PHASE 3 - Scope 1 Performance Measures€¦ · Web viewImplementing this alternative requires the introduction of monitoring equipment and software. PCEE: Personal Computers Energy

APPENDIX A: DESCRIPTION OF ALTERNATIVES

Table A1: The Scenarios Considered in Footprint Reduction AlternativesALTERNATIVE DESCRIPTION ECT: Emissions Control Technologies

Scenario under investigation involves the installation of 14 Diesel Oxidation Catalysts (DOCs) on 7 vessels, 2 per vessel.

EU: Engine Upgrades Scenario under investigation involves the installation of 40 kits on 12-cylinder engines, 36 kits on 16-cylinder engines, and 14 kits for 20-cylinder engines. Total of 80 kits being installed.

ER: Engine Replacements

Scenario under investigation involves the replacement of three 12-cylinder engines on each of three vessels along with the replacement of two 16-cylinder engines each of 4 vessels. Total of 17 engines of 5 vessels being replaced.

AF: Alternative Fuels Scenario under investigation involves the transition from ULSD to B20 biodiesel (20% biodiesel, 80% ULSD).

DER: Diesel Electric Retrofits

Scenario under investigation involves converting one line-haul boat into a diesel electric hybrid.

SREE: Server Room Energy Efficiency

Scenario under investigation involves installing server-monitoring equipment to improve temperature and humidity settings and equipment containment in one IT facility of the case study company.

PCEE: PC Energy Efficiency

Scenario under investigation involves managing 94 personal computers (PCs) and 174 liquid crystal display (LCD) monitors by installing software that can improve PC power use in an IT facility of the case study company.

ECT: Emissions Control Technologies

Under this category, we considered installing diesel oxidation catalysts (DOCs) that are targeted at minimizing emission of particulate matter and removing carbon monoxide and hydrocarbons from diesel fuel combustion in marine engines. A DOC consists of two main components; the housing constructed of carbon steel and the catalyst constructed of a stainless steel substrate. The size and weight of the DOC is dependent on what emissions targets are being pursued. Essentially, the higher the reduction requirements, the larger the system becomes, based on catalyst residence time (Washington State University Extension Energy Program 2010).

EU: Engine Upgrades

EMD, a primary manufacturer of marine diesel engines offers an emissions reduction kit for their engines. The emissions kit currently available involves replacing components with specific design changes to achieve emissions compliance with EPA 40 CFR Part 1042, which applies to marine diesel engines. Power assemblies, consisting of the unitized cylinder head, liner, piston, and connecting rod, along with fuel injectors are most commonly swapped out during an emissions reduction overhaul. A typical EMD emissions kit includes the appropriate number of power assemblies and injectors for the number of cylinders in an engine. EMD offers emissions kits with either new or unit exchange (UTEX) power assemblies. Weights of the power assemblies are the same between standard and emissions certified power assemblies. Effectively,

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no weight is added to the engine nor is the metallurgic content modified when an emissions kit is applied (EMD 2011)

ER: Engine Replacements

The case study company’s line-haul boats are fitted with diesel engines manufactured by EMD, Caterpillar, Detroit Diesel, and Cummins. Engine replacement is costly, yet inevitable for any vessel due to engine wear and to the need to comply with changed EPA emissions standards. Line-haul boats are usually equipped with 2-3 diesel engines, which have a lifetime of between 20-40 years. Newer engines offer increased fuel economy along with reduced emissions (EMD 2011).

AF: Alternative Fuels

Biodiesel is a renewable fuel derived from sources such as agricultural and animal products. These sources can include, but are not limited to, soybean oil, canola oil, sunflower oil, cottonseed oil, or animal fats. Biodiesel is produced domestically through a chemical process called trans-esterification and is a registered fuel by the EPA. The most common blend of biodiesel is B201. Substitution of ultra-low-sulfur diesel (ULSD) with a biodiesel blend reduces criteria pollutant emissions while offering similar power, torque, and fuel economy. In addition, biodiesel has lubricity properties and requires no major engine modifications. The cost of biodiesel is higher than petroleum diesel, resulting in increased fuel costs (ORNL 2003).

DER: Diesel Electric Retrofits

Hybrid power systems are a relatively new technology, and slowly being implemented in marine vessels. The power system of the hybrid tug includes main engines, auxiliary engines, and an array of lead acid batteries. The percentage of power drawn from the battery array is dependent upon the operational mode. The engine system is connected directly to an electrical generator. The system can have multiple generators and motors. To charge the batteries, the engine has to burn diesel. Overall, the fuel consumption of this system is much less compared a similar conventional engine (Jayaram et al. 2010).

