1. Spatial impacts and sustainability of farm biogas diffusion in Italy Oriana 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)

2. Backgroun d Innovating for Sustainable Growth: A Bioeconomy for Europe (EC, 2012) Environment, resource use, food supply, energy supply; sectors: 1.Agriculture and forestry 2.Fisheries and aquaculture 3.Bio-based industries 4.Food chain Biogas is the most consolidated “modern” bioenergy Disagreed spatial sustainability Mainly ex ante analyses Few data for ex post analyses

3. 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 plants e.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)

4. Aim of the study Estimating the footprint of agricultural biogas diffusion in Italy

5. Spatial propensity score analysis why? for drawing causal inference about spatial effects in observational studies for strenghtening ex post impact evaluations Methodology

6. IdeaExpected outcome = Y i Formal expression Treating i-th observation: T=1Average treatment effect on the treated ATT = Y i (T=1) Comaparing treated with the treated were it not treated: T=0 Average (non)treatment effect on the on the counterfactual TT = Y i (T=0) Comparing treated with a measurable proxy of the counterfactual Average treatment effect on untreated observations comparable to treated ones TT = Y i (T=0) Adjusting for potential outcome of no treatment Average treatment effectATE = ATT – TT Potential outcomes model Neyman (1923) [1990] – Statistical Science 5 (4) Rubin (1974) – Journal of Educational Psychology 76 Rubin …

7. 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)

8. Spatial analysis Potential impact of i on the neighbouring municipality j Y i = Wy j y j = 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 weights w i,j = 1 if j shares a border with i w i,j = 0 if i,j share no borders Anselin, 1988 – Spatial Econometrics: Methods and Models – Kluwer Academic Publishers

9. Treatment s Treatments: T = 1Controls: 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

10. 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 [#] 5.Livestock intensity index [LSU / ha] society economy environment

11. 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 plants Feedstock Rated power

12. operating biogas plants in 2010 operating plants fed with livestock waste operating plants fed with dedicated crops T2

14. Y = 2010 - 2000TreatmentATE Standard ErrorzP>z Confidence Interval 95% # working days from hired labour / year T1T1 1268.79212.215.980852.841684.73 T2T2 931.19343.532.710.007257.881604.51 # working days from household labour / year T1T1 -542.861740.52-0.310.755-3954.242868.50 T2T2 -1177.111235.36-0.950.341-3598.391244.16 ha UAA / year T1T1 39.5769.350.570.568-96.34175.50 T2T2 17.4089.250.190.845-157.54192.34 # farms / year T1T1 -47.2716.69-2.830.005-79.99-14.55 T2T2 -46.5816.60-2.810.005-79.13-14.04 LSU / ha / year T1T1 0.520.232.190.0280.050.98 T2T2 0.460.2558811.830.068-0.030.96 Results – Average effects of treatments T1, T2

15. Y = 2010 - 2000TreatmentATE Standard ErrorzP>z Confidence Interval 95% # working days from hired labour / year T1T1 1268.79212.215.980852.841684.73 T2T2 931.19343.532.710.007257.881604.51 # working days from household labour / year T1T1 -542.861740.52-0.310.755-3954.242868.50 T2T2 -1177.111235.36-0.950.341-3598.391244.16 Average effects of treatments T1, T2

16. Y = 2010 - 2000TreatmentATE Standard ErrorzP>z Confidence Interval 95% ha UAA / year T1T1 39.5769.350.570.568-96.34175.50 T2T2 17.4089.250.190.845-157.54192.34 # farms / year T1T1 -47.2716.69-2.830.005-79.99-14.55 T2T2 -46.5816.60-2.810.005-79.13-14.04 Average effects of treatments T1, T2

17. Y = 2010 - 2000TreatmentATE Standard ErrorzP>z Confidence Interval 95% LSU / ha / year T1T1 0.520.232.190.0280.050.98 T2T2 0.460.2558811.830.068-0.030.96 Average effects of treatments T1, T2

18. 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

19. Discussion\2 What is missingHow we suggest to improve Non-rural drivers of rural area Viability: sector mobility, off-farm income, availability of infrastructures Supplementing the dataset Spatial modulation of the treatmentGeneralised propensity score and dose-response model Further research

20. Thank you! oriana.gava@for.unipi.it This research was supported the by the EU 7 th Framework Program, grant No. 609448 IMPRESA – The Impact of Research on EU Agriculture http://www.impresa-project.eu/about.htmlhttp://www.impresa-project.eu/about.html