CO 2. Connecting the gaps: Energy demand projections and the disparity between current NDCs and the 2⁰C Paris target. Luís Fazendeiro, Sofia Simoes

Transcription

1 Energy Transitions Consumers Profiles & Energy Efficiency New Technologies & Low Carbon Practices Connecting the gaps: Energy demand projections and the disparity between current NDCs and the 2⁰C Paris target Integrative Energy City Planning Policy Support Climate Mitigation/ Adaptation 73 rd SEMI-ANNUAL ETSAP MEETING 17th 18th June 218 Chalmers University of Technology, Gothenburg, Sweden Luís Fazendeiro, Sofia Simoes ENERGY & CLIMATE CO 2

2 MAIN GOALS OF THIS WORK (a) Analysis of uncertainty in energy demand projections 1, renewable energy implementation (and how they compare with observed values) (b) How does this uncertainty differ from region to region? (e.g., OECD vs. Non-OECD; Developed vs. Developing nations, etc.) (c) What can these gaps teach us about current NDCs, national compliance and increased ambition levels? (e.g., BAU scenarios based on large energy demand projections) [1]- One of the exogenous factors with greater impact in least-cost optimization bottom-up technological (EEE) energy systems models, e.g., Simoes et al., Technol Forecast Soc. Change, 94: , 215.

3 GtCO₂e NDCS GAP - For 23 emissions level (with a chance > 66% of staying below 2ºC) the gap is: GtCO2eq (unconditional NDCs) 24% of the total 55.5Gt; - 11 GtCO2eq (conditional NDCs) - NDCs to be reviewed in 218. Source: UNEP Emissions Gap Report, 217, Fig. ES.2:

4 NDCS EMISSIONS UNCERTAINTY - Estimation of uncertainty in the NDCs emissions projections; Fig. 3c-d) of Rogelj, Fricko, et al., Nature Comms, 8:15748, 217.

5 PREVIOUS WORK ON ENERGY MODELLING ANALYSIS - Smil, V., 2. Perils of long-range energy forecasting: reflections on looking far ahead. Technol Forecast Soc. Change, 65(3): Morgan, M.G., Keith, D.W., 28. Improving the way we think about projecting future energy use and emissions of carbon dioxide. Clim Change, 9(3): Cornell, S., Constanza, R., et al., 21. Developing a systematic science of the past to create our future. Glob Environ Change, 2(3): Wilson, C., Grubler, A., et al., 213. Future capacity growth of energy technologies: are scenarios consistent with historical evidence? Clim. Change, 118: Trutnevyte, E., 214. The allure of energy visions: are some visions better than others? Energy Strategy Rev 2: Comparison of past projections of global and regional primary and final energy consumption with historical data Cabeza, Palacios, et al., Renew. Sustain Energy Rev 82: , 218.

6 PREVIOUS WORK II - Comparison of past projections of global and regional primary and final energy consumption with historical data Cabeza, Palacios, et al., Renew. Sustain Energy Rev 82: , Looked at OECD (North America, Europe, Pacific) + China - WEO: 1977, 1982, 1994, 1998, 24; - Population, Primary energy supply, FEC, energy intensity; SOME KEY FINDINGS: - For GDP projections were very optimistic, giving values higher than the real growth; - WEO projections greatly overestimated total final energy consumption for OECD countries from 1975; - BUT China FEC was greatly underestimated (particularly after 2 );

7 PREVIOUS WORK III Total FEC projection vs. reality for: all of OECD (left), China (right) Cabeza, L. F., Palacios, A., et al., Renew. Sustain Energy Rev 82: , 218. Figs. 2a, 2e

8 METHODOLOGY - 8 regions considered: World, OECD, OECD North America, OECD Europe, China, India, Russia and Africa; - Analyzed data for: TPES, CO2 emissions and RES electricity (for now)

9 TPES Millions GWh TPES Millions GWh TPES Millions GWh TPES OECD World measured WEO 1994 WEO 2 23 WEO China measured WEO 1994 WEO 2 WEO 24 Total Primary Energy Supply estimated in IEA-WEO, editions 1994, 2, 24, 21, 215, measured WEO 1994 WEO 2 23 WEO 24 25

10 % variation among TPES projections made in % variation among TPES projections made in TPES MAXIMUM VARIATION 12% 1% 8% 6% 4% 2% % -2% -4% % 2% 15% 1% % % -5% -1% - Max. variation (percentage) among WEO TPES projections for: (left) (right), for the years 22, 23, Positive variation: corrected upwards; - Negative variation: corrected downwards;

11 Mt CO2 Mt CO2 Mt CO2 CO2 EMISSIONS OECD World measured WEO 1994 WEO 2 23 WEO measured WEO 1994 WEO 2 WEO India - CO2 emissions from fossil fuel combustion (IEA-WEO, editions 1994, 2, 24, 21, 215, 217) measured WEO 2 WEO 24

12 % variation among CO2 projections made in % variation among CO2 projections made in CO2 EMISSIONS MAX. VARIATION 1% 5% % -5% -1% % 2% 1% % -1% -2% -3% Max. variation (percentage) among WEO CO2 emissions (fuel combustion) projections for: (left) (right), for the years 22, 23, Positive variation: corrected upwards; - Negative variation: corrected downwards;

13 % Renewable electricity % Renewable electricity % Renewable electricity RES ELECTRICITY OECD 35 3 World measured WEO 1994 WEO 2 WEO measured WEO 1994 WEO 2 23 WEO Africa - percentage of electricity generated from RES sources (hydro, geothermal, biomass, solar, wind, etc.) - IEA-WEO, editions 1994, 2, 24, 21, 215, measured WEO 2 WEO 24 Slide [13]

14 % variation among RE projections made in % variation among RE projections made in RES ELECTRICITY MAX. VARIATION 25% 2% 15% 1% 5% % % 8% 6% 4% 2% % Max. variation (percentage) among WEO electricity generated from RES projections for: (left) (right), for the years 22, 23, Positive variation: corrected upwards; - Negative variation: corrected downwards;

15 COMPARISON WITH NDCS - current gap between existing NDCs and reduction in GHG emissions (until 23) needed to comply with the Paris Agreement (2ºC) estimated at 13.5 GtCO2eq, or ~24% (UNEP, 217); - This work found variations of 45%, 47% and 47%, for OECD, OECD Europe and OECD North America regions, respectively, in CO2 emissions projected for 23 by the WEO (in the period ); - More relevantly, variations in CO2 projections for 23 of 2%, 25% and 27%, for Russia, Africa and India, respectively, in the period 21-17; - Many NDCs (e.g., Mexico, South Korea,Turkey) are made in relation to BAU scenarios (conditional NDCs), where typically very high economic growth is assumed, with a corresponding high increase in energy demand and emissions;

16 FUTURE WORK - Include more projections beside WEO (ETP, and perhaps others); - Account for GDP, population, energy intensity, improve data analysis; - Important regions that might be missing?: e.g., OECD Pacific, South America ; - Clarify relation/interaction between conditional NDCs and BAU scenarios/ energy demand projections!!!;

17 Consumers Profiles & Energy Efficiency It is very hard to make predictions specially about the future! - Niels Bohr Energy Transitions New Technologies & Low Carbon Practices THANK YOU! Policy Support Luís Fazendeiro l.fazendeiro@campus.fct.unl.pt ENERGY & CLIMATE Integrative Energy City Planning Climate Mitigation/ Adaptation CO 2 Sofia Simões sgcs@fct.unl.pt