Our literature review identified several established frameworks linking ecosystem goods and services to societal needs, sustainable resource use, and environmental protection. Among these, the DPSIR (Drivers, Pressures, State, Impact, Response) framework is particularly suited for analyzing causal chains between society and the environment and supporting adaptive management (Maxim et al. 2009; Kristensen 2004; Smeets and Weterings 1999), making it particularly interesting for integrating forest-based BE-monitoring into policy planning (Table 1).

Life-cycle assessment (LCA), as defined in ISO 14040/44 (ISO 2006a; 2006b), evaluates environmental impacts of products over their life cycle Its results inform industry, policymakers, and consumers, and highlights opportunities to reduce impacts. For example, net greenhouse gas balance assessment provides evidence of the climate benefits or drawbacks of bioenergy production (RED III, EU 2023; Table 1).

Environmental footprint (FP) analysis methods extend this to carbon emissions, land use, biodiversity, and water impacts, applicable from product to national scales (e.g., ISO 14067, ISO 14046, see Bringezu et al. 2021; Table 1). The HANPP (Human Appropriation of Net Primary Production) concept was reviewed but deemed too theoretical and vague in its determination, calculation, and interpretation and therefore ill-suited for forest BE monitoring.

Certification systems, often informed by LCA and FP, verify sustainability compliance of biomass production and use to social, economic and environmental standards through third party audits, with varying depth and scope. The ISO 13065 standard offers an internationally recognized meta standard for bioenergy assessments (ISO 2015; Table 1), while schemes such as FSC and PEFC also integrate social and governance criteria (Schleicher et al. 2019)

At the European level, the EU Bioeconomy Monitoring framework compiles indicators aligned with EU Bioeconomy Objectives, SDGs, and the Green Deal (Wydra and Kroll 2024; Robert et al. 2020; EC 2024; Table 1): For example, a “forest growing stock” indicator contributes simultaneously to the Green Deal’s goal of “Preserving and Restoring Ecosystems and Biodiversity,” the SDGs’ “Life on Land,” and the Bioeconomy Objective of “Managing Natural Resources Sustainably.” Forest related indicators include growing stock, roundwood removals, felling to increment ratio, bird and butterfly indices, Natura 2000 area, and LULUCF GHG emissions (European Commcission 2024a).

Further considered inputs included the Renewable Energy Directive (RED III, EU 2023) that outlines mandatory targets for renewable energy sources within the EU, and the pilot report of the “Systemic Monitoring and Modelling of the Bioeconomy (Symobio)” project (Bringezu et al. 2020; Bringezu et al. 2021) with exploratory approaches specifically tailored to BE-related challenges.

Aligning with DPSIR principles, our conceptual forest BE monitoring framework () defines societal needs as the main drivers of policy. Provisioning services such as wood for material and energy use are balanced with regulating, supporting, and cultural services, including LULUCF GHG targets, biodiversity, water protection, recreation, income and employment. These needs are translated into measurable targets that guide forest management strategies, mediating between policy objectives and actual forest states, generating trade-offs or synergies depending on chosen strategies.

Indicator-based monitoring incudes carbon stocks, biodiversity, soil, water, and wood supply. Quantitative ecological indicators for these groups derive both from empirical data (forest inventories, remote sensing, field surveys, harvest statistics) and FGMs simulating forest dynamics, which complements and extends data by filling spatiotemporal gaps and projects future developments under alternative management and climate scenarios. Continuous integration of empirical and modelled data supports feedback loops, using monitoring outcomes for adaptive policy updates and management adjustments through decisions of forest owners and managers.

The framework thus operationalizes monitoring of the German forest-based BE as a structured, evidence based, and adaptive system. By combining empirical data with dynamic FGM outputs across key ecological indicators, it provides a robust basis for evaluating sustainability trade-offs and guiding policy under changing environmental and societal conditions.

