A distributional analysis of the socio-ecological and economic determinants of forest carbon stocks
Forest carbon (C) sequestration is being actively considered by several states as a way to cost-effectively comply with the forthcoming United States (US) Environmental Protection Agency's rule that will reduce power plant C emissions by 32% of 2005 levels by 2030. However, little is known abou...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2016
- Institución:
- Universidad del Rosario
- Repositorio:
- Repositorio EdocUR - U. Rosario
- Idioma:
- eng
- OAI Identifier:
- oai:repository.urosario.edu.co:10336/23953
- Acceso en línea:
- https://doi.org/10.1016/j.envsci.2016.02.015
https://repository.urosario.edu.co/handle/10336/23953
- Palabra clave:
- Carbon
Article
Carbon sequestration
Ecosystem
Electric power plant
Environmental protection
Forest
Forest management
Landscape
Linear regression analysis
Multiple linear regression analysis
Priority journal
Statistics
United states
Carbon sequestration
Distributional impacts
Ecosystem services
Forest inventory and analysis
Quantile regression
- Rights
- License
- Abierto (Texto Completo)
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26df483b-680b-416a-a43c-90faacb1ead9-12d7c8bf2-67a1-46d2-a8e0-b82768ad86d9-165878e80-826f-4f78-9afd-6a08a68c8b69-19365b3f2-28ac-4ae4-b0b7-2571e0fc581f-12020-05-26T00:07:01Z2020-05-26T00:07:01Z2016Forest carbon (C) sequestration is being actively considered by several states as a way to cost-effectively comply with the forthcoming United States (US) Environmental Protection Agency's rule that will reduce power plant C emissions by 32% of 2005 levels by 2030. However, little is known about the socio-ecological and distributional effects of such a policy. Given that C is heterogeneous across the landscape, understanding how social, economic, and ecological changes affect forest C stocks and sequestration is key for developing forest management policies that offset C emissions. Using Florida US as a case study, we use US National Forest Inventory Analysis and Census Bureau data in both linear regression and quantile regression analyses to examine the socio-ecological and economic determinants of forest C stocks and its relationship with differing communities. Quantile regression findings demonstrate nonlinearity in the effects of key determinants, which highlight the limitations of regularly used mean-based regression analyses. We also found that forest basal area, site quality, stand size, and stand age are significant ecological predictors of carbon stocks, with a positive and increasing effect on upper quantiles where C stocks are greater. The effect of education was generally positive and mostly significant at upper quantiles, while the effects of income and locations with predominantly minority residents (as compared to whites) were negative. Upper quantiles were also affected by population age. Our findings underscore the importance of considering the broader socio-ecological and economic implications of compliance strategies that target the management of forests for carbon sequestration and other ecosystem services. © 2016 Elsevier Ltd.application/pdfhttps://doi.org/10.1016/j.envsci.2016.02.01514629011https://repository.urosario.edu.co/handle/10336/23953engElsevier Ltd3728Environmental Science and PolicyVol. 60Environmental Science and Policy, ISSN:14629011, Vol.60,(2016); pp. 28-37https://www.scopus.com/inward/record.uri?eid=2-s2.0-84960887698&doi=10.1016%2fj.envsci.2016.02.015&partnerID=40&md5=ab61869d750d536ebe73d51f4fee7725Abierto (Texto Completo)http://purl.org/coar/access_right/c_abf2instname:Universidad del Rosarioreponame:Repositorio Institucional EdocURCarbonArticleCarbon sequestrationEcosystemElectric power plantEnvironmental protectionForestForest managementLandscapeLinear regression analysisMultiple linear regression analysisPriority journalStatisticsUnited statesCarbon sequestrationDistributional impactsEcosystem servicesForest inventory and analysisQuantile regressionA distributional analysis of the socio-ecological and economic determinants of forest carbon stocksarticleArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501Soto, José R.Escobedo, Francisco J.Adams, Damian C.Blanco, German10336/23953oai:repository.urosario.edu.co:10336/239532022-05-02 07:37:21.309493https://repository.urosario.edu.coRepositorio institucional EdocURedocur@urosario.edu.co |
dc.title.spa.fl_str_mv |
A distributional analysis of the socio-ecological and economic determinants of forest carbon stocks |
title |
A distributional analysis of the socio-ecological and economic determinants of forest carbon stocks |
spellingShingle |
A distributional analysis of the socio-ecological and economic determinants of forest carbon stocks Carbon Article Carbon sequestration Ecosystem Electric power plant Environmental protection Forest Forest management Landscape Linear regression analysis Multiple linear regression analysis Priority journal Statistics United states Carbon sequestration Distributional impacts Ecosystem services Forest inventory and analysis Quantile regression |
