WO2007117233A1

WO2007117233A1 - Emission trading product and method

Info

Publication number: WO2007117233A1
Application number: PCT/US2006/012856
Authority: WO
Inventors: William L. Trout; Robert Broadfoot; Michael Hileman; Richard Jones
Original assignee: Hsb Solomon Associates, Llc
Priority date: 2006-04-07
Filing date: 2006-04-07
Publication date: 2007-10-18
Also published as: EP2013844A1; EP2013844A4

Abstract

The present invention provides a system and method for determining equivalency factors for use in comparative performance analysis of industrial facilities by determining a target variable and a plurality of characteristics of the target variable. Each of the plurality of characteristics is ranked according to value. Based on ranking value, the characteristics are divided into categories. Based on the sorted and ranked characteristics, a data collection classification system is developed. Data is collected according to the data collection classification system. The data is validated, and based on the data, an analysis model is developed. The analysis model then calculates the equivalency factors for use in one embodiment in performance measurement and equitable benchmarking of green house gas (GHG) emissions from industrial facilities for the purposes of allocating GHG emission allowances for permits, licenses, etc. Furthermore, a system and method is described for quantifying, transferring, managing, underwriting, or insuring the risk associated with emissions from manufacturing, operating a facility, or engaging in an activity that emits, produces, or potentially emits or produces permitted or regulated emissions. In addition to insuring against emissions that exceed permitted or regulated amounts, a system for pooling, managing, and trading emission credits is also described herein.

Description

EMISSION TRADING PRODUCT AND METHOD

The present invention relates to comparing the performance of refining, petrochemical, power generating, distribution, and other industrial facilities. More specifically, the invention relates to determining the equivalency factors that enable performance measurements and equitable benchmarking of green house gas (GHG) emissions, also known as carbon dioxide (CO₂) gas emissions from a facility to a form that can be directly compared to the GHG production from a another facility that has different characteristics for the purposes of allocating GHG emission allowances for permits, licenses, etc. Furthermore, the invention relates to the field of risk transfer systems and methods, in particular, to an emissions insurance system and method.

Negative environmental and health effects, such as global warming, smog, and respiratory problems in humans caused by the emission of harmful pollutants such as carbon dioxide (CO2), have resulted in countries, states, and territories throughout the world regulating the amount of emissions permitted by businesses and industries. Some scientists claim that the CO₂ emissions are causing global warming under the theory that the emissions create a green house effect. The source of these and other emission pollutants can come from a myriad of industries, including: energy industries, such as public electricity and heat production, petroleum refining, and the manufacturing of solid fuels. Various countries have agreed to reduce their CO2 emissions under the Kyoto Treaty.

The Kyoto Treaty commits industrialized nations to reducing emissions of greenhouse gases, principally CO₂, by around 5.2% below their 1990 levels over the next decade. To come into force, the treaty needs to be ratified by countries who are responsible for at least 55% of the world's CO₂ emissions. The agreement was dealt a severe blow in March 2001 when the United States announced it will not join.

A scaled-down version was drawn up four months later and finalized at climate talks in Bonn in Germany in 2002. If and when the revised treaty takes effect in 2008, it will require all signatories, including 39 industrialized countries, to achieve different emission reduction targets. With that aim, it will provide a complex system which will allow some countries to buy emission credits from others. For instance, a country in western Europe might decide to buy rights or credits to emit carbon from one in eastern Europe which could not afford the fuel that would emit the carbon in the first place. The Bonn agreement also reduced cuts to be made to emissions of six gases believed to be exacerbating global warming - from the original treaty's 5.2% to 2%.

Simplistic methods, such as GHG/tonne or bbl have been used in the past to determine emission allocation. However, such methods tend to be misleading and sometimes penalize efficient facilities.

Furthermore, in many cases, individual activities, processes, and pieces of equipment within an installation, such as an oil refinery for example, each have an individual permitted emission amount, often expressed in terms of tons or cubic meters. For example, Refining Facility X, having individual processes or activities A, B, and C, may have a NOx emission permit level of 120,000 tons per year for activity A, a CO₂ emission permit level of 200,000 tons per year for activity B, and a CO (carbon monoxide) emission permit level of 170,000 tons per year for activity C. Although in many cases emission permit levels may be combined for the same pollutant at a given location or installation, such as a power generation plant, significant risks that a company may exceed their permitted amounts still exist.

Exceeding permitted emission levels can carry significant fines or costs. Businesses have had to incur the costs associated with exceeding permitted levels, even if the cause of exceeding the permitted levels was beyond a company's control (e.g. damage caused by weather related storms, and equipment malfunction or failure). Heretofore, businesses have been unable to transfer any risks associated with exceeding permitted emissions regulations. The only options available to companies for emission risk management have been to install additional controls, invest in emission credit activities, purchase emission credits in the market, or reduce production. Implementing any of the foregoing options generally results in significant costs, and reducing production is typically not a viable economic alternative.

The present invention provides a new and unique system and method for determining equivalency factors for use in comparative performance analysis of industrial facilities by determining a target variable such as a green house equivalent gas standard (Standard GHG), and a plurality of characteristics of the target variable. The characteristics are sorted and a data collection classification system is developed. The data classification system is used to quantitatively measure the differences in characteristics. Data is collected according to the data collection classification system. The data is validated, and based on the data, an analysis model is developed to compare predicted target variable to actual target variable for a set of industrial facilities. The model is used to formulate equitable benchmarking of green house gases (GHG) emissions from industrial sources for the purposes of allocating GHG emission allowances, permits, licenses, etc.

Furthermore, the present invention generally relates to a system or method for quantifying, transferring, managing, underwriting, or insuring the risk associated with manufacturing, operating a facility, or engaging in an activity that emits, produces, or potentially emits or produces permitted or regulated emissions. As described herein, one general embodiment of the invention typically involves a unique combination of qualitative and quantitative functions combined in an novel fashion to develop a risk transfer system or method associated with insuring emission releases due to equipment malfunction, operator error, acts of terrorism, or force majeure events. In another embodiment of the invention, an insurer, trustee, or other entity accumulates each of its insured's emission credits in a bank or pooling system, wherein the insurer can use the bank of emission credits to pay claims, and/or trade emission credits in the market based on forecast data derived from emission calculation worksheets, realtime activity or installation data, or any other suitable source. BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

Fig. 1 is a flowchart illustrating the operation of an embodiment of the invention.

Fig. 2 is a flowchart illustrating the operation of another embodiment of the invention.

Fig. 3 is a flowchart illustrating the operation of another embodiment of the invention.

Fig. 4 is an example implementation of an embodiment of the invention.

Fig. 5 is another example implementation of an embodiment of the invention using example data.

Fig. 6 is an illustrative node for implementing a method of the invention.

Fig. 7 illustrates a block diagram of one embodiment of an Emission Risk Transfer System and Method

Fig. 8 is an illustrative table of an embodiment of the invention.

Fig. 9 is an illustrative table of an embodiment of the invention.

Fig. 10 is an illustrative table of an embodiment of the invention.

Fig. 11 is an illustrative table of an embodiment of the invention.

DETAILED DESCRIPTION OF THE INVENTION

The following disclosure provides many different embodiments, or examples, for implementing different features of a system and method for accessing and managing structured content. Specific examples of components, processes, and implementations are described to help clarify the invention. These are, of course, merely examples and are not intended to limit the invention from that described in the claims. Well-known elements are presented without detailed description in order not to obscure the present invention in unnecessary detail. For the most part, details unnecessary to obtain a complete understanding of the present invention have been omitted inasmuch as such details are within the skills of persons of ordinary skill in the relevant art.

Referring now to FIG. 1, an example 100 of the operation of one embodiment of a method for determining equivalency factors for use in comparative performance analysis of industrial facilities is shown. At step 102, a target variable ("Target Variable") is selected. The target variable is a quantifiable attribute (such as the metric tons of CO₂ emitted from stationary combustion sources). Target Variables could be in refining, chemical (including (petrochemicals, organic and inorganic chemicals, plastics, agricultural chemicals, and pharmaceuticals), Olefins plant, chemical manufacturing, power generating, distribution, and other industrial facilities. The Target Variables could also be for different environmental aspects (such ass non-CO₂ emissions and non-combustion CO₂ byproducts from specific processes). Target Variables could also be in other forms and types of industrial and commercial industries.

