Analyzing the Asymmetric Effects of Green Finance, Financial Development and FDI on Environment Sustainability: New Insights from Pakistan Based Non-Linear ARDL Approach
DOI:
https://doi.org/10.52131/joe.2023.0503.0151Keywords:
CO2 Emission, Green Finance, Fintech, Financial Development, FDIAbstract
This research examines the Asymmetric Effects of Green Finance, Financial Development, Financial technology, economic growth, foreign direct investment, and financial development on environmental sustainability. It accomplishes this by applying the Asymmetric Autoregressive Distribution of Lag (NARDL) approach on information obtained from Pakistan from 1997 to 2022. The Data is collected from the Economic Survey of Pakistan and WDI. By analyzing these factors' intricate dynamics within the context of Pakistan, the study seeks to ascertain the short- and long-term effects on environmental sustainability. Using the NARDL paradigm, we address the nonlinearities and asymmetric effects that may exist in this Model. Our findings demonstrate that Pakistan's attempts to protect the environment are aided or hindered by green finance initiatives, technology advancements, economic growth trends, FDI, and financial development. The study investigates how these linkages evolve over time, which aids in developing methodological and policy recommendations for promoting environmental sustainability across the country. This study adds to the body of previous research, Businesses, legislators, and other interested parties attempting to reconcile environmental protection with economic expansion in a world-changing world will find the results to be an invaluable resource. Policymakers should prioritize the development of green financial instruments, support sustainable economic growth, draw in foreign investments with an environmental focus, and use innovations to address the urgent problem of reducing carbon emissions and promoting a more sustainable future for Pakistan. The limitation of the study is that determining the validity of observed trends may be difficult because data for some variables may only be available for a brief period of time.
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Copyright (c) 2023 Amna Kanwal, Salman Khalid, Muhammad Zaheer Alam
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.