WEBVTT NOTE The Ecological Benefits of Smart Growth: Where is the Science? 1 00:00:00.000 --> 00:00:12.000 I am an economist by training, a strong free market economist, so I certainly share a lot in common with the von Mises Institute and the folks here who do such a good job. 2 00:00:12.000 --> 00:00:18.000 But I migrated several years ago over into the School of Forestry and Wildlife Sciences here at Auburn. 3 00:00:18.000 --> 00:00:23.000 This has allowed me to, it's a very sort of refreshing sort of thing to do in the middle of a career, 4 00:00:23.000 --> 00:00:33.000 is to take what you've always thought of as a model that applies pretty broadly into a completely new area where you haven't thought much about before. 5 00:00:33.000 --> 00:00:43.000 And it turns out there's a lot of applications for free market economics and public choice theory in the whole world of natural resources. 6 00:00:43.000 --> 00:00:45.800 of Sources. 7 00:00:45.800 --> 00:00:52.640 And one of the problems with the natural resource community is that there tends to be a mentality 8 00:00:52.640 --> 00:00:57.320 that focuses on command and control as a way of getting people to do what they know in 9 00:00:57.320 --> 00:01:02.400 their heart of hearts is the correct thing to do with the environment. 10 00:01:02.400 --> 00:01:11.160 And it's that sort of point in time that forms the point of departure for my discussion here. 11 00:01:11.160 --> 00:01:19.880 One of the probably biggest command and control-oriented programs or sets of principles that exist 12 00:01:19.880 --> 00:01:25.160 in the natural resources community, policy community, goes under a broad umbrella called 13 00:01:25.160 --> 00:01:33.680 smart growth, and that smart growth is essentially a set of principles by which the advocates 14 00:01:33.680 --> 00:01:38.160 of these policies want us to live our lives, and they're perfectly prepared to use the 15 00:01:38.160 --> 00:01:53.160 and the Apparatus of the Government, whether that comes in the form of tax incentives or various subsidies or outright regulation that tells you what you can and cannot do with your property and how you must live. 16 00:01:53.160 --> 00:02:04.160 The point is that it is a very, very widespread sort of movement. 17 00:02:04.160 --> 00:02:09.160 And one of the particular principles of this movement is that they like people to live in big cities. 18 00:02:09.160 --> 00:02:16.160 They publicly advocate high density human habitation. 19 00:02:16.160 --> 00:02:22.160 And the theory there is that if you cram everybody into the cities, 20 00:02:22.160 --> 00:02:26.160 then this leaves all the rest of the area pristine for all the critters. 21 00:02:26.160 --> 00:02:46.160 And so, at least in principle, the thought process is that there are ecological benefits that result from the pattern in which we conduct ourselves, the human populations. 22 00:02:46.160 --> 00:03:03.160 And here is wording from this principle within Smart Growth that tells you what this link is between the spatial organization of human populations and the sort of ecological consequences they expect. 23 00:03:03.160 --> 00:03:13.160 You build the buildings vertically rather than horizontally and you get what somebody refers to as efficient use of land and resources. 24 00:03:13.160 --> 00:03:20.800 and Resources, and I sort of wonder what they mean by efficient, but here's what it literally 25 00:03:20.800 --> 00:03:25.320 says, it reduces the footprint of new construction, preserving green space to absorb and filter 26 00:03:25.320 --> 00:03:33.320 rainwater, reduce flooding and storm water drainage needs, and so forth. And I've emphasized 27 00:03:33.320 --> 00:03:39.640 that because in effect what the argument here is, is that somewhere, somehow, by doing all 28 00:03:39.640 --> 00:03:43.640 and all this stuff, the world's a better place, all right? 29 00:03:43.640 --> 00:03:49.640 That just jumps out at me and I think that's got to be testable, okay? 30 00:03:49.640 --> 00:03:52.640 So I'm going to test that sucker. 31 00:03:52.640 --> 00:04:01.640 Just to give you a little bit of reference about how powerful this smart growth set of philosophies and advocates has become, 32 00:04:01.640 --> 00:04:07.640 I did a Google search on myself this morning, 149,000 references, Google search on Ludwig von Mises, 33 00:04:07.640 --> 00:04:18.640 Google search on smart growth, 12 million references. Google search on free market economics, 12.3 million. 34 00:04:18.640 --> 00:04:24.640 So the smart growth advocates, there's a lot of attention being paid to them. 35 00:04:24.640 --> 00:04:35.640 So what I'm interested in pursuing then is the question, what's wrong with a picture? 36 00:04:35.640 --> 00:04:44.640 The Smart Growth Advocates are perfectly happy to use the public sector to guide, as it were, how we live our lives. 37 00:04:44.640 --> 00:04:49.640 And their willingness to do that, it seems to me, is founded on two very, very strong assumptions. 38 00:04:49.640 --> 00:04:59.640 The first is that in fact there is this link between how we live our lives in a spatial context and ecological consequences. 39 00:04:59.640 --> 00:05:04.640 And then secondly, if there is some link, let's assume that number one is correct, 40 00:05:04.640 --> 00:05:25.640 If there is some link that command and control policies or subsidies or the sort of standard instruments of government intervention in the economy are, you know, sort of the efficient way of doing that, I'm not going to deal with number two because I don't need to deal with number two if I can show that number one has no basis to it. 41 00:05:25.640 --> 00:05:41.640 So that's what I want to explore. I want to explore that link between how we organize ourselves spatially and some indicator of ecological wellness as it were. 42 00:05:41.640 --> 00:05:54.640 So let's explore this link. In forestry, I can tell you exactly what the argument is because my colleagues in the School of Forestry and Wildlife Sciences make this argument. 