Policies to decarbonize
household heating systems inEast Asia :A simulation with FTT:HeatSoocheol Lee
Meijo University

Florian Knobloch, Unnada Chewpreecha, Seonghee
Kim, Yanmin He, Li-chun Chen, Aileen Lam, JeanFrançois Mercure and Soocheol Lee

Simulating the decarbonisation of buildings in East Asia
1. Background

Relative importance of residential heating (space and water):• Households are 3rd largest energy-related emitter of CO2 (behind power and transport):around 2.2GtCO2/y in direct emissions• 2/3 of global (direct) household emissions are due to heating (40% of which for water heating)• Decarbonisation of heating therefore key in mitigation scenarios, and even more important in scenarioswith high ambition (1.5C target) or limited use of negative emission technologies
Global carbon emissions by sector, current trends

Households

Road transport

Electricity
Source: E3ME-FTT

Direct CO2 emissions by residential heating

Simulating the decarbonisation of buildings in East Asia
1. Background

Options for the decarbonisation of residential heating
1. Improved insulation of buildings• In OECD countries, 75% of current building stock will still be in use in 2050
• Therefore, retrofitting of existing buildings is of central importance• Does hardly reduce demand for water heating
2. More efficient heating technologies• Replace old heating systems by more efficient ones, such ascondensing boilers• However, this can only reduce emissions by a limited extent
3. Renewable heating technologies• Heat pumps: mostly based on electricity• Solar thermal: ideal for water heating in countries with high levels of solar
irradiation• Biomass: modern biomass heating can be highly efficientSimulating the decarbonisation of buildings in East Asia
2. Model description

Technological change in heating depends on decisions by households

t t

t +t +ΔtΔt• Households replace their heating systems in time intervals related to technical lifetimes• Choices are based on Levelised Costs of Heating (LCOH):
combined measure of investment costs, fuel costs, and maintenance costs• Calibration of these costs to observed preferences and trends:
accounts for different comfort levels, local variations, existing policies etc.
• Behavioural assumptions: no switching back to less comfortable technologies

Simulating the decarbonisation of buildings in East Asia
2. Model description

Technological learning within FTT:HeatAdoption rate

(same principle as in FTT:Power and FTT:Transport)

CostTime

Diffusion andlearning interact

Time
FTT:Heat simulation of future solar thermal heating costs, by country(assuming a continuation of current policies).

Simulating the decarbonisation of buildings in East Asia
3. Results

Dynamics in underlying costs per technology, example of Japan

Coal, oil and gas:
Increase due to carbon tax
Renewables:
decrease due to learning effectsRenewables: combined effect oflearning and subsidies

Figure: Projected average heating costs per technology in Japan (in Euro per kWh of heat), without
technology policies (left), under a carbon tax (middle), and under subsidies for renewable heating (right).

Simulating the decarbonisation of buildings in East Asia
2. Model description

FTT:Heat – a micro-model of technology choice and substitution• Initially developed for the European Commission, then expanded to global coverage

The model simulates:• The future replacement and diffusion ofheating technologies (combined for spaceand water heating)• By households worldwide(59 world regions)• Based on dynamical shares equations(the FTT method – no optimisation)• Exogenous driver: trends of residential
heat demandSimulating the decarbonisation of buildings in East Asia
2. Model description

FTT:Heat as a part of E3ME-FTT-GENIE
Energy Resources
Natural resource use
and depletion

Policy Assumptions
Economic feedbacks
GHG Emissions

Energy Prices /Resource costs

GHG Concentrations
Environmental Changes
Land productivity

Policyassumptions

FTT:PowerPower sector
technology model

FTT:HeatTechnological changein residential heating

FTT:AgricultureCompeting usesof land

LPJmL-em
Land productivity

FTT:Transport
Transport technology
modelFTT:SteelGlobal warming

Steel sector
technology model

Precipitation
anomalies

E3ME
Macroeconometric model of theglobal economy

GENIE1
Carbon cycle

Concentrations

Env. Impacts

Figur e 1 : I n tegr ation of FTT:H eat w ith E 3 M E -FTT-GE N I E , a sim u lation -based I n tegr ated A ssessm en t
M od el for clim ate ch an ge p olicy r esear ch . 1 ,2

• Simulation of macroeconomic feedbacks (GDP, employment, etc.)• Dynamic integration with FTT:Power allows simulation of effects on the power sector,
such as the increase of indirect emissions from electricity production

GENIE1 - PLASIM
Climate system

Other climateimpacts

Simulating the decarbonisation of buildings in East Asia
3. Scenario simulations

Policy scenarios.

