Supporting Information

Quantifying the economic impact of changes in energy demand for space heating and cooling systems under varying climatic scenarios

Tomoko HASEGAWA1), a), Chan PARK2), b), Shinichiro FUJIMORI1),

Kiyoshi TAKAHASHI1), Yasuaki HIJIOKA1), and Toshihiko MASUI1)

1) Center for Social & Environmental Systems Research, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki, 305-8506, Japan

2) Department of Landscape Architecture, College of Urban Science, University of Seoul, Baebong Hall #6223,163 Seoulsiripdaero Dongdaemun-gu, Seoul 02504, Korea

a) Corresponding author at: Center for Social & Environmental Systems Research, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki, 305-8506, Japan, Tel: +81-29-850-2510; Fax: +81-29-850-2710; E-mail address: hasegawa.tomoko@nies.go.jp

b) Corresponding author at: Department of Landscape Architecture, College of Urban Science, University of Seoul, Baebong Hall #6223,163 Seoulsiripdaero Dongdaemun-gu, Seoul 02504, Korea, Tel: +82-2-6490-2849; Fax: +82-2-6490-2839; E-mail address: chaneparkmomo7@uos.ac.kr

S1. The Asia-Pacific Integrated Model/Computable General Equilibrium (AIM/CGE) model

1.1. Overall AIM/CGE modelThe CGE model used in this study was a one-year-step recursive-type dynamic general equilibrium model that includes 17 regions and 42 classifications (Fujimori et al., 2014b). The production sectors are assumed to maximize profits under multi-nested constant elasticity substitution (CES) functions at each input price. There are several power generation sectors, and the power generation output from several energy sources is combined with a logit function. This method was adopted to consider the energy balance because the CES function does not guarantee a material balance. Household expenditures on each commodity are described by a linear expenditure system (LES) function. The parameters adopted in the LES function are recursively updated in accordance with income elasticity assumptions. The savings ratio is endogenously determined to balance savings and investment, and capital formation for each good is determined by a fixed coefficient. The Armington assumption is used for trade, and the current account is assumed to be balanced. Land use is determined by the logit function (Fujimori et al., 2014a). The details of the model structure and mathematical formulas are described in the AIM/CGE basic manual (Fujimori et al., 2012).

To describe the energy supply and selection and stock changes of end-use technologies, we used the same approach as Fujimori et al. (2014b), where an energy end use model was incorporated into the CGE model. This approach has the advantage that it can deal in detail with energy end-use technologies, whereas the traditional method treats energy demand as an aggregated value. The basic concept in this approach is that household and other energy end-use sectors require several types of energy services such as cooling, heating, cooking, and lighting. A variety of selected technologies supply these services to meet the demand. The selection of an energy technology is determined by the distribution of its share of all the energy devices within a logit function (Supplementary Figure 1). One of the determinants of the share of an alternative energy technology is its cost, including both the investment cost and the operation and management costs (energy cost). The investment cost is annualized with specified discount rates over the device’s lifetime. The logit selection has two parameters. One of the parameters represents price elasticity to each device cost and the other is its basic share, which is related to preference, inertia, and behavioral and cultural aspects. The detailed technological information was taken from the

AIM/Enduse database (Akashi and Hanaoka, 2012).

Energy consumption of device l1

Device l1 operation

Energy service i1

Device l1 stock

Output

Device m1 Device l2 operation

・・・

Energy consumption of device l2

・・・

・・・

Investment cost of device l1

Other manufacturing goods

Energy service i2

Logit

i denotes a type of energy service and l denotes a type of device

Supplementary Figure 1: A schematic diagram of energy device selection and energy

demand structure (Fujimori et al., 2014b)

1.2. Energy service demand specificationWe followed essentially the same methodology as Fujimori et al. (2014b) to

determine the energy service demands, but special care is required in determining space heating and cooling. Thus, the respective calculations were modified, and the space demands for heating and cooling in commercial and household sectors were respectively determined as shown below (Schipper and Meyers, 1992):

where r ∈ R is a set of regions, i ∈ I is a set of energy service categories (space heating

and cooling), and are the energy service demand in region r and

energy service I for the commercial and residential sectors, respectively, POPr is the population in region r, LFr is the labor force in the commercial sector, fsar,i and fsbr,i are the floor space areas for each sector, respectively, ddr,i is population-weighted degree days, dpr,i is the device penetration ratio (for heating dp = 1), and ar,i and br,i are calibrated parameters.

The basic activity level is driven by the population and labor force for each sector. The floor space is also the basic structural driver of the demand for space heating and cooling. Degree days are climate driven parameters and the device penetration rate is affected by both climatic conditions and income level. Next, we explain how we determined heating and cooling degree days, floor space, and the device penetration rate.

1.3. Heating and cooling degree days (HDD and CDD)The mean daily temperature at a geographic resolution of 0.5° × 0.5° was used to calculate yearly degree days. We took the base temperature to be 18°C, for the whole period of the analysis for both heating and cooling. Although there are various criteria to meet the demand for human heating and cooling, for simplicity we adopted this single criterion, in accordance with Isaac and van Vuuren (2009).

