The club labeled “iron” is my current iron, and “driver” is my current driver. All other clubs are the fitter trying out different variations of shafts or club heads. Some of the names don’t make much sense to me, but are probably variations of shaft types or club head brands. Also worth noting is that all clubs are a 7 iron (except the club labeled driver).

The end result, I ended up going with Zxi5 (Srixon). Some of the data on Zxi5 below is pretty sparse (n=2). So don’t read too much into it. However, there was a second day of fitting that generally confirms the data below (or near it) perhaps I’ll post as a followup.

Summary Table

Best Overall Chart

Club Comparison Box Plot

Scatter Plots

Today’s post is to share about what I’m doing for Peoria Aggregate pricing with Homefield Energy for the period of 2025-2026.

The Short version:

For a recap of last year’s decision.

• I opted out. I may have spent $60 more than I would have it I didn’t opt out.

For Next Years’s Decision.

• I will probably opt out again. It’s a gamble though.

• Primary rationale -n HomeField Energy’s increases by 40% in June 2026 to May 2027, a much steep increase than Harbor Energy’s last period.

The Longer Version.

Here’s a chart explaining last year’s decision:

Analyzing Last Year’s decision. Was it a good decision?

I chose to opt out and go with Ameren (blue line).

My rates were neutral to Harbor for the first 6 months. (the Orange Line is on top of Blue line). The second 7 months (or so) my choice to “Opt out” and go with Ameren paid off (Blue line Ameren is below Harbor Energy’s). However for the last 3 summer months Ameren’s rate shot up per kWh. We used quite a bit more those moths..so it could be possible that those 3 months off set any gains from previous months…but I’m gonna take the simple view that more months under is pretty good.

For the future 2025-2026, Opt out or not?

I’m going to Opt out. Though much like last years post its a roll of the die. A reminder that my decision last year to opt out cost me about $60 or so for the entire 18 months.

This years bet is a little different in my opinion because From June 2026 to June 2027 it’s a much larger jump on Homefield Energy (vs Harbor Energy last year). The rate increases approximately 40% more in June 2026 to June 2027.

The center argument is that Ameren Rates are already at $11-$12, it’s foreseeable then could continue higher. But I see the recent rates due to excessively hot summer. And I like to gamble so i’ll take the variation in price.

However, It would be fine to stay in the agreement. Your rates will be a clearly better deal for the next 6 months..but might go up a lot in June 2026-May 2027.

Best of Luck!

Here’s a link to last year’s post:

Recently I watched Inside Out 2 with my family.  It’s safe to say I’m a fan.  My entire crew loved the movie, especially my three daughters ages 10, 8, & 4.   I’m not ashamed to admit that it got me (and my wife) a little teary eyed.  Perhaps the reason for the connection that many had to this movie is because it’s just so relatable.  Everyone experiences the chaos of various emotions “at the controls”. The movie allows us to to reflect on similar times we all have been in similar emotional experiences to the ones Riley goes through.   

The movie is also extremely valuable to parents of small children.  I know speaking from personal experience I’ve already used the movie multiple times already.  I’ll ask my kids, “Okay, who's at the controls right now?”  Their answer, “the purple guy” (or some other character).   I think it helps them identify and label their own emotions, which is just so useful on so many levels. 

As I started thinking about the movie, I started with a simple question. “I wonder what’s in store for a sequel (i.e. Inside Out III)?   What emotions would they use?”  

As I dug into the psychology research I realized just how deep the rabbit hole goes.  As I read the research as soon as one question was answered, three more were created.   But first, let me first give you an answer to my initial question,  “What are the potential emotions they might use in Inside Out 3?”  The answer,  based on scientific research, would be:  

• Surprise - Experienced when something unexpected happens. It can interrupt current activity, triggering heightened awareness and attention to the new information.

• Anticipation - The emotion of looking forward to a future event, which can be positive or negative. It plays a crucial role in motivation and preparation for what is coming.

• Trust - An emotion that involves the belief in the reliability, truth, ability, or strength of someone or something. Trust is fundamental to social interactions and relationships.

• Contempt - A feeling that a person or a matter is beneath consideration or deserving of scorn. Contempt often involves a mixture of disgust and anger. [This last emotion has differing opinions as to whether it is a ‘core emotion’] 

How did I arrive at these answers?   Well, of course, I used the basic search engines: Google, and yes ChatGPT.  I asked about the top research theories in emotions and I first came across these 4 points of views.  A warning, I’m no scientist; so I may be leaving out some key theories, but these were the ones that the internet seemed to raise to the highest ranks.  

1. Universal “Big Five” - Coined as “Largely agreed upon by researchers.” by the Centre for Clinical Psychology

2. Paul Ekman - A consultant for the movie, heavily influenced the development of the film

3. Robert Plutchik - Developed an “emotional Wheel”

You can see there is considerable overlap on the first five emotions.  However, the next 4 emotions are not agreed upon by all three views. Contempt is an emotion Ekman believes is a primary emotion, while Plutchik would call contempt a combination of Anger and Anticipation. On this chart there are 8 (or 9) total emotions that are considered ‘primary emotions’.  Later we’ll talk about  Brené Brown, who would likely add many more emotions.  My guess is these emotions are not entirely agreed upon by the scientific community.

