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Scholarship on Nature-based Solutions (NbS) primarily focuses on the potential for NbS to deliver multiple benefits to humans and biodiversity from networked natural systems. These approaches, if enacted without sensitivity to local contexts and histories, can deepen long standing injustices resulting from the destruction of complex self-organizing ecological systems, the usurpation of Indigenous governance and knowledge, and the prioritization of technical managerial approaches transforming nature into infrastructure. Here we review, synthesize, and critically reflect on existing scholarship on the rise of NbS in New York City, USA, to inform environmental policy in support of just transformations of complex urban systems. To do so, we examine NbS within the context of the social-ecological-technological system (SETS) of NYC. We organize our review and synthesis around three interrelated concepts of justice: Ecological, Indigenous Environmental, and Infrastructural Justice. Ecological Justice entails addressing the harms, needs, and desired futures of ecological actors while identifying synergies with human focused environmental justice concerns and movements. Indigenous Environmental Justice requires restoring Indigenous systems of governance and knowledge while making space for a diversity of social-ecological practices of marginalized communities. Infrastructural Justice addresses the historical and ongoing injustices perpetuated through mainstream infrastructure policy and design practice – including Environmental Justice concerns – which have increasingly turned towards NbS. Without embedding these principles within emergent NbS focused environmental policy agendas seeking just transformations, they will likely recreate utilitarian, anthropocentric, and colonial modes of managing nature as infrastructure. We conclude with a research-to-action agenda for meeting the interdependent needs of urban ecosystems and humans. • Dominant NbS approaches do not consider social or ecological justice. • NbS can be made more just by integrating concepts of Ecological, Indigenous Environmental, and Infrastructure Justice. • Applying these concepts within case study of NYC, USA, we identify opportunities for improving research and practice. • Explicitly including justice goals in NbS reorients policy at multiple levels. [ABSTRACT FROM AUTHOR]

• Partnerships between cities can play an important role in cloudburst knowledge systems. • There is a need for improved projections of cloudburst intensity with climate change. • There is a need for social science research to improve cloudburst risk dimensioning. • Policymakers need support in decision-making under climate change associated uncertainties. Cities are particularly vulnerable to cloudbursts - short-duration, intense rainfall events – which are often inadequately addressed through conventional stormwater and flood management policy. Climate change is projected to increase the frequency and intensity of cloudbursts in many cities. As minor cloudburst events become more frequent and extreme events more severe, cities will need to rapidly transform their stormwater drainage and interdependent systems, and the knowledge systems that guide their infrastructure decisions and policy. In this paper, we discuss the evolution of knowledge systems to address these challenges, using three diverse cities (Phoenix, USA; Copenhagen, Denmark; and New York City, USA) as case studies. We found that partnerships between cities – even across national boundaries – can be a particularly important component of cloudburst knowledge systems. We also identified limitations in knowledge systems related to non-stationary climate, the vulnerability of private property and the representation of cloudburst infrastructure in integrated water management, which present opportunities for future research to support decision-making. [ABSTRACT FROM AUTHOR]

Cities and urbanized regions are complex, dynamic, and highly integrated systems linking social, ecological, and technical infrastructure domains in ways that create deep challenges for good governance, policymaking, and planning. The combination of impacts from climate change in cities, air pollution, rapid population growth, multiple sources of development pressure and overall urban system complexity make it difficult for decision-makers to develop and guide development trajectories along more livable, equitable, and at the same time, more resilient pathways. Advancing urban sustainability and resilience agendas requires expanding the scope of inter- and trans-disciplinarity approaches, moving beyond the historically separate social–ecological and socio-technical approaches to jointly study social–ecological–technical infrastructure systems in cities. We take urban complexity as a given and suggest that in both research and practice we need to better capture and understand feedbacks, interdependencies, and non-linearities which create uncertainties and challenge the efficacy of governance practices to achieve normative goals for society. Here, we explore new methods, tools, and approaches to advance our understanding of urban system complexity through a series of journal special issue articles that examine urban structure–function relationships, urban sustainability transitions, green space availability, social–ecological memory, functional traits, and urban land use scenarios. [ABSTRACT FROM AUTHOR]

Mapping, modeling, and valuing urban ecosystem services are important for integrating the ecosystem services concept in urban planning and decision-making. However, decision-support tools able to consider multiple ecosystem services in the urban setting using complex and heterogeneous data are still in early development. Here, we use New York City (NYC) as a case study to evaluate and analyze how the value of multiple ecosystem services of urban green infrastructure shifts with shifting governance priorities. We first examined the spatial distribution of five ecosystem services – storm water absorption, carbon storage, air pollution removal, local climate regulation, and recreation – to create the first multiple ecosystem services evaluation of all green infrastructure in NYC. Then, combining an urban ecosystem services landscape approach with spatial multicriteria analysis weighting scenarios, we examine the distribution of these ecosystem services in the city. We contrast the current NYC policy preference – which is focused on heavy investment in stormwater absorption – with a valuation approach that also accounts for other ecosystem services. We find substantial differences in the spatial distribution of priority areas for green infrastructure for the valuation scenarios. Among the scenarios we examined for NYC, we find that a scenario in which only stormwater absorption is prioritized leads to the most unevenly distributed ES values. By contrast, we find least variation in ES values where stormwater absorption, local climate regulation, carbon storage, air pollution removal, and recreational potential are all weighted equally. We suggest that green infrastructure planning strategies should include all landscape components that contribute to the production of ecosystem services and consider how planning priority alternatives generate different ecosystem services values. [ABSTRACT FROM AUTHOR]

