
calibrated preferences.
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2010 2015 2020 2025 2030 2035 2040 2045 2050
ExaJou
les
Aust&NZElectricGenera5on:Example
nCHP
mSolar
lWind
kHydro
jGeothermal
iNuclear
gBiomass
eOil
cGas
aCoal0
2
4
6
8
10
12
2010 2015 2020 2025 2030 2035 2040 2045 2050
ExaJou
les
SEAsiaElectricGenera5on:Example
nCHP
mSolar
lWind
kHydro
jGeothermal
iNuclear
gBiomass
eOil
cGas
aCoal
The Energy System: Energy Demand
Industrial Technologies
Transport Technologies
Buildings Technologies
Liquids Market
Bioenergy Market
Natural Gas Market
Hydrogen Market
Electricity Market
Buildings Sector
Industrial Sector
Transport Sector
Coal Market We have detailed representations of
transportation & buildings in all regions.
The Energy System: Transportation
! Per capita transportation service demands (measured in km/yr) are a function of income and the prices of services.
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
20000
Air
LDV
Rail
Bus
Non-motorized
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
20000
Air
LDV
Rail
Bus
Non-motorized
0
5000
10000
15000
20000
25000
30000
35000
40000
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
20000
2010
2020
2030
2040
2050
2060
2070
2080
2090
2100
Freight/shippingto
nne-km
/person/yr
Passen
gerk
m/person/yr
Passenger
Freight
Interna8onalShipping
The Energy System: Transportation
! The choice among modes of transportation in the passenger sector is a function of the cost of travel, the time it takes, and income.
The Energy System: Transportation
! The choice among fuels within a mode is a function of cost (including capital cost and the cost of fuel)
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2010 2030 2050 2100
USACompactCarSales
Hydrogenfuelcell
Ba;eryelectric
Naturalgas
ICE-Hybrid
ICE
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
Ba.e
ryelectric
Hydrogen
fuelcell
ICE-H
ybrid
ICE
Naturalgas
Ba.e
ryelectric
Hydrogen
fuelcell
ICE-H
ybrid
ICE
Naturalgas
2010 2050
2005$/veh-km
USACompactCarCosts
Fuel
Capitalandnon-fuelO&M
0
200
400
600
800
1000
1200
1400
2010 2030 2050 2070 2090
Vehiclesperth
ousand
persons
LDVOwnershipratesNorthAmerica
Europe
Russia
Brazil
OthLatAm
Sub-SahAfrica
MidENAfrica
India
OtherS&CentAsia
China
PacificOECD
OtherSEAsia
The Energy System: Transportation
! A wide variety of detailed output variables can be reported for the transportation sector
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
USA Russia Brazil China India
LDVagedistribu/onin2030
<5yr
5-10yr
10-15yr
15-20yr
>20yr
0
10
20
30
40
50
60
70
80
90
100
2010 2030 2050 2070 2090
km/hr
Weightedaveragetransitspeed
USA
Russia
Brazil
China
India
The Energy System: Energy Demand
Industrial Technologies
Transport Technologies
Buildings Technologies
Liquids Market
Bioenergy Market
Natural Gas Market
Hydrogen Market
Electricity Market
Buildings Sector
Industrial Sector
Transport Sector
Coal Market We have detailed representations of
transportation & buildings in all regions.
The Energy System: Buildings
Per-capita Residential and Commercial Energy Use in 2010
0" 5" 10" 15" 20" 25"
USA"Canada"Russia"Europe"
Oth"Lat"Am"Brazil"
Sub?Sah"Afr"Mid"E"N"Afr"Other"Asia"Pac"OECD"
India"China"USA"
Canada"Russia"Europe"
Oth"Lat"Am"Brazil"
Sub?Sah"Afr"Mid"E"N"Afr"Other"Asia"Pac"OECD"
India"China"USA"
Canada"Russia"Europe"
Oth"Lat"Am"Brazil"
Sub?Sah"Afr"Mid"E"N"Afr"Other"Asia"Pac"OECD"
India"China"
Other"
Cooling"
HeaI
ng"
GJ#/#person#
Residen.al#
District"Heat"
Biomass"
Coal"
Liquids"
Natural"Gas"
Electricity"
0" 5" 10" 15" 20" 25"
USA"Canada"Russia"Europe"
Oth"Lat"Am"Brazil"
Sub?Sah"Afr"Mid"E"N"Afr"Other"Asia"Pac"OECD"
India"China"USA"
Canada"Russia"Europe"
Oth"Lat"Am"Brazil"
Sub?Sah"Afr"Mid"E"N"Afr"Other"Asia"Pac"OECD"
India"China"USA"
Canada"Russia"Europe"
Oth"Lat"Am"Brazil"
Sub?Sah"Afr"Mid"E"N"Afr"Other"Asia"Pac"OECD"
India"China"
Other"
Cooling"
HeaI
ng"
GJ#/#person#
Commercial#
District"Heat"
Biomass"
Coal"
Liquids"
Natural"Gas"
Electricity"
The Energy System: Buildings
! Future evolution of building energy use is shaped by... ! Residential and commercial floorspace
! Population, GDP, and exogenous per-capita floorspace satiation levels
0"
50"
100"
150"
200"
250"
300"
350"
400"
450"
2005"2015"2025"2035"2045"2055"2065"2075"2085"2095"
Billion
&m2&
Total&Building&Floorspace&
USA"
Canada"
Russia"
Europe"
Other"La@n"America"
Brazil"
SubGSaharan"Africa"
Middle"East"&"N."Afr."
