Scenarios of building energy demand for China with a detailed regional representation

Energy 67 (2014) 284e297

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Scenarios of building energy demand for China with a detailed regional representation Sha Yu a, *, Jiyong Eom b, Yuyu Zhou a, Meredydd Evans a, Leon Clarke a a b

Pacific Northwest National Laboratory, 5825 University Research Court, Suite 3500, College Park, MD 20740, USA Graduate School of Green Growth, KAIST Business School, 85 Hoegiro, Dongdaemun-gu, Seoul 130-722, Republic of Korea

a r t i c l e i n f o

a b s t r a c t

Article history: Received 14 August 2013 Received in revised form 27 December 2013 Accepted 30 December 2013 Available online 7 February 2014

Building energy consumption currently accounts for 28% of China’s total energy use and is expected to continue to grow induced by floorspace expansion, income growth, and population change. Fuel sources and building services are also evolving over time as well as across regions and building types. To understand sectoral and regional difference in building energy use and how socioeconomic, physical, and technological development influence the evolution of the Chinese building sector, this study developed a building energy use model for China downscaled into four climate regions under an integrated assessment framework. Three building types (rural residential, urban residential, and commercial) were modeled specifically in each climate region. Our study finds that the Cold and Hot Summer Cold Winter regions lead in total building energy use. The impact of climate change on heating energy use is more significant than that of cooling energy use in most climate regions. Both rural and urban households will experience fuel switch from fossil fuel to cleaner fuels. Commercial buildings will experience rapid growth in electrification and energy intensity. Improved understanding of Chinese buildings with climate change highlighted in this study will help policy makers develop targeted policies and prioritize building energy efficiency measures. Ó 2014 Elsevier Ltd. All rights reserved.

Keywords: China Building energy use Integrated assessment Downscaled analysis Climate change

1. Introduction China has experienced rapid economic growth in the past two decades with an annual GDP (Gross Domestic Product) growth rate of 10% [1,2]. China’s buildings sector has also grown rapidly and Chinese building energy use has increased by 40% from 1990 to 2009. It demanded 443 Mtoe in 2009, accounting for about 28% of the country’s total final energy demand [3]. China is the largest consumer in residential building energy use and the third in commercial building energy use [3]. This growth is not likely to wane anytime soon [4,5]. The consensus in the literature is that the shares of electricity and natural gas will continue to expand, displacing currently intensive use of traditional biomass [5,6]. Underlying the growth in the Chinese buildings sector is a continued expansion in building floorspace. Over the last several years, China has added 1.8e2.0 billion m2 of floorspace annually, establishing the world’s largest market for new construction [7,8]d urban and rural residential buildings expanded at the average annual rates of 7% and 2%, respectively, in the past decade [9]. In

* Corresponding author. Tel.: þ1 301 314 6736; fax: þ1 301 314 6719. E-mail address: [email protected] (S. Yu). 0360-5442/$ e see front matter Ó 2014 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.energy.2013.12.072

2010, the size of building floorspace in China was about 59 billion m2 with rural and urban residential buildings consisting of 41% and 35%, respectively [2]. Given the broad agreement that strong economic growth will continue in the foreseeable future, the trend in the Chinese buildings sector is likely to persist, putting upward pressure on building energy demand. The trends and characteristics of building energy use in China were examined in many studies, which broadly fall into three groups. The first group focuses on the historical trends of building energy use in China. Tonooka et al. [10] and Nakagami et al. [11] attempted to decompose historical residential energy consumption into service and fuel types; they then estimated emissions based on these data. Chen et al. [12] split building energy demand into various services and estimated its monthly variation at the national level. These studies, however, did not consider possible differences in climate conditions across regions. The second group of studies specifically addresses the issue of regional heterogeneity by identifying the structure of building energy consumption based on the surveys of several cities or sectors in China (see Refs. [13e 18]). A few studies provide a more comprehensive picture of the status of Chinese building energy demand at the regional level. Zhang [19] divided China into seven regions according to climatic characteristics and showed substantial differences in the demands

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for residential fuels (including district heat) across the regions between 1990 and 2000. Tsinghua University has been publishing the Annual Report on China Building Energy Efficiency since 2007, which details historical building energy use by building type and heating energy demand [20]. The last group of studies offers nearterm projections for the development of the Chinese building sector, based on a set of assumptions on economic growth and enduse technologies [6,21e23]. However, none of these projection studies were conducted at the sub-national level, and they did not address the influence of regional heterogeneities in socioeconomic development and climate change, which we consider to be equally important. This study builds on Eom et al. [5], which proposed a servicebased building energy model for China, nested in the GCAM (Global Change Assessment Model) integrated assessment framework. The model captures key drivers of building energy demand, including urbanization, economic growth, the expansion of building floorspace and energy service demands. While our study employs essentially the same modeling framework, it distinguishes itself from the work of Eom et al. [5], contributing to the body of literature in two ways. First, we disaggregate China into four subregions with different socioeconomic and climate conditions to examine the long-term evolution of the buildings sector at the regional level. This is a major analytical and methodological advance as the process of constructing the model required us to consider regional differences in socioeconomic development and preferences for building energy services and fuels. In this way, the responses to socioeconomic development, energy efficiency improvement, and building energy policy would be better represented at the regional level, providing a better platform for analysis of the Chinese building sector in general. Second, we analyze the potential impacts of climate change on the Chinese building sector at the regional level. There is general agreement in the literature that climate change will increase cooling energy demand (mostly electricity) and decrease heating energy demand [24e30]. However, only a few studies provides analyses on the impact of climate change on the Chinese building sector, and they are national-level aggregated analysis [25], city-level statistical analysis [31], or short-term building simulation analysis [29,32]. Instead, we examine the responses of the Chinese buildings sector to long-term climate change, based on a detailed representation of heating and cooling requirement and its interaction with climate change at the regional level. This study highlights four major findings. First, relatively cold regions in China, such as the C (Cold) and HSCW (Hot Summer Cold Winter) regions, will continue to account for a major portion of building energy demand and floorspace in China. Second, climate change will help reduce total final energy consumption in Chinese buildings. The impact of climate change on heating energy demand will remain significant in China with the greatest impact in the C and HSCW regions. Climate change will influence cooling energy use as well with the impact most profound in the HSCW region. The combined effect is that the decrease in heating energy use offsets the increase in cooling energy use in most regions with the exception of the HSWW (Hot Summer Warm Winter) region. Third, due to continued rapid urbanization, rural households in China will have a rapid decrease in total energy consumption over the course of the century, as well as fuel substitutions from traditional biomass to coal and then to cleaner fuels such as electricity and gas. Fourth, urban residential buildings in the SC (Severe Cold) and Cold (C) regions will also experience significant fuel transition characterized by decreased coal use and increased electricity and gas use. Overall, EUI (energy use intensity) in urban residential and commercial buildings will continue to rise at least for the next several decades, taking a different path than its developed economy counterparts.

