Transcript Africa’s bioenergy potential: a new resource curse? 16th

AFRICA’S BIOENERGY POTENTIAL: A NEW
RESOURCE CURSE?
16th ICABR Conference
June 24-27, 2012
Giorgia Giovannetti (University of Firenze
and European University Institute)
Elisa Ticci (University of Siena)
Outline of the presentation
 Africa’s biofuel energy potential
 The state of biofuel development in the continent
 Econometric estimates of drivers of large scale land
FDI for biofuel crops in SSA
 Conclusions
Africa’s biofuel energy potential
 Africa accounts for the largest share
Potentials for currently grassland and woodland (Mha)
800
of the world’s non protected grassland
and woodland areas potentially suitable
for biofuel feedstocks
700
 Southern Africa has been described
600
as a potential ‘Middle East of biofuels’
500
 National plans to support biofuel
400
sector in Ghana, Angola, Mozambique,
Nigeria, South Africa, Tanzania,
Zambia, Zimbabwe, Uganda, Benin,
Mali, Malawi, Senegal
300
200
100
0
Sugarcane
Maize
Cassava
North America, Europe and Russia
Soybean
Asia and Pacific
Source: IIASA 2009
Jatropha
LAC
Africa
The state of biofuel development in SSA
 Sources of data and information
 Case studies
 Global Biofuel Information Tool of the Center for International
Forestry Research
 Renewable Fuels Association

Land Matrix
The state of biofuel development in SSA/1
Great investors’ interest: SSA accounts for more than half of worldwide
farmland area involved in large scale land deals for biofuel crops since 2001
Large land deals for crops than can be used as biofuel feedstocks by region
250
14,000,000
12,000,000
200
10,000,000
150
100
8,000,000
6,000,000
4,000,000
Number of deals
50
2,000,000
0
Source: authors’ elaborations based on Land Matrix
Hectares
0
The state of biofuel development in SSA/2
 The role of Africa in the biofuel market is expected to
increase, but so far it has been marginal.
Fig: World fuel ethanol production in 2006, million liters
Swaziland , 17
Oceania, 24
Zimbabwe , 25
South America,
17756
North and Central
America, 20933
Africa, 606
Asia, 6961
Australia, 149
Other, 75
South Africa ,
388
Egypt , 30
Kenya , 17
Malawi , 15
Mauritius, 9
Nigeria, 30
Europe, 4631
Authors' elaboration from F.O. Licht estimates reported in Renewable Fuels Association (2007)
The state of biofuel development in SSA/3
 Future trends are very uncertain, problems in the start-
up of the production.
 Main barriers include:







local resistance
financial problems
unexpected technical difficulties
uncertain market and regulatory conditions
long lag between investment and production
building capacity in proper planting, caring and processing is crucial
but it is expensive and takes time
small farmers report low yields, processing difficulties, problems
with pests and in accessing inputs (Mozambique, Swaziland, Kenya)
The state of biofuel development in SSA/4
 Only first-generation biofuels
 In most cases, biofuel projects are large-scale commercial
plantations financed by big corporations
According to data from Global Biofuel Information Tool
of the Center for International Forestry Research
(CIFOR-GBIT), about nine to ten biofuel investments
recorded in 22 Sub-Saharan African countries until 2011
(181 projects) have been planned for cultivation, and only
ten percent are under outgrowing schemes.
The state of biofuel development in SSA/5
 Foreign based investments are a very important component
of biofuel projects in Sub-Saharan Africa
Planned and executed biofuel investments. Area of land accessed (in ha)
1200000
1000000
800000
600000
400000
200000
0
Foreign, non OECD
Foreign, OECD
National
Source: Authors’ elaboration from CIFOR - GBIT
Foreign OECD + non OECD
The drivers of land FDI for biofuel investments in SSA
 Focus on land demand for biofuel crops
 Dependent variable: number of deals concluded in SSA since
2001 with the purpose to cultivate crops that can be used as
biofuel feedstocks (Land Matrix)
 Gravity model framework
 Zero-Inflated Poisson model: in the first stage a logit
regression estimates the probability that land FDI for biofuel
projects are not affordable or profitable. The second stage
estimates the potential count of land investments for the pairs
of countries with a non-zero probability of concluding an
international land deal.
Independent variables (and expected sign)
Logit component (probability to
encounter zero-values)
 Biofuel producer (-)
 Regional dummy:
 Gulf States (+),
 High income OECD
countries (-)
 Emerging economies (-)
 Land scarcity (-)
Poisson regression component
 Bilateral variables


Distance (-)
Past colonial ties (+)
 Origin country variables


Population (+)
Per capita agricultural imports (+)
 Destination country variables





freshwater resources (+)
Agricultural land (+)
Land governance (-)
Public land (+)
General institutional conditions (?)
Related literature
 Arezki, Deininger and Selod (2011):
 Main findings: negative and hardly significant effect of
conventional governance variables, robust and negative effect
of land governance, positive role of land availability.
 Main differences with respect to our estimates:
Data source for the number of land deals
 Different focus: biofuel crops and SSA
 Slightly different econometric technique: ZIP vs standard Poisson
 Closer attention to the role of land governance and water-seeking
behavior

