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Energy Service Demand Projections and CO2 Reduction Potentials in Rural Households in 31 Chinese Provinces Rui Xing *,† , Tatsuya Hanaoka † , Yuko Kanamori † , Hancheng Dai † and Toshihiko Masui † Received: 30 September 2015; Accepted: 19 November 2015; Published: 27 November 2015 Academic Editors: Masaharu Motoshita and Marc A. Rosen Center for Social and Environmental Systems Research, National Institute for Environmental Studies, 16-2, Onogawa, Tsukuba, Ibaraki 305-8506, Japan; [email protected] (T.H.); [email protected] (Y.K.); [email protected] (H.D.); [email protected] (T.M.) * Correspondance: [email protected]; Tel./Fax: +81-29-850-2955 † These authors contributed equally to this work.

Abstract: Until 2012, most of China’s population lived in rural areas with markedly different patterns of household energy consumption from those in Chinese cities. The studies so far done on residential energy use in rural Chinese households have been limited to questionnaire surveys and panel data analyses. Hardly any studies on energy demand in rural areas have considered both the climatic and economic disparities across Chinese regions. In this study we conduct a systematic analysis of the rural Chinese residential sector on a regional basis. We begin by developing a macro-model to estimate energy service demands up to 2050. Next, we apply the AIM(Asia-Pasific Integrated Model)/Enduse model, a bottom-up cost-minimization model with a detailed mitigation technology database, to estimate the mitigation potential of low-carbon technologies in rural China. Our results show that energy service demand in the rural household sector will continue to increase in regions with growing population or income conditions. However, after 2030, the rural residential energy service demand will start to decline in most Chinese regions. The impacts of efficient technologies will vary from one region to the next due to regional climatic and economic disparities. Throughout all of China, the penetration of efficient technologies can reduce CO2 emissions by 20% to 50%. Of the technologies available, efficient lighting, biomass water heaters, and efficient electronics bring the most benefit when implemented in rural households. Keywords: China; rural household; Shared Socioeconomic Pathways; CO2 emission; efficient technology

1. Introduction Most of China’s people lived in rural areas until 2011, when the urban population surpassed the rural population for the first time due to the country’s rapid urbanization. As of now there are still over 650 million people and 210 million households [1] in the countryside. The types of housing and household energy structures markedly differ between urban and rural areas in China. The differences are so substantial that most of the studies done so far on Chinese energy demand have treated urban and rural households as two different sectors [2–4]. In the last century only a few studies looked into the energy service demand of China’s rural households. Technology share and energy consumption were rather unclear when compared to the urban households. With China’s rapid economic growth, living standards have sharply risen and the gap between the urban and rural economies has steadily narrowed (Figure 1, right). Rural residential service demand is drawing more attention from energy researchers. Several of the first attempts to open the “black box” in the research have relied on questionnaire survey methodologies [4–6]. While

Sustainability 2015, 7, 15833–15846; doi:10.3390/su71215789

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Sustainability 2015, 7, 15833–15846 Sustainability 2015, 72015, 7, page–page

questionnaire surveys cover rural households incompletely, incompletely, they provide a representative energy questionnaire surveys cover rural households theydodo provide a representative energy consumption profile of Chinese rural residences in certain areas. consumption profile of Chinese rural residences in certain areas. 14,000

Population (million)

1,400 1,200 1,000

49.4% 62.3%

Rural

800 600 400 200 37.7%

50.6% Urban

Per capita energy consumption (GJ)

1,600

12,000 10,000 8,000

Rural

32.9%

Urban

67.1%

27.8%

6,000 4,000 72.2%

2,000 0

0 "01 "02 "03 "04 "05 "06 "07 … "12 Year

"01

"02

"03

"04

"05

"06

"07

Year

Figure 1. China’s population (left, [7]) and per capita residential energy consumption (right, [8]). Figure 1. China’s population (left, [7]) and per capita residential energy consumption (right, [8]).

