METHOD AND APPARATUS FOR COMPARING THE EFFICIENCY OF OPERATORS

An apparatus and method for comparing the efficiency of operators is disclosed. The apparatus including at least a processor and a memory containing instructions configuring the at least a processor to receive operator data, calculate a carbon emission rate of the at least an operator as a function of the at least a carbon emission datum, calculate a carbon efficiency score of the at least an operator as a function of the carbon emission rate, and generate an operator ranking as a function of the carbon efficiency score of the at least an operator.

FIELD OF THE INVENTION

The present invention generally relates to the field of carbon efficiency. In particular, the present invention is directed to a method and apparatus for comparing the efficiency of operators

BACKGROUND

With rising carbon emissions and increased global warmings, it is important to decrease carbon emissions. A large contributor to carbon emissions are the transportation and cargo industries. It can be difficult to calculate the carbon efficiency of an actor within these industries due to the diverse tasks that they may perform. Existing solutions to this problem are not sufficient.

SUMMARY OF THE DISCLOSURE

In an aspect, an apparatus for comparing the efficiency of operators, the apparatus including at least a processor and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to receive operator data, wherein the operator data includes at least an operator associated with at least a carbon emission datum. The memory also containing instructions further configuring the processor to calculate a carbon emission rate of the at least an operator as a function of the at least a carbon emission datum. The memory also containing instructions further configuring the processor to obtain a carbon efficiency score of the at least an operator as a function of the carbon emission rate. The memory also containing instructions further configuring the processor to generate an operator ranking as a function of the carbon efficiency score of the at least an operator.

In another aspect, a method for comparing the efficiency of operators, the method including receiving, by a processor, operator data, wherein the operator data comprises at least an operator associated with at least a carbon emission datum. The method further including calculating, by the processor, a carbon emission rate of the at least an operator as a function of the at least a carbon emission datum. The method further including obtaining, by the processor, a carbon efficiency score of the at least an operator as a function of the carbon emission rate. The method further including generating, by the processor, an operator ranking as a function of the carbon efficiency score of the at least an operator.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed to systems and methods for comparing the efficiency of operators. In an embodiment, a carbon efficiency score for one or more operators may be calculated from one or more carbon emission rates. In an embodiment, carbon efficiency score may be calculated using a carbon efficiency machine-learning model.

Aspects of the present disclosure can be used to calculate a forecasted carbon efficiency score. In an embodiment, forecasted carbon efficiency score may be calculated using operator data and a forecasted task. In an embodiment, forecasted carbon efficiency score may be calculated using a forecast machine-learning model

Aspects of the present disclosure allow for the generation of an operator ranking based on carbon efficiency score and/or forecasted carbon efficiency score. In an embodiment, operator ranking may be used to choose an operator, which may be known as an operator selection. Exemplary embodiments illustrating aspects of the present disclosure are described below in the context of several specific examples.

With continued reference toFIG.1, apparatus100and/or computing device104includes at least a processor108. The at least a processor108may be consistent with any processor discussed with reference toFIG.8. Apparatus100and/or computing device104includes a memory112communicatively connected to the at least a processor108, wherein the memory112contains instructions configuring the processor108to preform tasks in accordance with this disclosure.

With continued reference toFIG.1, memory112contains instructions configuring processor108to receive operator data116. In some embodiments, operator data116may be received from operator database120. Operator database120is further described with reference toFIG.2. For the purposes of this disclosure, “operator data” is data concerning the historical performance of an operator. An “operator,” for the purposes of this disclosure, is a person that uses a transport vehicle. The transport vehicle may be used to transport objects from one location to another. Objects may include, as non-limiting examples, cargo, goods, produces, livestock, non-fungible goods, fungible goods, produce, cargo containers, oil, liquids, gasoline, food, meals, people, and the like.

With continued reference toFIG.1, A “transport vehicle” as used in this disclosure is a machine capable of moving one or more objects between one or more locations. In some embodiments, a transport vehicle may include, but is not limited to, a freight carrier, a truck, a car, a boat, a plane, a motorcycle, and the like. A transport vehicle may be configured to operate through, but is not limited to, air, land, sea, and the like. A transport vehicle may be configured to engage in one or more steps of a transport. In some embodiments, a transport vehicle may engage in pickup, delivery, and/or line haul operations. In some embodiments, a transport vehicle may include, but is not limited to, Less than Truckload (“LTL”) and/or Full Truckload (“FTL”) freight delivery.

With continued reference toFIG.1, operator data116includes at least an operator associated with at least a carbon emission datum124. A “carbon emission datum,” for the purposes of this disclosure is a datum describing the carbon emission of an operator. In some embodiments, operator data116may include greenhouse gas data associated with an operator. “Greenhouse gas data” as used in this disclosure is a metric associated with a pollutant that contributes to the greenhouse effect. A “pollutant” as used in this disclosure is a substance that degrades environmental quality. In some embodiments, greenhouse gas data may include, but is not limited to, carbon emissions, water vapor, methane, nitrous oxide, ozone, chlorofluorocarbons, hydrofluorocarbons, perfluorocarbons, and the like. Greenhouse gas data may include measurements associated with an amount of greenhouse gas generated. Carbon emission datum124may include an amount of greenhouse gas generated. An amount of greenhouse gas generated may be represented in, but is not limited to, metric tons, pounds, kilograms, cubic meters, and the like. As a non-limiting example, greenhouse gas data may include data showing 4 metric tons of carbon have been generated by an operator. In some embodiments, greenhouse gas data may include data from one or more pollutant sources. A “pollutant source” as used in this disclosure is any originating source of a pollutant. A pollutant source may include, but is not limited to, transport vehicles, power grids, combustion from boilers, furnaces, transport vehicle emissions, emissions from processes performed by or products manufactured by a transport vehicle, and the like. In some embodiments, carbon emission. In some embodiments, carbon emission datum124may be a component of greenhouse gas data; that is, carbon emission datum124may include a portion of greenhouse gas data pertaining to carbon emissions.

Still referring toFIG.1, carbon emission datum124and/or greenhouse gas data may be represented in energy and/or fuel consumed by a transport vehicle, total fuel consumed of a transport, and the like. Fuel may include, but is not limited to, gasoline, diesel, propane, liquefied natural gas, and/or other fuel types. In some embodiments, a transport vehicle may use alternative fuel. An “alternative fuel” as used in this disclosure is any energy source generated without a use of fossils. A “fossil” as used in this disclosure is preserved remains of any once-living organism. Alternative fuels may include, but are not limited to, nuclear power, compressed air, hydrogen power, bio-fuel, vegetable oil, propane, and the like. In the instance of alternative fuel, an energy conversion factor may be included. In some embodiments, an energy conversion factor may include, but is not limited to, gallons to electric equivalent for a hybrid or electric transport vehicle. Greenhouse gas data may be consistent with any greenhouse gas data disclosed in U.S. patent application Ser. No. 17/749,535, filed on May 20, 2022, and entitled “SYSTEM AND METHOD FOR GREENHOUSE GAS TRACKING,” the entirety of which is incorporated by reference herein in its entirety.

