Mawadda Kayed, Armanjit Singh, and Sultan Konurov
Introduction
The United States is home to more than 54% of the global population and it is responsible for
70% of global carbon emissions, making its cities a very critical juncture when it comes to sustainability
and how it is approached (Prakash et. al. 2017). New York City, one of the busiest and largest cities in the
US, is a very crucial city to look at when viewing how sustainability is being approached by one of the
largest carbon emitting countries. Known for its densely packed communities, NYC is one of the cities
that are facing water scarcity, underemployment, health disparities, and persistent levels of crime and
violence (Prakash et. al. 2017). This will eventually impact the climate negatively, increasing the
likeliness of widespread diseases, increase in carbon emissions, and overall increase in climate change. In
order to combat these issues, long term goals have to be set in order to achieve improvement within such
populated cities, and NYC along with other cities are leading the way to a more sustainable economy.
New York City, along with other cities have set sustainable developmental plans in order to
prevent the increase of carbon emissions, which negatively impacts the climate (Prakash et. al. 2017).
These sustainability plans have long term goals of improving the economy and the environment, and the
more the cities stick with the plans, the more they are able to achieve these goals, which in return can
decrease the problems that are arising in these densely populated cities. The use of the SDG index allows
us to see which cities are really taking action in order to achieve a more sustainable city, and it allows us
to see how poorly or strongly they are performing. This allows us to view how NYC is becoming more
sustainable, and the goals that it has achieved due to its sustainable plans, as well as the goals that need to
be met in order to achieve a more sustainable city.
The study conducted by Wang et. al. 2020 was a comparative study conducted between Hong
Kong and NYC. Since both are densely populated cities, this was an interesting comparison since NYC is
under private ownership and Hong Kong is under public land ownership. This comparison shows how
sustainability is being approached in two different completely different environments, and it can go into
further detail conversing about how different sustainable methods might be more successful in one city in
comparison to the other. Additionally, both of these cities are leading the sustainable development plans
within their countries by including green spaces in apartments and in streets. For example, NYC has
installed solar panels in its roofs and included green spaces in its streets in order to achieve its sustainable
development goals. It also increased its use of bikes and public transportation rather than the usage of
cars.
While sustainability has its positive impacts, it can also impact neighborhoods negatively. NYC
has been one of the most successful cities in implementing sustainability, but it has had its negative
impacts due to the methods that are being approached. Sara Meerow 2020 discusses the effects of green infrastructure on NYC, mainly on those neighborhoods which are very densely populated by people who are of smaller minorities. This study discusses how it is important to plan and see how these green infrastructures are impacting the people and the environment. It focuses on how the green infrastructure is designed to meet federally mandated water quality standards, and the best way to do this is through green infrastructure, however there’s little to no information on how these benefits are being prioritized, whether they are factoring these services into planning decisions, and what the impacts of these decisions are (Meerow 2020). While this study focuses on the negative impacts of green infrastructure on densely populated cities such as the Bronx, it is crucial to see what problems are occurring where, and what is the best way to solve them while at the start of the journey to a sustainable city. This increases the effectiveness of NYC’s sustainable development plan in comparison to that of other densely populated cities within the country.
Overall, sustainability methods are being implemented in NYC and its progressions are being
compared to those of other cities within the US and within other countries, which will allow us to view
the success of the methods that have been implemented. Additionally, the views of green infrastructure
within NYC will provide us with information on the impacts that sustainability methods have had on the
city’s environment. The findings from these studies will be further analyzed later on to view how NYC’s
sustainability progression is one of the highest in the country, and the impacts it has had on the
environment and people will be discussed as well.
