Download Integrated Assessment Modelling and Analysis Study Tour Workshop, Canberra 12

Integrated Assessment Modelling and Analysis
David Newth, Don Gunasekera and YiYong Cai
Study Tour Workshop, Canberra
12th September 2013
INTEGRATED GLOBAL MODELLING AND ANALYSIS
Our vision
“To understand the net socio-economicenvironmental benefits of action on
global-change issues”
David Newth| Integrated Global Modelling and Analysis | Page 2
Purpose of IAMs
• IAMs treat biophysical, social and economic processes in
a single platform to:
– Capture feedbacks between natural and human systems
– Generate policy-relevant information,
– Our IAMs, GIAM and NIAM, have a modular structure and so
can use different biophysical models, eg. SCCM or ACCESS
– They can assess regional and global climate processes and
their impacts on:
–
–
–
–
–
crop productivity,
labour productivity,
human health,
food and energy security
Bio-security
3 | Integrated Assessment Modelling and Analysis | David Newth, Don Gunasekera and YiYong Cai
Emissions
Pathway
Forcing,
Concentrations
Emissions, and
Land use
Socio-economic
Pathways
Earth System
Simulations
Emissions drivers
Mitigative capacity,
Exposure, Risks,
Shocks, and
Adaptive capacity
Climate Change
and
Climate variability
Integrated
Analysis
Mitigation,
Adaptation and
Impacts
4 | Integrated Assessment Modelling and Analysis | David Newth, Don Gunasekera and YiYong Cai
Increasing Science Capabilities
Science Capacity
• Understand global and regional
consequences and drivers of
change;
• Describe Energy-Water-Carbon
interactions;
• Build collaborations with key
groups within and beyond ICP;
• Explore alternative frameworks
and methodologies
• Identify and assemble key
elements
• Topical modelling modules
• First generation IAM
• Define integrated needs
• Identify knowledge and data gaps
• Expand “customer” requirements
• Transition to coupled analysis
• Reverse engineering of existing tools
• Modelling of nonlinearities and
tipping points in HES
• First generation coupling framework
• Coupled model formalisation
• System properties and phenomena
• Coupled IAM-ESM Modelling
• Coupled System Studies
• Climate-economy links
• Human-climate-ecosystem
links
• Coupled Analysis
• Delivery into ACCESS
• Global and regional IAM
• Combined energy, environment
carbon, water security analysis
• National, regional and global
analysis
global change issues
• New Economic Model
• Fuse NIAM and GIAM
• Water Availability Model
• Population demographics
• Land-Surface Scheme
• Anthropogenic LUC
• HPC framework
• Next generation IAM tools.
• Uncertainty analysis
•Science translation to risk analysis
and economics
•Integrated decision science
• Integrated Research
• Agile modelling and analysis
framework
• Coupled policy analysis
• Socio-Economic-Integrated
Policy Intelligence
• Unified Modelling Environment
• Fuse NIAM and GIAM
• Water Availability Model
• Land-Surface Scheme
• Anthropogenic LUC
• HPC framework
• Next generation IAM
Increasing Integration and Functionality
5 | Integrated Assessment Modelling and Analysis | David Newth, Don Gunasekera and YiYong Cai
Global Integrated Assessment Model
Global Trade and
Environment Model
Economic Impacts
Regional and sectoral
coverage, tax and
trade, emissions,
technological
innovation
Climate Economy
Interactions
Sector specific
impacts, rare events,
human health,
infrastructure
Simple Carbon
Climate Model
Emissions
Change in
temperature and
precipitation
6 | Integrated Assessment Modelling and Analysis | David Newth, Don Gunasekera and YiYong Cai
Change in
Environmental
Variables
Topics
•
•
•
•
•
•
Energy and Emissions Scenarios
