Global Forestry and Agricultural Model Initial Results EMF-22 - PowerPoint PPT Presentation

Global Forestry and Agricultural Model Initial Results EMF-22 Meeting, Tsukuba, Japan December, 2006 Brent Sohngen, Suk-won Choi, Bin Sun Ohio State University Tom Hertel, Alla Golub, Huey-Lin Lee(Purdue), Roger Sedjo (RFF), Robert Mendelsohn (Yale) Massimo Tavoni (FEEM) Funding: U.S. Department of Energy, Office of Biological and Environmental Research U.S. Environmental Protection Agency, Climate Analysis Branch;

Two Issues: • Are Forests a Transition Tool? – Results from Link with Integrated Assessment Model assessing a 500 ppm constraint. – WITCH Model (FEEM) linked to Global Timber Model, with Massimo Tavoni (FEEM) • Toward Development of PE Forest and Agr. LU Model – PE Forest and Agricultural LU model – Structure of global forestry and agriculture model – Data – Analysis so far – Next Steps

Are Forests a Transition Tool? • Link an IAM with Forestry/LU Model. • WITCH – A dynamic model to study the economics of climate change – TOP DOWN optimization framework, with a energy sector description and a game theory set up. • World, 12 regions • Economy: optimal growth • Energy: Energy sector specification • Climate: damage feedback • The 12 regions interact strategically • Analysis: What Impact does forestry have on “optimal” carbon prices under a stabilization scenario? – 550 ppm

Emission Profile Policy: 550 ppm constraint. Without Forestry World Industrial Carbon Emissions (GtC) 25 20 15 BAU 550 10 5 0 2 2 2 2 2 2 2 2 2 2 2 0 1 2 3 4 5 6 7 8 9 0 0 0 0 0 0 0 0 0 0 0 1 2 2 2 2 2 2 2 2 2 2 2

Energy Abatement Options 550 PPM; Without Forestry Policy induces significant reduction in carbon and energy intensities Trajectories in the energy intensity/carbon intensity wrt first period 25% Reduction in Carbon Intensity 20% 15% wrt first period 10% 550 w/out forest BAU 5% 0% 0% 10% 20% 30% 40% 50% 60% -5% Reduction in Energy Intensity wrt first period

Energy Abatement Options 550 PPM; Without Forestry 4 W orld Electric ity Generation x 10 5 4.5 4 Bac k 3.5 Nuc lear Hydroelectric 3 Oil TWh 2.5 Gas Adv Coal+CCS 2 Coal deSOX deNOX Old Coal 1.5 W ind&S olar 1 0.5 0 2000 2020 2040 2060 2080 2100

Adding Forestry Reduces Carbon Prices by ~ 56% over Century & Reduces average rate of price growth from >7% to 3.8% per year. Price of Carbon 900 iter 1 800 700 600 1995USD/tC 500 400 iter11 300 200 100 0 2 7 2 7 2 7 2 7 2 7 2 7 2 7 2 7 2 7 2 7 2 0 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

Reduces Near-Term Income Losses W orld Percentage GW P loss 0.5 0.4 0.3 0.2 0.1 550 with Forestry 0 % 550 w/out Forestry -0.1 -0.2 -0.3 -0.4 -0.5 2000 2020 2040 2060 2080 2100

2025 2055 2095 Million TCE/year (TgC) Where USA 42 144 193 Does OLDEURO 37 82 132 NEWEURO 8 18 29 the KOSAU 25 27 36 Sequest. CAJAZ 31 115 125 Trans. Ec. 179 117 134 Occur? ME/NA 73 49 31 SubSahA 270 175 106 Red Areas are S. ASIA 34 57 32 Largely due to CHINA 109 155 431 Reducing Defor. E. ASIA 451 481 371 LACA 391 326 330 Total 1649 1746 1950 C Price $57 $113 $271

Increases the Trade of Permits Net Import of Permits (cumulative to 2050) 20 15 10 5 GtC 0 OLDEURO USA NEWEURO KOSAU MENA CHINA CAJAZ SASIA EASIA TE SSA LACA -5 -10 550 w/out forest -15 550 with forest -20

Delays Abatement in Energy World Energy Intensity 300 250 200 Mtoe/US$ Trillions 150 100 550 w/out forest 550 with forest 50 BAU 0 2 2 2 2 2 2 2 2 2 2 0 1 2 3 4 5 6 7 8 9 0 0 0 0 0 0 0 0 0 0 2 2 2 2 2 2 2 2 2 2

Forest In “Transition” • Forests delay energy abatement. Are forests a transition tool? – Forest Carbon worth $1.1 trillion ($55 billion/yr AEA) – Total Economic cost: $2.5 – 12 Trillion (Richels et al.) • Transition tools: – Allow forest projects to enter CDM? • Continue efforts to build projects, even though project based approach suboptimal… – Develop rules for reducing deforestation • Project based? • National targets? • Indirect programs? – Use under national caps in developed countries • Current approach – but rules for forestry slow to evolve. • Competition with Biomass will be a key feature of future.

