Regional Differences, Dynamic Evolution and Driving Factors Analysis of PM2.5 in the Yangtze River Economic Belt
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Areas
2.2. Methodology
2.2.1. Dagum Gini Coefficient and Its Decomposition
2.2.2. Kernel Density Estimation
2.2.3. Spatial Autocorrelation Analysis
- (1)
- Global spatial autocorrelation
- (2)
- Local spatial autocorrelation
2.2.4. Spatial Quantile Regression Model
2.3. Data and Data Source
2.3.1. Data of Annual Average PM2.5 Concentration
2.3.2. Data of Driving Factors
- (1)
- Economic development level (PGDP): Some studies believed that the energy consumption and pollutant emissions caused by economic development had aggravated the environmental pollution situation [85]; Nevertheless, some other studies confirmed that economic development indirectly alleviated environmental pollution by promoting the optimization and use of advanced environmental protection technologies [86]. To verify the impact of economic development on PM2.5, the economic development level expressed by per capita GDP is included as an explanatory variable.
- (2)
- (3)
- Population density (PD): PD was measured by the population per square kilometer. It has a dual impact on the environment [33,40]. For one thing, higher population density would lead to more domestic garbage and sewage, which positively affects PM2.5; For another, higher population density negatively affects PM2.5 through scale effect and agglomeration effect, so the impact of PD on PM2.5 is uncertain.
- (4)
- Technology level (TEC): TEC, expressed by science and technology expenditure, is a vital factor in mitigating haze pollution [89]. It can not only improve energy efficiency and productivity and prevent haze pollution from the source, but also strengthen pollution control and alleviate haze pollution from the terminal. Hence, the coefficient is expected to be positive.
- (5)
- Financial expenditure scale (FES): FES was denoted by the proportion of fiscal expenditure in GDP. Financial expenditure, especially environmental protection expenditure, provides special funds for preventing and controlling environmental pollution and improving environmental quality [90]. Therefore, FES can play a crucial role in haze pollution mitigation.
- (6)
- Greening level (GL): Greening level was defined as the green coverage rate in built-up. GL can effectively purify sewage, reduce noise, and absorb pollutants and radioactive materials [82]. Hence, high green coverage is a significant way to purify the air and mitigate PM2.5 pollution.
- (7)
- Intensity of public transportation (TN): TN was measured by Annual public bus (tram) passenger traffic. Studies showed that TN hindered PM2.5 pollution by alleviating the pressure of energy use and reducing air pollutant emissions [91]. Thus, the impact of TN on PM2.5 is expected to be negative.
3. Results and Discussion
3.1. Temporal Variation Characteristics
3.2. Spatial Distribution Characteristics
3.3. Spatial Correlation Analysis
3.4. Regional Difference and Its Decomposition
3.5. Dynamic Evolution Characteristics
4. Discussion
4.1. Suggestions for PM2.5 Mitigation Measures
4.2. Future Research Direction
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Variables | Meaning | Unit |
---|---|---|
PM2.5 | The average concentration of PM2.5 | mg/m3 |
The level of economic development (PGDP) | Per capita GDP | 104 yuan |
Industrial structure (IS) | The proportion of tertiary industry output value in GDP | % |
Population density (PD) | Population per square kilometer | persons/square kilometer |
