Air Pollution Management: A Multivariate Analysis of Citizens' Perspectives and Their Willingness to Use Greener Forms of Transportation

Int J Environ Res Public Health. 2022 Nov 7;19(21):14613. doi: 10.3390/ijerph192114613.

Abstract

The present research aims to understand how air pollution can be managed by public authorities, both central and local, starting from citizens' perspectives on the issue. Air quality is a real problem, affecting people at multiple levels. Thus, we introduced the following variables to better understand the problem and to be able to formulate theoretical and practical implications for public management: the involvement of authorities in reducing air pollution; the involvement of citizens in reducing air pollution; financial incentives for citizens and companies for adopting behaviors that reduce air pollution; green investments in the city; the impact of air pollution on the community; and the need for independent bodies to monitor air pollution. The research methodology used is partial least squares structural equation modelling (PLS-SEM) and the required data were gathered from issuing a survey to citizens from the most important cities in Romania where pollution poses important challenges for the community and for the authorities. The results are useful to public managers in local and central institutions for creating better strategies meant to reduce air pollution, increase air quality, and improve the quality of the citizens' lives.

Keywords: air pollution; air pollution management; air quality; electrical cars; financial incentives; green investments; multivariate analysis; public management; public transportation; urban gardens.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Air Pollutants* / analysis
  • Air Pollution* / analysis
  • Air Pollution* / prevention & control
  • Cities
  • Environmental Pollution / analysis
  • Humans
  • Multivariate Analysis
  • Transportation

Substances

  • Air Pollutants

Grants and funding

This project has received partial funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No. 887544.