Enhancing carbon sinks in China using a spatially-optimized forestation strategy

Domain: biology
Published: 2026-01-12
Editorial outcome: major_revision

Abstract
China plans expanding 49.5 million hectares of new forests by 2050 to strengthen carbon sequestration. However, estimates of the carbon benefits from this expansion rarely consider the effect of ‘forest edge’, where tree mortality increases under intensified stress from wind, drought, pests, and fire. Here we show that proximity to forest edges substantially reduces biomass carbon storage, and develop a spatial optimization strategy that prioritizes planting in areas that minimize edge effects. Our projections show that forestation optimized for edge effects results in a 51% increase in carbon gain (986 ± 22 Tg by 2060), with approximately half of the total gain driven by reduced edge effects. These findings demonstrate that ignoring edge effects can significantly overestimate carbon sink potential and highlights spatially optimized forestation as a pathway to maximize climate mitigation and ecological benefits.

Introduction
China severely suffered from ecological and environmental deterioration caused by the overexploitation of forest resources due to rapid economic development1,2. Hence, large-scale greening projects, such as the Grain for Green Program and the Three-North Shelter Forest Program, were implemented to address the challenges of land degradation, desertification, and water and soil retention3. Intensive forestation and management activities helped to expand China’s forest coverage from 12% in 1979 to about 23% in 20194. Forestation practices were prioritized in environmentally vulnerable areas or areas with critical ecological functions initially (i.e., soil retention). As China made progress on environmental goals and the urgency of climate action increases, forestation activities increasingly focus on carbon sequestration. However, initially forestation aimed at environmental enhancement may result in a landscape of disconnected forest patches. For example, the Grain for Green Program sharply increased the number of forest patches in northwest China5.

From an ecological standpoint, the growing patchwork of forests is a sign of fragmentation. This process fragments large habitats into smaller ones and creates more disturbed, unhealthy forest edges6,7. Different from natural edges (adjacent to a relatively permanent non-forest ecosystem devoid of human activity), disturbed forest edges (both natural and developed) may suffer from increased tree mortality and reduced biomass8–11. Moreover, although species diversity and richness were 50–56% greater at both natural and developed forest edges compared to the interior, the abundance of invasive species was nearly twice as high in developed edges11. This indicates that while edges are diverse, those created by development carry a disproportionately high risk of biological invasion. Hence, strategic planning is required to enhance carbon sequestration by mitigating forest edge degradation. This strategic planning is especially important in China, where a substantial proportion of forest edges are shaped by human development, and effective land-use strategies could simultaneously boost carbon storage and curb the threat of invasive species.

The vast majority of the world’s forests are influenced by their edges12. Defined as the boundary between woodland and another land type13, these edges are common; over 70% of global forests are within 1 km of non-forest edges12. Furthermore, a substantial portion of these edges are human-made, created by development and deforestation14. A majority (over 50%) of global forests underwent an increase in fragmentation between 2000 and 202015, implying growing forest edge areas worldwide. Although the continuous forestation projects were not specifically designed to reduce forest edges in China, the expanding forest areas have eased forest fragmentation between 2000 and 202016. However, China still exhibits high forest fragmentation due to extensive, disturbed edges. These anthropogenic edges differ fundamentally from natural forest boundaries16, indicating significant potential for reducing the negative impacts of edge areas through targeted interventions.

Forestation plays a predominant role in retaining land carbon stocks and is the largest contributor to the recent increases in China’s land carbon sink17–19. China committed to a massive forestation effort between 2021 and 2050, aiming to create 49.5 million hectares (Mha) of new forest to strengthen its forest carbon sink in pursuit of its 2060 carbon neutrality goal20. However, important uncertainties persist regarding the prioritization of locations, the selection of appropriate tree species, and the potential carbon sequestration gains achievable through a spatially optimized strategy. Forest fragmentation reduces carbon storage in edge areas14,21, suggesting a significant potential for carbon enhancement through edge-reduction strategies16.

China’s forestation plan is a pivotal turning point, shifting its focus from broad, target-driven expansion for environmental improvement to targeted carbon enhancement. Therefore, smart forestation is essential for forest resilience and climate adaptation. In this work, we hypothesize that fragmentation reduces biomass carbon stocks in China’s edge forests. To address these considerations, here we develop a smart forestation strategy to enhance carbon sequestration, utilizing data from the 9th National Forest Inventory (NFI), which includes more than 3 million measurement records of individual trees from in situ plots (see “Materials and Methods”). Using this dataset, we first calculate carbon stocks for individual trees using provincial, species-specific allometric equations (i.e., biomass-diameter at breast height [DBH] relationships; Supplementary Table 1) and biomass-to-carbon conversion factors22–27. We aggregate these values to the plot level to analyze the spatial patterns of carbon stock variation along forest edge proximity gradients. Using these plot-level data, we develop a machine-learning model to predict forest biomass carbon stocks, incorporating forest age, climate impacts, and edge effects under different forestation and climate scenarios. We then assess the additional carbon benefits of an optimized forestation strategy by comparing its sequestration potential against other scenarios.

Results
Edge proximity and biomass carbon stock
Based on the NFI data, we found that natural forests were predominantly located within 2.5 km of edges, compared to 1.2 km for planted forests (Fig. 1a). This distribution suggests that forestation efforts have been concentrated either on the periphery of existing forest stands or in newly created, fragmented forest patches. Interestingly, tree biomass carbon stock increased significantly with distance from the forest edge; it was approximately 40% lower in planted forests than in natural forests (Fig. 1a). This aligns with previous findings of lower biomass carbon stock at edges in tropical forests10,21,28 but contrasts with observations from temperate U.S. forests, where the biomass carbon stock was higher at edges13,29. The dominant negative impact of forest edge on biomass carbon is further evidenced by a recent global remote-sensing study revealing consistently lower aboveground biomass in edges compared to interiors30. In addition, using logarithmic fitting, we found that forest stand age increases with distance from the edge to the interior (Fig. 1b, R2=0.69), a pattern similar to that of forest biomass carbon. However, the age of the planted forest stands shows little variation with increasing distance from the edges (Fig. 1b, R2<0.01, p>0.05), suggesting the increasing carbon density in planted forests is not due to the stand age (linear regression: R2=0.25, p>0.05).

To clarify the mechanisms shaping the biomass carbon patterns, we examined the types, frequencies, and intensities of different disturbances across distance gradients (Fig. 2). We found that pests and diseases were the primary stressors in both forest types across distance gradients, followed by climate disturbances and fires (Fig. 2). In addition, the frequency and intensity of disturbances soared with decreasing distance to edges (Fig. 2), indicating much higher disturbance in areas closer to edges. Similarly, we found that the human footprint (the direct and indirect human pressures on the environment due to buildings, population, croplands, roads, railways, etc.) rises sharply from the interior to the edge in both forest types, with a higher human footprint31 in planted forests than in natural forests (Fig. 3a). Moreover, tree mortality rates were higher at the edges in both forest types, with planted forests exhibiting a greater rate (7%–13%) than natural forests (4%–7%) within 1 km of the edge (Fig. 3b). In addition, tree establishment rates, as measured by the ratio of trees reaching the DBH threshold of 5 cm between the two inventories, were lower at the edge, which might relate to higher photoinhibition32. Therefore, forest edges are stressed by both human and natural disturbances. These support our hypothesis that China’s edge forests have lower biomass carbon stocks due to fragmentation.

Previous studies indicate that edges artificially created by disturbance in young tropical forests negatively affect trees10, whereas trees in the inherent, long-standing edges of temperate forests often adapt, leveraging increased resource availability for enhanced growth13. Therefore, the two contrasting findings observed in the carbon stock patterns between edges and interior areas10,13 can be attributed to the varied, disturbance-related, stress levels. The relationship between proximity to forest edges and lower carbon stocks implies a strong link to human disturbances as the primary driver of these low-carbon-density zones.

Forest edges could experience abiotic and biological effects, such as changes in edaphoclimatic conditions and species interactions33. The negative edge effect on biomass carbon identified in this study can be attributed to the high tree mortality and disturbances between the interior and edges (Figs. 2, 3). For example, forest edges were generally adjacent to lands with low structural complexity (i.e., lower surface roughness), receiving more solar radiation during the daytime but higher re-radiation to the atmosphere at night34, resulting in a higher wind speed, larger vapor pressure deficit, and greater diurnal fluctuation of soil and air temperature35. Therefore, high mortality rates, especially for tall trees, occur in edge areas36–38. More importantly, the sharp increase in tree mortality rates is related to higher activities of pest insects and diseases at the forest edges, as found in this study (Fig. 2). The high tree mortality might also relate to increasing wind damage and intensified stress of human activity39–41.

Forest edge might also be beneficial for forest stability, especially in natural forest edges. For example, natural forest edges are more stable and therefore might provide more resources to support the growth of vegetation42–46, which could offset potential negative impacts of fragmentation on tree growth47. However, such benefits were not confirmed in this study based on a large number of surveyed plots. Consequently, the influence of edge proximity on China’s forest biomass carbon stocks more closely resembles the negative relationship observed in tropical forests rather than the positive one documented in U.S. temperate forests10,48. This might be related to the high stress from disturbances at China’s forest edges. Thus, our results imply that reducing the forest edge area via a spatially optimized forestation plan would help to enhance the biomass carbon sequestration capacity. This is particularly important in China, which has a long-term strategy of intensive forestation set to continue until 205020.

Bio-climatically constrained forestation plans
The spatial distribution of potential forest lands must be identified before forest biomass carbon can be accurately estimated. In this study, suitable areas for forestation were identified by applying constraints based on climatic, topological, biological, and edaphic factors, using a random forest (RF) model (Supplementary Fig. 1 and Supplementary Table 3). We found that the potential forestation land was 94.8 Mha in China, with the majority distributed in southwestern (33%), northwestern (29%), and northern areas (15%) (Supplementary Table 4). This differs from the study of Yao et al.49, which primarily aimed at carbon stock enhancement through tree densifications in existing forests in southern China. In comparison, our study focuses on establishing new forests through area expansion. Despite differing approaches, our spatially optimized afforestation strategy confirmed southwestern China (39%) as a priority region, consistent with the Hengduan Mountains region (southwestern China) highlighted by Yao et al.49.

Our results also showed large contiguous areas of potential forestation lands in the central and northeast regions (Supplementary Fig. 1), which is consistent with previous studies3,27,29. Additionally, our analysis incorporated indigenous species selection under changing climates. To do so, we examined the distribution suitability of 24 species and species groups (e.g., large monoculture species such as Pinus massoniana and mixed forests such as broadleaf mixed forests) under future climate scenarios50, which has been widely used in China’s forestation projects. For each forestable grid cell, we identified the three most climatically suitable tree species, thereby generating three different forestation schemes for biomass carbon stock estimation (Supplementary Fig. 4).