At step 104, the first principle characteristics are identified. First principle characteristics are the physical or fundamental characteristics of a facility or process that are expected to determine the Target Variable. Common brainstorming or team knowledge management techniques can be used to develop the first list of possible characteristics for the Target Variable. In one embodiment, all of the characteristics of an industrial facility that may cause variation in the Target Variable when comparing different manufacturing facilities are identified as first principle characteristics.

Target variables for refineries with typically be established for each of the permitted or licensed sources without limitation, catalytic cracking units, catalytic refining units, sulfur recovery units, storage vessels/tanks, fluid coking units, wastewater treatment units/streams, cooling towers, equipment leaks, blowdown systems, vacuum units, crude units, steam boilers, furnaces/heaters, compressors, turbines, vessel unloading/loading facilities, flares/thermal oxidizers, pipelines, and gasoline racks.

Target variables for power generation facilities will similarly be established for each of the permitted or licensed sources, including but not limited to, turbines, steam boilers, cooling towers, fuel storage tanks, pipelines, wastewater treatment units/streams, equipment leaks, compressors, and flares/thermal oxidizers. At step 106, the primary first principle characteristics are determined. As will be understood by those skilled in the art, many different options are available to determine the primary first principle characteristics. One such option is shown in FIG.

2.

At step 108, the primary characteristics are classified. Potential classifications include discrete, continuous, or ordinal. Discrete characteristics are those characteristics that can be measured using a selection between two or more states, for example a binary determination, such as "yes" or "no." An example discrete characteristic could be "Type of Crude Unit." The determination of "Type of Crude Unit" is "SCU" for a standard crude unit with a nominal TBP outpoint of bottoms greater than or equal to 600 degrees F or "MCU" for a mild crude unit with a nominal TBP outpoint of bottoms less than 600 degrees F. Continuous characteristics are directly measurable. An example of a continuous characteristic could be the "Feed Capacity," since it is directly measured as a continuous variable. Ordinal characteristics are characteristics that are not readily measurable. Instead, ordinal characteristics can be scored along an ordinal scale reflecting physical differences that are not directly measurable. It is also possible to create ordinal characteristics for variables that are measurable or binary. An example of an ordinal characteristic would be refinery configuration between three typical major industry options. These are presented in ordinal scale by unit complexity:

1.0 Atmospheric Distillation 2.0 Catalytic Cracking Unit 3.0 Coking Unit

Ordinal variables are in rank order, and generally do not contain information about any useful quality of measurement. In the above example, the difference between the complexity of the 1.0 unit and the 2.0 unit, does not necessarily equal the complexity difference between the 3.0 unit and the 2.0 unit. Variables placed in an ordinal scale may be converted to an interval scale for development of equivalency factors. To convert ordinal variables to interval variables requires the development of a scale upon which the differences between units are on a measurable scale. The process to develop an interval scale for ordinal characteristic data can rely on the understanding of a team of experts of the characteristic's scientific drivers. The team of experts can first determine, based on their understanding of the process being measured and scientific principle, the type of relationship between different physical characteristics and the Target Variable. The relationship may be linear, logarithmic, a power function, a quadratic function or any other mathematical relationship. Then the experts can optionally estimate a complexity factor to reflect the relationship between characteristics and variation in Target Variable. Complexity factors are the exponential power used to make the relationship linear between the ordinal variable to the target variable resulting in an interval variable scale.

At step 110, a data collection classification system is developed. For those characteristics categorized as continuous, a data collection system that allows a quantification of the characteristics is needed. A system of definitions will need to be developed to ensure data is collected in a consistent manner. For characteristics categorized as binary, a simple yes/no questionnaire is used to collect data. A system of definitions may need to be developed to ensure data is collected in a consistent manner. For characteristics categorized as ordinal, a measurement scale can be developed as described above.

To develop a measurement scale for ordinal characteristics, at least four methods to develop a consensus function can be employed. In one embodiment, an expert or team of experts can be used to determine the type of relationship that exists between the characteristics and the variation in Target Variable. In another embodiment, the ordinal characteristics can be scaled (for example 1,2,3 . . . n for n configurations). By plotting the target value versus the configuration, the configurations are placed in progressive order of influence. In utilizing the arbitrary scaling method, the determination of the Target Variable value relationship to the ordinal characteristic is forced into the optimization analysis, as described in more detail below. In this case, the general optimization model described in Equation 1.0 can be modified to accommodate a potential non-linear relationship.

In yet another embodiment, the ordinal measurement can be scaled as discussed above, and then regressed against the data to make a plot of Target Variable versus the ordinal characteristic to be as nearly linear as possible. In a further embodiment, a combination of the foregoing embodiments can be utilized to make use of the available expert experience, and available data quality and data quantity of data.

Once a relationship is agreed, a measurement scale is developed. For instance, a single characteristic may take the form of five different physical configurations. The characteristics with the physical characteristics resulting in the lowest impact on variation in Target Variable will be given a scale setting score. This value may be assigned to any non-zero value. In this example, the value assigned is 1.0. The characteristics with the second largest impact on variation in Target Variable will be a function of the scale setting value, as determined by a consensus function. The consensus function is arrived at by using the measurement scale for ordinal characteristics as described above. This is repeated until a scale for the applicable physical configurations is developed.

At step 112, the classification system is used to collect data. The data collection process can begin with the development of data input forms and instructions. In many cases, data collection training seminars are conducted to assist in data collection. Training seminars may improve the consistency and accuracy of data submissions. A consideration in data collection is the definition of the industrial facility boundaries being analyzed. Data input instructions will provide definitions of what facilities, costs and staffing are to be included in data collection. The data collection input forms may provide worksheets for many of the reporting categories to aid in the preparation of data for entry. The data that is collected can come for several sources, including existing historical data, newly gathered historical data from existing facilities and processes, simulation data from model(s), or synthesized experiential data derived from experts in the field. Additionally, no data at all can be used, in which case the determination of primary characteristics may be based on expert experience.

At step 114, the data is validated. Many data checks can be programmed into an interactive data collection system. The interactive data collection system should only accept data that passes the validation check or the check is over-ridden with appropriate authority. Validation routines may be developed to validate the data as it is collected. The validation routines can take many forms, including:

Range of acceptable data is specified

Ratio of one data point to another is specified where applicable

Data is cross checked against all other similar data submitted to determine outlier data points for further investigation

Data is cross referenced to any previous data submission

Judgment of experts

After all input data validation is satisfied, the data is examined relative to all the data collected in a broad "cross-study" validation. This "cross-study" validation may highlight further areas requiring examination and may result in changes to input data.

At step 116, constraints may be developed for use in solving the analysis model. These constraints could include constraints on the equivalence factor values. These can be minimum or maximum values, or constraints on groupings of values, or any other mathematical constraint forms. One method of determining the constraints is shown in FIG. 3.

At step 118, the analysis model is solved by applying optimization methods of choice with the collected data to determine the optimum set of complexity factors relating the Target Variable to the characteristics. In one embodiment, the generalized reduced gradient non-linear optimization method can be used. However, many other optimization methods could be utilized.

At step 120, developed characteristics may be determined. Developed characteristics are the result of any mathematical relationship that exists between one or more first principle characteristics and may be used to express the information represented by that mathematical relationship. In addition, if a linear general optimization model is utilized, then nonlinear information in the characteristics can be captured in developed characteristics. Determination of the developed characteristics form is accomplished by discussion with experts, modeling expertise, and by trial and refinement.

At step 122, the optimization model is applied to the primary first principle characteristics and the developed characteristics to determine the equivalency factors. In one embodiment, if developed characteristics are utilized, step 116 through step 122 may be repeated in an iterative fashion until the level of model accuracy desired is achieved.

