Utilize este identificador para referenciar este registo: http://hdl.handle.net/10362/172585
Título: Optimal forest management under climate change variability
Autor: Rosa, Renato
Simas, Constança
Ataíde, Rodrigo
Soares, Paula
Tomé, Margarida
Palavras-chave: Bioeconomic modeling
Climate change
Climate change adaptation
Climate variability
Forest management
Environmental Science(all)
Economics and Econometrics
SDG 13 - Climate Action
SDG 15 - Life on Land
Data: Nov-2024
Resumo: Ecosystems are likely to be severely affected by climate change. While the literature on this subject focuses primarily on climate variable means, increasing evidence has been gathered on the importance of changes in climate variability in determining ecosystem impacts. In this context, forests play a significant role. While, on the one hand, forests have often been identified to be a key element in mitigating greenhouse gas emissions, on the other, forests are also affected by changes in climate. However, the number of studies on optimal forest management under climate change remains limited and has overlooked the role of climate variability. This paper adds to that literature by developing a coupled ecological-economic forest stand model in which forest dynamics are a function of monthly climate variables. We show that accounting for changes in climate variability substantially changes earlier findings. In particular, ignoring climate variability may fail to adequately account for changes in optimal harvest age and lead to erroneous conclusions regarding the effects of climate change on forested land value.
Descrição: Funding Information: CeBER's research is funded by national funds through FCT \u2013 Funda\u00E7\u00E3o para a Ci\u00EAncia e a Tecnologia, I\u00B7P. Project UIDB/05037/2020 with DOI 10.54499/UIDB/05037/2020. This work was funded by Funda\u00E7\u00E3o para a Ci\u00EAncia e Tecnologia (UID/ECO/00124/2019, UIDB/00124/2020 and Social Sciences DataLab, PINFRA/22209/2016), POR Lisboa and POR Norte (Social Sciences DataLab, PINFRA/22209/2016). CENSE is financed by Funda\u00E7\u00E3o para a Ci\u00EAncia e Tecnologia, I.P. Portugal (UIDB/04085/2020). This work was supported by FCT \u2013 Funda\u00E7\u00E3o para a Ci\u00EAncia e Tecnologia, I.P. by project reference UIDB/00239/2020 of the Forest Research Centre and DOI identifier 10.54499/UIDB/00239/2020, Renato Rosa acknowledges funding from FCT under the Scientific Employment Stimulus (CEECIND/02230/2017) and the The FCT Investigator Programme (IF/01106/2012/CP0153/CT0003). Publisher Copyright: © 2024 The Authors
Peer review: yes
URI: http://hdl.handle.net/10362/172585
DOI: https://doi.org/10.1016/j.ecolecon.2024.108322
ISSN: 0921-8009
Aparece nas colecções:Home collection (NSBE)

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