Pareto Based Performance Framework for Urban Greening: Visualizing Trade offs between Cost, Carbon Sequestration, and Shading

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Offers a novel application of evolutionary multi-objective optimization to urban greening with interpretable Pareto analysis.

🏢実務担当者:Provides a decision aid for urban planners and construction firms to evaluate greening options against cost and carbon.

🏛政策担当者:Demonstrates how to quantify trade-offs in green infrastructure policy, supporting cost-effective climate adaptation.

Urban greening in construction planning often entails trade offs between cost and ecological performance. This study develops a multi-objective optimization framework that explicitly exposes and interprets the trade-offs among cost, carbon sequestration, and shading benefit. 3D parametric modeling is coupled with Wallacei evolutionary algorithms and a localized vegetation database integrating Taiwan public construction costs, species-specific carbon sequestration data, and measured canopy coverage. Across 800 solutions generated over 40 generations, 70 solutions constituted the Pareto optimal front, with the first Pareto solutions emerging at generation 17 and later generations (from generation 34 onward) consistently producing more than five Pareto solutions per generation. The optimizer identifies non dominated greening configurations under constraints on green coverage ratio and plant diversity. The Pareto front is further analyzed to identify representative solution archetypes aligned with distinct planning priorities cost oriented, carbon oriented, shading oriented, and balanced compromise strategies. Quantified marginal returns along the front reveal that advancing from the minimum cost solution ($148,469) to the carbon optimal solution ($246,750) requires a 66% cost increase, whereas moving from the carbon optimal to the shading optimal solution ($269,523) adds only 9% cost while improving shading by 14%, with a 6% reduction in carbon sequestration. Decision makers can therefore clearly understand what is gained and what is sacrificed when shifting priorities along the Pareto front. By tightly coupling evolutionary multi-objective optimization with interpretable Pareto analysis and visual decision aids, this framework bridges algorithmic results and practical design choices, enabling urban greening strategies that balance environmental benefits with economic feasibility for sustainability and climate adaptation.

• openalex https://doi.org/10.1051/e3sconf/202671609009/pdffirst seen 2026-06-17 04:59:12 · last seen 2026-06-17 07:10:38