What is clear is that the creators of Inside Out (the original) were right to focus on the “Big Five” as the core characters for the first movie.  However, what about the characters of Inside Out 2?  If you’ve seen the movie you’ll see that none of these 4 emotions are in the movie. 

Instead, Inside Out II  focused on these emotions.  With definitions from Meriam Webster

1. Anxiety -  an abnormal and overwhelming sense of apprehension and fear often marked by physical signs, doubt concerning the reality and nature of the threat, and by self-doubt about one's capacity to cope with it

2. Envy - painful or resentful awareness of an advantage enjoyed by another joined with a desire to possess the same advantage

3. Embarrassment - A feeling or showing a state of self-conscious confusion and distress or a disturbance of mind

4. Ennui  - a feeling of weariness and dissatisfaction. Boredom. 

It begs the next question, “Why didn’t the writers include these emotions in either the original or the sequel?” 

It’s hard to know what might have been in the minds of the creators of the movies.   It could be that since there is some disagreement about the next 4 (or 5) emotions, that the creators felt they had a bit of leeway to explore lesser known emotion.  It could also be that some of the “core emotions” are perhaps not as widely identified in the public.  I don’t think I would have identified Surprise, Trust, and Anticipation as a core emotion. Or perhaps the creators wanted to focus on emotions relevant to young teens.  Certainly the chosen emotions for the sequel fit with emotions commonly attributed to teens. 

However, according to Ekman and Plutchnik these emotions are not primary emotions, rather they are combinations of the primary emotions. To pull from Plutchnik specifically, here are the various emotions and their components: 

• Anxiety =  Anticipation + Fear

• Envy = Anger + Sadness

• Ennui = Disgust  (in a less intense form)

• Embarrassment = Fear + Sadness 

Ennui (according to Plutchnik) is really a less intense form of disgust.  Here is a visual that helps explain the concept.   Although it’s not explicitly stated below, ennui here is probably best represented as “boredom”.    On the wheel below it’s located in the pink category.

From Plutchnik’s research I learned that really there are multiple emotions that can be attributed to the “disgust” character.  Disgust is a sort of category, and the intensity with which you feel the emotion alters the words we would attribute to them.   It would be interesting to see this play out in the movie.  I could envision the disgust character changing in size or stature according to some meter on the control board. It’s fun to think about.  

You can also see from this emotional wheel that certain emotions live at the intersection of two emotional categories.   For example, love is the combination of Joy and Trust.   Can you imagine how this may have played out in the move?   A new character called trust enters, full of optimism and acceptance to all.   It gets together with its favorite buddy Joy, and together they have the most beautiful child, “Love”.   And at the same time Anger and Disgust get together and have an offspring called “Contempt”.    It’s not hard to imagine a new plot line here.    The possibilities are endless!

But what would it take to make this plot line?  They would need to roll back the emotions from Inside Out 2, and they would need to re-introduce the “core 8” emotions, adding Trust, Anticipation and Surprise.  

Another concept in Plutchik’s research is the idea that some of the emotions operate as opposites of one another.  In the picture above you can see that by examining any emotion, at the opposite side is the emotions counterpart.   This too could have been an interesting dynamic to explore in the movie.  In fact, I think the original Inside Out did just that, with Joy and Sadness in a battle over Riley’s state of mind.  I could imagine similar dynamics in future movies between say, Fear and Anger, or between Disgust and a newly introduced character Trust. 

Of course, in reality it’s too late for Pixar to go down this path, but it’s a fun thought exercise.   I did some basic research on Ekman, who was a consultant on both movies. Here is a link to a presentation he has on the original Inside Out.  It is loaded with some great examples of psychology concepts that got embedded in the movie.  In fact, a potentially future post might be “How many psychology theories are represented in both movies?”  Here are a few from my own observations:

• a) core memories are tinted by emotion

• b) bottling up and suppressing emotions doesn’t work

• c) emotions create beliefs, beliefs make sup the sense of self 

Toward the end of my research I also came across Brené Brown’s research, who is a self described “researcher, storyteller who studies courage, vulnerability, shame, and empathy”.  Perhaps Brown would add these emotions (courage, vulnerability, & shame) as core emotions?  I’m not sure.  I just ordered Brown’s book “Atlas of the Heart”. Perhaps a future post.  Based on what I know of Brown’s work, I think she groups the emotions more by function.   In Atlas of the Heart she discusses 87 different emotions, and groups them in 13 categories of “When we are feeling…”.   Some of these 13 categories seem to map well to the other “groups” from Pultchnik or Ekman. For example hurting (Brown’s term) could be mapped to sadness (Ekman and Pultchnik’s group). But other categories Brown uses like “self-assessing” is harder for me at least to map to an emotion Ekman or Pultchnik might use. What is clear though is with 87 total emotions, Brené Brown wants to equip her readers with a awareness of the various emotions we experience and provide us with an increased awareness of when we are feeling those emotions.

