Reexamining the relationships among urbanization, industrial structure, and environmental pollution in China—New evidence using the dynamic threshold panel model

Hao, Yu; Zheng, Shihui; Zhao, Meirong; Wu, Haitao; Guo, Yunxia; Li, Yunwei

doi:10.1016/j.egyr.2019.11.029

Cited by 180 publications

(69 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…There is no reverse urbanization process in China [69,70]. Therefore, it is assumed that the bright pixels detected in the nighttime light image of the previous year will not disappear in the next year nighttime light image [71].…”

Section: Interannual Series Correctionmentioning

confidence: 99%

An Improved Correction Method of Nighttime Light Data Based on EVI and WorldPop Data

et al. 2020

View full text Add to dashboard Cite

Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) data has the shortcomings of discontinuous and pixel saturation effect. It was also incompatible with the Soumi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) data. In view those shortcomings, this research put forward the WorldPop and the enhanced vegetation index (EVI) adjusted nighttime light (WEANTL) using EVI and WorldPop data to achieve intercalibration and saturation correction of DMSP/OLS data. A long time series of nighttime light images of china from 2001 to 2018 was constructed by fitting the DMSP/OLS data and NPP/VIIRS data. Corrected nighttime light images were examined to discuss the estimation ability of gross domestic product (GDP) and electric power consumption (EPC) on national and provincial scales, respectively. The results indicated that, (1) after correction, the nighttime light (NTL) data can guarantee the growth trend on national and regional scales, and the interannual volatility of the corrected NTL data is lower than that of the uncorrected NTL data; (2) on the national scale, compared with the established model of NTL data and GDP data (NTL-GDP), the determination coefficient (R2) and the mean absolute relative error (MARE) are 0.981 and 8.518%. The R2 and MARE of the established model of NTL data and EPC data (NTL-EPC) were 0.990 and 4.655%; (3) on the provincial scale, the R2 and MARE of NTL-GDP model under the provincial units are 0.7386 and 38.599%. The R2 value and MARE of NTL-EPC model are 0.8927 and 29.319%; (4) on the provincial scale, the R2 and MARE of NTL-GDP model on time series are 0.9667 and 10.877%. The R2 and MARE of NTL-GDP model on time series are 0.9720 and 6.435%; the established TNL-GDP and TNL-EPC models with 30 provinces data all passed the F-test at the 0.001 level; (5) the prediction accuracy of GDP and EPC on time series was nearly 100%. Therefore, the correction method provided in this research can be applied in estimating the GDP and EPC on multiple scales reliably and accurately.

show abstract

Section: Interannual Series Correctionmentioning

confidence: 99%

An Improved Correction Method of Nighttime Light Data Based on EVI and WorldPop Data

et al. 2020

View full text Add to dashboard Cite

show abstract

“…However, some scholars found that industrial restructuring had little or no significance on environmental pollution, and some provinces' environment improvement deteriorated the circumstances of others [20]. In addition, other scholars considered the impact of industrial structure adjustment on pollutant emission had significant heterogeneity of varying pollutants and varying regions [21][22][23]. There is no doubt that these studies have provided beneficial exploration and help to reveal the relationship between the industrial structure and ecological environment.…”

Section: Introductionmentioning

confidence: 99%

“…Most studies only took the proportion of the secondary industry as an evaluation index to measure the industrial structure and construct the econometric regression model [13,16,19], which might lead to certain deviation and incompleteness of the analysis results. Thus, it was difficult to systematically reveal the complex influence of industrial structure evolution on the environmental system; (2) Increasing studies have centered on the relationship between industrial structure and carbon dioxide emission [9][10][11]14,18,24], as well as the relationship between industrial structure and the ecological environment system [4,23], but there is a lack of detailed study on the dimension of the air environment system. On the basis of the pressure-state-response model, it was gradually pursued by scholars to treat the air environment as a whole system from the system theory [25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Investigating the Relationship between the Industrial Structure and Atmospheric Environment by an Integrated System: A Case Study of Zhejiang, China

