Anthropogenic land use and land cover change (LULCC) is projected to continue in the future. However, the influence of forest management on forest productivity change and subsequent LULCC projections remains under-investigated. This study explored the impacts of forest management-induced change in forest productivity on LULCC throughout the 21st century. Specifically, we developed a framework to softly couple the Global Change Analysis Model and Global Timber Model to consider forest management-induced forest productivity change and projected future LULCC across the five Shared Socioeconomic Pathways (SSPs). We found future increases in forest management intensity overall drive the increase of forest productivity. The forest management-induced forest productivity change shows diverse responses across all SSPs, with a global increase from 2015 to 2100 ranging from 3.9% (SSP3) to 8.8% (SSP1). This further leads to an overall decrease in the total area with a change of land use types, with the largest decrease under SSP1 (−7.5%) and the smallest decrease under SSP3 (−0.7%) in 2100. Among land use types, considering forest management-induced change significantly reduces the expansion of managed forest and also reduces the loss of natural land in 2100 across SSPs. This suggests that ignoring forest management-induced forest productivity change underestimates the efficiency of wood production, overestimates the managed forest expansion required to meet the future demand, and consequently, potentially introduces uncertainties into relevant analyses, for example, carbon cycle and biodiversity. Thus, we advocate to better account for the impacts of forest management in future LULCC projections.

Key Points

Forest management-induced productivity change has a significant impact on future land use and land cover change (LULCC)
Neglecting such impact could overestimate the managed forest expansion and natural land reduction, especially under SSP1 and SSP5
We advocate considering such impacts during LULCC projection to constrain the uncertainty

Plain Language Summary

Land use and land cover change has shown widespread social, economic, and environmental impacts. However, the influence of forest management on forest productivity change and subsequent land use and land cover change projections remains under-investigated. This study explored how changes in forest productivity caused by forest management practices impact land use and land cover throughout the 21st century under various social and economic scenarios. The results show that more intense forest management generally leads to more productive forests. This, in turn, results in smaller changes in land use and land cover. Increasing forest management reduces the need to expand managed forests and helps preserve natural lands by 2100. Ignoring the impact of forest management on forest productivity could lead to biases in projecting forest expansion and wood production and potentially induce uncertainties in carbon cycling and biodiversity. Our results emphasize the need to account for forest management in future projections of land use and land cover changes.

1 Introduction

Land use and land cover change (LULCC) is one key interface between human and Earth systems, and has crucial impacts on terrestrial energy (Duveiller et al., 2018), carbon and water cycles (Taylor et al., 2022), climate extremes (Findell et al., 2017), biodiversity (Semenchuk et al., 2022), and ecosystem services (Lawler et al., 2014) at regional and global scales. In response to changing demands for wood, food, and fiber fueled by the increasing global population and per capita consumption, LULCC has markedly transformed approximately 17% of Earth's surface over the last 60 years (Intergovernmental Panel on Climate Change, 2022; Winkler et al., 2019). This transformation is characterized by increased afforestation and cropland abandonment in the Global North, in contrast to the deforestation and agricultural expansion in the Global South (Winkler et al., 2019). The LULCC is projected to continue in the foreseeable future (Chen et al., 2020; Luo et al., 2022).

Forest productivity, represented by the roundwood production per unit forest area in the study, could impact the required forested area to meet the certain demand for wood products and further LULCC dynamics. Specifically, the change in forest productivity could alter expected returns per unit of forest and the relative returns compared to other land use types, which subsequently influence land resource competition and thus LULCC (Wise et al., 2014). For instance, the increase in forest productivity may reduce the area of forested land needed to meet the demand for timber, and create more spaces for other land use types, such as cropland and pasture or natural, unharvested forests, which will then impact the competition for limited land resources and the spatial distribution of different land use types. Besides, the variation in forest productivity across regions will change the economic comparative advantage (Koebel et al., 2016) that dictates the regional forest and land allocation (Wise et al., 2014). Therefore, there is a pressing need to account for spatio-temporal change in forest productivity when projecting future LULCC dynamics.

Forest productivity can be influenced by a range of factors: including tree species, elevated CO₂ concentration (Davis et al., 2022; Liu et al., 2019), climate change (i.e., the change of air temperature and precipitation) (Uribe et al., 2023), disturbance and extremes (Hicke et al., 2012; Ross et al., 2021), and forest management activities (Islam et al., 2024; Sedjo, 1989). Among these, forest management practices like thinning, coppicing, pruning, and fertilizer applications (Fox et al., 2007; Noormets et al., 2014; Powers et al., 2012; Sedjo, 2001) are commonly used in managed forest globally (Wingfield et al., 2015), and have been demonstrated to increase the forest productivity with suitable applications (Fox et al., 2007; Golub et al., 2022; Noormets et al., 2014; Powers et al., 2012). Recent studies have quantified the change in forest productivity due to forest management (Gonçalves et al., 2008; Noormets et al., 2014; Powers et al., 2012). For instance, Li et al. (2018) reported that through the combined application of appropriate thinning and fertilization techniques, forest productivity in coastal Douglas-fir forests can increase by 17%. The productivity of eucalyptus in Brazil has significantly increased, nearly doubling over the past 20 years due to the adoption of intensive management strategies (Gonçalves et al., 2013). However, how forest management activities will change forest productivity in the future is still under-investigated. The Global Timber Model (GTM), a dynamic forest sectoral model, has been widely used to simulate and project global forest use and management (Daigneault & Favero, 2021; Sedjo & Lyon, 1990; Sohngen et al., 1999). GTM considers the impacts of forest management including thinning, fertilizer, and other silvicultural practices on forest productivity (Austin et al., 2020; Sohngen et al., 1999). Such model capability allows us to systematically explore and project the potential change of forest productivity driven by forest management.