Other"Asia"
Pacific"OECD"
India"
China"
The Energy System: Buildings
! Future evolution of building energy use is shaped by... ! Residential and commercial floorspace ! Levels of building service demands per unit floorspace
! Climate, building shell conductivity, GDP, and exogenous satiation levels
0"
0.02"
0.04"
0.06"
0.08"
0.1"
0.12"
2010"
2020"
2030"
2040"
2050"
2060"
2070"
2080"
2090"
2100"
MJ#/#DD#/#m
2#
Residen.al#Cooling#
USA"
Canada"
Russia"
Europe"
Oth."Lat."Amer."
Brazil"
SubFSah."Afr."
Mid."E."&"N."Afr."
Other"Asia"
Pacific"OECD"
India"
China"0"
0.05"
0.1"
0.15"
0.2"
0.25"
2010"
2020"
2030"
2040"
2050"
2060"
2070"
2080"
2090"
2100"
MJ#/#DD#/#m
2#
Residen.al#Hea.ng#
The Energy System: Buildings
! Future evolution of building energy use is shaped by... ! Residential and commercial floorspace ! Levels of building service demands per unit floorspace ! Fuel and technology choices by consumers
0"
5"
10"
15"
20"
25"
30"
35"
40"
45"
2010"2050"2100" 2010"2050"2100" 2010"2050"2100" 2010"2050"2100" 2010"2050"2100" 2010"2050"2100"
Brazil" China" India" Russia" USA" EU:27"
EJ/yr&
Othr:"Electricity"
Othr:"Heat"
Othr:"Liquids"
Othr:"Gas"
Othr:"Coal"
Othr:"Biomass"
Clng:"Electricity"
Htng:"Electricity"
Htng:"Heat"
Htng:"Liquids"
Htng:"Gas"
Htng:"Coal"
Htng:"Biomass"
The Energy System: Calibration
! The current base year for the energy system is 2010.
! We use IEA energy balances as calibration data.
! The calibration procedure calculates “share weights” such that the dataset derived from the IEA energy balances is reproduced.
! These share weights reflect unmeasured and non-economic influences on decision-making.
! If a technology has low costs but nevertheless has low market share (e.g. coal furnaces), then the model will compute a low share weight. If this base-year share weight is applied to future periods, then the market share of the technology will remain low even if it remains a relatively low-cost option.
! In most cases, we retain these share weights in future years. In some cases (e.g. renewables in the electric sector, or alternative-fuel vehicles in the LDV sector), we have over-written them because the base-year shares do not reflect mature market equilibrium conditions.
The Energy System: Results
The Energy System: Results
Primary Energy Inputs to Industrial Electricity
0
0.5
1
1.5
2
2.5
3
0
20
40
60
80
100
120
140
160
180
2001990
2010
2020
2030
2040
2050
2060
2070
2080
2090
2100
Prim
ary/Fina
lCoe
fficien
t
EJ/yr
geothermal
solar
wind
hydro
biomass
nuclear
oil
gas
coal
intensity
Frequently Asked Questions
! Common question: ! Why are some of the energy
prices in GCAM lower than recent history or other projections?
! Answer: ! We are a long-term equilibrium
model. We do not attempt to capture short-term market fluctuations or market behavior.
! In the case of oil, we do not sustain higher oil prices because the cost of substitutes (e.g., CTL, GTL) is lower than the current market price.
Oil Price
0
20
40
60
80
100
120
1965 1975 1985 1995 2005 2015
$/ba
rrel
EIA Nominal EIA Real (2010$) GCAM Real (2010$)
AGRICULTURE AND LAND USE
63
The Global Change Assessment Model
The Agricultural System: Demand
! GCAM currently models supply and demand for 12 crops, 6 animal categories, and bioenergy: ! Crops: corn, rice, wheat, sugar, oil crops (e.g., soybeans), other grains
(e.g., barley), fiber (e.g., cotton), fodder (e.g., hay, alfalfa), roots & tubers, fruits & vegetables
! Animals: beef, dairy, pork, poultry, sheep/goat, other ! Forest: roundwood ! Bioenergy: switchgrass, miscanthus, jatropha, willow, eucalyptus, corn
ethanol, sugar ethanol, biodiesel (from soybeans and other oil crops)
! We account for both food and non-food demand, including animal feed.
! Demand is modeled at the 32 region level.
