Stacked Use and Transition Trends of Rural Household Energy in

Subscriber access provided by Gothenburg University Library

Energy and the Environment

Stacked use and transition trends of rural household energy in mainland China Xi Zhu, Xiao Yun, Wenjun Meng, Haoran Xu, Wei Du, Guofeng Shen, Hefa Cheng, Jianmin Ma, and Shu Tao Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b04280 • Publication Date (Web): 04 Dec 2018 Downloaded from http://pubs.acs.org on December 5, 2018

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 23

Environmental Science & Technology

1

Stacked use and transition trends of rural household energy in mainland China

2

Xi Zhu1, Xiao Yun1, Wenjun Meng1, Haoran Xu1, Wei Du1, Guofeng Shen1, *, Hefa Cheng1, Jianmin Ma1, Shu

3

Tao1,2

4

1. Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing

5

100871, China

6

2. Sino-French Institute for Earth System Science, Peking University, Beijing 100871, China

7 8

* Corresponding author: Guofeng Shen, Email: [email protected]

9 10

The authors declare no competing financial interests.

11 12 13

Word count: 6950 = 6050 (text) + 900 (3 figures)

ACS Paragon Plus Environment


15

Abstract

16

Household energy use is an important aspect of environmental pollution and sustainable development.

17

From a nationwide residential energy survey, this study revealed that household fuel “stacking”-mixed use

18

of multiple fuels-is becoming noticeable over the 20 years from 1992 to 2012, particularly in northern

19

China where space heating is needed in the winter. Approximately 28% of rural households used only a

20

single energy type in 1992, whereas the percentage declined to merely 11% in 2012. The number of energy

21

types correlated positively with the heating degree days and negatively with the household income in areas

22

with limited or no heating requirements. Combined use of biomass and fossil fuels may lead to extra energy

23

use, up to 40% for cooking and 20% for heating. Some fuels, as supplementary ones, are used more often

24

than others, and the energy consumption of coal and honeycomb briquette could be underestimated by 34%

25

and 22% if only the primary energy was accounted for. Generally, household energy is shifting from solid

26

fuels to cleaner ones, such as electricity or gas for both cooking and heating, but with different patterns and

27

transition rates. Transition pathways varied extensively from one region to another due to the imbalanced

28

development. Clean transitions initially occur in well-developed provinces and mega-cities, and then

29

extend to inland provinces approximately 5-10 years later. Rapid energy transitions and urbanization have

30

led to nearly 50% reduction in residential energy consumption over these two decades, resulting in

31

significant declines in emissions of most air pollutants. The updated residential emission of primary PM2.5

32

was 3100 Gg in 2014. Extensively fuel stacking and rapid energy transitions have led to complex

33

circumstances in energy use.


Page 2 of 23

Page 3 of 23

35


TOC

36



Page 4 of 23

37

1. Introduction

38

Ambient and household air pollution rank high among various risk factors that contribute to ill-health and

39

are estimated to cause more than one million premature deaths in China.1 Different from other major

40

sources of ambient air pollution such as power plants, industry, and transportation,

41

emission is a unique source because of direct emissions into household environments 3 and emissions into

42

ambient environment, thus, the source may be the most important contributor to air pollution in certain

43

contexts. Unfortunately, the importance of this source is not fully recognized and does not receive

44

appreciable attention from the public and decision makers compared to other major sources. 4

45

Partially due to lack of awareness, basic information and knowledge about air pollutant emissions from the

46

residential sector are poor. Another explanation for data and knowledge gaps is the difficulty in the

47

collection of energy and emission data from the household emissions sector with very complex human

48

activities. For example, biomass fuels used for residential cooking and heating are non-commercial and are

49

always reported by the residents themselves. Likewise, biomass fuel consumption data are primarily

50

reported based on simple estimations with large uncertainties. As a result, high-quality data in residential

51

fuel consumption are very scarce, and residential emissions have relatively high uncertainties compared to

52

other sources. 5

53

Mainland China has been experiencing rapid economic development and societal change over the past three

54

decades. 6 Among these changes, a rapid transition in rural residential energy from solid fuels to electricity

55

and gases (LPG, natural gas, and biogas) has occurred over the last several decades. For instance, according

56

to the China Family Panel Studies, the proportion of Chinese households cooking primarily with solid fuels

57

has dropped sharply from 50% to 39% in two years from 2010 to 2012.7 The China Health and Retirement

58

Longitudinal Study (CHARLS) estimated that about 17% of rural Chinese households switched from

59

traditional fuels to modern ones in a four-year period from 2008 to 2012.

60

singly consider the primary single cooking fuel only. An immediate consequence of this transition would

61

be significant reductions in air pollutant emissions, especially those predominantly from residential

62

combustion.

63

consumption in mainland China,

64

quantities and dynamic changes.12 Moreover, such transitions are often incomplete and there are usually

9

8

2

the residential

Note that all of these studies

However, this transition has largely been ignored in official data portals for rural energy 10-11

which leads to significant bias in both estimated national fuel


Page 5 of 23


65

multiple options for different energy types. Stacked energy use (mixed use of multiple energy) is common

66

in rural homes around the world.

67

water using electricity, prepare non-staple foods using LPG, heat the home using coal, heat the heating bed

68

(Kang) with fuelwood, and heat animal feed using crop residues. Such conditions make data compilation

69

for rural household energy use quite difficult and complicated. Unfortunately, these databases are essential

70

for emission inventory development, air quality modelling, along with other similar applications.

71

Recently, a nation-wide rural residential energy survey, together with a solid fuel weighing campaign, has

72

been launched to collect direct data and to compile a new database for Chinese rural residential energy use.

73

12

74

rates that those reported by the IEA and FAO. 12 In this paper, we further exploit the information from this

75

rich dataset, with different focuses mainly on factors affecting energy stacking, potential extra fuel use by

76

multi-fuel users, and potential bias of the common approach that surveys the primary energy only. We take

77

a closer look at the transition of detailed fuel categories and examined detailed spatial variations.

78

Additionally, we extrapolate household energy consumption to 2014 based on statistical models that were

79

established based on survey data from 1992 to 2012. Consequently, we update emission inventories of

80

multiple air pollutants, contributions of residential combustion emissions and that from different fuel

81

subgroups are discussed. These new results are expected to promote a better understanding of the transition

82

of rural Chinese household energy and its impacts on the environment.

13

In China, it is not unusual for a rural resident to cook rice and boil

This national survey found significant decreases in residential biomass consumption with much larger

83 84

2. Methodology

85

Household energy survey and fuel weighing campaign. Details of the survey and quality control can be

86

found in Tao et al., (2018),

87

households in mainland China have been surveyed for the energy mix, and daily fuel consumption was

88

quantified from more than 1,600 households. The energy types in this survey include honeycomb briquette,

89

coal (excluding honeycomb briquette), corncob, crop residues (excluding corncob), brushwood, fuelwood

90

(excluding brushwood), charcoal, LPG, biogas, and electricity (for rice cooker, induction stove, kettle, and

91

heater). The data were collected in 2012 and recalled for 1992, 2002, and 2007.