Still referring toFIG.1, in some embodiments, carbon emission datum124may be calculated from fuel consumption data. For the purposes of this disclosure, “fuel consumption data” is data pertaining to amounts of fuel consumed over a period of time. The period of time may be, as a non-limiting example, the career of an operator. As another non-limiting example, the period of time may be the last 3 days, 1 week, 3 months, 2 years, and the like. As another non-limiting example, the period of time may be the period of time it took to complete a particular task. As a non-limiting example, if a task took 5 hours to complete, the period of time may correspond to those 5 hours. A “task,” for the purposes of this disclosure is an item of work. In some embodiments, the task may be a task that is to be done or has been done by an operator. In some embodiments, the task may be a job for an operator, which includes moving one or more objects from one location to another. In some embodiments, the task may be a job for an operator, which includes moving one or more objects from one location to another using a transport vehicle. In some embodiments, the task may be a job for an operator to do using a transport vehicle.

Still referring toFIG.1, in some embodiments, carbon emission datum124may be calculated from mileage data. For the purposes of this disclosure, “mileage data” is data pertaining to a number of miles traversed by a transport vehicle. Mileage data may be measured in miles, kilometers, feet, yards, furlongs, leagues, and/or any other suitable distance unit. Mileage data may be measured over a period of time. The period of time may be, as a non-limiting example, the career of an operator. As another non-limiting example, the period of time may be the last 3 days, 1 week, 3 months, 2 years, and the like. As another non-limiting example, the period of time may be the period of time it took to complete a particular task. As a non-limiting example, if a task took 5 hours to complete, the period of time may correspond to those 5 hours. In some embodiments, other types of data may be used to calculate carbon emission datum124such as type of fuel, idling time, traffic data, and the like. A person of ordinary skill in the art would appreciate, after having reviewed the entirety of this disclosure, that a variety of data could be used in addition to or in place of the data mentioned here in order to calculate the carbon emission datum124.

With continued reference toFIG.1, in some embodiments, carbon emission datum may be calculated using a lookup table. A “lookup table,” for the purposes of this disclosure, is an array of data that maps input values to output values. A lookup table may be used to replace a runtime computation with an array indexing operation. As a non-limiting example, a carbon emission lookup table may relate fuel consumption data to carbon emission datum124. As a non-limiting example, computing device104may be configured to “lookup” a given fuel consumption datum in order to find a corresponding carbon emission datum124. As a non-limiting example, computing device104may be configured to “lookup” a given mileage datum in order to find a corresponding carbon emission datum124.

With continued reference toFIG.1, in some embodiments, computing device104may use a carbon emission machine-learning model to calculate carbon emission datum124. Carbon emission machine-learning model may be created using a machine-learning module128. Machine-learning module128may be consistent with any machine-learning module disclosed as part of this disclosure; particularly, machine-learning module may be consistent with machine-learning module600disclosed with reference toFIG.6.

With continued reference toFIG.1, in some embodiments, carbon emission machine-learning model may be trained using carbon emission training data. Carbon emission training data may include a plurality of examples of fuel consumption data and/or mileage data with associated carbon emission datums. In some embodiments, carbon emission training data may also include fuel type, traffic data, or other types of data that are correlated to carbon emissions.

With continued reference toFIG.1, in some embodiments, carbon emission datum124may be calculated using one or more rates. As a non-limiting example, a fuel emission rate may be used to calculate carbon emission datum124from a fuel consumption datum. In some embodiments, fuel emission rate may represent an amount of carbon emission per volume of fuel used. Fuel emission rate may be stored in a database and retrieved by computing device104. As a non-limiting example, a mileage emission rate may be used to calculate carbon emission datum124from a mileage datum. In some embodiments, mileage rate may represent an amount of carbon emission per distance traveled. Mileage rate may be stored in a database and retrieved by computing device104.

With continued reference toFIG.1, in some embodiments, operator data116may include a task datum. For the purposes of this disclosure, a “task datum” is an element of data associated with a task. In some embodiments, task datum may be associated with the at least a carbon emission data. Task datum may be an element of task data. Task data may include, as non-limiting examples, vehicle data, distance data, terrain data, time data, cargo data, speed data, fuel data, traffic data, route data, and the like. Task data is disclosed further with reference toFIG.2. In some embodiments, the task datum may comprise a vehicle datum. For the purposes of this disclosure, a “vehicle datum” is an element of data concerning the type of transport vehicle. In some embodiments, vehicle datum may pertain to the transport vehicle that was used to accomplish the relevant task. Vehicle datum may include a type of vehicle, such as, as non-limiting examples, a truck, a car, a tractor, a motorcycle, a bike, and the like. In some embodiments, vehicle datum may include a make of vehicle, such as VOLVO, MACK, PETERBILT, FORD, BMW, YAMAHA, and the like. In some embodiments, vehicle datum may include a model of vehicle, such as LR, TERRAPRO, F150, PRIUS, IMPALA, and the like. In some embodiments, vehicle datum may include a mile per gallon rating for a vehicle such as, 24 mpg, 30 mpg, 17, mpg, and the like. In some embodiments, the task datum may comprise a distance datum. For the purposes of this disclosure, a “distance datum” is an element of data concerning the amount of distance traversed during a task. As non-limiting examples, distance datum may be 50 miles, 10 miles, 5 miles, and the like. Distance datum may be expressed in any suitable distance unit, including but not limited to miles, kilometers, feet, yards, furlongs, leagues, and the like.

With continued reference toFIG.1, in some embodiments, carbon emission datum124may include a plurality of carbon emission datums124. In some embodiments, each of the plurality of carbon emission datums124may be associated with a task datum. As a non-limiting example, a first carbon emission datum124may be associated with a first task datum, a second carbon emission datum124may be associated with a second task datum, and so on.

With continued reference toFIG.1, memory112contains instructions configuring processor108to calculate a carbon emission rate132of the at least an operator associated with operator data116as a function of the at least a carbon emission datum124. For the purposes of this disclosure, a “carbon emission rate” is a measurement of carbon emissions measured against another quantity. In some embodiments, calculating carbon emission rate132may be as a function of a task datum. As a non-limiting example, carbon emission rate132may be calculated as a function of distance datum. In some embodiments, carbon emission rate132may be a measurement of carbon emissions over distance. In some embodiments, carbon emission rate132may be expressed in units of weight (or mass) over distance. As non-limiting examples, carbon emission rate132may be expressed in lbs./mi, kg/m, tons/km, and the like. In some embodiments, carbon emission rate132may be calculated as a function of time datum. For the purposes of this disclosure, a “time datum,” is a datum describing the amount of time that a task took to complete. In some embodiments, carbon emission rate132may be a measurement of carbon emissions over time. In some embodiments, carbon emission rate132may be expressed in units of weight (or mass) over time. As non-limiting examples, carbon emission rate132may be expressed in lbs./min, kg/hr., tons/hr., and the like. In some embodiments, carbon emission rate132may be calculated as a function of cargo datum. In some embodiments, carbon emission rate132may be a measurement of carbon emissions over cargo weight. In some embodiments, this may be a ratio. In some embodiments, carbon emission rate132may be expressed in units of weight (or mass) over cargo weight (or mass). As non-limiting examples, carbon emission rate132may be expressed in tons/lb., lbs./lbs., kg/kg, kg/g, and the like.