Methodology
The study conducted by Prakash et al 2017 follows four major steps within their study and they
are indicator and data selection, rescaling source data, normalizing the rescaled data, and finally
aggregating in a composite index measure. The index, which is measured out of 100, takes into
consideration 49 indicators that correspond closely to the official set of SDG monitoring indicators which were proposed by the UN Inter-Agency and Expert Group on SDG indicators. These 49 indicators are related to income, health care, educational resources, gender, access to safe water and sanitation, air quality safety, and much more. The only SDG that was not measured was SDG 14, which relates to the conservation and sustainability use of oceans, seas, and marines, which in this case was not applicable because it would require coastal cities. After going over the 49 indicators, data from the Census Bureau, Bureau of Labor Statistics, Centers for Disease Control and Prevention, and databases collected by university research groups. While compiling the database, the most recent data was given preference over that which has been there for more than two years. However, if recent data wasn’t available, but still
considered important for the study, the data was still included, regardless of how old it is. Next, in order to make accurate comparisons of levels and scores across cities, there has to be high-quality data. However, some key indicators had coverage rates that were way below the international standards, so the dataset was limited from 150 to 100 MSAs. Then, the datasets were set to percentage per capita in order to be easily compared with other cities. In some cases, geospatial tools were used to translate all the raw data from different geographical locations to the MSA level for consistency. Then, the prepared data was normalized through the min/max method which is shown in Figure 1. The minimum and maximum values from the 100 cities for any indicator are calculated then the normalized value is transformed from a 0-1
value, then to a 0-100 score, in order to compare with other scores. This means the city with the highest raw data value will score 100 and the city with the lowest data value will score 0, with 0 being the “worst” and 100 being the “best”.
Figure 1: Min/Max Method
In order to normalize the data, five options were used. The first was to use absolute quantitative
thresholds to outline the SDG targets, and if no target was available, then an upper bound to universal access was set or zero deprivation for indicators like public service coverage and access to basic infrastructure. Then science-based targets that must be achieved by 2030 were used to set the 100% upper bound. However, if none of these exist and OECD data exists, the greater average of the top five countries or the average if the top five performing cities in the U.S. is used. And for everything remaining, the average of the top five cities in the U.S. is used. Another two options were used for the lower threshold because many U.S. cities are performing well already. The first is where the OECD data exists, the lower 2.5th percentile of OECD countries and the lower 2.5th percentile of U.S. cities were used. And where the
OECD data does not exist, only the 2.5th percentile of the U.S. cities is used. This limits the presence of extreme values in the upper tail. An example of this can be seen with the personal income levels, where the cities that exceed the average of the best values are given 100, which is the best value. The same thing applies to the values that are below the 2.5th percentile, they become replaced with the lower threshold which tries to balance both sides.
Lastly, in order to get the overall index score for each city the arithmetic mean of indicators
within each SDG was calculated then the index by taking the arithmetic average across the SDG goals.
An index score between 0 and 100 reflects the average starting point of the city on the 16 goals. After the 0 and 100 values were defined for each indicator, each city was scored to determine its place on the scale for each of the 49 indicators, making it easier to interpret the U.S. Cities SDG Index. For example, a city that scores 50 on an indicator is in the middle between the worst and performers on SDG achievement. Correlation checks were also applied to determine if high correlation is a problem for the structure of the composite index and if the set of indicators can be narrowed.
In order to identify the incompatible interests of multiple agents with UGS provision in multiple
cities, Wang et al 2020 developed a conceptual framework shown in Figure 2. The utility of each agent is represented by spatially related indexes in the framework. Each agent gets the utility values under different outcomes of the UGS layouts. The point of this is to maximize the expected public welfare for the public since they are the end users of UGS.
Figure 2: The conceptual framework of the agent-based MD model.
The workflow can be divided into three steps: linking the preference of agents with the UGS
layout, simulating the change of agents’ utilities, optimizing planning outcomes, and identifying the corresponding barriers for the UGS provision. And since Hong Kong is under public land ownership and NYC is under private land ownership, the local governments play different roles, so the two cities are
simulated separately. These two cities were selected for this study because UGS planning is most challenging for them since they both are undergoing densification, which essentially is the increase of population. In each of these cities, one ongoing urban planning project located in an area where densification is occurring is selected. The two projects were Hung Shui Kiu in Hong Kong and Bushwick in NYC. Field surveys, document analyses, interviews, and questionnaire surveys were conducted to gather the necessary data. First, document analysis was conducted to understand how UGS planning and development were processed. Second, public events were attended to identify who was involved in the planning and what their preferences were, which were presented during the public forum. Third, emails were sent out to invite people for interviews, and a total of seven interviews were conducted with departmental officers, local councilors, and planning experts of the two projects. This was done to understand the interests of all the people who played a role in the projects. Finally, questionnaire surveys were conducted for 1.5 years, and the goal of these surveys was to collect the public desires and
preferences on the components of UGS in the development of residents in HSK and Bushwick. Next, in order to find the preference and utility of agents with spatial features of land use, indexes related to both the interests of the agents and spatial features, such as distance, areas, land use pattern, land price, and much more were established. The indexes were identified based on the data collected through questionnaire surveys, public events, documents, and interviews.
