From Measurement to Monitoring: Evaluating Privacy Risks in Carbon Footprint Tools
測定から監視へ：カーボンフットプリントツールにおけるプライバシーリスクの評価 (AI 翻訳)
Y. Alzoubi, A. Topcu, E. Elbasi
🤖 gxceed AI 要約
日本語
本研究は、カーボンフットプリント計測ツールにおけるプライバシーリスクを体系的に評価した初めての研究である。46のツールを5カテゴリに分類し、データ種類、粒度、保存、処理、二次利用、ユーザー主体性の5次元でリスク枠組みを開発した。消費者向け計算機は低リスクだが、企業向けプラットフォームやIoT/AI/ブロックチェーンシステムは高リスクであることを明らかにした。高精度な測定はより多くのデータ収集を必要とするプライバシー-精度トレードオフが確認された。
English
This study provides the first systematic evaluation of privacy risks in carbon footprint tools across five categories: individual calculators, corporate platforms, infrastructure tools, IoT/AI/blockchain systems, and research tools. A novel five-dimensional privacy risk framework is applied to 46 tools, revealing that higher measurement accuracy often requires more intrusive data collection. Consumer calculators are low-risk, while enterprise platforms and IoT/blockchain systems pose the highest risks due to sensitive operational and real-time data flows. The findings highlight a privacy-accuracy trade-off crucial for GX tool adoption.
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
日本のGX実践では、SSBJに基づくScope3開示やカーボンフットプリント活用が進む中、個人・組織のデータ取り扱いに関するプライバシー問題は重要な検討課題となる。本論文は、消費者向けツールと企業向けツールのリスク差を明確にしており、日本企業がツール選定時に考慮すべき指針を提供する。また、IoT/AI/ブロックチェーンを用いた次世代ツールの高リスク性は、導入前のガバナンス構築の必要性を示唆する。
In the global GX context
Globally, as carbon footprint tools proliferate under frameworks like TCFD and ISSB, privacy risks become a critical governance issue. This paper provides a structured framework to evaluate these risks, applicable to any organization adopting such tools. The identified privacy-accuracy trade-off informs tool selection and regulatory development for sustainable finance and disclosure. The cross-category analysis is particularly valuable for multinational enterprises managing diverse tools across jurisdictions.
👥 読者別の含意
🔬研究者:The five-dimensional privacy risk framework offers a foundation for further empirical studies and technical audits of carbon footprint tools.
🏢実務担当者:Corporate sustainability teams can use the risk categorization to evaluate and select carbon footprint tools that balance measurement accuracy with data privacy.
🏛政策担当者:Regulators should note the high risks in enterprise and IoT/blockchain tools, suggesting a need for privacy standards in carbon accounting software.
📄 Abstract（原文）
The increasing urgency of climate change mitigation has accelerated the adoption of tools to measure and monitor greenhouse gas emissions at individual, organizational, and infrastructural levels. While these tools enhance the accuracy and accountability of emissions measurement, they also raise privacy concerns due to the sensitive data they collect, the granularity of monitoring, and the potential for secondary use. Despite their growing adoption, no prior study has systematically evaluated the privacy implications of carbon footprint tools across categories or examined how data practices differ between consumer calculators, enterprise systems, and emerging Internet of Things (IoT), Artificial Intelligence (AI), and blockchain solutions. This study addresses this gap by conducting a structured, cross-category privacy evaluation of carbon footprint tools, reviewing 46 tools from academic and industry sources. A novel five-dimensional privacy risk framework, encompassing data type, granularity, storage, processing, secondary use, and user agency, is developed and applied across five tool categories: individual calculators, corporate platforms, infrastructure tools, IoT/AI/blockchain systems, and research tools. The findings reveal pronounced privacy-accuracy trade-offs: tools offering higher measurement precision often require more intrusive data collection. The study also identifies category-specific least- and highest-risk tools, showing that simple household calculators present low risk. In contrast, enterprise platforms and IoT/blockchain systems exhibit the highest exposure due to sensitive operational, financial, and real-time data flows. The study is limited by its reliance on literature and public documentation, suggesting the need for empirical field studies and technical audits. Future research should expand on these findings by investigating user perceptions, legal frameworks, and privacy-preserving design strategies for sustainable digital tools.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1109/access.2026.3654347first seen 2026-06-23 06:14:44
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