Heat Stress and Labour Productivity
Food Security in S.E. Asia
Foreign Direct Investment in Africa
Vector Bourne Diseases
Air Pollution
7 | Integrated Assessment Modelling and Analysis | David Newth, Don Gunasekera and YiYong Cai
Energy and Emissions Scenarios
Integrated
Assessment Modelling and Analysis | Don Gunasekera and David Newth
8 |
RCP Experiment
GCM
GCM Output
Output
Climate-Economy
Climate-Economy
Interaction
Interaction
Global
Global Trade
Trade and
and
Policy
Policy Database
Database
Economic
Economic Loss
Loss
Factors
Factors
Global
Global Trade
Trade and
and
Environment
Environment Model
Model
RCP
RCP Emission
Emission
Constraints
Constraints
Economic
Economic and
and
Welfare
Outcomes
Welfare Outcomes
9 | Integrated Assessment Modelling and Analysis | David Newth, Don Gunasekera and YiYong Cai
Regional
Regional Economic
Economic
Growth
Growth
RCP Experiment
GCM
GCM Output
Output
Climate-Economy
Climate-Economy
Interaction
Interaction
Global
Global Trade
Trade and
and
Policy
Policy Database
Database
Economic
Economic Loss
Loss
Factors
Factors
Global
Global Trade
Trade and
and
Environment
Environment Model
Model
RCP
RCP Emission
Emission
Constraints
Constraints
Economic
Economic and
and
Welfare
Outcomes
Welfare Outcomes
10 | Integrated Assessment Modelling and Analysis | David Newth, Don Gunasekera and YiYong Cai
Regional
Regional Economic
Economic
Growth
Growth
RCP Emission Constraints
11 | Integrated Assessment Modelling and Analysis | Don Gunasekera and David Newth
Energy Technology Bundle
Coal
Nuclear
Oil
Hydro &
Geothermal
Gas
Wind
Solar
Biomass
12 | Integrated Assessment Modelling and Analysis | Don Gunasekera and David Newth
Waste
Coal with Carbon
Capture and Storage
Other
Renewable
Gas with Carbon
Capture and Storage
Bio-mass/fuel with
Carbon Capture and Storage
Regional Coverage
13 | Integrated Assessment Modelling and Analysis | Don Gunasekera and David Newth
Regional Coverage
14 | Climate change mitigation under alternative RCPs | David Newth
Energy use of the RCPs
Global Electricity Generation by Energy Source
(R CP 4.5)
Global Electricity Generation by Energy Source
(RCP 8.5)
100
Gas CCS
90
Coal CCS
80
Other Renewables
70
Waste
60
Biomass
H
50
W
T
40
Solar
Wind
30
Hydro & Geothermal
20
Nuclear
10
Gas
0
Oil
0
1
0
2
5
1
0
2
0
2
0
2
5
2
0
2
0
3
0
2
5
3
0
2
0
4
0
2
5
4
0
2
0
5
0
2
5
5
0
2
0
6
0
2
5
6
0
2
0
7
0
2
5
7
0
2
0
8
0
2
5
8
0
2
0
9
0
2
5
9
0
2
0
0
1
2
350
Gas CCS
300
Coal CCS
O the r Re newables
250
W aste
Biomass
200
H
W
T
150
Solar
W ind
Hydro & Geothe rmal
100
Nuc le ar
50
0
Coal
Global Electricity Generation by Energy Source
(RCP 6)
Gas
O il
0
1
0
2
5
1
0
2
0
2
0
2
5
2
0
2
0
3
0
2
5
3
0
2
0
4
0
2
5
4
0
2
0
5
0
2
5
5
0
2
0
6
0
2
5
6
0
2
0
7
0
2
5
7
0
2
0
8
0
2
5
8
0
2
0
9
0
2
5
9
0
2
0
0
1
2
Global Electricity Generation by Energy Source
(R CP 2.6)
450
120
Gas CCS
Coal CCS
100
80
O the r Re newables
W aste
300
W aste
W ind
Hydro & Geothe rmal
Nuc le ar
20
Gas
O il
0
1
0
2
5
1
0
2
0
2
0
2
5
2
0
2
0
3
0
2
5
3
0
2
0
4
0
2
5
4
0
2
0
5
0
2
5
5
0
2
0
6
0
2
5
6
0
2
0
7
0
2
5
7
0
2
0
8
0
2
5
8
0
2
0
9
0
2
5
9
0
2
0
0
1
2
Coal CCS
350
Solar
40
Gas CCS
400
O the r Re newables
Biomass
H
60
W
T
0
Coal
Coal
15 | Climate change mitigation under alternative RCPs | David Newth
Biomass
H2 5 0
W
T2 0 0
Solar
W ind
150
Hydro & Geothe rmal
100
Nuc le ar
Gas
50
0
O il
0
1
0
2
5
1
0
2
0
2
0
2
5
2
0
2
0
3
0
2
5
3
0
2
0
4
0
2
5
4
0
2
0
5
0
2
5
5
0
2
0
6
0
2
5
6
0
2
0
7
0
2
5
7
0
2
0
8
0
2
5
8
0
2
0
9
0
2
5
9
0
2
0
0
1
2
Coal
Energy Sector Transition
Thousand barrels per day
16 | Climate change mitigation under alternative RCPs | David Newth
