Identifying sustainable pathways with GLOBIOM-Brazil model

(1)

Identifying sustainable pathways

with GLOBIOM-Brazil model

Aline Soterroni, INPE/IIASA

Fernando Ramos, INPE

Michael Obersteiner, IIASA

+ many colleagues

Systems Analysis and the Americas

Fundação Getulio Vargas

September 6

th

_{, 2019}

(2)

Conflicting interests

PRODUCTION

PROTECTION

(3)

Major Products

Brazil – Global ranking

Production

Exports

Sugar

1

o

₁

o

Coffee

1

o

₁

o

Orange juice

1

o

₁

o

Bovine meat

2

o

₁

o

Poultry meat

2

o

₁

o

Corn

3

o

₃

o

Soy grains

2

o

₁

o

Soy cake

4

o

₂

o

Soy oil

4

o

₂

o

Cotton

4

o

₂

o

Pig meat

4

o

₄

o

Brazilian position

Source: USDA, 2017 3

(4)

Where does Brazil export to? (2017)

https://atlas.media.mit.edu/

(5)

Brazil’s agriculture trade balance

23% 46% 25%

GDP

Exports

Employees

Brazil’s agribusiness

0 15 30 45 60 75 90 105 1 9 8 9 1 9 9 0 1 9 9 1 1 9 9 2 1 9 9 3 1 9 9 4 1 9 9 5 1 9 9 6 1 9 9 7 1 9 9 8 1 9 9 9 2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3 2 0 0 4 2 0 0 5 2 0 0 6 2 0 0 7 2 0 0 8 2 0 0 9 2 0 1 0 2 0 1 1 2 0 1 2 2 0 1 3 2 0 1 4 2 0 1 5 2 0 1 6 U S$ b ill io n

Exports Imports Net

Source: https://www.embrapa.br/visao/trajetoria-da-agricultura-brasileira

(6)

1/3 of world’s rainforest (2/3 of Amazon)

(7)

Amazon Deforestation

De for es ta ti on (k m 2 /y ear) Source: PRODES/INPE 7

(8)

The Paris Agreement on Climate Change

After 21 years of negotiations, the UN delivered a universal

(9)

The Paris Agreement on Climate Change

Contribution: Brazil

intends to commit to

reduce greenhouse gas

emissions by

37% below

2005 levels in 2025

Subsequent indicative

contribution: reduce

greenhouse gas

emissions by

43% below

2005 levels in 2030

After 21 years of negotiations, the UN delivered a universal

(10)

(11)

Brazil’s emissions by sector

0 0.5 1 1.5 2 2.5 3 3.5 4 1 9 9 0 1 9 9 1 1 9 9 2 1 9 9 3 1 9 9 4 1 9 9 5 1 9 9 6 1 9 9 7 1 9 9 8 1 9 9 9 2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3 2 0 0 4 2 0 0 5 2 0 0 6 2 0 0 7 2 0 0 8 2 0 0 9 2 0 1 0 2 0 1 1 2 0 1 2 2 0 1 3 2 0 1 4 2 0 1 5 B ill io n C O 2 e q /y r

Land Use Change Agriculture Waste

Industrial Processes Energy

Source: SEEG (System for the Estimation of Greenhouse Gases)

(12)

Brazil’s emissions by sector

0 0.5 1 1.5 2 2.5 3 3.5 4 1 9 9 0 1 9 9 1 1 9 9 2 1 9 9 3 1 9 9 4 1 9 9 5 1 9 9 6 1 9 9 7 1 9 9 8 1 9 9 9 2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3 2 0 0 4 2 0 0 5 2 0 0 6 2 0 0 7 2 0 0 8 2 0 0 9 2 0 1 0 2 0 1 1 2 0 1 2 2 0 1 3 2 0 1 4 2 0 1 5 B ill io n C O 2 e q /y r

Land Use Change Agriculture Waste

Industrial Processes Energy

~70% of Brazil’s emissions come from Land Use Change and Agriculture sectors

(13)

