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Electricity consumption, education expenditure and economic growth
in Chinese cities
Zheng Fang, Yang Chen*
Presented at IAEE, Singapore
June 19-21, 2017
1
Outline of the paper
• Introduction
—motivation
—research questions
—contribution
• Data and descriptive evidence
• Theoretical framework and empirical methods
• Main results
• Conclusion
2
Motivation
• Electricity is the fastest-growing form of end-use energy globally (International Energy Outlook, 2016). And the growth rate of power generation and consumption in China has been the highest in the world.
• Cities play important roles in shaping energy consumption and carbon emission, the growth-electricity nexus is unexplored mostly due to data unavailability.
• As the largest GHG emitter since 2007, 80% of emissions are generated from urban economic activities and this share keeps rising with China’s rapid urbanization and industrialization process taking place in different tiers of cities (Liu, 2015).
3
Data Source: Wind Info
Electricity Consumption: Total
Electricity Consumption: Industry
Electricity Consumption: Manufacturing
Electricity Consumption: Production and Supply of Electricity, Gas and Water
Electricity Consumption: Production and Supply of Electricity and Heat
Electricity Consumption: Agriculture, Forestry, Animal Husbandry, Fishery and Water Conservancy
Electricity Consumption: Construction
1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 201519930 0
7000 7000
14000 14000
21000 21000
28000 28000
35000 35000
42000 42000
49000 49000
56000 56000
100 million kwh 100 million kwh100 million kwh 100 million kwh
Figure 1: The composition of electricity consumption by sector (1994-2014)
4
Data Source: Wind Info
Power Generation: India Power Generation: USA Power Generation: Russia Power Generation: China Power Generation: Japan
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 201419900 0
800 800
1600 1600
2400 2400
3200 3200
4000 4000
4800 4800
5600 5600
Terawatt-hours Terawatt-hoursTerawatt-hours Terawatt-hours
Figure 2: The top five power generators in the world (1990-2012)
5
Data Source: Wind Info
Electricity Consumption: China Electricity Consumption: USA Electricity Consumption: World Total
1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 201319890 0
3000 3000
6000 6000
9000 9000
12000 12000
15000 15000
18000 18000
billion kwh billion kwhbillion kwh billion kwh
Figure 3: A comparison of China, U.S. and the World in total electricity consumption (1990-2012)
6
Data Source: Wind Info
GDP: YoY Electricity Consumption:YoY
1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 20141994
4 4
6 6
8 8
10 10
12 12
14 14
16 16
% %% %
Figure 4: The co-movement of output and electricity consumption
7
Year Method Model Finding Note
Cross-country studies including China
Chen et al.
(2007)
1971-
2001
Pedroni panel
cointegration;
panel VECM
bivariate Short run:
GDP → E;
Long run:
GDP ↔ E.
China, Hong Kong,
Indonesia, India, Korea,
Malaysia, Philippines,
Singapore, Taiwan,
Thailand
Niu et al.
(2011)
1971-
2005
Pedroni panel
cointegration;
panel VECM
bivariate
(pairs of GDP,
E, CO2)
Developing countries:
GDP → E
Developed countries:
GDP ↔ E
China, Australia, Japan,
New Zealand, Korea,
Indonesia, Thailand, India,
accounting for coal, oil
and gas,
Cowan et
al. (2014)
1990-
2010
Konya (2006)
bootstrap panel
causality
Tri-variate
(+CO2)
China: GDP o E Brazil, Russia, India,
China and South Africa
Karanfil
and Li
(2015)
1980-
2010
Pedroni panel
cointegration;
panel VECM
Four-variate
(+electricity
net import,
urbanization)
East Asia and Pacific
region:
GDP → E
(mixed results for
other sub-samples)
160 countries
Table 1: A brief survey on the electricity-growth nexus literature in China
8
Exclusively about China
-Time series
Shiu and
Lam
(2004)
1971-
2000
Johansen
cointegration;
VECM
bivariate Short run:
GDP ← E
Long run:
GDP ← E
-
Wolde-
Rufael
(2004)
1952-
1999
Toda and
Yamamoto (1995)
causality
bivariate GDP ← E Shanghai; considering
total energy, coal,
electricity, coke, and oil.