43 00:05:54.640 --> 00:06:09.640 If you plant trees very densely in what we call managed forests, so they've got all that spacing worked out to what the optimal spacing for planting is and you fertilize them and you grow them thick and you grow them fast and you cut them, 44 00:06:09.640 --> 00:06:22.640 that in effect what that permits you to do is with plantation forestry you cut the same amount of timber volume on a smaller amount of land which allows sort of a lot of other land to be freed up for natural purposes. 45 00:06:22.640 --> 00:06:28.640 And if you're not doing that, then you're cutting the same volume of timber off of a much larger area of land. 46 00:06:28.640 --> 00:06:38.640 So there's that issue of density. Grow them thick, grow them in a small place, and you can get what you need. 47 00:06:38.640 --> 00:06:45.640 And that somehow there's these ecological benefits compared to letting the trees grow naturally and cut out of that natural area. 48 00:06:45.640 --> 00:07:06.640 And here's, it seems to me, the suggestion that's being made there is that in the aggregate, and I've got aggregate emphasized here, is that in the aggregate, this sort of managed population of trees has ecological benefits overall as compared to just growing them naturally and cutting them naturally. 49 00:07:06.640 --> 00:07:19.640 So now, let's look at the smart growth analogy. The smart growth analogy to what I see in forestry is almost identical. 50 00:07:19.640 --> 00:07:23.640 So here's a passage from the smartgrowth.org website. 51 00:07:23.640 --> 00:07:31.640 As we build, we replace our natural landscape, forests, wetlands, grasslands with streets, parking lots, rooftops and other impervious surfaces. 52 00:07:31.640 --> 00:07:46.640 The effect of this conversion is that stormwater, runoff which prior to development is filtered and captured by natural landscape, is trapped above impervious surfaces and runs off into streams, lakes and estuaries picking up pollutants along the way. 53 00:07:46.640 --> 00:07:54.640 Runoff can be reduced through clustering of development. There's the analogy to intensively managed forests, right? 54 00:07:54.640 --> 00:07:59.720 and thereby leaving larger open spaces and buffers although compact development 55 00:07:59.720 --> 00:08:04.880 generates higher runoff and pollutant loads within the development total 56 00:08:04.880 --> 00:08:08.840 runoff and pollutant loads are offset by reductions in surrounding undeveloped 57 00:08:08.840 --> 00:08:15.120 areas that is a the perfect analogy and so implicitly here the arguments being 58 00:08:15.120 --> 00:08:22.040 made that when you look at the world overall there are better patterns of 59 00:08:22.040 --> 00:08:24.640 Development and Worst Patterns of Development. 60 00:08:24.640 --> 00:08:29.160 And the better pattern of development is this one, where you put all those people, cram 61 00:08:29.160 --> 00:08:36.320 them into big cities, and you leave the countryside untrammeled. 62 00:08:36.320 --> 00:08:40.360 I can tell you who a big proponent of this is, probably the most eminent biologist in 63 00:08:40.360 --> 00:08:46.080 the world today, Ed Wilson at Harvard, came down here a couple of years ago, and he told 64 00:08:46.080 --> 00:09:07.080 A couple of things that seems to me are fairly self-evident, and you'll see that they're borne out by the data that I'll talk through here in a minute. 65 00:09:07.080 --> 00:09:15.080 First of all, the ecological success of any species creates opportunities for some species and imposes costs on others. 66 00:09:15.080 --> 00:09:23.080 In the words of economics, a successful species crowds out. Species that are competitive with that species. 67 00:09:23.080 --> 00:09:29.080 Okay, so surely in terms of things that we're very familiar with, population density, 68 00:09:29.080 --> 00:09:33.080 the more people there are, yeah, it's going to create problems for other species. 69 00:09:33.080 --> 00:09:36.080 Okay, the more black bears there are, the more deer. 70 00:09:36.080 --> 00:09:42.080 How many of you have azaleas that are suffering at the moment under the onslaught from a bunch of hungry deer? 71 00:09:42.080 --> 00:09:52.080 The point is a pretty simple one. Yeah, we know that human population matters and economics gives us a convenient way of thinking about that through the crowding out effect. 72 00:09:52.080 --> 00:10:02.080 And population density is probably the most widely used measure of human presence in a particular location. 73 00:10:02.080 --> 00:10:09.360 and you'll see that indeed population density consistently matters in models 74 00:10:09.360 --> 00:10:13.920 we're looking at some measure of ecological disturbance or distress okay 75 00:10:13.920 --> 00:10:18.360 now here it seems to me is the probably the most critical line in my 76 00:10:18.360 --> 00:10:22.880 presentation is this next one population density by itself may not be a 77 00:10:22.880 --> 00:10:29.680 consistent metric of human presence of the impact of human presence okay and by 78 00:10:29.680 --> 00:10:59.680 By way of illustrating this, let me refer to a researcher at Michigan State named Jack Lu, he is an ecologist, and he makes the following argument, that if you look at two equivalent populations in two equivalent areas, so you got the same population densities, then would you expect those two identical population