Value Engineering Cities

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Value Engineering Cities

BY SCOT MACIVER

B.A., University of Colorado, 2010

THESIS

Submitted as partial fulfillment of the requirements for the degree of Master of Science in Civil Engineering

in the Graduate College of the University of Illinois at Chicago, 2018

Chicago, Illinois

Defense Committee:

Sybil Derrible, Chair and Advisor Thomas Theis

Amid Khodadoust

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ACKNOWLEDGEMENT

I would first like to thank the University of Illinois at Chicago for giving me the

opportunity to pursue a graduate degree in the field of engineering. It has been dream of mine to become an engineer, and I am forever grateful. Secondly, a special thanks to my committee members, Professor Thomas Theis, and Professor Amid Khodadoust. They both provided me with valuable experiences that I will take with me for the rest of my career. Lastly, a special thanks to my advisor, Professor Sybil Derrible. I would not be completing this thesis, or have even started it, without his inspiration and belief in my ideas. I came into graduate school to become an engineer of some sort, but it was through Professor Derrible’s course ‘Cities and Sustainability’ that I discovered what kind of engineer I wanted to be. I cannot thank him enough for the profound effect he has had on me.

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TABLE OF CONTENTS

ACKNOWLEDGEMENT………... ii

LIST OF FIGURES………. v

LIST OF TABLES………... vi

Abstract………....…. vii

1

Introduction……….. 1

2

Background……….. 5

2.1 State of American Infrastructure………... 5

2.2 Value Engineering……….. 6

3

Methods……….11

3.1 City Selection………... 11

3.2 Scoring and Ranking……….. 13

3.3 Validation……… 16

4

Results………... 18

4.1 VE Analysis………. 24

4.1.1 Individual Score: Performance (Infrastructure) ……….. 26

4.1.2 Individual Score: Acceptance (Emissions) ………... 27

4.1.3 Individual Score: Cost (Tax Burden) ………28

4.1.4 Sensitivity Analysis……… 28

5

Conclusion………. 31

5.1 Future Work……….……... 33

6

References………. 35

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7

Appendix……….. 36

8

Vita……….40 - End of Table of Contents -

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LIST OF FIGURES

Figure 1: Value Engineering Flow Chart……….. 8

Figure 2: City Distribution Map……… 11

Figure 3: 2016 Population Estimate………..12

Figure 4: 2016 City size (sqmi)………. 13

Figure 5: ASCE State grades vs Infrastructure scores……….. 18

Figure 6: Infrastructure Radar Graphs and Legend………. 19

Figure 7: MT CO2e per capita………... 22

Figure 8a: Debt 2016……….23

Figure 8a: Debt 2016……….24

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LIST OF TABLES

Table 2: ASCE Infrastructure Categories………..5

Table 2: Scoring breakdown……….. 15

Table 3: City Rankings………...25

Table 4: Infrastructure Scores………... 26

Table 5: CO2e per capita Scores………... 27

Table 6: Tax Burden Scores………... 28

Table 7: VE Sensitivity Analysis……… 30

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Abstract: Cities are large, complex systems that face several important issues. Some of these issues include aging infrastructure, population growth, resource depletion, energy generation, waste management, climate change, and transportation efficiencies. This study focuses on city infrastructure, and how cities compare across the United States (US) in terms of urban infrastructure, while assessing their greenhouse gas emissions and budgets. The main objective of this study is to adapt value engineering principles to investigate and evaluate the current state of US urban infrastructure in 20 cities based on their infrastructure assets, and then to factor in their debt and CO2e emissions to create an overall score for comparison through value engineering (VE). This research notably extends the value engineering methodology to processes and systems, and more importantly to complex ones like cities. The results showed the highest populated cities may offer the greatest boost in infrastructure, although it appears to come at a high cost. We find that the optimal zone appears to be the mid-to-large size cities, with a population around 700,000 people, with further investigation required. The results show that as cities grow in population their debts do as well but not linearly. Large city infrastructure is heavily used; thus, it costs more to maintain and replace, leading to potentially cascading future costs, which could explain their soaring debts. Nonetheless, as cities grow in population size they also tend to become denser, and therefore reduce their CO2e emissions per capita, although this reduction does not compensate for the trade-off in their accumulated debt. As cities will continue to grow in the coming years, this study highlights the importance for cities to correctly manage all their assets to be able to support their populations without putting an extra financial burden on them.

