EMERGY SYNTHESIS 6: Theory and Applications of the Emergy Methodology

Transcription

1 EMERGY SYNTHESIS 6: Theory and Applications of the Emergy Methodology Proceedings from the Sixth Biennial Emergy Conference, January 14 16, 2010, Gainesville, Florida Edited by Mark T. Brown University of Florida Gainesville, Florida Managing Editor Sharlynn Sweeney University of Florida Gainesville, Florida Associate Editors Daniel E. Campbell US EPA Narragansett, Rhode Island Shu-Li Huang National Taipei University Taipei, Taiwan Enrique Ortega State University of Campinas Campinas, Brazil Torbjörn Rydberg Centre for Sustainable Agriculture Uppsala, Sweden David Tilley University of Maryland College Park, Maryland Sergio Ulgiati Parthenope University of Napoli Napoli, Italy iii December 2011 The Center for Environmental Policy Department of Environmental Engineering Sciences University of Florida Gainesville, FL

2 9 Renewable Emergy in Earth's Biomes Sherry Brandt-Williams and Mark Brown ABSTRACT Emergy evaluations of the 13 major natural biomes to determine their contributions of environmental services are presented. Driving emergy supporting production of environmental services were computed from global spatial data coverages where available. From a global perspective, the open ocean provides the highest level of services, 1.58 E25 sej/yr,, which amounts to em $5.9 trillion/yr, followed by tropical forests which contribute 7,.62 E245 sej/yr ( em $ 2.8 trillion). Tidal marshes and mangrove ecosystems contribute the most environmental services on a hectare basis (1.o E16 sej/ha/yr; equaling em $3,847) while Estuaries, at 2.56 E16 sej/ha/yr, provide the services on a per hectare basis valued at em $3527 per hectare. In total this method of computing the emdollar value of environmental services provided by the earths biomes yielded a total value of em $37 trillion. Computed transformities of Net Primary Production (NPP) for each biome showed interesting results with most highly productive systems having moderate to high transformities and low productivity biomes having both high and low transformities. INTRODUCTION One of the primary complaints about emergy evaluations is the requirement for extensive data not typically collected for either natural or anthropogenic processes. Another concern is that acquiring data that is highly accurate is usually only possible at very small scales such as individual farms or processing plants for which a Life Cycle Assessment (LCA) has been completed. Few studies have been conducted that assess use of raw materials over a wide variety of process or environmental conditions and therefore are representative of an average or range of conditions. In the last fifteen years, a higher priority has been placed on development of spatial data sets by the U.S. government of low enough security that they can be provided to the general public. These data have been gathered using similar methods and assembled into global databases. While there is error inherent in the interpolations used to convert point data to polygons and isopleths, this approach provides a higher level of accuracy than using an average of data across an entire continent or from averaging one part of the same biome with another on different continent. Categorization of biomes is consistent from a holistic flora and soil perspective, but not necessarily consistent based on the environmental inputs they convert into primary productivity. Emergy evaluations for the 13 major natural biomes are presented using spatial data to calculate energy and emergy at a biome scale. Figure 1 illustrates the energy sources evaluated using global data, much of it spatially explicit, to determine the inputs to each biome on an annual basis. Figure 2 shows the classification and location of biomes used in this study. This study evaluates four key emergy values relevant to natural systems: the empower density (sej/ha/yr), useful for landscape evaluations of energy concentration; the total emergy flow per biome at a global scale; the monetary value based on a global sej per dollar ratio; and the Unity Emergy Values (UEVs; sej/j) of Net Primary Productivity (NPP) for each biome. 93

3 Figure 1. System diagram. N W E S Biomes Grasslands Tundra Lakes Wetlands Ice/Rock Marine Temperate Forest Tidal marsh/mangroves Tropical Forest ag/urban desert Miles Figure 2. Global map of biomes. METHODS Rather than determine energy and emergy at the small per hectare scale and then scale up to the biome, we approached this study from a global perspective. Global energy was allocated across the biomes using global coverages of data (see Figures 3-9), then adjusted to account for the energy that remains within regions or is transported without use to other biomes. The energy for biome fragments was summed for total global biome energy and converted to emergy using standard global transformities (Odum, 2000, and Odum et al. 2000). Monetary values were estimated using the global emergy per dollar ratio (NEAD, 2010). In this study, only the natural environmental systems are evaluated, thus the two developed biomes (cropland and urban lands) were not included. Since the renewable inflows to a local area are co-products from the same sources (solar, tidal momentum and geothermal heat), their emergy are not independent (Odum. 1996). All renewable emergy inputs to biomes were computed but only the largest was used to avoid double counting. A case is made in a separate chapter in these proceedings for evaluating the renewable basis for ecosystem services using a different mathematical algorithm (Brown and Brandt-Williams, 2011). 94

