[american society of civil engineers world environmental and water resources congress 2012 -...

Toward More Efficient Global Warming Policy Solutions: The Necessity for Multi-Criteria Selection of Energy Sources

Saeed Hadian1, Kaveh Madani2, Christopher Rowney3, Soroush Mokhtari4

Department of Civil, Environmental, and Construction Engineering, University of

Central Florida, Orlando, FL 32816; PH (407) 823-2317; FAX (407) 823-3313 [email protected]; [email protected];

[email protected]; [email protected] ABSTRACT Despite humans’ acknowledgement of the main drivers of global warming, greenhouse gas (GHG) emissions are still increasing dramatically worldwide. GHG emissions in the U.S. have increased by 17% during the 1990-2009 period and are expected to increase even more with the economic growth after the current recession. Although current dominant policy for combating global warming relies on substitution of fossil energy sources with the renewable ones, we argue that such a general policy may be inefficient. In fact, such a policy is only a short-fix solution to the problem and may be associated with unintended consequences or secondary effects on other sectors and valuable natural resources. This paper highlights the necessity of multi-criteria decision analysis to select energy sources which are not only efficient in terms of GHG emissions reduction, but also will have minimal adverse effects on other sectors and resources. By simultaneous consideration of performances of energy sources under different sustainability criteria, it is discussed why some renewable energy sources may be even less promising than some non-renewable energy sources in reducing GHG emissions without affecting other resources and violating the sustainability rationales. The three considered sustainability criteria in this study include carbon footprint, water footprint, and cost, reflecting the environmental efficiency, water use efficiency, and economic efficiency, respectively. The performances of a range of non-renewable and renewable energy sources are considered under the three sustainability criteria to rank the energy sources based on different social choice rules with respect to the uncertainty involved in estimating the performances. INTRODUCTION Global warming is recognized as one of the obstacles to sustainable development and planning (USAID, 2011; CIEL, 2002; McDonald, 2006), resulting in a range of problems including health and environmental risks (EPA, 2011; NRDC, 2011), rising sea levels (NCDC, 2011), changing rainfall patterns (Dore, 2005), and manipulated ecosystem productivity (Doll and Zhang, 2010). Various countries around the world have been developing policies in attempt to meet the present humans’ needs while preserving the valuable natural resource for

2884World Environmental and Water Resources Congress 2012: Crossing Boundaries © ASCE 2012

World Environmental and Water Resources Congress 2012

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future generation, recognizing greenhouse gas (GHG) emission reduction as an essential element to sustainable development. As a response to environmental deficiencies pertaining to the use of the current fossil fuel-based energy systems, global interest in development of alternative energy sources has increased dramatically. Such development has yielded a variety of renewable energy sources that are considered by different countries as effective resources for continued development. For example, Europe, in its 20/20/20 energy strategy, has set an overall mandatory target of 20% for the proportion of renewable energy in the gross domestic energy consumption by 2020 (European Union, 2011). Other countries such as the U.S and Russia have also set similar goals in their energy plans. Such goals are supposed to be met through introduction of renewable energies, in national energy profiles, as reliable substitutions for fossil fuels to meet the energy demand while decreasing GHG emissions. Nevertheless, what mainly ignored by most countries are the unintended consequences of such policies, especially with respect to their effects on other valuable natural resources in the long-run. Although renewable energies are effective in terms of GHG reductions, not all of them are cost effective and truly green, and may be associated with undesired effects on other scarce yet precious natural resources (Madani et al., 2011). For example, the amount of water needed to produce one unit of some types of biomass and hydropower energies is between 70 to 400 times larger than the water needed to produce other energies (Gerbens-Leens et al., 2009). One of the most considerable side effects of different sources of energy is the usage of water to produce energy. Large amount of energy is needed for extraction, treatment and distribution of water and large amount of valuable water is needed to produce energy (Dennen, 2007). For some energy sources, the amount of freshwater used to produce a unit of energy is so high that makes them inefficient and unreliable sources of energy in comparison to other sources. Therefore, water use efficiency of energy sources should be taken into account in energy efficiency analyses. Water footprint, defined as the total amount of fresh water used to produce different products (Hoekstra and Hung, 2002; Hoekstra and Chapagain, 2007 and 2008), is a reliable metric for this purpose. In addition, economics of different energy sources, for which the values are extracted from previous studies, play an important role in energy policy analysis. An energy source with low water and carbon footprints but large cost is not a suitable option to invest on. It is important to note that environmental costs, health costs, and etc. are not included in the values used in this paper as the cost of energy. Following Madani et al. (2011), carbon footprint (representing environmental efficiency), water footprint (representing water use efficiency), and cost (representing economic efficiency) of a range of energy sources are taken into consideration to perform a multi-criteria assessment that serves as the basis for judgment regarding the efficiency of energy sources. Since uncertainties are associated with the performances of the energy alternatives under the considered criteria, this study uses a stochastic multi-criteria analysis approach suggested by Shalikarian et al. (2011) to account for the uncertainty effects on the overall ranking of the energy sources with respect to the three criteria.