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2. Methodology and data 2.1. Modeling framework This study uses a detailed building energy model nested in the Global Change Assessment Model (GCAM) to analyze long-term building energy demands in China. GCAM is an energy sector focused partial equilibrium integrated assessment model capable of representing the development of the full energy system and its associated greenhouse gas emissions to the end of the century. The model is global in scale, and it comprises of 14 global regions and runs in 5-year time steps from 1990 to 2095. Details regarding GCAM can be found in the work of Edmonds and Reilly [33], Clarke and Edmonds [34], Clarke et al. [35], and Clarke et al. [36]. The building energy model used for this study is fully nested in GCAM, so that fuel prices in the buildings sector are cleared in the GCAM global or regional markets sharing the same set of socioeconomic assumptions. The model explicitly accounts for key forces that influence the evolution of the sector: (i) the increase in building floorspace with population and economic growth; (ii) the associated demand for building energy services (e.g., space heating, space cooling, and other services such as appliances and equipment); and (iii) the fuel and technology competition within the services. In particular, the model is capable of representing the changes in building energy services as a function of income, fuel prices, and climate change, while at the same time capturing regionally differentiated behavior of demand satiation. The model originally proposed by Eom et al. [5] consists of three distinct sectorsdurban and rural residential buildings and commercial buildingsdwith detailed representation of energy services, fuels, and end-use technologies. Our study extends the model by disaggregating each of the three building sectors into four climate regions, so that in total twelve different building types are represented either separately or collectively under the influence of climate change.

2.2. Disaggregation of the Chinese building sector To capture regionally differentiated responses to socioeconomic development, energy efficiency improvement, and climate change, we disaggregated the China region in GCAM to four distinct climate regions: SC, C, HSCW, and HSWW (Fig. 1). This is slightly different from Chinese official classification of SC, C, HSCW, HSWW, and temperate [37]. Because the temperate zone is small in geographic coverage and population size (less than 6% of China’s total population), we collapsed the zone into the HSWW region, which shares the same building energy codes as the temperate zone and roughly similar building characteristics. This approach required one-to-one mapping between each of the 34 Chinese provinces,1 from which regional socioeconomic and energy statistics are constructed, and the four climate regions. Those provinces geographically identified by multiple climate regions were placed into the associated climate region where the greatest portion of its population resides. Building stock in each of the four climate regions are divided into urban and rural residential buildings and commercial buildings, so that twelve different building sectors are represented. All major assumptions, including socioeconomic development, floorspace

1 Note that the China region in GCAM also includes former or current planned economies of Vietnam, Mongolia, Cambodia, and North Korea. The inclusion of these countries results in only a small deviation from national statistics, given that China accounted for more than 97% of the entire region’s GDP in 2010. In this study, the contribution of these other countries is assumed to be evenly distributed across the four climate regions.

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Fig. 1. Definition of climate regions used in this study.

expansion, climate change impact, technology improvement, and energy service expansion, are made at this level of resolution (Fig. 2). The following sections elaborate on these assumptions one by one. 2.3. Socioeconomic assumptions We employed the same socioeconomic assumptions made for the aggregate China as in Eom et al. [5] to develop a disaggregated socioeconomic representation of the four climate regions, each with urban and rural areas. Note that, in our analysis, urban and rural residential sectors in each climate region have different income and population pathways, whereas the commercial sector uses the representative income and population of the corresponding climate region. Following the downscaling approach proposed by van Vuuren et al. [38], urban and rural per capita incomes are assumed to grow at constant annual rates, approaching a preset level by the assumed convergence year of 2150. The income trajectories are then adjusted to make sure that the sum of GDP in all urban and rural regions equals our assumed GDP pathway at the national level.

Fig. 2. Four major drivers of energy service demands in each of the 12 building sectors.

These processes generate urban and rural per capita incomes in the four climate regions as indicated by Fig. 3. As shown, the variation of per capita income across the climate regions tends to diminish with the urban-to-rural income gap within the regions remaining through the end of the century. The population assumption of aggregate China follows the medium variant of UN (United Nations) World Population Prospects [39] and the assumptions of Eom et al. [5] and Clarke et al. [36]. Given the uncertainties about regional fertility and mortality profiles and population migration, we assumed that populations in all climate regions will increase at the same rate, consistent with the aggregate population assumption (Fig. 4). Future urban and rural populations for each region are determined by assumed urbanization rates, which are discussed below. Base-year income and population data for each climate region and its urban-rural compositions are from the China Statistical YearBook [40]. For further details of the downscaling method, see Appendix A.

Fig. 3. Urban and rural incomes per capita in the four climate regions.

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Fig. 4. Urban and rural populations in the four climate regions.

The representation of urbanization is important for long-term energy modeling as urban and rural areas exhibit very different socioeconomic characteristics and fuel consumption patterns [5,24,41]. Regional heterogeneity needs to be taken into account in representing urbanization as the regions may have different historical paths of urbanization, which may shape the rate and extent to which urbanization process might unfold. Indeed, four different provincial groups in China varied in the path of urbanization, which is to some extent associated with regional economic growth but still is not fully explained due to the presence of many regional idiosyncrasies. For example, the level of urbanization in HSCW region rose much more than that of SC region over the past three decades, even though their economic development has not been noticeably different (Fig. 5). We establish a non-structural relationship between urbanization and per capita income. That is, a climate region’s urbanization level increases with its average level of per capita GDP, converging to a pre-specified level2 with the parameters fitted to its historical urbanization trends, which were constructed from China’s national and provincial statistical yearbook and census data [9,42,43]. In this way, we attempted to represent urbanization in the four climate regions in an internally consistent manner while still capturing regional heterogeneities. Note that as the average per capita GDP is coupled with the urbanization level, which simultaneously determines the region’s average per capita GDP with the aforementioned sub-regional income convergence assumption, we conducted iteration until both the region’s urbanization level and average per capita GDP become stabilized. The results show that all of the four climate regions undergo fast urbanization during the first half of the century, and then urbanization starts to level out after the mid-century (Fig. 5). These trends in aggregate present rapid urbanization up to about 73% by 2050, which is about the same as UN’s urbanization projection for China [39], followed by a slow-down phase, ultimately taking the path of the U.S. (United States). 2.4. The expansion of building floorspace The expansion of building floorspace for each of the twelve sectors represented in this study is linked to regional supply and

2 For this study, the long-term urbanization asymptote (or limit) has been set at 86%. This number is drawn from the UN’s 2050 urbanization prospect for so-called ‘more developed regions’. As a comparison, the UN’s 2010 estimates of urbanization level are 89% for Australia, 83% for South Korea, 82% for the U.S., 80% for UK, 74% for Germany, and 73% for Russia.