With continued reference toFIG.1, in some embodiments, calculating carbon emission rate132may include calculating a plurality of carbon emission rates from a plurality of carbon emission datums. In some embodiments, calculating carbon emission rate132may include calculating a carbon emission rate132for each of a plurality of tasks that an operator has conducted. In some embodiments, calculating carbon emission rate132may include calculating a carbon emission rate132for each operator of a plurality of operators. In some embodiments, calculating carbon emission rate132may include calculating a carbon emission rate132for each vehicle of a plurality of vehicles. This may be done, for example, when task datum includes a vehicle datum. As a non-limiting example, this may include calculating a carbon emission rate132for each vehicle that an operator uses.

With continued reference toFIG.1, memory112contains instructions configuring processor108to calculate a carbon efficiency score136of an operator as a function of the carbon emission rate132. For the purposes of this disclosure, a “carbon efficiency score” is a score that represents an operator's carbon efficiency. In some embodiments, carbon efficiency score136may represent an operator's carbon efficiency over a single task. In some embodiments, the carbon efficiency score136may represent an operator's carbon efficiency over a plurality of tasks. In some embodiments, carbon efficiency score136may represent an operator's carbon efficiency of a certain type of class of tasks. As a non-limiting example, carbon efficiency score136may represent an operator's carbon efficiency when using a certain type of transport vehicle, such as the type of transport vehicle indicated by vehicle data. As a non-limiting example, carbon efficiency score136may represent an operator's carbon efficiency when transporting a certain type of cargo. In some embodiments, the type of cargo may be a category of cargo as disclosed above. In some embodiments, the type of cargo may be a weight (or mass) of the cargo, such as under 500 lbs., 500 lbs to a ton, 1 ton-3 tons, over 3 tons, and the like. In some embodiments, this information may be attained from a cargo datum. For the purposes of this disclosure, a “cargo datum” is a datum describing cargo transported during a task. In some embodiments, carbon efficiency score136may represent an operator's carbon efficiency when traversing a certain terrain, such as a terrain indicated by a terrain datum. For the purposes of this disclosure, a “terrain datum” is a datum describing the terrain traversed during a task. As a non-limiting example, the terrain may be a surface type, such as paved, dirt, gravel, ice, and the like. As a nonlimiting example, the terrain may be a total elevation change. As non-limiting examples, the total elevation change may be under 500 ft, 500 ft-1500 ft, over 1500 ft, −500 ft to 500 ft, and the like.

With continued reference toFIG.1, in some embodiments, memory112may contain instructions configuring processor108to train a carbon efficiency machine-learning model140. In some embodiments, processor108may use machine-learning module128to train carbon efficiency machine-learning model140. Training carbon efficiency machine-learning model140may include training carbon efficiency machine-learning model140using carbon efficiency training data144. Carbon efficiency training data144may include a plurality of inputs correlated to a plurality of outputs.

With continued reference toFIG.1, the “inputs” for carbon efficiency training data144may include carbon emission data and task data. In some embodiments, carbon efficiency training data144may include examples of carbon emission data. In some embodiments, carbon efficiency training data144may include examples of carbon emission data and associated examples of task data. In some embodiments, carbon efficiency training data144may include examples of carbon emission rate132. In some embodiments, carbon efficiency training data144may include examples of carbon emission rate132and associated examples of carbon emission datum124and task data.

With continued reference toFIG.1, the “outputs” for carbon efficiency training data144may include examples of carbon efficiency scores which are correlated to inputs of carbon efficiency training data144. As a non-limiting example, carbon efficiency training data144may include a carbon emission rate132associated with a carbon efficiency score136. As another non-limiting example, carbon efficiency training data144may include carbon emission datums124and task data associated with a carbon efficiency score136. As another non-limiting example, carbon efficiency training data144may include carbon emission rate132and task data associated with a carbon efficiency score136.

With continued reference toFIG.1, in some embodiments, tasks may be classified into task categories. Task categories may include, as non-limiting examples, heavy-load tasks, mountainous tasks, wide load tasks, and the like. In some embodiments, task categories may correspond to types of tasks with differing expected carbon emissions. As a non-limiting example, tasks that are “mountainous” or contain long periods of altitude change may be expected to have higher carbon emissions. On the other hand, tasks with little to no elevation change may be expected to have lower expected carbon emissions. As another non-limiting example, tasks with low variance in transport vehicle speed may have lower expected carbon emissions. On the other hand, tasks with high variance in transport vehicle speed (which may arise in high-traffic areas) may have higher expected carbon emissions.

With continued reference toFIG.1, in some embodiments, tasks may be classified into task categories using a task classifier. A “task classifier,” for the purposes of this disclosure, is a classifier configured to sort tasks into task categories. Classifiers are discussed further with reference toFIG.6. Task classifier may receive data concerning a task, such as task data as input, and may output a task category for the task. Task classifier may be trained by machine-learning module128. In some embodiments, task classifier may be trained using training data containing sets of task data with associated task categories. In some embodiments, task classifier may be trained using training data containing sets of task data correlated to associated carbon emission datum124or carbon emission rate132. In these embodiments, task classifier may be trained to group tasks with similar carbon emission datum124or carbon emission rate132into like task categories. In some embodiments, task categories may be used to calculate carbon efficiency score. As a non-limiting example, if a task is associated with higher carbon emissions, the carbon efficiency score may be increased or augmented to account for this fact. As a non-limiting example, if a task is associated with lower carbon emissions, the carbon efficiency score may be decreased or discounted to account for this fact. In some embodiments, the task data used to train carbon efficiency machine-learning model140may include associated task categories. In some embodiments, carbon efficiency machine-learning model140may receive as input task data including associated task categories.

With continued reference toFIG.1, in some embodiments, memory112may contain instructions configuring processor108to calculate a greenhouse gas ratio. The calculation of a greenhouse gas ratio is discussed further with reference toFIG.3.

With continued reference toFIG.1, memory112may contain instructions configuring processor108to generate an operator ranking148. For the purposes of this disclosure, an “operator ranking” is an ordered list of operators. The operator ranking148is a function of the carbon efficiency score136of an operator. As a non-limiting example, operator ranking148may include an ordered list of operators, wherein the list is ordered based on the carbon efficiency score136of the operators. In some embodiments, operator ranking148may be ordered in a decreasing order, such that the operator with the largest carbon efficiency score136is listed first and the operator with the smallest carbon efficiency score136is listed last. In some embodiments, operator ranking148may be ordered in an ascending order, such that the operator with the smallest carbon efficiency score136is listed first and the operator with the largest carbon efficiency score is listed last.

With continued reference toFIG.1, computing device104may display operator ranking148on a display device. A “display device,” for the purposes of this disclosure, is a device that is capable of displaying data in a visual manner. Display device may include, as non-limiting examples, a television, a computer monitor, an LCD screen, an OLED screen, a CRT screen, and the like. Display device may be communicatively connected to computing device104. In some embodiments, display device may be local (located on the same network) to computing device104. In some embodiments, display device may be remote (located on a different network) to computing device104. In some embodiments, operator ranking148may include operator data116associated with each operator in operator ranking148. As non-limiting example, operator ranking148may include the names, pictures, employment history, age, and the like of operators in operator ranking148.