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1. Introduction

Cities—especially megacities with populations of ten million or more—are at the forefront of civilization when considering population growth and migration as we progress through this century and beyond. Worldwide, more individuals inhabit cities than rural areas, and that proportion is expected to increase to almost two-thirds by 2050; a vast difference from the three percent that lived in urban areas in 1800 (WPH 2016). Since 1800, the worldwide population has grown from roughly one billion to over seven billion, with the most aggressive growth occurring in the past seventy years where the population grew by a billion roughly every thirteen years (OWD 2015). At the turn of the 19^th century, the United States (US) had two of the five largest cities in the world, New York and Chicago (ThoughtCo 2017). Since then, cities around the world have grown at extraordinary rates, especially in Asia (WPH 2016), with current projections also seeing huge growth occurring throughout Africa (WPH 2016). Currently, the US has zero of the top twenty-five most populated cities in the world, with New York falling to twenty-eighth and Chicago falling outside of the top one-hundred (CityMayors 2018). Los Angeles has become the second most populated city in the US, but only ranks seventy-first worldwide (CityMayors 2018). Technically, the US currently does not have any megacities, it is only once you consider neighboring communities that numerous metropolis’ in America have populations over ten million. However, the US is home to thousands of cities with fewer than 500,000 residents (Statista 2018), and nearly half of the worldwide urban population reside in cities of similar size or smaller (UN 2014). In 2014, John Wilmoth, the Director of United Nations Department of Economic and Social Affairs, stated the management of urban areas as

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one of the most important developmental challenges of the 21^st century (UN 2014), although this is not the case for American population numbers, and their cities, are not expected to grow at such high rates like Asia and Africa (WPH 2016). Nonetheless, a growth is projected, and it will need to be managed effectively to sustain the quality of life most citizens have come accustomed to in the US.

Worldwide, as cities grow and age, stakeholders attempt to understand the features enabling them to be successful, as well as, the ones that limit their potential. Cities themselves are large, complex systems that face several issues presently, as well as, into the future. Some of these issues are aging infrastructure, population growth/decline, resource depletion, energy generation, climate change, and transportation efficiencies. By providing a home to many people, they tend to require a lot of energy, material, and processes to have their systems operate effectively. These systems can be costly due to their complexity, durability needs, and usage rates. While these infrastructure systems were initially designed to handle their population sizes, many are outdated, or designed for a population size that has since changed significantly, as population growth data has shown. Due to this, cities are constantly playing catch up with minor repairs and fixes.

With migration trends showing a growing movement to city centers, added stress is being applied to every infrastructure system in which a city relies on to operate. Nonetheless, as data shows, in the US there is a general improvement in efficiency within cities because of shorter commutes, smaller living spaces, and overall reduction in carbon dioxide equivalent (CO2e) emissions per capita (Glaeser 2009). So, while there is a large overall amount of energy used, waste generated, and water needed to be treated, there is a silver lining in the reduction per user if the systems can handle the peak loads.

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Currently, cities around the United States are facing enormous challenges when it comes to their infrastructure, whether it is due to their aging components or the difficult task to keep up with the growing demand put on their systems. Contrary to their rural counterparts, cities are associated with larger populations, higher CO2e emissions, larger budgets, and more extensive infrastructure. This infrastructure is what drives cities and enables them to sustain the large populations they hold. From water systems to roads and bridges, these systems need to operate safely and effectively, otherwise their populations will be at risks.

This thesis extends and applies a value engineering methodology to entire urban infrastructure systems. This method will be used in a different manner than traditional value engineering projects. The process will be used to analyze the current state of various cities, rather than using the process to directly access alternative solutions to improve a product’s or service’s value. The results can then provide insights into each city's current state and therefore provide substantial information on how to potentially manage their assets going forward. In addition, this works enables a comparison of cities regardless of their differences in size, density, or any other factor. Each city is then capable of being individually tailored depending on their specific needs based on their input values like infrastructure, population, and other assets. This will allow cities to develop infrastructure investment strategies unique to their needs.

The objective of this study, through the lens of value engineering, is to determine if an optimal population size for a major city in America exists. This will be done by quantifying the quality of infrastructure in each city, while considering their amount of debt and environmental footprint.

