The Urban–Rural Transformation and Its Influencing Mechanisms on Air Pollution in the Yellow River Basin
Abstract
:1. Introduction
2. Analytical Framework
3. Materials and Methods
3.1. Study Area
3.2. Data Sources
- (1)
- PT, IT, and LT are set as the core explanatory variables (Table 2). PT is a process by which the rural population is transformed into an urban population through its agglomeration into the urban. The urbanization rate of the population is a measure of the extent to which people are moving from rural areas to cities. It is calculated by dividing the number of people living in cities permanently by the total number of permanent residents. It is a comprehensive process that involves direct or indirect adjustments to various aspects of the existing industrial structure, characterized by the proportion of non-agricultural industrial output to total regional output. LT results from a combination of urban land (LUT) and rural land (LRT) change. On the one hand, land transformation is reflected in the sprawling expansion of urban construction. Conversely, cultivated land is closely related to transforming urban–rural areas. This relationship is represented by the ratio of town dwelling, industrial, and mining land to cultivated land in the county. The population data are derived from the census data in 2000 (the fifth census year), 2010 (the sixth census year), and 2020 (the seventh census year). The economic data were mainly derived from the 2001, 2011, and 2021 China County Statistical Yearbooks, and the Qinghai, Sichuan, Gansu, and Ningxia Provincial Statistical Yearbooks, as well as the national economic and social development statistical bulletins in relevant cities and counties in the Yellow River Basin in 2000, 2010, and 2020. Some missing data were processed by replacing missing values using SPSS 26.0 software for mean and linear interpolation. The statistics for urban, industrial, mining, and agricultural land originate from the Resource and Environmental Science Statistics Center of the Chinese Academy of Sciences, accessible at http://www.resdc.cn/ (accessed on 3 June 2024). The data presented here are derived from Landsat TM/ETM remote sensing pictures. The necessary data are obtained through a process of supervised classification and reclassification.
- (2)
- Regarding the selection of control variables, electricity consumption (EL) represents not only the consumption of industrial energy in urban areas but also the reduction in the use of non-clean energy sources, such as coal in rural areas, as represented by per capita electricity consumption. The continuous expansion of population size (POP) leads to problems such as expanding construction land, traffic congestion, housing shortages, reduction in per capita public resources, and increased energy consumption intensity. In theory, the expansion of the population will exacerbate the increase in PM2.5, which is associated with the number of permanent residents. The intensity of social activity reflects the comprehensive intensity of human socioeconomic activities. Previous studies have shown a close relationship between nighttime light image data and energy consumption, urban population density, and total GDP characterized by nighttime light brightness (NTL) [46,47]. The vegetation index can accurately reflect the surface vegetation coverage status and is represented by the annual normalized vegetation index (NDVI). The data were derived from the China Annual Vegetation Index Spatial Distribution Dataset, managed by the Resources and Environmental Sciences and Data Center of the Chinese Academy of Sciences (http://www.resdc.cn, accessed on 3 June 2024).
3.3. Research Methods
3.3.1. Index of Urban–Rural Transformation
- Data standardization processing [48].
- 2.
- Calculate the weights.
3.3.2. Kernel Density Estimation
3.3.3. Spatial Autocorrelation Analysis
3.3.4. Spatial Metrology Model
4. Results
4.1. Characteristics of the Spatiotemporal Evolution of the Urban–Rural Transformation
4.2. Characteristics of the Spatiotemporal Evolution of PM2.5
4.2.1. Distribution of PM2.5 in the Yellow River Basin
4.2.2. Spatial Pattern of PM2.5 in the Yellow River Basin
4.2.3. PM2.5 Spatial Correlations
4.3. The Processes by Which PM2.5 Is Affected by the Urban–Rural Transition
4.3.1. Applicability Test of Model
4.3.2. Model Results and Influencing Mechanisms
- 1.
- Impact of PT on PM2.5.
- 2.
- Impact of IT on PM2.5.
- 3.
- Impact of LT on PM2.5.
- 4.
- Direct and indirect effects.
Explanatory Variable | Direct Effect | Indirect Effect |
---|---|---|
lnPT | 0.018 | −0.033 ** |
lnIT | 0.044 *** | 0.009 |
lnLUT | 0.032 | 0.211 *** |
lnLRT | 0.384 *** | 1.028 ** |
lnEL | −0.059 *** | 0.039 |
lnPOP | −0.005 | −0.122 *** |
lnNTL | 0.315 *** | 0.093 |
lnNDVI | −0.015 * | −0.062 ** |
5. Discussion
5.1. The Driving Force in Large Cities
5.2. The Influencing Mechanisms of PM2.5 by Different Subsystems of the Urban–Rural Transformation Vary
6. Conclusions and Policy Implications