SREE: Server Room Energy Efficiency

As the need for computing resources increases, the energy required to power and cool dedicated server rooms is increasing. Research has shown that the standard configuration and operating conditions of servers can be improved, resulting in substantial energy savings with little associated costs (Brey et al. 2011). This alternative considered the possibility that the servers, temperature, and humidity savings in a server room facility can be optimized to achieve direct electricity savings (Pakbaznia & Pedram 2009). Implementing this alternative requires the introduction of monitoring equipment and software.

PCEE: Personal Computers Energy Efficiency

1 B20 is comprised of 20% biodiesel and 80% petroleum diesel.

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Many personal computers (PC) and monitors used in business environments are not turned off during idle hours, wasting both energy and money. Software is now readily available to control the power settings of computers and monitors (Nordman et al. 2000; Roberson et al. 2002) when they are not in use. Many studies have shown that significant amount of energy savings could be achieved through improving PC management in companies as well as home environments. The analysis considered the implications of installing PC Energy Efficiency software on all of the IT facility’s desktops to control power settings and reduce energy usage.

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APPENDIX B: ANALYTIC HIERARCHY PROCESS

The AHP method facilitates systematic configuration and structuring of the overall goal, impact categories, objectives to be targeted under each impact category, and PMs that define objectives (Saaty 1987). This is done through constructing a VT (Figure B1). The overall goal for the project – prioritizing footprint reduction alternatives – is set at the beginning of study and is used as the basis for developing the VT. The first tier of the VT included the impact categories that indicate broad areas of interest and that are not directly quantified. For our application, the impact categories are the three pillars of sustainable development economic, environmental and social impacts. Under each of these categories, specific objectives are identified and placed in the second tier of the decision tree: maximize profits, minimize natural resource consumption, minimize environmental releases, and maximize internal benefits. These objectives characterizes the specific goals under each impact category (Accorsi et al. 1999a). The PMs are defined under each objective, and they made up the third tier of the VT.

To capture individual preferences of each of the criteria, we ask the participating stakeholders to perform pair-wise comparisons across and within each of the VT tiers. For example, when considering the first tier (i.e., comparing across impact categories) the individuals compare two of the impact categories at a time, with a total of three comparisons: economic versus environmental, economic versus social, environmental vs. social. The comparisons are performed using ratio judgments from the semantic scale for AHP (Table B1) given in Accorsi et al (1999b). With the assumption that the manager’s value of the importance of economic impacts to environmental impacts is the reciprocal of their importance of environmental impacts to economic impacts, etc., the three pairwise comparisons result in a matrix of an individual’s priorities, with the self comparisons on the diagonal set to unity. This process is repeated for objectives within each impact category and then again for the PMs within each objective.

As outlined in Accorsi (1998), performance weights are obtained by computing the normalized “principal eigenvectors” (λmax) associated with each of the matrices. As a result, the weights range from 0-1 and sum to 1 across the impact categories, objectives being compared, and PMs being compared. If there is only one objective under an impact category or one performance measure under an objective, it is assigned a weight of 1 (Figure B2).

Table B1: The semantic scale used for pair-wise comparisons (Source: Accorsi et al., 1999a, 1999b)

Scale Description1: If the two elements are equally important 3: If one element is weakly more important than the other element5: If one element is strongly more important than the other element7: If one element is demonstrably or very strongly more important than the other

element9: If one element is absolutely more important than the other elementNumbers 2, 4, 6, and 8 can be used to express compromise between slightly different judgments; e.g. choose 4 if your judgment is in between 3 and 5.

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To obtain the absolute or final weights for objectives and PMs, weights from each tier are multiplied downward. For example, the final weight for capital cost is the product of the economic impact category, maximize profit objective, and capital cost performance measure (Figure 3). Prior to this step, only the impact categories have absolute weights. Because objectives are compared within impact categories and PMs are compared within objectives, these weights are relative to the other objectives and PMs used in the pairwise comparisons. The relative values cannot be cross-compared until absolute weights are calculated. We use the absolute PM weights to calculate the PI for each manager for each alternative.