This study focuses on infrastructure assets, greenhouse gas emissions, and budgets. This study directly leverages state-level ASCE State Report Cards (ASCE 2017), converts them into

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city infrastructure scores, and then utilizes the respective city debts and CO2e emissions to determine how major US cities rank against one another. As systems and populations grow, their complexity also grows non-linearly, therefore taking an increasing amount of management to maintain balance (Dininni 2017). Due to this, it is hypothesized that the largest of the US cities—

with populations above 1.5 million—will rank towards the bottom, while the smaller cities—

with populations below 750,000—will score the best.

The thesis goes as follows. In the next chapter, I introduce the background of American infrastructure, and provide a breakdown of value engineering. Followed by methods section, that will cover the parts of value engineering that I chose to focus on, and the equations used to derive my results. Lastly, the results section will explain the significance of each score, and how they are compared city by city.

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2. Background

2.1 State of American Infrastructure

The American Society of Civil Engineers (ASCE) is a professional organization that represents the civil engineering field. It is the authoritative provider of guidelines and standards for the profession to follow to ensure safety and increase productivity for the industry and the public.

Since 2001, every four years, the ASCE generates the American Infrastructure Report Card, which grades the state of the country’s infrastructure. There is a score created for the entire country, as well as, a score for each individual state. Table 1 shows the categories the ASCE looks at when compiling its grades.

Table 3: ASCE Infrastructure Categories Aviation (A)

Bridges (B)

Drinking Water (DW) Energy (E)

Rail (Ra) Roads (Ro) Solid Waste (SW) Transit (T)

Wastewater (WW) Waterways* (W)

*Compilation of levees, inland waterways, and dams

In 2017, the ASCE gave the United States a D-plus (ASCE 2017); a score that has only increased from a D over the past ten years (ASCE 2017). Outside of the inaugural year they started scoring infrastructure, the overall grade for the United States has always been in the D range, with the inaugural year, 1988, only being slightly higher at a grade of C (ASCE 2017).

This report can be argued as over-critical and self-serving for the civil engineering field because more drastic scores can lead to more attention, and ultimately more funding, but ASCE does

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provide a valuable service that at least attempts to provide the public with an overview of existing conditions.

The ASCE Report Card grades every infrastructure system from bridges and roads to dams and water treatment. It shows how the entire country needs repairs, with only a few states earning grades above the national average (ASCE 2017). The US Department of Transportation estimates it could cost one trillion dollars to bring the US highway system up to date (US DOT 2017). Furthermore, costs can be compounded when inadequate infrastructure effects pricing in other industries. Inefficiencies can lead to drops in production rates, which can affect businesses of all sorts. Issues like this are common and need to be considered when estimating the cost and effect of aging infrastructures. Moreover, the quality of infrastructure cannot be separated from the monetary value attached to it, both in costs to repair and current operating costs. The ASCE estimates current costs to improve nationwide infrastructure to be over $4.5 trillion over the next ten years (ASCE 2017). Therefore, it is imperative to identify these areas of vulnerability from both an infrastructure and monetary perspective. More importantly, it is necessary to determine where the biggest assets are on a city level, instead of looking at issues nationally or statewide as it is often done.

2.2 Value Engineering

Value engineering, VE, is a method used to evaluate and improve the ‘value’ of a product, service, or process. It is an approach that utilizes scores and weights to analyze measurables and quantify non-measurables. VE is an analytical method used to access the function and cost of a process or item (Kasi 2009). It is a comparison analysis that utilizes an original, or baseline idea, and compares it with proposed alterations of the same idea. Functions

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are at the core of value engineering. They are elegant two-word phrases, specifically an active verb and descriptive noun, that explain a function. For example, a city may propose to add lanes to an existing highway to improve the flow of traffic through a narrow corridor; an obvious function for that project could be ‘improve traffic flow.’ However, looking deeper, one may find that ‘increase commuter flow’ or ‘decrease commute time’ may be a more accurate function, thus, leading to a reevaluation of the project where a rail line may be suggested instead.

Functions can be broken down further into two subcategories, performance functions and acceptance functions, while cost is directly related to any monetary price associated with a function. These values are then weighted and compared. Since entries are weighted, users, and/or owners, control how much influence each function or cost carries. This process is used to quantify and methodically identify the best ‘value’ idea.