So perhaps there are more emotions than 8, or 9, or 13, or even 87.  What I have learned is that our emotional world is much more complex than I had ever really thought. For me personally, I have found that for as much time as I spend immersed in emotions I don’t know that much about them.  It would behoove me to learn more, if nothing else than to try and recognize, “who is at the control center?” 

My wife and I also decided that we want to equip our children with this emotional awareness. So we purchased an “emotional wheel” (like this one). We have put this wheel up on our refrigerator with the idea that each member of the family will have their own magnet and each day we’ll place the magnet on the wheel we are feeling. This will help us know each other’s emotion state, but also to know our own state. Speaking personally, perhaps once I can become more emotionally self-aware I can then do what Riley did at the end of Inside Out 2, and request Joy to come to the controls more often.   

For more information check out these links and references:

References

My Personal emotion research Organizer

https://docs.google.com/spreadsheets/d/1zid72NHFDy2hPc3KgzSqm3LFoLU9FQHCZraOrvhwU8A/edit?usp=sharing

Basic  Emotions

• Centre for Clinical Psychology

  https://ccp.net.au/basic-emotions/#:~:text=Anger%2C%20Fear%2C%20Sadness%2C%20Disgust,feelings%20in%20these%20five%20domains.

Paul Ekman

• Source: https://www.paulekman.com/projects/inside-out/

• Atlas of Emotions: http://atlasofemotions.org/#actions/

Robert Plutchik

• PyPlutchik: Visualising and comparing emotion-annotated corpora

  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8409663/

• Putting Some Emotion into Your Design – Plutchik’s Wheel of Emotions

  • https://www.interaction-design.org/literature/article/putting-some-emotion-into-your-design-plutchik-s-wheel-of-emotions#:~:text=Trust-,The%20Wheel%20of%20Emotion,%2Dshaped%20model%20of%20emotion%E2%80%9D.

• What Are Basic Emotions?: Emotions such as fear and anger are hardwired.

  • https://www.psychologytoday.com/us/blog/hide-and-seek/201601/what-are-basic-emotions

• Emotional Wheel 

  • https://en.m.wikipedia.org/wiki/File:Plutchik-wheel.svg

Brené Brown

• Brené Brown’s webpage: https://brenebrown.com/hubs/atlas-hub/

  • (See Brown’s HBO Series Atlas of the Heart at the link above)

• Etsy Emotional Wheel - (based on Brené Brown’s research)

• https://www.etsy.com/listing/1485248410/atlas-of-the-heart-feelings-wheel

There are two difference audiences for this post.

1. Teachers in Peoria Public School District 150 - Use this Sheet here.

2. All Education professionals for state of Illinois - Use this Sheet here:

   1. The audience here is for Teachers outside District 150, Administrators, OT / PT providers, or all other category of employee in education.

My aim is to answers t2 critical questions.

1. As anTeacher (or Admin, or OT/PT), what is my estimated salary now and in the future?

2. As a Teacher (or Admin, or OT/PT), what is my likely pension when I retire?

You cannot make changes to the attached Google Sheet, you will need to download a copy to your own Google Drive.

IMPORTANT

Since there are two different calculators, the directions will vary slightly.

• If you are using the “Teachers of PPSD 150 Calculator” read the directions below.

• If you are using the “All Education professionals for state of Illinois”. Skip to the Section at the bottom of this page.

How to Use the Calculator for Teachers of District 150

Below is a Step-By-Step guide for those interested in using the Teacher of PPSD 150 Calculator .

Step 1. Read the Tab “Start Here” (pink Tab at the bottom)

This page will provide a high-level walkthrough of the Google Sheet. The instructions roughly mimic this blog. As you read each tab, progress to the next tab.

Step 2. Go to “Inputs” - Enter all Information in Gray Cells

On the tab labeled “Inputs” (pic below) you will see that the first 5 boxes are specific to your situation (Gray cells). In these cells you should enter your: Age, Your current “Step”, Years you plan to work, and Whether you are “Tier 1 or Tier 2”

The Light Red (Pink) boxes require a little bit of “guess work” or judgement. These numbers are not known, so you may want to play around with these. Perhaps start by leaving them as is on your first go around. Later you might come back and update these cells with various scenarios you see as plausible. Of course there is no way to know on these cells, but the idea is that we can likely know within a range. By updating the range from High end to low end you can play various scenarios out (either best case or worst case). The first Light red cell is “how much will future contract raises be?”. The second question is, “what will inflation be?”. Yes these are VERY unknown and VERY hard to predict. If you aren’t sure simply leave them be. Or again if you’d like to play with various scenarios then read the notes and punch in different numbers (guidelines in the spreadsheet)

Updating these boxes are probably the most difficult. The Goal is to determine your current Lane with a “1” (in Purple) and if you will advance into future Lanes to enter the Age you will advance (in Red).

Step 3 - Review the “Results - Salary” Tab

This Tab will use some math to project both your current and likely future salary expectations. It’s important to know that this info is only as good as the assumption you entered on Step 2. In other words, it assumes you attain the Lanes on the given years you anticipate. It also assume an increase in contract from light Red cells.

