Science-based Targets for agriculture and forestry commodities

Final stakeholder workshopScience-based Targetsfor agriculture and forestry commodities

08/09/2016

Giel Linthorst (Ecofys)

Detlef van Vuuren (PBL)

Pete Smith (University of Aberdeen)

© ECOFYS | |

Agenda for stakeholder workshop

PART 1 (50 minutes)

1. Introduction

2. Project structure and outline

3. Scope of project, selection of commodities

4. Method to calculate emission factors and targets

a) Decarbonisation pathways for selected commodities

b) Land-Use Change (LUC) pathways for selected commodities

5. SDA-methodology and Tool to set science-based targets

6. Q&A

PART 2 (40 minutes)

1. Interactive discussion based on methodology and results

2. Polling

CLOSURE

08/09/2016 Science-based Targets for agriculture and forestry commodities

© ECOFYS | | 08/09/2016 Science-based Targets for agriculture and forestry commodities

INTRODUCTION

© ECOFYS | |

Introduction to this project

> Agricultural and forestry commodities (ranging from meat, diary to rice, wheat

and to wood) have a large impact on the environment and on the other hand

suffer from climate change in many regions

> Growth of global population and welfare will lead to almost doubling of demand

for agriculture and forestry commodities in 2050

> Increase of productivity combined with mitigating of GHG emissions are

required to meet global development and climate goals

> Companies play an important role in increasing productivity while mitigating

GHG emissions

> However, there is no transparent methodology that guides companies to

improve efficiency and to reduce GHG emissions per unit of agricultural and

forestry commodity in line with keeping global warming well below 2 oC

t GHG/unit food productt GHG/ha/yr

e.g. Smith et al. (2008); IPCC

e.g. Macdiarmid et al.(2013)

t GHG/t commodity


© freeimages.com/Andreas Krappweis © freeimages.com/Simon Stratford

© ECOFYS | |

Science-based Targets setting for companies

Inspire and enable companies to set GHG emission reduction

targets in line with the level of decarbonization required to

keep global warming 2°C

a) By the end of 2015 over 100 leading multinational

companies will have commited to adopt science-based

emission reduction targets

b) By the end of 2018 this will be more than 250 companies

c) This initiative will also demonstrate to policy-makers the

scale of ambition among leading companies to reduce their

emissions and act as a positive influence on international

climate negotiations

Goal

Expected outcomes

Science-based Targets08/09/2016 Science-based Targets for agriculture and forestry commodities

Science Based Targets initiative

Launched by CDP, UN Global Compact, WRI and WWF

© ECOFYS | |

New methodology to set Science-based Targets


• For the Science Based Targets initiative, Ecofys

developed with CDP, WRI and WWF a new methodology,

called the Sectoral Decarbonization Approach (SDA)

• The SDA methodology is based on the least-cost

modelled 2oC scenario (2DS) developed by the

International Energy Agency (IEA) as part of its

publication, Energy Technology Perspectives 2014

• Development of the SDA methodology involved

intensive stakeholder consultation

• The SDA methodology is freely available and was

published in Nature Climate Change

• However, agriculture, forestry and land use (AFOLU)

was not covered in SDA methodology yet. In this

project we developed a methodology to extend the SDA

for this sectors.


PROJECT STRUCTURE AND OUTLINE

© ECOFYS | |

Project structure and outline


KR Foundation

University of Aberdeen ECOFYSPBL

FU

ND

ER

PR

OJE

CT

PA

RTN

ER

S

PR

OJE

CT

PH

AS

ES

CO

NS

ULT

AT

IO

N

Selection of

commodities

Tool &

Report

1 3

Literature review and MACC

One consistent framework PBL IMAGE model

Stakeholder

workshop

TAG meeting TAG meeting TAG meeting

Stakeholder

workshop

TAG meeting

2. Emission factors and targets

Agriculture LUC

2a 2b

© ECOFYS | |

Members of the Technical Advisory Group (TAG)

Members of Technical Advisory Group:

– Ms Tiffanie Stephani Fertilizer Europe

– Mr Jan Peter Lesschen Wageningen University & Research (WUR)

– Ms Mark Henson Monsato

– Mr Kevin Rabinovitch Mars

– Ms Lini Wollenberg University of Vermont/CCAFS

– Mr Pedro Faria CDP

– Mr Henry King Unilever

– Mr Petr Havlik IIASA

– Mr Shaun Ragnauth US EPA

– Mr Stephen Russell World Resources Institute (WRI)



SCOPE OF PROJECT, SELECTION OF COMMODITIES

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Breakdown of global GHG emissions


