-

Assessing the land-use environmental effects of biofuel

pathways

A logic-based model

Authors Edgard Gnansounou Luis Panichelli

WORKING PAPER REF. 437.104

ENAC - Faculté Environnement naturel architectural et construit ICARE - Institut des infrastructures, des ressources et de l'environnement BPE- Bioenergy and Energy Planning Research Group EPFL-ENAC-LASEN Tel : +41 21 693 06 27 Director Fax : +41 21 693 28 63 Bât. GC, Station 18 E-mail: edgard.gnansounou@epfl.ch CH - 1015 LAUSANNE http://lasen.epfl.ch/

…………………Lausanne, November 2009…………………………

Table of contents Abstract 3

1. Introduction 4

2. Land-use change modelling in biofuels production 5

3. Uncertain issues 7

3.1. Adaptation to changes and actors’ heterogeneity 8

3.2. Co-products, crops rotation and feedstock selection 9

3.3. Imperfect markets, simultaneous decision and emergence 10

3.4. Time and spatial dynamics 11

3.5. Technological change 11

4. Methodological approach 13

4.1. Theoretical basis 13

4.2. Definition of determinants and indicators 14

4.3. Definition of heuristic rules and scales 18

5. Illustration: Assessing the potential for indirect land-use changes 20

5.1. Context definition 20

5.2. Assessment of variables 21

5.2.1. Technological development 21

5.2.2. Relation with commodity markets 22

5.2.3. Biomass use dependency 23

5.2.4. Regulation weakness 23

5.2.5. Food security vulnerability 23

5.3. Result of the illustrative case 24

6. Discussion 25

6.1. Model strengths and limitations 25

6.2. Future developments and perspectives 26

7. Conclusion 27

References 28

Appendix 33 Tables 36 Figures 40

2

Abstract

In this paper a logic-based model (LBM) is proposed for generating scenarios in a

policy oriented problem related to biofuel production strategies at a country level.

The aim is to evaluate the potential environmental impact of direct and indirect land-

use changes linked to a given biofuel production strategy. The model is intended as a

decision support system that can help users to select biofuel production pathways

based on their potential impact on land-use. The paper describes the main uncertain

issues in estimating the impact on land-use changes constrained to the present state

of knowledge. The model architecture, the main determinants and indicators and the

applied heuristic rules for knowledge management are detailed. A case study

estimating the potential for activities displacement of a soybean-based biodiesel

production strategy is presented to illustrate the methodology. Further refinements

include development of an expert system as an extension of the logic-based model,

application to a real case study and accounting of actors’ behaviours, spatial patterns

and system dynamics.

Key words: land-use, biofuels policy, logic-based model

3

1. Introduction

Biofuel policies design is crucial to plan how and where the biofuel feedstock will be

produced in order to reduce negative impacts on food security, environmental

performance and socio-economical development. Some recent studies (Searchinger

et al., 2008; Fargione et al., 2008; Farrell and O’Hare, 2008; Panichelli et al., 2009)

showed up the significance of land-use changes (LUC) for qualifying biofuels

production. Direct and indirect land-use changes are key factors to assess the

sustainability of biofuels production primary because of their impact on the GHG

emission balance, the food security vulnerability and the local environmental impacts

(Panichelli & Gnansounou, 2008). Several macroeconomic and regional models are

being developed to estimate the direct and indirect land-use changes induced by

increased biofuels’ demand. However, much uncertainty is still present in these

estimations (RFA, 2008). Uncertainties stem from the incomplete data to assess

biofuels demand impact on land-use change, the limitations in current models, the

incomplete understanding and knowledge about the indirect effects of biofuels

production and the uncertainty related to the evolution of key variables and

parameters.

While the time frame to achieve the development of consistent and reliable

quantitative models is still large, policy makers are requiring scientific knowledge

inputs to efficiently take decisions about biofuel policy and land allocation for

biofuel feedstock production. A qualitative approach is proposed in order to provide

decision makers with a support tool for preliminarily assessing the land-use impact

of a national biofuels program.

4

This paper describes a logic-based model that evaluates the potential environmental

impact of land-use changes produced by a biofuel production strategy using given

feedstock types. The methodology aims to assist decision makers to select biofuel

production strategies based on their comparative impacts on land-use.

First, current approaches to model land-use changes in biofuels production are

described (Section 2) and an overview of the main uncertain issues and key

parameters is given (Section 3). Section 4 describes the theoretical basis of the logic-

based model and the model architecture. Section 5 illustrates the conceptual model

by a study focused on the evaluation of the activities displacement potential for a

strategy relating to a given feedstock-specific biofuel production at the national level.

In section 6 the model advantages and drawbacks are discussed and further

developments are described. Finally, the conclusion is given in section 7.

2. Land-use change modelling in biofuels production

Current approaches to analyze LUC in biofuels production at a global scale are based

on equilibrium economic models (Gnansounou and Panichelli, 2008). Equilibrium

models are state-of-the-art to analyze policy impacts on international trade and

market driven processes. They have been used, and were proven successful to

analyze climate change and agricultural reform policies and are now being applied to

determine the impact of biofuel mandates on commodity prices, land distribution and

GHG emissions.

