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

Aim:- 42%

So far:- 23% All other

sectors 81%

Agriculture19%

CO2

N2O

CH4

Scottish Government, 2013

33

Sources of agricultural GHG emissions

http://www.farmingfutures.org.uk/

• CH4: anaerobic decomposition of organic matter

– Enteric fermentation

– Manure management

– Rice

• N2O: microbial transformation of N in soils and manures

– Manure and organic matter application to land

– Synthetic fertiliser application to land

– Grazing (urine)

• CO2: 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))

Marginal abatement costMarginal benefit from

abatement

Abatement

Ma

rgin

al c

ost

M

arg

ina

l be

ne

fit

Optimal abatement

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

Mar

gina

l cos

t

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 / tCO2e))

• 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

N2O

CO2

1010

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 (N2O 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)

1212

Probability density functions

Inputs Limits Wide PDF Medium PDF Narrow PDF

GWP (0, ) Mode * 0.6 Mode * 0.4 Mode * 0.2

Land area (0, ) Mode * 0.6 Mode * 0.4 Mode * 0.2

Applicability (0, 1) 1.0 0.6 0.2

Abatement (0, ) or (-, ) Mode * 4 Mode * 2 Mode

Interactions (0, ) 1.0 0.6 0.2

Uptake (0, 1) 1.0 0.6 0.2

Net costs (-, ) Mode * 4 Mode * 2 Mode

1313

(kt CO2e/y)

Narrow PDFs

Medium PDFs

Wide PDFs

(2022, central feasible potential, all sources combined, truncated normal distributions)

Results: uncertainty of the optimal abatement

Original studypro

babili

ty

1414

Results: parametric model and uncertainty scenario

Narrow PDFs Medium PDFs Wide PDFs0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

Truncated normalCensored normalTriangular

95% CI of the mean of the economically optimal GHG abatement (2022, central feasible potential, all sources combined)

1515

Results: ranking of the mitigation options

(2022, central feasible potential, wide PDFs, all sources combined, truncated normal distributions)

1616

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)

Narrow PDFs

Medium PDFs

Wide PDFs0%

20%

40%

60%

80%

100%

120%

AllAbatement rateUptakeApplicabilityInteraction factorsGWPActivity levelNet costs95

% C

I o

f th

e m

ean

1717

Discussion

• Highly uncertain optimal abatement

• Robust ranking of the measures (especially regarding the threshold)

• Focus on the most important inputs in further research

1818

DiscussionC

ontr

ibut

ion

to o

utpu

t’s u

ncer

tain

ty

(opt

imal

aba

tem

ent)

unc

erta

inty

Hig

hLo

w

Level of uncertaintyHighLow

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

1919

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

2020

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: vera.eory@sruc.ac.uk

Additional slides

2323

Results: ranking of the mitigation options

Optimal abatement

0 1 2 3 4 5 6 7 8 9 10 11 12 13 140

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Timing of mineral N

Cmproved N-use efficiency plants

Better land drainage

Reduced tillage

Timing of organic N

Avoiding excess N

Using organic N to its full extent

Delay between slurry and mineral N

Using composts in preference to slurry

Nitrification inhibitors

Introduction of new species

Controlled release fertilisers

Reducing N fertiliser

Biological fixation

Systems less reliant on inputs(2022, central feasible potential, wide PDFs, all sources combined, truncated normal distributions)

2424

Uncertainty inventory

GWP

Agricultural land areas

Abatement by mitigation

optionsUptake 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