Can you give more context to where the phrasing is used? Coming from a computer science angle, there are different data types for different things. For instance, you would use a “float” (floating point) data type to store a number like 7.12. Likewise, you use an “int” to store a whole number (such as 7). Because computers use a certain number of bits to store information, this means there’s a max size to your data. int data types specifically have a “signed int” option as well as an “unsigned int” (the latter being a non negative integer). The benefit there is that by not storing a sign, the int can store numbers about 2x as large as a signed int.
If I dont need to ever store a negative value, I might explicitly call out that when writing out an algorithm
People are inherently bad at rating things. Why not run a “This or that?” style study instead?
Given a list of items to rate, pair them up randomly. Ask a person which item they like better out of each pair. Run through Final Four type eliminations until you get down to their number one preference.
Run through this process for each person, beginning with different random pairings every time.
Record data on all the choices - not just the final ones. You should be able to get good data like that.
For example, there will probably be a thing that is so disliked that it gets eliminated in the first round more frequently than anything else. The inverse will likely be true of a highly-preferred item. And I am sure you can identify other insights as well.
Sounds like a good idea, however my participants neither have the attention span nor do I have the resources to do anything else :) after all, like I said, it’s just a small personal thing :)
The answer is 1:1, conservation of energy means that 1kWh of energy going into the AC you introduce near as makes no difference 1kWh of heat energy into the world. The wonderful thing AC does is amplify that cooling or heating by moving heat from one place to another meaning modern systems can move 5kW of heat using only 1kW of energy. In this example 6kW is outside but you have removed 5kW from inside so it’s still just 1kW net.
If you want to reduce the impact of using your AC you need firstly insulate the crap out of your house to prevent heat escaping or entering. Then you can delve into the world of Mechanical Ventilation Heat Recovery (MVHR), sealing all the air gaps in your home, at least quad-glazing, and finally passive cooling (sun shades over your windows).
Edit: sorry forgot to say for solar powered AC - which is 100% of my systems. I’m ignoring the grid for the above as the only systems I install are combined with at least 3x kW of solar and batteries so they never use grid power.
I know this is kind of off topic but I wanted to point out that the refrigerant that escapes from air conditioners when they leak or are thrown away, is a bigger contributor to climate change than the electricity they use.
Good point! Freon (CFC-12, with 10800x warming potential of CO2) has thankfully been banned by Montreal Protocol of 1987, and HCFC-22 (5280x) is being phased out. We are using what now, HFC-32 at 2430x? How much refrigerant does an AC contain, about a mole? I’ve been taught that refrigerant should normally never leak throughout the lifetime of the appliance (technicians are even prohibited from “recharging” refrigerant without identifying and fixing the point of the leak first) and that all gas must be recovered after end-of-life, but we can’t be sure that’s really what happens every time.
In that case leaking 1 mole of HFC-32 would be equivalent to… running the 1kW AC for 360 hours?
In my experience with the automotive industry. AC systems leak frequently and it is very common for the leak to be so small that it is not always possible to find the source.
So the majority of the time a fluorescent dye is added to the system and it is recharged with refrigerant to help find the source when it gets low again.
It’s common to have a leak so slow and undetectable that no one notices a system is low on refrigerant until a year later when it is summer again.
Also, auto parts stores sell cans of refrigerant so anybody can just recharge a leaking system, which is often cheaper than actually fixing the leak. So these AC systems are just constantly leaking refrigerant and being recharged.
I wouldn’t be surprised if AC systems in buildings are handled similarly.
Even if a law is made that a failed part must be identified before the system can be recharged, the technician who can’t find a leak is going to just pick a part (randomly or educated guess) to replace if he can’t find the leak.
I won’t comment on the final accuracy, but I will note that this is an extremely roundabout path to your final answer, and some of the intermediate steps are…weird. Most notably, the speculation that every man, woman, and child on the planet might run a 1 kW appliance 24/7/365. This is 7e13 kWh or 70k TWh, about 3x current global energy use (not just electicity) before accounting for efficiency. The equation you cite for radiative forcing, specifically its ln(new/old) term is very non-linear, so you should get a much lower marginal effect from 70k TWh than from 1 kWh.
