Valuing the forest carbon stocks of the Valuing the forest carbon stocks of the Brazilian AmazonBrazilian Amazon

Universidade Federal Universidade Federal de Minas Geraisde Minas Gerais

UF GUF GWoods Hole Research Woods Hole Research

CenterCenter

IPAM – INSTITUTO DE PESQUISAAMBIENTAL DA AMAZÔNIA

Britaldo Soares-Filho, Daniel Nepstad, Frank Merry, Hermann Rodrigues, Maria Bowman, ,

Paulo MoutinhoPaulo Moutinho

CURRENT ECONOMIC MODEL FOR THE AMAZON CURRENT ECONOMIC MODEL FOR THE AMAZON FORESTFOREST

Small economic value for Small economic value for the standing forestthe standing forest

How to change this paradigm?How to change this paradigm?

Payment for ecosystem services..Payment for ecosystem services..

How to compensate developing countries to reduce emissions from deforestation?

Incorporating emissions reductions Incorporating emissions reductions from tropical land-use change into from tropical land-use change into the international climate regime the international climate regime (UNFCCC process)(UNFCCC process)

United States of America 15%

China 11%European Union (25)

11%

Indonesia 7%Brazil 5%

Others 35%

Russian Federation 5%

India 4%

Japan 3%Germany 2%

Malaysia 2%

65% deforestation65% deforestation

REDD = Reducing Emissions from Deforestation and Degradation

Some challenges: (1) establishing baseline; (2) monitoring; (3) estimating costs; (4) compensation mechanism

"readiness"

How do we estimate the costs?How do we estimate the costs?

Opportunity cost analysesOpportunity cost analyses

Forgone profits*Forgone profits**a fraction of the costs of a REDD program

Opportunity cost =Opportunity cost =

Rent from cattle raisingRent from cattle raising

Rent from agribusinessRent from agribusiness

Rent from sustainable Rent from sustainable logginglogging

Rent for other forestry products (Brazil nuts,

rubber, etc)

Rent from loggingRent from logging

Interdisciplinary Model for Soybean Yield

88 polygons

DATA SOURCES

Agricultural Census (1995-96)

Soil Parameters

Climate DataRooting Depth pH

Precipitation Temperature

Solar Radiation

Vera-Diaz, et al. An Interdisciplinary model of Soybean Yield in the Amazon Basin: the climatic, edaphic, and economic determinants. Ecological Economics, 2007.

Scenarios of Soy frontier expansion in response to Paving

Hydro Powers: Jiraú and Santo AntônioNorthern Corridor and Transoceanic HighwayPotential area with high rents = 976,000 km2 or increase of 13%Pando, El Beni, Acre, and Madre de Dios U$/hecU$/hec

RentRent

R

RR

R

RRR

R

R

R

AM PA

MT

MA

TO

RR

RO

AC

AP

Belém

Macapá

Manaus

Palmas

Cuiabá

Santarém São Luís

Boa Vista

Rio Branco

Porto Velho 45°W

45°W

50°W

50°W

55°W

60°W

0°

5°S

10°S

15°S

0°

70°W 65°W

60°W

55°W

5°N5°N

65°W

5°S

10°S

15°S

70°W

deforested

Cerrado biomePaved road

Unpaved roadU$ 0/hec

U$ 12000/hec

Soy Net Present Value for 30 yearsSoy Net Present Value for 30 yearsdiscount rate of 5%discount rate of 5%

Cattle modelCattle model

(Merry et al., submitted)

-100

-50

0

50

100

150

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

anos

U$

annual cattle rent

Spatial model at 1km2

R

RR

R

RRR

R

R

R

AM PA

MT

MA

TO

RR

RO

AC

AP

Belém

Macapá

Manaus

Palmas

Cuiabá

Santarém São Luís

Boa Vista

Rio Branco

Porto Velho 45°W

45°W

50°W

50°W

55°W

60°W

0°

5°S

10°S

15°S

0°

70°W 65°W

60°W

55°W

5°N5°N

65°W

5°S

10°S

15°S

70°W

deforested

Cerrado biomePaved road

Unpaved roadU$ 0/hec

U$ 1150/hec

Cattle Net Present Value for 30 years

discount rate of 5%discount rate of 5%

Logging rent modelLogging rent model

Rent= Wood_PriceRent= Wood_Pricejj * tax_deduction * processing_loss * tax_deduction * processing_loss

- (transportation_cost- (transportation_costxyxy + harvest_cost + harvest_costjj + milling_cost + milling_costjj)*interest_rate)*interest_rate

The model produces dynamic rent surfaces based on wood prices, harvest and The model produces dynamic rent surfaces based on wood prices, harvest and milling costs collected for 588 milling centers located throughout the Amazon milling costs collected for 588 milling centers located throughout the Amazon

harvest capacityharvest capacity

milling centersmilling centers

Update friction surface

Calculate transportation cost to milling center

Define milling shed

Milling centers

Harvest volume

Calculate profit for each raster cell within a

specific milling shedMilling center’s

capacity

Wood price at milling center

Processing costs at milling center

Harvesting costs at milling center

Rank cells by their profitability

Harvest wood

Deforest (deforestation model)