With continued reference toFIG.1, computing device104may transmit operator ranking148to a remote device. For the purposes of this disclosure, a “remote device” is a computing device that is located remotely to computing device104. As non-limiting examples, remote device may include a laptop, smartphone, tablet, desktop, and the like. In some embodiments, once remote device has received operator ranking148, remote device may display operator ranking using a remote display device. Remote display device may be consistent with display device as disclosed in this disclosure. In some embodiments, computing device104may command remote device to display operator ranking148.

With continued reference toFIG.1, in some embodiments, memory112may contain instructions further configuring processor108to generate a forecasted carbon efficiency score152. Generating the forecasted carbon efficiency score is a function of operator data116and a forecasted task. Forecasted task may be consistent with tasks as disclosed in this disclosure. For the purposes of this disclosure, a “forecasted task” is a task that has not occurred yet. In some embodiments, forecasted task may be a task that a company is scheduled to conduct at some point in the future. In some embodiments, forecasted task may be a purely hypothetical task.

With continued reference toFIG.1, in some embodiments, generating forecasted carbon efficiency score152may include training a forecast machine-learning model156. In some embodiments, machine-learning module128may be used to train forecast machine-learning model156. In some embodiments, forecast machine-learning model156may be trained using operator training data160. In some embodiments, operator training data160may include past operator data and past task data correlated to carbon efficiency data. As a non-limiting example past task data may include task data for previous tasks that have been completed by the operator for which the forecasted carbon efficiency score152is being generated. In some embodiments, past task data may include task data corresponding to tasks that the operator has completed. In some examples, operator training data160may include past operator data and past task data correlated to carbon efficiency scores. In some embodiments, forecast machine-learning model156may be trained on training data corresponding to a specific operator. In some embodiments, this specific operator may be the operator for which forecasted carbon efficiency score is being generated. In some embodiments, forecast machine-learning model may be trained using training data from a variety of operators, such as some or all of the operators working for a company.

With continued reference toFIG.1, in some embodiments, forecast machine-learning model156may be used to generate the forecasted carbon efficiency score152. Forecast machine-learning model156may take operator data116and forecasted task data as input. Forecast machine-learning model156may output a forecasted carbon efficiency score152. In some embodiments, forecast machine-learning model156may generate a forecasted carbon efficiency score152for an operator. In some embodiments, forecast machine-learning model156may output a plurality of forecasted carbon efficiency score152wherein each of the plurality of forecasted carbon efficiency score152corresponds to an operator of a plurality of operators. Forecast machine-learning model156may be implemented using any methodology as described below in more detail in reference toFIG.6.

With continued reference toFIG.1, in some embodiments, carbon efficiency machine-learning model140, operator training data160, and/or task training data may be received from a training data database. Training data database may be implemented, without limitation, as a relational training data database, a key-value retrieval training data database such as a NOSQL training data database, or any other format or structure for use as a database that a person skilled in the art would recognize as suitable upon review of the entirety of this disclosure. Training data database may alternatively or additionally be implemented using a distributed data storage protocol and/or data structure, such as a distributed hash table or the like. Training data database may include a plurality of data entries and/or records as described above. Data entries in a training data database may be flagged with or linked to one or more additional elements of information, which may be reflected in data entry cells and/or in linked tables such as tables related by one or more indices in a relational database. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which data entries in a database may store, retrieve, organize, and/or reflect data and/or records as used herein, as well as categories and/or populations of data consistently with this disclosure.

With continued reference toFIG.1, in some embodiments, training data database may be an employee database. An “employee database,” for the purposes of this disclosure is a database of employee data maintained by an employer. For the purposes of this disclosure, an employee database may also include information regarding independent contractors, agents, and the like. In some embodiments, employee database may include information regarding a plurality of employees, as well as task data concerning the tasks completed by those employees. In some embodiments, employee database may also include carbon emission data regarding the tasks of the employees.

With continued reference toFIG.1, in some embodiments, memory112may contain instructions configuring the processor108to select an operator of the at least an operator. Any operators that are selected may be part of operator selection164. In some embodiments, this may be as a function of the carbon efficiency score136. For example, the operator with the highest, or otherwise best, carbon efficiency score136may be selected. In some embodiments, this may be as a function of the forecasted carbon efficiency score152. For example, the operator with the highest, or otherwise best, forecasted carbon efficiency score152may be selected. In some embodiments, the operator may be selected using operator ranking148. For example, in some embodiments, the first (or last) placed operator in operator ranking148may be selected.

With continued reference toFIG.1, in some embodiments, memory112may contain instructions configuring the processor108to display operator selection164on a display device. Display device may be consistent with any display device disclosed as part of this disclosure. In some embodiments, memory112may contain instructions configuring the processor108to send operator selection164to a remote device. Remote device may be consistent with any remote device disclosed as part of this disclosure.

Referring now toFIG.2, a diagram of an exemplary embodiment of operator database120is shown. Operator database120may be implemented, without limitation, as a relational database, a key-value retrieval database such as a NOSQL database, or any other format or structure for use as a database that a person skilled in the art would recognize as suitable upon review of the entirety of this disclosure. Operator database120may alternatively or additionally be implemented using a distributed data storage protocol and/or data structure, such as a distributed hash table or the like. Operator database120may include a plurality of data entries and/or records as described above. Data entries in a database may be flagged with or linked to one or more additional elements of information, which may be reflected in data entry cells and/or in linked tables such as tables related by one or more indices in a relational database. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which data entries in a database may store, retrieve, organize, and/or reflect data and/or records as used herein, as well as categories and/or populations of data consistently with this disclosure.

With continued reference toFIG.2, operator database120may include operator data116. Operator data116in some embodiments, may include identifying information an operator or plurality of operators. Identifying information may include, a name, a photograph, a job title, an employee number, a license plate number, a social security number, and the like.

With continued reference toFIG.2, in some embodiments, operator database120may include carbon emission data200. In some embodiments, operator data116may include carbon emission data200. Carbon emission data200may include a plurality of carbon emission datum124. As a non-limiting example, in some embodiments, operator data116may include carbon emission data200associated with one or more operators.

With continued reference toFIG.2, in some embodiments, operator database120may include carbon emission data200. In some embodiments, operator data116may include carbon emission data200. As a non-limiting example, in some embodiments, operator data116may include carbon emission data200associated with various tasks completed by operators in operator data116. As a non-limiting example, operator data116may include carbon emission data200associated with an operator, wherein the carbon emission data200may include data concerning one or more tasks completed by that operator.

