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CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 1 Dottorato Climate Change and Policy Modelling Assessment: Impacts in Modelling Francesco Bosello.

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Presentation on theme: "CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 1 Dottorato Climate Change and Policy Modelling Assessment: Impacts in Modelling Francesco Bosello."— Presentation transcript:

1 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 1 Dottorato Climate Change and Policy Modelling Assessment: Impacts in Modelling Francesco Bosello

2 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 2 The typical structure of a IIA exercise Climatic drivers Environmental impacts Δ Temp. Δ Preci. Δ SLR ……… Δ Tourism Flows Δ Energy demand Social Economic impacts Economic Assessment Δ flood. land Δ desert. land Δ crop yield Δ mort./morb. …………….. Δ Agr. Prod. Δ Health care expenditure Δ Labour prod. ……………..

3 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 3 Steps before the economic assessment Quantify impacts Translate them into meaningful economic variables Choice of a convenient baseline on which impacts can be imposed. Assess changes respect to a no climate change scenarios Static baselines status quo Dynamic baselines - - evolving according to exhogenous storylines (IPCC SRES) - - evolving according to endogenous mechanisms

4 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 4 IPCC and exhogenous storylines A1: rapid economic growth and technological dev.pm. Low population growth. A2: heterogeneous world, preservation of local id, economic growth but more fragmented technological progr. High population growth. B1: convergent world, low population growth, development towards a high tech and service society. Emphasis on sustainability. B2: like B1, but with more emphasis on local solution. Source: IPCC, Climate Change 2001, The Scientific Basis

5 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 5 The scenario issue IPCC approach: emissions scenarios stem from exogenous storylines proposed by/incorporated in a set of soft-linked models. Replicating soft linked emissions with hard link models may => unrealistic economic assumptions; alternatively using model- consistent economic assumptions may => different emissions paths! Problematic for hard linked models to replicate those storylines as the storyline is endogenously embedded: in fact it is the model itself The same problem with model comparison and harmonization Crucial role of the baseline it determines the impact

6 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 6 Quantifying impacts (1) – Sea Level Rise, some literature Low land in coastal countries with elevation < 5m. (Source: EEA, 2005)

7 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 7 Quantifying impacts (1) – Sea Level Rise, some literature Land loss in Source Nicholls 2007 Population living in coastal flood plain in Nicholls (2004) SLR impacts (+1 m.) in selected EU countries

8 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 8 Quantifying impacts (1) – Sea Level Rise, some literature

9 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 9 Quantifying impacts. Sea level ICES Data set 1: Sq. Km. of land lost due to erosion, if there is no protection for different SLR scenarios. Country detail. Combining Areas at risk Basis is the 1993 Global Vulnerability Analysis by Delft Hydraulics and Land Loss Nicholls and Leatherman (1995). Aggregated for the regions of interest, calculated in 2050 for 25 cm of SLR

10 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 10 Quantifying impacts (2) – Health, some literature Possible Changes in the Distribution of Death Rates from Heat Related Mortality in Europe – 2000 to A2 Scenario 2100, based on the climate signal alone. Source: PESETA project (2007) at CEC (2007)

11 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 11 Quantifying impacts (2) – Health, some literature

12 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 12 Quantifying impacts (2) – Health, some literature

13 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 13 Quantifying impacts (2) – ICES Change in Morbidity (n° of years diseased) Mortality (n° of deceases) Health Care Expenditure Due to Climate Change (ΔT) Calculated for five classes of diseases: - Malaria, - Schistosomiasis, - Dengue, - Diarrhoea, - Cardiovascular and Respiratory. Meta Analysis

14 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 14 Quantifying impacts (3) – ICES Change in base mortality: additional n° of deceased people: Examples Vector Borne Diseases Diarrhoea Applied to UP > 65 Cardio Vascular

15 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 15 Quantifying impacts (4) – ICES Additional Mortality Additional years of life diseased Additional Health Care Expenditure Additional Health Exp. VBD+Diarr. Additional Health Exp. Cv and Resp. From the literature

16 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 16 Quantifying impacts (4) – ICES Additional Mortality (1000) in 2050 for °C wrt 2000 (static baseline) Additional years of life diseased in 2050 for °C wrt 2000 (static baseline)

17 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 17 Quantifying impacts (4) – ICES Additional Health Care Expenditure LEVELS Is split between public and private additional expenditure LEVELS (using WHO 2003) These then calculated as % of GDP consistent with the original database (Tol) % The % is reported to GTAP GDP LEVELS consitent with GTAP GDP These levels are calculated in % of GTAP public and private demand for Non Market Services shocks in % change

18 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 18 Quantifying impacts (4) – ICES Final impacts on labour productivity and health care expenditure as shocks for the ICES model (+1.5°C wrt average) old baseline static model

19 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 19 Quantifying impacts (5) -- Energy Heating effect: higher temperatures in cold seasons lead to a lower demand for energy for heating purposes Cooling effect: higher temperatures in warm seasons lead to a higher demand for energy for cooling purposes Climate Change affects energy demand through changes in temperature Both effects are likely to weight differently at different geographical locations Hot countries vs Cold countries Econometric investigation on panel data performed to identify the elasticity of energy demand to temperature