The VE process is based around function and cost, and on finding the best intersection of the two. Value is generated by either improving function and/or decreasing cost. Furthermore, function can be broken down into many different variables, with those variables ranging from measurables, like flow rates and weight, to non-measurables, like happiness, ease of use, and preference. Value engineering enables systems and processes to become more affordable while maintaining, or exceeding, previous performance ratings. When utilized correctly, it can merge engineering and psychology together to create a solution that enhances quality of life.

The traditional VE process utilizes many phases to gather information, generate scores, and provide analysis. Figure 1 provides an overview of the steps taken to complete a full VE project. First, there is the Information Phase, where stakeholders determine the needs, constraints, and desires for their project. This is where direction and future ‘value’ is determined for a project. Stakeholders will collectively decide what is important and needed, and what can

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be excluded. Stakeholders come in different shapes and sizes, all with different levels of influence and power, and can be broken down into three categories; users, owners, and others.

For cities, the users are the individuals that inhabit and occupy the area throughout each day.

They are the ones who literally use all the infrastructure created. The owners are the groups, or individuals, that own, manage, and/or fund the project. Cities are unique in this regard from other standard VE projects in terms of funding because of taxes. The entire US taxpayer population contributes to funds allocated for infrastructure development and improvement. Furthermore, city registered taxpayers contribute more to their respective city’s development fund.

Management of these city projects are then controlled by city governments and smaller

subsidiaries that help design

Figure 1: Value Engineering Flow Chart

and engineer solutions. The others in VE projects are the individuals and groups that are indirectly affected, positively or negatively. For cities, this could be individuals from neighboring communities that live downstream from dammed up rivers for power generation, to communities that get passed by due to mega highways for commuting, or populations that pay

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taxes for services they do not directly benefit from. Collectively, these stakeholders share the responsibility to determine the focus of the project. For this project, the stakeholders were primarily the ASCE and their respective scores for the cities represented in the study. For a deeper analysis, a look into appointed government heads would provide more insight on the values and direction of each individual city, and the nation as a whole.

The next step is the Speculation Phase and Evaluation (Screening) Phase where ideas and suggestions for improvement are created and screened. First, ideas that range from conservative to outside-the-box are generated. They are then evaluated and screened based on their alignment to the focus of the project laid out in the Information Phase. This step was in large part omitted from the study because of lack of resources and focus for this project.

The last step, and focus of this project, is the Evaluation (Ranking) Phase and Development Phase. The evaluation component is an analysis of three factors; performance (function), acceptance (function), and cost. Each factor receives a score ranging from (0-5), with 5 being the optimal score. Cost value is inverted, so high cost results in low score and vice versa.

Therefore, all designs are compared with their final score being an average of the three factors, again ranging from (0-5), and highest score representing the highest ‘value’ design. However, like mentioned above, stakeholders can value factors differently. For example, a project that has a strict budget may want to count the cost factor twice or more, which would drive down the overall score for projects with a high cost (low score). This is where the ‘value’ is derived in each idea being compared in the VE study. The Evaluation (Ranking) Phase compiles the different scores into a Sensitivity Analysis (SA), and then the Development Phase uses these combined scores to determine the best option, or best compilation of options. The SA is a key component in an VE project, and a focal point for drawing comparisons. For this project, we are

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simply using the three factors from our study (infrastructure, emissions, and debt) to run our SA.

A sensitivity analysis simply allows a researcher to set the importance of each factor; which is set by the inputs of stakeholders in standard VE projects. In addition, multiple importance levels are compared to allow stakeholders to see different points of view. For example, this project has three factors, and when evenly divided they account for 33.33%. However, during the Information Phase we could have discovered that many people valued these factors differently.

We then create multiple analyses that weight the factors. One analysis may view the first factor as insignificant, so the second and third factor become twice as important. Stakeholders views are not this simple, and rarely result in nice round numbers. However, for this project, the importance of each factor is kept simple.

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3. Methods

3.1 City Selection

The focus of the study is to analyze US urban infrastructure with respect to their performance, CO2e emissions, and debt. Cities are then compared by their population sizes, with consideration of their city size and densities. A VE method is used for analysis to determine the city with the highest overall ‘value’ and highest ‘value’ when weighting each category—

infrastructure, CO2e, and debt.

