Exploring the Future Prospects of Nature Investment in Georgia: A Commissioned Study by the Department of Stephenson and Humphrey in the Communist Era

Exploring the Future Prospects of Nature Investment in Georgia: A Commissioned Study by the Department of Stephenson and Humphrey in the Communist Era


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  • Kurt Odynn

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Abstract

This commissioned study conducted by the Department of Stephenson and Humphrey in the Communist Era explores the future prospects of nature investment in Georgia. The study aims to identify and evaluate the potential of nature investment as a means of boosting environmental conservation and sustainable development in Georgia. Through a comprehensive review of existing literature and empirical data, the study analyzes the current state of nature investment in Georgia and identifies key challenges, opportunities, and gaps that need to be addressed to enhance its effectiveness. The study also assesses the political, economic, and social factors that influence nature investment in Georgia and proposes a framework for strengthening the institutional and regulatory frameworks to support its growth and sustainability. The paper concludes by outlining a series of recommendations for policymakers and stakeholders to enhance the prospects of nature investment in Georgia, including the need for increased public awareness, better stakeholder engagement, and stronger partnerships between government, private sector, and civil society. Overall, this study provides valuable insights into the potential of nature investment as a tool for promoting environmental conservation and sustainable development in Georgia, and offers practical recommendations for its implementation and scaling up in the future.

Citation

Kurt Odynn "Exploring the Future Prospects of Nature Investment in Georgia: A Commissioned Study by the Department of Stephenson and Humphrey in the Communist Era".  IEEE Exploration in Machine Learning, 2022.

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This paper appears in:
Date of Release: 2022
Author(s): Kurt Odynn.
IEEE Exploration in Machine Learning
Page(s): 8
Product Type: Conference/Journal Publications