The optimized scenario demonstrated an advantage over the random-planting scenario through reduced fragmentation and edge effects. Here, priority was placed on planting trees in fragmented, low-edge proximity locations to offset their proximity-to-forest edges under optimized scenario (Fig. 4). An earlier study revealed that a large-scale revegetation program in the region greatly reduced water availability, and tree population could suffer water shortages if vegetation expansion is further intensified51. Therefore, our optimized forestation strategy was also less intensified in forestation in arid and semi-arid regions (Fig. 4a, b and Supplementary Table 4). Such optimization is achieved using an algorithm to minimize the introduction of new edge areas in contrast to the random forestation strategy (see examples in Supplementary Fig. 3). More specifically, even though the regions have large areas of available land that are bio-climatically suitable for forestation, the optimized forestation strategy would constrain forest expansion in the region and introduce fewer edge areas compared to the non-optimized scenario (Fig. 4).

This study emphasized bioclimatic and ecological constraints in its optimization. For the scenario to be practically implementable, subsequent analyses must integrate economic and accessibility considerations. Thus, the proposed provincial forestation areas differed from those according to the plan released in the National Forest Management Plan (2016-2050)20. However, as implied in this study, we advocate that future national forestation plan could be maneuvered to reduce forest fragmentation for additional benefits.

Project forest biomass carbon stock increment by 2060
The selection of forest species is crucial to accurately predict biomass carbon stock3,27,29,50. We classified the forests into 24 typical tree species and species groups widely adopted in China’s forestation projects. Each of the species and species groups was examined to identify the potential distributions under future climate scenarios, using NFI plot observations coupled with machine-learning models27. The top three tree species with the highest suitability for forestation in each forestable grid cell were selected, providing three schemes for forest biomass carbon stock estimates (Supplementary Fig. 4). The RF model was applied to predict the biomass carbon stock of newly planted forests by 2060 by accounting for the impacts of climate, soil, and topographic variables (Fig. 5; see Supplementary Fig. 5 and Supplementary Table 3 for details of the RF model applied). We estimated 986.3 ± 22.2 Tg C (25 Tg C yr−1, or 51.3%) greater forest biomass carbon stock under optimized-planting scenarios, compared to non-optimized scenarios, in which the newly established forests and existing forests contribute 68% and 32%, respectively.

This additional carbon at forest edges will enhance the annual biomass carbon sink by 12.2% from 2021 to 206027,52,53. Our simulation projects that forest biomass carbon stocks will reach approximately 2,633.3 ± 20.3 Tg C by 2060 in newly planted forests created with the optimized-planting strategy (Fig. 5c). This estimate is 34.2% higher than the carbon stock in forests planted with random forestation scenarios (1,962.3 ± 18.4 Tg C, p < 0.01, Fig. 5c). Such additional carbon benefits from spatially optimized forestation strategies remain consistent across climate scenarios, despite equivalent forestation areas between optimized and non-optimized strategies (Fig. 5 and Supplementary Fig. 6). We attribute the carbon stock benefits of newly planted forests to two factors, reduced edge effects and differing spatial distributions (i.e., environmental conditions). Quantitatively, the edge effect accounted for 207.6 ± 6.9 Tg C, while environmental conditions accounted for 463.5 ± 21.2 Tg C (Supplementary Table 5).

In addition to the carbon sink from natural growth in existing forests, spatially optimized forestation activities will also boost carbon sequestration in areas that benefit from reduced edge effects. We found that by 2060, existing forests will retain an additional 53.2 ± 3.9 Tg C under the random-planting approach, compared to non-forestation scenarios. Interestingly, we further found that optimized forestation with the purpose of reducing the edge effect greatly benefits the growth of existing forests, where the additional carbon stock reached a substantially higher level of 368.5 ± 26.7 Tg C (Fig. 6c). Thus, the existing forests will absorb an additional 315.2 ± 23.0 Tg C by 2060 if forestation activities were spatially optimized, compared to random forestation (Fig. 6c). Thus, our results confirm that forest edges are critical for accurately accounting for regional and global carbon balances, despite their reported and varying impacts13,48. In summary, spatially optimized forestation enhanced carbon stocks by 522.8 ± 28.9 Tg (207.6 Tg from newly planted forests and 315.2 Tg from existing forests) through reduced edge effects, compared to the random-planting scenario, while the remaining 463.5 ± 21.2 Tg increase resulted from allocating forests to locations with more favorable conditions (Supplementary Table 5).

Forests play a central role in achieving the United Nations Sustainable Development Goals, including the sustainable use of terrestrial ecosystems, forest management, combating desertification, reversing land degradation, and halting biodiversity loss54,55. From an ecological perspective, forestation prioritizing planting trees to reduce fragmentation promotes connectivity between forest patches (Supplementary Fig. 7). This would be beneficial for biodiversity conservation according to the percolation theory7,56,57, but it can also accelerate succession, enabling forest edges to develop the characteristics of intact forest more quickly58. Our forestation patterns suggest that the spatially optimized forestation significantly reduced the forest patch density (−57.8%, Supplementary Fig. 7) and the perimeter of the forest patch (−15.3%, Supplementary Fig. 7). Such a forestation strategy might also bring auxiliary benefits by mitigating species extinction59, carbon emissions58, and habitat isolation. Therefore, in addition to the carbon sequestration benefits for existing and newly planted forests, a spatially optimized forestation scheme could also be beneficial for species diversity by reducing pest- and disease-related extinction rates44,60 via minimizing forest fragmentation44,61.

In summary, our study revealed that the forest biomass carbon stock declined with an increasing edge effect, depending on the proximity to edges. This pattern was closely related to frequent disturbances, high tree mortality rates, and suppressed tree establishment at forest edges. We further demonstrated that forest biomass carbon stocks can be enhanced through a spatially optimized forestation strategy by reducing forest edge effects and allocating planted forests to locations with more favorable locations. In contrast, neglecting edge effects will lead to carbon stock loss, even though the forested area is virtually the same under different scenarios. It remains uncertain, however, whether edge forests will be more vulnerable to future shifts in disturbance regimes, including fires, and pest outbreaks. In addition, carbon stock predictions are complicated by the combined effects of ongoing forest succession, which continuously reshapes species composition, and climate change-driven shifts in tree competition62. Therefore, further studies are needed to better understand disturbances and successional processes to reduce the uncertainty in carbon stock projections. We recommend integrating edge effects, climate impacts, and spatially smart forest management into conservation and afforestation practices for effective progress toward carbon neutrality.

Methods
Forest plot data
Data from the 9th NFI, 2014–2018), comprising over 3 million measurement records of individual trees across 37,147 plots (Fig. 7), were used, including 28,366 plots from natural forests and 8781 from planted forests. Data on tree species, forest origin, disturbances, DBH, and stand age were essential for this study. Specifically, the disturbance histories were recorded, including the type (e.g., pests, disease, fire) and intensity (scaled from level 1 to 4 with increasing intensity rated based on the proportion of trees affected in a stand; see Supplementary Table 2). We calculated the biomass carbon stock of each tree using DBH and summarized it at the plot level. Tree biomass was estimated for each tree using species-specific allometric equations, which were developed by the State Forestry Administration and tailored to each province (Supplementary Table 1). The proximity of each forest plot to the forest edge was derived from their locations on a land cover map (see below).

Climate data
Where meteorological date (air temperature, precipitation) were missing from forest site measurements, the gaps were filled using gridded datasets acquired from the National Earth System Science Data Center (https://www.geodata.cn/) (Supplementary Table 3). The aridity for each site was identified from the Global Aridity Index map at a spatial resolution of 30 arc-seconds (~1 km at the equator) (https://csidotinfo.wordpress.com/; Global Aridity Index and Potential Evapotranspiration Database V3) (Supplementary Table 3). Moreover, Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) provided by the Coupled Model Intercomparison Project Phase 6 (CMIP6) were adopted and resampled with a matched spatial resolution (i.e., 250 m) to project forest biomass carbon63.

Topographic data
Missing elevation data in the surveyed forest sites were gap-filled using a 250 m resolution digital elevation model (DEM) from Friedland Sulla-Menashe64 (Supplementary Table 3). The DEM map was also applied to derive related topographical variables, including aspect, slope, and topographic relief. The aforementioned variables served as inputs for the machine-learning model training and the projections of forest biomass carbon (see the next sections).

Soil data and processing
We obtained soil organic carbon, total nitrogen, soil organic matter, and cation exchange capacity from the newly developed 90 m resolution database of the National Soil Information Grids of China (Supplementary Table 3). These variables were used as indicators of soil structure stability, nutrient availability, and the soil capacity to retain nutrients65,66.

Proximity to the forest edge
Two proximity-to-forest edge maps were constructed for this analysis. One map depicted distances from existing forest sites to the nearest edge to examine gradients in biomass carbon stocks. The other represented distances from potential forestation grids to the nearest edge, which was an essential variable for modeling biomass carbon stock projections. Using China Land Use and Cover Change (CNLUCC) data (250 m resolution; see details below), we created maps of proximity to the forest edge, defined as the Euclidean distance from each site/grid to the nearest edge. Analysis of edge effects using CNLUCC maps (2000, 2015, 2018) produced consistent results (Fig. 1 and Supplementary Fig. 8), indicating the robustness of our methodology. Euclidean distance is a commonly adopted definition described with Eq. (1)67–69:

d(p,q) = \sqrt{(p1-q1)^2 + (p2-q2)^2} (1)

where d is the linear distance between the center of a pixel and the center of the nearest source pixel; (p,q) is the coordinates of the source pixel, and (p1,q1) is the coordinates of the nearest source with an attribute value. Proximity-to-forest edge analyses were conducted in ArcGIS (ESRI, USA). The frequency of the number of plots along the proximity-to-edge gradients is shown in Supplementary Fig. 9. Note that forest edges may vary over time due to historically intensive afforestation activities. However, the model framework cannot readily accommodate the dynamic, year-to-year variation in proximity-to-edge metrics are difficult to represent within. Therefore, we used proximity-to-edge data derived from the 2018 CNLUCC map to display the biomass carbon pattern and for model training.

Statistical analysis
The differences in variables between groups were evaluated by independent-sample t-test at a significance level of α=0.01. All statistical results are presented as the arithmetic mean ± standard error of the mean. Data analyses were performed using SPSS 27.0 (IBM Corp., Chicago, IL, USA) and R language; the graphs were drawn in Origin 2018 (OriginLab, Northampton, MA, USA) and ArcMap 10.2.

Constraints on potential land for forestation
We delineated potential forestation lands by applying various filtering and limiting constraints to existing non-forest areas. First, we identified non-forest areas using the CNLUCC land cover dataset produced by the Institute of Geographic Sciences and Resources of the Chinese Academy of Science29. The CNLUCC dataset was developed using a combination of high-resolution spectral information at 15–30 m from Landsat TM (Thematic Mapper) and Huanjing-1 satellite images70. Second, based on the 30-m CNLUCC land cover data, we restricted forestable lands to grid cells of grassland, unused land, and tidal flats, while excluding those identified as cropland, urban, water bodies, and alpine steppe meadows. Third, areas were excluded based on climatic and topographic constraints derived from previous studies; specifically, those with an average annual precipitation of <350 mm or an elevation >4.9 km29,71,72.

