SYSTEMS AND METHODS FOR EMISSIONS DATA MANAGEMENT

A system includes one or more processors and one or more memory devices storing instructions thereon that, when executed by the one or more processors, cause one or more processors to receive high-level sustainability data associated with an entity; generate a plurality of sustainability indicators for a plurality of categories for the entity based on the high-level sustainability data and one or more modeling assumptions associated with the entity; aggregate the plurality of sustainability indicators into a single sustainability indicator; generate a graphical user interface including the single sustainability indicator; and cause one or more devices to perform an action to improve the single sustainability indicator.

BACKGROUND

The present disclosure relates generally to data management. More specifically, the present disclosure relates to managing emissions data.

SUMMARY

One implementation of the present disclosure is a system. The system includes one or more processors and one or more memory devices storing instructions thereon that, when executed by the one or more processors, cause the one or more processors to receive high-level sustainability data associated with an entity. The instructions further cause the one or more processors to generate a plurality of sustainability indicators for a plurality of categories for the entity based on the high-level sustainability data and one or more modeling assumptions associated with the entity. The instructions further cause the one or more processors to aggregate the plurality of sustainability indicators into a single sustainability indicator. The instructions further cause the one or more processors to generate a graphical user interface including the single sustainability indicator. The instructions further cause the one or more processors to cause one or more devices to perform an action to improve the single sustainability indicator.

Another implementation of the present disclosure is a method. The method includes receiving, by one or more processing circuits, high-level sustainability data associated with an entity. The method further includes generating, by one or more processing circuits, a plurality of sustainability indicators for a plurality of categories for the entity based on the high-level sustainability data and one or more modeling assumptions associated with the entity. The method further includes aggregating, by the one or more processing circuits, the plurality of sustainability indicators into a single sustainability indicator. The method further includes generating, by the one or more processing circuits, a graphical user interface including the single sustainability indicator. The method further includes causing, by the one or more processing circuits, one or more devices to perform an action to improve the single sustainability indicator.

Another implementation of the present disclosure is one or more memory devices storing instructions thereon, when executed by one or more processors, cause the one or more processors to select one or more modeling assumptions for modeling low-level sustainability data based on high-level sustainability data of multiple entities. The instructions further cause the one or more processors to receive high-level sustainability data associated with an entity. The instructions further cause the one or more processors to generate a plurality of sustainability indicators for a plurality of categories for the entity based on the high-level sustainability data and the one or more modeling assumptions. The instructions further cause the one or more processors to aggregate the sustainability indicators into a single sustainability indicator. The instructions further cause the one or more processors to generate a graphical user interface including the single sustainability indicator. The instructions further cause the one or more processors to cause one or more devices to perform an action to improve the sustainability data.

DETAILED DESCRIPTION

Referring generally to the FIGURES, systems and methods for emissions data management is shown, according to various exemplary embodiments. The system and methods described herein can manage emissions related data for emissions tracking and reduction. An emissions system can, in some embodiments, collect activity data of a corpus or group of entities (e.g., of a user or group of users, a family, a company, a city, a state, a country, etc.). The activity data can be used to identify emissions production resulting from the activities of the activity data. The emissions production information can be used by the emissions system to establish an emissions footprint, e.g., carbon footprint, indicating emissions associated with a particular user or group of users. The data collected can be high-level data. For example, the data can represent general activities, behaviors, or preferences of the entities of the corpus or group of entities.

An emissions system that collects granular low-level data for every entity of a corpus or group of entities and determines emissions indicators for multiple emissions categories based on the granular low-level data may encounter various problems. The granular low-level data may directly describe consumption (e.g., energy consumption, fuel consumption, food consumption, water consumption, etc.). For example, the corpus or group of entities may be very large, e.g., hundreds, thousands, millions, or even billions of entities. Furthermore, the granular data points or data features that could be collected for each entity of the corpus or group of entities to determine emissions indicators may be even larger. These granular data points can indicate real-time or historical activities of users, specific granular descriptions of commuting routes of the users, granular descriptions of vehicle engine types or fuel efficiencies, etc. The amount of data storage needed to store the granular data points for the corpus or group of entities may be very large. Furthermore, processing and managing this large volume of data can require significant amounts of computational resources (e.g., processor and memory resources) and require significantly long processing times. These long processing times can cause computational resources to be in an operational state causing significant amounts of power to be drawn from a power source. Furthermore, entities of the corpus or group of entities may not wish to provide granular data to the emissions system for security reasons and therefore collecting the granular data from entities may have challenges.

To solve these, and other technical challenges, the systems and methods discussed herein can manage the large volume of data for the corpus or group of entities in a manner that reduces data storage resources used, reduce processor and memory resources used, reduce an amount of power consumption needed by the computing systems that implement the systems and methods, and allow for emissions indicators to be generated faster than conventional methods. For example, the emissions system can collect high-level data for the corpus or group of entities instead of, or in addition to, low-level data. The high-level data can indicate general behaviors, habits, or activities of the corpus or group of entities. The emissions system can generate emissions indicators based on the high-level data. However, because the high-level data is less granular, without correction or accounting of this lack of granularity, an accuracy of the emissions indicators could be reduced. To address the lack of granularity (e.g., to retain accuracy notwithstanding the lack of granularity of the high-level data), the emissions system described herein implements modeling assumptions that model low-level data based on the collected high-level data. This allows the emissions system described herein to quickly and efficiently determine emissions indicators while maintaining a high accuracy for the emissions indicators.