With continued reference toFIG.2, carbon emission data200may include a plurality of different data types concerning tasks. A person of ordinary skill in the art, after having read the entirety of this disclosure, would appreciate that carbon emission data200may include a variety of different types of data concerning tasks. In some embodiments, carbon emission data200may include vehicle data208. Vehicle data208may include a plurality of vehicle datums. Vehicle datums are described further with reference toFIG.1. In some embodiments, vehicle data208may include a type, make, or model of a transport vehicle that was used for a particular task. In some embodiments, carbon emission data204may include distance data212. Distance data212may include a plurality of distance datums. Distance datums are further described with reference toFIG.1. In some embodiments, carbon emission data200may include terrain data216. Terrain data216may include a plurality of terrain datums. Terrain datums are further described with reference toFIG.1. In some embodiments, carbon emission data200may include time data220. Time data220may include a plurality of time datums. Time datums. Time datums are further described with reference toFIG.1. In some embodiments, carbon emission data200may include cargo data224. Cargo data224may include a plurality of cargo datums. Cargo datums are further described with reference toFIG.2. Cargo data224may include, as non-limiting examples, the weight of cargo, quantitative data regarding the cargo, qualitative data regarding the cargo, a type of cargo, dimensions of cargo, and the like. In some embodiments, carbon emission data200may include speed data228. For the purposes of this disclosure, “speed data” is data related to the speed of a transport vehicle conducting a task. Speed data228may include a plurality of speed datums. As non-limiting examples, speed data228may include, average speed, top speed, lowest speed, and the like.

Referring now toFIG.3, apparatus304may be consistent with apparatus100. Apparatus304may be configured to receive first greenhouse gas data308. Apparatus304may receive first greenhouse gas data308through an external computing device, user input, and the like. In some embodiments, apparatus304may receive first greenhouse gas data308through one or more computing devices and/or sensors. In some embodiments, first greenhouse gas data308may include, but is not limited to, carbon emissions, water vapor, methane, nitrous oxide, ozone, chlorofluorocarbons, hydrofluorocarbons, perfluorocarbons, and the like. First greenhouse gas data308may include measurements associated with an amount of greenhouse gas generated. An amount of greenhouse gas generated may be represented in, but is not limited to, metric tons, pounds, kilograms, cubic meters, and the like. As a non-limiting example, first greenhouse gas data308may include data showing 4 metric tons of carbon have been generated by a user. In some embodiments, first greenhouse gas data308may include data from one or more pollutant sources. A pollutant source may include, but is not limited to, transport vehicles, power grids, combustion from boilers, furnaces, transport vehicle emissions, emissions from processes performed by or products manufactured by a transport vehicle, and the like. In some embodiments, a transport vehicle may include, but is not limited to, a freight carrier, a truck, a car, a boat, a plane, a motorcycle, and the like. A transport vehicle may be configured to operate through, but is not limited to, air, land, sea, and the like. A transport vehicle may be configured to engage in one or more steps of a transport. In some embodiments, a transport vehicle may engage in pickup, delivery, and/or line haul operations. In some embodiments, a transport vehicle may include, but is not limited to, Less than Truckload (“LTL”) and/or Full Truckload (“FTL”) freight delivery.

Still referring toFIG.3, in some embodiments, first greenhouse gas data308may include data of a pollutant emission source that may not be directly related to a transportation entity. As a non-limiting example, first greenhouse gas data308may include data from energy used in electronic invoicing of a transport. A “transportation entity” as used in this disclosure is a being involved in a transportation of a component. In some embodiments, first greenhouse gas data308may include degrees of separation from a transportation entity. A “degree of separation” as used in this disclosure is a measure of relation between two or more one entities and/or objects. For instance and without limitation, a degree of separation of first greenhouse gas data308may include two degrees of separation from actions of a transportation entity, with a first degree being fuel consumption of a transport vehicle and a second degree being pollution generated from transport component packaging. Greenhouse gas emission sources that may be one degree of separation away from actions of a transportation entity may include, but are not limited to, greenhouse gas emissions produced in generating electricity used during operations related to a transport process. Operations related to a transport process may include, but are not limited to, computational power, conveyor use, manufacturing machine use, crane use, light sources, and the like.

Still referring toFIG.3, first greenhouse gas data308may be represented in energy and/or fuel consumed by a transport vehicle, distance traveled of a transport vehicle, total fuel consumed of a transport, and the like. Fuel may include, but is not limited to, gasoline, diesel, propane, liquefied natural gas, and/or other fuel types. In some embodiments, a transport vehicle may use alternative fuel.

Still referring toFIG.3, apparatus304may be configured to receive second greenhouse gas data312. “Second greenhouse gas data” as used in this disclosure is a metric associated with a pollutant source. In some embodiments, second greenhouse gas data312may include data of an identical greenhouse gas emission source of first greenhouse gas data308. In other embodiments, second greenhouse gas data312may include data from a different greenhouse gas emission source than first greenhouse gas data308. In some embodiments, apparatus304may be configured to receive first greenhouse gas data308and/or second greenhouse gas data312from an external computing device, such as, but not limited to, a desktop, laptop, smartphone, server, and the like. In some embodiments, first greenhouse gas data308and/or second greenhouse gas data312may be generated from an on-board computing device of a transport vehicle.

Still referring toFIG.3, apparatus304may be configured to calculate first greenhouse gas metric316. A “greenhouse gas metric” as used in this disclosure is a metric pertaining to a pollutant emission contributing to the greenhouse effect. First greenhouse gas metric316may be calculated as a function of first greenhouse gas data308. In some embodiments, first greenhouse gas metric316may include an amount of emission generated. An amount of emission generated may include, but is not limited to, volumes, weights, masses, ratios, and the like. In some embodiments, first greenhouse gas metric316may include a measurement pertaining to a specific user. In some embodiments, first greenhouse gas metric316may include data of an amount of emission generated by a specific user. In some embodiments, first greenhouse gas metric316may include data of a type of emission generated by a user. In some embodiments, first greenhouse gas metric316may include data of a ratio of emission generated by a user. A ratio may include, but is not limited to, user emissions to average emissions, pollution emissions to clean energy emissions, and the like.

Still referring toFIG.3, apparatus304may be configured to calculate second greenhouse gas metric320. “Second greenhouse gas metric” as used in this disclosure is any measurement pertaining to an auxiliary emission of pollutant of a user. In some embodiments, apparatus304may calculate second greenhouse gas metric320as a function of second greenhouse gas data312. In some embodiments, second greenhouse gas metric320may be generated from a plurality of metrics. Second greenhouse gas metric320may include a measurement of an indirect source of pollutant emission, such as, but not limited to, distance traveled of a transport vehicle, transport type, invoicing, new hires, electricity used, and the like. In some embodiments, second greenhouse gas metric320may include, but is not limited to, a plurality of utility resources that are expended during a transport including water, electricity and other forms of energy consumed or expended by the transport vehicle. Second greenhouse gas metric320may include, but is not limited to, generation of electricity, a consumption of natural gas, propane, and oil. In some embodiments, second greenhouse gas metric320may include data of an aerial transport. An aerial transport may include, but is not limited to, an aircraft, helicopter, plane, drone, and the like. In some embodiments, data of an aerial transport may include distances traveled via aerial transport. In some embodiments, second greenhouse gas metric320may include manual data entered and/or recorded by a computing system of a transporter. In some embodiments, second greenhouse gas metric320may include emissions during a loading of components, such as a loading of components in a transport vehicle. In some embodiments, second greenhouse gas metric320may include emissions generated by equipment used in loading components to be transported. Equipment may include any machine configured to move one or more components. Equipment may include, but is not limited to, a forklift or other equipment used at a shipping terminal. In some embodiments, second greenhouse gas metric320may include utility expenditures associated with buildings and/or structures that may be associated with a transport vehicle and/or transport. In some embodiments, a first plurality of inputs may include an amount of fuel consumption and/or a number of miles driven by at least one vehicle associated with a freight carrier.

