WEBVTT 00:00:00.760 --> 00:00:00.820 DR JOHN ELLIS: Alright. 00:00:00.820 --> 00:00:04.733 Well, I appreciate the chance to share this info with you. 00:00:04.733 --> 00:00:09.800 As many of you know, the socioeconomic impact 00:00:09.800 --> 00:00:12.187 analysis, we do it for each of the regions. 00:00:12.187 --> 00:00:14.320 They make the request. 00:00:15.520 --> 00:00:19.400 We have some pretty good goals in there, provide a reasonable 00:00:19.400 --> 00:00:23.400 approximation of adverse economic impacts should 00:00:23.400 --> 00:00:26.200 the drought a record conditions prevail for a single year. 00:00:26.760 --> 00:00:29.760 And that's under the assumption that no water management 00:00:29.760 --> 00:00:31.000 strategies are employed. 00:00:31.000 --> 00:00:34.680 So this is if the planning group and the state really do nothing 00:00:34.680 --> 00:00:38.960 in the interim other than what's already been assumed when we 00:00:38.960 --> 00:00:40.840 made the projections. 00:00:40.840 --> 00:00:47.111 This is required by statute to do this level of detail, and it 00:00:47.111 --> 00:00:54.440 ought to embed that one year repeat of the drought of record. 00:00:54.440 --> 00:00:57.560 Secondary goal would be theoretically sound from 00:00:57.560 --> 00:00:58.662 an economic standpoint. 00:00:58.662 --> 00:01:02.190 Have a reasonable amount of research and effort to 00:01:02.190 --> 00:01:03.630 put together the analysis. 00:01:04.550 --> 00:01:06.790 We do try to do this at the best possible level of 00:01:06.790 --> 00:01:11.630 geographic specificity that gets down to the work, as you 00:01:11.630 --> 00:01:15.657 know, from municipal works, that can be it's primarily 00:01:15.657 --> 00:01:19.163 utility-based, and for the four or five other water use 00:01:19.163 --> 00:01:23.337 categories, irrigation, mining, etc, those are county-based. 00:01:23.337 --> 00:01:26.710 And it's also comparable in approach across planning 00:01:26.710 --> 00:01:29.950 regions so that we can, the results can be comparable. 00:01:32.630 --> 00:01:36.150 So those water use categories I was alluding to, as you all 00:01:36.150 --> 00:01:39.150 well know, are irrigated agr, livestock, manufacturing, 00:01:39.150 --> 00:01:41.910 mining, steam-electric power generation. 00:01:41.910 --> 00:01:45.177 And then the municipal is divided up into two categories, 00:01:45.177 --> 00:01:47.797 residential and then commercial water-intensive. 00:01:47.797 --> 00:01:51.868 And this that last category has about five or six different 00:01:51.868 --> 00:01:56.790 NAICs, or North American Industrial Classification 00:01:57.270 --> 00:02:03.967 includes health, hospitals, etc, some educational facilities, 00:02:03.967 --> 00:02:07.180 and things like car washes that are water intensive. 00:02:07.180 --> 00:02:10.180 But yeah, the majority of the time are classified 00:02:10.180 --> 00:02:11.860 under municipal water use. 00:02:13.340 --> 00:02:16.780 So we have three flavors of impact measures. 00:02:16.780 --> 00:02:19.580 The first one that we deal with are economic impacts. 00:02:20.100 --> 00:02:23.140 Primary one, the most everyone is interested in is income. 00:02:23.900 --> 00:02:28.780 And it's more analogous to GDP or gross state product either 00:02:28.780 --> 00:02:30.460 way, you want to package that. 00:02:30.460 --> 00:02:33.700 But that's the value of production plus all the costs. 00:02:35.340 --> 00:02:38.900 And then when we adjust those for impacts on the input supply 00:02:38.940 --> 00:02:40.500 sectors and resulting impacts. 00:02:40.500 --> 00:02:44.020 So whenever I produce something, if that production 00:02:44.020 --> 00:02:47.140 goes away, then I'm no longer buying all my inputs. 00:02:47.140 --> 00:02:49.460 And so there's adverse impacts for everything 00:02:49.460 --> 00:02:52.020 that fed into whatever it is I happen to be making. 00:02:52.780 --> 00:02:56.060 These IMPLAN multipliers help us calculate what the lost 00:02:56.060 --> 00:03:00.730 income is to those sectors, as well as if some people in 00:03:00.730 --> 00:03:02.130 those sectors get laid off. 00:03:02.130 --> 00:03:04.290 Well, they're not drawing as much money. 00:03:04.530 --> 00:03:06.810 They're not spending money at the Dairy Queen. 00:03:07.250 --> 00:03:10.530 So their reduction in spending is also accounted for. 00:03:11.970 --> 00:03:15.930 Then the second component of those economic impacts is 00:03:15.930 --> 00:03:18.343 electrical power purchase costs. 00:03:18.343 --> 00:03:26.157 And we just assumed that given a connection base for 00:03:26.157 --> 00:03:29.810 ERCOT, it covers about 90% of the electricity purchased 00:03:29.810 --> 00:03:31.717 in the state goes to ERCOT. 00:03:31.717 --> 00:03:36.370 These are representative of purchase, let's say, power 00:03:36.370 --> 00:03:39.560 purchase price from ERCOT data. 00:03:39.560 --> 00:03:44.610 And if those electrical power plants run short on water for 00:03:44.610 --> 00:03:49.690 cooling, then they have to buy power on the open market 00:03:49.690 --> 00:03:53.437 from other suppliers within the ERCOT chain. 00:03:53.437 --> 00:03:57.000 So this takes into account those additional purchase prices. 00:03:57.360 --> 00:04:00.600 Then the third one that most people are (AUDIO DISTORTS). 00:04:00.600 --> 00:04:02.340 Pardon me. 00:04:02.340 --> 00:04:04.700 Pardon me. 00:04:04.700 --> 00:04:07.060 Pardon me. 00:04:07.060 --> 00:04:10.800 And then a third component here is at risk jobs. 00:04:11.280 --> 00:04:14.000 This is the number of people associated with those particular 00:04:14.000 --> 00:04:16.560 sectors that are adversely impacted by the drought. 00:04:17.160 --> 00:04:19.000 We kind of repackage what we call this. 00:04:19.040 --> 00:04:22.440 In the past, we would just allude to it as lost 00:04:22.440 --> 00:04:23.680 employment or lost jobs. 00:04:23.680 --> 00:04:27.240 But in reality, you can't say with certainty just because 00:04:27.280 --> 00:04:29.880 a one year drought of record would result in the loss of 00:04:29.880 --> 00:04:33.800 a job, it could be a well-heeled that particular farm was going 00:04:33.800 --> 00:04:34.680 into the drought. 00:04:34.720 --> 00:04:37.720 If they have a large bank account, they might not lay 00:04:37.720 --> 00:04:42.400 you off as quickly and so we just repackage what 00:04:42.400 --> 00:04:44.840 we call it a little bit and call them at risk jobs. 00:04:44.840 --> 00:04:48.200 And that characterization will spill over to a couple other 00:04:48.200 --> 00:04:50.520 categories here in a minute that I'll talk about. 00:04:52.120 --> 00:04:55.918 Are there any questions of what I've described thus far? 00:04:56.937 --> 00:04:57.434 Alright. 00:04:57.434 --> 00:05:01.790 Then we have two additional flavors, I call them measures. 00:05:01.830 --> 00:05:04.590 The one in the left-hand column are financial transfers. 00:05:05.390 --> 00:05:09.390 If you have a drought of record comes into play, 00:05:09.390 --> 00:05:14.390 then if you're a utility, that bottom one kicks in. 00:05:14.430 --> 00:05:17.030 You're not gonna, if you're selling less water, you're 00:05:17.030 --> 00:05:19.790 not going to collect as much taxes for the state and for 00:05:19.790 --> 00:05:21.310 your particular utility. 00:05:21.870 --> 00:05:24.990 You also have lower revenue if you're selling less water. 00:05:26.470 --> 00:05:27.910 I'll jump back up to the taxes. 