[MUSIC] Hi, welcome back to the second session of Lesson 1. And we were getting in this session to do an analysis of three conceptual points that make the study of the nexus different in relation to the conventional approach in reduction in size of the conventional way we do numbers. First of all, the nexus requires the ability to characterize patterns over quantities of flows at different scales, and not just numbers. And the second point is that the nexus requires the ability of integrating information across both scales and dimensions. And the third one is that the nexus required the ability of handling impredicativity, since it is better to give practical examples of this concept to understand methods. So when we are dealing with a nexus, we are not dealing with individual numbers but about patterns. The different flows of water, energy, and food entangled, so we cannot then just analyze one of them at a time. And one very good example to explain the nature of the entanglement would be the example of the diet. This is something we are all familiar with. Let's imagine that we are considering in our diet three flows, energy, water, and protein. A specific nutrient will be [INAUDIBLE]. Food, in the analogy between water energy and food. And then these three flows are entangled when coming to typology of food item of your diet. So for instance, if you get 100 grams of milk, you get 140 kilo calories of energy, 70 grams of water, and 3 grams of protein. If you get beef, it would be 100, 70, and 21. If you get butter, you have a different profile, if you get bread, you get a different profile. What is the issue here? It's that we can not talk about energy or water. Or protein, we have to talk about milk, or beef, or bread. And when we are using one of these, we are getting a pattern of the three flows. You'll get one flow at a time. Only if you're looking at drinking water, then it would be easy, because then, drinking water is only water. But for the rest, it's not that simple. So when we are talking about the input of our diet, we are talking about possible typology of nutrient carriers that give you different profiles. Let's imagine now that we are going on the demand side, I want to know how much I have to eat of this type of flows per day for my diet. And I get another profile. This is the profile of the requirements. These are how much I have to eat of the quantity of this flow per day. It is clear now that we have a situation in which you have a pattern of requirements they want on the top. That is at one scale is the whole body, and another pattern of nutrient flows that is on the bottom line. But this time, these are not expressed in extensive variable, in quantitative bodies in typologies, quantity per 100 gram. It would go intensify. So what happen is that in order to get from the entanglement pattern of flows on the individual items, so the entanglement flow required by your diet, you have to do an operational scaling. So you have to combine these factors in a way that match this pattern. Of course, it's not always possible to have a perfect matching, and you will have some extra deficiency or extra surplus that is unused, because it is not always possible to have a 100% match. What you do if you want to do this, in general, you use a linear program, you use computers for doing this type of analysis. You don't do just any question. So when we are dealing with this problem, we cannot having a model that look only at the requirement of energy, or only the requirement of water, or only the requirement of proteins. You have to go for a pattern that you have to achieve and a pattern that you have as the sources for input. So the second point is that a quantitative characterization of the nexus, it is not using a scalar dimension of time but you have to handle different scale and different elements. In this case, we use another examples of the nexus and would be the production of a monoculture grain in the great plains in the United States in the '30s. So lets imagine we have a community of 1,000 people that are cultivating of 2,500 hectares. And they require 400 tons of grain for their diet, and 200 hectares of grain for producing them. Then they require 2,850 tons of grain for the market, for the income. And they require, for energy, the activity of 364 horses or mules. That is another 700 hectares of land for oats. So basically, this explain why they are using 2,500 hectares. These are for food, these are for money, and these are for energy, for the power that they require. So an important point is already, you'll see that there is an internal loop. So this is proportional to the quantity of hectare, and itself is requiring hectares, so this implies internal loop. And just to give you an example of what we are talking about is that at the beginning of the century, the 20th century before the arrival of mechanical harvester, this was a 30 horse power harvester. And of course, this was a pretty expensive things because the horses were needed at the moment of the harvest was the sea of grain. But then, after the harvesting season, basically, all these horses were many around eating and not being particularly useful, so the horse power was very expensive. So what do we have here? We could have a similar example that we saw before. You have an amount of energy, an amount of food, an amount of grain for the market that is consumed by the societies would be equivalent to what we saw before, not the mix of flows needed by the society. And you are below the combination of hectare and water required to give power, to give one horsemule activity, one tonnes of grain, or one tones for the food or for the market. And then by combing this information, we can also get how much land and how much water is required for this operation. Basically, you are multiplying the tonnes of water by the hectares of production, tonnes per hectare. And then you can do the same for The horses and the mule. So basically, in this way, you can obtain a relation between how much energy is required, how food is they required, how much market is required, and how much land and how much water. Of course, we can imagine other combinations of these characteristics, we can imagine different technical coefficient. We can imagine a different level of expenses or a different diet for the people, but that doesn't matter. You will have this set of relations that makes it possible to follow how these different numbers are correlated. And of course, in order to be capable of translating the requirement of biophysical quantities in an income. In terms of money, you will have to use economic variable here, nutritional variable here, and then technical variable here about the efficiency of the harvester or thresher that they use. The metabolic pattern of a social-ecological system describes how a society is producing and using the flows of food, water, and energy required to express its functions. So in a human society, this pattern is determined by different factors. Economic