92

Other data and emission inventories. Socioeconomic parameters, such as population and per-capita

12

and in the Supporting Information (S1). Briefly, more than 34,000 rural



93

income in rural areas, were obtained from the China Statistical Yearbook. 14 Heating degree days (HDD),

94

defined as accumulated degree deviations from predefined base temperatures and room heating is needed,

95

follow the methods developed by Chen et al., (2016).

96

energy consumption, EFs) were from the PKU-FUEL database compiled for parallel studies. 7, 16-18 Rural

97

residential emissions of major air pollutants, including primary PM2.5, PM10, TSP, SO2, NOx, NH3, CO2,

98

CO, BC, OC, and 16 polycyclic aromatic hydrocarbons (PAHs), were calculated for 1992, 2002, 2007,

99

2012, and 2014 based on activities (fuel consumption quantities) and corresponding EFs. Emissions were

100

calculated at the prefecture level, and then interpolated to grid points according to the grid population and

101

urban mask data.17 For electricity, consumption data were first converted to primary energy consumed

102

based on a power generation efficiency,

103

petroleum-, and natural gas-fired power plants, and non-fossil fuel power generation in each province. 20

104

Conversion coefficients of electricity to fossil fuel and proportion of different power generation units for

105

different years are listed in Table S1. The emissions of other sectors were from the PKU emission

106

inventories.18

107

Quantification of extra fuel use. Based on the data for single fuel users, the average daily consumption of

108

each individual fuel per household was derived. For multi-fuel users, equivalent days for individual fuel

109

types were calculated based on the quantities of individual fuels consumed and the average daily

110

consumption from the single-user category. In the case of no extra fuel use, the sum of equivalent days for

111

the multi-fuel users should be close to 1.0 on average. If the summed equivalent day is significantly higher

112

than 1.0, extra fuel use from the multi-fuel users can be identified and quantified. The calculated equation

113

is as follows:

114

De = Wi/Ti + Wj/Tj,

115

where De is the equivalent day of a household using mixed fuel i and j; Wi and Wj are the daily

116

consumption amount of fuel i and j (kg/household), respectively, in the multi-fuel use household; Ti is the

117

daily average consumption of the household using only fuel i (kg/household), and Tj is the daily average

118

consumption of the household using only fuel j (kg/household).

119

Data Analysis. Comparison and correlation analyses at a significance level of 0.05 were conducted using

120

SPSS 24.0. Excel 2016 and Matlab 2016 were used to conduct the pathway analysis. Maps were created

19

15

Pollutant emission factors (emissions per unit of

and the EFs were weighted by the proportions of coal-,


Page 6 of 23

Page 7 of 23

121


using ArcGIS10.2 (Esri, Redlands, CA).

122 123

3. Results and Discussion

124

3.1 Stacked energy use and influencing factors

125

Traditional energies are being transited to modern fuels, such as electricity, LPG, and natural gas, mainly

126

due to increasing affordability and improved accessibility.

127

complete and there are usually multiple options to use different energy types, consequently stacked energy

128

use is common in rural homes around the world. 13 In mainland China, biomass fuels, including fuelwood

129

and crop residues, dominated rural households for cooking and heating before the 2000s and were later

130

replaced partially and gradually by non-traditional energies. However, though coal, electricity, and LPG

131

have become increasingly affordable and accessible, biomass fuels were still used extensively due to both

132

tradition and unbalanced development in rural societies. 12 Despite the fact that many households can now

133

afford new and cleaner fuels, elder residents of these homes often prefer free biomass fuels to supply

134

specific indoor energy services. Transitions are thus complicated for a number of reasons, including, for

135

instance, household educational level, habits, awareness of indoor pollution, adverse health impacts, and

136

willingness to change, etc. 23-25 Though living conditions in rural China have improved quickly, millions of

137

rural residences in western China and mountainous regions are still too poor to use clean energy. 26

138

Generally, those who need to heat their home in winter often use multiple energies, as demonstrated by a

139

significantly positive correlation between the average number of energy types used and the HDD (Fig. 1a).

140

The HDD is proportional to heating energy consumption.

141

temperatures are always below zero and heating is required for 4-6 months.27-28 Because both electricity

142

and natural gas are too expensive to be used for heating during long winters without government subsidies,

143

solid fuels are often the dominant heating energies. In fact, 85% of households in rural China rely on solid

144

fuels for winter heating, 12 which leads to greater diversity in household energy types when cooking energy

145

is quickly shifting to electricity and LPG. The number of energy types used is found to be negatively

146

correlated with per-capita income for the provinces with no, or limited, heating requirements (Fig. 1b),

147

which is explained by the rapid cooking energy transition.

21-22

However, such transitions are often not

15

In Northern provinces, mean winter



b 5.0

Number of energy types

Number of energy types

a y = 2.1176e0.1871x R² = 0.5375 4.0

3.0

2.0 0.0

148 149 150 151 152

Page 8 of 23

1.0

2.0

3.0

4.0

4.0

y = 167.63x-2.979 R² = 0.3664

3.0

2.0 3.6 3.7 3.8 3.9 4.0 4.1 4.2 4.3

logHDD

logIcap

Fig. 1 (a) The positive correlation between the average number of energy types used in 2012 and the log-transformed heating degree day (HDD) for all provinces. (b) The positive correlation between the average number of energy used in 2012 and log-transformed per-capita income (Icap) for the provinces with no or limited heating requirement.

153 154

3.2 Extra fuel consumption due to “stacking”

155

By comparing the fuel consumption data between single and multiple energy users, excessive energy

156

consumption was found for households using mixed biomass and fossil fuels during the same period (see

157

details on calculation in the methodology and results in Table S1). For example, in a household using only

158

wood or briquette for cooking, the average daily consumptions of wood and briquette were 8.62 and 4.13

159

kg per household (family size adjusted), respectively, but in a household using both wood and briquette, the

160

household consumed 5.58 kg wood and 2.62 kg briquettes per day, of which the calculated De was 1.28.