Energy Technology Bundle
Global Electricity Generation by Energy Source
(RCP 8.5)
100
Gas CCS
90
Coal CCS
80
Other Renewables
70
W aste
60
Biomass
H
50
W
T
40
Solar
W ind
30
Hydro & Geothermal
20
Nuclear
10
Gas
0
Oil
0
1
0
2
5
1
0
2
0
2
0
2
5
2
0
2
0
3
0
2
5
3
0
2
0
4
0
2
5
4
0
2
17 | Climate change mitigation under alternative RCPs | David Newth
0
5
0
2
5
5
0
2
0
6
0
2
5
6
0
2
0
7
0
2
5
7
0
2
0
8
0
2
5
8
0
2
0
9
0
2
5
9
0
2
0
0
1
2
Coal
Energy Technology Bundle
Global Electricity Generation by Energy Source
(R CP 4.5)
350
Gas CCS
300
Coal CCS
O th e r Re ne wable s
250
W aste
Biomass
200
H
W
T
150
Solar
W ind
Hydro & Ge o the rmal
100
Nuc le ar
50
0
Gas
O il
0
1
0
2
5
1
0
2
0
2
0
2
5
2
0
2
0
3
0
2
5
3
0
2
0
4
0
2
5
4
0
2
0
5
0
2
18 | Climate change mitigation under alternative RCPs | David Newth
5
5
0
2
0
6
0
2
5
6
0
2
0
7
0
2
5
7
0
2
0
8
0
2
5
8
0
2
0
9
0
2
5
9
0
2
0
0
1
2
Coal
World Economic Growth
19 | Climate change mitigation under alternative RCPs | David Newth
World Economic Growth – Cost of Mitigation
20 | Climate change mitigation under alternative RCPs | David Newth
“Push” on Key Energy Technology
Solar and Wind
Widespread preference for wind and solar over other renewable
technology.
Carbon Capture and Storage
Reduced barries to entry. High technological viability. Early adoption.
Nuclear
Widespread usage. Rapid transition to nuclear from dirty technology.
21 | Climate change mitigation under alternative RCPs | David Newth
Heat Stress and Labour Productivity
Integrated
Assessment Modelling and Analysis | Don Gunasekera and David Newth
22 |
Example-Climate Change and Labour
Productivity
• Inspired by Dunne et al (2013) in Nature Clim. Change (24.02.13)
• Link between change in WBGT and Labour Productivity
• Can include additional factors such as indoor/outdoor/PPE
• What is missing:
– Implications for labour intensive sectors
– Economic effects on regional economies
• GIAM has a capacity to study shifts in labour use and productivity
in different sectors
• Provides a platform to assess economic welfare and adaptation
strategies
23 | Integrated Assessment Modelling and Analysis | David Newth, Don Gunasekera and YiYong Cai
Wet Bulb Globe Temperature
24 | Integrated Assessment Modelling and Analysis | David Newth, Don Gunasekera and YiYong Cai
RCP Emission Constraints
25 | Integrated Assessment Modelling and Analysis | David Newth, Don Gunasekera and YiYong Cai
Ten-year maximum monthly mean WBGT
ACCESS1.0 2001-2010
ACCESS1.0 RCP4.5 2091-2100
26 | Integrated Assessment Modelling and Analysis | Don Gunasekera and David Newth
ACCESS1.0 RCP8.5 2091-2100
Labour Capacity
27 | Integrated Assessment Modelling and Analysis | Don Gunasekera and David Newth
GDP
28 | Integrated Assessment Modelling and Analysis | Don Gunasekera and David Newth
Damages Avoided
29 | Integrated Assessment Modelling and Analysis | Don Gunasekera and David Newth
China
30 | Integrated Assessment Modelling and Analysis | Don Gunasekera and David Newth
Food Security in S.E. Asia
Integrated
Assessment Modelling and Analysis | Don Gunasekera and David Newth
31 |
Outline
• 1. Policy background of climate change and food security
• 2. Details of the integrated assessment framework
• Climate change database
• Crop yield database, planting calendar
• Econometric model for projection of future crop land productivity
• 3. Simulation results of land productivity, production,
consumption and GDP per capita
32 | Integrated Assessment Modelling and Analysis | David Newth, Don Gunasekera and YiYong Cai
Food Security under Climate Change
• Global climate change is real and is accelerating.
