Endogenous Targeting and the Additionality of Conservation
保全の内生的ターゲティングと追加性 (AI 翻訳)
Sarah Meier, Ben Balmford, Ville Inkinen
🤖 gxceed AI 要約
日本語
ボリビアの保護区の森林減少抑制効果をランダムサバイバルフォレストで分析。平均0.19ポイント減少（68%削減）だが、高リスク地域では0.50ポイントと効果が大きく、低リスク地域では追加性なし。保護区は低リスク地域に偏在し、全体の効果を制限。高リスク地域への優先配分の重要性を示唆。
English
This paper evaluates the additionality of protected areas in Bolivia using a Random Survival Forest model to predict deforestation risk. On average, protected areas reduce deforestation by 0.19 percentage points (68%), but effects are highly heterogeneous: no additionality in low-risk areas, up to 0.50 pp reduction in high-risk areas. The study finds that protected areas are disproportionately sited where deforestation pressure is low, limiting overall impact. The findings highlight the need to prioritize conservation in high-risk regions to enhance additionality.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本企業の森林関連Scope 3排出量算定やREDD+クレジットの追加性評価に示唆を与える。内生的ターゲティングの問題は、サプライチェーン森林減少対策にも適用可能。
In the global GX context
This paper offers an empirical approach to evaluating conservation additionality using ML, relevant for global forest carbon crediting (e.g., REDD+) and corporate deforestation commitments under SBTi FLAG. The finding that protected areas underperform due to endogenous targeting stresses the need for outcome-based conservation strategies.
👥 読者別の含意
🔬研究者:Novel use of Random Survival Forest for treatment effect heterogeneity in conservation policy evaluation; important for causal inference in environmental economics.
🏢実務担当者:Highlights that siting protected areas based on low deforestation risk undermines additionality — useful for designing conservation programs or carbon offset projects.
🏛政策担当者:Demonstrates that endogenous targeting dilutes conservation impact; supports prioritizing high-risk areas for protection to maximize climate and biodiversity benefits.
📄 Abstract（原文）
The world has lost one-third of its forests, with those in the tropics facing the most rapid decline despite their substantial ecological and climate benefits. Protected areas (PAs) have become the primary policy instrument to curb deforestation, yet they are often established where deforestation pressure is relatively low and potential conservation gains are limited. We evaluate how endogenous policy targeting shapes the additionality of PAs established in Bolivia between 1991 and 2023. We employ a staggered difference-in-differences design, matching treated and control units on a novel measure of predicted deforestation risk in the absence of protection, generated using a Random Survival Forest model. This framework allows us to evaluate treatment effects across the distribution of baseline deforestation risk. Our estimates indicate that, on average, PAs reduce deforestation by approximately 0.19 percentage points (pp), corresponding to a 68% reduction relative to the national deforestation rate over the study period. However, average treatment effects mask substantial heterogeneity across the deforestation risk distribution, with no evidence of additionality in low-risk areas and the largest effects emerging under high deforestation pressure, where PAs reduce deforestation by up to 0.50 pp. Furthermore, while PAs are disproportionately established in low-risk areas, we find limited evidence that this reflects systematically greater biodiversity or carbon gains, or that protection in high-risk areas substantially hinders economic development. Overall, our findings suggest that endogenous targeting is a key determinant of conservation additionality, highlighting the importance of prioritising conservation in high-deforestation-risk regions.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3929/ethz-c-000801581first seen 2026-06-17 04:46:47 · last seen 2026-06-17 07:07:34
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