At step 124, the Target Variable (e.g., GHG Standard) is evaluated for each industrial facility. At step 126, the GHG Standard is used to allocate industry wide permits (licenses, etc.) to individual industrial facilities.

Referring now to FIG. 2, one embodiment 200 of determining primary first principle characteristics 106 is shown. At step 202, the effect of each characteristic on the variation in the Target Variable between industrial facilities is determined. In one embodiment, the method is iteratively repeated, and an analysis model can be used to determine the effect of each characteristic. In another embodiment, a correlation matrix can be used. The effect of each characteristic may be expressed as a percentage of the total variation in the Target Variable in the initial data set. At step 204, each characteristic is ranked from highest to lowest based on its effect on the Target Variable. It will be understood by those skilled in the art that other ranking criteria could be used. At step 206, the characteristics may be grouped into one or more categories. In one embodiment, the characteristics are grouped into three categories. The first category contains characteristics that effect a Target Variable at a percentage less than a lower threshold (for example, 5%). The second category are those characteristics with a percentage between the lower percentage and a second threshold (for example, 5% and 20%). The third category are those characteristics with a percentage over the second threshold (for example, 20%). Additional or fewer categories and different ranges are also possible.

At step 208, those characteristics with Target Variable average variation below a specific threshold may be removed from the list of characteristics. For example, this could include those characteristics in the first category (e.g., those characteristics with a percentage of less than 5%). It will be understood by those skilled in the art that other thresholds could be used, and multiple categories could be removed from the list of characteristics. In one embodiment, if characteristics are removed, the process is repeated starting at step 202 above. In another embodiment, no characteristics are removed from the list until determining whether another co-variant relationships exist, as described in step 212 below.

At step 210, the relationships between the mid-level characteristics are determined. Mid-level characteristics are characteristics that have a certain level of effect on the Target Variable, but individually do not influence the Target Variable in a significant manner. Using the illustrative categories, those characteristics in the second category are mid-level characteristics. Example relationships between the characteristics are co-variant, dependent, and independent. A co-variant relationship occurs when modifying one characteristic causes the Target Variable to vary, but only when another characteristic is present. For instance, in the scenario where characteristic "A" is varied, which causes the Target Variable to vary, but only when characteristic "B" is present, then "A" and "B" have a co-variant relationship. A dependent relationship occurs when a characteristic is a derivative of or directly related to another characteristic. For instance, when the characteristic "A" is only present when characteristic "B" is present, then A and B have a dependent relationship. For those characteristics that are not co-variant or dependent, they are categorized as having independent relationships.

At step 212, characteristics displaying dependence on each other may be resolved to remove dependencies and high correlations. There are several potential methods for resolving dependencies. Some examples include: (i) grouping multiple dependent characteristics into a single characteristic, (ii) removing all but one of the dependent characteristics, and (iii) keeping one of the dependent characteristics, and creating a new characteristic that is the difference between the kept characteristic and the other characteristics. After the dependencies are removed, the process may be repeated from step 202. In one embodiment, if the difference variable is insignificant it can be removed from the analysis in the repeated step 208.

At step 214, the characteristics are analyzed to determine the extent of the interrelationships. In one embodiment, if any of the previous steps resulted in repeating the process, the repetition should be conducted prior to step 214. In some embodiments, the process may be repeated multiple times before continuing to step 214.

At 216, the characteristics that result in less than a minimum threshold change in the impact on Target Variable variation caused by another characteristic are dropped from the list of potential characteristics. An illustrative threshold could be 10%. For instance, if the variation in Target Variable caused by characteristic "A" is increased when characteristic "B" is present; the percent increase in the Target Variable variation caused by the presence of characteristic "B" must be estimated. If the variation of characteristic "B" is estimated to increase the variation in the Target Variable by less than 10% of the increase caused by characteristic "A" alone, characteristic "B" can be eliminated from the list of potential characteristics. Characteristic "A" can also be deemed then to have an insignificant impact on the Target Variable. The remaining characteristics are deemed to be the primary characteristics. Referring now to FIG. 3, an example embodiment 300 for developing constraints for equivalency factors is shown. Constraints are developed on the equivalency factors, step 302. The objective function, as described below, is optimized to determine an initial set of equivalency factors, step. 304.

At step 306 the percent contribution of each characteristic to the target variable is calculated. There are several methods of calculating the percent contribution of each characteristic. One method is the "Average Method," which is a two step process where the Total Average Impact is calculated and then the percent contribution of each characteristic is calculated. To calculate the Total Average Impact, the absolute values of the equivalency factors times the average value of each characteristic are summed as shown below:

Average Method Equation: TAI = ^T .| oi_j * avg_] [F^ )

TAI=Total Average Impact individual record referring to the facility j=individual first principle or developed characteristic θ_j=equivalency faction for the jth characteristic

F=is a function of the measured first principle characteristics or developed characteristic for a facility. In the case where the first principle characteristic is used directly, F may be 1 * characteristics. In the case of a developed characteristic, F can be any function of the first principle characteristic(s) and other developed characteristic^). avgj (Fi_j)=the average value of the measured first principle characteristics or developed characteristic over all facilities (over all j) in the analysis dataset

Following the calculation of the Total Average Impact, the percent contribution of each characteristic is then calculated as shown below: α , * avg,(F,, )\

Percent Contribution Equation: AI, = — - ^{J J} ^{H J} TAI

AI_j=Average Impact of jth first principle or developed characteristic

An alternate method is the "Summation of Records Method," which calculates the percent contribution of each characteristic by calculating the individual impacts from a summation of the impacts at each individual data record in the analysis dataset of facilities as shown below:

Summation of Records Equation: AI_j = averageover alii [j oO_j * F_y | /∑_k | a_k * F_jk \\

AI_j= Average Impact of jth first principle or developed characteristic i=the individual record referring to the facility j=individual first principle or developed characteristic k=individual first principle or developed characteristic ocj=equivalency faction for the jth characteristic

F=is a function of the measured first principle characteristics or developed characteristic for a facility.

The Summation of Records Method may be used if non-linearity exists in the impacts. It is contemplated that other methods to calculate impacts may be used.

With the individual percent contributions developed, the method proceeds to step 308, where each percent contribution is compared against expert knowledge. Domain experts will have an intuitive or empirical feel for the relative impacts of key characteristics to the overall target value. The contribution of each characteristic is judged against this expert knowledge.

At step 310 a decision is made about the acceptability of the individual contributions. If the contribution are found to be unacceptable the process continues to step 312. If they are found to be acceptable the process continues to step 316. At step 312, a decision is made to address how the unacceptable results of the individual contributions are to be handled. The options are to adjust the constraints on the equivalency factors to affect a solution, or to decide that the characteristic set chosen can not be helped through constraint adjustment. If the developer gives up on constraint adjustment then the process proceeds to step 316. If the decision is made to achieve acceptable results through constraint adjustment then the process continues to step 314.

At step 314, the constraints are adjusted to increase or decrease the impact of individual characteristics in an effort to obtain acceptable results from the individual contributions. The process continues to step 302 with the revised constraints.

At step 316, peer and expert review of the equivalency factors developed may be performed to determine the acceptability of the equivalency factors developed. If the factors pass the expert and peer review, the process continues to step 326. If the equivalency factors are found to be unacceptable, the process continues to step 318.

At step 318, new approaches and suggestions for modification of the characteristics are developed by working with experts in the particular domain. This may include the creation of new developed characteristics, or the addition of new first principle to the analysis data set. At step 320, a determination is made as to whether data exists to support the investigation of the approaches and suggestions for modification of the characteristics. If the data exists, the process proceeds to step 324. If the data does not exist, the process proceeds to step 322.

At step 322, additional data is collected and obtained in an effort to attempt the corrections required to obtain a satisfactory solution. At step 324, the set of characteristics are revised in view of the new approaches and suggestions.

