Integrating Digital Solutions into Urban Planning: A Computational Tool for CO₂ Storage and Green Space Management

The rapid urbanization of landscapes and the impacts of climate change are profoundly transforming urban ecosystems, with signifiant implications for ecosystem services that benefit human health and well-being. The restoration and conservation of urban ecosystems play a crucial role in enhancing climate resilience, as they address three interrelated dimensions: mitigation, multi-hazard adaptation, and the generation of socio-economic and environmental co-benefits. These actions also support additional ecosystem services essential to urban well-being. Among these, regulating services—such as carbon dioxide (CO₂) mitigation—are particularly critical in addressing the effects of climate change. In fact, trees and plants play a well-recognized role in sequestering CO₂ during their growth by storing it in woody biomass, including trunks, roots, and branches [1] [2].

In this context, one of the main challenges for urban designers and planners lies in effectively integrating vegetation into urban and neighborhood-scale projects. To address this, the implementation of designer-friendly digital tools in practitioners workflows can be very useful for several aspects, reducing  knowledge gaps, streamlining complex data management, and facilitating the application of environmental science principles in design workflows [3] [4].

The study presented led to the development of a computational tool in the Grasshopper3D working environment (McNeel). This tool allows users to quantify the CO₂ storage potential of specific tree species in urban environments, considering their growth stages and species-specific characteristics. This quantification represents a preliminary step toward creating a comprehensive tool for the design and management of urban green spaces. The tool is intended to guide professionals in adopting planning approaches that integrate ecosystem service evaluations. Additionally, it offers a foundation for assessing socio-economic and environmental co-benefits, such as improved public health, enhanced community inclusion, increased biodiversity, and better air quality.

An experimentation of the tool was conducted in the San Giovanni a Teduccio neighborhood as part of the Erasmus+ UCCRN_Edu project. This densely populated urban area faces significant environmental challenges. The analysis quantified the contribution of existing trees to CO₂ storage, providing critical data to improve environmental quality and enhance ecosystem services within the neighborhood.

 

References

• McPhearson, T., Karki, M., Herzog, C., Santiago Fink, H., Abbadie, L., Kremer, P., Clark, C. M., Palmer, M. I., and Perini, K. (2018). Urban ecosystems and biodiversity. In Rosenzweig, C., W. Solecki, P. Romero-Lankao, S. Mehrotra, S. Dhakal, and S. Ali Ibrahim (eds.), Climate Change and Cities: Second Assessment Report of the Urban Climate Change Research Network. Cambridge University Press. New York. 257–318
• European Environment Agency (EEA), (2022), 'Nature-based solutions in Europe: Policy, knowledge and practice for climate change adaptation and disaster risk reduction', Climate Change and Law Collection, pp. 40, 44-48, doi:10.1163/9789004322714_cclc_2021-0190-608
• Nocerino, G., Leone, M.F. (2024). WorkerBEE: A 3D Modelling Tool for Climate Resilient Urban Development. In: Calabrò, F., Madureira, L., Morabito, F.C., Piñeira Mantiñán, M.J. (eds) Networks, Markets & People. NMP 2024. Lecture Notes in Networks and Systems, vol 1189. Springer, Cham. https://doi.org/10.1007/978-3-031-74723-6_2
• Nocerino, G., Leone, M.F. (2023), Computational LEED: computational thinking strategies and Visual Programming Languages to support environmental design and LEED credits achievement. Energy Build. 278, 112626, https://doi.org/10.1016/j.enbuild.2022.112626