Potential

Mitigation of

GHG Emissions

in Brazil

Christophe de Gouvello

Lead AuthorThe World Bank

Supervision Team from the World Bank:

Adriana Gonçalves Moreira, AFOLU,

Alexandre Kossoy, Carb Finance

Barbara Farinelli, SD

Benoit Bosquet, AFOLU

Christophe de Gouvello, TTL,

Flavio Chaves, Transport,

Fernanda Pacheco, Program Assistant

Francisco Sucre, Oil dand Gas

Fowzia Hassan, Operation Analyst

Garo Batmanian, Amazon Region

Jennifer Chang, Finance

Mark Lundell, SD, sector leader

Mauro Lopes de Azeredo, LCC5C, External Communication

Pamela Sud, JPA

Paul Procee, Transport, Environment

Rogerio Pinto, Consultant, ETC

Team of Brazilian Specialists

CEAF Regis Manoel Liana

CETESB

João Wagner, Josilene Ticianeli Vanuzini

Ferrer,

Fátima Aparecida Carrara, Marcos Cunha

COPPE-

UFRJ

Roberto Schaeffer, Alexandre Szklo,

Bruno S. M. C.Borba,

André F.P.de Lucena, David C. Branco,

Amaro Pereira

CPTEC/

INPE

Saulo Freitas, Karla Longo, Ricardo

Siqueira

EMBRAPALuis Barioni, Geraldo Martha,

Bruno Alves, Magda Lima

UFMG Britaldo Soares, Letícia Hissa

ICONE André Nassar, Leila Harfuch

Iniciativa

Verde

Osvaldo Martins, Magno Castelo Branco,

Renato Toledo

INT Maurício Henriques

LOGITFuad Jorge Alves José, Ronaldo

Balassiano, Wagner C.Martins

Plantar Fábio Marques

UNICAMPArnaldo Silva, Gilberto Jannuzzi, Rodolfo

Gomes

USP Sérgio Pacca and Júlio Hato

18%

14%

5%64%

Energy

Transport

Waste

Land Use and Land Use Change

18%

14%

5%

41%

18%

5%Energy

Transport

Waste

Deforestation

Livestock

Agriculture

18%

14%

5%64%

Energy

Transport

Waste

Land Use and Land Use Change

Brazil GHG emissions profile is very unique

1.Considerable volumes of land

Agriculture and livestock have become key

sectors for growth

Leading to steady expansion over the

territory

Marginal expansion induces conversion of

native vegetation

Deforestation has become the main GHG

emissions source

2.Considerable efforts to explore

large renewable energy sources

Hydropower, Bio-ethanol from sugar cane

Low Carbon intensity of the Energy Matrix :

only 1.77 tCO2 vs 11.02 tCO2/cap in OECD

A – Land Use, Land Use Change and Forestry

Four main steps:

1) Calculation of the Available Area for agricultural expansion

2) Modeling of future spatial Land Use Change,including Deforestation (2010 - 2030)

3) Model GHG Emissions as a function of Land Use Change

4) Test technical options to reduce deforestation and emissions.

Land Use and Land Use

Change Modeling results

Land Use and Land Use

Change Modeling results

1. Act on Primary Causes: reduce need for new land

Free-up pasture for crop expansion by increasing

livestock productivity

2. Complementary Forest Protection :

Protect against illegal cuts Already being

implemented by the

Brazilian Government

1) Drastically reduce Deforestation

A DUAL STRATEGY

Technically feasible despite serious implementation challenges

(financing, extension services, innovative designs)

0

50000

100000

150000

200000

250000

2009 2012 2015 2018 2021 2024 2027 2030

Her

d si

ze p

er s

yste

m (h

ead)

Year

Complete cycle - degraded pastures

Complete cycle - extensive pastures

Extensive cow-calf + growing w/ supplementation + finishing on Integrated Crop-Livestock

Extensive cow-calf + growing w/ supplementation + finishing in feedlot

Increase Productivity of Livestock

to free-up land for expansion of

other activities to avoid deforestation

Without Policies to increase

Livestock Productivity

Increase Productivity of Livestock

to free-up land for expansion of

other activities to avoid deforestation

0

50000

100000

150000

200000

250000

2009 2012 2015 2018 2021 2024 2027 2030

Her

d si

ze p

er s

yste

m (h

ead)