• Agriculture, mainly food production, is vulnerable.
• Even without GCC, food demand is predicted to increase by 300%.
• Population and economic growth
• Increasing demand from bio-fuel
• Climate change will be a “multiplier” of existing threats.
33 | Integrated Assessment Modelling and Analysis | David Newth, Don Gunasekera and YiYong Cai
Historical Review
droughts
oil price spike (fertiliser)
bio-fuel (DC)
rising food demand (LDC)
34 | Food Security in South Asia | Yiyong Cai
Food Security in South Asia
• South Asia is even more vulnerable to climate change.
• 70% population living in rural areas, and relying on agriculture for livelihood
• weak economic infrastructure to resist food price shocks
• rapid urbanization and industrialization
• tropical and subtropical
• monsoon…
35 | Food Security| YiYong Cai
Further Impacts of Climate Change
• Crisis can spread to entire economy through rising food cost.
• Crisis can change production and consumption patterns.
• Not all countries are equally impacted.
• Food production moves from less productive regions to less productive.
• Food consumption shifts from less affordable crops to more affordable.
• It requires an integrated assessment approach.
36 | Food Security| YiYong Cai
An Integrated Assessment Modelling Framework
IPCC’
IPCC’s RCP8.5
GHG emissions
CMIP5 Database
FAO database of
global crop yields,
19611961-2007
Sacks et. al global
crop calendar
database
Econometric model of crop yield
responses to temperature and
precipitation changes by region
Alternative projections of future
land productivity shocks for 5
major crops by region
G-Dyn simulation of
production for 3 staple crop
sectors by region
37 | Food Security| YiYong Cai
CRU 3.1 database
of temperature and
precipitation
UN projection
for future
population by
region, medium
variant
Regional Coverage
• In total, 11 non-contiguous regions of the world
• In particular, 7 regions of Asia, including five individual countries
(Bangladesh, India, Nepal, Pakistan, and Sri Lanka) and two
campsite regions (North and East Asia, and Rest of South Asia)
• Additionally, 4 composite regions representing major regions of
the rest of the world (Oceania, North and South America, Europe
and the former Soviet Union, and Africa and the Middle-East)
38 | Food Security| YiYong Cai
Sectoral Coverage
• In total, 25 traded goods
sectors
• 5 agricultural sectors of
major food crops, namely,
rice, wheat, coarse grains,
oilseeds, and sugar
cane/beet
39 | Food Security| YiYong Cai
Sector Code
Pdr
Wht
Gro
v_f
Osd
c_b
Pfb
Ocr
Ctl
Oap
Rmk
Wol
frs
fsh
Mine
Cmt
Omt
Vol
Mil
Pcr
Sgr
Ofd
b_t
Mnfc
Serv
Sector Description
paddy rice
wheat
cereal grains nec
vegetables, fruit, nuts
oil seeds
sugar cane, sugar beet
cotton, plant-based fibers
crops nec
bovine cattle, sheep and goats, horses
animal products nec
raw milk
wool, silk-worm cocoons
forestry
fishing
mining industries
bovine cattle, sheep and goat meat products
meat products
vegetable oils and fats
dairy products
processed rice
sugar
food products nec
beverages and tobacco products
Manufacturing
Services
Crop Coverage
• 3 staple crops: rice, wheat and maize
• 2 cash crops: oilseeds and sugar cane/beet
• Beet, rather than cane, due to data constraint
GDyn SECTOR
RICE
WHEAT
CEREAL
OILSEEDS
SUGAR CANE/BEET
40 | Food Security| YiYong Cai
FAO CROP
RICE
WHEAT
MAIZE
GROUNDNUTS
BEET
Planting Calendar CROP
RICE
WHEAT
MAIZE
GROUNDNUTS
BEET
Future Carbon Pathway
• We assume global GHG concentration follows RCP8.5.