Twenty cities were chosen that ranged in city size, population size, city density, and geographic location. Figure 2 shows the geographic locations of the twenty cities selected. All geographic locations could not be represented equally due to data limitations. Multiple cities were selected from Texas and California because of their large state population and concentration of large cities with varying land sizes, emissions, and debts. All cities above one

Atlanta

Austin Houston

*

San Antonio *

*

**

*

* Nashville

Philadelphia New York

Washington DC Baltimore Chicago

Detroit

New Orleans Dallas

Denver

Las Vegas Los Angeles

San Diego San Francisco

Portland Seattle

City Distribution Map

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million were included except for Phoenix, AZ because there was not data available on CO2e emissions. Population sizes ranged from roughly half a million to nine million people, with over half the city population sizes falling below one million as seen in Figure 3.

Figure 3: 2016 Population Estimate

Population was the most available data for each city. The State Data Lab (ACS 2018) provided all the data for each city, which is directly pulled from the US Census Bureau. Initially, 2010 population data from the census was used because it provided the most accurate records.

However, 2010 population data did not accurately reflect the growth some cities had over the recent years, and therefore 2016 populations estimates from the census were used (ACS 2018).

For additional understanding, city size data was pulled from the 2016 US Gazetteer files (USG 2016), which provides land area data. City growth from 2010-2016, and 2016 population densities were then derived from a combination of these datasets. Figure 4 shows the breakdown in city sizes that ranged from roughly fifty square miles to nearly five hundred square miles.

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Figure 4: 2016 City size (sqmi)

3.2 Scoring and Ranking

Infrastructure assets were the focal point of this study, and a lot of attention was directed towards trying to convert state’s ASCE Infrastructure Report Card grades into city infrastructure scores. Since there are no standardized city scores, a formula had to be introduced based off the standardized state grades. Equation 1 uses the ratio of city population, PC, to state population, PS, to create an infrastructure ratio, IR. A city makes up a portion of a state’s population, and that proportion acts as an amplifier for infrastructure scores since higher populations will require more infrastructure.

𝐼𝑅 =^𝑃_𝑃^𝐶

𝑆 ( 1 )

CO2e emissions were the most difficult data to acquire. The Carbon Disclosure Project (CDP) has an open data portal of greenhouse gas (GHG) emissions for over 500 cities worldwide. However, US cities only made up roughly five percent of the cities from the CDP.

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The data collected for each city ranged from year 2010 to 2015 with various measurement methodologies used (CDP 2016). Therefore, measured data for each city came from varying years. The CDP data portal was chosen because it was the only source found that had at least ten cities from the US on the same database. Estimated CO2e emissions, CC, from each city were then divided by their respective populations, PC, to provide a per capita value shown in Equation 2. Since CO2e data ranged from 2010 to 2015, more population data was acquired from the State Data Lab for each city’s needed year (ACS 2018).

𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎 𝐶𝑂₂𝑒 = ^𝐶_𝑃^𝐶

𝐶 ( 2 )

City debt data was also acquired through the State Data Lab (ACS 2018), as well as taxpayer populations for each city. Initially, overall debt was chosen as the parameter to measure the impact it had on each city, but this approach weighed too heavily on the large cities in the study. Like CO2e emissions, the large populations have a profound effect when calculating per capita results. Therefore, tax burden, the amount of debt held by a city, DC, shared evenly by all taxpayers of that given city, TC, was chosen as the value used as seen in Equation 3. The number of taxpayers and population sizes differ because not all citizens work. This occurs for multiple reasons, but the data shows that roughly only a third of city populations have registered taxpayers. This number fluctuates city to city and provides a deeper understanding of a city’s assets.

𝑇𝑎𝑥 𝐵𝑢𝑟𝑑𝑒𝑛 =^𝐷_𝑇^𝐶

𝐶 ( 3 )

For the VE analysis, it was critical to normalize the three different factors to one another so they could be averaged together and compared city by city. As is common in VE, this was

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done by converting each factor into a graded scale, zero being the worst and five being the best.

The five-point scale was selected because the ASCE Report Card grades were already based off it. This scaling is the backbone of the SA within the study.

The infrastructure score (IS) was created by leveraging the ASCE State Report Card grades by their infrastructure ratio, IR, like mentioned above. This formula was used because densely populated areas require more intricate infrastructure to meet the needs of their population. Therefore, the larger the ratio, the bigger the increase in score from state to city. For example, New York City saw nearly a 50% increase in their score because their city population makes up almost half of the state population.