Technology level (TEC) | Expenditure for science and technology | 108 yuan |
Financial expenditure scale (FES) | The proportion of fiscal expenditure in GDP | % |
Greening level (GL) | Green coverage rate in a built-up area | % |
The intensity of public transportation (TN) | Annual public bus (tram) passenger traffic | 108 persons |
Variables | Mean | Std. Dev | Min | Max | Obs. |
---|---|---|---|---|---|
PM2.5 | 48.1816 | 15.7575 | 8.0915 | 101.1900 | 1728 |
PGDP | 3.5348 | 3.0292 | 0.0099 | 19.9017 | 1728 |
IS | 38.0672 | 8.1988 | 20.6600 | 77.4900 | 1728 |
PD | 481.6207 | 294.8146 | 52.7300 | 2305.6300 | 1728 |
TEC | 80,839.2100 | 258,354.8000 | 77.0000 | 4,263,655.0000 | 1728 |
FES | 16.4643 | 8.3178 | 4.9095 | 68.7608 | 1728 |
GL | 37.5647 | 8.6913 | 0.3600 | 93.8100 | 1728 |
TN | 2.0277 | 3.8147 | 0.0000 | 28.3800 | 1728 |
Year | Moran’s I | Z-Score | Year | Moran’s I | Z-Score |
---|---|---|---|---|---|
2003 | 0.176 *** | 14.659 | 2011 | 0.169 *** | 14.084 |
2004 | 0.173 *** | 14.492 | 2012 | 0.162 *** | 13.635 |
2005 | 0.169 *** | 14.167 | 2013 | 0.177 *** | 14.714 |
2006 | 0.17 2 *** | 14.454 | 2014 | 0.211 *** | 17.453 |
2007 | 0.196 *** | 16.285 | 2015 | 0.240 *** | 19.677 |
2008 | 0.175 *** | 14.618 | 2016 | 0.205 *** | 16.960 |
2009 | 0.206 *** | 17.063 | 2017 | 0.240 *** | 19.769 |
2010 | 0.178 *** | 14.789 | 2018 | 0.254 *** | 20.837 |
Variables | Spatial Durbin Model | Spatial Quantile Regression | ||||
---|---|---|---|---|---|---|
τ = 10 | τ = 25 | τ = 50 | τ = 75 | τ = 90 | ||
WY | 1.6962 *** | 0.8839 *** | 0.7802 *** | 0.7635 *** | 0.7834 *** | 0.7055 *** |
(0.0219) | (0.0255) | (0.0198) | (0.0134) | (0.0142) | (0.0172) | |
PGDP | −0.8459 *** | −0.0632 | 0.0050 | 0.2144 ** | 0.0975 | −0.1355 |
(0.1524) | (0.1946) | (0.1335) | (0.1010) | (0.1670) | (0.1336) | |
IS | −0.1979 *** | −0.1269 ** | −0.1343 *** | −0.1278 *** | −0.0763 * | −0.1226 ** |
(0.0358) | (0.0582) | (0.0284) | (0.0226) | (0.0416) | (0.0573) | |
PD | 0.0181 *** | 0.0163 *** | 0.0102 *** | 0.0108 *** | 0.0122 *** | 0.0105 *** |
(0.0011) | (0.0019) | (0.0015) | (0.0011) | (0.0015) | (0.0011) | |
TEC | −0.0185 | −0.0278 *** | −0.0457 ** | −0.0366 *** | −0.0396 | −0.0302 *** |
(0.0120) | (0.0101) | (0.0189) | (0.0076) | (0.0259) | (0.0079) | |
FES | −0.5255 *** | −0.3226 *** | −0.3394 *** | −0.2425 *** | −0.2475 *** | −0.3646 *** |
(0.0358) | (0.0520) | (0.0406) | (0.0309) | (0.0328) | (0.0371) | |
GL | −0.0115 | 0.0193 | −0.0124 | −0.0081 | 0.0001 | 0.0128 |
(0.0268) | (0.0431) | (0.0258) | (0.0182) | (0.0217) | (0.0315) | |
TN | −0.4326 *** | −0.4603 *** | −0.0406 | −0.0395 | 0.0520 | 0.1141 |
(0.0957) | (0.1490) | (0.1171) | (0.0586) | (0.1149) | (0.1466) | |
Cons | 54.1386 *** | 41.7269 *** | 44.1979 *** | 54.1068 *** | 55.1763 *** | |
(0.1810) | (0.2582) | (0.1122) | (0.2103) | (0.2213) | ||
Obs | 1728 | 1728 | 1728 | 1728 | 1728 | 1728 |
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Wang, W.; Wang, Y. Regional Differences, Dynamic Evolution and Driving Factors Analysis of PM2.5 in the Yangtze River Economic Belt. Sustainability 2023, 15, 3381. https://doi.org/10.3390/su15043381
Wang W, Wang Y. Regional Differences, Dynamic Evolution and Driving Factors Analysis of PM2.5 in the Yangtze River Economic Belt. Sustainability. 2023; 15(4):3381. https://doi.org/10.3390/su15043381
Chicago/Turabian StyleWang, Weiguang, and Yangyang Wang. 2023. "Regional Differences, Dynamic Evolution and Driving Factors Analysis of PM2.5 in the Yangtze River Economic Belt" Sustainability 15, no. 4: 3381. https://doi.org/10.3390/su15043381