Impacts of climate change on heating and cooling: a

worldwide estimate from energy and macro-economic

perspectives∗

Maryse Labriet1, Santosh R. Joshi2, Amit Kanadia3, Neil R. Edwards4,

and Philip B. Holden4

1Eneris Environment Energy Consultants, Madrid, Spain.

2REME, Ecole Polytechnique Federale de Lausanne, Switzerland.

3KanORS-EMR, Noida, India.

4Environment, Earth and Ecosystems, Open University, Milton Keynes, UK.

Abstract

The energy sector is not only a major driving force of climate change, it is

also vulnerable to future climate change. In this paper, we analyze the impacts of

changes in future temperature on the heating and cooling services both in terms of

global and regional energy impacts and macro-economic effects. For this purpose,

the technico-economic TIMES-WORLD and the general equilibrium GEMINI-E3

model are coupled with a climate model, PLASIM-ENTS, to assess the regional and

seasonal temperature changes and their consequences on the energy and economic

systems. One of the main insight of the analysis is the absence of climate feedback

induced by the adaptation of the energy system to future heating and cooling needs,

since the latter represent a limited share of total final energy consumption and

emissions, and the heating and cooling changes tend to compensate each other, at

the global level. However, significant changes may be observed at regional levels,

more particularly in terms of additional power capacity required to satisfy the

new cooling demands. In terms of macro-economic impacts, welfare gains comes

from the decrease of energy for heating and to welfare loss due to an increase

of electricity for space cooling. For energy exporting countries welfare gain is

∗The research leading to these results has received funding from the EU Seventh Framework Pro-

gramme (ERMITAGE FP7/2007-2013) under Grant Agreement no265170.

1

reduced (or lossed) due to losses of revenue coming from less energy export while

for non-energy exporting countries welfare gains is linked to the decrease of energy

needs for heating overcompensate the cost coming from the increase of electricity

consumption.

Keywords: Climate change, Heating, Cooling, Adaptation, Energy system,

Bottom-up model, Top-down model.

1 Introduction

Energy sector is not only a driver of climate change, given the large contribution of

the sector to greenhouse gas emissions, it is also vulnerable to the effects of climate

change. While the focus of interest until recently has mainly been on the emission

mitigation of the energy sector rather than on the climate vulnerability and resilience

of the sector, awareness of the implications of impacts and adaptation for energy is

increasing (Ebinger and Vergara, 2011; CCSP, 2007; Schaeffer et al., 2012; Mideksa

and Kallbekken, 2010). There are several possible impacts of climate change on the

energy demand and production (i)changes in cooling efficiency of thermal and nuclear

power generation, resulting in modified availability and efficiency of the plants (Linnerud

et al., 2011; Rubbelke and Vogele, 2011); (ii) changes in the seasonal river flows and

in their variability, affected hydropower potential and generation (Lehner et al., 2005;

Hamududu and Killingtveit, 2010; Iimi, 2007); (iii)changes in productivity of crops for

bio-energy (Haberl et al., 2011); (iv) changes in space heating and cooling requirements

for buildings (Isaac and van Vuuren, 2009; Mima and Criqui, 2009); (v)vulnerability of

energy-related infrastructure to extreme events and sea level rise (Craig, 2011).

Without surprise, several studies have shown the potential increase of cooling de-

mands and decrease of heating demands given future climate change, although the range

of changes will depend on the regional and seasonal temperatures (Dolinar et al., 2010;

Frank, 2005; Christenson et al., 2006; Wang et al., 2010; Ward, 2008). Such changes may

lead to different energy and emission patterns, which may result in a feedback between

the climate and the energy system. Since two opposite effects may happen (decrease

of heating and increase of cooling), the net balance in energy use might be positive or

negative depending on regional climatic conditions (Isaac and van Vuuren, 2009). How-

ever, since these changes usually occurring at different seasons, an annual compensation

of the increase of cooling by a decrease of heating will not avoid the possibility of new

peaks of demands during the warm seasons. In terms of emission balance, the net im-

2

pact will depend on the energy used for heating purposes and on the source of electricity

for cooling. The global effect on the climate system is not expected to be important

since heating and cooling don’t represent a high share of total end-use demands at the

World level; however, energy and economic impacts at regional and country levels may

be major, depending on both local energy and climate characteristics.

The objective of this paper is to assess the energy and economic implications of

changes in heating and cooling dynamics in the context of climate change using an em-

ulated version of the climate model PLASIM-ENTS, coupled with the TIAM-WORLD

techno-economic model TIAM-WORLD and the general equilibrium model GEMINI-E3.

Next section presents the three models used in the exercise, as well as how energy de-

mands for heating and cooling are estimated, taking into account future climate changes.

2 Methodological Framework

2.1 Climate model: PLASIM-ENTS

One of the principal obstacles to coupling complex climate models to impacts models is

their high computational expense. Replacing the climate model with an emulated version

of its input-output response function circumvents this problem without compromising

the possibility of including feedbacks and non-linear responses (Holden and Edwards,

2010). The climate model used in this application is PLASIM-ENTS, the Planet Sim-

ulator (Fraedrich et al., 2005) coupled to the ENTS land surface model (Williamson

et al., 2006). The resulting model has a 3D dynamic atmosphere, flux-corrected slab

ocean and slab sea ice, and dynamic coupled vegetation. The slab sea ice is held fixed

in these simulations. The model is run at T21 resolution (around 5◦).

The PLASIM-ENTS simulations as well as its emulations generally compare favourably

with the AR4 predictions (Figure 1). One significant shortcoming is the understated

DJF warming in the Arctic, a consequence of the neglect of the sea-ice feedback in these

PLASIM-ENTS simulations. Although caution will be required, the error dominantly

affects temperatures in sparsely-populated high-northern latitudes and so may not be

problematic for large-scale human impact studies. Sea-ice feedback will be introduced

into the ensemble for the next generation emulator. The emulator clearly performs very

well in capturing the spatial variability and magnitude of the warming. These compar-

isons illustrate that any errors introduced by the emulation of temperature are unlikely

to be significant compared to the errors in the simulator itself.

3

The climate data required for the assessment of heating and cooling changes due

to climate changes are Heating Degree Days (HDDs) and Cooling Degree Days. HDDs

provide a measure that reflects heating energy demands, calculated relative to some

baseline temperature: on a given day, the average temperature is calculated and sub-

tracted from the baseline temperature; if the value is less than or equal to zero, then

that day has zero HDDs (no heating requirements); if the value is positive, then that

number represents the number of HDDs on that day. The sum of HDDs over a month

provides an indication of the total heating requirements for that month. CDDs are di-

rectly analogous, but integrate the temperature excess relative to a baseline and provide

a measure of the cooling demands for that month.