Step 4 - Review the “Results - Pension” Tab

At the bottom of this page (in Dark Green) you can see you final Pension calculation

The darker Green number is you Pension Number at Regular Retirement

The Lighter Green number is you Pension Number at Early Retirement

Remember that there is a penalty of 6% for every year you take your retirement early.

For most people, you will enter a “0” in the inputs section under early retirement years. This is because you don’t want to take the 6% penalty (it’s quite a penalty). In which case the numbers will be the same. It is only if you want to examine how much different it would be that the numbers will differ. If you want to see the penalty, then go back to the “inputs” section and enter a number (e.g. 1, 2, 3..) for early retirement years. The number displayed shows the pension if you take the retirement early.

Optional - Review Other Tabs

If you want to see how the sausage is made, go ahead and review the other tabs. However, I would recommend not changing any information on those tabs. I have put some protection on the cells, where it will warn you if you End up making a change. However, if you ignore the errors you CAN make changes. However fair warning…changing those cells will most likely result in breaking the spreadsheet. So only do it if you are really sure.

How to Use the Calculator for Teachers of District 150

Below is a Step-By-Step guide for those interested in using the calculator for Educators for the State of Illinois. This includes: Teachers, Adminstators, OT/PT, and other education professionals working in the State of Illinois. [Many of the steps are the same, except 1-2 extra Steps]

Step 1. Read the Tab “Start Here” (pink Tab at the bottom)

This page will provide a high-level walkthrough of the Google Sheet. The instructions roughly mimic this blog. As you read each tab, progress to the next tab.

Step 2. Go to “Inputs” - Enter all Information in Gray Cells

On the tab labeled “Inputs” the first section is under the subheading “basic info”. In the pic below you will see that the first 5 boxes are specific to your situation (Gray cells). In these cells you should enter your: Age, Your current Salary, Years you plan to work, and Whether you are “Tier 1 or Tier 2” employee.

The Sub-Heading called “Predictions” contains the Light Red (Pink) boxes. These boxes require a little bit of “guess work” or judgement. The are not known, so you may want to play around with these. Perhaps start by leaving them as is on your first go around. Later, you might come back and update these cells with various scenarios you see as plausible. Of course, there is no way to know for sure the exact correct value on these cells, but the idea is that we can likely know within a range. By updating the range from “High-end” value to “Low-end” value you can play out various scenarios (either best case or worst case). The first Light red cell is “how much will future contract raises be?”. The second question is, “what will inflation be?”. Yes these are VERY unknown. If you aren’t sure simply leave them be or read the notes and punch in a range of different numbers (guidelines in the spreadsheet)

Step 3. Go to “Inputs and Calculations By Year” - Enter all Information in Light Red Cells

This is the one step that differs from those using the PPSD 150 Calculator. The reason for this extra step is that I do not know the salary schedule for all the districts across the state of Illinois. So this puts a tad more work on you as the user. I am betting that the salary structure probably has simliarities to PPSD in that you likely get increases based on your education level (called “Lanes” in District 150), and you also probably have the similar increases for years of experiences (called “steps in District 150). However, because I don’t know the exact values you would need to update them manually on the cells below.

To make things easy. The first column of Light Red cells “Increase Based on Education”, should only be altered if you plan on getting more education and getting and increase as a result. You likely won’t get this increase every year. But you will get it the FIRST year you move into a higher level of education. For PPSD 150 this value is 4% for an increase in Educations, However make sure to check what the increase is in your own School District.

For the next Light Red column of Cells “Increase Based on Steps”, this represents the increase you get for each additional year of service. The value show in this pic represent the default values for a Teacher at PPSD 150 starting in their first year. However, your values are likely to be different for your school district. Make sure to update as necessary.

Step 4 - Review the “Results - Salary” Tab

This Tab will use some math to project both your current and likely future salary expectations. It’s important to know that this info is only as good as the assumption you entered on Step 2. In other words, it assumes you attain the Lanes on the given years you anticipate. It also assume an increase in contract from light Red cells.

Step 5 - Review the “Results - Pension” Tab

At the bottom of this page (in Dark Green) you can see you final Pension calculation

The darker Green number is you Pension Number at Regular Retirement

The Lighter Green number is you Pension Number at Early Retirement

Remember that there is a penalty of 6% for every year you take your retirement early.

For most people, you will enter a “0” in the inputs section under early retirement years. This is because you don’t want to take the 6% penalty (it’s quite a penalty). In which case the numbers will be the same. It is only if you want to examine how much different it would be that the numbers will differ. If you want to see the penalty, then go back to the “inputs” section and enter a number (e.g. 1, 2, 3..) for early retirement years. The number displayed shows the pension if you take the retirement early.

Optional - Review Other Tabs

If you want to see how the sausage is made, go ahead and review the other tabs. However, I would recommend not changing any information on those tabs. I have put some protection on the cells, where it will warn you if you End up making a change. However, if you ignore the errors you CAN make changes. However fair warning…changing those cells will most likely result in breaking the spreadsheet. So only do it if you are really sure.