Understanding why some parks are used more regularly or intensely than others can inform ways in which urban parkland is developed and managed to meet the needs of a rapidly expanding urban population. Although geolocated social media (GSM) indicators have been used to examine park visitation rates, studies applying this approach are generally limited to flagship parks, national parks, or a small subset of urban parks. Here, we use geolocated Flickr and Twitter data to explore variation in use across New York City's 2143 diverse parks and model visitation based on spatially-explicit park characteristics and facilities, neighborhood-level accessibility features and neighborhood-level demographics. Findings indicate that social media activity in parks is positively correlated with proximity to public transportation and bike routes, as well as particular park characteristics such as water bodies, athletic facilities, and impervious surfaces, but negatively associated with green space and increased proportion of minority ethnicity and minority race in neighborhoods in which parks are located. Contrary to previous studies which describe park visitation as a form of nature-based recreation, our findings indicate that the kinds of green spaces present in many parks may not motivate visitation. From a social equity perspective, our findings may imply that parks in high-minority neighborhoods are not as accessible, do not accommodate as many visitors, and/or are of lower quality than those in low-minority neighborhoods. These implications are consistent with previous studies showing that minority populations disproportionately experience barriers to park access. In applying GSM data to questions of park access, we demonstrate a rapid, big data approach for providing information crucial for park management in a way that is less resource-intensive than field surveys. Highlights • We analyze geographic human visitation dynamics in all New York City parks using Twitter and Flickr data • Park visitation increases with proximity to public transportation, water bodies, athletic fields and impervious surfaces • Park visitation decreases with green space and proportion of minority ethnicity and minority race in neighborhoods of parks • Location-based social media data can be used as a rapid indicator for citywide park visitation to inform management [ABSTRACT FROM AUTHOR]

Defining landscape structure and key relationships between landscape structure and function is challenging in urban areas characterized by density and patchy spatial patterns. In order to trace the spatial and temporal patterns of urban landscape structures, compare patterns across cities, or inform urban design principles, we need to classify the landscape in a way that captures context and landscape heterogeneity, but can be broadly applied across different cities or landscape variations within a city. In this study, we introduce a simple and reproducible approach for classifying the structure of urban landscapes (STURLA) that utilizes heterogeneous, composite classes which represent combinations of built and natural features, and examine the response of a landscape function – surface temperature. This classification approach is unique in that it develops composite (as opposed to homogeneous) classes, which are defined a posteriori, based on compositions of adjacent structural elements that emerge in the urban landscape, using a cellular grid to define units of analysis. We test the separability of classes that emerge from this approach, and find that it is possible to discern classes – comprised of the mix of land and building covers common in urban areas – which have meaningfully distinct temperature signatures. This classification approach may be extended to multiple cities and ecological indicators in order to offer insight into the relationship between urban landscape structure and ecosystem response, in a way that accounts for interactions among different types of urban landscape surfaces. We suggest that this approach can support spatial prioritization of landscape function needs in urban development and design approaches for improving particular types of functioning, such as reductions in urban heat. [ABSTRACT FROM AUTHOR]

Cellular automata (CA) models have increasingly been used to simulate land use/cover changes (LUCC). Metaheuristic optimization algorithms such as particle swarm optimization (PSO) and genetic algorithm (GA) have been recently introduced into CA frameworks to generate more accurate simulations. Although Markov Chain Monte Carlo (MCMC) is simpler than PSO and GA, it is rarely used to calibrate CA models. In this article, we introduce a novel multi-chain multi-objective MCMC (mc-MO-MCMC) CA model to simulate LUCC. Unlike the classical MCMC, the proposed mc-MO-MCMC is a multiple chains method that imports crossover operation from classical evolutionary optimization algorithms. In each new chain, after the initial one, the crossover operator generates the initial solution. The selection of solutions to be crossed over are made according to their fitness score. In this paper, we chose the example of New York City (USA) to apply our model to simulate three conflicting objectives of changes from non-urban to low-, medium- or high-density urban between 2001 and 2016 using USA National Land Cover Database (NLCD). Elevation, slope, Euclidean distance to highways and local roads, population volume and average household income are used as LUCC causative factors. Furthermore, to demonstrate the efficiency of our proposed model, we compare it with the multi-objective genetic algorithm (MO-GA) and standard single-chain multi-objective MCMC (sc-MO-MCMC). Our results demonstrate that mc-MO-MCMC produces accurate simulations of land use dynamics featured by faster convergence to the Pareto frontier comparing to MO-GA and sc-MO-MCMC. The proposed multi-objective cellular automata model should efficiently help to simulate a trade-off among multiple and, possibly, conflicting land use change dynamics at once. • This paper presents a multi-objective CA land use/cover change model. • The model uses a novel multi-objective Markov chain Monte Carlo (MO-MCMC). • We compare MO-MCMC with a multi-objective genetic algorithm (MO-GA). • We apply our model to simulate the expansion of multi-density urban areas in New York. • The results show that the MO-MCMC outperformed MO-GA in terms of accuracy and speed. [ABSTRACT FROM AUTHOR]