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一个新的森林砍伐风险和基线分配模型,用于下一代嵌套的REDD+项目
Jeremy Freund1, Maren Pauly2, Will Gochberg1
1Wildlife Works, Mill Valley, CA, USA.
Scientific reports
|July 2, 2024
概括
一种新方法,评估风险的基线分配 (BAAR),从空间上预测森林砍伐,并为REDD+项目分配森林参考排放水平 (FREL). 这种方法有助于将碳融资集中在高风险地区,与国家气候目标保持一致.
科学领域:
- 环境科学环境科学
- 减缓气候变化减缓气候变化减缓
- 林业管理 林业管理 林业管理
背景情况:
- 基于自然的气候缓解解决方案依赖于准确的基线方法.
- 减少森林砍伐和森林退化排放 (REDD+) 传统上使用参考区域进行基线估计.
- 雷达+市场正在转向嵌套方法,需要正式的国家基线分配方法.
研究的目的:
- 引入和验证一种新的方法,即评估风险的基线分配 (BAAR),用于绘制森林砍伐风险的地图,并将国家森林参考排放水平 (FREL) 分配给项目.
- 通过34个REDD+项目在刚果民主共和国展示BAAR的应用.
- 突出基于风险的FREL分配的重要性,以平衡REDD+倡议中的科学严谨性和实际应用.
主要方法:
- 开发了BAAR,使用动态向量进行空间森林砍伐风险预测.
- 实施了一种方法,在项目层面分配FREL到具有差异化森林砍伐风险的地区.
- 在刚果民主共和国的34个REDD+项目中应用了BAAR.
主要成果:
- BAAR提供了未来森林砍伐的空间预测.
- 该方法允许根据评估的森林砍伐风险将FREL分配给项目区域.
- 基于风险的FREL分配被证明对于平衡科学准确性和实际实用性至关重要.
结论:
- 在REDD+中,BAAR为国家基线分配提供了一个强大的框架.
- 政府可以利用BAAR战略性地将自愿碳市场融资引导到森林砍伐风险最高的地区.
- 根据巴黎协定,BAAR方法支持与国家自主贡献 (NDC) 保持一致.
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