Brazil’s emissions by sector

0 0.5 1 1.5 2 2.5 3 3.5 4 1 9 9 0 1 9 9 1 1 9 9 2 1 9 9 3 1 9 9 4 1 9 9 5 1 9 9 6 1 9 9 7 1 9 9 8 1 9 9 9 2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3 2 0 0 4 2 0 0 5 2 0 0 6 2 0 0 7 2 0 0 8 2 0 0 9 2 0 1 0 2 0 1 1 2 0 1 2 2 0 1 3 2 0 1 4 2 0 1 5 B ill io n C O 2 e q /y r

Land Use Change Agriculture Waste Industrial Processes Energy 78% 46%

13

(14)

Brazilian territory

Protected Areas per biome

• Area: 850 million ha (Mha)

• Protected areas: 243 Mha (28%)1

• Private properties: 569 Mha (67%)2

• ~50% of Brazil’s native forests are inside private properties3 1 _{MMA, 2012} 2_{MDA, 2011} 3_{Soares-Filho et al, 2014}

Amazon

Cerrado

Caatinga

Atlantic

Forest

Pantanal

Pampa

14

(15)

2012 Brazil’s Forest Code

New Forest Code

Yas u yo sh i Ch ib a/A FP/ G ett y Ima ge s

CRA

(Environmental Reserve Quotas)

SFA

(Small Farms Amnesty)

CAR

(Rural Environmental Registry) 15

(16)

16

• Is the 2012 Forest Code enough to reconcile protection and

production in Brazil?

• Can Brazil achieve its INDC commitments by enforcing the

2012 Forest Code?

(17)

GLOBIOM

Model

2000 2010 2020 2030 2040 2050 Base Year Validation Projections

Time Resolution

GLOBIOM-Brazil

can be run up to

2100

www.globiom.org

(18)

GLO

bal

BIO

sphere

M

anagement

Model

Time Resolution

GLOBIOM-Brazil

can be run up to

2100

www.globiom.org

(19)

Time Resolution

GLOBIOM-Brazil

can be run up to

2100

(20)

Time Resolution

GLOBIOM-Brazil

can be run up to

2100

Croplands

Livestock

Bioenergy

Forestry

www.globiom.org

(21)

Time Resolution

GLOBIOM-Brazil

can be run up to

2100

Croplands

Livestock

Bioenergy

Forestry

www.globiom.org

(22)

GLOBIOM-Brazil: regional version of GLOBIOM

Regional zooming allows detailed spatial representation of land (50x50km) and introduction

of regional policies

30

(23)

Time Resolution

GLOBIOM-Brazil

can be run up to

2100

global –

bottom-up

– partial – equilibrium

www.globiom.org

Croplands

Livestock

Bioenergy

Forestry

(24)

Time Resolution

GLOBIOM-Brazil

can be run up to

2100

global –

bottom-up

– partial – equilibrium

www.globiom.org

Croplands

Livestock

Bioenergy

Forestry

recursive dynamic

DEMAND

(25)

30

25

(26)

30

26

Amazon

Cerrado

Atlantic Forest

Caatinga

Pantanal

Pampa

GLOBIOM-Brazil: 50 km x 50 km in Brazil

(27)

global – bottom-up – partial –

equilibrium

www.globiom.org

Croplands

Livestock

Bioenergy

Forestry

DEMAND

TRADE

Welfare = Producer surplus + consumer surplus Welfare (GLOBIOM) = integral of demand – costs of production

(28)

DEMAND

www.globiom.org Welfare = Producer surplus + consumer surplus Welfare (GLOBIOM) = integral of demand – costs of production Havlik et al. (2011)

(29)

DEMAND

www.globiom.org Welfare = Producer surplus + consumer surplus Welfare (GLOBIOM) = integral of demand – costs of production

global – bottom-up – partial –

equilibrium

demand

land management costs

processing costs trade cost function

livestock production costs

land-use change costs

demand

demand crop or wood quantity live prod quantity exports imports

processed quantity

(30)

DEMAND

www.globiom.org Welfare = Producer surplus + consumer surplus Welfare (GLOBIOM) = integral of demand – costs of production

global – bottom-up – partial –

equilibrium

demand

trade cost function production costs land-use change costs

demand demand exports imports crop livestock wood quantities demand target

(31)

