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.
|Rivista||RENEWABLE & SUSTAINABLE ENERGY REVIEWS|
|Stato di pubblicazione||Pubblicato - 2020|
- Carbon accounting
- Climate change
- Forest management
- Life cycle assessment
- Post-normal science