绿美肇庆生态建设下公益林质量精准提升的碳汇成效OA
Carbon Sequestration Performance of Precision Quality Improvement for Ecological Public Welfare Forests Under the Green and Beautiful Zhaoqing Ecological Construction
为评估"绿美肇庆生态建设"工程下生态公益林质量精准提升的碳汇成效,基于 2021-2024 年肇庆市森林资源二类调查数据,系统解析全市生态公益林碳储量的时空演变特征.结果表明,2022 年工程实施前,全市碳汇呈自然增长态势,总蓄积量年均增幅 4.84%,单位面积二氧化碳(CO2)吸收量年均增幅不足 1.50%.2022 年通过中幼林抚育、退化林修复等精准提升工程措施,碳汇能力稳步提升,单位面积CO2吸收量年均增长率最高达5.7%,总蓄积量由2022年的2.094×107 m3增长至2024年的2.277×107 m3,年均增幅4.30%.碳汇空间呈现"北强南弱、核心突出"的不均衡特征,广宁、怀集和封开为核心区,封开县增长最为显著,单位面积生物量2年累计增幅达11.40%,占4年总增幅的72.40%;高要区林分碳密度长期偏低,端州和鼎湖区受城市建成区限制提升有限.林龄结构上,中龄林、近熟林蓄积量稳步增长,但鼎湖、高要等幼龄林增长缓慢甚至负增长;怀集和德庆过熟林存在生态功能衰退风险;经济林生态服务功能长期处于较低水平,后备资源不足.未来肇庆市应优化林龄结构,强化幼龄林抚育,科学推进过熟林更新,分区分类深化精准提升,为"绿美肇庆"长效推进提供支撑.
To evaluate the carbon sequestration effectiveness of the precision improvement of ecological public welfare forests under the Green and Beautiful Zhaoqing Ecological Construction,this study systematically analyzed the spatiotemporal evolution of carbon stocks in the city's ecological public welfare forests based on data from the second-class forest resource inventory of Zhaoqing City from 2021 to 2024.The results show that before the implementation of the project in 2022,the city's carbon sequestration exhibited a natural growth trend,with an average annual increase in total standing volume of 4.84%and an average annual increase in CO2 uptake per hectare of less than 1.5%.After the implementation of precision improvement measures such as mid-and young-aged forest tending and degraded forest restoration in 2022,carbon sequestration capacity steadily increased.The average annual growth rate of CO2 uptake per hectare reached up to 5.7%,and total standing volume increased from 20.94 million m3 in 2022 to 22.77 million m3 in 2024,representing an average annual increase of 4.30%.The spatial pattern of carbon sequestration is characterized by notable unevenness,with strong performance in the north,weak performance in the south,and prominent cores.Guangning,Huaiji,and Fengkai form the core carbon sequestration areas.Among these,Fengkai showed the most significant increase,with cumulative biomass per hectare growing by 11.40%from 2022 to 2024,accounting for 72.4%of the total increase over the four-year period.In contrast,Gaoyao District consistently exhibited low carbon density,while Duanzhou and Dinghu Districts faced constraints in carbon sequestration potential due to limited availability of urban land.In terms of age structure,middle-aged and near-mature forests showed steady increases in standing volume and became the main sources of carbon sequestration.However,young forests in Dinghu and Gaoyao experienced slow or even negative growth.Meanwhile,in Huaiji and Deqing,over-mature forests face the risk of declining ecological functions due to natural succession.Additionally,economic forests within the public welfare forests have persistently weak ecological service functions,resulting in insufficient carbon sequestration reserves.Future efforts in Zhaoqing should focus on optimizing forest age structure,strengthening the tending and management of young forests,scientifically promoting the regeneration and transformation of over-mature forests,and imple-menting region-specific and forest-type-specific precision improvements to forest quality.These measures will provide long-term support for the sustainable advancement of the Green and Beautiful Zhaoqing initiative.
张硕;张峰;谭向平;王林华;吴建平;常中兵;钟文锋;范蓉思媛;俞梦笑
肇庆市林业局,广东 肇庆 526040肇庆市林业局,广东 肇庆 526040中国科学院华南植物园,广州 510650||华南国家植物园,广州 510650中国科学院华南植物园,广州 510650||华南国家植物园,广州 510650广东省科学院广州地理研究所,广州 510070广东省国土资源测绘院,广州 510663肇庆市林业局,广东 肇庆 526040中国科学院华南植物园,广州 510650||华南国家植物园,广州 510650中国科学院华南植物园,广州 510650||华南国家植物园,广州 510650
生态公益林碳汇时空演变森林蓄积量森林质量精准提升
Ecological public welfare forestCarbon sinkSpatiotemporal evolutionForest stock volumePrecision improvement of forest quality
《热带亚热带植物学报》 2026 (5)
519-528,10
国家重点研发计划项目(2024YFF1306600)国家自然科学基金项目(32301564,42430514)资助This work was supported by the National Key Research and Development Program of China(Grant No.2024YFF1306600),and the National Natural Science Foundation of China(Grant No.32301564,42430514).
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