気候・サステナビリティ報告における新興技術の応用

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:AI・ブロックチェーン・LLMをESG開示に統合する研究枠組みと、グリーンファイナンス政策の因果実証の両面で参考になる。

🏢実務担当者:開示データの信頼性・比較可能性を高める技術統合の可能性と、規制・保証上の課題を理解する手がかりとなる。

🏛政策担当者:グリーンファイナンス政策が研究開発投資を促す一方、特許産出には時間差がある点は、政策評価期間の設計に示唆を与える。

This thesis investigates the application of emerging technologies including blockchain, artificial intelligence and machine learning, and large language models (LLMs) in climate and sustainability reporting, with a particular emphasis on their integrated deployment as a complementary system for ESG disclosure. Employing a combination of systematic literature review and empirical analysis, this thesis explores how these technologies can enhance data integrity, transparency and comparability in sustainability reporting while also examining the regulatory challenges associated with their implementation.The systematic literature review, guided by the PRISMA framework and conducted using the Bibliometrix package in R on data extracted from Scopus and Web of Science, identifies three thematic clusters: the structural challenges of ESG reporting; the advantages, applications, limitations and future potential of blockchain, AI and machine learning, and LLMs in the disclosure process; and the regulatory and assurance challenges associated with their integrated deployment.The empirical findings demonstrate the real-world impact of China’s Green Finance Pilot Zone (GFPZ) policy, established in 2017. As an institutional framework designed to align financial systems with sustainability objectives, the GFPZ policy directly reflects the regulatory mechanisms discussed in the literature review. Using a difference-in-differences (DID) analytical framework and drawing on provincial-level data gathered from the China National Intellectual Property Administration (CNIPA) and the China Stock Market and Accounting Research (CSMAR) dataset from 2014 to 2024, the analysis effectively captures the causal impacts of this policy intervention on technology-driven sustainability outcomes. The findings reveal a divergence between two outcome variables: the GFPZ policy generated a statistically significant positive effect on research and development expenditures but no significant impact on green patent output, an outcome consistent with an innovation lag between investment and measurable output. These findings highlight the potential of targeted green finance policy interventions to stimulate innovation investment and underscore the importance of extended observation periods to capture the full impact on sustainable innovation output.This thesis makes three contributions to existing literature. First, it develops an integrated multi-technology framework for climate and sustainability disclosure, addressing a critical gap in existing literature where these technologies have largely been examined in isolation. Second, it provides empirical evidence on the causal relationship between green finance policy intervention and technology-driven innovation outcomes at the provincial level in China. Third, it offers actionable guidance for policymakers, regulators, and businesses seeking more effective frameworks that align financial innovation with sustainability objectives.

• openalex https://doi.org/10.25949/33868192first seen 2026-09-19 05:11:05 · last seen 2026-09-19 05:11:06