𝐼𝑆 = 𝐴𝑆𝐶𝐸 𝑆𝑡𝑎𝑡𝑒 𝑔𝑟𝑎𝑑𝑒 ∗ (1 + 𝐼𝑅) ( 4 )

The CO2e emissions per capita graded scale was created by using a value of 0MT CO2e per capita as the best score, a five, and a value of 100MT CO2e as the worst score, a zero. Tax burden followed the same strategy. A value of $0 owed per taxpayer was the best score, a five again, and a value of $100,000 per taxpayer was the worst score, a zero. Table 2 shows this breakdown, as well as the years considered for each category. These values provide the basis for the Sensitivity Analysis.

Table 2: Scoring breakdown

Data Population ASCE Report

Cards CO2e Emissions Tax Burden Year(s) 2016 2010-2018 2010-2015 2016

Grading N/A 0 to 5 0 to 5 0 to 5 Scale N/A 5=A

0=F

5=0MT per capita 0=100MT per capita

5=$0 tax burden 0=$100,000 tax burden

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In theory, CO2e and tax burden scores could exceed five if they had negative values, by capturing more CO2e than they emit or by having a surplus of funds instead of debt citywide.

These three values were then applied to the three categories in VE: performance, acceptance, and cost. Tax burden fell into the cost category, and infrastructure assets and CO2e per capita fell into the performance and acceptance categories, respectively. In a standard VE analysis, performance and acceptance have multiple criteria that make up each category, but for this study it was kept simple with each only having one. In general, performance components relate more to measurable factors like size, speed, and rates, and acceptance components are related to subjective criteria such as perspective, social approval, and preference. However, since there were only two factors, CO2e was placed in the acceptance category, even though technically it could be performance.

3.3 Validation

The full VE approach was not chosen because it was an unattainable task for all the cities in the study. Instead, only the VE analysis portion was used, specifically the Sensitivity Analysis (SA), that compares the three categories with different weighted values. The SA is an important component because it allows different metrics to be normalized and compared side-by-side.

There are multiple ways to normalize scores for comparison. For this study, everything was based on a scale from zero to five. As mentioned before, the SA was kept simple for this project and only four overall values were generated for each city: one that weights all categories the same (Equation 5), one that weights the performance categories double (Equation 6), one that

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weights the acceptance category double (Equation 7), and one that weights the cost category double (Equation 8).

𝑉₁ =^{𝑃+𝐴+𝐶}₃ ( 5 )

𝑉₂ = ^{𝑃+𝑃+𝐴+𝐶}₄ ( 6 )

𝑉₃ = ^{𝑃+𝐴+𝐴+𝐶}₄ ( 7 )

𝑉₄ = ^{𝑃+𝐴+𝐶+𝐶}₄ ( 8 )

It is through this SA that the value of each city and their components were truly understood. A city that values one facet over another can use the score that most aligns with their goals or strategy.

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4. Results

Data was collected around six main parameters: population, city size, density, infrastructure, CO2e per capita, and tax burden. As mentioned before, the last three parameters were derived from formulas, while the first three parameters came from data acquired from the US Census Bureau and State Data Lab.

City infrastructure scores were the focus of this study and leveraged from their respective ASCE State grades by their city-state population ratio. Figure 5 shows the difference in the two scores. Washington DC is the only city that did not change because it was given an ASCE grade to begin with. New York, Las Vegas, and Chicago show the largest increase in their score because they have the three largest city-state population ratios. However, Chicago still only ranks seventh overall for infrastructure score. Austin and San Francisco saw the smallest bump in their score. This was because both cities are relatively small in population in relation to California and Texas, the two highest populated states in the US. San Francisco fell to eighth ranked city, while Austin fell all the way to nineteenth for infrastructure.

Figure 5: ASCE State grades vs Infrastructure scores

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Figure 6a shows the graph legend and breaks down the different categories represented by the ASCE. Each graph shows how a city scores in terms of their ASCE State grade, and their city score. It also shows the values each city has for tax burden, CO2e emissions per person, and their city-state population proportion. These values are all color coordinated to easily show good and bad values; green representing a score in the top ten percent, and red representing the bottom ten percent.

Figure 6a: Infrastructure Radar Graphs Legend

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Figure 6b shows all twenty cities, and their respective graphs, which are arranged in decreasing order by city scores. New York City, Las Vegas, and Los Angeles were the top scoring cities. For New York City, the high score is due to the large city-to-state population ratio.