00:05:27.910 --> 00:05:31.150 If I'm not making output due to the water loss, 00:05:31.150 --> 00:05:32.630 then it doesn't get sold. 00:05:32.630 --> 00:05:35.710 So the state doesn't have as large of tax income. 00:05:36.310 --> 00:05:38.490 And then the third, the second component so 00:05:38.490 --> 00:05:39.870 there's water hauling costs. 00:05:39.910 --> 00:05:41.661 We have an assumption in there. 00:05:41.661 --> 00:05:47.350 If the needs within a particular county or WUG get above 80%, 00:05:47.790 --> 00:05:50.763 then all that water... 00:05:50.763 --> 00:05:54.620 the amount of needs that are above that 80% threshold 00:05:54.660 --> 00:05:56.980 has to be hauled in from somewhere else, and we just 00:05:56.980 --> 00:06:01.740 have an assumed cost per acre foot for hauling that in. 00:06:01.780 --> 00:06:04.100 So there's always additional costs. 00:06:04.100 --> 00:06:09.540 Anything that provides a pulse to the system, to the economy. 00:06:09.860 --> 00:06:12.380 There's always winners and there's losers. 00:06:12.420 --> 00:06:16.420 And what you know, the majority of this effort is trying to 00:06:16.820 --> 00:06:21.620 come up with a dollar value or some other impact measure of 00:06:21.620 --> 00:06:23.580 those particular losses. 00:06:24.460 --> 00:06:27.660 This column on the right, social impacts that includes 00:06:27.660 --> 00:06:29.300 consumer surplus losses. 00:06:29.620 --> 00:06:32.500 That's an economic term for how much? 00:06:32.540 --> 00:06:35.300 It's a monetary estimate of how much money it would 00:06:35.300 --> 00:06:38.580 take to put me back into my original standing before this 00:06:38.620 --> 00:06:40.140 adverse impact happened. 00:06:40.500 --> 00:06:43.140 From a residential standpoint, if I can only 00:06:43.140 --> 00:06:48.300 water my lawn once a week or zero times a week, then it's 00:06:48.300 --> 00:06:50.180 an estimate of the amount of money it would take to 00:06:50.500 --> 00:06:54.170 make me whole and feel like I hadn't gotten messed over. 00:06:54.930 --> 00:06:57.770 Then we have at risk population losses and at risk 00:06:57.770 --> 00:06:58.930 school enrollment losses. 00:06:58.930 --> 00:07:01.583 Those are both tied to that earlier measure. 00:07:01.583 --> 00:07:05.277 I spoke of the at risk job losses. 00:07:06.263 --> 00:07:09.290 So we have about ten measures we try to 00:07:09.290 --> 00:07:11.890 estimate in this analysis. 00:07:13.130 --> 00:07:15.130 A fair amount of those measures are tied to what's 00:07:15.130 --> 00:07:16.250 called the IMPLAN model. 00:07:16.250 --> 00:07:17.530 Impact planning model. 00:07:17.530 --> 00:07:20.090 We use the year 2021 as our baseline. 00:07:20.730 --> 00:07:24.330 And then basically it's a software that uses economic 00:07:25.610 --> 00:07:28.930 modeling technique known as input-output analysis. 00:07:29.450 --> 00:07:32.850 And it makes life a whole lot easier on trying to 00:07:32.890 --> 00:07:36.210 estimate these regional impacts of changes. 00:07:36.650 --> 00:07:38.250 And in the particular IMPLAN model, we're 00:07:38.250 --> 00:07:43.930 talking about there's 546 input or output sectors. 00:07:43.970 --> 00:07:48.850 I'm sorry that they have individual multipliers that 00:07:48.850 --> 00:07:53.467 track back what the lost income will be to those input sectors. 00:07:53.467 --> 00:07:58.738 So like I said, we use those to estimate potential lost income, 00:07:58.738 --> 00:08:00.960 taxes, and at risk jobs. 00:08:01.360 --> 00:08:04.480 And this is on a regional basis'cause that's what's required in 00:08:04.480 --> 00:08:05.960 the Texas Administrative Code. 00:08:06.000 --> 00:08:07.520 It is a regional focus. 00:08:08.080 --> 00:08:10.640 So we built 16 different implant models. 00:08:11.200 --> 00:08:15.200 We export significant amount of data out of each of those 00:08:15.200 --> 00:08:17.760 models manipulated off to the side, and come up with 00:08:17.760 --> 00:08:20.200 our visual estimates. 00:08:20.200 --> 00:08:22.973 I'll give you more detail on that in a second. 00:08:24.187 --> 00:08:30.471 Then we got, I suppose I'm jumping ahead a little bit here. 00:08:30.471 --> 00:08:35.520 Let's suppose that I come up with an estimate of the value 00:08:35.520 --> 00:08:38.360 added or the income that is associated, say, with 00:08:38.360 --> 00:08:43.560 the hog production in region A, and I also know how much water 00:08:43.560 --> 00:08:45.840 it took to produce that output. 00:08:45.880 --> 00:08:47.480 Then I could come up with an estimate of 00:08:47.480 --> 00:08:48.160 the value of water. 00:08:48.160 --> 00:08:51.550 And let's suppose it just for argument's sake, that's 00:08:51.550 --> 00:08:53.070 $1,000 per acre foot. 00:08:53.790 --> 00:08:56.550 And that's an upper bound that we use in our analysis. 00:08:57.070 --> 00:09:00.790 But we apply an elasticity adjustment to it so that we can 00:09:01.110 --> 00:09:04.430 roll in these impacts to where they don't hit all at once. 00:09:04.430 --> 00:09:07.710 We assume there's some flexibility in the system, that 00:09:07.710 --> 00:09:12.150 if I have low levels of needs, low levels of shortages, then 00:09:12.150 --> 00:09:13.110 there's flexibility. 00:09:13.110 --> 00:09:15.590 And I don't get hit with this full impact of $1,000 00:09:15.590 --> 00:09:16.950 an acre-foot right away. 00:09:17.310 --> 00:09:19.710 So we have some thresholds called B1 and B2 down there 00:09:19.750 --> 00:09:21.070 on the horizontal axis. 00:09:21.790 --> 00:09:25.830 They are thresholds where things start to kick in in between. 00:09:25.830 --> 00:09:29.190 In this particular example, 0 to 5% or saying no harm, 00:09:29.190 --> 00:09:33.230 no foul, I can have up to 5% needs that are shortages. 00:09:33.910 --> 00:09:37.190 There's no adverse impacts to that particular region for 00:09:37.230 --> 00:09:39.270 that particular shortage. 00:09:39.670 --> 00:09:43.070 But once I hit that 5% and, in this example, I could go 00:09:43.070 --> 00:09:46.510 all the way up to 40%, then it's gonna grow linearly 00:09:46.510 --> 00:09:51.860 along this diagonal line here until it reaches that 00:09:51.860 --> 00:09:53.780 full $1,000 per acre foot. 00:09:53.780 --> 00:09:57.300 So in this particular example, if I had a shortage of around 00:09:57.300 --> 00:10:03.020 22%, I would jump up here, read the amount of shortage, 00:10:03.020 --> 00:10:08.340 and I would say it was 50% of my adverse impact is applicable 00:10:08.340 --> 00:10:09.540 for that particular need. 00:10:09.740 --> 00:10:15.180 So, at 22% needs value would result in my estimate 00:10:15.180 --> 00:10:20.180 of the lost value for each acre foot would be 500. 00:10:20.220 --> 00:10:22.580 That would be 50% of that $1,000. 00:10:22.580 --> 00:10:26.460 So I'd be assuming that all the acre feet up to that 00:10:26.460 --> 00:10:30.740 amount, I'd value those at $500 bucks an acre foot in this 00:10:30.740 --> 00:10:32.340 particular production sector. 00:10:32.860 --> 00:10:38.700 And we have these types of analysis adjustment factors for 00:10:38.700 --> 00:10:42.740 every one of the six or five of the six water use categories 00:10:43.500 --> 00:10:44.360 with different parameters. 00:10:44.360 --> 00:10:48.730 Livestock is the most onerous because it's really, they've 00:10:49.530 --> 00:10:53.650 faced some very adverse, high losses very quickly in that 00:10:53.650 --> 00:10:54.730 particular sector. 00:10:55.250 --> 00:10:57.970 So this is going to be an overview of how we put 00:10:57.970 --> 00:11:00.450 those pieces together, I just described. 00:11:02.130 --> 00:11:06.303 So we have checked the value added from IMPLAN. 