The Agricultural System: Demand
! Non-food, non-feed demand: ! Base year demand for non-food, non-feed uses FAO statistics ! Future demand:
! Per capita demand for crops, animals, and forestry products is currently fixed. ! Thus, demand grows proportional to population, regardless of income or price.
! Feed demand: ! Base year demand for feed combines FAO statistics with data from the
IMAGE model (PBL) ! Future demand:
! Depends on the growth in animal consumption, as well as the change in relative prices of feed options
! Animal can either be grass-fed or grain-fed. The exact proportion of grass- vs. grain-fed depends on the price of pasture land as compared to the price of crops
! Grain-fed animals can shift their diet as the relative prices of various crops change. However, the elasticity is relatively low to prevent dramatic shifts that may comprise an unsustainable diet.
The Agricultural System: Demand
! Food demand: ! Base year demand for food uses FAO statistics ! Future demand in the baseline is calibrated to match FAO projections of
crop and meat demand through 2050. After 2050, we assume that per capita demand is constant.
! Meat demand in GCAM is price responsive. As the price of meat increases, meat demand will decline.
! The current price elasticity is very low (~0.25). This is consistent with USDA data for the USA and Australia. Developing countries typically have more elastic demand, but our default assumption is very conservative.
! Crop demand is not price responsive.
The Global Change Assessment Model
The Agricultural System: Technologies
! For each crop and region, we have started with a single production technology. ! The yield for this technology is calculated from GTAP/FAO statistics, by
dividing total production in a region by land area. ! GCAM results are production per year, not per harvest. Thus, we use
total physical crop land area to calculate yield and not harvested area. If a region actually harvests more than once a year, their “economic” yield (used by GCAM) will be larger than the actual physical yield.
! We exogenously specify technical change for agricultural technologies. ! We use FAO projections through 2050. ! After 2050, we assume that yields will improve by 0.25% per year for all
crops and regions.
The Global Change Assessment Model
! The world is divided into 283 regions
The Agricultural System: Basic Assumptions
71
The Agricultural System: Regions
The Agricultural System: Regions
Monfreda et al. (2009)
283 Different AgLU Supply Regions
! The world is divided into 283 regions ! Farmers allocate land across a variety of uses in order to maximize
profit ! There is a distribution of profits for each land type across each of the
283 regions ! The actual share of land allocated to a particular use is the probability
in which that land type has the highest profit ! The variation in profit rates is due to variation in the cost of production
! As the area devoted to a particular land use expands, cost increases ! Yield is fixed within each region for each crop management practice
The Agricultural System: Basic Assumptions
75
The Agricultural System: Nesting
Corn, wheat, etc.
Corn, wheat, etc.
Corn, wheat, etc.
Corn, wheat, bioenergy, etc.
Land
Non-Pasture
Grass and Shrubs
Pasture
Other Pasture
Intensively-Grazed PastureCrops All
Forests
ForestCommercial Forest
Tundra Rock, Ice,Desert UrbanArable Land
Shrubland
Grassland
Other arable land
Gray = ExogenousGreen = Non-commercialRed = Commercial
The Agricultural System: USA Wheat Yield
While yield is fixed within each subregion, there is a
distribution of yields across each of the 32 GCAM regions.
The Agricultural System: Calibration
! Currently, we calibrate to an average of 2008-2010 data. This is to avoid using an anomalous weather year as a benchmark.
! During the AgLU calibration process, the model computes the average profit rate required to reproduce the base year land allocations. We assume that the difference between this profit and the observed profit (yield * (p – c)) is a cost to production that also applies in the future.
! Thus, if you have a region with a high crop yield, but low land allocation in the base year (e.g., Wheat in Alaska), the model assumes that there are some additional costs that must be considered when expanding its land area. As a result, that crop will continue to have a low share in the future in the absence of a technology or policy change.
Wheat production in USA AEZ16
0.00000%
0.00002%
0.00004%
0.00006%
0.00008%
0.00010%
0.00012%
2000 2050 2100
% o
f glo
bal p
rodu
ctio
n
0%#10%#20%#30%#40%#50%#60%#70%#80%#90%#100%#
Base%Year%
σ%=%0%
σ%=%0.1%
σ%=%0.5%
σ%=%1%
σ%=%2%
σ%=%3%
σ%=%4%
σ%=%5%
σ%=%6%
σ%=%7%
σ%=%8%
σ%=%9%
σ%=%10%
σ%=%50%
σ%=%100%
Land#Type#1# Land#Type#2#
The Agricultural System: Land Competition
Source: Clarke and Edmonds (1993), McFadden (1974)
si =αiπ i( )σ
α jπ j( )σ
j∑
Change in land shares when land type 1’s profit increases by 20%
The Agricultural System: Land Competition
! Elasticities can be computed at each point, but ! By design, there is not a constant elasticity relationship with respect to
changes in profit
The Agricultural System: Land Cover Data
Potential Vegetation
Cropland area
Crop-specific harvested areas
Maize Area in 2010
Sub-national Harvested areas
! GCAM needs land cover by type (e.g., forest, grass, maize, wheat, etc.) for each region/AEZ combination in each historical year.
