LONG-TERM GLOBAL ENERGY DEMAND SCENARIOS FOR RESIDENTIAL SECTOR

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1 7-6 LONG-TERM GLOBAL ENERGY DEMAND SCENARIOS FOR RESIDENTIAL SECTOR Masahito TAKAHASHI 1 Gerhard TOTSCHNIG Ph.D 1 Central Research Institute of Electric Power Industry, Ohtemachi, Chiyoda-ku, Tokyo -816, Japan m-taka@criepi.denken.or.jp International Institute for Applied Systems Analysis, Schlossplatz 1, A-361 Laxenburg, Austria totsch@iiasa.ac.at Keywords: Residential sector, Final energy use, Structural change, Long-term trend, Scenario formulation Summary This paper illustrates one possible global and regional residential energy demand scenario for the 1 st century that are consistent with demographic and economic projections of IIASA s B scenario developed for Intergovernmental Panel on Climate Change (IPCC). An empirical analysis of per capita residential energy demand across fuel types and regions was performed using economic and energy data to develop energy demand projection formulas that represent long-term relationships between residential energy use and economic development. The past trend analysis shows that per capita energy demand is affected not only by income level but also by other factors such as climate condition, lifestyle and technological progress. Hence, the energy demand projection formulas were developed including possible future technological progresses and lifestyle changes as scenario parameters. For B scenario, global residential energy demand increases from 87 EJ in to 14 EJ by 5 and 156 EJ by and the share of developing regions in the global residential energy demand grow from 67% in to 8% by. Increase in electricity demand for nonthermal uses is the main factor boosting the global residential energy demand, while energy demand for thermal uses is expected to saturate in the latter half of the century. 1. Introduction It is often observed that, economic development causes sector shifts in final energy use from agriculture to industry and to services, and at the same time fuel shifts from solid biomass to fossil fuels and to grid electricity [1-3]. This structural change in final energy use has a significant influence on the trajectory of carbon emissions in the long run. This study focuses on final energy use in residential sector, which occupies about a quarter of total world energy use at present and has been growing at an annual rate of 1.%/yr in the past decade. The purpose of this study is to illustrate one possible global and regional residential energy demand scenario for the 1 st century that are consistent with demographic and economic projections of IIASA s B scenario developed for Intergovernmental Panel on Climate Change (IPCC) [4]. The world is disaggregated into eleven world regions (see Figure 1) defined both geographically as well as with respect to structural similarity in terms of economic and energy patterns; NAM (North America), WEU (Western Europe), PAO (Pacific OECD), FSU (Former Soviet Union), EEU (Central and Eastern Europe), LAM (Latin America and the Caribbean), MEA (Middle East and North Africa), AFR (Sub-Saharan Africa), CPA (Centrally Planned Asia and China), PAS (Other Pacific Asia) and SAS (South Asia). The residential energy demand scenario is formulated for each world region based on a set of regression equations representing the long-term trends of relationships between residential energy use and economic development at the regional level, which are developed through an empirical analysis of per capita residential energy demand using economic and energy data. Section explains briefly about methodology and data used in this study. Section 3 shows the past trends of relationships between per capita residential energy demand and per capita GDP, and the estimated regression curves for the per capita residential energy demand projections at the regional level. Section 4 shows the results of global and regional residential energy demand projections for the B scenario and discusses their implications. Section 5 is the conclusion of this paper

2 1 NAM LAM 3 WEU 4 EEU 5 FSU 6 MEA 7 AFR 8 CPA 9 SAS PAS 11 PAO 1 NAM North America LAM Latin America & The Caribbean 3 WEU Western Europe 4 EEU Central & Eastern Europe 5 FSU Former Soviet Union 6 MEA Middle East & North Africa 7 AFR Sub-Saharan Africa 8 CPA Centrally Planned Asia & China 9 SAS South Asia PAS Other Pacific Asia 11 PAO Pacific OECD Figure 1 Eleven world regions for global energy systems analysis. Methodology and Data Figure shows a basic structure of energy demand scenario generator for residential sector. Residential energy demand is separated into three categories, electricity demand for non-thermal uses, commercial fuel demand for thermal uses and non-commercial fuel demand for thermal uses. The electricity demand for nonthermal uses is defined as electricity consumed for non-heating purposes, which include lighting, air conditioner, television, refrigerator, microwave, personal computer, etc. Note that electricity consumed for heating purposes such as space heating, water heating and cooking is not included in this category. The commercial fuel demand for thermal uses is defined as the total sum of commercial