00:11:06.303 --> 00:11:10.890 We take the water use from the TWD water use survey data 00:11:10.890 --> 00:11:13.770 in our regular estimates. 00:11:14.490 --> 00:11:18.330 And then I'll get an initial value per acre foot. 00:11:18.330 --> 00:11:20.690 That would be analogous to that $1,000 an acre 00:11:20.690 --> 00:11:21.810 foot I just spoke of. 00:11:22.770 --> 00:11:26.090 Then we apply the IMPLAN multiplier so that we're 00:11:26.690 --> 00:11:30.930 also counting those indirect and induced impacts. 00:11:31.810 --> 00:11:35.770 We would then step number five, there at the bottom, we adjust 00:11:35.770 --> 00:11:36.650 for the number of firms. 00:11:36.650 --> 00:11:39.570 Sometimes there's a disconnect between the data we get from 00:11:39.570 --> 00:11:42.463 the water use survey as well as what's an IMPLAN. 00:11:42.463 --> 00:11:44.130 They have a different number of firms. 00:11:44.330 --> 00:11:47.040 So we were just with a number of firms, so we get a more 00:11:47.040 --> 00:11:50.760 realistic estimate of that baseline value of water. 00:11:51.960 --> 00:11:55.160 We apply that impact elasticity adjustment, which is where 00:11:55.200 --> 00:11:57.680 the degree of need comes in. 00:11:58.240 --> 00:12:01.813 That gives us an adjusted water value per acre-foot. 00:12:01.813 --> 00:12:08.760 And this is for each of the probably four-digit NAICs 00:12:08.760 --> 00:12:12.584 codes that are embodied in each of those five or six 00:12:12.584 --> 00:12:13.920 water use categories. 00:12:14.440 --> 00:12:16.040 We take that acre-foot of needs. 00:12:16.080 --> 00:12:19.080 We multiply that times that adjusted water acre value per 00:12:19.080 --> 00:12:22.440 acre-foot, and that gives us our lost income estimate for 00:12:22.440 --> 00:12:23.760 that particular sector. 00:12:24.560 --> 00:12:29.400 And we do this for every one of the pertinent sectors in 00:12:29.400 --> 00:12:30.600 a particular county. 00:12:31.560 --> 00:12:34.320 And we get some water use weighted average 00:12:34.320 --> 00:12:35.280 values for that. 00:12:36.080 --> 00:12:39.520 And the same process, not only we do apply this for 00:12:39.520 --> 00:12:43.050 lost income, but we do it for lost taxes as well as 00:12:43.050 --> 00:12:45.630 lost, for jobs at risk. 00:12:46.270 --> 00:12:49.910 So it's a fairly complicated process, but we're trying to get 00:12:49.910 --> 00:12:54.990 as much geographic specificity as we can and tailor-make 00:12:54.990 --> 00:12:58.990 our estimates so that they fit the region in the county 00:12:59.310 --> 00:13:03.737 and the WUG if we happen to be dealing with a municipal WUG. 00:13:03.737 --> 00:13:07.710 So, this there's a lot of work. 00:13:08.390 --> 00:13:12.070 It gives us estimates, which vary by the degree of shortage, 00:13:12.630 --> 00:13:16.110 also vary with the composition of the water use or the economic 00:13:16.110 --> 00:13:17.510 activity by county. 00:13:18.030 --> 00:13:21.310 If I have some high manufacturing firms like in 00:13:21.310 --> 00:13:25.790 Houston and they're heavy water users, then the value of that 00:13:25.790 --> 00:13:27.110 water is extremely high there. 00:13:27.110 --> 00:13:28.137 I want to... 00:13:28.137 --> 00:13:32.190 I would expect a water shortage there, but have 00:13:32.190 --> 00:13:38.550 higher impacts than if I was out, say in West Texas, 00:13:38.550 --> 00:13:41.110 trying to grow some alfalfa. 00:13:41.780 --> 00:13:44.940 So these vary with what I actually is going on in 00:13:44.940 --> 00:13:47.980 the composition of economic activity by county. 00:13:48.820 --> 00:13:51.700 And then we employ region-specific multipliers 00:13:52.860 --> 00:13:56.460 to get those other indirect and induced impacts. 00:13:57.940 --> 00:14:03.460 So if you read any of these reports in the past, 00:14:05.140 --> 00:14:07.140 you wonder, well, how do I interpret these numbers? 00:14:07.660 --> 00:14:10.620 It's good to understand what's going on in the background 00:14:10.620 --> 00:14:12.900 and what all went in to making these estimates. 00:14:13.340 --> 00:14:17.940 So just as a reminder, this slide gives us some of 00:14:17.940 --> 00:14:21.473 those reminders, baseline structure of the economy, 00:14:21.473 --> 00:14:25.220 where these baseline data from 2021, the IMPLAN model. 00:14:25.500 --> 00:14:28.340 So that's just a snapshot of the economy in time. 00:14:30.140 --> 00:14:33.300 If we looked at, if we looked back ten years, the structure of 00:14:33.300 --> 00:14:34.780 the economy would be different. 00:14:36.020 --> 00:14:37.620 We look forward, ten years. 00:14:37.620 --> 00:14:38.460 It'd be different. 00:14:38.820 --> 00:14:39.624 But that's one. 00:14:39.624 --> 00:14:44.770 The fact that we do use this in trying to evaluate water 00:14:44.770 --> 00:14:48.730 shortages in the future is one of the challenges we 00:14:48.730 --> 00:14:54.130 face because we can't, with much degree of reliability, 00:14:54.170 --> 00:14:55.850 try to project what the economy is going to be 00:14:55.850 --> 00:14:57.130 like 20 years from now. 00:14:57.330 --> 00:15:01.050 So we just rely and say today's structure is the best we can do 00:15:01.690 --> 00:15:02.877 for those particular analyses. 00:15:02.877 --> 00:15:06.130 So keep that as a caveat in the back of your mind. 00:15:06.890 --> 00:15:10.850 So the planning groups, as well as the engineering 00:15:10.850 --> 00:15:13.810 consultants, they go to great lengths, as well as the board. 00:15:14.890 --> 00:15:16.410 We update those supplies. 00:15:16.450 --> 00:15:19.250 The Gams models are updated, the desired future conditions 00:15:19.250 --> 00:15:23.730 are updated, Wham runs are made, infrastructure that's 00:15:23.730 --> 00:15:26.170 available for delivery and treatment is updated. 00:15:26.650 --> 00:15:30.530 All of that folds into what these final numbers will be. 00:15:31.570 --> 00:15:33.810 And then there'll be some other key parameters, like what's 00:15:33.810 --> 00:15:35.490 the cost of residential water? 00:15:35.530 --> 00:15:36.590 What's the water trucking cost? 00:15:36.590 --> 00:15:37.930 What are the electricity rates? 00:15:37.930 --> 00:15:41.320 All of those types of things fit into our final estimates. 00:15:42.640 --> 00:15:45.320 And as you know, population and municipal water demand 00:15:45.320 --> 00:15:48.880 projections are key to they really drive the boat as to what 00:15:48.880 --> 00:15:52.120 the adverse impacts would be if we did have that one year 00:15:52.120 --> 00:15:53.520 repeat of the drought of record. 00:15:54.320 --> 00:15:55.880 And we had some minor revisions in our 00:15:55.880 --> 00:15:59.720 methodology manufacturing, mining, and irrigation 00:15:59.720 --> 00:16:00.920 impact procedures. 00:16:00.920 --> 00:16:05.920 Some of the planning regions also provide some input on this, 00:16:05.920 --> 00:16:09.027 and they've got a better pulse beat on, especially on agr, 00:16:09.027 --> 00:16:12.400 what the practices are and what the spatial distribution of 00:16:12.720 --> 00:16:15.760 demands and needs are in their particular region. 00:16:16.640 --> 00:16:20.013 Then inflation alone is another key factor. 00:16:20.013 --> 00:16:25.120 All the estimates that we're going to come back with that 00:16:25.120 --> 00:16:28.520 I'll present today are all in year $2,023. 00:16:28.560 --> 00:16:29.987 That's actually September 23. 