Although a climate model can output degree-days explicitly, calculated from the

day-to-day temperature variability, such an approach is restrictive as it cannot be trans-

formed to a new baseline without repeating the underlying simulations, which can be

computationally prohibitive. Therefore, degree-days are not directly emulated but in-

stead derived from emulations of average temperature and daily variability, as defined

by the standard deviation of the daily temperature across the season, following the ap-

proach of Schoenau and Kehrig (1990). The assumption made is that daily temperatures

are scattered about the monthly mean with a normal distribution.

Seasonal HDDs and CDDs are computed at each PLASIM-ENTS cell from:

HDD =N

σ√

2Π

∫ BH

−∞(BH − T )e[−(T−µ)

2/2σ2]dT (1)

CDD =N

σ√

2Π

∫ −∞BC

(T −BC)e[−(T−µ)2/2σ2]dT (2)

where, N = number of days in the season, T = daily temperature, BH = HDD

baseline temperature, BC = CDD baseline temperature, µ = average daily temperature

across the season, σ = standard deviation of daily temperature across the season. For

the current analysis, BH = BC = 18oC is applied globally.

HDDs and CDDs are calculated at each of the 64x32=2048 GENIE gridcells. In order

to convert these onto TIAM regions, we derive a population-weighted average over the

grid cells that comprise a given region. We apply 2005 population data (CIESIN and

CIAT, 2005) at a 0.25o resolution which we integrate up onto the PLASIM-ENTS grid.

We note that moving to the lower resolution inevitably leads to approximations, most

notably when highly populated regions near ocean find themselves in grid cells which

are assigned to be ocean in PLASIM-ENTS, so that the coastal grid cells are likely to

be under-represented in the population weighted average.

4

2.2 Cooling and heating services

In the case without considering future climate change, energy demands for heating and

cooling do not consider any future temperature variations compared to the base year

2005. In this case, the drivers of future heating and cooling demands reflect changes

in socio-economical characteristics of the countries, but consider the same temperature

(HDD and CDD) as in the base year. Cooling deserves an additional comment. In-

deed, cooling demands depend on CDD but also on socio-economic factors influencing

the diffusion (purchase) of air-conditioning systems, which is usually described as an

S-shaped curve function of the level of income: penetration of air conditioners used to

start climbing steeply at a mensual income per household of about US$3300 (McNeil

and Letschert, 2008). Following the methodology and numerical assumptions proposed

by the authors, including a saturation effect guided by the level of penetration of air-

conditioners in the USA for a given CDD value, the demands for cooling services of

TIAM-WORLD were adjusted, given the GDP and POP assumptions and constant cli-

mate conditions as provided by PLASIM-ENTS in 2005. The availability rate, defined

as the share of population equipped with air-conditioning compared to the population

who need air-conditioning, reaches it maximum in all regions by 2050, except in Africa,

Central Asia and Caucase, Other Easter Europe and Indonesia, given GDP assump-

tions. More important, the climate factor influencing the purchase of air-conditioning

(without considering socio-economic constraints) already reaches its maximal value when

considering CDD as observed 2005 for all countries but Canada, Europe, Japan, Other

Eastern Europe, Russia and South Korea. The consequence is that future increase of

CDD would possibly raise the purchase of air-conditioners only in the countries above

(which are also the coldest countries), not in the others (which are the warmest ones),

where the dominating factor of air-conditioner purchase is the level of income. In other

words, future increase of CDD could accelerate the purchase of air-conditioners only in

the coldest countries; this effect is not considered in this study, the impacts on energy

would remain limited since CDD remain low. At the opposite, in the warmest countries,

future increase of CDD would have an impact on the use of air-conditioners, not on the

purchase dynamics.

This analysis supports the approach to compute the changes in heating and cooling

demands in the case with climate change: the impacts on demands for heating and

cooling services are calculated by adjusting these demands proportionally to the changes

of HDDs and CDDs of each region with respect to the values of the base year. In other

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words, energy services for heating with impact of climate change is given by:

EDHcct,r =

HDDt,r

HDDB.EDHt,r (3)

where EDHt,r is the energy service for heating without climate change at time t and

region r. B is the base year 2000 for GEMINI-E3 and 2005 for TIAM-WORLD.

Similarly, energy services for cooling with impact of climate change is given by

EDCcct,r =CDDt,r

CDDB.EDCt,r (4)

where EDCt,r the energy service for cooling without climate change at time t and region

r.

The analysis of the possible feedback between the energy and the climate systems

shows that such a retroaction can be neglected in the case of the impacts of heating and

cooling changes. In other words, the changes of heating and cooling won’t contribute to

additional changes in the future climate, and the one-time adjustment of the demands as

proposed above reflects in a relevant manner the future changes of heating and cooling.

Three groups of regions can be identified, based on the HDD and CDD dynamics

(Figures 1 and 2):

• colder regions, characterized by high levels of HDD and where the main expected

impact of climate change is a reduction of HDD (Russia, Canada);

• warmer countries characterized by high levels of CDD and where the main expected

impact of climate change is an increase of CDD (India, Other Developing India,

Middle East, Africa, Central and South America, Mexico, Australia);

• regions with intermediate climate where both heating and cooling appear to be im-

portant and the net impact of climate change may depend on each region (Europe,

China, Japan, USA, Caucase and Central Asia).

Sections 3 and 4 provide more details on how heating and cooling services are mod-

ified in each model.

2.3 Techno-economic model: TIAM-WORLD

The TIMES Integrated Assessment Model (TIAM-World) is a technology-rich model

of the entire energy/emission system of the World split into 16 regions, providing a

detailed representation of the procurement, transformation, trade, and consumption of

a large number of energy forms (Loulou, 2008; Loulou and Labriet, 2008). It computes

6

Figure 1: Spatial patterns of warming over the next century. Left hand panels are DJF

(December-January-February) and the right hand panels are JJA (June-July-August).

Three ensembles are compared: top) AR4 multi-model ensemble with SRES A1B forcing,

centre) PLASIM-ENTS simulated ensemble with RCP4.5 forcing and bottom) PLASIM-

ENTS emulated ensemble with RCP4.5 forcing.

