nserc strategic grant workshop 1 the university of western … · 2020-06-10 · treatment...

NSERC Strategic Grant Workshop 1

The University of Western Ontario

Research Team


2Simonovic August 18, 2008

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



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



Research goals

Examine how climate change affects long-term sustainability

Provide a tool to policy-makers

Stress importance of feedbacks



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



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.




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?




)()()( tqtutSdt

d−=

S

u q




Every decision is made within the feedback loop

system state decision

action




Feedback Processes:

Two kinds only Positive = reinforcing

Negative = balancing

But they combine …

1

1

1

1 1 1

1

1

1

money

200,000

150,000

100,000

50,000

0

0 10 20 30 40 50 60 70 80 90 100Time (Year)

State

100

75

50

25

0

0 5 10 15 20 25 30 35 40 45 50Time (Second)



Climate change modelling

The usual approach:

‘Drive’ complex model with emissions scenarios

The problem:

These systems are interdependent



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


13Davies August 18, 2008

Model description

Outline

Description of full model

Model components

Model goals/philosophy

Model use



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



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



Model sectors

1. Carbon Cycle

2. Climate System

3. Water Quantity

4. Water Quality

5. Surface Flow

6. Population

7. Land-use

8. Economy



Carbon: [Mass]

Legend

Decomposition

Oceanic Absorption

NPP

Emissions

Litter Fall

Atmosphere

Biomass

Litter

Humus

Stable Humus

Deep Ocean

Emissions

Land Use



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>



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=



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



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



Water quality: [Volume]

Returnable Water

Polluted fraction

Wastewater Treated Percentage

Treated Wastewater

Untreated Wastewater

Treated Wastewater Reuse

Water Withdrawal

Water Stress

Available Surface Water



Surface flow: [Volume]

Surface FlowMelt

Advection

Evaporation

Groundwater

Precipitation

Runoff

Legend

Terrestrial Atmosphere

Oceans

Marine Atmosphere

Groundwater

Ice

Land Surface



Population sector: [People]

Population

Population Growth

Deceleration in

Population Growth

Water Stress



Land-use sector: [Area]

Land Transfer[old][new]

Current Area[new]

Population Growth



Economic sector: [Dollars]

Output

CapitalInvestment Population

Temperature Changetfp

Emissions Controls Carbon Tax

Legend

Internal Feedbacks

External Variables

Savings Rate



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…



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



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



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



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



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



Other scenarios

Also assess effects of policies related to

Water treatment levels

Land-use change

Wastewater reuse

Or, biophysical uncertainties



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’



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



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




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




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

39Davies and MacGee August 18, 2008


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

39



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

40



41

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



Additional issues

Trade leakage and carbon pricing

Implications of energy price volatility for

optimal regulation/pricing

42



What is special about Canada?

Compared to other OECD countries, Canada is:

More energy intensive

More open to immigration

Net exporter of energy



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



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



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



47

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



48

Benchmark model: Canada

Calibrate model parameters to Canada

Calibration of damages and abatements costs key task.

Extension: add energy sector

World energy prices exogenous



Production

Output Yt produced using labour Lt and capital Kt

Damage coefficient: Ωt

Emissions control rate: Λt

αα −Λ−Ω= 1)1( tttttt LKAY



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



51

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?



Emissions control rate: Λt

Cost of controlling GHG emissions other key calibration

2

,1

ψµψ ttt =Λ



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

53



54

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?



55

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


McBean August 18, 2008 56

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?

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