Global climate-oriented building energy use scenarios

Energy Policy ∎ (∎∎∎∎) ∎∎∎–∎∎∎

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Global climate-oriented building energy use scenarios L.D. Danny Harvey n Department of Geography, University of Toronto, 100 St. George Street, Toronto, ON, Canada M5S 3G3

H I G H L I G H T S








A detailed model for generating global scenarios of building energy use is presented. Drivers of increasing energy use are population and per capita GDP in 10 regions. Heating, cooling and ventilation energy uses are projected using a stock turnover model. Global building fuel demand could decrease by 60–80% by 2100 relative to 2010. Global building electricity demand could be limited to a 100–200% increase.

art ic l e i nf o

a b s t r a c t

Article history: Received 25 July 2013 Received in revised form 14 December 2013 Accepted 16 December 2013

This paper explores the extent to which global fuel use in buildings could be reduced, and the growth in global electricity use in buildings limited, by applying stringent (factor of 3–4) improvements to recent building codes for new buildings worldwide and large (factor of 2–3) reductions in the energy use of existing buildings through renovations. The analysis is carried out for 10 different socio-economic regions of the world, taking into account existing building stock and energy intensities in each region and projected changes in population and income, which in most parts of the world will drive large increases in building floor area. A stock turnover model is applied to project changes in heating, cooling, service hot water (SHW) and non-thermal electricity demand with various rates of improvement in standards for new and renovated buildings, and various rates of renovation and demolition of existing buildings. For a scenario in which population peaks at about 9 billion and global average per capita GDP increases to twice the 2010 value by 2100, the global fuel demand could be reduced by a factor of four while limiting maximum annual electricity demand to twice the 2010 value. & 2013 Elsevier Ltd. All rights reserved.

Keywords: Building energy use scenario Reducing CO2 emissions Stock turnover model

1. Introduction The purpose of this paper is to assess the consequences for the global demand for fuels and electricity by the buildings sector of close to maximally-ambitious scenarios for the reduction in the energy intensity of new buildings and of existing buildings after renovation, in conjunction with a range of scenarios for the growth in building floor area and in the rates of renovation or replacement of existing buildings. This is part of a larger assessment involving transportation (Harvey, 2013a), industry and C-free energy sources that is directed toward articulating in considerable detail packages of measures at the regional level that succeed in eliminating global fossil fuel CO2 emissions by 2100 so as to substantially reduce the risk of catastrophic global warming. Ambitious plans for tightening the energy provisions of buildings codes have been announced by a number of countries,

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complemented by measures to require significant improvements in the energy performance of buildings at the time of major renovations. However, significant growth in building floor area over the coming decades is projected in many parts of the world. Without a sufficiently detailed accounting framework, it is impossible to know what the net effect of increasing building floor area and global extension of the proposed tightening of building codes for new and renovated buildings would have an overall fuel and electricity demand. This paper addresses this need by presenting a relatively detailed accounting model, and representative results, that takes into account many of the opposing factors that will affect future energy use in buildings. The model to be presented here builds upon and adds further end use detail to a set of global building energy scenarios presented in Harvey (2010, Section 10). Other recent scenarios at the global scale are Ürge-Vorsatz et al. (2012), the International Energy Agency0 s Energy Technology Perspectives 2012 (IEA (International Energy Agency), 2012a), and the Global Energy Assessment of the International Institute for Applied Systems Analysis (IIASA, 2012), which apply to all building energy

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uses, and McNeil et al. (2008a, 2008b) for appliances and office equipment. At the core of all the scenarios is a model of the turnover of building stock (through replacement or renovation), of the growth in total building floor area, and of the change in energy intensity over time of new and renovated buildings. As articulated by Coffey et al. (2009), the primary goals of this type of model are to ask “what-if” questions and to provide transparent answers that support goal-setting and high level policy design. The layout of this paper is as follows: Section 2 outlines recent trends in the energy provisions of building codes in various countries where dramatic improvements have occurred, and identifies countries where significant tightening of the energy provisions is expected over the next decade. The recent trends and proposed future improvements attest to the political feasibility (in at least some jurisdictions) of stringent tightening of building codes, and provide a good example for less ambitious jurisdictions. Section 3 describes the framework for developing scenarios of future building energy demand, including the drivers of increasing energy demand, and justifies assumptions concerning potential stringent energy performance standards for new and renovated buildings and for consumer goods and office equipment in buildings. Section 4 presents the resulting fuel and electricity use worldwide to 2100 for various assumptions concerning the growth of building floor area, rates of implementation of the proposed standards identified in Section 3, and rates of renovation of existing buildings, and compares the key results obtained here with those in other building energy use scenarios. Section 5 draws some policy implications. The scenarios presented here are generated through a combination of Excel worksheets and a Fortran program that reads input files generated by the Excel worksheets and then generates output that is pasted into the Excel worksheets for further analysis. To permit the interested reader to develop alternative scenarios with inputs of his or her own choosing, the Excel worksheets and Fortran program are available as supplemental online material.

2. Recent trends and future projections in the energy provisions of building codes 2.1. Recent trends An impressive strengthening of the energy provisions of building codes has occurred during the past decade in many jurisdictions. For example, the energy use relative to the stock average in 2003 for a sample of 1000 California buildings under the 2010 version of the building code developed by the American Association of Heating, Refrigeration and Air Conditioning Engineers (ASHRAE) for commercial buildings (ASHRAE 90.1-2010) is about 50% that of the 2003 stock average and about 40% less than if built according to ASHRAE 90.1-1999 (AEC-SCE, 2009), while the heating energy intensity allowed for medium and larger buildings under the 2012 German code (EnEV 2012) is about 1/3 that allowed prior to 2004. Harvey (2013b) presents data on heating requirements for residential dwellings built during successive decades in nine cold-climate countries; typical reductions per unit of floor area by a factor of 3–4 occurred over the past 50 years. 2.2. Planned and proposed strengthening of the energy provisions of building codes There have been many ambitious proposals for energy standards for new buildings in many countries. The US government0 s Building America program is promoting a drive toward net zeroenergy buildings by 2020, with 70% of the required reduction (relative to typical new construction in 2000) achieved through

reduced energy demand. The California Energy Commission has proposed that California work toward achieving net-zero energy for all new residential buildings by 2020 and for all new commercial buildings by 2030 (CEC (California Energy Commission), 2011). In Europe, energy performance standards for buildings are guided by the European Commission0 s Energy Performance in Buildings Directive (EBPD). Based on proposals presented by the European Commission in November 2008 concerning strengthening the EPBD, an update of the EPBD was adopted by the European Parliament and the Council of the European Union on 19 May 2010 that requires that all new public buildings be “nearly zero-energy” by 31 December 2018, and that all other buildings be nearly zeroenergy by 31 December 2020 (ECEEE (European Council for an Energy Efficient Economy), 2011). The interpretation of what constitutes “near zero-energy” is left to individual European states to determine in their implementation of the directive. The most stringent heating energy standard is the German Passivhaus Standard, which is an annual heat demand (or heating load) of no more than 15 kW h/m2 yr irrespective of the climate. This standard represents a reduction in heating energy use by a factor of 2–6 compared to the most recent code requirements for new residential buildings, and by a factor of 10–25 or more for existing residential buildings. As of the end of 2012, over 50,000 buildings (both residential and non-residential) had been built in Europe that met the Passive House Standard. A number of cities in Europe (foremost being Frankfurt) have adopted the Passivhaus Standard for some or all categories of municipal buildings, and effective January 2015, it will be the legally binding standard for all new buildings in Brussels (Daoud and Huytebroeck, 2011). In response to the European EBPD, referred to above, Austria has proposed that the Passive House standard be the new standard for all new buildings by 2014–2015, Finland has proposed that all new public buildings meet the Passive House standard by 2015, Denmark and The Netherlands are proposing a 50% reduction by 2015 compared to 2008, and Switzerland has considered its own Minergie-P voluntary standards (25–45 kW h/m2 yr on-site heatingþhot water energy use, depending on the type of building) as the mandatory standard by 2015 (BPIE (Buildings Performance Institute Europe), 2011a, Table 3). The European EBPD also requires member states to develop policies “to stimulate the transformation of buildings that are refurbished into near zero-energy buildings” (EPBD Article 9, Paragraph 2). Numerous case studies of renovation achieving 80–90% heating energy savings and 25–50% cooling energy savings are reviewed in (Harvey, 2013b). In 2007, the German Federal Government announced a program to bring all pre-1984 housing up to a heating standard twice as stringent as the standard for new buildings at that time by 2020 (this would be a heating requirement of 40 kW h/m2 yr, representing an average savings of about 80%) (Power, 2008).

