The importance of energy and activity data for technology policy modeling

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1 The importance of energy and activity data for technology policy modeling 2016 InterEnerStat Workshop: Energy efficiency and end-use data and Meeting of Organisations 14 December 2016, Paris Eric Masanet, PhD Energy Technology Policy Division International Energy Agency OECD/IEA 2015

2 Sizing the scale of the challenge and its technology and policy solutions Contribution of technology area to global cumulative CO 2 reductions DS Renewables 32% GtCO Energy efficiency 32% Fuel switching 10% Nuclear 11% DS CCS 15% The carbon intensity of the global economy can be cut by two-thirds through a diversified energy technology mix OECD/IEA, 2016 OECD/IEA 2016

3 Progress in clean energy needs to accelerate Technology Status today against 2DS targets by 2025 Electric vehicles Solar PV and onshore wind Other renewable power Nuclear More efficient coal-fired power Carbon capture and storage Biofuels Transport Industry Buildings Appliances and lighting Energy storage Not on track Accelerated improvement needed On track Clean energy deployment is still overall behind what is required to meet the 2 C goal, but recent progress on electric vehicles, solar PV and wind is promising OECD/IEA 2016

4 The challenge increases to get from 2 degrees to well below 2 degrees Energy- and process-related CO 2 emissions by sector in the 2DS Agriculture 2% 35 Buildings 8% GtCO Industry 33% Transport 24% Other transformation 4% Power 29% Industry and transport account for 75% of the remaining emissions in the 2DS in OECD/IEA, 2016 OECD/IEA 2016

5 Data disaggregation sound analysis +15% -35% Index: 1990=1. Data for IEA18 (Australia, Austria, Canada, Denmark, Finland, France, Germany, Ireland, Italy, Japan, Netherlands, Norway, Slovakia, Spain, Sweden, Switzerland, UK, USA). Source: IEA energy efficiency indicators database. TC: Temperature Corrected.

6 Selected IEA recommended indicators, by sector Sector Indicator (Level*) Coverage Energy data Activity data Residential L2: Space heating energy consumption per floor area (heated) All heating systems Total space heating energy consumption Total floor area L2: Cooking energy consumption per dwelling All cooking devices Total cooking energy consumption Total number of dwellings L3: Energy consumption per appliance unit By appliance type Energy consumption for all appliances of type A Number of appliances of type A Services L2: Space cooling energy consumption per floor area cooled All cooling systems Total space cooling energy consumption Total floor area cooled L3: Lighting energy consumption per unit of activity By service category Lighting energy consumption for service category A Unit activity of service category A L3: Other equipment energy consumption per unit of activity By service category Other equipment energy consumption for service category A Unit activity of service category A Industry L2: Energy consumption per unit of physical output Sub-sector Total sub-sectoral energy consumption Sub-sectoral physical output Transport L3: Passenger transport energy consumption per passenger-kilometre By mode / passenger vehicle type Energy consumption of passenger transport by mode / vehicle type A Number of pkm of passenger mode / vehicle type A L3: Freight transport energy consumption per tonne-kilometre By freight mode / vehicle type Energy consumption of freight transport by freight mode / vehicle type B Number of tkm of freight mode / vehicle type B * Levels in the IEA energy indicators pyramid : L2 (Level 2), L3 (Level 3) Source : IEA (EEI) 2014

7 The IEA energy indicators pyramid Robust energy technology modeling requires a high degree of disaggregation Source: IEA (2014). Energy Efficiency Indicators: Essentials for Policy Making.

8 ETP buildings model structure

9 ETP buildings model structure

10 ETP buildings model structure

11 ETP buildings model structure

12 ETP buildings model structure Level 2 indicators enable analytical linkages between drivers and end uses, which is critical for model calibration

13 ETP buildings model structure

14 EJ Technology-rich policy guidance Global buildings sector final energy savings by end use 200 Other 150 Appliances Cooking 100 Lighting Water heating 50 Space cooling 0 World energy consumption 6DS energy consumption Urban energy savings Non-urban energy savings 2DS energy consumption Space heating Urban buildings account for more than 75% of global building final energy savings in 2050, led by space heating and cooling demand reductions Source: ETP 2016

15 ETP buildings model structure Level 3 indicators enable analytical linkages between drivers and end uses, and end uses and technologies, which is critical for technology-rich energy systems modeling

16 ETP buildings model structure Level 3 indicators require a deep understanding of technologies and their relevant energy system characteristics Typically requires extensive data matching from often disparate sources Technology-rich energy surveys are uncommon, and often infrequent

17 GJ/t cement Level 3 technology data for industry Energy intensity of the Brazilian cement industry, % 10 Wet kilns 80% 8 60% 6 Dry kilns 40% 4 20% 0% Thermal energy intensity Robust modeling of industrial subsectors often critically depends upon sufficient Level 3 technology data

18 Million Btu per Metric Ton of Steel Shipped Structural and system boundary considerations Energy intensity of the US iron and steel industry, % % 40% 30% 20% 10% Energy intensity % electric arc process route 0% Disaggregation to capture structural effects is typically important, but must also be done with consistent system boundaries in mind

19 Argentina Australia Brazil Chile China France Egypt Germany India Indonesia Italy Japan Malaysia Mexico Portugal Russia outh Africa Spain Thailand Turkey UK Ukraine USA EU-27 World OECD Non-OECD l/100km Modeling and energy data systems: synergies ETP model uses the ASIF (activity structure intensity fuel) methodology Activity = Distance travelled Intensity = Fuel Economy Structure = Vehicle Stock

20 IEA data coverage and transparency Understanding energy use patterns Fuel use in Argentina

21 IEA data coverage and transparency Understanding energy use patterns Fuel use in China

22 Data matching can reveal opportunities for improved coordination U.S. Manufacturing Energy Consumption Survey (MECS) vs. U.S. GHG Reporting Program (GHGRP) Food Processing Industry, 2011 Source: Huang et al. 2013

23 Summary Technology-rich energy systems modeling provides critical guidance to the energy policy community But this is not possible without credible and comprehensive data! Detailed activity and technology-level data are very important for technology-rich energy systems modeling Requires coordination with subsector and technology expert communities Data matching is often required, but more disaggregated data and coordination among data collection institutions can help Level 3 data availability should be accelerated to meet the growing needs of the energy modeling, policy, and progress tracking communities Coordination and exchange between the modeling and data communities can be highly mutually beneficial

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