The gridded information was then aggregated into a CGE regional classification using a population density map (Center for International Earth Science Information Network - CIESIN - Columbia University and Centro Internacional de Agricultura Tropical - CIAT, 2005) using the formula:

where grid_HDD18l and grid_CDD18l are the HDD and CDD in grid l, Td,l is the daily mean temperature in grid l and day d, and POPl is the population in grid cell l.

1.4. Floor Space AreaFor residential sector, we adopted the log function used in Isaac and van Vuuren (2009) (Supplementary Figure 2). For the commercial sector, the floor area was calculated as the product of the labor population, proportion of total employees in the commercial sectors and floor area per employee(McNeil and Letschert, 2007). We used the exponential relationships between floor area per employee and income (shown in Supplementary Figure 3), and between the proportion of total employees in commercial sectors and income (shown in Supplementary Figure 4). The floor area was adjusted using the base-year data. Proportion of employees to the labor population and labor participation rate was simply assumed to be constant at the base-year level in the future.

1.5. Device Penetration RatioTo determine the impact of cooling energy technology, we considered the device penetration ratio (proportion of households owning air conditioners). We adopted the approach used in Isaac and van Vuuren (2009), where the device penetration ratio depends on climate and income, based on the assumption that climate conditions stimulate human demand for space cooling and that cooling technology (air conditioning) is basically accessible (available) at a certain income level. The device penetration ratio was calculated as the product of climate maximum saturation and

percentage availability of air conditioners. The climate maximum saturation was calculated as an exponential function of CDD (shown in Supplementary Figure 5), while the availability was calculated as a logistic function of income (shown in Supplementary Figure 5). See Isaac and van Vuuren (2009) for more detail. Using data for present day air conditioner penetration in various countries (McNeil and Letschert, 2007), we determined availability as a function of income.

1.6. DataThe base-year social accounting matrix was prepared by reconciling data from the Global Trade

Analysis Project (GTAP) database (Dimaranan, 2006), with data from industrial (OECD, 2005,

UNIDO, 2009), trade (UN, 2006), energy (IEA, 2009d, IEA, 2009c), and agricultural (FAO,

2009) statistics, national accounts (UN, 2007), and input-output tables (OECD, 2010). The

concept behind this reconciliation method was discussed by Fujimori and Matsuoka (Fujimori

and Matsuoka, 2011). The land database was prepared based on representative concentration

pathway (RCP) data (Hurtt et al., 2011) and the GTAP database (Lee et al., 2009).

CGE models generally use a social accounting matrix (SAM), which generally employs a monetary unit to calibrate the model’s parameters. To assess energy flows and greenhouse gas (GHG) emissions more precisely and more realistically, the CGE model should account not only for the original SAM but also for energy statistics. GTAP (Dimaranan, 2006) and an energy balance table (IEA, 2009b, IEA, 2009a) were used as the basis of the SAM and energy balance table, and the data were reconciled with other international statistics, such as national accounts (UN, 2007). Part of the concept behind this reconciliation method is described by Fujimori and Matsuoka (2011). Data from the year 2005 were used as the base year calibration data. GHG emissions were calibrated to the Emission Database for Global Atmospheric Research (EDGAR) version 4.2 (EC-JRC/PBL, 2012).

Energy services and technologies are listed in Supplementary SupplementaryTable 2. The list is based on the AIM/Enduse database (Akashi and Hanaoka, 2012), and includes many devices for which the levels of energy efficiency are differentiated. Moreover, the use of devices that use different energy sources (e.g., gas or electric powered vehicles) was assumed.

S2. Supplementary tables and figures

Supplementary Table 1: Regional classificationsCode 17 Regions 5 Regions Code 17 Regions 5 Regions

JPN Japan OECD90 TUR Turkey REF

CHN China Asia CAN Canada OECD90

IND India Asia USA United States OECD90

XSE Southeast Asia Asia BRA Brazil LAM

XSA Rest of Asia Asia XLM Rest of South America LAM

XOC Oceania OECD90 XME Middle East MAF

XE25 EU25 OECD90 XNF North Africa MAF

XER Rest of Europe REF XAF Rest of Africa MAF

CIS Former Soviet Union REF 　 　

Supplementary Table 2: Energy services and technologies Sector Category Technology options

Industry Cement Mill (e.g., ball mill, tube mill, vertical mill), kiln (e.g., wet kiln, semi-

wet/dry kiln, dry long kiln, vertical shaft kiln, SP/NSP1)

Other

industries2

Boilers (levels 1–53; e.g., efficient boilers [coal, oil, gas], boilers with

combustion control [coal, oil, gas], cogeneration [coal, oil, gas],

regenerative gas boilers), process heat (e.g., efficient industrial furnaces

[oil, gas]), motors (levels 1–2; e.g., motors with inverter controls,

efficient motors)

Residential

and

commercial

Cooling Efficient air conditioners (levels 1–3: most common device sold in

developed countries in 2005, longest running, highest performance)

Heating

Efficient air conditioners (levels 1–3: most common device sold in

developed countries in 2005, longest running, highest performance),

thermal insulation (e.g., wall insulation for detached houses, wall

insulation, low-emissivity (Low-E) double-glazed glass)

Hot water Efficient water heaters (kerosene, LPG, gas, coal), latent heat recovery

1 suspension preheater kiln or new suspension preheater kiln.