Prioritization (i,j) Economic (i,1) Environmental (i,2) Social (i,3) PM WeightsEconomic (1,j) 1 A B λmax1

Environmental (2,j) 1/A 1 B/A λmax2

Social (3,j) B/A 1/B 1 λmax3

Figure B2 Example matrix for impact category weights with manager responses and principal eigenvalues. Similar matrices are used for all value tree pair-wise comparisons, as well as, priorities of best-moderate-worst calculations. Because of the reciprocal rule, (2,1) is taken as 1/A (i.e., reciprocal of value in (1,2)).

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APPENDIX C: UTILITY EVALUATION

The next step is to evaluate the outcomes of PMs across alternatives as valued by each manager and express these outcomes in a common scale. This is referred to as the utility of alternatives to individual stakeholders and is evaluated by applying a modified version of a fuzzy logic technique (Accorsi et al., 1999b). According to this method, we first establish a numeric range of possible outcomes for each performance measure from “best” to “worst”. For example, if the performance measure is capital cost, the “best” is the lowest capital cost of an alternative, and the “worst” is the highest capital cost of an alternative (Figure A2.3). For PMs representing environmental releases, “best” would be the highest emissions reduction achievable by an alternative, and the “worst” would be the lowest emissions reduction or the maximum emissions increase from an alternative.

Figure B3 “Best”, “moderate”, and “worst” ranges with memberships for Employee Satisfaction PM.

According to the fuzzy theory, individual decision-makers have some sense of best, moderate and worst sub-ranges within a given range of possible outcomes (Zadeh, 1975). By eliciting these sub-ranges according to an individual’s knowledge and preferences for all PMs, we can to convert all the PMs into a common linguistic scale of best-moderate-worst2. There can be extreme sub-ranges where an individual can describe their range as “definitely best” or “definitely worst”. But in mid-ranges, it is unlikely that individuals can be very precise in their judgment, and thus, their responses may belong to best and moderate or moderate and worst ranges to a certain degree (referred to as “membership”). This means that a performance outcome of an alternative can belong to one or two sub-ranges with different memberships

2 There can be many forms of sub-ranges (Sadiq and Khan 2006; Liu 2007; Liu et al. 2009), but to maintain a simplistic approach, we choose a best-moderate-worst scale given in Accorsi (1999b).

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(Figure B3). These subsets can be plotted assuming linear functions, and are referred to as membership functions. For this, we ask the stakeholders to define their best and worst sub-ranges (Figure B3: B1-B2 and W1-W2) showing the full range of possible outcomes for each PM. At the extreme points of best and worst ranges, we give a membership of 1 (Figure B3: B1,W1), and assume that the membership linearly decrease to 0 across the sub-ranges (Figure B3: B2, W2). The moderate range is defined across the extreme points of the best and worst (Figure B3: B1-W1), taking the midpoint of the in between range (Figure B3: B2-W2) as the point with membership of 1; at the extreme points of the best and worst, we assume that membership linearly declines to 0.

In the next step, to derive the utility functions, we ask the stakeholders to express how much “best” is preferred over “moderate” and “worst” for each performance measure. We use the same semantic scale (Table B1) for pair-wise comparisons and organized responses in matrices to calculate the priorities placed on “best”, “moderate”, and “worst” by each manager. To accommodate time constraints, we elicit prioritization from each manager on only the best versus worst and best versus moderate ranges. We use fraction multiplication to estimate the moderate versus worst range (2):

The λmax of the matrices are calculated to obtain the priorities (Figure 4). This process is the same process as used in obtaining the PM weights. To link the best-moderate-worst performance values to utility values, we used inference rules (Klir and Yuan, 1995; Accorsi 1999b):

If Performance Value = Best, then Utility = Utility (Best) If Performance Value = Moderate, then Utility = Utility (Moderate) If Performance Value = Worst, then Utility = Utility (Worst)

Then, using priorities and membership values of each associate, the utility functions are developed for each performance measure (3). A utility is calculated for each alternative (x):

The result is a single, dimensionless value that represents the individual’s value of the alternative corresponding to the performance measure considered. This process is repeated for all alternatives and all PMs, and for all stakeholders interviewed.

APPENDIX D: THE INTERVIEW METHODOLOGY

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The method was implemented with the assistance of company stakeholders representing Administration, Strategic Planning, Engineering, and Sustainability Management, respectively. For the purpose of the following discussion, we name them the Administration, Strategic Planning, Engineering, and Sustainability Stakeholders.