Table 1 Approach used to model the various building energy end uses. STM¼stock turnover model. EI(t) ¼ energy intensity (kW h/m2 yr) as a direct function of time, EI(I, t) ¼energy intensity as a function of mean regional per capita income and time, EU(t)¼ energy use (GJ/person) as a function of time, EU(I, t)¼ energy use as a function of mean regional per capita income and time. Energy use

Residential buildings

Commercial buildings

Space heating Space cooling Ventilation Lighting Service Hot Water Cooking Consumer or office equipment

STMþ EI(I, t) STMþ EI(I, t) STMþ EI(I, t) Floor areaþ EI(t) Population þ EU(I, t) Population þ EU(I, t) Population þ EU(I, t)

STMþ EI(I, t) STMþ EI(I, t) STMþ EI(I, t) STMþ EI(I, t) Floor areaþEI(t) Floor areaþEI(t) Floor areaþEI(t)

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Table 2 The 10 world regions, initial population and GDP, peak population for low and high population scenarios, asymptotic GDP/P for low and high GDP/P scenarios, and the regional growth parameters adopted here for GDP/P. Region

PAO (Pacific Asia OECD) NAM (North America) WEU (Western Europe) EEU (Eastern Europe) FSU (Former Soviet Union) LAM (Latin America) SSA (Sub-Saharan Africa) MENA (Middle East and North Africa) CPA (Centrally planned Asia) SPA (South and Pacific Asia)

Population (millions)

GDP/P ($/person)

2010

2010

Maximum

201 348 484 119 286 586 813 424 1484 2148

Low

High

202 399 494 120 288 661 2171 608 1531 2570

205 530 526 120 289 750 3230 792 1582 3010

Table 3 Initial (2010) floor area and per capita floor areas (computed from the given floor area and population), and the floor area per capita that would be reached with arbitrarily large per capita income. Source of floor areas: Harvey et al. (2014). Region Residential buildings Floor area (billion m2)

Commercial buildings

Per capita floor area (m2)

Floor area (billion m2)

2010 Asymptotic PAO 8.1 NAM 22.6 WEU 17.5 EEU 2.9 FSU 7.0 LAM 9.4 SSA 8.0 MENA 6.9 CPA 41.4 SPA 28.1 Global 152.0

40 65 36 24 24 16 10 16 28 13 22.0

55 60 45 45 45 40 40 40 40 40

Per capita floor area (m2) 2010 Asymptotic

3.0 8.3 6.9 0.9 1.9 2.7 1.1 1.7 8.0 4.8 39.2

15.0 23.7 14.2 7.5 6.5 4.7 1.4 4.0 5.4 2.2 6.5

25 25 20 18 18 18 15 15 15 15

Asymptotic Low

High

34,000 40,000 30,000 30,000 27,000 25,000 22,000 22,000 27,000 22,000

50,000 55,000 50,000 45,000 45,000 40,000 30,000 35,000 45,000 40,000

0.02 0.01 0.02 0.04 0.04 0.04 0.04 0.04 0.06 0.06

Table 1 lists the various energy uses that are explicitly represented here and how they are modelled. Details are given later. The world is divided here into 10 different socio-economic regions, and Eq. (1) is applied separately to residential and commercial buildings in each region. Table 2 lists the 10 different regions considered here and their initial population and GDP/P. These regions are the same as the 11 world regions used in the Special Report on Emission Scenarios (SRES) of the Intergovernmental Panel on Climate Change (Nakicenovic and Swart, 2000, Appendix 3), except that South Asia and Pacific Asia have been combined into one region, and South Korea has been placed here in the Pacific-Asia OECD region alongside Japan, Australia and New Zealand. They can also be combined to form the five regions used for the IPCC Representative Concentration Pathways scenarios (IIASA (International Institute for Applied Systems Analysis), 2009). 3.2. Activity drivers The activity drivers are increasing population and gross domestic product (GDP) per person, which together drive increases in building floor area and energy use.

3. Projecting future building energy demand A model, described below, was developed to incorporate past trends in building energy intensity and possible future improvements in the performance of new and renovated buildings. 3.1. Decomposition of building energy demand Some energy uses in buildings, such as for heating, cooling, ventilation and lighting, depend on building floor area. Others, such as for residential service hot water (SHW, for showers and washing) and cooking, are more naturally related to population. Thus, the total energy use in region i can be computed as Ei ¼ P i Ai ðI i Þ∑j EI ij ðI i Þ þ P i ∑k EU ik ðI i Þ

33,990 45,516 30,923 16,489 11,412 10,966 2,232 8,930 7,270 4,127

GDP/P growth parameter

ð1Þ

where Pi is population in region i, Ai is the floor area per capita, EIij is the energy intensity (kW h per m2 of floor area per year) for energy end use j in region i, and EUik is the energy use per capita for energy end use k in region i, with Ai, EUij and EUik are all being functions of regional of mean per capita income Ii. Of those energy intensities that are given per unit of building floor area, some can be changed only through renovations of the building, while others can be changed without renovations. The former require a building stock turnover model in order to account for the lag between the introduction of new standards for new buildings or renovations, and changes in the mean energy intensity of the building stock.

3.2.1. Population and gross domestic product per person The scenarios presented here cover the period 2010–2100. Population for the period 2010–2100 is taken from the 2010 edition of the United Nations Population Division0 s World Population Prospects (UNPD (United Nations Population Division), 2010), summed over the specific countries that make up each of the regions given in Table 2. National GDPs in purchasing-powerparity terms in 2010 are taken from IMF (International Monetary Fund) (2012) and summed over the countries that make up each of the regions used here. The average GDP/P in each region, G, is assumed to follow a logistic function, whereby G(t) at some time t after 2010 is given by GðtÞ ¼

GU 1 þððGU  Go Þ=Go Þe  aðt  t o Þ

ð2Þ

where to ¼2010, GU is the assumed asymptotic GDP/P value in each region, and Go is the GDP/P person in 2010. As time progresses, G asymptotically approaches GU. Table 2 gives the UNDP population in 2100 for their low and medium population scenarios and the asymptotic GDP/P values adopted here for low and high GDP/P growth scenarios, and the growth parameter a in Eq. (2), which is assumed to be the same for low and high growth scenarios. For North America, a slight decrease in GDP/P has been assumed for the low GDP/P scenario,

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while GDP/P continues to increase in all regions for the high scenario. The difference in the regional per capita income between the poorest region (Sub-Saharan Africa) and the richest region (North America) decreases from a factor of 20 at present to a factor of about 2 by 2100 in both scenarios. These are arbitrary scenarios, meant only to illustrate the impact on global building energy demand, in conjunction with the GDP/P-floor area relationships introduced later, of alternative assumptions concerning regional population and GDP/P. Fig. 1a shows the resulting high and low global population and high and low global mean GPD/P scenarios, while Online Supplement Fig. S1 and S2 give the scenarios for individual regions. Global population peaks at 8.13 billion around 2045 and drops to 6.69 billion by 2100 in the low population scenario, and has risen to 10.12 billion by 2100 in the high scenario. Global average GDP/P increases by a factor of 2.1 (from $10800 to $22300) by 2100 in the low GDP/P growth scenario and by a factor of 3.2 (to $34600) in the high scenario. In the subsequent analysis, only the combination of low population with low GDP/P and high population with high GDP/P will be considered (referred to as the Low and High scenarios, respectively). The resulting variation in global GDP is shown in Fig. 1b (along with the variation in the annual rate of growth of global mean GDP/P for the two GDP/P scenarios). Under the low scenario, global GDP is 1.9 times larger in 2100 than in 2010, while under the high scenario it is 4.7 times larger. 3.2.2. Building floor area Residential and commercial building floor areas per person are assumed to increase with increasing income following a logistic function, in line with data presented in Schipper et al. (2001) and

