TEMPORAL-SPATIAL CHARACTERISTICS AND KEY INFLUENCING FACTORS OF PM2.5 CONCENTRATIONS IN CHINA BASED ON STIRPAT MODEL AND KUZNETS CURVE

2019 ◽  
Vol 18 (12) ◽  
pp. 2587-2604 ◽  
Author(s):  
Yigang Wei ◽  
Wenyang Huang ◽  
Huiwen Wang ◽  
Hongrui Zhao
Keyword(s):  
Influencing Factors ◽  
Kuznets Curve ◽  
Stirpat Model ◽  
Spatial Characteristics

scholarly journals Deciphering the Manufacturing Production Space in Global City-Regions of Developing Countries—a Case of Pearl River Delta, China

2019 ◽  
Vol 11 (23) ◽  
pp. 6850
Author(s):  
Bo Liu ◽  
Desheng Xue ◽  
Yiming Tan
Keyword(s):  
Developing Countries ◽  
Influencing Factors ◽  
Pearl River Delta ◽  
Global City ◽  
River Delta ◽  
Pearl River ◽  
Spatial Characteristics ◽  
Multi Scale ◽  
City Regions ◽  
Production Space

In the context of economic globalization, the manufacturing production space in the global city-regions of developing countries have presented significant spatial characteristics, attracting attention to the problems of intensive and sustainable development of production space. Taking global city-region in the Pearl River Delta (PRD) as an example, manufacturing production space based on remote sensing (RS) technology and point of interest (POI) data extraction was more precise and continuous, which had more advantages for further analysis of spatial characteristics and influencing factors in multi-scale, and precise policy recommendations. The results show that: (1) under different scales, the distribution characteristics of manufacturing production space and the agglomeration characteristics of spatial form are different. It is not simply extensive agglomeration or diffusion that can accurately explain its diversified spatial characteristics. Meanwhile, for the local manufacturing production space optimization control, the local government should apply advanced experience according to local conditions instead of simply and roughly promotion or containment. (2) Influencing factors show a strong positive correlation with the urbanization rate, the number of foreign direct investment (FDI) enterprises and gross industrial production, and which shows a weak negative correlation with fixed asset investment and the employment population. In conclusion, the spatial characteristics of manufacturing production space in global city-regions in developing countries is significantly different from that in Western countries, and its influencing factors have similarities and differences. Therefore, when conducting multi-scale space optimization and sustainable regulation, the government should consider more about the actual multi-scale spatial characteristics of manufacturing production space and its influencing factors instead of copy the Western experience.

Effect of economic growth, trade openness, urbanization, and technology on environment of Asian emerging economies

2018 ◽  
Vol 29 (6) ◽  
pp. 1123-1134 ◽  
Author(s):  
Kashif Munir ◽  
Ayesha Ameer
Keyword(s):  
Economic Growth ◽  
Emerging Economies ◽  
New Technologies ◽  
Trade Openness ◽  
Kuznets Curve ◽  
Stirpat Model ◽  
Causality Test ◽  
Content Type ◽  
Long Run ◽  
Short Run

Purpose The purpose of this paper is to analyze the long-run as well as short-run effect of economic growth, trade openness, urbanization and technology on environmental degradation (sulfur dioxide (SO2) emissions) in Asian emerging economies. Design/methodology/approach The study utilizes the augmented STIRPAT model and uses the panel cointegration and causality test to analyze the long-run and short-run relationships. Due to the unavailability of data for all Asian emerging economies, the study focuses on 11 countries, i.e. Bangladesh, Hong Kong, India, Indonesia, Iran, Malaysia, Pakistan, Philippines, Singapore, Sri Lanka and Thailand, and uses balance panel from 1980 to 2014 at annual frequency. Findings Results showed that the inverted U-shape hypothesis of the environmental Kuznets curve holds between economic growth and SO2 emissions. While technology and trade openness increases SO2 emissions, urbanization reduces SO2 emissions in Asian emerging economies in the long run. Unidirectional causality flows from urbanization to SO2 emissions and from SO2 emissions to economic growth in the short run. Practical implications Research and development centers and programs are required at the government and private levels to control pollution through new technologies as well as to encourage the use of disposed-off waste as a source of energy which results in lower dependency on fossil fuels and leads to reduce emissions. Originality/value This study contributes to the existing literature by analyzing the effects of urbanization, economic growth, technology and trade openness on environmental pollution (measured by SO2 emissions) in Asian emerging economies. This study provides the essential evidence, information and better understanding to key stakeholders of environment. The findings of this study are useful for individuals, corporate bodies, environmentalist, researchers and government agencies at large.

scholarly journals Spatial Characteristics of Life Expectancy and Geographical Detection of Its Influencing Factors in China

