Understanding the Impacts of Plant Capacities and Uncertainties on the Techno-Economic Analysis of Cross-Laminated Timber Production in the Southern U.S.

Abstract

Understanding the economic feasibility of cross-laminated timber (CLT), an emerging and sustainable alternative to concrete and steel, is critical for the rapid expansion of the mass timber industry. However, previous studies on economic performance of CLT have not fully considered the variations in the feedstock, plant capacities, manufacturing parameters, and capital and operating costs. This study fills this gap by developing a techno-economic analysis of producing CLT panels in the Southern United States. The effects of those variations on minimum selling price (MSP) of CLT panels are explored by Monte Carlo simulation. The results show that, across all the plant capacities from 30,000 to 150,000 m 3 /year, the MSP ranges from $345 to $609/m 3 with a ±6%-9% range caused by the variations in feedstocks, key manufacturing parameters, capital and operating cost. The MSP decreases significantly along the increasing capacities. A sensitivity analysis exhibits that the lumber price, lumber preparing loss, plant capacity, and the installed costs of layering and gluing, finishing, and miscellaneous, are the top driving factors to CLT MSP. Supported by Geographic Information System, this study also studies the transportation cost of delivering CLT to customers under three CLT demanding levels (1%, 5%, 15%). The results show that the transportation cost is 1%-8% of the MSP. Lower demanding level or higher plant capacity can increase the transportation cost due to average longer delivering distance. When considering the delivered cost that sums MSP and transportation cost, larger plant capacity does not necessarily generate lower delivered cost.

Emerging Engineered Wood for Building Applications

“…Techno-economic analysis (TEA) is a commonly adopted tool to evaluate the technical performance and economic feasibility of a process, product, or service. ,,− TEA can support decision-making processes by providing both technical and economic information Figure A shows the TEA framework proposed by Zimmermann et al., which has similar phrases as the LCA framework and includes goal and scope, inventory, calculation of indicators, and interpretation .…”

“…The goal and scope definition phase usually includes main questions, intended uses, limitations, audiences of the analysis, system boundary, indicator selection, analysis methods, and other elements . Typical TEA indicators include technical indicators (e.g., energy demand, energy efficiency, conversion rate, material demand, technological maturity, and technological advancement), economic indicators (e.g., capital expenditure, operating expenditure, net present value, internal rate of return, profit, minimum selling price, and life cycle cost), and techno-economic indicators (e.g., technology readiness level). ,,,− For instance, as shown in Figure C, Lan et al used the average delivered cost (minimum selling price plus transportation cost) of CLT as the indicator to explore the impacts of varied plant capacities and CLT demanding levels on the economic performance …”

“…328 Zhang and Lan also used sensitivity analysis and their results show that the minimum selling price of CLT is susceptible to lumber price change and lumber preparing loss. 257 Wijeyekoon et al conducted a TEA coupled with risk analysis for co-producing bark briquettes and tannin adhesive from Radiata pine bark in two systems, a standalone plant, and a colocated symbiosis plant. 331 The two process designs were simulated by the process-based models and coupled with a discounted cash flow analysis.…”

“…(B) The system boundary of LCA for engineered wood products; (C) an example of TEA results (delivered cost including minimum selling price (MSP) and transportation cost) of 1 m 3 CLT (cross-laminated timber) on varied plant capacities and demanding levels . Reproduced with permission from ref . Copyright 2022 Tech Science Press.…”

“…TEA has also been utilized to investigate the impacts of technical parameters and economic parameters on the economic performance of wood products. ,, Common studied technical parameters include plant capacity, , energy demand, , material usage, material loss/efficiency, , transportation distance, , feedstock quality, ,, product grade; economic parameters can include feedstock cost, ,, material and fuel price, ,, equipment price, , internal rate of return or discount rate, , labor cost, , maintenance cost, waste disposal cost, and market demand . These analyses of parameter impacts are often coupled with scenario analysis or sensitivity analysis. , …”