Assessing the impacts of the EU bioeconomy on third countries : potential environmental impacts in Brazil of EU biofuel demand to 2030.
Date: 2019-10-16
Institutions: Joint Research Centre
Subjects: Brazil, EU environmental policy, biofuel, economic indicator, environmental economics, environmental impact, environmental law, innovation, reduction of gas emissions, renewable resources, report, sustainable development, third country, waste management

Assessing  the  impacts  of  the  EU 
bioeconomy on third countries 

Potential environmental 
impacts in Brazil of EU 
biofuel demand to 2030 

Follador M., Philippidis G., 
Davis, J., Soares-Filho, B. 

2019 

EUR 29837 EN 

 
 
 
 
 
 
 
 
 
This publication is a Science for Policy report by the Joint Research Centre (JRC), the European Commission’s 
science and knowledge service. It aims to provide evidence-based scientific support to the European 
policymaking process. The scientific output expressed does not imply a policy position of the European 
Commission. Neither the European Commission nor any person acting on behalf of the Commission is 
responsible for the use that might be made of this publication. 

Contact information  
Name: Marco Follador 
Address: TP261, Via Fermi, 21027 Ispra (VA), Italy 
Email: marco.follador@ec.europa.eu 
Tel.: +39 0332 78 5393 

EU Science Hub 
https://ec.europa.eu/jrc 

JRC117364 

EUR 29837 EN 

PDF 

ISBN 978-92-76-09820-1 

ISSN 1831-9424 

doi:10.2760/304776 

Luxembourg: Publications Office of the European Union, 2019  

© European Union 2019 

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All content © European Union 2019, except: Cover page photo ©  Fernanda Renno’. 

How to cite this report: Follador, M., Philippidis, G., Davis, J. and Soares Filho, B. Assessing the impacts of the 
EU bioeconomy on third countries, Publications Office of the European Union, Luxembourg, 2019, ISBN 978-92-
76-09820-1, doi:10.2760/304776, JRC117364. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Contents 

Acknowledgements ................................................................................................ 2 

Executive summary ............................................................................................... 3 

1  Introduction ...................................................................................................... 7 

2  Policy context .................................................................................................... 9 

  Overview .................................................................................................... 9 

  EU Bioeconomy strategy ............................................................................... 9 

  EU Long term strategy 2050 ....................................................................... 11 

  EU Renewable Energy Directive (REDII) ....................................................... 12 

  Brazilian Bioeconomy ................................................................................. 13 

  Brazilian National Policy on Biofuels (Renovabio) ........................................... 14 

  EU-Mercosur Trade talks ............................................................................. 14 

  List of legislative tools ................................................................................ 16 

3  Impacts of EU biomass demand in third countries: state of play ............................ 20 

  Introduction .............................................................................................. 20 

  Macroeconomic indicators ........................................................................... 22 

3.2.1  Monitoring ........................................................................................ 22 

3.2.2  Modelling future socioeconomic pathways ............................................. 23 

  Global land use change............................................................................... 24 

  Sustainability of traded bio-commodities: existing assessment tools ................ 27 

  The need for a subnational impact assessment .............................................. 28 

4  Region of focus: Brazil ..................................................................................... 30 

  Sustainability of Europe-Brazil trade............................................................. 30 

  Land use changes in Brazil until 2017 ........................................................... 30 

  GHG emission profile .................................................................................. 34 

  The Brazilian Nationally Determined Contribution (NDC) ................................. 36 

5  Integrated impact assessment for Brazil case study ............................................. 38 

  Modelling framework .................................................................................. 38 

  EU Socioeconomic and policy scenarios ........................................................ 39 

6  Results ........................................................................................................... 41 

  Projections of demand and supply to 2030 .................................................... 41 

6.1.1  Ethanol and sugar .............................................................................. 41 

6.1.2  Main crops ........................................................................................ 43 

6.1.3  Wood and plantations ......................................................................... 44 

6.1.4  Forest recovery and deforestation rates ................................................ 45 

  Projected Land use changes in Brazil to 2030 ................................................ 46 

  Country net LULUCF emissions .................................................................... 49 

i 

 
  In depth analysis: sugarcane expansion ....................................................... 50 

6.4.1  Sugarcane expansion sorted by state ................................................... 50 

6.4.2  Land conversion to sugarcane ............................................................. 51 

6.4.3  GHG emissions from sugarcane cultivation ............................................ 52 

6.4.3.1  Emissions from changes in biomass carbon stock ............................. 52 

6.4.3.2  Emissions from changes in soil carbon stock .................................... 53 

6.4.3.3  Emissions from fertilizer and lime application .................................. 53 

6.4.3.4  Emissions from burning sugarcane straw ........................................ 54 

6.4.3.5  Total GHG emissions .................................................................... 54 

  Comparison with soybean expansion ............................................................ 56 

6.5.1  Soybean expansion sorted by states .................................................... 56 

6.5.2  Land conversion to soybean ................................................................ 57 

6.5.3  LULUCF  emissions due to soybean production ....................................... 58 

7  Conclusion ...................................................................................................... 60 

8  References ...................................................................................................... 64 

List of abbreviations and definitions ....................................................................... 70 

List of figures ...................................................................................................... 71 

List of tables ....................................................................................................... 74 

9  Supplementary Information .............................................................................. 76 

  Annex 1: MAGNET model ............................................................................ 76 

  Annex 2: OTIMIZAGRO ............................................................................... 78 

  Annex 3: Integrated modelling framework .................................................... 78 

  Annex 4:  land use transition matrix from POB scenario.................................. 79 

  Annex 5: Main factors shaping sugarcane expansion ...................................... 79 

Distribution of logistic infrastructure and ethanol plants ................................ 80 

Legislation and Agro-Ecological Zoning ....................................................... 80 

Land tenure ............................................................................................ 82 

Climate favourability ................................................................................ 82 

ii 

 
Editor 

Marco Follador 

Authors 

Marco Follador (JRC, D1) 

George Philippidis (JRC, D4) 

Juliana  Davis  (Universidade  Federal  de  Minas  Gerais/CSR  and  Centro  de  Inteligência 
Territorial CIT) 

Britaldo Soares-Filho (Universidade Federal de Minas Gerais/CSR) 

1 

 
 
 
 
 
Acknowledgements 

This work was developed within the JRC Biomass Study1 in support of the EU Bioeconomy 
Strategy and related policies. It is the result of a fruitful collaboration between European 
Commission  Departments  and  Brazilian  institutions.  We  would  like  to  acknowledge  the 
Brazilian Ministry of Energy and Mines and Ministry of Agriculture for sharing their data on 
projected trends in ethanol and crop production. 

Special thanks go to Robert M'barek, without whom the economic component of this study 
using  the  MAGNET  model  would  not  have  been  possible.  We  also  acknowledge  Sarah 
Mubareka and Andrea Camia for supporting  our work within the JRC Biomass Study and 
providing valuable advice on land use modelling.   Finally, we would like to thank Robert 
Edwards  for his interest in this study, his  technical  support and  feedback, which greatly 
enhanced the usability and significance of our results. 

1 https://ec.europa.eu/knowledge4policy/projects-activities/jrc-biomass-assessment-study_en  

2 

 
 
 
                                           
Executive summary 

The updated EU Bioeconomy Strategy of 20182 reinforced the importance of enhancing the 
knowledge  on  the  ecological  boundaries  of  the  bioeconomy  at  EU  and  global  level.  It  is 
recognized that the global context is highly relevant to align the EU bioeconomy targets 
with world-wide sustainable development goals. To achieve its decarbonisation targets and 
boost  the  bioeconomy,  the  EU  will  inevitably  consume  more  biomass.  The  EU’s  own 
biomass resources will meet part of the demand although these ambitious targets will also 
require reliable and sustained access to third country suppliers.  

Brazil is a key player in the global climate agenda since it is home to the largest tropical 
forest  and  the  seventh  largest  emitter  of  greenhouse  gases  (GHGs),  mainly  due  to 
deforestation and agriculture expansion. From the perspective of the EU, Brazil is also the 
biggest  exporter  of  agricultural  commodities  and  its  second  trading  partner  overall.  The 
environmental impacts arising from European consumption of bio-commodities are already 
evident  in  Brazil,  and  may  become  more  pronounced  under  a  weakened  environmental 
governance scenario. To integrate its goal of curbing climate change with the promotion of 
world-wide sustainable development, the  EU  needs to  guarantee the  sustainability of its 
supply  chains  also  by  ensuring  that  trade  between  Europe  and  Mercosur  respects  strict 
environmental  standards.  Until  now,  the  lack  of  clear  environmental  and  social  criteria 
associated with the production of traded biocommodities has hindered the definition of an 
internationally agreed approach to monitor the compliance of third country supply with EU 
standards. The recast Renewable Energy Directive (REDII) made a step in this direction by 
setting environmental criteria for conventional biofuel feedstock production, even though 
the final definition of these criteria and the technical rules to assess compliance with them 
remain under discussion. 

The tariff-rate quota (TRQ) for Brazilian ethanol has long been a sticking point in the EU-
Mercosur trade-talks: the European offer of 600 thousand tonnes (ca. 760 million litres) 
has been deemed too low by the Brazilian delegation, whose request was for one million 
tonnes (ca. 1.3 billion litres). This ex-ante  study assesses the potential impacts on land 
use changes, and associated GHG emissions, in Brazil resulting from increased EU demand 
for ethanol, and draws evidence-based conclusions to verify the compliance of sugarcane 
feedstock production with the REDII environmental criteria, by combining the computable 
general equilibrium model MAGNET with the land use model of Brazil OTIMIZAGRO. In the 
baseline  scenario  (business  as  usual,  BAU),  the  main  market  drivers  of  EU  demand  are 
economic growth and population projections to 2030 and the progressive implementation 
of  the  1st  and  2nd  generation  biofuel  mandates.  The  “high-import  scenario”  (phase-out 
biodiesel scenario, POB) deviates from the BAU by eliminating EU imports of palm oil from 
Asia by 2020, and progressively substituting biodiesel with bioethanol, leading to greater 
EU dependence on imports from Brazilian bioethanol. Projections for other crops’ expansion 
(including soybeans), plantations and deforestation scenarios have also been included to 
provide a more comprehensive picture of  plausible outcomes for Brazilian land use changes 
from 2017 to 2030. 

The results reveal that, under the BAU scenario, the Brazilian supply of ethanol reaches ca. 
51.5 billion litres by 2030 (from an estimated 33 billion litres in 2018), of which only 0.18 
billion litres are exported to the EU (i.e. 7% of Brazil’s ethanol exports). With the ethanol 
supply in Brazil rising to 52.2 billion litres under the POB scenario, Brazil’s export volume 
to the EU also rises rapidly to ca. 1.1 billion litres – close to the Brazilian TRQ request – 
representing ca. 30% of Brazil's total exports. To meet these demands the sugarcane area 
would increase to between 14.6 (BAU) and  14.8 (POB) million hectares by 2030,  with a 
marginal  difference  in  terms  of  land  use  changes  and  GHG  emissions  between  the 
scenarios.  Sugarcane  expansion  into  Amazonian  and  Cerrado  (savannah-like)  native 
vegetation  is  marginal  (less  than  2%)  resulting  in  limited  forest  biomass  loss  and 
associated  GHG  emissions.  The  conversion  of  other  croplands  (including  food  crops)  to 
sugarcane is also negligible and does not displace their production elsewhere or affect other 

2 https://ec.europa.eu/knowledge4policy/publication/updated-bioeconomy-strategy-2018_en  

3 

 
 
                                           
crop markets. Most of the sugarcane expansion occurs at the expense of pasturelands (ca. 
97%)  in  the  Southeast  and  Midwest  regions.  Conversion  of  pasture  to  sugarcane  and 
achieving the expected yield require the application of fertilizer and lime, which represent 
the  highest  source  of  GHG  emission  from  sugarcane  cropping.  Nonetheless,  restoring 
pasturelands could avoid further forest clearance and associated high GHG emissions from 
changes in biomass carbon stock, thereby representing an effective way to satisfy growing 
domestic  and  international  ethanol  demand  while  helping  to  achieve  Brazil’s  mitigation 
targets.  

This  study  points  out  that  sugarcane  feedstock  production  could  comply  with  REDII 
environmental criteria under both scenarios, given its marginal expansion into high carbon 
stock lands and limited displacement of other crops. Moreover, even though most of the 
sugarcane expansion is into pasturelands, it is possible – but far from certain - that new 
forest clearance in the northern regions is linked to the displacement of pasture from the 
Southeast and Midwest (uncertain risk of indirect land use change). 

By  comparison,  in  the  same  period,  production  of  soybean  –  the  largest  crop  in  Brazil, 
mainly used for animal feed – is projected to expand considerably into the native vegetation 
of the Amazon (ca 7%, i.e. 0.9 million hectares) and the Cerrado (ca 6%, i.e. 0.7 million 
hectares) resulting in high GHG emissions from biomass loss, not to mention that most of 
the  soybean  expansion  occurs  in  pasturelands  in  the  Midwest  and  Northeast  regions, 
leading  to  potential  pasture  displacement  and  new  forest  clearance  in  the  nearby 
Amazonian  and  Cerrado  biomes,  as  well  as  significant  loss  of  soil  carbon  stock  from 
conversion of the pasture itself.  

Accounting for all land use changes (i.e. the main 14 crops, plantations, forest regrowth 
and  deforestation),  the  country’s  cumulative  net  GHG  emission  balance  rises  steeply 
between 2017 and 2030, putting the Brazilian contribution to the Paris Agreements at risk, 
whilst  the  difference  between  the  country’s  Nationally  Determined  Contribution  (NDC) 
targets by 2030 (ca. 22 million CO2 tonnes) and our results is approximately an 
900 million CO2 tonnes. The higher emissions due to increasing deforestation rates – linked 
to lax enforcement of the Forest Code (the principal law regulating forest conservation in 
private  properties)  –  will  have  to  be  compensated  by  large  mitigation  efforts  in  other 
economic sectors so that Brazil can meet its NDC targets.  

Understanding  the  ecological  boundaries  of  the  bioeconomy  is  one  of  the  key-actions  of  the 
updated EU Bioeconomy Strategy of 2018. This includes quantifying the spillovers of EU trade 
with  the  main    biomass  suppliers,  like  Brazil,  to  assess  the  potential  impacts  of  the  EU 
bioeconomy  on  global  sustainable  development  goals.    The  results  reveal  that  sugarcane 
feedstock production could have limited impacts on GHG emissions through land use changes 
and farming practices, even under conditions of high EU demand for ethanol – a sticking point 
during  the  last  EU-Mercosur  trade  talks.  The  conversion  of  pasturelands  to  sugarcane  could 
represent an opportunity for the Brazilian sugar industry to meet the rising demand for ethanol 
and  sugar  while  achieving  sectoral  mitigation  targets  as  well  as  compliance  with  the  EU's 
environmental  criteria.  Pasture  displacement  towards  northern  regions  due  to  sugarcane 
expansion is possible but highly uncertain, and could be avoided through investments in cattle 
ranching intensification. In contrast, large-scale soybean expansion could lead to further loss of 
Amazonian forest and Cerrado native vegetation, through direct and indirect land use changes, 
i.e. pasture displacement.  

From  2005  to  2012,  Brazil  was  able  to  curb  deforestation  and  substantially  reduce  its  GHG 
emissions with strong improvements in environmental governance. Our modelling results from 
2017 to 2030 show that the contribution of all land use changes to country’s cumulative GHG 
emission  balance  could  be  an  additional  900  million  CO2  tonnes  above  the  NDC  target.  This 
implies that a dismantling of Brazil’s environmental protection could threaten uncontrolled rates 
of  deforestation  in  the  near  future,  thereby  risking  to  jeopardise  compliance  with  the  Paris 
Agreement.  A  firm  commitment ensuring that  a  trade  deal  with  Brazil  is  conditional  on  strict 
environmental  criteria  for  agricultural  commodities  would  be  an  effective  way  to  promote 
responsible sustainable development and avoid further deforestation directly or indirectly linked 
to  the  expansion  of  farming  and  ranching  activities,  in  line  with  the  commitments  of  the  EU 
“trade for all” strategy.    

4 

 
 
 
Main findings 

For  the  sake  of  simplicity,  we  provide  an  overall  analysis  of  the  land  use  changes  from 
2017 to 2030 and associated GHG emissions, due to the marginal differences between the 
scenarios’ outcomes.  

5 

IMPACTSUGARCANE:MAIN OUTCOMES, 2017-2030COMPLIANCE WITH EU ENVIRONMENTAL CRITERIAConvertingpasturelandtosugarcanedemandstheapplicationoflimeandfertilizers.ItrepresentsthelargestsourceofGHGemission(>35%)fromsugarcaneproduction–butfarlowerthanemissionsassociatedwiththeconversionofnativevegetationtocroplands•24.5MtonsCO2/year•2tonsCO2/hayearand2gCO2/MJ•Cumulativeemissions2018-2030:ca.312-323MtonCO2•GHGemissionprofile:1)Agriculturalpractices56%(38%fromfertilizerandlimeapplication;18%fromstrawburning);2)LULUCF44%(26%fromcarbonstockchangeinsoil;18%fromcarbonstockchangeinbiomass).StrawburningshouldbegraduallydismissedMarginalexpansion(<2%)intoforest(highcarbonstocklands)andsavannahnativevegetationMarginalexpansion(<1%)intoothercroplands(includingfoodcrops).NegligibledisplacementoffarmingactivitiesandassociatedILUC97%ofexpansionintopasturelandsintheSoutheastandMidwest.Uncertainlinkbetweensugarcaneexpansioninthesouthandpasturedisplacementintonorthernregions(andassociatedforestclearance)IMPACTSOYBEAN: MAIN OUTCOMES, 2017-2030COMPLIANCE WITH EU ENVIRONMENTAL CRITERIAOnlyLULUCFemissions:•73MtonCO2/year•1.8tonCO2/hayearand56gCO2/MJ•Cumulativeemissions2017-2030:ca.950MtonCO2(Bycomparison,sugarcaneLULUCF:ca.11MtonCO2/yearand0.85CO2/hayear;cumulativeemissions:ca.140MtonCO2)•SoybeanLULUCFemissions(tonCO2/hayear)aretwotimeslargerthanthesugarcaneones(higherconversionofforestandnativevegetationtocroplands)Significantexpansionintoforest(highcarbonstocklands,ca.7%=0.88Mha)andsavannahnativevegetation(ca.6%=0.73Mha).Marginalexpansion(<1%)intoothercroplands(includingfoodcrops).NegligibledisplacementoffarmingactivitiesandassociatedILUC87%ofexpansionintopasturelandsmainlyintheMidwestandnorthernregions.Soybeanlarge-scaleoccupationofnorthernpasturelandscoulddisplacepastureintoAmazonandCerradobiomes(withassociatednewforestclearance) 
 
 
 
 
 
 
It must be noted that the impacts reported here are only first order impacts. A comparison 
with  bioethanol  production  in  the  EU  or  emissions  associated  with  alternative  forms  of 
energy, are beyond the scope of this study. 

Quick guide 

Chapter 1 briefly introduces the key questions to be addressed to develop a sustainable EU 
bioeconomy in the mid to long term. 

Chapter  2  describes  the  policy  context  and  the  main  legislative  tools  considered  in  this 
study. Both European and Brazilian laws, directives and criteria have been used to support 
the simulations and to interpret the results. 

Chapter 3 summarises the state of play in monitoring and modelling the impacts of the EU 
bioeconomy and the sustainability of traded commodities at the global level. 

Chapter  4  briefly  describes  the  region  of  focus  and  the  impact  modelling  framework  to 
assess the sustainability of sugarcane feedstock production to satisfy varying EU demand 
for ethanol. 

Chapter 5 shows the model results for two projected scenarios of EU demand (baseline and 
high-import scenario) to 2030.  

Finally, chapter 6 summarises the main results of the study. 

6 

IMPACTCOUNTRY CUMULATIVE LAND USE CHANGES: MAIN OUTCOMES,2017-2030CONTRIBUTIONTO PARIS AGREEMENTThedifferencebetweenthecountry’sNationallyDeterminedContribution(NDC)targetsby2030(ca.22millionCO2tons)andourresultsisapproximatelyanadditional900millionCO2tons.DeforestationisthemainsourceoftheadditionalLULUCFemissions–weassumedthatthedeforestationratesobservedsince2013continueto2030(weakenedenvironmentalgovernancescenario) 
 
 
 
 
 
1  Introduction 

Demographic,  economic  and  environmental  questions  of  the  21st  century  present  major 
sustainability challenges for the biophysical boundaries of our planet. These concerns are 
fundamentally  incompatible  with  the  continued  usage  of  a  linear  'make-take-dispose' 
model of economic growth, and heavy reliance on limited carbon-based resources and their 
associated  environmentally  prejudicial  fossil-based  production  technologies.  As  a  result, 
socially  responsible  strategies  are  required  to  alleviate  the  pressures  on  the  biosphere, 
whilst harmonising environmentally friendly progress with a sustainable model of economic 
growth, employment and social inclusion. A key tenet underlying this thesis is the design, 
development and implementation of a symbiotic economy-environment 'circular economy' 
model of regenerative resource usage. An important component part of this paradigm is 
the conversion of biologically renewable resources and biological waste, i.e. biomass, into 
value-added streams (i.e. food, feed, industrial and energy applications).  

Against  this  background,  it is important  that policymakers  have  a  solid grasp  of  current 
and future biomass availability when prescribing multi-objective policies (i.e. higher value 
added growth and employment, lower emissions, energy self-sufficiency, biodiversity etc.). 
This in turn requires knowledge of the scope of those biomass-using activities that comprise 
the broad and diverse collective known as the bioeconomy, which in the EU is estimated to 
employ over 18 million people with a turnover of €2.3 trillion (Ronzon and M'barek, 2018). 
Indeed,  in  understanding  how  best  to  monitor  and  assess  the  performance  of  the 
bioeconomy  with  a  view  to  providing  high  quality  information  to  policymakers,  there 
remains considerable scope for improving our knowledge of the sector.  