The intensity of forest management activities is strongly related to the socioeconomic conditions. For instance, increased socioeconomic development (e.g., a higher consumption per capita) is likely to boost the wood demand, and, in turn, may lead to higher wood prices. Higher wood prices can incentivize greater investment in forestry through more intensive forest management practices, ultimately resulting in enhanced forest productivity (Austin et al., 2020; Daigneault & Favero, 2021; Favero et al., 2020). A set of the Shared Socioeconomic Pathways (SSP) scenarios (i.e., from SSP1 to SSP5) were developed to represent future socioeconomic conditions with divergent future population, income, and technology levels (O’Neill et al., 2014; Riahi et al., 2017). Forest management intensity may differ considerably under different SSPs, and the induced change in forest productivity will further influence land competition as well as LULCC. Notably, using GTM, Daigneault and Favero (2021) and Daigneault et al. (2022) showed the largest differences in the changes of forest management, forest productivity, and forest area across different SSPs over the 16 regions globally. Favero et al. (2021) used GTM to project forest productivity and forest area change driven by interactions of climate change and forest management under different SSPs combined with different climate forcing targets (Favero et al., 2021).

However, these existing studies only investigated LULCC with the land competition among limited land use types, for example, forest and farmland (cropland or pasture), despite the importance of LULCC with comprehensive land use types such as differentiating the cropland and pasture, grassland, and others. Grassland and pasture also play an important role in meeting the demand for livestock grazing, biodiversity conservation, and water regulation (Wang et al., 2022). Besides, cropland change can offer essential information for water usage (Foley et al., 2020) and facilitate estimations of changes in agricultural production (Wrigley & Nirmal, 2017), and food security analysis (Khoury et al., 2014). Therefore, understanding how forest management-induced forest productivity change will impact the LULCC dynamics with comprehensive land use types in the future is important to provide a holistic potential of future land resource allocation.

The future LULCC with land competition among more comprehensive land use types under diverse SSPs has been widely explored using the integrated assessment models (IAMs) (Popp et al., 2017). Doelman et al. (2018) used the Integrated Model to Assess the Global Environment (IMAGE) to explore how the global LULCC for 7 main land use types (forest, bioenergy plantation, grazing land, rainfed crop and irrigated crop, built-up area, and other land) will evolve under different SSPs (Doelman et al., 2018), Chen et al. (2020) and Luo et al. (2022) use the Global Change Analysis Model (GCAM) to show the global LULCC with 39 land use types including pasture, grassland and 29 detailed crop types under different SSPs. However, these existing studies did not consider the impact of forest productivity change driven by different forest management practices under diverse SSPs, since most IAMs including GCAM neglect the impacts of forest management on forest productivity (Golub et al., 2022). Simply neglecting the influence of forest management-induced forest productivity change could potentially bias the future LULCC projections.

Although Golub et al. (2022) investigated the forest productivity change caused by the combining impacts of climate change and forest management with the consideration of land competition among forest, cropland, and pasture in the future, the model used just contains the land use sector without the interactions with other sectors, and their analysis was just limited to SSP2, that is, the “Middle of the Road” scenario. Models that represent the interactions between land use and other sectors like energy, water, climate, and the macro-economy can better capture observed land system dynamics (Howells et al., 2013). IAMs like GCAM can capture those interactions and provide a better understanding of the LULCC. Besides, GCAM also belongs to the multi-sector dynamics model (Reed et al., 2022), which can well capture the detailed dynamics and interactions between land and other sectors. In addition, comprehensively investigating the future LULCC dynamics under the full set of SSPs (Riahi et al., 2017) could provide a wide range of possibilities and the associated uncertainties in the future. Integrating GTMs with the representation of forest management and IAMs with the capability to represent the detailed land use types and competitions provides a promising way to comprehensively investigate the impacts of forest productivity change induced by forest management under diverse SSPs on future LULCC dynamics (Daigneault et al., 2022).

This study aims to explore how forest management-induced forest productivity change will influence global LULCC with comprehensive land use types including managed forest, unmanaged forest, biomass, cropland grass, grazed pasture, other pasture, shrubs, rock and desert, tundra, and urban under five SSPs in the 21st century. In this study, the term “managed” land use type refers to those undergoing active management practices primarily for commercial exploitation, while “unmanaged” land use type denotes those without management activities and not used for commercial purposes. To achieve this goal, we softly coupled GTM and GCAM by passing the GTM-derived forest management-induced forest productivity change into GCAM. GCAM was then used to project future LULCC with and without changing forest productivity induced by forest management under five SSPs. Simulation outputs were then used to analyze the impacts of forest management on forest productivity and subsequent LULCC dynamics under all five SSPs by comparing the LULCC with and without considering the forest productivity change induced by forest management.

In the remainder of the paper, we describe the modeling framework and scenarios in Section 2. In Section 3, we analyze the impacts of forest management-induced forest productivity change on LULCC. In Section 4, we compare our results with the existing studies, explore the potential mechanisms of the LULCC induced by the forest management-induced forest productivity change, and discuss the implications and limitations in this study. In Section 5, we conclude the study.

2 Materials and Methods

In the study, we first used the global GDP per capita change and woody biomass quantity from GCAM to drive GTM for projecting future forest productivity change from 2015 to 2100, and the related forest management cost (we hereafter refer it to the cost related to forest management intensity levels) change under five reference SSPs (i.e., no climate change policies or impacts). We then integrated these GTM-projected changes into GCAM for projecting LULCC. Note that all the forest productivity change mentioned hereafter refers to that driven by forest management intensity change. We also performed a baseline projection of future LULCC without considering the change in forest management intensity and forest productivity using a recently released version of GCAM (v7.0) for analyzing the impact of forest management-induced forest productivity change on LULCC (Figure 1).

Details are in the caption following the image — **Figure 1**
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Schematic of the coupling framework between GTM and GCAM in this study. We compared the spatial-temporal pattern of land use and land cover change under the projections with and without the consideration of forest management change (see Section 2.4 Scenarios and Analysis for details).

2.1 Overview of SSPs Scenarios

SSPs describe a wide span of possible future social development and represent reference scenarios for climate change modeling and research (Moss et al., 2010; van Vuuren et al., 2012). Each SSP represents a combination of different levels of challenge to climate change mitigation and adaptation. Specifically, SSP1 represents a green growth “Sustainability” pathway that has low challenges in both mitigation and adaptation (van Vuuren et al., 2017). SSP2 represents the “Middle of the road” pathway which follows the historical pattern and has median challenges to both mitigation and adaptation (Fricko et al., 2017). SSP3 represents the “Regional Rivalry” pathway that is regionally heterogeneous and has high challenges in both mitigation and adaptation (Fujimori et al., 2017). SSP4 represents an “Inequality” pathway that breeds both geographical and social inequalities and has low challenge in mitigation and high challenge in adaptation (Calvin et al., 2019). SSP5 represents a “Fossil-fueled Development” pathway that largely uses fossil fuel energy and has high challenge in mitigation and low challenge in adaptation (Kriegler et al., 2017).