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Variable Name | Mark | Mean Value | Standard Error | Minimum | Maximum | |
---|---|---|---|---|---|---|
PM2.5 concentrations | PM2.5 | 45.158 | 16.988 | 1.265 | 88.114 | |
Population transformation | PT | 44.448 | 27.035 | 0.000 | 100.000 | |
Industrial transformation | IT | 79.689 | 15.549 | 19.978 | 100.000 | |
Land transformation | Construction land | LUT | 10.920 | 13.292 | 0.005 | 99.423 |
Cultivated land | LRT | 45.991 | 24.797 | 0.004 | 87.465 | |
Electricity consumption | EL | 4754.903 | 9823.977 | 202.344 | 159,999.200 | |
Population size | POP | 842.422 | 2214.075 | 1500.000 | 2,146,000.000 | |
Normalized vegetation index | NDVI | 0.820 | 0.100 | 0.300 | 0.920 | |
Nighttime light brightness | NTL | 9.046 | 12.407 | 0.000 | 63.000 |
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Variable Type | Variable Selection | Variable Definition | |
---|---|---|---|
Dependent variable | PM2.5 concentration (PM2.5) | Degree of air pollution | |
Explanatory variables | Population transition (PT) | The urbanization rate of the population | |
Industrial transformation (IT) | Non-agricultural development of industry | ||
Land transformation (LT) | Construction land (LUT) | The sprawling expansion of urban and town construction | |
Cultivated land (LRT) | The ratio of cultivated land in the county | ||
Control variables | Electricity consumption (EL) | The consumption of industrial energy and non-clean energy | |
Population size (POP) | Number of permanent residents | ||
Nighttime light brightness (NTL) | The night light brightness value of each county and district | ||
Normalized vegetation index (NDVI) | Urban average annual normalized vegetation index |
Explanatory Variables | Variable Selection | Indicator Weight | Estimate Properties | |
---|---|---|---|---|
Urban–rural transformation (URT) | Population transition (PT) | 0.23 | + | |
Industrial transformation (IT) | 0.22 | + | ||
Land transformation (LT) | Construction land (LUT) | 0.37 | + | |
Cultivated land (LRT) | 0.18 | − |
Test | Statistic | Likelihood Ratio (cχ²) | p-Value | Prob > χ² |
---|---|---|---|---|
Moran’s I | 5.123 | 0.000 | ||
LM-Spatial error | 740.439 | 0.000 | ||
RobustLM-Spatial error | 269.159 | 0.000 | ||
LM-Spatial lag | 584.981 | 0.000 | ||
RobustLM-Spatial lag | 113.701 | 0.000 | ||
LR-Ind | 279.77 | 0.000 | ||
LR-Time | 3633.36 | 0.000 | ||
LR-Spatial error | 21.55 | 0.0058 | ||
LR-Spatial lag | 29.44 | 0.0003 | ||
WALD-Spatial error | 21.41 | 0.0032 | ||
WALD-Spatial lag | 29.62 | 0.0001 | ||
Hausman | 244.49 | 0.000 |
Variables | Ind | Time | Both |
---|---|---|---|
W-lnPM2.5 | 0.870 *** | 0.396 *** | 0.546 *** |
Main | |||
lnPT | 0.00714 | 0.0710 *** | 0.00498 |
ln2PT | 0.000886 | 0.0375 * | 0.000612 |
lnIT | −0.446 | −9.864 ** | −1.739 |
ln2IT | 1.277 | 20.84 ** | 4.101 |
ln3IT | −0.807 | −10.93 ** | −2.352 |
lnLUT | −0.0141 | 0.253 *** | 0.0170 |
ln2LUT | −0.00450 | −0.153 *** | −0.000974 |
lnLRT | −0.0597 | 0.437 *** | −0.0382 |
ln2LRT | 0.126 *** | −0.0537 ** | 0.132 *** |
EL | −0.106 *** | 0.0286 | −0.103 *** |
POP | 0.00918 | 0.0290 * | 0.000774 |
NTL | 0.0810 *** | 0.105 *** | 0.0938 *** |
NDVI | 0.00885 | −0.0251 | 0.00987 |
Wx | |||
lnPT | −0.0728 | ||
ln2PT | −0.0346 | ||
lnIT | −3.447 * | ||
ln2IT | 7.768 * | ||
ln3IT | −4.327 * | ||
lnLUT | −0.0868 * | ||
ln2LUT | 0.192 *** | ||
lnLRT | 0.110 * | ||
ln2LRT | −0.0357 | ||
EL | 0.146 *** | ||
POP | 0.0101 * | ||
NTL | −0.0455 | ||
NDVI | −0.00931 | ||
Variance | |||
sigma2_e | 0.0126 *** | 0.0154 *** | 0.0123 *** |
R2 | 0.454 | 0.723 | 0.207 |
N | 1494 | 1494 | 1494 |
Explanatory Variable | Population Transformation (PT) | Industrial Transformation (IT) | Land Transformation (LT) | |
---|---|---|---|---|
Construction Land (LUT) | Cultivated Land (LRT) | |||
lnPM2.5 | 0.523 *** | 0.534 *** | 0.559 *** | 0.895 *** |
lnURT | 0.071 ** | −20.948 *** | −0.262 *** | −0.207 * |
ln2URT | 0.113 * | 45.114 *** | 0.232 *** | 0.166 ** |
ln3URT | −24.017 *** | |||
lnEL | −0.254 *** | −0.246 *** | −0.052 *** | 0.157 *** |
lnPOP | 0.138 *** | 0.130 *** | 0.037 ** | 0.046 |
lnNTL | 0.078 *** | 0.110 *** | 0.134 *** | 0.070 *** |
lnNDVI | −0.039 * | −0.041 ** | −0.029 * | −0.028 ** |
R2 | 0.469 | 0.536 | 0.618 | 0.266 |
N | 1494 | 1494 | 1494 | 1494 |
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Xu, C.; Yin, Z.; Sun, W.; Cao, Z.; Cheng, M. The Urban–Rural Transformation and Its Influencing Mechanisms on Air Pollution in the Yellow River Basin. Sustainability 2024, 16, 6978. https://doi.org/10.3390/su16166978
Xu C, Yin Z, Sun W, Cao Z, Cheng M. The Urban–Rural Transformation and Its Influencing Mechanisms on Air Pollution in the Yellow River Basin. Sustainability. 2024; 16(16):6978. https://doi.org/10.3390/su16166978
Chicago/Turabian StyleXu, Chen, Zhenzhen Yin, Wei Sun, Zhi Cao, and Mingyang Cheng. 2024. "The Urban–Rural Transformation and Its Influencing Mechanisms on Air Pollution in the Yellow River Basin" Sustainability 16, no. 16: 6978. https://doi.org/10.3390/su16166978