Still referring toFIG.3, apparatus304may include greenhouse gas ratio calculator324. Greenhouse gas ratio calculator324may include any computing system as described in this disclosure. Greenhouse gas ratio calculator324may be configured to apportion a greenhouse gas emission with a pollutant source. In some embodiments, greenhouse gas ratio calculator324may be configured to receive first greenhouse gas metric316and/or second greenhouse gas metric320. Greenhouse gas ratio calculator324may be configured to determine an estimation of greenhouse gas emissions from first greenhouse gas metric316and/or second greenhouse gas metric320. In some embodiments, greenhouse gas ratio calculator324may determine a conversion factor. A “conversion factor” as used in this disclosure is any ratio of energy used to greenhouse gas generated. In some embodiments, a conversion factor may include a carbon conversion rate for Liquefied Natural Gas (“LNG”), Kilowatts (“KW”), Diesel fuel, Compressed Natural Gas (“CNG”), Gasoline, biodiesel fuel, and/or air power. Determining greenhouse gas ratio328may include applying a conversion factor for fuel consumed by a transport vehicle and/or applying a conversion factor for a distance traveled by a transport. A “greenhouse gas ratio” as used in this disclosure is a proportion of a metric of a pollutant source to greenhouse gas emissions. In some embodiments, examples of a metric may include, but are not limited to, gallons of gasoline, diesel, or biodiesel fuel and number of miles driven by a vehicle. In some embodiments, a conversion factor for miles driven may be available from sources such as the Environmental Protection Agency (“EPA”). In some embodiments, a conversion factor may include grams of a GHG gas, such as carbon, emitted on a per mile basis. In some embodiments, a conversion factor may include a ratio of a pollutant emission to a greenhouse gas, such as, but not limited to, ozone, carbon, methane, propane, and the like. One of ordinary skill in the art would understand, after reviewing this disclosure in its entirety, how to determine a proper conversion factor to use for the calculation.

Still referring toFIG.3, first greenhouse gas metric316and/or second greenhouse gas metric320may be calculated by apparatus304with a conversion factor for a specific natural resource consumed during generation or use of a utility service. In some embodiments, a utility service may include, but is not limited to, natural gas, electricity, water, and/or oil. As a non-limiting example, a utility service may include water heating, sewage systems, lighting systems, heating and cooling systems, and the like. In some embodiments, a conversion factor may be associated with a generation of electricity. Carbon emissions may vary with an amount and type of energy source used in producing electricity. In some embodiments, a conversion factor may be calculated based on a plurality of factors such as, but not limited to, location, time of year, type of resource consumed during a generation of electricity, and the like. In some embodiments, a grid mix for a particular location may determine a conversion factor or factors that may be used. A type of resource consumed may include, but is not limited to, coal, natural gas, and/or other material that may be burned or used up during a generation of electricity. Additionally, renewable resources may be used during the generation of electricity and may allow for an offset of some carbon emissions that may be caused by a use of other resources.

Still referring toFIG.3, in some embodiments, a transport may include a plurality of components. In some embodiments, a plurality of components may include, but is not limited to, consumer goods. In some embodiments, each component of a plurality of components may be associated with one or more users. A user may include a transport recipient. Apparatus304may be configured to allocate an amount of greenhouse gas produced during a transport to a user based on a plurality of factors. In some embodiments, an amount of greenhouse gas produced may be allocated to a user based on factors such as, but not limited to, weight, volume, and/or fuel consumed during a transport of the plurality of components. This allocation process may be repeated based on multiple transports to provide a user with a total amount of carbon emissions associated with a user for a predetermined time period, such as, but not limited to, a year, month, week, and the like. In some embodiments, a portion of a total carbon amount may be allocated to a plurality of users. For instance and without limitation, a transport may include four transport recipients at four varying destinations. Apparatus304may determine that a transport has a total greenhouse gas emission of 24 metric tons of carbon. Apparatus304may be configured to determine a contribution of greenhouse gas emissions by each of the four transport recipients. Apparatus304may determine a first transport recipient contributed 4 metric tons of carbon emissions, a second transport recipient contributed 12 metric tons of carbon emissions, a third transport recipient contributed 6 metric tons of carbon emissions, and a fourth transport recipient contributed 2 metric tons of carbon emissions to a total carbon emission of a transport. Each determination of a contributed greenhouse gas emission of each transport recipient may be calculated by apparatus304through factors such as, but not limited to, transport component weight, transport component quantity, transport distance, transport routes, transport component packaging, electronic invoicing, and the like.

Still referring toFIG.3, in some embodiments, a first greenhouse gas data308and/or a second greenhouse gas data312may be stored in a database. A database may include, but is not limited to, Enterprise Resource Planning (“ERP”) databases, invoices records and/or other data sources. In some embodiments, a database may store and retrieve information automatically. In other embodiments, a database may be configured to receive manual inputs from a user. In other embodiments, information may be imported to a database. In some embodiments, a distance traveled, and/or energy consumed may be stored as transport vehicle miles and gallons of fuel consumed in separate databases for different transport vehicle categories. In some embodiments, a database may store information for a car category and a truck category separately. In other embodiments, a database may store carbon emission data of a car category and a truck category together. A database may include data from an ERP database. In some embodiments, a database may include airline transportation invoices. In some embodiments, a database may include utility data, transport invoices, and/or other data.

Still referring toFIG.3, apparatus304may include an objective function. An “objective function” as used in this disclosure is a process of minimizing or maximizing one or more values based on a set of constraints. Apparatus304may generate an objective function to optimize a greenhouse gas emission of a transport. In some embodiments, an objective function of apparatus304may include an optimization criterion. An optimization criterion may include any description of a desired value or range of values for one or more attributes of a transport; desired value or range of values may include a maximal or minimal value, a range between maximal or minimal values, or an instruction to maximize or minimize an attribute and/or a threshold value. As a non-limiting example, an optimization criterion may specify that a greenhouse gas emission of a transport should be less than 3 metric tons; an optimization criterion may cap a greenhouse gas emission of a transport, for instance specifying that a transport must not have a greenhouse gas emission greater than a specified value. An optimization criterion may specify one or more desired transport criteria. In an embodiment, an optimization criterion may assign weights to different attributes or values associated with attributes; weights, as used herein, may be multipliers or other scalar numbers reflecting a relative importance of a particular attribute or value. One or more weights may be expressions of value to a user of a particular outcome, attribute value, or other facet of a transport; value may be expressed, as a non-limiting example, in remunerative form, such as a material quality, a quickest transport, or the like. As a non-limiting example, minimization of greenhouse gas emissions may be multiplied by a first weight, while tolerance above a certain value may be multiplied by a second weight. Optimization criteria may be combined in weighted or unweighted combinations into a function reflecting an overall outcome desired by a user; a function may be a greenhouse gas emission function to be minimized and/or maximized. A function may be defined by reference to transport criteria constraints and/or weighted aggregation thereof as provided by apparatus304; for instance, a greenhouse gas emissions function combining optimization criteria may seek to minimize or maximize a function of greenhouse gas emissions.