2.2 GCAM Overview

GCAM is a major IAM for answering the central questions about human-Earth system interactions. It is one of the marker IAMs and has been widely used in many assessments at both national and international scales from the very first to the latest IPCC reports (Calvin et al., 2017; Houghton et al., 1990; IPCC, 2022). It is a market-equilibrium dynamic-recursive model representing the behavior of, and interactions between, the world's five sectors: macroeconomy, climate, water, energy, agriculture and land use (Calvin et al., 2019). The model reads external “scenario assumptions” about key drivers (population, economic activity, technology, and policies) and then assesses the implications of these assumptions on key scientific or decision-relevant outcomes (e.g., land use, commodity prices, energy use, emissions). In the model, representative agents use information on prices and other relevant factors to make decisions about resource allocation. The economic land allocation decision is based on a nested logit-sharing approach. Specifically, the share of each land use type is positively related to the relative value of mean profit rate. For instance, if the mean profit rate of one land use type is larger (smaller) than that of other types, this type tends to have a larger (smaller) share. When the mean profit rate of one land use type increases (decreases) and others keep constant, then the land share of this type will increase (decrease). Moreover, the nesting structure offers a flexible representation of land competition across different land use types through the logit exponent (Wise et al., 2014). We used the GCAM version 7.0 in this study (GCAM team, 2023). The agriculture and land use sector specifies production and land allocation with 34 land use types (Table S1 in Supporting Information S1) in 384 subregions by subdividing 32 global geo-political regions into the water-basin level. Among all the land use types, tundra, urban, rock and desert are exogenous and will keep constant in the future. In addition, GCAM v7.0 runs at a 5-year time step from the base year (2015) to 2100. GCAM considers the regional market for forest production and trade. In GCAM, the managed forest productivity can influence the price in the forest market and has a positive relationship with the mean profit rate (the net expected final profit) as well as the managed forest area under a certain roundwood demand. The management costs contribute to the total nonland cost (representing all expenses per volume of roundwood ($/m³) associated with producing roundwood, excluding the cost of acquiring or leasing the land, including management intensity cost and other costs, for example, transportation cost), and there is a negative relationship between the total nonland cost and the mean profit rate.

GCAM adopts the demographic and economic assumptions from Kc and Lutz (2017) and Dellink et al. (2017), and the technology and policy assumptions developed from the SSP narratives (O’Neill et al., 2017) to implement SSPs (Table S2 in Supporting Information S1). Specifically, population growth is the highest under SSP3, followed by SSP2, SSP4, SSP5, and SSP1. For SSP4, population growth is high in low-income countries, medium in medium-income countries, and low in high-income countries. GDP per capita increases the most under SSP5, followed by SSP1, SSP2, SSP4 (with a high, medium, and low increase for high, medium, and low-income countries, respectively), and SSP3. Detailed assumptions for the agriculture and land use sector can be found in Table S2 in Supporting Information S1.

2.3 GTM Overview

GTM is a dynamic economic model of the timber sector and optimizes the net welfare of the forest sector (Daigneault et al., 2012; Favero et al., 2017; Sedjo & Lyon, 1990; Sohngen et al., 1999, 2001; Tian et al., 2018) by selecting the harvest in each forest age class, management intensity, managed forest area in each time period. GTM relies on forward-looking behavior and solves decadally across a 200-year time frame. It is widely used in analyzing forest change (Daigneault & Favero, 2021; Favero et al., 2021; Tian et al., 2018) and bioenergy (Daigneault et al., 2012; Favero et al., 2020). GTM can generate forest management intensity and quantify its impacts on forest productivity at certain socio-economic levels. It represents more than 300 forest ecosystems across 16 regions of the world (Figure S1 in Supporting Information S1).

In GTM, the forest productivity at t − t₀ (t represents the current time step, and t0 represent the time step when the forest is planted) after planting is determined by a concave function: V_i(t − t₀; m_i(t₀)), where t − t₀ indicates the forest age, m_i(t₀) is the management intensity determined at the time of planting of forest type i. Forest management intensity influences future forest productivity, and a greater (lower) management intensity will enhance (decrease) future forest productivity. Combining the management intensity and the unit cost of management intensity can derive the management-associated cost.

To better consider the forest sector dynamics under five SSPs, we used the GTM version from Daigneault and Favero (2021), which quantifies the model parameters that are consistent with the SSP narratives (Table S3 in Supporting Information S1). Specifically, GTM use the same assumption of population and GDP per capita as GCAM in this study. The forest management intensity response (i.e., the forest productivity increase from investment in forest management) is 10% and 7.5% higher than the historical level, respectively under SSP1 and SSP5, keeps the historical level under SSP2, and is 10% lower than the historical level under SSP3. For SSP4, it is 7.5% more than the historical level for high-income countries, and is 7.5% lower than the historical level for low-income countries. The forest management cost (the unit cost of forest management intensity) is the highest under SSP3, followed by SSP2, SSP5 and SSP1. For SSP4, it is high for low-income countries, but low for high-income countries. The additional details can be found in Table S3 in Supporting Information S1.

2.4 The Soft Coupling Between GTM and GCAM

To consider the impacts of change in forest management intensity and forest productivity on multiple LULCC competitions in the future, we made a soft coupling between GTM and GCAM (Figure 1). To ensure consistency between the two models, particularly regarding fundamental socioeconomic conditions and the demand for woody biomass—a crucial variable influencing forest management changes in GTM (Daigneault et al., 2022), we employed a uniform GDP per capita growth rate as projected under the SSPs for both models. Additionally, we utilized the woody biomass quantities derived from GCAM as benchmarks for GTM.