0

1

2

3

4

5

6

Direct agriculture GHG emissions

Rest

Palm oil

Soy

Chicken

Maize

Wheat

Pigs

Dairy

Rice

Beef

0

1

2

3

4

5

6

FOLU (LULUCF) GHG emissions

(GtCO2eq)

Rest

Palm oil

Beef

Soy

0

5

10

15

20

25

30

35

40

45

50

Total GHG emissions (GtCO2eq)

Electricity and Heat Other Energy

Industry Transport

Buildings AFOLU

24%

6%

14%

21%

10%

25%

59%

41%

24%

76%

Sources: IPCC, 2014 & CEA, 2014

0

2

4

6

8

10

12

AFOLU GHG emissions (GtCO2eq)

Agriculture FOLU (LULUCF)

51%

49%

Land use

CO2

(LUC)

Agriculture

emissions

Electricity

and heat

Other

energy

Industry

Transport

Buildings

Agriculture,

Forestry, LU

© ECOFYS | |

Selection of agriculture and forestry commodities


The following commodities are selected:

1. Meat – Beef

2. Dairy

3. Poultry - Chicken

4. Meat – Pig

5. Rice

6. Maize

7. Wheat

8. Palm Oil

9. Soybeans

10.Round wood *)

The choice for these commodities was based on:

> Their large role in AFOLU emissions

> Their large volumes traded

Source: CEA 2014

*) Round wood will be treated on a more qualitative way in our final report

© ECOFYS | |

GHG emissions included


Meat - Beef and Dairy, Poultry - Chicken, Meat - Pig:

1. CO2 emissions from land use change associated with livestock and feed for livestock

2. Emissions from feed production i.e. direct and indirect N2O emission from application of

fertilizer, crop residues and deposition of manure on pastures; CH4 emission from

manure and flooded rice.

3. CO2 emission from machinery used on farm and for feed production.

4. CO2 and N2O emissions from fertilizer production needed for feed production

5. Enteric CH4 emission (Meat - Beef , Dairy and Meat - Pig)

6. CH4 emission from manure management

7. Direct and indirect N2O emission from manure management

Rice, Wheat, Maize, Palm oil, Soybean

1. CO2 emissions from land use change

2. CH4 emission from flooded soil (for Lowland rice only)

3. CO2 and N2O emission due to fertilizer production

4. Fertilizer-direct N2O emission from soil due to fertilizer application

5. Fertilizer-indirect N2O emission from leaching, runoff and volatilization

6. N2O emission from crop residue

7. CH4 and N2O emission from agricultural waste burning

8. CO2 emission from machinery on farmlemissions arising from land use change


Method for calculating emission factors and targets


Emissions

(2010-2050)

Activity

change

EF per

commodity

Marginal

Abatement

curves

Method for calculating emission factors of commodities

IMAGE 3.0 model

Con

sis

ten

t descrip

tion

of a

gric

ultu

re

Develo

pm

en

t with

/w

ithou

t clim

ate

po

licy


Emissions

(2010-2050)

Activity

change

EF per

commodity

Marginal

Abatement

curves


SSP2: Medium scenario (following medium

population, income, agriculture scenario –

similar to FAO baseline)

Crop production Lifestock production


Emissions

(2010-2050)

Activity

change

EF per

commodity

Marginal

Abatement

curves


Energy

Land-use

• Crop residues,

• agricultural waste

burning

• wetland rice,

• enteric fermentation

• animal waste,

• Fertilizer use,

• fertilizer production,

• Biological N-Fixation

• Deforestation ,

• CO2 from energy,


Emissions

(2010-2050)

Activity

change

EF per

commodity

Marginal

Abatement

curves


Energy

Land-use

2-degree decarbonisation pathway

Climate policy implemented by cost-

optimisation for all sectors, globally

Energy : energy efficiency, renewables,

CCS, nuclear

Deforestation: REDD, ARD

Non-CO2: Emission reduction via MACCs

2 deg C


Emissions

(2010-2050)

Activity

change

EF per

commodity

Marginal

Abatement

curves


Showing emissions per commodity

Calculate all emissions for each commodity

- Include inputs (e.g. feed)

- Following production chain for trade

Divide total emissions associated with

commodity by production level (cereal

emissions by cereal production)

A. Agriculture emissions (connectedto activity)

B. Land use change emissions (connected to activity change)

© ECOFYS | |

IMAGE model distinguishes between 26 regions



a. Agriculture emissions(non-CO2, CO2 for energy)