densities to draw the same resources from the natural resource base? 79 00:10:59.680 --> 00:11:25.680 The answer is not necessarily, and his argument is that in one case, if that population is organized into more households, smaller number per household, but more households, there's a larger draw on the natural resource base than if that population is organized into fewer numbers of households of a larger average size. 80 00:11:25.680 --> 00:11:30.320 okay because you end up with more houses in this one here more appliances 81 00:11:30.320 --> 00:11:35.240 more of everything which which implies a bigger draw on the resource base 82 00:11:35.240 --> 00:11:38.320 okay so he's the first one that I know of and his 83 00:11:38.320 --> 00:11:42.840 his Jack Lew's paper just came out in Nature about two years ago a little over 84 00:11:42.840 --> 00:11:43.920 two years ago 85 00:11:43.920 --> 00:11:49.120 he's the first one that I know of who has suggested that 86 00:11:49.120 --> 00:11:58.020 Not all population densities are created equal, okay? 87 00:11:58.020 --> 00:12:04.020 And indeed, I think there's a very simple way of thinking about the impact of population. 88 00:12:04.020 --> 00:12:10.320 So if you consider two states are identical in all respects except the distribution of their respective populations, 89 00:12:10.320 --> 00:12:16.620 would you expect the ecological footprint of man to be the same in both cases? 90 00:12:16.620 --> 00:12:25.620 Our knee-jerk reaction is to say, probably not. So, pick this example. You've got two states. They've both got equivalent populations of one million each. 91 00:12:25.620 --> 00:12:33.620 In one state, the urban population is 75% and in another state, the urban population is only 10%. 92 00:12:33.620 --> 00:12:42.620 Would you expect, they've got the same populations, same areas, same population densities. One is just more concentrated than the other. 93 00:12:42.620 --> 00:12:47.620 Would you expect the ecological consequences to be the same? 94 00:12:47.620 --> 00:12:52.620 And as I say, you need your reactions to say, well, of course not. 95 00:12:52.620 --> 00:12:55.620 Well, let's take that one step further then. 96 00:12:55.620 --> 00:13:01.620 Consider two states with identical urban populations. 97 00:13:01.620 --> 00:13:04.620 But in all other respects, they're identical. 98 00:13:04.620 --> 00:13:11.620 Would you expect this ecological footprint associated with urbanization to be identical in the two states? 99 00:13:11.620 --> 00:13:20.620 So here we've got those same two states, a million population, which means that for the state as a whole, they got the same population density, right? 100 00:13:20.620 --> 00:13:34.620 And in this case, the urban population is distributed entirely in one city of a million, and in that state, it's distributed in 50 cities, each with a population of 20,000. 101 00:13:34.620 --> 00:13:40.620 You expect the ecological effects to be the same of that human population? 102 00:13:40.620 --> 00:13:45.620 And my guess is most of us think about that and they say, well, there's got to be differences. 103 00:13:45.620 --> 00:13:53.620 And yet, you ought to just put the brake on your natural tendency to say, yeah, go get them. 104 00:13:53.620 --> 00:13:56.620 We've got to do something to save the environment. 105 00:13:56.620 --> 00:13:58.620 Those million people have got to eat. 106 00:13:58.620 --> 00:14:03.620 And it doesn't matter whether they're located in a city or in one city or located in 50 cities. 107 00:14:03.620 --> 00:14:05.620 They've still got to eat. 108 00:14:05.620 --> 00:14:08.620 They've got to get drinking water. 109 00:14:08.620 --> 00:14:15.820 They're probably all moving around. They're probably all wearing clothes, maybe. 110 00:14:15.820 --> 00:14:24.740 And so they're drawing on a resource base. They're both in principle drawing on at least 111 00:14:24.740 --> 00:14:29.620 some resource base that they share no matter where they're living. 112 00:14:29.620 --> 00:14:34.220 So your knee-jerk reaction gets tempered pretty quickly by the realization that they're drawing 113 00:14:34.220 --> 00:14:41.220 Coming from a resource base that probably is pretty similar no matter how the human population is configured. 114 00:14:44.660 --> 00:14:49.220 So here's what I set out to do, is I want to investigate whether the ugly facts actually 115 00:14:49.220 --> 00:14:55.960 support what the smart growth crowd believes is a very elegant theory, which is that we 116 00:14:55.960 --> 00:15:00.400 ought to cram all those people into a big city and that there are ecological benefits 117 00:15:00.400 --> 00:15:03.680 to doing so. 118 00:15:03.680 --> 00:15:07.000 And I'm going to do this in the following manner. 119 00:15:07.000 --> 00:15:09.240 I do not want to get into a shouting match 120 00:15:09.240 --> 00:15:13.000 with a bunch of ecologists about specific ecological effects 121 00:15:13.000 --> 00:15:14.160 of cities. 122 00:15:14.160 --> 00:15:16.800 So in other words, I'm willing to concede, 123 00:15:16.800 --> 00:15:20.160 yeah, big cities may have runoff associated 124 00:15:20.160 --> 00:15:21.560 with all that impervious surface. 125 00:15:21.560 --> 00:15:24.040 OK, I don't have a problem with that. 126 00:15:24.040 --> 00:15:29.680 To me, the issue is, are there broad aggregate indicators 127 00:15:29.680 --> 00:15:35.280 of Environmental Well Being that can tell us whether any little bitty effects, specific 128 00:15:35.280 --> 00:15:42.340 effects that may be occurring, end up somehow really mattering in terms of the environment. 