Machine learning and feature selection: Applications in economics and climate change
In one sentence. Reviews feature selection methods such as LASSO and causal selection for climate-economy studies, and applies them to find an optimal heatwave definition for economic outcomes.
Abstract
Abstract Feature selection is an important component of machine learning for researchers that are confronted with high dimensional data. In the field of economics, researchers are often faced with high dimensional data, particularly in the studies that aim to understand the channels through which climate change affects the welfare of countries. This work reviews the current literature that introduces various feature selection algorithms that may be useful for applications in this area of study. The article first outlines the specific problems that researchers face in understanding the effects of climate change on countries’ macroeconomic outcomes, and then provides a discussion regarding different categories of feature selection. Emphasis is placed on two main feature selection algorithms: Least Absolute Shrinkage and Selection Operator and causality-based feature selection. I demonstrate an application of feature selection to discover the optimal heatwave definition for economic outcomes, enhancing our understanding of extreme temperatures’ impact on the economy. I argue that the literature in computer science can provide useful insights in studies concerned with climate change as well as its economic outcomes.
Labels
- Topic
- Applications & impacts, Extremes & hazards
- Type
- Review, survey or tutorial
- Method
- Classical ML (trees, GP, linear, kernels)
- Domain
- Global
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.1017/eds.2023.36 OpenAlex:W4389819320
- Added
- 2026-10-22
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