fuel consumption for heating purposes that is supplied with a wide variety of commercial energy sources such as petroleum products, natural gas, district heating and electricity. The non-commercial fuel demand for thermal uses is biomass energy consumption for heating purposes, e.g. fuel woods and biomass wastes. For each category, a set of regression equations representing the long-term relationships between per capita energy demand and per capita GDP, were estimated using economic and energy data. Only one functional form was assumed for each category to develop this energy demand projection formula. The past trends of per capita energy demand were examined not only at the regional level, but also at the national level to see whether or not the assumed functional form is proper to describe the trends among and within regions. The past trend analysis shows that the per capita energy demand is affected not only by per capita GDP but also by other factors such as climate condition, lifestyle and technological progress in building insulation. Hence, the projection formulas were developed including future possible technological progresses and lifestyle changes as scenario parameters. Given future population and per capita GDP projections, the scenario generator calculates residential energy demands by demand category and by world region based on the projection formulas, and the total sum of them gives global residential energy demand in the future. Regression Equations Representing relationships between per capita residential energy demand and per capita GDP Storyline Income (GDP/capita), Population Electricity Demand for Nonthermal Uses Commercial Fuel Demand for Thermal Uses Non-commercial Fuel Demand for Thermal Uses Residential Energy Demand Figure A basic structure of energy demand scenario generator for residential sector

3 IEA energy statistics [5] is used as a primary energy database, which gives time-series data of sectoral final energy consumption for each country. Data on electrification of residential thermal demand was collected from other data sources such as national energy statistics in order to separate electricity consumption into non-thermal and thermal demand, since the IEA data set does not contain such disaggregate data at enduse level. The IEA data set was also used as population data source. The growth study datasets [6] created by Environmentally Compatible Energy Strategies (ECS) program of IIASA based on World Bank and IMF databases, was used as GDP data source, where the GDP data has a unit of US dollar converted in market exchange rates. Using these population and GDP data, per capita GDP, that is, income level was calculated for each country. 3. Past trends of relationships between residential energy use and economic development and estimation of energy demand projection formulas 3.1 Electricity demand for non-thermal uses Past trends of per capita electricity demand for non-thermal uses for fourteen developed and developing countries, United States, Finland, France, Italy, Netherlands, Czech Republic, Hungary, Poland, Russia, Japan, Chinese Taipei, Indonesia, Thailand and People s Republic of China, are shown in Figure 3. Note that a horizontal axis in the figure represents per capita GDP of the countries. The figure shows that, the non-thermal electricity demand grows steadily as income rises for every country and the growth paths as a function of per capita GDP are clearly different between developed and developing countries, that is, the developing countries grows more rapidly than the developed. For example, Czech Republic, Hungary and Poland follow more rapid growth pathways than European developed countries such as France, Italy and Netherlands. People s Republic of China and Thailand have been growing more steeply than Chinese Taipei as well as Japan. The same can be found out at the regional level as well in Figure 4. To describe this relationship between per capita non-thermal electricity demand and per capita GDP, a logistic function was assumed across regions as defined in Eqn. (1). Of three parameters of this function, A, B and C, C is the most important scenario parameter for a long-term energy demand projection because it gives the upper limits of per capita non-thermal electricity demand in the future when the income level goes higher. To develop the projection formulas, we assumed that two European developing regions, EEU and FSU, have the same upper limit as WEU, and eight other regions, CPA, PAS, SAS, MEA, LAM and AFR, have the same one as PAO. The results of curve fitting are shown in Figure 4. C PELE = 1 + exp( A + B PGDP) (1) where PELE is per capita electricity demand for non-thermal uses (GJ/person/yr), PGDP is per capita GDP (US$/person), and A, B and C are parameters to be estimated by regression analysis. 14 United States 14 PAO (actual data) Per Capita Electricity Demand for Non-Thermal Uses (GJ/person/yr) GDP per capita($/person) Finland France Italy Netherlands Czech Republic Hungary Poland Russia Japan Chinese Taipei Indonesia Thailand People's Republic of China Per Capita Electricity Demand for Non-thermal Uses (GJ/person/yr) GDP per capita($/person) CPA (actual data) SAS (actual data) PAS (actual data) MEA (actual data) AFR (actual data) WEU (actual data) EEU (actual data) FSU (actual data) NAM (actual data) LAM (actual data) PAO (fitted curve) CPA (fitted curve) SAS (fitted curve) PAS (fitted curve) MEA (fitted curve) AFR (fitted curve) WEU (fitted curve) EEU (fitted curve) FSU (fitted curve) NAM (fitted curve) LAM (fitted curve) Figure 3 Per capita electricity demand for non-thermal uses versus per capita GDP for fourteen developed and developing countries Figure 4 Regression curves of per capita electricity demand for non-thermal uses by world region