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7000

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Africa

Other Eastern Europ

e

Europe

Australia‐New

Zealand

Russia

China

Japan

India

Caucase and Ce

ntral A

sia

Other Develop

ing Asia

Middle East

USA

Canada

Central and

 Sou

th America

Mexico

South Ko

rea

Degree‐days ᵒC

Population‐weighted HDD from 2005 to 2100 (long‐term global average temperature increase 

of  3.3 ᵒC)

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1000

2000

3000

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6000

Africa

Other Eastern Europ

e

Europe

Australia‐New

Zealand

Russia

China

Japan

India

Caucase and Ce

ntral A

sia

Other Develop

ing Asia

Middle East

USA

Canada

Central and

 Sou

th America

Mexico

South Ko

rea

Degree‐days ᵒC

Population‐weighted CDD from 2005 to 2100 (long‐term global average temperature increase 

of  3.3 ᵒC)

Figure 2: . HDD and CDD corresponding to a long-term global average temperature

increase of 3.3oC - For each region, each column represents HDD and CDD for 2010,

2020, 2030, ... until 2100.

8

an inter-temporal dynamic partial equilibrium on energy and emission markets based

on the maximization of total surplus, defined as the sum of suppliers and consumers

surpluses. The model is set-up to explore the development of the World energy system

until 2100.

The model contains explicit detailed descriptions of more than 1500 technologies and

several hundreds of energy, emission and demand flows in each region, logically inter-

connected to form a Reference Energy System. Such technological detail allows precise

tracking of optimal capital turnover and provides a precise description of technology and

fuel competition.

TIAM-World is driven by demands for energy services in each sector of the economy,

which are specified by the user for the Reference scenario, and have each an own price

elasticity. Each demand may vary endogenously in alternate scenarios, in response

to endogenous price changes. Although the model does not include macroeconomic

variables beyond the energy sector, there is evidence that accounting for price elasticity

of demands captures a preponderant part of feedback effects from the economy to the

energy system (Bataille, 2005; Labriet et al., 2012).

TIAM-World integrates a climate module permitting the computation and modeling

of global changes related to greenhouse gas concentrations, radiative forcing and tem-

perature increase, resulting from the greenhouse gas emissions endogenously computed

(Loulou et al., 2009).

In the recent years, TIAM-WORLD has been used to assess the assessment of future

climate and energy strategies at global and region levels in full or partial climate agree-

ments and uncertain contexts (Loulou et al., 2009; Labriet and Loulou, 2008; Loulou

et al., 2012).

Although TIAM-WORLD, as any integrated assessment model, can be run in a stan-

dalone manner with global climate constraints (temperature, radiative forcing, green-

house gas concentration), the climate module of TIAM-WORLD does not compute the

regional nor seasonal temperature changes as needed for a relevant representation of the

possible heating and cooling adjustments due to climate change. The coupling of TIAM-

WORLD with an emulator of the climate PLASIM-ENTS provides this additional infor-

mation. Moreover, it adds realism in the way TIAM-WORLD simulates climate changes

thanks to the more detailed representation of climate dynamics in PLASIM-ENTS. The

global temperature increases obtained with PLASIM-ENTS tends to be slightly smaller

than the temperature increase obtained with the endogenous climate module of TIAM-

WORLD, reflecting a smaller equivalent temperature sensitivity of PLASIM-ENTS than

9

in TIAM-WORLD. Such differences are usual in climate modeling.

In essence, there is an iterative exchange of data between the two models, whereby

TIAM-WORLD sends to the climate emulator a set of total greenhouse gas concentra-

tions for the entire 21st century, computed in TIAM-WORLD, and the climate emulator

sends to TIAM-WORLD the seasonal and regional temperatures, converted in seasonal

heating and cooling degree-days for each of the regions of the model. These seasonal

and regional degree-days are used to compute new seasonal and regional heating and

cooling demands in TIAM-WORLD.

2.4 General Equilibrium model: GEMINI-E3

GEMINI-E3 (Bernard and Vielle, 2008)1 is a multi-country, multi-sector, recursive com-

putable general equilibrium model comparable to the other CGE models (EPPA, ENV-

Linkage, etc) built and implemented by other modeling teams and institutions, and

sharing the same long experience in the design of this class of economic models. The

standard model is based on the assumption of total flexibility in all markets, both

macroeconomic markets such as the capital and the exchange markets (with the asso-

ciated prices being the real rate of interest and the real exchange rate, which are then

endogenous), and microeconomic or sector markets (goods, factors of production). The

GEMINI-E3 model is built on a comprehensive energy-economy dataset, the GTAP-

8 database (Badri Narayanan et al., 2012). This database incorporates a consistent

representation of energy markets in physical units, social accounting matrices for each

individualized country/region, and the whole set of bilateral trade flows. Additional

statistical information accrues from OECD national accounts, IEA energy balances and

energy prices/taxes and IMF Statistics (Government budget for non OECD countries).

Carbon emissions are computed on the basis of fossil fuel energy consumption in phys-

ical units. For the modeling of non-CO2 greenhouse gases emissions (CH4, N2O and

F-gases), we employ region- and sector-specific marginal abatement cost curves and

emission projections provided by the US-EPA (U.S. Environmental Protection Agency,

2011, 2012). GEMINI-E3 describes now 24 sectors. Table 1 gives the definition of the

classifications used.

1All information about the model can be found at http://gemini-e3.epfl.ch, including its complete

description.

10

Table 1: Dimensions of the GEMINI-E3 modelRegions Sectors

Regions Energy

United States of America USA 01 Coal

Canada CAN 02 Crude Oil

Australia & New Zealand* AUZ 03 Natural Gas

Eastern European Countries EEU 04 Refined Petroleum

Western European Countries WEU 05 Electricity

Former Soviet Union FSU Non-Energy

China CHI 06 Forestry

India IND 07 Mineral Products

Rest of Eastern Asia REA 08 Chemical, rubber, Plastic

Rest of Southern Asia RSA 09 Metal and Metal products

South East Asia SEA 10 Paper products publishing

Middle East MID 11 Transport nec

Latin America LAT 12 Sea Transport

Africa AFR 13 Air Transport

14 Consuming goods

Primary Factors 15 Machinery and Equipment goods

Labor 16 Services

Capital 17 Dwellings

Energy resource (sectors 01-03) 18 Construction

Land (sectors 20-24) 19 Water

Other inputs 20 Paddy rice

21 Wheat

22 Cereals

23 Oilseeds

24 Rest of agriculture sectors

* The region Australia and New Zealand also includes Oceanic countries.

