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While green infrastructures (GIs) are used to address urban flooding and stormwater runoff and enhance sustainability, their implementation faces challenges such as limited funding and competing priorities. Existing studies generally focused on specific/individual GI practices, potentially overlooking the broader benefits or values of comprehensive GI plans that generally are interconnected networks of multiple GI practices. Thus, this paper developed an agent-based model to offer municipalities, planners, and decision makers a structured and systematic modeling approach to: (1) simulate the dynamic nature of GI implementation plans across spatial and temporal dimensions; (2) capture the interplay among regulations that govern GI plans; and (3) conduct scenario and sensitivity analyses to gain deeper insights into the various characteristics of GI plans under different scenarios. The proposed model considered different economic values of GI plans, including stormwater management, carbon sequestration, pollutant removal, energy cost savings, and increased property value, as well as the social values, such as the positive impact on nearby residents/communities. In addition, the developed model considered the following six GI practices: green roofs, rain gardens, community gardens, bioswales, permeable playgrounds, and permeable pavements. These GI practices were modeled across five submodels: school greening, private property greening, park greening, right-of-way property greening, and public housing properties greening. Scenario analysis was conducted to investigate the effect of financial limitations (i.e., funding priorities) on the model's behavior under two scenarios. Finally, the developed model was statistically validated. The results showed that increasing the number of GIs does not guarantee an increase in their economic value, but rather the implementation of GIs should be well planned according to a priority plan. The findings also provided valuable insights into the importance of considering physical and institutional limitations. This paper contributes a practical workflow for evaluating the costs and benefits of GI programs. [ABSTRACT FROM AUTHOR]

Greenery initiatives, such as green infrastructures (GIs), create sustainable and climate-resilient environments. However, they can also have unintended consequences, such as displacement and gentrification in low-income areas. This paper proposes an integrated machine learning (ML) approach that combines both unsupervised and supervised ML algorithms. First, 35 indicators that contribute to green gentrification were identified and categorised into 7 categories: social, economic, demographic, housing, household, amenities, and GIs. Second, data was collected for all census tracts in New York City. Third, the green gentrification susceptibility was modelled into 6 levels using k-means clustering analysis, which is an unsupervised ML model. Fourth, the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) was used to map the census tracts to their green gentrification susceptibility level. Finally, different supervised ML algorithms were trained and tested to predict the green gentrification susceptibility. The results showed that the artificial neural network (ANN) model is the most accurate in classifying and predicting the green gentrification susceptibility with an overall accuracy of 96%. Moreover, the outcomes showed that the Normal Difference Vegetation Index (NDVI), the proximity to GIs, the GIs frequency, and the total area of GIs were identified as the most important indicators to predict green gentrification susceptibility. Ultimately, the proposed approach allows practitioners and researchers to perform micro-level (i.e. on the census-tracts level) predictions and inferences about green gentrification susceptibility. This allows more focused and targeted mitigation actions to be designed and implemented in the most affected communities, thus promoting environmental justice. [ABSTRACT FROM AUTHOR]

With growing urbanization and increasing climate change-related concerns, green infrastructures (GIs) are recognized as promising solutions for mitigating various challenges and promoting sustainable development. Despite the important role of GIs, a comprehensive synthesis of the quantification of their full range of benefits and challenges is lacking in the current literature. To address this gap, a systematic literature review was conducted on the quantifiable environmental, economic, and social benefits and challenges of GIs. This paper followed the Preferred Reporting Items for Systematic Review (PRISMA) methodology, where 75 relevant articles were reviewed to present the various models and methods that could be used to quantify and assess the impacts of different GI types. The study further investigated existing knowledge trends and patterns, identified research gaps, and suggested future research directions. The results revealed that while existing research studies offer great insights into the impacts of GIs, a more holistic approach is necessary to balance the benefits and challenges of GIs. The findings also offered a comprehensive understanding of a wide range of environmental, economic, and social considerations of both natural and engineered GIs. Ultimately, the performed literature review serves as a comprehensive guide for researchers and practitioners and could be used in estimating and evaluating the benefits and challenges of GI plans and programs as well as in making informed decisions about GI projects. [ABSTRACT FROM AUTHOR]