At step 326, the reasoning behind the selection of characteristics used is documented. This documentation can be used in explaining results for use of the equivalency factors. Referring to FIG. 4, an example matrix 10 of a system for determining equivalency factors is illustrated. While matrix 10 can be expressed in many configurations, in this particular example, matrix 10 is constructed with the first principle characteristics 12 and developed characteristics 14 on one axis, and the different facilities 16 for which data has been collected on the other axis. For each first principle characteristic 12 at each facility 16, there is the actual data value 18. For each first principle characteristic 12 and developed characteristic 14, there is the equivalency factor 22 that will be computed with the optimization model. The constraints 20 limit the range of the equivalency factors 22. Constraints can be minimum or maximum values, or other mathematical functions or algebraic relationships. Moreover, constraints can be grouped and further constrained. Additional constraints on facility data, and relationships between data points similar to those used in the data validation step, and constraints of any mathematical relationship on the input data can also be employed. In one embodiment, the constraints to be satisfied during optimization apply only to the equivalency factors.

The target variable (actual) column 24 are the actual values of the target variable as measured for each facility. The target variable (predicted) column 26 are the values for the target value as calculated using the determined equivalency factors. The error column 28 are the error values for each facility as determined by the optimization model. The error sum 30 is the summation of the errors in error column 28. The optimization analysis, which comprises the Target Variable equation and an objection function, solves for the equivalency factors to minimize the error sum 30. In the optimization analysis, the equivalency factors (α._j) are computed to minimize the error (si) over all facilities. The non-linear optimization process determines the set of equivalency factors that minimizes this equation for a given set of first principle characteristics, constraints, and a selected value.

The Target Variable is computed as a function of the characteristics and the yet to be determined equivalency factors. The Target Variable equation is expressed as: Target Variable equation: TV₁ = ∑α .J ^{^}(characteristic) _{j + ε_;

TVi is the measured Target Variable for facility i characteristic is a first principle characteristic i is the facility number j is the characteristic number a,- is the jth equivalency factor

Si is the error of the model's TV prediction as defined by: Actual TV value-

-Predicted TV value for facility i

The objective function has the general form:

Objective Function: , P ≥ 1

i is the facility m is the total number of facilities p is a selected value

One common usage of the general form of objective function is for minimization of the absolute sum of error by using p=l as shown below:

Objective Function:

Another common usage of the general form of objective function is using the least squares version corresponding to p=2 as shown below:

1/2

Objective Function : Min ∑| 8,

/=1 Since the analysis involves a finite number of first principle characteristics and the objective function form corresponds to a mathematical norm, the analysis results are not dependent on the specific value of p. The analyst can select a value based on the specific problem being solved or for additional statistical applications of the objective function. For example, p=2 is often used due to its statistical application in measuring data and target variable variation and target variable prediction error.

A third form of the objective function is to solve for the simple sum of errors squared as given in Equation 5 below.

Objective Function: e,

While several forms of the objective function have been shown, other forms of the objective function for use in specialized purposes could also be used. Under the optimization analysis, the determined equivalency factors are those equivalency factors that result in the least difference between the summation and the actual value of the Target Variable after the model iteratively moves through each facility and characteristic such that each potential equivalency factor, subject to the constraints, is multiplied against the data value for the corresponding characteristic and summed for the particular facility.

For illustrative purposes, a more specific example of the system and method for determining equivalency factors for use in comparative performance analysis as illustrated in FIGS. 1-3 is shown. The example will be shown with respect to a major process unit in most petroleum refineries, known as a Fluidized Catalytic Cracking Unit (Cat Cracker). A Cat Cracker cracks long molecules into shorter molecules in the gasoline boiling range and lighter. The process in conducted at very high temperatures in the presence of a catalyst. In the process of cracking the feed, coke is produced and deposited on the catalyst. The coke is burned off the catalyst to recover heat and to reactivate the catalyst. The Cat Cracker has several main sections: Reactor, Regenerator, Main Fractionator, and Emission Control Equipment. Refiners desire to compare the performance of their Cat Crackers to the performance of Cat Crackers operated by their competition. This Cat Cracker example is for illustrative purposes and may not represent the actual results of applying this methodology to Cat Crackers, or any other industrial facility. Moreover, the Cat Cracker example is but one example of many potential applications of the used of this invention in the refining industry.

First, at step 102, the desired Target Variable will be "GHG emissions" in a Cat Cracker facility. At step 104, the first principle characteristics that may affect GHG emissions for a Cat Cracker might be:

FCC Unit Capacity Reactor Design Feedstock Classification

FCC Unit Utilization Reactor Temperature Feedstock Gravity

FCC Unit Age Catalyst Type Feedstock Metals

FCC Unit Location Percent Conversion Feedstock Temperature

FCC Unit Type Catalyst-to-Oil Ratio Feedstock Conradson

Carbon

Duplicate Equipment Maintenance Practices C₃ and C₄ Product Yield

To determine the primary characteristics, step 106, this example has determined the effect of the first characteristics. For this example, the embodiment for determining primary characteristics as shown in FIG. 2 will be used. Moving to FIG. 2, at step 202, each characteristic is given an variation percentage. At step 204, the characteristics from the Cat Cracker Example are rated and ranked. The following chart shows the relative influence and ranking for the example characteristics:

Characteristics Category Comment

FCC Unit Capacity 3 Little effect of scale on coke yield

FCC Unit Utilization 3 Little effect within normal ranges

FCC Unit Age N/A May affect GHG emissions performance, but not relevant to this analysis FCC Unit Location 3 Little effect on GHG emissions performance FCC Unit Type 2 Distinguishes between residuum, mild residuum and conventional FCC units Duplicate Equipment 3 Little effect on GHG emissions performance Reactor Design N/A May affect GHG emissions performance, but not relevant to this analysis Reactor Temperature 2 Correlated with Conversion below - select only one of these two variables

Catalyst Type 2 Significantly affects coke yield Percent Conversion 1 Significantly affects coke yield Catalyst-to-Oil Ratio 2 Significantly affects coke yield Feedstock Classification 1 Significantly affects coke yield Feedstock Gravity 2 Significantly affects coke yield Feedstock Metals 2 Significantly affects coke yield Feedstock Temperature 3 Little effect on GHG emissions performance

Feedstock Conradson 1 Significantly affects coke yield

Carbon

C₃ and C₄ Product Yield 2 Correlated with coke yield

Maintenance Practices N/A May affect GHG emissions performance, but not relevant to this analysis

In this embodiment, the categories are as follows:

Percent of Average Variation in the Target Variable

Between Facilities

Category 1 (Major Characteristics) >20% Category 2 (Midlevel Characteristics) 4-20% Category 3 (Minor Characteristics) <4%

A variable marked "N/ A" is not applicable to this analysis because its effects are intentionally disregarded for the purpose of this analysis.

It is understood that other embodiments could have any number of categories and that the percentage values that delineate between the categories may be altered in any manner.

Based on the above example rankings, the characteristics are grouped according to category, step 206. At step 208, those characteristics in Category 3 are discarded as being minor. Characteristics in Category 1 and 2 must be analyzed further to determine the type of relationship they exhibit with other characteristics, step 210. Each is classified as exhibiting either co-variance, dependence or independence, step 212. As an example:

Classification of Characteristics Based on Type of Relationship

Category 2 Type If Co-variant characteristics of Relationship or Dependent, Related Partner(s)

FCC Unit Type Independent Reactor Temperature Co-variant Percent Conversion Catalyst Type Co-variant Percent Conversion Percent Conversion Co-variant Catalyst Type, Reactor Temperature

Catalyst-to-Oil Ratio Independent Feedstock Classification Dependent Feedstock Conradson Carbon Feedstock Gravity Independent Feedstock Metals Independent Feedstock Conradson Independent Carbon

At step 214, the degree of the relationship of these characteristics is analyzed. Using this embodiment for the Cat Cracker example: FCC Unit Type, classified as having an Independent relationship, stays in the analysis process. Reactor Temperature and Catalyst Type are classified as having a co-variant relationship with Percent Conversion. Feedstock Classification is dependent upon Feedstock Conradson Carbon. A dependent relationship means Feedstock Classification is a derivative of Conradson Carbon. After further consideration, it is decided Feedstock Classification can be dropped from the analysis and the more specific characteristic of Feedstock Conradson Carbon will remain in the analysis process. The three characteristics classified as having a co-variant relationship must be examined to determine the degree of co-variance.