161

Such excessive consumption can be attributed to insufficient application of coal and biomass fuels. For

162

instance, extra fuel is needed to heat a cold stove at the beginning and more extra fuels are burned when

163

more stoves are used. Moreover, coal stoves are often damped, instead of extinguished, when they are not

164

in use, whereas extra biomass fuels are needed immediately prior to ignition and after (leftover) the

165

"effective burning activity". For a multiple-fuel user, individual fuels are often used in a relatively short

166

time. The amount of extra fuel use increases as the length of individual active use decreases. Based on the

167

data collected in our campaign, the De was 1.42 for those using mixed biomass and fossil fuels for cooking,

168

and 1.24 in heating fuel use, suggesting excessive consumption rates as high as 40% and 20% for cooking

169

and heating, respectively. Such extra fuel use would not be produced if two different biomass or fossil fuels

170

were used together by individual households, as the calculated De was 0.95 for cooking fuel, and 1.04 for


Page 9 of 23


171

heating fuel (Table S2). This might be partly explained by the practice that rural residents usually burn coal

172

and biomass in different stoves, while crop residues and wood could be burned in the same biomass stove.

173

However, it is important to note that due to limited data and potentially different habits between the two

174

groups, the quantitative estimates may be subject to probably high uncertainties and more field studies are

175

needed to improve the calculation and to evaluate the extra energy consumption during mixed use.

176

3.3 Comparison between single and multiple fuel surveys

177

Traditionally, most previous studies only collected the information of primary energy used in households. 7,

178

29-30

179

based on only primary energy can lead to a large bias. To demonstrate the difference between the detailed

180

energy survey and the primary energy survey, our survey database was re-sampled using the primary

181

energy concept, which is defined as the most frequently used energy type in a household. The time-sharing

182

contributions of various energy types derived from this study are compared with the results from the

183

re-sampled data for 2012. It was found that if only the primary energy was surveyed, the relative

184

contributions of coal, honeycomb briquette, LPG, biogas, and electricity would be underestimated by 34%,

185

22%, 11%, 22%, and 4%, respectively, whereas the contributions of wood and crop residue would be

186

overestimated by 19% and 18%, respectively. The reason for such a notable bias is that biomass fuels are

187

often used as a primary energy source in rural China, while others are more often used as supplementary

188

energies, which was ignored during a primary energy survey approach. If the primary as well as secondary

189

fuels were taken into consideration, the relative contributions of coal, honeycomb briquette, wood and

190

biogas would be underestimated by 14%, 12%, 1% and 10%, respectively, and the contributions of crop

191

residue, LPG and electricity would be overestimated by 1%, 3% and 1%, respectively, showing generally

192

smaller bias.

193

The proportion of household primarily using LPG for cooking in our study (22%) was close to that of 24%

194

in the CFPS2012.

195

CFPS2012’s result of 57%, and the proportion of households using electricity for cooking was considerably

196

higher in the present study (33%). In the CEERHAPS study,

197

biomass, gas and electricity for cooking were 48%, 29%, and 10%, respectively, which generally also

198

showed a higher biomass, but lower electricity user proportion, compared to the present study. The present

Because of complicated energy use habits and stacked energy selections, the energy consumption

7

However, the estimated proportion mainly using biomass (41%) was lower than the

31


the proportions of households using


Page 10 of 23

199

study and CEERHAPS covered the whole 31 provinces in mainland China, while the CFPS2012 covered

200

25 provinces of mainland China. Besides potential bias in surveys, one main cause for the difference here

201

might be associated with the question on the survey of primary cooking fuel (CFPS and CEERHAPS) or

202

times used for different fuels (like the present study). Electricity is frequently used for cooking, especially

203

in homes with a smaller family size, among younger people and in warmer seasons (e.g. summer), but field

204

observation showed that traditional solid fuels were also frequently used for daily cooking particularly in

205

winter and/or in preparation of some traditional Chinese foods. When only one single cooking fuel was

206

asked during the questionnaire, the answer might be biased with relatively higher uncertainties between the

207

choice of biomass or electricity, and possibly resulted in an underestimated fraction of electricity user. The

208

time-sharing fractions of biomass and electricity, from our present study, were very close, at about 35% for

209

each.

210

3.4 Trends in fuel “stacking”

211

In the past two decades, residential energy use in rural China has undergone tremendous changes, both in

212

energy types and energy consumption. Stacked fuel use is becoming noticeable over the 20-year period

213

studied. The increasing trend of multiple fuel use over time is observed in both cooking and heating

214

activities,

215

household level, is much more significant. In the early 1990s, approximately 28% of households used only

216

a single energy type, and the number of single-energy users decreased to merely 11% by 2012 (Fig.2a).

217

During the same period, the number of households using two or three energy types increased by 25%.

218

Generally, in all provinces, the numbers of energy types used exhibit an increasing trend, and the average

219

number of energy types used, at the household level, has increased at a similar rate for all provinces across

220

China, as seen in Fig. 2b, where they all appear as a group of parallel lines representing different provinces.

221

The spatial similarities in time trends were confirmed by the cluster analysis, as shown in Fig. S1.

222

The “stacking” phenomenon is common in most developing countries and is receiving growing concerns

223

worldwide. It necessitates a new set of household survey. For example, WHO is taking great efforts to

224

develop and update harmonized surveys to better characterize stacking.

225

recent studies evaluated both primary and secondary fuel use. As discussed above, when both primary and

226

secondary fuels were considered, the time-sharing fractions of different energy types generally showed

12

and when taking all different household activities into account, the “stacking”, at the


32, 33

It is good to see that more

Page 11 of 23


227

smaller bias compared to results from the only one primary fuel approach. Detailed information on the

228

consumptions of individual energy types is useful for decision making in terms of emission mitigation, as it

229

helps to reduce uncertainties in energy consumption and emission estimations. Detailed fuel categories can

230

also help to reduce the uncertainty in emission inventory as there could be orders of magnitude difference

231

in the EFs among them. 34

232 233 234 235 236 237

Fig. 2 (a) Percentage of rural households using different numbers of energy types, which are classified as coal, honeycomb briquette, charcoal, fuelwood, brushwood, corncob, other crop residues, LPG, biogas, and electricity, in 1992, 2002, 2007, and 2012, for either cooking or heating. (b) The time trends of average numbers of rural household energy types on the average (red line) and for each province (gray line) in mainland China from 1992 to 2012.

238 239

3.5 Two-decadal transitions in cooking and heating energies

240

The transition pathways in these two decades are illustrated in Fig. 3. The transition in cooking energy was

241

much faster than that in heating, showing a rapid replacement of solid fuels with either electricity or gases

242

(mainly LPG). As a result, biomass fuel use decreased from 84% in 1992 to 69% in 2012, and accelerated

243

after that, from 51% in 2007 to 36% in 2012. Although coal use increased slightly from 9% in 1992 to 12%

244

in 2002, because more households switched from biomass to coal than those from coal to clean energy, it

245

began to decrease after 2002, at a rate of about 2.5% drop every 5 years. With an increasingly greater

246

number of households replacing their biomass fuels with electricity and gases, coal use is expected to

247

decrease continuously after 2012. There is also a clear trend showing that some LPG users are likely to

248

adopt electricity for cooking and about 3% of time-sharing of gas switched to electricity from 2007 to 2012

249

as induction stoves became increasingly more popular.