Building on the review of existing frameworks (section 3.1.1) and the socio ecological context outlined above, we refined the indicator system leaning on the indicator groups defined in ISO 13065 (ISO 2015) – covering greenhouse gas emissions, water, soil, air, biodiversity, energy efficiency, and waste – to four ecological domains that are policy relevant and representable in FGMs. The four groups were selected based on:

The four environmentally relevant groups that chosen to inform on ecosystem service status in forests are (see Fig. 2):

By focusing on these domains, the indicator set balances the ability of FGMs to generate robust, quantitative outputs with the ecological dimensions most critical for sustainable BE-monitoring. Unlike broader sustainability frameworks (ISO 13065, RED III), our approach prioritizes those indicator groups that can be directly represented in forest simulation models and evaluated against national datasets (e.g., National Forest Inventory, soil surveys, meteorological records).

This focus ensures that the monitoring framework adds value beyond descriptive statistics: it allows continuous dynamic projection of forest states and services under alternative management and climate scenarios. The chosen groups therefore operationalize an ecologically-centered but model‑based perspective on monitoring, directly linking empirical and simulated information with adaptive forest‑BE governance.

Economy-related indicators quantifying wood extraction are well-established in German forestry statistics and inventories, providing direct measures of wood supply and forest management activity. They include wood quantities from harvest, thinning, salvage logging, as well as growing stock and net stock change and help assessing sustainable harvest levels to identify risks of overexploitation under the BE transition.

FGMs consistently represent these indicators, surpassing static yield tables by dynamically incorporating site- and climate-specific growth responses and allowing adjustments for climate and disturbance impacts. For monitoring, differentiation by species groups, or at least by broadleaf vs. needleleaf wood, and diameter classes should be provided, with the latter providing usage-independent perspectives. Quantities are ideally tracked in cubic meters under bark (m³ u.b.) alongside wood density data where available. Statistical data from the German “Einschlagsrückrechnung” (Jochem et al. 2023) and “Holzeinschlagsstatistik” (Destatis Code 41261) facilitate model calibration and validation, enabling fine-scale and long-term monitoring, but may exclude unregistered extractions, e.g., private use. In alignment with historical harvest statistics and surveys, FGM-derived wood extraction indicators should be tracked with annual resolution. Complementing harvest data, the National Forest Inventory (NFI) can provide estimates of forest growing stock, stock changes, growth increment, mortality and wood extraction at lower temporal resolution based on a 4 x 4 km resolution and partially at 2.83 x 2.83 km or 2 x 2 km. More details are provided in supplementary material S1.2.

Carbon-related indicators are central both for for tracking forest carbon stocks and national GHG reporting under UNFCCC/EU frameworks and for evaluating forest’s role in the BE-transition, supporting compliance with national targets. All assessed FGMs simulate C-storage and dynamics in biomass pools (stems, branches, leaves, roots, dead wood), supporting monitoring of stock dynamics influenced by harvests, climate-related diebacks and disturbances (storms, droughts, beetle outbreaks, fire), growth, and regeneration. Differentiation by stand development phase, species group, and biomass pool refines analysis for management and trend evaluation.

Annual indicator resolution allows capturing climate impacts across seasonal cycles. Volume-based units are easier to derive but can be converted to carbon units via representative wood densities. Although carbon stored in harvested wood products is relevant for GHG-reporting, it falls outside typical forest management influence and FGM scope and is excluded from our framework. Further details are provided in Supplementary Material S1.1.

Biodiversity is not a key focus of FGMs. Nonetheless, they can capture partial but informative proxies. Structural and compositional indicators such as tree species diversity, size distribution, habitat trees, and deadwood volume are recognized as meaningful for assessing habitat quality and resilience. Among the assessed models, five provide outputs on habitat tree potential, seven on deadwood, and several allow derivation of diversity metrics such as Simpson’s index or Gini coefficients. Suggested quantitative units for monitoring indicators include wood volume per species or species group (m³), or basal area (m²), with a differentiation by species, size, and for deadwood decay class and orientation where possible.

