A Study on the Factors Affecting China’s Direct Household Carbon Emission and Comparison of Regional Differences
Abstract
:1. Introduction
2. Methodology
2.1. The Methodology of Calculating Emissions
2.2. The Design of the Influencing Factor Model
3. Results
3.1. Calculation and Statistics of Direct Household Carbon Emissions
3.2. Regression Results and Comparison of Urban–Rural and Interprovincial Differences
4. Discussion and Conclusions
- (1)
- The average carbon emission of urban households is higher than that of rural households, and the average carbon emission of northeastern, eastern, western, and central households decreases in turn. In the case that other variables are controlled, the carbon emission of urban households is 17.4% higher than that of rural households.
- (2)
- Among all kinds of household energy consumption, central heating releases the highest amount of CO2.
- (3)
- Both personal background and household energy-consuming facility use have important influences on household carbon emissions, and their influence degree is higher in urban areas than in rural areas. In urban areas, continuous variables, such as the number of registration residents, average daily sunshine time in winter, and number of refrigerators, freezers, computers, incandescent lamps, water heaters, and air conditioners, all have significant positive impacts on household carbon emissions. The number of primary household cooking appliances has a significant negative impact. In rural areas, the number of cooking appliances, washing machines, and air conditioners has significant positive impacts on household carbon emissions. Household economic status has a significant negative impact, because poorer households use cheaper energy, such as coal rather than gas, thus emitting more carbon. Factors, such as age, income, family assets, and the individual perception of socioeconomic status, do not have significant impacts on household carbon emissions.
- (4)
- From an interprovincial perspective, household carbon emissions in Liaoning, Ningxia, Qinghai, Gansu, and Shanxi are relatively high, while those in Shanghai, Yunnan, Henan, Zhejiang, Hubei, and Anhui are relatively low.
- (5)
- Registration status, type of workplace (organization/company), ownership of the company/organization, marital status, and type of cooking appliances all show significant impacts on household carbon emissions. The carbon emissions of non-agricultural household registration residents and married people, especially people working in the Party and government organizations, enterprises, public institutions, and social organizations, are significantly higher. Rural households’ carbon emissions associated with cooking appliances are significantly higher than urban households’ emissions. Other classification independent variables, such as gender, nationality, the highest level of education, and political status, have no significant impact on household carbon emissions.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Highest Level of Education | Illiteracy | Primary School | Middle School | High School | Junior College | Undergraduate | Postgraduate |
---|---|---|---|---|---|---|---|
Mean | 1058.492 | 1305.333 | 1622.013 | 1764.098 | 1732.671 | 2030.683 | 1662.322 |
Overall | City | Rural | ||||
---|---|---|---|---|---|---|
Mean | Standard Error | Mean | Standard Error | Mean | Standard Error | |
Annual CO2 emission from honeycomb briquette/coal | 90.433 | 10.964 | 68.668 | 13.715 | 121.074 | 17.978 |
Annual CO2 emission from coal consumption | 242.186 | 21.468 | 126.159 | 21.399 | 405.530 | 41.612 |
Annual CO2 emission from gasoline consumption | 111.722 | 25.534 | 144.884 | 42.158 | 65.036 | 15.983 |
Annual CO2 emission from diesel fuel consumption | 22.787 | 7.598 | 20.229 | 11.274 | 26.389 | 9.101 |