1

Time-sharing fraction

a

0.8 0.6 0.4 0.2 0 1

Time-sharing fraction

b

251 252

2002

2007

2012

0.8 0.6 0.4 0.2 0

250

1992

1992 Electricity

Gas

Coal

2002 Wood

2007 Crop residue

2012 No-heating

Fig. 3 Transition pathways of average household time-sharing use for cooking (a) and heating (b) energy in rural mainland China from 1992 to 2012.

253 254

This transition trend was also elucidated for individual provinces. The transition varies extensively from

255

one place to another due to the imbalanced development across the country, as seen in Fig. S2. The

256

transition towards clean fuels initially occurred in those well-developed provinces and mega-cities. Similar

257

transitions extended to inland provinces approximately 5-10 years later. There is a general trend in

258

transition from solid fuels towards LPG in eastern China. The transition towards electricity was faster in

259

well-developed east China, and southwest areas with abundant hydropower and a large number of small

260

hydropower stations. 35

261

The transition towards clean energy also took place in heating, but with slower rates and different patterns.

262

In fact, the most obvious transition was the shift of non-heating households towards heating households. As

263

a large country, the temperature varies extensively from north to south. In northern China, winter heating is

264

demanded for four to six months, whereas no room heating is required in most southern provinces.

265

Nevertheless, in central China along the Yangtze River basin, especially in the high-altitude areas, there is

266

also a short period of chilly weather. Local residents in these areas did not heat their households in the past

267

but begin to do so with the improvement in living conditions. As a result, the fraction without room heating


Page 12 of 23

Page 13 of 23


268

decreased from 68% in 1992 to 62% in 2002, and further down to 59% in 2007. The rate of decline

269

accelerated during the last 5 years, sharply down to 45% in 2012. Provinces experiencing intermediate

270

temperatures in central China were among those transferred rapidly from non-heating to heating, especially

271

during the last five years. Among various energy types, electricity use experienced a fast growing with

272

fractions of 0.6%, 1.1%, 3.6% and 8.2% in 1992, 2002, 2007 and 2012, respectively. Similarly in coal use,

273

the fractions were 9.1%, 13.6%, 16.3% and 24.1% for the studied years, respectively. Although the fraction

274

of biomass fuels remained stable at about 23% during the studied period, it was considerably an

275

intermediate step of the overall transition from non-heating to coal and electricity heating.

276

3.6 Factors affecting LPG and electricity adoption

277

Theoretically, rural residents tend to use more expensive clean energy immediately once their living

278

condition improves. 22 When LPG, electricity and biogas were examined individually in this study, it was

279

found that only LPG use was promoted by increases in per-capita income, but the electricity use was

280

independent of income. Using the 2012 data as an example, the correlation coefficient between the fraction

281

of LPG use and log-transformed per-capita income is 0.86 (95% CI of the coefficient: 0.70~0.94),

282

indicating that living condition is a predominant factor affecting residential LPG use. In rural China, LPG

283

price is very much market driven without government subsidies 36 and the price (over 100 RMB per 15 kg)

284

is still expensive for many rural residents, especially when free biomass fuels are easily accessible.

285

Meanwhile, the electricity price in the rural residential sector is subsidized and almost fixed for decades. 37

286

Rice cookers, induction stoves, and electric kettles are not expensive in terms of rural household income

287

and can be used for years. As a result, the choice of using electricity is mainly due to its convenience and

288

affordability. Therefore, there is no significant correlation (r=0.04, 95% CI: -0.35~0.41) between use of

289

electricity and per-capita income (Fig. S3). This also applies to provinces without heating demand.

290

For biogas, a weak but negative correlation was identified (r=-0.24, 95% CI: -0.49~-0.05). It was found

291

that even though many biogas facilities have been installed in rural China over the past few decades,

292

dominantly promoted by government campaigns with subsidy, many biogas tanks are no longer in

293

operation. 12 One reason for the abandonment of biogas tanks is that the facilities are not well maintained

294

after installation due to the lack of qualified facility maintenance technicians in most rural villages. A likely

295

more important reason is that only a few rural residents still raise pigs at home, which was popular and



38

Page 14 of 23

296

provided manure regularly to maintain the biogas tanks in the past.

297

negative correlation between biogas use and per-capita income because poorer families still tend to raise

298

pigs themselves at home. The extensive use of clean fuels and electricity in the residential sector would

299

reduce emissions of various air pollutants to ambient air and mitigate household (indoor) air pollution

300

greatly.

301

3.7 Energy consumption amounts and extrapolation to 2014

302

Energy consumption amounts during the two decades, in thermal units (PJ) (calorific values for fuel

303

transition to thermal units are listed in Table S3), are shown in Fig. S4. The most prominent change

304

occurred in electricity use, which has increased more than eight times over the past two decades. Given the

305

relatively high efficiency of household electric cookware application, the relative contribution of electricity

306

to energy consumption, in thermal unit, is much less than those in time sharing.12 In recent years, rice

307

cookers became so popular that in 2012 they were used in nearly 80% of the households surveyed, whereas

308

in 1992 the percentage was merely 8%. Similarly, the households using induction stoves increased 17 times

309

from 2% in 1992 to 34% in 2012. The rapid promotion of electric cooking is not only due to the

310

convenience but also due to the low electricity price for rural residents, which has not changed for

311

decades.37 A similar trend can be seen for LPG, which increased from 52 PJ in 1992 to 214 PJ in 2012. A

312

nationwide LPG distribution system covering most rural areas has been in operation for many years in

313

mainland China, which provides good accessibility for rural households. 36 As the consumption of clean

314

energy increased, use of solid fuels decreased correspondingly. Reduction in biomass fuel use was much

315

faster than that in coal. In fact, use of coal even increased slightly before 2002, and then started to decrease.

316

The quantity of fuel wood consumption in 2012 was only 36% of that in 1992. In terms of thermal units,

317

the total energy consumption decreased almost 50% from 1992 to 2012. Aside from relatively higher

318

efficiencies in LPG and electricity, a rapid decline in rural population due to rapid urbanization is another

319

reason for the decrease in energy consumption. Since the rural population has decreased by approximately

320

20%, the energy consumption per capita declined approximately 37.5% due to energy transition. It is noted

321

that even with the rapid transition, there was almost 40% of time that rural residents used solid fuels in

322

2012, indicating a large potential for further transition towards clean fuels or electricity.