Suitable temporal resolutions for biodiversity indicators are annual or longer, reflecting the typically slow changes in forest composition and biodiversity, except under disturbance events. The proposed model-based proxies align with national and EU biodiversity policy needs (e.g. Nature Restoration Law) and complement empirical data from the NFI. While not comprehensive, they provide operational entry points for linking forest management and biodiversity outcomes. More details from expert discussions are provided in Supplementary Material S1.3

Forest soils provide vital ecosystem services by regulating carbon, water, and nutrient cycles. Soil carbon stocks often represent a significant portion of total forest carbon (Grüneberg et al. 2019). Currently available national soil inventories provide snapshots of soil-related indicators, albeit at low temporal resolution, highlighting the value of continuous model-based projections. Most process-based and some empirical FGMs can simulate soil carbon stocks and their dynamics with varying degrees of process simplification. These indicators have policy relevance for GHG reporting and ecosystem resilience assessments. Suggested indicator units for monitoring are t C, tons C ha⁻¹, tons C yr⁻¹, and tons C ha⁻¹ yr⁻¹.

Compared to soil carbon aspects, representation of soil nutrient cycles in FGMs is less consistent but could provide indicators to quantify nutrient constraints on productivity, site-specific N-loads, and risk of N₂O emissions and nitrate leaching. C/N ratios can derive insights on decomposition, organic matter quality and microbial activity. Currently, nitrogen dynamics is only included in a minority of the assessed FGMs, while phosphorus and biological processes are even less commonly represented. Analogous to carbon, monitoring of nutrient-stocks and fluxes should use mass-based units (e.g., t ha⁻¹, t ha⁻¹ yr⁻¹), enabling aggregation at regional or national scales. Standardized reference soil depths are necessary for comparability. Challenges related to soil indicators discussed during expert evaluation are provided in detail in Supplementary Material S1.4.

Water availability and drought stress influence forest growth, productivity, mortality, C-sequestration and species composition and are increasingly important for assessing forest resilience under climate change. Water availability depends on climatic conditions, stand properties, and soil characteristics including rooting depth, soil texture, organic matter content and compaction. Suggested monitoring indices include the Standardized Precipitation Evapotranspiration Index (SPEI, Vicente-Serrano et al. 2010) and the ratio of actual to potential evapotranspiration (annual water deficit, aET/pET), which additionally accounts for plant water processes.

Using meteorological input data from weather stations or climate models external to FGMs in combination with input on soil characteristics, a subset of process-based FGMs can simulate water balances and evapotranspiration dynamics, while the assessed empirical models currently lack this capacity. Indicators such annual water deficit (aET/pET) or standardized drought indices can therefore be derived in some cases. Indirect proxies hinting on water stress potential include stand basal area or stand density and structure, combined with information on species composition and site-specific environmental conditions. Suggested temporal resolutions for water indicators range from growing season to annual. Further background information from the expert exchange on water-related indicators is provided in Supplementary Material S1.5.

As highlighted in previous sections, all analyzed FGMs are principally oriented towards wood production indicators, providing robust simulations for sustainable yield management, trend detection, and policy support. These indicators, including harvest volume and standing stock stratified by species and dimensions, align with international reporting standards and the ecological focus of the presented monitoring framework (see also (Barreiro et al. 2016; Blujdea et al. 2021). Importantly, FGMs advance beyond traditional yield tables by capturing the dynamic effects of mixed and uneven-aged stands and climate variability, enhancing the accuracy of projections in scenarios such as those underpinning Germany’s “Climate-adapted Forest Management” compensation schemes.

The strong compatibility with empirical forest inventory data underpins their value for long-term policy evaluation, but also delineates the boundaries of this approach, which currently excludes broader economic indicators like value-added generation, employment effects, and market mechanisms – a limitation that becomes evident by comparison to the studies of Jose et al. (2023) and Kalogiannidis et al. (2022). Closing this gap requires integration with economic and social datasets and modeling approaches outside the scope of ecological FGMs. Thus, the effective application of economy-related indicators represents both a strength and a constraint of FGM-based bioeconomy monitoring.

