Download Technologies and deforestation (Angelsen and Kaimowitz 2001)

Analytical perspectives:
causes of deforestation



Sources and causes
Agents and motivations
Market failures





Efficiency  markets  ownership
Natural resource abundance  open access
Property rights not well defined  predatory
competition
Externalities  ecological functions
Institutional failures


Lack of government institutions
High costs of monitoring and fiscalization
Environmental functions
(services) of tropical forests






Global climate stability  carbon
sequestration  greenhouse effect (GWP
intact forest nearly balanced)
Preservation of biodiversity  scientific
and aesthetic benefits (global)
Regional climate stability (~national)
Hydrological balance and watershed
protection (local)
Recycling of soil nutrients (local)
Protection against fire susceptibility
(local)
Scanty evidences on
Brazilian biodiversity
Global estimates 13 m (3 to 100)
species of which 1.5 m catalogued
 Brazil share is 10 to 20% or 150 a
300 thousand (megadiversity)

20% of world vegetation
 10% of vertebrate animals

15 to 30 thousand catalogued
 1% scientifically prospected

Amazon Deforestation and Carbon
Dioxide Emissions, 1978-2003
Period
Annual
deforest
Km2
CO2 emissions
109 ton
Min.
Max.
(136 ton/ha)
(198 ton/ha)
% of World
Min.
Max
1978-88
22.273
0,31
0,45
4,4
6,3
1988-98
17.614
0,24
0,35
3,6
5,3
1998-03
20.133
0,27
0,40
4,5
6,6
Source: Author’s estimates based upon Inpe’s deforestation data
Net carbon emissions from land use
changes in Brazilian biomes, 1988-94
(MCT 2004)
Net emissions
Biome
Amazonia
Cerrado (shrub)
Atlantic forest
Caatinga (arid)
Pantanal (wetlands)
Total
TgC/yr TgCO2/yr
%
116,9
428,6
59
51,5
11,3
188,7
41,3
26
6
10
7,5
197,1
36,5
27,4
722,5
5
4
100
North Region: Secular growth
performance, 1840-2000
North Region: GDP per capita (2000 R$) and growth rates,1840-2000
GDPg%p.a.
GDP per capita
5
Rubber boom
1840-1912
4
2,7
2000 R$
3
2,7
2,7
2,7
Rubber Demographic and
crisis
economic lethargy
1912-1960
2,7
2,7
2,7
2,2
Stabilization
1994
15
Macro crisis
and stagnation
Regional 1980-2000
10
policies 9,0
4,3
1960-80
4,1
3,8
4,6
3,8
5
2,2
1,1
-0,5
1840
1860
1880
1900
2
2,3
1920
1940
-1,3
1960
1980
0
2000
-5
1,7
1
1,6
1,3
0,5
1,0
0
0,4
0,5
0,6
0,8
1,1
0,6
-13,7
0,8
-10
0,7
-15
% p.a.
GDPpcg%p.a.
Drivers of deforestation in
Brazilian Amazon




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
Macroeconomic factors: growth and exports
Accessibility to markets (transport cost) and geoecological conditions (topology and rainfall) are crucial
determinants of profitability and deforestation
Agricultural research (Embrapa)  soybean
Profits derived from productive activities -- logging,
cattle ranching and commercial crops (soybean) -drives deforestation;
Government incentives and subsidies were important in
the 70s not anymore; but federal transfers still make a
significant contribution to urban income
Land price speculation play a temporary role in remote
areas with costly access to markets;
Amazon deforestation x growth of
Brazilian GDP (1988-2005)
Def = 775*%GDP + 16.600 km2
Crescimento do PIB no Brasil e desflorestamento da Amazônia, 1978-2005
Desflorestamento
Cresc. PIB %
25.000
3,0
20.000
1,0
15.000
-1,0
10.000
-3,0
5.000
Ano agrícola (Sep-Aug)
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
-5,0
1989
0
Cresc. PIB (% a.a.)
5,0
30.000
1977/88
Desflorestamento (km2/ano)
35.000
Regional differences in real land
prices: cleared areas, 1966-2002
Brasil: Regional average real land prices for unplowed fields,
1966-2002 (R$ 2000/ha)
North
Northeast
East
South
West
Brazil
3000
13.901
2500
1500
1000
500
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
1986
1985
1984
1983
1982
1981
1980
1979
1978
1977
1976
1975
1974
1973
1972
1971
1970
1969
1968
1967
0
1966
2000 R$/ha
2000
Regional differences in real land
prices: forest, 1966-2002
Brasil: Regional average real land prices for natural forest area,
1966-2002 (2000 R$/ha)
North
Northeast
East
South
West
Brazil
6000
5000
3000
2000
1000
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
1986
1985
1984
1983
1982
1981
1980
1979
1978
1977
1976
1975
1974
1973
1972
1971
1970
1969
1968
1967
0
1966
2000 R$/ha
4000
Transport costs ($/ton) to
São Paulo, 1968
Transport costs ($/ton) to
São Paulo, 1980
Transport costs ($/ton) to
São Paulo, 1995
Transport costs ($/ton) to
nearest state captital, 1968
Transport costs ($/ton) to
nearest state captital, 1980
Transport costs ($/ton) to
nearest state captital, 1995
Reduction in transport cost to
national markets (São Paulo),
1968-80
Reduction in transport cost to
national markets (São Paulo),
1980-95
Reduction in transport cost to
local markets (State capital),
1968-80
Reduction in transport cost to
local markets (State capital),
1980-95
Costs and benefits: roads
(Fuller et al 2002, McVey 2002)





