| Literature DB >> 35682259 |
Xuechao Xia1,2, Hui Sun1,2, Zedong Yang1,2, Weipeng Yuan1,2, Dianyuan Ma1,2.
Abstract
With the accelerated development of urbanization in China, rural permanent population has declined, while rural electricity consumption has increased, resulting in a significant waste of electricity resources. Based on the provincial panel data of China from 2007 to 2020, this paper comprehensively used the decoupling model and the coordination degree model to analyze the temporal change characteristics, spatial distribution characteristics, and the degree of deviation of rural permanent population and rural electricity consumption. Firstly, according to the decoupling model, the type of decoupling between rural electricity consumption and rural permanent population was strong negative decoupling. At the provincial level, Beijing and Tibet belonged to expanding negative decoupling. Tianjin and Liaoning belonged to recession link. The other 27 provinces, including Hebei, Shanxi, and Shandong, belonged to strong negative decoupling. Secondly, according to the coordination degree model, the coordination type of the national rural permanent population and rural electricity consumption was uncoordinated. The areas that can be coordinated include 20 provinces, including Shanghai, Inner Mongolia, Jilin, Jiangsu, Anhui, Fujian, and Jiangxi. The basic coordination areas included Beijing and Tibet. Finally, according to the comprehensive measurement model, the provinces with strong negative decoupling included Shanxi, Zhejiang, and Chongqing. Sichuan, Hebei, Shandong, and Shaanxi belonged to moderately strong negative decoupling groups.Entities:
Keywords: coordination; decoupling; electricity consumption; rural permanent population
Mesh:
Year: 2022 PMID: 35682259 PMCID: PMC9180248 DOI: 10.3390/ijerph19116676
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 4.614
Definition of degree of decoupling.
| Decoupling State | RE | RL |
| |
|---|---|---|---|---|
| Negative decoupling | Expansion and negative decoupling | >0 | >0 | >1.2 |
| Strong negative decoupling | >0 | <0 | <0 | |
| Weak negative decoupling | <0 | <0 | 0 <
| |
| Decoupling | Weak decoupling | >0 | <0 | 0 < |
| Strong decoupling | <0 | >0 | <0 | |
| Recession decoupling | <0 | <0 | >1.2 | |
| Link | Expansion contact | >0 | >0 | 0.8 < |
| Recession contact | <0 | <0 | 0.8 < | |
The Type of Coordination Degree.
| Cxy | x, y | Coordination Degree Type |
|---|---|---|
| Cxy = 1.414 | x = y, and x > 0, y > 0 | More coordinated |
| 1.2 ≤ Cxy < 1.414 | x ≈ y | Coordinated |
| 1.0 ≤ Cxy < 1.2 | x > 0, y > 0 and x > y | Basically coordinated |
| 0.5 ≤ Cxy < 1.0 | x > 0, y < 0 | Reconcilable |
| −1.414 ≤ Cxy < 0 | x < 0, y < 0, or x > 0, and y < 0 | Uncoordinated |
Figure 1Change trend of rural permanent population and electricity consumption from 2007 to 2020.
Figure 2Spatial pattern of absolute increase and average annual decrease rate of rural permanent population from 2007 to 2020.
Figure 3Spatial pattern of the absolute growth rate and the average annual growth rate of rural electricity consumption from 2007 to 2020.
Decoupling coefficient and coordination between rural electricity consumption and rural residents.
