This paper presents an analysis of heating expenses in a large social housing stock in the North of France. An artificial neural network (ANN) approach is taken for the analysis of heating consumption data collected over four years in 84 social housing residences containing 13,179 dwellings that use collective heating. Analysis provides an understanding of the influence of both physical and socio-economic parameters on heating expenses and proposes a predictive model for these expenses. The model shows that the heating expenses are influenced by both the buildings' physical parameters and social indicators. Concerning the physical parameters, the most important indicators are the area of the dwellings, followed by the building age and the DPE (energy performance diagnostic). The family size as well as tenant age and income have an important influence on heating expense. The model is then used for establishing a data-based strategy for social housing stock renovation.

The study aims at investigating the environmental and economic effects of paper consumption and computer usage in the Banking sector in Palestine. The research mainly depends on the descriptive and quantitative method. In addition, it collects the data annually and it covers the period from 2016 to 2018. The study has found out that the total cost of using paper in the banks in Palestine is about 3.24 million dollars because they consume about 658.4 tons of paper. Looking at paper production process, this amount of paper needs about 16 thousand pine trees, 25 million btus of energy, and 13.5 million gallons. Looking at the total costs of using computers in the banks in Palestine, it costs about 7.6 million dollars and about 122 tons of different minerals and materials which are required to manufacture computers in the banks in Palestine. In addition, it needs about 1.52 GW per year to operate all computers. Hence, this causes the release of greenhouse gases which is equivalent to the consumption of 2495 barrels of oil.