The estimation of energy service demand on a national scale requires more systematic methods. The estimation of energy service demand on a national scale requires more systematic methods. Krey Krey et al. [9] used three assessment models to evaluated ruralrural energy use inuse Asian countries et al. [9] used integrated three integrated assessment models to evaluated energy in Asian and carried out a comparison study based on their results. Fan et al. [2] and Feng et al. [10] analyzed countries and carried out a comparison study based on their results. Fan et al. [2] and Feng et al. [10] panelanalyzed data to examine COto2 emissions and energy use China’suse rural households, panel data examine CO 2 emissions andinenergy in China’s rural respectively. households, All All three these studies were conducted country in disparities spite of the in huge threerespectively. of these studies wereofconducted at the country levelatinthe spite of thelevel huge income disparities in income and climate across Household regions and energy provinces. Household energy service demand with and climate across regions and provinces. service demand is often associated is often associated with incomeZhao and expenditure [11–14].household Zhao et al. energy [4] surveyed household income and expenditure [11–14]. et al. [4] surveyed consumption inenergy four kinds consumption in four kinds of Chinese regions and examined the impact of the population scale and of Chinese regions and examined the impact of the population scale and per capita income on per per capita income on per household energy consumption. Their method, however, resulted in the household energy consumption. Their method, however, resulted in the same per household energy same per household energy consumption in cold and warm areas with the same per capita income consumption in cold and warmThis areas withclearly the same per from capitathe income Beijing (e.g., Beijing and Shanghai). result diverges reality,(e.g., because cold and areasShanghai). have This result clearly diverges from the reality, because cold areas have much higher energy consumption much higher energy consumption due to space heating services in the winter season. Yu et al. [15] due to space heating services in the winter season. et al. demand [15] proposed a regional approach proposed a regional approach of estimating buildingYu energy by dividing China into four of estimating building energy demand by dividing China into four climatic areas. However, climatic areas. However, their study put Beijing, a highly developed region with a much their higherstudy disposable income to expendregion on energy the same climaticincome area as to some of theonless put Beijing, a highly developed withservices, a much in higher disposable expend energy developed regions of the west. services, in the same climatic area as some of the less developed regions of the west. China has third the third largest territory theworld worldand andan an extremely extremely diverse country China has the largest territory inin the diverseclimate. climate.The The country is is divided into five climatic areas ([16], Supplementary Figure S1) for designing the built divided into five climatic areas ([16], Supplementary Figure S1) for designing the built environment: environment: severe cold; cold; hot summer/cold winter; temperate; and hot summer/warm winter. severe cold; cold; hot summer/cold winter; temperate; and hot summer/warm winter. Buildings in Buildings in cold and severe cold areas are heated by central heating systems, while those in the other cold and coldheated areas mainly are heated by centralair heating systems, those in the2,other three areas threesevere areas are by standalone conditioners. Aswhile shown in Figure the heating are heated standalone air conditioners. Figure[17]. 2, the heating inequality, degree day 18 degreemainly day 18by gradually decreases from north As to shown south ininChina Economic gradually decreases from north to south in China [17]. Economic inequality, meanwhile, posed a meanwhile, has posed a major challenge to Chinese society in recent years. Accordinghas to The majorEconomist challenge[18], to Chinese society in recent years. According Economist [18], the per capita GDP in some coastal provincestoofThe China is already as the highper as capita that in GDP some European countries. However, in someaswestern the per capita GDP is still at the in some coastal provinces of China is already high asprovinces, that in some European countries. However, same level as provinces, some of the the world’s developed countries [18]).as some of the world’s least in some western per least capita GDP is still at the(Figure same 2, level this complex background, researchers cannot expect to evaluate household service developedGiven countries (Figure 2, [18]). demands in China solely on the basis of socioeconomic indicators or climate characteristics. Hardly Given this complex background, researchers cannot expect to evaluate household service demands any of the studies conducted so far have fully considered the regional disparities in climate or in China solely on the basis of socioeconomic indicators or climate characteristics. Hardly any of economy. The present study disaggregates China to the provincial level and looks into the patterns the studies so far have fully considered theregion. regional inaclimate or economy. of ruralconducted residential energy demand in each provincial Wedisparities first develop macro-model to The present study disaggregates China to the provincial level and looks into the patterns estimate future outlooks on rural residential energy service demand up to 2050. Then we use of therural residential energymodel demand in each provincial region.reduction We first develop to estimate future AIM/Enduse to evaluate the CO2 emission potential ainmacro-model order to analyze the effects outlooks on rural residential energy service demand up to 2050. Then we use the AIM/Enduse model 2 to evaluate the CO2 emission reduction potential in order to analyze the effects of energy-efficient

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technologies. The studytechnologies. focuses on rural buildings and offers suggestions concerning future of energy-efficient Theresidential study focuses on rural residential buildings and offers trends in CO emission. suggestions 2 concerning future trends in CO2 emission.

Heilongjiang Jilin Xinjiang

Tibet 0-1062 1063-2123 2124-3184 3185-4245 4246+

2686-4875 4876-7065 7066-9255 9256-11444 11445+

(HDD18)

(2011US$)

Inner-M.

Beijing Liaoning Gansu Tianjin Hebei Ningxia Shandong Shanxi Qinghai Henan Jiangsu Shaanxi Sichuan Anhui Shanghai Hubei Chongqing Zhejiang Jiangxi Hunan Guizhou Fujian Yunnan Guangxi Guangdong Hainan

Figure 2. Heating degree of 31 31Chinese Chineseprovinces. provinces. Figure 2. Heating degreeday day1818(left) (left)and andper percapita capita GDP GDP (right) (right) of

TheThe remainder isstructured structuredinto into Section 2, Section Section2 3describes and Section 4. Section 2 remainderofofthis this paper paper is Sections 2–4. the methodology describes the methodology fordemand estimating service demand and reduction evaluating the CO2Section emission for estimating future service and future evaluating the CO 2 emission potential. 3 presents the results of our estimations of future demand,ofenergy andenergy CO2 reduction potential. Section 3 presents the results of service our estimations futureconsumption, service demand, emissions atand the CO provincial level.atSection 4 discusses theSection technology selectionthe in technology detail and presents consumption, the provincial level. 4 discusses selection 2 emissions key conclusions. in detail and presents key conclusions. 2. Estimation ServiceDemand Demand 2. Estimation ofofService