McNeil et al. (2008a). That is, future floor area per person is given by AðIÞ ¼

A1 1 þ ððA1  A2010 Þ=A2010 Þe  aðI  I2010 Þ

ð3Þ

where I2010 and A2010 are the regional average GDP and floor area per person in 2010 and A1 is the saturation (asymptotic) value of the average floor area per person. A growth parameter a of 0.0002 is used for illustrative purposes. Table 3 gives the regional floor areas and per capita floor areas in 2010, and asymptotic per capita floor areas adopted here. Regional floor areas in 2010 were deduced from a variety of sources, as detailed in Harvey et al. (2014), while justification for the asymptotic floor areas per capita is given in the Online Supplement; it will simply be noted here that asymptotic floor areas are assumed to be smaller in regions with high population density than in regions with lower density. Fig. 2 shows the resulting low and high scenarios for the variation in floor area for residential and commercial buildings, summed over regions 1–5 (‘Cold’ regions) and 6–10 (‘Hot” regions). Cold region residential floor area decreases by 20% for the Low scenario (due to falling population) and increases by about 40% for the High scenario, while Hot-region residential floor area increases by factors of 2 and 4 for the same scenarios. Coldregion commercial floor area is unchanged or increases by 50%, and Hot-region commercial floor area increases to 4 and 7 times the 2010 area for the Low and High scenarios, respectively. As with other drivers, these are intended merely as illustrative scenarios.

350

40

Population (billions)

8

32

6

24 16

4 GDP/capita

2

8

Residential Floor Area (billion m2)

Population

10

Average GDP/capita (1000s 2005$)

48

12

300 250

High, hot Low, hot High, cold Low, cold

200 150 100 50

0

0

0 400

4

3

250 200

2

150 100 50 0 2000

1 Rate of growth in world average GDP/capita

2020

2040

2060

2080

0 2100

Commercial Floor Area (billion m2)

300

Rate of Growth in GDP/P (%/yr)

World GDP (trillions 2005$)

140 World GDP

350

120 100

High, hot Low, hot High, cold Low, cold

80 60 40 20

Year Fig. 1. (a) Variation in global population and in global average GDP/P for the high scenario (solid lines) and low scenario (dashed lines). (b) Variation in gross world product (GWP) for the combination of high population with high GDP/P growth (solid line) and low population with low GDP/P growth (dashed line). Also shown is the variation in the rate of growth of world average GDP/P for the low and high GDP/P scenarios.

0 2010

2040

2070

2100

Year Fig. 2. Variation in global (a) residential and (b) commercial building floor area in ‘Cold’ and ‘Hot' regions (regions 1–5 and 6–10 from Table 3, respectively) for the Low and High scenarios.

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3.3. Present stock-average energy intensities A four-dimensional breakdown of building energy use in 2010, disaggregated by region, sector (residential and commercial), end use (space heating, space cooling, ventilation, water heating, lighting, cooking and other) and energy source (fossil fuels, biofuels, district heat, solarþ geothermal, and electricity) is presented in Harvey et al. (2014). This dataset was calculated by further disaggregating the partly disaggregated datasets available from the International Energy Agency (IEA) Energy Balances reports for 2010 (IEA (International Energy Agency), 2012b, 2012c) and the Energy Technology Perspectives 2012 report (IEA (International Energy Agency), 2012a), supplemented by fully disaggregated regional data where available. The details concerning the disaggregation procedure are given in Harvey et al. (2014), along with complete 4-D results and a discussion of the data sources used and of uncertainties. Table 4 gives the estimated average energy intensities in 2010 by end use, sector, and region (but summed over all energy types), while Table S1 gives the proportions of different energy sources for various end uses. Average residential energy intensities for HVAC (heating, ventilation and air conditioning)þlighting range from less than 10 kW h/m2 yr in SubSaharan Africa and south Asia to over 150 kW h/m2 yr in EEU and FSU. Regional annual per capita energy use for service hot water ranges from 1.3 to 6.5 GJ, while annual per capita cooking energy uses ranges from 1.0 to 6.9 GJ, with high values in developing countries associated with heavy reliance on inefficiently used biomass energy. Annual miscellaneous residential electricity uses (for appliances and consumer electronics) range from about 20– 2450 kW h/person. Average commercial building energy intensities summed over all end uses range from about 100 kW h/m2 yr in CPA to about 380 kW h/m2 yr in FSU. On a global basis, biomass accounts for about 20%, 50% and 70% of the energy used for residential space heating, water heating and cooking, respectively, but plays a very minor role in the commercial sector (see Table S1).

cooling þventilation, as well as for commercial lighting. This model requires specification of a distribution of energy intensities for the 2010 building stock in each region, specification of the variation in regional building floor area and in the rates of demolition and renovation over time, and specification of the change over time in the energy intensity of existing, newly built and renovated buildings. The stock turnover model is described in detail in the Online Supplement, but the key qualitative features needed to understand the strengths and weaknesses of the approach used here are as follows:


allowance is made for the heating and cooling energy use of
















3.4. Future energy intensities simulated with a stock-turnover model A stock-turnover model is used to simulate future energy intensities for residential and commercial space heating and space

5




existing buildings to increase with increasing income in those regions of the world where indoor temperatures are not maintained at comfortable levels today due to lack of income; a transition to very high energy performance standards for new and renovated buildings is assumed to occur in all regions, with the heating and cooling energy intensities that are assumed to be eventually achieved dependent on the population-weighted mean number of annual heating degree days (HDD) and cooling degree days (CDD) in each region; the energy performance standards assumed for advanced new buildings and renovations are based on a comprehensive review (Harvey, 2013b) of what has been achieved in state-of-the-art buildings around the world today, and correspond to the case where comfortable indoor temperatures are maintained; the entire building stock that existed in each region in 2010 is assumed to be either renovated or replaced over the period 2010–2055, with a second renovation-or-replacement cycle over the period 2055–2100; the 2010 building stock in each region is divided into 45 cohorts that span a distribution of heating and cooling energy intensities centred around the mean energy heating and cooling energy intensities for that region; building cohorts are assumed to be renovated or replaced starting with the oldest cohort and working to toward the youngest cohort; the impact on total energy use when a given cohort is renovated or replaced therefore depends on the shape of the

Table 4 Estimated energy intensities in 2010 by end use, region and sector. Source: Harvey et al. (2014). The total for residential buildings excludes cooking, as cooking energy use on a per floor area basis is not meaningful but can be very large in the residential sector. Residential buildings Space heating

Cool-ing

Venti-lation

Light-ing

2

Region (kW h/m yr) PAO 40 NAM 67 WEU 134 EEU 150 FSU 165 LAM 7 SSA 2 MENA 26 CPA 29 SPA 0 Commercial buildings (all end uses as

2.8 8.1 2.2 1.5 1.5 7.9 1.1 4.6 1.2 1.5 kW h/m2 yr)

PAO NAM WEU EEU FSU LAM SSA MENA CPA SPA

33 55 18 19 29 44 18 32 9 13

126 123 106 145 203 15 1 50 42 1

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 37 21 8 8 24 11 4 8 2 7

6.3 9.1 4.7 3.3 5.9 5.5 2.0 9.0 4.5 8.3 57 47 25 26 39 17 9 21 6 17

Hot Water (GJ/person-yr) 3.8 6.0 3.1 2.4 6.3 1.4 6.5 4.2 3.6 2.5 41 29 34 47 21 10 20 28 15 21

Cook-ing

1.3 1.3 1.0 1.7 2.5 3.7 6.9 3.5 2.7 3.4 17 9 12 14 35 15 23 40 10 28

Plug load

Total

(kW h/p-yr) 1329 2450 725 470 289 133 22 171 135 121

(kW h/m2 yr) 108 148 185 202 256 52 189 122 75 72

45 63 47 49 30 13 11 24 6 26

357 347 251 306 382 125 86 204 90 112

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Region

PAO NAM WEU EEU FSU LAM SSA MENA CPA SPA PAO NAM WEU EEU FSU LAM SSA MENA CPA SPA PAO NAM WEU EEU FSU LAM SSA MENA CPA SPA