129 00:15:42.340 --> 00:15:49.680 And so I'm going to use a very aggregate indicator which is the presence or the intensity of 130 00:15:49.680 --> 00:15:54.220 ecologically imperiled species because they'll tell you whether or not there's something 131 00:15:54.220 --> 00:16:21.220 And what I'm going to do then is I've got data, this will be more apparent as we go on, but I'm going to essentially run multivariate regression analysis of a following general relationship, the species fragility in an area, as dependent on a number of ecological factors and a number of human induced factors. 132 00:16:21.220 --> 00:16:33.100 Among the ecological factors that I might potentially look at, if I'm looking at the 133 00:16:33.100 --> 00:16:39.540 number of endangered species, then you surely want to control for the total number of species. 134 00:16:39.540 --> 00:16:45.140 You almost certainly want to control for the number of endemic species. 135 00:16:45.140 --> 00:16:49.900 And those of you who do not know what endemics are, endemics are species that are unique, 136 00:16:49.900 --> 00:16:53.700 only found in a particular area and nowhere else. 137 00:16:53.700 --> 00:16:58.460 It turns out there's a very strong relationship between the number or 138 00:16:58.460 --> 00:17:02.800 percent of endemic species in a country or in a state or in a location 139 00:17:02.800 --> 00:17:05.540 and the number that are ecologically imperiled 140 00:17:05.540 --> 00:17:09.780 because the definition of endemic means that if they're found here and nowhere else it means 141 00:17:09.780 --> 00:17:13.300 they've carved out a little niche for themselves which means there probably aren't a lot of them, 142 00:17:13.300 --> 00:17:19.100 which automatically sort of clues you in maybe they're sort of ecologically fragile. 143 00:17:19.100 --> 00:17:34.500 And then you can look at a lot of indicators about those ecological niches, like annual rainfall, miles of coastline, average height above sea level. 144 00:17:34.500 --> 00:17:44.700 There are a lot of geophysical indicators that might clue you in to the extent of environmental diversity within a location. 145 00:17:44.700 --> 00:17:51.100 And then in terms of human sorts of variables, population density would be at the top of your list. 146 00:17:51.100 --> 00:17:55.220 We crowd out other species, no doubt. 147 00:17:55.220 --> 00:18:00.820 The urban population, the degree of urbanization. 148 00:18:00.820 --> 00:18:06.500 Here's the one I'll be particularly interested in, which is the distribution of that urban population. 149 00:18:06.500 --> 00:18:12.980 And then things like per capita income, education, maybe the more highly educated a population you have, 150 00:18:12.980 --> 00:18:18.980 The more interest there is in being environmentally friendly. 151 00:18:18.980 --> 00:18:23.220 I don't know. 152 00:18:23.220 --> 00:18:25.260 Here's some issues that I had to deal with. 153 00:18:25.260 --> 00:18:32.860 First issue is, what am I going to use as a measure of species fragility? 154 00:18:32.860 --> 00:18:37.620 Second is, what am I going to use as a measurement of the distribution of urbanization? 155 00:18:37.620 --> 00:18:46.340 and the third is, what level am I going to be permitted to conduct the analysis at? 156 00:18:46.340 --> 00:18:49.820 This will become more apparent in a little while. 157 00:18:49.820 --> 00:18:54.020 The ecologists all want you to conduct an analysis at a very, very micro scale, a very 158 00:18:54.020 --> 00:18:59.540 small scale, because they're convinced if you pour concrete here and you're covering 159 00:18:59.540 --> 00:19:05.020 up some little flower, that by definition, that means the world's a worse place. 160 00:19:05.020 --> 00:19:08.020 And they don't like to think in terms of larger scales. 161 00:19:08.020 --> 00:19:10.300 And so the scale issue, it turns out, 162 00:19:10.300 --> 00:19:14.700 is a very, very big issue in ecology. 163 00:19:14.700 --> 00:19:20.420 The guy I did the first part of this analysis with, Roger Brown, 164 00:19:20.420 --> 00:19:21.860 when he defended his dissertation, 165 00:19:21.860 --> 00:19:23.940 this was a chapter of it. 166 00:19:23.940 --> 00:19:27.180 And all of my colleagues just absolutely came unglued. 167 00:19:27.180 --> 00:19:29.940 We had a shouting match in his defense. 168 00:19:29.940 --> 00:19:32.700 And the dean of forestry was just shocked, 169 00:19:32.700 --> 00:19:35.300 because nothing like this had ever happened before. 170 00:19:35.300 --> 00:19:37.700 But it all boiled down to a scale issue. 171 00:19:37.700 --> 00:19:40.940 And so we've had to be pretty sensitive to this. 172 00:19:40.940 --> 00:19:44.440 So the metric for species fragility. 173 00:19:44.440 --> 00:19:46.780 I can tell you, because I've done the work, 174 00:19:46.780 --> 00:19:50.100 that if you look at a conventional measure 175 00:19:50.100 --> 00:19:53.460 of ecological fragility of species like listings 176 00:19:53.460 --> 00:19:55.720 under the Endangered Species Act, 177 00:19:55.720 --> 00:20:02.100 that those listings are not completely unbiased. 178 00:20:02.100 --> 00:20:06.140 They are subject to political forces. 179 00:20:06.140 --> 00:20:09.420 There have been several papers that demonstrate this. 180 00:20:09.420 --> 00:20:15.940 And so we rely on a listing put out by a company called NatureServe. 181 00:20:15.940 --> 00:20:19.580 NatureServe is a derivative organization 182 00:20:19.580 --> 00:20:21.380 of the Nature Conservancy. 183 00:20:21.380 --> 00:20:23.800 They split away from the Nature Conservancy several years ago 184 00:20:23.800 --> 00:20:28.420 to concentrate on purely monitoring and providing 185 00:20:28.420 --> 00:20:31.660 statistical information about endangered species. 