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Journal of Environmental Accounting and Management
António Mendes Lopes (editor), Jiazhong Zhang(editor)
António Mendes Lopes (editor)

University of Porto, Portugal


Jiazhong Zhang (editor)

School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710049, China

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Renewable Empower Distribution of the World

Journal of Environmental Accounting and Management 7(1) (2019) 11--26 | DOI:10.5890/JEAM.2019.03.002

Dong Joo Lee, Mark T. Brown

Department of Environmental Engineering Sciences, University of Florida, USA

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Large spatial scale emergy analysis can benefit from the latest spatial datasets that are the result of satellite-based models. This study utilized global spatial datasets developed by NASA and others to perform a global scale emergy analysis, producing global coverages of renewable energy sources leading to global coverages of empower for the renewable inputs to the geobiosphere. Total Aerial Empower Intensity (AEI) was computed for each 1 arc degree cell of Earth by taking the maximum value between the sum of the solar, tidal and geothermal inputs and the largest of the secondary inputs (wind, rain, runoff) for each cell. An important issue related to spatial emergy analysis was highlighted during this study. Previous methods of computing Unit Emergy Values (UEVs) (or transformities) for secondary and tertiary renewable flows (wind, precipitation, and chemical and geopotential exergy of runoff) relied on global average data. With these spatially explicit global data, new UEVs were computed that rely on spatially explicit fluxes of wind, rainfall, elevation, and modeled total dissolved solids data. Using these data and GIS it is possible to compute regional UEVs for chemical and geopotential exergy of runoff. Finally, we identify an interesting and well-known phenomenon in spatial analysis known as the Modifiable Areal Unit Problem (MAUP) and explore how it impacts spatial emergy evaluations. The MAUP results from spatial aggregation and scale of analysis and ultimately results in statistical bias when summary statistics are computed from spatial data. Our recommendation is first to be aware of MAUP when dealing with analysis of spatial data and second, when aggregating data over regions it is best to minimize subdivisions. Considering this outcome we suggest that evaluations that employ the max emergy algebra and GIS be done at the coarse scale of the regional boundary, rather than on a cell by cell basis.


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