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Poster
in
Workshop: Tackling Climate Change with Machine Learning: Global Perspectives and Local Challenges

Robustly modeling the nonlinear impact of climate change on agriculture by combining econometrics and machine learning

Benedetta Francesconi · Ying-Jung Deweese

Keywords: [ Time-series analysis ] [ Climate science and climate modeling ] [ Societal adaptation and resilience ] [ Public policy ] [ Agriculture and food ]


Abstract:

Climate change is expected to have a dramatic impact on agricultural production; however, due to natural complexity, the exact avenues and relative strengths by which this will happen are still unknown. The development of accurate forecasting models is thus of great importance to enable policy makers to design effective interventions. To date, most machine learning methods aimed at tackling this problem lack a consideration of causal structure, thereby making them unreliable for the types of counterfactual analysis necessary when making policy decisions. Econometrics has developed robust techniques for estimating cause-effect relations in time-series, specifically through the use of cointegration analysis and Granger causality. However, these methods are frequently limited in flexibility, especially in the estimation of nonlinear relationships. In this work, we propose to integrate the non-linear function approximators with the robust causal estimation methods to ultimately develop an accurate agricultural forecasting model capable of robust counterfactual analysis. This method would be a valuable new asset for government and industrial stakeholders to understand how climate change impacts agricultural production.

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