This is precisely the aim of the European Commission’s Knowledge Centre for Bioeconomy, 
which is housed at the  Joint Research Centre.  Moreover, ongoing consultations between 
the policy and academic communities reveals a clear need to plough additional resources 
into removing knowledge gaps through further data collection and construction (Gurria et 
al.,  2017)  to  reduce  existing  levels  of  uncertainty  surrounding  biomass  availability  and 
usage  within  the  European  Union.  Indeed,  access  to  detailed  (i.e.  time  and  space)  data 
sources  not  only  provides  a  basis  for  formulating  relevant  indicators  to  conduct  ex-post 
monitoring and performance evaluation of the bioeconomy, but is also an essential input 
for  ex-ante  modelling  impact  assessments.  To  draw  insights  from  these  data  trends  for 
modelling  exercises  and  policy  formulation,  one  also  requires  an  appreciation  of  the 
economic, societal and biophysical driving forces behind them (i.e. technological change, 
biomass  availability,  consumer  preferences,  policy  coherence  or  conflict,  etc.)  and  how 
they may be expected to continue shaping the progress of this collective of activities in the 
coming years. 

On this latter point, the focus of the EU's evolving internal energy requirements has been 
an important driver of biomass markets over the past 15 years. The role of biomass, as a 
part-solution for decarbonising EU energy markets  whilst ensuring self-sufficiency in the 
coming decades, plays a dominant role on the policy agenda. According to the Knowledge 
Centre  for  Bioeconomy,  biomass  for  bioenergy,  largely  from  forestry,  constitutes 
approximately  60%  of  the  EU's  renewable  energy.  Of  this  total,  75%,  13%  and  12%  is 
dedicated to heating and cooling, bioelectricity and liquid biofuels, respectively, whilst 96% 
of the EU's biomass requirement for energy is internally sourced (KCB, 2018).  

Turning the focus toward first-generation biofuel markets, the EU has an internal market 
requirement  of  approximately  16  billion  litres  of  first-generation  biodiesel,  of  which 
approximately  14%  was  sourced  from  extra-EU  imports,  principally  from  Argentina  and 
Malaysia  (USDA,  2018).  Ethanol  for  fuel  usage  in  the  EU  is  approximately  one-third  the 
size  of  the  biodiesel  market,  whilst  ethanol  imports  for  fuel  totalled  approximately  150 
million litres, or 3% of the EU's internal market.  

Whilst these statistics reveal a steady picture in terms of EU biomass for energy availability, 
of particular pertinence to the production of first-generation biofuels is the ongoing debate 

7 

 
 
 
 
which  encompasses  sustainability  criteria  regarding  indirect  land  use  change  (ILUC), 
reliable access to trade sources, and feed and food security. This, in turn, gave rise to an 
EU  roadmap,  known  as  the  Fuel  Quality  Directive  (FQD)  (EC,  2009)  for  the  adoption  of 
sustainable biofuel usage. In terms of securing stable trade access to third country imports 
of biomass for energy, the EU's reliance on Latin American sources would be consolidated 
further with a successful conclusion to the longstanding Mercosur-EU trade negotiations. 
Perhaps of more immediate concern is the ILUC issue, which has cast the spotlight very 
much on biodiesel feedstocks, particularly EU palm oil imports from Asia, which have been 
deemed  as  a  higher  deforestation  risk,  leading  to  potential  policy  conflicts  with  global 
environmental and food security objectives. This has resulted in the expected phasing out 
of palm oil imports for biodiesel feedstock by 2030.   

In seeking a more sustainable solution to meet the EU's internal commitments regarding 
biomass  usage  in  liquid  biofuels,  the  adoption  of  advanced  generation  biofuels  avoids 
conflicts with competing food crops whilst offering a potential solution for minimising ILUC 
effects.  Notwithstanding,  in  the  short-  to  medium-term,  there  is  a  high  degree  of 
uncertainty  regarding  the  economic  viability  at  mass  scale  of  currently  available 
technologies.  As  a  result,  with  conventional  EU  biofuels  facing  stricter  sustainability 
requirements,  the  use  of  first-generation  ethanol  is  touted  as  a  possible  medium-term 
solution.  As  noted  above,  biodiesel  currently  represents  a  considerably  larger  biofuel 
market within the EU, where the switch to bioethanol would require reliable supplementary 
access  to  third  country  imports.  With its  dominance  in  global  bioethanol  markets,  Brazil 
would be a key player in supporting such a policy initiative.  

Accordingly, the aim of this study is to quantitatively assess the impacts of a hypothetical 
switch in EU conventional biofuels in favour of bioethanol to meet the EU's internal market 
requirements. A key focus of the work is to assess the ramifications of this policy on the 
pattern of land use in Brazil.  

We use a global economic trade model called MAGNET to assess the EU's ethanol import 
requirements. As noted in EC 2018c (SWD(2018)431), with its state-of-the-art coverage 
of bio-based activities, MAGNET includes various features for assessing policy coherence in 
the  context  of  the  bioeconomy  and  Sustainable  Development  Goals  (SDGs),  and  can  be 
connected to sector-specific models that capture in detail the impacts of increased biomass 
usage. In the context of this research, MAGNET is linked with a spatially explicit land use 
model of the Brazilian regions, called OTIMIZAGRO, which measures the resulting impacts 
on  the  pattern  of  demand  for  crops,  the  direct  and  indirect  land  use  changes  (LUC  and 
ILUC) and the implications for CO2 emissions as a result of land use, land use change and 
forestry (LULUCF). 

8 

 
 
 
 
 
2  Policy context 

   Overview  

Europe  and  many  other  world  regions  are  boosting  their  bioeconomy  –  the  part  of  the 
economy that uses biologically renewable resources from land and sea  – as an engine of 
sustainable industrialisation, discovering innovative markets and creating wealth across all 
economic  sectors.  Its  crosscutting  nature  offers  a  great  opportunity  to  address  global 
environmental  and  societal  challenges,  such  as  (inter  alia)  ensuring  food  security, 
managing  land  uses  for  competing  biomass  feedstocks  production,  mitigating  climate 
change, while achieving long-term sustainable economic growth. The bioeconomy sectors 
are central to at least half of the Sustainable Development Goals (SDGs ), but conflicting 
national priorities and a fragmented definition of bioeconomy boundaries hinder progress 
toward the statement of a common agreed alignment on how bioeconomy could contribute 
to the SGDs on a global scale (El-Chichakli et al., 2016). In Europe, the Long-Term Strategy 
2050 confirmed the increasingly important role of bio-based sectors in achieving energy 
and  climate  targets.  Nonetheless,  the  recent  review  of  the  2012  Bioeconomy  Strategy 
pointed to the existing gaps concerning the monitoring and evaluation framework (M&E), 
calling for the development of a comprehensive M&E system of bioeconomy performances 
and impacts at national, European and global level .  

The recast Renewable Energy Directive (REDII) recognizes the overall direct and indirect 
land  use  change  impacts  associated  with  the  production  of  biofuels  and  bioliquids.  In 
particular,  it  introduces  a  new  approach  to  address  emissions  from  indirect  land  use 
changes (ILUC) by limiting the amount of high ILUC-risk fuels that can be counted towards 
the 2030 energy targets. Therefore, it is important to create a strong knowledge base to 
support  the  identification  of  low  ILUC-risk3  fuels  exempted  from  these  limits  when 
calculating  the  overall  national  share  of  renewables  and  the  share  of  renewables  in  the 
transport  sector.  To  an  extent,  this  new  approach  could  lead  to  long-standing  trade 
negotiations between Europe and its trading partners about the certification rules for low 
ILUC-risk commodities. In order to address this issue, the Commission is expected to adopt 
a Delegated Act (COM,2019) setting out the criteria for certifying low ILUC-risk biofuels, 
bioliquids and biomass fuels.  

The  EU  underlines  its  firm  commitment  to  promote  sustainable  development  globally 
through its trade policy by setting high binding environmental and social standards (EP, 
2016). Therefore, assessing the sustainability of traded bio-commodities is a milestone for 
the definition of any agreement between the EU and its trading partners - together with its 
potential  business  opportunities.  The  implementation  of  the  agreements  should  be 
monitored periodically to ensure compliance with the EU standards.  

  EU Bioeconomy strategy  

The  European  bioeconomy  encompasses  a  wide  range  of  productive  sectors  –  i.e., 
agriculture, forestry, fisheries, food, bio-energy and bio-based industry – with an annual 
turnover of ca. €2 trillion, over 18 million people employed and €621 billion in value added. 
These numbers account for ca. 4.2% and 8% of the EU’s GDP and workforce, respectively 
(Ronzon et al, 2018). Traditional agriculture and its manufactured products represent by 
far the most important contribution to EU bioeconomy,  whilst new services and products 
(e.g., bio-based chemicals or bioenergy) are still in their early stages (Figure 1). 

3 See the chapter 2.4 for the explication of “low ILUC-risk fuels” 

9 

 
 
                                           
Figure  1:  Quantified  Socioeconomic  Indicators  of  the  EU  Bioeconomy  in  2015.  Source:  based  on 
Ronzon and M'barek (2018). 

The  2018  update4  of  the  2012  Bioeconomy  Strategy  reinforced  the  scope  of  its  action 
towards the 2030 Agenda and SDGs5, as well as the Paris Agreement targets6, by setting 
new action plans to: i) Strengthen and scale up bio-based sectors, unlock investments and 
local  bioeconomies  rapidly  across  Europe;  
increase  market  uptake; 
iii)Understand the ecological boundaries of the bioeconomy (EC, 2018d). 

ii)  Deploy 

The  third  action  aims  at  increasing  the  knowledge  base  on  bioeconomy  sustainability 
dimensions  through  improved  observation,  measurement,  monitoring  and  modelling 
capacities (Figure 2). 

4https://ec.europa.eu/research/bioeconomy/pdf/ec_bioeconomy_strategy_2018.pdf#view=fit&pagemode=none  
5 https://www.un.org/sustainabledevelopment/development-agenda/  
6 https://unfccc.int/process-and-meetings/the-paris-agreement/the-paris-agreement  

10 

 
 
 
                                           
Figure 2: Third action of the Bioeconomy strategy  

Forward-looking  modelling  tools  are  essential  for  ex-ante  assessment  of  environmental, 
social  and  economic  trends  and  trade-offs  under  different  policy  scenarios  and 
socioeconomic pathways, to help drive sectoral policy coherence towards EU bioeconomy 
sustainability targets. This action recognizes the importance of modelling and quantifying 
direct and indirect land use changes resulting from increasing demand for different biomass 
feedstock and bio-based products at EU28 and global level, to guarantee the optimal use 
of available natural resources and limit biodiversity, carbon stock and ecosystem services  
degradation. 

Bioenergy is by far the largest EU renewable energy source, supplying 12% of the EU28's 
final energy demand, whilst the Bioeconomy strategy foresees a major role for bioenergy 
in  achieving  the  2020,  2030  and  2050  EU  energy  and  climate  targets.  Nonetheless,  the 
environmental impacts of bioenergy production could negate some or all the benefits of its 
usage. To mitigate this risk, after 2020 the EU sustainability criteria will be applied not only 
to  biofuels  and  bioliquids  but  also  to  solid  biomass  and  biogas  for  heating  and  power 
generation (according to the EU bioenergy sustainability framework reinforced under the 
recast  REDII).  Monitoring  and  evaluating  the  environmental  compliance  of  these  energy 
sources  ensures  a  credible  global  transition  to  a  bioeconomy  by  confirming  sustainably 
responsible producer behaviour, whilst creating a deterrence mechanism for the offenders. 

  EU Long term strategy 2050 

In  November  2018,  the  Commission  presented  its  strategic  vision  for  a  long-term 
competitive and climate-neutral economy by 2050 (EC, 2018a). The strategy covers nearly 
all EU policies and is in line with the Paris Agreement targets. It recognizes the important 
role  played  by  the  bioeconomy  sectors  towards  the  decarbonization  of  the  European 
economy.  It  estimates  that  by  2050,  the  current  EU  consumption  of  biomass  could  be 
doubled,  intensifying  the  pressure  on  EU  natural  resources  and  leading  to  steeply 
increasing biomass imports. The access to third country trade would also raise concerns 
related  to  emissions  from  indirect  land  use  change.  To  achieve  a  gradual  reduction  of 
import dependency on third country markets, most of the biomass used in 2050 to fulfil 
the  EU  demand  for  bioenergy  could  be  produced  domestically  by  improving  farmland 
productivity  and  agroforestry  techniques,  by  switching  from  first-generation  biofuel 
croplands to lignocellulosic grass, and by re-introducing abandoned land into cultivation, 
among others (Figure 3). This assumption raises some concerns, since Europe is already 
one of the most  intensively used continents on the globe and an increasing pressure on 
land resources could lead to a decline in the delivery of ecosystem services, including those 
related to climate change (e.g., carbon sink). 

11 

 
 
 
 
Figure 3: Projected land use changes 2020-2050 (Mha) for different scenarios. Source: EC, (2018b) 

  EU Renewable Energy Directive (REDII) 

The recast Renewable Energy Directive7 (REDII) entered into force on 24th December 2018 
by setting an EU-wide renewable energy target of at least 32% by 2030 (with a review for 
increasing this figure in 2023). It limits the amount of conventional biofuels consumed in 
the transport sector that can be considered when calculating  the national overall share of 
renewable  energy,  to  the  levels  existing  in  each  member  State  in  20208.  Under  REDII 
biofuels  are  required  to  reduce  GHG  emissions  by  at  least  50%  compared  to  the  use  of 
fossil  fuels9.  Thus,  the  REDII  paves  the  road  for  the  decarbonization  of  the  EU  energy 
system through a number of low-carbon measures in transport, heating and cooling and 
electricity  sectors  to  reduce  their  GHG  emissions,  improve  energy  security  and  provide 
energy at affordable prices, strengthen EU industrial and technological leadership, create 
new  opportunities  for  employment  and  regional  development,  and  provide  certainty  for 
investors (EU, 2018). 

The REDII also reinforces the EU sustainability framework for bioenergy to guarantee GHG 
emission  savings  and  minimize  unintended  environmental  impacts.  In  particular,  it 
recognizes that the magnitude of GHG emission-linked indirect land use changes (ILUC10) 
due to biofuel feedstock production is capable of negating some (or all) emission savings 
of biofuel consumption. To address this issue, the REDII sets national limits for high ILUC-
risk biofuels - produced from food and feed crops for which a significant expansion of the 
production  areas  into lands  with  high  carbon  stock  is  observed  -  at  2019  levels  starting 
from 2020, and then gradually reduces their contribution to zero by 2030 at the latest. Low 

7 

https://ec.europa.eu/info/news/new-renewables-energy-efficiency-and-governance-legislation-comes-force-
24-december-2018-2018-dec-21_en  

8 Some flexibility is allowed: the national limits can be increased by 1%, with an overall limit of 7% of the 2020 

final consumption of road and rail transport sector 

9 Based on a LCA that covers only direct emissions. The threshold for new installations rises to 60% in 2020 and 

65% in 2021  

10 Indirect land use changes (ILUC) occur when biofuel expansion into pasture or cropland previously destined for 
food and feed market causes the displacement of this production elsewhere, resulting in further conversion 
of native vegetation into new agricultural land or pasture, and associated GHG emissions. 

12 

 
 
 
                                           
ILUC-risk fuels are exempted from these limits when calculating the overall national share 
of renewables and the share of renewables in transport sector. 

COM  (2019a)  and  COM  (2019b)  define  the    rules  to  assess  the  sustainability  of  biofuel 
feedstock  production,  by  determining  what  constitutes  a  ”significant”  expansion  of 
feedstock into high carbon stock lands11 and by criteria for low-ILUC risk biofuels12. 

  Brazilian Bioeconomy  

 The national Strategy of Science, Technology and Innovation 2016-2022 (MCTIC, 201713) 
presented the guidelines for the development  of the Brazilian bioeconomy.  In 2016, the 
Brazilian bioeconomy sectors totaled ca. US$ 40 billion and ca. US$ 286 billion on the global 
and  domestic  markets,  respectively  (BNDES,  2018),  accounting  for  ca.  14%  of  country 
GDP in the same period. Agribusiness represented more than 50% of domestic sales, with 
food and beverage and tobacco sectors ranked second (20%), whilst the sugarcane sector 
totaled ca. BR$ 164 billion. The MCTIC (2018)14 report detailed the country action plan for 
bioeconomy by defining measures and targets for different sectors to stimulate economic 
activities that add value to biological processes and natural resources through technological 
solutions  to  produce  food,  feed,  materials,  chemicals,  fuels  and  energy.  The  plan  is 
structured into three thematic areas, namely i) biomass, ii) processing and biorefinaries, 
iii) bioproducts (Figure 4). 

Figure 4: thematic areas of the Brazilian action plan in bioeconomy. Source: Nunes, B. (2018). 

11  Absolute  magnitude  of  crop  expansion  and  its  share  compared  to  total  production  area  of  the  feedstock: 
Indicative values of 100000 ha/year is considered a sizeable expansion, and 1% of total production area have 
been suggested. 2. Share of the expansion into high carbon stock land: A conservative threshold of 10% of 
total expansion is suggested. 3. 
Type  of  crops  and  type  of  land  with  high  carbon  stock  must  be 
considered.  

12 Preventing land displacement through increased productivity or by cultivation feedstock on previously unused 
lands (abandoned or severely degraded). But there is an exemption for production by smallholders with <2ha 

13http://www.finep.gov.br/images/afinep/Politica/16_03_2018_Estrategia_Nacional_de_Ciencia_Tecnologia_e_I

novacao_2016_2022.pdf  

14 

http://www.mctic.gov.br/mctic/export/sites/institucional/ciencia/SEPED/Arquivos/PlanosDeAcao/PACTI_BIO
ECONOMIA_web.pdf  

13 

 
 
 
 
                                           
Given  the  importance  for  the  Brazilian  economy  and  its  decarbonization  targets,  the 
strategy for biofuels has been presented in a separate action plan15 (MCTIC, 2018b).  In 
2017,  sugarcane  biomass  had  a  significant  contribution  toward  the  renewable  energy 
targets of Brazilian NDC, accounting for ca. 18% of domestic energy supply. Recently,  two 
new biorefinaries for the production of second generation ethanol have been built in Sao 
Miguel  dos  Campos  (AL)  –  GranBio  company,  with  a  potential  capacity  of  82  million 
litres/year)  –  and  in  Piracicaba  (SP)  –  Raizen  company,  with  a  potential  capacity  of  40 
million  litres/year  –  but  they  still  represent  a  very  small  percentage  of  Brazil's  ethanol 
production. Moreover,  remaining challenges continue to limit the scaling up of these  bio 
refineries,  mainly  due  to  the  uncertainty  about  policies  and  mandates  for  advanced  low 
carbon fuels, the lack of recognition and proper pricing of advanced biofuel environmental 
services  and  positive  externalities  and  the  lack  of  cost-effective  feedstock  management 
strategy16. 

  Brazilian National Policy on Biofuels (Renovabio) 

On the 26th December 2017, the Brazilian Ministry of Mines and Energy published the new 
National Policy on Biofuels – called Renovabio17 – that aims at boosting the production and 
distribution of biofuels in Brazil to decarbonize the energy matrix and meet the country’s 
climate commitments. The expected starting date is 2020. Key elements of this program 
are: i) the certification of emissions associated to the production/import of biofuels, where 
authorized producers/importers are rated according to the  GHG emission saving of each 
biofuel  compared  to  the  conventional  fossil  fuel  alternative;  ii)  the  issuance  of 
decarbonization  credits  (CBIO)  and  their  distribution  to  biofuel  producers  and  importers 
according  to  the  volume  of  biofuels  produced  or  imported.  The  annual  mandatory  GHG 
emission reduction target for fuel sales is split into individual targets and applied to all fuel 
distributors by the National Agency of Petroleum, Gas, and Biofuels, in proportion to their 
market share in fossil fuel sales in the previous year. Distributors can achieve their targets 
through the direct purchase of certified biofuels, or by trading CBIO in the stock market. 
Trading CBIO will represent an additional revenue for biofuel producers and a stimulus for 
new  investments  in  this  sector.  As  regards  ethanol,  the  Brazilian  Ministry  of  Mines  and 
Energy aims to double the current production up to 50 billion litres by 2030, in line with 
the  projections  of  our  study.  The  environmental  criteria  set  by  the  Renovabio  initiative 
avoid  the  production  of  biofuel  feedstock  on  lands  converted  from  forest  areas  after 
December  2017,  and  limit  the  lands  for  sugarcane  expansion  within  the  demarked 
Agroecological Zoning. 

  EU-Mercosur Trade talks 

With a combined GDP of US$ 2.7 trillion in 2017, Mercosur18 is the largest trading bloc of 
Latin  America  and  Caribbean  regions.  In  2017,  the  EU  lost  its  long-standing  position  to 
China, as the largest trading partner of Mercosur. China accounts for 21.8% of Mercosur 
trade (EPRS, 2018), whilst the EU makes up for 20.3%, ahead of the USA (14.9%). Indeed, 
trade between the EU and Mercosur reached €111.6 billion in 2011 shortly before the end 
of the commodities boom, but declined to €84.9 billion in 2017 (Figure 5). 

15  https://www.mctic.gov.br/mctic/export/sites/institucional/tecnologia/tecnologiasSetoriais/Plano-de-Ciencia-

Tecnologia-e-Inovacao-Para-Energias-Renovaveis-e-Biocombustiveis.pdf  

16 http://biofutureplatform.org/about/  
17http://www.mme.gov.br/web/guest/secretarias/petroleo-gas-natural-e-combustiveis-

renovaveis/programas/renovabio/principal  

18 Mercosur: the common market of the south was founded in 1991 by Argentina, Brazil, Paraguay and Uruguay. 

In 2012 Venezuela joined the trading bloc but the country was temporarily suspended in 2016. 