As resilience planning gains prominence in urban sustainable development, green infrastructures (GIs) are widely recognized as effective solutions to address climate risks and environmental challenges. While urban greening initiatives have multiple benefits, they can also lead to unintended consequences such as environmental injustice, increasing property values, and the displacement and gentrification of vulnerable residents in the affected areas. While gentrification remains a significant concern for urban sustainability initiatives that aim to promote environmental justice, a consensus upon approaches and methods for precisely identifying communities undergoing gentrification is still lacking. While most of the existing research studies focused on examining the relationship between GIs and gentrification, little-to-no research was conducted to quantify the green gentrification vulnerability of different communities. To this end, and to bridge this knowledge gap, this paper proposes and develops a novel index called the green gentrification vulnerability index (GGVI). First, an extensive list of 35 indicators that contribute to green gentrification was identified and grouped into 7 categories: economic, social, housing, household, demographic, amenities, and GIs. Second, data were gathered on the 35 indicators for all the 2325 census tracts across New York City (NYC) in the US. Third, the data were preprocessed to ensure their suitability for implementing Principal Component Analysis (PCA). Fourth, PCA was implemented for each of the 7 categories separately to weight their respective green gentrification indicators and calculate vulnerability scores. Finally, these vulnerability scores were used to develop the GGVI. The research findings provided insights on the importance of the 35 indicators as well as the 7 green gentrification-related vulnerability categories in relation to their contribution to the GGVI. The 7 categories were ranked as follows: amenities, GIs, economic, demographic, social, housing, and households. In addition, proximity to parks, frequency of parks, proximity to GIs, and total area of parks were found to be the most important GIs-related indicators that could lead to green gentrification. Overall, this paper contributes to the growing body of research that addresses green gentrification by being the first study that proposes and develops a GGVI that can be used to identify at-risk communities from green gentrification and ultimately help practitioners, urban planners, decision-makers, and researchers in developing new policies and strategies aimed to mitigate the unintended consequences of green gentrification (i.e., displacement of vulnerable communities) and promote environmental justice and urban sustainability. • Green gentrification vulnerability was studied based on 35 indicators. • The importance of the different indicators was determined. • Principal Component Analysis was used to develop a GGVI. • The proposed index allows census-track green gentrification vulnerability analysis. [ABSTRACT FROM AUTHOR]

Various stormwater best management practices and green infrastructures (GIs) are recommended to address flooding, stormwater runoff, water quality, and sustainability. While detention basins are considered one of the main GI strategies, their benefits cannot be fully realized without properly maintaining them and making sure that they stay operational. Therefore, this paper used agent-based modeling (ABM) to devise an optimal maintenance program for detention basins to ensure that they function properly and continue to perform their water quality and flood control functions. More specifically, the following 2 agent types were incorporated in the model: 1) the detention basins were considered as static agents, and 2) the service teams responsible for the operation (maintenance, repair, and replacement) of the detention basins were considered as active agents. The developed ABM was applied for the entire network of stormwater detention basins in Newark, NJ. Sensitivity analysis was conducted to identify the most critical variables affecting the total cost of operating the network of detention basins as well as the functioning percentage of detention basins. In addition, optimization was implemented to determine the best maintenance program or policy that minimizes the total cost of operations, while also making sure that a desired functionality level or threshold is achieved for the entire network of detention basins. Finally, the ABM was statistically validated using a total of 10,000 Monte Carlo runs and 99% confidence intervals. The optimization results showed that, in order to minimize the total cost of maintaining the entire network of detention basins and ensure that at least 80% of the basins are in a functioning state at the end of the planning horizon, the decision-maker should implement the following maintenance program or strategy: have 2 service teams for the operations of the detention basins, follow a replacement policy, and replace detention basins after 3 maintenance periods. Also, the identified optimal maintenance program or strategy would result with an average total annual cost of around $4,085,000, where the average annual repair cost is around $2,572,200, the average annual maintenance cost is around $19,700, the average annual replacement cost is around $763,100, and the average annual service team cost is around $730,000. The proposed ABM for detention basins can be extended to other GIs as well as to different geographical areas. The usage of ABM has the advantage to reduce the subjectivity in developing plans for managing GIs. • Agent-based modeling could be implemented for deciding optimal maintenance strategies for a network of detention basins. • The optimal maintenance strategy was based on minimizing total cost and ensuring at least 80% of the basins are functional. • The identified best maintenance program leads to an average total annual cost of around $4,085,000. • The determined optimal maintenance strategy was statistically validated using Monte Carlo runs and 99% confidence intervals. • Using agent-based modeling has the advantage of reducing the subjectivity in developing plans for managing detention basins. [ABSTRACT FROM AUTHOR]