Using Predictive Analytics to Optimize Future Federal Sustainability Investment Allocation

Using Predictive Analytics to Optimize Future Federal Sustainability Investment Allocation | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 31 March 2026 V1 Latest version Share on Using Predictive Analytics to Optimize Future Federal Sustainability Investment Allocation Author : Emmanuel Oluwagboyega 0009-0006-4245-5013 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.177499350.05927900/v1 35 views 23 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Federal sustainability initiatives across the United States demand optimized investment strategies to ensure environmental progress, economic viability, and long-term resource management. With evolving global climate pressures and budgetary constraints, predictive analytics has emerged as a strategic instrument in refining capital allocation for sustainability programs. This study investigates how machine learning, real-time data modeling, and AI-driven analytics can enhance the precision, transparency, and impact of future federal sustainability investment decisions. Empirical insights demonstrate increased forecasting accuracy, improved project prioritization, and superior financial performance when predictive analytics tools are integrated into decision frameworks. Supplementary Material File (using predictive analytics to optimize future federal sustainability investment allocation (1).pdf) Download 145.59 KB Information & Authors Information Version history V1 Version 1 31 March 2026 Copyright This work is licensed under a Creative Commons Attribution 4.0 International License Keywords analytics federal financial investment sustainability Authors Affiliations Emmanuel Oluwagboyega 0009-0006-4245-5013 [email protected] View all articles by this author Metrics & Citations Metrics Article Usage 35 views 23 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Emmanuel Oluwagboyega. Using Predictive Analytics to Optimize Future Federal Sustainability Investment Allocation. Authorea . 31 March 2026. DOI: https://doi.org/10.22541/au.177499350.05927900/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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