00:16:29.987 --> 00:16:32.920 That's what it's required in Exhibit C. 00:16:33.640 --> 00:16:36.560 These adverse impacts are valued in those same dollars so 00:16:36.560 --> 00:16:39.830 that they'll be comparable to what the mitigation costs are 00:16:39.870 --> 00:16:42.910 for those engineering projects that each of the regional water 00:16:42.910 --> 00:16:47.150 planning groups put forth in their cost estimates. 00:16:47.150 --> 00:16:49.080 So we're having an apples-to-apples 00:16:49.080 --> 00:16:50.590 comparison there. 00:16:51.630 --> 00:16:54.990 So I've given you a lot of info, I'm going to tell you 00:16:54.990 --> 00:16:56.417 how it all comes together. 00:16:56.417 --> 00:17:05.190 There's about 25 individual tables, and they cover those 00:17:05.190 --> 00:17:06.830 impact elasticity functions. 00:17:06.830 --> 00:17:10.230 We got bridge tables, which help all the data talk to each other. 00:17:11.030 --> 00:17:14.350 The income, jobs and tax data that comes from IMPLAN. 00:17:14.350 --> 00:17:17.030 I alluded to the consumer surplus estimates. 00:17:17.030 --> 00:17:18.910 We do those off to the side as well. 00:17:19.750 --> 00:17:21.630 We jump back to the top, the water use 00:17:21.630 --> 00:17:23.750 survey data and splits. 00:17:23.750 --> 00:17:27.763 That's just dividing things up by region and county, 00:17:27.763 --> 00:17:29.110 and by river basin. 00:17:30.070 --> 00:17:32.150 And then we got some additional parameters that 00:17:32.870 --> 00:17:35.670 probably the biggest piece of data that goes into this 00:17:35.710 --> 00:17:41.860 is the demands and needs, that implicitly implies that 00:17:41.860 --> 00:17:43.020 we've got the supplies. 00:17:43.620 --> 00:17:49.300 Those needs are calculated just as supplies minus demands. 00:17:49.820 --> 00:17:52.180 If that number happens to be negative, then 00:17:52.180 --> 00:17:53.500 we say we have needs. 00:17:53.620 --> 00:17:56.660 So all that gets, all these 25 tables are provided for 00:17:56.660 --> 00:18:00.980 the whole state, all 254 counties, and the impact model. 00:18:01.020 --> 00:18:05.060 That is the access database which processes it, spits out 00:18:05.060 --> 00:18:08.100 the final impact estimates for each of the ten measures 00:18:08.100 --> 00:18:17.460 for each of the 1,900 municipal WUGs and 254 County 00:18:17.460 --> 00:18:20.500 WUGs, and five categories. 00:18:20.500 --> 00:18:23.300 Not all counties have steam-electric power. 00:18:23.300 --> 00:18:27.500 So there's not 254 steam-electric power 00:18:27.860 --> 00:18:30.340 estimates that actually have non-zero values. 00:18:31.020 --> 00:18:34.260 Anyway, we get the output, we repackage it, put it in 00:18:34.260 --> 00:18:37.797 these reports that ended up in your IPPs. 00:18:37.797 --> 00:18:42.290 And so that's all that comes together. 00:18:42.330 --> 00:18:44.570 It's almost fully automatic. 00:18:44.610 --> 00:18:47.570 The vast majority of our effort goes in to generate those 25 00:18:47.610 --> 00:18:51.290 input data tables that we feed into this access database. 00:18:51.610 --> 00:18:53.090 It spits out the result. 00:18:53.090 --> 00:18:57.770 And then we generate those reports and send them on to you. 00:18:59.010 --> 00:19:01.830 Any questions up to this point? 00:19:01.830 --> 00:19:11.010 OK, so I'm just, we've had some peer review. 00:19:11.450 --> 00:19:14.130 Most of this, the two that I'm alluding to here, took 00:19:14.170 --> 00:19:17.121 just before the DB22 effort. 00:19:17.121 --> 00:19:20.690 BBC Research Consulting took a hard look at our methodology, 00:19:20.730 --> 00:19:24.730 made some minor suggestions for revisions, but they were happy 00:19:24.730 --> 00:19:26.690 with the way we approached all this. 00:19:27.050 --> 00:19:30.650 And the office of the State Comptroller does similar work 00:19:30.650 --> 00:19:34.200 using IMPLAN, and they gave us their blessing as well. 00:19:34.800 --> 00:19:38.840 And in each planning cycle, each of the regional planning groups 00:19:38.840 --> 00:19:43.160 have their own opportunity to comment on the methodology. 00:19:43.960 --> 00:19:50.000 And so every year, also our team, the population 00:19:50.000 --> 00:19:53.440 and socioeconomic analysis team, we are always looking for ways 00:19:53.440 --> 00:19:55.520 to improve on this analysis. 00:19:55.560 --> 00:19:58.080 And so it's been well vetted. 00:19:58.920 --> 00:20:02.480 And we're confident that this is probably the best available 00:20:02.680 --> 00:20:06.000 estimates that could be made for this particular context. 00:20:08.360 --> 00:20:10.160 So I'm going to turn to what's in actually in 00:20:10.160 --> 00:20:11.880 those regional reports. 00:20:11.920 --> 00:20:15.520 This is just a on the front overview what's there. 00:20:15.520 --> 00:20:19.080 So the front end of those packages is 00:20:19.080 --> 00:20:20.440 an executive summary. 00:20:20.440 --> 00:20:24.680 Basically, that's item A there, the year 2021 00:20:24.680 --> 00:20:26.000 primary production sectors. 00:20:26.000 --> 00:20:28.520 That's just a picture for each of the regions. 00:20:28.520 --> 00:20:32.080 What's a big players are and their economic activity 00:20:32.190 --> 00:20:33.461 within their region. 00:20:33.461 --> 00:20:35.790 There's a water use summary by sector. 00:20:35.790 --> 00:20:39.830 Each of those water use sectors and then the projections that 00:20:40.070 --> 00:20:43.270 the board OKs by decade. 00:20:44.870 --> 00:20:47.470 And then there's a description of those impact measures, much 00:20:47.470 --> 00:20:49.697 like what I've described here. 00:20:49.697 --> 00:20:53.550 A brief overview of the methodology, and then you 00:20:53.550 --> 00:20:54.590 get to the results section. 00:20:54.590 --> 00:20:58.350 I'm going to just give you some example ones for a region picked 00:20:58.350 --> 00:21:00.550 at random here in a second. 00:21:00.870 --> 00:21:04.310 And then in Appendix A of every one of those reports, 00:21:04.310 --> 00:21:08.403 all 16 of them are county level results by WUG. 00:21:09.990 --> 00:21:13.590 So the as for both the municipal and non-municipal, so all 00:21:13.630 --> 00:21:16.110 the municipal ones, if it's a utility and it's been 00:21:16.110 --> 00:21:19.750 designated as a WUG, there's entries in that county in that 00:21:19.750 --> 00:21:24.230 Appendix A that show the needs and the lost impact estimates 00:21:24.230 --> 00:21:26.470 for each of the measures that we've been talking about. 00:21:28.750 --> 00:21:33.100 So here is a sample of this is from region L, just like 00:21:33.100 --> 00:21:34.220 I said, picked at random. 00:21:34.940 --> 00:21:40.460 If you took their top, I think there's ten in this sectors, 00:21:40.460 --> 00:21:44.060 then finance and insurance are the big is the big players. 00:21:44.860 --> 00:21:47.900 Please make note of the units. 00:21:47.900 --> 00:21:49.180 These are millions of dollars. 00:21:49.180 --> 00:21:55.060 So that's $19 billion for that first one, finance and insurance 00:21:55.060 --> 00:21:56.260 for the value added. 00:21:57.020 --> 00:22:00.922 And this is what the output is in a... 00:22:00.922 --> 00:22:02.580 was in 2021. 00:22:02.580 --> 00:22:05.740 So this is getting you familiar with how big is 00:22:05.740 --> 00:22:10.740 that particular production sector within the economy 00:22:10.740 --> 00:22:12.473 of that particular region. 00:22:12.473 --> 00:22:13.860 Manufacturing is number two. 00:22:14.420 --> 00:22:18.073 Healthcare is big, big player with them, and it goes on down. 00:22:18.073 --> 00:22:21.180 So, this top there's generally in each of those 00:22:21.180 --> 00:22:23.900 regional reports, there's about 20 of these that end 00:22:23.900 --> 00:22:27.340 up in these summary tables. 