Feasibility of enhancing carbon sequestration and stock capacity in temperate and boreal European forests via changes to management regimes

Dalmonech, D.; Marano, Gina; Amthor, Jeffrey S.; Cescatti, Alessandro; Lindner, Marcus; Trotta, Carlo; Collalti, Alessio

doi:10.1016/j.agrformet.2022.109203

Cited by 30 publications

(58 citation statements)

References 119 publications

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“…Intensive forest management is one way to increase the carbon sequestration capacity of fast-growing tree species. Extensive forest management increases forest carbon sequestration capacity and soil carbon storage through partial cutting (Dalmonech et al, 2022). Due to the strong spatial heterogeneity of China's forest carbon, it is very necessary to implement corresponding management strategies for forest conditions in different regions.…”

Section: Spatial Distribution Of Carbon Storage and Carbon Sequestrat...mentioning

confidence: 99%

The spatial and temporal distribution of China’s forest carbon

Cheng

Tian

et al. 2023

Front. Ecol. Evol.

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IntroductionChina’s forests have sequestrated a significant amount of carbon over the past two decades. However, it is not clear whether China’s forests will be able to continue to have as much carbon sequestration potential capacity in the future.MethodsIn order to research China’s forest carbon storage and carbon sequestration potential capacities at spatial and temporal scales, we built a digital forest model for each province of China using the data from The China Forest Resources Report (2014– 2018) and calculated the carbon storage capacity and sequestration potential capacity of each province with the current management practices without considering natural successions.ResultsThe results showed that the current forest carbon storage is 10.0 Pg C, and the carbon sequestration potential in the next 40 years (from year 2019 to 2058) will be 5.04 Pg C. Since immature forests account for the majority of current forests, the carbon sequestration capacity of the forest was also high (0.202 Pg C year−1). However, the forest carbon storage reached the maximum with the increase of stand maturity. At this time, if scenarios such as afforestation and reforestation, human and natural disturbances, and natural succession are not considered, the carbon sequestration capacity of forests will continue to decrease. After 90 years, all stands will develop into mature and over-mature forests, and the forest carbon sequestration capacity is 0.008 Pg year−1; and the carbon sequestration rate is ~4% of what it is nowadays. The change trend of forest carbon in each province is consistent with that of the country. In addition, considering the large forest coverage area in China, the differences in tree species and growing conditions, the forest carbon storage and carbon sequestration capacities among provinces were different. The growth rate of carbon density in high-latitude provinces (such as Heilongjiang, Jilin, and Inner Mongolia) was lower than that in the south (Guangdong, Guangxi, or Hunan), but the forest carbon potential was higher.DiscussionPlanning and implementing targeted forest management strategies is the key to increasing forest carbon storage and extending the service time of forest carbon sinks in provinces. In order to reach the national carbon neutrality goals, we recommend that each province have an informative strategic forest management plan.

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Section: Spatial Distribution Of Carbon Storage and Carbon Sequestrat...mentioning

confidence: 99%

The spatial and temporal distribution of China’s forest carbon

Cheng

Tian

et al. 2023

Front. Ecol. Evol.

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“…The study was conducted in three even-aged, previously managed European forest stands: i) the Boreal For more detailed information on the study site characteristics and climate data, see also Collalti et al (2018, Reyer et al (2020), Mahnken et al (2022) and Dalmonech et al (2022). The chosen sites have been selected due to their long monitoring history and the availability of a wide range of data sources for both carbon fluxes and biometric data for model evaluation, and bias-corrected climate scenarios for simulations under climate change scenarios.…”

Section: Study Sites and Virtual Standsmentioning

confidence: 99%

“…In addition, these stands: i) cover a wide climatic gradient, ii) their current state is the results of the legacy of forest management iii) they are mainly mono-specific and therefore represent interesting «living labs» to study the effects of climate change on single-species and their productivity, reducing confounding effects which otherwise make models struggle to predict forest growth and carbon dynamics (e.g. Marèchaux et al, 2021,Vacchiano et al2012 they have already been investigated in the context of climate-smart-forestry silvicultural scenarios (Dalmonech et al, 2022) (2022). The 3D-CMCC-FEM v.5.6 has been run at each site to cover the rotation period of each species (from 1997 to 2099) amid the current climate scenario (fixed atmospheric CO 2 concentration at the year 2000 of 368.8 μmol mol -1 ) consisting of detrended and repeated cycles of the present-day observed meteorology from 1996 to 2006 and the BAU management practices observed at the site.…”

