Replacing fossil fuels wtih solar energy in an SME in UK and Kurdistan, Iraq: Kansas fried chicken case study

Energy management and analysis are more common in large companies since they have the resources and commitment to assign such tasks to employee compared to SMEs. Only a very small proportion of the overall business costs pertains to energy requirements and therefore SMEs pay little attention to energy analysis and management. Fossil fuels, which cause issues related to global warming, can viably be replaced with renewable energy sources such as solar energy. Trends in solar cell development are likely to yield a potential solution to problems generated by an over reliance on fossil fuels. Solar solutions are relatively simple to implement in SMEs than in large corporation and the combined impact small businesses is likely to be much greater. A micro-business has been utilized as a cases study for the purposes of illustration in the UK and Kurdistan-Iraq. Even though Kurdistan-Iraq is abundant in oil and gas, its climatic favour the implementation of solar cells which can replace the existing use of non-renewable fossil fuel. Our comparative study suggests that solar can replaced a reasonable amount of the energy needs even in the UK and a much higher amount in Kurdistan-Iraq. Using 20% efficient solar, can replace 23% and 70% of the energy requirements of the microbusiness in UK and Kurdistan-Iraq respectively

Dynamics of spatial entropy and dissimilarity index against number of cells.

<p>These results were extracted from a single illustrative simulation run.</p

Parallel coordinate plot of squared residuals for a limited set of model parameter configurations for the Singapore model (Left: unconstrained; Right: constrained).

<p>Using the range values determined in preliminary experiments (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0080309#pone-0080309-g004" target="_blank">Fig. 4</a>), we conducted a limited set of experiments to find the best-fit model parameters. Each column reports the squared residual for each target spatial entropy and dissimilarity indexes values extracted from the actual land use map of Singapore. From these sets of experiments, we identify the sets and as the best-fit parameters for the unconstrained and constrained models respectively. Similarly, further experiments were conducted to identify the best-fit model parameters for the remaining cities.</p

Reconstructed versus actual city land use maps.

<p>Simulated figures is randomly chosen representative sample of 10 runs whose statistical resemblance to actual land use is reported in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0080309#pone-0080309-t001" target="_blank">Tables 1</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0080309#pone-0080309-t003" target="_blank">3</a>.</p

The Emergence of Urban Land Use Patterns Driven by Dispersion and Aggregation Mechanisms

<div><p>We employ a cellular-automata to reconstruct the land use patterns of cities that we characterize by two measures of spatial heterogeneity: (a) a variant of <i>spatial entropy</i>, which measures the spread of residential, business, and industrial activity sectors, and (b) an <i>index of dissimilarity</i>, which quantifies the degree of spatial mixing of these land use activity parcels. A minimalist and bottom-up approach is adopted that utilizes a limited set of three parameters which represent the forces which determine the extent to which each of these sectors spatially aggregate into clusters. The dispersion degrees of the land uses are governed by a fixed pre-specified power-law distribution based on empirical observations in other cities. Our method is then used to reconstruct land use patterns for the city state of Singapore and a selection of North American cities. We demonstrate the emergence of land use patterns that exhibit comparable visual features to the actual city maps defining our case studies whilst sharing similar spatial characteristics. Our work provides a complementary approach to other measures of urban spatial structure that differentiate cities by their land use patterns resulting from bottom-up dispersion and aggregation processes.</p></div

The Singapore model.

<p>Highlighted in the right figure are the areas that remains (yellow color) after non-developable lands are removed.</p

The Singapore land use patterns.

<p>Actual versus simulated city maps with and without compartmental constraints. Blue, green and red coloured pixels correspond to residential, business and industrial areas respectively. The actual map of Singapore was adapted from the Singapore Urban Redevelopment Authority master plan 2008 in which we discarded land use categories that are not directly related to the residential, business and industrial sectors.</p

The Singapore master plan.

<p>The actual land use map depicted in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0080309#pone-0080309-g003" target="_blank">Fig. 3</a> was extracted from the URA ((Urban Redevelopment Authority)) Singapore master plan 2008 where the residential, business and industrial land use sectors are the aggregations of relevant sub-categories. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0080309#pone.0080309.s001" target="_blank">Appendix S1</a> for details and data sources.</p

Simulation results summary - dissimilarity index.

<p>The mean and standard deviation values were computed over 10 individual repeat simulation runs using unique seeds. Results for all cities are based using the compartmental constrained models, except for Singapore land use which was also reconstructed using the unconstrained approach. Note that the standard deviations of the different trial measurements in the average is 1.14% of the mean value (1.71%, 1.14%, 0.58% for , , respectively), indicative of the robustness of the evolved patterns.</p

Cities differentiated by ranges of influence.

<p>The bubble size denotes the total land area of the city.</p