Air pollution is one of the serious environmental problems facing the world. This paper systematically investigates the impact and transmission mechanism of the construction of national eco-industrial parks (NEDPs) on urban air pollution based on Chinese city-level panel data from 2003 to 2021 using a staggered difference-in-differences (staggered DID) model. It is found that the construction of NEDP significantly reduces urban air pollution, a conclusion supported by the negative weight diagnostic test and two types of robust DID estimators. Mechanism analyses indicate that NEDP construction reduces urban air pollution mainly by improving regional environmental regulation, promoting green technology innovation and improving energy structure. In addition, the mitigation effect of NEDP construction on urban air pollution is heterogeneous by policy intensity, city resource endowment, city size and administrative status. Further tests show that the institutional environment enhances the air pollution mitigation effect of NEDP construction and that the better the degree of marketization, property rights system, legal system and market development in the place where the policy is implemented, the more conducive it is to amplify the air pollution suppression effect brought about by NEDP construction. Developing economies should take complete account of the characteristics of different regions when implementing place-based green policies to achieve synergistic development of the environment and the economy.
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http://dx.doi.org/10.1007/s11356-023-31168-2 | DOI Listing |
Water Sci Technol
January 2025
Department of Engineering, School of Engineering and Technology, Sunway University, Bandar Sunway, Petaling, Jaya 47500, Malaysia.
Coal power plants adversely impact air pollution, but they also pose a risk to our water sources. Discharge wastewater from power plants may degrade the quality of nearby water bodies. This study evaluates the potential water-related environmental impacts of electricity generation at an ultra-supercritical coal power plant in Malaysia using the life cycle assessment method.
View Article and Find Full Text PDFScand J Public Health
January 2025
Norwegian Institute of Public Health, Oslo, Norway.
Socioeconomic conditions remain an important factor in determining health outcomes in Northern Europe. In this commentary, we argue for evidence-based temperature-related climate adaptation policies in Northern Europe that account for disparities in socioeconomic conditions and aim at universal health coverage. We highlight the role of spatial and occupational disparities in urban areas that can be important factors in increased physical and mental health impacts related to heat and cold.
View Article and Find Full Text PDFEnviron Epigenet
December 2024
Institute of Clinical Science B, Royal Victoria Hospital, Centre for Public Health, Queens' University Belfast, Grosvenor Rd, Belfast BT12 6BA, United Kingdom.
The increasing prevalence of neurodegenerative diseases poses a significant public health challenge, prompting a growing focus on addressing modifiable risk factors of disease (e.g. physical inactivity, mental illness, and air pollution).
View Article and Find Full Text PDFWellcome Open Res
November 2024
Indian Institute of Public Health-Bengaluru, Public Health Foundation of India, Bangalore, India.
Background: Over 250 million children are developing sub-optimally due to their exposure to early life adversities. While previous studies have examined the effects of nutritional status, psychosocial adversities, and environmental pollutants on children's outcomes, little is known about their interaction and cumulative effects.
Objectives: This study aims to investigate the independent, interaction, and cumulative effects of nutritional, psychosocial, and environmental factors on children's cognitive development and mental health in urban and rural India.
Front Big Data
January 2025
Climate and Environmental Physics, Physics Institute, University of Bern, Bern, Switzerland.
Atmospheric ozone chemistry involves various substances and reactions, which makes it a complex system. We analyzed data recorded by Switzerland's National Air Pollution Monitoring Network (NABEL) to showcase the capabilities of machine learning (ML) for the prediction of ozone concentrations (daily averages) and to document a general approach that can be followed by anyone facing similar problems. We evaluated various artificial neural networks and compared them to linear as well as non-linear models deduced with ML.
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