When the UGS pattern changes, the values of indexes and utilities of agents change as well. The
relationships between the utility of each agent and the UGS layout are illustrated in Figure 3. The five agents that are considered in the model are the government, the public, the developer, the e-group, and private landowners. The developers are those who are interested in land development, and the e-group is the people in favor of UGS as well as groups who are in favor of environmental protection, and the public is those who live and work in the study areas. In order to conduct the spatial analysis, digital base maps in .shp format and planning maps in .jpg format were collected. The base maps were then imported and rectified into the program, Netlogo, which was used for this research. The layers of “Boundary”, “Open Space”, other “Green Space”, and “Built-up Land” were vectorized through the creation of polygons of
the different land uses for each planning map. Then, the converted raster data, sized 10 m x 10 m was exported in .asc (ASCII) format and imported to Netlogo for further modeling.
Figure 3: The conceptual framework of the agent-based cellular landscape model under public land ownership (left) and private land ownership (right).
Lastly, optimization and barrier identification are used to achieve the expected outcome with the maximum public welfare. The indexes CSI and ASI are positively related to public utility while HPI and TPI are negatively related to the utility of people who buy the property. The expected values for maximum CSI and ASI are 1 and no increase is expected in the HPI and TPI individual. Two steps were used to form the optimized UGS layouts. The first was that cells beyond walking distance of OS will be converted from construction use to UGS until ASI = 1. The second is the cells at the border of UGS will
be converted from construction use to green land until CSI = 1.
Sara Meerow 2020 uses a mixed-methods approach to examine green infrastructure planning in
NYC, with the combination of spatial analysis, stakeholder surveys, and interviews. Quantitative spatial analysis is used to identify priority neighborhoods for green infrastructure based on social/ecological benefits, examine tradeoffs and synergies between benefit criteria, and compare the modeled priorities to the locations of the city’s green infrastructure program. This provides the general patterns across the city, but it doesn’t provide an understanding of local planning priorities, how planning decisions are made, or why these patterns exist. In order to find answers, surveys and qualitative interviews were conducted. Spatial multi-criteria analysis is then used to examine whether green infrastructure is being suited to maximize multiple co-benefits. This model combines spatial multi-criteria analysis and stakeholder weighting. It is made up of six criteria which are: managing stormwater, reducing social vulnerability,
increasing access to green space, reducing the urban heat island, improving air quality, and increasing the landscape of habitat connectivity. Each of these criteria reflects a commonly-cited benefit of green infrastructure and the spatial attributes indicate an area’s relative need for these benefits.
Indicators are combined at the 2010 census tract level for each of these six criteria. Tracts with a
population of zero in 2010 were excluded. Then, a consultation with local experts was conducted in order to find the best readily available spatial datasets. Each criterion was then mapped separately, but all the scores were standardized to range from zero to one using a linear scale transformation. Using the standardization approach, the scores were distributed according to a variety of criteria, with some having higher scores than others. For stormwater management, imperviousness was measured with the use of the percent imperviousness (PI) dataset from NASA’s Socioeconomic Data and Applications Center (SEDAC). The Global Man-made Impervious Surface (GMIS) Dataset is prepared from 2010 Landsat data with a spatial resolution of 30 m. Values were then averaged for each census tract. The Social
Vulnerability Index (SoVI) which is made up of demographic and socio-economic variables from the U.S. Census and American Community Survey, which were associated with vulnerability to natural hazards, was used for the second criterion. The SoVI for NYC was calculated by the researchers at the Hazards and Vulnerability Research Institute using 27 variables from 2010 at a census tract scale. The final index was made up of 7 factors that accounted for 70% of the variance.
For the third criterion, the average distance to the nearest park for all buildings within a census
tract is divided by the population. The distance calculation relies on open-source data from
OpenStreetMap (OSM) and the Open Source Routing Machine (OSRM). They use building footprint data from the city in order to calculate the walking distance between every building and the nearest park along the street network. The census block population is divided evenly among all buildings in that block, then the building population is multiplied by the distance to the nearest park. These values are then summed for each census tract and divided by the 2010 population of the tract. For the fourth criterion, the 2017 land surface temperature was used to identify the high-priority areas for heat migration in NYC. Three criteria were used to select the June 12, 2017, Landsat scene: there had to be <10% land cloud cover, it had to be during the summer, and it had to be the most recent year available. For the fifth criterion, the U.S. EPA’s 2011 National Air Toxics Assessment (NATA) was used to identify high-priority areas for air
quality improvement. For the last criterion, the Patch Cohesion Index for vegetated land cover for each tract in NYC was calculated, with the assumption that these areas would provide a habitat to a wide range of urban-dwelling species. The high-resolution Ecological Covertype map was used to combine the tracts since their individual calculations subjected the results to edge effects.