Expand and improve infrastructure

(deforestation model)

Increase/decrease capacity, update costs and price, and

milling centers

emerge or deactivate

Regenerate forest Sojourn time

Initial land use and cover Logging Model Structure

(Merry et al., in press)

R

RR

R

RRR

R

R

R

AM PA

MT

MA

TO

RR

RO

AC

AP

Belém

Macapá

Manaus

Palmas

Cuiabá

Santarém São Luís

Boa Vista

Rio Branco

Porto Velho 45°W

45°W

50°W

50°W

55°W

60°W

0°

5°S

10°S

15°S

0°

70°W 65°W

60°W

55°W

5°N5°N

65°W

5°S

10°S

15°S

70°W

deforested

Cerrado biomePaved road

Unpaved roadU$ 0/hec

U$ 550/hec

Sustainable Logging Net Present Value for 30 years

Opportunity Cost

Carbon stock

Rent from logging

Rent from cattle raising

Rent from agribusiness

Supply curve

+

+

-

/

max

Opportunity Cost

Carbon stock

Rent from logging

Rent from cattle raising

Rent from agribusiness

Supply curve

+

+

-

/

max

0 tons/hec.

275 tons/hec.

Adapted from Saatchi et al., 2007

Brazilian Amazon forest 47 ± 9 billions of tons of Carbon

Carbon of above and bellow ground biomassCarbon of above and bellow ground biomass

All models are integrated on the same modeling platform

Custo de oportunidade

R

RR

R

RRR

R

R

R

AM PA

MT

MA

TO

RR

RO

AC

AP

Belém

Macapá

Manaus

Palmas

Cuiabá

Santarém São Luís

Boa Vista

Rio Branco

Porto Velho 45°W

45°W

50°W

50°W

55°W

60°W

0°

5°S

10°S

15°S

0°

70°W 65°W

60°W

55°W

5°N5°N

65°W

5°S

10°S

15°S

70°W

deforested

Cerrado biomePaved road

Unpaved road

US$ 3-0 per ton

US$ 0 per ton

US$ 8-13 per ton

US$ 3-8 per ton

> US$ 13 per ton

Opportunity costsOpportunity costs

Opportunity costs for reducing deforestation

05

10

15202530

35404550

55606570

75808590

95100

0 5 10 15 20 25 30 35 40 45

GigaTonsTons of Carbon in the Amazon forest (3.3 million Km2)

U$

47.9E+9 Total Carbon in tons

142 average ton/hec

3,385,537 forest area km2

232.26E+9 Total cost U$

4.85 U$ Average Cost Carbon

U$ 5.20 without loggingU$ 5.20 without logging

Calculating the op cost along the potential Calculating the op cost along the potential deforestation frontierdeforestation frontier

years

30

29

28

27

26

25

24

23

22

21

20

19

18

17

16

15

14

13

12

1110

9

8

7

6

5

4

3

2

1

0

100

200

300

400

500

600

700

800

900

1,000

1,100

1,200

1,300

1,400

1,500

1,600

0 50 100 150 200 250

Million tons of C

U$

30

29

28

27

26

25

24

23

22

21

20

19

18

17

16

15

14

13

12

11

10

9

8

7

6

5

4

3

2

1

Simulated deforestation Simulated deforestation by 2038by 2038

As the frontier advances opportunity costs fall

0.000.100.200.300.400.500.600.700.800.901.001.101.201.301.401.501.601.701.801.902.00

1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627282930

year

Mil

lio

n h

ecta

res

0.00

0.50

1.00

1.50

2.00

2.50

Bil

lio

n U

$

Reduced deforestation

Opportunity costs

A scenario of deforestation reduced to 0 in 10 years

(Not all op cost should be compensated)(Not all op cost should be compensated)

Carbon Cost

0.00

2.00

4.00

6.00

8.00

10.00

12.00

1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627282930

year

U$

Carbon cost over time (U$ ton)

Nepstad et al. 2007

THE COSTS AND BENEFITS OF REDUCING CARBON EMISSIONS FROM

DEFORESTATION AND FOREST DEGRADATION IN THE BRAZILIAN AMAZON

http://www.whrc.org/balihttp://www.climaedesmatamento.org.br/biblioteca

/

A proposal for a REDD program

0200400600800

1000120014001600

1 3 5 7 9 12 14 16 18 20year

US

$ (

mill

ion

)GovernPrivate sector

Forest People

$

Thank youThank you

Britaldo Soares Filhobritaldo@csr.ufmg.br

LBAThe Gordon and Betty Moore Foundation

The David and Lucille Packard Foundation NSF CNPq

British Embassy The Linden Trust/Roger and Victoria Sant/Joseph Gleberman

Sponsored by

www.csr.ufmg.br