Computable general equilibrium (CGE) models are top-down models that link

general equilibrium theory with realistic data of a global economy in order to find the

5

supply, demand and price levels that support equilibrium across interconnected

markets of an opened economy (Wing, 2004). Few CGE models have recently

included biofuels in their modelling structure and have determined the impact of

biofuels demand on land-use changes worldwide. Some of them are the extended

GTAP-E model (Birur et al., 2008), the EPPA model (Reilly and Paltsev, 2008) and

the LEITAP model (Woltjer et al., 2007), all based on the GTAP database. Within

partial equilibrium models several efforts have been recently done and a certain

number of models have been used to study the effect of biofuels demand on land-use

distribution. These models focus mainly on the agricultural markets and give a more

detailed description of a particular sector of the national economy. They include

among others the FAPRI (Searchinger et al., 2008) and the GLOBIOM (Havlík et al.,

2008) models. At the national level, some efforts are being carried on to further

disaggregate global partial equilibrium models to account for national specificities

(Nassar, 2008).

Spatial explicit approaches based on the integration of georeferenced and statistical

data are being developed to estimate land-use dynamics of specific feedstocks

(Sparovek et al., 2009; Schaldach et al., submitted). At the local level, land-use

modelling is based on satellite images processing and statistical analysis of land-use

structures, supported with additional data such as agricultural census and field

surveys (Montragio Pires de Camargo et al., 2008; Morton et al., 2006). Some efforts

are being done to account for the decision making process of land owners when

evaluating land-use options (Wu et al., 2007). These approaches are based on multi-

agent simulations that allow capturing the dynamics of land-use by specific actors

(Bao et al., 2008).

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Finally, integrated approaches aim at linking global and regional models. These

models’ integration allows to determine the impact of macro-economic variables on

land-allocation and to account for regional specificities. Some approaches link global

economic models to land-use spatial explicit models, especially, cellular automata

models (Hellman and Verburg, 2008). Other ongoing approaches try to link partial

equilibrium models to agent-based models to account for human behaviour

heterogeneity in land-use decisions (Fernandez, 2008).

Even though much effort has been done to improve modelling of biofuels and land-

use changes in these models, many problems are still unsolved. These problems

derived from incomplete information and ambiguity in present land-use databases,

the modelling limitations and assumptions in current models, the partial

understanding and knowledge about the indirect effects of biofuels production and

the uncertainty related to the evolution of key variables and parameters when

projecting biofuels’ supply-demand responses.

3. Uncertain issues

Estimating the environmental impact of land-use changes due to biofuels productions

requires making assumptions about the evolution of specific variables and

parameters. Due to the complex characteristic of economic interactions, spatial and

temporal dynamics, the interconnection of commodity markets, the technological

development, the adaptive capacity of actors and the multi-stakeholders implications

of decision outputs, uncertainties arise about how biofuels will be supplied and how

7

will land distribution be affected. Some of these uncertainties deal with issues that

are analyzed hereafter.

3.1 Adaptation to changes and actors’ heterogeneity

Food security policies may decrease the amount of biomass diverted to biofuels

production, encouraging other regions to increase biofuels production.

Environmental protection policies may limit the amount of available agricultural

land. Protectionist measures for small agricultural producers may encourage biomass

production for biofuel, displacing previous production to other regions. How biofuel

suppliers will react to these changes will depend on their own expectations and

capabilities. Even the same actor reacts differently to the same perturbation during

time, as it has the ability to learn from experiences and from others (Arthur, 2005).

Large experienced biofuel producers may design their production strategy in a

different way compared with emerging un-experienced producers. Accounting for

the diversity of behaviours in biofuels actors is a key issue in modelling social

decision making process.

Compliance with low-carbon requirements and other sustainability criteria may

increase/decrease land competition in good/bad carbon performing regions. Under

that framework, biofuel sustainability criteria may lead to a redistribution of biofuels

supply and a re-adaptation of the supply strategy previously designed by biofuel

producers. Biofuels’ trade agreements may stimulate production, constrain market

opportunities, favour technology transfer and international cooperation.

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3.2 Co-products, crops rotation and feedstock selection

Increased availability of co-products may reduce land pressure for animal feed and

extensive grazing, encouraging livestock intensification or provide additional

feedstock for biofuels production. However, much uncertainty exists about the

complex dynamic interaction between co-products and biofuels production,

agricultural intensification and cattle ranching dynamics.

Rotation crops are usually employed in order to relieve the soil quality and preserve

biodiversity. Soybean is produced in rotation with wheat in Argentina and in rotation

with corn in the United States. Demand for biofuels may increase feedstock prices,

encouraging farmers to produce that feedstock and reduce rotation with other crops

or activities. Decrease of rotation may increase the price of the rotation crop

encouraging production again. However, rotation schemes differ from one country to

another. At present, there is a lack of data and knowledge about the interaction of

biofuel feedstock efficiency with rotation crops and their influence on potential

activities displacement.

Feedstock substitution at national level may redistribute land pressure locally and

induce land-use changes of different land types. Research on alternative feedstocks

for biofuels production may change the medium and long-term production patterns.

Farmers may be able to choose between multi-function crops, food or energy crops,

agricultural and industrial residues. This selection will depend on farmers’

preferences for a specific feedstock.

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3.3 Imperfect markets, simultaneous decision and emergence

Biofuels market is imperfect. The number of biofuel producers is not high and

production is somehow monopolized by a few countries (mainly Brazil, the US and

Germany). Biofuel industry is relatively new and trade agreements and barriers

distort the market. The access to technology is heterogeneous and biofuels differ

from the sustainability point of view. These conditions characterize the biofuel sector

as an oligopoly market with imperfect competitions.

Moreover, the planned supply strategy by a biofuel producer is a dynamic process.

Each producer will readapt his strategy based on market signals and other producers’

decisions. Consequently, actors are not completely capable of responding to the

incentives introduced by a new policy due to the existence of structural barriers and

institutional impediments that constrains supply. Equilibrium assumptions and the

representation of the economy in a static initial state may be unsuitable to study

developing or transition economies that are undergoing rapid and substantial changes

and fast growing economic sectors.