Overall, while the current scope provides actionable, measurable indicators crucial for national and EU sustainability strategies, existing FGMs should be complemented by economic models and interdisciplinary tools to capture the full spectrum of the forest-based bioeconomy (Bouma and van Beukering 2015; Koetse et al. 2015).

Carbon and biomass indicators are core variables in FGMs, essential for modeling forest growth and ecosystem dynamics (Barreiro et al. 2016; Blujdea et al. 2021; Zald et al. 2016). They are consistently represented, though models vary in spatial and temporal resolution, species coverage, and biomass compartment detail. This broad representation enables inter-model comparison, supports uncertainty assessment, and builds confidence where results converge.

Carbon indicators are central to national and EU greenhouse gas (GHG) accounting and reporting. In Germany, reporting under the UNFCCC relies on the National Forest Inventory (NFI), yet its decadal cycle leaves gaps after the most recent 2022 survey. FGMs can bridge these gaps by projecting annual forest growth and carbon stock changes based on emerging climate data series, supporting timely and more accurate forest GHG reporting aligned with the EU LULUCF Regulation (EU 2018) and national climate protection targets.

Model-derived carbon indicators also meet SMART criteria: they are specific (biomass pools), measurable (e.g., t C ha⁻¹ yr⁻¹), and directly linked to inventories and reporting systems. Data availability is strong, models capture key pools such as stems, branches, and deadwood, and outputs are well suited for scenario analysis. Their policy relevance is high, as they feed directly into LULUCF and UNFCCC instruments. A notable limitation is the omission of harvested wood products (HWP) and downstream life-cycle impacts, which remain outside the scope of most FGMs and require complementary approaches for a full carbon balance.

Biodiversity spans multiple spatial scales (alpha, beta, gamma diversity) and taxonomic, genetic, and functional dimensions, which interact with geodiversity (Read et al. 2020; Scholes et al. 2008). Identifying robust and policy-relevant indicators at national scales therefore remains challenging (Geijzendorffer et al. 2016; Navarro et al. 2017). Rare forest structures support threatened species and unique functions (Leitão et al. 2016; Mouillot et al. 2013), while higher structural and functional diversity strengthens ecosystem resilience under stress through complementarity and facilitation (Niklaus et al. 2017; Trogisch et al. 2021). Germany’s shift from Norway spruce monocultures to mixed-species forests exemplifies synergies between climate adaptation, carbon storage, and biodiversity protection (Pörtner et al. 2021).

FGMs face limitations in representing complex functional diversity, fungal, faunal und understory communities and landscape connectivity due to simplified ecological processes and model resolutions (Blanco and Lo 2023; Lexer et al. 2000; Leidinger et al. 2021; Puumalainen 2001). However, measurable proxy indicators compatible with FGMs, such as deadwood volume, vertical heterogeneity, tree species composition, and management intensity are recognized practical biodiversity indicators in European forestry, provide actionable stand-level insights, are available from inventories, and already established in monitoring frameworks (Feld et al. 2010; Oettel and Lapin 2021; Read et al. 2020; Scholes et al. 2008; Ćosović et al. 2020). Large broadleaf tree abundance (e.g., diameter > 60 cm) can serve as proxy indicator for rare microhabitats in German forests (Paillet et al. 2019; Spînu et al. 2022), and associations between tree species composition and non-woody taxa allow indirect assessment of broader ecosystem diversity (Schneider et al. 2021).

The chosen biodiversity indicators can support goals of the EU Biodiversity Strategy and EU Green Deal targets (EC 2024), which require systematic reporting on ecosystem condition and restoration progress. They meet SMART principles insofar as they are specific, measurable, and operational for monitoring and modeling, providing quantifiable information relevant for restoration efforts and identification of synergies with climate protection and adaptation policy decisions. Yet, current proxies only partially cover the ambitions of advanced biodiversity strategies. Future frameworks will need integration of complementary datasets, cross-scale monitoring, and novel methods to close existing gaps.