323

As shown in Fig. S5, the changes in most energy types (in PJ) were relatively slow from 1992 to 2002, and


This can also explain the weak

Page 15 of 23


324

accelerated after 2002. The trends were almost linear from 2002 to 2012. Thus, the consumption of

325

individual energy types is represented by linear equations from 2002 to 2012 with the mean and standard

326

deviation of R2 values at 0.9730.038 for the 10 energy types. A similar trajectory was found for the

327

log-transformed per-capita income with R2 of 0.996, suggesting again that the energy mix transition

328

occurred along with socioeconomic development. Fig. S6 compares the slopes of the equations with

329

cooking and heating separately. In most cases, solid fuels are associated with negative slopes with the

330

highest values in fuelwood (-160 and -42 PJ/year for cooking and heating activities, respectively), followed

331

by crop residue and brushwood, whereas positive slopes were obtained for all clean fuels and electricity

332

(6.4 and 2.2 PJ/year for cooking and heating activities, respectively). The only exception is coal for heating

333

with a positive slope (14 PJ/year), mainly because many previous non-heating households started to heat by

334

coal, which is in line with the pathways discussed above. The cooking transition, either positive or negative,

335

was faster than that for heating because using clean energy for household heating is very expensive and is

336

hardly affordable under current living conditions in rural China. 39

337

From the statistical relationships derived for both cooking and heating, it is interesting to extrapolate

338

household energy consumption (PJ) to 2014, which is considered as an important baseline year for

339

evaluating the effectiveness of a series of mitigation actions, especially some measures on the residential

340

sector, taken by the central and local governments since 2014. 40-41 In addition to the importance of 2014,

341

the sample sizes for the regressions are too small to predict future energy consumption for a longer period.

342

The relative contributions of various fuels and electricity to the total energy use in 2014, based on predicted

343

residential energy consumption, are shown in Fig. S7. Although a rapid clean energy transition could be

344

observed, solid fuels still dominate the rural residential sector in the absolute quantity. This is partly

345

attributed to very low thermal efficiencies of traditional fuels, especially biomass fuels, which are less than

346

one-third of those of LPG or natural gas.

347

accounted for merely 6% of the total residential energy consumption in quantity, their contributions in

348

time-sharing are much higher (approximately 59% and 18% for cooking and heating, respectively). It is

349

expected that, accompanying continued future socioeconomic development, relative contributions of

350

various energy types will further shift towards clean LPG and electricity.

351

3.8 Air pollutant emissions from rural households

38

For the same reason, although LPG, biogas, and electricity



352

Based on data collected in this study, annual emissions of various air pollutants from the rural residential

353

sector were compiled and calculated for the relative contributions of residential sector to the national total

354

anthropogenic emissions (Fig. S8). 18 Primary PM2.5 from the residential sector was estimated at 3,100 Gg,

355

contributing to 27% of the total emissions from all sources in mainland China in 2014, ranking second after

356

industrial sources. The contribution from the residential sector was as high as 60% in 1992. The step

357

decline was due to both a rapid transition in residential energy mix as elucidated in this study, as well as a

358

rapid increase in emissions from other major sectors, including power generation, industry, and

359

transportation, also driven by economic growth. 2 The estimated residential contribution to the total national

360

emissions was 34% in 2005 by Lei et al., (2011), 42 and 39% in 2010 by Li et al., (2017). 43 These estimates

361

were considerably higher than our results here, mainly due to updated fuel consumption based on direct

362

surveys, as well as the inclusion of many localized emission factors in the present study.

363

Compared to primary PM2.5, relative contributions from the residential emission to the national total were

364

much lower for SO2 (7.8%), NOx (3.4%), and NH3 (5.4%), which are vital in the formation of secondary

365

aerosol. 44 The major sources of these pollutants in China are industry, power generation, or agriculture.43

366

As such, the residential sector contributes mostly to the primary, rather than secondary, PM2.5 in the

367

ambient air. The rural residential sector is also a major source of other incomplete combustion products.

368

For example, the rural residential sector contributed 36%, 54%, and 37% to the total emissions of BC, OC,

369

and BaP in 2014, respectively. The particularly high contribution to BaP emission is partially due to the

370

phase-out of beehive coke ovens in China driven by the implementation of the Coal Law. 45

371

Residential emissions are further categorized into detailed fuel sub-types (Fig. S9). Among the total

372

residential emissions, nearly 80% were from rural residential sources. Although the urban population

373

account for more than half of the total population, biomass fuels are usually not accessible in cities, and the

374

use of coal has to a large extent been replaced with pipelined natural gas, LPG, electricity, and centralized

375

heating systems in cities.

376

emissions (1480 Gg and 1600 Gg), whereas emissions from LPG and biogas accounted for only a

377

negligible quantity (12 Gg). On the other hand, the majority of residential SO2 emission is from coal

378

burning (90%) as the sulfur contents of biomass fuels are very low. In terms of contribution to primary

379

PM2.5, residential coal and biomass fuels are equally important. While residential coal burning has received

380

increased attention from governments, and various measures have been taken to replace coal heating stoves

17, 21

Coal and biomass fuels contributed similar amounts to primary PM2.5


Page 16 of 23

Page 17 of 23


381

with electricity and natural gas heaters in North China Plain during the last several years,46 biomass fuel use

382

needs to be given more attention before this fuel is terminated. The good news is that biomass fuel

383

consumption, which only occurs in rural areas, has reduced subsequently during the last several decades.

384

The total consumption of biomass fuels decreased approximately 68% from 804 Gg in 1992 12 to 261 Gg in

385

2014, and this trend is expected to be continuing. This decrease was mainly driven by the following two

386

reasons: rapid socioeconomic progress in rural China makes coal and other clean fuels more affordable for

387

rural residents; 22 and more than 280-million rural residents have moved to cities 14 where biomass fuels are

388

simply not accessible. 21 Significant reductions in air pollutant emissions from 1992 to 2014 can be seen

389

across the entire mainland China (Fig. S10).

390

While most past inventories are based on the residential energy consumption of national or provincial

391

statistics, which are limited and usually roughly estimated, it is important to note that this problem has been

392

recognized in a few past studies in which field surveys were conducted to improve the estimation of

393

residential emissions. 46-47 Based on compiled results from the field survey and few literature data, Zhang et

394

al., (2018)

395

slightly lower than our result here (744±236 Gg). Cheng et al., (2017),

396

the Beijing-Tianjin-Hebei (BTH) region, estimated that residential emissions of BC, OC and PM2.5 in the

397

BTH area were 64, 86 and 217 Gg, respectively. Set side by side with our results, the primary PM2.5

398

emission was comparable, but the BC and OC emissions were considerably lower in the present study (96,

399

113 and 208 Gg BC, OC and PM2.5, respectively). Fuel consumption amounts were generally lower in our

400

present survey especially for crop residues, e.g. residential wood, straw and coal consumptions were 84, 57,

401

and 71 Mtce in this study, while they were 108, 165, and 101 Mtce in Zhang et al (2018). Note the fuel

402

consumption from Zhang et al., (2018) was based on field surveys in three provinces and extrapolated to

403

the other provinces.

404

studies.