2.4.1 Household energy demand

In the standard version of GEMINI-E3 (Bernard and Vielle, 2008) the households con-

sumption is derived from an utility function based on a Stone-Geary (Stone, 1983) (called

also a Linear Expenditure System (LES)). We have replaced this functional form by a

nested CES utility function, that allows us to describe more precisely the household en-

ergy consumption. Figure 3 shows the nested CES structure now used in GEMINI-E3.

We have split energy consumption in two parts:

• those used for transportation purposes,

• and the other consumed for residential purposes (heating, cooling, cooking, light-

ing...).

In each nest, energy can be substituted by a capital good representing in the first case

cars and in the second one shelters.

2.4.2 Baseline scenario

Baseline scenarios in GEMINI-E3 models are built from i) forecasts or assumptions on

population and economic growth in the various countries/regions, ii) prices of energy in

world markets, in particular the oil price and iii) national (energy) policies. We have

11

Consumption

σhc

Housing (HCRESr)

σhres

Transport

σhtra

Other

σhoth

Energy (HCRESEr)

σhrese

Shelter Purchased

σhtrap

Private

σhtrao

Forestry Mineralproducts

... Rest of Agriculturalproduct

Fossil Energy (HCRESEFr) Electricity

Gas Coal Oil Petroleumproducts

Sea Air Othertransports

Equipment Energy

σhtraoe

Petroleumproducts

Naturalgas

Electricity

Figure 3: Nested structure of household consumption

used UN median variant to forecast population, the data from International Energy

Outlook 2011 (Energy Information Administration, 2011) and TIAM-WORLD is used

to forecast GDP growth, and prices of energy2 are taken from TIAM-WORLD. We

build a reference baseline for the period 2007-2100 with yearly time-steps. Our reference

baseline is closely related to RCP 6 and baseline climate year is 2000.

Figure 4 summarizes the methodology used to analyse the impacts on the energy

sector.

3 Impacts on the energy system

3.1 Assumptions related to future temperature increases

Two complementary questions are at the heart of the proposed analysis. First, what

are the impacts of future climate changes on the energy consumption and emissions

of heating and cooling services, and their possible consequences on the entire energy

2Supply prices on oil, coal and natural gas.

12

GEMINI-E3

PLASIM-ENTSem

Computation and regional mapping of

seasonal HDD & CDD

TIAM-WORLD

GHG concentrations

Seasonal local temperatures

Regional and seasonal heating and cooling services

Computation of heating and cooling services*

HDD/CDD

Population

GHG concentrations

Regional heating and cooling services

Baseline calibration - Energy prices, Heating & cooling services

Figure 4: Framework to compute impacts on the energy sector

system? Second, what are the possible feedbacks on the climate system of the changes

observed in the energy system?

Given the endogenous detailed representation of the energy system, including heating

and cooling in both residential and commercial sectors of TIAM-WORLD, the modeling

of several future temperature trajectories contributes to the better understanding of the

changes in both total and specific energy consumed for heating and cooling, endoge-

nously assessed by the model, as well as the possible consequences on other sectors such

as power generation, expected to increase to satisfy electricity demands for cooling.

For this purpose, a series of 14 scenarios was built and modeled, representing a

range of long-term global mean temperature increase from 1.6 to 5.6oC (Figure 5).

The long term temperature increase of the Reference case of TIAM-WORLD is 3.3oC,

corresponding to HDD and CDD as illustrated in figure 2.

It is important to remind that no mitigation climate strategies are assessed in the

current exercise and the focus is on the adaptation of the energy system to the impacts

of future climate change on heating and cooling. Such changes in heating and cooling

behavior changes would represent spontaneous adaptation reactions by households and

companies when facing variable local temperatures.

13

0

1

2

3

4

5

6

7

2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

Degree  C

CC1.6CC1.9CC2.2CC2.9CC3.3CC3.6CC3.8CC4.1CC4.4CC5.0CC5.4CC5.7

Figure 5: Range of global mean temperature increase over pre-industrial period used to

assess the impacts of climate change on heating/cooling

Notation: In all figures, CCx.x corresponds to a scenarios with a long term mean

temperature increase of x.x oC at the global level. Regional temperature increases may

be different and are computed by PLASIM-ENTS and used to assess the changes in

regional heating and cooling.

3.2 Global impacts and climate feedback

3.2.1 Climate feedback

The main result is that climate appears to be independent from the changes in heating

and cooling due to future climate change at the global level, on the time horizon consid-

ered (2100) even in cases of higher temperature increase (Figure 6. In other words, the

feedback between the energy and climate systems due to changes in heating and cooling

services at global level is negligible. However, this does not mean that the impacts of

climate change on heating and cooling are negligible, neither at the global level nor at

regional levels (next section). The characteristics of the global energy system and its

changes under heating and cooling adaptation to climate change explain this result.

14

350

400

450

500

550

600

650

700

750

2005 2010 2020 2050 2070 2080 2090 2100

CO2 concen

tration (ppm

)

NoCCCC1.6CC1.9CC2.2CC2.9CC3.3CC3.6CC3.8CC4.1CC4.4CC5.0CC5.4CC5.7

Figure 6: CO2 concentration under changing heating and cooling demands

3.2.2 Heating and cooling in the total energy balance

First, the share of heating and cooling energy consumption is small at the global level

compared to the total energy consumption of the system (less than 12%, Figure 7).

The decrease of this share in the case where impacts of climate change are not consid-

ered reflects both the socio-economic drivers of the demands for energy services and the

technology choices: demands for other energy services (other residential and commer-

cial uses and other sectors) increase more than heating and cooling services, and more

efficient technologies penetrate in the heating and cooling subsectors; more particularly,

coal/oil heating systems are substituted by more efficient gas/electricity technologies.

The difference with the shares observed in the case where impacts of climate change are

considered illustrates the net decrease of energy consumed for heating/cooling at the

global level due to future climate change. More severe long term increase of tempera-

ture does not affect the shares of heating and cooling in total final energy consumption

(Figure 7).