14 

 
 
 
 
                                           
Figure 5: EU trade in goods with Mercosur. Source: EPRS, 2018 

Since 1999, the European Union and Mercosur have been negotiating a bilateral agreement 
governing trade relations19. In May 2004, the EU made a formal market access proposal 
for  the  concession  of  additional  quotas  for  agricultural  products20  (Kume  et  al.,  2010). 
Unfortunately,  the  parties  were  not  able  to  reach  an  agreement  since  the  outcome  of  a 
follow-up  trade  negotiators’  meeting  at  ministerial  level  determined  that  the  EU's  offers 
were not ambitious enough, especially in agriculture and service sectors (SIA, 2008).  The 
negotiations have regained momentum since 2016, where rounds in March and July 2017 
made considerable progress on a wide range of chapters, although division between the 
parties still remains on a number of particularly sensitive issues. Opening the EU market 
to agricultural imports has long been a sticking point, since many EU products - including 
sugar,  ethanol  and  beef  -  would  come  under  pressure.  During  the  negotiation  round  in 
October 2017 the EU completed its quota offers on beef and ethanol, which the Mercosur 
countries deemed as too low21. On June 28th, the EU and Mercosur reached a provisional 
free trade agreement – the final text and the market access schedules have not determined 
yet.  According  to  the  released  details,  the  EU  will  establish  a  duty-free  tariff  rate  quota 
(TRQ)  of  562  million  litres  of  ethanol  for  industrial  use  per  year,  and  an  additional  250 
million litres at reduced tariff rates (USDA, 2019). The EU will also increase the TRQ for 
cane sugar to 180000 tons. 

Even though Brazil is one of the world's top producers and exporters of sugar and ethanol, 
it supplies a small share of the European market. In 2016, Brazilian ethanol accounted for 
only ca. 5% of EU purchases (Figure 6) due to the high tariff of 0.19 euro/l, which renders 
Brazilian exports to the EU as uncompetitive. The Brazilian sugarcane industry complained 
that other countries benefited from favorable duty-free sugar and ethanol agreements and 
insisted on keeping these commodities on the table within the ongoing Mercosur-EU trade 
negotiations (UNICA, 2016).  

19 http://trade.ec.europa.eu/doclib/press/index.cfm?id=1769  
20 The EU proposal included a TRQ for ethanol of 1.000.000 tons. 
21 The last EU offer included a 600,000 tonne-quota for ethanol, of which 400,000 tonnes for industrial use. The 
Brazilian counterpart wants to achieve a volume of ca. 1,000,000 tons, as in the EU 2004 proposal (Reuters, 
2017). 

15 

 
 
 
                                           
Figure 6: main countries of origin of ethanol imports to EU. Source: Eurostat and ePURE, 2017. 

The new planned Sustainability Impact Assessment in support of the association agreement 
negotiations  between  the  European  Union  and  Mercosur  (SIA,  2018)  will  provide  a 
knowledge  base  on  the  potential  consequences  for  the  economic,  social,  human  and 
environmental  dimensions  in  the  EU  and  Mercosur  of  the  awaited  trade  agreement.  Its 
scope, sectoral coverage and resolution significantly differ from the objective of our study. 

  List of legislative tools 

The  questions  addressed  by  this  study  can  be  categorized  into  different  European  and 
international policy areas related to energy, environment, and climate change (Table 1, 2 
and 3). 

Table 1: list of EU documents and legislative tools considered in this study 

Policy sector 

Legislative tool 

Main issues relevant to this study 

Energy/Environment 

Directive 
2009/28/EC22  of  the 
European  Parliament 
and  of  the  Council  of 
23  April  2009  on  the 
promotion  of  the  use 
from 
of 
renewable 
sources 
and  amending  and 
subsequently 
repealing  Directives 
2001/77/EC 
and 
2003/30/EC (RED) 

energy 

  Sets 

impacts 

sustainability 

criteria 
(including 
on 
biodiverse lands and lands with 
high  carbon  stocks)  and  GHG 
saving  criteria  to  comply  with 
Union’s  commitments  under 
the  Kyoto  Protocol  and  with 
Union 2030 energy and climate 
agenda; 

  Does  not  cover  the  issue  of 

ILUC 

  Sets a 20% binding  target for 
the  overall  share  of  energy 
from  renewable  sources  and 
10%  minimum  target  for  the 
share of biofuels in transport by 
2020 

  Points  out  the  role  of  biofuel 
imports  to achieve the correct 
domestic 
between 
balance 
production and production in 3rd 

22 https://eur-lex.europa.eu/legal-content/EN/ALL/?uri=celex%3A32009L0028  

16 

 
 
 
 
 
                                           
Energy/Environment 

Energy/Environment 

Directive 
(EU) 
2015/151323  of  the 
European  Parliament 
and of the Council of 9 
September 
2015 
amending  Directive 
98/70/EC  relating  to 
the  quality  of  petrol 
and  diesel  fuels  and 
amending  Directive 
2009/28/EC  on  the 
promotion  of  the  use 
from 
energy 
of 
renewable sources 

(EU) 
Directive 
2018/200124  of  the 
EU  parliament  and  of 
the  Council  of  11 
December  2018  on 
the  promotion  of  the 
use  of  energy  from 
renewable 
sources 
(REDII) 

countries,  taking  into  account 
negotiations, 
trade 
environmental, 
and 
social 
economic considerations (16) 

  Recognizes that the magnitude 
of  GHG  emissions  from  ILUC 
can  negate  some  of  GHG 
emission  savings  of  individual 
biofuels 

  Define an overall limit of a 7% 
maximum  contribution  of  fuels 
produced  from  cereals,  sugar 
and oil crops, starch-rich crops 
on  agricultural  lands,  towards 
the final consumption of energy 
in  rail  and  road  transport  in 
each Member State 

  Sets 

sustainability 

criteria 
(including  impacts  on  lands 
with  high  carbon  stocks)  and 
GHG  saving  criteria  to  comply 
with  the  EU’s  commitments 
under the Paris Agreement and 
its  2030  energy  and  climate 
agenda; 

  Limits 

biofuels 

contribution  of 
the 
conventional 
and 
bioliquids towards 2030 energy 
target in rail and road transport 
to the 2020 national share, with 
the  possibility  of  increasing 
them by +1% up to a maximum 
of 7% 

  Recognizes  the  magnitude  of 
GHG emissions due to ILUC 

bioliquid 

  Limits  high  ILUC-risk  biofuel 
contribution 
and 
towards 2030  energy target to 
the  2019  country  consumption 
level; 
their 
contribution  will  be  gradually 
reduced  to  zero  by  2030  at 
latest 

2023 

from 

  Points  out  the  need  of  a  clear 
definition of high and low ILUC-
risk feedstock 

23 https://eur-lex.europa.eu/legal-content/EN/TXT/?qid=1550150723129&uri=CELEX:32015L1513  
24 https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32018L2001&from=EN  

17 

 
 
 
 
 
 
 
 
 
 
                                           
Energy/Environment 

Commission 
Delegated  act  (EU) 
2019  (COM  2019)- 
The proposal is made 
to  Article 
pursuant 
26(2) of the REDII  

  Set criteria for determining the 
feedstock 
ILUC-risk 
high 
(including 
and 
soybean) for which a significant 
expansion  of  the  production 
area into land with high carbon 
stock is observed; 

sugarcane 

  Set  criteria  for  certifying  low 
and 

biofuels 

ILUC-risk 
bioliquids 

  Reinforces 

sustainability 
criteria in line with the Union’s 
commitments  under  the  Paris 
Agreement and SDGs (e.g, land 
degradation neutrality) 

  States  the  need  for  improving 
the 
knowledge  base  and 
understanding  the  bioeconomy 
ecological  boundaries,  through 
forward 
collection, 
data 
looking, 
sectoral 
cross 
assessments,  modelling  and 
scenarios 

  Points  out  existing  gaps  in 
monitoring 
bioeconomy 
performances and impacts and 
overarching 
an 
for 
calls 
evaluation 
and 
monitoring 
framework 
different 
on 
bioeconomy sectors at national, 
European and global level. 

  Estimates 

that  meeting  EU 
decarbonization 
targets  will 
double  the  EU  consumption  of 
biomass.  Part  of  this  demand 
will  be  satisfied  by  increasing 
access to 3rd country markets 

high 

  Points  out  the  EU’s  global  role 
as    major  import  market:  the 
EU’s 
environmental 
standards  can  have  effects  on 
3rd 
productive 
systems  –  EU  trade  policy 
should  be  used  to  promote 
sustainability 

country’s 

Bioeconomy/Bioenergy   COM(2018) 

673 
final25,  11.10.2018. 
Communication  from 
the  Commission 
to 
European 
the 
the 
Parliament, 
European 
economic 
and  social  committee 
and the committee of 
A 
region. 
the 
sustainable 
Bioeconomy 
Europe: 
the 
strengthening 
connection  between 
economy, society and 
the environment.  

for 

Energy/climate  

COM(2018) 
773 
final26,  28.11.2018. 
Communication  from 
the commission to the 
European  Parliament, 
the European Council, 
the 
Council, 
the 
European 
economic 
and social committee, 
the  committee  of  the 
the 
regions 
European  Investment 
Bank.  A  Clean  Planet 
for  all,  a  European 
long-term 
strategic 
a 
for 
vision 
prosperous,  modern, 
and 
competitive 

and 

25 https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52018DC0673  
26 https://eur-lex.europa.eu/legal-content/EN/TXT/?qid=1550150941903&uri=CELEX:52018DC0773  

18 

 
 
 
                                           
climate 
economy (LTS 2050) 

neutral 

Table 2: list of Brazilian legislative tools considered in this study 

Policy sector 

Legislative tool 

Main issues relevant to this study 

Energy/Environment 

Renovabio program – 
law 
National 
on 
biofuels, 
nº13.576, 
on  26th    December 
2007 

Environment 

Revised  Forest  Code, 
2012 
N° 
4.771/65) 

(Law 

  Set  environmental  criteria  for 
the production of sugarcane: i) 
avoid  the  production  of  biofuel 
feedstock  on  lands  converted 
after 
from 
December  2017;  ii)  limit  the 
lands  for  sugarcane  expansion 
within 
the  demarked  Agro-
ecological Zoning. 

forest 

areas 

  Certify 

the  sustainability  of 

feedstock productive systems 

  Set 

legal 
landowners 
conservation 
properties)  

requirements 

(i.e., 
in 

for 
forest 
private 

Agriculture 

Agro-Ecological 
Zoning (AEZ), 2009 

  Set limits of the available areas 
for the  expansion of  sugarcane 
in Brazil 

Table 3: EU-Mercosur trade negotiation considered in this study 

Policy sector 

Legislative tool 

Main issues relevant to this study 

Trade 

Trade 
Mercosur 2018/19 

talks 

EU-

  Will define the duty-free volume 
of  Brazilian  ethanol  sold  in  the 
EU market 

  Sets  binding  environmental  
criteria  and  working  standards 
for  EU  suppliers  of  agricultural 
commodities 

  Commits 

the  signatories 

to 
emission-reduction  targets  and 
to deforestation combat 

19 

 
 
 
 
 
 
 
 
3  Impacts of EU biomass demand in third countries: state of 

play 

  Introduction 

In a recent report by EEA (2018), it was acknowledged that increasing demand for food, 
feedstock, biomaterials, and bioenergy will exacerbate the pressure on natural resources 
and  potential  demand/supply  conflicts,  which  in  turn  requires  coordinated  action  and 
careful consideration of the trade-offs. Nevertheless, there is a relative dearth of literature 
examining the global impacts of EU bioeconomy activity on indirect land use changes (LUC 
and ILUC), and their GHG emissions. 

Studies on the impacts induced by the demand for biomass feedstocks differ depending on 
how  the  bioeconomy  is  defined  in  the  respective  strategies  and  which  sectors  are 
considered being part of it, since a commonly agreed definition currently does not exist.  

Differences  in  the  sectors  and  subsectors  included  into  bioeconomy  strategies  mainly 
reflect  the  priorities  and  competitive  advantages  of  each  country  or  economic  block. 
Examples are given in Table 4 .  

Table  4:  Sectors  included  into  bioeconomy  strategies  of  countries  examined.  “X”:  included  into 
bioeconomy strategy; “XX”: included into bioeconomy strategy and monitored. Source:  FAO 2018 
and Bracco et al., 2018. 

Sector 

Argentina 

Australia  Malaysia  South 
Africa 

USA  EU  Germany 

Agriculture 

xx 

Chemistry 
bioplastic) 

(incl. 

xx 

Biofuels/bioenergy 

xx 

Biorefining 

Feed 

xx 

Consumer 
(cosmetics…) 

goods 

xx 

Fisheries 

xx 

Food and beverage 

xx 

Forestry 

xx 

Construction/building 
industry 

x 

x 

x 

x 

x 

x 

x 

x 

Xx 

Xx 

Xx 

xx 

xx 

xx 

xx 

xx 

x 

x 

x 

x 

x 

x 

x 

x 

x 

xx 

xx  xx 

xx 

xx  xx 

xx  xx 

xx 

xx 

xx  xx 

xx 

xx  xx 

xx  xx 

xx 

xx  xx 

xx 

Therefore, this uncertainty in the bioeconomy boundaries and its sectoral targets hinders 
the definition of a globally agreed framework to identify potential impacts of the transition 
to a bioeconomy (Table 5). Most countries measure the contribution of bio-based activities 
to sectoral macroeconomic indicators – e.g., GDP, turnover, value added and employment 
(Ronzon  and  M’Barek,  2018;  FAO,  2018)  –  whilst  regulatory,  social  and  environmental 

20 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
dimensions are not systematically covered (Bracco  et al., 2018; Rodriguez et al., 2017; 
Maes et al., 2016). This knowledge gap calls for international guidelines to assist countries 
to monitor bioeconomy sustainability aspects in a systematic and harmonized way (FAO, 
2016;  O’Brien  et  al.,  2017).  LUC  and  ILUC)  are  not  an  exception  and  their  global 
assessment  for  the  main  sectors  of  the  bioeconomy  still  remains  relatively  neglected, 
coarsely aggregated at regional level and highly uncertain (Giljum et al., 2016; Hertel et 
al., 2012; O’Brien et al., 2015; Philippidis et al., 2016).  

Table 5: Main studies reviewed on global monitoring and impact assessment of bioeconomy sectors 

Study 

Description 

Geographical 
scope 

Sectors 
and 
products 

Output 

Bracco  et 
al., 2018 

Assessment  of  the 
bioeconomy 
contribution 
total economy 

to 

6 pilot countries 
in  Africa,  Latin 
America,  USA, 
Europe, Asia 

in 

Sectors 
the 
bioeconomy 
strategies 

Sectoral 
macroeconomic 
indicators, 
GDP, 
employment 

mainly 
turnover  and 

FAO, 
2018 

Assessment  of  the 
bioeconomy 
contribution  select 
national 
economies 

7 pilot countries 
in  Africa,  Latin 
America,  USA, 
Asia, 
Europe, 
Australia 

in 

Sectors 
the 
bioeconomy 
strategies 

Sectoral 
macroeconomic 
indicators, 
GDP, 
employment 

mainly 
turnover  and 

Giljum  et 
al, 2016 

Hertel  et 
al., 2012 

O’Brien et 
al., 2017 

O’Brien et 
al., 2015 

of 
Assessment 
land 
global 
demand  for  non-
food  products  of  
the 
EU 
bioeconomy 

land  use 
Global 
implications 
of 
biofuels  expansion 
(2006-2035) 

General evaluation 
about  monitoring 
modelling 
and 
tools  of  the  EU 
bioeconomy 
(gaps, 
criteria)  

needs, 

Assessment  of  the 
land  footprint  of 
EU 
the 
bioeconomy 

Global  

Bioplastic, 
biofuels 

Global 

Biofuels 

EU and global 

EU 
bioeconomy 
sectors 

footprint. 

Cropland 
Qualitative 
assessment  of  social 
and 
environmental 
impacts  

Cropland 
footprint. 
GHG  emissions  from 
land conversion 

General  consideration 
on  a  dashboard  of 
indicators  to  assess 
the 
on 
impacts 
environment,  society 
and  economy.  Focus 
on land footprint 

Global 

Agriculture  Cropland 

footprint, 
use 
imported 
exported 

land 

global 
(including 
and 
commodities) 

Parisi and 
Ronzon, 
2016 

report 
Technical 
on  the  workshop 
“a  global  view  of 

Global 

Bio-Based 
Industry 

General 
considerations  on  the 
for  methods, 
needs 

21 

 
 
 
bio-based 
industry” 

Philippidis 
et 
al., 
2016 

CGE 

Ex-ante 
impact 
assessment 
potential 
bioeconomy 
drivers 
a 
baseline  scenario 
to 2030  

of 
EU 

in 

Global  with  a 
focus on EU28 

EU 
bioeconomy 
sectors 

data and indicators to 
monitor 
socioeconomic 
impacts 
of 
bioeconomy 

the 

Macroeconomic 
indicators; 
GHG 
emissions  by  sectors; 
land  use  change  by 
regions 

Rodriguez 
et 
al., 
2017 

Overview 
about 
the  main  policy 
that 
frameworks 
the 
drive 
bioeconomy 
in 
Latin  America  and 
the Caribbean.  

Global  with  a 
focus  on  Latin 
and 
America 
Caribbean 

Bioeconomy 
sectors  of 
Latin 
American 
and 
Caribbean 
countries 

Policy frameworks and 
institutions  linked  to 
bioeconomy 
development;  export 
profile  for  the  main 
sectors  included  into  
bioeconomy strategy  

  Macroeconomic indicators 

3.2.1  Monitoring  

Most  countries  assess  the  contribution  of  the  bioeconomy  to  their  economy  through 
changes  in  typical  macroeconomic  indicators,  such  as  Gross  Domestic  Product  (GDP), 
employment rates, turnover and value added. This approach provides a coarse picture of 
the progress of the bioeconomy toward the targets set by the country strategy, whilst it is 
unable to capture faithfully the complexity and heterogeneous nature of country-specific 
bioeconomy sectors and products. Moreover, socioeconomic data are often only available 
at different degrees of granularity, which limits the direct comparison between countries 
or sectors. 

Bracco et al., (2018) and FAO, (2018) compare how seven pilot countries: i) define their 
bioeconomy; ii) set their bioeconomy strategy objectives; and iii) monitor and report the 
contribution  of  the  bioeconomy  to  their  economy  and  objectives.  Most  countries  rely  on 
traditional  statistical  accounts  on  production  and  consumption  (e.g.,  Germany  and 
Netherlands)  but  lack  a  systematic  approach  and  metrics  fully  implemented  to  measure 
the  impacts  of  the  bioeconomy.  In  contrast,  Malaysia,  for  example,  developed  a 
Bioeconomy  Contribution  Index  to  assess  the  contribution  of  bioeconomy  on  overall 
economy, based on 5 indicators: value added, bio-based exports, investment in bio-based 
activities, employment and productivity performance. 

Table 6 lists some of the most common indicators adopted to measure the impact of the 
transition  to  a  bioeconomy,  even  though  an  international  agreed  indicator  framework 
cannot  be  stated  due  to  the  current  fragmentation  of  the  research  on  bioeconomy 
boundaries (FAO,2018; Ronzon et M’Barek, 2018; SAT-BBE, 2014). 

22 

 
 
 
 
 
Table  6:  Most  common  indicators  used  to  assess  the  contribution  of  bioeconomy  sectors  to  total 
country economy 

Indicator 

Description 

Number of persons employed 

Turnover 

Value added 

Sectoral GDP  

Primary production  

Trade flows 

Production/Consumption  

Total  number  of  persons  working  in  the 
bioeconomy sectors 

Total  invoiced  by  the  bioeconomy  sectors 
(revenues from sales) 

Gross  income  from  operating  activities 
after adjusting for operating subsidies and 
indirect taxes 

Contribution  of  the  bioeconomy  and  its 
sectors to the macroeconomy 

Country production in agriculture, forestry, 
residues, fisheries, waste sectors 

Import/export  of  biomass  and  bio-based 
products 

Production  and  use  of  bioeconomy 
products (in volume and value) 

3.2.2  Modelling future socioeconomic pathways 

In the economic analysis of bioeconomy pathways, the modelling literature is broadly split 
between 'bottom-up' and 'top-down' approaches (Angenendt et al., 2018). The appropriate 
choice of approach is contingent upon the nature and scale of the research question. For 
example, the need to analyse very specific technological processes and the behaviour of 
detailed  agents  within  the  supply  chain  (i.e.,  farms,  refineries  etc)  favours  the  use  of  a 
bottoms-up approach. On the other hand, given their spatial limits, they cannot deal with 
broader  questions  relating  to  biomass  resource  competition  across  different  biobased 
activities,  the  interactions  and  resource  competition  effects  that  ensue  with  the  non 
biobased part of the economy and the resulting structural economic change over time to 
which these dynamic effects give rise. These type of 'big-picture' questions are therefore 
more suited to top-down economy-wide approaches. 

For  example,  input-output  (I/O)  analysis  and  social  accounting  matrices  (SAM)  are  top-
down tools often used to explore in a short term (inter alia) the wealth and employment 
generation impacts arising from assumed different demand shocks. The use of multiplier 
analysis  generates  useful  insights  on  the  structure  and  strength  of  the  interlinkages 
between sectors in an economy and their contribution to employment, value added, and 
energy use, among others (SAT-BBE, 2013). 

Global computable general equilibrium (CGE) models take an additional step by allowing 
for technological and structural economic  changes within an economy. The timeframe is 
therefore  rather  medium  to  long  term,  whilst such  ex-ante  assessments  can  be  used  to 
model biomass demand and supply under different policy, socioeconomic or technological 
scenarios, and evaluate their potential impacts on sector macroeconomic indicators, such 
as output, prices, trade and employment (Figure 7). 

23 

 
 
 
Figure 7: Employment (1000 head) in EU28 bio-based sectors. Source: Philippidis et al., 2018 

Thus, these models, by their very nature, are ideally suited to questions regarding policy 
trade-offs  and  synergies  in  biomass  usage,  not  only  within  countries,  but  also  across 
international boundaries. For example, CGE models which are calibrated to the well-known 
GTAP (Global Trade Analysis Project27) database, have coverage of 57 tradable activities 
across 140 world regions, whilst more advanced biobased variants such as MAGNET extend 
the activity coverage even further.  

