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Study on residential carbon lock-in
GAO Ran and ZHANG Zhen �
(Department of Environmental Science and Engineering, Fudan University) �
Contents
• Introduction - background and significance - concept of residential carbon lock-in • Review of literature - carbon lock-in - influence factors • Empirical study - data description - influence of income - influence of area • Conclusions
Background and significance
carbon dioxide emissions per unit of GDP of 2020 drop by 40% to 45% compared with that of 2005
Background and significance
• With the industrial restructuring and economic transformation,
Lock-in effect
Concept of residential carbon lock-in
• Socio-economic condition makes residential energy consumption and carbon emissions at a high level through the formation of a certain mode of lifestyle, which greatly weakens the effectiveness of daily energy-saving behaviors and energy-efficient appliances.
Electricity billHousehold expenditure
Housing area
Literature on carbon lock-in
• Carbon lock-in - production sector by Unruh - consumption sector by Jackson, Druckman,
Maréchal • Few researches on the formation process of
the lock-in effect in residential area
Literature on influence factors
• Influence of household appliances by Aydinalp et al., Lv, Ning et al.
• Distinguish between necessities and luxuries • Influence of housing area and income by
Holden and Norland, Ewing and Rong, Liu and Sweeney, Huo et al., Druckman and Jackson
• Separate and study respectively
Data description
• Electricity bill • Household expenditure • Housing area • Ownership of appliances “Survey Data of Shanghai Residents’ Carbon Consumption in 2013”
Table 1 Comparison of housing area between Type 1 and Type 5
Housing Area
Most ExtremeDifferences
Absolute 0.100
Positive 0.100
Negative -0.086 Kolmogorov-Smirnov
Z 0.635
Asymp. Sig. (2-tailed) 0.815
Type 1: villa, townhouse and ohira layer Type 5: farmer’s detached villa
Table 2 Comparison of annual electricity bill between Type 1 and Type 5
Annual Electricity Bill
Most ExtremeDifferences
Absolute 0.325
Positive 0.325
Negative 0.000 Kolmogorov-Smirnov
Z 2.000
Asymp. Sig. (2-tailed) 0.001
Type 1: villa, townhouse and ohira layer Type 5: farmer’s detached villa
Appliances selection
• annual electricity bill as dependent variable • ownership of appliances as dummy
variable • multiple linear regression
Table 3 Result of multiple linear regressionStandardized Coefficients Sig.
CollinearityStatistics
Beta Tolerance VIF (constant) 0.730
central air conditioning 0.295 0.000*** 0.594 1.684 wall-mounted air
conditioning 0.113 0.017** 0.867 1.154
electric fan 0.041 0.371 0.918 1.089 central ventilation system -0.087 0.158 0.502 1.993
floor heating 0.130 0.009*** 0.773 1.294 fan heater -0.069 0.165 0.779 1.284 small solar 0.065 0.177 0.813 1.230
electric blanket -0.021 0.662 0.866 1.154 electric oven 0.127 0.017** 0.677 1.477
balneal electric radiator -0.051 0.301 0.794 1.259 side-by-side combination
refrigerator 0.098 0.092* 0.561 1.783
*Significant at 10% significance level; **Significant at 5% significance level; ***Significant at 1% significance level.
Table 3 Result of multiple linear regressionStandardized Coefficients Sig.
CollinearityStatistics
Beta Tolerance VIF (constant) 0.730
central water purifier 0.020 0.728 0.591 1.692 dishwasher 0.217 0.000*** 0.531 1.884 disinfection 0.011 0.829 0.729 1.371
sweeping machine -0.127 0.017** 0.678 1.475 gas water heater 0.020 0.706 0.692 1.444
electric water heater 0.019 0.700 0.759 1.318 stereo -0.095 0.087* 0.615 1.626
home theater 0.013 0.822 0.561 1.782 video game console 0.084 0.092* 0.780 1.282
projector 0.044 0.418 0.645 1.550 double-door refrigerator 0.002 0.975 0.671 1.491
fitness equipment 0.059 0.330 0.522 1.916 aquarium 0.201 0.000*** 0.754 1.326
*Significant at 10% significance level; **Significant at 5% significance level; ***Significant at 1% significance level.
Influence of expenditure
• ownership of appliances as dependent variable • monthly expenditure as independent variable • logistic regression
Table 4 Result of logistic regressionPercentage Correct B Constant
Sig. Expenditure Constant
central air conditioning 98.2 0.000308 -5.24081 0.000787** 3.77E-15
wall-mounted air conditioning 87.2 1.68E-05 1.857314 0.803759 5.7E-11
floor heating 93.4 6.4E-05 -2.89358 0.294421* 1.17E-18 electric oven 89.0 0.000219 -2.91806 0.000707** 1.46E-19 dishwasher 99.1 0.000119 -5.21034 0.197925* 1.32E-11
sweeping machine 97.3 0.000162 -4.42213 0.011739** 1.92E-18 aquarium 97.0 8.23E-05 -3.79899 0.264089* 4.93E-17 central air
conditioning+floor heating
99.5 3.88E-05 -5.58434 0.138629* 1.03E-37
*Significant at 30% significance level; **Significant at 5% significance level.