The emergence of new producers and mandates may affect the supply and demand

dynamics as well as the competitivity of a specific biofuel pathway. As it may be

expected, if the demand for biofuels increases, new countries/regions will be

encouraged to produce biofuels. Moreover, biofuels producing countries may choose

to export their production or consolidate the biofuel industry by fixing national

mandates. Conversely, security of domestic biofuel supply for national mandates

may limit the amount of biofuel available for commercialization in a global market,

increasing biofuel prices and encouraging other regions to produce.

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3.4 Time and spatial dynamics

Time has implications in the analysis of land-use transitions, land allocation and

discounting of GHG emissions. Land-use is a dynamic process and consequently in a

given period a sequence of land-uses occurs. These transitions are essential to

allocate LUC GHG emissions between different land-uses in function of the period

that a biofuel crop occupies the land. Part of the uncertainty in present evaluations is

related to the incomplete information due to the lack of harmonization in land-use

databases. Data on unproductive and marginal lands availability and productivity are

missing.

Spatial representation is a key feature in modelling land-use change due to several

reasons. Firstly, it enables modelling the spatial correlation between different land-

uses. Moreover, it gives the capacity to downscale land distribution and land-use

changes further detailed than the country level based on the bio-physical

characteristics of feedstock production; and finally, it allows to estimate logistic

costs and to define optimal biofuel plants locations. Spatial explicit representation of

biofuels feedstock and crops productivity at the national and global scale is still

rough.

3.5 Technological change

Biofuel industry is booming. Current research in second and third generation biofuels

will allow these new technologies to enter into the market, as efficiency is improved

and costs are reduced. New biofuels technologies are emerging that reduce

competition for land. This issue has some implications on the assessment of biofuels

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impact. However, high uncertainty is linked to the point in time when these

technologies will be available and competitive. At present, this dynamic innovation

process is poorly accounted for. Agricultural intensification is one of the main

current research areas and it is still not known how price respond to yield increments

and how to model this relation, despite several approaches exist. More efforts should

be done to integrate time-dynamics and the geographical heterogeneity of land

productivity in modelling agricultural intensification. Second generation (2GB)

biofuels may reduce land competition and biomass allocation for biofuels leading to

a decrease of indirect land-use changes (iLUCs). While, higher yields can be

obtained from lignocellulosic crops and they can be planted on degraded land, on the

other hand, they may reduce co-products availability used as animal feedstock and

consequently more coarse grain may be diverted to feed. Intensification of

potentially displaced activities may reduce pressure on less productive and un-

productive land.

All in all, current uncertain issues and data imprecision call for the development of a

more flexible and dynamic approach in order to estimate the environmental impact of

land-use changes in biofuels production. Next section proposed such a

methodological framework.

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4. Methodological approach

4.1 Theoretical basis

In an attempt of understanding why land-use changes occur, where and in which

quantity and which the consequences are, models represent the real world process in

a simplified manner. However, gain in precision is not straightforward. More close to

the real problem is the model, more complex becomes the system (Zadeh, 1973) and

trickier is uncertainty. Uncertain issues and the imprecision of the available data

make the diagnosis of the environmental impact of land-use changes an intractable

problem.

Due to the present state-of-the-art approaches in biofuels land-use impact, it is

difficult to determine exact values of direct and indirect land-use change and their

potential environmental impact. Rule-based models can deal with these uncertainties

through a qualitative approach by providing a framework where all the rules and

assumptions are completely explicit and can be changed or updated as our

knowledge about the system improves (Ferraro et al., 2003). Rule-based expert

systems deals with decision making processes under uncertainty and so should be all

alternative reliable approaches for dealing with direct and indirect land-use change.

They should provide a simple way to achieve a definite decision based upon vague,

ambiguous, imprecise, noisy, or missing input information (Kaehler, 1998).

Expert systems, as an artificial intelligence-based technique, are computer programs

that simulate the problem-solving skills of one or more human experts in a given

field and provide solutions to a problem (De la Rosa et al., 2004). These systems

express inferential knowledge. The expert decision rules are based on scientific

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background and results of experiences and discussions with human experts, and

thereby reflect available expert knowledge.

The proposed approach is a first step in the development of an expert system devoted

to land-use changes implications of biofuels policy and their impacts. It consists in

design and construction of a Logic Based Model (LBM). The LBM is structured as a

decision tree where a set of indicators are aggregated in upper-level determinants.

The indicators are low-level input data and variables that allow estimating the

significance of a determinant. A determinant is an upper-level statement that, by

aggregating a set of indicators, expresses the degree of significance of a variable. The

highest level determinant (HLD) is the ultimate variable.

Once this latter determinant is defined, its main explaining factors (global

determinants) are determined. The global determinants (GD) are then further defined

using specific determinants (SD) that represent the main variables that explain their

states and evolution. For each specific determinant a set of global indicators (GI) is

defined. Finally, a set of specific indicators (SI), the low level indicators, is also

defined. Each SI is assessed using support data (ST).

A rule-based approach allows constructing the relational tree. Throughout heuristic

rules the indicators and determinants are linked between each other to evaluate the

performance of the highest level determinant (HLD). Each determinant/indicator has

a specific scale of evaluation that determines its relative importance within the set it

belongs to. Given a feedstock-specific biofuel production strategy, the user is asked

to introduce for each specific indicator a value that evaluates the performance of the

biofuel production strategy, based on the given evaluation scale. Given this input

data, the model scales-up through the rule-based relational tree to give the

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performance of the main determinant. In this way, the user can compare different

feedstock-specific production strategies based on their impact on land-use change.

