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NSERC Strategic Grant Workshop 1
The University of Western Ontario
Research Team
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Research team
Systems modelling - engineering Prof. Slobodan P. Simonovic (Project Lead) Dr. Evan G. R. Davies Mr. Khaled Akhtar
Climate Policy Prof. Gordon A. McBean
Economics Prof. James B. Davies Prof. Karen A. Kopecky Prof. James C. MacGee Prof. John J. Whalley Ms. Andrea Sweny Mr. Jacob Wibe
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Workshop agenda
Introduction to research Prof. Slobodan P. Simonovic
Model presentation Dr. Evan G. R. Davies
NSERC strategic research grant project Prof. Slobodan P. Simonovic Prof. James B. Davies Prof. James C. MacGee
Open discussion Prof. Gordon A. McBean - moderator
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Research goals
Examine how climate change affects long-term sustainability
Provide a tool to policy-makers
Stress importance of feedbacks
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Methodology
System Dynamics (modelling)
Explicit modelling of feedbacks
For systems with dynamic complexity
Improves understanding of system behaviour
Models the most important processes
Focuses on understanding, not on prediction
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System dynamics modelling
A rigorous method of system description, which facilitates feedback analysis usually via a simulation model of the effects of alternative system structure and control policies on system behavior.
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System dynamics modelling
An approach for addressing complexity A practical tool for policy makers A worldview, a paradigm Complexity and system behavior are caused by system structure (causal relationships)
The feedback Closed loops - what does the structure look like? Operational solutions - what are loops composed of?
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System dynamics modelling
)()()( tqtutSdt
d−=
S
u q
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System dynamics modelling
Every decision is made within the feedback loop
system state decision
action
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System dynamics modelling
Feedback Processes:
Two kinds only Positive = reinforcing
Negative = balancing
But they combine …
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money
200,000
150,000
100,000
50,000
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0 10 20 30 40 50 60 70 80 90 100Time (Year)
State
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0 5 10 15 20 25 30 35 40 45 50Time (Second)
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Climate change modelling
The usual approach:
‘Drive’ complex model with emissions scenarios
The problem:
These systems are interdependent
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Climate change modelling
The reality:
Interaction between socio-economic and natural systems causes climate change
Interaction determines the entire system’s evolution
Climate Change Social Adaptation
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Model description
Outline
Description of full model
Model components
Model goals/philosophy
Model use
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Model structure Model components (8):
Carbon cycle
Climate
Water Quantity
Water Quality
Surface Flow
Population
Land Use
Economy
Clearing
and
Burning
Land Use
Emissions
+
CarbonCarbon
ClimateClimate
+
+
+
Land UseLand Use
+
+
Temperature
Atmospheric
CO2
Water
Stress
Industrial
Emissions
−
Surface Water
AvailabilityWater
Consumption
PopulationPopulation
EconomyEconomy
Surface FlowSurface Flow
Temperature
Consumption
and Labour
+
+ −
GDP
per
capita
+
Water QualityWater Quality
Water QuantityWater QuantityWastewater
Treatment
Wastewater
Reuse
Wastewater
Treatment and
Reuse
−
−
+
+
−
Carbon Absorption
Atmospheric [CO2]Temperature Change
Water Use
Wastewater treatment
and reuse
Water scarcityRenewable flow in
changing climatePopulation growth
= f(water scarcity)Biome coverage
Human actionGDP change
Carbon tax
Emissions
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Model characteristics
Number of Model Elements: 740 variables
‘Variables’: ~1600 (incl. arrays)
Constants: ~470 (incl. arrays)
230 Stocks (many in arrays)
2300 total
600 equations 99 major equations
Thousands of feedbacks Population: 4468 loops
Water stress: 2756 loops
Economic output: 203 loops
Industrial emissions: 47 loops
Sector # of VariablesCarbon: 130
Economy: 115Climate: 80Water Treatment: 50Water Demand: 45Hydro. Cycle: 45Land Use/Change: 15Population: 10
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Model sectors
1. Carbon Cycle
2. Climate System
3. Water Quantity
4. Water Quality
5. Surface Flow