Based on the potential forestable map, we further calculated the suitability index for each grid cell using a machine-learning model by taking into account the climatic, topological, biological, and edaphic factors. Specifically, we flagged forestation failure plots and normal plots in model training, representing low and high forestation suitability, respectively. We classified lands as forestation failure sites if tree survival rates fell below 41%, a threshold that necessitates reforestation efforts. The trained RF model was then applied to the potential forestable map delineated in the previous step, producing a suitability map for locating the planted area (Supplementary Figs. 1 and 2).

Biomass carbon predictions
This study focuses on the impact of forestation on biomass carbon stock. Therefore, the biomass carbon stocks in the areas affected by forestation were examined, including the newly forested lands and the existing forests in close proximity to the newly forested areas. An RF model was constructed to predict the biomass carbon stocks using in situ measurements for model training73–75. Machine-learning models, such as RF, SVR, artificial neural networks, and gradient boosted regression trees, are used to predict the spatial characteristics of forest cover patterns. Among these models, the RF model consistently outperforms others when modeling complex environmental data67,76. To construct the regression tree, the RF model utilizes bootstrap samples and selects a subset from the original data set to split the tree nodes, which ultimately refines the regression tree75. Note that a few parameters, including ntree, which indicates the number of trees generated, usually set at 500 individuals. The model ranks the importance of variables by % IncMSE and IncNodePurity73 (Supplementary Fig. 5).

In our simulations, we screened the environmental variables used for training and identified 15 predictor variables for inclusion in the prediction model, covering five categories: vegetation, terrain, climate, soil, and other data (Supplementary Table 3). During the model simulation, 70% of the plots were used for model training, while the remaining 30% were used for model validation. The accuracy of the predicted model was R2=0.78 and RMSD=29.08 Mg C ha−1 (Supplementary Fig. 5d). Furthermore, we found that stand age and tree density contributed significantly to the predictive model (Supplementary Fig. 5a and c). In addition, a bootstrap sample set was randomly drawn with replacement from the original training data, and the number of trees (ntree) was set to 500 (Supplementary Fig. 5b).

Design of simulation experiments
We conducted three groups of experiments to simulate carbon stock changes under various forestation scenarios in China (2021–2060), using a pre-trained machine-learning model. The first group of experiments was based on the assumption that no forestation activities would be implemented under five climate scenarios (i.e., no climate change scenarios and four Shared Socioeconomic Pathways, namely SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). The second represented a baseline of unplanned (random) forestation, while the third applied a spatially smart forestation strategy to the same total land area, with the objective of reducing proximity to forest edges. Both the second and the third groups were designed to achieve the forestation goal of expanding the forest area by 49.5 Mha from 2021 to 2050 as mandated by the State Forestry Administration20. All experimental groups employed machine-learning models to predict the biomass carbon of existing forests in 2040, 2050, and 2060 (Fig. 6), and assess edge effects on current forest growth. For each simulation, the proximity shall be recalculated after forestation to ensure that the continuous forestation activities follow the rules of reducing edge effects (Supplementary Fig. 2). We quantified the contributions of random and smart forestation strategies to carbon stock by comparing the simulation results from the first and second groups, and then the second and third groups.

