Pathways to Sustainable Agriculture in Emerging Markets: Firm Level Evidence
新興市場における持続可能な農業への道筋:企業レベルのエビデンス (AI 翻訳)
Sania Asad, Amiya Bhaumik
🤖 gxceed AI 要約
日本語
本研究は、パキスタンの農業企業を対象に、グリーンファイナンスと技術進歩が持続可能性パフォーマンスと企業成長に与える影響を検証した。固定効果パネル回帰と媒介分析を用いた結果、技術進歩は持続可能性に正の影響を与えるが、グリーンファイナンスは直接的な効果を持たないことが判明した。持続可能性パフォーマンスは企業成長の媒介変数として機能し、気候スマート農業フレームワークの重要性が示唆された。
English
This study examines the impact of green finance and technological advancement on sustainability performance and firm growth using a balanced dataset of agri-firms listed on the Pakistan Stock Exchange. Fixed-effects panel regression and mediation analysis reveal that technological advancement positively affects sustainable performance, while green finance has no direct impact. Sustainability performance fully mediates the effect of technological advancement on firm growth, highlighting the importance of climate-smart agriculture.
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
本論文はパキスタンを対象としており、日本のGX文脈への直接的な関連性は低い。しかし、グリーンファイナンスの実効性や技術革新の重要性を示す点で、日本の農業分野における脱炭素化政策やESG投資の議論に示唆を与える可能性がある。
In the global GX context
This paper provides firm-level evidence from an emerging market on the limited direct impact of green finance on sustainable agriculture, contrasting with the stronger role of technology. Globally, it underscores that financial instruments alone may not drive regenerative practices without complementary investments in innovation and infrastructure, relevant for climate finance and sustainable supply chain discussions.
👥 読者別の含意
🔬研究者:Provides empirical evidence on the technology-sustainability-growth nexus in an understudied context.
🏢実務担当者:Highlights that green finance needs integration with technological investments for effective sustainability outcomes in agri-business.
🏛政策担当者:Suggests that policies should combine financial incentives with support for digital farming and agri-innovation to achieve climate resilience.
📄 Abstract(原文)
In order to achieve climate resilience and food security by 2030 the transition towards sustainable and regenerative food systems is critical, yet firm-level evidence investigating the drivers of sustainable agriculture is missing in emerging economies. The study investigates the impact of green finance and technological advancement, sustainability performance and firm growth opportunities. The study investigates a balanced dataset of Agri-filled firms listed on the Pakistan Stock Exchange. The study allocates fixed-effects panel regression and mediation analysis, predicting that technological advancement has a statistically strong and positive impact on sustainable performance. However, green finance possesses no direct impact. Sustainability performance exerts a significant positive impact on firm growth. The mediation analysis predicts that sustainability performance fully transmitted effects of technological advancement on firm growth whereas, green finance remains having no effect at all on firm growth in the presence of sustainability performance as a mediator. The insignificance of green finance suggests that green labelled financial resources do not necessarily translate into regenerative practices in agriculture domain, likely due to weak operational integration, lack of infrastructure, institutional barriers and limited absorptive capacity. These findings also highlight the importance of Climate Smart Agriculture Framework as; climate smart technologies lay out the basis for sustainability practices for regenerative agriculture. The study is highly effective for policy insights emphasizing that green financial instruments alone are not enough to create climate resilient and equitable food systems. However, investments in digital farming, Agri- based technological advancement and agricultural innovation could produce more effective approach for climate resilience.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.31674/ijbb.2025.v02i04.005first seen 2026-05-15 21:13:32 · last seen 2026-06-16 05:18:07
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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。