Global Forest & Ag Model - Model Structure Dynamic Optimization- Three Sectors ⎧ ⎫ QF QAg 16 18 6 16 18 ∑ ∑ ∑ ∑ ∑ ∫ ∫ + ⎪ ⎪ D ( Y ( a , H , m ) ) dQF D ( Y ( X , K , L ) dQ F F Ag Ag Ag Ag ⎪ ⎪ T ∑ region AEZ timber region AEZ ρ ⎨ ⎬ Max QLv ⎪ ⎪ 16 18 ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∫ + − − − D ( Y ( X , K , L ) ) dQ C C C ⎪ ⎪ Lv Lv Lv Lv F Ag Lv ⎩ ⎭ region AEZ region AEZ timber region regiion Subject to: • Forestry Production (Dynamic) • Crop Production • Livestock Production • Land Supply

Structure of Production ∑∑ i Y i H Q F (·) = Forestry a , t a , t i a Crops Livestock Livestock output Livestock output Crops output Value added nest Intermediate Value added nest Value added nest inputs Intermediate inputs Intermediate inputs Capital Land Labor σ = 0.24 σ = 20 AEZs σ = 0.24 Capital Capital Land&Feed Land&Feed Labor Labor σ = 0.5 Land Land Feed Feed σ = 20 AEZs AEZs

Production units are denominated by AEZ’s and 16 regions

AEZs F. Types US 10 6 CHINA 12 6 BRAZIL 6 5 CANADA 8 4 RUSSIA 7 5 EU ANNEX I 9 5 EU NON-ANNEX 7 4 Regions SOUTH ASIA 9 5 CENT AMERICA 12 5 REST SOUTH AM 18 5 SUB SAHARAN AF 9 4 SOUTHEAST ASIA 5 3 OCEANIA 9 5 JAPAN 5 2 AF MIDDLE E 3 2 EAST ASIA 4 3

Land Supply • Land imperfectly L F1 L F2 L F3 mobile across crops, L L L C t = - 0.9 livestock and forests. Total Land in AEZ • Use CET function • Calibrated on initial ⎛ τ ⎞ α ⎜ ⎟ C , j XE ⎜ ⎟ τ j R ⎝ ⎠ areas & rents = C , j Ag XL [ ] τ L ⎛ ⎞ ⎜ ⎟ τ − τ τ − τ τ − τ α + α + α 1 1 1 τ − R R R ⎝ ⎠ 1 c , j c , j L , j L , j F , j F , j • t = 0.9

Land Supply Calibration • Land Supply calibration based on current area of accessible land • Can purchase new units of endowment in regions with substantial inaccessible land – Brazil, RSAM, Central Amer., S. Asia, SE Asia, Sub Saharan Africa

Baseline Information • Demand Functions − = e n Q ( A )( I ) ( P ) i , t i i , t i , t t i , t • Demand Functions e Crops = 0.17 ↓ 0.06 n Crops = 0.25 e Livestock = 0.62 ↑ 0.76 n Livestock = 0.71 e Forest = 0.88 ↑ 0.93 n Forest = 1.10

GDP/Capita Projections 40,000 35,000 GTAP Projection 30,000 Adapting Mosaics GDP/Capita 25,000 20,000 15,000 10,000 5,000 0 2005 2015 2025 2035 2045 2055 2065 2075 2085 2095 2105 Year

Crops Livestock US 2.50 1.20 Technical CHINA 0.70 1.80 BRAZIL -0.45 -0.95 Change CANADA 2.50 1.20 RUSSIA 0.55 -0.20 % Ann. EU ANNEX I 2.50 1.20 EU NON-ANNEX 1.55 0.63 Chg. TFP SOUTH ASIA -1.70 0.83 CENT AMERICA 0.03 0.83 Nin et al. REST SOUTH AM 0.98 0.52 SUB SAHARAN AF -0.32 -0.01 (2003) SOUTHEAST ASIA -0.53 1.32 OCEANIA 2.50 1.20 JAPAN 1.00 1.00 AF MIDDLE E 0.20 0.01 EAST ASIA -0.53 1.30

Caution: ALL RESULTS ARE PRELIMINARY Comments Welcome….

Results: Prices 250 200 CropsGT Price Index (2005 = 100) LiveGT 150 ForGT CropsAM 100 LiveAM ForAM 50 0 2005 2015 2025 2035 2045 2055 2065 2075 Year

Results: Outputs Crop Output Index (2005 = 100) Livestock Output Index (2005 = 100) 450 2000 400 1800 US US CHINA CHINA 350 1600 BRAZIL BRAZIL OECDO 1400 300 OECDO ROW ROW TOTAL 1200 250 TOTAL Index Index 1000 200 800 150 600 100 400 50 200 0 0 2005 2015 2025 2035 2045 2055 2065 2075 2005 2015 2025 2035 2045 2055 2065 2075 Year Year Feed Proportion of Total Crop Output 1.20 US CHINA 1.00 BRAZIL OECDO ROW 0.80 TOTAL Proportion 0.60 0.40 0.20 0.00 2005 2015 2025 2035 2045 2055 2065 2075 Year

Results: Global Land Area 3,500 3,000 2,500 CrGT Million Hectares LvGT 2,000 ForGT CrAm 1,500 LvAm ForAm 1,000 500 0 2005 2015 2025 2035 2045 2055 2065 2075 Year

Results: Regional Land Areas GTAP GDP & Population Crop Area Livestock Area 1800 3500 1600 US CHINA 3000 BRAZIL OECDO 1400 US CHINA ROW TOTAL Million hectares 2500 1200 BRAZIL OECDO Million hectares ROW TOTAL 1000 2000 800 1500 600 1000 400 200 500 0 0 2005 2015 2025 2035 2045 2055 2065 2075 2005 2015 2025 2035 2045 2055 2065 2075 Year Forest Area Year 3500 3000 2500 US CHINA Million hectares BRAZIL OECDO ROW TOTAL 2000 1500 1000 500 0 2005 2015 2025 2035 2045 2055 2065 2075 Year