4 Figure 3. Albedo. Figure 4. Insolation. Figure 5. Wind. 95

5 Figure 6. Heat. Figure 7. Coastal length for tide determination. Figure 8. Global rainfall. 96

6 Figure 9. Potential Evapotranspiration. The emergy inflows supporting production of environmental services within each biome were evaluated separately using spatial datasets where available. Spatial data were available for the following driving emergies: insolation/albedo, wind, geothermal heat, tide, rain chemical potential. Spatial data for waves, ocean currents were not available. Each renewable energy was multiplied by its respective UEV using the E24 sej/yr baseline (Odum et al. 2000). Emdollar values were determined by dividing total renewable emergy per biome by the global emergy to dollar ratio (3.6 E12 sej/$) for 2000 (NEAD, 2010). Market values derived by Costanza et al. (1997) for a variety of human services, originally given in 1994 US$, were converted to 2000 US$ using a consumer price index calculator (BLS, 2010). Secondary Data Sources Solar Energy - Solar insolation energy was determined using raster GIS to multiply biome area by average annual insolation and albedo for the same areas (NASA EOS). Total insolation was derived by subtracting the coverage of albedo (Figure 3) from the insolation coverage (Figure 4). Wind Energy - Wind energy (Figure 5) was determined using raster GIS to multiply biome area by average annual wind velocity for the same areas (NASA EOS). Wave Energy No spatial data existed for wave energy. Thus total global wave energy data from Odum, (2000) was allocated to biomes based on aerial coverage as follows: Ocean shelf, 91%; Tundra, 1%; Ice/rock, 7%; reef, 1%. Ocean Currents - Total ocean current energy was assigned to open ocean. The estimate for the total current energy was taken from Odum et al. (2000). Geothermal Heat - Geothermal heat energy (Figure 6) was derived in the same way as insolation and wind using modified heat data from International Heat Group. The continuous spatial data for heat were modified by kriging point data using equal weighting factors so that the estimate of global heat was equal to the value used in Odum s (1996) original global evaluation 6.72 E20 J/yr (Sclater et al. 1980). Tidal Energy - Tidal energy was derived by measuring total coastline along those biomes with tidal influence (NOAA web) and using the appropriate percentage of the total tidal energy received by earth (Munk and Wunsch 1998). Biome coast length is illustrated in Figure 7. Rainfall Rainfall coverage is from Sustainable Development Department of the Food and Agricultural Organization of the United Nations (SDD-FAO, 2010). A rainfall coverage (SDD-FAO, 2010) and Potential Evapo-Transpiration (PET) coverage (CGIAR CSI, 2010) were used to derive rainfall used (Transpiration) and runoff (rainfall minus PET). Figures 8 and 9 show rainfall and PET. The resulting coverage was adjusted using a spatial weighting factor so that total runoff equaled that 97