Furthermore, for the second and third experimental groups, we conducted simulations with and without edge effects. According to the government guidelines20, both forestation scenarios are implemented to achieve a phased expansion: 28.4 Mha within 250 m of the forest edge by 2030, followed by an additional 13.9 Mha within 500 m by 2040 and 7.6 Mha within about 700 m by 2050 (Supplementary Fig. 2). For each group simulation, we included three species-selection-based forestation schemes (Supplementary Fig. 4), which were used to estimate the biomass carbon stock of newly established forests by 2060 (Fig. 5). All the simulations were performed considering the changes in forest age, species, and climate change impacts at a resolution of 250×250 m. For SSPs in second and third experimental groups, we conducted three replicate experiments per pathway using the top three climatically suitable species.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In their article “Spatially-optimized forestation enhances carbon sink by reducing edge effects in China”, Dong and colleagues quantified the negative effects of forest edges on carbon stocks in both natural and planted forests in China, based on more than 9000 field measurements. They also designed an optimized forestation strategy to reduce forest edges, and used a random forest model to predict the potential gains in carbon stocks from such a strategy. Their results suggest that this optimized forestation strategy can lead to higher carbon sequestration compared to random forestation, under various future climate scenarios. While their findings highlight the potential underestimation of future carbon sequestration due to overlooked edge effects, I have significant concerns regarding their methodology, particularly the method edge effects were separated in the measurement data and the approach to predicting carbon stocks. These issues prevent me from recommending the acceptance of this paper at its current stage.\nFirst, regarding the decline in forest biomass carbon stocks near forest edges, I am concerned about the role of forest age in this relationship. As is well known, forests closer to edges are more likely to be younger, often planted more recently. While the authors attempt to control for age by categorizing forests into young-, mid-, and old-aged groups, I am sure that even within these categories, forests near the edges are generally younger than those further inland. The parabolic curve of young forests in the paper also confirm this concern. The authors explain the low carbon stocks at large edge distances (4,700 m) as being due to the presence of very young trees in the interior. This definitely suggests that their edge distance-carbon stocks curve is very easily affected by forest age, rather than purely by edge effects. It is important to disentangle edge effects from forest age (also species composition). The authors' direct attribution of lower carbon stocks near edges to microclimate and resource availability seems inappropriate.\nSecond, for their random forest model used for predicting future carbon stocks, I noticed that forest age has the highest importance score, far outweighing other variables. This makes me skeptical of their conclusion that optimized forestation leads to higher carbon stocks. Their reported difference in carbon stocks between optimized and random forestation strategies could simply be due to older forests being planted in regions with more favorable environmental conditions, as the authors themselves note: “the optimized forestation strategy covers much less area in the central region due to reduced water availability.” In this case, the difference in carbon stocks between optimized and random forestation may not reflect the impacts of reducing forest edges. In addition, in Figure S1, the edge distance is a low-ranking factor, so its changes due to forestation may have minor impact on carbon stocks in their random forest model. Moreover, the authors should check whether the relationship between edge distance and carbon stocks in their random forest model align with that in their field measurements.\nThird, in their prediction of carbon stocks, the flowchart in Figure S2 suggests that existing forest disturbance maps were included in the analysis. However, I did not find any details of this in the paper. I think that accounting for natural disturbances is very important when predicting future carbon sequestration, as disturbances can significantly reduce carbon stocks, particularly in older forests, which are more vulnerable to events like droughts, heatwaves, fires, storms, and pest outbreaks. Otherwise, their model could overestimate future carbon sequestration due to forestation. Additionally, I wonder whether the authors considered tree competition after forestation, which can also influence carbon stocks.", "reviewer_2": "General Comments:\nThe site-level analyses have revealed that reducing the edge area can enhance forest biomass carbon, suggesting potential carbon benefits from mitigating the edge effect through a planned forestation strategy. The definition of a forest plot is crucial for scaling up site-level edge effects to national-scale studies. In this context, a critical analysis of the uncertainty surrounding the main conclusion should be thoroughly addressed. Specifically, it is important to consider the uncertainty associated with the enhancement of biomass carbon stock based on the applied data. Additionally, clarification is needed on what the national forestation scenario with edge effects included actually represents. If it refers to dense forests in the southwestern, northwestern, and northern areas, this is not a novel concept. Furthermore, it should be determined whether this scenario can help establish the spatial extent of afforestation in different regions of China. The manuscript's writing should be improved to ensure it is logical and clear.\nSpecific Comments:\nThe introduction section should be compacted to ensure the logic.\nWhat is the purpose of Figure 1, panel d?\nLines 139-143 are a repetition of Lines 50-52 and are more appropriately placed in the introduction section.\nLines 145-162 in Section 2.2 discuss widely known results, particularly water and nutrient recycling mechanisms associated with forestation, as well as typical forestation in the northeast region.\nLine 170: How are fragmented patches defined, especially in pixel-based national-scale cases?\nLine 175: Use the phrase \"forestation scenario with edge effects included\" instead of \"the optimized forestation scenario,\" and clarify the meaning of \"unplanned forestation scenario.\"\nLines 175-177: Clarify the time frame referenced by the phrase \"in the early period, and then...\"\nLines 180-184 are a repetition of Lines 145-162.\nFurther discussions are needed, including but not limited to the uncertainty associated with the enhancement of biomass carbon stock.", "reviewer_3": "Review for Manuscript: Spatially-optimized forestation enhances carbon sink by reducing edge effects in China\nIn my opinion, the manuscript needs too many corrections because it was based on too poor a methodology. This leads to huge errors in the calculation of biomass and other parameters of research.\nHere I suggest some corrections\nIntroduction:\nThe introductory part is too general description and hazy.\nIn line number 45 and 46 you mentioned that China had long suffered from deteriorated ecological and environmental conditions due to economic development. In the next line, no 49 to 50 you made a contrasting statement that, the intensive forestation activities increased China's forest covers from 12% in 1979 to about 23% in 2019. These two contrasting statements confuse the audience a lot.\nIn line number 62 you mentioned that about 84% of the global edge area is human-made. This sentence needs lots of supportive references because edges are naturally made it is a boundary between forest and land.\nAt forest edges a different kind of ecosystem is working you may have to have a plan to protect this ecosystem instead of planting a tree, due to plating this natural ecosystem will be highly disturbing.\nIn this research you identify a smart forestation strategy to enhance carbon sequestration potential in practice but what will be the possibilities that your strategy will accepted by policymakers?\nMachine learning algorithms are not widely accepted instead of forest inventories, so how would be useful to other budding researchers? Also, without ground truth, there is no sense of another type of data.\nIn the forest edge, the habitats of two ecosystems work together and due to the ecotone, the area is rich in biodiversity but poor in biomass and carbon stock. My advice to you is to plan a sustainable solution to protect the edge instead of planting a tree.\nThe title and introduction look a very big sense but the ecosystem will be disturbing if you disturb the edge.\nMethod:\nA ten-year-old data from four different earlier datasets were used in your research which will produce more error in predicting results.\nIn Figure 7 (Figure 7 Spatial distribution of the forest plots collected and used in this study) the blue circle represents natural forest while the red circle represents planted forest but in writing you mentioned that “The red and blue circles refer to natural forests and planted forests” Its opposite sentences check it. In your methodology you only used various online and software datasets you did not make ground truthing or ground-based measurements it is too difficult to provide accurate predictions of your result. There is a huge error in processing the data of other earlier studies. According to my opinion, the methodology is quite difficult for the common audience or new researchers and it is not too good.\nSo, your methodology is also not so good and with lots of errors. Therefore I strongly recommend that you kindly improve your methodology because faulty methodology leads to faulty results and conclusions.\nResult: These sections have plenty of mistakes first in Figure 1 At “a Panel” the blue dot represents a natural forest while the red dot represents a planted forest but in writing you wrote that “The red dots represent natural forests, and the blue dots represent planted forests” it was opposite meaning at figure and writing.\nIt was already proved that at the edge the tree biomass is always poor and increased at the interior region of the forest what will novel you represent? Why did you compare tropical forest edge with U.S. temperate forest? Both habitats are different.\nThe hypothesis and results are miss matches also here hypothesis of research is not clear. The result section was not so good written. In discussion too, many points are clubbed together and it seems be like a small review. There is no strong comparison and connection of present investigation and other earlier literature was found.\nThere was limited reference in this section and no good comparison of results with other same studies.\nMay have strongly recommended that authors simplify this manuscript. Overall, according to my opinion, the manuscript is not well written."}, "author_response": "Response to reviewer comments\nWe sincerely thank the reviewers for their valuable and constructive suggestions to improve the\nmanuscript. We found that these comments are greatly helpful and we re-examined our findings using\nan updated dataset – the 9th national forest inventory conducted in 2014-2018 consisting of >37,000\nforest plots collected in China. Based on the updated observational data, we are able to elucidate the\nmechanisms shaping the pattern of biomass carbon stock along the proximity gradients to forest edge\nin China. We have thoroughly revised the manuscript with updated results and discussion. We\naddressed all the comments raised by the three reviewers. Please find our point-by-point response\nbelow.\nPlease note that the line numbers refer to the newly revised manuscript, which has undergone\nsubstantial changes. Due to the extent of the revisions, tracked changes were not enabled in the\nmanuscript file. We have almost completely rewritten the manuscript and are now submitting this fully\nupdated version for your review. Additionally, we have provided a comparison PDF highlighting the\ndifferences between the two versions, which we believe will be helpful during the review process.\nReviewers' comments:\nReviewer #1 (Remarks to the Author):\nIn their article “Spatially-optimized forestation enhances carbon sink by reducing edge effects in China”,\nDong and colleagues quantified the negative effects of forest edges on carbon stocks in both natural and\nplanted forests in China, based on more than 9000 field measurements. They also designed an optimized\nforestation strategy to reduce forest edges, and used a random forest model to predict the potential gains\nin carbon stocks from such a strategy. Their results suggest that this optimized forestation strategy can\nlead to higher carbon sequestration compared to random forestation, under various future climate\nscenarios. While their findings highlight the potential underestimation of future carbon sequestration due\nto overlooked edge effects, I have significant concerns regarding their methodology, particularly the\nmethod edge effects were separated in the measurement data and the approach to predicting carbon\nstocks. These issues prevent me from recommending the acceptance of this paper at its current stage.\nResponse: We thank the reviewer for the comments. In the revised version, based on the reviewer valuable\ncomments and suggestions, we used the 9th National Forest Inventory (NFI) data comprising a much larger\nnumber of forest plots (>37,000 collected following the consistent protocol) than the previous version, and\nupdated the methods used to separate the edge effects. We found that the edge effects were evident in\nChina’s forests, but the effects differed in forests with different origins. We have made intensive changes\nand kindly ask the reviewer to check our updated results. Hence, we believe that these substantial\namendments fully resolve the reviewer’s reservations.\n1)First, regarding the decline in forest biomass carbon stocks near forest edges, I am concerned about the\nP\nA\nG\nE\n\\nrole of forest age in this relationship. As is well known, forests closer to edges are more likely to be younger,\noften planted more recently. While the authors attempt to control for age by categorizing forests into young-,\nmid-, and old-aged groups, I am sure that even within these categories, forests near the edges are\ngenerally younger than those further inland. The parabolic curve of young forests in the paper also confirm\nthis concern. The authors explain the low carbon stocks at large edge distances (4,700 m) as being due\nto the presence of very young trees in the interior. This definitely suggests that their edge distance-carbon\nstocks curve is very easily affected by forest age, rather than purely by edge effects. It is important to\ndisentangle edge effects from forest age (also species composition). The authors' direct attribution of lower\ncarbon stocks near edges to microclimate and resource availability seems inappropriate.