3.2.3 Respective contributions of heating and cooling adaptation

Additional to the low share of heating and cooling in total energy consumption, changes

in global energy consumption remain at levels of less than 1% (Figure 8) because the

15

12.1%10.4%

8.8% 8.5% 8.4% 8.3% 8.3% 8.3%

0%

10%

20%

30%

2005 2010 2020 2050 2070 2080 2090 2100

Share of heating and cooling energy consumption in total final energy consumption ‐ no climate change impacts ‐

(long‐term global average temperature increase of 3.3ᵒC)

Max (country‐based)

Min (country‐based)

World

12.1%10.4%

8.7% 7.9% 7.6% 7.4% 7.3% 7.5%

0%

10%

20%

30%

2005 2010 2020 2050 2070 2080 2090 2100

Share of heating and cooling energy consumption in total final energy consumption ‐ with climate change impacts ‐

(long‐term global average temperature increase of 3.3ᵒC)

Max (country‐based)

Min (country‐based)

World

12.1% 10.4% 8.6% 7.7% 7.2% 7.1% 7.2% 7.5%

0%

10%

20%

30%

2005 2010 2020 2050 2070 2080 2090 2100

Share of heating and cooling energy consumption in total final energy consumption‐ with climate change impacts ‐

(long‐term global average temperature increase of 5.7ᵒC)

Max (country‐based)

Min (country0based)

World

Figure 7: Share of heating and cooling in total final energy consumption

increase of cooling compensates to a certain extent the decrease of heating (Figure 9).

It is interesting to notice that increased future global temperature translates in lower

energy consumption for heating and cooling only in the mid-term (Figure 9): when ag-

gregated in total final energy consumption, changes due to decreased heating dominates

over the increased cooling in the intermediate time horizon. In the longer term, the

increase of cooling dominates over the decrease of heating, resulting in total energy for

heating and cooling very close to the case with a lower future temperature increase.

3.2.4 Fuel perspective

Figures 10, 11 and 12 illustrate the impacts of climate change on the different energy

commodities consumed for heating (various energy commodities) and cooling (electric-

ity) purpose. While the consumption of all energy commodities is reduced given the

decrease of heating needs (with gas dominating the fuels consumed for heating pur-

poses), electricity consumption is strongly dominated by the increase in cooling needs.

This additional demand for electricity is supplied mostly by coal and gas power

plants (Figure 13), corresponding to an additional capacity of up to 1 GW in the worst

temperature case (0.4 GW in the Reference case with 3.3C) versus the case without cli-

mate change impacts), followed by renewable (up to +0.5 GW in the worst temperature

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900

1000

2005 2010 2020 2050 2070 2080 2090 2100

EJ

Total final energy World level (average long term temperature increase of 3.3ᵒC over pre‐industrial)

No CC CC

Figure 8: Total final energy with and without climate change impacts

0

10

20

30

40

50

60

70

80

90

NoCC

CC3.3

CC5.7

NoCC

CC3.3

CC5.7

NoCC

CC3.3

CC5.7

NoCC

CC3.3

CC5.7

NoCC

CC3.3

CC5.7

NoCC

CC3.3

CC5.7

NoCC

CC3.3

CC5.7

2005 2020 2050 2070 2080 2090 2100

EJ

Energy consumption for cooling and heating at World level (average long term temperature increase of 3.3 and 5.7ᵒC over pre‐industrial)

Heating

Cooling

Figure 9: Global energy consumption for cooling and heating with and without climate

change impacts

17

case, +0.3 GW in the Reference case with 3.3oC).

0

5

10

15

20

25

Biom

ass

Coal

Electricity Gas

Heat Oil

Biom

ass

Coal

Electricity Gas

Heat Oil

Biom

ass

Coal

Electricity Gas

Heat Oil

Biom

ass

Coal

Electricity Gas

Heat Oil

Biom

ass

Coal

Electricity Gas

Heat Oil

2005 2020 2050 2080 2100

EJ

Final Energy Consumption ‐ Heating   No CC

  CC1.6

  CC1.9

  CC2.2

  CC2.9

  CC3.3

  CC3.6

  CC3.8

  CC4.1

  CC4.4

  CC5.0

  CC5.4

  CC5.7

Figure 10: Impacts of climate change on fuels used for heating at World level

3.2.5 Emission perspective

Although the changes in heating and cooling don’t affect the climate, it is interesting

to observe the changes at the sector level, which could have consequences on mitigation

policies. Indeed, while emissions from buildings decrease (up to -1.2 GtCO2 in 2100

the worst temperature case) thanks to the reduction of heating, the increased demand

for cooling translates in an increase of emissions of up to in the power sector, given

the additional coal and gas installed capacities (up to +3.7 GtCO2 in 2100 in the worst

temperature case). The net balance is positive (up to +2.5 GtCO2 in 2100). The climate

system remains insensitive to these additional emissions, as mentioned above, given both

their small magnitude and late occurrence.

18

0

5

10

15

20

25

30

35

40

45

50

2005 2020 2050 2080 2100

EJ

Final Energy Consumption (all electricity) ‐ Cooling  NoCC

  CC1.6

  CC1.9

  CC2.2

  CC2.9

  CC3.3

  CC3.6

  CC3.8

  CC4.1

  CC4.4

  CC5.0

  CC5.4

  CC5.7

Figure 11: Impacts of climate change on fuels used for cooling at World level

0

10

20

30

40

50

Biom

ass

Coal

Electricity Gas

Heat Oil

Biom

ass

Coal

Electricity Gas

Heat Oil

Biom

ass

Coal

Electricity Gas

Heat Oil

Biom

ass

Coal

Electricity Gas

Heat Oil

Biom

ass

Coal

Electricity Gas

Heat Oil

2005 2020 2050 2080 2100

EJ

Final Energy Consumption ‐ Heating and Cooling   No CC

  CC1.6

  CC1.9

  CC2.2

  CC2.9

  CC3.3

  CC3.6

  CC3.8

  CC4.1

  CC4.4

  CC5.0

  CC5.4

  CC5.7

Figure 12: Impacts of climate change on fuels used for heating at World level

19

0

20

40

60

80

100

120

140

Biom

ass

Coal

Gas

Other Ren

ew

Nuclear Oil

Biom

ass

Coal

Gas

Other Ren

ew

Nuclear Oil

Biom

ass

Coal

Gas

Other Ren

ew

Nuclear Oil

Biom

ass

Coal

Gas

Other Ren

ew

Nuclear Oil

Biom

ass

Coal

Gas

Other Ren

ew

Nuclear Oil

2005 2020 2050 2080 2100

EJ

Electricity Supply  NoCC

  CC1.6

  CC1.9

  CC2.2

  CC2.9

  CC3.3

  CC3.6

  CC3.8

  CC4.1

  CC4.4

  CC5.0

  CC5.4

  CC5.7

Figure 13: Impacts of climate change on electricity production at World level

0

5

10

15

20

25

30

35

40

Power

Indu

stry

Res&

Com

Supp

ly Fossil

Transport

Power

Indu

stry

Res&

Com

Supp

ly Fossil

Transport

Power

Indu

stry

Res&

Com

Supp

ly Fossil

Transport

Power

Indu

stry

Res&

Com

Supp

ly Fossil

Transport

Power

Indu

stry

Res&

Com

Supp

ly Fossil

Transport

2005 2020 2050 2080 2100

GtCO2

CO2 emissions NoCCCC1.6CC1.9CC2.2CC2.9CC3.3CC3.6CC3.8CC4.1CC4.4CC5.0CC5.4CC5.7

Figure 14: Impacts of climate change on sector CO2 emissions by sector at World level

