Purpose of Review: Forests are integral to global ecological stability, climate regulation, and economic resilience. They function as major carbon sinks, act as biodiversity reservoirs, and provide ecosystem services. Accurately modeling forest growth is essential to predict ecosystem responses to climate change and optimize ecosystem services. However, predicting forest growth remains challenging due to complex interactions between ecological processes, external drivers like climate change, and intrinsic dynamics, such as legacy effects and emergent properties, that influence forest responses over time. This work provides a systematic in-depth analysis of both established and emerging theories as found in the literature, exploring their integration into modern forest growth modeling with a special focus on new approaches, as implemented in 18 forest growth models which vary in their structure, objectives, and overarching goals. Recent Findings: Forest modeling requires a deep understanding of forest growth theories driven by multiple interacting processes. Over time, numerous eco-physiological theories have been developed to predict forest growth under both current and future climatic conditions via dynamic vegetation models. While some were established in the past, new approaches continue to emerge, refining the complexity, predictive accuracy, and practical applicability of models. This ongoing evolution has resulted in models that are theoretically diversified but also increasingly relevant for real-world case studies dealing with both anthropogenic and natural disturbances. Machine learning, trained on increasingly large datasets, is emerging as a powerful complement to traditional forest models. Rather than replacing process-based approaches, it can be combined with them in hybrid frameworks that integrate mechanistic understanding with data-driven flexibility. This combination improves predictive performance, extends model applicability, and supports more robust decision-making in forest management. Summary: Amid the ongoing’chicken-and-egg’ debate on whether photosynthesis drives growth or growth drives photosynthesis, our review synthesizes key interconnected theories, including Functional Balance, Local Determination of Growth, and Optimality Principles of forest growth. By integrating these perspectives, we offer a clear and comprehensive overview of the main frameworks governing growth and resource allocation in plants. As multiple studies emphasize the importance of integrating different and recent theories to better capture growth dynamics, we build on a state-of-the-art multi-modelling comparison to discuss what the implications of different theories might be at different temporal and spatial resolutions. Finally, we explore how emerging technologies, such as machine learning, can enhance predictive accuracy and help address current modeling limitations.

Harvested wood products (HWPs) have a pivotal role in climate change mitigation, a recognition solidified in many Nationally Determined Contributions (NDCs) under the Paris Agreement. Integrating HWPs' greenhouse gas (GHG) emissions and removals into accounting requirements relies on typical decision-oriented tools known as wood product models (WPMs). The study introduces the TimberTracer (TT) framework, designed to simulate HWP carbon stock, substitution effects, and emissions from wood decay and bioenergy. Coupled with the 3D-CMCC-FEM forest growth model, TimberTracer was applied to Laricio Pine (Pinus nigra subsp. laricio) in Italy’s Bonis watershed, evaluating three forest management practices (clearcut, selective thinning, and shelterwood) and four wood-use scenarios (business as usual, increased recycling rate, extended average lifespan, and a simultaneous increase in both the recycling rate and the average lifespan) over a 140 year planning horizon, to assess the overall carbon balance of HWPs. Furthermore, this study evaluates the consequences of disregarding landfill methane emissions and relying on static substitution factors, assessing their impact on the mitigation potential of various options. This investigation, covering HWPs stock, carbon (C) emissions, and the substitution effect, revealed that selective thinning emerged as the optimal forest management scenario. In addition, a simultaneous 10% increase in both the recycling rate and half-life, under the so-called “sustainability” scenario, proved to be the optimal wood-use strategy. Finally, the analysis shows that failing to account for landfill methane emissions and the use of dynamic substitution can significantly overestimate the mitigation potential of various forest management and wood-use options, which underscores the critical importance of a comprehensive accounting in climate mitigation strategies involving HWPs. Our study highlights the critical role of harvested wood products (HWPs) in climate change mitigation, as endorsed by multiple Nationally Determined Contributions (NDCs) under the Paris Agreement. Utilizing the TimberTracer framework coupled with the 3D-CMCC-FEM forest growth model, we identified selective thinning as the optimal forest management practice. Additionally, enhancing recycling rates and extending product lifespan effectively bolstered the carbon balance. Moreover, this study emphasizes the necessity of accounting for landfill methane emissions and dynamic product substitution, as failing to do so may significantly overestimate the mitigation potential of implemented projects. These findings offer actionable insights to optimize forest management strategies and advance climate change mitigation efforts.