Still referring toFIG.3, apparatus304may use an objective function to compare first greenhouse gas metric316and/or second greenhouse gas metric320with an ideal greenhouse gas metric. An “ideal greenhouse gas metric” as used in this disclosure is an optimal value of pollutant emissions. An ideal greenhouse gas metric may include, but is not limited to, a range of a quantity of pollutant emissions. For instance and without limitation, an ideal greenhouse gas metric may include a range of about between 1 to 5 metric tons of carbon. Generation of an objective function may include generation of a function to score and weight factors to achieve a process score for each feasible pairing. In some embodiments, pairings may be scored in a matrix for optimization, where columns represent transports and rows represent greenhouse gas emissions potentially paired therewith; each cell of such a matrix may represent a score of a pairing of the corresponding transport to the corresponding greenhouse gas emission. In some embodiments, assigning a predicted process that optimizes the objective function includes performing a greedy algorithm process. A “greedy algorithm” is defined as an algorithm that selects locally optimal choices, which may or may not generate a globally optimal solution. For instance, apparatus304may select pairings so that scores associated therewith are the best score for each order and/or for each process. In such an example, optimization may determine the combination of processes such that each object pairing includes the highest score possible.

Still referring toFIG.3, an objective function may be formulated as a linear objective function. Apparatus304may solve an objective function using a linear program such as without limitation a mixed-integer program. A “linear program,” as used in this disclosure, is a program that optimizes a linear objective function, given at least a constraint. For instance, and without limitation, objective function may seek to maximize a total score Σr∈RΣs∈Scrsxrs, where R is a set of all transports r, S is a set of all greenhouse gas emissions s, crsis a score of a pairing of a given transport with a given greenhouse gas emission, and xrsis 1 if a transport r is paired with a greenhouse gas emission s, and 0 otherwise. Continuing the example, constraints may specify that each transport is assigned to only one greenhouse gas emission, and each greenhouse gas emission is assigned only one transport. Matches may include matching processes as described above. Sets of processes may be optimized for a maximum score combination of all generated processes. In various embodiments, apparatus304may determine a combination of transports that maximizes a total score subject to a constraint that all transports are paired to exactly one greenhouse gas emission. Not all transports may receive a greenhouse gas emission pairing since each greenhouse gas emission may only pair to one transport. In some embodiments, an objective function may be formulated as a mixed integer optimization function. A “mixed integer optimization” as used in this disclosure is a program in which some or all of the variables are restricted to be integers. A mathematical solver may be implemented to solve for the set of feasible pairings that maximizes the sum of scores across all pairings; mathematical solver may be implemented on apparatus304and/or another device in apparatus100, and/or may be implemented on third-party solver.

With continued reference toFIG.3, optimizing an objective function may include minimizing a loss function, where a “loss function” is an expression an output of which an optimization algorithm minimizes to generate an optimal result. As a non-limiting example, apparatus304may assign variables relating to a set of parameters, which may correspond to score components as described above, calculate an output of mathematical expression using the variables, and select a pairing that produces an output having the lowest size, according to a given definition of “size,” of the set of outputs representing each of plurality of candidate ingredient combinations; size may, for instance, included absolute value, numerical size, or the like. Selection of different loss functions may result in identification of different potential pairings as generating minimal outputs. Objectives represented in an objective function and/or loss function may include minimization of transport times. Objectives may include minimization of greenhouse gas emissions. Objectives may include minimization of long idle times. Objectives may include minimization of cost. Objectives may include minimization of resources used.

Still referring toFIG.3, apparatus304may determine one or more factors contributing to first greenhouse gas metric316and/or second greenhouse gas metric320. As a non-limiting example, apparatus304may determine a transport to a user includes a long travel time as first greenhouse gas metric316and a large amount of fuel consumed as a second greenhouse gas metric320. Apparatus304may also determine a plurality of other contributing factors, such as, but not limited to, long idle percentages, amounts of stops in a transport, transport weight, transport path efficiency, and the like. Apparatus304may compare two or more contributing factors using an objective function to minimize an amount of greenhouse gas produced. As a non-limiting example, apparatus304may compare a transport distance to a number of stops in a transport path. Apparatus304may determine that a total amount of greenhouse gas produced by a transport may be offset by reducing a number of stops in the transport. Apparatus304may utilize a machine learning model to predict greenhouse gas generation of a transport. A machine learning model may be trained on training data correlating transport factors to greenhouse gas generation. A machine learning model may be configured to input transport factors and output estimated greenhouse gas emissions. In some embodiments, apparatus304may be configured to estimate greenhouse gas emissions of particular users, transports, individual contributing factors, and the like.

Still referring toFIG.3, apparatus304may be configured to display, but is not limited to displaying, greenhouse gas data, greenhouse gas metrics, greenhouse gas ratios, and the like to a user. In some embodiments, apparatus304may display greenhouse gas data and/or metrics through a graphical user interface (GUI). In some embodiments, apparatus304may be configured to display greenhouse gas data to a user through, but not limited to, a smartphone, tablet, desktop, laptop, and the like. Apparatus304may display alternative options for a transport of a user.

Still referring toFIG.3, in some embodiments, apparatus304may be configured to provide greenhouse gas emission feedback332through a display as a function of a calculation of greenhouse gas ratio328. “Greenhouse gas emission feedback” as used in this disclosure is information pertaining to greenhouse gas emissions of an individual, object, and/or entity. Greenhouse gas emission feedback332may include, without limitation, historical trends, daily emissions, hourly emissions, and the like. In some embodiments, greenhouse gas emission feedback332may include a comparison of a user to one or more other users. For instance, and without limitation, a user may drive inefficiently, causing an extra 0.4 metric tons of greenhouse gas emissions. A second user may drive efficiently, causing no extra greenhouse gas emissions. Greenhouse gas emission feedback332may show a first user a comparison of a second user driving efficiently and/or may show steps to increase driving efficiency of the first user. Greenhouse gas emission feedback332may include a greenhouse gas reduction plan. Apparatus304may generate a greenhouse gas reduction plan as a function of a tracking of a transport. A “greenhouse gas reduction plan” as used in this disclosure is a step or steps of preventing excessive pollutant emissions. A greenhouse gas reduction plan may be generated for a transport recipient, which may include, but is not limited to, recommended fuel types, transport times, fewer transport component packages, less frequent transports, and the like. For instance, and without limitation, apparatus304may present a greenhouse gas reduction plan to a user through an external computing device, such as a smartphone, laptop, desktop, and the like. A greenhouse gas reduction plan generated for a transport recipient may include environmentally friendly options such as using alternate fuels, using recyclable materials, using biodegradable packaging, and the like. Apparatus304may display an estimated amount of greenhouse gas emissions reduced through selecting environmentally friendly options of a greenhouse gas reduction plan. In some embodiments, apparatus304may be configured to generate costs associated with choosing environmentally friendly options, such as, but not limited to, costs of fuel, costs of transport component packaging, costs of transport duration, and the like.