Conclusion

Hopefully this was helpful. Please let me know what you think. I’ve listed some resources and links below to try and cite my work. If you’d like to know more information on the TRS System, you might also find this presentation from Michelle Wolfe helpful (below). Michelle has done a lot of research on the TRS system and other savings tools available to teachers in Illinois. Please take some time to read her presentation below: Michelle Wolfe’s Presentation on TRS, 403b’s, and 457b’s

Trs And Retirement Savings Options

5.86MB ∙ PDF file

Download

Download

Sources

• PUBLIC - Peoria Public School District 150 - Teacher Salary And Pension Calculator v1.6. - This is my own spreadsheet. a view only copy. To Edit, download a copy to your Google Drive and Store as your own version.

• Peoria Public School District Employee Contracts - PPSD 150 multiple employee contracts

• Teacher Salary Schedules - FY23 through FY25 - PPSD 150 teacher specific salary information 2023 to 2025

• SERS Tier 2 Maximum Salary Cap - TRS calculator for determine the Max Salary allowable for Tier 2 employees.

• TL;DR: Financial Literacy for Illinois Teachers’ Retirement System Members: Optimizing Financial Decisions Based on Your TRS Benefits. Purchase on Amazon at this link:

If you’re like me, you had no idea what this meant initially.  I did some research and hopefully I can save you some time. 

Quick Background

This post isn’t meant to cover all the history, rather just to get to a decision.   ( See the links at the bottom for additional background.)   

The short background on this topic is that the City of Peoria negotiated a community price for electrical (aka ‘Community Aggregation’).  This price is stable for the contracted period through Dec 2025.  This means it won’t change (except 1 step in July 2025).  The positive is that it is well known and stable.  The negative is that it MAY or MAY NOT be lower than Ameren’s electrical price.    So it’s a trade off: Stablity or Variabilty?   

So the Decision before you is: 

1. Do nothing at all.  Accepted the Harbor Energy ‘Community Aggregation’ Price and have a stable guaranteed price  

2. Opt out of Harbor Energy Community Aggregation Price AND use Ameren,  but it fluctuates

3. Opt out of Harbor Energy  Community Aggregation Price AND use another provider, and it’s work to shop and signup for new provider

The Data

I research this history, because that informs us on the present.  Here is a chart representing a few important prices of electricity. 

Analysis

• Ameren had a wide range of prices:  a low of 4.4 cents per KWH up to 12. 

• Ameren had especially highprices  2 years ago [ June 2022 to May 2023]  Average price of 11.2 

• Ameren has come down in the last year, [Jun 2023 to May 2023] an average of 8.27

• ComEd used as a reference, has very similar movement to Ameren

• Ameren provided a forecast of prices between 8.4 to 8.6 cents per KWH [dotted green line] Source: this information sheet (City of Peoria)

• Harbor Energy has a guaranteed price of 9.95 rising to 10.04 

• Harbor Energy could rise a bit at the end of 2025, above the 10.04. The charge is TBD.
  

The Key Factors to Weigh

This is a bit of personal preference.  Risk vs reward. Stability vs Fluctuation. 

If you put credence in Ameren’s projection, recent data shows you could probably save money by OPTING OUT.  However, this is a gamble, Ameren could be wrong on their estimates.   Ameren rates were higher between June 2022 and May 2023. 

If you believe the Ameren Forecast about 2024 and 2025, you would save approximately 15% on your electrical bill. Remember that this doesn't apply to your heating bill. My average electrical bill is about $145.  So I’d stand to save $22.75 per month.  Or about $261 per year.  Enough to matter, but perhaps not enough to compensate you for the risk of potential fluctuations.

My Decision / Conclusion

Personally I opted out.  I feel like the data tell me something.  The fact that the recent price per KWH is relatively low vs Harbor is important.  Plus the forecasts of Ameren. Unfortunately, I don’t have much evidence to say how good Ameren’s forecasts are.  So I could be putting too much credence on their forecasts.   However, I’ll take the risk.  

Furthermore, I haven’t shopped other providers (let me know if you have). Although this seems like a lot of work (it probably isn’t) at least it preserves optionality. In other words, I could change providers in the future from Ameren if I choose to (though not to Harbor Energy).

However, there is a good case to stick with Harbor Energy. Especially if you…

• Like stability on your bill and

• Feel there could be spikes in demand on the energy market

A Couple Notes to consider…

1. If you have solar, and use “net metering” you may want to dig more into this. (I’m not a solar customer, so I didn’t research, but I’ve seen comments in other forums that it is relevant)

2. If you go with Harbor Energy, you’ll still receive a Bill from Ameren. Ameren is the electrical distributor and still handles your account. It’s just that Ameren will supply it from Harbor. [You don’t need to setup new accounts or anything with Harbor]

Let me know what you decided.