title_short |
A distributional analysis of the socio-ecological and economic determinants of forest carbon stocks |
title_full |
A distributional analysis of the socio-ecological and economic determinants of forest carbon stocks |
title_fullStr |
A distributional analysis of the socio-ecological and economic determinants of forest carbon stocks |
title_full_unstemmed |
A distributional analysis of the socio-ecological and economic determinants of forest carbon stocks |
title_sort |
A distributional analysis of the socio-ecological and economic determinants of forest carbon stocks |
dc.subject.keyword.spa.fl_str_mv |
Carbon Article Carbon sequestration Ecosystem Electric power plant Environmental protection Forest Forest management Landscape Linear regression analysis Multiple linear regression analysis Priority journal Statistics United states Carbon sequestration Distributional impacts Ecosystem services Forest inventory and analysis Quantile regression |
topic |
Carbon Article Carbon sequestration Ecosystem Electric power plant Environmental protection Forest Forest management Landscape Linear regression analysis Multiple linear regression analysis Priority journal Statistics United states Carbon sequestration Distributional impacts Ecosystem services Forest inventory and analysis Quantile regression |
description |
Forest carbon (C) sequestration is being actively considered by several states as a way to cost-effectively comply with the forthcoming United States (US) Environmental Protection Agency's rule that will reduce power plant C emissions by 32% of 2005 levels by 2030. However, little is known about the socio-ecological and distributional effects of such a policy. Given that C is heterogeneous across the landscape, understanding how social, economic, and ecological changes affect forest C stocks and sequestration is key for developing forest management policies that offset C emissions. Using Florida US as a case study, we use US National Forest Inventory Analysis and Census Bureau data in both linear regression and quantile regression analyses to examine the socio-ecological and economic determinants of forest C stocks and its relationship with differing communities. Quantile regression findings demonstrate nonlinearity in the effects of key determinants, which highlight the limitations of regularly used mean-based regression analyses. We also found that forest basal area, site quality, stand size, and stand age are significant ecological predictors of carbon stocks, with a positive and increasing effect on upper quantiles where C stocks are greater. The effect of education was generally positive and mostly significant at upper quantiles, while the effects of income and locations with predominantly minority residents (as compared to whites) were negative. Upper quantiles were also affected by population age. Our findings underscore the importance of considering the broader socio-ecological and economic implications of compliance strategies that target the management of forests for carbon sequestration and other ecosystem services. © 2016 Elsevier Ltd. |
publishDate |
2016 |
dc.date.created.spa.fl_str_mv |
2016 |
dc.date.accessioned.none.fl_str_mv |
2020-05-26T00:07:01Z |
dc.date.available.none.fl_str_mv |
2020-05-26T00:07:01Z |
dc.type.eng.fl_str_mv |
article |
dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 |
dc.type.spa.spa.fl_str_mv |
Artículo |
dc.identifier.doi.none.fl_str_mv |
https://doi.org/10.1016/j.envsci.2016.02.015 |
dc.identifier.issn.none.fl_str_mv |
14629011 |
dc.identifier.uri.none.fl_str_mv |
https://repository.urosario.edu.co/handle/10336/23953 |
url |
https://doi.org/10.1016/j.envsci.2016.02.015 https://repository.urosario.edu.co/handle/10336/23953 |
identifier_str_mv |
14629011 |
dc.language.iso.spa.fl_str_mv |
eng |
language |
eng |
dc.relation.citationEndPage.none.fl_str_mv |
37 |
dc.relation.citationStartPage.none.fl_str_mv |
28 |
dc.relation.citationTitle.none.fl_str_mv |
Environmental Science and Policy |
dc.relation.citationVolume.none.fl_str_mv |
Vol. 60 |
dc.relation.ispartof.spa.fl_str_mv |
Environmental Science and Policy, ISSN:14629011, Vol.60,(2016); pp. 28-37 |
dc.relation.uri.spa.fl_str_mv |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84960887698&doi=10.1016%2fj.envsci.2016.02.015&partnerID=40&md5=ab61869d750d536ebe73d51f4fee7725 |
dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
dc.rights.acceso.spa.fl_str_mv |
Abierto (Texto Completo) |
rights_invalid_str_mv |
Abierto (Texto Completo) http://purl.org/coar/access_right/c_abf2 |
dc.format.mimetype.none.fl_str_mv |
application/pdf |
dc.publisher.spa.fl_str_mv |
Elsevier Ltd |
institution |
Universidad del Rosario |
dc.source.instname.spa.fl_str_mv |
instname:Universidad del Rosario |
dc.source.reponame.spa.fl_str_mv |
reponame:Repositorio Institucional EdocUR |
repository.name.fl_str_mv |
Repositorio institucional EdocUR |
repository.mail.fl_str_mv |
edocur@urosario.edu.co |
_version_ |
1814167534457323520 |