2 Other industries includes five energy services: boilers, process heat, motors, electrochemistry, and other.

3 Level means energy efficiency level.

Sector Category Technology options

water heaters (kerosene, LPG, gas), CO2 refrigerant heat pump water

heaters (levels 1–3), solar thermal water heaters, electric water heaters,

fuel cells

Cooking Efficient cooking stoves (kerosene, LPG, gas, coal), electric induction

heating (IH) cookers

Lighting Efficient fluorescent lamps (levels 1–2), efficient compact fluorescent

lamps (levels 1–2), light-emitting diode (LED) lamps

Refrigerator Efficient refrigerators (levels 1–3: average sold in developed countries in

2005, top runner, highest performance)

TV

Efficient TVs (levels 1–3: most common device sold in developed

countries in 2005, longest running, highest performance), liquid crystal

display TVs

Transport 4 Passenger

cars

Efficient vehicles (levels 1–3; gasoline, diesel; e.g., weight reduction,

engine friction reduction, aerodynamic drag reduction, rolling resistance

reduction, brake drag reduction, continuously variable transmission

[CVT], variable valve life and time [VVLT] and cylinder reactivation,

direct injection), hybrid electric vehicles (HEV; gasoline, diesel), plug-in

HEVs (gasoline, diesel), electric passenger vehicles (EV), fuel-cell

passenger vehicles (FCV), biofuels

Trucks

Efficient small trucks (e.g., rolling resistance reduction, engine

improvement, weight reduction, aerodynamic drag reduction), small

HEVs, small EVs, small FCVs, efficient large trucks, large HEVs,

natural gas powered vehicles, biofuels

Passenger

buses

Efficient buses (e.g., rolling resistance reduction, engine improvement),

HEV buses, biofuels

Ships Efficient ships, biofuels

AircraftEfficient aircraft (e.g., engine improvement, weight reduction, drag

reduction), biofuels

RailEfficient trains (electricity, diesel; e.g., regenerative braking systems with

VVVF5)

4 Rail, navigation, and aviation including freight and passenger services.

5 Variable Voltage Variable Frequency

Supplementary Table 3: Global climate models used in this studyGFDL-ESM2MHadGEM2-ESIPSL-CM5A-LRMIROC-ESMNorESM1-M

Supplementary Figure 2: Residential floor area per capita vs. GDP per capita adapted from Isaac and van Vuuren (2009)

Supplementary Figure 3: Exponential relationships between floor area per employee and income developed by McNeil and Letschert (2007)

Supplementary Figure 4: Exponential relationships between the proportion of employees in commercial sectors and income developed by McNeil and Letschert (2007)

Supplementary Figure 5: Climate maximum air conditioner saturation as a function of cooling degree days (CDD) (left); and availability (% of maximum saturation) as a function of income (right) adapted from Isaac and van Vuuren (2009)

Supplementary Figure 6: Changes in GDP in five regions due to changes in the demand for space heating and cooling under different climate conditions compared to a scenario with no climate change. The lines show the median values and the ranges show the uncertainty of global climate models from the highest to the lowest values.

Supplementary Figure 7: Heating (top) and cooling (bottom) degree days for RCP4.5 and RCP6.0 in five regions. Boxes and lines show the uncertainty range across the five GCMs. Boxes represent the first–third quartile range and the plain line indicates the median; (OECD90: United Nations Framework Convention on Climate Change (UNFCCC) Annex 1 countries, REF: Eastern Europe and Former Soviet Union, ASIA: Asia except OECD90 countries, MAF: Middle East and Africa, LAM: Latin America)

Supplementary Figure 8: Differences in temperature and radiative forcing change among the RCPs (Stocker et al., 2013)

Supplementary Figure 9: Sensitivity analyses: the economic loss caused by the changes in energy demand in five regions in 2100 for RCP8.5 in different scenarios considering three uncertain parameters:　 (i) population and income, (ii) a parameter representing the price elasticity of the technology selection, and (iii) technology costs. The reference value represents the median value (GCM is IPSL-CM5A-LR) and the black line shows the ranges of five climate models.

Supplementary Figure 10: Sensitivity analyses: the economic loss caused by the changes in energy demand in 17 regions in 2100 for RCP8.5 in different scenarios considering three uncertain parameters: (i) population and income, (ii) a parameter representing the price elasticity of the technology selection, and (iii) technology costs. The reference value represents the median value (GCM is IPSL-CM5A-LR) and the black line shows the ranges of five climate models of five climate models.

Supplementary Figure 11: Energy consumption in the building sector for RCP8.5 (GCM is IPSL-CM5A-LR); (OECD90: United Nations Framework Convention on Climate Change (UNFCCC) Annex 1 countries, REF: Eastern Europe and Former Soviet Union, ASIA: Asia except OECD90 countries, MAF: Middle East and Africa, LAM: Latin America)

Reference