To involve these stakeholders in eliciting the importance of PMs and utility of alternatives, we design and conduct a structured interview methodology, taking insights from Accorsi (1998). As time is an important factor in a business setting, and the MCA methodology is new to the stakeholders, the interview framework has to be carefully designed and revised to obtain the responses from the stakeholders in the most time-effective manner. We prepare a set of material – an introductory presentation, interview scripts, and visuals of PM ranges with anchors to provide context to the ranges – to help communicate our methods and improving the quality of responses; refer to Appendix E for the detailed interview script.

When performing the interview, at least two interviewers must be present; one to conduct the interview while the other one to check for inconsistencies in responses to pair-wise comparisons. For example, if economic impacts are strongly more important than environmental impacts, and economic impacts are weakly more important than social impacts, then logically, social impacts should be more important than environmental impacts (using fraction multiplication – similar to Equation 2). Consistent responses are at times difficult for stakeholders; in these cases, the “checker” intervenes and encourages a discussion to point out the inconsistency and possibly correct it. We deviate from rigorous inconsistency checking as prescribed in Accorsi et al (1999b), as follow-up interviews to correct inconsistencies were not possible.

Some components of the MCA methodology are simplified to accommodate the interview time limitation (one hour). For each PM, we ask for: the best and worst ranges, excluding responses for the moderate range, and how important it is to achieve best versus worst and best versus moderate, excluding responses for moderate versus worst (fractional multiplication served as an approximation for actual responses).

REFERENCES FOR APPENDICES A-D

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Brey, T., Lembke, P., Prisco, J., Abbott, K., Cortese, D., Hazelrigg, K., Larson, J., et al., 2011. Case Study: The Roi of Cooling System Energy Efficiency Upgrades. Beaverton. Retrieved from http://www.thegreengrid.org/en/Global/Content/white-papers/CaseStudyROIofCoolingSystemEnergyEfficiencyUpgrades.

Electro-Motive Diesel, Inc. (EMD), 2011. Personal Communication with C. McKeen. Nashville, TN: EMD.

Jayaram, V., Khan, M. Y., Miller, J. W., Welch, W. A., Johnson, K., & Cocker, D., 2010. Evaluating Emission Benefits of a Hybrid Tow Boat Final Report. Sacramento, CA. Retrieved from http://www.arb.ca.gov/ports/marinevess/harborcraft/documents/hybridreport1010.pdf

Nordman, B., Meier, A., & Piette, M. A., 2000. PC and Monitor Night Status : Power Management Enabling and Manual Turn-off. Information and Electronic Technologies, 7, 89-100.

Oak Ridge National Laboratory (ORNL)., 2003. Bioenergy Conversion Factors. Energy. Oak Ridge. Retrieved from http://bioenergy.ornl.gov/papers/misc/energy_conv.html, accessed February 2011

Pakbaznia, E., & Pedram, M., 2009. Minimizing data center cooling and server power costs. Proceedings of the 14th ACM/IEEE international symposium on Low power electronics and design (pp. 145-150). ACM.

Roberson, J. A., Homan, G. K., Mahajan, A., Nordman, B., Webber, C. A., Brown, R. E., Mcwhinney, M., et al., 2002. Energy Use and Power Levels in New Monitors and Personal Computers. Environmental Protection, (July).

Washington State University Extension Energy Program., 2010. Diesel Oxidation Catalyst (pp. 1-4). St. Louis. Retrieved from www.cleanairconstruction.org/content/research/WSU Off-Road Engine Technologies.pdf, accessed February 2011.

Accorsi, R, Apostolakis, G. & Zio, E., 1999a. Prioritizing Stakeholder Concerns in Environmental Risk Management. Journal of Risk Research, 2(1), pp.11-29.

Accorsi, R, Zio, E. & Apostolakis, G.., 1999b. Developing Utility Functions for Environmental Decision-making. Progress in Nuclear Energy, 34(4), pp.387-411.

Accorsi, Roberto, 1998. Prioritizing Stakeholder Concerns in Environmental Risk Management. MIT.

Carnegie Mellon University, 2008. Economic input-output life cycle assessment (EIO-LCA), U.S. 2002 industry benchmark model. Carnegie Mellon University Green Design Institute. Available at: www.eiolca.net [Accessed February 2012].

Ewing, A. et al., 2011. Insights on the Use of Hybrid Life Cycle Assessment for Environmental Footprinting. Journal of Industrial Ecology, 15(6), pp.937-950.

Frischknecht, R. et al., 2005. The ecoinvent Database: Overview and Methodological Framework. The International Journal of Life Cycle Assessment, 10, pp.3-9.

