spatial impacts and sustainability of farm biogas diffusion in italy oriana gava, fabio bartolini...
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Spatial impacts and sustainability of farm
biogas diffusion in ItalyOriana Gava, Fabio Bartolini and Gianluca Brunori
150th EAAE Seminar‘The spatial dimension in analysing the linkages between agriculture, rural development and the environment’
Department of Agriculture, Food and Environment – University of Pisa (Italy)
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Background
Innovating for Sustainable Growth: A Bioeconomy for Europe (EC, 2012)•Environment, resource use, food supply, energy supply; sectors:
1. Agriculture and forestry2. Fisheries and aquaculture3. Bio-based industries4. Food chain
•Biogas is the most consolidated “modern” bioenergy•Disagreed spatial sustainability•Mainly ex ante analyses•Few data for ex post analyses
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Rationale
Italian state’s incentives (2009)
• € 0.28 / kWh electricity plugged into the national grid, flat for 15 years• Rated power < 1000 kWh• Auto-produced feedstock ≤ 51%• Purchased feedstock within 70 km
Outcomes (2010)
• Diffusion of farm plantse.g. Carrosio, 2013 – Energy policy;
Chinese et al., 2014 – Energy policy
• Impact on the demand for land and agricultural labour in plants’ neighbourhood => spillover effects
Producing agroenergy belongs to farming activities (2006)
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Aim of the study
Estimating the footprint of agricultural biogas
diffusion in Italy
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Spatial propensity score analysis
why?
•for drawing causal inference about spatial effects in observational studies
•for strenghtening ex post impact evaluations
Methodology
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Idea Expected outcome = Yi Formal expression
Treating i-th observation: T=1 Average treatment effect on the treated
ATT = Yi(T=1)
Comaparing treated with the treated were it not treated: T=0
Average (non)treatment effect on the on the counterfactual
TT = Yi(T=0)
Comparing treated with a measurable proxy of the counterfactual
Average treatment effect on untreated observations comparable to treated ones
TT = Yi(T=0)
Adjusting for potential outcome of no treatment
Average treatment effect ATE = ATT – TT
Potential outcomes model
Neyman (1923) [1990] – Statistical Science 5 (4)Rubin (1974) – Journal of Educational Psychology 76Rubin …
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Propensity score method
• Pre-treatment difference variables (x): treated and control systematically diverge
• Propensity score = p(x) is f(x) [logit] that associates each i to its relative probability (Pr) to be among the treated (T=1)
p(x) ≡ Pr(T=1|x)
Rosenbaum & Rubin (1983) – Biometrika 70(1)
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Spatial analysis
Potential impact of i on the neighbouring municipality j
Yi = Wyj
•yj = outcome in j-th municipality•Yi =outcome in j-th municipality generated by i, if j shares a border with i
•W = spatial contiguity matrix made of i weightswi,j= 1 if j shares a border with i
wi,j= 0 if i,j share no borders
Anselin, 1988 – Spatial Econometrics: Methods and Models – Kluwer Academic Publishers
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Treatments
Treatments: T = 1 Controls: T = 0
T1 = municipality hosts ≥ 1 farm biogas plant
municipality hosts 0 farm biogas plants
T2 = municipality is under T1 AND the plant is fed with livestock waste and dedicated crops only
municipality is under T1 AND the plant is fed with any feedstock
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Sustainability indicators
Estimating Y in terms of:
1. Hired labour [# working days]
2. Household labour [# working days]
3. Utilised agricultural area [ha UAA]
4. Number of farms [#]
• Livestock intensity index [LSU / ha]
society
economy
environment
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Data
Italian Statistical Institute (ISTAT)• Census of Agriculture 2000 and
2010• Population Census 2001 and
2011
Reserach Centre on Animal Production (CRPA)• Biomass-to-energy census 2010
Number of biogas plantsFeedstockRated power
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operating biogas plants in 2010
operating plants fed with livestock waste
operating plants fed with dedicated crops
T2
