* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download Integrated Assessment Modelling and Analysis Study Tour Workshop, Canberra 12
Climate change adaptation wikipedia , lookup
German Climate Action Plan 2050 wikipedia , lookup
Effects of global warming on human health wikipedia , lookup
Climatic Research Unit documents wikipedia , lookup
Attribution of recent climate change wikipedia , lookup
Climate sensitivity wikipedia , lookup
Solar radiation management wikipedia , lookup
Climate change feedback wikipedia , lookup
Low-carbon economy wikipedia , lookup
General circulation model wikipedia , lookup
Media coverage of global warming wikipedia , lookup
Effects of global warming on humans wikipedia , lookup
Scientific opinion on climate change wikipedia , lookup
Climate change and agriculture wikipedia , lookup
Effects of global warming on Australia wikipedia , lookup
Global Energy and Water Cycle Experiment wikipedia , lookup
Public opinion on global warming wikipedia , lookup
Economics of climate change mitigation wikipedia , lookup
Carbon Pollution Reduction Scheme wikipedia , lookup
Economics of global warming wikipedia , lookup
Climate change and poverty wikipedia , lookup
Surveys of scientists' views on climate change wikipedia , lookup
Politics of global warming wikipedia , lookup
Mitigation of global warming in Australia wikipedia , lookup
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) 71 | Integrated Assessment Modelling and Analysis | David Newth, Don Gunasekera and YiYong Cai Absenteeism 72 | Integrated Assessment Modelling and Analysis | David Newth, Don Gunasekera and YiYong Cai 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 83 | Integrated Assessment Modelling and Analysis | Don Gunasekera and David Newth 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