Bamboo as a Nature-Based Solution (NbS) for Climate Change Mitigation: Biomass, Products, and Carbon Credits

Pan, Chunyu; Zhou, Guomo; Shrestha, Anil Kumar; Chen, Jialu; Kozak, Robert; Li, Nuyun; Li, Jinliang; He, Yeyun; Sheng, Chunguang; Wang, Guangyu

doi:10.3390/cli11090175

Cited by 20 publications

(7 citation statements)

References 70 publications

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“…This constitutes a critical aspect of the better management (utilization or conservation) of the species [43]. Bamboo forests are recognized as important resources in mitigating climate change [11], encompassing approximately 0.8% of the total global forest area [44]. Q. tumidinoda, an endemic bamboo species in Southwest China, holds significant potential for mitigating climate change and alleviating poverty through wood and bamboo shoot utilization.…”

Section: Influence Of Climate Changementioning

confidence: 99%

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Redistribution of Qiongzhuea tumidinoda in Southwest China under Climate Change: A Study from 1987 to 2012

Gui,

Dong,

Wen

et al. 2024

Forests

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Qiongzhuea tumidinoda stands out as an endemic bamboo species of significant conservation importance in Southwest China, particularly in the upper reaches of the Yangtze River. It holds a pivotal role in poverty alleviation through the commercialization of its wood and bamboo shoots. However, the suitable area of this species is undergoing rapid changes due to climate change, resulting in species redistribution and potential losses for bamboo farmers. We utilized 209 presence records and 25 environmental variables from 1987 to 2012 to predict the potentially suitable habitats for Q. tumidinoda using MaxEnt, ArcGIS, and R. We rigorously screened the recorded data for reliability and accuracy through expert consultations and observer interviews. We performed pre-processing to select the variables with high contributions for modeling, and 11 variables were selected for the final modeling. Our findings reveal that the top three most influential variables associated with Q. tumidinoda's distribution were the mean monthly potential evapotranspiration (Pet), annual range of air temperatures (Bio7), and mean diurnal air temperature range (Bio2), and the rates of contributions from 1987 to 2012 were 4.8333, 3.5833, and 1.7000. There was a southeastward shift and an elevation increase in the potentially suitable habitats for Q. tumidinoda. The area of potentially suitable habitats in the study region exhibited fluctuating growth, expanding from 3063.42 km2 to 7054.38 km2. The mean monthly potential evapotranspiration (Pet) emerged as a critical determinant shaping the distribution of potentially suitable habitats for Q. tumidinoda. Our study sheds light on the response of Q. tumidinoda to climate change, offering valuable insights for the development and management of plantation industries associated with this species. In the future, to enhance prediction accuracy, researchers could equally consider both organic and inorganic environmental variables. For better preservation of environment and development, Q. tumidinoda could be introduced into nature restoration projects in areas with a suitable habitat or as a commodity that participates in forest carbon sink trading.

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Section: Influence Of Climate Changementioning

confidence: 99%

“…Bamboos play a significant role as woody grass species in mitigating climate change and are considered a nature-based solution (NbS) to climate change [11]. Similar to many understory plants [12,13], understory bamboo species constitute a vital component of various forest ecosystems [14,15].…”

Section: Introductionmentioning

confidence: 99%

Redistribution of Qiongzhuea tumidinoda in Southwest China under Climate Change: A Study from 1987 to 2012

Gui,

Dong,

Wen

et al. 2024

Forests

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“…In October 2023, the program gathered more than 35 thousand acres of family forests [117], and one interviewee said nearly all the forest landowners now in the US understand forest carbon credits very well, due to the presence of the program. Similarly, the government-led Liangshan Bamboo Forest Carbon Program in Anji County, China, collects the forest land rights of smallholders, implements the management activities uniformly by a professional team, and pays the carbon credits back to the farmers [118]. The experts also suggested that the provincial and federal governments expand their capacity in the forest carbon teams, host more workshops and training sessions, work more closely with the academia, and establish more forest carbon programs.…”

Section: Addressing Social Barriersmentioning

confidence: 99%

“…Government subsidies are efficient incentives for project implementation, especially for afforestation-type projects, to cover the significant initial cost [121]. Also, programs aggregating small forest holders are emerging across the world, such as the Family Forest Carbon Program (US) [116] and the Liangshan Bamboo Forest Carbon Program (China) [118]. They provide annual payments to the small forest owners, merge the individual land pieces into large projects, and mitigate the cost issues by the economy of scale.…”