PAO NAM WEU EEU FSU LAM SSA MENA CPA SPA

Current average

Income- saturated

Residential space heating 40 101 67 67 134 134 150 150 165 165 7 16 2 6 26 31 29 105 0 17 Residential space cooling 3 7 8 11 2 4 2 3 1 4 8 24 1 23 5 33 1 10 2 26 Residential space ventilation 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Demolition fractions First round

New buildings

Renovated

Current average

2010

Future

2010

Future

26 44 87 97 107 4 2 26 29 0

13 15 17 20 22 2 1 3 12 2

32 54 107 120 132 5 1 21 23 0

26 29 34 39 43 4 1 4 24 0

7 11 4 3 4 24 23 33 10 26

4 4 1 0 1 8 11 7 4 12

7 11 4 3 4 24 23 33 10 26

5 5 5 5 5 12 15 11 8 17

4 4 4 4 4 4 4 4 4 4

2 2 2 2 2 2 2 2 2 2

8 8 8 8 8 8 8 8 8 8

4 4 4 4 4 4 4 4 4 4

Second round Demolition of previously renovated

Demolition of new from 1st round

Res

Comm

Res

Comm

Res

Comm

0.10 0.10 0.10 0.10 0.30 0.60 0.90 0.60 0.60 0.90

0.10 0.20 0.10 0.10 0.30 0.60 0.60 0.60 0.60 0.60

0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2

0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2

0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05

0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05

Commercial 126 123 106 145 203 15 1 50 42 1 Commercial 33 34 10 11 5 33 14 24 7 7 Commercial 33 21 8 8 24 13 4 16 2 7 Commercial

space heating 126 123 106 145 203 15 5 50 83 13 space cooling 33 37 18 14 17 73 100 68 43 111 ventilation 33 21 20 20 24 20 20 20 20 20 lighting

Current average

57.2 46.8 24.9 25.6 39.4 17.5 9.1 21.0 6.2 17.0

Income-saturated

Income-saturated

60 50 30 30 45 30 30 30 30 30

New buildings

Renovated

2010

Future

2010

Future

101 62 85 116 162 15 5 50 83 13

10 11 13 15 17 1 1 2 10 1

114 111 96 130 182 15 5 50 83 13

20 23 26 30 33 3 1 5 19 3

27 37 18 14 17 73 100 68 43 111

8 7 2 1 2 12 16 12 8 17

30 30 18 14 17 51 70 48 30 78

12 10 3 1 3 18 24 19 12 26

33 20 20 20 24 20 20 20 20 20

8 8 8 8 8 8 8 8 8 8

26 20 20 20 20 20 20 20 20 20

45 15 12 12 15 12 12 12 12 12

New buildings

Renovated

2010

Future

2010

Future

30 50 30 30 30 30 30 30 30 30

12 12 12 12 12 12 12 12 12 12

30 30 25 25 25 25 25 25 25 25

18 18 18 18 18 18 18 18 18 18

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Please cite this article as: Harvey, L.D.D., Global climate-oriented building energy use scenarios. Energy Policy (2014), http://dx.doi.org/ 10.1016/j.enpol.2013.12.026i

Table 5 The current stock average building energy intensities (kW h/m2 yr) for end uses that are modelled through a stock-turnover model, and the energy intensities that would occur with arbitrarily large income and no change in building characteristics or relative spatial distribution within each region (income-saturated), and for present (2010) and advanced (future) new buildings and renovations. Also given are the fractions of buildings that are demolished during the first (2011–2055) and second (2056–2100) renovation cycles. Current averages are from Harvey et al. (2014).

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variation in building energy intensity with building age and on the energy intensity of new or renovated buildings in the year that buildings in the given cohort are renovated or replaced; allowance is made for the latest buildings in many regions of the world to be many times more energy intensive than the existing stock average in these regions, and for renovation and replacement to increase rather than decrease the building energy intensity in regions where the provision of services is currently low.

The stock average heating, cooling and ventilation energy intensities (from Table 4), the energy intensities of existing buildings that are assumed to be achieved with arbitrarily large income, referred to as the “income-saturated” energy intensities, the energy intensities of newly built and renovated buildings, and the energy intensities of advanced new buildings and renovations are presented in Table 5. The energy intensities of new and renovated buildings are assumed to decrease by a further 0.5%/yr after the first renovation cycle, reflecting further slow technological and behavioural improvements. The overall HVAC þ lighting energy intensity for future new residential and commercial buildings are assumed to be 20– 32 kW h/m2 yr and 36–45 kW h/m2 yr, respectively. These energy intensities represent a factor of 3–7 reduction compared to current stock averages, but are achievable using generally low-tech, readily-available and reliable technologies and techniques. The larger energy savings involve a significant reliance on passive solar energy for one or more of heating, cooling, ventilation and lighting, and also require enlightened occupant behaviour but no sacrifice in comfort or convenience. Replacement of the conventional, largely linear design process with the integrated design process (Lewis, 2004; Pope and Tardiff, 2011) is a key to achieving dramatic energy savings in commercial buildings. Specific technical measures to achieve the low energy intensities assumed here are discussed in great detail in Harvey (2006). and in more condensed form in Harvey (2010, Section 4) and Harvey (2009). To simplify the calculations and because of insufficient data in all regions except NAM, the stock turnover model uses the total space heating energy use in a given region and the total floor area as input, rather than the energy uses and floor areas for buildings heated with different energy sources. The output of the stock turnover model gives the changing energy intensity under the assumption of no change over time in the relative amounts of different energy sources. However, in the Excel spreadsheets, heating with traditional biofuels is assumed to exponentially approach zero, with the lost market share combined with that of fossil fuels to produce an “Advanced fuels” category that may include some portion of advanced biofuel systems. Some portion of the advanced fuels energy demand in turn is optionally allowed to be gradually shifted to electric heating with heat pumps, as explained later. 3.5. Residential service hot water, cooking, lighting, appliances and consumer electronics Energy demand for residential SHW, cooking, and appliances þconsumer electronics is projected based on population and per capita energy requirements. Water heating equipment and appliances have a relatively short lifespan (15–20 years), consumer electronics are rapidly replaced, and water-using fixtures and behaviour can be changed at will, so a time variation in these energy intensities is directly specified without the use of a stock turnover model. The variation in the per capita SHW energy requirement is projected by specifying the variation in the per capita energy content of delivered hot water (i.e., water arriving at a hot-water

7

faucet), the proportions of different energy sources for SHW, and the efficiencies of water heating equipment (including standby losses in the case of systems with storage tanks, and distribution losses) for each energy source. Online Supplement Table S3 gives fossil fuel, district heat, biomass, solarþ geothermal and electricity energy use for SHW (as estimated in Harvey et al., 2014), conversion efficiencies (based on Ürge-Vorsatz et al. (2012)), the resulting delivery of hot water, the per capita energy use for SHW and per capita energy content of delivered SHW, and the fraction of delivered SHW provided by the various energy sources. Note that although there is a large difference in the per capita energy use for SHW between PAO, NAM and WEU, there is relatively little difference in the per capita delivery of hot water among these three regions. The much larger per capita energy use for SHW in NAM is due to the much lower delivery efficiency, related to the greater use of storage tanks in NAM instead of the predominantly tankless point-of-use systems used in WEU and PAO (which eliminate standby losses). Future energy demand for SHW is projected as follows: the demand for delivered SHW is allowed to increase as a logistic function of income, and to decrease as a logistic function of time to reflect the implementation of various water-conserving measures (such as low-flow showerheads, heat recovery from hot wastewater (reviewed in Harvey, 2006, Section 8.4) and waterconserving behaviour (such as cold-water clothes washing, switching from bathing to showering, and shorter showering times)). The supply of delivered hot water from traditional biofuels (given in Table S3 for 2010) is assumed approach zero following a logistic function, with the lost market share combined with that of fossil fuels to produce an “Advanced fuels” category that may include some portion of advanced biofuel systems. The gap between the initial fraction of SHW supplied by solar heat and by electricity and an assumed longterm fraction decreases following a logistic function of time. Efficiencies in supplying SHW with fuels and electricity increase as a logistic function of time. Table 6 gives the per capita delivery of hot water in 2010 (from Table S3), the delivered hot water demand assumed to be reached with arbitrarily large per capita income in the absence of any efficiency measures or conservation behaviour (‘Income’), and after accounting for more efficient water-using equipment and waterconserving behavioural changes (‘Efficiency’), the eventual shares of solar and electricity energy, and the asymptotic efficiencies assumed for water supply by fuels and electricity. Justification for