A simpler approach is to calculate the CO2 required for your 1 kWh AC, i.e.: 1kWh * 3600 kJ/kWh / 0.6 efficiency / 890 kJ/mol = 6.7 mol CO2. Current atmospheric CO2 is 75 Pmol. From that, I get radiative forcing of ln((7.4e16 + 6.7)/7.4e16)/ln(2)3.7 * 4pi*(6.4e6^2). Numpy won’t tell me what ln(74000000000000006.7/74000000000000000). It will tell me the forcing from 10 kWh is ~2.5W, or the same 0.25W/kWh you got. I guess ln is not that nonlinear in the 1+1e-16 to 1+1e-4 range, after all.
0.25W/kWh seems improbably high. 1 kWh is about 0.1 W running 24/365. At 60% efficiency, that’s burning 0.2W of natural gas and implies that the radiative forcing from CO2 is much greater than the energy to produce the CO2 in the first place. I get that the energy source for heating is different from the energy source for electricity, but it feels wrong, even without the 1000 year persistence. I don’t know where the radiative forcing equation came from nor its constraints, so I’m suspicious of its application in this context. There’s a lot of obscenely large numbers interacting with obscenely small numbers, and I don’t know enough to say whether those numbers are accurate enough for the results to be reasonable. Then there’s the question of converting the energy input to temperature change.
Numpy won’t tell me what ln(74000000000000006.7/74000000000000000).
Ran into exactly this problem for individual calculation 😆. Which is also why I multiplied by 8 billion and divided in the end - make the calculator behave. ln is linear enough around 1±epsilon to allow this.
implies that the radiative forcing from CO2 is much greater than the energy to produce the CO2 in the first place
That’s what I wanted to find out and it does appear to look exactly that way. Makes sense in retrospect since the radiative forcing is separate from the energy content of CO2 itself, same way as a greenhouse gets hot for no energy expended on its own.
Numpy won’t tell me what ln(74000000000000006.7/74000000000000000). Ran into exactly this problem for individual calculation
Trouble is that 74000000000000006.7/74000000000000000 ~ 1.000 000 000 000 000 1 and double-float precision is 0.000 000 000 000 000 2. Needs a 96 or 128 bit float. The whole topic of estimating one’s personal contribution to global phenomena is loaded with computer precision risks, which is part of what makes me skeptical of the final result, without looking far more closely than my interest motivates. Like calculating the sea level rise from spitting in the ocean - I believe it happens, but I’m not sure I believe any numerical result.
Your skepticism is excessively cautious 😁. You can work around precision limits perfectly fine as long as you are aware they exist there. Multiplying your epsilon and then dividing later is a legitimate strategy, since every function is linear on a small enough scale! You can even declare that ln(1+x) ~= x and skip the logarithm calculation entirely. Using some random full precision calculator I get:
<span style="color:#323232;">ln(1+x) ~= x
</span><span style="color:#323232;">6.7/74e15 = 9.0540540...e-17
</span>
You are worried about differences in the final answer of less than 1 part in a million! I try to do my example calculations in 3 significant figures, so that’s not even a blip in the intermediate roundoffs.
A/C for cooling is one of those things that highly correlates with the availability of solar power.
So your assumption that they are running on coal is suspect.
Heatpumps running in the winter are a more pressing concern, since those are highly correlated with the unavailability of solar and therefore often run on coal or gas (unless wind or hydro is available)
Even so, burning gas in a power plant and then running a heat pump is more efficient than burning gas to warm a house.
You are probably talking about US. For instance, in Europe, places where A/C is more needed and used have also lower share of energy produced from renewable sources.
Yes, ideally all AC will be running off solar, but that’s not the case at the moment. My state has thankfully closed its last coal powerplant, but also shut down one of its nuclear plants, using gas to replace both. We are now running at 50% gas 20% nuclear 20% hydro and 10% wind/solar. Which is why I wanted to focus on methane in this specific calculation: when deciding “is it OK for me to run the AC now, or is the longterm global heating side-effect too great?” natural gas is what is relevant to me.
How “great” that is is precisely the question here, and apparently it’s 2.2x. If you are really a stickler for exact real-life electricity production piechart distribution, multiply that by 50% gas and call it 1.1x. That is, for every year that I run my 1kW AC, that’s as if I am airdropping a 1.1kW heater to a random location on Earth that will heat it up at 1.1kW forever. 10 years = 11 random heaters. 8 billion people = 88 billion random heaters. Is that “too great”? I dunno.
