In recent years, the threats of climate change to households, businesses, and the financial industry have become imminent. There are more frequent and severe natural disasters, such as heat waves, wildfires, droughts, hurricanes, snow storms, storm surges, and hurricanes, causing physical damages and disrupting firms’ operations. As the society is transitioning to a low-carbon economy, business models of some companies have to change. Environmental regulations around the world are tightening, and climate awareness of the public is going up. Governments and climate organizations are actively promoting cooperation and tracking corporations' environmental efforts. Financial investors are pushing companies to reduce their carbon footprints through divestment and shareholder engagement.
We seek to understand the impact of climate change and how to manage climate and other environmentally related exposures encountered by households, firms, and investors. Big data and machine learning techniques are valuable tools because of the many dimensions and complex nature of the impact and exposures. Climate change is not only manifested in the increase in average global temperature but also in other extreme weather conditions. A simple linear model can hardly capture the complicated effects of climate on businesses and investments. Firm managers, investors, and social planners face three major sources of climate risk exposure: physical risk, as climate events can damage production plants, injure employees, and cause supply chain disruptions; transitional risk, as companies adopt new technologies and production methods that are more environmentally friendly and sustainable; and regulatory risk, when governments’ changing climate policies require firms to reduce emissions and disclose their risk management strategies. These impacts and exposures can only be measured by the nonlinear and complex interactions of many variables.
In this Research Topic, we welcome submissions using relevant climate and real-world variables and machine learning algorithms to quantify the climate and environmental impact on firms, examine the joint effort by companies and investors to reduce carbon emissions, construct strategies that manage climate risks and improve sustainability, or design new insurance or financial derivative products that hedge climate and other related exposure. Submissions spanning multiple disciplines are welcome. Only when we understand and manage climate risk can we build a more resilient and sustainable business sector and financial system to help fight climate change.
Keywords:
Climate change, Climate risk exposure, Physical climate risk, Transitional risk, Regulatory risk, Big data, Machine learning, Financial Solutions
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
In recent years, the threats of climate change to households, businesses, and the financial industry have become imminent. There are more frequent and severe natural disasters, such as heat waves, wildfires, droughts, hurricanes, snow storms, storm surges, and hurricanes, causing physical damages and disrupting firms’ operations. As the society is transitioning to a low-carbon economy, business models of some companies have to change. Environmental regulations around the world are tightening, and climate awareness of the public is going up. Governments and climate organizations are actively promoting cooperation and tracking corporations' environmental efforts. Financial investors are pushing companies to reduce their carbon footprints through divestment and shareholder engagement.
We seek to understand the impact of climate change and how to manage climate and other environmentally related exposures encountered by households, firms, and investors. Big data and machine learning techniques are valuable tools because of the many dimensions and complex nature of the impact and exposures. Climate change is not only manifested in the increase in average global temperature but also in other extreme weather conditions. A simple linear model can hardly capture the complicated effects of climate on businesses and investments. Firm managers, investors, and social planners face three major sources of climate risk exposure: physical risk, as climate events can damage production plants, injure employees, and cause supply chain disruptions; transitional risk, as companies adopt new technologies and production methods that are more environmentally friendly and sustainable; and regulatory risk, when governments’ changing climate policies require firms to reduce emissions and disclose their risk management strategies. These impacts and exposures can only be measured by the nonlinear and complex interactions of many variables.
In this Research Topic, we welcome submissions using relevant climate and real-world variables and machine learning algorithms to quantify the climate and environmental impact on firms, examine the joint effort by companies and investors to reduce carbon emissions, construct strategies that manage climate risks and improve sustainability, or design new insurance or financial derivative products that hedge climate and other related exposure. Submissions spanning multiple disciplines are welcome. Only when we understand and manage climate risk can we build a more resilient and sustainable business sector and financial system to help fight climate change.
Keywords:
Climate change, Climate risk exposure, Physical climate risk, Transitional risk, Regulatory risk, Big data, Machine learning, Financial Solutions
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.