Learning equilibria in growth-pollution models
Autor(a) principal: | |
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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. |
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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 |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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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 |
|
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1799134846097817600 |