Table 6 Initial per capita delivered hot water (from Online Table S2) and the delivered hot water demand assumed to be reached with arbitrarily large per capita income in the absence of any efficiency measures or conservation behaviour (‘Income’) and after accounting for more efficient water-using equipment and water-conserving behavioural changes (‘Efficiency’), and asymptotic efficiencies assumed for water supply by fuels and electricity. TrBF¼ traditional biofuels. Region Per capita delivered hot water (GJ/yr)

PAO NAM WEU EEU FSU LAM SSA MENA CPA SPA

Final shares

Final efficiencies

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Income

Efficiency

Solar Electricity Fuels TrBF Electricity

3.09 3.76 2.50 1.96 4.27 0.95 1.25 3.04 1.35 0.62

5.0 6.0 5.0 5.0 5.0 4.0 4.0 4.0 4.0 4.0

2.5 3.0 2.5 2.5 2.5 2.0 2.0 2.0 2.0 2.0

0.1 0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.2 0.2

0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2

0.90 0.80 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90

0.70 0.70 0.70 0.70 0.70 0.70 0.70 0.70 0.70 0.70

0.95 0.90 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95

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the delivered SHW amounts in the ‘Efficiency’ scenario are given in the Online Supplement (Section 5.2). Note that dramatic potential increases in the energy used to produce SHW are allowed for nonOECD regions of the world, although the increase in any given scenario is limited by the increase in per capita income. A similar approach is used for projecting future energy demand for residential cooking: the per capita demand for energy supplied to food is optionally allowed to increase based on income, energy supplied by traditional biofuels (which accounted for 63%, 87% and 99% of the 2010 residential cooking energy use in CPA, SPA and SSA, respectively) is assumed to exponentially approach zero while being replaced with fossil fuels or advanced fuels that are themselves used more efficiently over time, the efficiency of remaining traditional biofuel uses is allowed to increase from 0.17 to 0.70 following a logistic function, and a small share of solar cooking is allowed in warm regions. With regard to residential lighting energy use, oil products (likely kerosene) account for one quarter of all energy used for residential lighting worldwide (largely in SPA and CPA). Fouquet and Pearson (2006) indicate an efficacy for kerosene lamps in the UK in 1900 of 250 lm-h/kW h (0.25 lm/W), compared to 25,000 lm-h/kW h for average UK electric lighting in 2000. Thus, as kerosene lighting is replaced by electric lighting, energy use per unit of light produced will drop by a factor of 100 (more if the replacement electric lighting is more efficacious than the 2000 UK average). Here, we assume an exponential decline in kerosene use for lighting (with 20 or 40 year time constants), and neglect the small increase in electric lighting energy use by its replacement. Residential electric lighting energy intensity is assumed to increase with income (to a saturation value of 8 kW h/m2 yr), but to tend asymptotically toward a final energy intensity of 2 kW h/ m2 yr in all regions. With regard to energy use by appliances and consumer electronics, it is assumed that per capita levels would approach 1500 kW h/Pyr (about twice the present WEU level) with arbitrarily large per capita income in all regions except NAM (where it remains at the 2010 level of 2450 kW h/Pyr). The income-driven level is multiplied by a specified time-dependent efficiency factor. The savings potential compared to the income-driven increase in consumption is assumed to reach 50% (based on studies by summarized in Online supplement Table S4) by 2035 (Fast) or 2055 (Slow). Beginning in 2035 or 2055, all the energy intensities discussed here are assumed to decrease a further 0.5%/yr, as none of the efficiency improvements discussed above are likely to exhaust the technical and behavioural potential for reducing energy use.

3.6. Commercial service hot water, cooking and miscellaneous loads Energy demand for commercial SHW, cooking, and miscellaneous loads (largely office equipment but also including elevators, escalators and transformers) is projected in a similar manner to that for residential SHW, cooking and consumer goods, except that energy intensity per unit of floor area rather than energy use per person is used as a driving variable. In particular, commercial miscellaneous electricity loads are assumed to increase with arbitrarily large income to 50 kW h/m2 yr in all regions except NAM (which is already at 63 kW h/m2 yr), multiplied by a factor that exponentially approaches 0.5 with time constants of 20 or 40 years. Commercial SHW and cooking energy uses are assumed to be independent of income, but to decrease by 40% and 25%, respectively, from their present regional values with time constants of 20 or 40 years. As with residential appliances and consumer electronics, a further 0.5%/yr decrease is assumed after 2035 or 2055.

3.7. Summary The assumptions concerning energy intensities are summarized in Fig. 3. Shown, for each region, are four columns of energy intensities for each end use resolved here. The four columns, from left to right, pertain to the 2010 stock average, buildings built in 2010, future advanced renovations, and future advanced new buildings. Fig. 3 shows the extent to which new (2010) buildings are assumed to have been modestly less energy intensive than the stock average in PAO, NAM, WEU, EEU and FSU, and more energy intensive than the stock average in the other regions. The unusually low energy intensity of commercial buildings in CPA (largely China) is also evident. In LAM, SSA and SPA, advanced new commercial buildings are assumed to be only 10–30% less energy intensive than the current stock average, and in CPA they are assumed to have 65% higher energy intensity. The overall energy intensity of advanced new residential buildings is assumed to be less than half that of the existing stock average in all regions except LAM (where the reduction is only 45%) and CPA (where the reduction is about 30%, due to the assumed substantial increase in energy services). 3.8. Comparison with other models Key features in the structure of other models used to project future energy use of buildings are compared with the model presented here in the Online Supplement, Tables S5 and S6. The present model has a greater disaggregation of end uses and of energy sources compared to most other global scale models, is fully transparent (with the full Excel package and Fortran code used to generate the results available in the Online Supplement and documented in detail), and explicitly considers traditional and advanced biomass energy, district heat and heat pumps. It is formulated in terms of key drivers (population, per capita floor area and energy intensity or per capita energy use) and in terms of actionable policy measures (such as performance standards for new and renovated buildings, rates of renovation, and equipment energy use standards). As such, it is a user-friendly tool for investigating the integrated effect on global building energy use of a wide variety of assumptions concerning drivers and policy measures at a regional scale.

4. Results The impact of the assumptions outlined above on global demand for fuels and electricity by residential and commercial buildings is presented next. This is followed by a consideration of a partial shift from fuels to electricity for space and water heating using heat pumps. 4.1. Space heating energy and cooling þventilation energy demand Fig. 4 shows the variation in globally aggregated total space heating and cooling þventilation energy demand for residential and commercial buildings for the Low GDP scenario. Results are shown for the following cases:


Current-Slow, whereby the energy intensities of new and





renovated buildings are frozen at their assumed 2010 values, and complete renovation or replacement of the 2010 building stock occurs by 2100. Current-Accelerated, same as above except that all pre-existing buildings are renovated or replaced by 2055. Slow Improvement, in which the advanced energy standards for new and renovated buildings are achieved by 2055, with the

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300 Cooking Other

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Fig. 3. Energy intensity (from left to right within each region) of the stock average in 2010, new buildings in 2010, advanced renovations, and advanced new buildings, as adopted here.




renovation rate accelerating between 2015 and 2025 (Early) or 2025 and 2035 (Delayed). Fast Improvement, in which the advanced energy standards for new and renovated buildings are achieved by 2025, with Early or Delayed acceleration in the rate of renovation.

As seen from Fig. 4a, global residential space heating energy use rises to a peak by about 2040 and then decreases to about 3/4 its 2010 value by 2100 for the Current-Slow case. Part of this decline is due to the assumed decrease in cold-region floor area by

about 30% from its peak as population declines in the Low scenario. For any of the Low scenarios with advanced standards, global residential space heating energy use in 2100 is about one third that in 2010. The relative variation in commercial space heating energy use (Fig. 4c) is similar to that for residential buildings but does not drop below its 2010 level. The situation is different with regard to cooling þventilation (C þV) energy use (Fig. 4b and d), which grows by factors of 5 and 8 for commercial and residential buildings, respectively, for the Current-Slow case. For Fast-Early, demand in 2100 is about 1.7 and

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Fig. 4. Variation in total (fuels þelectricity) global space heating and cooling þ ventilation energy demand for residential and commercial buildings, for various assumptions concerning rate of improvement in the energy intensity of new and renovated buildings, and in the rate of renovations, for the Low GDP scenario. (a) Residential space heating, (b) Residential Cooling þ Ventilation, (c) Commercial space heating, (d) Commercial cooling þventilation.