Soybean trucked to markets (~ 800 miles) in
very poor road conditions  2 x US costs
Paving of roads is a hot policy issue (Avança
Brasil)
Mechanical extrpaolations Laurance et. al
2002  catastrophic results; 35-50% of
Amazonia deforested
Econometric models Andersen et al. 2002 Pfaff
et al. 2005  more reasonable impacts
Roads lead to land use intensification (logging
and cattle rising)  deforestation impact
depends on the elasticity of demand in
relevant markets (Angelsen 1999): local x
national markets
The Carajás investment program: the
railway corridor (EFC), steel mills (+)
and the impact area (AIC)
Trends and projections



Geometric trend extrapolations of deforestation
are untenable. Land prices and land use
intensification (short fallow) act as deterrents of
deforestation. Systemic effects are important
The indirect long run effects of Carajás on
deforestation are relatively small. Urban
concentration of population increases land prices
and reduces fertility rates
Policy trade-offs: deforestation x growth favors
subsidized credit credit; deforestation x equity
favor roads; land prices are crucial
Policy issues: impacts of
Carajás



Demographic transition and urbanization
 smaller long run rates of population
growth
Population density  higher price of land
 intensification of land use  saturation
effects
Roads  increased commercialization 
intensification of land use
Brazil: R&D expenditures on
agriculture, 1973-1993
Brazil: Embrapa expenditure on R&D in agriculture (real terms and as % of Agricultural GDP)
%Ag Gdp
0,6%
7
0,5%
6
4
0,3%
3
0,2%
2
0,1%
1
0,0%
0
1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993
1993 BR$
5
0,4%
% Agr.GDP
Milhões
Real
Soybean yield in AML and the rest
of Brazil, 1975-2004 (ton/ha)
Soybean yield in Legal Amazonia and in the rest of Brazil, 1975-2004
(ton/ha)
Legal Amazonia
Rest of Brazil
3,5
3,0
ton/ha
2,5
2,0
1,5
Fonte: IBGE - PAM
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
1986
1985
1984
1983
1982
1981
1980
1979
1978
1977
1976
1975
1,0
Spatial dynamics of
deforestation in Amazon



Squatter doing shifting cultivation and
loggers are leading agents of (small scale)
deforestation in wild areas
Cattle ranchers and large scale deforestation
come in the second stage of frontier
settlement
Commercial crops (soybean) penetrate in the
third stage replacing pasture area with
relatively small impact on deforestation in
consolidated areas
Cattle herd in Legal Amazonia and in the rest of Brazil, 1975-2003
(million heads)
Rest of Brazil
Legal Amazonia
200
180
160
Million heads
140
120
100
80
60
40
20
Fonte: IBGE - PPM
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
1979
1977
1975
0
Rondônia
1981
1983
Tocantins
Maranhão
Acre
Amazonas
Roraima
Amapá
2002
Pará
1999
Mato Grosso
1977
Legal Amazonia: Cattle herd by State, 1977-2003
60
Million heads
50
40
30
20
10
Fonte: IBGE - PPM
2003
2001
2000
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
1986
1985
1984
1982
1980
1979
1978
1976
1975
0
Cattle herd density
(heads/km2), 1975
Cattle herd density
(heads/km2), 1980
Cattle herd density
(heads/km2), 1985
Cattle herd density
(heads/km2), 1990
Cattle herd density
(heads/km2), 1995
Cattle herd density
(heads/km2), 2000
Cattle herd density
(heads/km2), 2003
Cost and benefits: cattle raising
(Margulis 2002, Faminow 1999, Andersen
2002)