It is determined that the change in GHG emissions is related to Reactor Temperature, Catalyst Type and Percent Conversion but that, since these are correlated, the optimization model constraints in Step 116 must be constructed to select either Percent Conversion to the exclusion of Reactor Temperature and Catalyst Type or Reactor Temperature and/or Catalyst Type to the exclusion of Reactor Temperature.

Continuing with the Cat Cracker example, and returning to FIG. 1, the remaining characteristics are categorized as continuous, ordinal or binary type measurement, step 108.

Classification of Remaining Characteristics Based on Measurement Type

Remaining characteristics Measurement Type

FCC Unit Type Discrete

Reactor Temperature Continuous

Catalyst Type Discrete

Percent Conversion Continuous Catalyst-to-Oil Ratio Continuous Feedstock Classification Discrete Feedstock Gravity Continuous Feedstock Metals Continuous Feedstock Temperature Continuous Feedstock Conradson Carbon Continuous C₃ and C₄ Product Yield Continuous

At step 110, a data collection classification system is developed. In this example, a questionnaire is developed to assess the FCC Unit Type, Catalyst Type and Feedstock Type. The questionnaire includes clear definitions to assure that data are collected in a consistent manner. The data are used to classify each FCC Unit in one of several discrete categories.

For illustrative purposes with respect to the Cat Cracker example, at step 112, data was collected and, at step 114, validated as follows for the first five of over two hundred cat crackers studied as identified below:

Cat Cracker Data

Number Feed

And FCC Feed Feed Cat-to- Con- Feed C₃+C₄ Coke-on-

Unit Type Conversion Density OiI Ratio carbon Metals Yield Catalyst

Unit of Measurement

Vol. kg/m^J kg/kg Wt. ppm Vol. kBtu /

% % % bbl

#1 FCC 83.1 899.0 7.1 2.5 11.8 4.0 307

#2 FCC 77.0 905.3 5.5 0.3 11.6 8.0 201

#3 FCC 74.9 911.1 6.7 0.5 11.6 3.0 250

#4 MRCC 76.0 892.7 8.6 0.8 11.8 2.0 277

#5 FCC 76.0 914.0 6.9 0.8 11.8 5.0 255 Constraint ranges were developed for each characteristics to control the model so that the results are within a reasonable range of solutions.

Cat Cracker Model Constraint Ranges

Feed Feed Cat-to- Feed Con- C₃H-C₄ Conversion Density Oil Ratio carbon Feed Metals Yield

Minimum 0.00 0.00 -1.0 0.0 0.0 -4.0 Maximum 4.00 1.00 200 40.0 50.0 4.0

Additional constraints prevent inclusion of co-variant variables as described in paragraph [0099] above. For this embodiment, the Feed Metals content is squared to improve the accuracy of the model. Constraints on both Feed Metals squared and Feed Metals are then removed.

At step 116, the results of the model optimization runs are shown below.

Model Results

Characteristics Equivalency Factors

Conversion 1.10 FCC Type: FCC -19.91 FCC Type: MRCC -21.96 Feed Density 0.53 Catalyst-to-Oil Ratio 10.13 Feed Conradson Carbon 27.93 Feed Metals 66.21 Feed Metals Squared -5.60 The model indicates other variables are not significant drivers of variations in coke yields between different Cat Crackers. This is indicated by the model finding zero values for equivalency factors for these two characteristics.

A sample model configuration for the illustrative Cat Cracker example is shown in FIG. 5. The data 18, actual values 24, and the resulting equivalency factors 22 are shown.

This process is applied to all refinery process units that inherently combust a particular fuel. Fuel quantities and GHG emissions equivalents are determined for each such refinery process unit.

A similar process is applied to determine an energy standard for the entire refinery complex. For energy requirements that do not inherently require a particular fuel, a standard fuel type is used to evaluate the GHG emissions standard. The GHG emissions standard for purchased electric power and purchased steam can be determined by a variety of methods provided that the method selected is consistent with the method used to determine actual GHG emissions.

Standard GHG emissions from refinery flaring are determined by the industry average flaring rate.

Standard GHG emissions from hydrogen plant by-product carbon dioxide are determined by stoichiometric relationships plus allowances for losses.

Standard GHG emissions from fugitive losses are evaluated by industry standard procedures.

In step 124, a comprehensive GHG standard for the refinery is determined by summing the GHG emission standard for refinery units requiring a particular type of fuel, the GHG emission standard from the standard fuel for the refinery's other fuel requirements, the GHG emission standard for refinery flaring, the GHG emission standard for hydrogen production, and the GHG emission standard for fugitive losses. Additionally, the GHG emission standard for purchased electric power and purchased steam are included in this example to capture indirect as well as direct GHG emissions in the refinery's GHG emissions standard.

In step 126, standard GHG emissions thus calculated are summed for all refineries operating within national , regional or state boundaries. Nationwide permits for emissions to the refining industry are then allocated to individual refineries according to each refinery's GHG emissions standard. Alternatively, a business entity operating more than one refinery could receive a permit based upon the sum of GHG emissions standards determined for each of its refineries.

Referring to FIG. 6, an illustrative node 40 for implementing the method is depicted. Node 40 can be any form of computing device, including computers, workstations, hand helds, mainframes, embedded computing device, holographic computing device, biological computing device, nanotechnology computing device, virtual computing device and or distributed systems. Node 40 includes a microprocessor 42, an input device 44, a storage device 46, a video controller 48, a system memory 50, and a display 54, and a communication device 56 all interconnected by one or more buses or wires or other communications pathway 52. The storage device 46 could be a floppy drive, hard drive, CD-ROM, optical drive, bubble memory or any other form of storage device. In addition, the storage device 42 may be capable of receiving a floppy disk, CD-ROM, DVD-ROM, memory stick, or any other form of computer- readable medium that may contain computer-executable instructions or data. Further communication device 56 could be a modem, network card, or any other device to enable the node to communicate with humans or other nodes.

As shown in Figure 7, an emissions risk transfer system and method according to one embodiment of the present invention is shown. As illustrated, an emissions regulatory agency issues an emissions permit for an installation 700, such as an oil refining facility. Typically, the emissions permit is an annual operating permit, which prescribes the maximum amount of allotted emissions for a particular installation, and its associated activities, processes, or pieces of equipment. Each regulated emission, such as CO₂, hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafhioride (SF6), methane (CH₄), nitrous oxide (N₂O), carbon monoxide (CO), nitrogen oxides (NOx), and non-methane volatile organic compounds (NMVOCs), can have an individual permitted amount. The permitted amounts are typically expressed in terms of tons or cubic meters of regulated material. In some cases, a given country or territory has an overall maximum emissions amount for a pollutant, such as CO₂, and provides permits for installations to industries, individual activities, or businesses based on the historical emission rates for the particular industry, activity, or business. It should be noted that for ease of describing various embodiments of the invention, as used throughout the specification, reference will be made to CO₂ as being the regulated pollutant. However, the embodiments of the invention described herein are applicable to a variety of emission regulated or potentially emission regulated pollutants, including but not limited to CO₂, hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF6), methane (CH₄), nitrous oxide (N₂O), carbon monoxide (CO), nitrogen oxides (NOx), and non-methane volatile organic compounds (NMVOCs).