5.0 times the 2010 demand for commercial and residential buildings, respectively, there being a greater relative savings potential in the commercial sector. Accelerated renovation with fixed standards (Current-Accelerated) leads to lower near-term heating energy use but not lower C þV energy use, because in some regions, renovation with current standards leads to greater rather than smaller Cþ V energy use (as services are increased). There is no long term energy penalty if the acceleration in renovation rate begins before the final standards for renovation are reached (Slow-Early vs Slow-Delayed), while near term energy use is reduced more quickly with an earlier acceleration in the renovation rate. However, as discussed by BPIE (Buildings Performance Institute Europe), (2011b), an earlier acceleration in the renovation rate leads to greater integrated costs because there is less time for unit costs to decrease through learning before renovation rates increase. Further insight into the behaviour seen in Fig. 4 is provided in the Online Supplement. Fig. 5 shows the same results as in Fig. 4, except for the High scenario (high population and GDP/P). Global heating energy demand in the Current-Slow case now grows by about 100% and 60% by 2050 in the commercial and residential sectors, respectively, while Cþ V energy grows by factors of 12 and 22, respectively. For the Fast-Early case, global commercial and residential heating energy uses in 2100 are about one-third and 40% that in 2010, respectively, while C þ V energy uses are 4 and 9 times the 2010 values. 4.2. Service hot water, cooking, lighting and miscellaneous energy demand Fig. 6 gives the demand for SHW, cooking, lighting and miscellaneous (electrical) energy uses for the Low and High GDP scenarios for cases where all efficiencies are frozen at their 2010 levels and for the Slow (40-year time constant) and Fast (20-year

time constant) implementation of the efficiency improvements detailed above for these end uses. Global residential SHW energy demand (Fig. 6a) for the Frozen cases increases by about a factor of three compared to 2010 by 2080 for the Low scenario but by a factor of six by 2100 for the High scenario. Under the most aggressive efficiency scenario (Fast), the total savings is almost 60%, which is sufficient to almost stabilize global SHW energy demand at the 2010 value for the High GDP scenario and produce a small longterm decrease in the Low GDP scenario. A little over half of the reduction from Frozen to Slow or Fast is due to end use efficiency measures, the balance being due to supply efficiency improvement. Because of the much greater potential to reduce space heating demand, and the very low initial SHW energy demand in parts of the world with strong population and GDP/P growth, the global SHW energy demand in 2100 is about twice the space heating demand in the Low scenario and about 2.6 times the space heating demand in the High scenario. Residential cooking energy use shows a rapid initial decrease (Fig. 6c) that is related to the assumed transition, largely over either a 20- or 40-year period, from inefficient traditional biomass cookstoves to efficient cookstoves (recall, that on a global basis, biomass accounts for about 70% of residential cooking energy use). Subsequent reductions in residential cooking energy use, and in commercial cooking energy use relative to the Frozen scenario, are relatively small by assumption. Residential lighting energy use is roughly stabilized under the High GDP, efficiency scenarios and decreases in the Low GDP scenarios. Miscellaneous electrical loads in both the residential and commercial sectors (Fig. 6e and f) grow to over 5 times the 2010 demand in 2100 for the High GDP, Frozen efficiency scenario, and to just over half that for the Low GDP, Frozen scenario. In the advanced scenarios, energy demand in 2100 is about half that for the Frozen scenarios, by assumption, and so is roughly stabilized for the Low GDP scenario and roughly doubles for the High scenario.

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Fig. 5. Same as Fig. 4, except for the High GDP scenario.

4.3. Scenarios with increasing use of electric heat pumps Heat pumps can be used to transfer heat from the outside to the inside of a building (during the heating season) and vice versa during the cooling season. The key performance parameter of a heat pump is the coefficient of performance (COP), which is the ratio of heat delivered to energy used. It depends on the magnitude of the required temperature lift (from a relatively cold heat source to a warm heat sink). In heating mode, heat pumps can draw heat from the outside air (forming an air-source heat pump, ASHP), from the ground (forming a ground-source heat pump, GSHP), or from ventilation exhaust air (forming an exhaust-air heat pump, EAHP). As the ground will be warmer than the outside air during winter, a GSHP requires a smaller temperature lift than an ASHP and so will operate with a higher COP. All three heat pumps will operate with a higher COP the cooler the temperature at which heat is required (i. e., distributing heat at 30 1C will result in a larger COP than if heat is distributed at 60 1C). At any given moment, COPs will be lower for the production of SHW (required at 50–55 1C) than for lowtemperature space heating. A higher COP is also possible in better insulated buildings, as adequate heat transfer is possible with lowtemperature radiators in a well-insulated building. In a comparison of heat pump performance in the UK, Switzerland and Germany, DEE (Delta Energy and Environment), (2011) reports higher COPs in Germany because of the generally higher insulation levels in the buildings. The German data are summarized in Online Table S5. The best COP for ground-source heat pumps used for a mixture space and water heating is 4.2 in retrofitted buildings and 5.0 in new buildings. For air-source heat pumps, the best COPs are 3.1 and 3.4 for retrofitted and new buildings, respectively. BPIE (Buildings Performance Institute Europe), (2011a, Table 22) provide separate COPs for hot water and space heating (also given in Table S7); the best COPs using ASHPs are greater for hot water than for space heating (4.3 vs 4.1), but are smaller for hot water than space heating using GSHPs (4.3 vs 5.4).1 IEA (International Energy Agency) (2011) recommends R&D targeting a 20% improvement in heat pump COPs by 2020 and a 50% improvement by 2030.

In an extension of the scenarios presented above, it is assumed that 50% of the space heating loads in existing buildings that would be met by fuels other than traditional biofuels, 70% of similar space heating loads of new buildings, and 50% of the SHW load that would be met by fuels, is instead provided by electric heat pumps. Similar proportions of the loads that would be met by electric resistance heating are also assumed to be eventually met by heat pumps. The average COP for space heating of existing buildings is assumed to increase from 3.0 in 2005 to 4.0 in by about 2035 (Fast) or 2055 (Slow), that for space heating of new buildings is assumed to increase from 4.0 to 5.0 over the same time frame, and that for SHW is assumed to increase from 2.5 to 3.5 (these COPs would represent some average of air-source, ground-source, and exhaust-air heat pumps). Fig. 7 compares the variation in total (residential and commercial) fuel and electricity uses over time for the Low-Fast scenario for cases with (i) no heat pumps, (ii) a partial shift of only fuel use to electric heat pumps, and (iii) a partial shift of both fuels and electric resistance heating to heat pumps. The savings in electricity demand in shifting existing electric resistance heating to heat pumps is, in the global aggregate, about half the increase due to shifting of fuels to heat pumps. The net increase in total building electricity demand due to increasing use of heat pumps peaks at about 4% near 2050. 4.4. Global demand for fuels, traditional biofuels and electricity Fig. 8 gives the time evolution in total (residential plus commercial) energy use by fuels þheat and electricity for the Current standards-Slow turnover scenario and for the Slow and 1 The larger COP for SHW than for space heating using ASHPs, in spite of the required higher final temperature for SHW, is due to the fact that SHW is required in summer as well as in winter, and the warmer temperatures in summer cause the annual mean temperature lift to be smaller than when the heat pump is used for space heating only. With GSHPs, there is little seasonal variation in the temperature of the heat source, so the final temperature is the main determinant of the required mean annual temperature lift.

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Fig. 6. Variation in various global residential (left) and commercial (right) energy demands for for cases with no improvement in efficiency compared to 2010, and for slow and fast implementation of the suite of behavioural and efficiency measures assumed here, for the Low and High GDP scenarios. (a) Service hot water, (b) service hot water, (c) cooking, (d) Cooking, (e) lighting, (f) lighting, (g) miscellaneous electrical and (h) miscellaneous electrical.