2.1.2.1. Socioeconomic Framework Socioeconomic Framework research usethe theShared SharedSocioeconomic Socioeconomic Pathways Pathways (SSPs), In In thisthis research wewe use (SSPs), aa set setofofqualitative qualitativeand and quantitative narrativesthat thatdescribe describefuture futuresocioeconomic socioeconomic conditions, conditions, to quantitative narratives to project project socioeconomic socioeconomic indicators such population, GDP, GDP, and and the share. TheThe SSPsSSPs are translated into five indicators such asaspopulation, theurban urbanpopulation population share. are translated into storylines based on worlds with various challenges for mitigation and adaption [19,20]. Each SSP five storylines based on worlds with various challenges for mitigation and adaption [19,20]. Each SSP storyline describes a different socioeconomic circumstanceofofthe thefuture. future.In Inthis thisstudy studywe we use use SSP2 SSP233,, a storyline describes a different socioeconomic circumstance a storyline describing a “business-as-usual” the typical trends ofdecades recent decades storyline describing a “business-as-usual” worldworld wherewhere the typical trends of recent continue, continue, with some progress toward achieving development goals. with some progress toward achieving development goals. The original SSP database [21] only provides quantification data at the country level. To conduct The original SSP database [21] only provides quantification data at the country level. To our analysis at the provincial level, we disaggregate the original national value to provincial values conduct our analysis at the provincial level, we disaggregate the original national value to provincial ([22], Supplementary File S1). Consistent with SSP2’s “business-as-usual” storyline, the difference values ([22], Supplementary File S1). Consistent with SSP2’s “business-as-usual” storyline, the between the provincial value and national value in a base year is assumed to stay the same up to 2050. difference between the provincial value and national value in a base year is assumed to stay the The compound population growth rate and urban population share of each province are both same up to 2050. The compound growth rate and share of each province disaggregated in this manner.population Figure 3 (left) shows the urban resultspopulation of a regional rural population are projection. both disaggregated in this manner. Figure 3 (left) shows the results of a regional rural population With the progress of urbanization in China, the rural population is expected to continue projection. With the of out, urbanization thebetween rural population expected to continue falling. As Krey et progress al. [9] point differencesininChina, behavior urban andis rural households may falling. As Krey et al. [9] point out, differences in behavior between urban and rural households lead to different future emission pathways with high or low urbanization levels. Urbanizationmay is lead to different future emission pathways with high or low urbanization Urbanization considered a key driver of energy service demand in this study. Meanwhile,levels. the household size is is considered a key energy service demand in this study.Households Meanwhile, household size is extrapolated updriver to 2050ofbased on the past trend (Figure 3, right). arethe larger in rural areas extrapolated up toin2050 based past trendPolicy” (Figureis3,generally right). Households in rural The areas than in cities China, as on thethe “One-child waived in are thelarger countryside. projection, that households will keep shrinking even in rural areas. The projection, than in cities inhowever, China, asshows the “One-child Policy” is generally waived in the countryside. The growth rate of per capita GDP is shrinking another principal future energy demand, but one however, shows that households will keep even indriver rural of areas. that to distinguish between urbanisand rural families. this study we use per capita income Thefails growth rate of per capita GDP another principalIndriver of future energy demand, but as one an economic indicator instead. Specifically, provincial per capita GDP is disaggregated from SSP2 that fails to distinguish between urban and rural families. In this study we use per capita income as (Figure 4, left) and a instead. linear regression is performed to per convert theGDP GDPistodisaggregated rural income (Figure 4, an economic indicator Specifically, provincial capita from SSP2 right). (Figure 4, left) and a linear regression is performed to convert the GDP to rural income (Figure 4, right).

3

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600

Household sizesize (persons) Household (persons)

Rural population (millions) Rural population (millions)

600

400 400

200 200

0 0

2010

2020

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2010

2020

2030

2040

2050

4 4

3 3

2 2 2010 2010

2020

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2020

2030

2040

2050

Figure 3. Rural population (left) and household size (right) of 31 Chinese provinces. Figure 3. Rural population (left) and household size (right) of 31 Chinese provinces. Figure 3. Rural population (left) and household size (right) of 31 Chinese provinces.

50000

1000

Estimated regional value Estimated regionalvalue value SSP national

1000

PerPer capita income in rural China (2011US$) capita income in rural China (2011US$)

50000

SSP national value PerPer capita GDP (2011US$) capita GDP (2011US$)

40000 40000 30000 30000

20000 20000

10000 10000

2010

2020

2030

2040

2050

2010

2020

2030

2040

2050

y = 0.1229x+302.78 0.9541 yR2==0.1229x+302.78 R2 = 0.9541

800 800

600 600

400 400

2000

3000

4000

5000

2000

3000

4000

5000

Per capita GDP (2011US$) Per capita GDP (2011US$)

Figure 4. 4. Regional (1997–2010)rural ruralincome income(right, (right, [7]). Figure Regionalper percapita capitaGDP GDP(left) (left) and and historical historical (1997–2010) [7]). Figure 4. Regional per capita GDP (left) and historical (1997–2010) rural income (right, [7]).