186 00:20:31.660 --> 00:20:51.660 and they have their own metrics for endangerment and are, I think, are widely regarded as these essentially unbiased source of information about what they call species that are at risk of extinction. 187 00:20:51.660 --> 00:20:55.660 The distribution of urbanization. 188 00:20:55.660 --> 00:21:04.660 Economics has a lot to offer actually in this discussion because we've got a very convenient measure of concentration in economics. 189 00:21:04.660 --> 00:21:12.160 It's called the Gini coefficient and the Gini coefficient ranges on a scale from 0 to 1. 190 00:21:12.160 --> 00:21:17.660 This is formally how it's calculated. I'm not going to march through the calculation. 191 00:21:17.660 --> 00:21:20.960 I'll show you a nice diagram that will depict it in a minute. 192 00:21:20.960 --> 00:21:39.960 But the point is that on a scale from 0 to 1, with 1 being highly concentrated and 0 being very diffuse, there's a very nice measure which tells you whether the population is spread out evenly over an area, uniformly, or whether the population is highly concentrated into particular locations. 193 00:21:39.960 --> 00:21:43.960 So now I hope it becomes apparent what I'm going to do. 194 00:21:43.960 --> 00:21:53.960 I'm going to look at the population and I'm going to apply that measure to it and I can tell for a state, all across a state or all across any unit I want to look at, 195 00:21:53.960 --> 00:21:58.960 whether the population of humans is concentrated or whether it's diffused. 196 00:21:58.960 --> 00:22:03.960 And I can tell whether that has an impact on species fragility. 197 00:22:03.960 --> 00:22:05.960 So that's where I'm headed. 198 00:22:05.960 --> 00:22:11.960 Here's what the Gini coefficient looks like, diagrammatically. 199 00:22:11.960 --> 00:22:22.960 This diagonal line here, in effect, represents purely uniform distribution of humans across whatever area they inhabit. 200 00:22:22.960 --> 00:22:30.960 So 20% of the people would be spread over 20% of the area, 40% of the people would be spread over 40% of the area, and so forth. 201 00:22:30.960 --> 00:22:37.960 So this would represent purely uniform distribution of people across the area you're talking about. 202 00:22:37.960 --> 00:22:42.960 Deviations from that, concentration would follow this curved line. 203 00:22:42.960 --> 00:22:49.960 So what this would really mean here is if this line was really spread all the way over here, 204 00:22:49.960 --> 00:22:54.960 then you'd end up with 95% of the people in 20% of the area. 205 00:22:54.960 --> 00:22:58.960 And so the population would be very skewed and very concentrated. 206 00:22:58.960 --> 00:23:14.960 And in the limit, if you went all the way along the edge here, and you had 100% of your people in, you know, 0.01% of the land area, then you'd have an extremely concentrated population. 207 00:23:14.960 --> 00:23:19.800 So this Gini coefficient is a sort of a convenient way that economists have 208 00:23:19.800 --> 00:23:24.400 to measure how concentrated the population, human population is in any 209 00:23:24.400 --> 00:23:33.400 geographic area you choose to consider. 210 00:23:33.520 --> 00:23:36.840 And the question is how are we going to measure 211 00:23:36.840 --> 00:23:39.580 human presence as it were? Well, 212 00:23:39.580 --> 00:23:43.560 we can measure population directly from the census, 213 00:23:43.560 --> 00:23:54.560 We can measure the number of households from the census, which sort of gets more towards incorporating Jack Lew's concept of the humankind's impact on the environment. 214 00:23:54.560 --> 00:24:01.560 We can look at roads. We can look at the distribution of nighttime lights. 215 00:24:01.560 --> 00:24:09.560 A lot of mapping satellite imagery has now become available, which would allow you, in effect, to take a given area, look at all the pixels, 216 00:24:09.560 --> 00:24:18.560 Add up all the pixels that are in a certain light category and divide by the total number of pixels in the area and you get a measure of nighttime light concentration. 217 00:24:18.560 --> 00:24:23.560 And impervious surface area, we're actually going to look at the first four of those. 218 00:24:23.560 --> 00:24:28.560 So I wasn't content just to look at the number or the distribution of population. 219 00:24:28.560 --> 00:24:37.560 I wanted to deliberately look at alternative measures to see whether I got the same results. 220 00:24:37.560 --> 00:24:43.360 Here's the scale, part of the scale issue is a data issue. 221 00:24:43.360 --> 00:24:48.960 Ideally the ecologists want us to keep looking more and more and more and more closely, 222 00:24:48.960 --> 00:24:51.360 but the problem is a data problem. 223 00:24:51.360 --> 00:24:56.760 At the state level in the U.S. I can get data on all of the variables I need. 224 00:24:56.760 --> 00:25:03.360 But the particular variable that you have to have in a model is the number of endemic species to an area. 225 00:25:03.360 --> 00:25:06.560 And that data only exists at the state level. 226 00:25:06.560 --> 00:25:15.360 It's slowly becoming available at the county level, but we don't have a lot of them yet. 227 00:25:15.360 --> 00:25:22.600 At the sub-county level, the data problems become overwhelming. 228 00:25:22.600 --> 00:25:27.560 It's possible, I would guess, within five years to do a county-level analysis. 229 00:25:27.560 --> 00:25:32.000 You can also do this on a transnational level, which means you can look at countries as your 230 00:26:02.000 --> 00:26:09.260 or at-risk species in each state against all these explanatory variables why do we 231 00:26:09.260 --> 00:26:16.960 exclude Hawaii because islands are just different everything about an island is 232 00:26:16.960 --> 00:26:23.400 different the in particular the critters can't escape if some exotic being comes 233 00:26:23.400 --> 00:26:26.360 in from the outside that the species aren't prepared to deal with 234 00:26:26.360 --> 00:26:29.360 Ecologically speaking, they can't escape. 