Specifically, we used the GCAM-derived GDP per capita change rate to drive GTM (see Text S1 in Supporting Information S1 for details). For the woody biomass, we used the GCAM's outputs under five SSPs as targets to iteratively adjust a woody biomass quantity forcing parameter in GTM to approach the given targets under each SSPs, separately (Daigneault & Favero, 2021). Then, we used both the GDP per capita and the tuned woody biomass quantity forcing parameter to drive GTM for a simulation from 2015 to 2205 at a 10-year interval, with the outputs of future forest management cost, forest productivity, and total nonland cost.

GTM simulation outputs were incorporated into GCAM after harmonizing the differences in forest age classes and forest types, since GTM has multiple forest types with multiple age classes in each region, while GCAM only has managed and unmanaged forests, and considers the forest age change implicitly. Specifically, we first carried out the forest age class and forest type aggregations for forest productivity and management cost for each GTM region. The forest age class aggregation methods generally followed the forest-related processes in GCAM. Specifically, for forest productivity, we averaged all economically mature age classes, since roundwood is only harvested at a mature age in GCAM. For the forest management cost, we calculated the average value for all age classes, considering the forest is managed as a whole, or across all age classes. For the forest type aggregation, the two variables are estimated as the area-weighted average of each forest type. Specifically, we use the area proportion of each forest type in 2015 as the weight. This aggregation is consistent with GCAM's method of using the area proportion of plant functional types in the base year as the weights to aggregate the carbon content at the regional scale (Kyle et al., 2011).

Considering that GTM and GCAM use different geographic regions during the future projection, we also performed the region harmonization (see Text S2 in Supporting Information S1 for details). Then we linearly interpolated the GTM-derived forest productivity and forest management cost from a 10-year interval to a 5-year interval, since GCAM needs input at a 5-year interval. We further estimated the forest productivity relative change and total nonland cost (see Text S3 in Supporting Information S1 for details), and we ran GCAM with these forest productivity changes and forest total nonland cost estimates.

2.5 Scenarios and Analysis

We projected the future LULCC using GCAM under two sets of scenarios. First, the simulations use the default GCAM settings without considering the forest management-induced forest productivity change under the five SSPs (SSP1-5). This set of the simulation is used as a baseline, and we thereafter refer to this set as the baseline scenarios with the LULCC projected by this set as LULCC_Baseline. Note that our baseline simulations align with the GCAM v7.0 default results. Second, the set of simulations that consider the forest management-induced forest productivity change under the same five SSPs are referred to as the FMC scenarios with the associated LULCC as LULCC_FMC. We then compared the scenarios to quantitatively analyze the impacts of forest management-induced forest productivity change on LULCC across 12 broad land use types (Table S1 in Supporting Information S1). Among these broad types, managed forest, grazed pasture, cropland, and biomass belong to managed land use types, while others belong to unmanaged land. Among the unmanaged land, grass, unmanaged forest, other pasture, and shrub are natural lands. Note that other natural but exogenous land use types, including tundra, urban, rock, and desert are time-unvarying in the future, which are not our focus in the study.

We analyzed the GTM-derived forest management intensity change at economically mature ages, and forest productivity relative change through the harmonization (Figure 1). For the area of each land use type, we analyzed the temporal evolution and spatial patterns of the differences between FMC and baseline. To quantify the impacts of forest management, we analyzed the absolute difference (D_abs) in the area of each land use type between FMC and baseline as well as the relative difference calculated as the ratio of D_abs to the area of each land use type in baseline. To further compare the impacts of forest management and LULCC from 2015 to 2100, we calculated the relative difference as the ratio of D_abs to LULCC from 2015 to 2100:

{D}_{rel,2100}^{k}=\frac{{(A}_{FMC,2100}^{k}{-A}_{FMC,2015}^{k})-{{(A}_{Baseline,2100}^{k}-A}_{Baseline,2015}^{k})}{\vert {A}_{Baseline,2100}^{k}-{A}_{Baseline,2015}^{k}\vert }

(1)

where A represents area, and k represents the land use type.

{A}_{FMC,2100}^{k}

and

{A}_{Baseline,2100}^{k}

represent the area of land use type k under FMC and baseline scenarios in 2100, respectively.

{A}_{FMC,2015}^{k}

and

{A}_{Baseline,2015}^{k}

represent the area of land use type k under FMC and baseline scenarios in 2015, respectively.

{A}_{Baseline,2100}^{k}-{A}_{Baseline,2015}^{k}

and

{A}_{FMC,2100}^{k}{-A}_{FMC,2015}^{k}

represent the change in the area of land use type k from 2015 to 2100 under the baseline and FMC, separately.

{D}_{rel,2100}^{k}

represents the differences between FMC and baseline relative to the area change under baseline for land use type k. This ratio can be used to quantify the uncertainty of LULCC projections in 2100 from the neglect of forest management representation. To better quantify the overall impact of forest management-induced forest productivity change on LULCC, we estimated the relative difference in the total area with LULCC between FMC and baseline compared to that in the baseline.

{TA}_{relative,2100}=\frac{\frac{\sum\limits _{k=1}^{n}\vert {A}_{FMC,2100}^{k}-{A}_{FMC,2015}^{k}\vert }{2}\,-\,\frac{\sum\limits _{k=1}^{n}\vert {A}_{Baseline,2100}^{k}-{A}_{Baseline,2015}^{k}\vert }{2}}{\frac{\sum\limits _{k=1}^{n}\vert {A}_{Baseline,2100}^{k}-{A}_{Baseline,2015}^{k}\vert }{2}}

(2)

where

\frac{\sum\limits _{k=1}^{n}\vert {A}_{FMC,2100}^{k}-{A}_{FMC,2015}^{k}\vert }{2}

and

\frac{\sum\limits _{k=1}^{n}\vert {A}_{Baseline,2100}^{k}-{A}_{Baseline,2015}^{k}\vert }{2}

represent the total area with LULCC in 2100 compared to 2015 under FMC and baseline scenarios, respectively. TA_{relative,2100} represents the relative difference in the total area with LULCC between FMC and baseline compared to that in the baseline.

Besides, for the region-based analysis, we also calculated the relative difference as the ratio of D_abs to total area for each region, to perform a relative comparison among regions.

