D3.2a: Regional case study on
Explaining Land Use Transitions in Andalusia under
Different Climate Policy Scenarios
Alejandro Caparrós and José L. Oviedo
Institute for Public Goods and Policies
Spanish National Research Council (CSIC).
Madrid (Spain)
[email protected]
[email protected]
Introduction
• European agricultural and environmental policies
have had a deep impact on land uses in the past, and
future policies will further influence them.
• We estimate reaction functions for Andalusia that
quantifying the impact of different policy scenarios:
 climate policies focused on carbon sequestration
 or renewable energy policies promoting biofuels.
• The final objective is to evaluate the possibility to
extend the methodology to the rest of Europe.
Previous research
• Existing models predict that carbon sequestration in forests
and bioenergy produced in Europe can have a significant
contribution (Ovando and Caparrós, 2009). These Computable
General Equilibrium models do not consider:
 Land-owners inertia or liquidity constraints
 Non-commencial values (aestethic and recreation)
 Wish to retain options for future land-use decisions
 Benefits and costs of which the analyst is unaware
• Lubowsky et al (2006) applies econometric models to land use
decisions in the US based on commercial net returns.
LUC model description
• LUC (land use choice) is an econometric model based on
landowner’s revealed preferences about competitive
agroforestry land uses:
 Crop
 Grassland
 Forest
• The model estimates the effects of different factors on
land use choice and land use transitions between two
years (1990-2006).
Drivers of land use choice
• Market-related factors: landowner net income
(returns) associated to alternative land uses.
• Subsidy-related factors: landowner net subsidies
associated to specific land uses and related
commodities.
• Environmental-related factors: protection figure,
slope, distance urban center, altitude, ha of
municipality, public property, average temperature,
average rainfall, population of municipality,
orientation, urban settlements in municipality.
Econometric specification
• For K potential land uses (j, k = 1,…,K), a landowner will
choose for parcel in use j the use k at time t that provides the
highest utility after land conversion costs.
Landowner choose land
use for parcel i
Parcel i starting
in use j
Crops
Grassland
Forest
• Conditional, nested and mixed logit models
Pilot study: Andalusia region
• The Andalusia region is the case study selected for the
pilot application of the LUC – Andalusia model.
• Andalusia is located in the south of Spain:
Model inputs
• Dependent variable: panel data observations of land
uses. Sample: 9,937 points.
 CORINE LAND COVER database
• Explanatory variables:
 Net income and subsidies for crop and range: FARM
ACCOUNTANCY DATA NETWORK .
 Net income and subsidies for forest: own studies
(GEA research group) and legislation.
 Environmental data for all: parcel-specific using GIS
databases.
• Data at parcel-level for some observed land uses and at
municipality-level for others.
Probabilities of land-use transition
• Probability of choosing land use k (out of all the possible
alternatives K) when starting in land use j in parcel i:
Vijk
exp
Pijk 
Vijh
hK exp
• With Uijk =Vijk+Ɛijk , where Vijk includes proxy variables for
market (MRK), subsidy (SUB) and environmental (ENV)
factors:
Vijk   jk   jk MRKijk  jk SUBijk   jk ENVijk
• βjk, λjk, γjk are vectors of parameters, and αjk is a vector of
specific intercepts for the k land use starting in land use j.
Table 1.
Variable
Conditional logit models for the period 1990-2006
(1)
Crop initial use
Grassland initial use
Model I
Model II
Model I
Model II
9.6513
20.3504
-0.5357
-4.5220*
0.0018
0.0034*
0.0001***
-0.0001
-0.0009
-0.0009
Forest initial use
Model I
Model II
Crop
Intercept
Net income
Net subsidies
Protection figure
Slope
Distance urban
• Model I:
only monetary variables.
• Model II:
includes non-monetary variables.