The emissions system further solves technical challenges in the display of emissions indicators for a large corpus or group of entities. Displaying the causes of emissions production for a corpus or group of entities may be difficult to summarize since there are a significant amount of possible emissions causes. To solve these, and other technical problems, the emissions system described herein is capable of generating emissions indicators based on the collected high-level data and modeling assumptions in multiple categories. The emissions system can generate, based on the modeling assumptions and the high-level data for each entity of the corpus or group of entities, an emissions indicator for each entity in each category. The emissions system can sort the emissions indicators into buckets of data such that the emissions data is organized by category. The emissions system can aggregate the emissions indicators of each bucket into a single emissions indicator for each category. The emissions indicators can, in some embodiments, be a timeseries of emissions indicators, e.g., emissions indicators for multiple points in time. In this regard, the emissions system can generate a set of emissions indicators for each point in time for a set of points in time for each category. The emissions system can generate a total emissions indicator for the corpus or group. The total emissions indicator can be an aggregate for emissions indicators of each category.

The emissions system can generate a user interface that displays the total emissions indicator for the corpus or group of entities. The user interface could be a trend or bar graph. The emissions system can cause the user interface to include a selectable element that allows a user to select between the categories. The user interface can update based on a selection of the user and drill down from the total emissions indicator to category level emissions indicators down to entity level emissions indicators. This user interface can allow a user to grasp, within a single interface, the breakdown of emissions indicators for the large corpus or group of entities which would normally require multiple different types of presentation formats.

Furthermore, the emissions system can aid a user or group of users to reduce their emissions footprint and track the performance of emissions reduction. The emissions system can help a user set carbon footprint goals, e.g., zero emissions goals or near zero emissions goals (e.g., net zero emissions goals, including offsets/investments). The emissions system can provide projects or carbon offsets that allow the user or group of users to reduce their carbon footprint and meet the carbon footprint goals that they have set.

Referring now toFIG.1, a block diagram of a system100including an emissions system102tracking and reducing emissions of a user or company is shown, according to an exemplary embodiment. The emissions system102can be a computer system (e.g., desktop computer, database system, server system, a cloud computing platform, etc.) that is configured to communicate with the wearable device114and/or user device118. The user interfaces and interface elements ofFIGS.4-63can be generated by the emissions system102and displayed on the wearable device114and/or the user device118. Furthermore, the emissions system102can receive user input from the user interfaces ofFIGS.4-63via the wearable device114and/or the user device118.

The wearable device114can be a smartwatch, a smart ring, smart glasses, a smart necklace, a pacemaker, etc. The wearable device114can collect data associated with travel, heart rate, blood pressure, etc. The user device118can be a smartphone, a tablet, a laptop, a desktop computer, a mobile device, etc. The user device118can include a display device for displaying user interfaces to a user (e.g., a LED screen, an OLED screen, etc.). The user device118can include input devices for receiving user input. For example, a touch screen, a mouse, a keyboard, etc. The wearable device114can include a similar display device and/or an input device.

A network can be used by the emissions system102to communicate with the wearable device114and/or the user device118. The network can be a Local Area Network (LAN), a Wide Area Network (WAN), the Internet, a cellular network (e.g., 3G, 4G, 5G), a Bluetooth connection, a Wi-Fi network, and any other type of wired or wireless form of communication. The emissions system102can include one or more processors104and one or more memory devices106.

The processor(s)104can be a general purpose or specific purpose processor, an application specific integrated circuit (ASIC), one or more field programmable gate arrays (FPGAs), a group of processing components, or other suitable processing components. The processor(s)104may be configured to execute computer code and/or instructions stored in the memories or received from other computer readable media (e.g., CDROM, network storage, a remote server, etc.).

The memory device(s)106can include one or more devices (e.g., memory units, memory devices, storage devices, etc.) for storing data and/or computer code for completing and/or facilitating the various processes described in the present disclosure. The memory device(s)106can include random access memory (RAM), read-only memory (ROM), hard drive storage, temporary storage, non-volatile memory, flash memory, optical memory, or any other suitable memory for storing software objects and/or computer instructions. The memory device(s)106can include database components, object code components, script components, or any other type of information structure for supporting the various activities and information structures described in the present disclosure. The memory device(s)106can be communicably connected to the processor(s)104and can include computer code for executing (e.g., by the processors) one or more processes described herein.

The emissions system102includes a company emissions service108, a user emissions questionnaire service110, and a user emissions service112. The services108-112can be stored as instructions on the memory devices106and run by the processors104. The services108-112can provide information to the wearable device114and/or the user device118. Similarly, the services108-112can receive information recorded and/or input by users of the wearable device114and/or the user device118.

The company emissions service108can be configured to record emissions information of a company, generate emissions indicators, and generate user interfaces including the emissions indicators for the company. In some embodiments, the company emissions service108can generate user interfaces for any other type of group of individuals, e.g., a company, a school, a college, a university, a family, a state, a city, a country, etc.

The user emissions questionnaire service110can be configured to provide a user with a series of questions to determine a carbon footprint of a user, e.g., via the wearable device114and/or the user device118. The emissions questionnaire service110can generate the carbon footprint based on the responses received from the user. The user emissions questionnaire service110can generate the user interfaces ofFIGS.27-42. The user interfaces ofFIGS.27-42can be displayed on the wearable device114and/or the user device118. The user emissions service112can be configured to record emission information specific to a user and generate emissions user interfaces for the user. The user emissions service112can receive tracking data (e.g., global positioning system data) and/or user input from the wearable device114and/or the user device118and generate the emissions user interfaces based on the recorded data. The emissions system102, or various components of the emissions system102, can generate data that causes the wearable device114or the user device118to display the interfaces described with references atFIGS.4-63.