Besides total population andand per capita GDP, GDP, the dwelling floor area andarea ownerships of household Besides total population per capita the dwelling floor and ownerships of Besides total population and per capita GDP, the dwelling floor area and ownerships of electronics are also key indicators in estimating the future energy service demand. To begin determining household electronics are also key indicators in estimating the future energy service demand. To electronics also key indicators in estimating the future energy service To thehousehold dwelling floor area, regression analysis File(Supplementary S2, Table S5) is performed panel begin determining theaare dwelling floor area, a(Supplementary regression analysis File S2,demand. Tableon S5) is begin determining the dwelling floor area, a regression analysis (Supplementary File S2, Table S5) is data of the per household floor area between 2000 and 2012 [7]. Next, the regression equation is used performed on panel data of the per household floor area between 2000 and 2012 [7]. Next, the to performed on panel of the per household floor area 2000 and 2012 [7].2050. Next, the extrapolate the past trend of per household floor area up to 2050. These projections follow the story line regression equation isdata used to extrapolate the past trend of perbetween household floor area up to These regression equation is used to extrapolate the past trend of per household floor area up to 2050. These of SSP2—“a development pathway that is consistent with typical patterns experience” [19]. projections follow the story line of SSP2—“a development pathway thatofishistorical consistent with typical projections follow theexperience” story linedata of SSP2—“a development pathway that is determine consistent typical patterns [19]. data are thenof synced with data on the totalwith number of These data of arehistorical then synced with onThese the total number households to the total floor patterns of historical experience” [19]. These data are then synced with data on the total number of households to determine the total floor area. Regional projections of floor area are listed in area. Regional projections of floor area are listed in Supplementary Table S1. The household electronics households to determine the total floor area. Regional projections of floor area are listed in Supplementary The household electronics component of our model consists four types component of our Table modelS1. consists of four types of electric appliances (white goods and of brown goods): Supplementary Table (white S1. Thegoods household electronics component of our model consists of four types of electricrefrigerators, appliances and brown goods): televisions, washing machines, televisions, washing machines, and computers. Therefrigerators, ownership projections for these of electric appliancesownership (white goods and brown goods): televisions, refrigerators, washing machines, and computers. projections goods are listed inThe Supplementary Table S2. for these goods are listed in Supplementary Table S2. and computers. The ownership projections for these goods are listed in Supplementary Table S2. Energy Service Demand 2.2.2.2. Energy Service Demand 2.2. Energy Service Demand previous research designed a macro-model [22,23] to estimate the future energy In In ourour previous research we we designed a macro-model [22,23] to estimate the future energy service In demand our previous research we designed a macro-model [22,23] demand to estimate thepresent futurestudy energy service of the urban residential sector. Rural energy service in the is demand of the urban residential sector. Rural energy service demand in the present study is estimated service demand ofsame the urban residential sector. Rural energy service demandsuch in the present study of is estimated in the manner, while the input databases on indicators as the number in the same manner, while the input databases on indicators such as the number of households, estimated infloor the same manner, while the on indicators such as the number of households, area, etc., are replaced withinput rural databases ones. In urban areas, space cooling service is also floor area, etc., floor are replaced rural ones. urban areas, spaceareas, cooling service is also considered households, area, etc.,with are replaced withInrural ones. In urban space cooling service is also as a major residential energy service in China’s warm regions [22]. However, cooling devices are 4 rarely used in rural families. Although the usage 4of cooling services is highly influenced by income 15836

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considered as a major residential energy service in China’s warm regions [22]. However, cooling devices are rarely used in rural families. Although the usage of cooling services is highly influenced income level, through the past 20 years is no evidence suggesting that cooling services have level,by through the past 20 years there is nothere evidence suggesting that cooling services have widely widely penetrated in China’s rural households [2,5,6,24]. Therefore, in this study, cooling service is penetrated in China’s rural households [2,5,6,24]. Therefore, in this study, cooling service is excluded excluded and energy service demand in the rural residential sector is categorized into five and energy service demand in the rural residential sector is categorized into five applications: heating, applications: heating, hot water, cooking, lighting, and electronics. Figure 5 describes the general hot water, cooking, lighting, and electronics. Figure 5 describes the general modeling process and modeling process and main exogenous inputs of the household applications.

main exogenous inputs of the household applications. Space heating Heating load per

m2

Domestic hot water supply

Other appliances

Unit of water use

Rated working and standby power

・Usage time per day ・Days of each month ・Number of households ・Average household size ・Penetration rate of each appliance

・Days of each month ・Average temperature of each month ・Number of households ・Average household size ・Usage frequency

・Total floor area

Annual heating service demand

Annual DHW service demand

Annual cooking, lighting, electronics service demand

Annual rural residential service demand (satiated demand)

Satiation factors

Per capita income

Calibrated rural annual residential service demand (economic behavior corresponded)

Figure 5. Flow demandestimation estimation process. Figure 5. Flowchart chartofofthe the service service demand process.

The annual space heating service usingEquation Equation annual heating The annual space heating servicedemand demand is is calculated calculated using (1).(1). TheThe annual heating 2 ) is 2) is service demand per per unitunit (m(m taken et al. al.[17]. [17]. service demand takenfrom fromZhang Zhang et HLr,t ,“=HLUr × ˆ DS, r,t

(1)

(1)

where

where

: r: : t: HLr,t : HLUr : DSr,t :

The region

The region The year The year The annual heating service demand , : The heating : annual The annualservice heatingdemand service demand (ktoe/m2) per unit (m2) The annual heating service demand (ktoe/m2 ) per unit (m2 ) The rural residential floor area , : The rural residential floor area

The domestic hot water (DHW) service demand for face-washing, cooking, and showering is

calculated usinghot Equation The usage of hot water per and hot water supply temperature The domestic water(2). (DHW) service demand foractivity face-washing, cooking, and showering is are taken from standard(2). household models [25]. The per tap water temperature is calculated the calculated using Equation The usage of hot water activity and hot water supplyfrom temperature average seasonal temperature. are taken from standard household models [25]. The tap water temperature is calculated from the average seasonal temperature. = ( − , , × , ) × , , (2) ÿÿ DLr,t “ POPr,t ˆ cρVi pTSi ´ TTr,s q ˆ Fr,s,i (2) where

where :

: c: ρ:

i: s: c: ρ: DLr,t : Vi : POPr,t :

s

i

DHW activity (face-washing, showering, and cooking) The season (summer, winter, and others) DHW activity (face-washing, showering, and cooking) The heat capacity of water ((kJ/kg·°C) The season (summer, The density ofwinter, water and others)

The heat capacity of water ((kJ/kg¨ ˝ C)5 The density of water The annual DHW service demand (ktoe) The usage of hot water per activity (L) The population

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TSi : TTr,s : Fr,s,i :

The hot water supply temperature (universal) The average tap water temperature (regional, seasonal) The activity frequency per season

