## 3 Results and Discussion
### Optimized GREI-Only and GGI Frameworks with Varying DDL and Resilience Goals  
GGI offers the potential to enhance system performance by streamlining O&M procedures. The associated costs, expressed as a percentage of the total LCC of the GGI, exhibit variability depending on the network's targeted goals and anticipated outcomes. Estimating O&M costs requires the application of localized unit price indices, encompassing activities such as routine cleaning and the inspection of network components. As ancillary operations to primary construction, O&M activities contribute significantly to LCC, partic  
Figure 3: Spatial representation of network elements in optimized GREI-only systems, categorized by varying degrees of decentralization. ularly for GGI systems in highly centralized districts, as illustrated in Figure 4. Figure 4 presents a multi-objective trade-off matrix plotting LCC, Tech-R, and Oper-R across various _DDL_. Each point represents a GGI configuration, forming a Pareto front where no single objective can be improved without compromising another. The chart reveals that higher resilience tends to increase cost, while decentralized layouts (higher _DDL_) yield better Tech-R and Oper-R performance, particularly under failure scenarios. The complexity of LCC arises from its coupling with diverse GGI objectives, including efficient drainage, aesthetic enhancements, structural integrity, Tech-R, and Oper-R, along with maintenance, restoration, replacement, inspection, and surveillance requirements. This multidimensional LCC matrix integrates considerations of service lifespan, physical infrastructure integrity, and various levels of Tech-R and Oper-R implementation. Each cell in the matrix corresponds to a specific objective and the associated LCC, forming a comprehensive framework for decision-making. The optimization of GGI configurations relies on the Pareto front, which represents a set of non-dominated solutions balancing multiple cost contributors. The Pareto front encapsulates the trade-offs between competing objectives, reflecting optimal configurations that maximize Tech-R and Oper-R while minimizing LCC. Figure 5 illustrates the optimized solutions for 24 GREI-only and GGI configurations, each optimized for a specific _DDL_ to achieve resilience goals. Figure 5 shows the LCC values for each _DDL_ under different decision preferences (e.g., maximizing Tech-R, Oper-R, or a balanced approach). The steep rise in LCC under resilience-focused preferences highlights the financial trade-offs of robustness. For instance, at _DDL_ = 100%, the LCC increases to USD 44.7 million when both Tech-R and Oper-R are maximized. The findings underscore the economic advantages of decentralized systems in achieving superior resilience performance. These results align with previous studies demonstrating the cost-effectiveness of decentralized GGI across diverse urban and climatic contexts [47]. For example, research indicates that GGI can fulfill essential urban drainage requirements under varying rainfall scenarios [48]. This reinforces the multifunctionality of decentralized GGI systems in addressing the growing complexities of urban water resource management, particularly in the face of climate change challenges [45]. Urban environmental constraints, such as limited open space availability and the finite capacity of soil water storage, present significant challenges to the effective implementation of GI [25]. Enhanced drainage capacity through expanded GREI designs offers a robust, albeit costlier, solution for managing extreme rainfall events [48]. For example, Solutions 1 and 4, which prioritize resilience within combined strategies, yield substantial  
Figure 4: Total life cycle costs (LCC) of optimized GGI configurations based on 24 selected solutions, illustrating their performance in terms of (**a**) operational resilience (Oper-R) and (**b**) technological resilience (Tech-R). Oper-R improvements of 33%, accompanied by a modest increase in LCC of less than 9% (Table 2).