Update Marginal Abatement Cost Curves

> New MAC curves for agricultural CH4 and N2O emissions

> Built up out of sets of mitigation measures found in literature (non-mutually

exclusive)

> Estimated reduction for a specific measure: average of all the RE values for this

measure found in literature

-60

-50

-40

-30

-20

-10

0

10

Nitrificationinhibitors

Controlledrelease

fertilisers Reduced NImproved

placement of N

Improved landmanure

application

Improvedagronomypractices

Fertilizer freezone

Spreadermaintenance

Sub-optimalfertilizer

applications

Ch

an

ge i

n N

2O

em

issi

on

(%

)

Average

Example: Fertilizer N2O measures


Update Marginal Abatement Cost Curves

> Included in analysis:

– Regional differences

– Increased implementation potential in time:

● Due to technology diffusion, applicability of measures, removal of

barriers, technology improvement

– Interaction between multiple measures

● Resulting in: marginal costs ꜛ, reduction potential per measure ꜜ

Example MAC: CH4 from rice - Asia


Global decarbonization pathways for selected commodities

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

beef beef dairy dairy pork pork poultrymeat &

eggs

poultrymeat &

eggs

rice rice soy, palmoil

soy, palmoil

maize maize wheat wheat

2015 2050 2015 2050 2015 2050 2015 2050 2015 2050 2015 2050 2015 2050 2015 2050

Mt

CO

2 e

qu

ival

en

ts /

Mt

DM

co

mm

od

ity

Global commodity emission factors (total except LUC CO2)

CO2

N2O

CH4


Global decarbonization pathways for selected commodities

0.0

2.0

4.0

6.0

8.0

10.0

12.0

dairy dairy pork pork poultrymeat &

eggs

poultrymeat &

eggs

rice rice soy, palmoil

soy, palmoil

maize maize wheat wheat

2015 2050 2015 2050 2015 2050 2015 2050 2015 2050 2015 2050 2015 2050

Mt

CO

2 e

qu

ival

en

ts /

Mt

DM

co

mm

od

ity

Global commodity emission factors (beef not shown)

CO2

N2O

CH4


Regional decarbonization pathway for Soy in Brazil

Totals excluding LUC CO2

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

2015 2030 2050

t C

O2

eq

/ t

so

y

EF Soy - Brazil

biomass burning fertilizer production machinery

biological N-fixation irrigation agricultural waste burning

indirect fertilizer crop residues fertilizer use


Regional decarbonization pathway for Beef in Brazil

Totals excluding LUC CO2

0

10

20

30

40

50

60

70

80

2015 2030 2050

t C

O2

eq/t

bee

f

EF Beef - Brazil

enteric fermentation animal waste FEED: fertilizer production

FEED: biomass burning FEED: machinery FEED: irrigation

FEED: crop residues FEED: indirect fertilizer machinery

FEED: fertilizer FEED: agricultural waste burning FEED: biological N-fixation

FEED: wetland rice


b. LAND-USE CHANGE (LUC) PATHWAYS

© ECOFYS | |

CO2-emissions from land-use change (“LUC-CO2”)


> Other than CH4 and N2O from agriculture, the level of these emissions is not related to the size of production, but to a change in production / change in agricultural area.

> LUC-CO2 emissions cannot easily be attributed to certain land uses of commodities (e.g. due to indirect land-use change)

> There is no consensus methodology on how to account for LUC-CO2

in Life Cycle Assessments.

> We have explored 4 different methods for accounting for LUC-CO2.

> Advantages and disadvantages of the methods will be discussed in detail on the website of the tool and in the report. Choice of methodalso depends on available information about region of origin, andreported recent direct land-use changes.

© ECOFYS | |

LUC-CO2: methods


A. LUC marginal (“LUC marginal”): The emissions related to the additional production of

one unit of product, calculated by comparing this scenario to A counterfactual world where

this additional production would not have occurred.

B. Historic, weighted for area (“Hist, area”): Allocating LUC-CO2 which occurred over a

certain period (historic period, or future period) to all commodities (crops and grass) on a

per ha basis, i.e. constant average LUC-CO2 per ha agricultural area in one specific region/

country.

C. Historic, weighted for area expansion (“Hist, expansion”): Allocating LUC-CO2

which occurred over a certain period (historic period, or future period) to the commodities

which have increased in area, on a per ha basis (e.g. attributing the LUC-CO2 emissions to

palm-oil only if only palm oil has expanded). This method has been applied by FAO in

allocating LUC-CO2 to livestock, and is similar to the “direct LUC-CO2 emission factor

applied in the LCA methods by Blonk Consultants.

