GCAM GAINS Scenario Comparison

Transcription

1 GCAM GAINS Scenario Comparison STEVEN J. SMITH 2030 Global Emission Scenarios Workshop International Institute for Applied Systems Analysis Laxenburg, Austria October 8, 2012 PNNL- SA- xxxx

2 OUTLINE In the spirit of model inter-comparison Will compare results from the GCAM reference and RCP4.5 scenarios to the GAINS-V0 scenarios to GCAM Overview Ø Level of detail Ø Non-CO 2 representation Comparison of near-term RCP Reference to GAINS Ø Drivers Ø Global Emissions Uncertainty Ø Examine the impact of updating a few key assumptions Some Questions The authors are grateful for research support provided by EPA. We also thank the Integrated Assessment Research Program in the Office of Science of the U.S. Department of Energy for long-term support that enabled the development of the Global Change Assessment Model, which was used to develop the RCP scenarios.

3 GCAM Integrated Assessment Model Work with GCAM has historically focused on longterm dynamics. " " " " " " " " Builds on the energy/economic model of Edmonds and Reilly completed three decades ago Combines economics-based energy, agriculture and land-use, and integrated climate (MAGICC) models Dynamic-recursive energy-agriculture market equilibrium 14 geopolitical regions Explicit energy technologies in all regions Emissions of 14 greenhouse gases and short-lived species Now runs to 2095 in 5-year time steps (RCP-version 15-year time steps) Now open source. October 8,

4 GCAM Technologies The GCAM model includes a wide range of energy consumption and generation technologies. Explicit end-use technologies. Transportation: cars, 2-wheel, rail, freight trucks, aircraft, shipping; Buildings (GCAM 3): heating, cooling, lighting, other; US industry: sub-sectors, process heat, motor drive, boilers, Electricity Supply Technologies. Conv Coal, IGCC, gas CC, gas turbine, oil, biomass, fossil and biomass with CCS, nuclear, hydro, wind, solar, geothermal. US supply by load segment (peak, intermediate, off-peak). Energy Transformation Technologies. Coal and gas to liquids, biomass liquids, coal to gas, hydrogen, with CCS options Reside biomass and dedicated energy crops Under a carbon policy, carbon in land is valued the same as fossil carbon, leading to substantial re-forestation. Traditional biomass, defined as biomass in developing country buildings, modeled separately. October 8,

5 GCAM Non-CO 2 Representation Emissions Factor (Tg / EJ)! 0.06! 0.05! 0.04! 0.03! 0.02! 0.01! Road Transport BC Emissions Factors! China! Central Europe! India! Southeast Asia! USA! Western Europe! Africa! 0.00! 2000! 2020! 2040! 2060! 2080! 2100! Year! Emissions factors are assumed to improve over time, as a function of income, generally converging to some lower value. October Smith SJ, 8, 2012 JJ West and P Kyle Economically Consistent Long-Term Scenarios for Air Pollutant Emissions. Climatic Change, 5 108: DOI: /s

6 GCAM Ref Scenario Construction Max Seasonal PM Levels Max Seasonal O3 Levels 60 Max Seasonal PM (ug/m 3 ) NorthCent-Europe Southern Europe Eastern USA Japan Australia Middle East South America Former Soviet U East China Indian Subcontinent Southeast Asia North Africa Mexico Southern-Cent Africa Max Seasonal O 3 (ppb) Income (1000 $/capita-mer) Income (1000 $/capita-mer) Surface pollutant levels for in the reference scenario were estimated in 2005, 2050, and 2095 using MOZART. Emissions control assumptions were adjusted in in order to achieve a more consistent long-term scenario. October Smith SJ, 8, 2012 JJ West and P Kyle Economically Consistent Long-Term Scenarios for Air Pollutant Emissions. Climatic Change, 6 108: DOI: /s

7 COMPARISON TO GAINS V0 CLE

8 GCAM Reference vs RCP 4.5 Will focus on comparing ref-case to GAINS since this is most comparable Emissons (Reltiave to GAINS 2005)! 1.4! 1.3! 1.2! 1.1! 1.0! 0.9! 0.8! 0.7! 0.6! 0.5! Pollutant Emissions (GCAM Ref & RCP4.5)! Emissions shown relative to GAINS 2005 GCAM-NOx! GCAM-BC! GCAM-CO! GCAM-SO2! GCAM-NOx! GCAM-BC! GCAM-CO! GCAM-CH4! GCAM-CH4!!""#$!"%#$!"!#$!"&#$!"'#$ ()*+$ Climate Policy Results in lower pollutant emissions Relatively small changes in 2020 By 2030 Substantial changes in SO 2 and CH 4, moderate changes in BC and NO x, little change in CO.