  Global land use change  

A  few  studies  on  measuring  the  global  land  use  impacts  of  the  EU  bioeconomy  exist 
although all of them present a coarse assessment of land use changes through a sectorial 
lens. 

Philippidis et al., (2016) present changes in land use (Km2) of EU28 bio-based sectors for 
different countries/regions and based on policy driven narratives defined by the degree of 
EU engagement with bioeconomy and sustainability objectives. Interestingly, the authors 
note, for example, that greater EU efforts to decarbonise its economy and turn to biomass 
as a source of sustainable economic growth, would imply a significant land expansion in 
Mercosur and North American economic blocs, compared to the reference scenario (Figure 
8). 

27 https://www.gtap.agecon.purdue.edu/  

24 

 
 
 
 
                                           
Figure 8: Changes (Km2 and %) from 2020 to 2030 in global land use by regions due to different 
policy narratives (IL and OL) compared with the reference scenario (RS). Source: Philippidis et al., 
2016. 

Giljum  et  al,  (2016)  assess  the  global  cropland  demand  related  to  EU  non-food  sectors 
(e.g., oil crops, fibers, rubber, cereals for biofuels and bioplastic) 28, focusing on two key 
products,  i.e.  biofuels  and  bioplastic.  The  former  has  the  highest  current  land  demand 
between  the  non-food  products,  whilst  the  latter  is  expected  to  exhibit  a  high  potential 
degree  of  sectoral  growth.  The  study  reveals  that  65%  of  the  land  area  (18.3  Mha) 
employed to meet EU demand for non-food products, are located in other world regions, 
notably  in  Asia  (e.g.,  soybean,  palm  oil),  stressing  the  high  EU  dependency  rate  on 
imported biomass and bio-based products. According to the same study, a significant part 
of  land  conversion  to  biofuel  crops  will  occur  in  Asia  and  Latin  America.  In  general,  the 
ambitious targets of bioeconomy strategies around the world could lead to an intensification 
of  land  competition  at  global  level,  calling  for  a  systemic  monitoring  of  LUC/ILUC  both 
domestically  and  abroad  (O’Brien  et  al.,  2017).  The  EU  was  a  net  importer  of  virtual 
agricultural land between 2000 and 2013 (O’Brien et al., 2015), even though the trend of 
the ratio of imported to exported lands is declining, suggesting an increased independence 
of  Europe  from  imported  biomass  (Figure  9).  For  example,  the  EU-27  demanded  ca.  45 
Mha of agricultural land from global suppliers in 2011, of which 42 Mha were cropland. In 
the same year, exported EU land totaled 19 Mha, of which ca. 17 Mha were cropland. The 
global cropland footprint of the  EU-27 was  on  average ca. 0.3 (ha/cap) with a declining 
trends over the period. 

28 Wood and wood-products were not considered in the assessment 

25 

 
 
 
                                           
Figure 9: Global cropland use of EU-27 between 2000 and 2013. Source: O’Brien et al., 2015 

Table 7 presents the common indicators adopted to measure changes in land use due to 
bioeconomy sectors/subsectors; most of the countries account for croplands. 

Table 7: some common indicators used to assess the impacts of bioeconomy sectors on land use 

Indicator 

Description 

Cropland footprint29 

of 

specific 

Productive areas (Km2) required to meet a 
specific  demand  for  biomass  or  bio-based 
products.  Gross  production  land:  land  for 
(e.g., 
production 
sugarcane). Net production land: allocation 
of  land  to  different  products  produced  on 
the  same  land  (e.g.,  sugar  and  ethanol). 
Net consumption land=domestic croplands 
+  net  traded  land  requirement  (imports-
exports); it defines the global land use for 
domestic consumption  

crop 

Intensity of land use 

Land productivity (ton/ha or head/ha) 

Land conversion share 

Virtual land (import/export) 

Productive lands obtained from conversion 
of  other  land  uses  (e.g.,  forest,  pasture, 
abandoned lands) 

Land required in third countries for biomass 
production 
domestic 
consumption  (virtual  land  import).  Land 
used  to  produce  biomass  for  export  to 
other countries (virtual land export).  

satisfy 

to 

26 

 
 
 
 
                                           
 
 
  Sustainability 

of 

traded 

bio-commodities: 

existing 

assessment tools 

Several data sharing and data visualization tools to assess and monitor the sustainability 
of bio-commodities’ trade at the global level exist. These tools allow for a broad exploratory 
analysis of the consequences of the demand for agricultural goods and services in the place 
of origin, through a limited number of environmental indicators. With only a few exceptions, 
their outputs are mainly aggregated at country level, which hinders a subnational impact 
assessment.  

The  GRAS  Project30  aims  at  supporting  the  establishment  and  monitoring  of  sustainable 
and deforestation-free supply chains of agricultural products. It provides information about 
land use changes, biodiversity, carbon stock and social indices with varying resolutions for 
16  countries  and  regions.  Some  indices  are  coarsely  aggregated  at  country  level  (e.g., 
social  indices)  and  can  be  useful  only  for  inter-country  or  inter-regional  comparison, 
without allowing for a subnational assessment. Georeferenced raster data on biophysical 
attributes  are  provided  with  a  better  resolution  (e.g.,  biomass  carbon,  pixel  1x1  Km2) 
according to the quality of publicly available datasets  

The TRASE31 platform seeks to describe the links between agricultural commodities supply 
chains and environmental and social risks in tropical forest regions. TRASE uses publicly 
available data to map trade flows (via trading companies) from the place of production to 
the  consumer  and  provide  a  picture  of  potential  impacts,  offering  a  knowledge  base  to 
move towards a more sustainable production, trade and consumption for the major forest-
risk  agricultural  commodities.  TRASE  covers  only  Latin  America  soy,  beef  in  Argentina, 
Brazil and Paraguay, palm oil in Indonesia and Colombia and coffee in Colombia. The scale 
of  analysis  is  defined  by  the  availability  of  country’s  production  data;  trade  volumes  of 
commodities  and  financial  flows  are  quantified  at  national  level,  whilst  only  information 
about Brazil and Paraguay soy production are disaggregated at municipality level. Flows of 
traded commodities and their environmental impacts - from production to final destination 
- are described in sankey diagrams. 

The ATLAS32 of Economic Complexity is a visualization tool that allows one to explore global 
trade flows over time for 250 countries and territories, classified into 20 categories of goods 
and 5 categories of services (covering ca. 6000 products). Raw trade data on goods are 
derived from COMTRADE33 (UN Statistical division), whilst raw data on services are from 
the international Monetary Fund34. It should be noted, however, that ATLAS does not assess 
any kind of environmental and socioeconomic impact due to trade flows. 

The  EORA35  global  supply  chain  database  consists  of  a  multi-region  Input-Output  table 
(MRIO)  model  working  with  a  common  26-sector  classification  across  190  countries.  It 
provides high-resolution IO tables and environmental satellite accounts, covering a 1990-
2015 time window. EORA produces spatially explicit environmental and carbon footprints 
associated  with  the  consumption  in  a  given  country  (domestic  resource  use,  resources 
embodied  in  imports  and  exports)  and  allows  for  linking  consumers  to  the  upstream 
hotspots of their purchases. 

30 https://www.gras-system.org/about-gras/the-gras-project/  
31 https://trase.earth/? 
32 http://atlas.cid.harvard.edu/ 
33 https://comtrade.un.org/ 
34 http://data.imf.org/?sk=9D6028D4-F14A-464C-A2F2-59B2CD424B85 
35 https://www.worldmrio.com/  

27 

 
 
 
 
 
 
 
                                           
  The need for a subnational impact assessment  

“ […] limit for biofuels, bioliquids and biomass fuels produced from food and feed crops for 
which  a  significant  expansion  of  the  production  area  into  land  with  high  carbon  stock  is 
observed”.  (Directive (EU) 2015/1513, EU Council 2018, Pag. 33). 

The main limitation of the above-mentioned studies is their coarse spatial resolution, which 
cannot fully address key-questions set by European Directives (e.g., REDII environmental 
criteria)  and  international  commitments  (e.g.,  Paris  Agreement).  Indeed,  aggregated 
results at country/regional level hide the causal links between productive systems and their 
impacts within a site-specific context (Brinkman et al., 2018). 

For  example,  understanding  if  a  cultivation  of  crops  for  biofuels  is  expanding  into  lands 
with a high carbon stock or results in high ILUC risk elsewhere requires the use of spatial 
indicators with a proper spatial resolution, able to capture the geographic heterogeneity of 
the socioeconomic, environmental, climate and ecosystem dimensions (Maes et al., 2016). 
In large countries such as Brazil - hosting different biomes with very different biophysical 
attributes–  the  average  estimation  of  carbon  emissions  and  removals  and  biodiversity 
losses at national level, does not make sense.  Agriculture practices and deforestation in 
the Amazon biome have more severe consequences in terms of CO2 emissions per hectare 
than  in  the  Cerrado,  for  example,  due  to  the  considerable  differences  in  carbon  stocks 
(Figure 10).  

Figure 10: Biomass content (tons C per hectare) across Brazilian biomes (Aguiar et al., 2015). The 
Amazon and Cerrado biomes recorded the highest deforestation rates due to agriculture and livestock 
expansion and land grabbing. 

Therefore, a spatially-explicit tool that provides estimates of the subnational allocation of 
projected  land  use  changes  across  the  country  biomes  due  to  varying  demand  for  bio-
commodities  is  needed  to  (i)  reduce  the  uncertainty  about  the  estimation  of  LULUCF 
emissions  and  biodiversity  losses  and  (ii)  to  offer  a  strong  scientific  knowledge  base  to 
assess  the  compliance  with  the  international  environmental  criteria  and  country  climate 
commitments (Figure 11).  

28 

 
 
 
 
 
 
Figure  11:  a  multilevel  assessment  translates  the  EU  bioeconomy  demand  into  potential 
environmental consequences at local scale. Example of Brazilian bioethanol 

29 

 
 
 
 
4  Region of focus: Brazil 

  Sustainability of Europe-Brazil trade 

Brazil  is  the  biggest  economy  of  Latin  America  and  the  largest  exporter  of  agricultural 
products to Europe. The EU is the Brazil’s second-biggest trading partner, accounting for 
ca.  18%  of  its  total  trade,  importing  from  Brazil  mainly  primary  products  (70%)  and 
exporting to Brazil manufacturing goods (84%) (Figure 12). That said, understanding the 
impacts of EU demand in Brazil means, above all, assessing the sustainability of Brazilian 
agribusiness sectors. 

Figure 12: EU28 exports to and imports from Brazil in 2008 and 2018 by product group (EUR billion). 
Source: Eurostat Comext, 201836. 

Concerns  have  been  raising  about  the  potential  environmental  damages  of  predatory 
agriculture  practices  in  Brazil  under  scenarios  of  lax  enforcement  of  environmental laws 
(Fuchs at al., 2019; Rochedo et al., 2018; Tollefson, 2018; Gibbs et al., 2015). A recent 
letter to the journal Science signed by more than 600 European scientists urged the EU to 
make  the  trade  negotiations  with  Brazil  conditional  to  strict  social  and  environmental 
criteria, in order to halt deforestation, contribute to curb climate change and guarantee the 
respect of human rights of indigenous communities (Kehoe et al., 2019). 

The need for guaranteeing a sustainable supply-chain governance by linking producers and 
consumers through an awareness on the potential consequences of traded products in the 
place  of  origin  has  been  discussed  on  27  June  2018  during  the  first  EU-Brazil  Fair  and 
Ethical Trade Consultation Forum37.   

  Land use changes in Brazil until 2017 

Between 1985 and 2017 Brazil lost ca. 71 million hectares of its native vegetation due to 
the  rapid  expansion  of  croplands  and  pasturelands  (Mapbiomas,  2019),  mainly  in  the 
Cerrado and Amazon biomes (Figure 13). 

36 https://webgate.ec.europa.eu/isdb_results/factsheets/country/details_brazil_en.pdf  
37 

https://eeas.europa.eu/delegations/brazil/47546/first-eu-brazil-fair-and-ethical-trade-consultation-forum-

held-rio-de-janeiro-21st-june-2018_en  

30 

 
 
 
 
                                           
Figure 13: contribution of Brazilian biomes to country area (%). 

Land  use  changes  in  Brazil  are  strongly  influenced  by  federal  government’s  policies.  As 
suggested by Rochedo et al., (2018), the environmental governance in Brazil can be divided 
into 3 periods: pre 2005, with a very poor governance and the highest deforestation rates; 
2005-2011,  with  an  improved  governance  that  reduced  deforestation  by  78%  and  the 
associated    GHG  emissions  by  54%;  and  a  post  2012  period,  when    deforestation  rates 
have levelled up again, especially in the Cerrado biome (Figure 13), due to lax enforcement 
of the Forest Code. In the period 1985-2017 the Amazon lost ca. 36 million hectares (10%) 
of native  vegetation, while the Cerrado lost 24 million hectares (18%) due to predatory 
farming practices and land grabbing (Figure 14 and Figure 15). 

31 

 
 
 
 
 
 
Figure 14: Trends of forests, croplands and pasturelands in the Amazon biome from 1985 to 2017 
(million hectares). Source: MapBiomas, 2019. 

32 

 
 
 
Figure 15: Trends of forests, croplands and pasturelands in the Cerrado biome from 1985 to 2017 
(million hectares). Source: MapBiomas, 2019. 

The most important crops in Brazil are soybean, sugarcane and corn, representing around 
85% of total production (Fuchs et al., 2019). Sugarcane is considered as a semiperennial 
crop; its expansion in the 1985-2017 period occurred mainly into the Cerrado and Mata 
Atlantica38 pasturelands (Figure 16). The contribution to sugarcane production of the other 
biomes is very limited.  

38 The Mata Atlantica biome has been widely deforested in the past. Nowadays, it conserves less about 20% of 

its native vegetation. Most of expansion of sugarcane and other crops occurs into pasturelands. 

33 

 
 
 
 
 
 
                                           
Figure 16: Expansion of semiperennial crops, notably sugarcane, into the Brazilian biomes. Source: 
Mapbiomas, 2019 

Soybean and corn are the largest annual crops in Brazil; they expanded mainly into  the 
Cerrado and Mata Atlantica biomes (Figure 17). Since 2001, the areas with annual crops 
have shown a  steeply increasing trend in the Amazon biome (about  a tenfold increase), 
which raises concerns about soybean expansion into the Amazon rainforest, even though 
most of the increase occurred on lands cleared before 2008 (starting year of Brazil’s Soy 
Moratorium). 

Figure 17: Expansion of annual and perennial crops, notably soybean and 2nd corn, into the Brazilian 
biomes.  Source: MapBiomas, 2019. 

  GHG emission profile 

Brazil  is  the  7th  largest  emitter  of  GHG,  accounting  for  ca.  3.4%  of  the  world's  CO2 
emissions. From 1990 to 2017, almost 64% of Brazil's GHG emissions came from land use 
change and forestry (LULUCF), whilst in 2017 this value dropped off to ca. 46% (Figure 

34 

 
 
 
 
18).  Between  2005  and  2012,  Brazil’s  GHG  emissions  were  reduced  by  54%,  mostly  by 
reducing deforestation by 78% (Rochedo et al., 2018). Most Brazilian GHG emissions are 
linked  to  agribusiness  activities;  in  2017  about  70%  of  GHG  emissions  came  from 
agriculture and livestock (SEEG Brazil, 201939). 

Figure 18: Gross GHG emission profile of Brazil from 1990 to 2017. Source: SEEG Brazil, 2019. 

Figure 19 shows the distribution of GHG emissions and removals, sorted by regional areas. 
In  the  Northern  region,  most  of  the  emissions  came  from  land  use  changes  (mainly 
deforestation). The high removals are linked to the large Amazon conservation units and 
indigenous lands. In the Centerwest – one of the most important regions for commodity 
production – the emissions are associated to farming activities, whilst in the Southeast – 
the  most  industrialized  and  urbanized  area  –  the  energy  sector  is  the  largest  source  of 
GHG. 

39 http://seeg.eco.br/en/ (April 2019). 

35 

 
 
 
 
                                           
Figure 19: GHG emissions (dark red) and removals (dark green) sorted by IBGE macroregions. 
Source: SEEG Brazil, 2019. 

  The Brazilian Nationally Determined Contribution (NDC) 

Brazil’s NDC40 committed to reduce its GHG emissions by 37% below 2005 levels in 2025, 
and by 43% below 2005 levels in 2030. The national climate plan includes actions, targets 
and legislative tools relevant to this study, in particular (FRB, 2015): 

  Biofuels: increasing the share of sustainable biofuels in the Brazilian energy mix to 
approximately 18% by 2030, by expanding biofuel consumption, increasing ethanol 
supply  by  increasing  the  share  of  advanced  biofuels  (second  generation),  and 
increasing the share of biodiesel in the diesel mix; 

  Land use change and forests: i) strengthening and enforcing the implementation of 
the  Forest  Code,  at  federal,  state  and  municipal  levels;  ii)  strengthening  policies 
and measures aimed to achieve, in the Brazilian Amazonia, zero illegal deforestation 
by 2030 and compensating for greenhouse gas emissions from legal suppression of 
vegetation by 2030; iii) restoring and reforesting 12 million hectares of forests by 
2030, for multiple purposes; 

  Agriculture: i) strengthen the Low Carbon Emission Agriculture Program (ABC) as 
the  main  strategy  for  sustainable  agriculture  development;  ii)  restoring  an 
additional 15 million hectares of degraded pasturelands by 2030 and enhancing 5 
million hectares of integrated cropland-livestock-forestry systems (ICLFS) by 2030. 

40 http://mma.gov.br/clima/ndc-do-brasil  

36 

 
 
 
 
 
 
                                           
In 2017, renewables accounted for ca. 43%  of  Brazil's energy matrix, thanks also to an 
important  contribution  of  sugarcane  biomass  (ca.  17%),  according  to  the  last  National 
Energy Balance report (BEN, 2018)41. 

41 http://www.epe.gov.br/pt/publicacoes-dados-abertos/publicacoes/balanco-energetico-nacional-2018  

37 

 
 
 
 
 
                                           
5  Integrated impact assessment for Brazil case study 

  Modelling framework 

To  calculate  the  environmental  impacts  of  varying  EU  demands  for  ethanol  in  Brazil  we 
established a comprehensive methodological procedure by combining two well-respected 
models: the global economic market simulation model, MAGNET, and the spatially explicit 
land-use  model  OTIMIZAGRO  (Figure  20).  This  multilevel  assessment  framework  allows 
moving from a regional/country outlook on socioeconomic trends to a subnational analysis 
of land use impacts, with the proper spatial resolution to assess the compliance with EU 
environmental criteria for biofuels production. 

Figure 20: Theoretical modelling framework. MAGNET projects the total and EU demand for Brazilian 
ethanol  from  different  EU  scenarios  (Consumer  side).  OTIMIZAGRO  calculates  and  allocates  the 
croplands to satisfy the demand at subnational level with a proper resolution to correctly quantify 
environmental impacts (Producer side). 

To estimate the trade impact of changes in EU biofuel policies on Brazilian production of 
bioethanol requires an appropriate system-wide market modelling tool which can capture 
the  interrelationship  between  economic  policy  changes  in  region  'A'  and  their  resulting 
repercussions on prices, trade and ultimately, production decisions in region 'B'. MAGNET 
projected  the  EU  demand  for  ethanol  until  2030  from  a  baseline  and  a  “conventional 
biodiesel  phase  out”  scenario.  Future  crop  production  across  13  commodities  (including 
soy) is derived from MAPA (2017)42, whilst deforestation and regrowth  rates come from 
Rochedo et al., (2018). The projected demand for wood (plantations) and meat (livestock) 
are taken from MCTIC and ONU (2017). All these exogenous estimations are inputted into 
OTIMIZAGRO,  which  translates  a  demand  for  a  commodity  into  production  areas,  and 
allocates them to agro climatic and soil aptitude maps for each crop. Concurrent allocation 

38 

 
 
 
 
                                           
 
 
of crops at a cell level of 25 ha is a function of biophysical attributes, land use constraints, 
crop  profitability  (calculated  using  regional  selling  prices,  production  and  transportation 
costs) and agro-climatic suitability. The probability of deforestation is a function of spatial 
determinants, such as the distance from paved roads and previously deforested areas. Net 
LULUCF emissions account for carbon stock changes in biomass and soils due to land use 
changes,  such  as  crop  and  pasture  expansion,  deforestation,  forest  plantation  and 
regrowth.  To  fully  assess  the  impact  of  sugarcane  crop  expansion  into  degraded  and 
marginal lands, we also estimated  GHG  emissions associated with agricultural practices, 
fertilizer and lime application and burning sugarcane straw.  

A  more  detailed  description  of  the  integrated  modelling  framework  is  included  in  the 
Supplementary Information (Annexes 1, 2 and 3). 

  EU Socioeconomic and policy scenarios 

The  construction  of  the  Baseline,  or  business  as  usual  (BAU),  scenario  for  the  MAGNET 
model is conducted over three time periods from 2011 to 2030 (2011–2015; 2015–2020; 
2020–2030). Employing secondary data and assumptions, the main market drivers over 
this period are macroeconomic (real GDP, population), biophysical (land productivities) and 
energy related (fossil fuel prices, energy consumption and production trends). To further 
refine the developments on biobased markets and the resulting impacts on third country 
trade  trends,  additional  exogenous  impacts  are  introduced  relating  to  environmental- 
(worldwide  GHG  reductions),  EU  agricultural  support,  bioenergy  and  trade  policies.  A 
detailed discussion of all these assumptions and the modelling approach is available online 
in  Philippidis  et  al.  (2018).  In  the  context  of  the  current  research,  the  key  baseline 
assumptions regarding the biofuel market trends are discussed briefly here (Table 8).  

Table  8:  Baseline  assumptions  over  the  three  time  periods  disaggregation  of  commodities  and 
regions. 