405

3.9 Implications

406

The socioeconomically driven transition of the rural residential energy mix towards clean fuels and

407

electricity in mainland China have led to significant emission reductions in most air pollutants, especially

408

in primary PM2.5, which contributes significantly to ambient air pollution and adverse health effects.

47

estimated that BC emission from rural solid fuel use in 2014 was 640±245 Gg, which was

47

48

based on a household survey in

Different sets of EFs also contributed to different emission quantities among these



409

Additionally, emissions of BC, which is an important climate-forcing pollutant and is largely attributable to

410

residential fuel use, have declined significantly. This implies that the transition benefits both health and

411

climate. Another potential benefit of the energy transition is the improvement of household air quality in

412

rural areas, which has been seriously overlooked in the past, despite that nearly 0.61 million premature

413

deaths in 2016 in mainland China were attributable to household air pollution. 48

414

It is necessary to note that aside from the small size of regressions restricting a long term prediction,

415

estimates in pollutant emissions and relative contributions of rural residential emissions are associated with

416

uncertainties due to air pollution control measures that took place around 2013-2014, although most actions

417

are still focusing on other sources but limited controls on rural residential emissions.50 These

418

countermeasures, if being effectively and sustainably carried out, would cause obvious changes in energy

419

consumptions and pollutant emissions from residential sources as well as other sectors. Despite of this, fast

420

clean transitions and significant co-benefits in environment, health and climate change are expected. A

421

campaign launched recently by the Chinese government provided large subsidies to help rural residents in

422

more than 20 municipalities in the North China Plain to shift heating energy sources from solid fuels to

423

electricity or natural gas. 46 It was estimated that a total of 4.7 million households in the region has been

424

involved during the last year alone. 51 This campaign is surely affecting the rural residential energy mix in

425

this region. However, such an effort is mainly focused on this part of China and may be difficult to apply to

426

other regions, such as northeast and northwest China, due to much high costs and limited gas resources.

427

More research is needed to identify the feasibility of alternative solutions, such as clean stoves and less

428

polluted solid fuels. It is worthwhile, both scientifically and politically, to address the influences of this

429

action on energy, emissions, air pollution, and health.

430 431

The residential sector contributes significantly to the total air pollutant emissions and needs to be addressed

432

in an effort to mitigate air pollution. Compared with other sectors, residential emissions are more

433

complicated not only in EFs but also activities. Based on direct data from a nationwide survey, this study

434

details stacked fuel use and identifies key influencing factors in rural household energy transitions. The

435

“stacking” has become more notable and has spatial similarities in time trends across different provinces.

436

Household energy is becoming cleaner. Rapid energy transition and urbanization led to almost halving


Page 18 of 23

Page 19 of 23


437

residential energy consumption from 1992 to 2012. However, transition patterns and rates varied

438

extensively from one place to another due to the imbalanced development. Socioeconomic conditions play

439

an important role in deriving such a transition particularly for some fuels like LPG. Compared with a rapid

440

energy transition in household cooking, the transition in room heating is much slower, mainly because of

441

its high cost. Excessive energy consumption was revealed in multiple fuel use, and there are probable

442

overestimations in biomass consumption and underestimations in other fuels when relying on the “primary

443

energy” carrier in household energy surveys. The detailed quantitative fuel-use data collected in this study

444

provides a unique opportunity to compile emission inventory for various air pollutants from rural

445

residential sources with detailed fuel categories. Results from this study help to reduce uncertainties in

446

emission estimation and modeling, and also provide more evidence and support in making scientific

447

recommendations to decision makers on this underappreciated source.

448 449

Acknowledgments

450

This work was funded by the National Natural Science Foundation of China (Grants 41390240, 41830641,

451

41571130010, and 41821005). The authors thank all participants and volunteers in the field study. The

452

authors would like to thank anonymous reviewers and Emily Li for their valuable comments and detailed

453

edits to clarify and to improve the manuscript.

454 455

Supporting Information. Field survey and data processing; Tables listing data of conversion

456

coefficients between different fuels and electricity, daily cooking and heating fuel consumption in homes

457

using single and multiple fuels and calculation of equivalent days, calorific values for different fuels; and

458

Figures showing cluster analysis results of the number of energy types in 31 provinces; spatial distributions

459

of different fuels in 1992 and 2012; fuel consumption amounts (PJ) in the four study years; dependence of

460

LPG, electricity and biogas on per capital income; slopes of the fitting equations for ten different energy

461

types; contributions of different energies to the total household energy consumption; contributions of

462

pollutant emissions from residential sector and different fuel groups in 2014; and spatial distribution of

463

rural residential emission contribution of PM2.5 in 1992 and 2014.



Page 20 of 23

465

References

466

1. Cohen, A.J.; Braure, M.; Burnett, R.; Anderson, H.R.; Frostad, J.; Estep, K.; Balakrishnan, K.;

467

Brunekreef, B.; Dandona, L.; Dandona, R.; Feigin, V.; Freedman, G.; Hubbell, B.; Jobling, A.; Kan, H.;

468

Knibbs, L.; Liu, Y.; Martin, R.; Morawska, L.; Pope III, C. A.; Shin, H.; Srtaif, K.; Shaddick, G.;

469

Thomas, M.; van Dingenen, R.; van Donkelaar, A.; Vos, T.; Murrary, C. J. L.; Forouzanfar, M. H.

470

Estimates and 25-yer trends of the global burden of disease attributable to ambient air pollution: an

471

analysis of data from the Global Burden of Diseases Study 2015. The Lancet 2017, 389, 1907-1918.

472

2. Huang, Y.; Shen, H. Z.; Chen, H.; Wang, R.; Zhang, Y. Y.; Su, S.; Chen, Y. C.; Lin, N.; Zhuo, S. J.;

473

Zhong, Q. R.; Wang, X. L.; Liu, J. F.; Li, B. G.; Liu, W. X.; Tao, S., Quantification of Global Primary

474

Emissions of PM2.5, PM10, and TSP from Combustion and Industrial Process Sources. Environ. Sci.

475

Technol. 2014, 48, 13834-13843.

476

3. Liu, J.; Mauzerall, D. L.; Chen, Q.; Zhang, Q.; Song, Y.; Peng, W.; Klimont, Z.; Qiu, X. H.; Zhang, S.

477

Q.; Hu, M.; Lin, W. L.; Smith, K. R.; Zhu, T., Air pollutant emissions from Chinese households: A

478

major and underappreciated ambient pollution source. P. Natl. Acad. Sci. USA 2016, 113, 7756-7761.

