Environmental Engel curves: A neural network approach
Environmental Engel curves: A neural network approach
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 MCdropout. 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.
environmental Engel curves, neural networks, prediction uncertainty
1543-1568
Mancini, Tullio
3e5a59a2-e184-4996-a7d6-7b4394bec08c
Calvo-Pardo, Hector
07a586f0-48ec-4049-932e-fb9fc575f59f
Olmo, Jose
706f68c8-f991-4959-8245-6657a591056e
November 2022
Mancini, Tullio
3e5a59a2-e184-4996-a7d6-7b4394bec08c
Calvo-Pardo, Hector
07a586f0-48ec-4049-932e-fb9fc575f59f
Olmo, Jose
706f68c8-f991-4959-8245-6657a591056e
Mancini, Tullio, Calvo-Pardo, Hector and Olmo, Jose
(2022)
Environmental Engel curves: A neural network approach.
Journal of the Royal Statistical Society, Series C (Applied Statistics), 71 (5), .
(doi:10.1111/rssc.12588).
Abstract
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 MCdropout. 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.
Text
Royal Stata Society Series C - 2022 - Mancini - Environmental Engel curves A neural network approach
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More information
Accepted/In Press date: 20 July 2022
e-pub ahead of print date: 31 August 2022
Published date: November 2022
Additional Information:
Funding Information:
Tullio Mancini acknowledges financial support from the University of Southampton Presidential Scholarship and Jose Olmo from ‘Fundación Agencia Aragonesa para la Investigación y el Desarrollo’ and project PID2019‐104326GB‐I00 from the Spanish Secretary of Science and Innovation.
Funding Information:
Spanish Secretary of Science and Innovation, University of Southampton Presidential Scholarship, Fundación Agencia Aragonesa para la Investigación y el Desarrollo, Grant/Award Number: PID2019‐104326GB‐I00 Funding information
Publisher Copyright:
© 2022 The Authors. Journal of the Royal Statistical Society: Series C (Applied Statistics) published by John Wiley & Sons Ltd on behalf of Royal Statistical Society.
Keywords:
environmental Engel curves, neural networks, prediction uncertainty
Identifiers
Local EPrints ID: 470771
URI: http://eprints.soton.ac.uk/id/eprint/470771
ISSN: 0035-9254
PURE UUID: 3e6d9ac7-76b5-48ef-a946-6b1ada5c27da
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Date deposited: 19 Oct 2022 17:06
Last modified: 17 Mar 2024 03:32
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Author:
Tullio Mancini
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