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demand dynamics. The demand for floorspace increases as regional income grows with its sensitivity relying on region-specific demand preferencesdfor example, warmer regions tend to show greater demand for floorspace per capita than colder regions, all else being equal. Also, as floorspace expands, its supply becomes increasingly costly because of various institutional and resource constraints, which is also region-specific. We decided not to use supply and demand elasticities estimated from recent historical data as they are not suitable for a century-long projection. Instead we specified them for each building sector, ensuring that per capita floorspace for each sector increases gradually with income and ultimately reaches the level of per capita floorspace in presently developed economies.3 Year-2005 per capita floorspace numbers from the China Statistics Yearbook [40] was used to calibrate the model for floorspace expansion. According to the projection, per capita floorspace continues to increase in all sectors with the overall growth rates of 66e79% in urban buildings, 60e80% in rural buildings, and 80e93% in commercial buildings over the century (Fig. 6). Residential per capita floorspace varies substantially by climate region and by urban-rural distinction, eventually reaching the current levels of most industrialized countries (Fig. 6a). By contrast, China’s per capita floorspace in commercial buildings in the four climate regions tends to be tightly bunched, except for the SC zone, and reaches to the current levels of Sweden and New Zealand (Fig. 6b). The general trend is that per capita floorspace is greater in warmer regions than in colder regions. It needs to be made clear that the projection should be interpreted as a reasonable set of assumptions, establishing a likely relationship between our socioeconomic pathways and building floorspace in the future, not as a prediction of future real estate trends. The projection of per capita building floorspace, in combination with the population assumptions, indicates a contrasting picture for urban and rural buildings. The rural share of total Chinese building floorspace declines from 45% in 2005 to 21% in 2050, while the urban share increases from 32% to 55% over the same period (Fig. 7). Although commercial buildings expand rapidly, their share remains nearly unchanged. Notably, HSCW and C regions combined continue to account for approximately two-thirds of total building stock in China. 2.5. Population-weighted heating/cooling degree days Degree-days are a well-known metric indicating heating or cooling service requirements in a particular region [44]. When combined with spatially explicit distribution of population, they can be used to help understand the aggregated requirement of heating and cooling service across a greater region or a country. Zhou et al. [25] proposed a method to estimate populationweighted HDDs/CDDs (heating and cooling degree days) for China at the national level using the gridded temperature and population data. This study extends the research by developing populationweighted HDD/CDDs over time for each of the four climate regions in China. As found in Zhou et al. [25], the estimation of HDD/ CDDs from the CCSM3 (Community Climate System Model Version 3) generally situates in the middle of the range of outcomes from the three climate models they used. We chose temperature projections from CCSM3 in the Intergovernmental Panel on Climate Change’s Special Report on Emissions Scenario, specifically, the A2 scenario, which is broadly consistent with GCAM reference

3 The price elasticity of supply, the price elasticity of demand, and the income elasticity of demand range from 0.3 to 0.5, from 0.5 to 0.04, and from 0.13 to 0.5, respectively, which are in similar ranges of those in Chaturvedi et al. (2013).

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Fig. 5. Selected countries’ historical urbanization [74], historical and projected urbanization in four climate regions, and resulting urbanization projection for China in aggregate.

Fig. 6. International comparison of residential (a) and commercial (b) building floorspace [69,75].

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improvement in shell efficiency. To reflect the urban-to-rural difference in shell efficiency while maintaining the consistency with the historical estimates, we assumed that each region’s rural stockaverage U-factor had steadily decreased from its 1980 estimate at half the rate of improvement of its urban counterpart (Fig. 9). 2.7. Energy service demands

Fig. 7. China building stock by sector (2005, 2020, 2050, 2095).

scenario in terms of CO2 emissions [25]. For the calculation, we assumed that future population distribution across China, not population size, remains fixed to the year-2000 distribution, which was provided by the IIASA (International Institute for Applied Systems Analysis) database.4 The result indicates that HDDs decrease while CDDs increase over time, suggesting decreasing requirement for space heating and increasing requirement for space cooling, regardless of the climate regions. Another interesting finding is that global climate change has different regional implications: SC experiences the greatest decline in HDDs, and HSWW has the greatest increase in CDDs (Fig. 8). 2.6. Building shell efficiency The shell efficiency of building envelope is measured in practice by U-factor (or thermal conductance) which is defined as the rate of heat transfer through a unit area of building shell. Lower U-factor indicates the building envelope being better insulated from outdoor temperature variations and thus lower requirements for heating and cooling service demands, all else being equal. Of course, the overall level of shell efficiency in China will improve over the time, driven either by technology improvement or regulatory requirements. The current level of U-factor differs by climate region. According to a survey by the China Academy of Building Research, the average U-factor values in 1980 were 1.28, 1.7, 2.0, and 2.35 W/m2 K for SC, C, HSCW, and HSWW regions, respectively (based on personal communication). As there is not enough historical evidence supporting a particular trajectory for U-factor improvement in Chinese buildings, we assumed an annual stock-average U-factor improvement rate 0.47% and 0.41% in urban residential and commercial buildings respectively, following the estimates for the U.S. used by Kyle et al. [45]. It is worth noting that Chinese rural buildings exhibit lower thermal performance than Chinese urban residential buildings, as well as residential buildings in advanced countries. Yang et al. [46] and Evans et al. [47] point out rural buildings in China often lack thermal insulation, and they are characterized by slow

4 Ideally, changes in urban extent consistent with our urbanization assumptions need to be developed and linked with climate change to account for the influence of population migration on HDD/CDD at the regional level. Because this adds an additional complexity to the assessment and, as Zhou et al. point out, the influence of population migration is likely to be small, we decided not to account for the influence in this study.

The expansion in energy service demands driven by rising energy service intensities (or energy services per unit of floorspace), combined with population and per capita floorspace. Five different energy servicesdspace heating, space cooling, water heating and cooking, lighting, and appliancesdrepresent mutually-exclusive energy consumption in the buildings sector. Following the specification developed by Eom et al. [5] and Chaturvedi et al. [24], demands for space heating and cooling energy services are affected not only by changing per capita income and service prices but also by changing space conditioning requirements represented by heating and cooling degree days, building shell improvements and technological change, and internal heat gains from unused portion of energy from other services. Heating and cooling energy demands are primarily affected by heating and cooling degree days and floorspace expansion, while demands of other services (water heating and cooking, lighting, and appliance) are based on population and income growth. Per unit floorspace energy service demands are represented as follows:

" #  ln 2 i dH ¼ kH ,ðHDD,h,r  lH ,IGÞ, 1  exp  mH PH

#  ln 2 i dC ¼ kC ,ðCDD,h,r þ lC ,IGÞ, 1  exp  mC PC "

" dj ¼ kj ,qj , 1  exp

ln 2 i  mj Pj

!#

where the first coefficient (kH, kC, kj) are calibrated parameters, the second terms are satiated demands, and the third terms are economic factors that impact building energy services. Regarding satiation demands, HDD and CDD are heating and cooling degree days [h  C], which change over time and differ across climate zones; h is thermal conductance [GJ/m2 h  C], known as the U-factor for buildings; r is the floor-to-surface ratio; IG is internal gains that could impact building’s thermal comfort and heating and cooling energy requirements, and lH and lC are internal-gain scalars accounting for the potential mismatch of the time when space conditioning is required and the time when the internal gains are produced. Regarding the economic choice term, qj represents the effect of economic decision making, i is per capita income, Pj is the price of an energy service, mj is referred to as saturation impedance of the service. The saturation impedance represents the extent to which the saturation of an energy service is impeded, given the affordability of the service, i/Pj in the process of prioritizing various energy services within the budget. The model detailed was discussed in the paper of Eom et al. [5] and Chaturvedi et al. [24] and downscaled heating and cooling degree days for different climate zones are discussed in Section 2.5. Demands for other services (water heating and cooking, lighting, and appliance) are based only on per capita income and service prices. The demand representation ensures that energy services per unit of floorspace do not exceed certain pre-specified levels, or socalled satiation levels, which are assumed to be the same as the levels of current U.S. buildings adjusted by heating and cooling degree day ratios.