DEMAND

www.globiom.org

global – bottom-up – partial –

equilibrium

demand

trade cost function production costs land-use change costs

demand demand exports imports crop livestock wood quantities demand target

(32)

★

SSP 3

SSP 2

SSP 1

(High Challenges)

Regional Rivalry

Middle of the Road

(Intermediate Challenges)

Sustainability

Socio-economic challenges

for adaptation

So

cio

-econo

m

ic

challeng

es

for

m

it

ig

ati

on

(Low Challenges)

(33)

★

SSP 3

SSP 2

SSP 1

(High Challenges)

Regional Rivalry

Middle of the Road

(Intermediate Challenges)

Sustainability

Socio-economic challenges

for adaptation

So

cio

-econo

m

ic

challeng

es

for

m

it

ig

ati

on

(Low Challenges)

High yield

growth

Moderate yield

growth

Slow yield

growth

Crop yields

(34)

Time Resolution

GLOBIOM-Brazil

can be run up to

2100

GLOBIOM Model

www.globiom.org

(35)

Time Resolution

GLOBIOM-Brazil

can be run up to

2100

GLOBIOM Model

www.globiom.org RUMINANT G4M

EPIC

(36)

Cropland sector

EPIC

Crop model

18 crops – IBGE

(including Soya, Corn, SugC, BeaD, Barl, Cass, Whea, etc)

4 different systems (HI, LI, IR, SS) + Double cropping soy/corn

36

(37)

Cropland sector

EPIC

Crop model

18 crops – IBGE

(including Soya, Corn, SugC, BeaD, Barl, Cass, Whea, etc)

4 different systems (HI, LI, IR, SS) + Double cropping soy/corn

EPIC outputs are input data for

GLOBIOM model. Costs are

derived from EPIC and

harmonized with FAO data.

Yield increase:

 Exogenously (breeding, new

varieties, etc)

 Endogenously (response to

price)

37

Potential

yields

Fertilizer

needs (N, P)

Water

requirements

Costs

EPIC: Environmental Policy Integrated Climate Model

(38)

38

2000

2005

2010

2015

2020

2050

Base

Year Validation Scenarios Projections

(39)

39

2000

2005

2010

2015

2020

2050

Base

Year Validation Scenarios Projections

(40)

40

GLOBIOM model uses Global Land Cover (GLC) 2000 by JRC

(41)

41

(42)

11/09/2019 42 Amazon Caatinga Atlantic Forest Pampa Pantanal Cerrado

(43)

Changing the time steps

2000

2010

2020

2030

Validation

Base

Year

2000

2005

2010

2015

2020

2025

2030

Validation

Base

Year

Projections

10-yr 5-yr

 DEFAULT

43

(44)

Double cropping – New management system

44

Double cropping soy-corn

Two crops are grown in sequence within an agricultural year on the same

piece of land

2000

2015

IBGE/PAM*

Double cropping area

soy-corn in Brazil increased

400% between 2000 and

2015

(45)

Internal Transportation Costs

(per product and destination)

Bovine Meat

Pulp Biomass

Roads Nearest state capital Nearest sea port

Costs to state

capitals

Costs to

sea port

(46)

Model steps

2000

2005

2010

2015

2020

2050

Base

(47)

Soybean harvested area and production

47 0 5 10 15 20 25 30 35 2005 2010 2015 So yb e an ar e a (m ill io n h e ct ar e s)

IBGE/PAM Baseline (GLOBIOM-Brazil)

(a) Soybean harvested area (b) Soybean production Brazil’s aggregated numbers of (a) soybean harvested area and (b) soybean production for the years 2005, 2010 and 2015 according to IBGE/PAM and as projected by the baseline scenario of GLOBIOM-Brazil. The differences between IBGE/PAM and GLOBIOM-Brazil for the year 2015 are less than 2% for either area or

(48)

Soybean harvested area in 2015

48

IBGE/PAM Baseline (GLOBIOM-Brazil)

Spatial distribution of the soybean harvested area in 2015 according to IBGE/PAM (left) and the baseline scenario of GLOBIOM-Brazil (right). Color bar values are

expressed in thousands of hectare per cell.