Yuan et
al. (2007)
1978-
2004
Johansen
cointegration;
VECM
bivariate GDP ← E -
Yuan et
al. (2008)
1963-
2005
Johansen
cointegration;
VECM
Four-variate
(+K, L)
Short run:
GDP ← E
Long run:
GDP ↔ E
Considering total
energy, coal, oil, and
electricity
Chang
(2010)
1981-
2006
Johansen
cointegration;
VECM
Tri-variate GDP ← E Considering crude oil,
natural gas, coal,
electricity
Yalta and
Cakar
(2012)
1971-
2007
Vinod (2004)
meboot
bivariate
Four-variate
(+K, L)
GDP o E Considering total
energy, electricity, fossil
fuel, combustible
renewables and waste,
alternative and nuclear.
Long et al.
(2015)
1952-
2012
Johansen
cointegration;
Granger causality
five-variate
(+K,L,CO2)
GDP ↔ E Considering coal, oil,
gas, electricity, hydro,
and nuclear energy.
9
-Panel data
Herrerias
et al.
(2013)
1995-
2009
Panel
cointegration,
panel DOLS,
Dumitrescu and
Hurlin (2012)
short-run
causality, Canning
and Pedroni
(2008) long-run
causality
bivariate Short run:
GDP ↔ E
Long run:
GDP ↔ E
Considering total
energy, coal, oil, coke,
and electricity.
10
Research questions
• What are the impacts of electricity consumption and human capital on city economic growth?
• Are the impacts homogeneous across regions and city tiers?
• What are the causal relations between electricity consumption and growth of cities?
11
Contribution
• Focus on the growth-electricity nexus exclusively in China, accounting for commonly observed cross-sectional dependence and heterogeneity of causal relations using city-level panel data.
• Adopt the multi-variable framework in the energy-augmented neoclassical production function including human capital.
• Apply the continuously updated fully-modified estimator to investigate the magnitude and elasticity of the covariates with regard to economic growth and extend the analysis to different groups of cities classified by three macro regions and by city tiers.
12
Data and descriptive evidence
Mean S.D. Min Max N
lnGDP 10.224 0.814 5.307 13.018 269*10
lnK 9.603 0.921 4.412 11.831 269*10
lnEdu 6.223 0.837 0.277 9.055 269*10
lnElectricity 7.583 1.134 3.043 11.013 269*10
East China (n=100*10) Middle China (n=98*10) West China (n=71*10)
Mean S.D. Mean S.D. Mean S.D.
lnGDP 10.644 0.695 10.054 0.711 9.867 0.848
lnK 9.973 0.757 9.460 0.893 9.280 0.993
lnEdu 6.559 0.777 6.056 0.793 5.978 0.828
lnElectricity 8.041 0.807 7.454 1.021 7.117 1.410
Tier 1 & 2 (n=39*10) Tier 3 (n=101*10) Tier 4 (n=129*10)
Mean S.D. Mean S.D. Mean S.D.