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Fig. 8. Heating and cooling degree days for different climate zones.

Fig. 9. U-Factor improvements over time for each of 12 different sectors.

3. Base-year energy consumption Base-year building energy demand in each climate region is constructed from Chinese energy statistics reported at the provincial level with adjustments [9,48e51]. The problem is that due to the difference in reporting practices, the sum of province-level energy consumption in the CEDB (China Energy Databook) [52] or the China Energy Statistical Yearbook does not exactly match with the total China building energy consumption reported in the same source or in the International Energy Agency’s energy balance. To reconcile these differences, the shares of fuels in building energy demand for individual climate region in 2005 have been constructed based on province-level energy data and then adjusted to match with the year-2005 national-level data in CEDB. Then such energy demand estimates at the climate-region level have been scaled up to the year-2005 data for GCAM’s China region obtained from IEA (International Energy Agency) energy balance. Traditional biomass, widely used in rural China, is excluded in the official residential statistics from domestic sources. Assuming traditional biomass is used only in rural areas, we incorporated CEDB’s non-commercial energy consumption estimates into building energy consumption in rural areas. The result is rural energy demand in 2005 accounting for 67% of total building final energy consumption in China. As most Chinese studies do not take into account of the intensive use of traditional biomass, the rural energy share ranges only about 20e40%, depending on climate

zones. The survey from Tsinghua University, for example, indicates the aggregate rural energy share of 35% [20].5 We found that the pattern of building energy consumption is very different across the climate regions (Fig. 10). Cold and Severe Cold zones, which are characterized by an intensive use of space heating energy and thus had been the focus in the early stage of building energy policy development, represent 44% of total building energy consumption in China [53]. In particular, the SC zone has highest energy consumption per unit of floorspace although the region only accounts for 17% of total energy consumption in China. The intensity of building energy consumption tends to decline from colder to hotter regions. Urban and rural fuel consumption profiles present a contrasting picture because of their differences in fuel access and socioeconomic status. The fuel source used in rural areas is primarily traditional biomass, followed by coal, electricity, and oil, and this pattern in rural areas is similar for all climate regions. By contrast, urban residential sectors tend to rely on more modern fuels with district heating accounting for as much as 36% and 22% in severe cold and

5 Another issue is that commercial biomass, existing in urban residential and commercial buildings in our energy balance based on IEA statistics, is not included in any Chinese statistical categories. According to IEA statistics, commercial biomass use in buildings is only 0.0006 EJ, less than 0.0001% of total building energy use in China (IEA 2010). Based on this, commercial biomass was divided into 12 sectors based on the share of total energy use and share of traditional biomass consumption in each climate zone.

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Fig. 10. Energy use intensity by fuel and climate regions (2005). Fig. 11. Energy use intensity by service and climate region (2005).

cold regions. Oil is also intensively used both in the urban residential and commercial sectors although commercial buildings tend to demand more oil than residential buildings. Note that, differently from other fuels, coal is widely used both in urban and rural settings perhaps due to its advantage as a cheap and accessible option particularly for regions with poor energy infrastructure. Interestingly enough, the rural residential sector is most energy intensive when expressed in terms of final energy demand per unit of floorspace. This is due to the sector’s heavy reliance on traditional biomass, which is used to deliver residential heating and cooking services but in a very inefficient manner. Because of the intensive use of traditional biomassd10 EJ out of the total building energy demand of 18 EJ in 2005drural buildings accounts for about two-thirds of total building energy consumption in China. Service-based modeling of building energy consumption necessarily requires breakdown of energy consumption by service type. Yet, neither Chinese statistics nor IEA data specifies energy consumption by service, which posed challenges to this detailed assessment. There are only a few surveys that report current service profiles of urban residential energy consumption. The Annual Report on China Building Energy Efficiency [20] examines electricity used in cooling, lighting and appliances, shares of fuels and technologies for district heating, and oil and gas used in water heating and cooking at the province level. Another survey for five urban cities with varying climate conditions provides coal and electricity shares in cooking and heating energy consumption [13]. Based on these reports, we have developed base-year 2005 final energy consumption profiles by fuel type and service for the four urban residential sectors. The combined results are largely consistent with studies of Yu et al. [54], Hu and Jiang [21], and Tonooka et al. [10]. Rural energy consumption profiles are even less well understood. Although there are a few surveys on rural energy consumption, these cover only small geographical areas typically a couple of villages or counties, and their findings are different from one to another [17,18,55e60]. Given limited understanding of rural energy use profiles, we assumed that the service shares of rural fuel consumption for a given fuel are the same as those in urban areas, and the result turned out to be consistent with Lin et al. [61], which examines rural development and energy use in rural areas, including buildings. Because energy service profiles for each of the four commercial sectors were not available, shares of energy services in urban areas have been applied to fuel use in each of the sectors along with region-specific multipliers to ensure that the resulting service shares at the national level are consistent with energy services profiled by Zhou et al. [22].

This process resulted in service shares varying widely across building types and climate regions (Fig. 11). Three points are worth making regarding this result. First, space heating alone accounts for more than half of the building energy use in the SC and C zones and is at least one of the major energy services in all climate regions except for HSWW region. The result indicates a strong correlation between heating energy intensity and heating degree daysdheating energy intensity is higher for colder climate regions. Second, cooling energy use is currently very limited particularly in residential buildings, although its contribution will continue to increase with rising standards of living. Third, life essentials, such as space heating, water heating, and cooking services, account for nearly all of the building energy consumption, although commercial buildings exhibit a greater variety in energy services than residential buildings. The suggestion is that the Chinese buildings sector will experience a dramatic shift in building energy consumption toward energy services that have been so far considered non-essential, such as appliance and space cooling services. 4. Results 4.1. Building energy use trends in China According to the model, building energy use in China will increase from 18 EJ in 2005 to 46 EJ in 2095. The near-term projection of 27 EJ in 2020 and 31 EJ in 2030 is slightly greater than IEA’s projection [62]d24 EJ in 2020 and 26 EJ in 2030dand lower than Eom et al. [5]d27 EJ in 2020 and 34 EJ in 2030. Although EIA (U.S. Energy Information Administration) also provides its numbersd15 EJ in 2020 and 19 EJ in 2030 [63]dthey are not directly comparable as the demand for traditional biomass is not taken into account. The dramatic increase in final energy consumption is driven by both the increasing demand for floorspace and the intensification of energy services, such as appliances and, to a lesser extent, cooling, water heating, cooking, and lighting services (Fig. 16a). Not surprisingly, this transformation involves rapid electrification of the building energy system and the phase-out of solid fuels (Fig. 16b), a trend also indicated by previous studies on the buildings sector in developing economies [5,24]. 4.2. Trends in final energy demand by climate region The Chinese buildings sector presents considerable heterogeneity in its development across the four climate regions

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Fig. 12. Total building energy use (a) and energy use intensity (b) by climate region (with climate change).