Periods: (2011-2015; 2015-2020; 2020-2030) 

  Real  GDP  and  population  growth  projections  from  European  Commission  (EC, 

2016). 

  Land productivity growth:  projections from von Lampe et al., (2014). 

  Global fossil fuel price projections for coal, crude oil and gas (World Bank, 2017) 

for each period. 

  Greenhouse gas emissions reductions from European Commission (EC, 2016) for 

each period. 

(2011-2015 period) 

Trade Policy (Trade) 

  EU28 Enlargement elimination of tariffs between the EU and Croatia  

  Extension to Croatia of an EU common external tariff (CET) on third country trade 
and reciprocal third country CETs extended to Croatia as an EU28 member. 

Agricultural Policy  

  Continued  phasing  in  of  decoupled  payments  for  2004  and  2007  accession 

members 

  Targeted removal of specific pillar 1 coupled support payments: Seeds, beef and 
veal  payments  (except  the  suckler  cow  premium)  decoupled  by  2012,  Protein 
crops, rice and nuts decoupled by 1 January 2012 

39 

 
 
 
 
  Re-coupling of support under the article 68 provision 

  Greening of 30% of first pillar payments  

  Pillar 2 payments to the EU Member States under the financial framework  

  Abolition of raw milk (2015) quota  

EU Biofuels Policy (BF) 

  1st generation EU average bio-fuel mandate of 5.75% 

(2015-2020 period) 

Trade Policy (Trade) 

  EU-Canada trade shocks with HS6 product exceptions tariffs  

  EU-Vietnam trade shocks with HS6 product exceptions tariffs  

Agricultural Policy (CAP) 

  First and second pillar payments follow financial framework budget envelopes.  

  Abolition of raw sugar (2017) quotas 

EU Biofuels Policy (BF) 

  1st generation bio-fuel mandate of 7 %  

  Elimination of all palm oil imports flows from Asia to the EU. 

(2020-2030 period) 

Agricultural Policy (CAP) 

  2%  p.a.  reductions  in  CAP  budget  payments.  Pillar  1  (coupled/decoupled)  and 
pillar 2 (by rural development measure) payment structures assumed unchanged 
from 2020. 

Bio-energy Policy (BF) 

  EU28-wide 1st generation bio-fuel mandate of 7 % EU28-wide 2nd generation bio-

fuel mandate of 3.5% 

Following Banse et al., (2008), fiscal-neutral first- and second-generation biofuel mandates 
consistent with the recent EU energy package finalized in June 2018, are assumed. Thus, 
in the baseline scenario, the EU-wide average first generation biofuel mandate reaches 7% 
by  2020  and  is  maintained  to  2030.  Taking  a  time-linear  approach,  advanced  biofuel 
blending mandates of close to zero in the benchmark year (2011) to  3.5% in 2030, are 
implemented.   

Given  the  specific  focus  on  EU-Brazil  trade  relations,  additional  shocks  to  the  baseline 
description in Philippidis et al., (2018) have been introduced. In seeking to reinforce the 
sustainability criterion underlying the EU’s first-generation biofuels policy in the EU Energy 
Package agreed in June 2018, all EU imports of palm oil from Asia are eliminated by 2020. 
Employing  data  from  the  Brazilian  Ministry  of  Mines  and  Energy  (EPE,  2018),  additional 
Brazilian  bioethanol  export  trade  share  shocks  (principally  to  the  EU,  USA,  South  Korea 
and Japan) have been introduced to mimic the export quantity trends from official Brazilian 
government  sources  and  projections.  To  reflect  their  key  status  as  determinants  of 
marginal changes in land usage in Brazil, further baseline shocks have been imposed on 
historical and projected production trends for Brazilian bioethanol (billions of litres) based 
on data from (EPE, 2018). Further examination of the baseline scenario revealed that the 
outcomes  for  Brazilian  oilseeds  (i.e.,  soybean)  and  sugar  tracked  reasonably  well  the 
production and export trends reported by official Brazilian sources (MAPA, 2017). 

40 

 
 
 
6  Results 

  Projections of demand and supply to 2030 

6.1.1  Ethanol and sugar 

MAGNET  projected  the  total  supply  and  the  EU  demand  for  Brazilian  ethanol  under  two 
policy scenarios (Figure 21) 

Figure 21: Total supply of Brazilian ethanol to 2030. Source: MAGNET, 2018 

In  the  BAU  scenario,  the  total  country  supply  of  ethanol  (51.6  billion  litres)  tracked 
reasonably well the official projections of the Brazilian Ministry of Mines and Energy (i.e., 
49.4 billion litres) from a scenario of intermediate growth of sugar-energy sector due to 
the  implementation of Renovabio (EPE, 2018). The EU share of total Brazilian export slowly 
increases from ca. 5% (0.11 billion litres) in 2018 to  6.8% (0.18 billion litres), far below 
the  EU  proposal  of  duty-free  import  quota  (tariff  rate  quotas,  TRQ)  for  ethanol  of  600 
thousand tons43 (Figure 22).  

In the POB scenario, EU imports of ethanol rise rapidly after 2020, due to the progressive 
substitution of conventional biodiesel with bioethanol. By 2030 the EU share of Brazilian 
bioethanol exports is expected to be ca. 30% (ca. 1.13 billion litres). From a trade policy 
perspective, EU bioethanol imports cross the TRQ line (2017 proposal) in 2026, but remain 
below the EU offer made in May 2004.  

43 About 0,76 billion litres. EU-Mercosur trade talks , October 2017. 

41 

20302018Source: MAGNET33 bl51.6 bl52.2 blBAUPOB14,8 MH14,6 MH10.2 MHBAUPOB20302017 
 
 
 
 
                                           
Figure 22: Projections of EU import of Brazilian ethanol from a baseline (BAU) and phase out biodiesel 
(POB) scenario. The dotted lines identify two different Tariff Rate Quotas (TRQ) for ethanol, under 
discussion in the ongoing trade talks between EU and Mercosur. Source: MAGNET, 2018. 

The Brazilian production of sugar has been derived from official projections of the Brazilian 
Ministry of Agriculture (Figure 23).  

Figure 23: Total sugar supply to 2030 (million tons). Source: MAPA, 2017. 

The total area of sugarcane to meet the demand for ethanol and sugar has been calculated 
by  considering  an  average  Total  Recoverable  Sugar  (ATR)  of  129  kg  sugar  per  ton  of 
sugarcane,  an  82%  effectiveness  of  milling  and  productive  processes  and  an  increasing 
land productivity trend (EPE, 2018). From 2017 to 2030 the sugarcane cropland increases 
by 45% (4.6 million hectares) in the POB scenario and by 43% (4.4 million hectares) in 
the BAU (Figure 24). 

42 

38.7 M tons52.1 M tonsSugar Projection20302017Source: MAPA, 2017 
 
 
 
 
 
Figure 24: Projections of sugarcane area (million hectares) to satisfy the supply of sugar and 
ethanol from a baseline (BAU) and a phase out biodiesel (POB) scenario. Source: own elaboration. 

6.1.2  Main crops 

Land use changes with respect to sugarcane production are driven also by the competition 
with  other  commodities  for  viable  agricultural  lands.  Therefore,  it  is  necessary  to 
simultaneously  allocate  the  expansion  (or  reduction)  of  the  other  croplands  to  fully 
represent the direct and indirect land use changes due to agricultural activities, including 
the  displacement  of  marginal  farming  and  ranching  systems  in  favour  of  more  lucrative 
energy crops. The projections to 2030 for the main crops (Table 9) have been derived from 
the official estimations of MAPA, (2017).  

Soybean areas rise rapidly to satisfy internal and external demands. According to MAPA, 
(2017) exports will represent more than 60% of total production (without considering the 
potential additional demand from China due to the tariff trade war with US44), moving from 
ca. 73 million tons in 2017 to 100 million tons in 2030. The increase of soybean production 
to satisfy China’s appetite and substitute the entire US shortfall is raising many concerns 
about its potential environmental consequences, especially in terms of tropical forest loss 
(Fuchs et al., 2019).   

Double cropping systems that combine first-crop soybeans and then second crop corn are 
the main reason for the expansion of the 2nd-crop corn (61%) and the progressive reduction 
of first-crop corn. Other crops show limited changes, flat or decreasing trends (e.g., beans, 
rice, and coffee).  

Soybean, sugarcane and second crop corn (2nd corn)  areas, which represented more than 
70% of total cropland in Brazil in 2018,  record the highest increments by 2030 – i.e, 29% 
(ca. 10 million hectares), 44% (ca. 4 million hectares, BAU scenario) and 61% (ca. 8 million 
hectares) respectively. 

44 The export to China of soybean increased in the first semester 2018 (+6% compared to previous year); many 
farmers decided to move part of their sugarcane productive area to soybean (Reuters, 2018). There are not 
official projections about this new scenario yet. 

43 

20302018Source: MAGNET33 bl51.6 bl52.2 blBAUPOB14,8 MH14,6 MH10.2 MHBAUPOB20302017 
 
 
 
 
                                           
Table 9: projection of productive areas for the main crops from 2018 to 2030. Source: MAPA, 2017 

Crop 

 Soybean 

 Corn (2st crop) 

 Corn (1st crop) 

 Bean 

 Wheat 

 Rice 

 Coffee 

 Manioc 

 Feather cotton 

 Cocoa 

 Banana 

 Orange 

 Tobacco 

Area 2018 (Mha) 

Area 2030 (Mha) 

35.2 

12.9 

5.64 

3.65 

2.02 

2.39 

1.92 

1.38 

1.08 

0.63 

0.53 

0.61 

0.39 

47.1 

20.9 

3.28 

3.19 

2.27 

2.10 

1.80 

1.19 

1.16 

0.66 

0.53 

0.49 

0.42 

6.1.3  Wood and plantations 

The projected demand to 2030 for wood from plantations (to produce charcoal, firewood, 
panels, lumber, and pulp) has been derived from MCTIC and ONU (2017). The consumption 
of wood from plantations increases from 192.2 million cubic meters in 2012 to 350.8 million 
cubic meters in 2050  with an average annual increment of 1.55%.  To satisfy this growing 
demand for wood, plantations’ area – mainly eucalyptus and pine – will increase by 1,7% 
per  year,  on  average.  The  estimated  plantation  expansion  is  ca.  138  thousand  hectares 
per  year  in  the  period  2015-2025,  and  159  thousand  hectares  to  2050  (Figure  25).  In 
2030, plantations will cover ca 9 million hectares. 

44 

 
 
 
Figure 25: Projected area (ha) of forest plantations to 2050. Source: MCTIC and ONU, 2017. 

6.1.4  Forest recovery and deforestation rates 

Starting from the targets of the National Plan for Native Vegetation Recovery (Planaveg, 
2014)45 - which set an ambitious objective of 12.5 million hectares of forest restoration by 
2035  -  we  projected  the  area  of  recovered  forest  to  2030  by  including  in  the  modelling 
some land use  restrictions (i.e, available pasture areas for recovery purpose). That said, 
the results showed a gradual increase of the areas for native forest recovery from ca. 1.9 
million hectares in 2017 to ca. 7 million hectares in 2030. 

The projection to 2030 of deforestation rates for the Amazon and Cerrado biomes is derived 
from  Rochedo  et  al.,  (2018).  We  selected  the  intermediate  environmental  governance 
scenario, which assumes the maintenance of current deforestation policies and considers 
a  growing  political  support  for  predatory  agriculture  practices,  land-grabbing  and  a 
progressive  undermining  of  protected  areas  legislation  and  the  Forest  Code.  The  annual 
deforestation rates for the other biomes, i.e. Caatinga, Pantanal, and Pampas, come from 
Aguiar et al., (2015), and for Atlantic Forest from SOS Mata Atlantica (Figure 26). 

45 

http://www.mma.gov.br/florestas/pol%C3%ADtica-nacional-de-recupera%C3%A7%C3%A3o-da-

vegeta%C3%A7%C3%A3o-nativa.html  

45 

 
 
 
 
                                           
Figure 26: Deforestation trends  in the Brazilian biomes to 2030. Sources: Rochedo et al., (2018) for 
Cerrado and Amazon biomes [1000 Km2]; Aguiar et al., (2015) and SOS Mata Atlantica, for the other 
biomes [average 1000 Km2/year] 

  Projected Land use changes in Brazil to 2030 

Finally,  Otimizagro  uses  as  input  the  exogenous  projections  for  the  main  crops,  forest 
plantations, forest  recovery, and deforestation rates to 2030. The model allocates these 
quantities according to biophysical, climate, governance, and economic explicative factors 
to  simulate  land  use  and  land  use  changes  under  the  baseline  (BAU)  and  phase  out 
biodiesel  (POB)  scenarios.  Otimizagro  outputs  yearly  land  use  raster  maps  with  a  cell 
resolution of 25 ha (Figure 27).  

Figure 27: Projected land use change map in 2030 (BAU). 

46 

HydrographyUrban areaPasturePasture in protected areaSavannahSavannah in protected areaForestForest in protected areaRegenerationPlanted forestSoySugarcaneOther crops 
 
 
 
The scenarios’ transition matrices and the final 2030 maps slightly differ, due to the limited 
increase of sugarcane croplands between BAU and POB scenario (by 1%). For the sake of 
simplicity, we provide an overall analysis of the land use changes from 2017 to 2030 in the 
BAU scenario only (Figure 28). The land-use transition matrix of the POB scenario can be 
found in the Supplementary Information section (Annex 4). 

Figure 28: Land use changes from 2017 to 2030 under the baseline (BAU) scenario. 

According to the land use transition matrix (Figure 29), most of agricultural lands in 2017 
continue to be agricultural in 2030 (ca. 75%). Agriculture expansion always puts pressure 
on native vegetation conversion as it raises land prices locally and elsewhere, the latter 
due to land speculation (Miranda et al., 2019). The new croplands (ca. 18 million hectares) 
expand  mainly  into  pasture  (ca.  93%  of  expansion),  whilst  only  ca.  4%  coming  from 
conversion  of  forests  and  3%  from  savannah.  In  2017  pasturelands  are  more  than  230 
million hectares, whilst the restoration of  degraded land represents a huge opportunity for 
Brazilian  agriculture  to  meet  domestic  and  international  demand  without  the  need  of 
clearing new forest areas.  

47 

HydrographyUrban areaPasturePasture in protected areaSavannahSavannah in protected areaForestForest in protected areaRegenerationPlanted forestSoySugarcaneOther crops20172030 (BAU) 
 
  
 
Figure 29: Transition matrix representing the cumulative land use changes (hectares) from 2017 to 
2030 in the BAU scenario. 

The expansion of pasture into the forest and savannah (more than 40 million hectares) is 
mainly linked to predatory land grabbing and livestock. Pasture displacement into Amazon 
and  Cerrado  biomes  can  be  associated  with  the  large-scale  expansion  of  soybean  into 
northern ready-cleared areas (Gibbs et al., 2015).  The cause-effect relation between the 
agricultural  expansion  into  existing  pasturelands  in  the  southern  regions  and  the 
deforestation trends in the northern region of Brazil to open new pasture areas, is uncertain 
and hard to define as indirect land use changes (ILUC). 

In  the  Amazon  biome,  newly  cleared  lands  will  be  marginally  occupied  with  productive 
activities by 2030 and can be mostly explained as quest for land (Miranda et al., 2019). 
Subsequently,  in  the  mid-term  horizon  (usually  more  than  7  years  after  deforestation 
(MAPA, 2017)) those lands could be cropped with soybean, for example, or dedicated to 
cattle ranching activities. The magnitude and rate of these land use transitions are affected 
by the governmental commitment to preserve Amazonian forest through a full enforcement 
of the Forest Code and the implementation of penalties for illegal deforestation (Tollefson, 
2018).  

48 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  Country net LULUCF emissions 

We considered that the deforestation trend observed since 2012 continues to 2030, which 
reflects the political support for  predatory agriculture and land-grabbing practices under 
an intermediate environmental governance scenario (Rochedo et al., 2018).  This implies 
increasing  losses  in  carbon  stock  and  net  cumulative  GHG  emissions  that  can  put  the 
country's contribution to the Paris agreement at risk (Table 10). 

Table 10: LULUCF net emissions (Mtons CO2) - BAU scenario 

Million tons CO2 

2018 

2019 

2020 

2021 

2022 

2023 

2024 

2025 

2026 

2027 

2028 

2029 

2030 

 GROSS EMISSIONS 

- Deforestation 

755 

714 

- Other LUC emissions 

41 

800 

755 

45 

845 

796 

49 

892 

839 

53 

937 

880 

57 

983 

922 

61 

1031 

1080 

1126 

1176 

1222 

1273 

1321 

966 

1012 

1054 

1100 

1142 

1189 

1234 

65 

68 

72 

76 

80 

84 

88 

 REMOVALS  
(including CU/IL) 
 - Regeneration 

- Plantation    expansion 
and other LUC removals 
-  Conservation  Units/ 
Indigenous 
lands 
(CU/IL) 
 NET 
(not including CU/IL) 
 NET 
(including CU/IL) 

EMISSIONS 

EMISSIONS 

-365 

-368 

-371 

-374 

-377 

-380 

-383 

-388 

-392 

-395 

-398 

-401 

-402 

-17 

-30 

-19 

-31 

-22 

-31 

-25 

-32 

-28 

-32 

-30 

-32 

-33 

-32 

-37 

-32 

-41 

-33 

-45 

-33 

-48 

-33 

-50 

-33 

-52 

-33 

-318 

-318 

-318 

-318 

-318 

-318 

-318 

-318 

-318 

-318 

-318 

-318 

-318 

709 

749 

791 

836 

877 

920 

965 

1011 

1052 

1099 

1142 

1190 

1237 

391 

432 

474 

518 

559 

603 

648 

693 

734 

781 

824 

872 

919 

Figure  30  displays  the  GHG  emissions  recorded  from  1990  to  2017,  and  the  divergence 
between  the  National  Determined  Contribution  target  (NDC)  and  the  results  of  our 
simulation. The NDC curve shows a decreasing trend that falls to ca. 22 million tons CO2 
in 2030 (Grassi et al.,  2017).  In contrast, the LULUCF emissions from the BAU  scenario 
rise  after  2017  because  of  the  projected  deforestation  trends,  diverging  from  the  NDC 
target in 2030 by ca. 900 million tons CO2. 

Figure  30:  LULUCF  CO2  emissions  from  changes  in  biomass  and  soil  carbon  stocks.  2017-2030 
projections from a BAU scenario versus the country NDC targets (Grassi et al., 2017). 

49 

050010001500200025003000Brazil NDCBAUHistorical trendMillion tons CO2LULUCF emissions and Brazil NDC targetSource NDC target: Grassiet al., 2017Contribution to Paris Agreement at risk 
 
 
 
 
  In depth analysis: sugarcane expansion 

6.4.1  Sugarcane expansion sorted by state 

The largest sugarcane expansion in absolute terms is expected in the State of Sao Paulo 
(ca. 2,2 million hectares on average), followed by Mato Grosso (0,9 million hectares) and 
Minas  Gerais  (0,7  million  hectares).  Mato  Grosso  (122%)  and  Minas  Gerais  (83%)  also 
record the highest average relative increments46 (Table 11). A detailed description of the 
main  factors  shaping  the  sugarcane  expansion  can  be  found  in  the  Supplementary 
Information (Annex 5) 

Table 11: sugarcane area in 2017 and projected expansion to 2030, sorted by Brazilian states. Total 
area (1000 ha) and relative increment (%) vs. 2017 area, under the BAU and POB scenarios 

Sugarcane 
Area (kha) 

Sugarcane expansion  
Area (kha) vs.2017 

Sugarcane expansion  
Change (%) vs.2017 

 State 

2017 

2030 BAU 

2030 POB 

2030 BAU  2030  

 Acre 

 Alagoas 

 Amapa 

 Amazonas 

 Bahia 

 Ceará 

 Distrito Federal 

 Espirito Santo 

 Goiás 

 Maranhão 

 Mato Grosso  

2.95 

411 

0.15 

5.65 

121 

42.6 

0.93 

78.1 

730 

49.9 

229 

 Mato Grosso do sul  778 

 Minas Gerais 

 Pará 

 Paraíba 

 Paraná 

 Pernanbuco 

 Piauí 

 Rio de Janeiro 

 Rio  Grande 
Norte 
 Rio Grande do Sul 

do 

 Rondonia 

 Roraima 

 Santa Catarina 

 São Paulo 

 Sergipe 

Tocantins 

905 

13.1 

115 

652 

342 

15.8 

105 

59.8 

37.0 

4.55 

0.58 

12.2 

5395 

51.7 

26.2 

 - 

0.7 

 - 

 - 

28.6 

1.35 

1.3 

10.6 

369 

0.1 

12 

875 

666 

 - 

0.3 

338 

3.38 

0.2 

0.5 

 - 

0.4 

 - 

 - 

0.3 

2145 

2.375 

0.25 

 -    

0.80 

 -    

 -    

32.4 

2.77 

1.67 

13.6 

463 

0.17 

18.5 

891 

731 

 -    

0.26 

354 

4.79 

0.33 

0.46 

0.02 

1.02 

 -    

 -    

0.37 

2051 

2.69 

 -    

- 

0.2 

- 

- 

23.7 

3.2 

140 

13.6 

50.6 

0.2 

5.2 

112 

73.6 

- 

0.3 

51.8 

1.0 

1.3 

0.5 

- 

1.1 

- 

- 

2.5 

39.8 

4.6 

1.0 

POB 
 -    

 0.20  

 -    

 -    

 26.8  

 6.51  

 180.4  

 17.4  

 63.5  

 0.35  

 8.10  

 115  

 80.8  

 -    

 0.23  

 54.3  

 1.40  

 2.05  

 0.43  

 0.04  

 2.76  

 -    

 -    

 3.02  

 38.0  

 5.20  

 -    

46 We disregard the Federal District value, since its absolute (ha) contribution to sugarcane area is very low. 

50 

 
 
 
 
  
                                           
BRAZIL 

10184 

4455 

4570 

43.7 

44.9 

6.4.2  Land conversion to sugarcane 

Land  use  conversions  to  new  sugarcane  areas  from  2017  to  2030  mainly  occur  in  the 
regions of the Southeast and Midwest (Figure 31).  