The Logic-based model is developed as a Microsoft Excel platform with Visual-

Basic macros.

4.2 Definition of determinants and indicators

The highest level determinant (HLD) is the land environmental footprint of a biofuel

pathway within a national biofuel program. The land environmental footprint is

defined as a measure of the environmental impact of using a specific amount of land

to produce biofuel feedstock at the national scale and the potential amount of land

use to relocate the displaced activities. The environmental footprint is a function of

the land-use change intensity and the environmental risk, i.e. the global determinants

respectively GD-1 and GD-2 (Figure 1).

A biofuel pathway is defined as the combination of a type of fuel, a type of

feedstock and a type of conversion technology. The national biofuel program is the

set of strategic governmental actions that determines the national framework of the

biofuel industry development. It includes the definition of the set of biofuels

pathways, the logistics schedule, the feedstock production technology, the

production scale, the destination and use of the production, the trade agreements and

the governmental policies to encourage the development of the specific biofuel

pathways.

The land-use change intensity (GD-1), that is, the amount of land required to

produce the biofuel feedstock and relocate the displaced activities, depends on the

required direct and indirect land-use change (specific determinants for GD-1).

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While, the direct land-use change (SD-11) represents the required area (m2) to

produce a given amount of dry matter biofuel feedstock, the activities displacement

potential (SD-12) measures the potential area required to re-locate the

activities/land-uses displaced by the biofuel feedstock production activity.

The environmental risk (GD-2), that is, the potential environmental impact of the

land-use intensity, is a function of the LUC carbon intensity and the potential local

environmental impacts (specific determinants SD-21 and SD-22 for GD-2,

respectively). SD-21 accounts for the GHG emissions from direct and the indirect

land-use change and use. SD-22 measures the potential environmental damage to

soil, water and biodiversity from direct and the indirect land-use change and use.

A set of key variables (global indicators) influences the results of each of the specific

determinants, as follows:

SD-11 is determined by the biofuel production scale (GI-111), the feedstock

dependency (GI-112), the feedstock and biofuel intensification potentials (GI-113)

and the feedstock expansion potential (GI-114).

GI-111 indicates the magnitude of the biofuel production scale in function of the

domestic consumption and the export opportunities. GI-112 measures the availability

of a specific feedstock, the feedstock diversification potential (alternative uses) and

the potential to change from one feedstock to another, depending on the feedstock

competitiveness and maturity. GI-113 estimates the potential to increase productivity

of the feedstock production and the efficiency of the conversion technology, in

function of the local technological development, private and public investments and

technology transfer agreements. Finally, GI-114 represents the availability to

increase area for biofuel feedstock production, depending on the agricultural land

16

availability, the agro-ecological suitability of the feedstock and the possibility to shift

the production activity.

SD-12 value is constructed upon the values of the following global indicators: the

technological development in potentially displaced products (GI-121), the relation

with commodity markets (GI-122), the biomass-use dependency (GI-123), the

regulation weakness (GI-124) and the food security vulnerability (GI-125).

GI-121 measures the potential to increase productivity of the food crops and cattle

production potentially displaced by the biofuel feedstock, in function of global

productivity increment trends and the degree of adoption of intensification practices

in the country. GI-122 represents the connection between the biofuel feedstock and

the food commodity markets based on he interaction with co-products, with rotation

crops and the expected demand for potentially displaced commodities. The

availability of alternative-uses of the biomass and the use-shift potential is estimated

by GI-123, depending on the feedstock multi-functionality and the potential for

functional change. Moreover, the vulnerability of the regulation system with regard

to land-use policies (GI-124) depends on the vulnerability of the agricultural

expansion frontier and the potential for land concentration. Finally, the risk of food

shortage and food price increments is measured by GI-125 based on the food stock

capacity, the tendency in consumption patterns and the significance of food-crops

subsides.

In the environmental risk assessment module, SD-21 is determined by the conversion

of high carbon stock areas (GI-211) and the intensity of the management practices

(GI-212). GI-211 defines the potential to convert areas of high carbon stock in

function of the regulation weakness, the market pressure and the spatial correlation

17

between land-uses. GI-212 measures the adoption, frequency and use of tillage and

fertilizers.

On the other hand, the soil degradation potential (GI-221), the water pollution

potential (GI-222) and the biodiversity vulnerability (GI-223) determines the value

of SD-22. While the risk of soil degradation depends on the intensity of the

agricultural field practices and the adoption of rotation schemes, the risk of water

quality degradation is a function of the fertilizers intensity and the availability and

use of water sources. The proximity to high conservation units, the availability of

species and actions affecting them defines the risk of biodiversity loss.

Moreover, for each global indicator, a number of factors can be defined in order to

assess the significance of each global indicator. These factors are the specific

indicators that determine the significance of the global indicator. Finally, supporting

data (ST) is used to assess the degree of relevance of the specific indicators.

Supporting data can be quantitative information based on statistics, georeferenced

data, projections from deterministic models’ results or qualitative information based

on experts’ opinion about a specific issue. The relation between specific indicators

and the supporting data, with a proposed measurement unit, is shown in Tables 1-3.

The measurement unit and its relation with the indicator scale can be adapted

according to data availability, the experience and preferences of the evaluator. The

definitions of the specific indicators are listed in the Appendix 1.