Although the regulatory agency may issue a CO₂ emissions permit for an installation 700 based in part on the installation's historical data, typically the emissions permit while documenting the projected CO₂ emissions from all activities in an installation covered under the applicable law, will routinely result in the tons of CO₂ allotted to the installation being less than the projected CO₂ emissions. As shown in Figure 7, a business or installation operator will calculate, measure, or determine the emissions forecast for an installation 702. This emissions forecast can be computed by any entity capable of computing the forecast, including but not limited to the regulatory agency, as previously described herein, the installation operator or business, a third party, an underwriter, and the insurer. The emissions forecast can be determined by many methods. In the Commission of the European Communities' Decision of 29/01/2004 ("Commission Decision"), in regard to the Directive 2003/87/EC of the European Parliament and of the Council of 13 October 2003, herein incorporated by reference in its entirety, describes various methods of calculating and measuring emissions forecast. Referring by way of illustration to the Commission Decision, detailed calculation, measurement, and error data for determining CO₂ emissions in a variety of industries, using a variety of measurement devices is disclosed. For example, calculation of CO₂ emissions include the formula:

CO₂- emissions = activity data * emission factor * oxidation factor

As illustrated in the Commission Decision, several tables are provided for documenting installation information, calculation and measurement data. For example, such reports include the identification of the installation plant, the type of activity performed at the plant, emissions' information, such as CO₂, combustion and process emissions (Figures 8-11).

In one embodiment of the invention, the insurer evaluates the installation and compares the permitted CO₂ emissions amount to the forecast amount 704. The insurer can obtain this information from a variety of sources. For example, in some European Communities, the permitted emission amounts for installations are publicly available via the internet. Additionally, although as shown in Figure 7, this embodiment refers to the insurer evaluating the installation 704, this evaluation and comparison can be accomplished by a third party, an independent body, the insured, or any suitable entity. The evaluating and comparing function 704 referenced in Figure 7 can also include an assessment, evaluation or review of the installation's regulatory- permitted-emissions calculations and data, previous operating years and forecast years emission data, previous operating and forecast installation unit production data, previous and planned modifications to the installation, safety reviews, activity/process hazard analyses, and failure mode effect analyses for the installation and the myriad of sequence of events within an installation's activities. Evaluating this type of data and information aides the insurer in performing a thorough evaluation of the probable maximum loss (PML) and maximum foreseeable loss (MFL) associated with emissions insurance for an installation. Thereby, providing a thorough basis for the emission insurance's deductible, policy, and premium limits. Because of the myriad of factors affecting the potential profitability to an insurer providing any of the embodiments of the invention, those of ordinary skill in the art will recognize the need for insurer based or affiliated engineering activities associated with the risk management of emission exposures. Technical information, technical experience, and analyses are applied to provide underwriters the risk evaluation data required to conduct risk quantification, risk acceptance, and pricing analyses. Not only are these engineering skills provided prior to underwriting an emission insurance policy, the same skills are also applied after a loss for loss control and claims administration.

In another aspect of an embodiment of the invention, part of the documentation for the emission insurance, includes the calculations performed to compute the forecasted emissions over the policy period, such as the worksheets and reports referenced in the Commission Decision (Figures 8-11). Additionally, if forecasted emissions exceed the permitted emissions, the evaluation and comparison function 704 can also include a determination of the amount of energy credits, or other emission allowance producing investments/projects the insured can acquire and use to offset any excess forecast emissions. For example, if an insurer's evaluation of activities within an installation reveals that the insurer's calculated forecast emissions exceed the permitted emissions, and/or exceeds the insured's calculated forecast, the insurer can require the insured purchase emission credits or engage in other emission allowance producing investments/projects so that the permitted emissions exceed the forecast emissions. If needed, the insured can purchase emission credits from the marketplace, the insurer, or another entity. The emission credits are generally based on for example, tons of CO₂. Additionally, emission allowance projects, such as "Joint Implementation," JI, or "Clean Development Mechanism," CDM, projects are applied as debits to the insured's overall emissions and may be required to yield net emissions levels below the specified allowances. As incorporated by reference herein and attached hereto as Appendix B is the 24 February 2004 "Opinion of the Committee on Industry, External Trade, Research and Energy for the Committee on the Environment, Public Health and Consumer Policy on the proposal for a European Parliament and Council directive amending the Directive establishing a scheme for greenhouse gas emission allowance trading within the Community, in respect of the Kyoto Protocol's project mechanisms," which discusses allowing credits from the JI and CDM project-based activities under the Kyoto protocol to be converted in to emission allowances. Also, incorporated by reference herein and in its entirety is the "Directive 2003/87/EC of the European Parliament and of the Council of 13 October 2003 establishing a scheme for greenhouse gas emission allowance trading within the Community and amending Council Directive 96/6 I/EC." Also, incorporated by reference in its entirety is Document ID 52003DC0830, "Communication from the Commission on guidance to assist Member States in the implementation of the criteria listed in Annex III to Directive 2003/87/EC of the European Parliament and of the Council of 13 October 2003 establishing a scheme for greenhouse gas emission allowance trading within the Community and amending Council Directive 96/61/EC, and on the circumstances under which fore majeure is demonstrated."

The evaluation and comparison function 704, which includes review of any associated documentation, including the reports and worksheets illustrated in the Commission Decision as illustrated in Figures 8 - 11, constitute a part of the engineering role in the emission insurance. The evaluation and comparison 704 will verify the insured's calculations for accuracy, analyze the deterministic projections for practical realism, and run standardized risk models to test the sensitivity in results. The evaluation and data review functions 704 require engineers knowledgeable in the industry, region, and activity involved. For example, in power generation, the engineer must understand the generation technology, fuel types, and heat rates in comparison to the projected amounts expected to be consumed in that region, in order to thoroughly determine an emission forecast.

In yet another embodiment, because the energy conversion factors, calculation formats, and formulas for the covered activities' emission calculations are typically specified by or on behalf of the regulatory authority (e.g. the European Union's Commission Decision), this provides some standardization for these determinations in that the forms submitted to the regulatory agency, can also be provided to the insurer as part of the submission data. Once the emissions forecast is verified by the insurer, the information can be cross checked with any property insurance policy and site inspection data, if available, as another check on the installation composition and risk quality. Since the emissions coverage can be dependent on property perils (e.g. fire, lightning, windstorms, etc.) the property risk evaluations serve an additional purpose as providing insights on the risk quality for emission releases.

Additionally, as part of the evaluation function 704, based on risk models developed for the activities covered by the regulatory agency's emission limits, the engineer can develop a range of sensitivity estimates, estimating the likelihood that the insured will achieve emissions levels above the installations or group of installations covered in the policy allowances. The insured may also include in the submitted documentation, certified emission reduction units produced from projects, such as JI or CDM projects, that are linked to their overall emission levels and are applied as debits to the insured's overall emissions and may be required to yield net emissions levels below the specified allowances.

As an additional aspect of the evaluation and comparison function 704, either based on a risk model computed score, engineering judgment, or both, some installations can be identified for on-site inspections. The on-site inspection can review property risk evaluation data, but also can examine the events or sequence of events that determine the installation-level and policy-level PML and MFL. This work will require analysis of installation operational inter-dependencies of the covered locations. For example, suppose an insured had four power generation installations covered by the emission policy. An equipment breakdown loss at one facility may suspend operations locally for several months. Although the annual emissions at that non-operating installation are much lower, because the other facilities have to increase output to make up for the non-operating installation's loss, the aggregate emissions can possibly be in excess of the maximum permitted allowance and any applied emission deductible. These types of regional interdependency factors, external to a single installation can play an important role and be an important part of the policy's value to the insured. This inter-dependency risk is a key underwriting issue, and requires a detailed analysis by the insurer in the evaluation and comparison function of an embodiment of the inventive system and method described herein.

As also shown in Figure 7, taking into consideration the evaluation and comparison functions 704 described previously, an emissions insurance policy 706 is created. In addition to policy premium amounts, an exemplary emissions insurance policy according to one embodiment of the invention can also contain information (or have such information as an attachment or appendage) on the installations or activities within an installation covered by the policy. Such information can include the activities' production capacity, and average, lowest, or highest CO₂ emissions within a certain time period. The policy can also include details of the coverage. For example, the policy can specify that the coverage is applicable only to emission occurrences caused by equipment failure, acts of terrorism, or force majeure events; and that the coverage extends only to those emission sources documented in any attached worksheets or schedules, which worksheets or schedules are those associated with permitted emissions, such as those described in Appendix A, the Commission Decision at pages 38 - 41. The policy may also provide for coverage of expenses incurred by the insured as a result of the covered emission occurrence, such as expenses expended by the insured to reduce the loss (e.g. the leasing of equipment to replace failed or damage equipment that caused the emission occurrence) and expenses incurred by the insured for professional services that are necessary and reasonable in order to certify the details of a claim.