The city makes up over forty percent of the entire state’s population, which supplies a major boost to their city score. Las Vegas and Los Angeles benefit from their population ratio, too, but their greatest asset is a great starting ASCE State Report Card grade. Nevada and California are two of the top performing states on the Report Card. New Orleans has an average population ratio, at roughly ten percent, but Louisiana is the worst performing state from the ASCE Report Card. Thus, their city score is the worst of all cities studied. Solid Waste (SW) and Aviation (A) were the two best scoring ASCE categories across the board, while Roads (R) were the worst average scoring category.

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Figure 6b: Infrastructure Radar Graphs

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CO2e emissions results vary significantly from overall citywide emissions to emissions per capita. This follows the logic that dense, highly populated areas emit the most GHG, but significantly reduce their per capita usage. Figure 7 shows this reduction as New York ranks best in per capita usage even though they produce the most emissions. Four of the seven densest cities saw the greatest change from their citywide emissions to their per capita, with New York jumping from twentieth all the way to first. Seattle, DC, and San Francisco, the other three densest cities, were already highly ranked because of their small city size so they could not improve their ranking too much. Las Vegas is the outlier of the group because it has more than double the per capita emissions of the next worst city. It is the fourth lowest populated city, but emits the similar amounts of emissions citywide as Los Angeles does, the second highest populated city.

Figure 7: MT CO2e per capita

Tax burden did not have the same significant shift as CO2e emissions. Los Angeles, Houston, and San Diego—three of seven most populated cities—saw the greatest positive

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change in their rank. However, three other cities in the top seven—Philadelphia, New York, and Chicago—saw no change in their score. This is due to all three cities having significantly larger overall debt than the rest of the cities. Figure 8a shows Philadelphia, the third worst with regards to debt, having almost double the debt of the next worst city, Los Angeles, but with less than a third of the population. Chicago, the second worst city with debt, has more than double that of Philadelphia. But New York is in a league of its own. They have more debt than all the other nineteen cities combined, plus eighty billion dollars. New York does carry the largest population by a lot, but their overall debt keeps them as the highest tax burden for their taxpayers by almost

$20,000, more than most cities tax burden altogether, as seen in Figure 8b.

Figure 8a: Amount of total recorded debt at the end of 2016

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Figure 8b: Amount of burden split evenly by a city’s taxpaying population 4.1 VE Analysis

For each city, the results of the study were broken down into their four values (V1, V2, V3, V4) from the SA, and their individual scores from each of the categories, totaling seven scores for comparison. Table 3, below, provides an overall breakdown of each parameter from this study.

Each city is simply ranked from first to twentieth because it is easier to see the differences when rescaled. For example, New York has twice the population of Los Angeles, over four million people, but only scores one rank better. While Seattle scores the same rank better than Denver, and only has twenty thousand more people. Regardless, this table provides a good overview of how each city compares to one another.

$70,000

$50,000

$30,000

$10,000 New York City, NY

Chicago, IL Philadelphia, PA San Francisco, CA Dallas, TX Portland, OR New Orleans, LA Nashville, TN Houston, TX Atlanta, GA Baltimore, MD Detroit, MI Los Angeles, CA Seattle, WA San Diego, CA Denver, CO Austin, TX San Antonio, TX Washington, DC Las Vegas, NV

Tax Burden 2016

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Table 3: City Rankings

Atlanta,GA19197161616258181510 Austin,TX910171818111912171817 Baltimore,MD18151918877116101011 Chicago,IL3318933107191222 Dallas,TX888514151117141465 Denver,CO1213311125697131716 Detroit,MI1414201291313189151312 Houston,TX449115121114181659 LasVegas,NV171812131123217202020 LosAngeles,CA221536813163413 Nashville,TN151711220944101998 NewOrleans,LA20205101911142019127 NewYorkCity,NY111681112120111 Philadelphia,PA551714461515151133 Portland,OR161610151049104786 SanAntonio,TX6764171411161361618 SanDiego,CA761461317161151115 SanFrancisco,CA1091320219183274 Seattle,WA1112217710812241414 Washington,DC13114195N/A513581919 1=Highest1=Highest1=Highest1=Largest1=Highest1=Largest1=Highest1=Highest1=Lowest1=Lowest1=Highest1=Highest *ColorschemeflippedforDebtandTaxBurden Debt2016* CityPopulation 2016Taxpayers 2016Pop.Growth 2010-2016Citysize 2016CityDensity 2016City/State Ratio2016ASCEState ScoreInfrastructure ScoreCO2e EmissionsCO2e/personTaxBurden 2016*