4.3 Definition of heuristic rules and scales

The Logic-based model is constructed through the definition of heuristics rules that

link each set of indicators or determinants with its higher level aggregate. Heuristic

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rules are a well-know method to represent knowledge, causal relationships and

hierarchical decision processes. Each indicator is associated to a linguistic scale of

measurement, e.g. very low, low, medium, high, very high. Each linguistic value has

as well an associate numerical scale, e.g. from 0 to 4. So, heuristic rules are defined

as follow:

IF A is high AND B is low AND C is very high

THEN C is very high

A fuzzy measure can also be associated to the heuristic rule, e.g., C is likely to be

very high. That means we introduce an uncertainty factor of e.g. 0.7 and so if the

conditions are true then the consequence is likely to occur with a given grade of

fuzziness.

For each set of indicators/determinants, all combinations of values from each

numerical scale are generated via a Visual-Basic Excel macro. Then, a mapping

function is computed that matches each combination to a value of the upper-level

scale.

The user is asked to enter, as input data, the assessment of the supporting data based

on the reference scale of each support. That is, the expert evaluates the performance

of the biofuel production strategy with respect to each variable. Then the LBM,

based on hierarchical linkage between supporting data, indicators, determinants and

the computing of upper-level values, scales up until a value for the main determinant

is obtained.

Due to the modular approach, the user can focus on a particular effect or assess the

performance of the whole system.

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5. Illustration: Assessing the potential for indirect land-use changes

5.1 Context definition

A hypothetical case study is given in this section in order to illustrate the

methodological approach. The case study is limited to the analysis of the potential

displacement effects induced by a soybean-based biodiesel production strategy at the

national level.

Given a country with export opportunities the government is seeking to support the

best biofuel pathways in order to develop rural economies. The government has

developed a biofuels production strategy willing to export 30% of the total

production in 2020. Soybean is the main feedstock candidate because of its

availability, the production efficiency, the low production costs, the maturity of the

technology and the supply chain and the current trade opportunities. However, as a

food crop, the feedstock production may entail some drawbacks. A key issue for

international biofuels trade is that the biofuel produced has to be sustainable, and

therefore, the promoted biofuel pathway should benefit from greenhouse gases

emissions reduction. A significant factor to assure this is that high carbon stock areas

are not converted to other uses. This implies the avoidance of producing biofuel

feedstock in these areas and the minimization of the displacement of other activities

into these areas.

The LBM model is then used to estimate the potential for indirect land-use changes

that may lead to the conversion of high carbon stock areas. The activities

displacement potential (a specific determinant in the LBM) is then estimated based

on the significance of the global and specific indicators.

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5.2 Assessment of variables

The values of the specific indicators are generated from the aggregation of the

supporting data. The same scale is applied for all the variables and a linguistic value

is associated to each number (from 0=very low performance to 4=very high

performance). The performance of the system with respect to each support data is

assessed by the user and introduced in the model as input data. The supporting data is

evaluated based on the following information and the results are given in Table 4.

5.2.1. Technological development

Improving technological development allows reducing pressure on land

requirements. The government, in collaboration with the national bank, has an

effective credit program for agricultural producers. However, the program is limited

to the acquisition of agricultural machinery (ST-12111 = 1). Due to the high

importance of the soybean industry in the country significant funds are destined to

research projects on productivity increment. Research programs are efficiently

ongoing in the national research institutes, in the academic institutions and in the

private sector (ST-12112 = 0). Cropland price has significantly increased in recent

years, motivated by the high price of agricultural commodities. Increased demand for

food products is assumed to have a high positive effect on projected land prices

improving the intensification process (ST-12113 = 1). Until present pasture land has

been highly under-utilized. However, as the country is a net meat exporter and the

meat demand is projected to moderately increase the government and the big cattle

rangers are strongly investing in the development for new production techniques

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(ST-12121 = 0). Motivated by a higher meat demand, pasture land price are assumed

to follow meat price enhancing the intensification process (ST-12122 = 0).

5.2.2. Relation with commodity markets

Commodities interacting with the soybean sector may have positive or negative

impacts on land-use. While crops in rotation may be displaced to other regions,

biofuel co-products may reduce land requirements for other products. Half of the

soybean production is done in rotation with corn. Increased soybean production may

partially displace corn to other regions (ST-12211 = 2). Soybean-pasture rotations

are implemented in 20% of the soybean production, and mainly dominated by large-

scale producers. The displacement of pastures by soybean crops is almost absorbed

by pasture intensification (ST-12212 = 0). Meat demand is assumed to moderately

increase but beef demand is assumed to remain stable (ST-12221 = 0). Apart from

the rotation corn, soybean may directly displace wheat, beans, rice and cassava and

indirectly displace sugarcane, oranges, or coffee. Some of these products have a high

projected demand and other land may be required to produce it (ST-12222 = 2).

Soybean meal is a main driver of soybean production as animal feedstock. It can

replace corn and other cereals in animal feeds generating land-use reductions, that is,

land credits (ST-12231 = 0). Glycerine can be used to generate biogas that can

eventually displace fossil fuel use in the soybean-based biodiesel industry. However,

at present no bio-energy is produced from biodiesel co-products in the country. On

the other hand, soybean meal production largely overpass land credits compared with

avoided glycerine production (ST-12232 = 1).

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5.2.3. Biomass-use dependency

Biomass-use dependency may increase land-use requirement as alternative products

will have to be produced from other sources. Soybean is a crop that can be used as

grain or oil for multiple food purposes (ST-12311 = 4). As 20% of the produced

soybean oil is expected to be used for biofuel production more land is assumed to be

put under oil seeds production elsewhere to counteract the moderate reduction in

soybean oil used for food (ST-12312 = 2). The biodiesel industry is highly dependent

on soybean production as main feedstock. Due to the policy targets and market

opportunities, better perspectives are forecasted for the use of soybean oil as biofuel

feedstock. However, in order to avoid competition with food products, soybean use

as biofuel feedstock will be moderately reduced in the future when alternative cost-

effective feedstocks become available (ST-12321 = 2). The soybean sector is well

structured and is easy to change from one biomass-use to another (ST-12322 = 0).