! We have similar methodologies in other sectors: ! Population: IIASA, US Census ! Energy: IEA, EIA, country
studies ! Agriculture: FAO, GTAP, MIRCA ! Emissions: EDGAR, EPA, RCP
The Global Change Assessment Model
The Agricultural System: Supply
! Yield is exogenously calculated. ! Base year derived from GTAP/FAO production and land area. ! Yields increase over time based on exogenously specified technical
change.
! Land area is endogenously calculated. ! Each land types share of area in its region is the probability its profit is the
highest in that region.
! Supply = land * yield
The Agricultural System: Results
Global Beef Feed Beef Consumption by Region
0
20
40
60
80
100
120
140
160
1990
2010
2020
2030
2040
2050
2060
2070
2080
2090
2100
PCal
USA Southeast Asia South Korea South Asia South America_Southern South America_Northern South Africa Russia Pakistan Middle East Mexico Japan Indonesia India European Free Trade Association Europe_Non_EU Europe_Eastern EU-15 EU-12 Colombia China Central Asia Central America and Caribbean Canada Brazil Australia_NZ Argentina Africa_Western Africa_Southern Africa_Northern Africa_Eastern
0
1000
2000
3000
4000
5000
6000
1990
2005
2010
2015
2020
2025
2030
2035
2040
2045
2050
2055
2060
2065
2070
2075
2080
2085
2090
2095
2100
Mt
Scavenging_Other Pasture_FodderGrass
FodderHerb_Residue FeedCrops
The Agricultural System: Results
Wheat Production by Region Wheat Production in the USA
0
100
200
300
400
500
600
700
800
900
1000
1990
2010
2020
2030
2040
2050
2060
2070
2080
2090
2100
Mt/y
r
USA
Southeast Asia
South Korea
South Asia
South America_Southern
South Africa
Russia
Pakistan
Middle East
Mexico
Japan
India
European Free Trade Association
Europe_Non_EU
Europe_Eastern
EU-15
EU-12
Colombia
China
Central Asia
Central America and Caribbean
Canada
Brazil
Australia_NZ
Argentina
Africa_Western
Africa_Southern
Africa_Northern
Africa_Eastern 0
20
40
60
80
100
120
1990
2005
2010
2015
2020
2025
2030
2035
2040
2045
2050
2055
2060
2065
2070
2075
2080
2085
2090
2095
2100
Mt/y
r
WheatAEZ16 WheatAEZ15 WheatAEZ14 WheatAEZ13 WheatAEZ12 WheatAEZ11 WheatAEZ10 WheatAEZ09 WheatAEZ08 WheatAEZ07
The Agricultural System: Results
0
100
200
300
400
500
600
700
800
900
1000
1990 2015 2030 2045 2060 2075 2090
Mt/y
r NonFoodDemand_Crops
FoodDemand_Crops
FeedCrops
Wheat Consumption by Use
The Agricultural System: Results
Global Land Allocation
The Global Change Assessment Model
! While we can explain the energy and agricultural systems separately, these two systems cannot be separated in practice. Choices made in one sector affect outcomes in another sector.
! This is true both in the real world and in GCAM. You cannot run the different components of the model separately.
! GCAM currently has three means of linking the energy and agriculture systems: ! Bioenergy: supplied by the agricultural system, demanded by the energy
system ! Fertilizer: supplied by the energy system, demanded by the agricultural
system ! DDGS: supplied by the energy system, demanded by the agricultural
system
The Agricultural System: Linking the Energy & Agricultural Sectors
89
Fertilizer Supply
! We are modeling synthetic fertilizer production for use in the agricultural sector. We do not include non-agricultural uses of fertilizer or natural fertilizer.
! Production by technology is from IEA.
Global fertilizer production by fuel
0
20
40
60
80
100
120
140
160
180
1990
20
05
2010
20
15
2020
20
25
2030
20
35
2040
20
45
2050
20
55
2060
20
65
2070
20
75
2080
20
85
2090
20
95
2100
MtN
refined liquids
gas
coal
Fertilizer Demand
! Consumption by country (and therefore region) are from FAO ResourceSTAT.
! Consumption by region is first downscaled to crops according to a dataset put together by the International Fertilizer Industry Association working in collaboration with the FAO, and then downscaled to AEZ on the basis of crop production.
Global fertilizer consumption by crop
0
20
40
60
80
100
120
140
160
180
1990
20
05
2010
20
15
2020
20
25
2030
20
35
2040
20
45
2050
20
55
2060
20
65
2070
20
75
2080
20
85
2090
20
95
2100
MtN
biomass
Wheat
SugarCrop
Root_Tuber
Rice
PalmFruit
OtherGrain
OilCrop
MiscCrop
FodderHerb
FodderGrass
FiberCrop
Corn
Joint GCAM Community Modeling Meeting and GTSP Technical Workshop Joint Global Change Research Institute College Park, Maryland, USA
Introduction to the Global Change Assessment Model (GCAM) THE JGCRI GCAM TEAM
Thursday, December 3, 2015
PNNL-SA-90713
Outline
! Brief introduction to Integrated Assessment Models
! Overview of GCAM
! Detailed information on GCAM’s ! Economic assumptions, ! Energy system, ! Agriculture and land use system, ! Emissions, ! Policies, ! Climate system, and ! Solution algorithm.