6. Population
7. Land-use
8. Economy
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Carbon: [Mass]
Legend
Decomposition
Oceanic Absorption
NPP
Emissions
Litter Fall
Atmosphere
Biomass
Litter
Humus
Stable Humus
Deep Ocean
Emissions
Land Use
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Biomass
Litter
Humus
Stable Humus
and Charcoal
CO2 in Atmosphere
NPP
Litterfall
Decay
to
HumusDecayfromLitter
Decayfrom
Humus
Carbonization
HumificationDecayfrom
Charcoal
<Pjk>
<Tao(Bjk)>
<Lambda j>
<Tao(Lj)>
<Phi j>
<Tao(Hj)>
<Tao(Kj)>
<Sigma (NPPj)>
Unburnt
Wood
Biomassto
CharcoalLitter to
Charcoal
Burnt
BiomassBurntLitter
<Biomass to Atm>
<Burnt Biomass to
Charcoal>
<Dead biomass to
Humus>
<Litter to Atm>
<Litter Burnt into
Charcoal>
Internal Humus
Flows
<Internal Humus
Flows Calculation>
Internal Charcoal
Flows
CO2 in Mixed
Layer
CO2 in Deep
Ocean
Diffusion Flux
Th
Concentration
Edd
Mixe
Equil CO
Mixing Time
<Init CO2 in Mixed
Ocean>
<Init CO2 in Deep
Ocean>
Flux Atm to
Ocean
Turn On Human
Land Use
Atmospheric CO2
Concentration
Biome Area
<Current Biome
Area>
<Init Biome Area>
<Litter Q10>
<Humus Q10>
<Charcoal Q10>
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Sample of carbon equations
Atmosphere
Biomass
Net Primary Productivity
Root Decay
( ) dtFEBBNPPDDDDA OLBKHLB ⋅−+++−+++= ∫
( )∫ ⋅−−−−−= dtUBBFKFHFLNPPB jkjkBjkBjkBjkBjkjk
15101)( ×⋅⋅= jjjkjk SANPPpNPP σ
( )( )00 ln1)()( AANPPNPP jj βσσ +×=
)( 4
44
j
j
jB B
BFH
τ=
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Climate: [Heat]
Atmosphere
Mixed Layer
Ocean[h1]
Ocean[h20]
……
Solar Radiation
Forcing Space
Radiative Forcing
Solar Radiation
Longwave Radiation
Latent & Sensible Heat
Advective Heat
Diffusive Heat
Legend
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Water quantity: [Volume]
Total Withdrawals
Domestic Industrial Agricultural
Technology
Water Intensity Water Intensity Irrigated Area Efficiency
Population
GDP capita-1 Electricity
Reuse
Internal Factors
Feedbacks
Legend
Desalination Groundwater
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Water quality: [Volume]
Returnable Water
Polluted fraction
Wastewater Treated Percentage
Treated Wastewater
Untreated Wastewater
Treated Wastewater Reuse
Water Withdrawal
Water Stress
Available Surface Water
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Surface flow: [Volume]
Surface FlowMelt
Advection
Evaporation
Groundwater
Precipitation
Runoff
Legend
Terrestrial Atmosphere
Oceans
Marine Atmosphere
Groundwater
Ice
Land Surface
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Population sector: [People]
Population
Population Growth
Deceleration in
Population Growth
Water Stress
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Land-use sector: [Area]
Land Transfer[old][new]
Current Area[new]
Population Growth
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Economic sector: [Dollars]
Output
CapitalInvestment Population
Temperature Changetfp
Emissions Controls Carbon Tax
Legend
Internal Feedbacks
External Variables
Savings Rate
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Wastewater
Reuse
Water QualityWater Quality
Water QuantityWater Quantity
CarbonCarbon
ClimateClimate
Land UseLand Use
PopulationPopulation
EconomyEconomy
Surface FlowSurface Flow
Intersectoral feedbacks
Clearing
and
Burning
Land Use
Emissions
Temperature
Atmospheric
CO2
Water
Stress
Industrial
Emissions
Surface Water
AvailabilityWater
Consumption
Temperature
Consumption
and Labour
GDP
per
capita
Wastewater
Treatment
Wastewater
Treatment and
Reuse
Tie all of the sectors together to get…
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Key variables
Atmospheric CO2
Available surface water Biome areas CO2 emissions Economic output (GDP) Land use change Population Surface temperature Water withdrawals and consumption Water stress Wastewater treatment and reuse
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Model experimentation
Goals of simulation exercises
Model validation
Gain trust in model
Policy simulation
Identify basic model behaviours
Investigate causes of those behaviours
Identify key structures and feedbacks
Tie model behaviour to real world
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Model terminology
A policy is a prescription of alternative parameter values from the ‘business-as-usual’ case
Policy changes represent scenarios
Alternative simulations allow scenario analysis
Case 1) X = 1
Case 2) X = 3
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Scenario analysis example
Simulation Set-up
Set parameter values
Change one or more in 2 runs…
Regular run called “Base Case”
Alternative is the “Policy”
Example: Carbon Tax Change one parameter
Climate sensitivity = 4 W/m2