20 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 20 Quantifying impacts (6) -- Energy - 31 countries (OECD and non-) from 1978 to The dataset includes: Real GDP per capita (IEA) Residential demand for oil products, electricity and gas (IEA) Fuel prices (IEA) Seasonal Temperature (Hadley Center UEA High Resolution Gridded Dataset) Balanced panel with the following observations: - Electricity: 550 (T = 22; N = 25) - Natural gas: 418 (T = 19; N = 22) - Oil products: 418 (T = 19; N = 22) Data

21 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 21 Quantifying impacts (7) -- Energy Cluster analysis used to identify temperature clusters GROUP 1 – mild Austria, Belgium, Denmark, France, Germany, Ireland, Luxembourg, Netherlands, New Zealand, Switzerland, Greece, Hungary, Italy, Japan, Korea, Portugal, South Africa, Spain, Turkey, United Kingdom, United States; GROUP 2 – hot Australia, India, Indonesia, Mexico, Thailand, Venezuela; GROUP 3 – cold Canada, Finland, Norway, Sweden.

22 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 22 Quantifying impacts (8) -- Energy Cooling effect for electricity is present in hot and mild countries in summer and spring Heating effect for all fuels in winter and mid-seasons

23 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 23 Quantifying impacts (9) – Tourism, some literature Source: PESETA project (2007) at CEC (2007) Green => Increased climatic attractiveness Red => reduced climatic attractiveness Europe: Changes in Tourism Climate Index (climate attractiveness) rt A2 scenario

24 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 24 Quantifying impacts (9) – ICES Using a World tourism Model (HTM13, Tol et al., 2005) Which assesses changes in domestic and international tourist flows with a country detail The model is calibrated on 1995 data and explains tourism flows with: population, income, temperature, coastal lenghts, travel distance.

25 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 25 An example for Italy (% changes wrt no climate change) International Arrivals Domestic Tourist Trips Total Tourism Demand

26 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 26 Formulas for tourism

27 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 27 Quantifying impacts (12) – Agriculture, some literature Source: IPCC, (2007)

28 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 28 Quantifying impacts (10) -- Agriculture Rosenzweig and Hillel (1998) report detailed results from an internally consistent set of crop modelling studies Wheat, maize, rice, soybean Australia, Brazil, Canada, China, Egypt, France, India, Japan, Pakistan, Uruguay, USSR, USA 3 GCMs; with and without CO2 fertilisation 3 levels of adaptation Data extended to the regions of the economic model and to different climate change scenarios main yield drivers: regional T and CO2 concentration parameterization as reported by Tol (2002).

29 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 29 Quantifying impacts (10) -- Agriculture Source: Rosenzweigh and Hillel, (1998)

30 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 30 Quantifying impacts – ICES

31 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 31 Quantifying impacts – ICES Changes in agricultural productivity, without adaptation for 1.5°C increase and 600 ppm in 2050 r.t average

32 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 32 How to introduce these impacts into a CGE Sketching the structure of ICES Database (xx.HAR) Key parameters (xx.PAR) The model equations (xx.TAB) Command File (xx.CMF) Output in % change (xx.SOL) Output in Levels (xx.UPD) Instructions + which variables are exogenous and which endogenous (closure)

33 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 33 How to introduce these impacts into a CGE The nature of the impact Supply side impacts on stocks or productivity (e.g. health labour productivity, agriculture land productivity, sea level rise land stock) They affects variables which are typically exhogenous, easy to accommodate direct inputs to the command file Demand side impacts changes in preferences (e.g. health health care demand, energy energy demand, tourism recreational services demand) They affect variables which are typically endogenous, this is a tricky issue

34 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 34 Explaining demand-side shock modeling Equation PRIVDMNDS # private consumption demands for composite commodities (HT 46) # (all,i,TRAD_COMM)(all,r,REG) qp(i,r) - pop(r) = sum{k,TRAD_COMM, EP(i,k,r)*pp(k,r)} + EY(i,r)*[yp(r) - pop(r)]; Equation PRIVDEGYCOM # private consumption demands for energy commodities (HT 46) # (all,i,EGYCOM)(all,r,REG) qp(i,r) - pop(r) = adsp(i,r)+ sum{k,TRAD_COMM, EP(i,k,r)*pp(k,r)} + EY(i,r)*[yp(r) - pop(r)]; Equation PRIVDNEGYCOM # private consumption demands for non-energy commodities (HT 46) # (all,i,NEGYCOM)(all,r,REG) qp(i,r) - pop(r) = adsnec(r) + sum{k,TRAD_COMM, EP(i,k,r)*pp(k,r)} + EY(i,r)*[yp(r) - pop(r)]; Equation NEWBUDGET # eplicit budget costraint # (all,r,REG) INCOME(r)*y(r) = sum(i,TRAD_COMM, VPA(i,r)*(pp(i,r)+qp(i,r)) + VGA(i,r)*(pg(i,r)+qg(i,r))) + SAVE(r)*(psave(r)+qsave(r));

