Search

• Substitution factors for forest products for the Canadian context were developed. • Substitution factors ranging from 0.51 to 0.91 tC/tC were found. • They will lead to more accurate assessments of the forest GHG mitigation potential. • These factors will help governments to manage forest and fight climate change. Evaluating the climate change mitigation potential of the forest sector requires a holistic approach based on forest carbon (C) sequestration, C storage in harvested wood products (HWP) and substitution on markets. High uncertainty is associated with substitution factors, that express avoided fossil greenhouse gas (GHG) emissions from the use of forest-based products in replacement of GHG-intensive materials and fossil fuels. Few studies have focused on the development of substitution factors in Canada, resulting in the use of unrepresentative generic data. Here, we provide a framework to reduce uncertainties related to substitution factors for primary wood products in a Canadian context. A life cycle assessment framework is used to quantify fossil GHG emissions for a baseline and a wood-intensive scenario. For solid product substitution, we focused on the construction sector and analyzed a range of innovative wood buildings with steel and reinforced concrete as alternative materials. We found non-weighted averages of 0.80 tC/tC for sawnwood and 0.81 tC/tC for panels. For energy substitution, we analyzed cases with different specifications on biomass product, facility type and alternative fossil fuel source in non-residential heat production and biofuel transportation sectors. We found a non-weighted average of 0.80 tC/tC for non-residential heat production and 0.51 tC/tC for biofuel transportation, that can be interpreted as 0.91 tC/tC for heavy fuel oil, 0.69 tC/tC for light fuel oil and 0.68 tC/tC for natural gas substitution. These results provide a benchmark for substitution factors in Canada, to help guide forest management strategies for climate change mitigation. [ABSTRACT FROM AUTHOR]

The success and rate of forest regeneration has consequences for sustainable forest management, climate change mitigation, and biodiversity, among others. Systematically monitoring forest regeneration over large and often remote areas is challenging. Remotely sensed data and associated analytical approaches have demonstrated consistent and transparent options for spatially-explicit characterization of vegetation return following disturbance. Moreover, time series of satellite imagery enable the establishment of spatially meaningful recovery baselines that can provide a benchmark for identifying areas that are either under- or over-performing relative to those baselines. This information allows for the investigation and/or prioritization of areas requiring some form of management intervention, including guiding tree planting initiatives. In this research, we assess recovery following stand replacing disturbances for the 650 Mha forested ecosystems of Canada for the period 1985–2017, wherein ~51 Mha of Canada's forested ecosystems were impacted by wildfire, and ~ 21 Mha were impacted by harvesting. For quantification of forest recovery, we implement the Years to Recovery or Y2R metric using Landsat time series data based on the Normalized Burn Ratio (NBR) to relate the number of years required for a pixel to return to 80% of its pre-disturbance NBR value. By the end of the analyzed period, 76% of areas impacted by wildfire were considered spectrally recovered compared to 93% of harvested areas. On average, we found that harvest areas had more rapid spectral recovery (mean Y2R = 6.1 years) than wildfire (mean Y2R = 10.6 years) and importantly, that Y2R varied by ecozone, disturbance type, pre-disturbance land cover, and latitude. We used airborne laser scanning data to assess whether pixels that were considered spectrally recovered had attained United Nations Food and Agricultural Organization benchmarks of canopy height (>5 m) and cover (>10%) across four geographic regions representing different forest types. Overall, 87% and 97% of recovered pixels sampled in harvests and wildfires, respectively, had achieved at least one of the benchmarks, with benchmarks of height more readily achieved than benchmarks of cover. By analyzing spatial patterns of Y2R, we identified areas that had significant positive or negative spatial clustering in their rate of spectral recovery. Approximately 3.5–4% of areas disturbed by wildfire or harvest had significant positive spatial clustering, indicative of slower spectral recovery rates; these areas were also less likely to have attained benchmarks of height and cover. Conversely, we identified significant negative spatial clustering for 0.94% of areas recovering from harvest and 1.93% of areas recovering from wildfire, indicative of spectral recovery that was more rapid than the ecozonal baseline. Herein, we demonstrated that remote sensing can provide spatial intelligence on the nature of disturbance-recovery dynamics in forested ecosystems over large areas and moreover, can retrospectively quantify and characterize historic forest recovery trends within the past three decades that have implications for forest management, climate change mitigation, and restoration initiatives in the near term. • Landsat time series monitored forest disturbance and recovery for 32 years across 650 Mha. • Harvests spectrally recovered more rapidly (6.1 years) than wildfire (10.6 years). • Lidar-derived benchmarks of canopy cover (>10%) and height (>5 m) used for validation. • 87% and 97% of recovered harvest and wildfire samples achieved one of the benchmarks. • Nationally, 3.5%–4% of wildfire and harvest areas had slower than expected recovery. [ABSTRACT FROM AUTHOR]

The demand for wood-based energy is foreseen to grow as energy and climate policies around the world promote the use of bioenergy for climate change mitigation. However, the carbon impacts of forest bioenergy range widely in the literature. The value-choices made on the response of forest management to bioenergy demand have a major influence on the results obtained from modeling exercises and may actually change the contribution of forest bioenergy from climate worsening to climate change mitigation. Despite their relevance, there is very little information, or transparency, as to the empirical basis by which these assumptions are chosen and evaluated against. This study aims to fill in this crucial knowledge gap through a mix of critical review, analysis of historical statistical data, and expert judgement. Several prominent studies reporting climate change mitigation from forest bioenergy in three countries are reviewed: Canada, Sweden, and Southeast USA. This analysis shows that the studies rely on assumptions that bioenergy demand will spur supply responses aimed at more efficient forest management and/or relative increases in forest area. Confronting literature assumptions with trends in historical data, we present the most extensive reality-check of bioenergy literature assumptions to date. We find that studies projecting a large role of forest bioenergy in climate change mitigation rely on assumptions that are too optimistic, at times outright unrealistic. We believe scientists could avoid the misinterpretation of their results and improve the policy relevance of their work by more transparent reporting of: i) value-laden assumptions, ii) their influence on the results, iii) the process and rationale behind such assumptions, especially distinguishing between assumptions that reflect incremental changes in management from assumptions requiring transformational change in several industrial sectors. • Accounted carbon benefits of forest bioenergy depend on underlying assumptions. • Forest bioenergy mitigates climate change only when additional biomass is produced. • Forest management responses assumed in the literature are overly optimistic. • Post Normal Science tools can help scientists avoid misinterpretation of results. • Scientists should better convey that model results are contingent on value-choices. [ABSTRACT FROM AUTHOR]