• RCP8.5 is leading to 8.5 W/m2 radiative forcing in 2100.
• RCP8.5 assumes no active mitigation action.
• This is not an unreasonable assumption for 2007 to 2030.
41 | Food Security| YiYong Cai
Future Projections for Climate under RCP8.5
• We use CMIP5 database for future climate change (83 in total).
Model
ACCESS1-0
ACCESS1-3
bcc-csm1-1
bcc-csm1-1-m
CanESM2
CanESM2
CanESM2
CanESM2
CanESM2
CCSM4
CCSM4
CCSM4
CCSM4
CCSM4
CESM1-BGC
A lot more…
42 | Food Security| YiYong Cai
Experiment
r1i1p1
r1i1p1
r1i1p1
r1i1p1
r1i1p1
r2i1p1
r3i1p1
r4i1p1
r5i1p1
r1i1p1
r2i1p1
r3i1p1
r4i1p1
r5i1p1
r1i1p1
Model
CESM1-CAM5
CESM1-CAM5
CESM1-CAM5
CESM1-WACCM
CMCC-CESM
CMCC-CM
CMCC-CMS
CNRM-CM5
CNRM-CM5
CNRM-CM5
CNRM-CM5
CNRM-CM5
CSIRO-Mk3-6-0
CSIRO-Mk3-6-0
CSIRO-Mk3-6-0
Experiment
r1i1p1
r2i1p1
r3i1p1
r2i1p1
r1i1p1
r1i1p1
r1i1p1
r10i1p1
r1i1p1
r2i1p1
r4i1p1
r6i1p1
r10i1p1
r1i1p1
r2i1p1
Historical Regression of Crop Productions
• We estimate a cubic crop yield (output per unit of land) model
• regress rice, wheat, maize, ground nuts, and sugar beet data from FAO
• on temperature and rainfall data from CRU 3.1, from 1961 to 2007
• aggregated by global crop planting dates from Sacks et. al (2010)
 Y 
2
3
2
3
log i ,t  = β iT,1∆Tt + β iT, 2 (∆Tt ) + β iT,3 (∆Tt ) + β iP,1∆Pt + β iP, 2 (∆Pt ) + β iP, 3 (∆Pt )
 Yi ,t −1 
+ β iI,1∆Tt ∆Pt + β iI, 2 (∆Tt ) ∆Pt + β iI,3 (∆Pt ) ∆Tt + ci
2
43 | Food Security| YiYong Cai
2
Projections for Future Land Productivities
• We plug future climate projections into the econometric
model, and generate 83 trajectories of land productivities.
• We bound the annual projections from above by 30% and
from below by -30%, to ensure regularity.
• We rank them by cumulative impact, and only pick the
highest, medium and lowest trajectories to simply analysis.
44 | Food Security| YiYong Cai
Food Production (Rice)
Climate change induced competitive advantage: Bangladesh and Pakistan produce more
rice than they would otherwise do with high and medium land productivity.
45 | Food Security| YiYong Cai
Foreign Direct Investment in Africa
Integrated
Assessment Modelling and Analysis | Don Gunasekera and David Newth
46 |
FDI in African agriculture
• Agriculture FDI accounts for less than 5 per cent of
overall FDI in Africa
• FDI has grown on average by 17 per cent during 2003-10
period showing an upward trend
• Considerable scope for raising agricultural productivity in
currently cultivated land in Africa
• African agriculture could play a potentially important
role in meeting the growing global food demand as well
as reducing local poverty.
