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Predictive analytics in climate finance: Assessing risks and opportunities for investors

Authors: Onyeka Chrisanctus Ofodile; Adedoyin Tolulope Oyewole; Chinonye Esther Ugochukwu; Wilhelmina Afua Addy; Omotayo Bukola Adeoye; Chinwe Chinazo Okoye;

Predictive analytics in climate finance: Assessing risks and opportunities for investors

Abstract

Predictive analytics is increasingly recognized as a pivotal tool in climate finance, offering investors invaluable insights into both the risks posed by climate change and the opportunities for sustainable investment. This Review delves into the burgeoning field of predictive analytics within climate finance, emphasizing its significance in aiding investors to navigate the multifaceted landscape of climate-related risks and opportunities. By leveraging advanced data analytics techniques, predictive analytics empowers investors to anticipate and mitigate climate-related risks, ranging from physical risks such as extreme weather events and sea-level rise to transition risks associated with regulatory changes and technological shifts. Moreover, predictive analytics enables investors to identify emerging opportunities in sectors poised for sustainable growth, such as renewable energy, clean technology, and climate resilient infrastructure. This Review also sheds light on the methodologies and data sources utilized in predictive analytics for climate finance, encompassing climate models, satellite imagery, socioeconomic indicators, and financial data. Through the analysis of historical trends and future projections, predictive analytics provides investors with actionable insights to inform their investment decisions and align their portfolios with climate-related goals and mandates. Despite its potential benefits, the adoption of predictive analytics in climate finance is not without challenges. This Review examines the hurdles associated with data quality, model uncertainty, regulatory complexities, and the integration of climate-related factors into financial decision-making processes. Addressing these challenges necessitates interdisciplinary collaboration, robust risk assessment frameworks, and ongoing innovation in predictive analytics methodologies. In conclusion, this Review underscores the critical role of predictive analytics in climate finance and its transformative potential in enhancing the resilience and sustainability of investment portfolios. By harnessing the power of data-driven insights, investors can proactively manage climate-related risks, capitalize on sustainable investment opportunities, and contribute to the transition towards a low-carbon economy. As climate change continues to exert profound impacts on financial markets, the integration of predictive analytics represents a strategic imperative for investors seeking to navigate the evolving landscape of climate finance effectively.

Keywords

Opportunities, Climate Finance, Predictive Analytics, Risks, Investors

  • BIP!
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    citations
    The total number of citations received by all articles of the researcher of interest.
    16
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
The total number of citations received by all articles of the researcher of interest.
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
16
Top 10%
Top 10%
Top 10%
Green