\nResponse: We sincerely appreciate the reviewer’s insightful and constructive comments. Taking into\naccount these precious insights, in the revised version, we use the forest age instead of instead of age group to show\nthe edge impacts (see Fig.1 below). Regarding the concern about forest age and its potential confounding\neffect on edge effects, we conducted further analysis to explore the relationship between tree age and\nedge distance. Indeed, as the reviewer pointed out, in natural forests, a clear positive relationship between\nedge distance and forest age was observed (R2 = 0.69, p<0.05), indicating that stand age played a role in\nedge effects but other factors might also have contributed. However, we found that, for planted forests,\nthere is no significant positive correlation between edge distance and forest age (R2 = 0.25, p>0.05),\nindicating that the edge effects are determined by other factors other than stand age.\nFig.1 Changes in forest biomass carbon stock and stand age along the gradient of the proximity-to-forest\nedge.\nTo address this issue more rigorously and directly, we made a further step and performed an additional\nanalysis using a Random Forest + SHAP model to examine the relationship between edge distance and\nforest growth. Shapley Additive exPlanations (SHAP) is a tool proposed based on the Shapley value in\ngame theory to provide transparency for model outputs (Rozemberczki et al., 2022). SHAP quantifies the\nmarginal contribution of each feature to the model's prediction, offering both global and local interpretability\nP\nA\nG\nE\n\\n(Bokati et al., 2024). This property makes SHAP highly suitable for complex nonlinear models, enabling it\nto capture the interactions between features (Borgonovo et al., 2024; Yan et al., 2024). The results show\nthat in both natural and planted forests, the edge distance has a positive relationship with carbon stocks\n(Fig.2).\nWe believe the above additional analyses and substantial revisions strengthen our conclusions and help\nto eliminate the potential influence of forest age from the edge effects. Hence, we believe that this issue\npointed out by the review has also been resolved thanks to the reviewer’s constructive comments.\nFig.2 Edge impacts on forest carbon stock derived from Shapley Additive exPlanations (SHAP) analysis.\nPanels a and b represent natural forest and planted forest, respectively.\n2)Second, for their random forest model used for predicting future carbon stocks, I noticed that forest age\nhas the highest importance score, far outweighing other variables. This makes me skeptical of their\nconclusion that optimized forestation leads to higher carbon stocks. Their reported difference in carbon\nstocks between optimized and random forestation strategies could simply be due to older forests being\nplanted in regions with more favorable environmental conditions, as the authors themselves note: “the\noptimized forestation strategy covers much less area in the central region due to reduced water availability.”\nIn this case, the difference in carbon stocks between optimized and random forestation may not reflect the\nimpacts of reducing forest edges. In addition, in Figure S1, the edge distance is a low-ranking factor, so\nits changes due to forestation may have minor impact on carbon stocks in their random forest model.\nMoreover, the authors should check whether the relationship between edge distance and carbon stocks in\ntheir random forest model align with that in their field measurements.\nResponse: We appreciate the reviewer’s thoughtful comments regarding the random forest model and\nthe potential confounding effects of forest age and environmental conditions on the predicted carbon\nstocks. While we acknowledge that forest age plays a significant role in influencing carbon stocks, it is\nP\nA\nG\nE\n\\nimportant to emphasize that forest age is considered to be changed equally between different scenarios.\nMore specifically, in optimized forestation scenario and non-optimized forestation scenario, the areal\nexpansion of newly planted forest and the age increment were both equal. The differences in the carbon\nstock between the two scenarios are contributed from the forestation locations (i.e. the edge effect and the\nenvironmental conditions). Besides, since our aim is to locate the most suitable area for forestation and\ncarbon enhancement, the optimized forestation strategy benefits from both reduced edge effect and its\ninteraction with location-dependent environmental factors. Indeed, as the reviewer pointed out, the\ndifference in carbon stocks between optimized and random forestation is not contributed from reduced\nforest edges only but also from spatially more favorable conditions. Therefore, we conducted additional\nexperiments for both spatially-optimized and random-planting scenarios by setting the edge\ndistance to a value of 9999 m. These additional experiments provide carbon stock increments free from\nedge effects. Our simulation revealed that spatially optimized forestation enhanced carbon stocks by 522.8\n± 28.9 Tg (207.6 Tg from newly planted forests and 315.2 Tg from existing forests) through reduced edge\neffects compared to the random planting scenario, while the remaining 463.5 ± 21.2 Tg increase resulted\nfrom allocating forests to more favorable conditions. Therefore, spatially optimized forestation could\nenhance biomass carbon stocks by 986.3 ± 22.2 Tg C by 2060—a 51.3% increase (~12.2% of the annual\nforest biomass sink) compared to non-optimized scenarios, with reduced edge effects contributing 52% of\nthe carbon stock enhancement. These results were added to the discussion and abstract. We also\nrephrased our descriptions throughout the entire manuscript to attribute such benefits to spatially optimized\nplanting strategy, instead of attributing the benefits to solely edge effects.\nWe also believe that forest carbon stock is more closely related to stand structural and climatic parameters\n(e.g. stand age) than edge effect. However, our target is to quantify the additional carbon benefit from\nspatially optimized forestation strategy given the equivalent forestation area. Therefore, although the edge\ndistance is a low-ranking factor, the comparison between scenarios show a large, additional benefits from\nspatially optimized strategy (i.e. ~12.2% of the annual forest biomass sink incensement). To sum up, our\nresults demonstrate that edge effect still plays an essential role to bring direct and indirect carbon\nsequestration benefits.\nFurthermore, regarding the reviewer’s concern on the relationship between optimized forestation\nstrategies and carbon stocks, we would like to clarify that optimizing afforestation strategies reduced forest\nfragmentation (Fig. S3&S7). Further analysis revealed that the lower biomass carbon at China’s\nforest edge were closely related to higher disturbances (e.g. fires, pest and diseases, see Fig. 2&3\nin the main text) and mortality, thus the distance to forest edge also represents the degree of\ndisturbance and mortality from the outside to the inside of the forest (Fig. 2&3). We hope this\nadditional clarification addresses the reviewer’s concerns and enhances the robustness of our findings.\nThus, overall, we believe we have greatly addressed the aforementioned concerns of this reviewer.\n3)Third, in their prediction of carbon stocks, the flowchart in Figure S2 suggests that existing forest\ndisturbance maps were included in the analysis. However, I did not find any details of this in the paper. I\nthink that accounting for natural disturbances is very important when predicting future carbon sequestration,\nas disturbances can significantly reduce carbon stocks, particularly in older forests, which are more\nvulnerable to events like droughts, heatwaves, fires, storms, and pest outbreaks. Otherwise, their model\nP\nA\nG\nE\n\\ncould overestimate future carbon sequestration due to forestation. Additionally, I wonder whether the\nauthors considered tree competition after forestation, which can also influence carbon stocks.\nResponse: We sincerely appreciate the reviewer’s valuable feedback regarding the inclusion of natural\ndisturbances in our carbon sequestration predictions. In this study, we utilized in-situ survey data to build\nthe relationship between edge proximity and the forest carbon stock. Our model training used all the plots\navailable, which include plots disturbed by various factors with varying level of disturbance. Therefore, the\ntrained model included the impacts of these disturbances. We also found that disturbances gradually\ndecrease from the forest edge towards the interior. However, these disturbances (such as fire, storms, and\npest outbreaks) could be change in future, while prediction of these disturbances is challenging. Therefore,\nour prediction assumes that the disturbance intensity stays unchanged in the future. We agree that the\nfuture carbon stock could be systematically overestimated. However, due to lack of data, we provide\nuncertainty in the discussion to remind reader. Please see revisions in Lines 264-270.\nRegarding tree competition post-forestation, we totally agree that forest succession will lead to changes in\nthe species composition and therefore the carbons stocks. Actually, we have published a paper elaborating\nforest succession’s impacts on carbon stock/sink prediction (see Dong et al. 2024). Succession will result\ninto changes in species composition and even the type of forests, which might not visible in short- and\nmid-term predictions but essential for long-term carbon prediction (Dong et al., 2024). For short- and mi-\nterm prediction, tree competition majorly results into thinning and our trained model are robust to capture\nthe carbon accumulation under such scenarios. However, although our predictions cover a relatively short\nperiod of 40 years from 2020 to 2060, we are fully aware that change of tree composition could also affect\nthe carbon stock prediction for a period of 40 years. Therefore, we added these uncertainties in discussion\n(see Lines 264-270). We suggest that future research could incorporate these dynamics for a more\naccurate predictions of forest stock changes (Dong et al., 2024).\nWe believe that the revisions made have addressed the reviewer’s concerns and strengthened the\nmanuscript, and we greatly appreciate the reviewer’s contributions to this improvement.\nReferences:\nBokati, L., Kosheleva, O., Kreinovich, V., & Thach, N. (2024). Why Shapley Value and Its Variants Are\nUseful in Machine Learning (and in Other Applications). In (pp. 169-174).\nBorgonovo, E., Plischke, E., & Rabitti, G. (2024). The many Shapley values for explainable artificial\nintelligence: A sensitivity analysis perspective. European Journal of Operational Research, 318(3),\n911-926. doi:https://doi.org/10.1016/j.ejor.2024.06.023\nRozemberczki, B., Watson, L., Bayer, P., Yang, H.-T., Kiss, O., Nilsson, S., & Sarkar, R. (2022). The\nShapley Value in Machine Learning: Proceedings of the Thirty-First International Joint Conference on\nArtificial Intelligence.\nYan, Y., Piao, S., Hammond, W. M., Chen, A., Hong, S., Xu, H., . . . Allen, C. D. (2024). Climate-induced\ntree-mortality pulses are obscured by broad-scale and long-term greening. Nature Ecology &\nEvolution, 8(5), 912-923. doi:http:s//doi.org/10.1038/s41559-024-02372-1\nDong, Y., Yu, Z., Agathokleous, E., Zhou, G., & Liu, S. (2024). Pitfalls in forest carbon sink\nprojection. Journal of Forestry Research, 35(1), 87.\nP\nA\nG\nE\n\\nReviewer #2 (Remarks to the Author):\nGeneral Comments:\nThe site-level analyses have revealed that reducing the edge area can enhance forest biomass carbon,\nsuggesting potential carbon benefits from mitigating the edge effect through a planned forestation strategy.\nThe definition of a forest plot is crucial for scaling up site-level edge effects to national-scale studies. In\nthis context, a critical analysis of the uncertainty surrounding the main conclusion should be thoroughly\naddressed. Specifically, it is important to consider the uncertainty associated with the enhancement of\nbiomass carbon stock based on the applied data. Additionally, clarification is needed on what the national\nforestation scenario with edge effects included actually represents. If it refers to dense forests in the\nsouthwestern, northwestern, and northern areas, this is not a novel concept. Furthermore, it should be\ndetermined whether this scenario can help establish the spatial extent of afforestation in different regions\nof China. The manuscript's writing should be improved to ensure it is logical and clear.\nResponse: We sincerely appreciate the reviewer’s valuable feedback, and we made a thorough work and\nto address these issues, as explained below:\nFirst, to improve the robustness of our findings, we have re-conducted the experiments using updated and\nmore comprehensive data from the National Forest Inventory (NFI) comprising >37,000 forest plots (Lines\n284-297). The NFI is a systematic and long-term forest resources survey project, which aims to provide a\ncomprehensive and accurate understanding of the current state, changing trends, and ecological functions\nof national forest resources. The survey plots are designed at 667-800 m2 covering China. Note that plot\nareas might slightly differ between provinces but the carbon stock were converted to the unit of Mg ha-1.\nThe distribution of the forest plots is showed in Fig. 3.\nFig.3 Spatial distribution of the forest plots collected and used in this study.\nSecond, as the reviewer pointed out, uncertainties should be addressed in forest carbon stock prediction.\nIn combined with other reviewer’s comments, we have added uncertainties of carbon predictions in the\ndiscussion to improve the manuscript. Please see our changes in Lines 264-270.\nP\nA\nG\nE\n\\nThird, regarding “clarification is needed on what the national forestation scenario with edge effects included\nactually represents”, it concerns the phrase “In the forestation scenario with consideration of reducing edge\neffects”. The sentence has been deleted in the revised version. Additionally, to avoid confusion, we\nexplained the two forestation scenarios here. In this study, the national forestation scenario with\nconsideration of edge effects indicates forestation with priority given to available land to reduce edge area.\nMore specifically, since the Chinese government committed to forest 49.49 Mha land from 2021 to 2050\n(see National Forest Management Plan 2016-2050 released by the State Forestry Administration of the\nPeople’s Republic of China), there is a need to locate the best areas to be forested. How to implement\nthe afforestation plan and where to implement it are the key points that the scientific community is\nparticularly concerned about. Here, we identified 94.8 Mha of land with forestation potential, subject to\nbioclimatic limitations. Thus, the critical challenge lies in identifying which 49.49 Mha within the 94.8 Mha\nof available land should be prioritized for reforestation to optimize carbon sequestration. We found that\nspatially optimized approach to reduce edge effect could help attain such goal. We developed two\nforestation scenarios based on government guidelines and our findings regarding the relationship between\nedge gradient and biomass carbon. One scenario is a spatially optimized forestation strategy aimed at\nreducing edge effects and forest fragmentation. This approach has created larger and more continuous\nforest areas comparing to the other (i.e. non-optimized scenario) (Fig.S3& S7), which would be more\nbeneficial for sustainable forest growth and ecological conservation (e.g. reduce fragmentation).