Figure 31: land use conversions to sugarcane from 2017 to 2030. Example of BAU scenario 

Most of the sugarcane croplands in 2017 continue to be productive in 2030, representing 
on  average  68%  of  total  sugarcane  area  (Table  12).  By  focusing  only  on  sugarcane 
expansion area, we notice that almost the totality of new sugarcane croplands come from 
the conversion of pastureland (97%), which also account for more than 30% of the total 
production area. Sugarcane expansion into pasture accounts for ca. 25% of the cumulative 
expansion  of  Brazilian  agriculture  into  pasturelands  from  2017  to  2030  (4.4-5.2  versus 
16.4 million hectares). 

The  conversion  of  native  vegetation  and  other  croplands  (including  food  crops)  to 
sugarcane is very limited, being less than 3% in both scenarios.  

51 

26Pasture⇨Sugarcane Other crops⇨Sugarcane Savannah ⇨Sugarcane Forest ⇨Sugarcane Unaccounted areasSugarcane ⇨Sugarcane Land use conversion 2017-2030 
 
 
 
 
 
 
 
Table 12: land use conversions to sugarcane cropland from 2017 to 2030 under the BAU and POB 
scenarios. 

Area (Mha) 

Conversion 
(% vs. tot. area) 

ratio  

BAU  

POB  

BAU 

POB 

Conversion 
(%vs. 
expansion) 
BAU  

POB  

ratio 
tot. 

TRANSITIONS  
2017-2030 
Sugarcane permanence 

Pasture to Sugarcane 

 10.2  

 4.35  

Other crops to Sugarcane 

 0.03  

Savannah to Sugarcane 

 0.05  

Forest to Sugarcane 

Total sugarcane area 

Total expansion area 

 0.03  

 14.6  

 4.46  

10.2 

4.46 

0.04 

0.05 

0.03 

14.8 

4.57 

69.5 

29.6 

0.23 

0.32 

0.19 

100 

- 

65.9 

33.3 

0.28 

0.37 

0.21 

100 

- 

- 

97.5 

0.76 

1.04 

0.63 

100 

- 

- 

97.5 

0.82 

1.07 

0.61 

100 

- 

We could conclude that the direct impacts on land use arising from sugarcane expansion 
are  very  limited,  resulting  in  a  small  loss  of  native  forest  (high  carbon  stocks)  and 
displacement of other cropping activities. Even though most of the sugarcane expansion is 
met from pastureland (97%), it is far from certain that new forest clearance in the northern 
regions  is  linked  to  the  displacement  of  pasturelands  from  the  Southeast  and  Midwest 
(indirect land use change). Pasture displacement for livestock is not the only solution to 
meet the steeply increasing demand for meat. In addition to a partial substitution of beef 
for  pig  meat,  and  in  particular  chicken,  in  the  past,  from  1996  to  2008  –  sugarcane 
expansion  led  to  an  increased  pasture  intensification  rate  (head  per  hectares)  and  beef 
production (Amorim et al., 2010; Kozumi, 2014). 

6.4.3  GHG emissions from sugarcane cultivation 

As mentioned above, the largest expansion of sugarcane crops is expected to be met by 
pasturelands. This should result in limited GHG emissions from living biomass due to the 
low carbon content (Mello et al., 2014). However, cultivating degraded pasture demands 
the  use  of  fertilizer  and  lime  to  prepare  the  soil  for  farming  and  achieve  the  expected 
sugarcane productivity per hectare, representing an additional source of GHG emissions. 
Some regions of Brazil, notably in the northern states, rely on burning sugarcane straw to 
facilitate  the  manual  harvesting    by  cleaning  the  fields.  A  detailed  description  of  the 
methods and parameters for estimating the GHG budget can be found in MCTI and ONU 
(2017). 

6.4.3.1  Emissions from changes in biomass carbon stock 

Table 13 and Table 14 show the emissions from changes in biomass carbon stocks. Each 
row  represents  the  progressive  transition  from  a  land  use  type  in  2017  to  sugarcane  in 
2030. For example, the forest area in 2017 converted to the sugarcane by 2020 emits ca. 
593000  tons  of  CO2  (usually  through  a  forest  to  pasture  and  pasture  to  sugarcane 
transition).  

Table  13:  GHG  emissions  (Million  tons  CO2)  from  living  biomass  carbon  stock  change  due  to 
sugarcane expansion from 2017 to 2030. BAU scenario 

Land use in 2017 
BAU scenario 
Pasture 

Forest 

Savannah 

2018 

2019 

2020 

2021 

2022 

2023 

2024 

2025 

2026 

2027 

2028 

2029 

2030 

TOT 

3.22 

3.20 

3.19 

3.20 

3.17 

3.16 

3.16 

3.13 

3.12 

3.10 

3.08 

3.07 

3.07 

40.89 

0.76 

0.60 

0.59 

0.59 

0.57 

0.52 

0.60 

0.52 

0.49 

0.37 

0.27 

0.30 

0.08 

6.24 

1.17 

1.04 

1.25 

0.90 

0.90 

0.78 

0.82 

0.75 

0.61 

0.52 

0.42 

0.31 

0.13 

9.59 

Permanent crops 

TOT 

0.06 

5.20 

0.07 

4.90 

0.06 

5.10 

0.07 

4.75 

0.06 

4.70 

0.06 

4.52 

0.06 

4.65 

0.05 

4.45 

0.04 

4.27 

0.05 

4.04 

0.05 

3.82 

0.04 

3.71 

0.03 

3.30 

0.69 

57.42 

52 

 
 
  
Table  14:  GHG  emissions  (Million  tons  CO2)  from  living  biomass  carbon  stock  change  due  to 
sugarcane expansion from 2017 to 2030. POB scenario 

Land use in 2017 
POB scenario 
Pasture 

Forest 

Savannah 

2018 

2019 

2020 

2021 

2022 

2023 

2024 

2025 

2026 

2027 

2028 

2029 

2030 

TOT 

3.30 

3.29 

3.27 

3.27 

3.25 

3.25 

3.22 

3.22 

3.19 

3.19 

3.17 

3.14 

3.13 

41.89 

0.93 

0.69 

0.69 

0.59 

0.76 

0.56 

0.44 

0.48 

0.42 

0.36 

0.27 

0.27 

0.14 

6.61 

1.15 

1.18 

1.16 

0.97 

1.09 

0.76 

0.72 

0.63 

0.61 

0.41 

0.32 

0.33 

0.17 

9.51 

Permanent crops 

TOT 

0.06 

5.44 

0.06 

5.23 

0.05 

5.18 

0.05 

4.87 

0.05 

5.15 

0.05 

4.61 

0.05 

4.44 

0.02 

4.36 

0.04 

4.26 

0.03 

3.99 

0.03 

3.79 

0.03 

3.77 

0.02 

3.46 

0.53 

58.54 

6.4.3.2  Emissions from changes in soil carbon stock 

Table 15 and Table 16 show the emissions from changes in soil carbon stocks. Each  row 
represents the progressive transition from a land use type in 2017 to sugarcane in 2030. 

Table 15: GHG emissions (Million tons CO2) from soil due to sugarcane expansion from 2017 to 2030. 
BAU scenario 

Land use in 2017 
BAU scenario 
Pasture 

Forest 

Savannah 

2018 

2019 

2020 

2021 

2022 

2023 

2024 

2025 

2026 

2027 

2028 

2029 

2030 

TOT 

0.91 

1.80 

2.69 

3.58 

4.48 

5.38 

6.26 

7.15 

8.03 

8.91 

9.76 

10.62 

11.41 

80.98 

0.00 

0.00 

0.00 

0.01 

0.01 

0.01 

0.02 

0.03 

0.03 

0.04 

0.05 

0.07 

0.07 

0.35 

0.00 

0.01 

0.01 

0.01 

0.02 

0.03 

0.04 

0.05 

0.06 

0.07 

0.09 

0.11 

0.13 

0.63 

Annual crops 

-0.01 

-0.01 

-0.02 

-0.02 

-0.02 

-0.02 

-0.01 

-0.01 

0.00 

0.01 

0.02 

0.04 

0.05 

0.01 

Permanent crops 

TOT 

0.00 

0.90 

0.00 

1.80 

0.00 

2.69 

0.00 

3.59 

0.00 

4.50 

0.01 

5.41 

0.01 

6.31 

0.01 

7.23 

0.01 

8.14 

0.02 

9.05 

0.02 

9.94 

0.02 

0.03 

0.14 

10.86 

11.69 

82.11 

Table 16: GHG emissions (Million tons CO2) from soil due to sugarcane expansion from 2017 to 2030. 
POB scenario 

Land use in 2017 
POB scenario 
Pasture 

Forest 

Savannah 

2018 

2019 

2020 

2021 

2022 

2023 

2024 

2025 

2026 

2027 

2028 

2029 

2030 

TOT 

0.93 

1.85 

2.78 

3.71 

4.63 

5.55 

6.46 

7.36 

8.26 

9.17 

10.06 

10.93 

11.73 

83.42 

0.00 

0.00 

0.01 

0.01 

0.01 

0.02 

0.02 

0.03 

0.03 

0.04 

0.05 

0.06 

0.07 

0.36 

0.00 

0.01 

0.01 

0.02 

0.02 

0.03 

0.04 

0.05 

0.07 

0.08 

0.10 

0.12 

0.13 

0.69 

Annual crops 

-0.01 

-0.02 

-0.02 

-0.02 

-0.03 

-0.03 

-0.02 

-0.02 

-0.01 

0.00 

0.02 

0.04 

0.06 

-0.05 

Permanent crops 

TOT 

0.00 

0.93 

0.00 

1.85 

0.00 

2.78 

0.00 

3.71 

0.00 

4.64 

0.01 

5.58 

0.01 

6.51 

0.01 

7.44 

0.01 

8.37 

0.01 

9.31 

0.02 

0.02 

0.02 

0.12 

10.24 

11.17 

12.02 

84.55 

6.4.3.3  Emissions from fertilizer and lime application 

For fertilizing sugarcane areas, an average volume of 100 kg N per hectare per year was 
considered  (MCTIC  and  ONU,  2017).  Table  17  and  Table  18  show  the  emissions  (CO2e) 
from fertilizer application. 

Table 17: GHG emissions (Million tons CO2e) from fertilization of sugarcane crops. BAU scenario 

Mtons CO2e 
BAU scenario 

2018 

2019 

2020 

2021 

2022 

2023 

2024 

2025 

2026 

2027 

2028 

2029 

2030 

TOT 

53 

 
 
 
 
 
 
 
 
Direct emissions 

2.81 

2.90 

2.99 

3.08 

3.17 

3.26 

3.35 

3.45 

3.54 

3.63 

3.72 

3.81 

3.90 

43.60 

Indirect emissions 

3.33 

3.44 

3.55 

3.66 

3.77 

3.88 

3.99 

4.10 

4.21 

4.32 

4.43 

4.54 

4.65 

51.87 

TOT 

6.14 

6.34 

6.54 

6.74 

6.94 

7.14 

7.34 

7.54 

7.75 

7.95 

8.15 

8.35 

8.55 

95.46 

Table 18: GHG emissions (Million tons CO2e) from fertilization of sugarcane crops. POB scenario 

Mtons CO2e 
POB scenario 
Direct emissions 

2018 

2019 

2020 

2021 

2022 

2023 

2024 

2025 

2026 

2027 

2028 

2029 

2030 

TOT 

2.82 

2.93 

3.04 

3.13 

3.21 

3.30 

3.39 

3.48 

3.57 

3.66 

3.75 

3.84 

3.93 

44.07 

Indirect emissions 

3.35 

3.48 

3.61 

3.71 

3.82 

3.92 

4.03 

4.14 

4.24 

4.35 

4.45 

4.57 

4.67 

52.34 

TOT 

6.17 

6.41 

6.65 

6.84 

7.03 

7.23 

7.42 

7.62 

7.81 

8.01 

8.21 

8.41 

8.60 

96.41 

For soil preparation, an average volume of 2 tons of limestone per hectare per year was 
considered  (MCTIC  and  ONU,  2017).  Table  19  shows  the  GHG  emissions  from  lime 
application. 

Table 19: GHG emissions (Million tons CO2e)  from lime applications 

Mtons CO2e 

2018 

2019 

2020 

2021 

2022 

2023 

2024 

2025 

2026 

2027 

2028 

2029 

2030 

TOT 

BAU scenario 

1.27 

1.57 

1.76 

1.55 

1.26 

2.47 

1.2 

1.57 

1.87 

2.06 

1.85 

1.56 

2.77 

22.76 

POB scenario 

1.32 

1.62 

1.81 

1.60 

1.30 

2.48 

1.22 

1.64 

1.92 

2.09 

1.89 

1.60 

2.75 

23.25 

6.4.3.4  Emissions from burning sugarcane straw 

The state of Sao Paulo approved the Law n11.241 in 2002, which regulates the burning of 
sugarcane  straw  -  and  will  completely  remove  this  practice  by  2021.  Other  producers, 
notably  the  northeast  states,  have  not  yet  defined  their  position,  and  continue  to  use 
burning  as  a  mean  to  facilitate  manual  harvesting.  Table  20  shows  the  emissions  from 
burning sugarcane straw (CO2e). 

Table 20: GHG emissions (Million tons CO2e) from burning sugarcane straw 

Mtons CO2e 

2018 

2019 

2020 

2021 

2022 

2023 

2024 

2025 

2026 

2027 

2028 

2029 

2030 

TOT 

BAU scenario 

4.45 

4.32 

4.21 

4.08 

4.16 

4.22 

4.26 

4.29 

4.31 

4.29 

4.26 

4.21 

4.16 

55.21 

POB scenario 

4.93 

4.54 

4.49 

4.43 

4.39 

4.52 

4.46 

4.56 

4.47 

4.53 

4.42 

4.45 

4.32 

58.52 

6.4.3.5  Total GHG emissions 

Total GHG emissions are calculated by adding the emissions associated with biomass and 
soil carbon stock changes (LULUCF), and agricultural practices (Table 21 and Table 22). 

Table 21: total sugarcane area (million hectares) and GHG emissions (million tons CO2) associated 
with sugarcane production from 2017 to 2030. BAU scenario 

BAU scenario 

2018 

2019 

2020 

2021 

2022 

2023 

2024 

2025 

2026 

2027 

2028 

2029 

2030 

TOT 

area 

Sugarcane 
(Mha) 
TOT GHG emission 
(Mtons CO2) 
- LULUCF 

10.5 

10.9 

11.2 

11.6 

11.9 

12.2 

12.6 

12.9 

13.3 

13.6 

14.0 

14.3 

14.6 

18.0 

18.9 

20.3 

20.7 

21.6 

23.8 

23.8 

25.1 

26.3 

27.4 

28.0 

28.7 

30.5 

313 

6.10 

6.70 

7.79 

8.34 

9.20 

9.93 

11.0 

11.7 

12.4 

13.1 

13.8 

14.6 

15.0 

140 

    - Biomass 

5.20 

4.90 

5.10 

4.75 

4.70 

4.52 

4.65 

4.45 

4.27 

4.04 

3.82 

3.71 

3.30 

57.4 

    - Soil 

0.90 

1.80 

2.69 

3.59 

4.50 

5.41 

6.31 

7.23 

8.14 

9.05 

9.94 

10.9 

11.7 

82.1 

- Fertilizer and lime 

7.41 

7.90 

8.30 

8.29 

8.20 

9.61 

8.54 

9.11 

9.61 

10.01 

10.0 

9.91 

11.3 

118 

- Burning  

 4.45  

 4.32  

 4.21  

 4.08  

 4.16  

 4.22  

 4.26  

 4.29  

 4.31  

 4.29  

 4.26  

 4.21  

 4.16  

55.2 

54 

 
 
 
 
 
 
Table 22: Total sugarcane area and GHG emissions associated with sugarcane production from 2017 
to 2030. POB scenario 

POB scenario 

2018 

2019 

2020 

2021 

2022 

2023 

2024 

2025 

2026 

2027 

2028 

2029 

2030 

TOT 

area 

Sugarcane 
(Mha) 
TOT GHG emission 
(Mtons CO2) 
- LULUCF 

11.62 

11.37 

11.86 

12.37 

12.36 

12.88 

12.90 

13.41 

13.44 

14.02 

14.11 

14.66 

14.76 

19.4 

19.9 

21.2 

21.8 

22.7 

24.7 

24.2 

25.8 

26.9 

28.1 

28.6 

29.5 

31.2 

323.8 

6.4 

7.1 

8.0 

8.6 

9.8 

10.2 

10.9 

11.8 

12.6 

13.3 

14.0 

14.9 

15.5 

143.1 

   - Biomass 

5.44 

5.23 

5.18 

4.87 

5.15 

4.61 

4.44 

4.36 

4.26 

3.99 

3.79 

3.77 

3.46 

58.5 

   - Soil 

0.93 

1.85 

2.78 

3.71 

4.64 

5.58 

6.51 

7.44 

8.37 

9.31 

10.24 

11.17 

12.02 

84.6 

- Fertilizer and lime 

8.10 

8.25 

8.73 

8.81 

8.51 

9.99 

8.75 

9.45 

9.75 

10.27 

10.11 

10.15 

11.35 

122.2 

- Burning 

4.93 

4.54 

4.49 

4.43 

4.39 

4.52 

4.46 

4.56 

4.47 

4.53 

4.42 

4.45 

4.32 

58.5 

LULUCF emissions account for less than 50% of total GHG emissions (Figure 32); only ca. 
20% came from changes in live biomass carbon stock – e.g, deforestation and other land 
use transitions. The most relevant CO2 source is associated with fertilization and limestone 
applications (38%). Burning straw represents ca. 18% of total GHG emissions; this practice 
is  progressively  disappearing  with  the  gradual  substitution  of  manual  with  mechanical 
harvesting. 

Figure  32:  Profile of  the  cumulative  GHG  emissions  (million  tons  CO2)  from  sugarcane  production 
from 2018 to 2030. 

On  average,  the  GHG  emission  is  ca.  2  tons  of  CO2  per  hectare  per  year.  The  LULUCF 
component  can  be  translated  into  an  equivalent  land  use  change  emission  per  MJ  of 
additional ethanol. The additional annual LULUCF emissions in the POB scenario compared 
to BAU rise to 0.48 million tons CO2 per year in 2030. The additional ethanol production in 
the POB scenario compared to BAU amounts to 0.66 billion litres per year. Conventionally, 
in Europe, LUC emissions are spread over 20 years’ of production. Considering also that 
ethanol  has  a  Lower  Heating  Value  energy  content  of  about  20  MJ  per  litre,  the  LUC 
emissions work out to be about 2gCO2/MJ. 

55 

 
 
 
 
 
 
 
 
 
  Comparison with soybean expansion 

We  calculated  the  LULUCF  emissions  coming  from  the  expansion  of  soybean  in  order  to 
compare the impacts of sugarcane cultivation with the impacts of the largest crop in Brazil.  

6.5.1  Soybean expansion sorted by states 

By comparison, Table 23 displays soybean croplands, sorted by state. This crop is mainly 
used for animal feed, whilst only a small share (ca. 1% in 2016) of the total production is 
used for biodiesel (Fuchs et al., 2019). 

In  absolute  terms,  according  to  our  projections,  the  largest  expansion  occurs  into  Mato 
Grosso (ca. 3.5 million hectares), Goiás (ca. 2 million hectares), Mato Grosso do Sul (ca. 
1,4 million hectares), Tocantins e Minas Gerais (ca. 1 million hectares). Mato Grosso will 
be, by far, the largest producer in 2030 (ca. 13 million hectares). Most of these new areas 
are within the Cerrado and Mata Atlantica biomes. However, three out of the four highest 
rates  of change are  observed in the Amazon states, namely  Pará (138%), Acre (128%) 
and Rondonia (69%), even though, in absolute terms, only Pará will contribute with more 
than one million hectares to 2030 soybean area. 

Table 23: Soybean area in 2017 and projected expansion to 2030, sorted by Brazilian states. Total 
area (1000 ha) and relative increment (%) vs. 2017 area. 

Area 

Soybean 
(kha) 
2017 

Soybean 
expansion  
Area (kha)  
2030 vs. 2017 

Soybean 
expansion  
Change (%)  
2030 vs. 2017 

Total 
(kha) in 2030 

area 

 STATE 

 Acre 

 Alagoas 

 Amapá 

 Amazonas 

 Bahia 

 Ceará 

 Distrito Federal 

 Espirito Santo 

 Goiás 

 Maranhão 

 Mato Grosso  

11.2 

- 

- 

0 

1486 

- 

60 

- 

4152 

813 

9349 

 Mato Grosso do sul  2747 

 Minas Gerais 

 Pará 

 Paraíba 

 Paraná 

 Pernanbuco 

 Piauí 

 Rio de Janeiro 

 Rio  Grande 
Norte 
 Rio Grande do Sul 

do 

 Rondonia 

1830 

468 

- 

4848 

- 

668 

- 

- 

4659 

348 

128  

- 

- 

- 

40  

100 

18  

- 

48  

52  

38  

51  

56  

138  

- 

7  

- 

54  

- 

- 

17  

69  

14.4 

- 

- 

601 

0.03 

10.9 

- 

1991 

421 

3558 

1399 

646 

- 

350 

- 

361 

- 

- 

777 

239 

56 

25.6 

- 

- 

0.48 

2087 

0.03 

71 

- 

6144 

1234 

12907 

4146 

2860 

1114 

- 

5198 

- 

1029 

- 

- 

5435 

587 

 
 
 
 
  
 
 
 
 
 
 
 Roraima 

 Santa Catarina 

 São Paulo 

 Sergipe 

 Tocantins 

Brazil 

4.03 

602 

851 

- 

1083 

33981 

0.6 

154 

452 

- 

1066 

13070 

15  

26  

53  

- 

98  

38 

5 

756 

1302 

0 

2149 

47051 

6.5.2  Land conversion to soybean 

Deforestation  due  to  soybean  expansion  accounts  for  almost  7%  of  new  croplands  (ca. 
886450 ha), the second main land conversion after pasture to soy (Table 24). In 2030, the 
largest share of  soybean will be grown  on croplands coming from areas already planted 
with soybean in 2017 (72% of the total area).  