Source: 

1. Plug-In Illinois - Use as a source for Comparing Prices of Utilities for Ameren and Com-Ed Cost of Electricity 

2. City of Peoria Community Aggregation Informational Sheet

3. WCBU Article on Community Aggregation dated 6/20/2023

4. Ameren Homepage - used to retrieve my personal bills and confirm the electric rates on Plug-In Illinois.

5. Harbor Energy Website: used to research Harbor and ultimately opt-out

Click this link to just jump to the tool and play around with data.

https://jlocke1979.github.io

About this Tool

Awhile back I was very interested in Illinois educational data. My wife and I had just moved back to Central Illinois. Presently (2023) we have 3 girls, although at the time we had just 2. My oldest daughter was turning 5, about to enter Kindergarten. And we’re trying to make a decision on where to live, which inevitably invovles assessments about the school districts. So I set about learning about the various school districts. The first site significant site I came across was the Illinois State Board of Education. They have a great tool called the Illinois State Report Card. I spent hours on it. However, I felt like it was missing one key thing. This was the ability to compare various districts against one another at a glance.

So I dove into the data and started pulling the raw files. v I wrote scripts to summarize the data. I then combined it with geographic schools district boundaries (geojsons). Lastly, I enlisted the help of a web designer Brandon D’Adams who helped me with the deployment and design of the tool site. The end result is this interactive tool.

Warning! This information is from 2016

Yes this post is very dated. As of this writing of this blog posts it’s 2023. However, I feel it’s better to share than not share as was a pretty involved data exploration project and I hate to see it sit all alone in my GitHub repository. Better for it to be shared with the world (even if dated).

Insights

Here’s a few screenshots of the data I found most interesting.

Classroom Size

Some obvious clusters of larger classroom sizes in green around Chicago and St. Louis.

Teacher Salary

Teacher salaries are notably higher (green) in and around Chicago and St. Louis suburbs. Feel free to explore the tool and hover over individual school districts to see more data about a particular district.

Percent Ready for College

This indicates the percentage of studies ready for college. Darker orange means more student prepared, lighter orange means fewer.

Percentage of Student that are Homeless

This map was perhaps the most heartbreaking. I was shocked at many school districts having upwards of 7% of students are unhoused! That means in a school of 200 there could be 7-8 students unhoused (or even 14 in some districts). I can only imagine how difficult it must be for a children to excel in education while without housing.

Correction / Error. The “Legend” here is labels the color legend from 0% to 24% (this is wrong). It should read 0% to 7%.

Operational Expenditure Per Pupil (OEPP)

This metric measures the overall operational expenditure in each district per student. Higher amounts spent per pupil in Green, lower amounts in purple.

An obvious bias of expenditure toward the northern part of the state. I am curious of the correlation between expenditure per student and student performance. To what degree does spending more achieve higher results? Of course, correlation doesn’t mean causation. My sense is there is a lot going on here. I’m sure there are people who spend their careers studying these questions of optimal expenditure. However, my sense is that school district operation expenditure per student is closer related to local funding source (i.e. local property taxes). It makes me wonder if states that rely more on local school district funding have a wider range of student performance. My guess is they would. My thinking is this would be due to the fact that with more local funding there would be certain districts with higher property taxes who are able to contribute more to their local schools, resulting in a “haves and have not” situation. It would be interesting to compare against states like Minnesota and Massachusetts that relay more on State funding sources. I have read articles that this can result in a ‘flatter’ amount of spending per student across the state, which I would think results in less disparate outcomes. Comment if you know of any such research or if you have thoughts of your own.

Conclusion

This project is meant to experiment with mapping tools and to use chrolopleth to visualize data in new ways. Perhaps future posts will …

1. Refresh the data to more recent info (2023)

2. Explore correlations between funding and performance

3. Add time elements to visualize changes over time.

4. Connect education / researches studying the effect of Local vs State funding sources.

Sources

Those interested in exploring the code…

• Here is a link to my GitHub account for the data processing portion of the code

• Here is a link to my GitHub account for the website build (Thanks again Brandon!)

• Primary data comes from Illinois State Board of Education Report Card. https://www.illinoisreportcard.com

Attributions

A special thanks to Brandon D’Adams who did much of the heavy lifting on the website and design elements of the interactive data visualization.

The source of the information is largely the U.S. Environmental Protection Agency, although it’s supplemented with The U.S. Department of Transportation and US Department of Agriculture. For the full code in R along with a PDF of the final representation please see my GitHub account here.

For sake of Definitions sake, Particulate Matter of less then 2.5 micrograms a (aka PM_2.5) is described as fine inhalable particles, with diameters that are generally 2.5 micrometers and smaller.  For more information on PM2.5, what they are and why they are dangerous see this link from the US EPA. The EPA regulates particulate that are inhalable (defined as 10 micrograms or smaller). It does not regulate particles larger particulates of 10 micrograms or larger. Here is a picture from EPA’s website to illustrate the relative size of the particles.

The remaining content is in screenshots from my original powerpoint. Apologies, in advance for the “slide show” format. While not for everyone, hopefully it’s a bit more visual. I suppose I haven’t completely kicked my addiction to slides from my ‘consultant’ days.

Future Blog posts could update this information to more recent data sets and assess the change over time. Data from the EPA at this site indicate an overall downward trend in PM2.5 however it would be interesting to see if the same worst offending US counties are still on the same list. Additionally, wildfires are a large contributor to PM2.5 emissions, they are likely to have increased since 2014 to cause more severe density especially in the west.

Feel free to like and/or comment on interesting things you see in the data.

For the episode “Found in Translation” the portion of the interview focused on Ralph Chami starts at 20:00.