lnGDP 11.015 0.650 10.464 0.618 9.797 0.734
lnK 10.337 0.653 9.793 0.766 9.233 0.925
lnEdu 6.728 0.828 6.341 0.767 5.977 0.805
lnElectricity 8.117 0.757 7.998 0.752 7.097 1.269
13
0
1000
2000
3000
4000
5000
6000
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
East Middle West
0
1000
2000
3000
4000
5000
6000
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Tier 1 Tier 2 Tier 3 Tier 4
Figure 6: Electricity consumption per capita for 2003-2012 by regions and city tiers (kWh)
14
-
20,000
40,000
60,000
80,000
100,000
120,000
140,000
160,000
180,000
200,000
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Tier 1 Tier 2 Tier 3 Tier 4
-
20,000
40,000
60,000
80,000
100,000
120,000
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
East Middle West
Figure 7: Output per capita for 2003-2012 by regions and city tiers (yuan)
15
Beijing
Tianjin
Shanghai
Guangzhou City
Shenzhen City
.14
.16
.18
.2.2
2
grow
th o
f out
put (
%)
.07 .08 .09 .1 .11growth of electricity consumption (%)
Shijiazhuang City
Tangshan City
Taiyuan City
Hohhot City
Baotou City
Shenyang CityDalian City
Changchun City
Harbin City
Nanjing City
Wuxi City
Suzhou City
Hangzhou City
Ningbo City
Hefei City
Fuzhou City
Xiamen CityQuanzhou City
Nanchang City
Ji'nan City
Qingdao CityYantai CityZhengzhou City
Wuhan City
Changsha City
Foshan City
Dongguan City
Nanning City
Chongqing
Chengdu City
Guiyang City
Kunming City
Xi'an City
Lanzhou City
Urumqi City
.1.1
5.2
.25
grow
th o
f out
put (
%)
0 .05 .1 .15 .2 .25growth of electricity consumption (%)
Figure 10: Annual growth rate of output and electricity consumption for Tier-1 and Tier- 2 cities (2004-2012)
18
Theoretical framework and empirical methods
• 𝑌𝑡 = 𝐴𝐾𝑡𝛼 𝐿𝑡
1−𝛼−𝛽 𝐻𝑡𝛽 𝐸𝑡
𝛾
• 𝑦𝑡 = 𝑐 + 𝛼𝑘𝑡 +𝛽ℎ𝑡 +𝛾𝑒𝑡
• The cross-sectional dependence test:
— Frees (1995): 𝑁
2
𝑁 𝑁−1 𝑟 𝑖𝑗
𝑁𝑗=𝑖+1
𝑁−1𝑖=1 −
1
𝑇−1
𝑆𝐸(𝑄)→ 𝑁(0,1)
— Pesaran (2004): 2𝑇
𝑁(𝑁−1) 𝜌 𝑖𝑗
𝑁𝑗=𝑖+1
𝑁−1𝑖=1 → 𝑁(0,1)
19
• Panel unit root test: the cross-sectionally augmented IPS test (Pesaran, 2007);
• Pedroni’s panel cointegration test (Pedroni, 1999, 2004);
• The continuously-updated and fully-modified estimator (Cup-FM) developed by Bai et al. (2009);
• The heterogeneous panel Granger non-causality test (Dumitrescu and Hurlin, 2012):
—𝐻0: no Granger causal relation for any units of the panel;
—𝐻1: some of the units have a causal link and some other units do not have a causal link among the variables examined.
20
𝑦𝑖,𝑡 = 𝛼𝑖 + 𝛾𝑖,𝑘𝑦𝑖,𝑡−𝑘
𝐾
𝑘=1
+ 𝛽𝑖,𝑘𝑥𝑖,𝑡−𝑘
𝐾
𝑘=1
+ 𝜀𝑖,𝑡
• The steps to test whether the null hypothesis that 𝑥𝑖,𝑡 do not Granger cause 𝑦𝑖,𝑡, i.e., 𝛽𝑖,𝑘 = 0 are summarized below when there is cross-sectional dependence:
Step 1: Estimate the parameters for each unit 𝑖 and compute
𝑍𝑁,𝑇 =𝑁
2𝐾(𝑊𝑁,𝑇 − 𝐾) and 𝑍 𝑁 =
𝑁[𝑊𝑁,𝑇−𝐸(𝑊 𝑖,𝑇)]
𝑉𝑎𝑟(𝑊 𝑖,𝑇) where 𝑊𝑁,𝑇
is the average of N individual Wald statistics 𝑊 𝑖,𝑇 for each cross-section unit. Step 2: Estimate the model under the null hypothesis that 𝛽𝑖,𝑘 = 0 and obtain 𝛼 𝑖, 𝛾 𝑖,𝑘 and residuals 𝜀 𝑖,𝑡. Step 3: Resample the residuals with replacement and construct a series {𝑦 𝑖,𝑡} by 𝑦 𝑖,𝑡 = 𝛼 𝑖 + 𝛾 𝑖,𝑘𝑦𝑖,𝑡−𝑘
𝐾𝑘=1 + 𝜀 𝑖,𝑡.