Scenario Policies targeted at technology uptake

Insulation policies

Baseline

No new policies

No improved insulation

S1

No new policies

Improved thermal insulation of buildings, lowering
the demand for space heating to50-150 kJUE/m2/

HDD by 2100
S2Carbon tax of US$29/tCO2 (from 2020 onwards),

Improved thermal insulation (as in S1)linearly increasing to US$114/tCO2 by 2050
S3

Carbon tax of US$57/tCO2 (from 2020 onwards),

Improved thermal insulation (as in S1)linearly increasing to US$229/tCO2 by 2050

S4

Subsidy payments of 25% on upfront investment costs of Improved thermal insulation (as in S1)modern renewables, paid from 2020–30, phased out
afterwards

s5

s5

S3+S4

Improved thermal insulation (as in S1)

Simulating the decarbonisation of buildings in East Asia
3. Results

Scenario projections for China• Large increase of heat demand, even under improved

building insulation, due to increasing incomes• Decreasing use of coal and biomass• Increasing uptake of renewables, already observable

under current trends• Global leader in solar thermal heating• Peak of CO2 emissions around 2030, -14% in 2050
• Policies could lead to -75% in 2050

Figure: Projected technology diffusion (left) and CO2 emissions(right) in China’s residential heating sector, scenarios (a-e).

Simulating the decarbonisation of buildings in East Asia
3. Results

Scenario projections for Japan• Gradual decrease of heat demand with improved

insulation (-29% in 2050)• Decreasing market shares of oil heating• Increasing uptake of heat pumps, already undercurrent trends• When power sector is decarbonised, total CO2emissions could see a decrease by 64% undercurrent trends• Policies would allow reduction by -81% in 2050

Figure: Projected technology diffusion (left) and CO2 emissions(right) in Japan’s residential heating sector, scenarios (a-e).

Simulating the decarbonisation of buildings in East Asia
3. Results

Scenario projections for South Korea• Slightly increasing heat demand• Continued dominance of gas and oil heating,increasing share of district heating• Only very slow uptake of renewable heating• Still, more efficient gas heating could reduce CO2emissions by 24% in 2050
• Policies could reduce total emissions by -58% in

2050, but net effect highly depends on power
sectorFigure: Projected technology diffusion (left) and CO2 emissions(right) in South Korea’s residential heating sector, scenarios (a-e).

Simulating the decarbonisation of buildings in East Asia
3. ResultsScenario projections for Taiwan• Heat demand dominated by water heating,therefore not affected by improved insulation• Large share of direct electric heating• Increasing uptake of solar thermal heating• Policies could increase the uptake of solar

thermal, and induce phase-out of fossil fuels• Key for reduction of overall emissions is thedecarbonisation of the power sector

Figure: Projected technology diffusion (left) and CO2 emissions(right) in Taiwan’s residential heating sector, scenarios (a-e).

Simulating the decarbonisation of buildings in East Asia
3. Results• Savings from energy expenses exceed theadditional costs of new heating systems inall countries• Net reduction in heating costs of 5% inSouth Korea, 20% in Japan, and up to40% in China• Subsidies lead to more efficient systems
and lower overall costs• Costs for households would increase
initially due to carbon taxes

Figure: Projected changes in average heatingcosts per country, relative to scenario a (in %).
Left side: engineering cost estimate.
Right side: cost as perceived by households(including intangibles).

Simulating the decarbonisation of buildings in East Asia
4. Conclusion

Dcarbonisation of residential heating in East Asia is achievable in 2050,
based on existing technologies
1. Substantial policy effort required: such as carbon taxes and subsidy payments
2. Policy mixes more effective than carbon tax on its own
3. Initial cost increases for households, followed by savings in the medium term

Even under strong policies, an ambitious decarbonisation likely takes decades
1. Slow turnover: technology lifetimes of 20+ years, 75% of buildings still in use in 2050
2. Diverse preferences: households behave differently, no ‘representative agent’
3. Inertia: households and industry need time to learn and adjusta) Imperfect information: lack of knowledge, relevance of social influence, ‘wait and see’b) Industry constraints: limited capacities and know-how for new technologies

Waiting for too long can result in a ‘lock in’ situation,
which may require the ‘scrapping’ of heating systems later in time.
This would require even more stringent policies.

Simulating the decarbonisation of buildings in East Asia

Thanks for your attention!
Questions?
Remarks?
Florian Knobloch
Department of Environmental Science
Faculty of ScienceRadboud University Nijmegen
PO Box 9010, NL-6500 GL Nijmegen, The Netherlands+31 (0)24 - 356 23 93
f.knobloch@science.ru.nl
http://www.ru.nl/environmentalscience/staff/individual-staff/knobloch/
https://www.ceenrg.landecon.cam.ac.uk/people/florian-knoblochSimulating the decarbonisation of buildings in East Asia

References
Knobloch, F., Mercure, J.-F., Pollitt, H., Chewpreecha, U., & Lewney, R. (2017). FTT:Heat - A simulation model fortechnological change in the residential heating sector. European Commission, DG Energy. Available under:
https://ec.europa.eu/energy/en/data-analysis/energy-modelling/macroeconomic-modelling

Knobloch, F., Pollitt, H., Chewpreecha, U., Daioglou, V., & Mercure, J. F. (2019). Simulating the deep decarbonisationof residential heating for limiting global warming to 1.5 C. Energy Efficiency.
Mercure, J. F., Pollitt, H., Edwards, N. R., Holden, P. B., Chewpreecha, U., Salas, P., Lam, A., Knobloch, F., &Vinuales, J. (2018). Environmental impact assessment for climate change policy with the simulation-based integrated
assessment model E3ME-FTT-GENIE. Energy Strategy Reviews.