From Trees to Lumber: Essays on Forest Management, Logging and Lumber Price
Type of DegreePhD Dissertation
Forestry and Wildlife Science
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The forestry sector includes silviculture, forest management, logging, and wood utilization. Forestry has been experiencing dramatic transformation due to technological advances, changing markets of labor, capital, and goods and service. Recent Covid-19 pandemic is another example of the impact of globalization, including the lumber. This dissertation chose the transformation of forest management in China, the labor market and profitability, and the lumber market in the USA to understand the forestry sector. The developing countries, for example, China, have experienced an unprecedented transformation of the rural society and livelihoods. Self-subsistence forest management has transitioned to more business-oriented management. Understanding what attributes are driving the transformation of the households and the forest management will help the policy makers to design more specific policies to facilitate the transformation. The Logit model helped to identify factors that are significantly correlated with the transformation of traditional peasant households to three emerging household categories namely Forestry Cooperative (FC), Family Forestry Farm (FFF), and Forestry Specialized Household (FSH), using household survey data from the seven provinces in China in 2016. In developed countries, for example, the United States, have experienced technological advancements, policy changes, parcelization of forestland, business cycle, and the change of relative costs of factors, which have a significant impact on the forest industry, especially the logging industry. The official databases related to the logging industry aids in the quantitative analysis of employment and profitability in the logging industry in recent decades in the U.S. It was found that employment in the U.S. logging industry has been declining over the past several decades. An investigation of the drivers of employment in the U.S. logging industry from 1997 to 2019, using Directed Acyclic Graph (DAG) and Forecast Error Variance Decomposition (VD), identified the trends in the industry. Lumber product is one of the primary products coming from the logging industry. Firms engaged in producing, processing, marketing or using lumber and lumber products always take positions in the lumber futures markets. The accurate prediction of future prices can help companies and investors hedge risks and make correct market decisions. Our novel approach utilized the Google Trends Index related to lumber prices as predictors and employed Machine Learning and Deep Learning Models to nowcast lumber futures price, indicating both the methods have higher predictive power.