D. Foregone sequestration (“Foregone Sequ.”) : The sequestration that would have been

achieved, or the avoided emissions by no longer producing one unit of product, calculated

by comparing a baseline with a scenario where a certain amount of production would not

have occurred. This method has been applied implicitly by Nguyen et al. 2010, and a

slightly modified variant has been proposed by Müller-Wenk and Brandão 2010.

© ECOFYS | |

Emission factors across commodities & comparison to literature

Crop commodities, method D

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© ECOFYS | |

Emission factors across commodities & comparison to literature

Livestock commodities, method D

08/09/201608/09/2016 Science-based Targets for agriculture and forestry commodities

beef WRI =440

© ECOFYS | |

LUC-CO2 in Science-based target setting


> LUC-CO2 emissions under a 2 degree scenario are zero or negative on the

long term

> This would result in negative emission factors per commodity

> Difference in emission factors across regions are very scenario-dependent

> Therefore: impementation of a zero-deforestation target (in 2030), i.e.

zero LUC-CO2 emission intensity.


Final emission factors (non-CO2 + CO2)

© ECOFYS | |

Comparison with literature

Emission factor including all sources

08/09/2016 Science-based Targets for agriculture and forestry commodities08/09/2016 Science-based Targets for agriculture and forestry commodities

0

10

20

30

40

50

60

70

80

IMAGE (2015) FAO (2013)

t C

O2

eq

/ t

be

ef

(CW

)

EF - beef Brazil (base year)

Energy

Feed - Fertilizer + residues

Feed - energy

Animal waste

LUC CO2(Method C - Hist, expansion)

Enteric fermentation

© ECOFYS | |

Comparison with literature

Emission factor including all sources

08/09/2016 Science-based Targets for agriculture and forestry commodities08/09/2016 Science-based Targets for agriculture and forestry commodities


SDA METHODOLOGY & TOOL

© ECOFYS | |

Company-specific targets on agricultural commodities are

based on convergence towards regional intensity level in 2050

Homogeneous sectors Physical allocation

The SDA methodology assumes that the carbon intensity for the commodity tend to converge in 2050. The rate of convergence depends on the differential between the carbon intensity of the company’s production and the 2ºC carbon intensity of the average regional production in 2050 and the predicted regional growth of sourcing of the company.

Data levels

Climate targets for production/sourcing of agricultural commodities can be defined based on three levels of data

- Tonne of products purchased in regions1

2- GHG emissions

based on secondary data- Tonne of products purchased in regions

3- GHG emissions

based on primary data- Tonne of products purchased in regions

- Targets will be defined based on average decarbonization pathway in specific regions- Performance of company can not be compared to average

Pros and cons

- Targets will be defined based on convergence of GHG intensity of company towards regional intensity level in 2050- Performance of company can be compared to average, but effects of actions are not measurable

- Targets will be defined based on convergence of GHG intensity of company towards regional intensity level in 2050- Performance of company can be compared to average and effects of actions are measurable

Company data



Tool to set science-based targets per commodity and region

© ECOFYS | |

Key characteristics of new methodology for agriculture and

forestry commodities


1. The methodology is based on a new least-cost modelled 2oC scenario

taking into account latest insights in mitigation potential and costs of

climate-smart solutions.

2. The methodology is based on intensity pathways per commodity, using

physical indicators (tonne of product) for the 10 major agriculture and

forestry commodities

3. Both carbon intensity and absolute targets can be set by the methodology

4. The methodology can be applied by farmers up to retailers to reduce GHG

emissions during production and to green the supply chain

5. The methodology is flexible to set targets for each year until 2050

6. The methodology will be improved continuously based on new insights

© ECOFYS | |

Questions on Part 1


Please feel free to ask your questions:

- by raising your hand in the WebEx or

- by typing your question in the Chat box

After raising your hand, we will unmute you to hear your questions and we will

monitor the Chat box to answer the main questions.

After this short Q&A we will

address more specific points for

debate in part 2 of this workshop

© ECOFYS | |

CLOSURE AND NEXT STEPS


Please contact Giel Linthorst of Ecofys if;

• You have any questions

• You have valuable feedback on the project results

• You want to pilot the methodology for your company.

© ECOFYS | |

Giel Linthorst

Programme leader Science-based Targets

T: +31 (0)30 662 3322

M: +31 (0)6 11 366 935

E: [email protected]


http://www.ecofys.com/en/experts/linthorst

mailto:[email protected]?subject=SBT%20AgriFor%20presentation

Science-based Targets for agriculture and forestry commodities

Technology

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