(49)

Cattle herd in Brazil in 2010

Cattle herd in Brazil and major biomes in million heads for the year 2010 according to IBGE/PPM and as projected by the baseline scenario of GLOBIOM-Brazil. The difference

between IBGE/PPM and GLOBIOM-Brazil at national level in 2010 is smaller than 2%.

(50)

Accumulated deforestation (2001-2015)

50

0 50 100 150 200 250 300 0 50 100 150 200 250 300

PRODES/INPE Baseline (GLOBIOM-Brazil)

19.3 million hectares 17.9 million hectares

Spatial distribution of accumulated deforestation in the Amazon biome from 2001 to 2015 given by PRODES/INPE (left) and as projected by the baseline scenario of GLOBIOM-Brazil (right). The difference between PRODES/INPE and GLOBIOM-Brazil is

(51)

Model steps

2000

2005

2010

2015

2020

2050

Base

(52)

Impacts of Brazil’s Forest Code

Legal Reserve Requirement Amnesty of Small Farms IP AM 1934 Decret 23.793 1965 Federal Law 4.771 2012 Federal Law 12.651 IB GE So ar es -F ilh o et al (2 01 4) CRA 52

(53)

Scenarios

Measures of the Forest Code

No

enforcement of

the Forest Code

NoFC

Full

enforcement of

the Forest Code

FC

Illegal deforestation control

no

yes

Small farms amnesty (SFA)

no

yes

Environmental Reserve Quota (CRA)

no

yes

Forest regrowth

no

yes

11/09/2019 53

Counterfactual Scenario

(54)

Cropland expansion x Forest stabilization

11/09/2019 54

2000 2010 2020 2030 2040 2050

Cropland expansion (bar charts) and native vegetation area evolution (line charts) as projected by the FC and NoFC scenarios

0 30 60 90 120 300 350 400 450 500 2000 2020 2040 Year C ro p la n d s ( M h a ) N a tiv e V e g e ta tio n ( M h a ) Croplands (FC) Croplands (NoFC) Native Vegetation (FC) Native Vegetation (NoFC)

(55)

Cropland expansion x Forest stabilization

0 30 60 90 120 300 350 400 450 500 2000 2020 2040 Year C ro p la n d s ( M h a ) N a tiv e V e g e ta tio n ( M h a ) Croplands (FC) Croplands (NoFC) Native Vegetation (FC) Native Vegetation (NoFC)

2000 2010 2020 2030 2040 2050

4% smaller

under FC

scenario

FC could

prevent a net

loss of 53.4

million

hectares of

deforestation

Cropland expansion (bar charts) and native vegetation area evolution (line charts) as projected by the FC and NoFC scenarios

(56)

Forest loss (orange) or gain (blue)

−300 −250 −200 −150 −100 −50 0 50 100 −300 −250 −200 −150 −100 −50 0 50 100

No Forest Code

Forest Code

Loss: 25.1 Mha Gain: 12.9 Mha Loss: 65.5 Mha Gain: 0 Mha 11/09/2019 56

Spatial distribution of cumulative loss (orange) or gain (blue) of native vegetation for the scenarios NoFC (left) and FC (right) between 2010 and 2050.

(57)

Cropland and pasture loss/gain

Forest Code Scenario (2010 – 2050)

−300 −200 −100 0 100 200 300 −300 −200 −100 0 100 200 300

Cropland loss/gain

Pasture loss/gain

(58)

0 0.2 0.4 0.6 0.8 1 1.2 1.4 2010 2020 2030 2040 2050 LU C F e m is si o n s (G tC O 2 e q /y r)

No Forest Code Forest Code

Emissions from land-use change and forestry

Brazil’s NDC commitment: reduction of 1.2 GtCO2eq (from 2.8 GtCO2eq to 1.6 GtCO2eq)

- 1.03 GtCO2eq

- 0.32 GtCO2eq

(59)

Emissions

59 0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 1990 1995 2000 2005 2010 2015 2020 2025 2030 EM IS SI O N S [M tC O 2 e/ ye ar )

(60)

Emissions (1% increase)

(61)

(62)

Emissions – FC scenario

(63)

Brazil’s NDC



_{Land Use Change and Forests}



_{Zero illegal deforestation}

_{in the Amazon biome by 2030}



_{Restoring and reforesting}

_{12 million hectares (Mha) of}

forests

by 2030



_{Enhancing sustainable native forest management system}

(64)

GLOBIOM-Brazil model and the Brazil’s INDC

64

“The Brazilian Government has benefited from the cooperation

between IIASA

and leading Brazilian public institutions INPE

and IPEA. The

results of the

GLOBIOM-Brazil model were quite

informative and have

provided

science-based evidence

that has

contributed to

Brazil’s INDC.”