! Frequently asked questions
POLICY MARKETS
94
The Global Change Assessment Model
Other Markets: Climate Policy
! Carbon or GHG prices: ! Users can specify the price of carbon or GHGs directly ! Emissions will vary depending on other scenario drivers
! Emissions constraints: ! Users can specify the total amount of emissions (CO2 or GHG) ! Model will calculate the price of carbon needed to reach the constraint
! Climate constraints: ! Users can specify a climate variable (e.g., concentration or radiative forcing) target
for a particular year ! Users determine whether that target can be exceeded prior to the target year ! Model will adjust carbon prices in order to find the least cost path to reaching the
target ! (This type of policy increases model run time significantly)
Other Markets: Energy Policy
! We can impose constraints (lower & upper bounds) on energy consumption. ! The model will solve for the tax (upper bound) or subsidy (lower bound)
required to reach the given constraint. ! Within an individual sector, these constraints can be share constraints
(e.g., fraction of electricity that comes from solar power). ! This allows us to model renewable portfolio standards and biofuels standards.
! Across sectors, these must be quantity constraints.
Other Markets: Land-Use Policy
! REDD: ! In this policy, we set aside some land from economic competition. This land
cannot be converted to crops, pasture, or any other land type. ! Currently, this is the core assumption in GCAM when running a carbon policy.
! We have protected 90% of non-commercial ecosystems.
! Valuing carbon in land: ! In this policy, we assume that land use change emissions are taxed at the
same rate as fossil fuel and industrial emissions. ! Land owners receive a subsidy proportional to their carbon content.
! Bioenergy constraints (upper or lower): ! We can also constrain biomass to a particular level. This is implemented in
GCAM as a tax or subsidy on bioenergy consumption. The tax/subsidy is adjusted until the constraint is met.
! Bioenergy taxes: ! We can impose a tax on bioenergy that is linked to the carbon price.
! Imposing a climate policy affects the cost of energy production for carbon-intensive fuels. This induces a shift toward lower emitting technologies.
Other Markets: The Effect of Climate Policy on the Energy System
Cost of Electricity Generation in China
Electricity Generation in China in 2050
0
5
10
15
20
25
30
35
40
Reference Tax ($25/tC in 2020)
EJ/y
r
m Solar
l Wind
k Hydro
j Geothermal
i Nuclear
h Biomass w/CCS
g Biomass
f Oil w/CCS
e Oil
d Gas w/CCS
c Gas
b Coal w/CCS
a Coal
0
2
4
6
8
10
12
14
16
18
20
coal (IGCC) coal (IGCC CCS) Gen_III
1975
$/G
J
Reference
Policy
99
! Under the default assumption in GCAM, 90% of non-commercial ecosystems are protected in GCAM. This means that they cannot be used for crop or bioenergy production.
Other Markets: The Effect of Climate Policy on the Agriculture & Land-Use System
100
0
20
40
60
80
100
120
140
0.0 1.0 2.0 3.0
$ / t C
A b a te m e n t (G tC / y r )
0
1
2
3
4
5
6
7
8
9
10
0 20 40 60 80 100
Pric
e
Quantity
Other Markets: Climate Policy Cost
! GCAM can compute the cost of a climate policy endogenously. ! The cost metric used is the area under the marginal abatement cost (MAC)
curve. This area under the MAC curve commonly referred to as “deadweight loss” (i.e., the change in producer and consumer surplus.)
! Currently, we are not modeling this cost as affecting GDP in GCAM.
Marginal Abatement Cost “Deadweight loss”
Tax Revenue
Supply
Demand
Original Quantity
New Quantity
Frequently Asked Questions
! Common question: ! Does the GCAM reference scenario include other climate and energy
policies?
! Answer: ! To the extent that these exist in the base year, they will be calibrated into
the GCAM reference scenario. ! However, we do not explicitly include any proposed climate or energy
policies in the reference scenario.
TRADE
The Global Change Assessment Model
Current Approach to Modeling Trade
! In general, we model Heckscher-Ohlin trade. We have not focused on bilateral trade.
! This means that for many products, we assume that trade occurs freely into and out of global markets. These products include coal, gas, oil, bioenergy, food, and fiber. ! A region’s net trade position is dynamic depending on economics,
technical change, demand growth, resources, and other changing factors. ! In simplest terms – given a global market price – each region computes
demand and production, and net imports are the difference.
! For other products, we have fixed or static interregional trade. These products include solar, wind, geothermal, meat, and dairy. ! For some products, like solar resources, trade is physically impractical if
not infeasible. ! For other products such as beef, our basic economic modeling approach
makes dynamic trade complicated, and the fixed trade assumption based on historical data is a conservative approach.