Gregory, R. & Keeney, R L, 1994. Creating Policy Alternatives Using Stakeholder Values. Management Science, 40(8), pp.1035-1048.

Jolliet, O. et al., 2003. {IMPACT} 2002+: A new life cycle impact assessment methodology. The International Journal of Life Cycle Assessment, 8(6), pp.324-330.

Keeney, R L, 1981. Analysis of Preference Dependencies among Objectives. Operations Research, 29(6), pp.1105-1120.

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Keeney, R. L. & Raiffa, H., 1976. with Multiple Objectives: Preferences and Value Tradeoffs, New York, NY: John Wiley & Sons, Inc.

Keeney, Ralph L & McDaniels, T.L., 1999. Identifying and Structuring Values to Guide Integrated Resource Planning at BC Gas. Operations Research, 47(5), pp.651-662.

Keeney, Ralph L, 1982. Decision Analysis: An Overview. Operations Research, 30(5), pp.803-838.

Liu, K., 2007. Evaluating Environmental Sustainability: An Integration of Multiple-Criteria Decision-Making and Fuzzy Logic. Environmental Management, 39(5), pp.721-736.

Liu, K.F.R. et al., 2009. A Qualitative Decision Support for Environmental Impact Assessment Using Fuzzy Logic. Journal of Environmental Informatics, 13(2), pp.93-103.

Saaty, R.W., 1987. The analytic hierarchy process—what it is and how it is used. Mathematical Modelling, 9(3–5), pp.161-176.

Sadiq, R. & Khan, F.I., 2006. An integrated approach for risk-based life cycle assessment and multi-criteria decision-making: Selection, design and evaluation of cleaner and greener processes. Business Process Management Journal, 12(6), pp.770-792.

Suh, S. et al., 2004. System Boundary Selection in Life-Cycle Inventories Using Hybrid Approaches, Environmental Science and Technology. Environment Science and Technology, 38(3), pp.657-664.

Swiss Center for Life Cycle Inventories, 2012. ecoinvent v2.2 Database. Available at: http://www.ecoinvent.ch/ [Accessed April 12, 2012].

Thabrew, L. et al., 2011. Indirect Emissions Reduction Opportunities for Freight Carriers. In In Proceedings of LCA XI Conference, October 4-6, 2011. Chicago, IL.

Yuan, Y. & Shaw, M.J., 1995. Induction of fuzzy decision trees. Fuzzy Sets and Systems, 69(2), pp.125-139.

Zadeh, L.A., 1975. The concept of a linguistic variable and its application to approximate reasoning—I. Information Sciences, 8(3), pp.199-249.

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APPENDIX E: THE INTERVIEW SCRIPT

This Appendix shows the script used by the Vanderbilt Team to interview the case study company stakeholders. In addition, we prepared an introductory power point presentation on MCA, and a handout to the interviewees that included the semantic scale, definitions of PMs, decision tree, and visual of PM ranges.

Multi Criteria Analysis on Environmental Footprint Reduction AlternativesThe Decision ContextThe case study company has been working with Vanderbilt researchers to assess possible ways to reduce its environmental footprint. We are using a method to assess multiple criteria across environmental, economic, and social impact areas. This will result in a prioritization of varying interventions to reduce the company’s footprint.

Multi Criteria AnalysisThere are two major components of this analysis: (1) establishing the relative importance of a set of PMs (Fig. 1) that will be used to help prioritize alternatives, and (2) assessing each associate’s idea of “acceptable” (best) and “not acceptable” (worst) ranges for each performance measure. Both steps will involve evaluating the preferences and judgments of the case study company stakeholders.

Step 1: Assessing weights for PMsThis exercise will take you through a process where you will be asked to reveal the relative importance you place on impacts, objectives and PMs (Fig. 1) that have been developed to assess possible ways to reduce the company’s footprint over the next two years. It is understood that all of these impacts, objectives, and PMs are important to you. Nevertheless, when you go through the first part of this exercise, consider each pair of PMs only in relation to one another. This will allow us to assign relative weights to each element of the value tree. All elements will be factored into the final analysis.

Please review the weights from this semantic scale. You will be using these values to express you’re your judgment of the comparisons. It is important to note that you can:

Use the numbers as many times as you would like, Use 2, 4, 6, and 8 to indicate your preference if it falls in between the defined values, and Switch the order of the pairwise comparisons (i.e., economic v. environmental: economic

is weakly more important than environmental or environmental is weakly more important than environmental).

