Decoding India’s Lean–Green Financing Architecture for MSMEs: A Scheme-Wise Descriptive Analysis of Credit Flows, Sustainability Incentives, and Policy Alignment
インドのMSME向けリーングリーンファイナンスアーキテクチャの解読：スキーム別の信用フロー、持続可能性インセンティブ、政策整合性の記述的分析 (AI 翻訳)
Ashwani Kumar, Pranjal Yadav, Dr. Vaibhav
🤖 gxceed AI 要約
日本語
本論文は、インドのMSME（零細中小企業）向けのリーン（効率化）とグリーン（持続可能性）を促進する融資制度を分析した。プラダン・マントリ・ムドラ・ヨジャナ、CGTMSE、SIDBIのグリーンイニシアチブ、ZED認証などの制度を対象に、融資額の拡大を確認する一方、効率化とグリーン目標の統合にギャップがあることを指摘。信用インセンティブ、グリーンファンドへのアクセス、リスク分担メカニズムが重要であると結論づけ、政策提言を行っている。
English
This paper analyzes India's lean-green financing schemes for MSMEs, including PMMY, CGTMSE, SIDBI's green initiatives, and ZED certification. It finds growth in lending but gaps in integrating lean efficiency with green sustainability. It emphasizes the need for credit incentives, green funds, and risk-sharing mechanisms, and provides policy recommendations.
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文脈では直接的な関連は限られるが、中小企業向けグリーンファイナンスの制度的枠組みの国際比較として参考になる可能性がある。
In the global GX context
This paper contributes to the global discourse on green finance for SMEs by examining India’s policy architecture. It highlights challenges in integrating lean and green practices, which are relevant to many developing and emerging economies.
👥 読者別の含意
🔬研究者:Researchers interested in green finance for SMEs will find the integrated framework and policy analysis useful.
🏢実務担当者:Corporate sustainability teams can learn about incentive structures for green adoption in MSMEs.
🏛政策担当者:Policymakers can draw lessons on designing green credit lines and risk-sharing mechanisms for SMEs.
📄 Abstract（原文）
In India, Micro, Small, and Medium Enterprises (MSME) has played a major role in India’s economic growth, generating employment, as well as in expanding India’s industries. In recent times, promoting MSME in India regarding “lean” management as well as “green” sustainability through specialized funding initiatives has been a major focus of Indian authorities. This present study aims to explore emerging patterns in India’s “lean green” MSME funding, particularly in major government programs promoting MSME’s “lean” and “green” practices. Through the application of another data-driven approach to analysis, the authors leverage secondary data by using official government documents, Reserve Bank of India documents, Ministry of MSME documents, as well as performance indicators. Scheme selection criteria form the foundation upon which the authors have identified specific schemes. These schemes include the Pradhan Mantri MUDRA Yojana Scheme, the CGTMSE Scheme, SIDBI’s green initiative through MSE-GIFT and MSE-SPICE Scheme, as well as the ZED Certification Scheme. The findings indicate a notable expansion in both the volume and scope of MSME lending; however, they also reveal significant gaps in the strategic integration of lean efficiency practices with the long-term sustainability objectives of green initiatives. Credit incentives, access to green funds, and effective risk-sharing mechanisms emerge as critical enablers for encouraging MSMEs to transition toward sustainable business models. At the same time, the study identifies persistent disparities in MSMEs’ access to green initiatives, levels of awareness, cost burdens associated with green adoption, and managerial and executive capacity gaps. By addressing these dimensions collectively, the study contributes to the existing literature through the development of an integrated framework linking MSME finance, lean practices, green motivators, and performance outcomes. The study concludes with policy-oriented recommendations emphasizing the need for coordinated institutional support, dedicated green credit lines, and results-based financing mechanisms to enhance the resilience and sustainability of MSMEs.
🔗 Provenance — このレコードを発見したソース
- openaire https://doi.org/10.47191/afmj/v11i1.16first seen 2026-05-14 21:53:36
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。