! Collection and Processing ! Pelletizing important to
increase the energy density of the fuel and facilitate transportation
! Average cost to transport to local collection facility and pelletize of $2.18/GJ (2005$) ! 85% of cost is in pelletizing ! compare to $1.33/GJ for
Coal (Edwards). ! International transport cost of
$0.31/GJ (2005$) added to all regions (assumes large ocean bulk carriers)
Interregional Trade: the case of Bioenergy
(Van Vliet, 2009, consistent with Wolf 2006)
We model large scale bioenergy systems
Future Goals and New Approaches in Development for Modeling Trade
! We have several recent efforts where we have either goals or plans to go beyond the simple global market approach. ! Dynamically combine regionally differentiated product markets with global
or multiregional markets. ! Allow ability to model effect of regional isolation or difficulty in participating
in inter-regional markets (e.g., short-term constraints to imports). ! Unlike some Armington implementations, allow long-term dynamic
change of regional product import/export positions in response to economics.
! We have initial implementations differentiating demand from domestic and global (or extra-regional) markets. ! Combines intra-regional production as currently modeled with statistical
(logit) parametric sharing of interregional trade in global market. ! Still requires more development and vetting, possibly simplifying. ! Natural gas production and markets to address need for LNG for global. ! Would allow dynamic trade in current fixed trade products like beef. ! Project task for DOE to model trade limits/implications of climate impacts
on food demand in vulnerable regions.
EMISSIONS
108
Emissions: Modeling in GCAM
109
Emissions = Em_ factor•Activity_ Level • 1−Em_Controls(GDPper−capita )( )
In an IAM we need to represent future emission trajectories and how those trajectories might vary under different drivers and policies.
CO2: GCAM is a process model for CO2 emissions and reductions ! Emissions depend on specific technologies, whose use is explicitly determined by the
model, and can be modified through carbon prices. ! The GCAM, in effect, produces a Marginal Abatement Curve for CO2
Non-CO2 GHGs: are modeled as
Air pollutant emissions: (SO2, NOx, etc.) are modeled as:
Non-CO2 emissions (both GHGs & air pollutants) originate from many sources and can be controlled using multiple abatement technologies
! This is too much detail for us to include explicitly at the process level ! We calibrate to base year inventories and use parameterized functions for future emissions
controls and Marginal Abatement Cost (MAC) curves to change emission factors over time. ! Technology shifts still play a role, since emission factors differ between technologies.
Emissions = Em_ factor•Activity_ Level • 1−MAC(Carbon - Price)( )
Emissions: Base Year Emissions
GCAM tracks emissions for a number of greenhouse gases and air pollutants ! CO2, CH4, N2O, CF4, C2F6, SF6, HFC23, HFC32, HFC43-10mee, HFC125, HFC134a,
HFC143a, HFC152a, HFC227ea, HFC236fa, HFC245fa, HFC365mfc, SO2, BC, OC, CO, VOCs, NOx, NH3
! We calculate CO2 from fossil fuel & industrial uses, as well as from land-use change
! CO2: ! Energy system: we read in global carbon contents for fossil fuels (e.g., coal, gas,
oil). These are chosen so we match global emissions from CDIAC in the base year. These carbon contents are used to compute emissions in all years (including the base year).
! LUC: we read in carbon density, growth parameters, and historical land allocation and compute emissions in all years (including the base year).
! Non-CO2: ! 2005 emissions calibrated to match the EDGAR* data set (except BC & OC, where
we use RCP inventories). In some cases (e.g., electricity), we supplement EDGAR with EPA to get technology-specific emissions.
! We plan to update GCAM calibration to the newly released CEDS historical emissions dataset, which currently extends to 2014. globalchange.umd.edu/ceds
Emissions: Vegetation CO2 Emissions
! First, we determine the total change in carbon stock for each land type and region. ! Δ C Stock = [Land Area (t)]*[C density (t)] - [Land Area (t-1)]*[C density
(t-1)]
! Then, we allocate that change across time. ! If change in land area decreases the carbon stock (e.g., deforestation),
then all carbon is released into the atmosphere instantaneously. ! If the change in land area increases the carbon stock (e.g., afforestation),
then carbon accumulates slowly over time, depending on an exogenously specified mature age.
! The mature age varies by land type and region.
Emissions: Forest Carbon Uptake
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 20 40 60 80 100
35 years
50 years
75 years
100 years
Years since land conversion
% o
f eve
ntua
l car
bon
stoc
k
Emissions: Soil CO2 Emissions
! First, we determine the total change in carbon stock for each land type and region. ! Δ C Stock = [Land Area (t)]*[C density (t)] - [Land Area (t-1)]*[C density (t-1)]
! Then, we allocate that change across time. ! Whether carbon stock increases or decreases, we use the same formula.
! ! The half life, λ, varies by region. ! In general, colder regions have longer soil carbon half lives.
!"#$%&'(") ! = !!"#$%&'(") 0 + !∆!"#$%&'(")!"#$%!,! ∙ (1− !!!")!