4 3. Commercial fuel demand for thermal uses Figure 5 shows past trends of per capita commercial fuel demand for thermal uses for the fourteen developed and developing countries, and indicates two important points in making a global thermal demand projection. Firstly, commercial fuel demand for thermal uses seems to reflect climate conditions or in other words, latitudes of the countries rather than per capita GDP. For example, United States and Finland meet higher thermal demand than Japan, because the first two have colder climates than the second one. Czech Republic and Poland meet similar thermal demand to Netherlands, while they have a wide income gap. Secondly, the per capita commercial fuel demand for thermal uses is likely to have a peak or, in some cases, a decline in the future as shown in Figure 5. European and North American developed countries show convergence of per capita thermal demand among the countries and it seems to decline in recent years regardless of increasing per capita GDP. This is in part due to extensive efforts to improve insulation efficiency of residential buildings in European and North American developed countries since the oil shocks of 197s. For example, in Germany, thermal insulation regulations has been revised four times since the first introduction of 1977 and the present regulation values for annual heating demand per square, 37-9 kwh/m/yr, is no more than a half of the first regulation value of 1977, 18-5kWh/m/yr [7]. Although Japan shows steadily increasing, but lower than WEU, demand, which is considered due to longer heating hours, larger heating area in houses and decreasing population per household, it is also likely to have a peak in coming decades as can be seen in Europe and North America. To describe the long-term trends at the regional level as shown in Figure 6, one function as defined in Eqn. () was assumed, which has a peak demand of Py at an income level of Px and a tail demand of Ty when the income level is much higher than Px. Ty is a key scenario parameter to determine per capita thermal demand in the future and reflect climate condition or latitude of the region as discussed above. So we assumed that, EEU and FSU have similar tail demand to WEU, LAM and CPA similar to PAO, and SAS, PAS, MEA and AFR similar to Singapore or Oman that are high-income countries near the equator, supposing future convergence of per capita thermal demand according to the climate zones. The results of curve fitting are shown in Figure 6. PTHMC P ( P I ) ( PGDP I Y X X X = ( PX I X ) + ( PGDP I X ) X TX PX ( PX + ( PGDP PX ) ) ( PY TY ) = + T T Y X PX + ( PX + ( PGDP PX ) ) PGDP P X ) if PGDP < P if () where PTHMC is per capita commercial fuel demand for thermal uses (GJ/person/yr), PGDP is per capita GDP (US$/person), and Px, Py, Tx, Ty and Ix are parameters to be estimated by regression analysis. 5 United States 5 PAO (actual data) Per Capita Commercial Fuel Demand for Thermal Uses (GJ/person/yr) GDP per capita($/person) Finland France Italy Netherlands Czech Republic Hungary Poland Russia Japan Chinese Taipei Thailand Indonesia People's Republic of China Per Capita Commercial Fuel Demand for Thermal Uses (GJ/person/yr) GDP per capita($/person) CPA (actual data) SAS (actual data) PAS (actual data) MEA (actual data) AFR (actual data) WEU (actual data) EEU (actual data) FSU (actual data) NAM (actual data) LAM (actual data) PAO (fitted curve) CPA (fitted curve) SAS (fitted curve) PAS (fitted curve) MEA (fitted curve) AFR (fitted curve) WEU (fitted curve) EEU (fitted curve) FSU (fitted curve) NAM (fitted curve) LAM (fitted curve) Figure 5 Per capita commercial fuel demand for thermal uses versus per capita GDP for fourteen developed and developing countries Figure 6 Regression curves of per capita commercial fuel demand for thermal uses by world region

5 3.3 Non-commercial fuel demand for thermal uses Figure 7 and Figure 8 show past trends of non-commercial fuel demand for thermal uses at the national level and regional level respectively, giving two important points in a residential biomass energy demand projection. The first is that, biomass energy is used as the main energy source for heating purposes in Asian, African and Latin American developing regions, CPA, PAS, SAS, LAM and AFR, and at the same time it tends to decline as the income level goes up. On the contrary, the situation in European developing regions, EEU and FSU, is different, where a similar amount of biomass energy to WEU is consumed. The second is that, a certain amount of biomass energy still remains consumed even in developed countries and the degree of biomass