Referring now toFIG.2, the emissions system102generating emissions indicators228from high-level entity data206based on modeling assumptions204is shown, according to an exemplary embodiment. The emissions system102includes a modeler210. The modeler210can model high-level entity data206with modeling assumptions204to generate low-level consumption data226via an engine212. The modeling assumptions204can indicate low-level consumption data226that results from certain high-level entity data206. The modeling assumptions204can be global modeling assumptions or customer specific assumptions. For example, the high-level entity data206could describe general characteristics or behaviors of a an entity (e.g., a user, a group of users, a family, etc.), for example, commuting characteristics, residential heating, cooling, or electrical consumption, eating tendencies, etc. In some embodiments, one or more of the characteristics or behaviors of an entity can have a first characteristic corresponding to working from home, and a second characteristic corresponding to not working from home (e.g., commuting to an office). For example, the commuting characteristics, residential heating, cooling, electrical consumption, eating tendencies, etc., can have a first a work from home commuting characteristic may be different than a non-work from home commuting characteristic. The high-level entity data206can be non-specific, e.g., the high-level entity data206could indicate an eating preference (e.g., meat, vegan, vegetarian, pescatarian, etc.). Similarly, the high-level entity data206could indicate characteristics of a vehicle of the user or commute of the user, e.g., a size (e.g., small, medium, or large) of the vehicle and a fuel type of the vehicle (e.g., gas, electric, hydrogen, etc.).

The modeling assumptions204can model the low-level consumption data226with the high-level entity data206. For example, the modeling assumptions204can indicate expected consumption levels of a vehicle of a particular size (e.g., small, medium, or large). The modeling assumptions204can indicate expected food consumption levels of particular eating habits (e.g., meat, vegan, vegetarian, pescatarian, etc.). The modeling assumptions204can indicate expected low-level consumption data226of shopping habits, e.g., amount of merchandise purchased that result from in-person shopping, online shopping, etc. The modeling assumptions204can indicate expected low-level consumption data226that results from certain types of HVAC equipment for certain sizes of a home, e.g., certain run times, energy consumptions, fuel consumptions, etc. The modeling assumptions204can be region specific, in some embodiments. For example, different geographic regions may have different weather patterns and residential homes in different geographic regions can consume various amounts of energy based on their location, e.g., extreme hot or cold climates can cause HVAC equipment to consume more energy than mild or temperate climates. In some embodiments, the modeling assumptions204can indicate low-level consumption data226of a work from home setup. For example, the modeling assumptions204may indicate an expected low-level consumption data226, based on an indication regarding the frequency of working from home (e.g., days/week, days/month, etc.), a work from home duration (e.g., hours/day, etc.), the number of computers (e.g., desktop computers, laptop computers) utilized while working from home, the number of external monitors (e.g., external displays, screens, graphical display devices) utilized while working from home, and other remote work quantifiers. In some embodiments, the modeling assumptions204may indicate an expected low-level consumption data226of additional home heating and cooling emissions, workstation electronics that are added onto existing residential footprint, etc. In some embodiments, the modeling assumptions204may be based on temporary or lasting statuses of societal or regional health emergencies and/or government orders (e.g., stay-at-home orders). For example, different geographic regions may have different restrictions on commuting and social engagements, which can cause the amount of energy consumed due to working from home or by a building to vary. For example, additional heating and cooling emissions may be based on increases or decreases in residential energy consumption. For example, the modeling assumptions204may produce low-level consumption data226based on data associated with pre-pandemic circumstances, ongoing-pandemic circumstances, or post-pandemic circumstances.

The modeler210can receive telemetry data from a telemetry data source208. The telemetry data source208can be data storage element (e.g., a database) that collects data of devices. The devices could be a vehicle telematics system, airline flight data, a wearable, a smart thermostat, etc. The telemetry data source208could be an Internet of Things (IoT) event hub. The telemetry data source208could establish a communication connection with an edge device and collect telemetry data from the edge device via the communication connection. The communication connection could be established via a network, e.g., the Internet, a cellular network, MQ Telemetry Transport (MQTT), etc. The network could be the network described with reference toFIG.1. The devices could be smartphones (e.g., the user device118), vehicle systems, health tracking devices (e.g., the wearable device114), etc. In addition to the high-level entity data206, the modeler210can model the low-level consumption data226based on the telemetry data of the telemetry data source208. While the high-level entity data206might indicate that a user commutes on average five times a week in a compact vehicle, the telemetry data could be telemetry data received from a telematics system of the vehicle that indicates specific fuel efficiencies, distances traveled, etc.

In some embodiments, a user can provide low-level consumption data226to the emissions system102via the user device118via a questionnaire. The questionnaire can collect one, tens, or hundreds of attributes that describe a consumption profile of the user. Via the modeling assumptions204, the modeler210and the engine212can generate the low-level consumption data226for the user based on the profile built for the user. The modeler210can identify attribute values that are appropriate for each user. The attribute values can be modeling assumptions204. The modeler210can analyze the profile built for an individual and select the modeling assumptions204most appropriate for the individual. For example, if the individual rides the bus to work and cats meat, the modeler210can select modeling assumptions204that model consumption for riding the bus for a commuting category and eating meat for an eating category.

