循環経済と持続可能な開発：G7諸国におけるグリーンイノベーション、エネルギー転換、グリーン投資が生態学的フットプリントに与える影響の証拠
Circular Economy and Sustainable Development: Evidence From Green Innovation, Energy Transition, and Green Investment on Ecological Footprint in G7 Countries (原題)
Sohidul Islam, Reday Chandra Bhowmik, H. G. Sulimany, Md. Mustaqim Roshid, Abdulrahman Atllah Alharbi, Shah Asadullah Mohd. Zobair
🤖 gxceed AI 要約
日本語
G7諸国（1995-2023年）を対象に、グリーンファイナンス、グリーンイノベーション、廃棄物リサイクル、エネルギー転換、経済成長、人口が生態学的フットプリントに与える影響を分位点回帰で分析。グリーンファイナンスは一貫してフットプリントを削減するが、イノベーションやリサイクルは逆効果の分位点もあり、エネルギー転換の効果は不均一。政策・経営への示唆を提示。
English
Using quantile-on-quantile regression on G7 data (1995-2023), this study finds green finance consistently reduces ecological footprint, while green innovation and recycling show positive effects in most quantiles, and energy transition has heterogeneous effects. Provides targeted guidance for policymakers and business leaders on circular economy investments.
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
日本では、循環経済への移行が政策課題となっており、グリーンファイナンスの効果を実証した本研究成果は、日本の企業や金融機関が環境投資の優先順位を検討する際の参考となる。また、SSBJ開示や統合報告書において、環境パフォーマンスの評価指標を選定する際のエビデンスとして活用できる。
In the global GX context
This study contributes to global circular economy and green finance literature by providing empirical evidence from G7 countries. It offers insights for policymakers and businesses on the heterogeneous effects of circular economy drivers, relevant to ISSB and CSRD disclosure frameworks that require assessing environmental impacts.
👥 読者別の含意
🔬研究者:Provides empirical evidence on the heterogeneous effects of green finance, innovation, and energy transition on ecological footprint, useful for further research on circular economy policies.
🏢実務担当者:Highlights the need to evaluate environmental performance of green investments and innovation to ensure circular economy initiatives reduce ecological pressure.
🏛政策担当者:Offers targeted guidance on how different circular economy drivers affect ecological footprint across quantiles, informing policy design for sustainable development.
📄 Abstract（原文）
Achieving sustainable development in advanced economies requires accelerating the transition toward a circular economy while mitigating escalating ecological pressures. Despite growing policy attention, evidence remains limited on the joint effects of green finance, green innovation, municipal waste recycling, energy transition, economic growth, and population on the ecological footprint in the G7 countries during 1995–2023. Using a multivariate quantile‐on‐quantile regression framework, this study captures distributional heterogeneity in the relationships between these determinants and the ecological footprint. The results show that green finance consistently reduces the ecological footprint, with stronger mitigation effects beyond the middle quantiles. In contrast, green innovation and municipal waste recycling show positive effects across most quantiles, while the effect of energy transition is heterogeneous, remaining positive at lower and middle quantiles before becoming slightly negative at the highest quantiles. Economic growth and population also increase the ecological footprint, with the impact of economic growth becoming substantially stronger at higher quantiles. To address potential endogeneity, instrumental variable quantile regression with lagged instruments is employed, and the results remain robust. For managers, the results highlight the importance of directing green finance toward projects with demonstrable environmental benefits while systematically evaluating the environmental performance of innovation, recycling, and energy transition initiatives to ensure that circular economy investments translate into lower ecological pressure. By revealing how the effects of circular economy drivers vary across ecological footprint regimes, this study provides targeted guidance for policymakers and business leaders seeking to strengthen long‐term ecological sustainability and advance the Sustainable Development Goals.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1002/sd.71560first seen 2026-08-21 05:02:44 · last seen 2026-09-22 05:08:00
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