A0326
Title: A data dependent spatial panel climate model on harmful emissions
Authors: Alexia Ventouri - Kings College London (United Kingdom) [presenting]
George Kapetanios - Kings College London (United Kingdom)
Stylianos Asimakopoulos - University of Stirling (United Kingdom)
Vasilis Sarafidis - Brunel University London (United Kingdom)
Abstract: Spatial cross-sectional dependence is introduced to a panel climate model on harmful emissions. It builds on a novel methodology for identifying and estimating spatial and network models using large panel data, which addresses the challenge of estimating the spatial interactions between individual units by developing a boosting algorithm that relies on the statistical significance of individual neighboring covariates tested one at a time. The method is referred to as boosting one neighbor at a time multiple testing (BONMT). It allows for the flexible selection of neighboring units in the presence of high-dimensional networks, even in cases where the cross-sectional dimension of the panel is larger than the number of time series observations available. BONMT is applied to facility-level data from 1992-2023 on toxic emissions, employment, and sales at over 12,000 facilities. Results suggest that pollution imposes costly externalities on human health and the economy, forcing policymakers to make environmental protection a top priority.