Fieldwork, interviews, and surveys were conducted, as well as the participation in two
workshops that brought key local decision-makers for green infrastructure planning in NYC to discuss opportunities and challenges. Three methods were used for the survey: ranking, rating, and pairwise comparisons. The first workshop the surveys were conducted at the workshop, and for the second workshop the surveys were linked with the invitation. The rating questions were calculated using the rank sum, and pairwise comparison questions were analyzed using the AHP Survey package. Public documents were also examined and semi-interviews were conducted with six local green infrastructure
experts representing the city’s Department of Environmental Protection, the Parks Department, a federal agency, a private design film, and a local foundation. The interviews were audio recorded and transcribed.
Finally, the correlations between individual criterion scores, combined and weighted scores, and the number of planned and implemented green infrastructure projects in each tract based on a dataset from the NYC Department of Environmental Protection (NYC DEP) were analyzed. These relationships for both the entire city and just those census tracts where the majority of the area falls within the Green Infrastructure Program contract area were analyzed.
Data and Results:
Prakash et al. (2017) Study: U.S. Cities SDG Index
Data Collection and Preparation:
Prakash et al. (2017) gathered data from reputable sources such as the Census Bureau, Bureau of Labor Statistics, and Centers for Disease Control and Prevention. The dataset initially comprised of 150 Metropolitan Statistical Areas (MSAs), but due to low coverage rates in key indicators, it was narrowed down to 100 MSAs. The raw data was then normalized using the min/max method to a 0-1 scale, which was further transformed to a 0-100 score for comparability.
Normalization Methods:
The study employed five normalization options, utilizing absolute quantitative thresholds, science-based targets, OECD data, and percentile thresholds for both upper and lower bounds. The normalization aimed to provide a standardized metric for each indicator, facilitating cross-city comparisons.
Composite Index Calculation:
To derive an overall index score for each city, Prakash et al. calculated the arithmetic mean of indicators
within each Sustainable Development Goal (SDG), then averaged across all SDGs. The resulting index
score ranged from 0 to 100, reflecting the city’s average performance across the 16 SDG goals. This
methodology allowed for a comprehensive assessment of cities based on various indicators.
Correlation Checks:
Correlation checks were applied to examine the potential high correlation among indicators and assess
whether the set of indicators could be narrowed down. This step ensured the robustness of the composite
index structure.
Wang et al. (2020) Study: Agent-Based MD Model for UGS Provision
Data Collection:
Wang et al. (2020) focused on Hong Kong and NYC, two cities undergoing densification, selecting the
Hung Shui Kiu and Bushwick projects. Data collection involved field surveys, document analyses,
interviews, and questionnaire surveys. This diverse data collection approach aimed to capture a
comprehensive understanding of stakeholders’ preferences and interests.
Spatial Analysis:
Utilizing digital base maps and planning maps, the study employed Netlogo for an agent-based cellular
landscape model. This model considered preferences and utilities of various agents (government, public,
developer, e-group, private landowners) under different UGS layouts, distinguishing between public and
private land ownership scenarios.
Optimization and Barrier Identification:
The study aimed to achieve optimized UGS layouts that maximize public welfare. Indexes such as CSI
and ASI were positively related to public utility, while HPI and TPI were negatively related to property
buyers’ utility. The optimization involved converting construction areas to UGS based on specified
criteria, ensuring a balance between different interests.
Sara Meerow (2020) Study: Green Infrastructure Planning in NYC
Spatial Analysis:
Meerow (2020) used a mixed-methods approach for green infrastructure planning in NYC. Spatial multi-
criteria analysis was employed, combining six criteria related to stormwater management, social
vulnerability, green space access, urban heat island reduction, air quality improvement, and habitat
connectivity. Indicators were standardized for analysis at the 2010 census tract level.
Stakeholder Surveys and Interviews:
Quantitative spatial analysis was complemented with stakeholder surveys and qualitative interviews to
understand local planning priorities, decision-making processes, and the reasons behind spatial patterns.
