This review explores climate smart forestry's definitions and trade-offs, highlighting its implications for ecosystem management.
In this article, we review the definition of Climate Smart Forestry (CSF), its developmental history, and the challenges, trade-offs, and synergies associated with the use of this term. Our review was compiled by systematic searches of the English literature using the terms “climate smart forestry” “climate adaptive forestry” “forestry and climate mitigation” in the accessible English literature. CSF is a relatively recent term that is defined as forestry practice that strives to; (i) remove greenhouse gas (GHG) emissions through mitigation; (ii) adapt and build forest resilience to climate change; and (iii) sustainably increase forest productivity and socio-economic incomes and benefits. In general, trade-offs must occur with the use of this definition because mitigation and adaptation practices are often counteracting, and because land managers and landowners have other forest values that take precedence over “climate smart practice” such as immediate sources of income, products, and services for livelihood. We suggest when CSF is often promoted with a singular focus by policy makers and practitioners without consideration of the whole ecosystem, and especially the social aspects of place, it is usually ignored. In addition, businesses, NGOs, and policy makers often appropriate the term as a brand to market their own specific climate agendas often facilitating a focus on carbon credits, tree planting, and the promotion of vulnerable overstocked high-carbon forests. In response, the academic community must champion an approach that is more nuanced and synergistic with other management values. To provide examples of these tradeoffs, we describe regional place-based applications of CSF practice chiefly from North America and from Europe where CSF has been mostly promoted. In summary, we conclude that the most prudent approach to CSF should be risk-averse, accounting and managing for multiple values in an unpredictable world.
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Ashton et al. (2025) studied this question.
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