The modeling structure of the other three applications builds on the operating characteristics of each energy-consuming appliance. It can generally be expressed as Equation (3). OTLr,t “ Ur ˆ Ar,t “ f pworking power, operation time, etc.q ˆ Ar,t

(3)

where OTLr,t : Ur : Ar,t :

The service demand (ktoe) of lighting, cooking, and electronics The annual service demand per unit (cooking range, etc.) or per m2 The amount of service end users (total electronic units or total floor area)

Table 1 summarizes the operating characteristics of lighting, cooking, and the four white/brown goods composing household electronics. Most of the designed working powers and standard operation times are taken from the SCHEDULE program [25]. The service demand for cooking stoves is a function of the average household size. As explained in Section 2.2, we extrapolate the numbers of durable consumer goods per household in 2050, with the exception of cooking stoves, which are always set to one unit per household with no future variation. The usage patterns listed in Table 1 describe the general conditions in a standard family of a developed country. For Chinese rural families, the usage patterns need to be adjusted to cope with their lower income level (Section 2.3). Table 1. Operation conditions of household appliances. Appliance

Working Power (W)

Standby Power (W)

Operation Time (H/Day)

Washing machine Refrigerator TV set (color) Computer Cooking range Lighting device

86.00 20.802 60.50 100.00 234.6 ˆ HS + 253.2 5.00 (W/m2 )

0.00 0.00 0.40 10.00 0.00 0.00

1.00 24.00 8.00 8.00 6.00 6.00

HS: household size (persons per household).

2.3. Model Calibration The frequency of hot water activity, electronics operation time, and other energy usage settings described above more or less represent the standard lifestyle in a developed country. In a less developed region such as rural China, limited financial resources may prevent these settings from being fully applied. Service demand should increase with income and decrease with service price [9,26]. In economic terms we can identify a satiation point for energy service comfort: below the satiation point the marginal utility of an energy service is positive; above it, the marginal utility turns negative [27,28]. In the current study we evaluate income-induced service demand in the future by introducing satiation factors to calibrate our model estimates. The satiation factor of each energy service can be described as a logistic function with per capita income as the explanatory variable ([22]; Equation (4)). When the per capita disposable income reaches the OECD (Organisation for Economic Co-operation and Development) average of 25,596 US$ (year 2011, current prices and purchasing power parity [29]) in the future, the satiation factor is manually set as 100%. SFr,sv,t “

25596 ` asv PCIr,t ˆ PCIr ă 25596 PCIr,t ` asv 25596 SFr,sv,t “ 100% PCIr ě 25596

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(4)


where sv: asv : SFr,sv,t : PCIr,t :

Energy service, e.g., heating, hot water, cooking, etc. A logistic model parameter of energy services sv The satiation factor Per capita income

In the macro-model from our previous study (Xing et al., 2015 [22]) we determined the satiation factors for certain energy services using the least squares method, as shown in Equation (5). Rural households, however, rely on non-commercial biomass, most of which is collected directly from the fields. The accurate total amount of non-commercial biomass consumption is difficult to tally, and no energy consumption data for the base year (2010) are available in the statistical yearbooks. Without this information, we must assume that the per capita income induces service demand just as it does in urban households and use the same satiation functions to calibrate the model results (Equation (6)). Minimizing: ÿ ε“

2

stat estm pECUr,sv,t0 ´ ECUr,sv,t0 q

(5)

r,sv

Subject to: stat estm ECUr,sv,t0 “ ECUr,sv,t0 ˆ SFr,sv,t0

where sv: t0: stat : ECUr,sv,t0 estm : ECUr,sv,t0

Energy service, e.g., heating, hot water, cooking, etc. The base year (2010) The annual energy consumption (urban) from statistics (NBSC, 2011) The estimated annual energy consumption (urban) estm 1 ˆ SFr,sv,t “ ESDr,sv,t ESDr,sv,t

(6)

where 1 ESDr,sv,t : estm : ESDr,sv,t

The calibrated annual energy service demand (rural) The estimated annual energy service demand (rural)

2.4. AIM/Enduse We select effective and efficient technologies for the rural residential sector by estimating the mitigation potential of sustainable policies using the AIM/Enduse model [30,31], a bottom-up cost-minimization model with a detailed mitigation technology database. In the base year (2010) the estimated service demands are disaggregated to the energy usage of existing technologies, while in the target year (2050) service demands are satisfied with a combination of existing technologies and efficient technologies. For each household appliance we set an existing (EXT) technology with a low efficiency and one or more technologies with high efficiencies (NEW and BAT). NEW technology is considered more efficient and BAT (best available technology) is considered the most efficient. The technological energy efficiencies in the base year are average values for widely used electric appliances [32]. The technology shares in the base year are taken from empirical studies [33–37]. Table 2 gives an example of the technology levels. A total of 83 existing, new, and best available technologies are prepared for the estimation of emission reduction potentials in the AIM/Enduse model (Supplementary Table S3). The model selects a technology combination for each province based on a least total system cost. The emission factors that are used to estimate CO2 emission are taken from AIM/Enduse-Global ([38], Supplementary Table S6).