factor, demographic factor, technical factor, ecological factor. And can be studied only by adopting different scales, different dimensions or analysis. So rather than having models, they are putting everything into the system of accounting, give you just number, this is better than that. It is much better to keep the central relation of the tables, so you can see what are dereliction, and play with them. So the last point that I would like to discuss is the discussion of impredicativity. Impredicativity is a weird name. Again, it's a very exotic name, but basically, that we cannot, when dealing with complex systems, [INAUDIBLE] way, clear direction of causality that A is there because of B. It seems to be, again, a very term it is not. For instance, the government is ruling on the cities on one scale, their daily life, but the citizens are ruling on the government at the election time. The number of predator is affecting the number of the population of preys at one scale. But on another scale, the size of the preys is effecting the population of predators. This is commonly known as the chicken egg paradox. You must have a chicken to have eggs, or have an eggs to have a chicken. Which is the cause or what? So in general, these are impredicativity is not welcome in reduction because this is not something you can model with conventional models. An example of how these could be applied to what we've been discussing so far, and let's see what are the condition and define the sustainability of the community producing grain in the plain, in the US that we saw before, the example that we saw before. So basically, you have a problem of feasibility. If you have external biophysical consraints, you have processed outside human control, they can decide what you can do or you cannot do. You have enough soil, enough land. There is an appropriate climate. There is water. Is it raining enough. Then we have another factor. Viability is internal biophysical and economic constraints. Can you do, given the fact you're going to have external biophysical constraints, what you want to do? Is the profit okay? Are the cost affordable? Do you have the required know-how? Do you have the appropriate technology? So these are internal constraints. And then you have a third set of facts of their relevance. This is about desirability. What you are doing is compatible with the institutional normative values. It is acceptable, according to normal values, do we like it? And let's see what we mean with impredicative relation. For instance, in this case, in the case of the intensive farming in US plains, they didn't have external biophysical constraints. They have plenty of agricultural resources. So basically, they add an institutional system already. So they were maximizing economic returns. So they were, as a matter of fact, getting more than three tonnes of grain per capita. So we're selling this grain. So what was the problem? The problem, in this case, was internal constraints. It was called viability. There was a decreasing return to scale. The more they were enlarging the area production, the more they had to feed mules that they were used only at the moment of the harvesting. And the second point, they are right to avoid that 40% of the arable land, what used to feed the mules. Therefore, in this case, you have a case of an internal constraints limiting the expansion of the system. Let's imagine that we have another situation which you have a very severe shortage of agricultural resources. So basically, the institution and normative value define [INAUDIBLE] just trying to remind our life to die hard in your area. And then, basically, you are adopting a system of traditional knowledge that guarantee you to survive there. In this situation, the standard constraints is due to feasibility. You have something which is outside human control that is preventing the system for expanding further. Let's mention you have a third case in which you have a lot of land, and you would be capable of handling the things. But you are living in a religious community that they don't want to have fancy things, they don't want to have more money. They are happy with 400 kilos of grain per capita. And basically, adults traditional knowledge to guarantee their survival. But in this case, it is a cultural constraints affecting the impossibility of expanding of this community. What is the point of all this discussion? That this type of analysis can not be done by using just a model. Or at least, you should have to include all these discussion as exogeny input in the model. But there is another case that we could consider. Let's imagine that you have a situation in which the society has a very serious shortage of water, energy, and land. And less than 0.01 hectare per person, and still, they want to have a very high level of economic income that's more than $40,000 per capita. You can be able to do that, but you have to change the type of economies that you are running. For instance, an economy based on financial sector and trade. And this would be the case of Hong Kong. That in order to do that, us to rely in enormous amount of imports for providing the input required for the. So this a solution base and what is called, in general, externalization. You are externalizing the biophysical constraints to someone else. So in this way, you could escape the constraints of feasibility. Now, why is this important? Because as we will see in the other classes, this is the real issue, the way we are running the economy in many of the developed countries. Okay, so in conclusion, we can say that the quantitative analysis of the nexus between water, energy, and food is challenging because different flows are entangled into patterns. They are defined on different scales of analysis. So for instance, the profile of nutrients found in food products versus the profile of nutrients required in the diet. Second, you have different dimensional of analysis. You have an economic dimension, a social dimension, demographics dimension, technical dimension, ecological dimension. And we need to keep track in relation among numbers. They are defined of different scales and using different dimensions of analysis. There are impredicative relation over characteristics of the metabolic pattern in relation to feasibility. At times, it's the external constraints that are affecting what you do, or internal constraints, or cultural constraints. And then can affect each other in an impredicative way. It is not clear always which one is affecting determining the final configuration of the button. And the last that we see in the last example of Hong Kong are that all the metabolic system are open, and really, it matters to understand first of all how open is the system. Because one thing is that if you are having a problem with [INAUDIBLE] hectare, if you are a close subsistence society, or if you are [INAUDIBLE] hectare if you are in Hong Kong or in other big cities that is trading and in banking. [BLANK_AUDIO[