Year

Complete cycle - degraded pastures

Complete cycle - extensive pastures

Extensive cow-calf + growing w/ supplementation + finishing on Integrated Crop-Livestock

Extensive cow-calf + growing w/ supplementation + finishing in feedlot

With Policies to increase

Livestock Productivity

1 a 2,0 ua/ha/ano

Integrated Livestock and

Agriculture Systems

1rst Year:

Rice + Eucalyptus

2nd Year:

Eucalyptus + Soyabean

3rd Year:

Eucalyptus + Pasture3rd to 10th Year:

Eucalyptus + Pasture + Animals

Without Policies to

increase Livestock

Productivity

With Policies to

increase Livestock

Productivity

-

25

50

75

100

125

150

175

200

225

250

275

300

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030

SOYBEAN

CORN

FOREST

BEAN

SUGAR-

CANE

RICE

COTTON

PASTURE

-

25

50

75

100

125

150

175

200

225

250

275

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030

REFORES-

TATION

SOYBEAN

CORN

FOREST

BEAN

SUGAR-

CANE

RICE

COTTON

PASTURE

Potential Impact of Increased Livestock productivity

to free-up pastures for expansion of other activities

without deforesting (Millions of Hectares)

Without Policies to increase

Livestock Productivity

With Policies to increase

Livestock Productivity

1. Native Forest Recovery

Brazil Law is unique: Mandatory Restoration of

Legal Forest Reserves

2. Plantations for Renewable Charcoal for Steel Industry

2) Sequestration :

Large Opportunities

Trade-off between legal compliance and risk of Carbon Leakage

(less land remains available for crop expansion)

3) Livestock:

Alternatives for mitigation of

GHG emissions

Improvement of

forage quality

Genetic improvement

of the cattle herd

Expansion of the

feedlot sector

Recovery of degraded

pastures

Adoption of integrated

systems (Crop-

Livestock, Crop-

Livestock-Trees)

Increased stocking rates

Decreased demand for

grazing lands

Improvement of

performance indices

Decreased age at

slaughter

Decrease in cow herd

size needed to supply

calves

Decrease in greenhouse

gas emissions

4) AgricultureGreenhouse Gases

(GHGs) Emissions from

Agricultural Systems

I. Soil EmissionsII. Emissions from fossil

fuels

CO2 – Changes in soil C stock.

N2O – Fertilizer, crop residues and soil

C losses (N2O from soil N

mineralization )

CH4 – Biomass burning and

waterlogged rice

CO2eq – Based on the GHGs generated

from diesel oil combustion to produce

the energy required for field operations

(fertilization, disc plough, seeding…).

Accelerate Zero Tillage

Electricity Supply

Wind Energy

Biomass Cogeneration

Demand Energy Efficiency, Demand Side Management

Oil and Gas Supply

Refineries design and Flared Gas to Liquid (GTL)

Demand Energy Efficiency in the Industry

Fuel switch and Substitution for Biomass Charcoal from Renewable Biomass

B – Mitigation Options for the Energy Sector

Based on Energy Modeling used for Plano National de Energia 2030 (EPE)

Industrial Consumption of Energy from

Fossil Fuels Technical options cover the following five main areas:

Energy Efficiency (optimization of combustion, heat recovery inindustrial processes, steam recovery, furnace heat recovery,implementation of new technologies and processes, and other measures)

Recycling and Reducing Materials Used

Inter-energy substitution 1 (Fossil Fuels for Fossil Fuels)

Inter-energy substitution 2 (Fossil for Renewable Alternatives)

Reduction in the Use of Non-renewable Biomass (charcoal fromrenewable biomass from native forests)

Sub-sectors from considered industries: Cement, Iron andSteel, Minerals, Chemicals, Non-Iron Metals, Textiles, Food andDrink, Celluloses and Paper, Ceramics and other (based on the NationalEnergy Balance – BEN 2008)

Regional Transport

of cargo and

passengersUrban Transport

of cargo and

passengers

C – Mitigation Options for the Transport Sector

Transport Planning and Emissions Models:

TransCAD, EME, MANTRA, COPERT

19

Example: Modal Shift Regional Transport

Without Mitigation

Measures (2030)

All the load is

transported by

trucks

20

Example: Modal Shift Regional Transport

With Mitigation

Measures (2030)

New East

Bahia

Railway

BEFORE: all the

load was

transported by

trucks

AFTER: most of

the load is shifted

to new cargo

trains

Metro BusBRT

Passenger Loading

5000 10000 15000 20000 25000

Without Mitigation

Measures (2030)