21
Step 4: Estimate the model using the resampled data 𝑦 𝑖,𝑡 and compute the test statistics.
Step 5: Repeat Step 3 and Step 4 a number of times and compute the empirical critical values from the distribution of test statistics at a given significance level.
Step 6: Compare test statistic in Step 1 with empirical critical values obtained in Step 5 and make a decision.
22
Test China East China Middle China West China
Frees’(1995) Q 25.820*** 8.823*** 8.011*** 5.831***
Pesaran (2004) 45.464*** 15.763*** 18.449*** 15.766***
N 269 100 98 71
Table 2: Cross-sectional dependence test results
23
Level First difference
Samples Intercept Intercept and trend Intercept Intercept and trend
China lnGDP -1.958 -1.982 -2.461** -2.735*
lnK -2.114 -2.093 -2.615*** -3.061***
lnEdu -2.291* -2.388 -2.977*** -3.284***
lnElectricity -2.114 -2.461 -2.897*** -3.058***
East
China
lnGDP -1.802 -2.220 -2.633*** -2.869**
lnK -2.166 -2.278* -2.818*** -3.108***
lnEdu -2.275* -2.263 -2.845*** -2.962**
lnElectricity -2.042 -2.609 -3.047*** -3.409***
Middle
China
lnGDP -2.072 -1.827 -2.421** -2.553
lnK -1.950 -1.823 -2.413** -3.007**
lnEdu -2.261* -2.130 -2.901*** -3.234***
lnElectricity -1.950 -2.308 -2.649*** -2.667
West
China
lnGDP -2.202 -2.398 -2.857*** -3.174***
lnK -2.276* -2.033 -2.463** -2.935**
lnEdu -2.187 -2.291 -2.941*** -3.388***
lnElectricity -2.011 -2.156 -2.549*** -2.383
Table 3: Panel unit root test results with cross-sectional dependence
24
Test China East China Middle China West China
Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 Model 1 Model 2
Panel v -4.633 -3.983 -4.406 -2.345 -2.587 -2.930 -0.186 -1.452
Panel rho 13.767 7.572 8.476 4.319 8.451 4.968 6.751 3.736
Panel PP -28.596*** -20.686*** -15.958*** -12.635*** -16.139*** -10.621*** -18.908*** -13.336***
Panel ADF -14.649*** -13.091*** -8.331*** -8.284*** -8.083*** -6.610*** -9.551*** -8.112***
Group rho 19.756 14.993 12.017 8.667 11.865 9.429 10.252 7.820
Group PP -47.852*** -32.874*** -27.038*** -21.022*** -30.956*** -18.006*** -24.685*** -17.885***
Group ADF -16.133*** -13.622*** -10.528*** -9.937*** -9.618*** -5.967*** -7.608*** -7.710***
No. of obs. 2690 1000 980 710
Table 4: Pedroni’s panel cointegration test
25
Test China
East China Middle China West
China
Coefficient t-
statistics Coefficient
t-
statistics Coefficient
t-
statistics Coefficient
t-
statistics
lnK 0.077*** 12.996 0.039*** 4.260 0.052*** 5.369 0.131*** 12.979
lnEdu 0.072*** 14.540 0.147*** 17.203 0.028*** 3.143 0.076*** 10.586
lnElectricity 0.055*** 12.068 0.081*** 8.560 0.088*** 10.813 0.011 1.703
Table 5: Panel estimation results Cup-FM
26
Test China
East China Middle China West China
Test
statistic p-value
Test
statistic p-value
Test
statistic p-value
Test
statistic p-value
Electricity→GDP 1.158 0.247 1.734 0.083 -0.623 0.533 0.929 0.353
GDP→Electricity 6.717 0.000 2.328 0.020 6.089 0.000 3.157 0.002
Electricity→Edu 5.350 0.000 5.417 0.000 1.441 0.150 2.292 0.022
Edu→Electricity 5.791 0.000 0.434 0.664 5.663 0.000 4.103 0.000