In some embodiments, the modeling assumptions204can be modified for individual entities of a corpus or group of entities. For example, if a user knows the average annual temperature, the average summer temperature, or the average winter temperature for their geographic region, the user could provide this information to the emissions system102via the user device118. The emissions system102could set the modeling assumptions204for energy consumption to heat or cool the home of the user based on the average temperatures provided by the user. These specific details entered by one user could be used by the modeler210for another user. For example, if a first user provides temperature data for a geographic region of the user, the modeler210could identify that a second user is in the same geographic region. The modeler210could select the modeling assumptions204for the second user to be the same as the modeling assumptions204determined to be used for the first user based on the temperature data provided by the first user for the geographic region. Similarly, the user could provide an average monthly bill for energy for their home to the emissions system102.

In some embodiments, for a corpus or group of entities, the telemetry data source208can sort and organize telemetry data for various entities of the corpus or group of entities. For example, the telemetry data source208can store an indication of each entity of the corpus or group of entities and store relationships between each entity and the telemetry devices of each entity. In this regard, the telemetry data source208can sort, filter, or tag data based on the relationships between the entities and the edge devices.

The modeler210can, in some embodiments, execute machine learning and/or an artificial intelligence algorithm to tune the modeling assumptions204. For example, because the telemetry data of the telemetry data source208is granular and specific to the activities of an entity, the low-level consumption data226that is generated from the telemetry data can be highly accurate. The modeler210can execute the machine learning and/or artificial intelligence algorithm based on the telemetry data to learn modeling assumptions204. This allows the emissions system102to collect a small amount of telemetry data for a small portion of entities of the corpus or group of entities and utilize the learned modeling assumptions204to make accurate determinations of the low-level consumption data226for entities of the corpus or group of entities that do not have telemetry data. In this regard, data storage reductions, processing resource reductions, processing speed improvements can be realized. For example, instead of storing and processing telemetry data for an entire corpus or group of entities, the emissions system102may only store and process telemetry data for a small portion of the corpus or group of entities. Instead of storing telemetry data for the other entities of the corpus or group of entities (which would require a large amount of storage resources) or perform a lengthy and resource intensive processing of the telemetry for the other entities, the modeler210can model the low-level consumption data226with the high-level entity data206and the learned modeling assumptions204.

The engine212can generate the low-level consumption data226based on the output of the modeler210. The low-level consumption data226can be consumption values in one or multiple categories. The categories could be commuting to work, commuting home from work, residential heating, residential cooling, residential electric consumption, food consumption, additional residential energy consumption attributable to working remotely (e.g., computing devices energy consumption, communications network system energy consumption, desktop computer energy consumption, computer monitor energy consumption, heating, ventilating and air conditioning energy consumption, lighting energy consumption, etc.). The low-level consumption data226could be generated by the engine212for one or multiple times. For example, the low-level consumption data226could be generated to indicate the consumption value of each entity of a corpus or group of entities in each category on a daily, weekly, bi-weekly, monthly, or yearly basis. The low-level consumption data226could indicate an amount of fuel consumed to commute to work on a particular day, a number of bus rides taken, a number of train rides taken, a length of time that a vehicle charged, an amount of energy consumed to heat or cool a building, an amount of meat, fish, vegetables, or grains consumed, etc.

The emissions identifier214can generate the emissions indicator228based on the low-level consumption data226. The emissions identifier214can generate the emissions indicator228by determining an amount of emissions, e.g., carbon dioxide (CO2) or carbon dioxide equivalent (CO2e) that results from each particular consumption value of the low-level consumption data226. The emissions identifier228can generate an emissions indicator228for each entity of a corpus or group of entities. The emissions identifier228can sort the emissions indicators228into buckets based on the category of the emissions indicators228. For example, a commuting related emissions indicators228for the corpus or group of entities could be sorted into a commuting bucket. All shopping related emission indicators228can be sorted into a shopping bucket.

The emissions identifier214can further aggregate the emissions indicators228in each of the buckets to generate an emissions indicator for each category. For example, for a corpus or group of entities, the emissions identifier214could aggregate (e.g., sum, average, weight, etc.) the emissions indicators of each category into a single category emissions indicator228. Furthermore, the emissions identifier214can aggregate (e.g., sum, average, weight, etc.) the emissions indicators228of each category into a total emissions indicator228for the corpus or group of entities. The individual emissions indicators228for each category, the category level emissions indicators228, and the total emissions indicator can be time correlated data (e.g., timeseries data). For example, each emissions indicator228could be a series of emissions values for points in time, e.g., for days, weeks, months, years. The emissions identifier214can store trends of the emissions indicators228and update each trend as new emissions indicators228are generated.

The emissions identifier214can determine lifecycle emissions, in some embodiments. The emissions indicators228can include lifecycle emissions indicators. Lifecycle emissions can attribute carbon emissions back to the source of the original energy that is being consumed in a downstream activity. For example, the emissions identifier214can determine carbon emission from the generation of electric power at a plant flowing into a residential home. If the power plant sources energy from 50% nuclear and 50% coal, the emissions identifier214can determine emissions indicators that accurately reflect not only the emission from the use of appliances in a home, but the emission associated with the actual generation of power via coal and nuclear production.

A user interface portal218can allow a user to access and view the emission indicators228. The user interface portal218can generate user interfaces, e.g., the user interfaces ofFIGS.4-64. The user interface portal218can populate various user interface elements of the user interfaces ofFIGS.4-64based on the emissions indicators228stored in the data storage216. Furthermore, recommendations generated by the recommendation engine220can be displayed in the user interfaces of theFIGS.4-64. The user interface portal218can retrieve the recommendations from the data storage216stored in the data storage216via the recommendation engine220. The user interface portal218can populate user interface elements (e.g., the user interfaces ofFIGS.4-64) with the recommendations.

