This study analyzed logging industry employment and profitability in recent decades in the U.S. based on Occupational Employment Statistics (OES), Quarterly Workforce Indicators (QWI), Quarterly Census of Employment and Wages (QCEW), and Timber Product Output (TPO) Reports. The logging industry in the U.S. has experienced reduced employment with an aging workforce over the past two decades. The changes might be related to increased productivity from mechanization, combined with reduced demand for logging, but estimates of capital and labor productivity for logging are not available. To overcome the data limitation, a simple and cost-effective economic model, Economic Input-Output Life Cycle Assessment (EIO-LCA) Model, was applied to estimate the profitability of the entire industry at a state level. It was found that the reduced demand and increased operating costs led to poor profitability and a wave of closures of logging firms but also accelerated the adjustment in the logging industry. Serious challenges facing the forestry sector include the lack of an effective monitoring tool for the logging industry, structural shortage of logging labor, and rising operating costs.

Firms engaged in producing, processing, marketing, or using lumber and lumber products always invest in futures markets to reduce the risk of lumber price volatility. The accurate prediction of real-time prices can help companies and investors hedge risks and make correct market decisions. This paper explores whether Internet browsing habits can accurately nowcast the lumber futures price. The predictors are Google Trends index data related to lumber prices. This study offers a fresh perspective on nowcasting the lumber price accurately. The novel outlook of employing both machine learning and deep learning methods shows that despite the high predictive power of both the methods, on average, deep learning models can better capture trends and provide more accurate predictions than machine learning models. The artificial neural network model is the most competitive, followed by the recurrent neural network model.

Employment in the US logging industry has been declining over the past few decades and fell to a 20-year low following the 2008 economic recession. This study investigates the drivers of employment in the US logging industry from 2007 to 2017, using a directed acyclic graph (DAG). This approach is applied for the first time to disclose the contemporaneous causal relations among employment, wages, mechanization, production level, and product prices in the logging industry. Forecast error variance decomposition (VD) is further used to examine the long-run dynamic relationships between these variables. The results show that the product price directly affects employment and indirectly promotes employment through wages. The results of VD show that mechanization has an increasing long-term effect on employment.

China’s economy has shifted from high-speed development to high-quality development as the marginal benefits of the traditional growth model decline. This study explores how new quality productive forces (NQPFs) drive industrial structure upgrading via factor endowment upgrading and factor synergy, with the goal of addressing development bottlenecks. Using 2013–2024 provincial panel data, NQPFs are measured through the entropy weight-TOPSIS method (factor endowment upgrading) and a coupled coordination model (factor synergy), integrated into a comprehensive index, and analyzed via two-stage least squares (2SLS) regression. The empirical results show that China’s NQPFs are in their initial stage, with factors still in the process of accumulation and adaptation. NQPFs significantly promote industrial structure advancement and rationalization, with factor synergy outperforming factor endowment upgrading. Its promotion of advancement is universal, but its impact on rationalization is regionally heterogeneous, with inadequate transmission in underdeveloped and high-environmental-regulation provinces and ineffective dual mechanisms in manufacturing-led provinces. Notably, the driving effect of NQPFs on manufacturing upgrading is stronger than that on services. This study provides guidance for NQPF cultivation and contributes to the theory and Chinese practice of industrial structure upgrading from a dual-factor perspective.