Table 24: Land use conversions to soybean cropland from 2017 to 2030. 

Area (Mha) 

Conversion 
(% vs. total area) 

ratio  

Conversion 
(% vs. total expansion) 

ratio 

TRANSITIONS 2017-2030 

Soy to Soy 

Pasture to Soy 

Other crops to Soy 

Savannah to Soy 

Forest to Soy 

Total Soy area 

Total Soy expansion 

33.9 

11.3 

0.09 

0.73 

0.89 

47.05 

13.1 

72.2 

24.1 

0.18 

1.56 

1.88 

- 

- 

- 

86.9 

0.65 

5.60 

6.78 

- 

- 

In  the  period  2009-2016  soybean  has  been  responsible  for  1,2%  of  the  loss  of  Amazon 
rainforest  (GTS,  2018),  even  though  its  cultivation  in  this  biome  accounts  for  ca.  13% 
Brazil’s  production  in  2017  (ca.  4,5  million  hectares).  Indeed,  most  of  the  soybean 
expansion in the past was met from pasturelands converted from forest before 2008, the 
reference year of the Soy Moratorium47 

New land conversion to soybean mainly takes place in the Mid-West and northern states, 
which presents a worrying trend in terms of the potential threat of soybean expansion into 
the  Amazon  rainforest  and  Cerrado  native  vegetation  (Figure  33).  The  occupation  of 
pastures in the north of Mato Grosso and Tocantins, Maranhão, Rondonia and Pará could 
lead to new forest clearance through displacement of pasturelands into the Amazon biome. 
Moreover, from 2017 to 2030, ca 731,750 ha of native vegetation in the Cerrado will be 
converted to soybean. Even though in this biome there are large cleared areas suitable for 
soybean expansion (ca. 42 Mha), these lands are not located in the regions with the most 
rapid recent growth (Gibbs et al., 2015).  The projected large-scale expansion of soybean 
into  the  Matopiba  region  could  raise  both  direct  and  indirect  (i.e,  through  pasture 
displacement) land conversion of the native vegetation of Cerrado biome, which is highly 
vulnerable since it is not safeguarded by the Soy Moratorium. The large-scale expansion 
of soybean into northern states is driven by the low price of land – in the Matopiba region 
lands are 50% cheaper than in Mato Grosso (MAPA, 2017). 

47 The Soy moratorium is a voluntary zero-deforestation trade-based agreement implemented in Brazil to avoid 
the purchase of soybean grown on land deforested after 2008 (the agreement was signed in 2006 by major 
soybean trades) 

57 

 
 
 
  
 
 
 
 
                                           
Figure 33: Land use conversions to soybean from 2017 to 2030. 

6.5.3  LULUCF  emissions due to soybean production 

Table 25 shows the LULUCF emissions due to soybean expansion between 2017 and 2030. 
We  did  not  calculate  emissions  from  agricultural  practices  associated  with  soybean 
cultivation. 

Table 25: LULUCF emissions (million tons CO2) from soybean expansion from 2018 to 2030. 

BAU scenario 

2018 

2019 

2020 

2021 

2022 

2023 

2024 

2025 

2026 

2027 

2028 

2029 

2030 

TOT 

Soybean area (Mha) 

LULUCF  emission 
(Mtons CO2) 
    - Biomass 

    - Soil 

35.1 

36.2 

37.2 

38.3 

39.4 

40.5 

41.5 

42.6 

43.7 

44.8 

45.9 

46.1 

47.1 

65.5 

76.9 

75.7 

77.6 

77.8 

78.9 

76.8 

77.0 

74.9 

72.6 

68.3 

65.6 

61.9 

949 

49.6 

58.4 

54.7 

54.0 

51.7 

50.3 

45.6 

43.3 

38.7 

33.9 

27.1 

21.8 

15.6 

545 

15.9 

18.5 

21.0 

23.6 

26.1 

28.6 

31.2 

33.7 

36.2 

38.7 

41.2 

43.8 

46.3 

405 

The  expansion  of  soybean  into  the  rainforest  and  Cerrado  native  vegetation  determines 
the  high  values  of  CO2  emissions  from  biomass  loss.  The  average  LULUCF  emission  per 
hectare for soybean is two times larger than that for sugarcane (1.76 ton CO2/ha per year 
versus 0,85 ton CO2/ha per year). Figure 34  shows the difference between the soybean 
and sugarcane LULUCF emission magnitudes employing trends from 2018 to 2030. 

58 

 
 
 
 
Figure 34: soybean and sugarcane LULUCF emission trends from 2018 and 2030 

The  LULUCF  emissions  per  hectare  of  soybean  expansion  can  be  translated  roughly into 
the equivalent land use change emissions per MJ of soya biodiesel that could be produced 
from that land. Between 2017 and 2030, this expansion results in 949 million tons of CO2. 
Conventionally, in Europe, land use change emissions are divided by the production on the 
land over 20 years; then the emissions attributed to a ton  of additional annual soybean 
production work out to be 1.06 tons of CO2 per ton of soybean per year. Considering the 
energy content of soybean (ca. 20 MJ/kg at 13% moisture), and the slight energy losses 
in the processing to biofuel, the emissions per MJ of biodiesel that could be made from the 
additional soybean correspond to 56gCO2/MJ. 

59 

 
 
 
 
 
7  Conclusion 

To  achieve  its  decarbonisation  targets  and  boost  the  bioeconomy,  the  EU  will  inevitably 
consume more biomass. Part of the demand will be met by EU resources, but this will also 
require sustained access to third country markets. In order to reconcile its economic growth 
with  the  goal  of  mitigating  climate  change  and  promoting  globally  sustainable 
development, the EU trade agreements with third countries should be conditional to strict 
social  and  environmental  criteria.  This  is  of  particular  importance  for  Brazil,  the  largest 
exporter of agricultural products to the EU. In 2017, the EU-Mercosur trade-talks failed to 
conclude a political agreement on a few sensitive agricultural products – including ethanol. 
Guaranteeing  the  competitiveness  of  European  farmers  and  concerns  about  the 
environmental  sustainability  of  Brazilian  ethanol  feedstock  supply,  have  long  been  key-
issues during the negotiations. This study provides insights about the potential impacts on 
land  use  change  and  associated  greenhouse  gas  (GHG)  emissions  from  sugarcane 
cropping, and assesses its compliance with the European environmental criteria for biofuel 
feedstock production. 

Our  projections  to  2030  show  that  sugarcane  expansion  into  the  Amazon  and  Cerrado 
native vegetation  could be marginal (less than 2%) if Brazil upholds its ban  on growing 
sugarcane  in  the  Amazon,  and  hence  would  entail    limited  forest  biomass  loss  and 
associated GHG emissions (Figure 35). The conversion of other croplands – including food 
crops – to sugarcane would also be negligible (less than 1%), thereby avoiding significant 
indirect land use changes elsewhere or affecting other crop markets. Although most of the 
sugarcane expansion occurs at the expenses of pasturelands (97%) - because ranching is 
in general economically less competitive than sugarcane - it is difficult and very uncertain 
to forge a direct link between new forest clearance in the northern regions (Amazon and 
Cerrado)  and    the  displacement  of  pasturelands  from  the  Southeast  and  Midwest. 
Converting pasture to sugarcane and  achieving the expected yield demand a substantial 
application  of  lime  and  fertilizers,  which  represents  the  largest  source  of  GHG  emission 
from  sugarcane  cropping  (ca.  35%).  However,  these  emissions  are  far  lower  than  the 
emissions from deforestation to open new areas for crop expansion, making this transition 
an opportunity for the Brazilian sugarcane industry to meet the rising demand for ethanol 
and sugar while achieving country sectoral mitigation targets and the compliance with the 
EU's environmental criteria. Nevertheless, this solution also requires investments in cattle 
ranching intensification to yield land for agricultural expansion, especially in regions with 
easy  access  to  grain  production  given  that  improved  and  supplemental  feeding  both  on 
pasture and in feedlots is essential to beef intensification. 

This  study  did  not  assess  the  potential  impacts  of  varying  ethanol  import  quotas  on 
European  producers  of  biofuels  that  would  come  under  pressure.  We  only  judged  the 
environmental  sustainability  of  Brazilian  sugarcane  feedstock  production  within  different 
scenarios, showing that its potential impacts on GHG emissions through land use changes 
could be considered limited even under conditions of high EU demand - which represents 
a small share of Brazilian total supply. 

60 

 
 
Figure 35: main outcomes about potential impacts of sugarcane expansion from 2017 to 2030. The 
GHG emission profile includes LULUCF emissions and emissions from agricultural practices. The check 
mark identifies the compliance with the environmental criteria set by the EU REDII, whilst the caution 
sign draws attention to warning bells that could require further analysis 

By comparison, soybean – the largest crop in Brazil with expected 48 million hectares in 
2030 – will expand considerably into the Amazon (ca. 7% of total expansion i.e. 0.9 Mha) 
and Cerrado native vegetation (ca. 6%, i.e. 0.7 Mha), resulting in high GHG emissions from 
loss of biomass and soil carbon (Figure 36). This crop is mainly used for animal feed and 
only 1% of total production is currently used for biofuel. 

The  lower  land  prices  in  the  Matopiba  region  has  the  potential  to  drive  large-scale 
expansion of soybean into northern areas. This soybean growth on pasturelands of Mato 
Grosso,  Tocantins,  Maranhão,  Rondonia  and  Pará  could  lead  to  further  forest  clearance 
through displacement of pasturelands into the nearby Amazon and Cerrado biomes. While 
a  strict  enforcement  of  the  Soy  Moratorium  could  halt  the  expansion  of  soybean  into 
forested  areas  of  the  Amazon  biome,  the  Cerrado’s  native  vegetation  remains  highly 
vulnerable to soy conversion due to the absence of effective environmental governance for 
this biome. 

61 

IMPACTSUGARCANE:MAIN OUTCOMES, 2017-2030COMPLIANCE WITH EU ENVIRONMENTAL CRITERIAConvertingpasturelandtosugarcanedemandstheapplicationoflimeandfertilizers.ItrepresentsthelargestsourceofGHGemission(>35%)fromsugarcaneproduction–butfarlowerthanemissionsassociatedwiththeconversionofnativevegetationtocroplands•24.5MtonsCO2/year•2tonsCO2/hayearand2gCO2/MJ•Cumulativeemissions2018-2030:ca.312-323MtonCO2•GHGemissionprofile:1)Agriculturalpractices56%(38%fromfertilizerandlimeapplication;18%fromstrawburning);2)LULUCF44%(26%fromcarbonstockchangeinsoil;18%fromcarbonstockchangeinbiomass).StrawburningshouldbegraduallydismissedMarginalexpansion(<2%)intoforest(highcarbonstocklands)andsavannahnativevegetationMarginalexpansion(<1%)intoothercroplands(includingfoodcrops).NegligibledisplacementoffarmingactivitiesandassociatedILUC97%ofexpansionintopasturelandsintheSoutheastandMidwest.Uncertainlinkbetweensugarcaneexpansioninthesouthandpasturedisplacementintonorthernregions(andassociatedforestclearance) 
 
 
Figure  36:  main  outcomes  about  potential  impacts  of  soybean  expansion  from  2017  to  2030  and 
compliance with the environmental criteria set by the EU REDII (Note: only a small share of soybean 
(1% in 2016) is dedicated to biofuel production – the REDII criteria should be applied only to this 
volume). The GHG emission profile only includes LULUCF emissions. The check mark identifies the 
compliance  with  the  environmental  criteria  set  by  the  EU  REDII,  whilst  the  caution  sign  draws 
attention to warning bells that may require further analysis 

The overall land use changes – due to the allocation of all projected supplies of the main 
agricultural commodities, wood, together with deforestation and forest regeneration rates 
to  2030  -          imply  increasing  LULUCF  net  cumulative  emissions  that  put  the  Brazilian 
contribution to the Paris Agreement at risk. The LULUCF emissions in both scenarios rise 
rapidly  as  we  move  to  2030,  which  contradicts  the  decrease  foreseen  by  Brazil's  NDC 
targets (ca. 22 million tons of CO2). This additional emission (ca. 900 million tons of CO2) 
is mainly due to our assumption that the deforestation rising trends since 2012 continue 
unabated  to  2030,  with  a  growing  support  for  predatory  agricultural  and  land-grabbing 
practices and the lax enforcement of the Forest Code (Figure 37). 

Figure 37: impacts of cumulative land use changes in Brazil from 2017 to 2030 on LULUCF emissions, 
which could put the country contribution to the Paris Agreement at risk. 

From 2005 to 2012, Brazil was able to curb deforestation and substantially reduce its GHG 
emissions  with  strong  improvements  in  environmental  governance.  However,  with  the 
dismantling of Brazil’s environmental protection, there is no safeguard that deforestation 

62 

IMPACTSOYBEAN: MAIN OUTCOMES, 2017-2030COMPLIANCE WITH EU ENVIRONMENTAL CRITERIAOnlyLULUCFemissions:•73MtonCO2/year•1.8tonCO2/hayearand56gCO2/MJ•Cumulativeemissions2017-2030:ca.950MtonCO2(Bycomparison,sugarcaneLULUCF:ca.11MtonCO2/yearand0.85CO2/hayear;cumulativeemissions:ca.140MtonCO2)•SoybeanLULUCFemissions(tonCO2/hayear)aretwotimeslargerthanthesugarcaneones(higherconversionofforestandnativevegetationtocroplands)Significantexpansionintoforest(highcarbonstocklands,ca.7%=0.88Mha)andsavannahnativevegetation(ca.6%=0.73Mha).Marginalexpansion(<1%)intoothercroplands(includingfoodcrops).NegligibledisplacementoffarmingactivitiesandassociatedILUC87%ofexpansionintopasturelandsmainlyintheMidwestandnorthernregions.Soybeanlarge-scaleoccupationofnorthernpasturelandscoulddisplacepastureintoAmazonandCerradobiomes(withassociatednewforestclearance) 
 
 
 
 
will be under control in the near future. Making trade negotiation with Brazil conditional on 
strict  environmental  criteria  for  agricultural  commodities  could  be  an  effective  way  to 
promote  sustainable  development  and  avoid  further  deforestation  directly  or  indirectly 
linked to farming and ranching activity expansion.   

63 

 
 
 
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69 

 
 
 
 
 
 
 
 
 
 
List of abbreviations and definitions 

BAU 

Business as usual scenario 

EEA 

EPE 

European Environmental Agency 

Brazilian Ministry of Energy (Energy Research Office) 

FAO 

Food and Agriculture Organization (UN) 

GHG 

Greenhouse gas  

ILUC 

Indirect Land Use Change 

LUC 

Land Use Change 

LULUCF Land Use, Land Use Change and Forestry 

MAPA  Brazilian Ministry of Agriculture 

MMA 

Brazilian Ministry of Environment 

MCTIC  Brazilian Ministry of Science, Technology, Innovation and Communication 

NDC 

Nationally Determined Contributions 

POB 

Phase out biodiesel scenario 

REDII  Renewable Energy Directive (EU, 2018) 

SDGs  Sustainable Development Goals  

TQR 

Tariff Rate Quota  

70 

 
 
 
 
List of figures 

Figure 1: Quantified Socioeconomic Indicators of the EU Bioeconomy in 2015. Source: 
based on Ronzon and M'barek (2018). ................................................................... 10 

Figure 2: Third action of the Bioeconomy strategy ................................................... 11 

Figure 3: Projected land use changes 2020-2050 (Mha) for different scenarios. Source: 
EC, (2018b) ........................................................................................................ 12 

Figure 4: thematic areas of the Brazilian action plan in bioeconomy. Source: Nunes, B. 
(2018). .............................................................................................................. 13 

Figure 5: EU trade in goods with Mercosur. Source: EPRS, 2018 ................................ 15 

Figure 6: main countries of origin of ethanol imports to EU. Source: Eurostat and ePURE, 
2017. ................................................................................................................. 16 

Figure 7: Employment (1000 head) in EU28 bio-based sectors. Source: Philippidis et al., 
2018 .................................................................................................................. 24 

Figure 8: Changes (Km2 and %) from 2020 to 2030 in global land use by regions due to 
different policy narratives (IL and OL) compared with the reference scenario (RS). 
Source: Philippidis et al., 2016. ............................................................................. 25 

Figure 9: Global cropland use of EU-27 between 2000 and 2013. Source: O’Brien et al., 
2015 .................................................................................................................. 26 

Figure 10: Biomass content (tons C per hectare) across Brazilian biomes (Aguiar et al., 
2015). The Amazon and Cerrado biomes recorded the highest deforestation rates due to 
agriculture and livestock expansion and land grabbing. ............................................ 28 

Figure 11: a multilevel assessment translates the EU bioeconomy demand into potential 
environmental consequences at local scale. Example of Brazilian bioethanol ............... 29 

Figure 12: EU28 exports to and imports from Brazil in 2008 and 2018 by product group 
(EUR billion). Source: Eurostat Comext, 2018. ........................................................ 30 

Figure 13: contribution of Brazilian biomes to country area (%). ............................... 31 

Figure 14: Trends of forests, croplands and pasturelands in the Amazon biome from 1985 
to 2017 (million hectares). Source: MapBiomas, 2019. ............................................. 32 

Figure 15: Trends of forests, croplands and pasturelands in the Cerrado biome from 1985 
to 2017 (million hectares). Source: MapBiomas, 2019. ............................................. 33 

Figure 16: Expansion of semiperennial crops, notably sugarcane, into the Brazilian 
biomes. Source: Mapbiomas, 2019 ........................................................................ 34 

Figure 17: Expansion of annual and perennial crops, notably soybean and 2nd corn, into 
the Brazilian biomes.  Source: MapBiomas, 2019. .................................................... 34 

Figure 18: Gross GHG emission profile of Brazil from 1990 to 2017. Source: SEEG Brazil, 
2019. ................................................................................................................. 35 

Figure 19: GHG emissions (dark red) and removals (dark green) sorted by IBGE 
macroregions. Source: SEEG Brazil, 2019. .............................................................. 36 

Figure 20: Theoretical modelling framework. MAGNET projects the total and EU demand 
for Brazilian ethanol from different EU scenarios (Consumer side). OTIMIZAGRO 
calculates and allocates the croplands to satisfy the demand at subnational level with a 
proper resolution to correctly quantify environmental impacts (Producer side). ........... 38 

Figure 21: Total supply of Brazilian ethanol to 2030. Source: MAGNET, 2018 .............. 41 

Figure 22: Projections of EU import of Brazilian ethanol from a baseline (BAU) and phase 
out biodiesel (POB) scenario. The dotted lines identify two different Tariff Rate Quotas 

71 

 
 
(TRQ) for ethanol, under discussion in the ongoing trade talks between EU and Mercosur. 
Source: MAGNET, 2018. ....................................................................................... 42 

Figure 23: Total sugar supply to 2030 (million tons). Source: MAPA, 2017. ................. 42 

Figure 24: Projections of sugarcane area (million hectares) to satisfy the supply of sugar 
and ethanol from a baseline (BAU) and a phase out biodiesel (POB) scenario. Source: 
own elaboration. .................................................................................................. 43 

Figure 25: Projected area (ha) of forest plantations to 2050. Source: MCTIC and ONU, 
2017. ................................................................................................................. 45 

Figure 26: Deforestation trends  in the Brazilian biomes to 2030. Sources: Rochedo et al., 
(2018) for Cerrado and Amazon biomes [1000 Km2]; Aguiar et al., (2015) and SOS Mata 
Atlantica, for the other biomes [average 1000 Km2/year] ......................................... 46 

Figure 27: Projected land use change map in 2030 (BAU). ........................................ 46 

Figure 28: Land use changes from 2017 to 2030 under the baseline (BAU) scenario. ... 47 

Figure 29: Transition matrix representing the cumulative land use changes (hectares) 
from 2017 to 2030 in the BAU scenario. ................................................................. 48 

Figure 30: LULUCF CO2 emissions from changes in biomass and soil carbon stocks. 2017-
2030 projections from a BAU scenario versus the country NDC targets (Grassi et al., 
2017). ................................................................................................................ 49 

Figure 31: land use conversions to sugarcane from 2017 to 2030. Example of BAU 
scenario ............................................................................................................. 51 

Figure 32: Profile of the cumulative GHG emissions (million tons CO2) from sugarcane 
production from 2018 to 2030. .............................................................................. 55 

Figure 33: Land use conversions to soybean from 2017 to 2030. ............................... 58 

Figure 34: soybean and sugarcane LULUCF emission trends from 2018 and 2030 ........ 59 

Figure 35: main outcomes about potential impacts of sugarcane expansion from 2017 to 
2030. The GHG emission profile includes LULUCF emissions and emissions from 
agricultural practices. The check mark identifies the compliance with the environmental 
criteria set by the EU REDII, whilst the caution sign draws attention to warning bells that 
could require further analysis ................................................................................ 61 

Figure 36: main outcomes about potential impacts of soybean expansion from 2017 to 
2030 and compliance with the environmental criteria set by the EU REDII (Note: only a 
small share of soybean (1% in 2016) is dedicated to biofuel production – the REDII 
criteria should be applied only to this volume). The GHG emission profile only includes 
LULUCF emissions. The check mark identifies the compliance with the environmental 
criteria set by the EU REDII, whilst the caution sign draws attention to warning bells that 
may require further analysis ................................................................................. 62 

Figure 37: impacts of cumulative land use changes in Brazil from 2017 to 2030 on 
LULUCF emissions, which could put the country contribution to the Paris Agreement at 
risk. ................................................................................................................... 62 

Figure 40: modelling framework developed for the Brazil case-study. Steps: 1) Projection 
of EU demand for ethanol to 2030 (with MAGNET model); projections of the other crops’ 
growth to 2030 (MAPA, 2017). 2) Allocation of all productive croplands (with 
OTIMIZAGRO model) and calculation of land use changes. 3) Estimation of GHG emission 
from LULUCF and farming (with  OTIMIZAGRO model). ............................................ 79 