Considering both forest productivity and total nonland cost changes can potentially affect LULCC projections, we added an additional set of scenarios to uncover the contribution of forest productivity and total nonland cost change. These scenarios only considered the change in forest productivity and kept the total nonland cost same as the baseline scenario. We refer to it as FMC_nocost scenarios and the derived LULCC as LULCC_{FMC_nocost}. We calculated the contribution (f_FMC) of forest productivity change on LULCC as the differences between LULCC_{FMC_nocost} and LULCC_Baseline. By comparing LULCC_{FMC_nocost} and LULCC_FMC, we evaluated the contribution (f_ncc) of total nonland cost change on LULCC. To better understand the potential mechanism of the impact of forest productivity and total nonland cost change on LULCC, we also analyze the mean profit rate among FMC, FMC_nocost, and baseline.

3 Results

3.1 Future Forest Productivity Change Induced by Forest Management

Globally, forest management intensity for the forest at mature age shows an overall increasing trend under SSPs from 2015 to 2100 with significant temporal and spatial variation among SSPs (Figures S2–S3 in Supporting Information S1). The increase in forest management intensity across SSPs is driven by the increase in roundwood demand resulting from increasing income and population throughout the 21st century (Tables S2–S3 in Supporting Information S1), since the increasing roundwood demand would result in higher roundwood price that incentivizes more investment through higher intensity of forest management. Temporally, SSP1 shows the largest relative increase, followed by SSP5, SSP2, and SSP4, while SSP3 shows the lowest relative increase. In 2100, the relative increases are 83.4%, 70.3%, 40.3%, 64.4%, and 71.9%, respectively for the five SSPs. The differences among SSPs are caused by the combined effects from the divergent assumptions of socioeconomics-driven roundwood demand, forest management intensity responses, unit costs of forest management, and technological change under different SSP scenarios (Tables S2–S3 in Supporting Information S1). For example, SSP5 and SSP1 have higher economic growth than other SSPs (Tables S2–S3 in Supporting Information S1), and they also have relatively higher forest management intensity response, relatively lower unit cost of forest management, and relatively stronger technological improvement. All these factors together result in the relatively higher increase in forest management intensity under SSP1 and SSP5 than other SSPs.

In terms of spatial patterns, in 2100, there are similar trends among regions under SSPs such as Canada, USA, Central America and the Caribbean, and Russia, while most differences are shown in most Europe and Africa regions. In addition, for the forest management intensity for all forest ages, there is also an overall increasing trend and heterogeneous spatial patterns across five SSPs (Figures S4–S5 in Supporting Information S1). The spatial variations are mainly driven by different ecological and economic advantages in each region under different SSPs (Tian et al., 2018). For example, in Central America and the Caribbean, the increase in forest management intensity is related to the relatively high ecological productivity for the sub-tropical and tropical forests (Cramer et al., 1999; Hidalgo et al., 2011) and the relatively low cost in establishing plantation and purchasing land, which could potentially attract more investments through forest management. These spatial variations show that the forest management intensity is spatially heterogeneous and is dependent on the specific SSP scenario.

Forest productivity under the five SSPs shows diverse changes driven by the divergent increase of forest management intensity across space and time (Figure 2). Temporally, it shows an overall increasing pattern across SSPs. Although it follows the same order (i.e., SSP1 > SSP5 > SSP2 > SSP4 > SSP3) under different SSPs as the forest management intensity relative changes at mature age in 2100, it shows different trends from the forest management intensity relative changes at mature age across SSPs. Specifically, at the global scale, the relative increases are 8.8%, 6.4%, 3.9%, 5.9%, and 7.8%, respectively for the five SSPs. Overall, the spatial pattern is similar under different SSPs. Some countries show similar changes across the five SSPs. Notably, Canada, China, Mexico, Central America, the Caribbean, and Japan show the largest increase in 2100, while Russia and Brazil show the lowest changes under the five SSPs. However, the differences among SSPs also occur in both northern and southern regions of South America (South America_Northern and South America_Southern; countries within each region can be found in Table S4 in Supporting Information S1), USA, and Argentina. For example, the northern region of South America (South America_Northern) shows a 29.4% relative increase under SSP4, while there is almost no change under SSP3. The large regional variation among SSPs is related to the differences in the increases in forest management intensity among SSPs. For example, South America and Argentina show relatively larger forest management intensity increases under SSP1, SSP4, and SSP5, while there are relatively smaller increases under SSP2 and SSP3. The forest productivity change pattern overall matches the patterns of change in forest management intensity pattern across SSPs. Besides, the different forest growth curves among different species in different regions could also influence the spatial pattern of forest management-induced forest productivity change.

Given the impacts on forest management intensity and forest productivity, the change in total nonland cost also shows significant spatial and temporal variations among SSPs (Figure 3). Overall, it shows a decreasing trend from 2015 to 2100 at the global scale under the five SSPs. Among them, SSP1 and SSP5 decrease the most, SSP3 decreases the least, and SSP2 and SSP4 are in between. Spatially, most regions, especially China, Mexico, Central America, and the Caribbean, overall show a large decrease. Some other regions, especially Russia and Brazil, show a small to slight increase in 2100. We found the relative change in total nonland cost shows large spatial variations under different SSPs. For example, the USA shows a 2.4% increase, while Mexico shows a decrease of 17.7%, under SSP3. Some countries also experience very divergent changes under different SSPs. For example, Argentina has a 20% decrease under SSP4, while almost showing no change under SSP3. The variation in total nonland cost can be attributed to the change in forest management cost and forest productivity (Equation S3 in Supporting Information S1; Figure S4 in Supporting Information S1). As shown in Equation S3 in Supporting Information S1, the total nonland cost ($/m³) is estimated by dividing all costs except the land cost for each unit of forest area by the forest productivity. When forest management intensity increases in a region, both the forest management cost and forest productivity will increase. However, since forest management intensity cost is a relatively small part (<5.9% in the study) of the total nonland cost per area, there is a decreasing trend of total nonland cost in most regions. The spatial differences in the total nonland cost change are determined by the spatially-varying change of forest management intensity cost and forest management-induced forest productivity across SSPs.