479 480 481 482 483 484

4. Fan, J. L.; Zhang, Y. J.; Wang, B., The impact of urbanization on residential energy consumption in China: An aggregated and disaggregated analysis. Renew. Sust. Energ. Rev. 2017, 75, 220-233. 5. Fisher-Vanden, K.; Jefferson, G. H.; Liu, H. M.; Tao, Q., What is driving China's decline in energy intensity? Resour. Energy Econ. 2004, 26 (1), 77-97. 6. Zhang, X. P.; Cheng, X. M., Energy consumption, carbon emissions, and economic growth in China. Ecol. Econ. 2009, 68, 2706-2712.

485

7. Chen, Y. L.; Shen, H. Z.; Zhong, Q. R.; Chen, H.; Huang, T. B.; Liu, J. F.; Cheng, H. F.; Zeng, E. Y.;

486

Smith, K. R.; Tao, S., Transition of household cook fuels in China from 2010 to 2012. Appl. Energ.

487

2016, 184, 800-809.

488 489

8. Hou, B. D., Tang, X., Ma, C., Liu, L., Wei, Y. M., & Liao, H., Cooking fuel choice in rural China: Results from microdata. J. Cleaner Product. 2017, 142, 538-547.

490

9. Secrest, M. H.; Schauer, J. J.; Carter, E. M.; Baumgartner, J., Particulate matter chemical component

491

concentrations and sources in settings of household solid fuel use. Indoor Air 2017, 27, 1052-1066.

492 493 494 495

10. International

Energy

Agency.

IEA

World

Energy

Statistics

and

Balances.

http://www.oecd-ilibrary.org/statistics. Accessed June 6, 2018. 11. National Bureau of Statistics of People's Republic of China. China Statistical Yearbooks Database. http://tongji.cnki.net/overseas/engnavi/navidefault.aspx. Accessed June 6, 2018.

496

12. Tao, S.; Ru, M. Y.; Du, W.; Zhu, X.; Zhong, Q. R.; Li, B. G.; Shen, G. F.; Pan, X. L.; Meng, W. J.;

497

Chen, Y. L.; Shen, H. Z.; Lin, N.; Su, S.; Zhuo, S. J.; Huang, T. B.; Xu, Y.; Yun, X.; Liu, J. F.; Wang,

498

X. L.; Liu, W. X.; Cheng, H. F.; Zhu, D. Q., Quantifying the rural residential energy transition in China

499

from 1992 to 2012 through a representative national survey. Nature Energy 2018, 3, 567-573.

500 501 502 503

13. Ruiz-Mercado, I.; Masera, O., Patterns of Stove Use in the Context of Fuel-Device Stacking: Rationale and Implications. Ecohealth 2015, 12 (1), 42-56. 14. National Bureau of Statistics of the People's Republic of China, China Statistical Yearbook 2013. China Statistics Press: 2013.


Page 21 of 23


504

15. Chen, H.; Huang, Y.; Shen, H. Z.; Chen, Y. L.; Ru, M. Y.; Chen, Y. C.; Lin, N.; Su, S.; Zhuo, S. J.;

505

Zhong, Q. R.; Wang, X. L.; Liu, J. F.; Li, B. G.; Tao, S., Modeling temporal variations in global

506

residential energy consumption and pollutant emissions. Appl. Energ. 2016, 184, 820-829.

507

16. Ru, M. Y.; Tao, S.; Smith, K.; Shen, G. F.; Shen, H. Z.; Huang, Y.; Chen, H.; Chen, Y. L.; Chen, X.;

508

Liu, J. F.; Li, B. G.; Wang, X. L.; He, C. F., Direct Energy Consumption Associated Emissions by

509

Rural-to-Urban Migrants in Beijing. Environ. Sci. Technol. 2015, 49, 13708-13715.

510

17. Shen, H.; Tao, S.; Chen, Y.; Ciais, P.; Güneralp, B.; Ru, M.; Zhong, Q.; Yun, X.; Zhu, X.; Huang, T.,

511

Urbanization-induced population migration has reduced ambient PM2.5 concentrations in China. Sci.

512

Adv. 2017, 3, (7).

513 514

18. Peking

University.

Emission

Inventories

of

Major

Air

Pollutants

(PKU-FUEL).

http://inventory.pku.edu.cn/home.html. Accessed June 6, 2018.

515

19. Smith, K. R., In praise of power. Science 2014, 345, 603-603.

516

20. Chandrasekaran, S. R.; Hopke, P. K.; Newtown, M.; Hurlbut, A., Residential-Scale Biomass Boiler

517 518

Emissions and Efficiency Characterization for Several Fuels. Energ. Fuel 2013, 27, 4840-4849. 21. Cai, J.; Jiang, Z., Changing of energy consumption patterns from rural households to urban households

519

in China: An example from Shaanxi Province, China. Renew. Sust. Energ. Rev. 2008, 12, 1667-1680.

520

22. Sovacool, B. K., Conceptualizing urban household energy use: Climbing the "Energy Services Ladder".

521

Energ. Policy 2011, 39 (3), 1659-1668.

522

23. Wu, J. W.; Hou, B. D.; Ke, R. Y.; Du, Y. F.; Wang, C.; Li, X. Z.; Cai, J. W.; Chen, T. Q.; Teng, M. X.;

523

Liu, J.; Wang, J. W.; Liao, H., Residential Fuel Choice in Rural Areas: Field Research of Two

524

Counties of North China. Sustainability 2017, 9, 609.

525

24. Shen, G.; Lin, W.; Chen, Y.; Yue, D.; Liu, Z.; Yang, C., Factors affecting the adoption and sustainable

526

use of clean fuels and cookstoves in China-a Chinese literature review. Renew. Sust. Energ. Rev. 2015,

527

51, 741-750.

528

25. Rehfuess, E.; Puzzolo, E.; Stanistreet, D.; Pope, D.; Bruce, N., Enablers and barriers to large scale

529

uptake of improved solid fuel stoves: a systematic review. Environ. Health Perspect. 2014, 122, (2)

530

120-130

531 532 533 534

26. Wu, W. H.; Zhu, H. Y.; Qu, Y. H.; Xu, K. Y., Regional Disparities in Emissions of Rural Household Energy Consumption: A Case Study of Northwest China. Sustainability 2017, 9, 726. 27. Mei, S.; Yaxu, Z.; Xuguang, H., Status and Development Suggestions of Wind Heating in Northern China. Energy Procedia 2017, 142, 105-110.

535

28. Duan, X.; Wang, B.; Zhao, X.; Shen, G.; Xia, Z.; Huang, N.; Jiang, Q.; Lu, B.; Xu, D.; Fang, J.,

536

Personal inhalation exposure to polycyclic aromatic hydrocarbons in urban and rural residents in a

537

typical northern city in China. Indoor Air 2014, 24, 464-473.

538 539 540 541

29. Chen, Q.; Yang, H.; Liu, T.; Zhang, L., Household biomass energy choice and its policy implications on improving rural livelihoods in Sichuan, China. Energ. Policy 2016, 93, 291-302. 30. Wang, R.; Jiang, Z., Energy consumption in China's rural areas: A study based on the village energy survey. J. Cleaner Product. 2017, 143, 452-461.

