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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


Absolute 0.325

Positive 0.325

Negative 0.000 Kolmogorov-Smirnov

Z 2.000


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.


CollinearityStatistics


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


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.


P(y)= 1/(1+e^(-constant-B*expenditure)) (1) y: kind of appliances P(y): probability to own the appliances


•  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) )


•  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


Absolute 0.234

Positive 0.046

Negative -0.234

Kolmogorov-Smirnov Z 1.465


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


Collinearity Statistics


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


Table 7 Result of multiple linear regressionStandardized Coefficients Sig. Collinearity Statistics


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


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.


P(y)= 1/(1+e^(-constant-B*area)) （2）Y: kind of appliances P(y): probability to own the appliances


• 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) )


•  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!

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