Annual CO2 emission from bottled liquefied gas consumption | 285.604 | 30.198 | 295.674 | 36.285 | 271.428 | 51.758 |
Annual CO2 emission from pipeline natural gas consumption | 89.362 | 6.418 | 145.787 | 10.634 | 9.926 | 2.607 |
Annual CO2 emission from pipeline gas consumption | 4.570 | 0.991 | 7.630 | 1.687 | 0.263 | 0.166 |
Annual CO2 emission from domestic livestock and poultry manure consumption | 0.689 | 0.457 | 0.001 | 0.001 | 1.658 | 1.101 |
Annual CO2 emission from straw consumption | 36.761 | 10.556 | 0.680 | 0.398 | 87.557 | 25.352 |
Annual CO2 emission from fuelwood consumption | 95.286 | 16.412 | 13.987 | 5.593 | 209.740 | 38.519 |
Annual CO2 emission from electricity consumption | 140.255 | 3.342 | 162.484 | 4.941 | 108.961 | 3.891 |
Annual CO2 emission from coal consumption for central heating | 416.015 | 16.785 | 668.280 | 25.947 | 60.872 | 11.844 |
Overall | City | Rural | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Sample Size | Mean | Standard Error | Chi-Square | Sample Size | Mean | Standard Error | Chi-Square | Sample Size | Mean | Standard Error | Chi-Square | |
Shanghai | 502 | 563.934 | 81.142 | 63,472.542 ** | 502 | 563.934 | 81.142 | 42,007.862 ** | - | - | - | 41,875.822 ** |
Yunnan | 385 | 431.312 | 109.268 | 93 | 432.576 | 230.452 | 292 | 430.977 | 124.858 | |||
Inner Mongolia | 99 | 2148.507 | 430.850 | 25 | 2444.166 | 768.832 | 74 | 2027.556 | 528.35 | |||
Beijing | 547 | 2890.335 | 411.938 | 519 | 2852.278 | 413.583 | 28 | 4517.27 | 3914.308 | |||
Jilin | 465 | 2086.345 | 266.333 | 178 | 2568.291 | 359.685 | 287 | 1761.665 | 371.945 | |||
Sichuan | 566 | 685.667 | 59.624 | 275 | 758.297 | 72.848 | 291 | 597.372 | 97.413 | |||
Tianjin | 288 | 1791.549 | 93.018 | 288 | 1791.549 | 93.018 | - | - | - | |||
Ningxia | 94 | 3848.488 | 765.211 | 47 | 3759.822 | 1113.41 | 47 | 3966.709 | 1047.567 | |||
Anhui | 397 | 849.748 | 165.76 | 119 | 1018.621 | 312.868 | 278 | 775.867 | 195.673 | |||
Shandong | 575 | 1621.078 | 166.792 | 315 | 1839.414 | 218.597 | 260 | 1290.621 | 254.466 | |||
Shanxi | 280 | 2503.978 | 205.697 | 189 | 2928.688 | 200.255 | 91 | 1518.648 | 448.442 | |||
Guangdong | 531 | 998.398 | 143.13 | 531 | 998.398 | 143.130 | - | - | - | |||
Guangxi | 393 | 944.638 | 244.071 | 194 | 990.616 | 349.036 | 199 | 910.551 | 339.695 | |||
Jiangsu | 499 | 1748.379 | 360.575 | 321 | 1521.577 | 338.841 | 178 | 2197.783 | 842.309 | |||
Jiangxi | 476 | 959.256 | 219.712 | 284 | 873.694 | 202.375 | 192 | 1074.697 | 440.045 | |||
Hebei | 295 | 2705.470 | 366.161 | 99 | 4176.916 | 842.420 | 196 | 1841.287 | 255.453 | |||
Henan | 582 | 630.339 | 184.061 | 216 | 554.151 | 82.867 | 366 | 681.132 | 302.232 | |||
Zhejiang | 462 | 804.428 | 181.933 | 341 | 617.490 | 166.611 | 121 | 1222.579 | 453.276 | |||
Hubei | 600 | 1029.383 | 229.263 | 350 | 1139.935 | 377.321 | 250 | 902.247 | 235.862 | |||
Hunan | 475 | 788.081 | 110.976 | 240 | 840.126 | 150.085 | 235 | 738.705 | 163.523 | |||
Gansu | 195 | 2254.467 | 290.209 | 50 | 2180.543 | 553.103 | 145 | 2276.645 | 341.671 | |||
Fujian | 294 | 1182.625 | 364.78 | 194 | 1202.781 | 503.759 | 100 | 1134.849 | 307.779 | |||
Guizhou | 249 | 1009.139 | 203.509 | 177 | 734.973 | 286.453 | 72 | 1447.806 | 246.531 | |||
Liaoning | 395 | 2867.547 | 295.736 | 345 | 2832.957 | 286.600 | 50 | 3116.595 | 1313.229 | |||
Chongqing | 265 | 1807.233 | 556.672 | 79 | 973.932 | 187.346 | 186 | 2126.664 | 764.541 | |||
Shaanxi | 369 | 1563.291 | 201.159 | 106 | 2017.001 | 214.252 | 263 | 1366.285 | 270.846 | |||
Qinghai | 101 | 2486.825 | 331.347 | 75 | 2263.146 | 298.489 | 26 | 3269.67 | 1074.684 | |||
Heilongjiang | 589 | 2401.231 | 233.904 | 318 | 2613.66 | 203.645 | 271 | 2161.686 | 442.081 |