Oceanic parameters: w = m/yr, κ = 1890 m2/yrAtmosphere-Ocean mixing time = 1.5 yrCO2 fertilization (β) = 0.5NPP partition, P[j][k] = 6 x 4 matrixBiomass residence time, τ(Bjk) = 6 x 4 matrixDepreciation, δ(t) = 10%/yrSavings rate = prescribed annual valuesPrecipitation multiplier = 3.4%/ºCStable runoff percentage = 37%Annual irrigation expansion rate = prescribedWater Demand terms, DSWImin, ISWImin, γd, γi
And so on…
Climate sensitivity = 4 W/m2
Oceanic parameters: w = m/yr, κ = 1890 m2/yrAtmosphere-Ocean mixing time = 1.5 yrCO2 fertilization (β) = 0.5NPP partition, P[j][k] = 6 x 4 matrixBiomass residence time, τ(Bjk) = 6 x 4 matrixDepreciation, δ(t) = 10%/yr
Case Selector = 0,1Savings rate = prescribed annual valuesPrecipitation multiplier = 3.4%/ºCStable runoff percentage = 37%Annual irrigation expansion rate = prescribedWater Demand terms, DSWImin, ISWImin, γd, γi
And so on…
Carbon Tax
80
0
1960 1988 2016 2044 2072 2100
Time (Year)
Carbon Tax : Base $/kton
Carbon Tax : Optimal Tax $/kton
Effect
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Scenario analysis example
Industrial Carbon Emissions E(t)
13.98
8.226
2.469
1960 1988 2016 2044 2072 2100
Time (Year)
"Industrial Carbon Emissions E(t)" : Base Gt C/Year"Industrial Carbon Emissions E(t)" : Optimal Tax Gt C/Year
Output Q(t)
96.95
51.19
5.446
1960 1995 2030 2065 2100
Time (Year)
"Output Q(t)" : Base trillion $/Year"Output Q(t)" : Optimal Tax trillion $/Year
Direct Results: GDP and Emissions Biophysical results: Temperature and CO2
Surface Temperature Change
1.599
0.7995
0
1960 1988 2016 2044 2072 2100
Time (Year)
Surface Temperature Change : Base CelsiusSurface Temperature Change : Optimal Tax Celsius
Atmospheric CO2 Concentration
623.52
465.92
308.32
1960 1988 2016 2044 2072 2100
Time (Year)
Atmospheric CO2 Concentration : Base ppmvAtmospheric CO2 Concentration : Optimal Tax ppmv
Base Scenario vs. Optimal Tax in 2100
Abatement Cost = $150 BEnvironmental Benefit = $82 BTotal Economic Cost = $68 B
Averted Temperature Change: 0.14°C
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Other scenarios
Also assess effects of policies related to
Water treatment levels
Land-use change
Wastewater reuse
Or, biophysical uncertainties
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Other scenarios
Investigation One
Investigation Two
Climate Sector Sensitivity
Irrigation Area Sensitivity
Wastewater and Land-use
Irrigation, Fertilization,
and Climate Sensitivity
Carbon Tax Policy
Physical Change
1. Monte Carlo One
2. Monte Carlo Two
3. Reduced WastewaterTreatment and Reuse
4. Reduced Land-use
5. Increased Irrigation Area
6. Increased CO2-Fertilization
7. Increased Radiative Forcing
8. Optimal Carbon Tax
9. Temperature Limit Tax
10. Ramp Tax
11. Low Change
12. High Change
Sensitivity Analysis
‘What If’
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Other scenarios
Novel Findings
Novelty of Approach
Combined Policies
Water Stress Calculations
Water Stress and Population
Alternative Population Drivers
Wastewater Treatment and Reuse
Land-use Policies
13. Traditional wta
14. Novel wta
13. Traditional wta (repeat)
15. Novel wta (ε = 0.0245)
16. GDP per Capita Ratio
17. Output Ratio
18. No Reuse
19. No Treatment, No Reuse
20. Conservation
21. Exploitation
22. Plunder
23. Environment-first
24. Increased Realism
Base Run Only
Extreme Policies
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NSERC strategic research grant
Objectives1. Improve climate-relevant biophysical model components
2. Develop and incorporate into model any missing socio-economic components relevant to climate change
3. Couple modified biophysical and new socio-economic model components
4. Develop framework for communication between science and policy communities
5. Implement model to examine effects of climate change on socio-economic and environmental sustainability
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Tasks1. Evaluation of appropriate temporal and spatial
scales for biophysical sectors2. Expansion of economic sector, addition of
energy sector Identification of critical feedbacks Identification of suitable temporal and spatial scales Approaches to deal with scale mismatches
3. Representation of critical feedbacks4. Selection of policy-relevant variables5. Simulation of policy options
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Additions Energy sector
Electricity generation is fossil-fuel intensive Transportation is oil-dependent
Regionalization From global aggregation to national blocs Configuration of blocs already determined
Modifications Population Land use Economy
Nations/Blocs (12)• Canada• US• EU• Former USSR & E. Europe• China• Latin America• N. Africa and Middle East• Sub-Saharan Africa• Indian Subcontinent• Japan & Asian Tigers• SE Asia• Oceania
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Economists’ role in project
