Graduate Project
 

Utilizing economic indicators in a machine learning platform to create an interdisciplinary forecasting framework for natural resource management and policy planning

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https://ir.library.oregonstate.edu/concern/graduate_projects/8c97kx57k

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  • Abstract: Resource managers and policy face challenges in forecasting the demand for natural resources replenishment (where possible) of natural resources due to changes in human lifestyle, population change and economic development. The year 2020 demonstrated this challenge on several fronts, including the realm of natural resources. Additionally, much competition for resources by all living organisms, carbon cycling, and effects from human behavior and our ever-changing climate, pose significant risks and future depletion or damage to renewable and finite resources. Managing risks and forward planning, requires several methods by which we can measure system variables and discern the most likely future outcomes and plan for them accordingly. Proposed is the use of a disciplined collection of economic indicators, coupled with data from the wood products industry, which have a statistical significance strong enough to make forecasts for wood resource commodities (adaptable to most natural resource commodities). Therefore, with back-tested data used to create forecast accuracy high as 86% at times, policy and adaptive management strategies can be supplemented to assist in making informed decisions three to twenty-four months, or more into the future. When possible, forecasting may allow us to effectively intervene into ecological systems for greater contribution and sound management of our social responsibilities for finite and renewable resources. The purpose of this paper is to describe, demonstrate, and provide evidence that indeed this can be accomplished through use of publicly available open-source data, and to perhaps enhance this ability through utilization of cloud based Artificial Intelligence (machine learning) methods which are becoming more common and accessible. Ultimately, the long-term vision provides a foundation database, where interdisciplinary users can add specialized topic data which builds higher level functions, shared openly, allowing all users full access to develop an ever growing collective of resource data. This “collective” allows for anticipation of events, instead of just simply reacting to them. This combined with the power of constantly integrated A.I. algorithms, data analytics, GIS, and real time data, all with the ability to contain nearly limitless variable and case data (limited only by local or cloud server capacity), could provide a powerful natural resource forecasting framework on a local, national, or even global scale. There are few, if any, interactive dashboards fully categorized and binned, that can be viewed on portable and desktop connected hardware, in the public domain for use by natural resource personnel. A tool enhancing our ability to anticipate possible changes, is an asset long overdue.
  • Executive Summary: This document outlines a detailed and disciplined method for anticipating natural resource use in forward time frames applied to the wood products industry. This strategy could be implemented synergistically with ecological, biological, geographic, demographic, and regional planning when invoking short and long-term forecasting, existing policy, management of natural resources, and creation of public policy. If we fail to effectively forecast the future, opportunities to plan, manage, and act could be compromised with greater downside risk. Year 2020 provided an opportunity to investigate effects on the system, and has thus far provided a wealth of relevant data. The United States Federal Reserve Bank has revised year 2020 G.D.P. growth estimates downward, owing to global pandemic effects. Separating the near-term effects of COVID-19, from longer term trends will be key to making better natural resource management and policy decisions. In summary, adoption of a strategic disciplined system to monitor the economic issues driving natural resources and public policy, is knowledge for all persons engaged in managing renewable and non-renewable resource commodities, and increases clarity. More clarity equates to better decisions.
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file details Polinsky_JM_Capstone2021.pdf 2021-06-29 Öffentlich Herunterladen