235 00:26:29.360 --> 00:26:32.480 And so the effects are magnified. 236 00:26:32.480 --> 00:26:36.200 This is very, very well known in the ecological literature. 237 00:26:36.200 --> 00:26:40.240 And so we have omitted Hawaii from our analysis of the states. 238 00:26:40.240 --> 00:26:44.280 I'll show more of this to you presently. 239 00:26:44.280 --> 00:26:46.440 So what do we find? 240 00:26:46.440 --> 00:26:50.000 Across the 49 states, here's our means. 241 00:26:50.000 --> 00:26:53.920 I particularly want to point out a couple of things. 242 00:26:53.920 --> 00:26:56.160 The Gini coefficients. 243 00:26:56.160 --> 00:27:03.280 Genie coefficients are very, very consistent for three of the four measures of human presence. 244 00:27:03.280 --> 00:27:08.620 The mean genie coefficient is at about 0.8 or so. 245 00:27:08.620 --> 00:27:13.440 With roads, it's different just because roads don't necessarily always follow the human population. 246 00:27:13.440 --> 00:27:20.320 Sometimes they just go off because the roads are all decided by politicians' pork barreling. 247 00:27:20.320 --> 00:27:25.080 But the other thing I want to point out about these genie coefficients is the minimum. 248 00:27:25.080 --> 00:27:32.480 The minima Gini coefficient on those three indicators is up close to between 0.55 and 0.6. 249 00:27:32.480 --> 00:27:45.680 So even the minimum degree of concentration is fairly concentrated. 250 00:27:45.680 --> 00:27:47.980 And here's our regression model. 251 00:27:47.980 --> 00:27:52.980 We're going to, our dependent variables is the percent of endangered species in a state. 252 00:27:52.980 --> 00:28:01.880 We're going to control for the percent of endemic species in that state, the area of the state, the population. 253 00:28:01.880 --> 00:28:10.180 Once you've fixed area, then when you enter population, in effect, you've entered population density. 254 00:28:10.180 --> 00:28:17.180 Here's our measure of the concentration of that population in the state. 255 00:28:17.180 --> 00:28:22.280 And then we have a set of regional dummies, dummy variables. 256 00:28:22.280 --> 00:28:29.280 And then I will replace population density, I will replace systematically with the other measures of human presence. 257 00:28:29.280 --> 00:28:37.280 So I'll look at households, I'll look at roads, and I'll look at nighttime lights. 258 00:28:37.280 --> 00:28:42.280 And here is a very, very busy figure. So I'll explain it fairly simply. 259 00:28:42.280 --> 00:28:54.580 Firstly, first of all, the percent of endemic species is by far, in a way, the biggest explanatory variable with respect to the percent of ecologically fragile species in the state. 260 00:28:54.580 --> 00:29:05.580 Very consistently, you will get an extremely high, large coefficient and extremely significant statistical explanatory power. 261 00:29:05.580 --> 00:29:15.580 Look at these four variables. These are the indicators of population density or human presence. 262 00:29:15.580 --> 00:29:22.580 They all vary systematically, are highly significant indicators. No question about it. 263 00:29:22.580 --> 00:29:27.580 Humans crowd out other species or they stress other species. 264 00:29:27.580 --> 00:29:32.580 That's a very consistent finding with the literature. I'm not disputing that at all. 265 00:29:32.580 --> 00:29:37.580 Here, it seems to me, is what the issue is under dispute. 266 00:29:37.580 --> 00:29:44.580 The next four measures are measures of the spatial arrangement of those humans. 267 00:29:44.580 --> 00:29:51.580 And the smart growth folks claim that the spatial organization matters. 268 00:29:51.580 --> 00:29:58.580 Right? They say the more you put those folks into the big cities, the better off we ought to be ecologically. 269 00:29:58.580 --> 00:30:06.580 I can't find it. I can't find it and I don't care how you define the human population. 270 00:30:06.580 --> 00:30:15.580 I cannot get a statistically significant coefficient on a variable looking at the concentration of the human population. 271 00:30:15.580 --> 00:30:26.580 We note some regional effects that are pretty significant and I don't have yet a ready explanation for the regional effects. 272 00:30:26.580 --> 00:30:38.580 Let me turn now to our international analysis, which I'm still in the process of marching through with my doctoral student, Ron Pandit. 273 00:30:38.580 --> 00:30:52.580 We've got data from about 124 countries around the world where we can get data on endemic species, total species, threatened and endangered species, and all the other variables that we are looking at. 274 00:30:52.580 --> 00:31:02.580 What would you expect? Would you expect human populations to be more spread out or less spread out in other countries than the United States? 275 00:31:02.580 --> 00:31:06.580 What do you think would happen to the Gini coefficient around the world? 276 00:31:06.580 --> 00:31:14.580 I was expecting it to go down. I was expecting there to be some countries where all the people were still out in the bush living in the Stone Age. 277 00:31:14.580 --> 00:31:18.580 Okay, so they're all spread out. Well, it doesn't happen. 278 00:31:18.580 --> 00:31:28.580 That's why I focused on this a little while ago. Look at the minimum Gini coefficient for population for all the countries around the world. 279 00:31:28.580 --> 00:31:35.580 It's almost as high on average for all the countries of the world as it was just for the United States. 