Perceptions on Smart Gas Meters in Smart Cities for Reducing the Carbon Footprint
Abstract
:1. Introduction
2. Background
2.1. Smart Meters for Low Carbon
2.2. Data Privacy
3. Investigation Methodology
3.1. Data Overview
3.2. Multiple Regression and Predictor Variables
4. Results
4.1. Hypothesis 1 Evaluation—Reducing the Bill
4.2. Hypothesis 2 Evaluation—Enviromental Impact
4.3. Hypothesis 3 Evaluation—Behaviour Change
4.4. Hypothesis 4 Evaluation —Save Energy
4.5. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Label | Dwelling | Education | Employment Status | Social Class | Age |
---|---|---|---|---|---|
1 | Apartment | No formal education | An employee | AB | 18–25 |
2 | Semi-detached house | Primary | Self-employed (with employees) | C1 | 26–35 |
3 | Detached house | Secondary to Intermediate Cert Junior Cert level | Self-employed (no employees) | C2 | 36–45 |
4 | Terraced house | Secondary to Leaving Cert level | Unemployed (actively seeking work) | DE | 46–55 |
5 | Bungalow | Third level | Unemployed (not actively seeking work) | Farmer | 56–65 |
6 | Refused | Refused | Retired | Refused | 65+ |
7 | / | / | Carer Worker | / | Refused |
Estimate Std. | Error | t Value | Pr(>|t|) | |
---|---|---|---|---|
(Intercept) | 1.074785 | 0.130390 | 8.243 | 3.92 × 10−16 |
Question401 | 0.046504 | 0.020976 | 2.217 | 0.0268 |
Question310 | 0.008176 | 0.013244 | 0.617 | 0.5371 |
Question300 | 0.059517 | 0.018823 | 3.162 | 0.0016 |
Question5418 | −0.031983 | 0.019811 | −1.614 | 0.1067 |
Estimate Std. | Error | t Value | Pr(>|t|) | |
---|---|---|---|---|
(Intercept) | 1.395896 | 0.155932 | 8.952 | 2 × 10−16 |
Question401 | 0.028856 | 0.025085 | 1.150 | 0.2502 |
Question310 | −0.004334 | 0.015838 | −0.274 | 0.7844 |
Question300 | 0.037998 | 0.022510 | 1.688 | 0.0916 |
Question5418 | −0.025360 | 0.023692 | −1.070 | 0.2846 |
Likert Scale | Frequency | Percentage | Cumulative Percentage |
---|---|---|---|
1 | 613 | 44.908 | 44.91 |
2 | 328 | 24.029 | 68.94 |
3 | 216 | 15.824 | 84.76 |
4 | 91 | 6.667 | 91.43 |
5 | 117 | 8.571 | 100.00 |
Estimate Std. | Error | t Value | Pr(>|t|) | |
---|---|---|---|---|
(Intercept) | 1.76912 | 0.22537 | 7.850 | 8.39 × 10−15 |
Question401 | −0.01575 | 0.03626 | −0.435 | 0.66398 |
Question310 | 0.06026 | 0.02289 | 2.632 | 0.00858 |
Question300 | 0.07340 | 0.03253 | 2.256 | 0.02422 |
Question5418 | −0.01784 | 0.03424 | −0.521 | 0.60255 |
Estimate Std. | Error | t Value | Pr(>|t|) | |
---|---|---|---|---|
(Intercept) | 1.31163 | 0.14910 | 8.797 | <2 × 10−16 |
Question401 | 0.01416 | 0.02399 | 0.590 | 0.5551 |
Question310 | 0.01807 | 0.01514 | 1.193 | 0.2329 |
Question300 | 0.04118 | 0.02152 | 1.913 | 0.0559 |
Question5418 | −0.02655 | 0.02265 | −1.172 | 0.2414 |
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Hurst, W.; Tekinerdogan, B.; Kotze, B. Perceptions on Smart Gas Meters in Smart Cities for Reducing the Carbon Footprint. Smart Cities 2020, 3, 1173-1186. https://doi.org/10.3390/smartcities3040058
Hurst W, Tekinerdogan B, Kotze B. Perceptions on Smart Gas Meters in Smart Cities for Reducing the Carbon Footprint. Smart Cities. 2020; 3(4):1173-1186. https://doi.org/10.3390/smartcities3040058
Chicago/Turabian StyleHurst, William, Bedir Tekinerdogan, and Ben Kotze. 2020. "Perceptions on Smart Gas Meters in Smart Cities for Reducing the Carbon Footprint" Smart Cities 3, no. 4: 1173-1186. https://doi.org/10.3390/smartcities3040058
APA StyleHurst, W., Tekinerdogan, B., & Kotze, B. (2020). Perceptions on Smart Gas Meters in Smart Cities for Reducing the Carbon Footprint. Smart Cities, 3(4), 1173-1186. https://doi.org/10.3390/smartcities3040058