1: if the two elements are equally important3: if one element is weakly more important than the other element5: if one element is strongly more important than the other element7: if one element is demonstrably or very strongly more important than the other element9: if one element is absolutely more important than the other elementNumbers 2, 4, 6, and 8 can be used to express compromise between slightly differing judgments; e.g. choose number 4, if your judgment is in between 3 and 5.

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Figure 1: Decision Value Tree

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Descriptions of PMs Total capital cost: Capital investments for purchasing or installing environmental

footprint reductions alternatives. Net change in O&M costs: changes in any recurring costs or cost savings related to

operating and maintaining the environmental footprint reductions alternatives. Quantity of direct energy use: changes in energy use within the company boundary

(day-to-day operations) related to any environmental footprint reductions alternative (e.g., electric energy consumed by IMG facilities).

Quantity of indirect energy use: changes in energy use outside the company boundary (supply chain) related to any environmental footprint reductions alternative (e.g., energy consumed for fuel production or manufacture of engines).

Quantity of direct water use: changes in water use within company boundary related to a given environmental footprint reduction alternative, where relevant (NOTE: Direct water use was not relevant for any of the considered alternatives and will not be included in this exercise).

Quantity of indirect water use: changes in water use outside the company boundary related to a given environmental footprint reduction alternative, where relevant (e.g., water consumed for electricity production).

Quantity of direct GHG: changes to current level of GHGs being emitted within the company related to environmental footprint reductions alternatives (e.g. GHG emissions from retrofitted engines).

Quantity of indirect GHG: changes to current level of GHGs emitted from operations outside the company related to environmental footprint reductions alternatives (e.g. GHG emissions from manufacturing engine kits).

Quantity of direct criteria pollutants: changes in current level of criteria pollutants from operations within the company related to environmental footprint reductions alternatives.

Quantity of indirect criteria pollutants: changes in current level of criteria pollutants from operations outside the company related to environmental footprint reductions alternatives.

Level of stakeholder support: level of stakeholder support for implementing environmental footprint reduction alternatives.

Level of stakeholder satisfaction: stakeholder pride and retention as a result of company actions to reduce environmental footprint.

Stakeholder health and safety: changes in stakeholder health and safety related to environmental footprint reduction alternative (NOTE: is not included at this stage, but may be investigated in a later stage).

Perceived company image: How the public views your company in response to environmental footprint reduction alternatives adopted (NOTE: is not included at this stage, but may be investigated in a later stage).

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1) The overall agreed upon goal is to “Reduce the Company’s Environmental Footprint”. To achieve this specific goal, we will begin the process of defining relative importance by comparing the three broad impact categories of:

a. Economicb. Environmentalc. Social

In Matrix 1, please estimate the relative importance of the following: Economic goals v. Environmental Economic goals v. Social Environmental goals v. Social

Matrix 1 Economic Environmental SocialEconomic 1Environmental 1Social 1

[Consistency check]

2) The PMs related to maximizing profits are:a. Total capital costsb. Net changes in O&M costs In Matrix 2, please estimate the relative importance of:

Total net changes in O&M costs v. capital costsMatrix 2 Capital costs O&M costs Capital costs 1O&M costs 1

3) Under the larger impact category of “environmental", we will compare the following objectives:

a. Minimize natural resource use b. Minimize environmental releasesIn Matrix 3, please estimate the relative importance of:

Minimizing environmental releases v. minimizing natural resource useMatrix 3 Resource use Environmental releasesResource use 1Environmental releases 1

4) The PMs related to minimizing natural resource use are:a. Quantity of direct energy use b. Quantity of indirect energy usec. Quantity of direct water used. Quantity of indirect water useIn Matrix 4, please estimate the relative importance of:

Direct energy use reductions v. indirect energy use reductions

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Direct energy use reductions v. direct water use reductions Direct energy use reductions v. indirect water use reductions Direct water use v. indirect energy use reductions Indirect energy use reductions v. indirect water use reductions Direct water use reductions v. indirect water use reductions

Matrix 4 Direct Energy Indirect Energy

Direct Water Indirect Water

Direct Energy 1Indirect Energy 1Direct Water 1Indirect Water 1

[Consistency check] 5) The PMs related to minimizing environmental releases are:

a. Quantity of direct GHGsb. Quantity of indirect GHGsc. Quantity of direct criteria pollutants (SO2, NOx, PM, CO)d. Quantity of indirect criteria pollutants In Matrix 5, please estimate the relative importance of:

Direct GHG reductions v. indirect GHG reductions Direct GHG reductions v. direct criteria pollutants reductions Direct GHG reductions v. indirect criteria pollutants reductions Direct criteria pollutants reductions v. indirect GHG reductions Indirect GHG reductions v. indirect criteria pollutants reductions Direct criteria pollutants reduction v. indirect criteria pollutants reductions

Matrix 5 Direct GHGs

Indirect GHGs

Direct criteria pollutants

Indirect Criteria Pollutants

Direct GHGs 1Indirect GHGs 1Dir Crit pollutants 1Indirect CP 1

[Consistency check]

6) Under the larger impact category of “social", we will compare the following objectives:a. Maximize internal social benefitsb. Maximize external social benefitsIn matrix 6, please estimate the relative importance of:

Maximizing internal social benefits v. maximizing external social benefits

Matrix 6 Internal social benefits External social benefits

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Internal social benefits 1External social benefits 1

[Consistency check]

7) The PMs related to maximizing internal social benefits are:a. Level of personnel supportb. Level of personnel satisfaction c. Stakeholder health and safetyIn Matrix 7, please estimate the relative importance of:

Stakeholder satisfaction v. stakeholder support Stakeholder health and safety v. stakeholder support Stakeholder health and safety v. stakeholder satisfaction

Matrix 7 Stakeholder support

Stakeholder satisfaction

Health and Safety

Stakeholder support 1Stakeholder satisfaction 1Health and Safety 1

[Consistency check]

8) The PMs related to maximizing external social benefits are:a. Public health risk b. Perceived company imageIn Matrix 8, please estimate the relative importance of:

Perceived image of the company v. public health risksMatrix 8 Public health risk Perceived imagePublic health risk 1Perceived image 1

Step 2: Utility Evaluation of Performance MeasuresIn this exercise, please consider your company’s goal of reducing your environmental footprint. This is defined as reducing environmental emissions and resource use for one year of IMG operations.

For each of the PMs, you already worked with us to conduct pairwise comparisons to determine relative importance of one PM to another. This will allow us to calculate specific weights for each impact area, objective, and performance measure.

Now we will ask you to look at each performance measure in isolation, supposing that the performance measure is the only metric on which you are basing a decision. For each performance measure, we would like you to look at the given range of possible outcomes and tell us what you think are the unacceptable (worst) and acceptable (best) ranges. To save time, we will not be asking you for a moderate range, but it is important to know that we will be

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calculating a moderate range from your worst and best ranges. This middle range will have overlapping values with the worst and best ranges.

Again, during this exercise we ask that you consider each performance measure in isolation and that you keep in mind your goal of footprint reduction.

Please note that there is no right or wrong answer when picking these best or worst ranges. This should be based on your judgment for each PM.

1) Quantity of direct energy use: change in energy use within the company boundary (day-to-day operations).

a. To give you some context, electric energy used in a given year for the assessed IT facility was about ZZ kWh.

b. Given a range of possible reductions for direct energy from a minimum possible of XX to a maximum possible reduction of YY, what size reduction in energy use would be considered your:

i. Acceptable (Best) range?ii. Not acceptable (Worst) range?

PM Range of outcomes (kWh)

Best Range Worst Range

Direct Energy XX-YY

c. Looking at the ranges you established, and using the same semantic scale as in Part 1, please indicate how much more important it is for you to reach an outcome in your best range v. your worst?

d. Supposing the moderate range to be ___________, how much more important is it to reach your best range v. your moderate?

Comparing ranges Best Moderate WorstBest 1Moderate 1Worst 1

2) Quantity of indirect direct energy use: change in energy use outside the company boundary (supply chain) related to any footprint reduction.

a. The yearly fuel energy usage for an average line haul boat is about 28M kWh.b. Given a range of reductions for indirect energy use from a “worst” of increasing your

indirect energy use by XX to a maximum possible reduction of YY, what size reduction in indirect energy use would be considered your:


PM Range of outcomes (kWh)


Indirect Energy

XX-YY

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c. Looking at the ranges you established, please indicate how much more important it is for you to reach an outcome in your best range v. your worst?