Logit regression
A
B
C
D
E
F
G
-1.646**
-1.717**
-1.680**
-1.649**
-1.656**
-1.636*
-1.626**
(0.672)
(0.668)
(0.687)
(0.670)
(0.667)
(0.848)
(0.667)
-2.635***
(0.646)
-2.663***
(0.651)
-2.658***
(0.654)
-2.643***
(0.641)
-2.644***
(0.648)
-2.654***
(0.766)
-2.631***
(0.645)
-0.464
(1.151)
-0.568
(1.159)
-0.521
(1.162)
-0.455
(1.146)
-0.485
(1.156)
-0.464
(1.195)
-0.416
(1.151)
Land scarcity (dummy)
-1.720***
(0.500)
-1.731***
(0.499)
-1.721***
(0.502)
-1.711***
(0.498)
-1.720***
(0.502)
-1.695***
(0.498)
-1.722***
(0.504)
Biofuel producer
-19.37***
(1.305)
-38.96***
(0.882)
-17.41***
(4.264)
-22.43***
(1.875)
-34.52***
(1.359)
-9.852
(66.22)
-19.20***
(1.268)
Constant
5.555***
(0.786)
4622
-233.3
5.665***
(0.813)
4622
-233.3
5.492***
(0.813)
4622
-226.7
5.546***
(0.760)
4468
-233.1
5.453***
(0.774)
4468
-229.4
5.576***
(0.823)
4468
-235.5
5.355***
(0.766)
4468
-227.7
High income OECD
countries
Emerging countries
Gulf countries
Observations
Log pseudo-likelihood
Notes: Independent variables with negative coefficient estimates are associated with an
increase in the probability of biofuel land deals
Robust standard errors. * p < 0.10, ** p < 0.05, *** p < 0.01.
Poisson regression component – bilateral variables and
country-of-origin variables
A
B
C
D
E
F
G
0.428
0.494
0.573*
0.397
0.560*
0.462
0.521*
(0.327)
(0.343)
(0.316)
(0.314)
(0.338)
(0.349)
(0.313)
-0.603**
(0.256)
-0.440
(0.290)
-0.475*
(0.245)
-0.633***
(0.237)
-0.515**
(0.262)
-0.629***
(0.239)
-0.642***
(0.227)
Origin country variables
Population
0.303**
(0.146)
0.268*
(0.147)
0.290**
(0.144)
0.309**
(0.142)
0.300**
(0.143)
0.302*
(0.183)
0.333**
(0.138)
0.402***
0.341**
0.381*** 0.418***
0.398***
0.427**
0.441***
(0.147)
(0.139)
(0.138)
(0.191)
(0.136)
Bilateral variables
Colonial
relationship
Distance
Per capita
agriculture imports
(0.138)
(0.147)
Poisson regression component –destination country variables
Agricultural land (ha)
A
B
C
0.398**
0.414**
0.269*
Potential non forest land
Freshwater resources
D
E
F
G
0.323**
0.466***
0.419***
0.302**
0.938***
1.634***
0.993***
0.103
0.179*
Rural population density
1.001***
0.900***
1.246***
Investor protection index
0.288*
0.240*
0.488***
Enforcing contract rank
Security of land rights
0.00262
-0.798**
-0.865**
Importance rural public
property land
0.432*
Index of land tenure policy
Government Effectiveness
Political Stability
0.308*
-0.00309
-0.789**
-0.528*
-0.710*
0.532**
0.476*
0.0304***
0.0208***
-0.465**
Rule of Law
Control of Corruption
0.283**
0.0272***
0.0116*
0.0607***
0.0182***
Notes: Variables in logs. Refer to the main text for robust standard errors, constant included but not reported
Comments of econometric results
 Our evidence is consistent with the notion that biofuel producers
and land-scarce countries see transnational land investments as a
possible strategy to integrate and expand their access to biofuel
feedstocks for domestic energy
 Actual or “perceived” agricultural biocapacity of Sub-Saharan
African countries appears to act as a pull force
 Water abundance is another driving force of biofuel land demand
 Better institutional conditions increase Africa’s attractiveness
 Countries with weaker protection of land rights and higher share of
rural public property land are more likely to host a greater number
of land FDI for biofuel crops
Directions for future research
 Comparing the current results with alternative
methods of dealing with zero values (Eaton–Tamura
Tobit model and Heckman sample selection model)
 Estimating the number of projects for biofuel crops
in a destination country.
 Comparing drivers of biofuel and other agricultural
projects.
Conclusions
 Preliminary stage of development, promising signals but also
many barriers
 Some features of biofuel development in SSA raise concerns
on its equity and sustainability:



Specialization in first-generation biofuels
Prevalence of large plantations and of export-oriented foreign
corporations
Strict link between biofuel investment and large scale farmland
acquisitions.
 Biofuel-oriented land FDI seem mainly resource-seeking.
Land governance weaknesses may be regarded as a way to
access land and water resources at very favorable conditions.