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Sustainability assessment using STIRPAT approach to environmental quality: an extended panel data analysis.
- Source :
-
Environmental science and pollution research international [Environ Sci Pollut Res Int] 2021 Apr; Vol. 28 (14), pp. 18163-18175. Date of Electronic Publication: 2021 Jan 06. - Publication Year :
- 2021
-
Abstract
- The consequence of increasing economic activities is observable in the incidence of environmental deterioration. Many studies have explored the precedents of environment quality. In this regard, the proposed stochastic impacts by regression on population, affluence, and technology (STIRPAT) and environmental Kuznets curve (EKC) analysis are valuable not only for academic analysts, but also for policymakers. This study has focused on 80 selected countries between 1990 and 2017, which confirms the existence of EKC within the STIRPAT framework. The results are estimated with the help of dynamic ordinary least square (DOLS), which controls for the autocorrelation in long periods. According to the estimated results, this study confirms U-shaped EKC based on industrial-, agricultural-, and services-based economic activities. This means that over-reliance on one specific economic activity may harm the environment and create footprint. In this regard, urbanization is responsible for affecting carbon dioxide emissions. Moreover, governance and technology are protecting the environment. This quadratic function had classified the sample countries in terms of the degree of sustainability of their economic activity sectors. This study proposes that countries should work on a balanced composition of economic activity so that the lowest possible environmental deterioration is caused.
- Subjects :
- Carbon Dioxide
Technology
Urbanization
Data Analysis
Economic Development
Subjects
Details
- Language :
- English
- ISSN :
- 1614-7499
- Volume :
- 28
- Issue :
- 14
- Database :
- MEDLINE
- Journal :
- Environmental science and pollution research international
- Publication Type :
- Academic Journal
- Accession number :
- 33410004
- Full Text :
- https://doi.org/10.1007/s11356-020-12044-9