47 | Integrated Assessment Modelling and Analysis | David Newth, Don Gunasekera and YiYong Cai
Development funding sources/gaps in Africa
Source: Benin et al (2012)
48 | Integrated Assessment Modelling and Analysis | David Newth, Don Gunasekera and YiYong Cai
Yield gaps and land use in Africa
Source: Deininger et al (2011)
49 | Integrated Assessment Modelling and Analysis | David Newth, Don Gunasekera and YiYong Cai
Modelling framework
• GDyn : recursively dynamic global CGE model
• Calibrated to GTAP data base version 8
• Aggregated into 7 regions and 16 sectors
• Simulation period of 2007-30
50 | Integrated Assessment Modelling and Analysis | David Newth, Don Gunasekera and YiYong Cai
African map
51 | Integrated Assessment Modelling and Analysis | David Newth, Don Gunasekera and YiYong Cai
Scenario analysis
1. Reference case (baseline) scenario:
• land productivity of all food and cotton sectors and FDI in the five African
regions are assumed to increase at an average annual rate of 0.8 per cent
and 3.0 percent respectively based on recent trends
2. Land productivity growth scenario:
• land productivity of all food and cotton sectors in the five African regions is
assumed to increase at an annual average rate of 1.2 percent (50% ↑ CUM)
3. FDI growth scenario:
• FDI in the five African regions is assumed to increase at an annual average
rate of 10 percent (over 200% ↑ CUM)
4. Combined scenario: scenarios 2 plus 3
52 | Integrated Assessment Modelling and Analysis | David Newth, Don Gunasekera and YiYong Cai
Sectoral Output
53 | Integrated Assessment Modelling and Analysis | Don Gunasekera and David Newth
Percentage change in exports relative to Scenario 1
54 | Integrated Assessment Modelling and Analysis | Don Gunasekera and David Newth
Implications
1. The economic impact of land productivity improvement is minor
compared to that of foreign investment.
2. Growth of African agriculture does not necessarily improve local
food security, although it stimulates exports and GDP.
3. Who enjoys the harvest of Africa? Local poverty reduction and
food security?
4. How to extend the technological consequence of foreign
investment from land to labor and capital?
5. How to “translate” economic boom into local development?
55 | Integrated Assessment Modelling and Analysis | David Newth, Don Gunasekera and YiYong Cai
Vector Bourne Diseases
Integrated
Assessment Modelling and Analysis | Don Gunasekera and David Newth
56 |
Overview
• Climate change induced dengue spread
• Illustrative scenarios
• Baseline scenario
• Risk management scenarios
• EpiCast - Australia model
• Results and policy implications
57 | Integrated Assessment Modelling and Analysis | David Newth, Don Gunasekera and YiYong Cai
Climate variables affecting dengue
• Relative humidity
• Survival of Aedes aegypti influenced by moisture and
humidity levels
• Ambient temperature
• Ambient temperature influences the life cycle rate
and geographic distribution of Aedes aegypti (latitude
and altitude)
Source: Bambrick et al (2008), Beebe et al (2009) and Kearney et
al (2009)
58 | Integrated Assessment Modelling and Analysis | David Newth, Don Gunasekera and YiYong Cai
Climate change induced dengue spread
• Several recent studies highlight:
• Potential expansion of Aedes aegypti due to climate
change
• Possible extra risk due to adaptation to climate
change (e.g. inappropriate use of rainwater tanks)
• Focus on two key factors:
• Impact of regional warming
• Effect of rainwater tanks and unsecured water
containers
59 | Integrated Assessment Modelling and Analysis | David Newth, Don Gunasekera and YiYong Cai
Baseline scenario (reference case)
• Corresponds to:
• SRES Marker scenario A1B
• 1°C rise by 2030
• Represents the effects of:
• Projected spread of Ae. aegypti to northern,
eastern and southeast Australia by 2030 due to
climate change
• Estimated uptake of rainwater tanks by 2030
• No mitigation strategies
60 | Integrated Assessment Modelling and Analysis | David Newth, Don Gunasekera and YiYong Cai
Projected distribution of Aedes aegypti due to climate change in
2030
Source: Beebe et al (2009)
61 | Integrated Assessment Modelling and Analysis | David Newth, Don Gunasekera and YiYong Cai
Risk management scenarios
• Appropriate water storage hygiene is crucial as
an effective risk management measure
• Low response scenario – 25 % of the projected
no. of rainwater tanks and breeding sites in
2030 are properly maintained
• High response scenario – 75 % of the projected
no. of rainwater tanks and breeding sites in
2030 are properly maintained
62 | Integrated Assessment Modelling and Analysis | David Newth, Don Gunasekera and YiYong Cai
Four key ingredients of EpiCast - Australia
1. Australian census demographics (where people
live) and worker flow data (where they work) at
ABS Census District resolution
2. Community-level disease transmission between
people, through various contact groups
(household, work group, school..)
