Energy consumption in rural China:
Drivers behind household energy demand in
Shanxi province, China
Mette Wik (UMB) and Kristine Korneliussen (CICERO)
Final workshop of LDE project
7 May 2013
UMB School of economics and business
UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP
Introduction
 China accounts for almost half of global energy demand
 32% of the population use some form of traditional biomass
energy
 Shanxi province:
– Major coal provider
– Fuelwood use is negligible
– Other types of biomass (crop residues and straw) and coal are
important energy sources.
 Use of biomass/coal for cooking and heating can have severe
effects on:
2
– Environment
– Health
– Climate
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THEORIES ON HOUSEHOLD ENERGY USE
1. Poverty-environment hypothesis
2. Energy ladder hypothesis
3. Multiple fuel model
4. Environmental Kuznets Curve
 How does energy use respond to increased income?
– Biomass use will decrease due to substitution by other energy
sources (theories 1 and 2)
– New fuels will come in addition to old sources of energy (theory 3).
3
– Unclear at what level of income the turning point for emissions will
occur (theory 4).
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EMPIRICAL FINDINGS
 Mixed evidence
– Income important for total energy use
– Income need to increase substantially in order for biomass use to
decrease
 Many other factors than income are also found important
– Geography/topography and climate
– Infrastructure and access to markets
– Fuel prices
– Access to land and distance to forests
– Household size and education level
4
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GAPS AND LIMITATIONS IN EXISTING LITERATURE
 Energy transition and determinants of fuel use not fully
understood
 No general consensus regarding which factors determine fuel
switching.
 No similar studies from Shanxi – or from other coal rich areas in
China (???)
5
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DATA
 Questionnaire on rural energy consumption, 2010.
 Rural household survey, 2010
 10 different villages in Shanxi
 1000 households
 Excluded 3 villages (no information on bioenergy use)
 Excluded households with no income information and/or
zero household members.
6
 End up using a dataset of 571 households
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income
holds
(Yuan)
571
Village 1
101
Village 2
104
Village 3
88
Village 6
83
Village 7
95
Village 8
60
Village 9
40
25,521
(20,861)
23,325
(13,954)
17,866
(9,571)
29,200
(21,091)
17,528
(10,931)
43,765
(31,254)
27,251
(20,510)
13,537
(9,312)
Transfers
house-
Property rent
sample
Houshold
Wages
Total
No of
Family business
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7
INCOME SOURCES IN RURAL SHANXI
important
59%
36%
2%
3%
Maize
37%
88%
8%
2%
2%
Maize
42%
48%
45%
6%
Maize
41%
40%
55%
3%
1%
Maize
35%
21%
70%
5%
4%
Maize
13%
74%
22%
1%
2%
Fruits
64%
69%
29%
2%
Fruits
53%
29%
60%
1%
Maize
17%
10%
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Most
% income
from
crop
agriculture
Land area
(Mu)
9.3
(7.3)
9.4
(6.5)
11.1
(8.3)
9.1
(8.5)
11.8
(5.9)
10
(7.1)
4.9
(5.1)
4.3
(1.6)
69%
4%
27%
7%
29%
64%
Cooking +
395 kgce
Heating water
75
Lighting +
Biomass
appliances
(st.d)
Lighting
used pr year
Cooking +
using
heating
Mean amount
Heating
% of hhs
Cooking
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ENERGY USE
(580)
Coal
74
1120 kgce
(720)
Electricity
100
75 kgce
22%
78%
(62)
Gas
8
26
13 kgce
91%
(27)
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9%
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Energy sources used for cooking
Households
using:
%
Only one
source:
21
Two
sources:
Three
sources:
39
36
Energy source(s) used for cooking
Total
Bio
Coal
El
Gas
85
22
9
2
Bio/Coal
Coal/El
Bio/El
Bio/Gas
Gas/El
Coal/Gas
68
65
49
26
11
3
Bio/Coal
Bio/Coal/
Bio/El/
Coal/El/
/El
Gas
Gas
Gas
119
55
24
5
118
222
203
Four
9
sources
24
4
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Per capita energy use in income quartiles
Average per
Income quartiles
capita
Total Energy
yearly
Use
income
Kgce
Yuan
(st.d)
Bioenergy
Coal
Electricity
Gas
Kgce
Kgce
Kgce
Kgce
(st.d.)
(st.d.)
(st.d.)
(st.d.)
(st.d.)
Lowest
Medium low
Medium high
2,579
480
155
305
17
2
(809)
(228)
(216)
(210)
(12)
(6)
4,743
448
128
298
19
3
(544)
(287)
(194)
(180)
(12)
(8)
6,950
485
104
356
21
4
(848)
(312)
(134)
(279)
(12)
(7)
15,135
665
112
512
34
7
(8,199)
(455)
(148)
(422)
(32)
(13)
10
Highest
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Empirical specification
 Biomass mainly used for cooking. Coal, electricity and
gas are alternative sources.
 No markets for biomass
 Coal and modern energy purchased in the market
 Non-separable model
 Use the following reduced form equations:
 Qbm: quantity of biomass used
 Tbm : time spent collecting the biomass
 Qc : quantity of coal used
11
 Qmf : quantity of “modern fuels”, electricity and gas, used
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Summary statistics of regression variables
Dependent
variables
Obs
Mean
Std. Dev.
Min
Max
Biomass (kgce)
549
395
580
0
10,051
549
13.3
24
0
150
549
1120
720
0
3,572
549
88
73
3.7
780
Time collecting
biomass
(days/year)
Coal (kgce)
Modern fuels
(kgce)
12
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Summary statistics of regression variables (cont)
Explanatory variables
Obs
Mean
Std. Dev.
Min
Max
548
6.7
2.1
0
16
Household size
549
3.7
1.5
1
9
Labour force
549
2.8
1.4
0
7
549
9.2
7.3
0
50
549
15,275
20,327
0
161,595
Wage income (Yuan)
549
9,094
11,072
0
92,000
Transfer income (Yuan)
549
687
1,241
0
20,400
549
23,292
38,406
0
310,000
Mean education (years)
Land area (Mu)
Operating income (Yuan)
13
House value (Yuan)
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Expected signs of explanatory variables
Explanatory variables
Time
Biomass
consumption
collecting
biomass
Coal
Modern fuel
consumption consumption
Mean education
-
-
+
+
Household size
+
+
+
+
+
+
?
?
Land area
+
+
?
?
Household income
-
-
+
+
House value
-
-
+
+
Labour force/household
size
14
Dependent variables
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RESULTS
 Biomass consumption
 Time used to collect biomass
 Coal consumption
 Modern energy consumption
15
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DISCUSSION AND CONCLUSIONS
 Findings support fuel switching – higher income means less
use of biomass and higher use of coal and modern fuels.
 Biomass is not abandoned – support the multiple fuel model.
 Use of biomass is mainly decided by access to land and
agricultural resources
 Differences between villages. Access to agricultural resources
and markets seem important.
16
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