542

31. Duan, X. L.; Jiang, Y.; Wang, B. B.; Zhao, X. G.; Shen, G. F.; Cao, S. Z.; Huang, N.; Qian, Y.; Chen,

543

Y. T.; Wang, L. M., Household fuel use for cooking and heating in China: Results from the first



Page 22 of 23

544

Chinese Environmental Exposure-Related Human Activity Patterns Survey (CEERHAPS). Appl.

545

Energy 2014, 136, 692-703.

546 547 548 549

32. World

Health

Organization

(WHO).

Survey

harmonization

process.

http://www.who.int/airpollution/household/survey-harmonization/en/. Accessed Nov. 2018. 33. World Health Organization (WHO). New harmonized household energy survey questions finalized. http://www.who.int/airpollution/household/harmonized-survey/en/ Accessed Nov. 2018.

550

34. Shen, G. F.; Xue, M.; Chen, Y. C.; Yang, C. L.; Li, W.; Shen, H. Z.; Huang, Y.; Zhang, Y. Y.; Chen,

551

H.; Zhu, Y.; Wu, H. S.; Ding, A. J.; Tao, S., Comparison of carbonaceous particulate matter emission

552

factors among different solid fuels burned in residential stoves. Atmos. Environ. 2014, 89, 337-345.

553

35. Zhang, L. X.; Pang, M. Y.; Wang, C. B., Emergy analysis of a small hydropower plant in southwestern

554 555 556 557 558

China. Ecol. Indic. 2014, 38, 81-88. 36. Yang, C.-J.; Zhou, Y.; Jackson, R. B., China's fuel gas sector: History, current status, and future prospects. Utilities Policy 2014, 28, 12-21. 37. He, W.; Zhang, C.; Hao, R., Analysis of electricity price policy and economic growth. J. Sci. Industrial Res. 2015, 74, 11-18.

559

38. Shen, G. F.; Lin, W. W.; Chen, Y. C.; Yue, D. L.; Liu, Z. L.; Yang, C. L., Factors influencing the

560

adoption and sustainable use of clean fuels and cookstoves in China -a Chinese literature review.

561

Renew. Sustain. Energ. Rev. 2015, 51, 741-750.

562 563 564 565

39. Niu, S.W.; Hu, L.L.; Qian, Y.J.; He, B.L., Effective pathway to improve indoor thermal comfort in rural houses: Analysis of heat efficiency of elevated Kangs. J. Energy Engineer. 2015, 142, (4). 40. Zhang, H. F.; Wang, S. X.; Hao, J. M.; Wang, X. M.; Wang, S. L.; Chai, F. H.; Li, M., Air pollution and control action in Beijing. J. Cleaner Product. 2016, 112, 1519-1527.

566

41. Wang, L.; Zhang, F. Y.; Pilot, E.; Yu, J.; Nie, C. J.; Holdaway, J.; Yang, L. S.; Li, Y. H.; Wang, W. Y.;

567

Vardoulakis, S.; Krafft, T., Taking Action on Air Pollution Control in the Beijing-Tianjin-Hebei (BTH)

568

Region: Progress, Challenges and Opportunities. Int. J. Environ. Res. Pub. Health 2018, 15(2), 306.

569

42. Lei, Y.; Zhang, Q.; He, K. B.; Streets, D. G., Primary anthropogenic aerosol emission trends for China,

570

1990-2005. Atmos. Chem. Phys. 2011, 11, (3), 931-954.

571

43. Li, M.; Zhang, Q.; Kurokawa, J.; Woo, J. H.; He, K. B.; Lu, Z. F.; Ohara, T.; Song, Y.; Streets, D. G.;

572

Carmichael, G. R.; Cheng, Y. F.; Hong, C. P.; Huo, H.; Jiang, X. J.; Kang, S. C.; Liu, F.; Su, H.; Zheng,

573

B., MIX: a mosaic Asian anthropogenic emission inventory under the international collaboration

574

framework of the MICS-Asia and HTAP. Atmos. Chem. Phys. 2017, 17, (2), 935-963.

575

44. Zhang, J. K.; Sun, Y.; Liu, Z. R.; Ji, D. S.; Hu, B.; Liu, Q.; Wang, Y. S., Characterization of submicron

576

aerosols during a month of serious pollution in Beijing, 2013. Atmos. Chem. Phys. 2014, 14 (6),

577

2887-2903.

578

45. Xu, Y.; Shen, H. Z.; Yun, X.; Gao, F.; Chen, Y. L.; Li, B. G.; Liu, J. F.; Ma, J. M.; Wang, X. L.; Liu, X.

579

P.; Tian, C. G.; Xing, B. S.; Tao, S. Health effects of banning beehive coke ovens and implementation

580

of the ban in China. PNAS 2018, 115(11), 2693-2698.

581

46. Ministry of Environmental Protection, People’s Republic of China. Air pollution prevention and

582

control plan for Beijing, Tianjin and Hebei and surrounding areas in 2017.

583

http://dqhj.mee.gov.cn/zcfg/201709/t20170915_421697.shtml. Accessed June 6, 2018.


2017.

Page 23 of 23

584 585


47. Zhang, W. S.; Lu, Z. F.; Xu, Y.; Wang, C.; Gu, Y. F.; Xu, H.; Streets, D. G., Black carbon emissions from biomass and coal in rural China. Atmos. Environ. 2018, 176, 158-170.

586

48. Cheng, M. M.; Zhi, G. R.; Tang, W.; Liu, S. J.; Dang, H. Y.; Guo, Z.; Du, J. H.; Du, X. H.; Zhang, W.

587

Q.; Zhang, Y. J.; Meng, F., Air pollutant emission from the underestimated households' coal

588

consumption source in China. Sci. Total Environ. 2017, 580, 641-650.

589

49. Institute for Health Metrics and Evaluation (IHME). GBD Compare Data Visualization. Seattle, WA:

590

IHME, University of Washington, 2016. http://vizhub.healthdata.org/gbd-compare. Accessed June 6,

591

2018.

592 593

50. China’s

State

Council.

Air Pollution Prevention and

Control Action Plan.

2013.

http://www.gov.cn/zwgk/2013-09/12/content_2486773.htm. Accessed June 6, 2018.

594

51. Ministry of Ecological Environment of the People's Republic of China. The ministry of environmental

595

protection organized a special inspection on the winter heating security of Beijing, Tianjin and Hebei

596

and

597

http://www.mep.gov.cn/gkml/hbb/qt/201712/t20171224_428550.htm. Accessed June 6, 2018.

surrounding

"2+26"


cities.

2017.

Stacked Use and Transition Trends of Rural Household Energy in

Recommend Documents