7 used by Odum et al. (2010) in computing global runoff. The resulting spatial transpiration and runoff coverages were intersected with biome coverage to derive biome rain used and biome runoff. Runin Runin is the quantity of rainfall that runs off terrestrial lands which flows into downstream biomes, and was calculated as the difference between rainfall and PET (see above). Total terrestrial runoff was split between estuaries (60%), swamps & floodplains (32%) and and Lakes (8%) based on areas. Sediments - Continental sediments load computed by Odum, (2000) was used and assumed all to be deposited in estuaries. Net Primary Productivity Values for the biome s primary productivity varied widely between studies reported in the literature. Average NPP of biomes derived from Odum (1983) given in Table 1 was used to calculate the efficiency of converting renewable emergy into net primary productivity. Annual joules per hectare were divided into annual emergy flows per hectare to determine UEVs of NPP. In a broad sense this UEV represents an efficiency ratio. RESULTS The emergy of biomes are summarized in Table 2 through 4. Table 2 ranks the biomes based on the total supporting emergy flow (based on the largest input emergy). This table demonstrates the ocean influence on the planet. Marine biomes cover about 75% of the globe so it is not surprising that the emergy flow and emdollar value of marine biomes account for 76% of the total global flows. However, freshwater biomes only account for about 2% of the global surface, yet they their support is about 6% of the total global emergy flow. Aerial empower density of each biome is given in Table 3 in decreasing order. Also shown is the emdollar value of the annual environmental services (taken as the annual emergy support per hectare per year). Higher aerial empower densities are typically more complex systems and represent higher levels of information and services on a unit basis. Coastal wetlands and estuaries rank highest followed by floodplain wetlands. Table 4 illustrates the transformity of NPP by biome, which might be considered a measure of efficiency of each biome. Interestingly, the biome with the highest transformity is the one with the lowest Net Primary Productivity (NPP) suggesting that emergy support is utilized by productive functions other than primary production. A low emergy per NPP indicates a more efficient system at producing vegetative matter, while a high emergy per NPP might indicate that energy is being used for other things - storage of natural capital such as ice or peat, for example. It may also be indicative of a limiting factor. Table 1. Net primary productivity of biomes 1. Biome Ice/rock Tidal marsh, mangroves Estuaries Tundra Open ocean Lakes Coral reefs Swamps/floodplains Tropical Forest Ocean Shelf Grass/rangelands Temperate/Boreal Forest Desert 1. after Odum, 1983 NPP (J/ha/yr) 4.40E E E E E E E E E E E E E+10 98

8 Table 2. Ranking by total annual emergy flow into each biome and monetary value derived from global emergy per dollar ratio, Biome Emergy (sej/yr) EmDollars (2000), (Billion em $/yr) Open ocean 1.58E+25 $5,907 Tropical Forest 7.62E+24 $2,845 Tidal marsh, mangroves 1.70E+24 $635 Temperate/Boreal Forest 2.40E+24 $895 Estuaries 1.70E+24 $635 Grass/rangelands 1.85E+24 $690 Ocean Shelf 1.36E+24 $509 Ice/rock 1.54E+24 $575 Swamps/floodplains 7.35E+23 $274 Tundra 5.82E+23 $217 Coral reefs 3.84E+23 $143 Lakes 1.88E+23 $70 Desert 9.83E+22 $37 Table 3. Ranking of biomes by the aerial empower density (emergy flux per hectare) and the monetary value per hectare. Aerial Empower Density Biome (sej/ha/yr) EmDollars (2000), ( em $/ha/yr) Tidal marsh, mangroves 1.03E+16 $3,847 Estuaries 9.45E+15 $3,527 Swamps/floodplains 4.46E+15 $1,663 Coral reefs 6.19E+15 $2,309 Tropical Forest 4.01E+15 $1,497 Open ocean 5.26E+14 $196 Lakes 9.40E+14 $351 Ice/rock 9.39E+14 $350 Temperate/Boreal Forest 8.12E+14 $303 Tundra 7.83E+14 $292 Ocean Shelf 4.77E+14 $178 Grass/rangelands 4.74E+14 $177 Desert 5.11E+13 $19 Table 4. Ranking of biomes using the transformity of the net primary productivity for each biome. Biome Transformity (sej/j) NPP (J/ha/yr) Aerial Empower Density (sej/ha/yr) Ice/rock 2.14E E E+14 Tidal marsh, mangroves 4.40E E E+16 Estuaries 4.30E E E+15 Tundra 3.82E E E+14 Open ocean 2.87E E E+14 Lakes 2.57E E E+14 Coral reefs 1.69E E E+15 Swamps/floodplains 1.52E E E+15 Tropical Forest 1.24E E E+15 Ocean Shelf 6.51E E E+14 Grass/rangelands 5.39E E E+14 Temperate/Boreal Forest 4.62E E E+14 Desert 3.87E E E+13 99