Dr. José Domingos Miguez

Dr. José Domingos Miguez was the Director of the

Department of Environmental Evaluation in the

Ministry of the Environment, Brazil, and one of

the Brazil’s leading climate negotiators.

(65)

Example of policy evaluation with

GLOBIOM-Brazil

(66)

Brazil is a major producer of soybeans

(67)

Deforestation x Soy Expansion in the Amazon

(68)

Deforestation x Soy Expansion in the Amazon

68

Soy Moratorium (SoyM)  First zero-deforestation agreement signed in the tropics

April, 2006

Greenpeace Campaign

24 July, 2006

SoyM signed by ABIOVE and ANEC

(69)

Deforestation x Soy Expansion in the Amazon

April, 2006

Greenpeace Campaign

24 July, 2006

SoyM signed by ABIOVE and ANEC

(70)

70

21%

47% 32%

Amazon Cerrado Others

70% 30%

Matopiba Outside Matopiba

Soy expansion between 2000 and 2016

Source: IBGE/PAM

Where do the soybeans

grow in Brazil?

MATOPIBA Cerrado

(71)

Brazil’s

agricultural

boom

Cerrado context

Source: Carneiro-Filho & Costa, K Agroicone, INPUT 2016

19.8% of undisturbed remnants

8% of Cerrado is under protection

4,800 endemic species under threat

Between 2000 and 2014,

around 30% of the soy

expansion in the Cerrado

occurred in native vegetation

(72)

More than 70 companies have signed the Cerrado Manifesto, mostly in the consumer and retail sectors (not the big traders such as Cargill, Bunge and ADM, or entities such as

China which buys a third of the Cerrado soybeans )

Cerrado Manifesto

(73)

73

2020

2030

2040

2050

What would be the impacts of the SoyM

expansion to the Cerrado

(74)

Scenarios

74

Illegal deforestation control No deforestation control Soy Moratorium SoyM Amazon 2006 SoyM Amazon 2006 SoyM Cerrado 2020

Baseline

SoyMExp

(75)

Scenarios

75

Illegal deforestation control No deforestation control Soy Moratorium SoyM Amazon 2006 SoyM Amazon 2006 SoyM Cerrado 2020

Baseline

SoyMExp

Forest Code

SoyM Amazon 2006

(76)

Soybeans expansion in Brazil (2021-2050)

76 0 5 10 15 20 25 30 35 40 45 50 Baseline 2020 Baseline 2050 SoyMExp 2050 So yb e an ar e a (m ill io n h a)

Cerrado Amazon Other biomes

(77)

Area (million ha)

Production (million ton)

SoyM in the Cerrado would not prevent future

soybean expansion

(78)

Area (million ha)

Production (million ton)

2.1 4.0

7% of soybean area projected in the Cerrado by 2050

(79)

Area (million ha)

Production (million ton)

1.1 0.9

2%

of soybean area projected in Brazil by 2050

(80)

0 50 100 150 200 250 300 0 50 100 150 200 250 300

Avoided (direct) loss due to SoyM (2021–2050)

Spatial distribution of accumulated DIRECT avoided deforestation for soybean expansion from 2021 to 2050 due to FC (left) and the full compliance with the SoyM (right)

Co lo r b ar v alu es are ex p resse d in t h o u san d s o f h ect ares p er cell. 3.6 Mha 0.9 Mha

(81)

TRASE dataset

81

TRASE is an internet tool developed jointly by international non-profit organization

(NGO) Stockholm Environment Institute (SEI) and Global Canopy to improve

transparency in supply chains globally by tracking commodities supply chains in

(82)