00:22:27.540 --> 00:22:30.450 I also give the amount of tax collections that they give 00:22:30.450 --> 00:22:32.850 and the number of jobs that are in each one of those sectors. 00:22:32.850 --> 00:22:35.810 So this is kind of a picture of the year 2021. 00:22:35.810 --> 00:22:38.348 What was in the IMPLAN data? 00:22:38.348 --> 00:22:43.570 Which was what made that economy go in that particular year? 00:22:45.930 --> 00:22:49.437 And then I know all of you have seen these numbers. 00:22:49.437 --> 00:22:52.530 What is the total water demand? 00:22:52.530 --> 00:22:55.690 This is the year 2021. 00:22:55.970 --> 00:22:59.379 What's it comes from the water use survey and as well as 00:22:59.379 --> 00:23:03.090 PSA, our team's estimates for the annual water use 00:23:03.090 --> 00:23:08.890 within each of these sectors and the majority of regions, 00:23:09.530 --> 00:23:11.357 the big water user will be municipal. 00:23:11.357 --> 00:23:13.050 And all are a little different because they 00:23:13.050 --> 00:23:14.170 have so much irrigation. 00:23:14.210 --> 00:23:16.446 They can have bigger, bigger players here. 00:23:16.446 --> 00:23:19.530 In this particular one, irrs not too far behind because 00:23:19.530 --> 00:23:22.103 they do have some irrigation in the lower portions. 00:23:22.103 --> 00:23:24.730 It's number two water user. 00:23:25.330 --> 00:23:28.800 So once again, we're still in what is to get people 00:23:29.080 --> 00:23:32.200 acquainted with what's going on in that particular region. 00:23:33.480 --> 00:23:35.560 So then, when you get to the actual estimates 00:23:35.560 --> 00:23:38.667 of adverse impacts. 00:23:38.667 --> 00:23:46.080 So we first see the needs for irrigation is the one 00:23:46.440 --> 00:23:47.440 on this first panel. 00:23:48.160 --> 00:23:50.280 The irrigation needs are fairly constant. 00:23:51.080 --> 00:23:57.870 71 to 58 acre feet in 2030 goes up very slightly by 2080. 00:23:57.870 --> 00:24:01.240 Percentages, if you round them to zero decimal points, 00:24:01.280 --> 00:24:04.124 they all come across as 23%. 00:24:04.124 --> 00:24:05.520 Impact measures. 00:24:06.880 --> 00:24:10.920 If you go for 2030 to 2080, it jumps from 35 million to 36, 00:24:10.920 --> 00:24:13.040 which is not a huge impact. 00:24:13.080 --> 00:24:15.400 And that risk job losses are there. 00:24:15.760 --> 00:24:18.600 So these are the kind of things that we look at in 00:24:18.600 --> 00:24:21.760 each one of those 16 regions, and we do it for each of 00:24:21.760 --> 00:24:23.920 the six water use categories. 00:24:25.400 --> 00:24:27.973 So let's see what I got here next. 00:24:27.973 --> 00:24:29.996 OK, so here's manufacturing. 00:24:29.996 --> 00:24:36.560 A lot bigger amount at play dollar-wise. 00:24:36.560 --> 00:24:38.080 Let's see. 00:24:38.120 --> 00:24:45.120 So acre feet vary from 39 up to 58,000, with the shortages 00:24:45.120 --> 00:24:50.640 they went from 36% to 44% with actual needs. 00:24:51.680 --> 00:24:56.320 And then income losses went from 9 billion up to $12 billion over 00:24:56.320 --> 00:24:58.080 that time span frame. 00:24:58.080 --> 00:25:02.440 So, like I said, once again, this is just how they examine 00:25:02.440 --> 00:25:06.640 these trends over time, especially as, in general, water 00:25:06.640 --> 00:25:08.320 use supplies get less over time. 00:25:08.320 --> 00:25:11.600 Demands tend to go up depending on which sector 00:25:11.600 --> 00:25:12.480 you're looking at. 00:25:13.200 --> 00:25:16.880 And these are estimates of how adverse impacts would grow over 00:25:16.880 --> 00:25:19.840 time in the manufacturing sector for this particular region. 00:25:21.120 --> 00:25:23.640 So jump to the third example. 00:25:24.240 --> 00:25:25.950 This one's a little bit more complex. 00:25:25.950 --> 00:25:30.750 This is municipal, and we get some huge. 00:25:30.990 --> 00:25:34.870 This is all a ninefold increase in needs. 00:25:35.270 --> 00:25:42.630 You go from 38,000 to 361,000, and then the needs grow from 00:25:42.630 --> 00:25:47.030 7% of demands to 38%. 00:25:47.030 --> 00:25:48.870 So they're taking a pretty big hit. 00:25:49.510 --> 00:25:53.070 Now, one thing that's really key to remember is that 00:25:53.070 --> 00:25:56.390 the numbers that's presented here at the regional level, 00:25:56.830 --> 00:26:02.470 at 38% it says for the whole region the needs average 38%. 00:26:02.470 --> 00:26:05.510 But I may have some individuals' utilities in there 00:26:05.710 --> 00:26:08.870 too, where their individual needs are 70, 80, 90%. 00:26:09.710 --> 00:26:14.750 So I can have some very high loss utilities that kind of get 00:26:14.790 --> 00:26:17.470 glossed over when I'm looking at the numbers at this level. 00:26:17.510 --> 00:26:18.870 So just keep that in mind. 00:26:21.710 --> 00:26:25.780 So the income losses there at the regional level go 00:26:25.780 --> 00:26:32.087 from 319 million all up to 2.8 billion jobs. 00:26:32.087 --> 00:26:32.713 4,121. 00:26:32.713 --> 00:26:35.180 Up to 36,060. 00:26:35.860 --> 00:26:38.740 And note that we have some additional measures that we 00:26:38.740 --> 00:26:43.473 report for the municipal water use sector. 00:26:43.473 --> 00:26:46.820 We have tax losses, trucking costs. 00:26:47.580 --> 00:26:54.380 So the mere fact that I have some positive numbers in this 00:26:54.380 --> 00:26:56.740 trucking cost still tells me I have some individual 00:26:56.740 --> 00:27:00.540 utilities that were their needs values got above 80%. 00:27:00.540 --> 00:27:03.860 So we had to truck some water in, and that cost 00:27:03.860 --> 00:27:06.740 was $45,500 per acre-foot. 00:27:06.740 --> 00:27:09.020 And that was for assumed round trip. 00:27:09.820 --> 00:27:14.220 Anyway, the mere existence of these numbers, $107 million in 00:27:14.220 --> 00:27:17.900 2080 for the trucking costs, as I had some probably smaller 00:27:17.900 --> 00:27:20.900 utilities that didn't have as reliable water supplies, 00:27:20.900 --> 00:27:25.010 and they had some significant needs, projected needs where 00:27:25.010 --> 00:27:28.250 the needs were above 80% of their normal demands. 00:27:29.810 --> 00:27:33.081 And then we've got some utility loss, revenue losses, 00:27:33.081 --> 00:27:36.597 and tax revenue losses. 00:27:38.663 --> 00:27:41.734 So I'll give you a chance. 00:27:41.979 --> 00:27:44.010 I went through a lot of detail right there. 00:27:44.250 --> 00:27:47.370 I'm going to give a little demo of a dashboard here in a second. 00:27:47.370 --> 00:27:50.330 But before we go there, does anyone have any 00:27:50.330 --> 00:27:51.570 specific questions? 00:27:58.330 --> 00:27:59.130 Can you guys hear me? 00:27:59.170 --> 00:28:02.130 I want to make sure I have been talking for the last 20 minutes. 00:28:02.130 --> 00:28:02.797 JIM DARLING: Yeah. 00:28:02.797 --> 00:28:05.721 DR JOHN ELLIS: With no audience. 00:28:05.721 --> 00:28:09.950 So, like any good analysis, you should always be aware. 00:28:09.950 --> 00:28:10.970 HEATHER: You got a question, John. 00:28:11.490 --> 00:28:12.010 DR JOHN ELLIS: Pardon me. 00:28:12.610 --> 00:28:14.050 HEATHER: You got a question from Gail? 00:28:16.130 --> 00:28:16.570 DR JOHN ELLIS: Let me see. 00:28:19.690 --> 00:28:21.480 Well, I'm not getting to the chat. 00:28:21.520 --> 00:28:23.480 Let me see. 00:28:23.480 --> 00:28:29.373 HEATHER: Gail, do you just wanna ask your question? 