Exercise 1)a)1. We calculate the FCF for year 1-10 and for year 11 (which we use to calculate theterminal value). The FCF in the excel is calculated by FCF = EBIT*(1 – Reinvestment Rate), which yields the same FCF as when using FCF = EBIT*(1-t) + Depreciation – Capex – Change in WCProof of the relation:\(Rr=(NetCapex+ΔWC)/(EBIT(1-t)\)\(1-Rr=(EBIT(1-t)-(netcapex+ΔWC))/(EBIT(1-t))\)     \(EBIT(1-t)*(1-Rr)=EBIT(1-t)-netcapex-ΔWC\)  \(Netcapex=capex-depreciation\)2. We calculate the unlevered betas (by using the equity beta, leverage and the calculated debt beta) of the competing firms. We then take a weighted average of these to compute the unlevered beta for the firm we are valuing. 3. Using the unlevered beta, we calculate the unlevered return, which we use to calculate the cost of equity. We also calculate the cost of debt, and finally we calculate the WACC. 4. We use the WACC to calculate the net present value of the free cash flows, and theterminal value, before adding these to get an enterprise value of 48’434 (we don’t add the base year (year 0)).b)1. We compute the FCF for each of the scenarios of drop in EBIT depending on the leverage ratio. We use the formula FCF = EBIT*(1-t)*(1-Reinvestment Rate), so that the percentage drop in EBIT is the same as the percentage drop in FCF.2. We compute the WACC for all the scenarios of the leverage ratios by doing a sensitivity analysis in excel, where we adjust the leverage ratio when calculating the different WACCs. 3. We then compute the NPV of all the FCF for each of the leverage ratio scenarios. We now compute the enterprise values by adding the NPV of all the FCF. The highest enterprise value is with a leverage ratio of 44% (if we only look at leverage ratios between 0-60%). The enterprise value of the firm when having a leverage ratio of 44% is 55’099. If we include decimal points, it is 44.99% (just before the drop in EBIT for a leverage of 45%, with an enterprise value of 55’586).
The air pollution, waste management collection and other environmental hot spots  are proved by scientific papers to be the result of a lack of environmental policy but moreover of social policy. This paper is trying to illustrate the correlation between the environmental quality and the level of the social indexes. As this paper is only a slight introduction on the previously mentioned relation we’ll focus on the proximity to the roads. The roads are the origin of many pollution sources: the noise induced by the lack of non-reducing noise concrete, the emission of NO2 and particulate matter and many others. As mentioned \cite{Stewart_2015} the more deprived population are under high pollution conditions but one might ask if the conditions of life gets better as the distance to the main roads is increasing.  Data We will use the data provided for the commune of Vernier in Geneva by the open access portal of the canton of Geneva . We used the resources on the roads, the number and the location of subsidies and the population in the commune of Vernier.    Methods This study's aim is to show the strong correlation between the social deprivation value and the low environmental indexes. Many studies have been focused on this burden that the most deprived populations must endure. As we focus on a geographical relation we used as a GIS software QGIS to visualize and calculate our layers and data analysis Geoda to answer our hypotheses. To classify the different sub-regions in Vernier we will divide the municipality of Vernier in a grid to understand the local problematic. As our study aims to identify the aspects of the roads the data were divided as follow: roads, national road and cantonal road. We then used the distance from the centroids of the grid cells as the origin to compute the distance to the closest road, cantonal and national road (using the NNjoin function).  No distinction between the social’s or the accommodation subsidies was emitted to calculate the number of subsidies, the point count function in QGIS was used. This choice was made to find a global answer and in for the next studies to study the characteristics of these subsidies. Then the data provided by the previous steps was used to study the hypothetical link in Geoda. Results The map of Vernier helps us to understand that in more than the half of its distance the national road is underground. It allows us to understand the dense areas as the spread of the subsidies in the municipality. We observe that the mostly dense area are not the ones that receives the more subsidies, in the South-East of the municipality. In other words, the number of subsidies is not directly proportional to the population density.
Day map

Reda Tahiri

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Noise pollution and greenness index mutual influence  (Jullien Nicolas, Locatelli Maxence, Tahiri Reda) In the common sens green spaces have a positive influence on the inhabitants health and perception of their environment. Studies have proved that a traffic-noise pollution related have enormous consequences on psychological states of urban residents. According to a study conducted   \cite{Margaritis_2017}  on European cities, noise-traffic can be lowered thanks to the porosity of the green spaces. In addition, based on the effects of traffic-noise pollution, research driven in Iran \cite{Sakieh_2017} came to the conclusion that traffic-noise pollution have a significant influence on residents well-being  and potential effect on the health. However a spatial analysis based on the effects of traffic-noise on health can be important to understand the concepts and the warning situation that the authorities can take in account. Therefore, it might be interesting to question the statistics about the mutual influence of traffic-noise pollution and greenness index. Data Various kind of data were used to carry out the study :4 RVB images describing green spaces in the region of Vernier. They are orthophotos from 2014 provided by Swisstopo (https://www.swisstopo.admin.ch/) with a spatial resolution of 0.5m.One vector file containing borders of Vernier's region One raster file highlighting variation of noise pollution through Vernier's region during day time. Another similar file has been used for data during the night.  Those files incorporate SonBase data set with data collected by Federal Office for the Environment (FOEN) as well as several other offices such as Federal Offices for Spatial Development (ARE) or Roads (FEDRO).