Fast efficiency-improvement cases, the latter with and without a partial shift to heat pumps. For the Low GDP, Current-Slow scenario, fuel use peaks at about 90% above the 2010 demand by about 2070, then drops by about 10% by 2100. For the three advanced-buildings scenarios, fuel use drops to 23–43% of the 2010 level by 2100. For the High GDP, Current-Slow scenario, fuel use is almost three times the 2010 level by 2100, while for the advanced-buildings scenarios, fuel use is 40–80% the 2010 level by

2100. The situation is different for electricity, which exceeds the 2010 demand in 2100 for every scenario combination considered, although by only 52% for the Low-Fast case with heat pumps. A critical parameter with respect to the prospects for eliminating fossil fuel greenhouse gas emissions from the building sector is the degree of electrification of the building stock; greater electrification makes emission reduction easier, as long as the electricity supply can be decarbonized. Fig. 9 shows the fraction of

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total building energy use that is met by electricity for the low GDP scenarios (the fractions are almost identical for the high GDP scenarios). Even for the Current-Slow scenario case, with no efficiency improvement compared to new buildings and renovations in 2010, the electricity fraction increases by half—from about 30% in 2010 to 45% in 2100. This is due to greater growth in demand for energy services that are met with electricity than with fuels. Under the efficiency scenarios, greater percent reduction in heating demand (which is largely met with fuels) is possible than for most other end uses. Thus, the electricity fraction increases further, to 64% for the case with rapid efficiency gains. The partial transition to heat pumps for space and water heating further increases the electric fraction to 72%.

4.5. Comparison with other work Some key results of several models are compared in Table S8, and more detailed numerical comparisons of building energy use in 2050 as projected here and in Ürge-Vorsatz et al. (2012) and IEA (International Energy Agency) (2012a) for their most aggressive efficiency scenarios are presented in Table S9. One of the key differences between the present model and that of other models is the allowance, in the present model, for the electrical energy intensity of new commercial buildings in developing countries to be vastly greater than the average of existing buildings (whereas the electrical energy intensity of new buildings in OECD countries is assumed, based on the available evidence, to be slightly better than the stock average).

Fig. 8. Total building energy use as fuels or heat (including active solar and district heat) and as electricity, for the Current-Slow scenario and for slow and fast implementation of advanced standards, and for fast implement with a partial shift to heat pumps (HP) for (a) the Low GDP scenario and (b) the High GDP scenario.

0.8 0.7 0.6

Electricity Fraction

Fig. 7. Comparison of fuel and electricity demand for scenarios with no use of heat pumps, partial shifting of space heating and SHW loads from fuels only to electric heat pumps, and from fuels and electric resistance (ER) space and SHW loads to electric heat pumps for the Low-Fast-Early scenario.

0.5 0.4 Fast with Heat Pumps Fast (efficiency improvement) Slow (efficiency improvement) Current-Slow

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Year Fig. 9. Electricity share of total building energy for various scenarios.

The scenarios with the most detailed published documentation are those of BPEI (2011b) and Ürge-Vorsatz et al. (2012). In the BPEI scenarios, covering Europe (EU27 þSwitzerland and Norway), regulated building energy use (heating, ventilation, cooling, hot water and lighting) decreases by 34% by 2050 compared to 2010 for their Slow-Shallow scenario, by 48% for their Medium scenario, by 68% for their Deep scenario, and by 71% for their Two-stage scenario. In the global scenario of Ürge-Vorsatz et al. (2012), building floor increases area by 127% from 2005 to 2050 but

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global on-site energy use for space heating and cooling decreases by 34% for their ‘Deep efficiency’ (a savings of 67% compared to their ‘Frozen Efficiency’ scenario).

5. Concluding comments This paper has presented a regionally-disaggregated model to estimate the net effect on global fuel and electricity use by the building sector for various combinations of growth in population and GDP per capita (which, together, drive increases in building floor area), rates of improvement in the energy performance of new and renovated buildings and of equipment in buildings, and in the timing of an acceleration in the rate of renovation plus replacement of buildings such that all buildings existing in 2010 are renovated or replaced by 2055. A second renovation/replacement cycle is assumed to occur between 2055 and 2100, when the projection ends. In the ‘Low’ scenario, global population peaks at 8.1 billion around 2045 and declines to 6.7 billion by 2100 and global mean per capita GDP increases to 2.1 times the 2010 value, while in the ‘High’ scenario, population rises to 10.1 billion by 2100 (about 50% larger than for the Low scenario) and average per capita GDP is 3.2 times the 2010 value in 2100 (also about 50% larger than for the Low scenario). In all the scenarios presented here, dramatic reductions in the energy intensity of new buildings (by factors of 3–4 relative to typical new buildings, which is consistent with current best practice) and in the energy intensity of renovated buildings (by factors of 2–3, as achieved or exceeded in many projects) are assumed to be eventually achieved, either by 2025 (Fast) or 2055 (Slow). These assumptions are not intended as predictions, but rather, they are intended to show the consequences (in conjunction with other factors) of gradual attainment of regional mean energy intensities consistent with documented local best practice. Among the insights gained by this analysis are:


a large decline (by 70% without the use heat pumps, and by 80%













with heat pumps) in global fuel use for buildings relative to 2010 is possible for the Low scenario with universal Fast implementation of current best-practice standards, and by 40–60% relative to 2010 for the High scenario; peak global electricity use for buildings can be limited to about two times the 2010 demand for the Low scenario with universal Fast implementation of advanced building and equipment standards (compared to over three times the 2010 demand with all standards frozen at their 2010 values), and can be limited to about three times the 2010 demand for the High-Fast scenario (compared to six times the 2010 demand for the High-Frozen scenario); there is a strong tendency for service hot water (SHW) demand to increase sharply, although global SHW could be reduced from 2015 onward for the Low-Fast scenario and limited to about a 40% increase by 2100 for the High-Fast scenario; although total (fuel þelectricity) global space heating demand substantially exceeds global SHW energy demand at present (36 vs 25 EJ in 2010), SHW energy demand will likely become larger than space heating demand in the future if efficiency and conservation measures are vigorously pursued for both end uses; and if 50% of the space heating loads in existing buildings, 70% of the space heating loads of new buildings, and 50% of the SHW load that are or would be satisfied by fuels are shifted to electric heat pumps, global electricity use by buildings is about 10% larger by the time the shift is complete; however, if similar proportions of existing electric resistance heating are also shifted to electric heat pumps, the net increase in building electricity use is only about 5%.

The significant energy reductions that are forecast for the Fast scenarios, and the smaller but still substantial savings for the Slow scenarios, highlight the urgency of rapid and strong improvements in energy intensity standards for new and renovated buildings, but also indicate that current plans under the European Energy Performance in Buildings Directive (ECEEE (European Council for an Energy Efficient Economy), 2011) – which are comparable in magnitude and timing to the improvements assumed worldwide here under the Fast scenario – are on the right track. It is essential, however, that such standards be achieved in practice, and this in turn requires a strong program of training the relevant design and construction trades, enforcement through building inspections during construction, and follow-up monitoring of energy use. The results obtained here are entirely a function of the structure of the model and of the input assumptions, but to the extent that the model structure captures the most important features of the real world and the input assumptions represent close to maximum feasible energy saving measures, these insights should remain valid. On-site, building scale co-generation of heat and electricity (micro-cogeneration) has not been considered in the scenarios presented here, for several reasons. First, retrofits of existing buildings over the next 40 years are assumed here to reduce heating loads by up to 80–90 % (i.e., from 200 to 400 kW h/m2 yr to 40 kW h/m2 yr on average), while heating load standards for new buildings are set at 20–30 kW h/m2 yr. With such low heat loads, cogeneration is not viable except possibly using small units in large building complexes. Second, unless the fuel is biomass, cogeneration will increase rather than decrease overall CO2 emissions once the electricity supply system is completely decarbonized, something that could be done worldwide over a 40-year period (as will be shown in a forthcoming paper). Third, heat pumps powered by electricity from a C-free grid represent a viable means of eliminating heating-related emissions from much of the building stock, as well as providing a dispatchable electricity end use that in turn will permit greater use of intermittent renewable electricity sources (such as wind) to meet non-heating electricity needs. However, some scenarios for the UK assume a moderate use of micro-cogeneration in the buildings sector (i.e., Hinnells et al., 2007). The Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) is based on climate simulations that are driven by various scenarios of radiative forcing that are referred to as “Representative Concentration Pathways” (RCPs). The RCP with the lowest maximum radiative forcing, 2.6 W/m2, is referred to as the RCP2.6 pathway, and was developed for the IPCC by van Vuuren et al. (2011). In this pathway, fossil fuel emissions decline to zero by 2100. This paper articulates specific policy measures and goals (energy standards for buildings and equipment, fuel switching and renovation rates) that, in combination with measures in other sectors and on the supply side, could achieve this pathway for low and high scenarios of population and per capita economic growth. However, the Excel worksheets that are provided in the Online Supplement could equally be used to articulate combinations of policy actions sufficient to achieve the CO2 emissions pathways associated with any of the less stringent RCPs (described in Volume 109, Numbers 1–2 of Climatic Change and in Moss et al. (2010)).