Emissions: Non-CO2 Drivers
Energy System ! Emissions in the energy system can be driven by input (e.g., fuel
consumed by a particular technology) or output (e.g., fuel or service produced by a particular technology).
! Emissions information is technology-specific. As a result, different technologies that produce the same output can have different emissions per unit of activity.
! For most gases and species, we model drivers of emissions in detail. However, for some F-gases, the driver data (e.g., fire extinguishers) depends only on GDP.
Agriculture ! Emissions in the agricultural system can be driven by output (e.g., for
crop production) or land area (e.g., for open burning). ! Emissions information is crop and region specific in GCAM. However,
inventory data is region specific, but not crop specific (or AEZ specific).
GCAM Non-CO2 Emissions: Projections
115
Air Pollutant Emissions ! Projections currently use a global parameterization where emission
factors decline as a function of GDP per capita ! This species-specific parameterization captures the general trend of increasing pollutant
controls over time. ! This does not capture regional and technological heterogeneity. Future updates to this
are planned. ! Note that the GCAM implementation of the SSP scenarios used a different approach,
incorporating region, sector, and fuel specific pollutant emission factor pathways (Calvin et al 2016, Rao et al. 2016).
Non-CO2 GHG Emissions ! GHG emission factors only change due to MAC curves
! Where a MAC curve is present, the emissions factor changes in two ways • Below-zero (e.g. “no cost”) MAC mitigation (e.g. MAC reduction percentage is > 0 at
zero carbon price) are applied in the reference case. (can be turned off by setting no-zero-cost-reductions to 1 within a MAC curve).
• Under a carbon policy, the emission factor is reduced, as a function of the carbon price, as specified by the MAC curve.
Emissions: Fluorinated Gases
! Fluorinated gas emissions are linked either to the size of the industrial sector (e.g., semiconductors) or to GDP (e.g., fire extinguishers). As those drivers change, emissions will change. Additionally, we include abatement options based on the EPA’s most recent MAC curves.
! For HFC134a from cooling (e.g., air conditioners), we make additional adjustments to emissions factors in the developing regions to reflect their continued transition from CFCs to HFCs (see EPA report).
HFC134a Emissions
0
500
1000
1500
2000
2500
1990
2010
2020
2030
2040
2050
2060
2070
2080
2090
2100
Gg
HFC
134a
USA Taiwan Southeast Asia South Korea South Asia South America_Southern South America_Northern South Africa Russia Pakistan Middle East Mexico Japan Indonesia India European Free Trade Association Europe_Non_EU Europe_Eastern EU-15 EU-12 Colombia China Central Asia Central America and Caribbean Canada Brazil Australia_NZ Argentina Africa_Western Africa_Southern Africa_Northern Africa_Eastern
Emissions: Results
USA Methane Emissions USA Sulfur Emissions
0
10
20
30
40
50
60
70
80
1990
20
10 20
20 20
30 20
40 20
50 20
60 20
70 20
80 20
90 21
00
TgC
H4/
yr
Urban Processes
Industrial Processes
Industry
Transportation
Residential
Commercial
Refining
Hydrogen
Electricity
Energy Production
Forest/Savannah Burning
Other Crops
Rice
Livestock
0
5
10
15
20
25
30
1990
20
10 20
20 20
30 20
40 20
50 20
60 20
70 20
80 20
90 21
00
TgSO
2/yr
Urban Processes
Industrial Processes
Industry
Transportation
Residential
Commercial
Refining
Hydrogen
Electricity
Energy Production
Forest/Savannah Burning
Other Crops
Rice
Livestock
Emissions are produced at a region level (32 regions for energy, 283 regions for agriculture & land-use).
GCAM Non-CO2 Emissions: User Options
118
GHG Emissions ! Emissions prices of different GHGs can be linked together for a multi-gas
policy using the linked-ghg-policy object (for example, linked_ghg_policy.xml). The parameter price-adjust is used to convert prices (e.g., 100 year GWP) and demand-adjust is used to convert demand units (e.g., to common units of carbon equivalents). ! These can be changed by year if desired. ! Setting price-adjust to zero means that there is no economic feedback for the
price of this GHG. MAC curves, however, will still operate. This can be changed separately for energy/industrial/urban CH4, agricultural CH4 (CH4_AGR), and CH4 from agricultural waste burning (CH4_AWB), LUC CO2 emissions (e.g. CO2_LUC).
This flexibility allows CO2-only or CO2-equivalant markets/constraints for various “baskets” of emissions as needed.
New GCAM Non-CO2 User Options
119
These will be part of the next incremental release. ! MAC curves can be set for any emission species. (e.g., CH4-only market,
NOx market, etc.)
! Below zero MAC reductions phased in over several years (default 25 years, with optional user-defined time period).
! GHG objects can be added/changed via user input in any time period (currently GHG objects must first appear in 1975 and cannot be changed).
! MAC curves can be set to operate only before a specific vintage year (useful for combining pollutant emission MAC curves with new source standards).
! New linear-control object allows user to specify that an emission factor will go to a user-defined value over a specified time period.