3.2 Impact of Forest Productivity Change Induced by Forest Management on Future LULCC

Under the baseline scenarios, there is a large proportion of unmanaged pasture, grass, and unmanaged forest, all of which show a decreasing trend. By contrast, the cropland, managed forest, and managed pasture only take up a relatively smaller area, but show an increasing trend over the five SSPs except SSP1. There is a relatively larger LULCC under SSP3, and a smaller LULCC under SSP1 than other SSPs (Figures 4a–4e; Table S5 in Supporting Information S1). After considering the forest management impacts, there is an overall decreasing trend in managed forest area, but an increasing trend in the area of other land use types, especially natural land areas such as unmanaged forest, grass, and unmanaged pasture compared to baseline (Figures 4f–4j; Table S6 in Supporting Information S1). Managed forest shows the largest relative decrease under SSP5 (−4.3%), followed by SSP1 (−4.1%), SSP4 (−3.1%), SSP4 (−2.6%), and shows the least relative decrease under SSP3 (−1.5%) compared to the baseline. Natural land shows the largest relative increase under SSP5, where the unmanaged forest increases by 1.4% and unmanaged pasture increases by 0.8%. SSP1 also shows a large relative increase in unmanaged forest (0.9%), and unmanaged pasture (0.6%), respectively. These demonstrate the notable impact of forest management-induced forest productivity change on reducing the managed forest expansion and saving the natural land loss, especially the loss of natural forest.

We also compared the difference between LULCC_FMC and LULCC_Baseline relative to the change in LULCC from 2015 to 2100 under baseline for each type (Table S7 in Supporting Information S1) and the accumulated change of all land use types (Table S8 in Supporting Information S1). By 2100, we found that there is a significant relative decrease in area with LULCC especially under SSP1 (−7.6%), and SSP5 (−3.7%). For different land use types, there is a large relative decrease in managed forest (−14.56%), but a large relative increase for natural land, including unmanaged pasture (20.6%), shrub (13.8%), unmanaged forest (8.4%), and grassland (7.0%) under SSP1. There is also a large relative decrease in managed forest under SSP4 (−14.3%) and SSP5 (−10.5%). These demonstrate the significant impacts of forest management-induced forest productivity change on LULCC at a global scale.

We further analyzed the LULCC at the regional scale. Under the baseline (Figure S6 in Supporting Information S1), western, southern, and eastern regions of Africa (Africa_Western, Africa_Southern, Africa_Eastern), USA, Canada, and Russia show the largest LULCC especially under SSP3, while showing the least LULCC under SSP1. When considering the LULCC relative to the regional area, southern, eastern, and western regions of Africa (Africa_Southern, Africa_Eastern, and Africa_Western), South Asia, Central America and the Caribbean, and Mexico show a large relative LULCC, with the largest change under SSP3 and smallest change under SSP1(Figure S7 in Supporting Information S1). Compared to the baseline, USA, Canada, Russia, and China show the largest difference in LULCC. The largest differences are observed under SSP5 with the most variable difference among regions under SSP5, and the least difference under SSP3 (Figure 5 and Figure S8 in Supporting Information S1). Relative to the respective area of the regions, the differences between FMC and baseline in 2100 are large in Central America and the Caribbean, Mexico, EU-12, USA, Canada, the eastern region of Europe (Europe_Eastern), and China. Under SSP1, there are more regions experiencing obvious LULCC, while the changes are smaller under SSP3 (Figure S9 in Supporting Information S1).

To better understand the impact of forest productivity and total nonland cost induced by forest management on LULCC, we compared the results among LULCC_Baseline, LULCC_FMC, and LULCC_{FMC_nocost} (Figure S10 in Supporting Information S1). The difference between LULCC_FMC and LULCC_Baseline, and that between LULCC_{FMC_nocost} and LULCC_Baseline show a decreasing trend in managed forest area, but an increasing trend in cropland, biomass, and especially natural land. However, the difference between LULCC_FMC and LULCC_{FMC_nocost} shows the opposite direction for managed forest, biomass, and natural land under five SSPs at the global scale. Overall f_ncc and f_fmc show the same direction for pasture under SSP1, SSP2, and SSP5, but the opposite direction under SSP3-4. Moreover, the impact of non-land cost changes is relatively small compared to the impact of forest productivity changes on LULCC across the SSPs.

We also compared the mean profit rate difference among baseline, FMC, and FMC_nocost simulations to better understand the underlying mechanisms of the forest management impacts (Figures S11–S15 in Supporting Information S1). Both the changes induced by forest productivity only and the changes induced by both forest productivity and total nonland cost changes reduce the mean profit rate of managed lands including managed forest, biomass, cropland, and pasture, while the mean profit rates of the natural land keep constant. By contrast, the change caused by total nonland cost change overall shows the opposite effect. Specifically, the mean profit rate increases for managed forest and cropland under five SSPs. It also increases for biomass under SSP1-SSP4, while managed pasture shows an increase under SSP2, SSP3, SSP4, and SSP5. These demonstrate that the forest productivity change has a dominant effect on the mean profit rate compared to the total nonland cost, which further drives the LULCC difference.

4 Discussion

Previous studies stressed the wide variation of forest sector dynamics under alternative socioeconomic pathways (Daigneault & Favero, 2021) and the impacts of productivity change on LULCC under land competition (Wise et al., 2014). However, existing LULCC projections using IAMs usually neglect the impact of forest productivity change driven by different forest management practices under diverse SSPs (Golub et al., 2022). This study quantifies how the forest management-induced forest productivity change influences the LULCC dynamics with comprehensive land use types under five SSPs by the end of the 21st century, by softly linking GTM with GCAM. We found that forest management has a significant impact on forest productivity, which further significantly influences the spatial and temporal dynamics of LULCC among the five SSPs.

The forest management intensity and the induced forest productivity change show an increasing trend in the future under five SSPs. These broad findings align with previous studies that considered a detailed change in the forest sector. Previous studies projected an increasing global timer demand under all the five SSPs, driven by increasing GDP per capita as well as other socioeconomic assumptions (Daigneault & Favero, 2021). The increase in timber demand further causes an increasing wood price, and the higher wood price would incentivize more intense forest management through the management investment (Daigneault et al., 2022; Mendelsohn & Sohngen, 2019). This suggests a consistent uptrend in forest management across the SSPs, paralleling the trends observed in our results. The magnitude of management intensity change is also comparable to Favero et al. (2020). Given the higher management intensity could increase forest productivity (Gonçalves et al., 2013; Li et al., 2018), it is important to represent this source of productivity changes in the modeling, based on which our results show an overall increasing trend of forest productivity that is within observed ranges (e.g., Mendelsohn & Sohngen, 2019; Scholze et al., 2006).

