New York City mapped its trees and calculated each one’s economic benefits

The US Forest Service created algorithms to calculate ecological value based on rainwater each tree retains, the electricity conserved, and reduction of air pollution - that calculation just happens to be expressed in dollars. How they make that transition from ecological value to economic value is a mystery to me, but it is just something to keep in mind as you look over this. It is moreso a measure of ecological impact, not economic (though, of course, they are connected).

http://time.com/money/4389722/national-parks-economic-value/

There was an article I read not too long ago that showed the areas in NYC with more trees and greenery also command higher rents.

Also on a completely anecdotal level I feel more "at ease" and less anxious when I'm in parts of cities that have a lot of greenery, especially compared to areas that are barren and nearly all concrete/brick/whatever.

I imagine I can't be the only one who feels that way.

1. Has high reflectance in the infrared, so they reflect sunlight back to the sky before it is converted to heat. Built surfaces would absorb this radiation and heat up.

2. Reduce heat also by evaporating water.

3. Reduce ground level heat by providing shadow.

4. Remove pollution from air, such as sulfur oxide (SO2), nitrogen oxides (NOx), ozone (O3) and fine particles.

But yes, converting these to money is very indirect and involves a lot estimation.

When you say you're not convinced that it's possible to be really sure you've captured all of the benefits, do you mean captured their exact value? Or a hard upper bound of such? Or a soft upper bound with a given confidence interval? Or even just enumerated all of the benefits?

I think calculating an upper bound with confidence interval for the value of a tree in a city would be a really interesting exercise. To do it properly you'd have to account for everything from tangible benefits (shade, reduced CO2) to vague generalities such as residents who "feel happier living in a city with lots of trees". I think it'd be possible, but definitely not trivial.

As ^green infrastructure^.

Trees->flowers->insects->pollination. Lack of pollinators can be compensated by more trees. This is but one example ~ https://www.epa.gov/green-infrastructure/what-green-infrastr...

You can't expect to get in-depth, exhaustive explanations of complex concepts in a short article, but you should looks the primary sources cited therein and critique those if you feel they fall short.

More screenshots: http://imgur.com/a/ctLGx

https://en.wikipedia.org/wiki/General_Sherman_(tree)

Trees in background not catalogued

It's anyway kind of pointless to jam such data into OSM. Most rendering tools are happy to use multiple data sources, so someone interested in such specialized information can just get it from New York City.

That doesn't mean that it absolutely shouldn't be added to OSM, but there should be some clear motivations and benefits, not just helping (hypothetical) lazy people (that might be interested in the data) avoid a JOIN (which is hyperbole, for this data you don't even have to match anything up, just use it together with a base map).

https://hn.algolia.com/?query=by:wodenokoto%20tree&sort=byPo...

Search "comments" rather than "stories".

Even things with direct utility like food have weird economic values, not objective values tied only to their utility.

Deriving additional value from that data is hardly a case of trying to waste money.

Mapping of trees was going to be done by whoever was responsible for maintaining them, on a smaller basis by anyone required to work around them (say for sidewalk work), and probably by multiple other organizations.

That's not what your source says at all.

What it says is that there is a point where further increases in density will provide less positive economic effects than negative. It does not say what that point is, nor does it say that there aren't means that can move the interesection point further up. The point where a city becomes less efficient than a rural area is also much further up the density scale, because to get there you first need the inefficiencies to outweight all the benefits, not merely rise faster than the benefits.

The paper doesn't even try to quantify which cities might have reached or exceeded the threshold where further density would be detrimental. All it does is try to establish office rents as an indicator of the efficiency of land use policies.

The paper itself also points out a lot of caveats even for their very limited goals.