The emissions system102can include a recommendation engine220. The recommendation engine220can generate recommendations for improving the emissions indicators228. For example, the recommendation engine220can generate recommendations on a company level. The recommendation engine220can generate the recommendations on the company level based on category level emissions indicators228or total emissions indicators228for the corpus or group of entities. The recommendation engine220can generate recommendations for individual entities of the corpus or group of entities. For example, the recommendation engine220can generate a recommendation for a particular user based on the emissions indicators228for each user. The recommendation engine220can generate category based recommendations for the entire corpus or group of entities e.g., based on category level emissions indicators228. The recommendation engine220can generate category based recommendations for particular entities based on the emissions indicators228for the particular entities in particular categories.

The recommendations can be recommendations to adjust commuting, e.g., a suggestion to take a bus more frequently, invest in a more fuel efficient vehicle, work from home more frequently, etc. The recommendations could be recommendations to change water usage, e.g., take shorter showers. The recommendations could be recommendations to change eating habits, e.g., cat less meat, cat more vegetables, etc.

The emissions system102includes an offset manager224. The offset manager224can acquire carbon offsets that offset the emissions indicators228. The offset manager224can receive a section, by a user, to acquire a particular offset and communicate with an external system that manages the offset to acquire the offset. In some embodiments, the offset manager224receives votes or indications of interest of various types of offsets from a user via a mobile application222. The offset manager224can aggregate the votes or indications of interest to determine which offsets have the most votes or indications of interest. The offset manager224could identify which categories have a number of votes or indications of interest greater than a particular amount. The offset manager224can acquire an offset responsive to determining that the offset has the most votes or indications of interest. The offset manager224can acquire the offset responsive to determining that the offset has a number of votes or indications of interest greater than a particular amount.

The mobile application222can be a mobile application run on a user device such as the user device118or the wearable device114. The mobile application can include user interfaces, for example, the user interfaces ofFIGS.4-63. The mobile application222can generate user interfaces, e.g., the user interfaces ofFIGS.4-63. The mobile application222can populate various user interface elements of the user interfaces ofFIGS.4-63based on the emissions indicators228stored in the data storage216. Furthermore, recommendations generated by the recommendation engine220can be displayed in the user interfaces of theFIGS.4-63. The mobile application222can retrieve the recommendations from the data storage216stored in the data storage216via the recommendation engine220. The mobile application222can populate the user interface elements ofFIGS.4-63with the recommendations.

Referring now toFIG.3, a process300of generating the emissions indicators from the high-level data based on the modeling assumptions is shown, according to an exemplary embodiment. The emissions system102can be configured to perform the process300. For example, the process300can be performed by components of the emissions system102. For example, the modeler210, the engine212, the emissions identifier214, etc. of the emissions system102can be configured to perform the process300. Furthermore, any computing system described herein can be configured to perform the process300.

In step302, the process300can include receiving, by one or more processing circuits, high-level data for multiple categories for a corpus or group of entities. For example, the emissions system102can receive the high-level entity data206for the corpus or group of entities. The corpus or group of entities could be users of a group, e.g., employees of a company, members of a family, citizens of a city, state, or country, occupants of a building, etc. The high-level data can indicate high-level behaviors, characteristics, preferences, or a profile of consumption for the entities of the corpus or group of entities in various categories (e.g., commuting, food, shopping, business travel, additional consumption from remote work, remote work behaviors, work from home setups, work from home frequency, work from home durations, etc.). For example, the high-level data could indicate typical commute distance, typical commute day per week, average size of a vehicle driven, utilization of busses, trains, shopping habits, food habits, etc. As another example, the high-level data could indicate typical work from home frequency (e.g., days per week, days per month, days per year, work from home occurrences per unit time, etc.), work from home duration (e.g., hours per day, hours per week, hours per month, etc.), work from home hardware and energy consumption (e.g., energy consumptions attributed to desktop computer use, laptop computer use, external monitor use, home office lighting usage, home office air conditioning usage, home office internet usage, etc.).

In step304, the process300can include selecting, by one or more processing circuits, one or more modeling assumptions for the multiple categories that model low-level data based on the high-level data. The high-level data can be the high-level data received in the step302. The emissions system102can select one or multiple of the modeling assumptions204. The modeling assumptions204can model the low-level consumption data226based on the high-level entity data206. For example, the modeling assumptions204could indicate energy consumption for heating or cooling a building for certain ranges of square feet, geographic locations, equipment types, etc. The high-level entity data206could indicate an approximate residence size, geographic location of the residence (e.g., state, city, region, etc.), and/or an indication of a type of equipment (e.g., air conditioning unit and furnace, heat pump system, etc.). Furthermore, the modeling assumptions204could indicate the amount of meat, vegetables, or dairy products consumed based on different food consumption behaviors (e.g., meat cater, vegan, vegetarian, pescatarian, etc.) of an entity indicated by the high-level entity data206.

In step306, the process300can include generating, by one or more processing circuits, emissions indicators for the multiple categories for multiple points in time based on the one or more modeling assumptions and the high-level data. The engine212can generate the low-level consumption data226based on the modeling assumptions204and the high-level entity data206. The engine212can further generate the low-level consumption data226based on telemetry data of the telemetry data source208. The engine212can provide the low-level consumption data226to the emissions identifier214and the emissions identifier214can generate the emissions indicators228based on the low-level consumption data226.