\nAs the reviewer pointed out, tree densification in China is a good idea to enhance forest carbon stock as\nwell (Yao et al., 2024). However, as explained above, the spatial optimization proposed in this study is not\nto densify forests in China, but to smartly expand new forest in the future. Thus, the proposed scenario\ndoes provide the spatial extent of afforestation in different regions of China. Please see these contents in\nFig. 4 in the main text and Figure S1&S4 in the supplement of our new manuscript.\nTaken all together, we believe that, through substantial work and revisions based on reviewer comments,\nwe have resolved all indicated issues.\nSpecific Comments:\nThe introduction section should be compacted to ensure the logic.\nResponse: The introduction has been revised. Please check our revisions in the new manuscript. The\nintroduction is designed to follow the below logic:\n1) Firstly, we introduced that China's past forestation efforts focused on combating ecological\ndegradation through large-scale environmental restoration.\n2) Second, we explained that these efforts often created fragmented forests with extensive human-\ninduced edges, which can reduce biomass carbon stocks and increase vulnerability to\ndisturbances.\n3) Third, we stressed that China shifts toward carbon-focused forestation to meet climate goals,\nwhile this study proposes a smart forestation strategy that maximizes carbon sequestration using\nmachine learning and national forest inventory data.\nP\nA\nG\nE\n\\nLines 139-143 are a repetition of Lines 50-52 and are more appropriately placed in the introduction section.\nResponse: The sentence has been revised and placed in the last paragraph of the introduction section as\nsuggested.\nLines 145-162 in Section 2.2 discuss widely known results, particularly water and nutrient recycling\nmechanisms associated with forestation, as well as typical forestation in the northeast region.\nResponse: Revisions have been done. Lines 151-162 have been deleted and please see our revisions in\nLines 166-201.\nLine 170: How are fragmented patches defined, especially in pixel-based national-scale cases?\nResponse: The algorithm we designed will locate the grids close to existing forests, in which the small\nforestable patches (surrounded by existing forests) will be planted with priority.\nLine 175: Use the phrase \"forestation scenario with edge effects included\" instead of \"the optimized\nforestation scenario,\" and clarify the meaning of \"unplanned forestation scenario.\"\nResponse: Changes have been made. We have removed the term “unplanned forestation scenario” for\nclarity.\nLines 175-177: Clarify the time frame referenced by the phrase \"in the early period, and then...\"\nResponse: The sentence has been removed.\nLines 180-184 are a repetition of Lines 145-162.\nResponse: Revisions have been made.\nFurther discussions are needed, including but not limited to the uncertainty associated with the\nenhancement of biomass carbon stock.\nResponse: We sincerely appreciate the reviewer’s detailed comments. The results and discussion has\nbeen intensively revised and we also added discussion about uncertainties. Please kindly check and let\nus know what further revisions are needed.\nReference:\nYao, L., Liu, T., Qin, J., Jiang, H., Yang, L., Smith, P., . . . Piao, S. (2024). Carbon sequestration potential\nof tree planting in China. Nature Communications, 15(1), 8398. doi:http://doi.org/10.1038/s41467-\n024-52785-6.\nP\nA\nG\nE\n\\nReviewer #3 (Remarks to the Author):\nReview for Manuscript: Spatially-optimized forestation enhances carbon sink by reducing edge effects in\nChina. In my opinion, the manuscript needs too many corrections because it was based on too poor a\nmethodology. This leads to huge errors in the calculation of biomass and other parameters of research.\nHere I suggest some corrections.\nResponse: We thank the reviewer for the constructive comments. We have thoroughly revised the\nmanuscript using a totally new dataset – the 9th National Forest Inventory data comprising >37,000\nforest plots collected in China. The methods, results, discussions, and figures were all updated, and provide\nbelow point-by-point responses. Through these substantial revisions, we believe we have fixed these\nerrors, thanks to the reviewers constructive comments.\n1)Introduction:\nThe introductory part is too general description and hazy.\nIn line number 45 and 46 you mentioned that China had long suffered from deteriorated ecological and\nenvironmental conditions due to economic development. In the next line, no 49 to 50 you made a\ncontrasting statement that, the intensive forestation activities increased China's forest covers from 12% in\n1979 to about 23% in 2019. These two contrasting statements confuse the audience a lot.\nResponse: We sincerely appreciate the reviewer’s insightful comments. We have revised the manuscript\nto present a more coherent narrative. In the revised version, we rephrased the sentence to “China once\nseverely suffered from deteriorating ecological and environmental conditions caused by the\noverexploitation of forest resources, as a consequence of rapid economic development. …” to avoid\nconfusion. While China has indeed experienced significant environmental degradation due to rapid\neconomic development, this issue has been mitigated over recent decades through intensive forestation\nefforts. The increase in forest cover from 12% in 1979 to about 23% in 2019 is a direct result of these\nlarge-scale afforestation and reforestation initiatives. We hope this change help improve the logical flow\nand be clearer for the reader.\nIn line number 62 you mentioned that about 84% of the global edge area is human-made. This sentence\nneeds lots of supportive references because edges are naturally made it is a boundary between forest and\nland.\nResponse: We thank the reviewer for pointing this out and we made change as suggested. The figure was\nderived from Brinck et al. (2017). It reported that “we found that 84% of the edge area is anthropogenically\ncreated (see Supplementary Table 1).” However, as reviewer mentioned, the number could be different\nbetween studies. To avoid dispute, we rephrased the sentence to “a substantial amount of the global edge\narea is human-made”.\nP\nA\nG\nE\n\\nIn this research you identify a smart forestation strategy to enhance carbon sequestration potential in\npractice but what will be the possibilities that your strategy will accepted by policymakers?\nResponse: We appreciate the reviewer’s concerns regarding the potential acceptance of our proposed\nforestation strategy by policymakers. Our study primarily presents a theoretical forestation approach aimed\nat enhancing carbon sequestration. However, we acknowledge that the successful implementation of this\nstrategy depends on various factors, including policy priorities, resource availability, and regional\nenvironmental conditions. There is a long way from proposing a strategy to the ultimate stage of being\naccepted. We do not expect that the concept could be implemented immediately upon publication of our\npaper. However, it will bring this idea to attention.\nOur group work closely with the Forestry Administration of China, and we provide many suggestions\ndirectly to the government. For example, we proposed a spatial optimization plan for forestation in the\nupper Minjiang River by trade-off between water conservation and carbon sequestration benefits. The\nstrategy has been applied across >900,000 hectares of forests, providing scientific support for\nstrengthening the ecological security barrier of the Minjiang River Basin and the broader Yangtze River\nEconomic Belt (document available but in Chinese and could be provided if needed). Beside, we also\ncontributed directly to China’s Nationally Determined Contributions (NDCs) report relating to forest\nmanagement improvement in China for carbon stock enhancement (Chinese document also available).\nWe believe that our findings provide valuable evidence that can guide future discussions on forest\nmanagement and carbon sequestration strategies. We hope that the novel insights in our study will\ncontribute to the ongoing dialogue in policy circles on how to optimize forestation efforts for climate change\nmitigation.\nMachine learning algorithms are not widely accepted instead of forest inventories, so how would be useful\nto other budding researchers? Also, without ground truth, there is no sense of another type of data.\nResponse: We acknowledge the reviewer’s concern regarding the use of machine learning algorithms as\na substitute for traditional forest inventories. It is important to clarify that machine learning models do not\naim to replace ground-based forest surveys; rather, they are a powerful tool for predicting future trends\nbased on historical data. The primary advantage of using machine learning in our study is its ability to\nanalyze large datasets and generate predictions that can guide forest management and policy planning.\nTraditional age–biomass equations can introduce significant uncertainty when applied to such a large and\nheterogeneous dataset in using such a large dataset. However, we fully agree that ground-truth data is\nessential for the validation and calibration of any model. Thus, our approach relies on intensive calibration\nand validation using historical, field-measured data before applying to the predictions of future carbon\nstock. This methodology can provide valuable insights for budding researchers by offering a\ncomplementary approach to more conventional methods, enabling better-informed decision-making in\nforest planning.\nAt forest edges a different kind of ecosystem is working you may have to have a plan to protect this\necosystem instead of planting a tree, due to plating this natural ecosystem will be highly disturbing. In the\nforest edge, the habitats of two ecosystems work together and due to the ecotone, the area is rich in\nbiodiversity but poor in biomass and carbon stock. My advice to you is to plan a sustainable solution to\nP\nA\nG\nE\n\\nprotect the edge instead of planting a tree. The title and introduction look a very big sense but the\necosystem will be disturbing if you disturb the edge.\nResponse: We sincerely appreciate the reviewer’s insightful and constructive comments regarding the\npotential ecological disturbance caused by forestation at the forest edge and the topic of biodiversity\nconservation. First, China’s Forestry Administration places a lot of efforts to reduce the disturbances and\ntreat forestation as a process of enrich biodiversity (see page 93 of the book below). To achieve the goal,\nChina Forestry Administration has released a book “Interpretation of Afforestation Technical Regulations\n—Technical Regulations for Afforestation (GB/T 15776-2016)\", in which biodiversity protection has been\ndetailed in afforestation practices. Here are some rules to be followed during forestation (see page 93-94\nof the book):\n1. Protection of Local Ecological Features\nSpecial attention should be given to preserving ancient and rare trees, as well as local\necological habitats. Nesting grounds for birds and dens for mammals should be conserved.\n2. Conservation During Site Preparation\nWhen clearing land for afforestation, avoid disturbing areas with existing biodiversity value,\nsuch as old trees, nests, and wildlife habitats. Prevent large-scale planting of single-species\nor exotic species that could disrupt the local ecosystem.\n3. Control of Exotic Species Introduction\nUse native tree species whenever possible. Introduction of exotic species should follow strict\necological risk assessments.\n4. Protection of Rare and Endangered Tree Species\nRare and endangered species, including ancient trees, must be strictly protected. When\nclearing land or conducting other operations in afforestation areas, saplings and mother\ntrees of such species should be preserved. Favorable conditions should be created to\nsupport their survival, growth, and regeneration.\nSecond, China launched ambitious plan to forest 49.49 Mha land from 2021 to 2060 to enhance carbon\nstock as released in the National Forest Management Plan (2016-2050) issued by the State Forestry\nAdministration of the People’s Republic of China. Therefore, the potentially forestable land might be more\nor less affected, while an optimized approach will be needed to reduce the negative impacts and to attain\nthe expected goals. Here, we identified 94.8 Mha of land with forestation potential, subject to bioclimatic\nlimitations. Thus, the critical challenge lies in identifying which 49.49 Mha within the 94.8 Mha of available\nland should be prioritized for reforestation to optimize carbon sequestration. Under random forestation\nscenario, established forests will suffer from fragmentation and low potential in biodiversity conservation,\nand the forested area will be more disturbed comparing to optimized scenario.\nWe fully agree that forest edges are unique ecological zones where the interaction of ecosystems might\nresult in high biodiversity. However, it should also be noted that large edge area might also indicate high\nforest fragmentation, which is negative to biodiversity conservation. The past few decades of forestation\nprojects have introduced the large increase of man-made forest edge. Our proposed spatially optimized\nforestation strategy that reduce edge area is to give priority to fill the gap between forests, which is a way\nP\nA\nG\nE\n\\nto reduce forest fragmentation and abrupt edges. We believe that such strategy might not necessary lead\nto biomass loss, while in contrast, contiguous forest with reduced fragmentation could be beneficial to\nharbor more species.\nHowever, we also aware that forestation could introduce disturbance at naturally generated edge. To\naddress your concerns about edge afforestation in natural ecosystems, we can also add an additional\nexperiment aimed at avoiding afforestation at the edges of natural forests. In the new simulations,\nforestation will only be conducted to reduce man-made forest edge (Table S3). This additional experiment\nwill be based on a recently released forest map produced by (Cheng et al., 2023). However, this will also\nintroduce new uncertainties, and the impacts on biodiversity are unclear. Nonetheless, we can conduct the\nanalysis if the reviewer believes that will be helpful and necessary.\n2) Method:\nA ten-year-old data from four different earlier datasets were used in your research which will produce more\nerror in predicting results.\nResponse: We sincerely appreciate the reviewer’s valuable feedback. We acknowledge that the use of\nthe original data from four different earlier datasets in our previous version may have introduced errors,\npotentially affecting the accuracy of our predictions. To address this concern, we have re-conducted the\nexperiments using data from the 9th National Forest Inventory (NFI, 2014-2018) of China, which\ncomprising >37,000 forest plots and provides more extensively covered, more recent, and reliable\ninformation. The results from this updated dataset are significantly more robust and demonstrate clearer\ntrends compared to the previous analysis. We believe that this revision improves the overall accuracy and\nreliability of our findings.