Assist in development of regional model of world economy, including energy sector and other socio-economic elements, to provide inputs for physical modeling
Model Canada as a separate region, with appropriate detail and policy analysis
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The Canadian region
An example of what economists will contribute to project
First step: model Canada as a small open economy, with appropriate detail and policy analysis
Later: include Canada as a region in global model
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Canada: key questions
i. What are costs and benefits of different emission reduction targets for Canada?
ii. What carbon prices are required to achieve different emission path targets?
iii. Implications of immigration and population growth
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Additional issues
Trade leakage and carbon pricing
Implications of energy price volatility for
optimal regulation/pricing
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What is special about Canada?
Compared to other OECD countries, Canada is:
More energy intensive
More open to immigration
Net exporter of energy
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Energy intensity
Energy Intensity: BTU per 2000 US $ (PPP)
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
20,000
1980 1985 1990 1995 2000 2005
Canada
US
Australia
Norway
UK
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Population growth rates
Population Growth Rates
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
AUS
CAN
OEC
NOR
USA
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Static CGE models used to examine impacts of climate policy on Canada:
Hamilton and Cameron (1994), Jaccard and Montgomery (1996), ab Iorwerth et al. (2000), Dissou (2005), Wigleand Snoddon (2007), Boehringer and Rutherford (2008)
Sectoral models:
Jaccard and Montgomery (1996), Jaccard et al. (2000), Loulou et al. (2000) Jaccard and Rivers (2007)
Various other papers, less quantitative.
Selected literature: Canada
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Benchmark model: Canada
Start with small open economy version of Nordhaus (2007)
Exogenous growth model augmented to include GHG emission and climate damages
Small open economy means Return on capital fixed at 4 %
Path of CO2 exogenous: take projections from (more sophisticated) climate projections
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Benchmark model: Canada
Calibrate model parameters to Canada
Calibration of damages and abatements costs key task.
Extension: add energy sector
World energy prices exogenous
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Production
Output Yt produced using labour Lt and capital Kt
Damage coefficient: Ωt
Emissions control rate: Λt
αα −Λ−Ω= 1)1( tttttt LKAY
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Damage coefficient: Ωt
Calibration of damage function key element
Nordhaus (2007) models damage as quadratic in temperature
2
,2,11
1
tttt
tTT θθ ++
=Ω
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Carbon prices and emissions
Use calibrated version of model to
address:
What carbon prices are required to “hit”various emission targets?
What would the impact be on per capita GDP?
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Emissions control rate: Λt
Cost of controlling GHG emissions other key calibration
2
,1
ψµψ ttt =Λ
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Extension: energy sector
Distinguish major components; calibrate abatement costs separately; take vintage structure of capital into account
Investigate effects of volatility in world energy prices - on national income, energy output, emissions, and optimal carbon regulation/pricing
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Extension: population
Use calibrated version of model to explore effect of alternative population growth rates for cost of alternative CO2 emission targets
Policy question: Should immigrant receiving countries receive additional “emission credits” if they accept immigrants from countries worst hit by global warming?
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Extension: trade
Potentially important issue with carbon regulation/pricing is “leakages” via trade
Regulation/pricing in Canada may lead carbon intensive industries to relocate to countries with low (no) carbon prices and export back to Canada
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Discussion
Which policy issues related to global climate change do you see as key to Canadian policy studies and how can the model outputs that you think we could generate be of increased value to you?
Do you have concerns about features of existing climate/economy models that perhaps limit their value for Canadian policy analysis? Are some policy issues generally underrepresented in these studies?