00:28:29.373 --> 00:28:31.040 DR JOHN ELLIS: Yeah, just ask the question, please. 00:28:31.080 --> 00:28:31.920 It'd be easier. 00:28:32.080 --> 00:28:35.160 I'm having trouble getting to the chat. 00:28:35.160 --> 00:28:40.530 HEATHER: I think you're muted, Gail. 00:28:41.620 --> 00:28:42.160 SPEAKER: I think you're muted, Gail. 00:28:54.320 --> 00:28:56.400 SPEAKER: You're kind of coming in and out on my end. 00:28:57.520 --> 00:28:59.067 GAIL PEEK: Let me just put it in a chat. 00:29:00.155 --> 00:29:00.490 KEVIN S: Hey, well, we got you now. 00:29:00.490 --> 00:29:01.200 HEATHER: We can hear you now. 00:29:01.518 --> 00:29:01.947 KEVIN S: We've got you now. 00:29:01.947 --> 00:29:03.240 GAIL PEEK: Oh, you can. 00:29:03.280 --> 00:29:08.240 I was wondering in reviewing the various factors, if you did 00:29:08.240 --> 00:29:12.960 any review of the MAG numbers, which is always a bugaboo in 00:29:12.960 --> 00:29:16.360 our regional meetings with the desired future conditions. 00:29:17.990 --> 00:29:18.167 DR JOHN ELLIS: No. 00:29:18.167 --> 00:29:20.110 Our team we don't really play with that. 00:29:20.110 --> 00:29:21.870 We just take it as given. 00:29:21.870 --> 00:29:26.150 And that's why, that's beyond my expertise, especially in terms 00:29:26.150 --> 00:29:30.830 of I play with economics and not with that particular deal. 00:29:30.830 --> 00:29:34.310 Well, I do know how they fold into how they 00:29:34.310 --> 00:29:35.470 impact our numbers. 00:29:35.790 --> 00:29:39.430 But as far as the actual determining whether or not 00:29:39.430 --> 00:29:41.110 they're what they should be. 00:29:41.150 --> 00:29:42.870 No, I don't play that game. 00:29:43.570 --> 00:29:44.150 MATT NELSON: John, I can answer. 00:29:44.150 --> 00:29:45.580 I can answer that. 00:29:45.580 --> 00:29:46.410 DR JOHN ELLIS: OK. 00:29:46.410 --> 00:29:51.190 MATT NELSON: The MAGs are not MAGs are part of the planning 00:29:51.190 --> 00:29:55.470 process in determining what water is available and who 00:29:55.470 --> 00:29:58.630 will have enough, and where shortages may appear. 00:29:58.630 --> 00:30:00.723 So it's sort of the early stage. 00:30:00.723 --> 00:30:04.670 So, it has its role in the early stage. 00:30:04.670 --> 00:30:06.790 It doesn't actually have anything to do with 00:30:06.790 --> 00:30:07.790 the economic analysis. 00:30:07.790 --> 00:30:11.710 The economic analysis is really only based on once you 00:30:11.710 --> 00:30:15.830 identify your shortages, then the economic analysis begins. 00:30:15.830 --> 00:30:17.070 So they're very separate. 00:30:17.510 --> 00:30:20.198 Does that help? 00:30:20.198 --> 00:30:22.565 DR JOHN ELLIS: OK. 00:30:22.565 --> 00:30:24.933 Alright. 00:30:24.933 --> 00:30:30.380 I'll jump back to what's shown on the screen, the limitations. 00:30:30.380 --> 00:30:34.100 So, the analysis focuses on those sectors with adequate 00:30:34.100 --> 00:30:37.940 water use data, especially in the manufacturing sector. 00:30:37.980 --> 00:30:39.940 Sometimes we don't have very good the best 00:30:39.940 --> 00:30:40.940 water use data there. 00:30:40.940 --> 00:30:43.220 So we don't try to make estimates for those 00:30:43.220 --> 00:30:47.220 particular subset sectors within manufacturing. 00:30:47.820 --> 00:30:50.980 So in that regard, we probably underestimate some 00:30:50.980 --> 00:30:52.340 of the adverse impacts. 00:30:53.140 --> 00:30:57.340 It does have, it only considers one year of drought 00:30:57.340 --> 00:31:01.340 that's primarily due to what's in the administrative code. 00:31:03.060 --> 00:31:06.100 Plus it's very difficult to do analysis at this 00:31:06.100 --> 00:31:10.540 level and take into account stochastic aspects of drought, 00:31:10.580 --> 00:31:13.709 multi year aspects, etc. 00:31:13.709 --> 00:31:17.970 No consideration of impacts on the forwardly linked sectors. 00:31:19.130 --> 00:31:22.450 What that means is, if there's a drought in the Panhandle 00:31:22.450 --> 00:31:27.170 and they feed 500,00 fewer cattle, that does not. 00:31:27.210 --> 00:31:31.130 We do not track what the lost income is to 00:31:31.170 --> 00:31:32.330 the city of Houston. 00:31:33.010 --> 00:31:35.357 In fact, that they can't get as much beef at their local 00:31:35.357 --> 00:31:37.517 HEB, and prices went up, OK. 00:31:37.517 --> 00:31:40.890 We're only looking at the things that are adversely 00:31:40.890 --> 00:31:44.130 impacted, that fed into production of that beef, not 00:31:44.130 --> 00:31:46.210 the downstream adverse impacts. 00:31:49.370 --> 00:31:51.810 No consideration of backward linked impacts on 00:31:51.810 --> 00:31:53.090 other planning regions. 00:31:53.690 --> 00:31:58.788 So if, once again, I come from an agr background. 00:31:58.788 --> 00:32:03.854 So, I keep talking about Irr or regions. 00:32:03.854 --> 00:32:09.970 If they happen to buy some of their input feed from a region, 00:32:09.970 --> 00:32:17.160 say, other than Irr, maybe they get some from D or F, then 00:32:17.760 --> 00:32:22.400 these estimates are regionally focused, and so the adverse 00:32:22.400 --> 00:32:25.640 impacts on those other regions that aren't the parent region 00:32:25.960 --> 00:32:27.520 are not estimated here. 00:32:27.680 --> 00:32:30.720 There is a technique called multi-regional input analysis 00:32:30.720 --> 00:32:32.200 that would track those. 00:32:32.680 --> 00:32:36.240 If I got some of my input supplies from regions outside 00:32:36.240 --> 00:32:39.400 of my primary region of interest, and we would come 00:32:39.400 --> 00:32:42.400 up with some estimates of what the adverse impacts are on 00:32:42.400 --> 00:32:44.880 those, and we're hoping to fold that into the next go round 00:32:44.920 --> 00:32:49.880 of next state water round of regional state water plans. 00:32:49.880 --> 00:32:54.560 Analysis does not consider building moratoriums due to 00:32:54.600 --> 00:32:56.120 long term water shortages. 00:32:56.360 --> 00:33:01.000 Some cities are probably already have this in place, or we don't 00:33:01.000 --> 00:33:05.320 have that as one of the response functions in our modeling. 00:33:05.840 --> 00:33:08.636 Increased value of water over time is not considered. 00:33:08.636 --> 00:33:12.800 As Texas continues to mine more and more groundwater and use 00:33:12.800 --> 00:33:16.110 more of its surface water allocations, the price of water 00:33:16.110 --> 00:33:23.328 is going to go up and in what few areas that we do have water, 00:33:23.328 --> 00:33:25.230 especially water markets. 00:33:25.230 --> 00:33:27.510 There's some in the lower Rio Grande Valley where they 00:33:27.550 --> 00:33:31.057 sell surface water rights, one person to another. 00:33:31.057 --> 00:33:33.230 Those prices are going to go up. 00:33:33.230 --> 00:33:35.750 And we're not taking into account any kind of inflation 00:33:35.750 --> 00:33:40.190 in the cost of water over time in this analysis. 00:33:40.710 --> 00:33:43.510 And the biggest one is I alluded to this earlier, 00:33:43.510 --> 00:33:46.190 considers the structure of the economy as being static. 00:33:46.590 --> 00:33:49.768 We use that year 2021 as our baseline. 00:33:49.768 --> 00:33:53.550 When we do it again for the next state water plan, 00:33:53.550 --> 00:34:01.183 that will be year 2026 baseline IMPLAN numbers, we 00:34:01.183 --> 00:34:03.781 jump at five years every time. 00:34:03.781 --> 00:34:07.394 So, any questions on what I talk about? 