建立在地化物種指標於企業生物多樣性揭露

Abstract

隨著環境、社會、治理 (environment, social, and governance, ESG) 的發展，生物多樣性逐漸受到企業重視，自然相關財務揭露 (Taskforce on Nature-related Financial Disclosures, TNFD) 已成為企業管理生物多樣性風險的主流框架。然而 TNFD所建議之全球指標工具在臺灣適用性有限，因此有必要建立在地化指標。本研究聚焦TNFD LEAP方法學「定位」與「評估」步驟所使用的物種滅絕指標，第一部分採用物種分布模型模擬的陸域動物分布範圍與臺灣紅皮書名錄，建立在地化稀有加權豐富度指標 (rarity-weighted richness, RWR) 與物種威脅減緩與復育指標 (species threat abatement and restoration, STAR)，同時以全球尺度物種分布資料建立相對應指標，藉此評估指標在地化過程資料尺度的變化，並比較指標內不同生物類群與指標間表現之差異，以提供企業應用建議。第二部分為案例分析，應用在地化指標分析企業全臺據點生物多樣性重要度，並透過礦區現地調查資料驗證指標結果，探討指標使用於場址評估之適切性。 研究結果顯示，在地化RWR指標與STAR指標高分區域主要分布於沿海平原、流域與部分山區，比較使用全球與在地資料建立的指標，兩者分布截然不同。指標在地化過程有助於提高結果的精確性與空間解析度，並聚焦臺灣物種受脅情形，然而由於指標設計臺灣特有種的重要性則相對降低。此外，物種指標受到生物類群物種數之差異而產生偏頗，本研究指標整體結果與鳥類高度相似，建議指標工具可依照生物類群與物種呈現，提高工具使用之彈性。在地化RWR與STAR兩指標在地理空間上高度相關，部分區域則有較大差異，我們仍建議使用者根據保育目標應用指標。在案例分析，兩指標對於據點生物多樣性重要度的評估排序一致，提供TNFD「定位」步驟場址間分析之可比較性。針對場址尺度，物種指標提供礦區潛在出沒重要物種清單，作為影響減緩與保育行動之參考，而我們探討了現地調查資料與潛在分布預測差異之可能原因，包含模型資料限制、調查人力成本與野生動物調查本身之困難性等。本研究建立企業評估臺灣據點生物多樣性之可行工具，提供物種指標應用與發展之建議，未來建議持續建立與擴展物種分布資料，以期加強企業永續報告的科學基礎與數據支持。

With the growing emphasis on environmental, social, and governance (ESG), biodiversity has gained increasing attention from corporations. The Taskforce on Nature-related Financial Disclosures (TNFD) has emerged as the mainstream framework for managing biodiversity-related risks. However, the global metrics tools recommended by TNFD have limited applicability in Taiwan, highlighting the need for localized metrics. This study focuses on the species extinction metrics used in the "Locate" and "Evaluate" steps of the TNFD LEAP methodology. In the first part, we developed localized Rarity-Weighted Richness (RWR) and Species Threat Abatement and Restoration (STAR) metrics by integrating species distribution models of terrestrial animals with the National Red List of Taiwan. Corresponding metrics were also established to assess the impact of data scale during localization and comparisons between taxonomic groups and metrics were conducted, providing recommendations for corporate applications. The second part presented a case study that applied these localized metrics to assess biodiversity importance at corporate sites across Taiwan. Field survey data from mining sites were analyzed to validate the metric results and explore their suitability for site-level assessments. The study results indicate that the high-scoring areas of the localized RWR and STAR are primarily distributed across coastal plains, river basins, and certain mountainous regions. A comparison between metrics constructed of global and localized data reveals distinct differences in their spatial distribution. The localization process enhances the accuracy and spatial resolution of the results and allows a stronger focus on the threats faced by species in Taiwan. However, due to the metric design, the importance of Taiwan’s endemic species is relatively reduced. Additionally, species metrics may be biased due to differences in species numbers among biological groups. In this study, the overall metric results closely resemble those of birds, highlighting the importance of presenting metric tools separately for different biological groups and species to increase the flexibility of the tool. The localized RWR and STAR show a high degree of spatial correlation, although significant differences exist in certain areas. Therefore, we recommend that users select metrics based on their conservation objectives. In the case study, both metrics produce consistent rankings of biodiversity importance across operational sites, providing comparability for site-level assessments in the "Locate" phase of the TNFD framework. For mining sites, the species metrics offer a list of potentially important species present in these areas, serving as a reference for impact mitigation and conservation actions. We also examined the discrepancies between field survey data and potential distribution predictions, which may be influenced by factors such as model data limitations, survey costs, and difficulty in wildlife surveys. This study established a practical tool for businesses to evaluate biodiversity for sites in Taiwan and provided recommendations for further species metrics development. Additionally, we suggest continuously establishing and expanding species distribution data in the future to strengthen the scientific basis and data support for corporate sustainability reporting.