Fig. 13. Development in per capita building energy consumption (a) and energy use intensity (b) in Chinese urban residential buildings compared with the history of developed economies.

(Fig. 12). The C and HSCW regions dominate overall the other regions and account for about two-thirds of total Chinese building energy consumption (Fig. 12a). Recall that the two regions collectively account for 68% of total building floorspace in China (Fig. 7). From the perspective of the intensity of energy consumption in buildings (or, equivalently, EUI), the SC region is prominent (Fig. 12b). It is worth noting that while the region’s EUI starts to decline in 2020 due to its rapid switch away from inefficient uses of traditional biomass, the other regions maintain steady growth in EUI due to their faster rates of urbanization over the next several decades and lower reliance on traditional biomass in rural areas.6 Note that a sizeable gap in EUI between the regions continues to remain mainly due to the differences in climate conditions and floorspace demand.

6 The urbanization rate in the SC region is around 8% higher than the national average in 2012 (HBS and NBSC, 2012) and the future growth rate is slower than other regions (Fig. 4).

4.3. Energy use of urban residential buildings and its interaction with climate regions The trends and patterns of energy consumption also differ greatly by building type (Fig. 12a). Urbanization in China leads to a continued increase in final energy consumption in urban residential buildings and a rapid decrease in rural residential buildings after 2020dthe urban residential sector, which was responsible for 17% of the total final energy used in buildings, increases its share to almost half by 2050. The projected energy consumption for urban China is not unreasonable when compared with current building energy consumption in developed economies. The energy intensity of urban China will remain lower than those of currently developed countries (Fig. 13). Note that as the comparison is made between the future Chinese building sector and the history in developed countries, the latter generally constitutes the upper bound of the extent to which the energy intensity in the Chinese building sector might increase over time with the assumption that energy efficiency in the developed countries will continue to improve as it has done so far. Per capita energy consumption in the urban residential sector

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Fig. 14. Development of energy use intensity in Chinese commercial buildings compared with the history of developed countries.

Fig. 15. Energy use in rural buildings by fuel and climate region (with climate change).

Fig. 16. Impact of climate change on building energy service (a) and fuel use (b).

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will increase rapidly over the next several decades before it levels off and eventually triples its current level by the end of the century (Fig. 13a). A similar trend is observed in terms of EUI, although it presents greater regional variation due to the difference in climate conditions (Figs. 13b and 6)dcolder regions tend to exhibit higher EUI. The increases in EUI with comfort level are indicated by previous studies [64,65]. Space heating is the most energy-intensive end-use in residential buildings in China and accounts for around 46% of total final energy in urban buildings as of today. In 2010, heating energy use accounts for 70% and 59% of final energy consumption in urban residential buildings in SC and C regions, respectively; this share is less in warmer climatesd33% in HSCW and 4% in HSWW regions. The efficiency of heating devices is low and envelope insulation is not widely installed. The average heating energy use intensity in northern China is one to two times more than developed countries in the same latitude, and existing buildings have an enormous energy saving potential [66,67]. The future development of heating energy use in China depends on a number of factors such as the performance of the installed heating system and building envelope, climatic conditions, behavioral characteristics, and socioeconomic conditions. The floorspace as well as income level continue to increase in northern China (Figs. 3 and 7), which together will increase the demand for energy services; the increasing demand for comfort level will expand heating services to southern China. To curb the heating energy use, the Chinese government has started district heat reform and heating retrofits in urban residential buildings in northern China with policy and financial incentives [16,68]. In addition, because of better insulation, more efficient heating devices, and increasing temperature, the space heating energy use in urban residential buildings decreases in all regions in the futuredgreatest reduction happens in Northern China. 4.4. Energy use in commercial buildings Energy use of commercial buildings is different from that of urban households, as end-uses such as ventilation, heating, cooling, and equipment vary greatly between these two sectors. Based on our data, it is estimated that the average EUI of commercial buildings is 0.21 GJ/m2 in 2005. This is at least 14% greater than the equivalent value of the urban residential sector. EUI in commercial buildings grows fast in all regions. EUI of the SC region, because of its high heating energy intensity and demand, will surpass the current EUI of most developed countries but the United States by the mid-century, so does EUI in the C region (Fig. 14). In addition, EUI in commercial buildings in developed countries declines as income increases; however, the EUI in commercial buildings in China continue to grow rapidly. This deserves attention from policy makers, as it requires well-designed institutions and regulations to reverse this trend. By the end of the century, electricity consumption in Chinese commercial buildings will double and reach 74% of final energy consumption in commercial buildings, which is close to the development path in European countries [69]. This rapid electrification is driven by an increasing penetration of electricity-related services (equipment, lighting, and cooling). Therefore, it is important to regulate appliance efficiency and introduce mechanisms to encourage high performance in existing commercial buildings, in addition to design-stage energy efficiency measures, which current energy codes and voluntary program such as LEED (Leadership in Energy & Environmental Design) cover. In addition, the installation of smart energy management systems and renewable energy in non-residential buildings becomes more important due to their high share of electricity use [70,71].

4.5. Fuel substitution and energy use in rural residential buildings Energy consumption is highly unbalanced between rural residential, urban residential, and commercial sectors. Rural residential buildings account for 67% of building energy use in 2005, largely because of traditional biomass. According to IEA, rural houses used 10.26 EJ traditional biomass, about 57% of total building energy use in 2005 [3]. Induced by fuel switching and urbanization, the share of rural energy use significantly decreases, to 32% in 2035 and 22% in 2050 (Fig. 12a). With income growth, energy profiles in rural household evolve and traditional biomass is gradually substituted by commercial fuels. The fuel substitution happens at two stages. In the next two decades, the decline in traditional biomass is accompanied by the increasing use of coal in all regions, which may lead to high emissions and air pollution problems (Fig. 15). This has caught the attention of scholars and the Chinese government. Much progress has been made in southern China, for example, in switching from traditional biomass to commercial biogas, which burns more efficiently. The Ministry of Agriculture and the Ministry of Finance have invested over 61 billion Yuan in the construction of household biogas since 2003, and the number of biogas plants installed has doubled since 2000 [72]. Much less progress has been made regarding efficiency improvement in the buildings themselves. In recent years, there are pilot programs to help subsidize retrofits of farmers’ homes in specific parts of China [47]. The Chinese Ministry of Housing and Urban-Rural Development is also considering a more systematic approach based on a design standard for rural homes [73]. In addition, some alternative options could help lower the coal use such as better insulation and more efficient kangs.7 The second stage fuel substitution happens after 2030. As income further increases, rural households reduce the use of coal and switch to cleaner fuels such as electricity and gas in the second half of the century (Fig. 15). The transition in heating energy use in rural households is affected by the growing demand for thermal comfort and improvements in fuel efficiency, as well as fuel substitution. In SC, C, and HSCW regions, heating energy use will peak in 2015 and then decline rapidly as inefficient traditional biomass switches to coal and then to electricity; decline in heating service is due to both reduced rural population and improved heating efficiency. The HSWW region, which currently does not have heating service, observes direct decline in energy consumption from the beginning of the century because of fuel switching away from traditional biomass. Given the comfortable climate, the increase in heating demand in the HSWW region is small.