Table 6: Panel Granger non-causality test results
27
Test Tier 1&2 Tier 3 Tier 4
Frees’(1995) Q 6.324*** 9.976*** 10.553***
Pesaran(2004) 5.056*** 16.462*** 25.389***
N 39 101 129
Table A2: Cross-sectional dependence test results
28
Level First difference
Samples Intercept Intercept and trend Intercept Intercept and trend
Tier 1 &
2
lnGDP -1.409 -1.659 -2.154 -2.676*
lnK -2.019 -2.109 -2.442** -2.591
lnEdu -1.906 -1.933 -2.738*** -3.014**
lnElectricity -2.098 -2.204 -2.531** -2.554
Tier 3 lnGDP -1.783 -1.853 -2.279* -2.443
lnK -2.165 -2.069 -2.549*** -2.849**
lnEdu -2.281* -2.390 -2.981*** -3.093***
lnElectricity -1.975 -2.382 -2.785*** -3.069***
Tier 4 lnGDP -2.255* -2.218 -2.700*** -2.973**
lnK -2.076 -2.076 -2.622*** -3.031***
lnEdu -2.419** -2.452 -3.147*** -3.487***
lnElectricity -2.117 -2.419 -2.913*** -3.018**
Table A3: Panel unit root test results with cross-sectional dependence
29
Test Tier 1 & 2 Tier 3 Tier 4
Model 1 Model 2 Model 1 Model 2 Model 1 Model 2
Panel v -1.842 -1.645 -4.191 -2.614 -1.726 -2.525
Panel rho 5.676 3.08 8.163 4.63 9.561 5.139
Panel PP -8.798*** -5.745*** -19.183*** -12.198*** -19.316*** -16.172***
Panel ADF -3.956*** -3.705*** -10.183*** -7.847*** -9.941*** -9.996***
Group rho 7.33 5.647 12.15 8.963 13.747 10.615
Group PP -19.320*** -10.192*** -30.992*** -20.614*** -31.054*** -23.627***
Group ADF -7.614*** -3.719*** -10.440*** -9.229*** -9.872*** -9.460***
No. of obs. 390 1010 1290
Table A4: Pedroni’s panel cointegration test
30
Test Tier 1 & 2 Tier 3 Tier 4
Coefficient t-statistics Coefficient t-statistics Coefficient t-statistics
lnK 0.207*** 13.208 -0.007 -0.705 0.090*** 11.296
lnEdu 0.131*** 13.293 0.033*** 3.336 0.053*** 8.726
lnElectricity 0.181*** 12.091 0.046*** 5.452 0.056*** 9.732
Table A5: Panel estimation results Cup-FM
31
Test Tier 1 & 2 Tier 3 Tier 4
Test
statistic
p-
value
Test
statistic p-value
Test
statistic p-value
lnElectricity→lnGDP 1.363 0.173 0.340 0.734 0.622 0.534
lnGDP→lnElectricity 2.168 0.030 3.697 0.000 5.237 0.000
lnElectricity→lnEdu 3.604 0.000 3.270 0.001 2.850 0.004
lnEdu→lnElectricity 0.805 0.421 2.640 0.008 5.584 0.000
Table A6: Panel Granger non-causality test results
32
Conclusion
• For China as a whole physical and human capital have similar positive impacts as electricity consumption on local economic growth.
• Electricity consumption plays a dominant role to boost growth in the Center; human capital contributes most to growth in the East; physical capital facilitates growth in the West.
• We find a uni-directional causal relation running from economic growth to electricity consumption in central and western China and a feedback effects in eastern China.
33
Conclusion
• Electricity granger causes education expenditure in some eastern Chinese cities and a reverse relation is observed for cities in Middle China, while for western cities a bi-directional causal link is found.
• National and decentralized local policies and targets should be reexamined and coordinated across government agencies.
34