In one embodiment, the insured 's limits of liability (e.g. deductibles) can be based on a combined-emission-incident-aggregate limit for the policy period, be based on an each-emission-incident limit, or any other suitable insured-liability-limiting scenario. In still another embodiment, the policy can have various deductible methods, including for example, a monetary deductible amount, or a deductible in the form of tons or m³ of CO₂.

In addition to coverage details, in one embodiment, the policy includes details of exclusions, conditions, and/or subrogation of coverage. Exclusions can include for example, emission excursions based on war and the failure of the insured to follow maintenance or operating procedures for an activity or piece of equipment. Conditions can include for example, a requirement that the insured notify the insurer within a certain time period of knowledge of an occurrence, such as a twenty-four hour notification period.

For example, as illustrated in Figure 7, in one embodiment the insurer has access to the insured's activity data 712 via communication link 714. This communication link 714 can be via any suitable and preferably secure means, including through internet or intranet connections combined with the use of a computer operating system. In many activities, CO₂ emissions, in some amount, are a daily and inherent part of the normal operation of an activity. Because there may be a direct correlation between CO₂ emissions and unit output or fuel consumption for example, activity instrumentation that measures these variables can be an important gauge in determining CO₂ emissions. Additionally, instrumentation may be used to directly measure CO₂ emissions, or the activity control system (e.g. a distributed control system) may be configured to calculate CO₂ emissions based on activity variables, such as temperature, pressure, fuel consumption, or flow of product output. In this embodiment, the communication link 714 represents the ability of the insurer to access the insured's pertinent activity data, such as CO₂ emissions and other activity data that can be used to calculate or forecast CO₂ emissions. This aspect provides the insurer with the ability to not only monitor the activity 710 for previous emission occurrences and potential emission claims (e.g. excess CO₂ emissions) on some periodic basis, including continuous monitoring, but it also provides the insurer with a real-time status of CO₂ emissions by an insured, or all insureds when this system is used with all CO₂ emissions policy holders of an insurer. As will become evident in the succeeding portion of this detailed description, this unique system gives insurers the ability to forecast the overall need for emission credits on a going forward basis, as well as allowing the insurer to sell anticipated excess emission credits on the market at a premium, when the insurer acts on behalf of the insureds in an emission pooling, banking or trustee relationship. Additionally, a real-time communications link, such as link 714, also provides the insurer with the ability to monitor the activities 710 and update risk models and scenarios previously or contemporaneously developed for the activities covered by the regulatory agency's emission limits. This communications link 714 also gives the insurer or insured the ability to update scenarios, models, and sequence of events identified in safety reviews, activity/process hazard analyses, and failure mode effect analyses for the activities. Thereby providing the insurer or insured with an updated emission probability based on real-time operational data.

The embodiment shown in Figure 7 also shows the insured monitoring its activities 708 and notifying the insurer of any CO₂ excursions, modifications, and claims. In addition to notifying the insurer of any emission occurrences, the policy can provide that the insurer has the right to inspect the installation and associated activities and examine the risk. Also, another condition could include that prior to making any material change that would affect the emissions risk for a covered activity; the insured must notify and receive confirmation of continuance of coverage from the insurer. In addition to activity modifications, in one embodiment of the invention, emission insurance coverage is limited to a specific production output rate, wherein emission insurance coverage is lost if the activity exceeds either instantaneous or cumulative production rates.

In another embodiment, in the event of an emission occurrence, the insured should immediately notify the insurer, via the method(s) described in the emission insurance policy such as phone or e-mail. Once the insurer receives notification of an emission occurrence, the insurer can make a determination of whether or not to send a control specialist to the site, in an attempt to access any potential claim and attempt to reduce the emission claim potential. Additionally, as previously illustrated in Figure 1 and described herein, if the insurer has a communications link 714 with the insured's activity data 712, and has the several potential incident or risk scenarios modeled within a system, the insurer could use the appropriate models to determine the potential extent of an emission occurrence, as well as determine if there is a need to have an engineering representative visit the site. The magnitude of the identified emissions should be computed as quickly as possible. In some cases, using a communications link 714 to the insured's activity data 712 will allow for a real-time calculation or determination of an emission's magnitude. Consequently the insured, the insurer, or other professional service can determine or forecast the increased emissions from the covered peril. Based on these results the insurer may take additional proactive actions at one or more of the covered installations to reduce the forecasted emissions and possible claim severity. For example, the insurer or its representative may commission installation, at the insurer's expense, scrubbers to reduce the emissions if the insurer believes the installation benefits underwriting by eliminating a claim or lowering any reserve emission credits. Additionally, upon notification and evaluation of the emission occurrence, the insurer can react by purchasing additional emission credits in the market to compensate for any projected emission claims.

In another embodiment of the invention, several methods and combinations of methods are available for emission claims valuation and loss adjustment. For example, one method for computing a claim valuation can include the determination of total exceeded emissions by determining the actual CO₂ emissions and subtracting the allowed or permitted CO₂ emissions. Additionally, if for example, as described previously the insurer was required to purchase additional emission allowances or credits (e.g. based on a determination that the forecast emissions for the policy period exceeded the permitted or allowance emissions), the claims valuation could would include an additional subtraction from the total exceeded emissions by any purchased emission allowances or credits. The credits could also include those attributed to the insured because of emission credit projects, such as the JI and CDM projects previously described.

Still other claims valuation can include utilizing insured deductibles. As previously mentioned, the insured's limit of liability can be based on an aggregate amount, per incident amount, or any other suitable limit of liability. The insured's deductible can be based on a dollar amount or can be based in the form of tons or m³ of CO₂. When the deductible is based on a dollar amount, this amount can be subtracted from any "dollar loss amount" as described below in reference to payment of claims. When the deductible is based on tons or m³ of CO₂, the deductible can be subtracted from any determination of total exceeded emissions ("TEE"), in order to give an total exceed emissions prime (TEE'). Similarly, for determination of a "dollar loss amount" of the TEE or TEE', the TEE or TEE' can be multiplied by the cost of an emission credit, which can be determined by market rates at the time of the emission occurrence, or market rates at the time of reporting and payment, as described below in reference to payment of claims. Although these represent only a few emission claims valuation, it should be realized that any suitable method for claims valuation in conjunction with emissions insurance is considered to be within the scope of an embodiment of the invention.

Emission insurance claims should be identified before the end of the policy period. Since emissions are typically computed from fuel consumed and production volumes, quantifying exceedance amounts should be relatively straightforward. Also, since the insured normally must report these values to the regulatory authority, the calculations will most likely follow a standardized procedure.

Payment of claims can be in multiple forms. In one embodiment, payment is made in the form of the dollars required to purchase emission credits ("dollar loss amount"), or by the insurer supplying the credits directly. For example, the insurer could supply the emission credits from an emission credit bank, controlled by the insurer. Still in another embodiment, the insurer can go into the market to purchase the needed emission credits. In some cases reporting emissions and the subsequent payment of fees for exceeding permitted amounts, or using emission credits to apply towards exceedance amounts, does not occur until the end of a permitted period. When paying claims in the form of dollars required to purchase emission credits, one embodiment of the invention includes paying claims in the form of payment at the average trading market price during the period of the emission release event. Still, other scenarios for claim payments can include payment at market rates, at the time of reporting and payment to the appropriate regulatory agency. In another embodiment of the invention, when an insured provides a report of its yearly emissions data to the regulatory authority, the insured may be asked for verification of its emissions data by an independent third party, to be paid for by the insured. As an added feature of the emissions insurance, the insurer may have provisions in the policy that allow for endorsements to the policy by the insured, wherein the insurer can provide appropriate professionals to quantify emission losses for covered perils, if expertise does not exist in-house.