Still referring toFIG.6, machine-learning module600may be configured to perform a lazy-learning process620and/or protocol, which may alternatively be referred to as a “lazy loading” or “call-when-needed” process and/or protocol, may be a process whereby machine learning is conducted upon receipt of an input to be converted to an output, by combining the input and training set to derive the algorithm to be used to produce the output on demand. For instance, an initial set of simulations may be performed to cover an initial heuristic and/or “first guess” at an output and/or relationship. As a non-limiting example, an initial heuristic may include a ranking of associations between inputs and elements of training data604. Heuristic may include selecting some number of highest-ranking associations and/or training data604elements. Lazy learning may implement any suitable lazy learning algorithm, including without limitation a K-nearest neighbors algorithm, a lazy naïve Bayes algorithm, or the like; persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various lazy-learning algorithms that may be applied to generate outputs as described in this disclosure, including without limitation lazy learning applications of machine-learning algorithms as described in further detail below.

Now referring toFIG.7, an exemplary embodiment of a method700for comparing the efficiency of operators. Method700includes a step705of receiving, by a processor, operator data, wherein the operator data comprises at least an operator associated with at least a carbon emission datum. This may be implemented as described with reference toFIGS.1-6. In some embodiments, operator data may include a task datum associated with the at least a carbon emission datum. In some embodiments, the task datum may include a vehicle datum. In some embodiments, the task datum may include a distance datum. In some embodiments, the at least a carbon emission datum may include a plurality of carbon emission datums. In some embodiments, each of the plurality of carbon emission datums may be associated with a task datum.

With continued reference toFIG.7, method700includes a step710of calculating, by the processor, a carbon emission rate of the at least an operator as a function of the at least a carbon emission datum. This may be implemented as described with reference toFIGS.1-6. In some embodiments, step710may include calculating the carbon emission rate of the at least an operator comprises calculating the carbon emission rate of the at least an operator as a function of the task datum. This may be implemented as described with reference toFIGS.1-6. In some embodiments, step710may include calculating a plurality of carbon emission rates from the plurality of carbon emission datums. This may be implemented as described with reference toFIGS.1-6.

With continued reference toFIG.7, method700includes a step715of obtaining, by the processor, a carbon efficiency score of the at least an operator as a function of the carbon emission rate. This may be implemented as described with reference toFIGS.1-6. In some embodiments, step715may be a function of the plurality of carbon emission rates.

With continued reference toFIG.7, method700includes a step720of generating, by the processor, an operator ranking as a function of the carbon efficiency score of the at least an operator. This may be implemented as described with reference toFIGS.1-6.

With continued reference toFIG.7, in some embodiments, method700may include a step of generating, by the processor, a forecasted carbon efficiency score of the at least an operator as a function of the operator data and a forecasted task. This may be implemented as described with reference toFIGS.1-6. In some embodiments, generating the forecasted carbon efficiency score of the at least an operator may include training a forecast machine-learning model using operator training data, wherein the operator training data comprises at least past operator data and past task data correlated to carbon efficiency data. This may be implemented as described with reference toFIGS.1-6. In some embodiments, generating the forecasted carbon efficiency score of the at least an operator may include generating the forecasted carbon efficiency score of the at least an operator using the forecast machine-learning model. This may be implemented as described with reference toFIGS.1-6. In some embodiments, method700may further include a step of selecting, by the processor, an operator of the at least an operator for a forecasted task as a function of the forecasted carbon efficiency rating. This may be implemented as described with reference toFIGS.1-6. In some embodiments, method700may include a step of training, by the processor a carbon efficiency machine-learning model using carbon efficiency training data, wherein the carbon efficiency training data comprises examples of carbon emission data and associated examples of task data. This may be implemented as described with reference toFIGS.1-6. In some embodiments, step715may include calculating a carbon efficiency score of the at least an operator comprises using the carbon efficiency machine-learning model. This may be implemented as described with reference toFIGS.1-6.

Memory808may include various components (e.g., machine-readable media) including, but not limited to, a random-access memory component, a read only component, and any combinations thereof. In one example, a basic input/output system816(BIOS), including basic routines that help to transfer information between elements within computer system800, such as during start-up, may be stored in memory808. Memory808may also include (e.g., stored on one or more machine-readable media) instructions (e.g., software)820embodying any one or more of the aspects and/or methodologies of the present disclosure. In another example, memory808may further include any number of program modules including, but not limited to, an operating system, one or more application programs, other program modules, program data, and any combinations thereof.

Computer system800may also include a storage device824. Examples of a storage device (e.g., storage device824) include, but are not limited to, a hard disk drive, a magnetic disk drive, an optical disc drive in combination with an optical medium, a solid-state memory device, and any combinations thereof. Storage device824may be connected to bus812by an appropriate interface (not shown). Example interfaces include, but are not limited to, SCSI, advanced technology attachment (ATA), serial ATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and any combinations thereof. In one example, storage device824(or one or more components thereof) may be removably interfaced with computer system800(e.g., via an external port connector (not shown)). Particularly, storage device824and an associated machine-readable medium828may provide nonvolatile and/or volatile storage of machine-readable instructions, data structures, program modules, and/or other data for computer system800. In one example, software820may reside, completely or partially, within machine-readable medium828. In another example, software820may reside, completely or partially, within processor804.

Computer system800may also include an input device832. In one example, a user of computer system800may enter commands and/or other information into computer system800via input device832. Examples of an input device832include, but are not limited to, an alpha-numeric input device (e.g., a keyboard), a pointing device, a joystick, a gamepad, an audio input device (e.g., a microphone, a voice response system, etc.), a cursor control device (e.g., a mouse), a touchpad, an optical scanner, a video capture device (e.g., a still camera, a video camera), a touchscreen, and any combinations thereof. Input device832may be interfaced to bus812via any of a variety of interfaces (not shown) including, but not limited to, a serial interface, a parallel interface, a game port, a USB interface, a FIREWIRE interface, a direct interface to bus812, and any combinations thereof. Input device832may include a touch screen interface that may be a part of or separate from display836, discussed further below. Input device832may be utilized as a user selection device for selecting one or more graphical representations in a graphical interface as described above.

Computer system800may further include a video display adapter852for communicating a displayable image to a display device, such as display device836. Examples of a display device include, but are not limited to, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasma display, a light emitting diode (LED) display, and any combinations thereof. Display adapter852and display device836may be utilized in combination with processor804to provide graphical representations of aspects of the present disclosure. In addition to a display device, computer system800may include one or more other peripheral output devices including, but not limited to, an audio speaker, a printer, and any combinations thereof. Such peripheral output devices may be connected to bus812via a peripheral interface856. Examples of a peripheral interface include, but are not limited to, a serial port, a USB connection, a FIREWIRE connection, a parallel connection, and any combinations thereof.