Section: Reaching Cost-effectivenessmentioning

confidence: 99%

Canada’s Green Gold: Unveiling Challenges, Opportunities, and Pathways for Sustainable Forestry Offsets

Pan,

Li,

et al. 2023

Forests

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Forestry offsets, recognized for their diverse environmental and social co-benefits, are gaining a growing interest as nature-based solutions to combat climate change. Despite Canada’s extensive forest resources, its potential for carbon credit remains largely untapped. This study aims to unveil the prevailing challenges in developing forestry offsets in Canada and propose potential solutions, drawing on insights from in-depth semi-structured interviews (SSIs) with 23 experts in the field. A qualitative thematic analysis highlighted 14 challenges under four major frequently discussed themes: methodological (37%), social (29%), economic (22%), and implementation challenges (12%). Our findings highlighted the urgency of addressing key obstacles, including the impermanent nature of forestry carbon offsets, substantial public knowledge gaps, uncertainties in the cost-effectiveness impacting financial viability, and the need for enhanced capacity for project implementation. Building on the discussions on the identified challenges, this study further presented a comprehensive analysis of the future directions for Canada, emphasizing the importance of addressing key methodological issues, enhancing public and Indigenous education and engagement, and leveraging advanced technologies and innovative approaches like ton-year accounting for economic viability. This paper delivers pivotal insights that have the potential to shape the direction and integrity of the forestry offset markets in both Canada and globally.

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“…Moreover, amid the rapid growth of the modern bamboo industry, bamboo products are gaining popularity among a wider audience. They play a distinctive role in poverty alleviation and the revitalization of rural areas that produce bamboo (Pan et al, 2023). Moso bamboo forests play a vital role in the carbon cycle of forest ecosystems.…”

Section: Introductionmentioning

confidence: 99%

Estimating carbon sequestration potential and optimizing management strategies for Moso bamboo (Phyllostachys pubescens) forests using machine learning

Lv,

Yuan,

Sun

et al. 2024

Front. For. Glob. Change

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Estimating the carbon sequestration potential of Moso bamboo (Phyllostachys pubescens) forests and optimizing management strategies play pivotal roles in enhancing quality and promoting sustainable development. However, there is a lack of methods to simulate changes in carbon sequestration capacity in Moso bamboo forests and to screen and optimize the best management measures based on long-term time series data from fixed-sample fine surveys. Therefore, this study utilized continuous survey data and climate data from fixed sample plots in Zhejiang Province spanning from 2004 to 2019. By comparing four different algorithms, namely random forest, support vector machine, XGBoost, and BP neural network, to construct aboveground carbon stock models for Moso bamboo forests. The ultimate goal was to identify the optimal algorithmic model. Additionally, the key driving parameters for future carbon stocks were considered and future aboveground carbon stocks were predicted in Moso bamboo forests. Then formulated an optimal management strategy based on these predictions. The results indicated that the carbon stock model constructed using the XGBoost algorithm, with an R2 of 0.9895 and root mean square error of 0.1059, achieved the best performance and was considered the optimal algorithmic model. The most influential driving parameters for vegetation carbon stocks in Moso bamboo forests were found to be mean age, mean diameter at breast height, and mean culm density. Under optimal management measures, which involve no harvesting of 1–3 du bamboo, 30% harvesting of 4 du bamboo, and 80% harvesting of bamboo aged 5 du and above. Our predictions show that aboveground carbon stocks in Moso bamboo forests in Zhejiang Province will peak at 36.25 ± 8.47 Tg C in 2046 and remain stable from 2046 to 2060. Conversely, degradation is detrimental to the long-term maintenance of carbon sequestration capacity in Moso bamboo forests, resulting in a peak aboveground carbon stock of 29.50 ± 7.49 Tg C in 2033, followed by a continuous decline. This study underscores the significant influence of estimating carbon sequestration potential and optimizing management decisions on enhancing and sustaining the carbon sequestration capacity of Moso bamboo forests.

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Bamboo as a Nature-Based Solution (NbS) for Climate Change Mitigation: Biomass, Products, and Carbon Credits

Cited by 20 publications

References 70 publications

Redistribution of Qiongzhuea tumidinoda in Southwest China under Climate Change: A Study from 1987 to 2012

Redistribution of Qiongzhuea tumidinoda in Southwest China under Climate Change: A Study from 1987 to 2012

Canada’s Green Gold: Unveiling Challenges, Opportunities, and Pathways for Sustainable Forestry Offsets

Estimating carbon sequestration potential and optimizing management strategies for Moso bamboo (Phyllostachys pubescens) forests using machine learning

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