Georges FarinaInstituut voor Milieuvraagstukken
Georges Farina
Ph.D
Postdoctoral Researcher at the Institute for Environmental Studies (VU Amsterdam)
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Introduction
Publications
Publications (4)
Many cities are developing infiltration infrastructures aimed at restoring natural hydrological processes. Among those, Nature-Based Solutions (NBS) can generate co-benefits, which contribute to cities’ adaptation to climate change. However, choosing optimal locations for infiltration infrastructures within limited available urban space is a comple...
The Lez catchment is characterized by a rapid urbanization, due to the attractiveness of the city of Montpellier, and is exposed to a typical Mediterranean weather with high risk of flash flood and other emerging issues, such as air pollution, heat island effects and biodiversity losses. We present the evaluation of two types of NBS to address thes...
This article consists of a proposition of analysis of institutional (hidrological) risk-related speeches. From the perspective of reading the graphical imaginary dimension of risk atlases, I try to give basic analysis elements of risk construction, and its definition within the theoretical corpus produced by various institutions, specifically in th...
Questions
Questions (2)
Hi everyone,
I want to ask a (not so) quick question about ecosystem services economics, urban planning, sustainable water drainage and cost-benefit analysis.
Whenever comparing different scenarii of urban greening or urban planning, in a cost-benefit anaysis (CBA), opportunity costs have to be integrated to represent the whole array of costs associated with a plan. The definition by Buchanan (1992) is good enough here : "Opportunity cost is the evaluation placed on the most highly valued of the rejected alternatives or opportunities", which is to say, it is the value that is sacrificed in any choice in a decision making situation. This cost is usually valued as the highest benefits that could have been produced by a choice, but that was forgone by the actual choice.
Now, when the subjects of a CBA, that is, the scenarii, are firm or private indivdual investments, the notion of opportunity cost is pretty thorough : it is the net revenue from the best forgone investment. And that's it.
However when dealing with urban planning, as with many other real world economics, it becomes trickier. The opportunity cost of using urban space is almost exclusively represented in the literature with land or real estate prices. The underlying hypothesis there is that construction would always be the next best profitable option for urban land, and that land prices are its best reflection. Fair enough, although it may debatable, as not all the alternatives of land use for a given land have a clear net benefit flow, far from it. Who knows, perhaps, at times, the next most profitable option for public welfare would be represented by the net flow of ecosystem services generated by a natural conservation option ?..
Anyhow, what if we are valuating water drainage systems scenarios ? Small urban green bioswales alongside the roads, designed for managing sustainably rainwater and stormwater ? It does not really eats up property lands, but rather public space, streets and sidewalks. What about linear street parking spaces, if those are to be converted for creating urban parks and whatnot. Real estate prices would not be a fitting approximation for the lost foregone best benefit in these situations, it seems to me.
I have not found any alternatives in the literature for taking into account the opportunity costs of open or public space. Do you have any insights on this matter ? Any literature on geographic economics or urban economics modelling that deals differently with this ?
Many thanks for any contribution
Hi,
Ok so I'm all new to computer science and metaheuristics, and need to implement multi-objective optimization for an environemental economics problem with real world data.
The problem goes as this :
I have a large set of possible locations for an infrastructure within a city. Each location provides two benefits A and B, as well as costs (more benefits may be added in the future, depending on further research). Data points are unrelated, cost and benefit functions are not linear and not continuous.
I need a method for selecting a handful of locations that maximizes simultaneously A and B, under the constraint that the total costs < a budget parameter.
I went towards genetic algorithm (GA) for this problem, as it is is highly combinatorial, but I am facing the fact that most "traditional" GA I've looked at have fixed chromosome lengths, and ultimately only give out final individuals of n items. In my case, i am quite flexible on the quantity of best locations, as long as it either minimizes total costs, or handles it as a constraint. As a matter of fact, it would be quite interesting to have as a final result a pareto-front of individuals of different size (for example : in my problem, locations in city center have more "value" than locations in periurban areas, so a few centric locations could be as pareto-optimal as more numerous periurban locations). So I see the problem as a knapsack problem, where costs would be the "weight"; however there can't be any repetition of items here (a same location cannot be used twice).
Is there a way to handle costs constraint as to make a knapsack genetic algorithm that can provide a pareto front of individuals of heteogeneous length. I have been trying it with DEAP library but don't really find details in its documentation.
Many thanks
Georges Farina