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Category Variable Obs Mean Std.Dev. Min Max
Treatment Municipality with at least one biogas plants 7159 0.02 0.16 - 1.00
Municipality with at least one biogas plant using biomass from livestock or dedicated crops
7159 0.02 0.15 - 1.00
Outcome Difference in hired labour in neighbouring municipalities [# working days] 7159 371.47 25,220 -378,248 322,337.00
Difference in household labour in neighbouring municipalities [# working days] 7159 -58,356 75,284 -779,094 165,042.00
Difference in UAA in neighbouring municipalities [ha] 7159 -325.31 1,986.44 -15,855 23,645.18
Difference in farm number in neighbouring municipalities [#] 7159 -530.93 772.89 -7,974 1,050.00
Difference in livestock density index in neighbouring municipalities [LSU/ha] 7159 -8.98 64.88 -2,100 37.37
Municipality localisation
Municipality located in the mountains 7159 0.53 0.50 - 1.00
Municipality located on the coast 7159 0.08 0.27 - 1.00
Change in population density from 2000 to 2010 7159 16.34 56.24 -967.01 1,082.32
Urban 7159 0.03 0.16 - 1.00
Intermediate urban 7159 0.01 0.11 - 1.00
Urban belt 7159 0.43 0.49 - 1.00
Intermediate 7159 0.31 0.46 - 1.00
Periphery 7159 0.19 0.39 - 1.00
Remote area 7159 0.04 0.19 - 1.00
Central 7159 0.47 0.50 - 1.00
Marginal area 7159 0.53 0.50 - 1.00
Farm features
Average farm size [ha] 7157 0.34 0.24 - 1.00
Farms with arable land [#] 7157 94.57 163.44 - 2,186.00
Livestock size in 2000 [#] 7159 1,210.91 3,046.73 - 81,528.46
Mechanisation intensity [# tractors] 7157 276.35 441.58 1.00 6,458.00
UAA [ha] 7157 1,264.94 1,856.05 - 27,776.93
UAA rented [ha] 7157 99.73 281.13 - 7,106.20
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Y = 2010 - 2000Treatmen
t ATEStandard
Error z P>zConfidence Interval
95%# working days from hired labour / year
T1 1268.79 212.21 5.98 0 852.84 1684.73T2 931.19 343.53 2.71 0.007 257.88 1604.51
# working days from household labour /
year
T1 -542.86 1740.52 -0.31 0.755 -3954.24 2868.50
T2 -1177.11 1235.36 -0.95 0.341 -3598.39 1244.16
ha UAA / year
T1 39.57 69.35 0.57 0.568 -96.34 175.50T2 17.40 89.25 0.19 0.845 -157.54 192.34
# farms / year
T1 -47.27 16.69 -2.83 0.005 -79.99 -14.55T2 -46.58 16.60 -2.81 0.005 -79.13 -14.04
LSU / ha / year
T1 0.52 0.23 2.19 0.028 0.05 0.98T2 0.46 0.255881 1.83 0.068 -0.03 0.96
Results – Average effects of treatments T1, T2
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Y = 2010 - 2000Treatmen
t ATEStandard
Error z P>zConfidence Interval
95%
# working days from hired labour / year
T1 1268.79 212.21 5.98 0 852.84 1684.73T2 931.19 343.53 2.71 0.007 257.88 1604.51
# working days from household labour /
year
T1 -542.86 1740.52 -0.31 0.755 -3954.24 2868.50
T2 -1177.11 1235.36 -0.95 0.341 -3598.39 1244.16
Average effects of treatments T1, T2
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Y = 2010 - 2000Treatmen
t ATEStandard
Error z P>zConfidence Interval
95%
ha UAA / year
T1 39.57 69.35 0.57 0.568 -96.34 175.50T2 17.40 89.25 0.19 0.845 -157.54 192.34
# farms / year
T1 -47.27 16.69 -2.83 0.005 -79.99 -14.55T2 -46.58 16.60 -2.81 0.005 -79.13 -14.04
Average effects of treatments T1, T2
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Y = 2010 - 2000Treatmen
t ATEStandard
Error z P>zConfidence Interval
95%
LSU / ha / year
T1 0.52 0.23 2.19 0.028 0.05 0.98T2 0.46 0.255881 1.83 0.068 -0.03 0.96
Average effects of treatments T1, T2
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Discussion\1
• Biogas plant distribution is spatially uneven• Biogas diffusion has a marked impact on rural economy• Sustainability trade-offs:
+positive effects on income and job availability in rural areas- increased environmental pressure: agricultural intensification
and marginalisation of small farms• Legal constraints affect feedstock sourcing area and transport
costs
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Discussion\2
What is missing How we suggest to improveNon-rural drivers of rural areaViability: sector mobility, off-farm income, availability of infrastructures
Supplementing the dataset
Spatial modulation of the treatment Generalised propensity score and dose-response model
Further research
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Thank you!
This research was supported the by the EU 7th Framework Program, grant No. 609448
IMPRESA – The Impact of Research on EU Agriculture http://www.impresa-project.eu/about.html