5.2.4. Regulation weakness

Regulation weakness has a strong impact on land-use change as it increases the

vulnerability of the land expansion control process. Despite the restrictive legislation,

lack of effective regulations has lead to significant illegal deforestation (ST-12411 =

3). Natural grasslands have been significantly converted to agricultural land (ST-

12412 = 4). Large-scale producers strongly monopolized soybean production. No

land-tenure restrictions exist (ST-12421 = 4). No land price-control legislation exists

(ST-12422 = 4).

5.2.5. Food security vulnerability

23

Harming food security will enforce land expansion incentives. The country is a net

exporter of food products and so, no food imbalance occurs (ST-12511 = 0). Soybean

is a for-export product so no internal demand for this product exists (ST-12512 = 0).

Rice, sugarcane, beans and wheat are basic food crops for the country. Displacement

of these activities may significantly harm food security (ST-12521 = 4). Potentially

displaced food-crops are mainly for internal consumption, so little influence is

expected to have on international trade (ST-12522 = 0). The government has a

significant control on maximum prices of main food products through agreement

with producers and agricultural subsidies (ST-12531 = 1). Low-income agricultural

sectors benefit from important governmental subsidies to support domestically

consumed food products (ST-12532 = 0).

5.3 Result of the illustrative case

The potential for displacement of activities induced by the soybean-based biodiesel

production strategy results to be high (a value of 3). This is mainly due to the very

high vulnerability of the agricultural frontier, the very high potential for land

concentration and the very high multi-functionality of the feedstock. However, the

effect is attenuated by the high and very high potential for soybean and cattle

productivity increment, the very low food stocks vulnerability and the high credits

for co-products production. The assessment of the other specific indicators results in

medium performances, namely for dependence on crops rotation, expected demand

for potentially displaced commodities, potential for functional change, tendency in

consumption patterns and the significance of food crops subsidies, All in all, the

iLUC potential is driven by the very high regulation weakness, the high relation with

commodity markets, and the very high biomass-use dependency. The iLUC impact is

24

reduced by the high potential for technological development and the medium food

vulnerability.

Results show that in order to improve the performance of the biofuel pathway more

efforts should be directed to control land tenure and expansion, control the

destination of displaced rotation crops and support cattle intensification.

6. Discussion

6.1. Model strengths and limitations

The LBM is designed in a modular hierarchical structure. Scales can be changed and

adapted to account for different significance of each factor to the upper-level factor

or weights can be included to reflect the preference for one indicator among the

others. Model adaptation is a function of regional specificities of the pathway to be

evaluated, on the policy goals of the decision maker and on the expert knowledge. It

is easy to include new context-specific indicators or to eliminate less relevant

indicators of the system under evaluation, to re-arrange the determinants and

indicators and the relational links, and to modify the measurement scales.

The modular approach allows using the model to analyze a specific problem, as

shown in the illustrative case study. Uncertainty factors or probabilities can also be

associated to represent partial relationships between causes and consequences (e.g. a

0.2 probability to represent a variable that has little influence on the upper-variable).

One of the main drawbacks of the model is the subjective approach for knowledge

representation. It is however also its strength. Relational links and indicators are

25

designed based on the authors’ experience. However, this issue can be improved by

experts’ validation. Relevant experts in the research domain can be interviewed

through structured questionnaires where the experts list the main factors influencing

a variable and the relative importance between them. This data can be used to update

and feedback the model, adapting the relational links and the hierarchical structure to

the specific problem context and based on local stakeholders opinion.

Subjective evaluation can also lead to the determination of input values by a selected

user based on his preferences. A multi-stakeholder approach can also help in the

definition of input values, including the views of the main actors implicated in the

evaluation of the significance of the factor.

The LBM can be used as a tool to define the framework for developing scenarios in

quantitative models, as a way to restrain uncertain factors in deterministic models. It

can also be used to assess a global performance that requires the utilisation of both

qualitative and quantitative variables (Gnansounou, 2009).

6.2. Future developments and perspectives

The LBM is a fist step in the development of an expert system that will allow

evaluating the environmental impact of land-use changes induced by a biofuel

production strategy from a given feedstock. The system can be used as an

exploratory tool to compare governmental strategies for developing biofuel pathways

and to help policy makers in the design of new production strategies. The expert

system will further include actors’ behaviours. The expert system can then be applied

to a real case study in order to validate the approach in a specific regional context.

This will imply interactions with local experts in order to construct expert knowledge

26

for the country-specific implementation of the model. The expert system will require

the development of a knowledge-database about the specific problem and the

inference engine that will guide the user through the reasoning process to solve the

problem.

Multi-agents simulations can help the user in the determination of strategic input

values. Main actors in the design and implementation of the biofuel supply strategy

should be identified. Actors should be categorized and the influence of their

decisions on the value of the input data should be determined. Agent-based models

can be also linked to georeferenced data in other to study the spatial patterns of land-

use change induced by biofuels production. Geographic information systems (GIS)

can be useful to create integrated models than considers feedstock suppliers

behaviour accounting for spatial features. A special characteristic of land-use change

is its dynamic behaviour. System dynamics is then proposed as a complementary

modelling approach to improve comprehension of land flows and feedbacks over

time. Accounting for actors’ behaviour, spatial patterns on land-use and system

structure and feedbacks can significantly improve our comprehension of biofuels

impacts on land-use changes. Further research on this area is envisioned to better

understand land-use implications of biofuel policies.