\nIn Figure 7 (Figure 7 Spatial distribution of the forest plots collected and used in this study) the blue circle\nrepresents natural forest while the red circle represents planted forest but in writing you mentioned that\n“The red and blue circles refer to natural forests and planted forests” Its opposite sentences check it.\nResponse: We sincerely appreciate the reviewers' meticulous attention to details. We apologize for the\nincorrect statement in the Figure. Correction has been made in the new manuscript to ensure the\nconsistency and accuracy of the content.\nIn your methodology you only used various online and software datasets you did not make ground truthing\nor ground-based measurements it is too difficult to provide accurate predictions of your result. There is a\nhuge error in processing the data of other earlier studies. According to my opinion, the methodology is\nquite difficult for the common audience or new researchers and it is not too good. So, your methodology\nis also not so good and with lots of errors. Therefore, I strongly recommend that you kindly improve your\nmethodology because faulty methodology leads to faulty results and conclusions.\nResponse: We thank the reviewer for the technical comments. Indeed, there are multiple datasets used in\nthe analysis and the largest uncertainty might come from the biomass data collected from different studies.\nIn the revised version of the manuscript, we overcome this shortcoming by using field measured NFI plot\nP\nA\nG\nE\n\\ndata that collected following a strict and consistent protocol that adopt by the national forestry\nadministration. The NFI was conducted by province with over 670,000 permanent plots set up in China, in\nwhich >37,000 plots were forest plots. The NFI collects in situ information includes: tree species and origin\n(natural vs. planted), forest age, growing stock volume and biomass, disturbance history. Thus, NFI data\nprovides the most reliable, observational information that essential for model training, validation, and\npredictions. We believe the updated dataset significantly enhances the reliability of our findings.\nThe data was used to train the machine-learning model and validation has been showed in Figure S5.\nSpecifically, the model validation was performed by using 30% of the in-situ measurements, and the\naccuracy of the predicted model was R2 = 0.78, RMSD = 29.08 Mg C/ha (Figure S5). These results indicate\nthat the model performs well in carbon stock predictions.\nAs suggested, we have also updated the flowchart to show the data processing methods used (see Figure\nS2). We also revised the text to elaborate as clear as possible to the readers. Hence, by combining field-\nbased real-world forest measurements with machine learning and related statistical tools, we were able to\noffer a novel, integrated, multi-level analysis addressing the core question of the study. Thanks to all the\nreviewers’ comments, we applied extensive revisions and hope that everything is now clearer.\n3) Result:\nIt was already proved that at the edge the tree biomass is always poor and increased at the interior region\nof the forest what will novel you represent? Why did you compare tropical forest edge with U.S. temperate\nforest? Both habitats are different.\nResponse: We thank the reviewer for the comment. Actually, there is no consistent conclusion showing\nthat edge effects are always negative on tree biomass. Previous studies generally reported that “edge\nincreases the incident light which, in turn, promotes plant growth (Murcia, 1995).” This finding is also\nsupported by two of the recent studies in temperate forests (Morreale et al., 2021; Reinmann & Hutyra,\n2017). However, another study found that forest biomass is lower at tropical forest edge due to\ndisturbances (Brinck et al., 2017). Morreale et al. (2021) pointed out that temperate forest edges are less\nwind-threatened and less sensitive to the elevated temperatures and water stress that occur along all\nforest edges. Rather, increases in radiation may release the most-limiting biogeochemical constraints on\ntemperate forests (temperature and light) (Nemani et al., 2003; Reinmann et al., 2020; Smith et al., 2018).\nHowever, our study revealed a negative edge effect similar to tropical forest but contrast to temperate U.S.\nTherefore, we suspected that China’s forest edges were majorly newly created and susceptible to\ndisturbance-induced mortality. This is also why we compared our finding to the edges in tropical area\nand temperate U.S.: the edge effects are contrasting between the China and the U.S.\nThus, our findings are novel because: 1) China’s forest edge is lower in biomass, which is similar to highly\ndisturbed tropical forest edge but different from traditional temperate forest edge in the U.S.; 2) There is a\nlack of regional reported pattern of edge effects on forest biomass in China so far and our study is the first\none showing such edge effects using the most recent available, extensive covered in-situ surveyed plot\ndata; 3) Previous local and regional study stop at showing the edge effects, while our study is the first to\nquantify the carbon benefits by applying such results in forestation practice.\nP\nA\nG\nE\n\\nThe hypothesis and results are miss matches also here hypothesis of research is not clear. The result\nsection was not so good written. In discussion too, many points are clubbed together and it seems be like\na small review. There is no strong comparison and connection of present investigation and other earlier\nliterature was found.\nResponse: We thank the reviewer for the comment. In the previous version of the manuscript, we didn’t\nprovide a hypothesis to be tested. As suggested, in the updated new manuscript, we added the working\nhypothesis: “In this study, we hypothesize that China’s edge forests have lower biomass carbon stocks\ndue to fragmentation”. In previous version, the results were derived from different in-situ surveys, while the\ndisturbances information is not available, preventing us from in-depth examination of the sources of edge\neffects. Therefore, we made a lot of assumption in the discussion, which might be the reason that the\nreviewer found there is lack of strong connection between studies cited. In the new manuscript, based on\nthe large dataset provided by the 9th NFI, we are able to explain the cause of the negative impacts of edge\non forest biomass. We believe these are helpful to readers.\nThere was limited reference in this section and no good comparison of results with other same studies.\nResponse: We have updated the Results and Discussion section. We have added more explanations to\nsupport our findings and the comparisons were also updated.\nMay have strongly recommended that authors simplify this manuscript. Overall, according to my opinion,\nthe manuscript is not well written.\nResponse: We have updated the manuscript as suggested. Specifically, the new dataset was used and\nthe results and figures were all updated. Some of the figures have been moved to the supplement to simply\nthe manuscript. The overall logic is to show the edge effects on forest carbon stock and then applied the\nfinding to forestation activities for carbon stock enhancement. We are open to any further comments from\nall reviewers.\nReferences:\nBrinck, K., Fischer, R., Groeneveld, J., Lehmann, S., Dantas De Paula, M., Pütz, S., . . . Huth, A. (2017).\nHigh resolution analysis of tropical forest fragmentation and its impact on the global carbon cycle. Nat\nCommun., 8(1), 14855. doi:https://doi.org/10.1038/ncomms14855.\nCheng, K., Su, Y., Guan, H., Tao, S., Ren, Y., Hu, T., . . . Guo, Q. (2023). Mapping China’s planted forests\nusing high resolution imagery and massive amounts of crowdsourced samples. ISPRS Journal of\nPhotogrammetry and Remote Sensing, 196, 356-371.\ndoi:https://doi.org/10.1016/j.isprsjprs.2023.01.005\nMorreale, L. L., Thompson, J. R., Tang, X., Reinmann, A. B., & Hutyra, L. R. (2021). Elevated growth and\nbiomass along temperate forest edges. Nature Communications, 12(1), 7181.\ndoi:https://doi.org/10.1038/s41467-021-27373-7\nMurcia, C. (1995). Edge effects in fragmented forests: implications for conservation. Trends in Ecology &\nEvolution, 10(2), 58-62. doi:https://doi.org/10.1016/S0169-5347(00)88977-6\nP\nA\nG\nE\n\\nNemani, R. R., Keeling, C. D., Hashimoto, H., Jolly, W. M., Piper, S. C., Tucker, C. J., . . . Running, S. W.\n(2003). Climate-Driven Increases in Global Terrestrial Net Primary Production from 1982 to 1999.\n300(5625), 1560-1563. doi:http://doi.org/10.1126/science.1082750\nReinmann, A. B., & Hutyra, L. R. (2017). Edge effects enhance carbon uptake and its vulnerability to\nclimate change in temperate broadleaf forests. 114(1), 107-112.\ndoi:https://doi.org/10.1073/pnas.1612369114\nReinmann, A. B., Smith, I. A., Thompson, J. R., & Hutyra, L. R. (2020). Urbanization and fragmentation\nmediate temperate forest carbon cycle response to climate. Environmental Research Letters, 15(11),\n114036. doi:http://doi.org/10.1088/1748-9326/abbf16\nRozemberczki, B., Watson, L., Bayer, P., Yang, H.-T., Kiss, O., Nilsson, S., & Sarkar, R. (2022). The\nShapley Value in Machine Learning: Proceedings of the Thirty-First International Joint Conference on\nArtificial Intelligence.\nSmith, I. A., Hutyra, L. R., Reinmann, A. B., Marrs, J. K., & Thompson, J. R. (2018). Piecing together the\nfragments: elucidating edge effects on forest carbon dynamics. 16(4), 213-221.\ndoi:https://doi.org/10.1002/fee.1793\nP\nA\nG\nE\n\\nResponse to reviewer comments\nWe sincerely thank the reviewers for their valuable and constructive suggestions, which have been\nhelpful in further improving the manuscript. Here, we provide point-to-point responses to these\ncomments. A clean version of the manuscript is also provided to facilitate the review process.\nReviewer #2 (Remarks to the Author):\nThis manuscript presents an interesting and timely study examining the effects of forest edge\ndisturbances on carbon sinks in China. The authors employ extensive plot-based observations in\ncombination with a range of remote sensing datasets to quantify forest carbon dynamics. The\nresults are potentially significant. However, some methodological descriptions lack clarity and\nsufficient detail, which may impede readers’ ability to fully evaluate the robustness and reliability of\nthe findings.\nThere are some several specific comments and suggestions that may help improve the clarity and\nrigor of the manuscript.\nForest edges are the key focus of this study, and their accurate characterization is crucial to the\nvalidity of the results. The authors state that “The forest plots were mapped on a land cover image\nto derive the proximity-to-edge values.” However, the forest plot data from the 9th National Forest\nInventory (NFI) were collected between 2014 and 2018, while the land cover images used for\nmatching (CNLUCC) have discontinuous temporal coverage (e.g., 2013, 2015, 2018, 2020, 2023).\nAs noted in the Introduction, China has ambitious afforestation plans and continues to expand its\nforest area substantially every year, which means that forest edges are likely to undergo\nconsiderable changes annually. The authors should clarify how this temporal mismatch was\naddressed in their analysis. I also suggest that they evaluate, perhaps in the Supplementary\nMaterials, whether using remote sensing data from different years might affect forest edge\ndelineation, thereby providing stronger support for the robustness of their results.\nResponse: We thank the reviewer for this insightful comment. Indeed, the National Forest Inventory\n(NFI) was conducted every five years and the forestation activities will continuously alter the\nproximity-to-edge of some of the NFI plots. To reduce the impacts, we used the CNLUCC map of\n2018 to match the 9th NFI (2014-2018) surveyed plots assuming the survey dates close to the\nreleased year of NFI report. To examine whether the continuous forestation affects our results, we\nagree to the reviewer’s suggestion and recalculated the distance to forest edge information using\nthe 2015 CNLUCC map and a much earlier map in 2000 (Figure 1). Although the results remained\nconsistent, it shows that the pattern is more evident using CNLUCC map in 2015 than in 2000 (see\nFigure 1 below). These results also indicate that our findings are robust through time. For future\nscenarios, the proximity will be recalculated after forestation to ensure that the simulations capture\nthe reduced edge effects through time (see main text section 3.4).\nAs correctly noted by the reviewer, forest edges may vary substantially from year to year due to\nintensive afforestation activities. However, calculating annual proximity-to-edge metrics is\ncomputationally demanding and difficult to represent consistently within the model framework.\nTherefore, we used the proximity-to-edge data derived from the 2018 CNLUCC map for this\nanalysis. We acknowledge that this simplification may introduce certain biases, and we have added\na corresponding discussion in the revised manuscript to explicitly note this limitation and guide the\ninterpretation of our results. The following text was added:\n“Note that forest edges may vary over time due to historically intensive afforestation activities.\nHowever, annually varying proximity-to-edge metrics are difficult to represent within the model\nframework. Therefore, we used proximity-to-edge data derived from the 2018 CNLUCC map to\ndisplay the biomass carbon pattern and for model training.”\nWe followed the reviewer’s suggestion and added these results in the Supplementary Materials.\nPlease see our revisions in the main text Lines 357-359 and 365-368, and the Supplementary\nFigure S8.\nFigure 1. Changes in forest biomass carbon stock along the gradient of the proximity-to-forest edge\nusing (a) 2000 and (b) 2015 CNLUCC maps.\nIn addition, the frequency distribution of distances from forest edges of in situ plots the could also be\nincluded in the Supplementary Materials to provide readers with a more detailed understanding of\nthe data.\nResponse: We sincerely thank the reviewer for the constructive comment. We agree and have\nadded the plot distribution frequency of the distances in Figure S9 in the Supplementary\nMaterials (also see Figure 2 below).\nFigure. 2 The distribution frequency of distances from forest edges of in situ plots.\n2.The potential forestable lands reported in this study differ from those in previous research (Yao et\nal., 2024. Carbon sequestration potential of tree planting in China. Nature Communications.\nhttps://doi.org/10.1038/s41467-024-52785-6 ). In this manuscript, the potential forestable lands\nappear to be more concentrated in semi-arid to semi-humid regions (“large contiguous areas of\npotential forestation lands were distributed in the central and northeast regions” in Line 172-173)，\nwhereas earlier studies suggested that potential forestable lands are primarily located in southern\nChina where water and heat conditions are more favorable (“The remaining areas available for\nfuture afforestation in China are principally located in the Hengduan Mountains, the middle and\nlower reaches of the Yangtze River Plain, and the Qinba Mountains”. Yao et al., 2024). Authors\nshould provide a thorough and candid discussion of the discrepancies between their results and\nprevious findings and the underlying causes. This is particularly critical because the delineation of\npotential afforestation areas is fundamental to evaluating future forest carbon sinks and directly\ndetermines the reliability of the study’s conclusions.