Soil modules in FGMs can consistently represent indicators for carbon pools and fluxes. Model-based indicators are measurable, reported in national inventories, and directly relevant for GHG accounting. In Germany, the Yasso model is already applied to estimate carbon emissions and removals from mineral forest soils (Viskari et al. 2020; UBA 2025), but soil carbon indicators are also central to emerging policy frameworks, including the proposed EU Soil Monitoring Law and the revised LULUCF Regulation from 2023, which mandate higher-tier reporting. By providing annual projections of soil carbon, FGMs can move beyond default emission factors.

However, FGMs simplify soil process representation for feasibility, which can cause major sources of uncertainty, for example in flux assessments (Vereecken et al. 2016). Moreover, pronounced spatial heterogeneity and uncertainties in underlying databases used for model initializations, which often require interpolation for continuous coverage, can further obscure climate impact signals (Lark and Bolam 1997; Nachtergaele et al. 2012; Folberth et al. 2016).

Nutrient dynamics, particularly nitrogen, influence tree growth, decomposition, nitrate leaching, and N₂O emissions. Rising deposition since the 19th century and synthetic fertilizer use since 1913 have intensified N-related risks (Lamarque et al. 2013; Pretzsch et al. 2014). Monitoring soil N status thus is central to the critical loads concept (Aazem et al. 2022)d cycling is linked to key ecosystem services (Costanza et al. 1997; Kooch et al. 2022). Yet, nutrient cycling is only partially represented in FGMs, with limited estimates of productivity effects or leaching risk.

Phosphorus dynamics, soil biological activity, and soil organism diversity receive even less attention and remain largely outside model scope. Consequently, soil indicators only partially fulfil SMART criteria: carbon pools are specific and measurable, whereas nutrient and biological dimensions are incomplete. Data availability and resolution are sufficient for carbon but remain weak for N and P. Thus, while FGMs provide actionable, policy-relevant insights for soil carbon, integration of nutrient and biological indicators lags behind, constraining their use for monitoring of soils as multifunctional, biodiversity-supporting systems.

Climate change increases the need for water indicators, exemplified by the widespread Norway spruce dieback during the 2018–2022 droughts (Knapp et al. 2024; Anders et al. 2024). Water availability is a key predictor of drought-induced mortality and growth decline, with implications for carbon stocks, harvest potential, and linkages to carbon and nutrient cycling. Accordingly, water indicators are increasingly relevant to inform the development of adaptation and resilience strategies, such as forest conversion to more diverse stands, which is expected to generate co-benefits for water regulation (Obladen et al. 2021). Additional management interventions such as adjusted thinning can mitigate drought stress by reducing stand density and maintaining canopy microclimate (Bradford et al. 2022; Meyer et al. 2022).

Water indicators are challenging, as high temporal variability makes drought frequency and duration often more critical than annual water deficits (Lazoglou et al. 2024). Aggregation to national or annual scales tends to obscure such variability and complicates interpretation. Moreover, soil properties, including depth, porosity, texture, and organic matter content, strongly influence water availability (Minasny et al. 2021; Nemes and Rawls 2004; Saxton and Rawls 2006), and uncertainties in soil datasets directly affect indicator reliability (Bagnall et al. 2022). Process-based models can partially capture soil–plant–atmosphere interactions (Blyth et al. 2011; Fatichi et al. 2012) when explicitly representing transpiration, evaporation, percolation, and runoff (Bonan et al. 2014). Empirical FGMs currently lack water indicator representations. A particular difficulty remains the estimation of actual evapotranspiration (aET), as model outputs often depend on site-specific conditions.

Against SMART and policy criteria, water indicators align well with priorities on drought and adaptation. Specific indices, such as aET/pET ratios, are interpretable where data permit. However, quantifiability is limited due to model representation and data constraints. Actionability is high in targeted applications, such as drought risk assessment or management planning, but diminishes at broader scales due to temporal and spatial variability.