Risk of future deforestation

82 0 50 100 150 200 250 300 Bunge Cargill Dreyfus Domestic Consumption Abiove/ Anec China EU28 Market share from

TRASE and future deforestation for soy from GLOBIOM-BRAZIL

(83)

Risk of future deforestation

83

0 50 100 150 200 250 300 0 50 100 150 200 250 300

Louis Dreyfus Cargill

Soy sourced (2015): 1.8 Mton Soy sourced (2015): 3.6 Mton Risk of deforestation

0.01 Mha (0.3% of future deforestation to soy)

Risk of deforestation 0.43 Mha (12% of future deforestation to soy)

(84)

Deforestation risk (absolute and relative)

84 0 10 20 30 40 50 60 70 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

ADM Amaggi Bunge Cargill Cofco Louis Dreyfus

Big 6 Abiove & Anec Fu tu re re la ve d e fo re st a o n ri sk (h a/ 1 0 0 0 to n /y r) Fu tu re d e fo re st a o n ri sk (M h a )

(85)

Deforestation risk (absolute and relative)

85 0 10 20 30 40 50 60 70 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

ADM Amaggi Bunge Cargill Cofco Louis Dreyfus

Big 6 Abiove & Anec Fu tu re re la ve d e fo re st a o n ri sk (h a/ 1 0 0 0 to n /y r) Fu tu re d e fo re st a o n ri sk (M h a )

(86)

Deforestation risk (absolute and relative)

86 0 5 10 15 20 25 30 35 40 45 50 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

Brazil China EU28

Fu tu re re la ve d e fo re st a o n ri sk (h a/ 1 0 0 0 to n /y r) Fu tu re d e fo re st a o n ri sk (M h a )

(87)

Deforestation risk (absolute and relative)

87 0 5 10 15 20 25 30 35 40 45 50 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

Brazil China EU28

Fu tu re re la ve d e fo re st a o n ri sk (h a/ 1 0 0 0 to n /y r) Fu tu re d e fo re st a o n ri sk (M h a )

(88)

Overview of other policy studies

(89)

Biofuels

89 10.70 16.04 27.92 47.35 60.50 70.69 10.72 16.04 27.92 30.25 33.69 35.74 37.44 10.72 16.04 27.92 37.87 45.23 50.22 -10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 2000 2005 2010 2015 2020 2025 2030 New scenarios (billion litres)

(90)

90 RCP 2.6 RCP 8.5 HadGEM-ES NorESM1-M MIROC-ESM GFDL-ESM2M IPSL-CM5A

Anthropic

Disturbances

GHG Emissions

and RCPs

Radiative forcing and climatic scenarios

Climatic Impacts

Global Climate Model

– CMIP5/ISIMIP

Global projections of temperature, precipitation,

moisture

Global Crop Model

– ISIMIP

Global projections of crop productivity (crops

+ grass)

Biophysical Impacts

_{Economic Impacts}

GLOBIOM-Brazil

Global changes in land use, supply and demand,

bilateral trade

RESTORE+ BRAZIL

MODELING GROUP

EPIC LPJmL

(91)

Impacts on

soybean area

in Brazil

Name - Title 91

(a)

(b)

(c)

(92)

0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 2.25 2.50 2.75 3.00 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 D ef o re st a o n in m ill io n h a p er ye ar

Fragile Brazil’s NDC commitments?

(93)

Obrigada!

Thank you!

soterr@iiasa.ac.at

aline.soterroni@inpe.br

alinecsoterroni@gmail.com

“Essentially, all models are wrong, but some are useful”

George E.P. Box

(94)

0 Mha

18 Mha

Governance

Restoration

NoFC

FC FCnoCRA

Full IDC

NoFC

IDCImpf

IDCAmz

FCnoCRA

Very week

IDC

FC

No IDC Partial IDC Full IDC

12 Mha

(95)

Accumulated deforestation (2001-2020)

11/09/2019 95

(96)

Accumulated deforestation (2001-2020)

11/09/2019 96

(97)

Accumulated deforestation (2001-2020)

11/09/2019 97

(98)

Total emissions – LUCF sector – Brazil

98