\nResponse: We sincerely thank the reviewer for this valuable and insightful comment, which greatly\nhelped us to further improve the discussion section, as we understand other readers might have the\nsame questions. First, it is important to clarify that our study defines potential forestable lands as\nbio-climatically constrained non-forest lands. The forest expansion considered here, refers to the\nprocess of establishing new forests on previously non-forest lands (see Figure 3a below). In\ncontrast, Yao et al. (2024) majorly focused on the effect of tree densification on existing forests\nrather than the spatial expansion of new forests. They pioneered in developing an integrated\nmachine learning framework to determine the optimal planting density across China’s existing\nforests, and quantified carbon benefits from planting trees in established forests (see Figure 3b\nbelow). Besides, our results on the spatial distribution of potential forestable lands are largely\nconsistent with those reported by Yao et al. (2024). Their result shows that the remaining areas\navailable for future afforestation in China are principally located in the Hengduan Mountains, the\nmiddle and lower reaches of the Yangtze River Plain, and the Qinba Mountains (Yao et al., 2024).\nThe Hengduan Mountains are located in the southwestern part of China. The main part of the Qinba\nMountains is in Shaanxi Province, which belongs to the administrative region of the northwest.\nTherefore, the discrepancies were due to the differed definitions and research objectives between\nthe two studies. Therefore, our results of the spatial distribution of potential forestable lands remain\nbroadly consistent with those of Yao et al. (2024).\nAdditionally, our results also in consistent to previous study of Zhang et al. (2021), which\nreported that potential forestation lands are mainly situated along the Huhuanyong Line (also known\nas Heihe-Tengchong Line). Our results also agree with Yao et al. (2024)’s finding that southern\nChina is suitable for forest growth attributing to its favorable hydrothermal conditions. In our\nprevious study (Yu et al., 2023), we also demonstrated that forests accumulate more carbon in\nsouthern China due to favorable condition, yet large contiguous areas available for forestation is\nreducing. In comparison, the central and northeastern regions have larger potential in providing\ncontiguous area for forest expansion.\nWe have added more discussion on this issue in the revised manuscript. Please check\nour revisions in Lines 185-191 for more details.\nFigure.3 Illustration of the difference of implementation between afforestation and densification.\nReferences:\nYu Z, et al. China’s forestation on marginal land was less efficient in carbon sequestration compared\nwith non-marginal land. One Earth 6, 1692-1702 (2023).\nZhang, L., Sun, P., Huettmann, F., & Liu, S. (2022). Where should China practice forestry in a\nwarming world?. Global Change Biology, 28(7), 2461-2475.\n3.The description in Methods, Section 3.2 Constraints on potential land for forestation is insufficient\nand somewhat unclear. While Results and Discussion 2.2 states that a random forest (RF) model\nwas employed to delineate the spatial distribution of potential forest, the Methods section provides\nno details on how the RF model was implemented.\nResponse: We thank the reviewer for the careful and constructive comments. As suggested, we\nadded the following explanations:\n“Based on the potential forestable map, we further calculated the suitability index for each gridcell\nusing a machine-learning model by taking into account the climatic, topological, biological, and\nedaphic factors. Specifically, we flagged forestation failure plots and normal plots in model training,\nrepresenting low and high forestation suitability, respectively. Forestation failure sites are lands with\ntree survival rate below 41%, thus requiring reforestation. The trained RF model was then applied to\nthe potential forestable map delineated in the previous step, producing a suitability map for locating\nthe planted area (Figure S1&S2).”\nThese descriptions provide more details of identifying potential forestable lands. Please see our\nrevisions in the main text (Lines 385-392).\nBesides, some methodological details require further improvement. For example, the use of the\nHansen Global Forest Change dataset is mentioned (Table S3), but the specific role it played in the\nanalysis is not clearly explained.\nResponse: We sincerely thank the reviewer for pointing out this mistake. The Hansen Global Forest\nChange dataset was initially used in the early stages of this study and was removed. This is\nbecause the Hansen forest provides only the forest coverage information, while the potential\nforestable land (e.g. grassland) need to be identified from other products. To reduce uncertainties\nintroduced by multiple data products, we turned to CNLUCC dataset, which provide the distributions\nof both forest and other land covers. Please see our revisions in the updated Table S3.\n4.As mentioned in the Introduction, “China launched an ambitious forestation program, committing\nto creating an additional forest area of 49.5 million hectares (Mha) from 2021 to 2050.”, however, a\nsubstantial portion of this planned expansion lies outside the potential forestable lands identified by\nthe authors, for example, the additional forest area associated with the Three-North Shelter Forest\nProgram. This discrepancy should be clarified. Besides, afforestation in China is guided by\ngovernment policies and strongly influenced by policy frameworks, which are typically defined at the\nprovincial rather than regional level. Therefore, Table S4 may be of limited value and may not\naccurately represent the actual afforestation plans. To enhance the credibility and feasibility of the\nsimulation experiments proposed in this study, it would be helpful if the authors could compare the\nafforestation plans for different provinces used in their simulations with relevant official government\ndocuments, such as the primary references cited in the afforestation scheme discussed in this\narticle (State Forestry Administration of the People’s Republic of China, National Forest\nManagement Plan (2016–2050), 2016). In summary, the afforestation simulation experiments\nshould fully discuss their feasibility as well as the advantages and disadvantages compared with\nexisting policies.\nResponse: We sincerely thank the reviewer for the insightful and constructive suggestion. In this\nstudy, we first identified 94.8 Mha of the potential forestation area (Figure S1), and then located the\n49.5 million hectares (Mha) for forestation from the potential area. To do this, we employed a\ndistance gradient algorithm to screen out the most suitable 49.5 Mha for afforestation, aiming to\nmaximize carbon sequestration. It is worth noting that a substantial portion of potential afforestation\nareas overlap with the coverage of the Three-North Shelterbelt Program (see Figure 4 below). This\noccurs because we identified potential afforestation areas based on bioclimatic and ecological\nconstraints, without explicitly incorporating the influence of existing policy-driven planning\npreferences (e.g., accessibility, economic costs).\nWe agree to the reviewer and have now updated Table S4 accordingly. More specifically, we\nadded the information of potential forestable lands, spatially optimized forestation areas, and the\nfigures from the National Forest Management Plan (2016-2050) for each province in China. The\nresults showed that there were differences between the spatially optimized forestation areas in our\nstudy and those outlined in the national plan. The National Forest Management Plan places greater\nemphasis on afforestation in provinces north of the Qinba Mountains, such as Inner Mongolia,\nShanxi, and Gansu provinces. In comparison, our study was oriented more by the theoretical\nbenefits of carbon enhancement through spatially optimized plan. As suggested, we have added\nmore discussion regarding the feasibility and the advantages/disadvantages between different\nplans. Please see our revisions in Lines 224-228.\nFigure 4. The spatial distribution in the potential forestable lands, the spatial-optimization forestation\narea, and the Three North Shelterbelt Forest area.\nMethods, Section 3.4 Design of simulation experiments requires a more detailed description, as the\ncurrent explanation of the simulation experiments is somewhat confusing. In addition, the\ndescription of some simulation experiments needs to be moved from the Results section to the\nMethods section.\nResponse: We agree to the reviewer and made revisions accordingly. We have moved the\nsentences in Results section 2.2 that deal with the experimental details to Methods section 3.4.\nPlease see our revisions in Lines 422-437. In addition, we have also reorganized the Results\nsection 2.2 (Lines 181-228) to improve the logical flow and clarity of the manuscript.\nReviewer #3 (Remarks to the Author):\nThe overall manuscript is well revised. In revised manuscript authors try to provide answers of\nnearly all questions. Check the grammatical mistakes.\nResponse: We sincerely thank for the reviewer for the suggestions. We have asked a native\nEnglish-speaker to check our manuscript thoroughly to correct mistakes. Please check our revisions\nin the updated version."}, {"round": 2, "reviewer_comments": {"reviewer_2": "This manuscript presents an interesting and timely study examining the effects of forest edge disturbances on carbon sinks in China.. The authors employ extensive plot-based observations in combination with a range of remote sensing datasets to quantify forest carbon dynamics. The results are potentially significant. However, some methodological descriptions lack clarity and sufficient detail, which may impede readers’ ability to fully evaluate the robustness and reliability of the findings.\nThere are some several specific comments and suggestions that may help improve the clarity and rigor of the manuscript.\n1.Forest edges are the key focus of this study, and their accurate characterization is crucial to the validity of the results. The authors state that “The forest plots were mapped on a land cover image to derive the proximity-to-edge values.” However, the forest plot data from the 9th National Forest Inventory (NFI) were collected between 2014 and 2018, while the land cover images used for matching (CNLUCC) have discontinuous temporal coverage (e.g., 2013, 2015, 2018, 2020, 2023). As noted in the Introduction, China has ambitious afforestation plans and continues to expand its forest area substantially every year, which means that forest edges are likely to undergo considerable changes annually. The authors should clarify how this temporal mismatch was addressed in their analysis. I also suggest that they evaluate, perhaps in the Supplementary Materials, whether using remote sensing data from different years might affect forest edge delineation, thereby providing stronger support for the robustness of their results.\nIn addition, the frequency distribution of distances from forest edges of in situ plots the could also be included in the Supplementary Materials to provide readers with a more detailed understanding of the data.\n2.The potential forestable lands reported in this study differ from those in previous research (Yao et al., 2024. Carbon sequestration potential of tree planting in China. Nature Communications. https://doi.org/10.1038/s41467-024-52785-6 ). In this manuscript, the potential forestable lands appear to be more concentrated in semi-arid to semi-humid regions (“large contiguous areas of potential forestation lands were distributed in the central and northeast regions” in Line 172-173)，whereas earlier studies suggested that potential forestable lands are primarily located in southern China where water and heat conditions are more favorable (“The remaining areas available for future afforestation in China are principally located in the Hengduan Mountains, the middle and lower reaches of the Yangtze River Plain, and the Qinba Mountains”. Yao et al., 2024). Authors should provide a thorough and candid discussion of the discrepancies between their results and previous findings and the underlying causes. This is particularly critical because the delineation of potential afforestation areas is fundamental to evaluating future forest carbon sinks and directly determines the reliability of the study’s conclusions.\n3.The description in Methods, Section 3.2 Constraints on potential land for forestation is insufficient and somewhat unclear. While Results and Discussion 2.2 states that a random forest (RF) model was employed to delineate the spatial distribution of potential forest, the Methods section provides no details on how the RF model was implemented. Besides, some methodological details require further improvement. For example, the use of the Hansen Global Forest Change dataset is mentioned (Table S3), but the specific role it played in the analysis is not clearly explained.\n4.Methods, Section 3.4 Design of simulation experiments requires a more detailed description, as the current explanation of the simulation experiments is somewhat confusing. As mentioned in the Introduction, “China launched an ambitious forestation program, committing to creating an additional forest area of 49.5 million hectares (Mha) from 2021 to 2050.”, however, a substantial portion of this planned expansion lies outside the potential forestable lands identified by the authors, for example, the additional forest area associated with the Three-North Shelter Forest Program. This discrepancy should be clarified. Besides, afforestation in China is guided by government policies and strongly influenced by policy frameworks, which are typically defined at the provincial rather than regional level. Therefore, Table S4 may be of limited value and may not accurately represent the actual afforestation plans. To enhance the credibility and feasibility of the simulation experiments proposed in this study, it would be helpful if the authors could compare the afforestation plans for different provinces used in their simulations with relevant official government documents, such as the primary references cited in the afforestation scheme discussed in this article (State Forestry Administration of the People’s Republic of China, National Forest Management Plan (2016–2050), 2016). In summary, the afforestation simulation experiments should fully discuss their feasibility as well as the advantages and disadvantages compared with existing policies.\nIn addition, the description of some simulation experiments needs to be moved from the Results section to the Methods section.", "reviewer_3": "The overall manuscript is well revised. In revised manuscript authors try to provide answers of nearly all questions. Check the grammatical mistakes."}, "author_response": ""}, {"round": 3, "reviewer_comments": {"reviewer_2": "The authors have addressed all my concerns."}, "author_response": ""}]

Decision letter