Air pollution driving factors analysis: Evidence from economically developed area in China

Abstract

Rapid economic growth is usually accompanied by rising air pollution in China, especially in the economically developed regions. Beijing is the capital of China, which is selected as a study case in this article. The aim of this study is to authenticate if the Environmental Kuznets curve (EKC) hypothesis exists between air pollution and economic growth in Beijing over the period of 1990–2013, and to understand the driving factors of the air pollution by utilizing the Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model. To avoid the multicollinearity of the driving factors, ridge regression is utilized. CO2, SO2 and dust emissions are served as the air pollution indicators. Results show that, both CO2 and SO2 emissions, contribute to an agreement to the EKC hypothesis, while dust emissions do not. Besides, the turning point of SO2 emissions has already achieved in 1999, whereas that of CO2 emissions will be achieved in 2026. Population, urbanization, and industry structure are introduced into the STIRPAT model. In the STIRPAT model, CO2, SO2 and dust emissions all follow the EKC hypothesis, while only SO2 emissions achieved the turning point in 2006, nevertheless, that of CO2 and dust emissions will be achieved in 2023 and 2070, respectively. Population, urbanization, and industry structure factors all contribute a positive impact on CO2 emissions. However, the three factors all exert negative influence on the SO2 and dust emissions. Finally, policies on addressing air pollution problems in Beijing have been discussed. © 2016 American Institute of Chemical Engineers Environ Prog, 35: 1231–1239, 2016

Analysis on the regional difference and impact factors of CO2 emissions in China

“…Ridge regression was employed in this article to examine the impact factors of net CO 2 emissions, and the formal formulation of ridge regression is regarded as one of least squares subject to specific parameters . Linear least square fit is known as a standard method to solve the system of linear equations: and it seeks to minimize the residual: where Y represents m × 1 vector; X represents m × n matrix ( m ≥ n ); β represents n × 1 vector; refers to Euclidean norm.…”

Spatial Correlation, Influencing Factors and Environmental Supervision on Mechanism Construction of Atmospheric Pollution: An Empirical Study on SO2 Emissions in China

“…Guan et al found that population density was significantly related to smoke and dust in East China, India, North America and North Africa [4]. Li and Sun used a STIRPAT model to study the atmospheric pollution situation in Beijing from 1990 to 2013, and through empirical analysis they found that social and economic factors such as population, urbanization and industry structure have an impact on atmospheric pollutants [5]. Yang et al studied the impact of energy consumption, energy structure, and treatment technology on SO 2 based on the LMDI decomposition method [6].…”

Regional Inequality and Influencing Factors of Primary PM Emissions in the Yangtze River Delta, China

“…For instance, Wang et al applied ridge regression to eliminate multicollinearity for revealing the influence mechanism of socio-economic factors on energy-related carbon dioxide emissions in Guangdong Province, China [65]. Li et al utilized ridge regression to avoid the multicollinearity of independent variables for understanding the driving factors of the air pollution (SO 2 , CO 2 ) in Beijing, China [66].…”