et al. 2020

View full text Add to dashboard Cite

Under the dual pressure of industrial structure upgrade and atmospheric environment improvement, China, in a transition period, is facing the challenge of coordinating the relationship between the industry and the environment system to promote the construction of a beautiful China. Based on system theory and coupling coordination model, the interaction analysis framework between industrial structure (IS) and atmospheric environment (AE) was constructed. An integrated system with 24 indicators was established by the pressure-state-response (PSR) model of IS and level-quality-innovation (LQI) model of AE. Then, we analyzed trends observed in coupling coordination degree (CCD) and dynamic coupling coordination degree (DCCD) for 11 cities in Zhejiang Province, China, using statistical panel data collected from 2006 to 2017. Conclusions were as follows: (1) the 11 cities' comprehensive level of the IS system shows a trend of stable increase, yet the comprehensive level of AE demonstrated a trend of fluctuation and transition. There are significant spatial variations among cities; (2) The CCD analysis results found that Hangzhou, Ningbo, and Wenzhou take the lead in realizing the transformation from barely coordinated development to superior coordinated pattern, while other cities were still in the stage of barely coordinated development; (3) the DCCD phase of 11 cities can be roughly divided into three types: upgraded-utmost development type (only Hangzhou), stable-harmonious development type (Wenzhou, Lishui, and Zhoushan) and transitional-harmonious development type (the remaining seven cities). This means, for most cities, the contradiction between the transformation process of IS and the AE has become increasingly prominent and intensified. Finally, three necessary and sustainable strategies were proposed to environmental policy makers.

show abstract

“…Air pollution is not only related to natural phenomena such as seasons [ 22 , 23 , 24 ] and wind speeds [ 25 ], but also to social phenomena such as human activities [ 26 , 27 , 28 ]. In December 2019, an “unknown cause of pneumonia” appeared in Wuhan, China.…”

Section: Introductionmentioning

confidence: 99%

Analysis on the Temporal Distribution Characteristics of Air Pollution and Its Impact on Human Health under the Noticeable Variation of Residents’ Travel Behavior: A Case of Guangzhou, China

Wang

Zou

2020

IJERPH

View full text Add to dashboard Cite

During the large-scale outbreak of COVID-19 in China, the Chinese government adopted multiple measures to prevent the epidemic. The consequence was that a sudden variation in residents’ travel behavior took place. In order to better evaluate the temporal distribution of air pollution, and to effectively explore the influence of human activities on air quality, especially under the special situation, this study was conducted based on the real data from a case city in China from this new perspective. Two case scenarios were constructed, in which the research before the changes of residents’ travel behavior was taken as case one, and the research after the changes in residents’ travel behavior as case two. The hourly real-time concentrations of PM2.5, PM10, SO2, NO2, CO and O3 that have passed the augmented Dickey–Fuller (ADF) test were employed as a data source. A series of detailed studies have been carried out using the correlation method, entropy weight method and the Air Quality Index (AQI) calculation method. Additionally, the research found that the decrease rate of NO2 concentration is 61.05%, and the decrease rate of PM10 concentration is 53.68%. On the contrary, the average concentration of O3 has increased significantly, and its growth rate has reached to 9.82%. Although the air quality in the first week with fewer travels was in the excellent category, and chief pollutant (CP), as well as excessive pollutant (EP), were not found, as traffic volume increased, it became worse in the second and third weeks. In addition to that, special attention should still be paid to the development trend of O3, as its average hourly concentration has increased. The findings of this study will have some guiding significance for the study of air pollution prevention, cleaner production, and indoor environmental safety issues, especially for the study of abnormal traffic environments where residents’ travel behaviors have changed significantly.

show abstract

Reexamining the relationships among urbanization, industrial structure, and environmental pollution in China—New evidence using the dynamic threshold panel model

Cited by 180 publications

References 58 publications

An Improved Correction Method of Nighttime Light Data Based on EVI and WorldPop Data

An Improved Correction Method of Nighttime Light Data Based on EVI and WorldPop Data

Investigating the Relationship between the Industrial Structure and Atmospheric Environment by an Integrated System: A Case Study of Zhejiang, China

Analysis on the Temporal Distribution Characteristics of Air Pollution and Its Impact on Human Health under the Noticeable Variation of Residents’ Travel Behavior: A Case of Guangzhou, China

Contact Info

Product

Resources

About