Impacts of Industrial Restructuring and Technological Progress on PM2.5 Pollution: Evidence from Prefecture-Level Cities in China
Abstract
:1. Introduction
2. Methodology and Materials
2.1. Model Derivation
2.2. Variables Description and Data
2.2.1. Core Explanatory Variables
- (1)
- Industrial structural upgrading (ISU): The international community usually divides various industries into three categories. This paper takes the proportion of the added value of the tertiary industry as the indicator of the upgrading of the industrial structure [33].
- (2)
- Industrial structure rationalization (ISR): Theil introduced the concept of entropy in information theory into economics, and used it to measure income inequality, which better reflects the uneven distribution of income factors in regions [34]. Therefore, the Theil index can be used to evaluate the rationalization of the industrial structure. The calculation method of Theil index is as follows:
- (3)
- Technological progress (TP): This paper takes the change of total factor productivity (TFPCH) as the indicator of TP. Because there is no need to estimate parameters in advance, data envelopment analysis (DEA) can effectively reduce errors and avoid the influence of subjective factors, and is suitable for evaluating the relative effectiveness of multi-input decision-making units [35]. Therefore, DEA-Malmquist productivity index method is used to calculate the TFPCH. At the same time, this method can further decompose TFPCH into technical change (TC) and efficiency change (EC) [36]. All the calculation work is based on the index database with the time span from 2006 to 2017 and DEAP 2.1 platform. The input indicators set in this paper include capital, energy and labor. Specifically, this paper uses capital stock to represent capital investment. The former is calculated based on the price level in 2006 and the perpetual inventory method [37]; Convert all kinds of energy consumed by prefecture-level cities into standard coal and characterize energy input by this; Using the scale of employed persons to represent labor input. In this paper, the price index of 2006 is used to offset the GDP, and the processed GDP is used to characterize the output.
2.2.2. Control Variables
- (1)
- Economic development (ED): Most of the related researches on the correlation between economic development and PM2.5 pollution are based on the EKC (environmental Kuznets curve) theory [32]. This paper uses per capita GDP to measure the economic status of cities.
- (2)
- Urbanization (UR): The urbanization process has changed the original natural and social conditions, and therefore, cities have become spatial containers of PM2.5 pollution [18]. Many studies have confirmed the positive impact of urbanization on PM2.5 pollution [38,39,40]. This paper uses the proportion of urban population to the total population to measure the urbanization level.
- (3)
- Energy intensity (EI): The general view is that pollutants emitted by energy consumption constitute the important supplement of PM2.5 [41,42]. Considering the difference of city scale, energy intensity has a higher explanatory power for PM2.5 pollution. In this paper, all kinds of energy consumed by cities are converted into standard coal, and the energy intensity is measured by the amount of standard coal consumed per 10,000 yuan of GDP.
- (4)
- (5)
- Precision (PR): The precipitation process is an important way to remove particulate pollutants [45]. In this paper, the average value of annual precipitation recorded by urban meteorological monitoring stations is used to measure urban precipitation.
2.3. Data Source
3. Temporal-Spatial Characteristic of PM2.5 Pollution
3.1. Dynamic Evolution of PM2.5 Pollution
3.2. Spatial Autocorrelation of PM2.5 Pollution
4. Impacts of Influencing Factors on PM2.5 Pollution
4.1. Model Recognition
4.2. Analyses of Results
5. Discussion and Conclusions
5.1. Main Conclusions
5.2. Policy Implications
5.3. Research Prospects
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Variables | Mean | Std. Dev | Min | Max | Units | Sample Size |
---|---|---|---|---|---|---|
PM2.5 Concentration (PM) | 44.63 | 18.09 | 3.64 | 104.30 | μg/m3 | 3091 |
Industrial Structural Upgrading (ISU) | 37.93 | 9.43 | 8.58 | 80.81 | % | 3091 |