Early settlers capitalize gains in land appropriation
(land price speculation)
Large (capitalized) cattle ranchers appropriate most
of the gains of forest conversion:
 rates of return in cattle ranching are potentially
high (circa 10% p.a.)
Deforestation + small scale cattle ranching important
mechanism of social mobility  extensive land use
technologies
No ecological/precipitation constraint  penetrates
the rain forest
Economic/environmental sustainability of cattle
ranching still an open issue
Soybean cropped area in Legal Amazonia and in the rest of Brazil,
1980-2004 (million ha)
Milllion ha
Legal Amazonia
Rest of Brazil
22
20
18
16
14
12
10
8
6
4
2
Source: IBGE - PAM
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
1986
1985
1984
1983
1982
1981
1980
0
Legal Amazonia: Soybean cropped area by states, 1980-2004
Mato Grosso
Maranhão
Tocantins
Pará
Rondônia
6
Million ha
5
4
3
2
1
Fonte: IBGE - PAM
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
1986
1985
1984
1983
1982
1981
1980
0
Soybean: harvested area as
% of area, 1975
Soybean: harvested area as
% of area, 1980
Soybean: harvested area as
% of area, 1985
Soybean: harvested area as
% of area, 1990
Soybean: harvested area as
% of area, 1995
Soybean: harvested area as
% of area, 2000
Soybean: harvested area as
% of municipal area, 2004
Cost and benefit: soybean
(Ong 2004, Rezende 2004)





The role of Embrapa agricultural research was crucial
specially for soybean cultivation
Large scale mechanized technology leads to income
concentration but does not generate frontier
proletarians
Agro-business activities  urban employment
Strong precipitation restrictions  does not
penetrate the dense rain forest
Comes in a later stage of settlement 
mechanization requires no trunks and roots
Technology and deforestation
(Angelsen and Kaimowitz 2001)


Sustainable development requires both higher
productivity and forest conservation
Technological change (progress)




Increase in TFP (total factor productivity)
Embodied (in inputs) x disembodied (management)
Factor saving x neutral changes
Technological change x deforestation


Green revolution (Borlaug) hypothesis: fixed demand +
higher yield  less agricultural area (global level)
Subsistence hypothesis: new technology impact on land
requirements
• Full belly x increased aspirations)
• Land degradation hypothesis

Development hypothesis: dynamic feed backs are positive
(EKC, forest transition, Nerlove)
Technology and deforestation





Selective logging is important source of finance for
initial investment
Slash and burn technique is a rational response to
the relative scarcity of labor and capital in the early
stages of settlement
Cattle raising with extensive land use is a rational
product/technology choice given the low prices of
land and thus becomes the most important source of
deforestation
Intensification requires adequate infrastructure
(roads) and adequate topology
Geo-ecological (rainfall) barries to commercial crops
(soybean as suplementary feeding)
Above-ground carbon cycle
in-slash-and-burn agriculture
Carbon
stock in
Vegetation
(ton / ha)
initial carbon stock
1st burn
abandon
decomposition + use
secondary recovery
Time
Brazil: fire spots + forest
fires, 2003QI (IBGE 2005)
Brazil: fire spots + forest
fires, 2003QII (IBGE 2005)
Brazil: fire spots + forest
fires, 2003QIII (IBGE 2005)
Brazil: fire spots + forest
fires, 2003QIV (IBGE 2005)
Policy issues: technological
options



The impact of land use intensification (both for
logging and cattle ranching) on deforestation
depends on the importance of local and national
markets as destination of output (elasticity of
demand)
Intensification in remote frontier areas is restricted
by lack of transport infrastructure and by geoecological conditions (dense forest, topology, etc.)
Intensification will require technical government
research and assistance as well as comprehensive
campaigns of technology dissemination
Policy issues: fiscal and
environmental instruments