20

‐2

‐1

0

1

2

3

4

5

Power Res&Com Power Res&Com Power Res&Com

2050 2080 2100

GtCO2

Variation of CO2 emissions over Scenario without climate change impacts

CC1.6CC1.9CC2.2CC2.9CC3.3CC3.6CC3.8CC4.1CC4.4CC5.0CC5.4CC5.7

Figure 15: Variation of CO2 emissions in power and residential/commercial sectors over

the scenario without climate change impacts

3.3 Regional impacts

Impacts of heating and cooling adaptation to future climate change may have important

consequences at regional levels, depending on the characteristics of the local energy

systems and local climate changes. Such changes are illustrated in four contrasted cases:

India, characterized by a warm climate and a low energy budget allocated to heating and

cooling, Russia with a cold climate and a high energy budget allocated to heating and

cooling, and Europe and China, characterized by an intermediate climate and moderate

to high energy budget allocated to heating and cooling (Figure 2 , Table 2).

Table 2: Share of heating and cooling in total final energy consumption

India Russia Europe China

Range over 2005-2100 for long-term

temperature increase from 1.6 to 5.6oC 0.4% to 4% 14% to 24% 15% to 19% 4% to 13%

Adaptation of the heating and cooling services to future changes in temperature

translates in dominating increase of energy for cooling in India, dominating decrease

of energy for heating in Russia but also in China, where energy changes in heating are

stronger than in cooling, while changes in heating and cooling result as compensating

each other in Europe, when considering total energy uses (Figure 16). Of course, usual

energy system dynamics also occur, on top of the climate effects. For example, the

decrease of energy use observed in Europe between 2005 and 2020 is not climate related,

21

but reflects the substitution of inefficient oil heaters by more efficient gas and biomass

heaters. Impacts of climate change on specific energy commodities help understanding

the magnitude of the positive effects of climate change on the use of fossil fuels and

biomass for heating purpose in the four countries but India, where heating plays a

negligible role in energy consumption, and at the contrary, the possible pressure of

cooling on electricity generation in India, Chinas as well as Europe (Figures 17 and 18).

Not surprisingly, without any emission mitigation constraints, coal power plants appear

to be the most cost-efficient option, resulting in moderate increase of CO2 emissions

of India and Europe in the 3.3oC temperature case (up to 4% - but up to 10% in the

worst temperature case). At the opposite, a slight decrease of emissions in Russia may

be observed (up to -3.5%).

0

5

10

15

20

25

NoC

CCC

3.3

CC5.7

NoC

CCC

3.3

CC5.7

NoC

CCC

3.3

CC5.7

NoC

CCC

3.3

CC5.7

NoC

CCC

3.3

CC5.7

NoC

CCC

3.3

CC5.7

NoC

CCC

3.3

CC5.7

2005 2020 2050 2070 2080 2090 2100

EJ

Energy consumption for cooling and heating ‐ China

HeatingCooling

0

3

6

9

12

NoC

CCC

3.3

CC5.7

NoC

CCC

3.3

CC5.7

NoC

CCC

3.3

CC5.7

NoC

CCC

3.3

CC5.7

NoC

CCC

3.3

CC5.7

NoC

CCC

3.3

CC5.7

NoC

CCC

3.3

CC5.7

2005 2020 2050 2070 2080 2090 2100

EJ

Energy consumption for cooling and heating ‐ Russia

HeatingCooling

0

5

10

15

NoC

CCC

3.3

CC5.7

NoC

CCC

3.3

CC5.7

NoC

CCC

3.3

CC5.7

NoC

CCC

3.3

CC5.7

NoC

CCC

3.3

CC5.7

NoC

CCC

3.3

CC5.7

NoC

CCC

3.3

CC5.7

2005 2020 2050 2070 2080 2090 2100

EJ

Energy consumption for cooling and heating ‐ Europe

HeatingCooling

0

5

10

15

20

25

NoC

CCC

3.3

CC5.7

NoC

CCC

3.3

CC5.7

NoC

CCC

3.3

CC5.7

NoC

CCC

3.3

CC5.7

NoC

CCC

3.3

CC5.7

NoC

CCC

3.3

CC5.7

NoC

CCC

3.3

CC5.7

2005 2020 2050 2070 2080 2090 2100

EJ

Energy consumption for cooling and heating ‐ China

HeatingCooling

Figure 16: Total final energy consumed for heating and cooling with and without climate

change impacts in India, Russia, Europe and China

The seasonal impacts of climate change, more particularly on the generation, peak

management and price of electricity (and possible gas) deserves more analyses. The re-

sults and main insights will be added in the coming updated version of the manuscript,

taking into account also the impacts of temperature increase on the efficiency and avail-

ability of thermal power plants.