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Table 2. Efficiencies (provided/consumed) of three technology levels. Efficiency

Device Coal stove (heating) Refrigerator

EXT

NEW

BAT

0.39 0.63

1.00

0.80 1.43

2.5. Scenario Setting For the model simulation we prepare two scenarios to evaluate the CO2 emission growth and reduction potential: the Fix (FIX) and Cost-effective selection (CES) scenarios. In FIX the technologies can be regarded as frozen: the technology level remains at the base year (2010) level and no efficient technologies are implemented in the future. FIX is designed to offer a baseline picture of how CO2 emissions will increase without any efficient technology intervention. In CES, efficient technologies stored in technology databases are allowed to penetrate the future market. Traditional technologies and efficient technologies compete over cost-effectiveness. In other words, CES describes a natural implementation process for efficient technologies. The model basically allows the choice of any efficient technology to achieve a minimum total system cost. Two constraints take effect, however, during this process. The first constraint addresses the future share of non-commercial energy. Non-commercial energy sources such as firewood or straw currently account for over 80% of the rural residential energy demand [39]. Most of the non-commercial energy is collected by household members and has no commercial price, which makes it easy for the model to choose. However, the burning of these non-commercial fuels in indoor environments can cause serious air pollution problems. For this reason, CES does not encourage the use of non-commercial energy or set the share of non-commercial energy beyond the current (2010) level. The second constraint addresses the future share of renewable energy. The applications of renewable energy require expensive appliance costs without incurring costs for the energy supply itself. The minimized total system cost in the model analysis process can lead to a sudden increase in the renewable share within a short period, an outcome unlikely to occur in the real world. Exogenous constraints are therefore added for the renewables under study. In CES, the shares of these renewables will not surpass the IEA’s 450 scenario ([40]; Supplementary Table S4). Table 3 summarizes the definitions of the two scenarios. Table 3. Scenario definitions. Scenario

Definition

Fix (FIX)

Technology efficiency fixed at the 2010 level

Cost-effective selection (CES)

Efficient technologies naturally penetrated in the future market Share of non-commercial energy not to surpass the 2010 level Share of renewable energy not to surpass the IEA’s 450 scenario

3. Results and Discussion 3.1. Energy Service Demands Figure 6 shows the energy service demand of the whole of China and three Chinese regions with different urbanization levels. The urban and rural populations are disaggregated results from SSP2. The urban service demand is drawn from the results from our previous study [22]. Beijing and Tibet represent high and low urbanization cases, respectively. Different factors drive constant increases in the rural service demand in Beijing and Tibet. The share of the rural population in Beijing drops considerably by 2050, but the rural population grows in absolute terms because the total Beijing population doubles. The rural energy service demand thus increases intensively due to increases in

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both the population and income level. Tibet currently has the highest rural population share and is expected to keep this rank in 2050. The increase of the rural energy service demand in Tibet probably Sustainability 2015, 72015, 7, page–page stems from the slow but steady process of urbanization and rising income. Inner Mongolia represents a a middle urbanization case. As income rises the andrural the rural population energy service middle urbanization case. As income rises and population falls,falls, ruralrural energy service demand demand in Inner Mongolia grows to 2030, then over declines next two decades. The same in Inner Mongolia grows up to 2030,up then declines theover nextthe two decades. The same pattern is pattern is observed when aggregating all 31 regions intonational China’s national observed when aggregating all 31 regions into China’s results. results.

Population (mil.)

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Figure 6. 6. Energy of31 31Chinese Chinese provinces in SSP2. Figure Energyservice service demand demand of provinces in SSP2.

3.2. Energy Consumption and CO 2 Emission 3.2. Energy Consumption and CO 2 Emission Figure 7 shows the estimated results of energy consumption 2 emissions Figure 7 shows the estimated results of energy consumption (top(top row)row) andand CO2CO emissions (bottom (bottom row) in China’s rural households. We selected four representative regions with different row) in China’s rural households. We selected four representative regions with different building building codes and looked into their future projections. Overall, energy consumption and CO2 codes and looked into their future projections. Overall, energy consumption and CO2 emissions emissions both grow drastically in the future. In CES, efficient technologies penetrate the market—in bothsome growcases drastically inamounts—even the future. Inwithout CES, efficient penetrate the market—in in limited emissiontechnologies taxes or technology subsidies. In the targetsome casesyear in limited amounts—even without emission taxes or technology subsidies. appears In the target (2050), the FIX-compared reduction rate ((FIX-CES)/FIX) of energy consumption to be year (2050), rate ((FIX-CES)/FIX) consumption to be low lowthe in FIX-compared cold regions (6%reduction in Heilongjiang, China) and highofinenergy warm regions (19% inappears Guangdong, China). Reductions the consumption of coal and electricity energy contribute theGuangdong, bulk of the CO 2 in cold regions (6% in in Heilongjiang, China) and high in warm regions (19% in China). emission reductions. The FIX-compared reduction rate of CO 2 emissions in the target year ranges Reductions in the consumption of coal and electricity energy contribute the bulk of the CO2 emission from 21% cold regions (Heilongjiang, China) 54% in warm regions (Guangdong, China). reductions. TheinFIX-compared reduction rate of COto 2 emissions in the target year ranges from 21% in Electricity renewables (photovoltaic panels, etc.) have no reduction impact on the results. cold regions (Heilongjiang, China) to 54% in warm regionsobservable (Guangdong, China). Electricity renewables This tells us that the expensive upfront costs of electricity renewables will dissuade the regions from (photovoltaic panels, etc.) have no observable reduction impact on the results. This tells us that the adopting them unless a carbon tax, technology subsidies, or other financial supports are provided. expensive upfront costs of electricity renewables will dissuade the regions from adopting them unless Penetration of efficient technologies also has impacts on per capita energy/emission intensities. a carbon tax, technology subsidies, or otherdrops financial are provided. In Beijing, per capita energy consumption fromsupports 534.92 ktoe/cap (FIX) to 492.84 ktoe/cap (CES) Penetration of efficient technologies also has impacts on per energy/emission intensities. while per capita CO2 emissions drop from 105.98 tCO2/cap (FIX) to capita 86.03 tCO 2/cap (CES) by 2050. In In Beijing, per capita energy consumption drops from 534.92 ktoe/cap (FIX) to 492.84 ktoe/cap Guangdong, a warm region, per capita energy/emission intensities are much lower than Beijing’s(CES) Nevertheless, there is adrop marked reduction of energy/emission intensities in CES. Per by capita whilelevel. per capita CO2 emissions from 105.98 tCO tCO2 /cap (CES) 2050. In 2 /cap (FIX) to 86.03 energy consumption drops from 105.98 ktoe/cap (FIX) tointensities 86.03 ktoe/cap (CES) lower while per CO2 level. Guangdong, a warm region, per capita energy/emission are much thancapita Beijing’s emissions drop tCO2/cap (FIX) toof0.17 tCO2/cap (CES) by 2050. However, compared to the Nevertheless, therefrom is a 0.37 marked reduction energy/emission intensities in CES. Per capita energy results of the urban residential sector from our previous study (Xing, 2015 [22]), per capita consumption drops from 105.98 ktoe/cap (FIX) to 86.03 ktoe/cap (CES) while per capita CO2 emissions energy/emission intensities in rural households are much lower than in urban households. This