Section: Study Sites and Virtual Standsmentioning

confidence: 99%

Stand age diversity and climate change affects forests’ resilience and stability, although unevenly

Vangi,

Dalmonech,

Cioccolo

et al. 2023

Preprint

Self Cite

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Forest age is the result of population dynamics, disturbance regimes, silviculture and plays an important role in the global Carbon (C) cycle. To examine the shaping effects of forest age on Carbon budget under current and future climate conditions, we used a biogeochemical-based model in three managed forest stands and then modeled their development as undisturbed systems. The model was forced with climate outputs under four representative climate scenarios over a matrix of 11 age classes for each stand. We find that the Net Primary Production (NPP) peak was reached in the middle-aged class (42- to-70-year-old) regardless of the climate scenario, while total C-woody stocks (tCWS) increased with age with different trajectories in the three sites, but not linearly as expected. Under climate change scenarios, the beech forest shows as expected increasing NPP with increasing atmospheric CO2 and temperature as much for younger age classes as for older ones. Conversely, in the spruce and Scots pine dominated sites NPP and tCWS decrease under climate change scenarios. ANOVA analysis applied as factorial analysis to model outputs shows that age and climate scenarios have all effects on forest carbon budget only if taken singularly while their combination reduces - if not totally weakens - any effect.

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“…Forest ecosystems play an irreplaceable role in carbon sequestration (Lin and Ge 2019) and contribute signi cantly to the regulation of climate, the carbon cycle, and the mitigation of global warming (Chu et al 2019;Dixon et al 1994; Li et al 2022). However, due to China's uneven distribution of forestry resources (Li et al 2017), low utilization rate, and stagnant input-output e ciency, therefore, developing and implementing e cient forest ecosystem management strategies (Cai et al 2022; Lin and Ge 2020; SCHULZE and SCHULZE 2010; Dalmonech et al 2022;Griscom et al 2017); advancing research on the design of carbon sequestration afforestation policies (Gren and Aklilu 2016;Sedjo 1997;Sugden 2018); avoiding deforestation (Daigneault et al 2022;Kindermann et al 2006), increasing forest cover, working on forest restoration (Popkin 2020; Lewis et al 2019), and improving the supply capacity of forest ecosystem services (Puhlick et al 2020) will be a crucial step in maintaining the high quality of forest carbon sink development and increasing the e ciency of forest carbon sink inputs and outputs. (Wang et al 2022).…”

Section: Introductionmentioning

confidence: 99%

Dynamic Analysis and Trend Forecast of China's Forestry Carbon Sink Benefits Based on Dual Carbon Targets

Zhao

Shi

Qiao

et al. 2023

Preprint

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The efficient development of forestry is one of the most effective ways to deal with climate change and achieve carbon neutrality. This paper evaluates and analyzes the benefits of carbon sinks in 31 provinces of China and predicts their future development trends. Firstly, the forest volume expansion method was used to calculate the carbon sink volume and value of Chinese provinces from 2008 to 2018. In addition, the DEA model is used to dynamically analyze input-output efficiency at the provincial and national levels. Lastly, the GM (1, 1) prediction model was used to forecast the trend of China's forestry carbon sink input-output efficiency from 2019 to 2030. It is found that: (1) In terms of space, the benefits of China's forests as carbon sinks are "high in the north and south and low in the east and west," while in terms of time, the differences between regions are getting bigger. (2) Based on efficiency differences, provinces can be categorized into four gradients: high-efficiency, scale-effective, technologically effective, and low-efficiency. The forestry technology level and resource input scale in high-efficiency areas are relatively effective, whereas the indicators above in low-efficiency regions have a significant discrepancy with the optimal level. (3) Predictions indicate that between 2019 and 2030, the benefits of carbon sinks in forests will continue to decline. Based on the findings of this study and the current condition of input-output efficiency, specific recommendations for the long-term development of carbon sinks under the dual carbon objective are provided.

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Feasibility of enhancing carbon sequestration and stock capacity in temperate and boreal European forests via changes to management regimes

Cited by 30 publications

References 119 publications

The spatial and temporal distribution of China’s forest carbon

The spatial and temporal distribution of China’s forest carbon

Stand age diversity and climate change affects forests’ resilience and stability, although unevenly

Dynamic Analysis and Trend Forecast of China's Forestry Carbon Sink Benefits Based on Dual Carbon Targets

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