To assess the degree to which it has met its commitments under the Paris Agreement, Morocco is called upon to carry out carbon assessments and transparent evaluations. Within the forestry sector, little is known about the role of Morocco’s forests in contributing to carbon uptake. With this aim, we applied for the first time in the literature the 3-PG model to Cedrus atlantica ((Endl.) Manetti ex Carrière, 1855), which represents about 131,800 ha of Morocco’s forest area (i.e., Azrou forest). Through the Differential Evolution-Markov Chains (DE-MC) we tested and assessed the sensitivity and calibrated the 3-PG model. This process-based model provided significant results regarding the carbon sequestration capacity. The results showed the following: i. Parameters related to stand properties, canopy structure, and processes, as well as biomass partitioning, are the most important or sensitive for the performance of the model; ii. The DE-MC method optimized the values of the 3-PG parameters which was confirmed by the means of the Gelman–Rubin convergence test; iii. According to the predictions of the calibrated 3-PG, the Net Primary Production in the pure Azrou forest varies between 0.35 and 8.82 $\mathrm{tC}.{\mathrm{ha}}^{-1}.{\mathrm{yr}}^{-1}$, it is equal in average to 5.48 $\mathrm{tC}.{\mathrm{ha}}^{-1}.{\mathrm{yr}}^{-1}$, which given the total area corresponds to 7918 $\mathrm{tC}.{\mathrm{ha}}^{-1}$.

Maintaining or increasing forest carbon sinks is considered essential for mitigating the rise in atmospheric CO₂ concentrations. In contrast, harvesting trees is perceived as having negative consequences for both the standing biomass stocks and the carbon sink strength. However, the forest carbon sink needs to be examined from a forest stand canopy perspective, where assimilation predominantly occurs in temperate forests. Here we show that a threshold of leaf area exists beyond which additional leaves do not contribute to CO₂ uptake. The associated biomass can be harvested without affecting the forest carbon uptake. Based on eddy covariance measurements, we show that CO₂ uptake (gross primary production – GPP) and net ecosystem exchange (NEE) in temperate forests are of a similar magnitude in both unmanaged and sustainably managed forests, on the order of 1500–1600 $\mathrm{g}\mathrm{C}{\mathrm{m}}^{-\mathrm{2}}{\mathrm{yr}}^{-\mathrm{1}}$ for GPP and 542–483 $\mathrm{g}\mathrm{C}{\mathrm{m}}^{-\mathrm{2}}{\mathrm{yr}}^{-\mathrm{1}}$ for NEE. A threshold located between 3 and 4.5 m² m⁻² LAI (leaf area index) can be used for sustainable harvesting with regard to CO₂ uptake. Simulations based on the LPJ-GUESS (Lund–Potsdam–Jena General Ecosystem Simulator) model reproduce the saturation of GPP and NEP and the convergence on the LAI threshold range. Accordingly, in temperate managed forests, trees can be harvested while maintaining a high tree biomass and carbon sink of the remaining stand. In this case, competition between neighboring trees in unmanaged forests is replaced by harvest management and provision of wood products. No difference in the LAI productivity response was observed between managed and unmanaged sites.

Taper equations are indispensable tools for characterizing the stem profile of trees, providing valuable insights for forest management, timber inventory, and optimal assortments allocation. The recent progress in Terrestrial Laser Scanning (TLS) has revolutionized forest inventory practices by enabling non-destructive data collection. In this study, four taper models from three different model categories were established based on point cloud data of 219 Pinus nigra trees. The taper equations fitted with TLS data were used to predict the diameter at specific stem heights and the total stem volume. The results show that among fitted models, the Max and Burkhart segmented model calibrated by the means of a mixed-effects approach provided the best estimate of the diameter at different heights and the total stem volume evaluated for different diameter at breast height (DBH) classes. In numerical terms, this model estimated the diameter and the volume with a respective overall error of 0.781 cm and 0.021 m3. The predicted profile also shows that above a relative height of 0.7, the diameter error tends to increase due to the low reliability of data collected beyond the base of the crown primarily caused by interference from branches and leaves. Nevertheless, this study shows that TLS technology presents a compelling opportunity and a promising non-destructive alternative for generating taper profiles and estimating tree volume.

Maintaining or increasing forest carbon sinks is considered essential to mitigate the rise of atmospheric CO₂ concentrations. Harvesting trees is perceived as having negative consequences on both the standing biomass stocks and the carbon sink strength. However, harvesting needs to be examined from a forest stand canopy perspective since carbon assimilation occurs in the canopy. Here we show that a threshold of leaf area exists beyond which additional leaves do not contribute to ecosystem fluxes. The associated biomass can be harvested without affecting the forest carbon fluxes. Based on eddy covariance measurements we show that CO₂ uptake (GPP) and net ecosystem exchange (NEP) in temperate forests are of similar magnitude in both unmanaged and sustainably managed forests, in the order of 1500–1600 gC m^-2 y^-1 for GPP and 542 – 483 gC m⁻² y⁻¹ for NEP. A threshold of about 4 m² m^-2 LAI (leaf area index) can be used as a definition of sustainable harvesting with regard to CO₂ uptake. Simulations based on the LPJ-GUESS model reproduce the saturation of GPP and NEP and convergence on the LAI threshold range. Accordingly, in managed forests, trees can be harvested while maintaining a high tree biomass and carbon sink of the remaining stand. In this case competition between neighbour trees in unmanaged forests is replaced by harvest management. In unmanaged forests, competition for light, nutrient and water cause self-thinning, thereby limiting the carbon sink strength.