Indirect land use change: a guide for the perplexed

Indirect land use change: a guide for the perplexed!

Dr Chris Malins!

Biofuels International webinar!September 2014!

!  With thanks to Moses Maimonides and (more recently) Repetto and Austin!

!  An overview of the concept, science and evidence base of iLUC!

!  I’m not going to tell you that one number is ‘right’!

We naturally like what we have been accustomed to, and are

attracted towards it.... The same is the case with those opinions of

man to which he has been accustomed from his youth; he likes them, defends them, and

shuns the opposite views.!

A guide for the perplexed!

The International Council on Clean Transportation is an independent nonprofit organization founded to provide first-rate, unbiased research and technical and scientific analysis to environmental regulators. Our work is focused on the world’s largest vehicle markets.!

About the International Council on Clean Transportation!

Slide 4!

!  Biofuel feedstocks have to come from somewhere!!  That means some combination of:!

1.  Drawing down stocks !!  Cannot continue year-on-year!

2.  Reducing demand in other sectors!!  Food-vs.-fuel !

3.  Increasing average global yields!!  What is the mechanism for this?!

4.  Using un-utilised wastes !!  Irrelevant for the analysis of crop-based fuels!

5.  Expanding cultivated area !!  Causes indirect land use change (iLUC) emissions!

The logic of iLUC!

!  Predicting the balance of effects requires a model!

!  We are far from value consensus!

!  Almost all modeling suggests significant emissions though!

!  Uncertainty is an inevitable part of this type of modeling exercise!

iLUC factor estimates!

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ETHANOLBIODIESELETHANOLBIODIESELETHANOLBIODIESEL

European CommissionCARBEPA

Car

bon

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y (g

CO

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ILUC Indirect agricultural emissions Direct emissions

Fossil fuel comparator 50% saving

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Eth

ano

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iLUC factor (gCO2e/MJ)

The regulatory efforts aren’t the only scenarios…!

!  Models can be thought of as a bewildering array of equations, nests and elasticities!

!  Can be more instructive to focus on macro-level effects, the key factors determining iLUC!!  Demand change: Elasticity of food demand to price!!  Productivity change: Elasticity of yield to price!!  Productivity change: Crop choices!!  Productivity change: Utilization of co-products!!  Land use expansion: Elasticity of area to price!!  Emissions implications: Carbon stock of new land!

Understanding iLUC and iLUC modeling!

Demand change: Elasticity of food demand to price!

!  Roberts and Schlenker (2010) find one third to one half of response on demand side (consumption)!

!  2008 food price spike:!!  Broad consensus that biofuels contributed!!  Generally not considered the primary driver!

!  Models predict medium term price increases!!  Poverty increases have been predicted on order of tens of millions!

!  USDA (2003): “demand responses to price changes are also generally the largest for poorer countries and decrease with affluence” !!  The price elasticity of consumption of bread and cereals for

Tanzania: 0.62, Germany: 0.15, America: 0.05!!  Laborde (2011b): “rigid [biofuels] policies … contribute

significantly to price volatility and are potentially more toxic than traditional farm support or decoupled programs.”!

Food consumption changes!

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FAPRI GTAP

US Coarse grains

Ethanol

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(mix)

MalaysiaBiodiesel

% of feedstock from reduced consumption Reduction in ILUC emissions due to reduced food consumption

% o

f fe

edst

ock

gCO

2e/M

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Contribution of food consumption change to iLUC results (JRC, 2011)!

Productivity change: Elasticity of yield to price!

!  ADAS (2008): “Despite substantial variation in yield trends between countries, the world trends for most of the major crops show a remarkably consistent linear trend”!

Historical yield trends!

Example of U.S. maize yields!

Actual yields Linear yields Price

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ld (

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!  A key effect in most iLUC modeling!!  Higher prices could support:!

!  Increased inputs (e.g. fertilizer)!!  Investment in equipment!!  More labor!

!  Keeney and Hertel (2010) find a short run elasticity of yield to price of 0.25!

!  Higher prices may also support increased innovation!

Is price a strong driver of yield?!

!  Bouët et al. (2009): “One recent analysis concluded that relative price changes have not encouraged innovation in U.S. agriculture in the last 40 years … This finding cautions against the efficacy of policies based on the premise that price signals alone induce efficient technical change.”!

!  Berry (2011): “There is a long tradition in agricultural economics … that takes as obvious the notion that almost all of the price-elasticity of supply comes from land-use rather than yield.”!

!  Some researchers have argued that yield is low on new land coming into production!

