Sustainable lending strategies: a framework for enhancing climate resilience in industrial loan portfolios

被引:0
|
作者
Roy, Neha Chhabra [1 ]
机构
[1] SVKMs Narsee Monjee Inst Management Studies, Bangalore, Karnataka, India
关键词
EXTREME LEARNING-MACHINE; RISK-MANAGEMENT; CLASSIFICATION;
D O I
10.1007/s10308-024-00709-w
中图分类号
D81 [国际关系];
学科分类号
030207 ;
摘要
This paper underscores the critical significance of climate-related risks within the banking and industrial sectors, emphasizing the need for a climate-resilient response system and strategic loan portfolio planning. The study introduces an efficient methodology for identifying key Climate-Linked Risks (CLRs) across impacted sectors, capturing their diverse impacts, quantifying them, and subsequently designing optimal loan portfolios. A comprehensive review of literature and primary responses from CLR experts, coupled with secondary data sources, forms the basis of our analysis. Initially, CLR impacts were categorized into push and pull indicators based on empirical weighted averages. Subsequently, fuzzy logic theory was employed to quantify CLR in form of composite index across industries. Finally, the study proposes portfolio planning for banks using mean variance portfolio. The proposed control approach assesses sectoral severity, prioritizes sectors, identifies root causes, and recommends cost-effective strategies, enhancing the overall resilience of the banking ecosystem.
引用
收藏
页码:423 / 462
页数:40
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