2020 ◽  
Vol 17 (3) ◽  
pp. 906 ◽  
Author(s):  
Yafei Wu ◽  
Ke Hu ◽  
Yaofeng Han ◽  
Qilin Sheng ◽  
Ya Fang
Keyword(s):  
College Students ◽  
Environmental Factors ◽  
Life Expectancy ◽  
Influencing Factors ◽  
Health Improvement ◽  
Spatial Characteristics ◽  
Two Factors ◽  
Per Capita ◽  
Urbanization Rate ◽  
The Impact

Life expectancy (LE) is a comprehensive and important index for measuring population health. Research on LE and its influencing factors is helpful for health improvement. Previous studies have neither considered the spatial stratified heterogeneity of LE nor explored the interactions between its influencing factors. Our study was based on the latest available LE and social and environmental factors data of 31 provinces in 2010 in China. Descriptive and spatial autocorrelation analyses were performed to explore the spatial characteristics of LE. Furthermore, the Geographical Detector (GeoDetector) technique was used to reveal the impact of social and environmental factors and their interactions on LE as well as their optimal range for the maximum LE level. The results show that there existed obvious spatial stratified heterogeneity of LE, and LE mainly presented two clustering types (high–high and low–low) with positive autocorrelation. The results of GeoDetector showed that the number of college students per 100,000 persons (NOCS) could mainly explained the spatial stratified heterogeneity of LE (Power of Determinant (PD) = 0.89, p < 0.001). With the discretization of social and environmental factors, we found that LE reached the highest level with birth rate, total dependency ratio, number of residents per household and water resource per capita at their minimum range; conversely, LE reached the highest level with consumption level, GDP per capita, number of college students per 100,000 persons, medical care expenditure and urbanization rate at their maximum range. In addition, the interaction of any two factors on LE was stronger than the effect of a single factor. Our study suggests that there existed obvious spatial stratified heterogeneity of LE in China, which could mainly be explained by NOCS.

scholarly journals Effects of straw’s comprehensive utilization-technology on agricultural carbon emission in Jiangsu Province

2021 ◽  
Vol 267 ◽  
pp. 01014
Author(s):  
Xue Qin ◽  
Jun Yan ◽  
G.Y. Zhu
Keyword(s):  
Influencing Factors ◽  
Carbon Emissions ◽  
Emission Reduction ◽  
Carbon Emission ◽  
Explanatory Variable ◽  
Inhibitory Effect ◽  
Jiangsu Province ◽  
Stirpat Model ◽  
Comprehensive Utilization ◽  
The Core

Straw resources are abundant in Jiangsu province, the utilization and burning of straw is an important problem in agriculture carbon emission reduction. In order to analyze the effect of straw’s comprehensive utilization technology on agricultural carbon emission, the STIRPAT model is introduced, which takes straw utilization technology as the core explanatory variable while other influencing factors as control variables, and the ridge regression is adopted to conduct an empirical analysis on the influencing factors of agricultural carbon emission in Jiangsu province from 2008 to 2018. The results demonstrate that for every 1% increasing of straw’s comprehensive utilization technology, agriculture carbon emission will be reduced by 0.17%; the labor force is the biggest driver of agriculture carbon emissions; agriculture economic development, energy consumption takes a certain inhibitory effect on agriculture carbon emissions, but not very great.

scholarly journals Analysis of Spatial-Temporal Characteristics of the PM2.5 Concentrations in Weifang City, China

2018 ◽  
Vol 10 (9) ◽  
pp. 2960 ◽  
Author(s):  
Yixiao Li ◽  
Zhaoxin Dai ◽  
Xianlin Liu
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Influencing Factors ◽  
Human Activities ◽  
Contemporary Society ◽  
Spatial Characteristics ◽  
Temporal Characteristics ◽  
Temperature And Precipitation ◽  
Hourly Data ◽  
The Government

Air pollution, which accompanies industrial progression and urbanization, has become an urgent issue to address in contemporary society. As a result, our understanding and continued study of the spatial-temporal characteristics of a major pollutant, defined as 2.5-micron or less particulate matter (PM2.5), as well as the development of related approaches to improve the environment, has become vital. This paper studies the characteristics of yearly, quarterly, monthly, daily, and hourly PM2.5 concentrations, and discusses the influencing factors based on the hourly data of nationally controlled and provincially controlled monitoring stations, from 2012 to 2016, in Weifang City. The main conclusion of this study is that the annual PM2.5 concentrations reached a peak in 2013. With efficient aid from the government, this value has decreased annually and has high spatial characteristics in the northwest and low spatial characteristics in the southeast. Second, the seasonal and monthly PM2.5 concentrations form a U-shaped trend, meaning that the concentration is high in the summer and low in the winter. These trends are highly relevant to the factors of plantation, humidity, temperature, and precipitation. Third, within a week, higher PM2.5 concentrations appear on Mondays and Saturdays, whereas the lowest concentration occurs on Wednesdays. It can be inferred that PM2.5 concentrations tend to be highly dependent on human activities and living habits. Lastly, there are hourly discrepancies within the peaks and troughs depending on the month, and the overall daytime PM2.5 concentrations and reductive rates are higher in the daytime than in the nighttime.

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