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METHOD Table 1 indicates the carbon footprint, water footprint, and cost of twelve different energy sources considered in this study. While some values in the Table are unique, others show different ranges, due to the uncertainties involved. We use a range of social choice rules to find the overall ranking of each energy source for the three considered criteria. Since uncertainties are involved and some performances are not unique, we used a Monte-Carlo Social Choice Making approach developed by Shalikarian et al. (2011) to deal with the uncertainties. In social choice making, we assume each criterion represents an interest in the society. Thus, it is assumed that carbon footprint represents the interest of a group within the society that seeks reducing GHG emissions to deal with global warming, water footprint represents the interest of a group within the society that seeks protecting water resources, and cost represents the interest of a group within the society, concerned with the economic efficiency of energy use.

Table 1. Carbon Footprint, Water Footprint, and Cost of Different Energy Sources

Energy Source Type Carbon Footprint Water Footprint Cost Ethanol from corn

produced in the U.S. 81-85 g CO2/KWH

(Hill, 2006) 78 m^3/GJ (Gerbens-

Leens et al., 2009) 3-9 cent/KWH (Owen, 2006)

Ethanol from sugar cane produced in Brazil

19 g CO2/KWH (Oliveira, 2008)

99 m^3/GJ (Gerbens-Leens et al., 2009)

3-9 cent/KWH (Owen, 2006)

Biomass: wood-chip

25 g CO2/KWH (Parliamentary Office

of Science and Technology, 2006)



Biomass: miscanthus

93 g CO2/KWH (Parliamentary Office




Solar cell 50-100 g C02/KWH (Perry, 2008)

0.3 M^3/GJ (Gerbens-Leens et al., 2009)


Wind energy 5-30 g CO2/KWH (Lenzen, 2008)

0 (Gerbens-Leens et al., 2009)

3.3-8.7 cent/KWH (Neij, 2008)

Wave and tidal energy

25-50 gCO2 /KWH (Parliamentary Office


0 7.5 cent/KWH (Ocean Energy Council, 2011)

Hydropower 3-11 g CO2/KWH (Baratta, 2010)


6-12 cent/KWH Sims et al., 2008)

Coal 900-1000 g

CO2/KWH (Baratta, 2010)

0.2 M^3/GJ (Gerbens-Leens et al., 2009)

4.5-8 cent/KWH (Cheeseman, 2010)

Oil 893 g CO2/KWH (Fridleifsson, 2008)

1.1 m^3/GJ (Gerbens-Leens et al., 2009)


Gas

500 g C02/KWH (Parliamentary Office



4.9-6.9 cent/KWH Sims et al., 2008)

Nuclear energy 6-26 g CO2/KWH (Baratta, 2010)


3.9-8 cent/KWH Sims et al., 2008)



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The social choice is a fair selection of the central planner who is interested in satisfying all parties to the extent possible. Since the notion of fairness is not unique, several social choice rules have been developed and will be used in this study. Plurality rule and Hare rule. Based on the plurality rule, the option that receives the highest number of votes (most popular option) is selected as the best option. To develop a ranking order, after selection of the best option, we eliminate that option and repeat the process with the remaining options. The process is continued until the rankings of all energy sources are determined. Hare rule, defined as Plurality Rule with eliminations, yields the same ranking as well. Table 2 shows the overall ranking order of the energy sources based on the Plurality and Hare rules, with consideration of the uncertainty effects based on the method suggested by Shalikarian et al. (2011). Based on these rules, wind energy is the best and Ethanol (from sugar cane, produced in Brazil) is the worst energy options. Borda Score Rule. Based on this rule, scores are assigned to different alternatives based on their ranks, with better alternatives receiving higher scores. Eventually, each alternative receives n number of scores, where n is the number of criteria (here n=3). The summation of all scores of one alternative represents the Borda score of that alternative and the highest score belongs to the winner. For more information about this rule, see Baharad and Nitzan (2003), Sheikhmohammady and Madani (2008), and Shalikarian et al. (2011). Once the best alternative is determined, it is eliminated from the alternatives set and the process is repeated for the remaining alternatives. Table 2 shows the overall ranking order of the energy sources based on the Borda Score rule, with consideration of the uncertainty effects based on the method suggested by Shalikarian et al. (2011). Based on this rule, wind energy is the best and coal is the worst energy options. Median Voting Rule. This rule assumes that the most likely group choice is the most average choice (Congleton, 2002). Such a choice is the one that meets all the criteria with the least amount of compromise necessary by all parties. Similar to the previous rules, we eliminate the best option and continue the process for the rest of the options until all energy sources are ranked. Table 2 shows the overall ranking order of the energy sources based on the Median Voting rule, with consideration of the uncertainty effects based on the method suggested by Shalikarian et al. (2011). Based on this rule, wind is the best energy option and ethanol (from corn, produced in the U.S.) is the worst energy option. Majoritarian Compromise rule. As a refinement to the Median Voting rule, the Majoritarian Compromise rule (Sertel and Yilmaz, 1998) selects the alternative with the majority of support at the highest possible rank and ties are broken according to the number of supports. More information about this method may be found in Sheikhmohammady and Madani (2008), and Shalikarian et al. (2011). Similar to the previous rule, the best option is determined first. Then the process is repeated for the remaining options. Table 2 shows the overall ranking order of the energy sources based on the Majoritarian Compromise rule, with consideration of the uncertainty