Conventional

buses

Metro lines

Example: Modal Shift Urban Transport

(Belo Horizonte - no Pico da Manhã)

Metro BusBRT

Passenger Loading

5000 10000 15000 20000 25000 New Additional

Metro lines

New BRT

substituting

conventional

buses

Example: Modal Shift Urban Transport

(Belo Horizonte - no Pico da Manhã)

With Mitigation

Measures (2030)

D – Mitigation Options in the

sector of Urban Waste

Solid Urban Waste

Reduction

Recycling

Landfils

CH4

destruction

Composting

No emissions

Incinerating

CO2 from fóssil and

N2O

Not collected

CH4 - no method

to reduce

Projections 2008-2030

Main Mitigation Potentials

-800

-700

-600

-500

-400

-300

-200

-100

0

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030

Mt

CO

2

Reforestation

Scaling up no tillage cropping

Avoided deforestation and livestock

Landfill and wastewater treatment methane destruction

Urban Transport - mitigation measures

Regional Transport - mitigation measures

Energy Conservation – Industry (fossil fuels)

Gas to liquid (GTL)

Refineries

Energy Conservation Commercial/Industrial (Elec)

Energy Conservation Residential (Elec)

Sugarcane cogeneration

Wind

mitigation due to ethanol exports

Cumulative Emissions

Reductions

2010-2030

Inform the Decision Making Process

Example: Cogeneration from Sugarcane

Is there a low carbon option ? Extracting condensing turbine, 90 bars

What is the mitigation potential ? 158 MtCO2e (7.5MtCO2/year)

Does it make sense economically from a

planning perspective ?YES:

Marginal Abat. Cost = - $ 105 /tCO2

(8% social discount rate)

Would it happen spontaneously ? NO:

Sector Expected IRR is 18% > 8%

Incentive required = + $ 8 /tCO2

How much financing needed ? Additional investment = + $ 35 billion

(+$1.6 bi /year)

Key questions

Break-Even Carbon Price = +$8/tCO2

1

2

3

4

5

678

910

11

12

13

14

15

16

1718 19 20 2122 23 24 2526

2728

293031

32

33

-140

-120

-100

-80

-60

-40

-20

0

20

40

60

0 2000 4000 6000 8000 10000 12000

sugarcane cogen.

res. lighting

heat recovery sys

industrial lighting

ind. solar thermal applications

steam rec. system

optimizing traffic

gas to liquid (GTL)

avoided deforestation + livestock

no tillage cropping

bicycle lanesethanol exports

new ind.processes

biomassvs. other

fuels

renewable charcoal

existing refineries

new refineries

landfill CH4 destruction

rail + water vs. roads

residential solar heater

BRTReforestation

energy efficiency

TL Brazil-Venez.

NG vs. fuels

windpower

furnace heat rec.

comm. lighting

recycling

combustion opt.

electric motors

Refrigerators

ethanol displacing domestic gasoline

Marginal Abatement Cost / Break-Even Carbon Price Curves

Selection criteria:

MAC < U$50/tCO2e, (social discount rate 8%)

or

Other benefits would

trigger

implementation(transport, sanitation)

-500

-400

-300

-200

-100

0

100

200

300

400

0 500 1,000 1,500 2,000 2,500 3,000

sugarcane cogen.

res. lighting

heat recovery sys

ind. solar thermal applications

steam rec. system

gas to liquid (GTL)

ethanol exportsbiomass vs. other fuels

renewable charcoal

windpower

new refineries

residential solar heater

NG vs. fuels

electric motors

furnace heat rec.

combustion opt.

other energy efficiency measures

ethanol displacing domestic gasoline

BRT

bike lanes

optimizing traffic

existing refineries (energy integration)

commercial lighting

new industrial processesTL Brazil Venezuela

refrigerator

investing in railroads

Avoided deforestation + livestock:

6 billion tCO2 (2010-2030)

MAC = -US$0.48/tCO2

ECC = +US$5.6/tCO2

Sensitivity Analysis: Oil price:

+$40 to +$70 per barrel

For Incentive(to finance Break-Even Carbon Price)

For Investment

Additional Financing is needed

Not exorbitant, however new instruments are

required

Average Investment needed:

U$ 22 billion additional per year

(National Investment = U$ 250 bi/year, FDI = U$ 30 bi/year)