3. Disease transmission and natural history model
parameters
4. Dept of Transport statistics on long-distance travel
63 | Integrated Assessment Modelling and Analysis | David Newth, Don Gunasekera and YiYong Cai
Australian census district demographics
Family Type
64 | Integrated Assessment Modelling and Analysis | David Newth, Don Gunasekera and YiYong Cai
Australian census district demographics
Family Type
65 | Integrated Assessment Modelling and Analysis | David Newth, Don Gunasekera and YiYong Cai
Australian census district demographics
Family Type
Family Age Structure
66 | Integrated Assessment Modelling and Analysis | David Newth, Don Gunasekera and YiYong Cai
Australian census district demographics
Long distance travel
Time of year
Length
Destination
Day care: Private
Location: 10008808
Family Type
Family Age Structure
.
. and General
Industry: 632 Health
Insurance
Occupation: Manager
.
Income: $ 75,000
Location: 1008001
.
67 | Integrated Assessment Modelling and Analysis
Student: Government
School
Location: 1008122
Sector: 425 Clothing and
Footwear Retailing
Occupation: Retail Sales
Income: $ 26,000
Location:
1008102
| David Newth, Don Gunasekera and YiYong
Cai
Contact groups
School
Community
Childcare
68 | Integrated Assessment Modelling and Analysis | David Newth, Don Gunasekera and YiYong Cai
Workgroup
Workgroup
Disease Transmission
Rain Water Tank
Swimming Pool
69 | Integrated Assessment Modelling and Analysis | David Newth, Don Gunasekera and YiYong Cai
Key measures used
• Basic reproductive no. (R0)
• 1.5, 2.0 and 2.5
• Potential heath cost
• $2.80/exposed person/year
Disability adjusted life years (DALYs)
• Labour absenteeism
• 2 days (subclinical) and 10 days (clinical)
70 | Integrated Assessment Modelling and Analysis | David Newth, Don Gunasekera and YiYong Cai
Disability Adjusted Life Years (DALYs)
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Absenteeism
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Public health costs
73 | Integrated Assessment Modelling and Analysis | David Newth, Don Gunasekera and YiYong Cai
Policy implications
• Development of a dengue vaccine is long way off
• Public health controls need to focus on
• Reducing Ro
• Shortening mean vector lifespan
• Reducing mosquito bite rate
• Reducing vector breeding sites
• Improved water storage hygiene should be a key
priority given continuing adaptation to dry conditions
and water shortages
74 | Integrated Assessment Modelling and Analysis | David Newth, Don Gunasekera and YiYong Cai
Air Pollution
Integrated
Assessment Modelling and Analysis | Don Gunasekera and David Newth
75 |
Policy context
• Limited analysis of health/economic effects of pollution
control using Australian data/information and modelling
• Need better estimates of spatial variability/impacts of
pollutants
• Focus of CSIRO work – to develop an integrated analytical
framework (for policy evaluation) which incorporates:
•
•
•
•
•
•
Environmental and meteorological factors;
Spatial variation in air quality;
Population/demographic data;
Epidemiological data linking exposure and responses to pollutants;
Health and related effects;
Socio-economic impacts (e.g. absenteeism and labour productivity)
76 | Integrated Assessment Modelling and Analysis | David Newth, Don Gunasekera and YiYong Cai
CSIRO Integrated Analytical Framework
•Urban Form
•Topography
77 | Integrated Assessment Modelling and Analysis | David Newth, Don Gunasekera and YiYong Cai
Scenario analysis
• Illustrative only: to demonstrate the capability/usefulness of the CSIRO
Integrated Analytical Framework
• Study area and focus: PM10 in Sydney GMR
• Hospital admissions – respiratory diseases
• Baseline scenario:
• Assumed current pollution controls will remain unchanged
• Based on 2005 detailed pollution data
• Used health impacts data over 2003-06
• High pollution scenarios:
– 10% and 25% increase in PM10 relative to baseline levels
• Low pollution scenarios:
• 10% and 25 % decline in PM10 relative to baseline levels
78 | Integrated Assessment Modelling and Analysis | David Newth, Don Gunasekera and YiYong Cai
Spatial distribution of census collection districts in Sydney GMR
TAPM grid cells and EpiCast demographic data (based on ABS 2006
census)
80 | Integrated Assessment Modelling and Analysis | David Newth, Don Gunasekera and YiYong Cai
Distribution of PM10
81 | Integrated Assessment Modelling and Analysis | David Newth, Don Gunasekera and YiYong Cai
Dose response functions
• Linearized dose response functions, based on World Bank (1994)
analysis. Dose response captures the uncertainties around central
forecasts.