4.6. Impact of climate change on building energy service and fuel use by climate region Climate change leads to a slight decrease in total building energy consumption in China (Fig. 16). Relative to the no-climate-change case, total building energy consumption with warming climate decreases by 3% in 2020 and 5% in 2050.8 The modest impact is due to the counterbalancing effects of decreasing heating energy use and increasing cooling energy use (Fig. 16a). In terms of fuel uses, the impact results in the greater demand for electricity and the less

7 A Kang is a traditional heated bed used in Northern China; it could be used for space heating and cooking. 8 We only discuss final energy consumption in this paper, and primary energy consumption of the buildings sector may change differently. As discussed by Wan et al. [30], climate change also affects fuel composition of the power sector and associated primary energy use and emissions.

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Fig. 17. Impact of climate change by climate region on cumulative end-use fuel consumption (a) and energy services (b).

demand for coal and district heat (Fig. 16b). The modest decrease in total building energy consumption and resulting effect on the building energy system in China has been also discussed Zhou et al. [25], albeit at the national level. The impact of climate change on heating energy use is significant. With the likely climate change, the HDDs will decline (Fig. 8), which requires less heating service to meet the same comfort level. Although the heating energy demand in most regions will decline in the second half of the century without climate change, climate change could help further reduce heating energy use (Fig. 17). Compared to the no change case, climate change could help save one-third of the total heating energy use in China at the end of the century and the impact of climate change on heating energy savings is most significant in C and HSCW regions. For example, heating energy use in the HSCW region would reduce around 40% with climate change. Although heating energy use in the HSWW region is small, without climate change, heating energy use in this region will almost double by the end of the century. Climate change also leads to changes in cooling energy use. Compared to the no-climate-change case, climate change could lead to a 70% increase in cooling energy use, and the impact is most profound in the HSCW region, where cooling energy use almost doubles under the climate change scenario by the end of the century (Fig. 17a). The impacts of climate change differ by regions. Climate change would lower total building final energy use in SC, C, and HSCW regions as the reduction in heating energy use surpasses the increase in cooling energy use, but with climate change, HSWW region would consume more energy because the impact on cooling energy demand is dominant. Climate change also impacts usage of fuels in Chinese buildings. In all regions, use of coal, gas, oil, and district heating would decline with climate change, which corresponds to the decline in heating energy demand. Use of electricity, the major fuel for cooling, would increase in all regions; however, the impact on the SC region is not substantial because of low shares of cooling service demand in the region (Fig. 17b). 5. Conclusions This paper builds on previous studies and contributes in two aspects. First, this study develops a region specific building energy model, which divides China into four climate regions with three building sectors (rural residential, urban residential, and commercial) in each region. The assessment is conducted at the

regional level. It considers regional differences in socioeconomic development, regional preferences for building energy services and fuels due to different climate conditions and accessibility of resources, and potentially regionally differentiated climate feedbacks. The second contribution is that we analyze potential impacts of climate change on the buildings sector. Our study assesses the impact of climate change at sub-regional level and its interaction with other factors in the long term, which provides a holistic understanding of climate change’s impact on the Chinese buildings sector. Different climate zones in China may require different mitigation and adaptation measures given their respective responses. The results show that building energy use in China will continue to grow until the end of the century; specifically building energy use almost triples from its 2005 level. The Cold and Hot Summer Cold Winter regions are particularly important, and each region accounts for one-third of China’s building energy use. Among all regions, the Cold region also has the highest energy use in commercial buildings because of its high GDP and income level as well as high commercial floorspace. Meanwhile, commercial buildings in all regions will experience fast electrification and rapid increases in energy use intensity, whereas for most developed countries energy use intensity of commercial buildings declines as income grows. As of today, there is only one building energy code for commercial buildings effective in 2005 and the standard is less stringent than that of the prevailing U.S. and EU (European Union) standards for commercial buildings. Voluntary programs such as LEED and the Three-Star Rating started only recently in China. The rapid growth of EUI in commercial buildings deserves attention from policy makers as well as the green building community to work together and build stringent mandatory requirements and strong markets to improve energy efficiency in commercial buildings. Demand for space heating also continues to grow in China and expand rapidly to southern China. However, in southern China, heating is not a major concern and therefore the majority of buildings there lack insulation, which easily results in high heating energy demand. In addition, builders and the building community in Southern China do not have enough hands-on experience in using and installing heating-related techniques; this requires extensive training and education that would likely need to be led by local and national governments. Both rural and urban households will expect fuel substitution and the transition to cleaner fuels. For rural households, the transition will likely happen in two stages: from traditional biomass to coal, and then to electricity and gas. Southern China, compared to northern China, has higher income levels and thus started the fuel

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transition early and began to explore alternative fuels such as biogas. Urban households in SC and C regions will reduce the use of coal and then gradually decrease district heating, accompanied by an increase in the use of electricity and gas. As the fuel transition happens, the demand for commercial energy, especially electricity, will ratchet up and may require more infrastructure for electricity generation, or alternatively, more deployment of renewable energy in buildings. Finally, climate change will have greater impacts on the decrease in heating energy use than on the increase in cooling energy use, and thus reduce overall final energy use in the Chinese buildings sector. However, impacts on different regions are uneven. Climate change leads to a reduction in energy use in the SC, C, and HSCW regions, while it increases total energy consumption in the HSWW region. The HSCW and C regions are most affected in heating energy reduction and the HSCW region also has the greatest increase in cooling energy use. Climate change also leads to changes in fuel use, implicated by a reduction in heating fuels and an increase in cooling fuels. For example, electricity use, induced by increasing cooling energy demand, increases in all regions. Given climate change impacts are differently distributed among fuel types and different climate regions, policies for mitigating or adapting climate change in the building sector should also be region-specific and fuel-specific. There are limitations in this study. First, we only consider final energy consumption of buildings. The impact of climate change on primary energy use may differ. Our future studies will examine changes in primary energy use, especially fuel use in the power sector, and how this will affect total emissions of buildings. Second, the study period 2005e2095 is a long period of time with many uncertainties, and therefore, we will develop more scenarios in the future work to better understand impacts of policy development and technology improvement on building energy use. Acknowledgments The authors are grateful for research support provided by the Office of Energy Efficiency and Renewable Energy of the U.S. Department of Energy and the Global Technology Strategy Program. The authors acknowledge long-term support for GCAM development from the Integrated Assessment Research Program in the Office of Science of the U.S. Department of Energy. The Pacific Northwest National Laboratory is operated for the U.S. Department of Energy by the Battelle Memorial Institute under contract DEAC05-76RL01830. The views and opinions expressed in this paper are those of the authors alone. Appendix A We use an approach to downscaling similar to that proposed by van Vuuren et al. [38] and assume partial convergence in per capita GDP for eight regions in China, using 2150 as the CY (convergence year), and 2005 as the BY (base year). We determine a constant annual per capita income growth rate per sector(IncomeGR,S) leading to China’s per capita income level in the convergence year; and the indices C and S in this formula refer to China and building sector respectively.

