Realizing Mitigation Efficiency of European Commercial Forests by Climate Smart Forestry

Abstract

European temperate and boreal forests sequester up to 12% of Europe’s annual carbon emissions. Forest carbon density can be manipulated through management to maximize its climate mitigation potential, and fast-growing tree species may contribute the most to Climate Smart Forestry (CSF) compared to slow-growing hardwoods. This type of CSF takes into account not only forest resource potentials in sequestering carbon, but also the economic impact of regional forest products and discounts both variables over time. We used the process-based forest model 4 C to simulate European commercial forests’ growth conditions and coupled it with an optimization algorithm to simulate the implementation of CSF for 18 European countries encompassing 68.3 million ha of forest (42.4% of total EU-28 forest area). We found a European CSF policy that could sequester 7.3–11.1 billion tons of carbon, projected to be worth 103 to 141 billion euros in the 21st century. An efficient CSF policy would allocate carbon sequestration to European countries with a lower wood price, lower labor costs, high harvest costs, or a mixture thereof to increase its economic efficiency. This policy prioritized the allocation of mitigation efforts to northern, eastern and central European countries and favored fast growing conifers Picea abies and Pinus sylvestris to broadleaves Fagus sylvatica and Quercus species.

Tackling unresolved questions in forest ecology: The past and future role of simulation models

“…Forests provide important ecosystem services, such as timber production, carbon sequestration, recreation and protection against natural hazards, whose persistence or improvement is of high societal relevance (De Groot et al 2002, MEA 2005. Sustaining these ecosystem services is the focus of forest management (Nabuurs et al, 2017;Yousefpour et al, 2018). Forest IBMs have a long history in helping management planning (Courbaud et al, 2001;Hiltner et al, 2018;Huth & Ditzer, 2001;Huth et al, 2005;Keenan et al, 2008;Mäkelä et al, 2000;Porté & Bartelink, 2002;Pretzsch et al, 2008).…”

Evolving Integrated Models From Narrower Economic Tools: the Example of Forest Sector Models

“…Future efforts could also focus on reformulating the objective function as a multi-objective function where all objectives are integrated in a similar way, and where biophysical units can be used. For example, Yousefpour et al [163] follow a goal programming approach and use a multiobjective function composed of the net present value for commercial timber production and of carbon sequestration fluxes, to which different weights are attributed. Such multi-objective methods are commonly used in applied forest management [164] and could benefit the FSM research field and allow for the inclusion of objectives for which the evaluation of economic value is the main limitation.…”

Magnitude and timing of density reduction are key for the resilience to severe drought in conifer-broadleaf mixed forests in Central Europe

“…In landscapes experiencing drought, simulations of management intensity and harvesting types have been tested to evaluate the resilience of managed forests to climate change (e.g., Niinimäki et al 2012;Seidl et al 2017). Forest-enterprise, regional, and global assessments of adaptive management have also tested the effectiveness of forest growth simulation models to explore adaptive management options in decision-making (Thrippleton et al 2020;Yousefpour et al 2019;Yousefpour et al 2018). Despite these recent applications of mechanistic simulation models for promoting forest resilience, only a few model applications consider both ecological (i.e., improving resilience to drought) and economic aspects.…”