That’s the assumption we’ve made… Vera Eory, Kairsty Topp, Dominic Moran, Adam Butler 29/9/2015, Edinburgh Royal Statistical Society Seminar

22 Our mitigation targets CO 2 N2ON2O CH 4 Scottish Government, 2013

33 Sources of agricultural GHG emissions CH 4 : anaerobic decomposition of organic matter –Enteric fermentation –Manure management –Rice N 2 O: microbial transformation of N in soils and manures –Manure and organic matter application to land –Synthetic fertiliser application to land –Grazing (urine) CO 2 : fossil fuel combustion and soil carbon degradation –On-farm energy use (field operations, animal housing etc.) –Changes in above and below ground C stocks

44 Assessment of mitigation policies Aspects: –Economic: At what cost? How efficient? –Distributional: Who loses, who gains? –Environmental: How much mitigation? Are there any negative or positive co-effects? –Institutional: Transaction costs? How to monitor?

55 Marginal abatement cost curves Economic rationale How do they help? –Identify the most cost-effective ways of meeting the targets – within and between sectors –Identify options that cost less than the marginal benefit from abatement (e.g. Shadow Price of Carbon (SPC)) Pearce and Turner 1989

66 An example MACC UK MACC for 2022 (maximum technical potential) MacLeod et al. 2010

7 Optimal pollution reduction Marginal abatement cost curve, 95% confidence interval Marginal benefit from abatement, 95% confidence interval Abatement Marginal c ost Optimal abatement, 95% confidence interval

8 MACC uncertainty assessment Propagating estimated statistical uncertainty through an economic assessment model: Agricultural MACC (marginal abatement cost curve) Arable areas and managed grasslands (excl. livestock) Scotland (2012 to 2022) Output metrics: Optimal abatement (cumulative abatement below the shadow price of carbon (£29 / tCO 2 e)) Ranking of each option

9 Mitigation options Avoiding nitrogen application in excess Using manure nitrogen to its full extent Reducing N nitrogen fertiliser Improving the timing of mineral nitrogen application Improving the timing of slurry and poultry manure application Separating slurry applications from fertiliser applications by several days Using composts, straw-based manures in preference to slurry Using controlled release fertilisers Using nitrification inhibitors Using biological fixation to provide nitrogen inputs Introducing of new species (including legumes) Adopting plant varieties with improved N-use efficiency Adopting systems less reliant on inputs Using reduced tillage and no-till techniques Improving land drainage N2ON2O CO 2

10 Sources of uncertainty Main sources of uncertainty –Farmers’ uptake of mitigation practices, effects of policy instruments –Current emissions and mitigation effects of alternative practices (emission factors) –Costs of changing farming practices and transaction costs –Current and future agricultural activities and practices (effects of climate change, demographics, economics) Uncertainties in the… –unitary and total abatement of practices –costs and the cost-effectiveness of practices –ranking of the practices and the economically optimal level of abatement

11 Propagating statistical uncertainty Monte Carlo simulations for all combinations of: Year (2012, 2017, 2022) Uptake scenario (low feasible, central feasible, high feasible and maximum technical potential) Uncertainty source (N 2 O GWP, activity level, applicability, uptake, interaction factors, abatement rate, net cost, all seven sources combined) Uncertainty scenario (narrow, medium, wide) Parametric distribution (censored normal, truncated normal, triangular)

12 Probability density functions InputsLimitsWide PDFMedium PDFNarrow PDF GWP (0,  ) Mode * 0.6Mode * 0.4Mode * 0.2 Land area (0,  ) Mode * 0.6Mode * 0.4Mode * 0.2 Applicability(0, 1) Abatement (0,  ) or (- ,  ) Mode * 4Mode * 2Mode Interactions (0,  ) Uptake(0, 1) Net costs (- ,  ) Mode * 4Mode * 2Mode

13 (kt CO 2 e/y) Narrow PDFs Medium PDFs Wide PDFs (2022, central feasible potential, all sources combined, truncated normal distributions) Results: uncertainty of the optimal abatement Original study probability

14 Results: parametric model and uncertainty scenario 95% CI of the mean of the economically optimal GHG abatement (2022, central feasible potential, all sources combined)

15 Results: ranking of the mitigation options (2022, central feasible potential, wide PDFs, all sources combined, truncated normal distributions)

16 Results: importance of the uncertainty of individual groups of inputs The ratio of the width of the 95% CI to the mean of the economically optimal GHG abatement (2022, central feasible potential, truncated normal distributions)

17 Discussion Highly uncertain optimal abatement Robust ranking of the measures (especially regarding the threshold) Focus on the most important inputs in further research

18 Discussion Contribution to output’s uncertainty (optimal abatement) uncertainty High Low Level of uncertainty High Low Activity level Abatement rate Applicability rate Uptake rate GWP Interaction factors Net costs Must haves High effort, little return Low effort, little return Quick wins

19 Discussion MACCs are complex, accumulating many layers of uncertainty Uncertainty reporting should be an essential part of policy input (both quantifiable and deep uncertainty) Data gaps about the statistical uncertainty Uncertainty in the policy process

20 The policy context Scottish Government 2011

Acknowledgments Funded by the Scottish Government Rural and Environmental Science and Analytical Services division (RESAS) funding to SRUC and to ClimatexChange Contact:

Additional slides

23 Results: ranking of the mitigation options Optimal abatement (2022, central feasible potential, wide PDFs, all sources combined, truncated normal distributions)

24 Uncertainty inventory GWP Agricultural land areas Abatement by mitigation options Uptake of mitigation options Applicability of mitigation options Net cost of mitigation options Macro-economic drivers Farm management Discount rate GWP metric Weather and soil types Soil processes Farmers’ behaviour Agro- environmental policies