Additionally,

Here are two longer form interview with Ralph Chami at a different podcast.

Some key Ideas I took from Mr. Chami’s podcasts.

• Chami is a financial economist that provides a way to translate the value of natures services (by whales and Elelphants).

• Valuing services is a way to protect and confer value to nature.

• Our neo-classical way of viewing nature assume nature is infinite and has a price of zero, such assumptions create excessive consumption of the good.

• While we can theoretically value whale, elephants, or other animals, it is only the application of laws by governments that can create marketplaces for these animals. After establishing protection for theses animals, then markets begin to pop up.

Organizations where Ralph Chami is active:

• https://www.naturemarkets.net/secretariat/ralph-chami

• https://bluegreenfuture.org

• https://www.rebalance.earth

Scientific Papers

• “On Valuing Nature-Based Solutions to Climate Change: A framework with Application to Elephants and Whales”.

News Articles:

• https://www.washingtonpost.com/science/2019/11/04/living-whales-are-worth-an-enormous-amount-money/

• https://www.imf.org/Publications/fandd/issues/2019/12/natures-solution-to-climate-change-chami

I was particularly interested in projects focused on Mangroves and Seagrasses given their relative newness to the market. There is a lot more projects focused on Terrestrial Forrests, but this was less of my focus as they have been around for many years.. I did throw in the Rimba Ray Projects in Indonesia, which was mostly terrestrial forrest, but also has elements of mangrove conservation in it.

The primary sources on these projects are from either Verra or Plan Vivo. These are carbon verification organization that help to ensure natured based projects are valid and verified. These sites have portals that allow you to read about each project they verified. In a few cases I gather CO2 prices from published news articles. The CO2 prices were the hardest piece of information to source . I did deep research on 36 total projects, however I was only able to fill in all the columns of data on 12 projects.

Summary Data Table of Key Metrics

If you’re inclined to just read data tables here is a snapshot of the high-level metrics.

Scatterplot of the same data

If you’re like me you want to visualize the data table. This scatter plot below shows the same data minus the final column of Annual Financial Value per HA.

Seagrass Insights

It’s probably worth mentioning that there were relatively few Seagrass projects from which to pull. This is likely because the methodologies to measure seagrasses are relatively new. As a result, there are few seagrass projects. The chart above shows the seagrasses (barely). If you squint hard in the upper left corner you’ll see them. The reason they are so small is due to the very small size in hectares. Interestingly, the prices on these two seagrass projects are among the highest I recorded. This could be because the projects are based in Japan. Japan seems to have a relatively well developed market for CO2 credits. While I’m no expert, I would guess that Japan has done a bit more than the average country to create a national market for CO2 credits resulting in overall higher prices.

Mangrove Insights

It’s interesting to see the range of Prices on Mangroves CO2 per ton, anywhere from $3.90 to $27.80. This shows the high variability on each project. This is probably not a surprise to a wetland scientist, but was news to me. The variability is probably due to many factors including the type of projects, the geographies, the species of mangroves, whether it is a restoration or a conservation project, etc.

Rimba Raya Insights

For reference, I threw in the Rimba Raya project. It is a large project in Indonesia that has a lot of positive press (with good reason). It protects key forrest where the endangered Orangutan resides. It is also an extremely biodiverse tropical rain forrest. So probably not comparable to the other mangrove projects. The obvious uniqueness of the project may be the result for the unusual metrics, making it an outlier for multiple reasons. Future posts may need to expand the sourcing on other the terrestrial projects, and do analysis exploring the uniqueness of this project. Based on reading up on Rimba Raya, I would guess the project is unique based on the qualitative aspects (orangutan territory) as well as the size of the project (in hectares), and the the amount of CO2 sequestered per HA. This last variable is likely caused by the fact that the project is considered "avoided deforestation”, which inherently are able to claim larger amounts of CO2. However, the price of the project is likely not an outlier at $8.50.

Value Per Hectacres

Looking at the financial value per Hectacres provides an interesting high level summary of the ‘value’ of the land for CO2 Credits only. Of course this land has value over and above its ability to generate CO2 credits (such values resides in many areas including biodiversity, fishing, erosion protection, etc.). The range of value per HA are from $1 to $182. Of course, Rimba Raya has an outlier value of $620, due to its uniqueness.

Summary and a few Ideas

The primary purpose of this data investigation was to learn about wetlands and mangroves projects. It provides an interestingly large range of prices, roughly between $4 and $28. There also is a wide range of CO2 tons per HA (from .1 to 20). There’s also a wide range sizes, from 10 to 325,000.

As a future idea, I can see expanding this information and building out a software tool that essentially stores and organizes this information. I could imagine this information helping project developers, NGO, and even private investors in analyzing the economics of nature based Carbon Credit projects. To make this idea scalable, I would think that the verification companies like Verra and Plan Vivo would have to make the information more accessible, ideally via APIs, either that or lots of research. As it stands now, the information is buried in PDFs that require extensive reading and searching to find.

Let me know what you think.