The variation in projected forest productivity change is driven by different increases in forest management intensity across the SSPs, which can be attributed to different socioeconomic assumptions that align with the narratives of SSPs, particularly within the timber sector (Daigneault et al., 2022). We found the change in forest management intensity shows an overall increasing trend, despite some differences among SSPs, with the largest increase under SSP1 and SSP5, the lowest increase under SSP3, and SSP2 and SSP4 in between. The change in forest management intensity among different SSPs is caused by the combined effects of the socioeconomic and technological assumptions in GTM and GCAM under SSPs (Daigneault & Favero, 2021; Daigneault et al., 2022; Daigneault, 2019). As the sustainability pathway, SSP1 has the highest management intensity response, the highest technology change, the lowest management intensity unit cost, and relatively high GDP per capita caused by a high economic and low population growth (which can contribute to roundwood demand). These could result in the highest forest management intensity through investment. The SSP5 (fossil-fueled development pathway) has the highest GDP per capita, a relatively higher management intensity response and technological change, and a relatively low unit cost of forest management. All these could lead to a relatively higher increase of forest management intensity. By contrast, the regional rivalry pathway: SSP3 is the lowest in technological change and management intensity response, and GDP per capita (caused by a low economic growth and high population growth), but the highest in unit cost of forest management, jointly leading to the lowest increase in forest management intensity. SSP2 (middle-of-the-road pathway) and SSP4 (inequality pathway) are in between for all the factors and show a relatively modest increase in forest management intensity. Consequently, driven by the increased forest management intensity, forest productivity also shows an increasing trend under five SSPs, with the highest increase under SSP1, the lowest increase under SSP3, and the others are in between (Figure 2). In GTM, forest productivity is a function of management intensity, the elasticity of management inputs in forestry due to technological change, tree growth function, and initial forest stocking density (Sohngen et al., 2001; Tian et al., 2018). While forest management intensity, significantly influenced by diverse socioeconomic conditions, shows a marked increase in our findings, other factors remain constant or slightly increase for each forest type, unaffected by socioeconomic conditions during the simulation. Thus, changes in forest productivity over the future period among SSPs are primarily and directly influenced by forest management intensity. In addition, forest productivity has a positive, concave relationship with management intensity in GTM. These could explain why the change in forest productivity follows the same increasing trend and the same order as forest management intensity change in our results.

Forest management induces the relative decrease in managed forest area and the relative increase in other land use types of area, compared to the baseline scenarios. Previous studies suggest that global timber demand is more likely to be met through increased forest productivity (Daigneault & Favero, 2021). Our results of forest management-induced overall decrease in managed forest area align with the existing finding. In addition, the relative increase in areas of cropland, biomass, and natural land use types in our result is similar to Wise et al. (2014), which showed that compared to constant productivity, when the productivity increases in one land use type of area, it is likely to result in a decrease in price and area of that land use type, but increase in area of other types (Wise et al., 2014). Especially, the large relative increase in the area of natural land use types including other pasture, unmanaged forest, and grass could be explained by the relatively larger share in land induced by the relative decreased mean profit rate in managed types of land.

The projected LULCC and their differences across future scenarios between FMC and baseline can be explained by the relative change in the mean profit rate among land use types. The changes in forest productivity and associated total non-land costs significantly influence mean profit rates. Compared to the baseline, under FMC, the mean profit rate for managed land exhibits a decline, whereas it remains stable for natural land (refer to Figures S11–S15 in Supporting Information S1) under FMC compared to baseline. These relative changes in mean profit rates could be due to variations in the cost difference between price and total non-land costs, and the productivity associated with each land use type. Essentially, both the price-cost difference and land productivity positively correlate with mean profit rates (Wise et al., 2014). For managed forest, the forest productivity increase and the non-land cost decrease at the global scale (Figure 3) drives the price decrease, due to more supply and less cost. This further drives the decrease in managed forest's mean profit rate. For other managed land like biomass, managed pasture, and cropland, the price decreases accordingly due to more available land caused by the decrease in managed forest area (Zhao et al., 2021), leading to a decreasing mean profit rate. For natural land, the mean profit rate remain constant in the future simulation, because the mean profit rate of natural land is equal to the rental rate (Zhao et al., 2024) when missing a carbon price in GCAM v7.0, which is the case in our study. The relative change in the mean profit rate among the land use types drives the obvious increase in natural land, and decrease in managed forest, and various changes for other managed land. The difference between SSPs could be explained by the variation of the FMC driven by different socioeconomic assumptions under each SSP.

We further found that the independent impact of forest productivity change on LULCC is in the same direction of change in both forest productivity and total nonland cost, whereas the change in total nonland cost causes the change in LULCC in an adverse direction at the global scale (Figure S10 in Supporting Information S1). This is because when only the forest productivity increases, the induced lower price causes a larger decrease in mean profit rate by a decreasing difference between forest price and total nonland cost. This will leave more available land for other land use types, which further causes a larger decrease in the mean profit rate of other managed lands. The decreased total nonland cost increases the difference between forest price and total nonland cost compared to FMC_nocost. This further causes a relatively smaller decrease in mean profit rate compared to FMC_nocost, which leads to a smaller decrease in managed forest, and smaller increase in natural land and other managed land.

At a regional scale, there is a significant regional difference in LULCC between with and without considering the forest management-derived productivity change. Overall, Central America and the Caribbean, Mexico, EU-12 (including Bulgaria, Cyprus, Czech Republic, Estonia, Hungary, Lithuania, Latvia, Malta, Poland, Romania, Slovakia, Slovenia), Canada, and USA are projected to show less area expansion in the managed forest, combined with large reductions in unmanaged forest, unmanaged pasture, and grass loss compared to other regions. The potential reason for the larger impacts of forest management in these regions could be a combined effect of the forest management-induced productivity change driven by high management investment response and productive species, and forest management associated cost as well as the relative ecological and economic advantages over other regions (Sohngen et al., 1999; Wise et al., 2014). The relative decrease in managed forest and increase in natural lands in these regions highlight that the increase in forest productivity induced by forest management benefits more from forest management in terms of reducing deforestation, and the reduction in other natural lands.