35 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 35 Explaining demand-side shock modeling Variable (all,i,MASER_COMM)(all,s,REG) apd(i,s) # private cons. dem. shock parameter for market services in reg. r #; Equation PHLDDMAS # private consumption demand for market services. (HT 48) # (all,i,MASER_COMM)(all,s,REG) qpd(i,s) = apd(i,s)+qp(i,s) + ESUBD(i) * [pp(i,s) - ppd(i,s)]; Variable (all,s,REG) apdC(s) # private cons. dem. shock parameter for all non market in reg. r #; Equation PHLDDNMAS # priv. cons. demand for for all trad comm but market services. (HT 48) # (all,i,NOMASER_COMM)(all,s,REG) qpd(i,s) = apdC(s) + qp(i,s) + ESUBD(i) * [pp(i,s) - ppd(i,s)]; Equation NEWBUDGET # eplicit budget costraint # (all,r,REG) sum(i,TRAD_COMM, VPA(i,r)*(pp(i,r)+qp(i,r))) = sum(i,TRAD_COMM, VIPA(i,r)*(ppm(i,r)+qpm(i,r)))+ sum(i,TRAD_COMM, VDPA(i,r)*(ppd(i,r)+qpd(i,r)));

36 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 36 An IIA exercise example The model static & recursive dyn CGE 12 Regions: USA:United States NEWEURO:Eastern EU OLDEURO:EU 15 KOSAU:Korea, S. Africa CAJANZ:Canada, Japan, New Zealand TE: Transitional Economies MENA:Middle East and North Africa SSA:Sub Saharan Africa SASIA:India and South Asia CHINA:China EASIA:East Asia LACA:Latin and Central America 17 Sectors: Rice Wheat Cereal Crops Vegetable Fruits Animals Forestry Fishing Coal Oil Gas Oil Products Electricity Water Energy Intensive industries Other industries Market Services Non-Market Services Used for investi- gations on transi- tional dyna- mics

37 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 37 An IIA exercise example The baseline asumptions: % changes

38 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 38 An IIA exercise example The baseline results

39 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 39 CC impacts 1.5º C temperature increase in 2050 wrt average (% change wrt baseline)

40 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 40 Results

41 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 41 Comparison with the existing literature Source: IPCC, 2007 FAR In 2050 Damage = 0.3% of world (2050) GDP ~ 352 billions US $ 2001

42 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 42 Comparison with the existing literature $/tC 93 tutti 50 pr 51 prtp1% 16 prtp 3% 314 Stern 261 prtp<1% Intervalli di confidenza al 67% Survey di 108 stime (Tol, 2005)

43 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 43 Results, static vs dynamic

44 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 44 The sectoral picture Climate change impacts on production in 2050 (% change wrt base) GDP

45 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 45 Caveats in interpreting the results The climate scenario issue (uncertainty on the possible temperature increase) The Impact scenario issue (no irreversibility and or catastrophic events) The economic scenario issue: the geographical scale, transitional dynamics and frictions in substitution. The economic variable represented: stock vs flows (GDP as a welfare measure)

46 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 46 Stock vs flows, the case of sea level rise Source: Tol (2001)

47 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 47 Stock vs flows, the case of sea-level rise The implicit value of land ($ per km2)

48 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 48 Building damage functions A standard approach In a more or less sophisticated way, parameters of a given damage function, whose functional form is chosen with some ad hoc properties, are calibrated such that in a given time with a given temperature the total damage reaches a given level expressed as (%) loss of potential GDP. This amounts to: Assume exogenously the link between damage and temperature (linear, quadratic, cubic) A more or less additive procedure in the estimation of total damage

49 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 49 Examples of damage functions Nordhaus and Yang (1996) Nordhaus and Boyer (1999 -) Manne and Richels (1996 -) Peck and Teisberg (1992 -)

50 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 50 An example: CCDF calibration in RICE 2007 Source: Nordhaus (2007), lab notes on RICE 2007

51 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 51 An example: CCDF calibration in RICE 2007 Source: Nordhaus (2007), lab notes on RICE 2007

52 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 52 An example: CCDF calibration in RICE 2007 Source: Nordhaus (2007), lab notes on RICE 2007

53 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 53 An alternative methodology. Tol Source: Tol, (2002)

54 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 54 Using (static) CGE to calibrate the damage function Quantify all impacts for different ΔTs Plug them together into the CGE Estimate the parameters of the implicit regional damage functions The main advantage of this procedure is to consider autonomous adaptations and thus impact interactions.

55 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 55 Damages and damage coefficients

56 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 56 A new calibration Recall the RICE 99 (and subsequent) damage function

57 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 57 New damages by temperature

58 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 58 New damages by region

59 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 59 New emission path…

60 CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 60 Open questions: Is it legitimate to use a static model to calibrate a CC damage function? Is it legitimate to use a flow-based model to calibrate a CC damage function? Is it legitimate to use a market-based model to calibrate a CC damage function?


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