utilization seems to reflect availability of their own domestic biomass resources. For example, Finland and France consume nearly as much biomass energy as People s Republic of China at present in terms of per capita demand, while biomass energy consumption is nearly zero in Netherlands and Japan. A power function with a constant term as defined in Eqn. (3) was assumed to develop the projection formulas for per capita non-commercial fuel demand. If F is negative, per capita non-commercial fuel demand declines as an income level rises, and D gives a lower limit of the demand, which represents a region-specific constant demand for residential biomass energy use. The results of curve fitting are shown in Figure 8. PTHMNC F = D + E PGDP (3) where PTHMNC is per capita non-commercial fuel demand for thermal uses (GJ/person/yr), PGDP is per capita GDP (US$/person), and D, E and F are parameters to be estimated by regression analysis. Per Capita Non-commercial Fuel Demand for Thermal Uses (GJ/person/yr) GDP per capita($/person) Figure 7 Per capita non-commercial fuel demands for thermal uses versus per capita GDP for fourteen developed and developing countries United States Finland France Italy Netherlands Czech Republic Hungary Poland Russia Japan Chinese Taipei Indonesia Thailand People's Republic of China Per Capita Non-commercial Fuel Demand for Thermal Uses (GJ/person/yr) GDP per capita($/person) Figure 8 Regression curves of per capita non-commercial fuel demand for thermal uses by world region PAO (actual data) CPA (actual data) SAS (actual data) PAS (actual data) MEA (actual data) AFR (actual data) WEU (actual data) EEU (actual data) FSU (actual data) NAM (actual data) LAM (actual data) PAO (fitted curve) CPA (fitted curve) SAS (fitted curve) PAS (fitted curve) MEA (fitted curve) AFR (fitted curve) WEU (fitted curve) EEU (fitted curve) FSU (fitted curve) NAM (fitted curve) LAM (fitted curve) 4. Regional and Global Residential Energy Demand Scenario for the B Scenario 4.1 Demographic and Economic Development Projections for the B Scenario The residential energy demand scenario was formulated for IIASA s B scenario based on the projection formulas. The B scenario describes a future world with continuously increasing global population and intermediate level of economic development and is oriented toward local and regional solutions to economic, social and environmental sustainability [4]. Table 1 shows the regional and global population and income projections for the B scenario. The global population increases from 6.1 billion in to 9.4 billion by 5 and.4 billion by. The population growth in Asian, African and Latin American developing countries, CPA, SAS, PAS, AFR, LAM and MEA, accounts for most of this steep rise in the global population. Income differences among the regions are assumed to narrow throughout the 1 st century. Income ratio between three developed regions, NAM, PAO and WEU, and eight other developing regions decreases from 18.6 in to 5. by 5 and 3.6 by due to economic development in the developing regions.

6 Table 1 Regional and Global Population and Income Projections for the B Scenario Income (Per capita GDP) Population (million) Region (US thousand $/person) 5 5 NAM PAO WEU EEU FSU CPA SAS PAS LAM MEA AFR World Per Capita Residential Energy Demand Projections for Eleven World Regions Figure 9 shows the per capita residential energy demands for eleven world regions projected for, 5 and in units of GJ/person/yr. The per capita residential energy demand is highest for North American and European regions, NAM, WEU, EEU and FSU throughout the century. The thermal demand accounts for a large part of this high residential energy demand in these regions because of their colder climates. The per capita thermal demand in Asian, African and Latin American developing regions, CPA, SAS, PAS, LAM, MEA and AFR does not increase so much regardless of increasing income level, because most of these developing regions are located at warm or hot climate zones and so the energy demand for space heating and water heating are much lower than the cold climate zones. Another characteristic of the thermal demand is that, in these Asian, African and Latin American developing regions, increasing income level induces significant shifts in final energy demand for thermal uses from non-commercial fuel to commercial fuel. Per capita electricity demand for non-thermal uses grows rapidly in developing regions, CPA, PAS, LAM and MEA, and it becomes a large part of the residential final energy use in these developing regions by the end of the century. This structural change in final energy use might have a significant influence on the trajectory of carbon emissions in the long run. Per Capita Residential Energy Demand(GJ/person/yr) Electricity Demand for Non-thermal Uses Commercial Fuel Demand for Thermal Uses Non-commercial Fuel Demand for Thermal Uses NAM PAO WEU EEU FSU CPA SAS PAS LAM MEA AFR Figure 9 Regional per capita residential energy demand projected for, 5 and 4.3 Global Energy Demand Scenario for Residential Sector Toward Figure -(a) and -(b) show the global energy demand projections for the B scenario by region and by demand category respectively.