9 The individual components of each biome are presented in Tables 5 through 17. These are arranged in order of decreasing spatial empower density (as per Table 3). The global area covered by each biome is provided in the title of each table. Please note that this area is not contiguous; biomes of similar type are scattered across the earth surface and can be thought of as a large-scale ecological community. While the fauna and flora might be different, their overall characteristics and interactions with the dominant physical properties constitute unique assemblages fitting the definitions of the biome classification. Table 5. idal marsh mangroves 17 E7 ha. 1 Sun 8.21E E+21 2 Wind 6.17E E E+21 3 Geothermal Heat 1.75E E E+22 4 Rain 2.32E E E+23 5 Tide 2.72E E E+23 6 Runin 5.85E E E+23 7 Sediment 8.10E E E E E+16 Monetary value $7, E+07 Table 6. Estuaries (including V) 18 E7 ha. 1 Sun 8.04E E+21 2 Wind 1.57E E E+23 3 Rain 9.34E E E+22 4 Geothermal Heat 2.26E E E+22 5 Tide 2.96E E E+23 6 Runin 5.85E E E+23 7 Sediment 8.10E E E E E+16 Monetary value $6, E+07 Table 7. wamps/floodplains 17 E7 ha. 1 Sun 6.55E E+21 2 Wind 3.40E E E+20 3 Geothermal Heat 1.75E E E+22 4 Rain 1.28E E E+23 5 Runin 9.08E E E E E+15 Monetary value $2, E

10 Table 8. oral eef 6 E7 ha. 1 Sun 2.77E E+21 2 Wind 1.57E E E+23 3 Rain 3.22E E E+22 4 Geothermal Heat 8.99E E E+22 5 Waves 4.89E E E+20 6 Tide 1.02E E E E E+15 Monetary value $2, E+07 Table 9. ropical orest 190 E7 ha. 1 Sun 1.06E E+23 2 Wind 2.58E E E+21 3 Geothermal Heat 2.01E E E+23 4 Rain 2.46E E E E E+15 Monetary value $1, E+07 Table 10. Open ocean E7 ha. 1 Sun 1.34E E+24 2 Currents 8.5E E E+25 3 Wind 1.57E E E+23 4 Rain 1.56E E E+25 5 Geothermal Heat 4.36E E E+24 6 Tide 4.95E E E+25 7 Ocean waves 2.268E E E E E+15 Monetary value $ E+07 Table 11. a es 20 E7 ha. 1 Sun 8.21E E E+21 2 Wind 2.96E E E+20 3 Geothermal Heat 2.12E E E+22 4 Rain 5.23E E E+23 5 Runin 2.32E E E E E+15 Monetary value $ E

11 Table 12. ce/roc 164 E7 ha. 1 Sun 1.11E E+22 2 Wind 1.94E E E+22 3 Geothermal Heat 1.74E E E+22 4 Rain 4.97E E E+24 5 Waves 2.84E E E E E+14 Monetary value $ E+08 Table 13. emperate/ oreal orest 296 E7 ha. 1 Sun 9.66E E+22 2 Wind 8.49E E E+22 3 Geothermal Heat 3.13E E E+23 4 Rain 7.74E E E E E+14 Monetary value $ E+06 Table 14. undra 74 E7 ha. Sun 1.27E E+22 Wind 6.06E E E+22 Geothermal Heat 7.87E E E+23 Rain 1.78E E E+17 Waves 2.52E E E E E+14 Monetary value $ E+07 Table 15. Ocean helf 286 E7 ha. 1 Sun 1.28E E+23 2 Wind 1.57E E E+23 3 Rain 1.48E E E+24 4 Geothermal Heat 3.59E E E+23 5 Waves 4.36E E E+22 6 Tide 4.36E E E E E+14 Monetary value $ E

12 Table 16. rass/rangelands 390 E7 ha. 1 Sun 2.00E E+23 2 Wind 5.99E E E+22 3 Geothermal Heat 4.13E E E+23 4 Rain 5.96E E E E E+14 Monetary value $ E+06 Table 17. esert 193 E7 ha. 1 Sun 9.83E E+22 2 Wind 5.25E E E+22 3 Geothermal Heat 2.04E E E+22 4 Rain 2.05E E E E E+13 Monetary value $ E+06 DISCUSSION Beyond furthering our understanding of emergy methods, the societal need to place value on different land types is an important area of research. Although using expert ecological opinion to provide a value ranking for the services provided by different ecosystems is preferable to a largely uninformed public willingness to pay approach it is still dependent upon widely divergent views on the services provided and market driven economic pricing. Using reproducible and verified data in a consistent quantitative method will help provide scientific evidence of the real importance of these ecosystems to the function of the earth. While the methods in this study might introduce greater error overall, they provided greater consistency and results more applicable to all natural systems than typical when evaluating systems at a smaller, individual scale. From a global perspective (Table 2), the open ocean provides the highest level of services, 1.58 E25 sej/yr,, which amounts to em $5.9 trillion/yr, because it has the greatest areal global coverage. Tropical forests are next with 7.62 E24 sej/yr ( em $2845 trillion/yr). The sum of environmental services of all biomes is em $13.8 trillion. On an aerial basis (Table 3), tidal marshes and mangroves, provide the greatest environmental service on a per hectare basis (1.03 E16 sej/ha/yr) and have the highest emdollar value - em $3847 per hectare. Estuaries follow very closely with 9.45 E15 sej/ha/yr of environmental services equaling em $3527/ha/yr in emdollar value. The environments typically experiencing pulse events (floodplains, coral reefs, tropical rain forests) are in the E15 sej/ha/yr range. Not unexpected, desert biomes had the lowest areal empower intensity (5.1E13 sej/ha/yr; equaling em $19/ha/yr) and also contributed the least globally (Table 2) to the value of environmental services (9.83 E22; equaling em $37 billion). It is interesting to note that the highest NPP transformities are mixed between relatively high ecologically productive systems and those that with low productivities. For instance, the highest NPP transformity was computed for the ice/rock biome. This is the result of relatively high supporting emergy flows and obvious low ecosystem productivity. One might suggest low efficiencies if one assumed that all input emergy were used in primary production, but it is more likely that input emergy is adding to the production of other functions (ice making) rather than strictly ecological functions. Transformities calculated in this way are more a indicator of the full array of functions that are 103