22

 

Figure 17: Impacts of climate change on specific energy commodities consumed for

heating and cooling in India, Russia, Europe and China

23

 

Figure 18: Impacts of climate change on electricity supply in India, Russia, Europe and

China

24

4 Macroeconomic impacts

4.1 Incorporating energy demand changes in general equilib-

rium model

In GEMINI-E3, for households we describe only total energy consumption without de-

scribing the different purposes (heating, cooling, cooking, lighting, etc. see section 2.4.1),

following the nested CES functions represented in Figure 3. Total energy consumption

for residential needs (HCRESEr) is given by:

HCRESEr = HCRESr · λhresr · αhresr ·[

PHCRESrPHCRESEr · λhresr

]σhresr

, (5)

where HCRESr represents household consumption for residential purposes (i.e. ex-

penses related to the shelter and the energy), PHCRESr and PHCRESEr are re-

spectively the price of residential consumption and the aggregated energy price used for

residential purposes (heating, cooling, cooking,...). σhreser , αhreser and λhreser represent

the CES parameters, respectively the elasticity of substitution, the share parameter and

the technology shifter. Total energy is splited between coal, refined oil, natural gas and

electricity distinguished by GEMINI-E3 with the following CES functions:

HCRESEFr·θhresefr = HCRESEr·λhreser ·αhreser ·

[PHCRESEr

PHCRESEFr · λhreser /θhresefr

]σhreser

(6)

Where HCRESEFr represents total fossil energy consumption for residential pur-

pose, PHCRESEFr the price of this energy and θhresefr a technical progress associated

to this fossil energy. Electricity consumption by households is given by:

H05,r · θ05,r = HCRESEr · λhreser · (1− αhreser ) ·[

PHCRESErPHC05,r · λhreser /θ05,r

]σhreser

(7)

and fossil energies (where i=01,02,03,04) consumed for residential purpose are com-

puted by the following equation:

Hi,r = HCRESEFr · λhresefr · αhresefi,r ) ·

[PHCRESEFr

PHCi,r · λhresefr

]σhresefi,r

(8)

The impacts of climate change on heating and cooling demands are computed through

the HDD and CDD indicators. They are used to modify the parameters θ05,r and θhresefr .

We assume that the decrease of energy needs for heating coming from the decrease of

25

HDDs can be considered as an increase of the technical progress associated to this con-

sumption. The same hypothesis is applied for cooling but in this case we decrease the

technical progress. As GEMINI-E3 does not distinguish within the energy consumption

for residential between the different uses, we consider the budget shares (in monetary

unit) of heating and cooling coming from the TIAM-WORLD model for each period

and for the baseline. In the scenarios, we modify the technical progress by using the

following equations:

θt05,r =θt05,r

1 + βheating05,t,r ·(

HDDt,r

HDD2000,r− 1)

+ βcooling05,t,r ·(

CDDt,r

CDD2000,r− 1) (9)

θthresef,r =θthresef,r

1 + βheatinghresef,t,r ·(

HDDt,r

HDD2000,r− 1)

+ βcoolinghresef,t,r ·(

CDDt,r

CDD2000,r− 1) (10)

Where βheating05,t,r (βcooling05,t,r ) represent the share of electricity used for heating (for cool-

ing) in the total electricity consumption for household, and βheatinghresef,t,r (βcoolinghresef,t,r) rep-

resent the share of fossil energy used for heating (for cooling) in the total fossil energy

consumption for household. θthresef,r and θt05,r are the technical progresses used in the

reference case. These budget shares are presented in Figures 19 and 20. The budget

shares are computed in value from TIAM-WORLD figures and are equal to expenses in

energy heating (i.e. quantity × price) divided by total expenses of households in energy

including residential and transportation purposes. The final impact of climate change

on energy consumption will depend of course of the respective shares on heating and

cooling coming from TIAM-WORLD and the changes in the HDDs and CDDs.

We apply a same methodology for heating and cooling in commercial activities. In

this case we modify the technical progress associated with the services sector represented

in GEMINI-E3.

4.2 Results and Analysis

We run 3 scenarios related to the impacts of climate change on the energy demands:

• in the first one, we analyse the decrease in the heating energy consumption,

• in the second one, we study the increase of cooling energy consumption,

• in the final one, we simulate the both effects.

For each simulation we present the results for the year 2100.

4

26

0

500

1000

1500

2000

2500

3000

3500

4000

4500

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

USA CAN AUZ EEU WEU FSU CHI IND REA RSA SEA MID LAT AFR

Share

HDD

Figure 19: Budget share of heating energy consumption on total energy consumption in

percentage4(left axis) and HDD (right axis) in 2010

0

1000

2000

3000

4000

5000

6000

0%

2%

4%

6%

8%

10%

12%

14%

16%

USA CAN AUZ EEU WEU FSU CHI IND REA RSA SEA MID LAT AFR

Share

CDD

Figure 20: Budget share of cooling electricity consumption on total electricity consump-

tion in percentage (left axis) and CDD (right axis) in 2010

27

4.2.1 Decrease in the heating energy consumption

Figure 21 shows the changes of the HDDs in 2100 and the variations of household

total fossil energy consumption. The regions with the highest space heating energy

demands (refer to the budget shares represented in Figure 19) are European countries,

Former Soviet Union, China and north American countries. For these regions, energy

demand for space heating declines within a range of 10% to 30% in 2100, resulting in

significant energy savings. Nevertheless even if the decreases of energy consumption for

space heating are significant, the impacts on fossil energy consumption of households

(including energy used for private transportation) are rather limited and never higher

than 5.5% (see China). At the global level, the decrease of consumption in fossil energy

by households is equal to 2.6%. Analyses done with TIAM-WORLD coupled with

PLASIM-ENTS reaches similar conclusions in longer terms, and even in cases where the

future climate changes are increased.

It is also interesting to note that the ex-post decrease of heating energy computed by

GEMINI-E3 is always less important than the ex-ante change introduced in the model.

For example, for the USA we implement an ex-ante change of fossil energy equal to

8.2%5 but the ex-post variation computed by GEMINI-E3 is about 5.3%. This differ-

ence stems from a rebound effect, which is well-documented in the economic literature

(Dimitropoulos, 2007) explaining that when the cost of energy services is decreasing

(which is the case when we suppose that less energy is required to satisfy the same

level of confort) there is a tendency to increase the level of confort (i.e. increase the

temperature inside buildings) by using more energy which limits the ex-ante fall.

Of course the impact is much less important on energy consumption at the national

level even if we take into account the decrease of heating by the commercial activities.

Figure 22 shows that the most affected regions are the Eastern European countries where

the decrease of heating demand induces a fall of total energy consumption by 2.1%.

