Urban Greenprint: A Decision Support Tool for Optimizing Urban Forest Strategies in Sustainable Cities

Urban forests (UFs) play a crucial role in mitigating climate change, but their management presents complex trade-offs between environmental, economic, and social aspects. We developed a Decision Support Tool (DST) to simulate 27-year UF dynamics under six different management strategies, aiming to maximize socio-economic and environmental benefits while considering costs. Business as Usual (BaU), Yielding Scenario (YS), High Management (HM), Forest Development (FD), Social Boost (SB), and Cover Maximizing (CM) strategies were tested with the DST in the Vazzieri district of Campobasso, central Italy. The DST integrates CO2 removal, management expenditures and revenues, and the social usability of UFs. The findings show that while all the strategies contribute to climate change mitigation, FD and SB offer the best balance between the environmental and social sides. FD demonstrates significant CO2 removal with moderate expenditures, whereas SB maximizes CO2 removal despite its high management expenditures. Otherwise, YS and BaU show limited environmental benefits with beneficial economic outcomes. While achieving the highest environmental and social benefits, CM incurs the greatest economic costs. This study highlights the need for long-term, integrated UF strategies to harmonize climate change mitigation with economic viability and social inclusivity. The DST provides a valuable framework for urban planners and policymakers to optimize sustainable UF management.

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Publication Details

Journal
Urban Science
Published
2025-06-11
DOI
https://doi.org/10.3390/urbansci9060216
Citations
2
Primary Topic
Urban Green Space and Health
Type
article
Field-Weighted Citation Impact
2.40
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article

Urban Greenprint: A Decision Support Tool for Optimizing Urban Forest Strategies in Sustainable Cities

Marco di Cristofaro, Mauro Maesano, Federico Valerio Moresi, Giuseppe Scarascia Mugnozza et al.
2 citations
Urban Science
Urban Green Space and Health
2.40
article

Urban Greenprint: A Decision Support Tool for Optimizing Urban Forest Strategies in Sustainable Cities

Marco di Cristofaro, Mauro Maesano, Federico Valerio Moresi, Giuseppe Scarascia Mugnozza, Bruno Lasserre
article en
2 citations

Abstract

Urban forests (UFs) play a crucial role in mitigating climate change, but their management presents complex trade-offs between environmental, economic, and social aspects. We developed a Decision Support Tool (DST) to simulate 27-year UF dynamics under six different management strategies, aiming to maximize socio-economic and environmental benefits while considering costs. Business as Usual (BaU), Yielding Scenario (YS), High Management (HM), Forest Development (FD), Social Boost (SB), and Cover Maximizing (CM) strategies were tested with the DST in the Vazzieri district of Campobasso, central Italy. The DST integrates CO2 removal, management expenditures and revenues, and the social usability of UFs. The findings show that while all the strategies contribute to climate change mitigation, FD and SB offer the best balance between the environmental and social sides. FD demonstrates significant CO2 removal with moderate expenditures, whereas SB maximizes CO2 removal despite its high management expenditures. Otherwise, YS and BaU show limited environmental benefits with beneficial economic outcomes. While achieving the highest environmental and social benefits, CM incurs the greatest economic costs. This study highlights the need for long-term, integrated UF strategies to harmonize climate change mitigation with economic viability and social inclusivity. The DST provides a valuable framework for urban planners and policymakers to optimize sustainable UF management.

Urban ScienceVol. 9(6)
University of Molise (IT), Università degli Studi della Tuscia (IT), European University Institute (IT)
Openalex Percentile: Top 12%
Urban Green Space and Health
2.40
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Urban Greenprint: A Decision Support Tool for Optimizing Urban Forest Strategies in Sustainable Cities — Marco di Cristofaro, Mauro Maesano, et al. · Urban Science (2025) | TGRS Research Map | TGRS