drop from 0.37 tCO2 /cap (FIX) to 0.17 tCO2 /cap (CES) by 2050. However, compared to the results of the urban residential sector from our previous study (Xing, 2015 [22]), per capita energy/emission 9

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intensities in rural households are much lower than in urban households. This indicates that even indicates even with efficient technologies, emission reduction will notasbethat as substantial as that in with efficientthat technologies, emission reduction will not be as substantial in urban households urban households considering the future growth of the urban population. considering the future growth of the urban population.

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Figure 7. Energy consumptionand andCO CO22emissions emissions of China’s Figure 7. Energy consumption China’s rural ruralresidential residentialsector. sector.

Technology Selection 3.3.3.3. Technology Selection In this study we provide a choice of 83 residential energy service (Table 2) technologies within In this study we provide a choice of 83 residential energy service (Table 2) technologies within the AIM/Enduse model to keep the cost as low as possible while attaining CO2 emission reduction. the AIM/Enduse model to keep the cost as low as possible while attaining CO2 emission reduction. Figure 8 shows the efficient technologies that bring about the greatest CO2 emission reductions in Figure 8 shows the efficient technologies that bring about the greatest CO2 emission reductions in four four representative provincial areas. Although the reduction shares of each technology differ among representative provincial areas. Although the reduction shares of each technology differ among the the four regions, the technology selection seems to be unanimous: efficient lighting, biomass water four regions, the technology selection seems to be unanimous: efficient lighting, biomass water heaters, heaters, and efficient electronics. and efficient electronics. 10 15842

Sustainability 15833–15846 Sustainability 2015, 2015, 7, 72015, 7, page–page

The diffusion of the fluorescent lamp contributes over half of the CO2 emission reduction in all diffusion the fluorescent half of the COconsidered all four 2 emission reduction four The regions. The of fluorescent lamplamp and contributes LED lamp over are respectively a “NEW”inlighting regions. The fluorescent lamp and LED lamp are respectively considered a “NEW” lighting technology technology and an optimally efficient “BAT” technology in this category. Nevertheless, the model and an optimally technology in this category. Nevertheless, the no model simulation simulation resultsefficient suggest“BAT” a large penetration of the fluorescent lamp with accompanying results suggest a large penetration of the fluorescent lamp with no accompanying penetration of LED. penetration of LED. Though less efficient than LED, the fluorescent lamp has the appealing Though less efficient than LED, the fluorescent lamp has the appealing advantage of an affordable advantage of an affordable price. The fluorescent lamp can thus be seen as a bridge between the price. The fluorescent lamp canand thus be seen as a bridge between the traditional incandescent lamp traditional incandescent lamp LED, especially in rural households. Biomass (wood residuals) is and LED, especially in rural households. Biomass (wood residuals) is considered a carbon-neutral considered a carbon-neutral energy source important for CO2 emission reduction. The biomass water energy source COimportant reduction. in The biomass water ranks as thethe second 2 emissiontechnology heater ranks asimportant the secondfor most Heilongjiang andheater Guangdong, and third most important technology in Heilongjiang and Guangdong, and the third most important in Beijing most important in Beijing and Shanghai. and Shanghai. No emission tax or technology subsidy is included in the low-carbon scenario of this study. We No emission tax or technology subsidy is included in the low-carbon This scenario of this study. examine only the energy savings brought about by efficient technologies. necessarily limits We the examine only the energy savings brought about by efficient technologies. This necessarily limits the selectable technologies. In future studies we will also consider financial instruments when we design selectable technologies. In future studies we will also consider financial instruments when we to design our scenarios. We also expect regions to adopt different technology combinations best suited their our scenarios. We also regions to adopt different technology combinations best suited to their respective climates andexpect economies. respective climates and economies. Other electronics (NEW)

Biomass water heater

Other electronics (NEW)

Biomass water heater Heilongjiang (Severe cold) Refrigerator (BAT)

Beijing (Cold)

Fluorescent lamp

Refrigerator (BAT) Fluorescent lamp

Biomass water heater

Refrigerator (BAT)

Fluorescent lamp Other electronics (NEW) Biomass water heater


Refrigerator (BAT)


Other electronics (NEW) Fluorescent lamp

Figure 8. CO2 emission reduction contributors in four provincial areas. Figure 8. CO2 emission reduction contributors in four provincial areas.