Additional Data

A price forecast for Voluntary Carbon Credit Markets

Probably the single biggest takeaway from the report, is a forecast of where the voluntary Carbon Credit markets will be in the future (see page 34 of PDF). The forecasts provides 4 distinct scenarios of the future. Which is smart, because, obviously, there is a lot of uncertainty around how climate change will play out. Their report provides more detail on the various scenarios and the assumptions underlying, however for purposes of this blog. I simply wanted to get to a single value for each year. In their original report they provide a “central range”. I like their approach. In fact, most of the time I prefer ranges, but for the purposes of coming up with a ‘single forecast’ I chose to take the mid-point of E&Y’s central range. I added this value in orange.

So there you have it. An interesting projection of future Carbon Credit prices in the voluntary market.

How to use information?

Probably the best use of this information is to ncorporate it into financial models on various projects. This could be done for a variety of projects but most of the voluntary Carbon Credit markets right now focused on nature based solutions. Like restoring or conserving forests, or wetlands.

Global Marginal Abatement Cost Curve

This next Gem is located in the Appenix (p. 56), but I think is fascinating. It highlight all the available emissions reductions options and then orders it relative to the lowest cost options and the highest cost options.

This in effect shows how ‘difficult’ it will be to extract various emissions from the atmosphere. Notice that a good 60%+ of the missions can be extracted at $200 per ton of CO2. However, the cost curt takes a sharp upward turn as you stretch into the 90% range. The implication is that it will be very expensive to abate that last bit of CO2 (upwards of $300 to $800 US per ton).

Summary

Experts in these projects will tell you that there is still a wide range of prices even today on any given Carbon Credit project. Future blog posts might explore the variation in CO2 prices across different present day projects.

Sources

Ernst and Young Net Zero Centre - The group who published the report.

“Essential, Expensive and evolving: the outlook for carbon credits and offsets” - A PDF of the report.

C-ROADS stands for Climate Rapid Overview and Decision Support. As their website says, this tool is a “computer simulator that helps people understand the long-term climate impacts of national and regional greenhouse gas emissions reductions at the global level”

I must say the tool was very interesting. I could see spending hours playing with it. However, here are the first few scenarios I ran, and what I learned.

Scenario 0 - The Baseline.

The tool defaults to this, with a notably depressing 3.3 degree (Celsius) rise in Temperature by 2100. Safe to say this is climate crisis in full effect and assumes the status quo.

Scenario 1 - Emissions Peak Immediate Peak this Year

The natural first step if we could waver a magic wand globally. Let’s see the effect if we “Stop the Increase” in emission immediately. Yes this a bit of a “best case” scenario, but that is the point of this tool to play with what ifs. The result, a 3.1 degree (Celsius) rise. Translation, we’re still in trouble.

Scenario 2 - Bring the Reductions Earlier (2050) assume 2% reduction

Presumably 2050 is far away, and that’s the lesson here. If it take us til 2050 to get reductions we are still left with 2.6 temperature increase. This scenario is counter-intuitive if you think about it. And shows how even if we begin reductions in 2050, temperature will still climb, a bit jarring. Conclusion, 2% reductions aren’t enough.

Scenario 3 - Bring the Reductions Earlier (2040) assume 5% reduction

This scenario finally gets us close to the targeted range of 1.5 to 2.0 degree rise in celsius. So success! Unfortunately look at the chart, you can visually see a 5% reduction per year would be huge, and let’s be honest not going to fall off a cliff like this. Also this is only 13 years away. So let’s keep 2040, but go back to the 2% Reduction

Scenario 3 - Bring the Reductions Earlier (2040) assume 2% reduction

Okay, this is of course difficult (2% reduction), but maybe reasonable (who am I to say). However we are still at 2.5 degrees.

Scenario 4 - Prevent Deforrestation at 50%

Let’s play with preventing Deforrestation. Some effect. But not much

Scenario 5 - Add in significant reforestation at 100%

It helped a little, but still 2.3 degree! The model doesn’t got past 100%. So have to revert to the other levers.

Scenario 6 - Add in significant reforestation at 100%

The final model. It still only get us to 2.3 degree.

Scenario 7 - Increase the reduction to 4 %

Last scenario to illustrate the level of reduction needed.

Conclusion

I must say this model left me feeling a little overwhelmed. Of course we all hear about the difficulty to avert Climate Crisis, but these models seem to drive home the numbers that are needed. The model also make clear the “delays” and “buffers” that happen, which are counter intuitive. Reductions aren’t enough, but the timing and the other actions (like Reforestation and Foressation) matter too.

It’s clear that the challenges loom large. The assumptions of Reach Peak year in 2023. Start reducing in 2040 by 4% per year. Stop 100% of Deforestation, and promote 100% of Forrestation, are enough. But can society act to achieve this?

Sometimes the blog will be references to other content creators: website, blogs, podcasts, books, scientific papers, social media posts.  Other times it’ll be my own content either in written form or chart, tables, or perhaps even computer code.  In this way the blog is closely really more of a ‘data journal’.  

The intent is then to have a place to store and clarify my thought process.  It also provides me the tools to exercise (and hopefully improve) my writing skills and of course my hope is that I might find an audience of people that find the same stuff I find interesting. Thanks in advance  for reading!

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