In step308, the process300can include sorting the emissions indicators into buckets based on the categories. For example, the emissions identifier214can generate an emissions indicator228for each entity for a corpus or group of entities in each category. The emissions identifier214can sort the emissions indicators228into buckets. The buckets can be data groupings or regions of the data storage216for storing emissions indicators228of each category. The emissions identifier214can sort the emissions indicators based on category such that each bucket includes all of the emissions indicators of the corpus or group of entities for each category. The emissions identifier214can store the sorted data in the data storage216.

In step310, the process300can include generating data causing a computing device to display the emissions indicators sorted into the buckets. The emissions system102can generate data that causes user interfaces to be displayed on computing devices such as the wearable device114or the user device118. The user interfaces can be the user interfaces ofFIGS.4-63.

Referring now toFIG.4-7, user interfaces400-700for various roles, e.g., a vice president, a CEO, and a sales department representative are shown, according to an exemplary embodiment. The emissions system102can be user-specific based on a role of an individual that is logged into the user interfaces400-700, e.g., CEOs, vice presidents, sales representatives, etc. The emissions information displayed in various user interfaces described herein can be tailored based on the role of the logged in user. In some embodiments, a user can view the emissions tracking data of the user and the individuals of the user's team or employees.

FIG.4is a user interface400with an element202for a user Parker. Parker may be a vice president of sustainability at a company.FIG.5is a user interface500that includes an element502for a user John. John may be a CEO of a particular company.FIG.6is a user interface600that includes an element602for a user Lille. Lille may be a sales department representative or team leader.FIG.7is a user interface700that includes an element702for a user Lisa. Lisa may be a CEO of a particular company. The information displayed in the various following interfaces can be tailored based on the role of the user logged in, e.g., a user may view carbon information associated with their employees, e.g., a CEO may view all emission data of a company while a department leader may view emissions data associated with their department.

Referring now toFIG.8, user interface800of an emissions tracking dashboard is shown, according to an exemplary embodiment. InFIG.8, a user interface800includes elements802-808. The element802includes an indication of a total number of employees for a particular company that the emissions system102generates the interface600for. The element804includes an indication of percentage of employees that are participating in the emissions tracking and reduction of the emissions system102. The element804further includes an indication of a percentage increase since a previous month. The element806provides an estimated scope of emissions at tons of carbon dioxide (CO2). The element808provides an opportunity indication, a number of opportunities to reduce carbon and a number of tons of carbon that could be reduced.

The user interface800includes an element810that provides a carbon footprint score in an element812for a company, team, and/or individual. The score can be provided along with a trend of the carbon footprint score over time. The trend can further include a goal set by the company and/or a user for reducing carbon emissions to. The score and the trend can be filtered by various parameters, e.g., based on total footprint814, scope three emissions816, household818, commuting820, food822, etc.

The user interface800can further include elements824and826that describe actions for reducing the carbon footprint, e.g., to get the carbon footprint closer to zero and/or below the threshold set for the company. The element824can describe an investment, e.g., $5 per employee investment, that purchases carbon offsets or other carbon reducing financial derivatives that “zero out” employees for the company, e.g., reduce carbon scores below a threshold or make a carbon footprint zero. The element626can describe a renewable natural gas (RNG) reduction where utilizing RNG for the company would result in a specific carbon footprint reduction. The investments can be made by an employee or a company.

Referring now toFIG.9, an emissions trend900of emissions indicators is shown, according to an exemplary embodiment. Element904includes a trend line906indicating the emissions indicators228. The element904can indicate the emissions indicators228for various points in time, e.g., days, months, years, decades, etc. The emission indicators228can be carbon dioxide or carbon dioxide equivalent. The emissions trend900includes a target908. The target908can be set by a user via the user device118. The target908could be a group goal, e.g., the goal for a company, family, city, state, or country. The trend900includes a target908indicating zero emissions. The emissions trend900can include a zero emissions line910. A user can interact, via the user device118, with the element902to switch between a total footprint, e.g., as shown inFIG.9, to other categories, e.g., as shown inFIGS.10-14.

Referring now toFIG.10, the emissions trend900ofFIG.10drilled down to a commuting transportation category is shown, according to an exemplary embodiment. The element1002indicates emissions indicators228for months broken down into commuting categories. The element1002can indicate the emissions indicators228for various points in time, e.g., days, months, years, decades, etc. The categories can be automobile, train, subway, carpool, ferry, vanpool, motorcycle, bus, or any other method of commuting. Each bar of the element1002can include sub-components that indicate the amount of emissions attributed to each category.

Referring now toFIG.11, the emissions trend900drilled down to a business transportation category is shown, according to an exemplary embodiment. The element1102indicates emissions indicators228broken down into business travel categories. The element1102can indicate the emissions indicators228for various points in time, e.g., days, months, years, decades, etc. The categories can be business class air, economy class air, bus, train, subway, automobile or any other business travel. Each bar of the element1102can include sub-components that indicate the amount of emissions attributed to each category.

Referring now toFIG.12, the emissions trend900drilled down to a household category is shown, according to an exemplary embodiment. The element1202indicates emissions indicators228broken down into household categories. The element1202can indicate the emissions indicators228for various points in time, e.g., days, months, years, decades, etc. The categories can be electricity, heating, cooling, ventilation, or any other category associated with households. Each bar of the element1202can include sub-components that indicate the amount of emissions attributed to each category.

Referring now toFIG.13, the emissions trend900drilled down to a food category is shown, according to an exemplary embodiment. The element1302indicates emissions indicators228broken down into food categories. The element1302can indicate the emissions indicators228for various points in time, e.g., days, months, years, decades, etc. The categories can be vegan, vegetarian, meat lover, seafood, dairy, or any other category associated with households. Each bar of the element1302can include sub-components that indicate the amount of emissions attributed to each category.