00:34:07.394 --> 00:34:09.590 There are probably some other limitations, but these 00:34:09.590 --> 00:34:13.268 are the big the big ones. 00:34:13.268 --> 00:34:26.020 Alright, there is a dashboard that presents majority 00:34:26.020 --> 00:34:29.820 of these results that's available to the public, as 00:34:29.820 --> 00:34:33.767 well as anyone who wants to its forward public-facing. 00:34:33.767 --> 00:34:37.860 You can just Google TWBD and then the word 00:34:37.860 --> 00:34:41.567 socioeconomic, and it'll take you straight to this page. 00:34:41.567 --> 00:34:48.980 The actual see to it here. 00:34:50.580 --> 00:34:53.007 Jump to a different page. 00:34:53.007 --> 00:34:57.500 It has several components to it. 00:35:03.860 --> 00:35:08.513 I'm going to back out of this. 00:35:08.513 --> 00:35:11.730 Hopefully, I'll get back to dashboard here. 00:35:15.890 --> 00:35:18.290 Stop sharing my screen and then share again. 00:35:19.810 --> 00:35:27.180 Get this here just a second. 00:35:27.180 --> 00:35:37.970 Alright, there you go. 00:35:37.970 --> 00:35:43.730 Alright, stop sharing, and then I'm going to share again. 00:35:44.450 --> 00:35:46.090 See if I can get this out of the way. 00:35:54.450 --> 00:35:56.437 What's going on? 00:35:57.823 --> 00:36:00.170 Just a second. 00:36:00.170 --> 00:36:15.913 It's not getting me where I thought it would. 00:36:15.913 --> 00:36:43.187 Alright, here we go. 00:36:43.187 --> 00:36:48.040 OK, so can you see this socioeconomic impact 00:36:48.040 --> 00:36:49.240 analysis website? 00:36:51.320 --> 00:36:52.040 ELIZABETH: Not yet. 00:36:58.680 --> 00:36:58.840 DR JOHN ELLIS: How's that? 00:36:59.310 --> 00:36:59.692 ELIZABETH: Yes, I can see 00:36:59.692 --> 00:37:00.459 DR JOHN ELLIS: OK. 00:37:00.459 --> 00:37:05.040 Alright, so for those of you who are really interested in 00:37:05.040 --> 00:37:08.120 this thing, you can just go to TWBD main homepage. 00:37:08.120 --> 00:37:10.830 There's a water planning tab, this little up here. 00:37:10.830 --> 00:37:17.590 You can click on it, and then you can click on planning 00:37:17.590 --> 00:37:21.230 data and scroll down in the socioeconomic impact 00:37:21.230 --> 00:37:23.510 analysis screen is there. 00:37:24.910 --> 00:37:28.150 So there's four or five tabs that are really 00:37:28.150 --> 00:37:29.270 key components of this. 00:37:29.270 --> 00:37:30.470 One is Interactive Data. 00:37:30.470 --> 00:37:31.950 We'll take a look at that in a second. 00:37:32.590 --> 00:37:36.350 2026 Regional Water Impact Reports, those are what 00:37:36.630 --> 00:37:42.070 you guys are IPPs, and in the previous reports are from 00:37:42.070 --> 00:37:44.030 previous state water plans. 00:37:44.390 --> 00:37:49.230 There's an FAQ section which is both has a live link to 00:37:49.270 --> 00:37:51.110 around 18 or so key questions. 00:37:51.110 --> 00:37:54.590 If I scroll down like the first two or so, what 00:37:54.590 --> 00:37:56.977 is the purpose and scope of the impact analysis? 00:37:56.977 --> 00:37:59.310 Or what impact measures are included? 00:37:59.310 --> 00:38:03.310 So there's 18 of these in this parent, one, if you're 00:38:03.310 --> 00:38:06.838 really a glutton for punishment, you can click on 00:38:06.838 --> 00:38:10.563 this download detailed FAQs. 00:38:10.563 --> 00:38:11.940 It's a PDF. 00:38:11.980 --> 00:38:15.460 It has his original 18, as well as several more that 00:38:15.460 --> 00:38:18.780 are more detailed and drill down deeper into what all 00:38:18.820 --> 00:38:19.780 went into this thing. 00:38:20.980 --> 00:38:23.420 There is a one or two pager quick overview 00:38:23.420 --> 00:38:26.100 of the socioeconomic impact analysis. 00:38:26.500 --> 00:38:29.340 And then there's a user guide for this dashboard. 00:38:29.780 --> 00:38:34.260 So there's lots of additional info about how we 00:38:34.300 --> 00:38:37.500 came up with these numbers, how to interpret them, etc. 00:38:37.500 --> 00:38:39.580 So I'm going to just show you a little bit about 00:38:39.580 --> 00:38:40.820 the interactive data. 00:38:41.020 --> 00:38:44.540 Click on that and you can pick a particular 00:38:44.540 --> 00:38:46.100 region that of interest. 00:38:46.700 --> 00:38:48.900 And it brings up over here to the right. 00:38:50.180 --> 00:38:51.607 This happens to be with the region A. 00:38:51.607 --> 00:38:53.660 I'll tell you what, I'll jump to a slightly different 00:38:53.700 --> 00:38:56.260 region just to play. 00:38:56.620 --> 00:38:57.740 So let's jump to G. 00:39:00.060 --> 00:39:02.940 This top section over here gives you a kind of a mini 00:39:02.980 --> 00:39:06.100 summary of what is in the front end of those executive summary 00:39:06.170 --> 00:39:11.090 reports so part of the IPP that TWD sent back to you for that 00:39:11.330 --> 00:39:12.410 Socio-Economic. 00:39:13.410 --> 00:39:17.850 So GDP for part or region G is relatively small out 00:39:17.850 --> 00:39:21.050 of the state total, this 1.93 million. 00:39:21.410 --> 00:39:23.103 That's 1.93 million millions. 00:39:23.103 --> 00:39:27.130 So that's almost $2 trillion is what the total 00:39:27.130 --> 00:39:31.010 state GDP was for 2021. 00:39:31.010 --> 00:39:33.330 These baseline sample numbers. 00:39:33.610 --> 00:39:36.130 So it's important to keep these units in mind here. 00:39:36.530 --> 00:39:43.370 So region G was only $101,000 million. 00:39:43.570 --> 00:39:46.050 So it wasn't a very big proportion of the total state. 00:39:46.090 --> 00:39:47.690 Probably about 8 or 9%. 00:39:48.770 --> 00:39:51.010 Employment estimate is 1,000,055. 00:39:51.930 --> 00:39:57.370 And the population was 2.4 million for that particular. 00:39:57.410 --> 00:40:00.890 But then it gives you a summary here GDP loss to 00:40:00.930 --> 00:40:02.050 the region, the same numbers. 00:40:02.050 --> 00:40:04.365 We just looked at sample ones for region L. 00:40:04.365 --> 00:40:07.702 This is the same kind of table by decade. 00:40:07.702 --> 00:40:11.760 At risk job losses, at risk population outmigration due 00:40:11.760 --> 00:40:12.400 to job loss. 00:40:12.400 --> 00:40:14.560 We took some of the there was a measure that 00:40:14.560 --> 00:40:17.060 I haven't explained it yet. 00:40:17.060 --> 00:40:22.840 I took some historic studies that showed around I think 00:40:22.840 --> 00:40:32.040 it's 18% of people that lost jobs, would migrate out of 00:40:32.080 --> 00:40:33.840 from one location to another. 00:40:34.160 --> 00:40:37.720 It's kind of the best we could do because when people actually 00:40:37.720 --> 00:40:42.720 move from one city to another because of job loss, it's 00:40:43.320 --> 00:40:46.840 a whole lot more factors at play than what we have access 00:40:46.840 --> 00:40:50.520 to the data to ferret all that out for particular region. 00:40:50.520 --> 00:40:52.640 If I live in Dallas, I lose my job. 00:40:52.920 --> 00:40:55.040 Grandma and grandpa live two miles away. 00:40:55.040 --> 00:40:58.040 There's not a very high chance I'm gonna move out of that 00:40:58.040 --> 00:41:00.653 particular region or city. 00:41:00.653 --> 00:41:06.070 So some of these that, need some caveats on how 00:41:06.070 --> 00:41:07.310 realistic they might be. 00:41:08.030 --> 00:41:13.390 So you scroll on down, and you can see by decade 00:41:13.430 --> 00:41:15.750 there's the total demand. 00:41:15.790 --> 00:41:18.857 You've got the needs in the darker blue, 00:41:18.857 --> 00:41:20.750 and the percentages are shown. 00:41:20.790 --> 00:41:26.390 They vary from 16.85% needs all across to 38.42%. 00:41:26.390 --> 00:41:29.710 That's for the region as a whole across all water use sectors. 