| Province | Rural Permanent Population Growth Rate (%) | Growth Rate of Electricity Consumption (%) | Decoupling Coefficient | Type of Decoupling | Coordination Degree Cxy | Type of Coordination | Comprehensive Type |
|---|---|---|---|---|---|---|---|
| Nationwide | −0.0427 | 0.0263 | −0.6168 | Strong negative decoupling | −0.3262 | Uncoordinated | Strong negative decoupling and Uncoordinated |
| Beijing | 0.0038 | 0.0410 | 10.7236 | Expanding negative decoupling | 1.0885 | Basically coordinated | Expanding negative decoupling |
| Tianjin | −0.0167 | −0.0189 | 1.1278 | Recession link | −1.4117 | Uncoordinated | Recession link |
| Hebei | −0.0251 | 0.0132 | −0.5242 | Strong negative decoupling | −0.4214 | Uncoordinated | Strong negative decoupling and Uncoordinated |
| Shanxi | −0.0446 | 0.0283 | −0.6348 | Strong negative decoupling | −0.3083 | Uncoordinated | Strong negative decoupling and Uncoordinated |
| Inner Mongolia | −0.0331 | 0.0807 | −2.4389 | Strong negative decoupling | 0.5459 | Reconcilable | Strong decoupling |
| Liaoning | −0.0504 | −0.0274 | 0.5447 | Recession link | −1.3565 | Uncoordinated | Recession link |
| Jilin | −0.0364 | 0.0424 | −1.1655 | Strong negative decoupling | 0.1077 | Reconcilable | Strong decoupling |
| Heilongjiang | −0.0360 | 0.0621 | −1.7258 | Strong negative decoupling | 0.3639 | Reconcilable | Strong decoupling |
| Shanghai | −0.0220 | 0.1524 | −6.9245 | Strong negative decoupling | 0.8468 | Reconcilable | Strong decoupling |
| Jiangsu | −0.0358 | 0.0433 | −1.2105 | Strong negative decoupling | 0.1341 | Reconcilable | Strong decoupling |
| Zhejiang | −0.0554 | 0.0367 | −0.6618 | Strong negative decoupling | −0.2821 | Uncoordinated | Strong negative decoupling and and Uncoordinated |
| Anhui | −0.0344 | 0.0731 | −2.1235 | Strong negative decoupling | 0.4787 | Reconcilable | Strong decoupling |
| Fujian | −0.0264 | 0.0706 | −2.6767 | Strong negative decoupling | 0.5868 | Reconcilable | Strong decoupling |
| Jiangxi | −0.0292 | 0.0574 | −1.9640 | Strong negative decoupling | 0.4374 | Reconcilable | Strong decoupling |
| Shandong | −0.0473 | 0.0068 | −0.1438 | Strong negative decoupling | −0.8475 | Uncoordinated | Strong negative decoupling and Uncoordinated |
| Henan | −0.0288 | 0.0403 | −1.3953 | Strong negative decoupling | 0.2303 | Reconcilable | Strong decoupling |
| Hubei | −0.0298 | 0.0597 | −2.0038 | Strong negative decoupling | 0.4482 | Reconcilable | Strong decoupling |
| Hunan | −0.0297 | 0.0446 | −1.5042 | Strong negative decoupling | 0.2791 | Reconcilable | Strong decoupling |
| Guangdong | −0.0051 | 0.0351 | −6.8529 | Strong negative decoupling | 0.8451 | Reconcilable | Strong decoupling |
| Guangxi | −0.0214 | 0.1019 | −4.7727 | Strong negative decoupling | 0.7737 | Reconcilable | Strong decoupling |
| Hainan | −0.0200 | 0.1167 | −5.8379 | Strong negative decoupling | 0.8168 | Reconcilable | Strong decoupling |
| Chongqing | −0.0671 | 0.0418 | −0.6232 | Strong negative decoupling | −0.3198 | Uncoordinated | Strong negative decoupling and Uncoordinated |
| Sichuan | −0.0485 | 0.0404 | −0.8334 | Strong negative decoupling | −0.1280 | Uncoordinated | Strong negative decoupling and Uncoordinated |
| Guizhou | −0.0278 | 0.1104 | −3.9753 | Strong negative decoupling | 0.7258 | Reconcilable | Strong decoupling |
| Yunnan | −0.0205 | 0.0757 | −3.6903 | Strong negative decoupling | 0.7036 | Reconcilable | Strong decoupling |
| Tibet | 0.0024 | 0.1356 | 55.7806 | Expanding negative decoupling | 1.0178 | Basically coordinated | Expanding negative decoupling |
| Shaanxi | −0.0303 | 0.0169 | −0.5571 | Strong negative decoupling | −0.3869 | Uncoordinated | Strong negative decoupling and Uncoordinated |
| Gansu | −0.0286 | 0.0453 | −1.5817 | Strong negative decoupling | 0.3109 | Reconcilable | Strong decoupling |
| Qinghai | −0.0319 | 0.0730 | −2.2926 | Strong negative decoupling | 0.5168 | Reconcilable | Strong decoupling |
| Ningxia | −0.0231 | 0.0359 | −1.5502 | Strong negative decoupling | 0.2982 | Reconcilable | Strong decoupling |
| Xinjiang | −0.0096 | 0.0816 | −8.4583 | Strong negative decoupling | 0.8757 | Reconcilable | Strong decoupling |