280 00:31:35.580 --> 00:31:41.580 So it turns out that concentration of human populations is very high all around the world. 281 00:31:41.580 --> 00:31:47.660 in the world. And when I first started this, I had people very specifically say your analysis 282 00:31:47.660 --> 00:31:50.880 of the United States is no good because the United States is just not the same as it is 283 00:31:50.880 --> 00:31:57.880 everywhere else. Well, sorry for those ugly facts rearing their head again. It turns out 284 00:31:57.880 --> 00:32:01.440 the United States is a lot like the rest of the world. The rest of the world, human populations 285 00:32:01.440 --> 00:32:10.840 Cluster Together 286 00:32:10.840 --> 00:32:16.480 We have actually added one additional variable into the next set of models we ran internationally. 287 00:32:16.480 --> 00:32:21.560 We knew for each country the percentage of protected areas of the land base that had 288 00:32:21.560 --> 00:32:24.800 been set aside in protected areas for wildlife. 289 00:32:24.800 --> 00:32:30.200 And so we were able to enter that as an additional explanatory variable. 290 00:32:30.200 --> 00:32:38.200 It turns out that it never matters. 291 00:32:38.200 --> 00:32:42.200 Set that land aside and the poachers still get on there and kill the critters. 292 00:32:42.200 --> 00:32:51.200 So the percent of protected area in a country just doesn't seem to influence the fraction of endangered species in a country. 293 00:32:51.200 --> 00:32:58.200 But once again for all around the world when we look at the percent of endemic species, 294 00:32:58.200 --> 00:33:04.200 Exceptionally Highly Significant as an explanatory variable for the percent of endangered species. 295 00:33:08.200 --> 00:33:14.200 Population density matters, just the way you expect it to. More people, more endangerment. 296 00:33:16.200 --> 00:33:22.200 This is the Gini coefficient of population. Look at this. Gini coefficient matters. 297 00:33:22.200 --> 00:33:38.200 I'll come back to that. This is per capita GDP. This is like, this is a variable that we enter in to control for what's known as the environmental Kuznets curve. Anybody know what the environmental Kuznets curve is? 298 00:33:38.200 --> 00:33:53.200 Environmental Kuznets curve in a nutshell is a statistical relationship between the economic well-being of people in a country or a state and indicators of environmental stress. 299 00:33:53.200 --> 00:34:01.200 So the logic runs as follows. When you're absolutely dirt poor and you're struggling to survive, you eat those critters because you're just struggling to survive. 300 00:34:01.200 --> 00:34:17.200 25, as you develop economically, and you can feed yourself and you've got clothing and you've got houses, you start to appreciate the finer things in life, like saving all those critters because you like looking at them rather than eating them. 301 00:34:17.200 --> 00:34:22.200 So there ends up being this relationship between income or economic well-being. 302 00:34:22.200 --> 00:34:29.200 As you get more and more, you're willing to trash your environment to gain a little bit economically, over some range. 303 00:34:29.200 --> 00:34:36.200 And then as you continue to develop economically, your trashing of the environment diminishes, where you take better care of the environment. 304 00:34:36.200 --> 00:34:43.200 So there's what's known as an inverted U-shaped relationship between economic well-being and species fragility. 305 00:34:43.200 --> 00:34:51.200 And we have got a number of different formulations where we look at this relationship and we don't find much evidence of it. 306 00:34:51.200 --> 00:34:57.200 Now, back to this one. This is my variable of interest. 307 00:34:57.200 --> 00:35:04.200 Look what happens when we add a dummy variable for whether or not the country was an island. 308 00:35:04.200 --> 00:35:06.200 That's this set of results right here. 309 00:35:06.200 --> 00:35:15.200 Island countries matter. They got lots of fragile species. 310 00:35:15.200 --> 00:35:26.200 And it matters so much that it completely invalidates any notion that the distribution of people matters. 311 00:35:26.200 --> 00:35:35.200 The spatial distribution of people matters. And for that matter, it drives out of significance even population density. 312 00:35:35.200 --> 00:35:50.200 Okay, so you better, this is clearly a variable that matters in a model, and you better not just exclude it from your model because you think it doesn't matter and that island countries are somehow, you know, not worth including in your model. 313 00:35:50.200 --> 00:35:58.200 And here's why. Look at the differences between island countries and non-island countries. 314 00:35:58.200 --> 00:36:06.200 Population density in island countries is six times higher than population density in non-island countries. 315 00:36:06.200 --> 00:36:11.200 The percent of endemic species that live there and nowhere else is triple. 316 00:36:11.200 --> 00:36:17.200 The percent of ecologically fragile species is also more than triple, almost quadruple. 317 00:36:17.200 --> 00:36:23.000 quadruple. So these characteristics of island countries are just radically different than 318 00:36:23.000 --> 00:36:32.200 characteristics of countries that aren't island countries. So you've got to have them in there 319 00:36:32.200 --> 00:36:37.480 and when you put them in there, you just, you don't find any evidence that the spatial 320 00:36:37.480 --> 00:36:40.480 and the International Organization of Human Matters. 321 00:36:45.480 --> 00:36:48.480 So where do we go from here with our analysis? 322 00:36:48.480 --> 00:36:54.480 Well, first of all, we could address the scale issue. 