Some thoughts on addressing uncertainties in integrated

assessment models

Helen ApSimon and Tim OxleyImperial College London

Many techniques and approaches to uncertainty e.g. Sensitivity studies/scenario analysis statistical (Monte-Carlo) etc.

See NIAM web-site for assembled papers www.niam.scarp.se

*******************************************Emphasis now on:i) Systems approach (HAZOP) and robustness of

proposed ceilingsii) What we can learn from parallel more detailed

modelling to GAINS at the national scaleiii) Differences now and in 1999 re Gothenburg

Projected emissionsAQ & GHGs

Abatement options & costs

Atmospherictransport/ deposition

Environment: criteria protn/targets

Costs and benefits of different abatement scenarios

Proposed EMISSION CEILINGS

robust ????

Future activities energy/agriculture

Problem boundaries; space ,time, external factors

HAZOP risk techniques

Developed for chemical industry but widely used in environmental problems e.g. UK Royal Commission re GMOs

Examine each component of the system

i) Define intended function

ii) Apply guide words e.g.“NOT”, “LESS”, “MORE” ,“AS WELL AS”, “REVERSE”

iii) Identify deviations, their possible causes, and the consequences

Emission projections:magnitude (NB spatial distribution)

Emission factors: useful national consultations with IIASA still some big uncertainties for PM2.5 including sources covered (same applies to black C)

Other/new sources important?; e.g. electric vehicles, new fuels, CHP+biomass/waste etc; shipping

Other sources not included in national ceilings:

shipping; sources outside European map area (HTAP)

MARPOL agreement will result in >20% reduction in S deposition over the UK in 2020.

NOx from shipping also very important

Figures courtesy of CEH using the FRAME model

Emission projections:magnitude (NB spatial distribution)

Emission factors: useful national consultations with IIASA still some big uncertainties for PM2.5 including sources covered (same applies to black C)

Other/new sources important?; e.g. electric vehicles, new fuels, CHP+biomass/waste etc; shipping

Activity data (NB external data outside world): 2 scenarios currently (national and PRIMES + CAPRI)

Germany Italy Nlands Poland Spain UK0

50

100

150

200

250

300

350

400

450

500

SO2

PRIMES BLPRIMES MFRNAT BLNAT MFR

Germany Italy Nlands Poland Spain UK0

100

200

300

400

500

600

700

800

900

NO x

PRIMES BLPRIMES MFRNAT BLNAT MFR

Future emissions SO2 and NOx

Similar emissions both national & PRIMES many countries.

BUT further reductions (baseline –MTFR) smaller in comparison to totals

In some cases small or no overlap between range of emissions between scenarios.

Emission projections:magnitude (NB spatial distribution)

Emission factors: useful national consultations with IIASA still some big uncertainties for PM2.5 including sources covered (same applies to black C)

Other/new sources important?; e.g. electric vehicles, new fuels, CHP+biomass/waste etc; shipping

Activity data (NB external data outside world): 2 scenarios currently (national and PRIMES + CAPRI)? do these reflect the full uncertainty in projected activities?

Other influences as well: e.g. dietary trends re agriculture, population trends, economic/political changes, CCS/nuclear prospects, CDM, wild cards

Abatement options and costs

Technologies- do they deliver? e.g. Euro standards, HGV Euro V/VI in urban areas.

“as well as”- side effects: e.g. higher primary NO2, other pollutants and GHGs,NH3 abatement -> other components of the N cycle (TFRN)

Other ways of reducing emissions behavioural change-> activity data efficiency measures-> “What is assumed re technologies in energy projections?” e.g. domestic gas boilers-> transparency of data from PRIMES etc

BUT more ways of reducing emissions helps re ceilings.

Uncertainties in costs- illustration from TFRN

High efficiency measures

Slurry application techniques (e.g. deep injection)

Immediate incorporation (arable only)

Eu/kg NH3

1-3

0.1 - 4

Medium efficiency

Slurry application techniques (e.g. trailing shoe,hose)

Rapid incorporation <24 h (arable only)

1-6

0.3 - 8

NEW COST ESTIMATES LOWER; when as low as 1 eu/kgN then reduced need for fertilisers may make total cost zero or negative (costs to farmers v costs to contractors : costs to countries)-> spread sheet for costs with default values for key factors; transparent tool for use by countries, and by IIASA to use with GAINS modelling

Atmospheric transport-> exposure,depositionModel validation against measurements, intercomparisons, interannual variability (climate impacts). But SOURCE APPORTIONMENT?

GAINS uses derived source-receptor relationships country-> grid; adds up cumulative effect of reductions.(No differentiation which sources abated- see national IAM studies)

Cross-pollutant effects/non-linearities e.g. NH3 as limiting nitrate in PM

Protection of ecosystems, target setting.Critical loads well established; new O3 flux approach.

What is being “protected”?

Recent work by CEH on SSSIs and uncertainty range of critical loads. More breakdown on ecosystems protected would help in valuing benefits?

79%>min CL 58%>max CL 71%>CL nut N

A national perspective

Spatial resolution, treatment of individual sources e.g. tends to increase ecosystems above C loads,and more info re what is protected. NH3 and N deposition very much more patchy.

Other considerations driving which sources reduced to achieve ceilings. URBAN AIR QUALITY and limit values NO2, PM -> road-side and urban concentrations. e.g. Comparison 2 UK energy scenarios showed worse AQ with the lower CO2 and NOx emissions because of relocation of energy sources into urban areas.

Air quality & climate change; transition technologies.

Robust targets?

Need to allow for improvements in science e.g CAFE re N deposition and eutrophication. PM components re health effects (precautionary approach)

Gap closure; current situation(2010) and critical loads are less uncertain than Current Policy and MTFR.

? Geographical variability: Europe wide targets as compared with uniform gap closure; how much does this redistribute the effort/costs relative to the benefits?

20 40 60

Total cost (billion DM/yr)

% ecosystems “unprotected”

30

20

10

30%

40%50%

prioritised strategy from IAM

uniform reduction

SO MANY UNCERTAINTIES !!

IAM results can be surprisingly robust-> selecting same measures and similar levels of effort (cost per kg) for countries for different scenarios & targets

BUT experience from IAM for Gothenburg protocol

BAT