9 Drivers: Global Energy Consumption Energy Consumption (EJ)! 300! 250! 200! 150! 100! 50! 0! Global Energy Consumption (GCAM Ref & GAINS_CLE)! Emissions shown GAINS solid lines relative to GAINS 2005!""#$!"%#$!"!#$!"&#$!"'#$ ()*+$ GAINS-coal comb! GAINS-gas! GAINS-oil! GAINS-bio! GCAM-gas! GCAM-coal total! GCAM-bio! Base-year differences due to either different categories or different energy content assumptions. Generally similar trends GCAM has slightly higher growth in coal consumption post 2020 GCAM has larger growth in biomass consumption (growth is all commercial, not traditional, biomass) Would be useful to look at regional trends.

10 Global Emissions: Base-year differences Emissons (Reltiave to GAINS 2005)! 1.3! 1.2! 1.1! 1.0! 0.9! 0.8! 0.7! 0.6! Pollutant Emissions (GCAM Ref & GAINS CLE)!!""#$!"%"$ &'()$ GAINS-BC! GAINS-NOx! GAINS-SO2! GAINS-CO! GCAM-NOx! GCAM-BC! GCAM-CO! GAINS-CH4! GCAM-CH4! Emissions shown relative to GAINS 2005 GCAM is calibrated to year 2000 emissions from the RCP process (Lamarque et al. 2010). Emissions a the technology level are scaled such that sectoral emissions match in Some differences in 2005 GAINS has higher CH 4 emissions from fossil fuel extraction GCAM has higher CO emissions from building sector (biomass consumption) GCAM has lower NO x emissions from transportation (slightly higher other sectors) GCAM BC is lower in buildings, higher in industry

11 Emissons (Reltiave to GAINS 2005)! Global Emissions: Trends 1.3! 1.2! 1.1! 1.0! 0.9! 0.8! 0.7! 0.6! Pollutant Emissions (GCAM Ref & GAINS CLE)!!""#$!"%#$!"!#$!"&#$!"'#$ ()*+$ GAINS-BC! GAINS-NOx! GAINS-SO2! GAINS-CO! GCAM-NOx! GCAM-BC! GCAM-CO! GAINS-CH4! GCAM-CH4! Issue: is CLE sufficient out to 2030? GCAM is calibrated to year 2000 emissions from the RCP process (Lamarque et al. 2010). Some differences in trends CH 4 and NO x similar overall trend (GAINS NO x increases again by 2030)??? CO larger growth in GCAM (buildings, but also ind & transp) BC GCAM transport increases slightly, GAINS decreases (smaller difs in bldg and ind trends) SO 2 trends diverge (see next slides)

12 A New SO 2 Emissions Scenario 60000! Anthropogenic SO 2 Emissions! Emissions (Gg SO 2 )! 50000! 40000! 30000! 20000! 10000! Rest of World! International Shipping! North America & Europe! China! Dotted Lines, new nearterm SO2 assumptions 0! 1950! 1960! 1970! 1980! 1990! 2000! 2010! 2020! 2030! Year! Will use a new near-term SO 2 emissions scenario that assumes: China continues to install SO 2 controls on all power plants North American and European SO 2 controls continue on current trajectory MAROPOL limits on bunker fuel sulfur contents are implemented as planned Source: Smith et al. (2011). Klimont, Smith, & Cofala (2012) Smith et al. (2012) in preparation

13 Global Emissions: SO2 Trends Emissons (Reltiave to GAINS 2005)! 1.3! 1.2! 1.1! 1.0! 0.9! 0.8! 0.7! 0.6! SO 2 Emissions (GCAM Ref, Alt Ref & GAINS CLE)! GAINS-SO2! GCAM RCP Ref! GCAM 3-alt SO2!!""#$!"%#$!"!#$!"&#$!"'#$ ()*+$ Trend in modified GCAM scenario matches GAINS quite well GCAM 3 flattening after 2035 may be an artifact? Smith et al. (2012) in preparation

14 Global Emissions: BC Trends BC Emissions - Alt Base-Year EF Assumptions! Emissons (Reltiave to GAINS 2005)! 1.3! 1.2! 1.1! 1.0! 0.9! 0.8! 0.7!,-./0123$,3-4123$ 5) $ Alternative GCAM emissions scenarios generated by varying baseyear EF assumptions for only: road LDV & HDV, and traditional biomass. 0.6!!""#$!"%#$!"!#$!"&#$!"'#$ ()*+$ A range in emission magnitudes and pathways result from these different assumptions. -- Although the qualitative character of the trajectories remains. Smith et al. (2012) in preparation

15 Some Questions " Have uncertainty estimates for BC, OC and SO 2, but not for most others. " It is very important to provide some estimate of robustness of emissions! " Emissions are generally treated as known in chemistry modeling. " Are there robust differences in emission factors between traditional (ag wastes, dung, small wood, etc.) and commercial biomass (wood)? " Differences in emission factors between different road transport modes (light-duty vehicles, vs heavy trucks)? " Underlying driver data here is improving, but still needs work. " Need to account for vintage structure, performance degradation/super emitters. " What are reference case structural change assumptions? " Traditional biofuel use, enforcement of vehicle standards (and in different subsectors). More detailed qualitative narratives + summary (supplemental) tables. " Dealing with other sectors not explicitly modeled and have poor data " Non-road mobile (construction, agriculture, etc.), small industries, trad. biofuels.

16 Thank You