7. Conclusion

Estimating the environmental impact on land-use changes induced by biofuels

production is a complex issue that requires dealing with uncertain issues and

incomplete information. A logic-based approach can help to structure the decision

27

process by identifying the main factors influencing the system and their significance

on the magnitude of the output. The approach requires the input from experts and the

validation by the main actors involved in the development and implementation of

biofuel production strategies. Further developing an expert system on this basis may

help decision makers on the design and implementation of biofuel production

strategies.

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32

Appendix 1. Definition of specific indicators

SI-1111 National biofuel demand: Measure of the demand for biofuels at the

national scale.

SI-1112 Biofuel export opportunities: Measure of the projected demand for the

country specific biofuel.

SI-1121 Feedstock competitivity: Measure of the competitivity of the biofuel

feedstock in function of quality, price and sustainability criteria.

SI-1122 Feedstock maturity: Measure of the development and adoption of the

feedstock complex in function of the available infrastructure and logistics and the

tradition and experience developed on the feedstock cultivation.

SI-1131 Potential for conversion technology improvement: Estimation of the

capability to improve the feedstock transformation performance into biofuel.

SI-1132 Potential for feedstock productivity increment: Estimation of the capability

to increase yield in feedstock cultivation.

SI-1141 Land availability: Amount of land available for feedstock cultivation.

SI-1142 Feedstock suitability for agro-ecological conditions: Capacity of adaptation

of the feedstock to the local agro-climatic conditions.

SI-1143 Activity shifting potential: Measure of the capacity to change from one

land-use to another.

SI-1211 Potential for crops productivity increment: Measure of the probability to

increase yield in the production of the displaced crops.

SI-1212 Potential for cattle intensification: Measure of the possibility to increase

productivity in cattle production.

33

SI-1221 Dependence of crop-rotation: Measure of the degree of interaction between

the feedstock and other commodities in the use of the same land.

SI-1222 Expected demand for displaced commodities: Measure of the projected

country-specific demand of displaced products in the international market.

SI-1223 Co-products interaction: Measure of the production of co-products and their

ability to substitute high protein crops and co-products or to be used as feedstock for

biofuels production.

SI-1231 Feedstock multi-functionality: Measure of the quantity of alternative uses

of the biomass.

SI-1232 Potential for functional change: Measure of the probability to change the

use of the product from feedstock for biofuel to other use.

SI-1241 Vulnerability of agricultural expansion frontier: Measure of the risk of

agricultural expansion in illegal areas.

SI-1242 Potential for land concentration: Measure of the risk of land concentration

as a function of land-tenure regulation weakness and the weakness of land price

regulation.

SI-1251 Food stocks capacity: Measure of the availability of food in stock to

compensate an eventual food shortage.

SI-1252 Tendency in consumption patterns: Measure of the magnitude of the

projected demand for potentially displaced commodities.

SI-1253 Food-crops subsidies significance: Measure of the governmental control of

food prices at the national level and the spatial disaggregation of subsides for the

specific biofuel feedstock.

SI-2111 Regulation weakness: Measure of the potential for illegal conversion of

high carbon content areas.

34

SI-2112 Market pressure: Measure of the potential market induced conversion of

high carbon content areas in function of the price of the land and the projected price

of the potential displaced commodities.

SI-2113 Spatial correlation: Measure of the proximity of the agricultural frontier and

the feedstock production area to high carbon content areas.

SI-2121 Tillage intensity: Measure of the degree of adoption of reduced tillage and

the frequency of application.

SI-2122 Fertilizers intensity: Measure of the degree of adoption of N fertilizers and

the frequency of application.

SI-2211 Intensity of field practices: Measure of the intensity of tillage and fertilizers

application.

SI-2212 Rotation adoption: Measure of the degree of adoption of rotation schemes

including intercropping adoption, fallow land adoption and rotation with high

organic matter crops.

SI-2221 Fertilizers intensity: Measure of the frequency and use of nitrogen and

sulphur fertilizers.

SI-2222 Proximity to water sources: Measure of the presence and proximity of water

sources.

SI-2231 Proximity to high conservation units: Measure of the presence and

proximity of high conservation units.

SI-2232 Vulnerability of species: Measure of presence and quantity of endangered

species.

SI-2233 Pesticides intensity: Measure of the aquatic and terrestrial toxicity of

applied pesticides and the frequency of application and use.

35

Table 1. Indicators and Support data for direct land-use change estimation.

Specific indicator Support data

ST-11111 National blending targets

ST-11112 Flexi fuels development perspectives

SI-1111 National biofuel demand

ST-11113 Price difference with alternative fossil fuel

ST-11121 International trade agreements

ST-11122 Biofuel price competitivity

SI-1112 Export opportunities

ST-11123 Biofuel sustainability

ST-11211 Quality

ST-11212 Price competitivity (inc. Subsides)

SI-1121 Feedstock competitivity

ST-11213 Feedstock sustainability

ST-11221 Logistic and infrastructure facilities SI-1122 Feedstock maturity

ST-11222 Production experience and tradition

ST-11311 Public investment in research and infrastructure

ST-11312 Private investment supported by profitability of production

SI-1131 Potential for conversion efficiency increment

ST-11313 Technology transfer agreements and legislative support

ST-11321 Subsidies for agricultural inputs and machinery

ST-11322 Public and private research on yield improvement

SI-1132 Potential for feedstock productivity increment

ST-11323 Feedstock market price and land price induced intensification

ST-11411 Availability of agricultural land

ST-11412 Legal expansion restriction

SI-1141 Land availability

ST-11413 Land-use competition

ST-11421 Land suitability SI-1142 Suitability for agro-ecological conditions

ST-11422 Climatic suitability

ST-11431 Alternative commodity land rent improvement

ST-11432 Land-owner history on traditional land-use

SI-1143 Activity shifting potential

ST-11433 Risk perception

36

Table 2. Indicators and Support data for indirect land-use change estimation.