CLIMATE
The Global Change Assessment Model
The Climate System: Approach
* GCAM includes more HFCs than are included in MAGICC. We map missing gases to those with similar lifetimes based on GWP. Meinshausen et al., 2011
! GCAM default climate module is now Hector with the option to run MAGICC5.3 ! Inputs:
! GCAM passes emissions to the climate model ! Fossil fuel & Industrial CO2, Land-Use Change CO2, CH4, N2O, SF6, C2F6, CF4,
HFC125, HFC134a, HFC143a, HFC227ea, HFC245fa*, SO2, CO, NOx, NMVOCs, BC, OC
! Outputs: ! MAGICC and Hector compute concentrations and radiative forcing ! Computes atmospheric CO2, temperature change, air-land/air-sea fluxes, SLR
Why develop a new simple climate model?
! MAGICC ! Used across many scientific and policy communities –
instrumental in the IPCC ! Many strengths ! Old code to work with ! Not open source, legal issues unclear
! Developed Hector ! Free and open-source – community model
! www.Github.com/JGCRI/hector ! Easy to use and well documented
! Hartin et al., 2015 - GMD ! Hartin et al., 2016 - BGS
Hector philosophy and structure
! Complexity only where warranted ! Modular
! Components can be enabled/disabled via inputs ! E.g. you can test two different ocean submodels against each
other
! Modern, clean structure ! E.g. coupler enforces unit checking between submodels
Hector: open and object –oriented architecture
! Initialization ! Input data are routed to
model components via the model core
! Spin up ! the carbon cycle is in
equilibrium before the main run starts
! Main run
Hector: open and object –oriented architecture
126
! Components have a defined interface (API)
! They register their dependencies and capabilities with the core ! e.g., sea level rise depends on
temperature ! Core orders components by their
dependencies ! Components query the core for
data ! Core routes request to
appropriate component SLR Core
My name is SLR and I provide sea
level rise data
Got it
I depend on mean global temperature
Okay
Hello
During initialization
SLRCoreX
I need current sea level rise
Hold onSomeone needs
sea level riseI warned you I'd
need temperature for this
Temperature
Yes, it's already been calculated…here
Temperature please
Here you go
Here's sea level rise
Here you are
As the model runs
; Config file for hector model: RCP4.5 [core] run_name=rcp45 startDate=1745 endDate=2100 do_spinup=1 ; if 1, spin up model before running (default=1) max_spinup=5000 ; maximum steps allowed for spinup (default=2000) [onelineocean] enabled=0 ; putting 'enabled=0' will disable any component ocean_c=38000, Pg C [ocean] enabled=1 ; putting 'enabled=0' will disable any component
spinup_chem=0 ; run surface chemistry during spinup phase? tt = 72000000 ; 7.2e7 thermohaline circulation, m3/s tu = 49000000 ; 4.9e7 high latitude overturning, m3/s twi = 12500000 ; 1.25e7 warm-intermediate exchange, m3/s tid = 200000000 ; 2.0e8 intermediate-deep exchange, m3/s k = 0.2 ; ocean heat uptake efficiency (W/m2/K) [simpleNbox] ; Initial (preindustrial) carbon pools atmos_c=588.071 ; Pg C in CO2, from Murakami (2010) veg_c=550 ; Pg C detritus_c=55 ; Pg C soil_c=1782 ; Pg C
Sample Input File
Initial values for the ocean and land
components
127
Hector: Science
Hector: Atmosphere
! Well mixed globally averaged atmosphere ! Forced with emissions from RCP scenarios
! CO2 – anthropogenic & LUC
! BC/OC ! CH4/N2O ! 26 halocarbons ! Sulphate aerosols ! Volcanic emissions
! Calculate: ! Stratospheric H2O ! Tropospheric O3
! Radiative forcing ! include both indirect and direct effects on radiative forcing
Science: Land
130
! A classic simple design: five boxes
! NPP, RH, litter fluxes scaled by global temperature and CO2
! Optional biomes – ex. Boreal and tropical
! Continual mass balance to check for ‘leaks’
Atmosphere
Earth
Vegetation
Detritus
Soil
LAND
Science: Ocean
131
! 4 boxes ! 2 surface boxes (100m) ! Intermediate box ! Deep box (~3777m)
! Advection and water mass exchange
! Heat uptake in surface boxes ! Carbon chemistry in surface
boxes (e.g., atmosphere-ocean flux, pH, CaCO3 saturations)
The Climate System: Results
Hartin et al., 2015 - GMD
1950 2000 2050 2100 1900 1850
Atmospheric CO2 – RCP8.5
Radiative Forcing
The Climate System: Results
1900 1950 2000 2050 2100 -2
0
1850
2
4
6
1900 1850 1950 2000 2050 2100 1900 1850
Atmospheric Temperature – RCP8.5
Ocean pH – RCP8.5
Energy
Water
Land
Socioeconomics
Climate – Hector + capabilities
GCAM integration of Hector
QUESTIONS?