Environmental Engel curves: A neural network approach

Mancini, Tullio ; Calvo-Pardo, Hector ; Olmo, Jose

Resumen: Environmental Engel curves describe how households' income relates to the pollution associated with the services and goods consumed. This paper estimates these curves with neural networks using the novel dataset constructed in Levinson and O'Brien. We provide further statistical rigor to the empirical analysis by constructing prediction intervals obtained from novel neural network methods such as extra-neural nets and MC dropout. The application of these techniques for five different pollutants allow us to confirm statistically that Environmental Engel curves are upward sloping, have income elasticities smaller than one and shift down, becoming more concave, over time. Importantly, for the last year of the sample, we find an inverted U shape that suggests the existence of a maximum in pollution for medium-to-high levels of household income beyond which pollution flattens or decreases for top income earners.

Idioma: Inglés

DOI: 10.1111/rssc.12588

Año: 2022

Publicado en: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS 71, 5 (2022), 1543-1568
ISSN: 0035-9254

Factor impacto JCR: 1.6 (2022)

Categ. JCR: STATISTICS & PROBABILITY rank: 54 / 125 = 0.432 (2022) - Q2 - T2

Factor impacto CITESCORE: 2.6 - Mathematics (Q2) - Decision Sciences (Q2)

Factor impacto SCIMAGO: 0.916 - Statistics and Probability (Q1) - Statistics, Probability and Uncertainty (Q2)

Financiación: info:eu-repo/grantAgreement/ES/MICINN/PID2019-104326GB-I00

Tipo y forma: Article (Published version)