Appendix A. Supplementary materials Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.enpol.2013.12.026.

Please cite this article as: Harvey, L.D.D., Global climate-oriented building energy use scenarios. Energy Policy (2014), http://dx.doi.org/ 10.1016/j.enpol.2013.12.026i

L.D.D. Harvey / Energy Policy ∎ (∎∎∎∎) ∎∎∎–∎∎∎

References BPIE (Buildings Performance Institute Europe), 2011a. Principles for Nearly ZeroEnergy Buildings: Paving the Way for Effective Implementation of Policy Requirements. Buildings Performance Institute Europe, Brussels. BPIE (Building Performance Institute Europe), 2011b. Europe0 s Buildings Under the Microscope, A Country-by-country Review of the Energy Performance of Buildings. Buildings Performance Institute Europe, Brussels. CEC (California Energy Commission), 2011. Achieving Energy Savings in California Buildings: Saving Energy in Existing Buildings and Achieving a Zero-net-energy Future, California Energy Commission Draft Staff Report. Coffey, B., Borgeson, S., Selkowitz, S., Apte, J., Mathew, P., Haves, P., 2009. Towards a very low-energy building stock: modelling the US commercial building sector to support policy and innovation planning. Build. Res. Inf. 37, 610–624. Daoud I., Huytebroeck E., 2011. Adoption of Passivhaus as the Brussels city standard in 2015. In: UK PassivHaus Conference 2011, 24–25 October 2011. DEE (Delta Energy and Environment), 2011. Heat pumps in the UK: How Hot can they Get, Delta Energy and Environment, Edinborough, United Kingdom. ECEEE (European Council for an Energy Efficient Economy), 2011. Steering through the Maze #2, Nearly Zero-energy Buildings: Achieving the EU 2020 Target. 〈http://www.eceee.org/buildings/MazeGuide2-NetzeroEnergyBldgs.pdf〉. Fouquet, R., Pearson, J.G., 2006. Seven centuries of energy services: the price and use of light in the United Kingdom (1300–2000). Energy J. 27, 139–178. Harvey, L.D.D., 2006. A Handbook on Low-energy Buildings and District energy Systems: Fundamentals, Techniques and Examples. James & James, London. Harvey, L.D.D., 2009. Reducing energy use in the buildings sector: measures, costs, and examples. Energ. Effic. 2, 139–163. Harvey, L.D.D., 2010. Energy and the new reality. Energy Efficiency and the Demand for Energy Services, vol. 1. Earthscan, London. Harvey, L.D.D., 2013a. Global climate-oriented transportation scenarios. Energy Policy 54, 87–103. Harvey, L.D.D., 2013b. Recent advances in sustainable buildings: review of the energy and cost performance of the state-of-the-art best practices from around the world. Annu. Rev. Environ. Res. 38, 281–309. Harvey, L.D.D., Korytarova, K., Lucon, O., Roshchanka, V., 2014. Construction of a global disaggregated dataset of building energy use in 2010. Energy Build. (submitted). Hinnells, M., Boardman, B., Darby, S., Killip, G., Layberry, R., 2007. Transforming UK homes: Achieving at a 60% Cut in Carbon Emissions by 2050. In: ECEEE, 2007 Summer Study, 1105–1109. IEA (International Energy Agency) 2011. Technology Roadmap, Energy Efficient Buildings: Heating and Cooling Equipment, International Energy Agency, Paris. IEA (International Energy Agency), 2012a. Energy Technology Perspectives 2012, Pathways to a Clean Energy System, International Energy Agency, Paris. IEA (International Energy Agency), 2012b. Energy Balances of OECD Countries 2009–2010, International Energy Agency, Paris. IEA (International Energy Agency), 2012c. Energy Balances of Non-OECD Countries 2009–2010, International Energy Agency, Paris.

15

IIASA (International Institute for Applied Systems Analysis), 2009. RCP Database, Version 2.0. 〈http://tntcat.iiasa.ac.at:8787/RcpDb/dsd?Action=htmlpage&page= welcome#regiondefs〉. IIASA (International Energy Agency). 2012. Energy Technology Perspectives 2012, International Energy Agency, Paris. IMF (International Monetary Fund), 2012. International Monetary Fund, World Economic Outlook Database, April 2012. www.imf.org/external/pubs/ft/weo/ 2012/01/weodata/index.aspx. Lewis, M., 2004. Integrated design for sustainable buildings. Build. Future A Suppl. ASHRAE J. 46 (9), 22–30. McNeil, M.A., Letschert, V.E., de la Rue du Can, S., 2008a. Global Potential of Energy Efficiency Standards and Labeling Programs. Lawrence Berkeley National Laboratory, Berkeley, California. McNeil, M.A., de la Rue du Can, S., McMahon, J.E., 2008b. Enduse Global Emissions Mitigation Scenarios (EGEMS): A New Generation of Energy Efficiency Policy Planning Models. Lawrence Berkeley National Laboratory, Berkeley, California. Moss, R.H., Edmonds, J.A., Hibbard, K.A., Manning, M.R., Rose, S.K., van Vuuren, D.P., Carter, T.R., Emori, S., Kainuma, M., Kram, T., Meehl, G.A., Mitchell, J.F.B., Nakicenovic, N., Riahi, K., Smith, S.J., Stouffer, R.J., Thomson, A.M., Weyant, J. P., Wilbanks, T.J., 2010. The next generation of scenarios for climate change research and assessment. Nature 463, 747–756. Nakicenovic, N., Swart, S. (Eds.), 2000. Emission scenarios, Special Report of the Intergovernment Panel on Climate Change, Cambridge University Press, Cambridge. Pope, S., Tardiff, M., 2011. Integrated design process: planning and team engagement. ASHRAE Trans 117, 433–440. Power, A., 2008. Does demolition or refurbishment of old and inefficient homes help to increase our environmental, social and economic viability. Energy Policy 36, 4487–4501. Schipper, L., Unander, F., Murtishaw, S., Ting, M., 2001. Indicators of energy use and carbon emissions: explaining the energy economy link. Annu. Rev. Energy Env. 26, 49–81. UNPD (United Nations Population Division),2010. World Population Prospects: The 2010 Revision Population Database, UNPD. 〈www.esa.un.org/unpp〉. Ürge-Vorsatz, D., Petrichenko, K., Antal, M., Staniec, M., Ozden, E., Labzina, E., 2012. Best practice Policies for Low Carbon & Energy Buildings Based on Scenario Analysis, Centre for Climate Change and Sustainable Policy (3CSEP) for the Global Buildings Performance Network, Paris. Ürge-Vorsatz, D., et al., 2012. Energy end use: buildings. In: Johansson, T.B., Patwardhan, A., Nakicenovic, N., Gomez-Echeverri, L. (Eds.), Global Energy Assessment. International Institute for Applied Systems Analysis, Laxenburg, pp. 649–756. van Vuuren, D.P., Stehfest, E., den Elzen, M.G.J., Kram, T., van Vliet, J., Deetman, S., Isaac, M., Goldewijk, K.K., Hof, A., Beltran, A.M., Oostenrijk, R., van Ruijven, B., 2011. RCP2.6: exploring the possibility to keep global mean temperature increase below 2 1C. Clim. Change 109, 95–116.

Please cite this article as: Harvey, L.D.D., Global climate-oriented building energy use scenarios. Energy Policy (2014), http://dx.doi.org/ 10.1016/j.enpol.2013.12.026i