Industrial Structure Rationalization (ISR) | 0.14 | 1.40 | −0.08 | 50.13 | % | 3091 |
Technological Progress (TP) | 1.00 | 0.09 | 0.15 | 1.94 | % | 3091 |
Technical Change (TC) | 1.01 | 0.08 | 0.59 | 1.31 | % | 3091 |
Efficiency Change (EC) | 1.00 | 0.09 | 0.13 | 1.66 | % | 3091 |
Economic Development (ED) | 41,169.61 | 28,456.11 | 3418.00 | 224,147.00 | yuan | 3091 |
Urbanization (UR) | 49.78 | 17.89 | 12.00 | 98.70 | % | 3091 |
Energy Intensity (EI) | 1.05 | 0.64 | 0.08 | 7.67 | t/104 yuan | 3091 |
Temperature (TE) | 15.15 | 0.64 | 1.10 | 26.80 | °C | 3091 |
Precipitation (PR) | 1006.06 | 571.28 | 41.80 | 3202.50 | mm | 3091 |
Year | I | Z(I) | Year | I | Z(I) |
---|---|---|---|---|---|
2007 | 0.583 | 58.272 *** | 2013 | 0.560 | 55.926 *** |
2008 | 0.494 | 49.410 *** | 2014 | 0.529 | 52.913 *** |
2009 | 0.491 | 49.125 *** | 2015 | 0.565 | 56.465 *** |
2010 | 0.531 | 53.017 *** | 2016 | 0.588 | 58.720 *** |
2011 | 0.539 | 53.877 *** | 2017 | 0.499 | 49.872 *** |
2012 | 0.507 | 50.692 *** |
General Dynamic Panel Models | Spatial Static Panel Models | Spatial Dynamic Panel Models | ||||
---|---|---|---|---|---|---|
Model (a) | Model (b) | Model (c) | Model (d) | Model (e) | Model (f) | |
τ | 0.7388 *** | 0.7197 *** | 0.3271 *** | 0.3337 *** | ||
(0.0407) | (0.0687) | (0.0310) | (0.03184) | |||
ρ | 0.4639 *** | 0.4657 *** | 0.4966 *** | 0.5008 *** | ||
(0.0239) | (0.0240) | (0.0195) | (0.0208) | |||
ISU | 0.0041 * | −0.0542 ** | 0.0009 | −0.0037* | −0.0009 | −0.0078 *** |
(0.0029) | (0.0274) | (0.0009) | (0.0027) | (0.0009) | (0.0027) | |
ISR | −0.0226 | −2.2674 | −0.0015 *** | −0.0794 ** | −0.0011 * | −0.1112 *** |
(0.0548) | (2.6874) | (0.0006) | (0.0190) | (0.0006) | (0.0158) | |
TP | −2.0470 ** | −0.1801 ** | −0.2250 ** | |||
(0.9172) | (0.0887) | (0.0916) | ||||
TP × ISU | 0.0518** | 0.0040 * | 0.0060 *** | |||
(0.0260) | (0.0023) | (0.0022) | ||||
TP × ISR | 2.1332 | 0.0743 *** | 0.1047 *** | |||
(2.5335) | (0.0178) | (0.0151) | ||||
TC | 0.5328 *** | −0.0591 * | −1.1204 ** | |||
(0.1086) | (0.0320) | (0.0543) | ||||
EC | 1.2548 *** | 0.0216 | 0.0666 ** | |||
(0.2211) | (0.0263) | (0.0262) | ||||
ln ED | 0.0124 | 0.3708 *** | −0.0758 *** | −0.0779 *** | −0.1064 *** | −0.1082 *** |
(0.0359) | (0.0783) | (0.0143) | (0.0145) | (0.0294) | (0.0293) | |
UR | −0.0039 | −0.0076 | −0.0022 *** | −0.0021 *** | −0.0010* | −0.0010 * |
(0.0048) | (0.0051) | (0.0007) | (0.0007) | (0.0006) | (0.0005) | |
ln EI | 0.0167 | 0.3049 ** | 0.0704 *** | 0.0732 *** | 0.0517 *** | 0.0493 *** |
(0.0700) | (0.1296) | (0.0201) | (0.0201) | (0.0148) | (0.0145) | |
ln TE | 0.2362 *** | 0.2495 *** | 0.0958 | 0.0987 | 0.1777 * | 0.1758 * |
(0.0589) | (0.0789) | (0.0956) | (0.0956) | (0.0933) | (0.0952) | |
ln PR | −0.2280 *** | −1.1394 *** | −0.0248 *** | −0.0244 ** | −0.0207 ** | −0.0185 * |
(0.0330) | (0.0459) | (0.0092) | (0.0091) | (0.0098) | (0.0097) | |
Obs | 2810 | 2810 | 3091 | 3091 | 2810 | 2810 |
LM–Error | (0.129) | (0.128) | (0.123) | (0.122) | ||
Robust LM–Error | (0.144) | (0.150) | (0.138) | (0.145) | ||
LM–Lag | (0.018) | (0.013) | (0.016) | (0.011) | ||
Robust LM–Lag | (0.037) | (0.025) | (0.032) | (0.020) | ||
AR (1) | (0.000) | (0.000) | (0.000) | (0.000) | ||
AR (2) | (0.798) | (0.356) | (0.472) | (0.307) | ||
Hansen over-Identification test | (0.195) | (0.187) | (0.213) | (0.218) |
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Xu, N.; Zhang, F.; Xuan, X. Impacts of Industrial Restructuring and Technological Progress on PM2.5 Pollution: Evidence from Prefecture-Level Cities in China. Int. J. Environ. Res. Public Health 2021, 18, 5283. https://doi.org/10.3390/ijerph18105283
Xu N, Zhang F, Xuan X. Impacts of Industrial Restructuring and Technological Progress on PM2.5 Pollution: Evidence from Prefecture-Level Cities in China. International Journal of Environmental Research and Public Health. 2021; 18(10):5283. https://doi.org/10.3390/ijerph18105283
Chicago/Turabian StyleXu, Ning, Fan Zhang, and Xin Xuan. 2021. "Impacts of Industrial Restructuring and Technological Progress on PM2.5 Pollution: Evidence from Prefecture-Level Cities in China" International Journal of Environmental Research and Public Health 18, no. 10: 5283. https://doi.org/10.3390/ijerph18105283