Independent Variable | Overall | City | Rural | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
β | Sig. | Tolerance | VIF | β | Sig. | Tolerance | VIF | β | Sig. | Tolerance | VIF | ||
Constant | 0.235 | 0.959 | 2.729 | 0.609 | −1.404 | 0.865 | |||||||
Pb1 | age | 0.002 | 0.342 | 0.365 | 2.741 | 0.001 | 0.849 | 0.315 | 3.177 | 0.004 | 0.349 | 0.400 | 2.503 |
Pb2 | total individual income last year | 0.000 | 0.340 | 0.966 | 1.036 | 0.000 | 0.240 | 0.936 | 1.069 | 0.000 | 0.959 | 0.951 | 1.052 |
Pb3 | total family income last year | 0.000 | 0.829 | 0.882 | 1.134 | 0.000 | 0.911 | 0.827 | 1.209 | 0.000 | 0.396 | 0.836 | 1.196 |
Pb4 | number of registration residents | 0.045 | 0.019 | 0.739 | 1.353 | 0.043 | 0.058 | 0.706 | 1.417 | 0.046 | 0.169 | 0.706 | 1.416 |
Pb5 | number of houses owned by the family | 0.026 | 0.540 | 0.846 | 1.182 | 0.042 | 0.323 | 0.824 | 1.213 | −0.049 | 0.640 | 0.795 | 1.259 |
Pb6 | household economic status | −0.039 | 0.309 | 0.661 | 1.513 | 0.021 | 0.614 | 0.681 | 1.467 | −0.158 | 0.040 | 0.557 | 1.797 |
Pb7 | individual perception of socioeconomic status | −0.053 | 0.268 | 0.699 | 1.430 | −0.066 | 0.205 | 0.712 | 1.404 | −0.080 | 0.406 | 0.588 | 1.700 |
He1 | domestic living space | 0.000 | 0.164 | 0.707 | 1.414 | 0.001 | 0.115 | 0.704 | 1.420 | 0.000 | 0.669 | 0.683 | 1.464 |
He2 | the average daily sunshine time in winter | 0.045 | 0.010 | 0.332 | 3.016 | 0.063 | 0.002 | 0.316 | 3.167 | 0.040 | 0.211 | 0.338 | 2.958 |
He3 | the average daily sunshine time in summer | −0.016 | 0.343 | 0.331 | 3.024 | −0.027 | 0.153 | 0.313 | 3.198 | −0.021 | 0.514 | 0.353 | 2.834 |
He4 | the number of main cooking appliances | −0.015 | 0.122 | 0.788 | 1.269 | −0.020 | 0.021 | 0.787 | 1.271 | 0.097 | 0.016 | 0.491 | 2.037 |
He5 | the number of refrigerators | 0.236 | 0.000 | 0.621 | 1.611 | 0.253 | 0.003 | 0.619 | 1.615 | 0.150 | 0.156 | 0.618 | 1.617 |
He6 | the number of freezers | 0.155 | 0.049 | 0.787 | 1.271 | 0.186 | 0.066 | 0.807 | 1.240 | 0.088 | 0.511 | 0.654 | 1.530 |
He7 | the number of washing machines | 0.090 | 0.156 | 0.665 | 1.504 | −0.046 | 0.572 | 0.648 | 1.543 | 0.249 | 0.018 | 0.623 | 1.605 |
He8 | the number of dryers | 0.026 | 0.889 | 0.899 | 1.112 | 0.116 | 0.583 | 0.876 | 1.141 | −0.094 | 0.793 | 0.784 | 1.276 |
He9 | the number of TVs | 0.080 | 0.118 | 0.705 | 1.419 | 0.055 | 0.348 | 0.671 | 1.490 | 0.095 | 0.322 | 0.664 | 1.505 |
He10 | the number of computers | 0.036 | 0.172 | 0.768 | 1.302 | 0.095 | 0.038 | 0.582 | 1.717 | 0.003 | 0.933 | 0.849 | 1.178 |
He11 | the number of fluorescent lamps | 0.020 | 0.562 | 0.248 | 4.024 | 0.018 | 0.628 | 0.253 | 3.946 | −0.004 | 0.951 | 0.220 | 4.551 |
He12 | the number of incandescent lamps | 0.007 | 0.772 | 0.874 | 1.144 | 0.059 | 0.046 | 0.880 | 1.136 | −0.053 | 0.169 | 0.821 | 1.218 |
He13 | the number of water heaters | 0.159 | 0.005 | 0.583 | 1.716 | 0.221 | 0.001 | 0.628 | 1.592 | 0.092 | 0.373 | 0.630 | 1.588 |
He14 | the number of air conditioners | 0.079 | 0.037 | 0.446 | 2.241 | 0.067 | 0.080 | 0.467 | 2.143 | 0.163 | 0.099 | 0.488 | 2.050 |
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Fan, J.; Ran, A.; Li, X. A Study on the Factors Affecting China’s Direct Household Carbon Emission and Comparison of Regional Differences. Sustainability 2019, 11, 4919. https://doi.org/10.3390/su11184919
Fan J, Ran A, Li X. A Study on the Factors Affecting China’s Direct Household Carbon Emission and Comparison of Regional Differences. Sustainability. 2019; 11(18):4919. https://doi.org/10.3390/su11184919
Chicago/Turabian StyleFan, Jingbo, Aobo Ran, and Xiaomeng Li. 2019. "A Study on the Factors Affecting China’s Direct Household Carbon Emission and Comparison of Regional Differences" Sustainability 11, no. 18: 4919. https://doi.org/10.3390/su11184919