Winter heating is its own problem, but at least cold can always be dealt with by more insulation and clothing. Heat can literally make whole areas of Earth unsurvivable without electrical cooling. Would I rather feel more comfortable now or choose to be able to survive without mechanical aids later?
An A/C consumes more energy when the temperature difference is higher, which is when it’s sunny outside. At those points in time, the grid is receiving a lot of solar power.
So just saying a grid has 10% solar is too simplistic. That grid probably has 30% solar during summer noon and 0% solar on a winter morning.
If your goal is to save emissions, your best bet is to get some solar panels if you can, run the A/C when the sun is shining. Have a well insulated house that acts as a thermal battery and turn the A/C off during the peaks of the duck curve.
Your oversimplifying. No offense, but your calculation is a bit of a spherical cow in vacuum.
I am not gonna do the math, but the concept is simple: to cool a small amount of air you must heat a larger amount of air somewhere else. A/C is basically a heater overall, that consumes more “fuel” (whatever is your fuel) than normal, winter heating per identical volume of heated air.
That is why they say it is not great. Regarding the calculations, all co2 based calculations are not really accurate. It depends on the energy source, on the efficiency of energy production, on location of production, of supply chain… CO2 measures for a given product are extreme, inaccurate approximations not really meaningful on large scales. I won’t worry too much. I’d use A/C only when needed, with target temperature between 25 and 28, and you’ve done your part
Your situation reminded me of the way IMDB sorts movies by rating, even though different movies may receive vastly different total number of votes. They use something called a credibility formula which is apparently a Bayesian statistics way of doing it, unlike the frequentist statistics with p-values and null hypotheses that you are looking for atm.
You could use a few different null hypotheses here. One with minimal assumptions would be that the medians are equal. This can be tested using the Mann-Whitney U test. en.m.wikipedia.org/wiki/Mann–Whitney_U_test
Your null hypothesis is the thing you’re trying to disprove. For example, if I wanted to run a study to asses the effect of adding a certain growth hormone to a cell culture, my null hypothesis would be “there is no effect”. In your case, it would be “there is no difference in how much different things are liked”. From there, you’d run your study, and do your statistical analysis, for which there are different methods based on the type of data, number of groups your comparing, sample size, etc., and I’m not a statistician so I can’t say which methods are best for what you’re planning.
When it comes to p-value, to really simplify it, you can think of your p-value as the likelihood your null hypothesis is true. That’s not exactly what it means, but it’s an easy way to remember it.
That’s not really how quantum entanglement works. When particles are entangled, their quantum mechanical states cannot be described independently. So you couldn’t write down a waveform for just one particle and have it correctly describe reality, you would need the waveform of the entire state and therefore all entangled particles.
As a consequence, certain physical observables can be highly correlated between the particles. For example, if the spin of the overall entangled state of 2 particles is 0, then the spin of 1 particle will be exactly opposite the spin of the other. But these spins are only defined upon measurement (interaction with a system that is deterministic), and at that point the entangled state is collapsed. There’s no mechanism for transporting information while maintaining an entangled state.
Ignoring this fundamental issue, it still wouldn’t be possible to maintain an entangled state between particles in a pair of twins for any practical amount of time. Maintaining coherence in qubits (entailed bits) is one of the big challenges in quantum computing. If the qubits interact with the environment it breaks their entanglement. Even just thermal vibrations will destroy the state. So typically qubits are held at near absolute 0 in a dilution refrigerator. Even still, the longest a qubit has been kept coherent is 5 seconds.
Thanks, been studying a bit about entanglement, super determinism and all that. I thought it was an interesting thought about the twins but I realized it wasn’t likely for the reasons you gave. It’s almost like distance between objects is the weird part about our universe, not it’s quantum material, thus why the entanglement seems strange at a distance. The more I study about it, the more that our 3 dimensions isn’t fundamental, but only a result of wave collapse - this is why the photon doesn’t seem to care how much we try and passively manipulate it, only how it’s finally collapsed. Like how qubits can only exist in their uncertain phase for 5 secs - it’s hard to keep it from interacting/collapsing. Perhaps antimatter annihilates with our matter because of how differently the two matter types collapse their particles from each other?  It’s all so interesting…
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