Referring now toFIG.14, the emissions trend900drilled down to a shopping category is shown, according to an exemplary embodiment. The element1402indicates emissions indicators broken down into shopping categories. The element1402can indicate the emissions indicators228for various points in time, e.g., days, months, years, decades, etc. The categories can be online, local, in-person, big business, small business, or any other category associated with shopping. Each bar of the element1402can include sub-components that indicate the amount of emissions attributed to each category. For example, the element1402can include a sub-component or multiple sub-components that indicate the amount of emissions attributed to working from home. For example, the element1402can indicate a rate, total, difference, etc., of energy use attributed to workstation electronics of a home setup, energy use attributed to heating, cooling, ventilating, or air conditioning, and/or additional energy use attributed to lighting during working from home. The element1402can indicate a change in emissions over a first period of time and a change in emissions over a second period of time, or compare emissions from the first period of time to the emissions of the second period of time. For example, the element1402may indicate the change in emissions between working from home and not working from home. For example, an entity that has no work from home may have zero emissions attributed to working from home, but if the entity begins or increases the quantity of work from home, the emissions attributed to working from home may be non-zero and the element1402may indicate the change(s) via one or more sub-components of the element1402.

Referring now toFIG.15, a user interface1500of an emissions tracking dashboard is shown, according to an exemplary embodiment. The user interface1500includes elements1502-1508providing team investment actions for improving carbon emissions for the company. The element1502provides a solar project match while the element1504provides a regenerative farming improvement. The element1506provides a car pool challenge where users of the company can car pool to work to reduce their carbon footprint. The element1508provides a commuter walk challenge where a user can walk to work to reduce carbon emissions resulting from motor vehicle transportation.

Referring now toFIG.16, the user interface1500including a renewable natural gas offset element1602is shown, according to an exemplary embodiment. The element1602includes a projected impact1604describing the impact on carbon production that will result from the renewable natural gas investment. The element1602allows a user to accept and begin investing in the renewable natural gas program.FIG.17illustrates the interface1500including an element1702describing a solar energy program. The element1702includes a projected impact1704indicating a projected impact from investing in the solar energy program. The element1706allows a user to accept and begin investing in the solar energy program.

Referring now toFIG.18, a user interface1800including emissions indicators for a sales team is shown, according to an exemplary embodiment. Element1802indicates a carbon footprint for the sales team. The element1804indicates a current impact for the sales team. Element1806indicates a carbon reduction opportunity for the sales team. Element1808indicates a participation rate for the sales team. Element1810indicates a monthly carbon impact for the team in various categories. Element1812indicates impact categories while element1814indicates team members of the sales team.

Referring now toFIG.19, a user interface1900indicating an average emissions footprint for employees is shown, according to an exemplary embodiment. The element1302can indicate the emissions indicators228for various points in time, e.g., days, months, years, decades, etc. for employees of a particular company. The user interface1900indicates two categories of emissions, category 6 and category 7. Category 6 emissions relate to business travel while category 7 emissions relates to transportation of employees to and from work. However, any type of emissions category could be displayed in the user interface1900. The user interface1900includes a bar element for each point in time. The bar element can be broken into sub-components that represent the emissions attributed to category 6 and category 7 respectively.

Referring now toFIG.20, a user interface2000indicating budget allocations and carbon offset allocations is shown, according to an exemplary embodiment. The user interface2000includes an element2002indicating an annual budget. The element2002includes a pie chart representing the portion of the budget utilized for offsets and the remaining portion of the budget that has not yet been utilized. Of the utilized portion of the budget, the element2004of the user interface2000can indicate the portion of the utilized budget that is allocated (e.g., offsets that have been deployed to offset emissions of employees) and the portion of the utilized budget that is unallocated (e.g., the portion of offsets that are in a holding inventor). The element2004can be filtered based on currency or metric tons of carbon. The user interface2000includes an element2006indicating total investments for points in time, e.g., days, months, years, decades, etc. The investments can be shown year-to-date in the element2006based on a fiscal year of a company. The points in time include bars that indicate the total investment in various types of offsets. The element2006can be filtered based on currency or metric tons of carbon. The user interface2000includes an element2008indicating category investments indicating, for various points in time, a portion of the utilized budget that is allocated and the portion of the utilized budget that is unallocated. The element2008can display investments by category.

Referring now toFIG.21, a user interface2100for viewing solar carbon offsets is shown, according to an exemplary embodiment. A user can be prompted with the user interface2100responsive to a user requesting to view category investments for solar. If there are no solar related projects, the user interface2100can be displayed. A user can interact with the element2102, via the user device118. Responsive to interacting with the element2102, various available solar projects may be displayed via a user interface where a user can browse and/or purchase offsets.

Referring now toFIG.22, a user interface2200indicating emissions indicators for a user is shown, according to an exemplary embodiment. The user interface2200includes an element2202indicating a number of employees that have completed an onboarding process for the emissions system102. The user interface2200includes an element2204indicating a number of employees that have not completed the onboarding. The user interface2200includes an element2206indicating a total number of onboarding invitations sent to employees of the company. The user interface2200includes an element2208indicating engagement metrics for monthly users. The engagement metrics indicate the number of active and inactive users for various months. The user interface2200includes an element2210that indicates levels of profile completion for the employees. The element2210breaks down the profile into categories, e.g. commuting, housing, shopping diet, and indicates a percentage level that the employees have completed each category. The element2212indicates the interest that the employees have expressed in different categories of carbon offset. The categories can include solar, wind, energy demand, forestry, hydro, pipeline emissions, biomass, and waste disposal.