Influence of expenditure
P(y)= 1/(1+e^(-constant-B*expenditure)) (1) y: kind of appliances P(y): probability to own the appliances
Influence of expenditure
• P(central air conditioning) = 1/(1+e^(5.24081-0.000308*expenditure) )
• P(floor heating) = 1/(1+e^(2.89358-0.000064*expenditure) ) • P(electric oven) = 1/(1+e^(2.91806-0.000219*expenditure) ) • P(dishwasher) = 1/(1+e^(5.21034-0.000119*expenditure) ) • P(sweeping machine) = 1/(1+e^(4.42213-0.000162*expenditure) ) • P(aquarium) = 1/(1+e^(3.79899-0.0000823*expenditure) ) • P(central air conditioning + floor heating) = 1/
(1+e^(5.58434-0.0000388*expenditure) )
Influence of expenditure
• P(central air conditioning)=0.5,expenditure=17016
• P(floor heating)=0.5,expenditure=45212
• P(electric oven)=0.5,expenditure=13324
• P(dishwasher)=0.5,expenditure=43784
• P(sweeping machine)=0.5,expenditure=27297
• P(aquarium)=0.5,expenditure=46160
• P(central air conditioning + floor heating)=0.5,expenditure=143926
1.37% of Type 1 and Type 5
Table 5 Comparison of monthly expenditure between Type 1 and Type 2
Levene’s Test for Equality of Variances
t-test for Equality of Means
F Sig. t df Sig.(2-tailed)
Equal variances assumed 0.098 0.755 -1.173 465 0.241
Equal variances not assumed -1.128 35.289 0.267
Type 1: villa, townhouse and ohira layer Type 2: high-rise and small high-rise
Table 6 Comparison of winter electricity bill between Type 1 and Type 2
Winter Electricity Bill
Most ExtremeDifferences
Absolute 0.234
Positive 0.046
Negative -0.234
Kolmogorov-Smirnov Z 1.465
Asymp. Sig. (2-tailed) 0.027
Type 1: villa, townhouse and ohira layer Type 2: high-rise and small high-rise
Appliances selection
• winter electricity bill as dependent variable • ownership of appliances as dummy variable • multiple linear regression
Table 7 Result of multiple linear regressionStandardized Coefficients Sig.
Collinearity Statistics
Beta Tolerance VIF (constant) 0.009
central air conditioning 0.077 0.212 0.468 2.135
wall-mounted air conditioning -0.021 0.721 0.512 1.955
electric fan 0.019 0.663 0.936 1.068 central ventilation -0.060 0.216 0.763 1.310
floor heating 0.179 0.000*** 0.782 1.279 fan heater 0.084 0.061* 0.896 1.116 small solar 0.008 0.851 0.889 1.124
electric blanket -0.148 0.001*** 0.928 1.078 electric oven 0.140 0.002*** 0.874 1.144
balneal electric radiator -0.115 0.010** 0.918 1.089 side-by-side combination
refrigerator 0.108 0.054* 0.580 1.724
*Significant at 10% significance level; **Significant at 5% significance level; ***Significant at 1% significance level.
Table 7 Result of multiple linear regressionStandardized Coefficients Sig. Collinearity Statistics
Beta Tolerance VIF (constant) 0.009
central water purifier 0.054 0.244 0.844 1.185 dishwasher 0.067 0.148 0.844 1.185 disinfection 0.069 0.132 0.861 1.162
sweeping machine -0.083 0.070* 0.856 1.169 gas water heater -0.002 0.965 0.839 1.192
electric water heater 0.063 0.185 0.812 1.231 stereo -0.057 0.246 0.751 1.331
family theater 0.031 0.525 0.768 1.303 video game console 0.125 0.007*** 0.841 1.188
projector -0.077 0.107 0.800 1.250 double-door refrigerator 0.056 0.306 0.608 1.644
fitness equipment 0.041 0.377 0.847 1.181 aquarium 0.044 0.338 0.842 1.187
*Significant at 10% significance level; **Significant at 5% significance level; ***Significant at 1% significance level.
Influence of housing area
• ownership of appliances as dependent variable • housing area as independent variable • logistic regression
Table 8 Result of logistic regression
Percentage Correct
B Constant Sig.
area Constant
Floor heating 95.8 0.015049 -5.22453 4.73E-05** 2.39E-17
Electric blanket 50.6 0.000933 -0.10783 0.673384 0.710806
Electric oven 71.2 0.00644 -1.68856 0.007613** 1.98E-07
balneal electric radiator
78.8 0.002083 1.056039 0.467639 0.004258
video game console
71.8 0.004253 -1.47223 0.071882* 3.65E-06
*Significant at 10% significance level; **Significant at 1% significance level.
Influence of housing area
P(y)= 1/(1+e^(-constant-B*area)) (2)Y: kind of appliances P(y): probability to own the appliances
Influence of housing area
• P(floor heating)= 1/(1+e^(5.22453-0.015049*area) ) • P(electric oven)= 1/(1+e^(1.68856-0.00644*area) ) • P(video game console)= 1/
(1+e^(1.47223-0.004253*area) )
Influence of housing area
• P(floor heating)=0.5,area=347 • P(electric oven)=0.5,area=262 • P(video game console)=0.5,area=346
Conclusions
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
90.0%
100.0%
Type 1 Type 5 Type 2
central air conditioning
wall-mounted air conditioning
floor heating
income level
housing area
Conclusions
Conclusions
• When the area constraint is removed, Type 2 joins in the high-energy. • When the income constraint is removed, Type 5 joins in the high-energy.
Conclusions
• It is vital to propose a wealthy and frugal lifestyle fit for Chinese people.
Thanks for your listening!