基于自然的碳补偿项目边界的开放访问数据库。

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Akshata Karnik, Jack B Kilbride, Tristan R H Goodbody, Rachael Ross, Elias Ayrey
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引用次数: 0

摘要

基于自然的气候解决方案(NBS)已成为旨在减少大气二氧化碳和缓解气候变化影响的战略的重要组成部分。碳补偿已成为国家统计局最广泛实施的战略之一,然而,这些项目也因夸大补偿而受到批评。由于缺乏现成的地理空间边界数据,验证nbs衍生的碳抵消的有效性变得复杂。在这里,我们详细介绍了方法,并提出了一个基于自然的偏移项目边界数据库。该数据库提供分布在55个国家的575个国家统计局项目的位置。地理空间边界的聚合使用了从碳项目注册表中抓取数据(n = 433, 75.3%)以及人工地理参考和数字化(n = 127, 22.1%)。数据库条目包括三种类型的碳项目:避免毁林、造林、再造林和再植被以及改善森林管理。对地理参考和数字化过程的精度评估表明,该方法具有较高的精度(交叉比联合得分为0.98±0.015)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An open-access database of nature-based carbon offset project boundaries.

Nature-based climate solutions (NBS) have become an important component of strategies aiming to reduce atmospheric CO2 and mitigate climate change impacts. Carbon offsets have emerged as one of the most widely implemented NBS strategies, however, these projects have also been criticized for exaggerating offsets. Verifying the efficacy of NBS-derived carbon offset is complicated by a lack of readily available geospatial boundary data. Herein, we detail methods and present a database of nature-based offset project boundaries. This database provides the locations of 575 NBS projects distributed across 55 countries. Geospatial boundaries were aggregated using a combination of scraping data from carbon project registries (n = 433, 75.3%) as well as manual georeferencing and digitization (n = 127, 22.1%). Database entries include three varieties of carbon projects: avoided deforestation, afforestation, reforestation and re-vegetation, and improved forest management. An accuracy assessment of the georeferencing and digitizing process indicated a high degree of accuracy (intersection over union score of 0.98 ± 0.015).

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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