My climate change house hunt - Part 2

In my first post in this series, I examined the best and worst parts of the country to move to through a climate change perspective. Well, all least according to the Climate Vulnerability Index.

It yielded some interesting results, but I concluded that the factors and variables in that ranking did not fit my situation and preferences.

But luckily, the publishers of the Climate Vulnerability Index (CVI) made their data open-sourced and freely available. So, my next logical step is to use that data to make my own scorecard.

The CVI utilizes 184 data points roughly grouped into two categories - community vulnerabilities and climate risks. These are further broken out into sub-groups such as Health, Infrastructure, Extreme Events, etc.

To craft my own scorecard, I began by looking at each and every variable used, deciding whether or not to include it in my ranking. My goal was to focus the data on aspects that were most relevant to me and my house hunt. The trick was to pick variables that (to me) described risks and vulnerabilities associated with different locations specific to the geographic realities of climate change. As you will see, the CVI dumps a ton of data into the mixer, and many of the variables are frankly - a little weird. I wanted to pick data that informed me about that location and how it might adapt to climatic stress…NOT factors that are more general and mostly independent of the role of climate.

But to be fair, personal preferences did start to creep in. After all, this is MY scorecard!

So let’s go. Starting down the list - I included Life Expectancy, Mental and Physical Health, Cancer Rates, High Blood Pressure rates. After all, I wanted to live somewhere that has a generally healthy vibe. It’s hard to deal with climate change when you’re already sick, right?

But some of the health factors got overly specific. # of Mammograms? Cholesterol Rates? HIV and Hepatitis? No thanks - these don’t tell me much.

I did include COVID vaccination rates. I don’t want to live near a bunch of cavemen conspiracy nuts. Education rates - included. Prevalence of Mobile Homes - not included (that would probably artificially suppress rural areas…)

And on it went….

Homeless Population rates - included.

Walkability - included.

Percent of Unbanked Households - not included.

I had to often look at the data for each variable and think through what was it really telling me. Did the presence of Electric Vehicle charging stations really convey something about a community? (I ended up not-including this one, as it probably just correlated with locations in urban areas with large populations and interstate highways…)

What about Voter Turnout or Parks and Greenspace? (I included both).

It got a little easier moving to the climatic factors. This is at the core of the question, and I mostly included factors that correlated to increased climate stress - wildfire exposure, air quality, flooding, storms, etc.

After several iterations, I ended up selecting 53 of the 184 variables for my scorecard. You can find a full list here, with a notation on whether I included it.

Now to weight the variables and generate the rankings. As noted above, the original Index grouped all their data into two broad sections - basically community and climate - and within those sections had seven sub-categories. You can read all about it here.

This was a bit too complex for me. I decided to group all of my 53 selected variables into three categories:

1. Social Cohesion and Health - essentially whether or not a community has their shit together. (20%)

2. Economic and Geographic Features - locational advantages or disadvantages - things like economy, infrastructure, proximity to hazardous waste, etc. (20%)

3. Climate Risk Factors - an area’s exposure to heat, storms, wildfires, sea-level rise, etc. (60%)

You can see how I grouped the data here.

Next, I averaged the variables in each category, and then applied a weighting. After some consideration, I ended up with a 20/20/60 breakdown as indicated above. My rationale was that I wanted this to still be predominately about climate issues, but wanted to consider both the community and geographic realities of each location.

Then, add the weighted scores together, push the button, sort the results, and bingo…

Beaverton, Oregon.

Okay.

According to my newly refined Scorecard, out of 3,135 counties in the United States, the single best location from a climate risk perspective is Washington County, Oregon. AKA Beaverton.

A list of the Top 45 counties and the major cities can be found here.

I’m not dissing Beaverton. It has a lot going for it. Just seems coincidental that my ranking tool popped out a location less than a mile from where I currently live.

And in case you’re wondering, the WORST possible place to be from a climate vulnerability perspective is Beaumont, Texas. List of 15 worst counties located here.

This requires more exploration. SO, in my next post in this series I will dig further into the results of the Scorecard, compare my results to some other published lists, and consider the implications for our potential move.

Stay tuned…

Lyrical Truth Bomb

When musicians say what needs to be said:

Mundane Masterpieces

Everyday inventions that deserve more respect.

The classic lawn chair.

You keep your chair in a bag, with its fussy drawstrings, awkward pinch points, and clunky, saggy seat. Give me this masterpiece of mid-century design. Aluminum frame with nylon webbing. Light as air. Quick to deploy. Aesthetically pleasing and operationally superior, you’ll find these bad boys at my campsite for years to come.

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Disclaimer:

All content and opinions are solely those of the author (Jack), and not representative of my employer, former employers, anyone in Congress, my family, former college roommates, Baptists, the good citizens of Oregon, or my dog Mabel.