3) Quantity of indirect water use: change in water use outside the company boundary a. An average line haul boat is responsible for about 30 Mgal of indirect water use.b. Given the possible range of reductions for indirect water use, from a “worst” of an

increase of XX million gallons to a maximum possible reduction of YY million gallons, what size reduction in indirect (supply chain) water use would be considered your:


PM Range of outcomes (Mgal)


Indirect Water XX-YY




4) Quantity of direct GHG: change in current level of GHGs being emitted within the company related to direct operations.

a. An average line haul boat emits about 6,000 MTCO2eq of direct GHGs.b. Given a possible range of reductions for direct GHGs, from XX metric tons of CO2 to

a maximum possible reduction of YY metric tons, what size reduction in direct GHG emissions would be considered your:


PM Range of outcomes (MTCO2eq)


Direct GHGs XX-YY

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5) Quantity of indirect GHG: changes to current level of GHGs emitted from operations outside the company (e.g. manufacturing) related to footprint reductions alternatives.

a. An average line haul boat is responsible for about 1,000 MT of indirect GHGs.b. Given a possible range of reductions for indirect GHGs, from XX metric tons to a

maximum possible reduction YY million metric tons, what size reduction in indirect (supply chain) GHG emissions would be considered your:


PM Range of outcomes (MTCO2eq)


Indirect GHGs XX-YY




6) Quantity of direct criteria pollutants: change in current level of criteria pollutants (CO, PM, NOx and SOx) from operations within the company.

a. An average line haul boat emits about 2,000 kg PM2.5eq of direct criteria pollutant emissions.

b. Given a possible range of reductions for direct criteria pollutants, from a minimum reduction of XX to a maximum reduction of YY kg, what do you consider to be your:


PM Range of outcomes (kgPM2.5eq)


19

Direct CP XX-YY




7) Quantity of indirect criteria pollutants: changes in current level of criteria pollutants from operations outside the company (e.g. manufacturing).

a. An average line haul boat is responsible for about 1,500 kg of indirect criteria pollutant emissions.

b. Given a possible range of reductions for indirect criteria pollutants from an addition of XX kg to a maximum potential reduction of YY kg, what size reduction in indirect criteria pollutant emissions would be considered your:


PM Range of outcomes (kgPM2.5eq )


Indirect CP XX-YY




8) Your goal is to have the maximum stakeholder support for company actions that reduce emissions and resource consumption.

a. The range of outcomes for stakeholder support is XX-YY%. b. How great a percentage of total stakeholders supporting measures would be

considered your best range?c. What would you consider to be your worst range for total stakeholder support?

PM Range of outcomes


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Stakeholder Support

XX-YY

d. Looking at the ranges you established, please indicate how much more important it is for you to reach an outcome in your best range v. your worst?

e. Supposing the moderate range to be ___________, how much more important is it to reach your best range v. your moderate?

Comparing ranges

Best Moderate Worst

Best 1Moderate 1Worst 1

9) Your goal is to maximize stakeholder pride and retention as a result of company actions that reduce emissions and resource consumption.

a. The range of outcomes for stakeholder pride and retention is XX-YY%. b. What would be considered your best range for stakeholder satisfaction, in

percentages?c. What level of stakeholder satisfaction would you consider to be your worst range?PM Range of outcomes Best Range Worst Range

Stakeholder Satisfaction

XX-YY

d. Looking at the ranges you established, please indicate how much more important it is for you to reach an outcome in your best range v. your worst?

e. Supposing the moderate range to be ___________, how much more important is it to reach your best range v. your moderate?


10) Total capital cost: Costs over the life of the investment for purchasing or installing emissions reductions alternatives.

a. In 2011, your company paid capital costs of about $ZZ upgrading 11 engines to reduce emissions.

b. Given a possible range of capital costs from $XX to $YY, what would you consider to be your:

i. Best range (above which you would begin to question whether the cost was worth it)?

ii. Worst range?PM Range of Best Range Worst Range

21

outcomes ($ per year)

Capital Costs XX-YY




12) Net change in O&M costs: change in any recurring costs or savings.a. An average line haul boat consumes $ZZ worth of fuel annually.b. Given a possible range of O&M costs from a maximum savings of $XX to a

maximum increase in expenditure of ~$YY, what amount of change to O&M expenditure would be considered your:

i. Best range? ii. Worst range?

PM Range ($ per year) Best Range Worst RangeO&M Costs XX-YY




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PHASE 3 - Scope 1 Performance Measures€¦ · Web viewImplementing this alternative requires the introduction of monitoring equipment and software. PCEE: Personal Computers Energy