 IncomeGR;S ¼

IncomeC;CY IncomeS;BY

1 CYBY

Then we will calculate preliminary per capita income of a sector S at year T (IncomeS,T) based on income growth rate and per capita income in the previous year (T  1).

IncomeS;T ¼ IncomeS;T1 *IncomeGR;S

Next, the sector-level per capita income is adjusted to ensure the sum of total income for all sectors is consistent with China’s total GDP by 1) determine the difference between two items (DiffC,T) and 2) attribute error terms into sectors on the basis of their share in the income growth (IncomeSh_S,T).

1Þ Diff C;T ¼ IncomeC;T *PopC;T 

X

IncomeS;T *PopS;T

S

IncomeS;T *PopS;T IncomeS;T1 *PopS;T1  2Þ IncomeSh S;T ¼ P  IncomeS;T *PopS;T IncomeS;T1 *PopS;T1 CinC

Finally, the final per capita income for a sector can be calculated as

IncomeS;TðFinalÞ ¼ IncomeS;T þ

Dif fC;T *IncomeSh PopS;T

S;T

References [1] NBSC. Statistical Communiqué of the People’s Republic of China on the 2010 national economic and social development. Beijing, China: National Bureau of Statistics of China; 2011. [2] NBSC. China statistical yearbook 2010. Beijing, China: National Bureau of Statistics of China; 2010. [3] IEA. World energy outlook 2011; 2011. Paris, France. [4] Evans M, Shui B, Halverson M, Delgado A. Enforcing building energy codes in China: progress and comparative lessons. Richland, WA: Pacific Northwest National Laboratory; 2010. [5] Eom J, Clarke L, Kim SH, Kyle P, Patel P. China’s building energy demand: longterm implications from a detailed assessment. Energy 2012;46(1):405e19. [6] Jiang K, Hu X. Energy demand and emissions in 2030 in China: scenarios and policy options. Environ Econ Policy Stud 2006;7(3):233e50. [7] Wu Y, Liu C, Liu Y, Qu H. Study on China building energy management system. Beijing, China: China Construction Industry Publishing House; 2007. [8] Wu Y, Liu C. Policy study on China building energy incentive program. Beijing, China: China Construction Industry Publishing House; 2007. [9] NBSC. China statistical yearbook 2012. Beijing, China: National Bureau of Statistics of China; 2012. [10] Tonooka Y, Mu H, Ning Y, Kondo Y. Energy consumption in residential house and emissions inventory of GHGs, air pollutants in China. J Asian Archit Build Eng 2003;2(1):93e100. [11] Nakagami H, Murakoshi C, Iwafune Y. International comparison of household energy consumption and its indicator. In: ACEEE summer study on energy efficiency in buildings. Pacific Grove, CA: American Council for an EnergyEfficient Economy; 2008. pp. 214e24. [12] Chen S, Li N, Guan J, Xie Y, Sun F, Ni J. A statistical method to investigate national energy consumption in the residential building sector of China. Energy Build 2008;40(4):654e65. [13] Brockett D, Fridley D, Lin J, Lin J. A tale of five cities: the China residential Energy consumption survey. In: ACEEE summer study on building energy efficiency. Pacific Grove, CA: American Council for an Energy-Efficient Economy; 2002. pp. 29e40. [14] Tonooka Y, Liu J, Kondou Y, Ning Y, Fukasawa O. A survey on energy consumption in rural households in the fringes of Xian city. Energy Build 2006;38(11):1335e42. [15] Yang X, Tan H. Research on building energy consumption situation in Shanghai. In: International conference for enhanced building operations: building commissioning for energy efficiency and comfort, Shenzhen, China. Energy Systems Laboratory, Texas A&M University; vol. VII-2-4. [16] Zhao J, Zhu N, Wu Y. Technology line and case analysis of heat metering and energy efficiency retrofit of existing residential buildings in Northern heating areas of China. Energy Policy 2009;37(6):2106e12. [17] Wang X, Dai X, Zhou Y. Domestic energy consumption in rural China: a study on Sheyang County of Jiangsu Province. Biomass Bioenergy 2002;22(4):251e6. [18] Wang X, Feng Z. A survey of rural energy in the developed region of China. Energy 1997;22(5):511e4. [19] Zhang Q. Residential energy consumption in China and its comparison with Japan, Canada, and USA. Energy Build 2004;36(12):1217e25. [20] THUBERC. 2011 annual report on China building energy efficiency. Beijing, China: China Architecture and Building Press; 2011. [21] Hu X, Jiang K. Evaluation of technology and countermeasures for greenhouse gas mitigation in China [In Chinese]. Beijing, China: China Environmental Science Press; 2001. [22] Zhou N, Lin J. The reality and future scenarios of commercial building energy consumption in China. Energy Build 2008;40(12):2121e7. [23] Kang Y, Wei Q. Analysis of the impacts of building energy efficiency policies and technical improvements on China’s future energy demand. Int J Global Energy Issues 2005;24(3).