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4.1.1 Individual Score: Performance (Infrastructure)

There was no clear pattern for infrastructure in terms of population size, as seen in Table 3, below, but five of the nine most populated cities occupied five of the seven lowest scores. This is partly due to the huge bump in score cities like New York City, Los Angeles, and Chicago get from having multiple millions of people. However, Houston is also one of those cities and only placed fourteenth. The results also showed that the smallest cities, by city size, consistently fell in the middle of the pack for infrastructure scores (Table 4), showing that the larger a city becomes the more unstable the infrastructure outlook possibly becomes. City density showed favoritism towards denser cities, with outliers of Nashville and Atlanta placing in the top five.

Table 4: Infrastructure Scores

New York City, NY 1 8 1 1

Las Vegas, NV 17 13 11 2

Los Angeles, CA 2 3 6 3

Nashville, TN 15 2 20 4

Atlanta, GA 19 16 16 5

San Diego, CA 7 6 13 6

Chicago, IL 3 9 3 7

San Francisco, CA 10 20 2 8

Denver, CO 12 11 12 9

Portland, OR 16 15 10 10

Baltimore, MD 18 18 8 11

Seattle, WA 11 17 7 12

Washington, DC 13 19 5 13

Houston, TX 4 1 15 14

Philadelphia, PA 5 14 4 15

San Antonio, TX 6 4 17 16

Dallas, TX 8 5 14 17

Detroit, MI 14 12 9 18

Austin, TX 9 7 18 19

New Orleans, LA 20 10 19 20

Infrastructure Score City ^Population²⁰¹⁶ City size 2016 City Density

2016

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4.1.2 Individual Score: Acceptance (Emissions)

For CO2e, Table 5 shows a trend towards the higher populated cities scoring best, but more importantly, denser cities. So, places like Seattle and San Francisco with relatively large populations and small land sizes score near the top of the list. It can also be seen that the order for city size is flipped, with the smaller cities ranking well. City density is essentially the ratio of population to city size, and therefore its column in Table 5 closely matches the overall CO2e score, proving a high correlation between population density and CO2e per capita. This follows the logic that dense areas perform more efficiently than less dense areas.

Table 5: CO2e per capita Scores

Chicago, IL 3 9 3 12

Denver, CO 12 11 12 13

Dallas, TX 8 5 14 14

Atlanta, GA 19 16 16 18

Population

2016 City size 2016 City Density

2016 CO2e/person City

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4.1.3 Individual Score: Cost (Tax Burden)

For cost, Table 6 shows that population and density play a major role in tax burden.

However, unlike CO2e scores, where they mirrored density rankings, tax burden scores are best for cities in the middle of the pack in terms of density and population size. The four densest cities scored the worst, and four of the five most populated cities scored in the bottom half.

Table 6: Tax Burden Scores

4.1.4 VE Sensitivity Analysis

The scores for the Sensitivity Analysis are derived from the equations and scaling system cited in the methods section. Table 7, below, shows the results for each city calculated by

Denver, CO 12 11 12 16

Atlanta, GA 19 16 16 10

Dallas, TX 8 5 14 5

Chicago, IL 3 9 3 2

Tax Burden City ^Population2016 City size 2016 City Density 2016

2016

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Equations 5-8 (Grey: V1, Orange: V2, Blue: V3, Green: V4). The results showed three of the five highest populated cities finish in the bottom four of each analysis, these were Chicago, Philadelphia, and New York City. Los Angeles and San Diego were consistently first and second, respectively. Seattle, Washington DC, and Denver rounded out the top five for each analysis. The five smallest populations mainly fell between 7^th and 15^th. New Orleans, the lowest populated city, never finished higher that 14^th, while New York, the highest populated city, never finished higher than 17^th.

The results of the study partially disproved the hypothesis. While most of the highly- populated cities did score the worst, there were outliers (Los Angeles, San Diego and San Antonio) that proved highly-populated cities can excel if managed well. Excluding the outliers, the midsize cities did consistently show a better average scoring than the cities with populations exceeding one million in the United States.