Learning equilibria in growth-pollution models

Detalhes bibliográficos
Autor(a) principal: Gomes, O.
Data de Publicação: 2011
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://ciencia.iscte-iul.pt/public/pub/id/412
http://hdl.handle.net/10071/10078
Resumo: Purpose – This paper seeks to explain how inefficient learning rules may lead to a perception of economic and ecological realities that may be systematically distorted in the long run. Design/methodology/approach – The paper evaluates long‐term growth in standard growth‐pollution models. Expectations about future levels of pollution are formed under adaptive learning. Findings – Socio‐economic players (private agents, governments, non‐profit organizations and/or groups of states) may fail in understanding, with full accuracy, long‐term environmental conditions. The perception about environment threats acquires a cyclical nature, even when ecological problems evolve steadily. Research limitations/implications – Relevant policy implications emerge if the agent is unable to compute the true levels of environmental pollution that will persist in the steady state. Authorities of several kinds are likely to underestimate or overestimate ecological problems. Practical implications – The learning approach to the perception of the environment can be applied to other economic, social and biological issues, besides material growth. For instance, it can contribute to explain some cases of over‐exploitation of resources: even in the presence of a social planner capable of avoiding typical “tragedy of the commons” situations, this entity may fail in perceiving the reality and, thus, in applying the policies that prevent the exhaustion of resources. Originality/value – The paper contributes to the literature on growth and environmental issues, but takes a step forward: it approaches not only the observed relation between economy and ecology, but also the impact over the observed relation of a systematically incorrect interpretation of such a connection.
id RCAP_d95693e8ed9ed11117140ab81b1927cf
oai_identifier_str oai:repositorio.iscte-iul.pt:10071/10078
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling Learning equilibria in growth-pollution modelsEnvironmental pollutionEconomic growthAdaptive learningNonlinear dynamicsEnvironmental managementPurpose – This paper seeks to explain how inefficient learning rules may lead to a perception of economic and ecological realities that may be systematically distorted in the long run. Design/methodology/approach – The paper evaluates long‐term growth in standard growth‐pollution models. Expectations about future levels of pollution are formed under adaptive learning. Findings – Socio‐economic players (private agents, governments, non‐profit organizations and/or groups of states) may fail in understanding, with full accuracy, long‐term environmental conditions. The perception about environment threats acquires a cyclical nature, even when ecological problems evolve steadily. Research limitations/implications – Relevant policy implications emerge if the agent is unable to compute the true levels of environmental pollution that will persist in the steady state. Authorities of several kinds are likely to underestimate or overestimate ecological problems. Practical implications – The learning approach to the perception of the environment can be applied to other economic, social and biological issues, besides material growth. For instance, it can contribute to explain some cases of over‐exploitation of resources: even in the presence of a social planner capable of avoiding typical “tragedy of the commons” situations, this entity may fail in perceiving the reality and, thus, in applying the policies that prevent the exhaustion of resources. Originality/value – The paper contributes to the literature on growth and environmental issues, but takes a step forward: it approaches not only the observed relation between economy and ecology, but also the impact over the observed relation of a systematically incorrect interpretation of such a connection.Emerald Group Publishing Ltd2015-11-03T15:58:33Z2011-01-01T00:00:00Z20112015-11-03T15:56:03Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://ciencia.iscte-iul.pt/public/pub/id/412http://hdl.handle.net/10071/10078eng2040-802110.1108/20408021111162128Gomes, O.info:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-09T17:55:37Zoai:repositorio.iscte-iul.pt:10071/10078Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:28:22.406236Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Learning equilibria in growth-pollution models
title Learning equilibria in growth-pollution models
spellingShingle Learning equilibria in growth-pollution models
Gomes, O.
Environmental pollution
Economic growth
Adaptive learning
Nonlinear dynamics
Environmental management
title_short Learning equilibria in growth-pollution models
title_full Learning equilibria in growth-pollution models
title_fullStr Learning equilibria in growth-pollution models
title_full_unstemmed Learning equilibria in growth-pollution models
title_sort Learning equilibria in growth-pollution models
author Gomes, O.
author_facet Gomes, O.
author_role author
dc.contributor.author.fl_str_mv Gomes, O.
dc.subject.por.fl_str_mv Environmental pollution
Economic growth
Adaptive learning
Nonlinear dynamics
Environmental management
topic Environmental pollution
Economic growth
Adaptive learning
Nonlinear dynamics
Environmental management
description Purpose – This paper seeks to explain how inefficient learning rules may lead to a perception of economic and ecological realities that may be systematically distorted in the long run. Design/methodology/approach – The paper evaluates long‐term growth in standard growth‐pollution models. Expectations about future levels of pollution are formed under adaptive learning. Findings – Socio‐economic players (private agents, governments, non‐profit organizations and/or groups of states) may fail in understanding, with full accuracy, long‐term environmental conditions. The perception about environment threats acquires a cyclical nature, even when ecological problems evolve steadily. Research limitations/implications – Relevant policy implications emerge if the agent is unable to compute the true levels of environmental pollution that will persist in the steady state. Authorities of several kinds are likely to underestimate or overestimate ecological problems. Practical implications – The learning approach to the perception of the environment can be applied to other economic, social and biological issues, besides material growth. For instance, it can contribute to explain some cases of over‐exploitation of resources: even in the presence of a social planner capable of avoiding typical “tragedy of the commons” situations, this entity may fail in perceiving the reality and, thus, in applying the policies that prevent the exhaustion of resources. Originality/value – The paper contributes to the literature on growth and environmental issues, but takes a step forward: it approaches not only the observed relation between economy and ecology, but also the impact over the observed relation of a systematically incorrect interpretation of such a connection.
publishDate 2011
dc.date.none.fl_str_mv 2011-01-01T00:00:00Z
2011
2015-11-03T15:58:33Z
2015-11-03T15:56:03Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://ciencia.iscte-iul.pt/public/pub/id/412
http://hdl.handle.net/10071/10078
url https://ciencia.iscte-iul.pt/public/pub/id/412
http://hdl.handle.net/10071/10078
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2040-8021
10.1108/20408021111162128
dc.rights.driver.fl_str_mv info:eu-repo/semantics/embargoedAccess
eu_rights_str_mv embargoedAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Emerald Group Publishing Ltd
publisher.none.fl_str_mv Emerald Group Publishing Ltd
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
_version_ 1799134846097817600