The decrease of the energy needs for heating creates of welfare gain which are of

course correlated to decrease of the energy consumption (see Figure 22). For regions

that are energy exporting countries this welfare gain is reduced by losses of revenue

coming from less energy exports. This is the case of Middle East countries, Former

Soviet Union, Africa and Canada. Two regions experience a welfare loss, Middle East

and Africa. In these regions the need of space heating is rather limited and therefore

the benefit from a reduced demand of space heating can not offset the loss of energy

5the ex-ante change is equal with our notation to: 1− 1

1+βheatinghresef,usa

·(

HDDt,usaHDD2000,usa

−1

)

28

‐90%

‐80%

‐70%

‐60%

‐50%

‐40%

‐30%

‐20%

‐10%

0%

‐6.0%

‐5.0%

‐4.0%

‐3.0%

‐2.0%

‐1.0%

0.0%USA CAN AUZ EEU WEU FSU CHI IND REA RSA SEA MID LAT AFR

Fossil energy

HDD

Figure 21: Variation in household fossil energy in % (left axis) and HDDs change in %

(right axis) in 2100 - Scenario: impact of CC on heating demand

exports, in contrary for example to other energy exporting regions like Former Soviet

Union.

4.2.2 Increase in the cooling energy consumption

In this scenario we increase the energy demand for space cooling by changing the tech-

nical progress associated to electricity consumption for household consumption and the

services sector. Figure 23 gives the impacts of the climate change on total electricity

consumption and the changes in CDDs. For regions where the level of CDDs is signifi-

cant (i.e. India, South Asia, Middle East, Latin America and Africa), the need for space

cooling increases within a range of 10% to 30% in 2100. The final impact on electricity

consumption is explained by three factors:

• the initial level of the CDDs (i.e. without climate change),

• the diffusion and use of coolers linked to socio-economic factors (McNeil and

Letschert, 2008),

• the impact of the climate change on the CDDs.

The two first factors are represented in GEMINI-E3 by the parameter βcooling05,t,r which is

calibrated from the TIAM-WORLD Model. The last one is computed from PLASIM-

29

‐2.4%

‐1.9%

‐1.4%

‐0.9%

‐0.4%

0.1%

0.6%

USA CAN AUZ EEU WEU FSU CHI IND REA RSA SEA MID LAT AFR

Total energy consumption

Welfare change

Figure 22: Welfare changes in % of households consumption and percentage change of

energy consumption in 2100 - Scenario: impact of CC on heating demand

ENTS.

The increase of total electricity consumption remains moderate, at the global level.

Total electricity (of all sectors) demand increases by 1.2% in 2100, the highest increase

is equal to 2.7% in USA.

The increase in electricity consumption for cooling purpose has detrimental effects

on the economy but at seemingly low levels. The welfare impact expressed in percentage

of household consumption is always below 0.08%. Figure 24 shows these regional welfare

costs.

4.2.3 Impact of climate change on energy consumption

We combine in this scenario the decrease of energy for space heating needs and the

increase of electricity for cooling buildings. Figure 25 presents the impacts on fossil

energy consumption, electricity consumption and CO2 emissions. This figure shows

that although the variation in percentage of electricity is generally greater than the

percentage change in the consumption of fossil fuels, the final impact is a reduction in

CO2 emissions, except for Latin America and India, where the increase of electricity

induces an increase in CO2 emissions by the electricity sector. However the impact on

CO2 emissions is low, and never higher than a decrease of 2.0%, at the global level CO2

30

0%

10%

20%

30%

40%

50%

60%

70%

80%

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

USA CAN AUZ EEU WEU FSU CHI IND REA RSA SEA MID LAT AFR

Electricity

CDD

Figure 23: Variation in electricity consumption of all sectors in % (left axis) and CDDs

change in % (right axis) in 2100 - Scenario: impact of CC on cooling demand

‐0.12%

‐0.10%

‐0.08%

‐0.06%

‐0.04%

‐0.02%

0.00%

USA CAN AUZ EEU WEU FSU CHI IND REA RSA SEA MID LAT AFR

Figure 24: Welfare changes in % of household consumption in 2100 - Scenario: impact

of CC on cooling demand

31

emissions decreases by 0.6% in 2100. Analysis done with TIAM-WORLD coupled with

PLASIM-ENTS confirms that the global feedback between the energy and the climate

systems due to changes in heating and cooling remains very small even in 2100 (less

than 1% increase of global emissions), even if changes at national levels are not small in

some cases.

The welfare impacts are presented in Figure 26. They are equal to the sum of

the welfare gains coming from the decrease of energy for heating (except for energy

exporting countries which experiences loss of exports revenue see section 4.2.1) and to

the welfare losses due to an increase of electricity for space cooling. For non energy

exporting countries, the welfare gains linked to the decrease of energy needs for heating

overcompensate the cost coming from the increase of electricity consumption.

‐3.0%

‐2.0%

‐1.0%

0.0%

1.0%

2.0%

3.0%

USA CAN AUZ EEU WEU FSU CHI IND REA RSA SEA MID LAT AFR

Fossil energy

Electricity

CO2

Figure 25: Variation in fossil energy consumption, electricity consumption and CO2

emissions in %- Scenario: impact of CC on heating and cooling combined

5 Conclusion

Adaptation to future climate change includes changes in heating and cooling, being on

an autonomous or planned basis. The analysis, based on the coupling of a climate model

with a techno-economic energy model and a general equilibrium model, confirms that

the impacts on the energy system may be large at the regional levels, with decrease of

energy uses (mostly fossil fuels and biomass) for heating, and increase of electricity for

32

‐0.3%

‐0.2%

‐0.1%

0.0%

0.1%

0.2%

0.3%

0.4%

0.5%

USA CAN AUZ EEU WEU FSU CHI IND REA RSA SEA MID LAT AFR

Figure 26: Welfare changes in % of household consumption in 2100 - Scenario: impact

of CC on heating and cooling combined

cooling depending of course on the regions. The impacts on electricity generation may

reach up to several GW in some regions, often supplied by coal, resulting in additional

greenhouse emissions. However, the climate feedback is negligible, given the small share

of heating and cooling in total final energy use and also the balancing, at the global

level, of the increased emissions from the additional electricity supply by the decrease

of emissions by associated to space heating. (Range of % of changes in energy uses for

heating and cooling should be used carefully, and therefore are not provided as main

insights; they could indeed give wrong signals, since they may hide may small absolute

numbers and therefore not be significant).

At the macro-economic level, welfare gains and losses are observed, associated to

changes in energy consumption as well as in energy trade dynamics. They remain

however small.

Several other impacts of climate change on the energy system are being implemented

in the models, to be combined with the heating and cooling effects: the impacts of tem-

perature increase on the efficiency and availability of thermal plants, on the bioenergy

availability and price and on the hydroelectricity potentials. The impacts on electricity

prices and peak management will deserve more attention once the different possible ef-

fects of climate change will be combined in the models. Extreme events and vulnerability

of other energy resources are be covered by this study.

33

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