323 00:36:54.480 --> 00:36:58.480 So we could come down to finer levels of analysis and say, 324 00:36:58.480 --> 00:37:01.480 okay, we're pretty sure that across the United States as a whole, 325 00:37:01.480 --> 00:37:09.800 How you organize people spatially doesn't seem to have aggregate environmental effects, 326 00:37:09.800 --> 00:37:16.080 but in smaller locations the spatial organization of people may indeed matter. 327 00:37:16.080 --> 00:37:26.400 So we'd like to be able to bring that analysis down to a finer scale and see whether the conclusions are generalizable from a large scale down into a finer scale. 328 00:37:26.400 --> 00:37:35.400 We still remain for us to be able to get the data on that. That's coming, but it's not available yet. 329 00:37:35.400 --> 00:37:47.400 Here's what I'd really like to do. I'd really like to not work with established political jurisdictions. 330 00:37:47.400 --> 00:37:55.400 Countries are artificial constructs, right? Somebody just drew a line and said, that's my country. Take it if you can. 331 00:37:55.400 --> 00:38:25.400 But animals don't know that and species don't know that and I think really the best test of what we're looking at would be to take a big map of the world and throw darts all around the world as random points and take a radius around each of those points and identify endangered species and then identify the spatial distribution of the humans and see whether in a purely random analysis, purely random, you could link the spatial distribution 332 00:38:25.400 --> 00:38:35.400 and artificial distribution of humans with some measure of species fragility. 333 00:38:35.400 --> 00:38:38.400 Maybe, maybe this can be done. 334 00:38:38.400 --> 00:38:41.400 I can do this in the U.S. because NatureServe can actually get me the data. 335 00:38:41.400 --> 00:38:45.400 If I give them a latitude and a longitude point, they can draw a radius around it 336 00:38:45.400 --> 00:38:48.400 and give me the information on endangered species. 337 00:38:48.400 --> 00:38:53.400 But I can't do this for an international analysis. 338 00:38:53.400 --> 00:39:00.800 Lastly, I'd like to disentangle my dependent variable. 339 00:39:00.800 --> 00:39:04.900 Remember, my dependent variable is just the number of imperiled species in a country, 340 00:39:04.900 --> 00:39:07.100 or the number of imperiled species in a state. 341 00:39:07.100 --> 00:39:14.600 Well, the problem with that is that not all imperiled species are created equal. 342 00:39:14.600 --> 00:39:19.900 Okay, and let me show you what I mean by that. 343 00:39:19.900 --> 00:39:25.900 It turns out that the species that are tracked, they track them by major taxa. 344 00:39:25.900 --> 00:39:29.900 So they've got the amphibians, they've got birds, they've got reptiles, they've got plants, 345 00:39:29.900 --> 00:39:32.900 they've got fishes, they've got invertebrates. 346 00:39:32.900 --> 00:39:37.900 But look at this, look at the number, look at the sample size. 347 00:39:37.900 --> 00:39:42.900 Vascular plants in our sample dominate, there's almost 6,000 of them. 348 00:39:42.900 --> 00:39:46.900 Look at 139, 214, 65. 349 00:39:46.900 --> 00:39:52.100 The average country in terms of its flora and fauna, in terms of its fauna, 350 00:39:52.100 --> 00:39:55.980 the numbers are much lower than for the vascular plants. 351 00:39:55.980 --> 00:40:01.220 So I think one argument that you can make is to say, LeBan, 352 00:40:01.220 --> 00:40:05.220 your results are dominated by the vascular plants. 353 00:40:05.220 --> 00:40:11.060 And people, the impact of people and how humans are configured spatially 354 00:40:11.060 --> 00:40:14.540 may not have an impact because, well, the plants just don't 355 00:40:14.540 --> 00:40:19.100 seem to be very sensitive to that, but if you looked at the mammals, for example, or 356 00:40:19.100 --> 00:40:26.460 you looked at the amphibians, that there may be taxa-specific effects that indeed you could 357 00:40:26.460 --> 00:40:32.260 identify if you were just focused on those rather than on aggregating all of these different 358 00:40:32.260 --> 00:40:39.260 taxa of species together in an analysis. And so this is surely the next step that we will 359 00:40:39.260 --> 00:40:44.260 that we'll actually be able to take in Ron Pandit's dissertation work. 360 00:40:44.260 --> 00:40:49.260 We'll split that out and see how sensitive the individual tax results are 361 00:40:49.260 --> 00:40:54.260 or whether they're consistent with the aggregate results that we have. 362 00:40:54.260 --> 00:41:04.260 But the bottom line is that, at least at the moment, in an analysis that I'm perfectly happy to admit is a relatively aggregated level, 363 00:41:04.260 --> 00:41:14.260 We don't find any scientific basis for the very strong statements that are made in the smart growth literature 364 00:41:14.260 --> 00:41:22.260 to the effect that there are these ecological benefits to cramming people into very densely populated cities. 365 00:41:22.260 --> 00:41:32.260 Surely there are localized effects to doing that, but in the aggregate, when we look at the composition of species fragility, 366 00:41:32.260 --> 00:41:37.260 We don't find any evidence that the spatial organization of humans matters. 367 00:41:37.260 --> 00:41:46.260 So I've been pretty blunt in my criticism of the foundation of the smart growth prescriptions for human populations. 368 00:41:46.260 --> 00:41:55.260 And incidentally, Auburn, the city of Auburn, is a smart growth city, if you did not know that. 369 00:41:55.260 --> 00:42:02.260 So, it seems to me there's a lack of science that underpins what has become a very, very powerful movement. 370 00:42:02.260 --> 00:42:03.260 Thank you very much.