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effects based on the method suggested by Shalikarian et al. (2011). Based on this rule, wind energy is ranked as the best and coal is ranked as the worst options. Condorcet’s Practical Rule. This rule is a variation of Condorcet’s rule. As in Condorcet’s rule, pairwise comparisons determine the ultimate winner. However, in the case of Condorcet’s Practical rule, all potential pairs are explored. The ultimate winner of each round is allowed to continue on to the next round (Johnson, 2005). After selection of the best option based on this rule, this option is eliminated and the process continues for the remaining options. Table 2 shows the overall ranking order of the energy sources based on the Condorcet’s Practical rule, with consideration of the uncertainty effects based on the method suggested by Shalikarian et al. (2011). Based on this rule, wind energy is the best energy source while coal is the worst one. Anti-Plurality Rule. This rule eliminated the option with the least appeal. This is continued until only the winner remains (Baharad and Nitzan, 2011). Once the first winner (best option) is selected, the process is repeated for the other options, until all options are ranked. Table 2 shows the overall ranking order of the energy sources based on the Anti-Plurality rule, with consideration of the uncertainty effects based on the method suggested by Shalikarian et al. (2011). The Anti-Plurality rule selects wind as the best energy option and ethanol (from corn, produced in the U.S.) as the worst one. RESULTS AND DISCUSSION After finding the rankings of different energy sources based on different social choice rules (Table 2), we need to find the overall rank of the energy sources.

Table 2. Rankings of Energy Sources Based on Different Social Choice Rules

Energy Sources

Plurality and

Hare rules

Borda Score rule

Median Voting

rule

Majoritarian Compromise

rule

Condorcet’s Practical

rule

Anti-Plurality

rule

Wind 1 1 1 1 1 1 Nuclear 3 2 2 2 2 3

Wave and tidal energy 2 5 4 4 4 2

Gas 4 3 5 5 5 4 Solar cell 6 4 6 6 6 6 Biomass:

wood-chip 10 7 3 3 3 10

Hydropower 8 6 7 7 8 8 Oil 7 8 9 9 9 7

Coal 5 12 12 12 12 5 Biomass:

miscanthus 9 9 10 10 10 9

Ethanol from sugar cane 12 11 8 8 7 11

Ethanol from corn produced

in the U.S. 11 10 11 11 11 12



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Here we determined the overall ranking of the energy options (Table 3) based on their average ranking under all the rules applied (similar to what done in Madani et al. (2011) and Sheikhmohammady et al. (2011)).

Table 3. Final Ranking of Energy Sources Based on Social Choice Rules

Energy Source Average Rank Wind 1

Nuclear 2 Wave and tidal energy 3

Gas 4 Solar cell 5

Biomass: wood-chip 6 Hydropower 7

Oil 8 Coal 9

Biomass: miscanthus 10 Ethanol from sugar cane 11

Ethanol from corn produced in the U.S. 12

The final ranking suggests that wind power is the best energy source based on the three efficiency criteria considered in this analysis. Nuclear power is ranked as the second best energy source in this analysis due to its low footprints and cost. The next option, wave and tidal energy, has a zero water footprint and fairly low carbon emissions and cost. The results suggest that biomass may not be a perfect source of energy if water footprint and cost are considered in addition to carbon footprint to determine its overall efficiency. Despite the significant support of solar and hydropower in the national energy policies, our analysis suggests that gas, as a non-renewable energy source, is more efficient overall, due to its lower cost and water footprint. The procedure followed in this study is associated with some limitation. Thus, the resulting ranking may not be perfect. However, the obtained results are casting doubt on those policies and procedures that develop quick-fix solutions to the problem and are emerged as a result of incomprehensive decisions. For instance, while the outcomes of recent research suggest that the carbon footprint of biomass has been previously underestimated and may be even higher than the carbon footprint of coal (The Guardian, 2011; Jacobson, 2008), and while we know that the water footprint of biomass is relatively large, the current energy policies are actively encouraging large investments to expand the biomass-based industry. Such a gap between reliable analyses and policy making can result in undesirable conditions in the future and major damages to ecosystems and other valuable natural resources that may be irreversible. CONCLUSION Reducing GHG emissions is the main motive for active development of renewable energy options that can result in the overall reduction of the carbon



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