Our study primarily focuses on the impacts of forest management under the five SSP reference scenarios and provides a basis for comparison with emission mitigation scenarios. The potential impacts of forest management under mitigation scenarios could also be significant. To explore this, we carried out additional simulations under the SSP126 mitigation scenario, which represents a green growth pathway reaching a low forcing target (2.6 W/m²) by 2100. Under SSP126, forest management induced-productivity change could reduce managed forest expansion and natural land loss in the future, compared to the baseline scenarios without considering these productivity changes (Figure S16 in Supporting Information S1). With the same level of FMC, SSP126 shows a smaller impact on LULCC (Figure S16b in Supporting Information S1) than SSP1 (Figure 4f). Additionally, incorporating forest management-induced productivity changes results in a reduction in cumulative land use emission by over 1.5 Gt CO₂ (Figure S16c in Supporting Information S1) compared to the baseline. While comprehensive simulations and analyses are necessary to fully understand the impacts of forest management under various mitigation scenarios, such an in-depth exploration is beyond the scope of this study. Nonetheless, our results suggest that considering the forest management-induced productivity changes can facilitate the achievement of the mitigation target. This underscores the importance of incorporating forest management-induced productivity changes in the mitigation scenario projections and climate mitigation policy-making processes.

There are some limitations in our study. First, although both GTM and GCAM are calibrated to 2015 using historical data, there are some inconsistencies between GTM and GCAM in terms of model input, theory and scenario assumptions. To constrain the inconsistencies, we used the same GDP and population for both models, and calibrated the GTM's woody biomass quantity using GCAM outputs, and configured the SSP scenarios based on O’Neill et al. (2014). Second, we made a soft link between GCAM and GTM. Note that such one-way coupling excludes the feedback between forest and other sectors, so future research could develop a forest management module in GCAM to better account for its interaction with other processes/sectors in a synchronous, two-way coupling way. Third, some studies showed the long-term benefit of forest management on reducing forest fire (Coulston et al., 2023) and other climate change impacts (Zhao, Daigneault, et al., 2023; Zhao, Wild, et al., 2023), which is not considered in our study. Thus, our results may underestimate the benefit of forest management on forest productivity and the impacts on LULCC (Favero et al., 2021). Fourth, our study focuses on the impacts of forest management on forest productivity and LULCC dynamics. However, previous studies pointed out the importance of climate change (e.g., temperature and precipitation change and elevated CO2 concentration) on forest productivity (Cui et al., 2024; Zhang et al., 2022). Representing the combined impacts of both forest management and climate change on forest productivity is required. Despite these limitations, our investigations are primarily based on the relative differences rather than absolute magnitudes, mitigating the impact of the above-mentioned uncertainties.

Our results indicate that management-induced forest productivity reduces managed forest expansion and natural land reduction. These underscore the importance of forest management in enhancing roundwood production efficiency and saving the loss of natural land especially natural forest at both the global and regional scale. Given the significant impacts of human-driven forest management, we advocate considering the impacts of FMC in the future LULCC projections. Although the study mainly focuses on the impacts on LULCC, and the forest management-induced forest productivity change can have potentially wide impacts on other sectors. For example, the change in forest productivity and nonland cost could influence the agro-product price and trade (Daigneault et al., 2022; Nelson et al., 2014; Wise et al., 2014). The change in land allocation may also influence water demand (Graham et al., 2018; Kim et al., 2016) and carbon emissions (Friedlingstein et al., 2023; Houghton et al., 2012). In addition, previous studies found that natural land use types are important for biodiversity (Hua et al., 2022). Thus, when taking forest management into account, the resulting more natural lands could be beneficial for biodiversity and is promising to mitigate the climate impacts on biodiversity in a warmer climate (LeClère et al., 2020). We analyzed the region-scale LULCC patterns with a coarse spatial resolution. Further downscaling them into high-spatial-resolution gridded LULCC using Demeter (Chen et al., 2020; Luo et al., 2023) can provide more valuable spatial information, which is essential for Earth system modeling.

5 Conclusions

This study explores how future forest management-induced forest productivity change influences global LULCC, by softly linking GCAM and GTM. We found that human-driven forest management could lead to an overall increase in forest productivity across all five SSPs and the largest increase at the global scale could be 8.8% in 2100 under SSP1. This further leads to an overall decrease in LULCC over the next 80 years, with the largest decrease also occurring under SSP1 (7.5%). Among land use types, we project a significant reduction in the expansion of managed forest as well as a reduced loss in natural land area, especially the natural forest. Our results suggest that overlooking forest management's impact on forest productivity could lead to underestimations of wood production efficiency, overestimates of managed forest expansion, and excessive reductions in natural land. This overlook could potentially introduce uncertainties in further analyses, such as the carbon cycle and biodiversity, which are crucial for understanding climate change impacts. Therefore, it is essential to incorporate forest management changes into future LULCC projections to better account for human influence on the Earth system in a changing world.

Acknowledgments

This research was supported by the National Aeronautics and Space Administration (NASA) through Future Investigators in NASA Earth and Space Science and Technology (FINESST) program (Grant 80NSSC24K0028) and Terrestrial Ecology: Arctic Boreal Vulnerability Experiment (ABoVE) (Award 80HQTR19T0055) to Min Chen and the USDA National Institute of Food and Agriculture, McIntire-Stennis [project number ME041825], through the Maine Agricultural & Forest Experiment Station to Adam Daigneault. The authors thank Dr. Brent Sohngen for his valuable guidance in GTM, and Dr. Volker Radeloff and Dr. Hamid Dashti for their help in providing valuable comments on the manuscript.

Open Research

Data Availability Statement

The data sets used in the study are available at Luo (2024). GCAM model can be freely downloaded at Bond-Lamberty et al. (2023).

Supporting Information

References

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Volume12, Issue8

August 2024

e2024EF004878

Impacts of Forest Management-Induced Productivity Changes on Future Land Use and Land Cover Change

Abstract

Key Points

Plain Language Summary

1 Introduction