4. Conclusions and Future Work 4. Conclusions and Future Work 4.1. Conclusions 4.1. Conclusions This study highlights the importance of considering the socio-economic and climate diversity that study the importance of considering socio-economic and climate exist This across ruralhighlights areas in China while estimating the ruralthe energy demand. Future energy diversity demand that exist across rural areas in China while estimating the rural energy demand. Future energy in rural households is estimated using a well-grounded methodology. The results show that energy demand in rural households is estimated using a well-grounded methodology. The results show that service demand in China’s rural household sector will continue to increase in regions with growing energy service demandconditions. in China’s In rural household will to increase in regions with population or income most Chinese sector regions, thecontinue rural service demand will start to growing population or income conditions. In most Chinese regions, the rural service demand decline after 2030. The results show that the decoupling between energy and emission is higherwill in start toareas decline after 2030. The results show that the decoupling between energystudy and emission is warm compared to cold areas in rural households. Compared to our previous [22] related higher in warm areas compared to cold areas in rural households. Compared to our previous study to urban household energy demand estimation, emission intensity improvement is lower in rural [22] related in to urban household energy demand estimation, emission intensity improvement is lower households the competitive technologies selection scenario. The results indicate that the technology in rural households the competitive technologies The results indicate that the transition can achieveinemission reductions in the rural selection area in anscenario. efficient manner without any financial technology transition can achieve emission reductions in the rural area in an efficient manner without 15843 11


stimulus such as tax or subsidies. For example, for the lighting service, a switch happens towards the moderate technology such as fluorescent lamps in terms of both cost and energy efficiency. The key contribution of this study is to estimate the rural household energy demand at the provincial level which helps bring out the comparative dynamics between the warm and cold regions of China. For instance, the CO2 reduction potential ranges from 20% to 50% across all 31 regions and appears to be relatively larger in warm areas than in cold areas. Yet without certain policies in place, the diffusion of efficient technologies will be limited. Efficient lighting, biomass water heaters, and efficient electronics bring about the greatest benefits in rural households. 4.2. Future Work Future studies should be carried on the following aspects. First, due to limited data related to non-commercial biomass, the maximum share of non-commercial biomass in this study is manually fixed in future scenarios. Field surveys could provide more insights related to fuel-switching dynamics in the China’s rural household sector. This would help to assess the impact of income growth on energy structure in the household sector in rural areas. Second, this study focuses solely on the residential sector under the assumption that all 31 provinces use a national electricity grid. As such, an average emission factor for electricity is applied for all 31 provinces, and this value is fixed in the future in order to evaluate impact from the demand side only. To understand the impact of regional dynamics in a comprehensive manner, future study is required linking demand and supply sides at the regional level. Third, according to the empirical studies [2,5,6,24], cooling services are not widely used in the Chinese rural household. This study extends the current situation into the future and excludes the cooling services. Future studies can consider the possibility of using cooling services by designing other scenarios. For instance, for the cold regions in China, insulation retrofitting can be an efficient way to reduce the energy demand and CO2 emissions. However, due to data unavailability related to the technical details and deployment share of insulation retrofit technology, the technology is not included in the current study. Further research is required to be conducted for marking the profile of the insulation level in rural residential buildings in order to assess the impact from insulation retrofitting on the energy profile of the rural household sector. Last but not least, in this study the projections of all socioeconomic indicators are limited with SSP2’s “business-as-usual” storyline. For instance, projections of floor space and white/brown goods’ ownership are extrapolations of historical panel data, which implies a stable development in the rural households. Further research should be undertaken to account for the all the uncertainties related to the socio-economic indicators to have a better understanding of the rural energy sector dynamics. Note 1. The rural areas mentioned in this study are the national administrative divisions treated as “rural areas” in the “China Statistical Yearbook” [41]. 2. The working power of a refrigerator is an average value of refrigerator models that are commonly used in China’s rural families (108L-298L). 3. Only socioeconomic projections (total population, urban population share and per capita GDP) from SSP2 are considered. Mitigation or adaptation projections in SSPs are not taken into account in scenarios of this study. Supplementary Materials: Figure S1: China’s climate zones; Table S1: Regional residential floor area (rural, million m2 ); Table S2: Ownerships of household electronics (unit/household); Table S3: List of technologies considered in the AIM/Enduse model; Table S4: Building energy demand (China, 450 Scenario) [40]; File S1: Disaggregation of socioeconomic indicators [22]; File S2: Regression analysis of per capita floor area; Table S5: Coefficients of per capita floor area; Table S6: Electricity emission factor in Enduse/Global [38]. Acknowledgments: This research was supported by the Environment Research and Technology Development Fund (S-12-2) of the Ministry of the Environment, Japan. The authors are grateful to Toshiharu. Ikaga and Shivika. Mittal for providing valuable research suggestions. The authors also thank anonymous referees who reviewed and gave important comments to this paper.

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Author Contributions: Rui Xing, Tatsuya Hanaoka, Yuko Kanamori, Hancheng Dai, and Toshihiko Masui conceived and designed the research; Rui Xing collected the data and conducted the model simulation. Tatsuya Hanaoka, Yuko Kanamori, Hancheng Dai, and Toshihiko Masui supervised the data collection and model simulation, and verified the results. Rui Xing drafted the manuscript. Tatsuya Hanaoka, Yuko Kanamori, Hancheng Dai, and Toshihiko Masui reviewed, commented on, and edited the manuscript. Conflicts of Interest: The authors declare no conflict of interest.

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