-0.9968
-7.6057**
0.0001**
0.0002**
0.0002*
0.0009
-0.0054***
-0.0023
-1.7378**
-0.9245***
-0.3056
-0.0353*
-0.0351***
-0.0388**
-0.0017
0.0017
0.0034
Altitude
0.0006
Ha municipality
0.0000
Public property
-1.6684***
0.0000
-0.9618**
Temperature
-0.6161
0.2954**
0.5160***
Rainfall
-0.0006
-0.0021***
-0.0034**
Grassland
-13.5810
-27.5199
1.3390*
-0.3475
1.3187
1.3925
Net income
Intercept
0.0002
0.0005
-0.0001***
-0.0001**
-0.0004***
-0.0004***
Net subsidies
0.0677
0.0480
0.0057
0.0067
-0.0128**
-0.0086
Protection figure
Slope
Distance urban
0.1492
0.2210
-0.3555
0.0415
0.0165***
0.0271**
-0.0069
0.0018
-0.0014
Altitude
0.0009***
Ha municipality
0.0000
0.0000
Public property
0.3392
Temperature
Rainfall
0.6323
0.0707
-0.0710
0.0104**
-0.0015***
0.0011
Forest
Intercept
3.9297
7.1695
-0.8033
4.8695**
-0.3219
Net income
0.0016
0.0075*
0.0047**
0.0020
0.0050
6.2132***
0.0085
Net subsidies
0.0043
0.0171
0.0005
-0.0069*
0.0117**
0.0129**
Protection figure
1.5886*
0.7035***
Slope
-0.0061
0.0186**
0.0118
0.0086
-0.0035
-0.0020
Distance urban
Altitude
-0.0015***
Ha municipality
0.0000
Public property
1.6684***
0.6226**
-0.0162
-0.3661***
-0.0098**
0.0036***
Temperature
Rainfall
n
Pseudo r2
AIC
0.6611**
0.0000
-0.4451***
0.0023**
4607
4597
1907
1897
3340
3321
0.041
0.322
0.041
0.152
0.069
0.153
0.035
0.031
0.691
0.627
0.205
0.190
Factors affecting transition probability
Final use
Crop
Crop
Net income (+)
Protection figure (-)
Slope (-)
Public property (-)
Grassland
Forest
Rainfall (+)
Net income (+)
Protection figure (+)
Public property (+)
Rainfall (-)
Initial use
Grassland
Net income (+)
Protection figure (-)
Slope (-)
Public property (-)
Temperature (+)
Rainfall (-)
Net income (-)
Slope (+)
Altitude (+)
Rainfall (-)
Subsidies (-)
Protection figure (+)
Slope (+)
Altitude (-)
Public property (+)
Temperature (-)
Rainfall (+)
Forest
Net income (+)
Slope (-)
Temperature (+)
Rainfall (-)
Net income (-)
Slope (+)
Subsidies (+)
Protection figure (+)
Temperature (-)
Rainfall (+)
9,937 sampling points
Simulations from 2006 to 2030
Table 3. Simulation results under different scenarios (changes from 2006 to 2030)
Pear
Apple
Orange
Potato
Growing beyond limits
Growing within limits
New welfare
Turbulent decline
Variations in key variables
Crop net income (%)
Range income (%)
Forest income (%)
30.0
5.0
-5.0
5.0
0.0
5.0
0.0
5.0
10.0
0.0
-5.0
10.0
Crop subsidies (%)
Range subsidies (%)
Forest subsidies (%)
20.0
0.0
0.0
10.0
10.0
20.0
0.0
50.0
50.0
-100.0
-100.0
-100.0
Protection figure (%)
-10.0
10.0
20.0
-40.0
2.0
-0.2
0.5
0.0
0.5
0.0
0.5
-0.2
1.78
-1.55
-0.23
1.28
-1.63
0.35
Temperature variation (ºC)
Rainfall variation (mm dav)
Resulting changes in land uses (1)
Crop (%)
Range (%)
Forest (%)
(1) Percentages of changes refer to the total area.
-1.08
0.59
0.49
5.62
2.44
-8.05
Summary: Innovative approaches
• Our econometric approach takes into account:
 Land-owners inertia or liquidity constraints
 Non-commencial values (aestethics and recreation)
 Benefits and costs of which the analyst is unaware
Summary: Main research results
• Inertia is a key factor: landowners need long periods
of time to react to new incentives.
• Grassland has the highest probability of change.
• Income and subsidies are relevant, but land use
transitions are largely explained by non-monetary
factors.
• The methodology can be extended to the rest of
Europe with existing data.
Summary: Main research results
• There are no large changes in land use by 2030,
except in the Potato scenario.
• Crops are favored in the Pear and Apple scenarios as
crop subsidies increase.
• In the Orange scenario large subsidies for grasslands
and forests promote these uses.
• In the Potato scenario, subsidies disappear and
protection areas are reduced and this implies a
relevant reduction of the area covered by forests.
• Climate change variables have a moderate impact on
land use transitions (ranges analyzed are narrow).
Summary: Policy relevant conclusions
• Policy makers should take into account inertia when
designing land use policies.
• Carbon sequestration policies and biofuel production
are prime examples. These policies will need a long
period of time to change current land uses.
• Non-monetary factors are as important as net
income and subsidies.
Thank you for your attention
[email protected]
[email protected]