Specific indicator Support data

ST-12111 Subsidies for agricultural inputs

ST-12112 Research on yield improvement

SI--1211 Potential for crops productivity increment

ST-12113 Cropland price increment perspective

ST-12121 Credits and incentives for cattle intensification SI--1212 Potential for cattle intensification

ST-12122 Pasture land price increment perspective

ST-12211 Adoption of rotation agriculture SI--1221 Dependence on crop-rotation

ST-12212 Adoption of pasture rotation

ST-12221 Increment of meat/milk demand SI--1222 Expected demand for displaced commodities

ST-12222 Increment of displaced food-crops demand

ST-12231 Replacement of high protein crops for animal feed SI--1223 Co-products interaction

ST-12232 Production of biofuels

ST-12311 Potential for alternative uses SI-1231 Feedstock multi-functionality

ST-12312 Feedstock functional share

ST-12321 Opportunity cost SI-1232 Potential for functional change

ST-12322 Technical constraints

ST-12411 Potential for illegal deforestation SI-1241 Vulnerability of agricultural expansion frontier ST-12412 Potential for illegal grasslands conversion

ST-12421 Weakness of land-tenure regulation SI-1242 Potential for land concentration

ST-12422 Land price control weakness

ST-12511 Import/export imbalance SI-1251 Food stocks capacity

ST-12512 Internal demand for food-crops feedstock

ST-12521 Domestic demand for displaced food-crops SI-1252 Tendency in consumption patterns

ST-12522 Export share of displaced food-crops

ST-12531 Maximum price governmental control SI-1253 Food-crops subsidies significance

ST-12532 Spatial disaggregation of subsidies

37

38

Table 3. Indicators and Support data for environmental risk estimation.

Specific indicator Support data

ST-21111 Potential for illegal deforestation SI-2111 Regulation weakness

ST-21112 Illegal grasslands conversion

ST-21121 Natural land price SI-2112 Market pressure

ST-21122 Price of displaced commodity

ST-21131 Distance from agricultural frontier to natural area SI-2113 Spatial correlation

ST-21132 Distance from feedstock area to natural area

ST-21211 Reduce tillage adoption SI-2121 Tillage intensity

ST-21212 Tillage frequency

ST-21221 N fertilizers use SI-2122 Fertilizers intensity

ST-21222 Fertilization frequency

ST-22111 Tillage intensity SI-2211 Intensity of field practices

ST-22112 Organic matter input intensity

ST-22121 Intercropping adoption

ST-22122 Rotation with high OM crops

SI-2212 Rotation adoption

ST-22123 Fallow land adoption

ST-22211 Use of N fertilizers SI-2221 Fertilizers intensity

ST-22212 Use of S fertilizers

ST-22221 Distance to surface water SI-2222 Proximity to water sources

ST-22222 Distance to groundwater

ST-22311 Distance to undisturbed forests

ST-22312 Distance to nature protection areas

SI-2231 Proximity to high conservation units

ST-22313 Distance to high biodiverse grasslands

ST-22321 Number of endangered species SI-2232 Vulnerability of species

ST-22322 Population density of endangered species

ST-22331 Pesticides toxicity

ST-22332 Pesticides frequency of application

SI-2233 Pesticides intensity

ST-22333 Pesticides use

Table 4. Estimation of the activities displacement potential.

Specific Determinant

SD-12 Activities displacement potential 3

Global indicator Specific indicator Support data Performance GI-121 1 SI-1211 1 ST-12111 Subsidies for agricultural inputs 1 ST-12112 Research on yield improvement 0

Potential for crops productivity increment ST-12113 Cropland price increment perspective 1

SI-1212 0 ST-12121 Credits and incentives for cattle intensification 0

Technological development

Potential for cattle intensification ST-12122 Pasture land price increment perspective 0

GI-122 3 SI-1221 2 ST-12211 Adoption of rotation agriculture 2

Dependence on crop-rotation ST-12212 Adoption of pasture rotation 0

SI-1222 2 ST-12221 Increment of beef demand 0

Expected demand for displaced commodities ST-12222 Increment of displaced food-crops demand 2

SI-1223 1 ST-12231 Replacement of high protein crops for animal feed 0

Relation with commodity markets

Co-products interaction ST-12232 Production of biofuels 1

GI-123 4 SI-1231 4 ST-12311 Potential for alternative uses 4

Feedstock multi-functionality ST-12312 Feedstock functional share 2

SI-1232 2 ST-12321 Opportunity cost 2

Biomass-use dependency

Potential for functional change ST-12322 Technical constraints 0

GI-124 4 SI-1241 4 ST-12411 Potential for illegal deforestation 3

Vulnerability of agricultural expansion frontier ST-12412 Potential for illegal grasslands conversion 4

SI-1242 4 ST-12421 Weakness of land-tenure regulation 4

Regulation weakness

Potential for land concentration ST-12422 Land price control weakness 4

GI-125 2 SI-1251 0 ST-12511 Import-export balance 0

Food stocks vulnerability ST-12512 Internal demand for food-crops feedstock 0

SI-1252 2 ST-12521 Domestic demand for displaced food-crops 4

Tendency in consumption patterns ST-12522 Export share of displaced food-crops 0

SI-1253 1 ST-12531 Maximum price governmental control 1

Food security vulnerability

Food-crops subsidies significance ST-12532 Spatial disaggregation of subsidies 0

39

Figure 1. Model architecture.

40