13 characteristic of a biome (ie ice storage or wind generation for instance) where supporting emergy may be split between ecological production and exported or stored without contributing to ecological production. Several discrepancies arose when trying to compare these global data sets to data used for earlier emergy work. Deep heat is perhaps the greatest conundrum. When the continuous spatial data for heat were modified by kriging point data without any weighting, this method did not provide an estimate of global heat similar to the value used in Odum s original global process transformities (2000). We used overall and equal weighting that forced the total deep heat to be equal to 6.72 E20 J/yr (Sclater et al. 1980). However, it is possible that with new sensing equipment, more intensive global sampling and better access to the larger database that global estimates of energy flows might be changing. This is an important area of future emergy research because it changes the transformities we use, and not necessarily linear. In all cases we constrained the global data derived from aerial coverages to equal those given by Odum (2000). To do this we developed equal weighting factors that when applied to the spatial data yielded values equal to those of Odum s earlier wor. A much needed area of research is detailed evaluation of the spatial character of these global data and thus eventually updating the values used in calculating global transformities (Odum, 2000). REFERENCES BLS, Bureau of Labor Statistics, CPI Inflation Calculator. Accessed at on 6/10/10. Brandt-Williams S Evaluation of Watershed Control of Two Central Florida Lakes: Newnans Lake and Lake Weir. Ph.D. Dissertation, Environmental Engineering Sciences, Univ. of Florida, Gainesville. Brown and Brandt-Williams, Quantus Emergy. Emergy Synthesis. Proceedings from the 6 th biennial Emergy Research Conference. Center for Environmental Policy, University of Florida. Costanza, R., d Arge, R., de Groot, R., Farber, S., Grasso, M., Hannon, B., Naeem, S., Limburg, K., Paruelo, J., O Neill,R. V., Raskin, R., Sutton, P., van de Belt, M The value of the world s ecosystem services and natural capital. Nature 387, Munk, W., Wunsch, C., Abyssal recipes II: Energetics of tidal and wind mixing. Deep-Sea Research I 45, NASA EOS Surface meteorology and Solar Energy. Downloaded from: on 6/10/2010 NEAD, National Emergy Analysis Database. Center for Environmental Policy. University of Florida. Data obtained 6/10/10 from NOAA Geographic distribution of different tidal cycles along the earth's coastlines. Downloaded from on 6/10/10. Odum, E.P Basic Ecology. Sanders College Pub. New York. 613p. Odum, H.T Environmental Accounting, Emergy and Decision Making. Wiley, NY. Odum, H.T Folio #2: Emergy of global Processes. Handbook of Emergy Evaluation: A compendium of data for emergy computation issued in a series of folios. Center for Environmental Policy, Univ. of Florida, Gainesville. Odum, H.T., M.T. Brown, and S. L. Brandt-Williams Folio #1: Introduction and global budget. Handbook of Emergy Evaluation: A compendium of data for emergy computation issued in a series of folios. Center for Environmental Policy, Univ. of Florida, Gainesville. Sclater, J.F., G. Taupart, and I.D. Galson The heat flow through the oceanic and continental crust and the heat loss of the earth. Rev. Geophys. Space Phys. 18: