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The next experiment may seem counterintuitive, but it is very worthwhile. We are going to be measuring albedo radiation. Using your solar setup, point your PV panel at the floor and take a measurement (Figure 10-34). What did you expect A zero reading In fact, as you can see, there is still a lot of energy in indirect radiation which is reflected from other surfaces. We saw in the last experiment how bifacial solar cells are able to collect the solar energy reflected from two faces. Therefore, in the covered walkway they can collect energy reflected from the ground (albedo) as well as from direct radiation.

20.00 18.00 16.00 14.00 12.00 10.00 8.00 6.00 4.00 2.00 0.00 WDM MLM FBM FDM CHM PPM RST MIN CON TRM FDR ELM HTL REC ARG MED RTL AUT GOV EDU 20 Waste groups

Regression coefficient for landfill disposal costs ($/ton)

9 8 7 6 5 4 3 2 1 0 AUT RTL FDS MED CON CON WDM CHM MLM PPM GOV ARG ARG EDU FDM FBM TRM REC REC ELM RST HTL HTL MIN Landfill disposal cost is insignifcant in predicting annual solid waste generation at the 95% confidence level for these groups

FIGURE 6-1.

Figure 16.9 Waste group regression coef cient comparison for land ll disposal cost ($/ton).

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day or night. The power is stored locally in batteries located in the foundation of the sign. In addition to powering devices in remote locations with no access to the power grid, we can also construct large photovoltaic arrays, which generate a significant quantity of electricity which can be fed into the grid when it is not being used onsite. The great thing about photovoltaic cells, is that they can be used in place of things like roof tiles and shingles so although we cover the building with photovoltaic cells, which are expensive, we save on the cost of the roofing material.

In all waste groups, the number of employees was the most significant variable to predict annual waste generation. This is logical, because the number of employees should have a high correlation to the on size of a company direct impact on annual waste generation tonnages. The more employees working at a company, the more waste the company generates. The results of the regression

100 ISO 14001 Certification is insignificant in predicting annual solid waste generation at the 95% confidence level for these groups AUT WDM MLM FDM CHM PPM FBM ELM TRM EDU RST RTL MIN FDS MED GOV

on both cost and revenue at the same time not on just one or the other. What a breakthrough! This function is a building block of business management, and business owners must keep it in their subconscious all the time. As a new business survives or succeeds, it starts growing because of increased demand or the founder s ability to offer additional products or services. The founder can no longer run the business alone and hires the first employee. He or she probably rents some space and buys some tools of the trade. The control of the business now rests in the hands of the employee and the founder or the leader. Although the performance of each person is still clearly visible, the complexity of the business is increasing. The new costs that come with growth are added to the existing costs of the business. These additional costs may be ignored as the focus on the basic equation of profitability continues. However, the basic cost model, and its relationship with profitability, has changed as follows: Profitability f[cost(build/buy, building, tools, employee), revenue]

TABLE 16.4 RANKING AND COMPARISON OF SIGNIFICANT VARIABLES TO PREDICT SOLID WASTE BASED ON STANDARDIZED REGRESSION COEFFICIENT MAGNITUDE FOR THE NUMBER OF EMPLOYEES STANDARDIZED REGRESSION COEFFICIENT FOR THE NUMBER OF EMPLOYEES

1.32 1.26 1.20 1.16 1.16 1.12 1.03 0.97 0.93 0.92 0.91 0.91 0.91 0.90 0.90 0.89 0.89 0.89 0.88

We can see how a solar array can be made plain and large as in this solar array at the Centre for Alternative Technology, U.K. (Figure 10-37). Or with a little bit of thought, they can be integrated creatively into the building fabric as shown in Figure 10-38.

analysis reflected this fact. In order to compare the quantities of solid waste generated by the 20 remaining waste groups, the average solid waste per company in each of the 20 waste groups was calculated. This was completed to gain additional insights into waste generation and to identify trends. Table 16.7 displays the regression coefficients for the number of employees and the average waste generated per company. The graph in Fig. 16.11 displays the relationship between average annual solid waste generation per company for each waste group and the employee regression coefficient.

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