Figure 41: Transition matrix representing the cumulative land use changes (hectares) 
from 2017 to 2030 in the POB scenario. ................................................................. 79 

Figure 42: distribution of ethanol plants and main logistic infrastructure used for ethanol 
transportation in Brazil. Source: EPE, 2018. ............................................................ 80 

72 

 
 
Figure 43: distribution and land use categories of the areas identified by the 
AgroEcological Zoning for the expansion of sugarcane in Brazil. Source: Jaiswal et al., 
2017. ................................................................................................................. 81 

Figure 44: total private land under no legal obligations (share of municipality area). 
Source:  Jaiswal et al., 2017. ................................................................................ 82 

Figure 45: climate favorability map for sugarcane. Source: MCTIC and ONU, 2017. ..... 83 

73 

 
 
 
List of tables 

Table 1: list of EU documents and legislative tools considered in this study ................. 16 

Table 2: list of Brazilian legislative tools considered in this study ............................... 19 

Table 3: EU-Mercosur trade negotiation considered in this study ............................... 19 

Table 4: Sectors included into bioeconomy strategies of countries examined. “X”: 
included into bioeconomy strategy; “XX”: included into bioeconomy strategy and 
monitored. Source: FAO 2018 and Bracco et al., 2018. ............................................ 20 

Table 5: Main studies reviewed on global monitoring and impact assessment of 
bioeconomy sectors ............................................................................................. 21 

Table 6: Most common indicators used to assess the contribution of bioeconomy sectors 
to total country economy ...................................................................................... 23 

Table 7: some common indicators used to assess the impacts of bioeconomy sectors on 
land use ............................................................................................................. 26 

Table 8: Baseline assumptions over the three time periods disaggregation of commodities 
and regions. ........................................................................................................ 39 

Table 9: projection of productive areas for the main crops from 2018 to 2030. Source: 
MAPA, 2017 ........................................................................................................ 44 

Table 10: LULUCF net emissions (Mtons CO2) - BAU scenario .................................... 49 

Table 11: sugarcane area in 2017 and projected expansion to 2030, sorted by Brazilian 
states. Total area (1000 ha) and relative increment (%) vs. 2017 area, under the BAU 
and POB scenarios ............................................................................................... 50 

Table 12: land use conversions to sugarcane cropland from 2017 to 2030 under the BAU 
and POB scenarios. .............................................................................................. 52 

Table 13: GHG emissions (Million tons CO2) from living biomass carbon stock change due 
to sugarcane expansion from 2017 to 2030. BAU scenario ........................................ 52 

Table 14: GHG emissions (Million tons CO2) from living biomass carbon stock change due 
to sugarcane expansion from 2017 to 2030. POB scenario ........................................ 53 

Table 15: GHG emissions (Million tons CO2) from soil due to sugarcane expansion from 
2017 to 2030. BAU scenario .................................................................................. 53 

Table 16: GHG emissions (Million tons CO2) from soil due to sugarcane expansion from 
2017 to 2030. POB scenario .................................................................................. 53 

Table 17: GHG emissions (Million tons CO2e) from fertilization of sugarcane crops. BAU 
scenario ............................................................................................................. 53 

Table 18: GHG emissions (Million tons CO2e) from fertilization of sugarcane crops. POB 
scenario ............................................................................................................. 54 

Table 19: GHG emissions (Million tons CO2e)  from lime applications .......................... 54 

Table 20: GHG emissions (Million tons CO2e) from burning sugarcane straw ............... 54 

Table 21: total sugarcane area (million hectares) and GHG emissions (million tons CO2) 
associated with sugarcane production from 2017 to 2030. BAU scenario ..................... 54 

Table 22: Total sugarcane area and GHG emissions associated with sugarcane production 
from 2017 to 2030. POB scenario .......................................................................... 55 

Table 23: Soybean area in 2017 and projected expansion to 2030, sorted by Brazilian 
states. Total area (1000 ha) and relative increment (%) vs. 2017 area. ..................... 56 

Table 24: Land use conversions to soybean cropland from 2017 to 2030. ................... 57 

74 

 
 
Table 25: LULUCF emissions (million tons CO2) from soybean expansion from 2018 to 
2030. ................................................................................................................. 58 

Table 26: Study disaggregation of commodities and regions. .................................... 77 

75 

 
 
 
9  Supplementary Information 

  Annex 1: MAGNET model 

The  economic  analysis  of  this  research  is  conducted  using  the  global  neoclassical 
computable general equilibrium (CGE) simulation model MAGNET (Modular Applied GeNeral 
Equilibrium Modelling Tool) (Woltjer and Kuiper, 2014). As a principal source of data, the 
MAGNET model is calibrated to the Global Trade Analysis Project (GTAP) database (Aguiar 
et al., 2016), which has a coverage of 57 commodities and 140 regions. The most recent 
version  (version  9)  with  a  2011  benchmark  year,  includes  detailed  national  accounts 
information  from  statistical  offices,  as  well  as  gross  bilateral  trade  flows  by  commodity, 
with associated international transport costs and trade protection data.  

In  the  accompanying  standard  GTAP  model  (Corong  et  al.,  2017),  the  behavioural 
equations  characterising  economic  agents  (i.e.,  producers,  households,  investors)  follow 
the theoretical tenets of constrained optimization (cost minimization, utility maximization). 
Producers operate under conditions of perfect competition and constant returns to scale, 
whilst further market clearing and accounting equations ensure that supply equals demand 
in all markets and national-income, -expenditure and -output flows are equal within each 
country's  circular flow. A series of price linkage equations with exogenous tax (or tariff) 
variables capture the market distortions on domestic and imported markets. The propensity 
to save in each economy is a fixed share of regional income, whilst regional investments 
are allocated based on a regional rate of return mechanism. A neoclassical ‘closure’ rule is 
assumed which implies that imbalances on the capital account (i.e., regional savings less 
investment) are compensated by the current account (exports minus imports), such that 
the balance of payments nets to zero.   

For  multiregional  impact  assessments,  the  standard  GTAP  represents  a  solid  point  of 
departure. For the purposes of the current work, it does not, however, include an explicit 
treatment of bioenergy, whilst explicit modelling of relevant EU biomass policies is lacking. 
The MAGNET model remedies both of these omissions. MAGNET has been used widely in 
the area of natural resource, agricultural and environmental economics, in the academic 
(e.g., Banse et al., 2008; Rutten et al., 2013; Nelson et al., 2014; Schmitz et al., 2014; 
Boulanger  and  Philippidis,  2015,  Philippidis  et  al,  2018,  2019)  and  policy  literature 
(Boulanger and Philippidis, 2014; Philippidis et al., 2016; Boulanger et al., 2016; M'barek 
et al., 2017). 

As  an  advanced  ‘biobased’  derivative  of  the  standard  GTAP  database,  the  MAGNET  data 
incorporates further biomass and bio based activity sector splits from the 'parent' sector 
definitions  of  the  GTAP  database.  The  coverage  of  bioenergy  includes  conventional  and 
advanced  biofuels  and  biokerosene.  Aside  from  'traditional'  gas  and  coal  fired  stations, 
electricity  generation  also  includes  a  biobased  technology,  as  well  as  renewable  (solar, 
wind,  hydroelectric)  and  nuclear  alternatives.  To  service  the  bioenergy  activities  within 
MAGNET, non-standard additional lignocellulose biomass sources are also included within 
the data (i.e., crop and forestry residues, energy crops, pellets). The characterisation of 
the trade-offs and synergies relating to biomass usage is further enhanced through explicit 
recognition of biodiesel and bioethanol biofuel animal feed by-products (oilcake and DDGS, 
respectively),  as  well  as  further  sector  splits  to  characterise  specific  feed  and  fertiliser 
activities.    

With a rich treatment of sectoral detail beyond the standard GTAP database coverage, the 
MAGNET  model  is  able  to  characterise  differentiated  technologies  for  livestock  and  crop 
activities. Furthermore, the modelling of agricultural factor markets recognises immobilities 
in land transfer between alternative agricultural and non-feed/food activities, labour wage 
and  capital  rent  differentials  between  agricultural  and  non-agricultural  sectors,  and 
changes in agricultural land supply to reflect abandonment or uptake. In addition, explicit 
biofuel, EU domestic policy, and greenhouse gas emission modules are 'activated' to enrich 
the design of the baseline scenario (see next section). 

76 

 
 
Further details of the biobased variant of MAGNET can be found online in Philippidis et al., 
2018. 

For  this  study,  the  chosen  data  disaggregation  (Table  26)  encompasses  lignocellulose 
biomass  sources  (i.e.,  residues,  energy  crops,  pellets),  agriculture,  food,  animal  feed, 
conventional  biofuel  technologies,  advanced  biofuels  (including  biokerosene)  and 
biochemical  biomass  conversion  technologies,  and  bioelectricity.  Non  bio-based  energy 
markets  are  represented  by  crude  and  processed  fossil  fuels  and  several  electricity 
technologies (fossil, nuclear and non-biological renewables). The regional disaggregation 
includes  an  EU28  aggregate  region,  whilst  non-EU  regions  cover  ‘large’  third-country 
distributors  of  raw  and  processed  biomass  products  on  world  markets  and  a  European 
residual region of EU neighbours. 

Table 26: Study disaggregation of commodities and regions. 

Commodity Disaggregation (66 Commodities): 

Arable and horticulture (9): paddy rice, wheat; other grains; oilseeds; raw sugar; vegetables, fruits and nuts; 
other crops; plant fibers; crude vegetable oil 

Livestock and meat (7): cattle and sheep; wool; pigs and poultry; raw milk; cattle meat; other meat; dairy 

Fertilizer (1): fertilizer 

Other  food  and  beverages  (4):  sugar  processing;  rice  processing;  vegetable  oils  and  fats;  other  food  and 
beverages 

Other  ‘traditional’  bio-based  (5):  fishing;  forestry;  textiles,  wearing  apparel  and  leather  products;  wood 
products; paper products and publishing 

Bio-mass  supply  (10):  energy  crops;  residue  processing;  pellets;  by-product  residues  from  rice;  by-product 
residues  from  wheat;  by-product  residues  from  other  grains;  by-product  residues  from  oilseeds;  by-product 
residues from horticulture; by-product residues from other crops; by-product residues from forestry 

Bio-based  liquid  energy  (5):  1st  generation  biodiesel;  1st  generation  bioethanol;  2nd  generation  thermal 
technology biofuel; 2nd generation biochemical technology biofuel; bio-kerosene 

Bio-based  industry  (4):  lignocelluose  sugar;  biochemical  (fermentation)  conversion  of  sugar  biomass  to 
polylactic  acid  chemicals;  biochemical  (fermentation)  conversion  of  bioethanol  to  polyethylene  chemicals; 
thermochemical conversion of biomass to chemicals 

Bio-based and non-bio-based animal feeds (3): 1st generation bioethanol by-product distillers dried grains and 
solubles (ddgs); crude vegetable oil by-product oilcake; animal feed. 

Renewable electricity generation (3): bioelectricity; hydroelectric; solar and wind 

Fossil fuels and other energy markets (10): crude oil; petroleum; gas; gas distribution; coal; coal-fired electricity; 
gas-fired electricity; nuclear electricity; electricity distribution; kerosene 

Other sectors (5): chemicals, rubbers and plastics; other manufacturing; aviation; other transport; other services 

Regional Disaggregation (8 Regions): 

EU28; Rest of Europe; North America; Brazil, Rest of Central and South America; African continent; China, 

Rest of Asia and Oceania. 

77 

 
 
   
  Annex 2: OTIMIZAGRO 

We used the spatially explicit land use model Otimizagro48 to assess the impacts of future 
demands for  crops  on  direct and indirect land use changes (LUC and ILUC) and LULUCF 
CO2 emissions. The model has a national coverage and simulates land use change, forestry, 
deforestation,  regrowth  and  associated  carbon  emissions  under  various  scenarios  of 
agricultural  land  demand  and  environmental  policies  (Rochedo  et  al.,  2018).  The  model 
framework is structured into four spatial levels: i) Brazilian biomes; ii) micro-regions, as 
defined  by  the  Instituto  Brasileiro  de  Geografia  e  Estatistica  (IBGE);  iii)  Brazilian 
municipalities, and iv) a raster grid with a 25 ha pixel resolution. Otimizagro models nine 
annual crops (soybean, sugarcane, corn, cotton, wheat, beans, rice, manioc and tobacco), 
including multiple cropping, four permanent crops (coffee, oranges, bananas and cocoa) 
and  plantation 
the 
deforestation/forest  regrowth  rates  are  exogenous  to  the  model.  The  crop-expansion 
module calculates the productive area (ha or Km2) to satisfy the demand for a commodity 
(tons  or  litres);  concurrent  allocation  of  crops  at  pixel  level  is  a  function  of  a  suitability 
map,  defined  on  the  basis  of  crop  climate  suitability  and  profitability  (calculated  using 
regional selling prices, production and transportation costs) When available land in a given 
region  is  insufficient  to  meet  the  specific  land  allocation,  Otimizagro  reallocates  the 
distribution of remaining land demands to neighbouring regions, creating a spill over effect.  

for  crops,  wood  and  meat,  and 

forests.  The  demands 

The allocation of productive areas in 2012 (simulation starting point) was based on official 
datasets  on  cropped  areas  at  municipality  level  (IBGE,  2012)49and  forest  plantations  at 
State level (ABRAF, 2012)50. To improve the delimitation of soybean, sugarcane and corn 
croplands, the agriculture maps of Canasat (2016)51 have been used. Further details of the 
Otimizagro framework and modules can be found in Soares-Filho et al., (2016), Rochedo 
et al. (2018) and online52. 

  Annex 3: Integrated modelling framework 

The modelling framework (Figure 38) includes 3 different components that aim at: 

1)  Projecting  the  EU  demand  for  commodities  under  socioeconomic  and  policy 
scenarios to 2030 (through the MAGNET model), and the total country supply. This 
step prepares all the inputs for OTIMIZAGRO, including the projections of the other 
13  cropping  activities  (e.g,  soybean  and  corn),  plantations,  forest  regrowth  and 
deforestation to 2030.  

2)  Allocating the cropland areas to satisfy the projected demands from 2018 to 2030, 
and calculating  land use changes (through the OTIMIZAGRO model). The selected 
spatial resolution is 25 hectares and the temporal resolution is 1 year. 

3)  Estimating the GHG emissions due to LULUCF and farming practices (through the 

OTIMIZAGRO model). 

48 https://csr.ufmg.br/dinamica/otimizagro/  
49 Instituto Brasileiro de Geografia e Estatística (IBGE). Available at: http://www.sidra.ibge.gov.br  
50 Associação brasileira de produtores de florestas plantadas.  
Available at:  http://www.ipef.br/estatisticas/relatorios/anuario-ABRAF12-BR.pdf  
51 Monitoramento da cana de açúcar por imagens de satélite (Program for monitoring sugar cane areas through 

remote sensing images). Available at: www.dsr.inpe.br/laf/canasat. 

52 https://csr.ufmg.br/otimizagro/ 

78 

 
 
 
 
 
                                           
Figure  38:  modelling  framework  developed  for  the  Brazil  case-study.  Steps:  1)  Projection  of  EU 
demand for ethanol to 2030 (with MAGNET model); projections of the other crops’ growth to 2030 
(MAPA, 2017). 2) Allocation of all productive croplands (with OTIMIZAGRO model) and calculation of 
land  use  changes.  3)  Estimation  of  GHG  emission  from  LULUCF  and  farming  (with    OTIMIZAGRO 
model). 

  Annex 4:  land use transition matrix from POB scenario 

Figure 39 shows the land use transition matrix from 2017 to 2030 in the POB scenario. 

Figure 39: Transition matrix representing the cumulative land use changes (hectares) from 2017 to 
2030 in the POB scenario. 

  Annex 5: Main factors shaping sugarcane expansion  

Land use maps show little difference between BAU and POB scenarios, due to the limited 
increase  of  sugarcane  croplands  (by  1%  from  BAU  to  POB).  In  both  cases,  the  largest 
expansion of sugarcane is expected in the regions of the Southeast and Mid-West - notably 
in  the  state  of  Sao  Paulo,  Minas  Gerais  and  Mato  Grosso  do  Sul.  Therefore,  it  seems 
reasonable to suppose that the main drivers shaping sugarcane distribution are the same. 

79 

Categories of land usePastureSavannahForestRegenerationAgricultural areaPlanted forestLanduse 2030Pasture209,113,95020,302,25022,083,05002,410,0000253,909,250Savannah096,710,625000096,710,625Forest00378,365,250000378,365,250Regeneration4,194,275223,300526,7001,915,02516,30006,875,600Agricultural area16,541,283826,467952,046056,759,579075,079,375Planted forest1,162,65012,12514,875038,2507,839,9509,067,850Landuse 2017231,012,158118,074,767401,941,9211,915,02559,224,1297,839,95020172030 
 
 
 
 
 
Distribution of logistic infrastructure and ethanol plants 

The  concentration  of  sugar  and  ethanol  mills in  the  southeast  regions,  especially in  Sao 
Paulo  State,  together  with  the  well-developed  multimodal  system  for  transportation  of 
ethanol (Logum system53), makes this region more attractive for the sugar sector due to 
reduced transportation and production costs (Figure 40). This trend is expected to continue 
in the near future since most of the projects for new ethanol plants appear close to existing 
road infrastructure in the southern regions. The development of the railway Norte-Sul and 
Centro Atlântica should improve the transport links with the northern regions of the country 
making their ethanol production more competitive in the future (EPE, 2018). 

Figure  40:  distribution  of  ethanol  plants  and  main  logistic  infrastructure  used  for  ethanol 
transportation in Brazil. Source: EPE, 2018. 

Legislation and Agro-Ecological Zoning 

The limits of the available areas for the expansion of sugarcane in Brazil have been set by 
the Agro-Ecological Zoning (AEZ) in 2009 (Figure 41). That means ca. 64 million hectares 
of suitable land out of which only 10 million hectares have been already used for sugarcane 
in 2017. Almost all the viable land is located in the southern and mid-west regions, and, 
to  a  lesser  extent,  along  the  northeast  coastline.  Since  then,  there  were  several 
unsuccessful  attempts  to  extend  its  limits  into  the  Legal  Amazon  (Senate  bill 

53 www.logum.com.br/  

80 

PIPELINE (LOGUM)TERMINALS (LOGUM)MAIN ROADSMAIN RAILWAYSMILLS (OPERATIVE)MILLS (RE-OPENING)MILLS (NEW) 
 
 
 
                                           
PLS626/201154 ) - the last one at the end of 2018. It is important to stress than even the 
Brazilian Sugarcane Industry Association (UNICA) reinforces its commitment to adhere to 
the  AEZ  in  order  to  comply  with  national  and  international  environmental  standards. 
Indeed,  the  new  National  Policy  on  Biofuels  (Renovabio)  limits  the  lands  for  sugarcane 
expansion within the demarked AEZ and avoids the production of biofuel feedstock on lands 
converted from forest areas after December 2017. 

Figure 41: distribution and land use categories of the areas identified by the AgroEcological Zoning 
for the expansion of sugarcane in Brazil. Source: Jaiswal et al., 2017. 

Even though the sugarcane production in the Northeast has dropped off in the past, more 
recently it has risen again and it could benefit from new governmental programs aimed at 
boosting the sugar sector in this region (e.g., Programa Renovar55). Moreover, the Brazilian 
legislation  (law  9363/96,  Art.7056)  states  that    the  volume  of  ethanol  for  export  to 
preferential markets must be produced in the Northeast – one of the poorest  areas of the 
country – as a means to promote the social and economic growth of rural areas. This law 
is in line with the EU commitment of promoting sustainable development through its trade 
policy (EP, 2016). 

54 https://www25.senado.leg.br/web/atividade/materias/-/materia/102721  
55  https://asplanpb.com.br/2018/03/14/programa-renovar-promete-revitalizar-cultura-da-cana-de-acucar-no-

nordeste-e-recuperar-empregos-perdidos/ 

56https://presrepublica.jusbrasil.com.br/legislacao/107933/lei-9362-96  

81 

 
 
 
 
                                           
Land tenure 

In  the  Southern  and  Northeastern  municipalities,  there  is  a  high  share  of  private 
pastureland under no legal obligations (Figure 42), as defined by the Brazilian Forest Code 
– the principal law regulating forest conservation in private properties. This privately held 
land can be converted into agriculture and cattle production. However, in most cases, the 
progressive  degradation  of  pasturelands  has  decreased  traditional  extensive  ranching, 
leaving the door open for more lucrative sugarcane expansion. 

Figure 42: total private land under no legal obligations (share of municipality area). Source:  Jaiswal 
et al., 2017. 

Climate favourability 

Maps  delimiting  the  climate  suitability  for  each  crop  have  been  calculated  taking  into 
consideration the relationship between observed occurrences of the  crop and the spatial 
variability of climate attributes  – i.e, average annual temperature and rainfall  – and the 
soil moisture deficit and surplus, as detailed by (MCTIC and ONU, 2017). Figure 43 shows 
the probability of successful sugarcane growth under current and near future climates. It 
fits reasonably well with the results of Assad et al., (2013) that projected the impacts of 
climate  change  to  2030  on  the  viable  agricultural  area  for  the  main  crops,  including 
sugarcane.  Our  study  only  considered  rainfed  crops,  without  including  the  possibility  of 

82 

 
 
 
 
 
extending the suitable agricultural areas through irrigation (as an adaptation measure to 
counteract negative impacts of longer droughts).  

Figure 43: climate favorability map for sugarcane. Source: MCTIC and ONU, 2017. 

Jaiswal et al., (2017) assesses the impacts of mid-term climate change projections to 2045 
on Brazilian ethanol production, showing that significant losses are expected in the north, 
whilst the southern regions record smaller losses and even limited increases.  

83 

Favorability 
 
 
 
 
 
 
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ISBN 978-92-76-09820-1