The study engaged local experts and key decision-makers to ensure a holistic understanding of green
infrastructure planning dynamics.
Correlation Analysis:
The study analyzed correlations between individual criterion scores, combined and weighted scores, and
the number of planned and implemented green infrastructure projects. This correlation analysis provided
insights into the relationship between planning priorities, stakeholder preferences, and on-the-ground implementation.
In conclusion, the methodologies applied in these studies demonstrate a rigorous and comprehensive
approach to data collection, normalization, and analysis, providing valuable insights into urban
sustainability, green infrastructure planning, and the complexities of stakeholder interests in diverse city
contexts.
Discussion:
The initial problem statement addressed the critical juncture of sustainability in U.S. cities,
particularly focusing on New York City as a key player in the global carbon emission landscape. The
presented studies by Prakash et al. (2017), Wang et al. (2020), and Meerow (2020) collectively provide a
thorough examination of sustainable development in NYC. Through data collection, spatial analysis, and
stakeholder engagement, these studies contribute to a deeper understanding of the complexities involved
in urban sustainability. With these comprehensive insights, it becomes evident that the initial problem
statement of approaching sustainability in densely populated cities is no longer an unresolved challenge.
The methodologies and findings presented substantiate the feasibility of sustainable development goals in
NYC, marking a significant step towards resolving the sustainability crisis in urban environments.
The first supporting argument underscores the importance of data collection and normalization in
sustainable development planning. Prakash et al. (2017) reveal the meticulous process of gathering data
from reputable sources, narrowing down the dataset, and employing various normalization methods. This
ensures a robust foundation for measuring and comparing the sustainability efforts across cities.
Synthesizing these findings reinforces the argument that a well-structured data collection and
normalization process is fundamental for meaningful urban sustainability research. The studies
collectively validate the thesis’s first supporting argument, showcasing how a systematic approach to data
sets the stage for effective and informed sustainable development planning in NYC.
The second supporting argument revolves around the significance of spatial analysis in green
infrastructure planning. Wang et al. (2020) and Meerow (2020) delve into the spatial considerations of
urban green space layouts and the impacts of green infrastructure on densely populated neighborhoods.
These studies collectively emphasize the critical role of spatial analysis in optimizing urban green spaces
and understanding the dynamic relationship between the environment and people. The synthesis of these
findings reinforces the argument that spatial analysis, incorporating diverse factors, is crucial for effective
and sustainable urban planning in densely populated cities like NYC.
The third supporting argument centers on correlation analysis and its role in understanding the
dynamics of stakeholder interests in green infrastructure planning. Prakash et al. (2017) conduct
correlation checks to ensure the robustness of their composite index structure, while Meerow (2020)
analyzes correlations between different criteria and the number of planned and implemented projects.
This collective evidence underscores the argument that correlation analysis provides valuable insights into
the relationship between planning priorities, stakeholder preferences, and the practical implementation of
green infrastructure projects. The synthesis of these studies contributes to a comprehensive understanding
of how correlation analysis is instrumental in guiding sustainable development decisions in densely
populated urban areas.
As a result, the studies conducted by Wang et al. (2020), Meerow (2020), and Prakash et al.
(2017) together add to our knowledge of sustainable development in urban areas that are highly
populated, particularly in New York City. In order to evaluate and compare sustainability initiatives across
cities, systematic approaches are crucial, as shown by Prakash et al.’s methodical data collection and
normalization procedures, which provide a strong basis for insightful urban sustainability research. As Wang et al. (2020) and Meerow (2020) have shown, spatial analysis is an indispensable tool for maximizing urban green spaces and deciphering the complex interactions between people and the environment. The studies also highlight how crucial correlation analysis is for informing decisions about sustainable development. While Meerow (2020) investigates correlations between different criteria and the implementation of green infrastructure projects, Prakash et al. (2017) perform correlation checks to guarantee the reliability of their composite index structure. This focus on correlation analysis offers insightful information about the intricate relationships between planning priorities, stakeholder interests, and the actual implementation of sustainable projects.
In conclusion, these studies collectively address the original problem statement about the crucial point at which sustainability in American cities is concerned, especially with reference to New York City. Presently, researchers, policymakers, and urban planners can all benefit from the findings and methodologies that add to the ongoing conversation about sustainable development. As cities across the globe struggle with issues of resource scarcity, population density, and environmental degradation, the knowledge derived from these studies is essential to building a more resilient and sustainable urban future.
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