00:41:31.150 --> 00:41:35.030 So this is just a quick and dirty summary in visual 00:41:35.030 --> 00:41:39.150 form that you can examine, which is a lot, in many ways 00:41:39.150 --> 00:41:42.190 more palatable than just looking at individual tables. 00:41:42.790 --> 00:41:47.470 So if you click on this, go to detailed region report then it 00:41:47.470 --> 00:41:49.683 gives you additional detail. 00:41:49.683 --> 00:41:53.310 See the county outlines. 00:41:53.310 --> 00:41:59.737 You got utility taxes, tax losses, loss value to customers, 00:41:59.737 --> 00:42:02.030 GDP loss to the region. 00:42:02.060 --> 00:42:05.900 So eventually, you have all ten of those impact 00:42:05.900 --> 00:42:07.260 measures summarized there. 00:42:07.780 --> 00:42:13.353 It's always important to recognize that these... 00:42:13.353 --> 00:42:14.584 What the units are? 00:42:14.584 --> 00:42:17.740 These are million dollars if it's a dollar-based measure. 00:42:17.980 --> 00:42:21.260 One thing that I glossed over and should have emphasized 00:42:21.260 --> 00:42:29.300 earlier, all of the needs were calculated at the work level. 00:42:29.340 --> 00:42:31.460 Like I say, for especially for municipal, that's at 00:42:31.460 --> 00:42:33.620 the municipal level or the utility level. 00:42:34.500 --> 00:42:38.380 And so when those results get reported, especially, they'll 00:42:38.380 --> 00:42:42.380 get folded in to the regional level as well as the some of 00:42:42.380 --> 00:42:48.100 the county level results that say, supposedly, 1,000 acre foot 00:42:48.100 --> 00:42:52.780 need, then the losses associated with that, the dollar impact 00:42:52.780 --> 00:42:53.500 measures. 00:42:53.500 --> 00:42:56.300 Those are the lost value to the region, not to 00:42:56.340 --> 00:42:59.860 the individual utility, because we folded in 00:42:59.860 --> 00:43:01.683 those IMPLAN multipliers. 00:43:01.683 --> 00:43:06.250 They calculate those total impact losses to the region 00:43:06.250 --> 00:43:10.130 as a whole, not just the particular WUG where 00:43:10.130 --> 00:43:12.530 the drought happened to occur. 00:43:13.170 --> 00:43:16.463 So keep that in mind as you're looking at these numbers. 00:43:16.463 --> 00:43:18.410 Lower left here. 00:43:19.410 --> 00:43:20.530 KEVIN S: Hey, John, I'm sorry to interrupt. 00:43:20.530 --> 00:43:21.130 It's Kevin. 00:43:21.130 --> 00:43:24.450 We had one question in the chat from Dr Rainwater. 00:43:24.610 --> 00:43:28.490 I can read his question, but he asked, "Are the impact numbers 00:43:28.490 --> 00:43:29.650 for the entire decade shown?" 00:43:31.730 --> 00:43:34.210 DR JOHN ELLIS: Yeah, they represent the decade, but it's 00:43:34.210 --> 00:43:36.810 just a one year estimate. 00:43:36.850 --> 00:43:37.357 Oh, OK. 00:43:37.357 --> 00:43:38.570 Let me restate that. 00:43:39.010 --> 00:43:41.250 It's not for the entire decade. 00:43:41.250 --> 00:43:42.410 It's not cumulative. 00:43:42.450 --> 00:43:46.410 It says if you happen to be cruising along at 2030 hit 00:43:46.410 --> 00:43:49.010 and you had a one year repeat of the drought of record, 00:43:49.170 --> 00:43:52.690 that is our point estimate of what the lost impact would be 00:43:52.690 --> 00:43:54.303 for that particular year. 00:43:55.037 --> 00:43:59.920 If you jump forward to 2040, then the needs are a different 00:43:59.920 --> 00:44:03.680 set of needs because we have different assumed supplies, 00:44:03.680 --> 00:44:06.378 the aquifers drawn down, etc. 00:44:06.378 --> 00:44:09.836 We maybe we got a new, we might have a new reservoir in place. 00:44:09.836 --> 00:44:13.280 Maybe those supplies came on if we knew about it today, when 00:44:13.280 --> 00:44:15.680 we were making the projections. 00:44:16.480 --> 00:44:19.400 So they just happen to be markers in time. 00:44:19.920 --> 00:44:22.960 They're not a cumulative ten year decadal loss. 00:44:23.000 --> 00:44:26.200 It's a one year impact loss. 00:44:26.960 --> 00:44:28.480 Does that answer your question? 00:44:32.240 --> 00:44:34.880 KEVIN S: I think he's affirmed that it did in the chat. 00:44:34.880 --> 00:44:38.680 DR JOHN ELLIS: Well, I can't see the chat. 00:44:40.200 --> 00:44:41.760 KEVIN S: Oh, I think you did, John. 00:44:41.760 --> 00:44:42.520 DR JOHN ELLIS: OK. 00:44:42.520 --> 00:44:46.760 Alright, so if you look further back on the graph on 00:44:46.760 --> 00:44:49.840 the lower left there, you see for this particular region 00:44:49.840 --> 00:44:55.200 G, that majority of that pie chart is municipal, the red. 00:44:55.600 --> 00:44:59.643 So there the most of the water user in the baseline 2021, 00:44:59.643 --> 00:45:02.968 irrigation was second, and then steam electric. 00:45:02.968 --> 00:45:05.950 G has a fair amount of steam electric power production. 00:45:05.990 --> 00:45:10.110 There's a fair amount of mustard colored pie in 00:45:10.110 --> 00:45:12.323 that particular pie chart. 00:45:12.323 --> 00:45:16.070 OK, look a little bit to the right. 00:45:16.070 --> 00:45:19.450 You see acre feet of demands and acre feet of needs, 00:45:19.450 --> 00:45:22.670 and those are projected out there by particular water use. 00:45:23.390 --> 00:45:26.990 So this gives you some regional level yields. 00:45:27.310 --> 00:45:30.550 If you're really, like I say, a super glutton for punishment. 00:45:30.550 --> 00:45:32.030 You can go to county level. 00:45:32.590 --> 00:45:35.963 You can actually select a county, Shackleford, 00:45:35.963 --> 00:45:38.030 and it updates the table. 00:45:38.430 --> 00:45:39.630 This one's pretty boring. 00:45:40.790 --> 00:45:44.230 Must not have been any needs at all 'cause it 00:45:44.230 --> 00:45:45.550 ended up all zeros. 00:45:46.950 --> 00:45:48.390 Not sure it's updating here. 00:45:48.430 --> 00:45:49.350 Something's wrong. 00:45:52.510 --> 00:45:54.390 Let me see if I can get this to update. 00:45:56.660 --> 00:45:57.300 There we go. 00:45:57.700 --> 00:45:58.940 I had to punch it some more. 00:45:58.980 --> 00:45:59.900 That didn't make sense. 00:46:00.940 --> 00:46:03.940 So particular county, Shackleford, you can look that 00:46:03.940 --> 00:46:07.220 up and see what those estimates are for that particular county. 00:46:07.620 --> 00:46:15.380 If you jump back to the parent, Rio, at the bottom here, you 00:46:15.380 --> 00:46:18.700 can download the digital versions of the data that fed 00:46:18.700 --> 00:46:20.020 into these dashboards. 00:46:21.180 --> 00:46:27.362 So any questions on this part? 00:46:27.362 --> 00:46:38.180 OK, then let me see if I can get back to this. 00:46:40.740 --> 00:46:45.458 Let's see. 00:46:45.458 --> 00:46:56.470 No clue what's showing. 00:46:56.470 --> 00:47:03.963 Alright, I'm going to... 00:47:03.963 --> 00:47:09.530 Alright, so are you seeing a question mark? 00:47:11.583 --> 00:47:11.997 JIM DARLING: Yeah, we can. 00:47:11.997 --> 00:47:15.570 DR JOHN ELLIS: OK, so that's my alleged contact info, 00:47:16.650 --> 00:47:19.450 and I'm happy to answer any other questions that pop up 00:47:20.130 --> 00:47:22.930 later when you've had time to think about this. 00:47:22.930 --> 00:47:30.223 I threw a lot of terms and data at you all at once, but anyway. 00:47:30.223 --> 00:47:36.810 So any additional questions? 00:47:36.810 --> 00:47:40.530 Alright. 00:47:40.530 --> 00:47:46.970 Well, I appreciate the opportunity to do 00:47:46.970 --> 00:47:47.770 the show and tell. 00:47:47.810 --> 00:47:50.130 And like I said, if anything pops up later, don't 00:47:50.130 --> 00:47:53.250 hesitate to ping me with an email or give me a call.