S. Yu et al. / Energy 67 (2014) 284e297 [24] Chaturvedi V, Eom J, Clarke LE, Shukla PR. Long term building energy demand for India: disaggregating end use energy services in an integrated assessment modeling framework. Energy Policy. http://dx.doi.org/10.1016/j.enpol.2012. 11.021. [25] Zhou Y, Eom J, Clarke L. The effect of global climate change, population distribution, and climate mitigation on building energy use in the U.S. and China. Clim Change 2013:1e14. [26] Amato A, Ruth M, Kirshen P, Horwitz J. Regional Energy demand responses to climate change: methodology and application to the commonwealth of Massachusetts. Clim Change 2005;71(1e2):175e201. [27] Wilbanks TJ, Romero-Lankao P, Bao M, Berkhout F, Cairncross S, Ceron J-P, et al. Industry, settlement and society. In: Parry ML, Canziani OF, Palutikof JP, van der Linden PJ, Hanson CE, editors. Climate change 2007: impacts, adaptation and vulnerability e contribution of working group II to the fourth assessment report of the Intergovernmental Panel on climate change. Cambridge, UK: Cambridge University Press; 2007. pp. 357e90. [28] Isaac M, van Vuuren DP. Modeling global residential sector energy demand for heating and air conditioning in the context of climate change. Energy Policy 2009;37(2):507e21. [29] Li DHW, Yang L, Lam JC. Impact of climate change on energy use in the built environment in different climate zones e a review. Energy 2012;42(1):103e12. [30] Wan KKW, Li DHW, Liu D, Lam JC. Future trends of building heating and cooling loads and energy consumption in different climates. Build Environ 2011;46(1):223e34. [31] Wan KW. An investigation of the impact of climate change on energy use in buildings in different climate zones across China. Hong Kong: City University of Hong Kong; 2011. [32] Olonscheck M, Holsten A, Kropp JP. Heating and cooling energy demand and related emissions of the German residential building stock under climate change. Energy Policy 2011;39(9):4795e806. [33] Edmonds J, Reilly J. A long-term global energy-economic model of carbon dioxide release from fossil fuel use. Energy Econ 1983;5(2):74e88. [34] Clarke JF, Edmonds JA. Modelling energy technologies in a competitive market. Energy Econ 1993;15(2):123e9. [35] Clarke L, Edmonds J, Jacoby H, Pitcher H, Reilly J, Richels R. CCSP synthesis and assessment product 2.1, part A: scenarios of greenhouse Gas emissions and atmospheric concentrations. U.S. Government Printing Office; 2007. [36] Clarke L, Kyle P, Wise M, Calvin K, Edmonds J, Kim S, et al. CO2 emission mitigation and technological advance: an updated analysis of advance technology scenarios. Richland, WA: Pacific Northwest National Laboratory; 2008. [37] MOC. Standard of climatic regionalization for architecture. Beijing, China: Ministry of Construction; 1993. [38] van Vuuren DP, Lucas PL, Hilderink H. Downscaling drivers of global environmental change: enabling use of global SRES scenarios at the national and grid levels. Glob Environ Change 2007;17(1):114e30. [39] UN. World urbanization prospects: the 2010 revision. Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat; 2010. [40] NBSC. China statistical yearbook 2006. Beijing, China: National Bureau of Statistics of China; 2006. [41] Krey V, O’Neill BC, van Ruijven B, Chaturvedi V, Daioglou V, Eom J, et al. Urban and rural energy use and carbon dioxide emissions in Asia. Energy Econ 2012;34(Suppl. 3(0)):S272e83. [42] NBSC. Comprehensive statistical data and materials on 50 years of new China. Beijing, China: China Statistics Press; 1999. [43] Shen J. Estimating urbanization levels in Chinese provinces in 1982e2000. Int Stat Rev 2006;74(1):89e107. [44] Büyükalaca O, Bulut H, Yilmaz T. Analysis of variable-base heating and cooling degree-days for Turkey. Appl Energy 2001;69(4):269e83. [45] Kyle P, Clarke L, Rong F, Smith S. Climate policy and the long-term evolution of the U.S. building sector. Energy J 2010;31(2). [46] Yang X, Jiang Y, Yang M, Shan M. Energy and environment in Chinese rural housing: current status and future perspective. Front Energy Power Eng China 2010;4(1):35e46. [47] Evans M, Yu S, Song B, Deng Q, Liu J, Delgado A. Building Energy efficiency in rural China. Energy Policy 2013. http://dx.doi.org/10.1016/j.enpol.2013.06.040.

297

[48] GBS. Guangdong statistical yearbook. Guangzhou, China: Guangdong Bureau of Statistics; 1980e2010. [49] HBS. Heilongjiang statistical yearbook. Harbin, China: Heilongjiang Bureau of Statistics; 1980e2010. [50] JBS. Jiangsu statistical yearbook. Nanjing, China: Jiangsu Bureau of Statistics; 1980e2010. [51] TBS. Tianjin statistical yearbook. Tianjin, China: Tianjin Bureau of Statistics; 1980e2010. [52] LBNL. China energy databook v. 7.0. Berkeley, CA: Lawrence Berkeley National Laboratory; 2008. [53] IEA. 2008 Energy balances. Paris, France: International Energy Agency; 2010. [54] Yu L, Watanabe T, Yoshino H, Gao W. Research on Energy consumption of urban apartment buildings in China. J Environ Eng Archit Inst Jpn 2008;73(624):183e90. [55] Chen L, Heerink N, van den Berg M. Energy consumption in rural China: a household model for three villages in Jiangxi Province. Ecol Econ 2006;58(2): 407e20. [56] Wang X, Feng Z. Rural household energy consumption with the economic development in China: stages and characteristic indices. Energy Policy 2001;29(15):1391e7. [57] Wang X, Feng Z. Study on affecting factors and standard of rural household energy consumption in China. Renew Sustain Energy Rev 2005;9(1):101e10. [58] Wang X, Feng Z, Gao x, Jiang K. On household energy consumption for rural development: a study on Yangzhong County of China. Energy 1999;24(6): 493e500. [59] Wang X, Li J. Influence of using household biogas digesters on household energy consumption in rural areasda case study in Lianshui County in China. Renew Sustain Energy Rev 2005;9(2):229e36. [60] Zhou Z, Wu W, Wang X, Chen Q, Wang O. Analysis of changes in the structure of rural household energy consumption in northern China: a case study. Renew Sustain Energy Rev 2009;13(1):187e93. [61] Lin E, Dudek DJ, Sea Fang. Rural development and its energy, environment and climate change adaptation policy. Beijing, China: Science Press; 2011. [62] IEA. Key world energy statistics. Paris, France: International Energy Agency; 2011. [63] EIA. International energy outlook 2011. Washington, D.C.: U.S. Energy Information Administration; 2011. [64] Glicksman LR, Norford LK, Greden LV. Energy conservation in Chinese residential buildings: progress and opportunities in design and policy. Annu Rev Energy Environ 2001;26:83e115. [65] Li M. Sustainable design optimization of rural houses in North China. West Lafayette, IN: Purdue University; 2011. [66] Zhao J, Zhu N, Wu Y. The analysis of energy consumption of a commercial building in Tianjin, China. Energy Policy 2009;37(6):2092e7. [67] Lv S, Wu Y. Targets and obstacles of heat retrofits in existing residential buildings in northern China. HVAC 2007;9:20e5. [68] IEA. The International Combined Heat and Power/District Heating Cooling Collaborative: advancing near-term low carbon technologies. Paris, France: International Energy Agency; 2007. [69] Odyssee. Database for Energy efficiency indicators in Europe. http://www. odyssee-indicators.org/ [accessed 15.12.11]. [70] Annunziata E, Frey M, Rizzi F. Towards nearly zero-energy buildings: the state-of-art of national regulations in Europe. Energy 2013;57(0):125e33. [71] Li DHW, Yang L, Lam JC. Zero energy buildings and sustainable development implications e a review. Energy 2013;54(0):1e10. [72] Chen Y, Yang G, Sweeney S, Feng Y. Household biogas use in rural China: a study of opportunities and constraints. Renew Sustain Energy Rev 2010;14(1): 545e9. [73] MOHURD. Design standard for energy efficiency of rural residential buildings. Beijing, China: Ministry of Housing and Urban-Rural Development; 2013. [74] World Bank. World development indicators. Washington, D.C.: The World Bank; 2011. [75] DOE. US building energy data book. Washington, D.C.: U.S. Department of Energy; 2009.