But not everyone agrees!

!  Berry and Schlenker find no significant econometric evidence!

!  There is more consensus on elasticity of area than yield !

!  Most studies that consider both yield and area elasticities find that area is more responsive than yield!

!  Some researchers focus on the micro-economic logic rather than historical data!

Comparison of yield and area elasticity is crucial!

Roberts and Schlenker (2010); Berry and Schlenker (2011)!

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World 4

-commod

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US 4-co

mmodity!

Brazil 4

-commod

ity!

China 4

-commod

ity!

Thailan

d 4-co

mmodity!

US corn !

US soy!

Shor

t run

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stic

ity to

pric

e!

Statistically insignificant results!

Yield elasticity!

Area elasticity!

Demand elasticity!

Productivity change: Crop choices!

!  Some locations have higher typical yields than others!!  Some crops have higher typical yields than others!!  Crop expansion in some locations has higher carbon

implication than others!!  The location where models forecast expansion is

crucial!!  Some models also predict improved production

efficiency by switching!!  E.g. Laborde (2011a) forecasts reduced area of ‘other

oilseeds’ and increase in (higher yielding) oilseed rape!!  This is a side of iLUC that has not been so widely

discussed!

Crop and location can be important!

Palm EU rapeseed EU wheat US maize

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AGLINK

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Change in area requirement because of crop shifting (ha/toe)!

For this report, based on the Witzke decomposition !

Productivity change: Utilization of co-products!

!  Most biofuels are produced alongside co-products, notably distillers’ grains (ethanol) and oil meals (biodiesel)!

!  These can offset some land demand, e.g.:!!  ~40% of U.S. corn -> ethanol plants!!  ~1/3 of that is leftover as distillers’ grains after fermentation!!  Distillers’ grains are returned to feed market reducing net

impact!!  This is included in all major iLUC models!!  Key questions:!

!  What is displaced?!!  How much is displaced?!

More than one product!

!  Some studies have suggested that 1 kg of DDGS could replace more than 1 kg of other feed!!  Klasing (2012) argues that this would not be the case in

well-specified diets!!  DDGS have higher protein content that straight

corn/wheat and thus could displace other feeds, e.g. soy!!  This is likely correct, especially in Europe, and soy may

have a larger iLUC footprint!!  ICCT feed formulation studies suggest many

ingredients could be displaced in livestock diets !

Could co-products avoid disproportionate iLUC?!

Land use expansion: Elasticity of area to price!

!  JRC (2010): “All models are sensitive to the ratio of yield to area elasticity in different countries”!

!  Responsiveness of area to price signals can be reduced by government interventions e.g. Common Agricultural Policy!

!  Taheripour, Tyner, and Wang (2011): “In previous decades, crop acreages (distribution of cropland among alternative crops) were much more responsive to changes in government programs,” but “it seems that farmers now respond to the relative crop prices more than what we observed in the past.”!

!  There is a paucity of quality literature to set values – often developing world parameters are based on developed world data!

!  Some models (e.g. IFPRI) use calculated rather than observed values!

Expanding the agricultural frontier!

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How many hectares for a hectare worth of feedstock?!

Emissions implications: Carbon stock of new land!

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Grassland Soil Foregonesequestration

(x 30yrs)

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A what price a hectare?!

Average carbon stocks can make a big difference!

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GTAP US Coarse

grains Ethanol

GTAP EU Wheat

Ethanol

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Ethanol

FAPRI EU Biodiesel

gCO

2 MJ-1

yr-1

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ears

)

Calculated from model JRC estimate (40 tC/ha) with peat oxidation

The ICCT simple macro-model!

iLUC from high to low – illustration!

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100% land use change

Elasticityof food

consumption to price

Elasticity of yield to price

Crop choice

Utilization of

co-products

Carbon stock of new land

iLU

C (

gCO

2e/M

J)

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In conclusion…!

!  There’s no question that some iLUC happens – only how much and where!

!  The models are developing over time!!  If all biofuels came from new land, the LUC emissions

would be high!!  Market mediated effects are crucial to the environmental

proposition of biofuels!!  There’s no single right answer – there are subjective

elements even in good modeling!!  Expected iLUC emissions are too significant to ignore!!  New technologies could help shift focus to feedstocks

where iLUC can be avoided or minimized!!  Wastes and residues; low-quality land; algae etc. !

Feeling less perplexed yet?!

Thanks!Questions?

http://www.theicct.org/guide-perplexed-indirect-effects-biofuels-production !