We developed models of suppression expenditures for individual extended attack fires in British Columbia using parametric and nonparametric machine-learning (ML) methods. Our models revealed that suppression expenditures were significantly affected by a fire’s size, proximity to the wildland–urban interface (WUI) and populated places, a weather based fire severity index, and the amount of coniferous forest cover. We also found that inflation-adjusted individual fire suppression expenditures have increased over the 1981 to 2014 study period. The ML and parametric models had similar predictive performance: the ML models had somewhat lower root mean squared errors but not on mean average errors. Better specification of fire priority as well as resource constraints might improve future model performance.

Burned area, fire severity, and suppression expenditures have increased in British Columbia in recent decades with climate change. Approximately 80% of suppression expenditures are attributable to wildfires near the Wildland–Urban Interface (WUI). Evaluating the potential for fuel management to reduce suppression expenditures is essential to mitigating demands on fire response resources and reducing impacts on communities. One management approach is to increase the proportion of deciduous tree species, which have a lower propensity for crown fire. Using fire suppression expenditure data from 1981 to 2014, we applied the machine learning method causal forests (CFs) to estimate the effect of the proportion of conifer forest cover on suppression expenditures for WUI fires and how these effects varied with other influential factors (i.e., heterogenous treatment effects). Across all fires, the effect of conifer cover on suppression expenditures was stronger on private land compared to public land, under high fire danger measured by daily severity ratings (DSRs), which reflect wind speed and fuel moisture, and for fires igniting earlier in the calendar year, based on Julian day. These findings provide insights into prioritizing wildland fuel treatment when budgets are limited. The CFs approach demonstrates potential for broader applications in fire risk mitigation and analysis beyond the scope of the current data. CFs may also be valuable in other areas of forest research where heterogenous treatment effects are common.

Forest products in Canada contribute significantly to the Canadian trade balance. Canadian producers depend heavily on export markets raising the question: how do exchange rate fluctuations impact Canada’s competitiveness in foreign markets? The paper applies a fixed-effects model with individual slopes to investigate this question. Twenty years of monthly data are employed to study the pricing-to-market (PTM) behaviour of Canadian softwood log, lumber and pulp exports as the exchange rate changes. We find a great degree of incomplete exchange rate pass-through, with PTM being apparent for Canadian exporters, particularly in major markets. The export price adjustment tends to mitigate the effect of exchange rate fluctuations on foreign currency prices of Canadian products in most cases. This pricing behaviour reflects exporters’ desire to stabilize their share of the destination market.