The determinants of environmental quality in the SAARC region: a spatial heterogeneous panel data approach
Khaizran Khalid1, Muhammad Usman2, Muhammad Abuzar Mehdi1
1Department of Economics, Government College University, Faisalabad, 38000, Pakistan.
Abstract:
In recent years, financial development, trade policies, and energy performance have attracted attention due to their behavior on environmental quality. Therefore, the current study examines the impact of financial development, trade openness, primary and renewable energy utilization, and economic growth on the ecological footprint in South Asian Association for Regional Cooperation (SAARC) countries from 1990 to 2017. This article progresses the proficiency of financial development by utilizing the comprehensive and multidimensional index of financial sector development based on their depth, access, and efficiency of their financial institutions and markets. In order to estimate the robust results, this study employed the cross-sectional dependency tests that allow the second-generation unit root, Westerlund cointegration, augmented mean group (AMG), error correction model (ECM), and Dumitrescu-Hurlin (D-H) panel non-causality tests. The results revealed a very weak effect of financial development in a panel of SAARC countries, while country-specific results reveal that financial development significantly enhances the pollution level in the case of Bangladesh and Sri Lanka. However, it improves the environmental quality in Nepal. Furthermore, trade openness only improves the environmental quality in the case of Nepal. Additionally, the findings explore that primary energy consumption enhances the ecological footprint in Bangladesh, Nepal, and Sri Lanka and reduces in case of Bhutan. On the contrary, renewable energy consumption significantly improves the environmental quality in all countries except Bangladesh. Finally, consistent with these findings, a number of suitable policy implications are expressed in the angle of SAARC economies.
Related Concept Videos
Sampling Plans
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
Scatter Plot
Selected Data About Geographic Locations
Response Surface Methodology
The process of RSM involves several key steps:
Mechanistic Models: Compartment Models in Individual and Population Analysis
Statistical Methods for Analyzing Epidemiological Data