Still another aspect of an embodiment of this invention is the development of an emissions credit bank by the insurer. In this embodiment, the insurer arranges to have all or some of the emission credits, allowances, or project credits for all or some of its insureds assigned, transferred, or provided to the insurer, an entity affiliated with the insurer, or a trustee. In another aspect of an embodiment of this invention, the insurer provides investments in approved energy and emission reduction projects (e.g. emission reduction projects in specified countries) and acquires additional emission credits for its investments. Some or all of the credits assigned, transferred, or provided to the insurer from its insureds and any additional emission credits acquired by the insurer through emission reduction projects, or purchased in the market for example, form an emission credit bank or pool. The insurer can use this emission credit bank as a primary source for paying claims of its insureds. Additionally, using the risk modeling techniques previously mentioned, the insurer can also forecast the need for emission credits, and projected price for emission credits in order to establish trading operations of emission credits.

In other embodiments of the invention, the insurer engages in forecasting the cost effectiveness of making emission reduction engineering or design changes in covered activities. In this aspect, the insurer determines if the engineering or design change would result in excess emission credits, which if sold in the marketplace could yield a higher return for the insurer based on the difference between the cost to implement the emission reduction engineering or design changes and the projected revenue from the sale of the excess emission credits at market rates. In another embodiment of the invention, the insurer provides reduced emission insurance premiums based on the insureds using equipment or a vendor listed on recommended equipment or vendors lists.

In still another embodiment, an Engineering entity, which can be associated or affiliated with the insurer, provides engineering design and/or installation services to business for new, modified, or re-designed activities within an existing or new installation. The Engineering entity provides the business with engineering services that provide the activity will function or operate at or below an emission level. This Engineering entity can implement the evaluating, comparison, monitoring, pooling, and equipment/vendor recommendation functions previously described herein, in order to modify or design an emission-efficient activity. Additionally, the Engineering entity can provide engineering design and/or installation services and forecasts for emission-reducing projects to the insurer and/or insured. In another aspect of this embodiment, the insurer can provide insureds with reduced rates for coverage and policy premiums based on the insureds use of the Engineering entity in any modification, design, or installation of a covered or potentially covered activity.

While the invention has been shown and described with reference to the preferred embodiment thereof, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention.

Claims

WHAT IS CLAIMED:

1. A method for determining equivalency factors in an industrial facility, comprising: determining an industry target variable; determining a plurality of characteristics of the industry target variable; classifying the plurality of characteristics; collecting data with respect to the characteristics; determining an equivalency factor for each of the plurality of characteristics using an optimization model.

2. The method of claim 1, wherein the industry target variable is selected from the group consisting of a catalytic cracking target, a catalytic reforming target, a sulfur recovery unit target, a storage vessel target, a fluid coking unit target, a wastewater target, a cooling tower target, a equipment leak target, a blowdown system target, a vacuum unit target, crude unit target, a steam boiler target, a flare/thermal oxidizer target, a pipeline target, a turbine target a furnace target, a compressor target, a vessel loading/unloading target, and a gasoline rack target.

3. The method of claim 1, wherein the optimization model is any nonlinear optimization method.

4. The method of claim 1, wherein the optimization model is a linear optimization method.

5. The method of claim 1, further comprising: determining a percentage variation value for each of the plurality of characteristics; dividing the plurality of characteristics into at least two categories based on the percentage variation value; and grouping characteristics in one of the at least two categories based on a relationship of the characteristics.

6. The method of claim 1, further comprising: dividing the plurality of characteristics into a first category, a second category and a third category; determining a relationship between the characteristics in the first category; and grouping the characteristics in the first category that have a common relationship.

7. The method of claim 1, further comprising: creating a developed characteristic by determining a mathematical relationship between a first one of the plurality of characteristics and a second one of the plurality of characteristics.

8. The method of claim 1, further comprising: using the equivalency factor to compare a first facility and a second facility.

9. The method of claim 1, further comprising: adjusting a target variable of a first facility using the equivalency factor; adjusting a target variable of a second facility using the equivalency factor; and comparing the adjusted target variable of the first facility against the adjusted target variable of the second facility.

10. The method of claim 1, further comprising: selecting a benchmark facility.

11. The method of claim 9, further comprising: calculating a performance gap value between a first facility and the benchmark facility.

12. The method of claim 1, further comprising: calculating a performance gap value between a first facility and a second facility using the equivalency factor.

13. The method of claim 1, further comprising: classifying a first facility into a performance subgroup in accordance with the ratio of the first facility's actual target variable to the first facility's actual target variable adjusted using the equivalency factor.

14. The method of claim 1, further comprising: ranking a first facility and a second facility in accordance with the first facility's actual target variable adjusted using the equivalency factor and the second facility's actual target variable adjusted using the equivalency factor.

15. The method of claim 1, further comprising: calculating performance gaps using subgroups derived through the use of the equivalency factor.

16. A method for determining equivalency factors in a power generation facility, comprising: determining a power generation target variable; determining a plurality of characteristics of the power generation target variable; classifying the plurality of characteristics; collecting data with respect to the characteristics; determining an equivalency factor for each of the plurality of characteristics using an optimization model.

17. The method of claim 16, wherein the power generation target variable is selected from the group consisting of a turbine target, a steam boiler target, a cooling tower target, a fuel storage tank target, a pipeline target, a wastewater target, a equipment leak target, a compressor target, and a flare/thermal oxidizer target.

18. The method of claim 16, wherein the optimization model is any nonlinear optimization method.

19. The method of claim 16, wherein the optimization model is a linear optimization method.

20. The method of claim 16, further comprising: determining a percentage variation value for each of the plurality of characteristics; dividing the plurality of characteristics into at least two categories based on the percentage variation value; and grouping characteristics in one of the at least two categories based on a relationship of the characteristics.

21. The method of claim 16, further comprising: dividing the plurality of characteristics into a first category, a second category and a third category; determining a relationship between the characteristics in the first category; and grouping the characteristics in the first category that have a common relationship.

22. The method of claim 16, further comprising: creating a developed characteristic by determining a mathematical relationship between a first one of the plurality of characteristics and a second one of the plurality of characteristics.

23. The method of claim 16, further comprising: using the equivalency factor to compare a first facility and a second facility.

24. The method of claim 16, further comprising: adjusting a target variable of a first facility using the equivalency factor; adjusting a target variable of a second facility using the equivalency factor; and comparing the adjusted target variable of the first facility against the adjusted target variable of the second facility.

25. The method of claim 16, further comprising: selecting a benchmark facility.

26. The method of claim 25, further comprising: calculating a performance gap value between a first facility and the benchmark facility.

27. The method of claim 16, further comprising: calculating a performance gap value between a first facility and a second facility using the equivalency factor.

28. The method of claim 16, further comprising: classifying a first facility into a performance subgroup in accordance with the ratio of the first facility's actual target variable to the first facility's actual target variable adjusted using the equivalency factor.

29. The method of claim 16, further comprising: ranking a first facility and a second facility in accordance with the first facility's actual target variable adjusted using the equivalency factor and the second facility's actual target variable adjusted using the equivalency factor.

30. The method of claim 16, further comprising: calculating performance gaps using subgroups derived through the use of the equivalency factor.

31. A method for providing an emissions risk transfer system comprising: evaluating forecast emission data; evaluating permitted emission allowances; comparing the forecast emission data to the permitted emission allowances; and developing an emission risk transfer policy based on financial goals.

32. The method of claim 31 wherein the forecast emission data is CO₂ data.

33. The method of claim 31 wherein the forecast emission data is hydrofluorocarbons (HFCs) data.

34. The method of claim 31 wherein the forecast emission data is perfmorocarbons (PFCs) data.

35. The method of claim 31 wherein the forecast emission data is sulphur hexafluoride (SF6) data.

36. The method of claim 31 wherein the forecast emission data is methane (CH₄) data.

37. The method of claim 31 wherein the forecast emission data is nitrous oxide (N₂O) data.

38. The method of claim 31 wherein the forecast emission data is carbon monoxide (CO) data.

39. The method of claim 31 wherein the forecast emission data is nitrogen oxides (NOx) data

40. The method of claim 31 wherein the forecast emission data is non- methane volatile organic compounds (NMVOCs) data.