The reduction of federal government transfer (
fiscal responsibility) will indirectly induce lower
deforestation through increased taxation and lower
disposable income
• Taxation of land at municipal level is an
important policy issue
• Transfer linked to deforestation performance
• International compensation
Effective regulation of land use (forest reserve) is an
important instrument to halt deforestation in critical
environmental areas (rainforest, etc.)
Government investment in infrastructure (roads)
Total economic value (TEV) of the
standing forest
Use values
Existence
I. Direct
II. Indirect
III. Option
1. Sustainable
logging and and
extraction of
other forets
products
1. Local climate
stability
1. Future uses of
I. and II.
2. Tourism and
other
recreational
activities
2. Nutrient
recycling
2. Insurance
premium for the
future use of
biodiversity
3. Sicentific and
educational
services (genetic
material)
3. Hydrological
balance and
protection of
aquifers
4. Global climate
stability (carbon
sequestration )
1. Preservation of
cultural and
aesthetic
inheritance
Empirical problems
Dificulties of distinguishing indirect
use value, option values and
existence values
 Uncertainties in estimates are
significant
 The order of magnitude of benefits
and beneficiaries

Rate of discount



Benefits of future generations which are
more rich and better endowed with
technology
Rate of discount = pure rate of
intertemporal discount + elasticity of the
marginal utility of consumption x growth
rate of consumption
Value used are 2%, 6% and 12% a.a.
Total Economic Value (GDP) of
deforested areas in Legal Amazonia
from 1985-95 in US$ de 1995/ha
(Andersen et al 2002)
Net present value of
Rural GDP
Total
GDP
Total Economic Value
Private
benefits
Local public benefits
Global
public
benefits
Discount rates
2%
6%
12%
p.a.
p.a.
p.a.
1..657
553
276
2.406
3635
802
1.418
401
481
1.425
590
475
163
237
74
1620
790
170
Policy issues in the PostKyoto environment


Need of international compensation
Avoided deforestation






Project x national level
Non-permanence issue
Sovereignity
Leakages
Externality problems
Transfer of technology: intensification will
require technical research and assistance
as well as massive investments on
technology dissemination
Policy issues in the PostKyoto environment



The reduction of federal government transfer (fiscal
responsibility) will indirectly induce lower
deforestation through increased taxation of
economic activity
• Taxation of land at municipal level could play
some role
• Transfer linked to deforestation performance
Effective regulation of land use (forest concession
and reserves) is an important instrument to halt
deforestation in critical environmental areas
(rainforest, biodiversity niches etc.)
Government investment in infrastructure (roads)
Brazilian geographic regions
(Ibge 2005)
Table 3. Simulation of Percent Change in Converted
Land (ARALT) per Hectare of MCA Land for the IPCC
Scenarios A2 for the Timeslices 2050s and 2080s –
Model C, Weighted
2050 A2
Region
North
E1
2080 A2
E2
E1
E2
-13
-11
-27
-26
Northeast
11
12
27
30
Southeast
11
11
15
15
South
24
22
32
29
CentralWest
12
15
2
5
Brazil
12
13
14
15
Table 3. Simulation of Percent Change in Converted
Land (ARALT) per Hectare of MCA Land for the IPCC
Scenarios B2 for the Timeslices 2050s and 2080s –
Model C, Weighted
2050 B2
Region
North
Northeast
Southeast
South
CentralWest
Brazil
E1
2080 B2
E2
E1
E2
-21
1
8
18
9
-18
2
9
16
12
-41
9
11
19
-1
-39
11
12
17
3
6
7
5
7
Table 3. Simulation of Percent Change in Converted
Land Value per Hectare for the IPCC Scenarios A2 for
the Timeslices 2050s and 2080s – Model C, Weighted
2050 A2
Region
North
Northeast
Southeast
South
CentralWest
Brazil
E1
2080 A2
E2
E1
E2
-32
6
28
202
84
-31
7
28
201
82
-63
-5
22
602
134
-63
-5
20
592
126
90
89
221
216
Table 3. Simulation of Percent Change in Converted
Land Value per Hectare for the IPCC Scenarios B2 for
the Timeslices 2050s and 2080s – Model C, Weighted
2050 B2
Region
North
Northeast
Southeast
South
CentralWest
Brazil
E1
2080 B2
E2
E1
E2
-29
2
18
134
42
-28
4
18
133
41
-53
1
24
194
56
-52
2
23
192
54
57
56
79
78