• PM10 dose responses for
•
•
•
•
•
•
•
•
Premature mortality
Respiratory hospital admission
Asthma attacks
Lower respiratory infections
Emergency Room Visits
Reduced Activity Days
Chronic Bronchitis
Respiratory symptoms
• Map to
• Absenteeism
• Sectoral impacts
• Health costs
82 | Integrated Assessment Modelling and Analysis | David Newth, Don Gunasekera and YiYong Cai
Level of PM10
Mortality
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Respiratory Hospital Admissions
84 | Integrated Assessment Modelling and Analysis | Don Gunasekera and David Newth
Emergency Room Visits
85 | Integrated Assessment Modelling and Analysis | Don Gunasekera and David Newth
Results / key findings
• Results are illustrative and indicative only - based on available data and
assumptions.
• Empirically based, disaggregated and improved data and parameters will help to
improve the analysis.
• % people exposed to Very Good AQI tends to decline under High pollution
scenarios relative to the baseline case.
• Total estimated health cost: $297m under High pollution scenario, compared to
$201m under the baseline case. Under Low pollution scenario, the estimated
health costs are $136m.
• Changes in PM10 pollutant levels strongly affect:
• Exposure to each AQI per day;
• Potential hospitalisation cases and health costs; and
• Economic implications (e.g. absenteeism, labour productivity)
86 | Integrated Assessment Modelling and Analysis | David Newth, Don Gunasekera and YiYong Cai
Key messages
• CSIRO’s Integrated Analytical Framework can help promote
the development, analysis and synthesis of better
information and knowledge about:
• Pollutants, exposure and impacts to improve public policy making in
pollution control
• Capability demonstrated through scenario analysis using
PM10 as a case study.
• Key strengths of the CSIRO Integrated Analytical Framework:
• Treatment of individuals as a heterogeneous group;
• Explicit treatment of different features of individuals (gender, age,
family structure, occupation, location of residence and work, travel etc)
• Evaluates responses to external stimuli (e.g. withdrawal from outdoor
activities due to high levels of pollution)
87 | Integrated Assessment Modelling and Analysis | David Newth, Don Gunasekera and YiYong Cai
Potential directions for development
• Environmental:
• Detailed urban canopy modeling to improve spatial resolution of pollutant
predictions, and to
• Produce peak concentrations as well as averages
• Health
• Use of EpiCast capability to include joint susceptibility of individuals with
other medical conditions to air pollution effects
• Economic impacts
• Linking health impacts to economic models such as MMRF
• Embedding of the system in a national integrated assessment model
(e.g. CSIRO’s NIAM) to project effects of urban and population
developments in Sydney and other regions
Thank you
CSIRO Marine and Atmospheric
Research
David Newth, Don Gunasekera, and
YiYong Cai
CLIMATE AND ATMOSPHERE
Global Integrated Assessment Model
GCM
GCM Output
Output
Climate-Economy
Climate-Economy
Interaction
Interaction
Global
Global Trade
Trade and
and
Policy
Policy Database
Database
Economic
Economic Impact
Impact
Factors
Factors
Global
Global Trade
Trade and
and
Environment
Environment Model
Model
RCP
RCP Emission
Emission
Constraints
Constraints
Economic
Economic and
and
Welfare
Outcomes
Welfare Outcomes
90 | Integrated Assessment Modelling and Analysis | Don Gunasekera and David Newth
Regional
Regional Economic
Economic
Growth
Growth