7 The global residential energy demand nearly doubles from 87 EJ in to 156 EJ in as seen in Figure -(a). The eight developing regions, EEU, FSU, CPA, SAS, PAS, LAM, MEA and AFR grow in residential energy demand at a higher rate of.76%/yr than that of three developed regions, NAM, PAO and WEU. As a result, the share of the developing regions in the global residential energy demand increases from 67% in to 74% by 5 and 8% by. The growth rate of residential energy demand is highest for LAM and MEA due to both their increasing population and income level. PAO is expected to be the only region that decreases in residential energy demand during the century and, based on our demand projection, the residential energy demand for PAO begins to decline around 3 and nearly halves by the end of the century mainly due to decreasing population in Japan. Increase in electricity demand for non-thermal uses is the main factor boosting the global residential energy demand as seen in Figure -(b), which leads to rising share of non-thermal electricity demand in the global residential energy demand from 13% in to 36% by. This is mainly caused by rapidly increasing non-thermal electricity demand in Asian, African and Latin American developing regions as shown in Figure 9. Thermal energy demand, which is defines as the total sum of commercial and non-commercial fuel demand for thermal uses, is expected to grow steadily in the former half of the century and then grow slowly or even saturate in the latter half of the century as seen in Figure -(b). This reflects our finding that, unlike non-thermal electricity demand, thermal energy demand is mostly determined by climate condition rather than income level. The fuel shifts in thermal energy demand from non-commercial fuel to commercial fuel in the developing regions, also contribute in part to this slowdown in thermal energy demand growth. Global Residential Energy Demand(EJ/yr) AFR MEA LAM PAS SAS CPA FSU EEU WEU PAO NAM Year Figure -(a) Global residential energy demand projection toward by world region Global Residential Energy Demand(EJ/yr) Electricity Demand for Nonthermal Uses Commercial Fuel Demand for Thermal Uses Noncommercial Fuel Demand for Thermal Uses Year Figure -(b) Global residential energy demand projection toward by demand category

8 5. Conclusion This paper presents one possible global and regional residential energy demand scenario for the 1 st century that are consistent with demographic and economic projections of the IIASA s B scenario for IPCC, based on the energy demand projection formulas developed through the empirical analysis of per capita residential energy demand across fuel types and regions. For the B scenario, the global residential energy demand increases from 87 EJ in to 14 EJ by 5 and 156 EJ by. Increase in electricity demand for non-thermal uses in developing countries of Asia, Africa, Latin America and the Middle East, boosts the global residential energy demand. Increasing income level as well as population growth causes this increase through rising ownership of household electric appliances and lifestyle changes in these developing countries. As a result, the share of the developing countries in the global residential energy demand rises from 67% in to 74% by 5 and 8% by, and the non-thermal electricity demand occupies a larger share of 4% by 5 and 36% by than that of 13% in. Unlike the non-thermal electricity demand, the growth of thermal energy demand is expected to slow down or even saturate during the century. Increasing income level does not induce a significant growth of per capita thermal energy demand, but a structural change in final energy demand for thermal uses in the developing countries from non-commercial fuel, e.g. fuel woods, to commercial fuel, e.g. petroleum products and natural gas. Technological enhancements in residential building insulation also contribute to this saturation of global thermal energy demand. The results imply that, long-term efforts to curb greenhouse gases emissions from residential energy use in developing countries are needed to achieve the global sustainable economy. Acknowledgements The authors gratefully acknowledge Dr. Keywan Riahi for his encouragement and helpful comments on this work. References [1]Schaefer, A., 5, Structural change in energy use, Energy Policy, Volume 33, Issue 4, pp []Gruebler, A., 1995, Industrialization as a historical phenomenon, WP-95-9, IIASA [3]International Energy Agency, 1997, Indicators of energy use and efficiency: Understanding the link between energy and human activity, OECD/IEA [4]Intergovernmental Panel on Climate Change (IPCC),, Emissions Scenarios: A Special Report of IPCC Working Group III [5]International Energy Agency, 3, Energy Balances of OECD and Non-OECD Countries, OECD/IEA, Paris [6]Miketa, A., 4, Technical description on the growth study datasets, Environmentally Compatible Energy Strategies (ECS) Program, IIASA, [7]World Energy Conference (WEC), Energy Efficiency Working Group, 1, Energy Efficiency Policies and Indicators, WEC