Referring now toFIG.23, a user interface2300indicating enrollment and engagement of users is shown, according to an exemplary embodiment. The user interface2300includes an element2302indicating the number of employees of a company that are enrolled in the emissions system102. The user interface2300includes an element2304that indicates percentages of employees that are active and inactive. The user interface2300includes an element2306that indicates category participation. The user interface2300includes an element2308that indicates a trend of category engagement over time for a user selected category.

Referring now toFIG.24, a user interface2400of a marketplace interface where offsets and projects can be reviewed for reducing emissions production is shown, according to an exemplary embodiment. The user interface2400includes lists of all projects that the company could participate in. The interface2400includes projects such as methane collection, wind energy, renewable natural gas, reforestation offset, regenerative farming, solar energy, off shore wind, etc. The user interface2400could include the user interface2500ofFIG.25A. The user interface2500can indicate a featured project for the interface2400. In some embodiments, the interface2500is a featured project scroll in another user interface that highlights new projects available. In some embodiments, a user can toggle to view some of all categories of offsets available.

Referring now toFIGS.25B-25C, user interfaces2520-2540are shown for allocating offsets, according to an exemplary embodiment. Via the user interfaces2520-2540, a user can purchase offsets through and then allocate those offsets to specific carbon emissions. For example, the offsets could be allocated to offset their business travel expenses for a particular month, offset the footprint of one or more employees for the past year, etc. The user can select the carbon emission from a source, (e.g. business travel, commuting, personal travel, residential heating, etc.), the timeframe in which the emission was generated (e.g. month, quarter, year) and then either a numeric value or % of the total for that time period can be offset using the inventory of offsets that have been purchased.

Referring now toFIG.25D, a user interface2560including emissions indicators for a company is shown, according to an exemplary embodiment. The user interface2560includes an element2562indicating employee carbon footprint for the company. The element2564indicates a total number of employees for the company. The user interface2560indicates a top category of engagement in element2566. A total budget of the company for purchasing carbon offsets is shown in element2568. The user interface2560includes an element2570indicating investments of the company for various months plotted over time. The user interface2500includes an element2572indicating categories of interest. The categories of interest can be categories that the employees of the company are interested in purchasing carbon offsets in. The user interface2500includes an element2574including a geographic map and a location of a building of the company on the map. The user interface2500further includes a list of active offset projects in element2576. A user can further view retired projects in the user interface2500, e.g., offset projects that the company was previously participating in.

Referring now toFIG.26, a schematic diagram of a wearable device2600displaying an emissions tracking interface is shown, according to an exemplary embodiment. The wearable2600may, in some embodiments, be the wearable device114ofFIG.1. InFIG.11, the device1100displays an interface2602. The interface2602can display a carbon footprint for a wearer of the wearable device1100for a particular day and/or time period. In this regard, as the user makes decisions, e.g., drives a car, orders meat, etc. and the decisions are provided to the emissions system102, the score of the wearable device2600can update.

Referring now toFIG.27, a flow diagram of a process2700of collecting high-level entity data206for multiple categories for a corpus or group of entities is shown, according to an exemplary embodiment. The emissions system102can be configured to perform the process2700. For example, the process2700can be performed by components of the emissions system102. For example, user emissions questionnaire service110of the emissions system102can be configured to perform the process300. Furthermore, any computing system described herein can be configured to perform the process300.

In step2702, the process2700can include beginning onboarding for a user. The service110can receive a command to start the onboarding from the user device118. A user, via the user device118, could open an application on the user device118for a first time causing the process2700to be executed. The user could select a start element displayed on the user device118to start the process2700.

In step2704, the process2700can include generating data that causes the user device118to display an element asking the user if they work primarily remotely. Responsive to the user selecting yes, the indication of the user working primarily remotely can be saved as the high-level entity data206for the user and the process can proceed to step2710. Responsive to the user responding no via the user device118, the process can proceed to step2706.

In step2706, the process2700can include generating data that causes the user device118to display an element prompting a user to select the modes of transportation that the user uses to commute to work. The user may be presented with options2708on the user device118. The options2708can include a car, bus, subway, bike, motorcycle, train, ferry, walk, carpool, vanpool, etc. The service110can save the selections of modes of transportation as the high-level entity data206for the user. In some embodiments, the service110can cause the user device118to display the user interface2800ofFIG.28. The user interface2800includes elements2802that allow a user to select one or multiple modes of transportation of the options2708. A user can interact with element2804of the user interface2800to confirm the selection made by the user.

In step2710, the process2700can include generating data that causes the user device118to display an element prompting a user to select a diet that best reflects the daily eating habits of the user. The user may be presented with options2718on the user device118. The options2718can include a vegan diet, a vegetarian diet, a pescatarian diet, an omnivore diet, and mostly meat diet, etc. In some embodiments, the service110can cause the user device118to display the user interface2900ofFIG.29. The user interface2900can include an element2902allowing a user to select between the options2718.

In step2712, the process2700can include generating data that causes the user device118to display an element prompting a user to select an indication of how frequently they shop online. The user can be presented with options on the user device118to indicate their shopping habits. The shopping habits could indicate that the user never shops online, sometimes shops online, often shops online, or always shops online. In some embodiments, the service110can cause the user device118to display the user interface3000ofFIG.30. The user interface3000can include elements3004allowing a user to select between the various shopping habits.

In step2714, the process2700can include generating data that causes the user device118to display an element prompting a user to indicate how many square feet their home is. The user can be presented with options on the user device118to select the square footage of their home. For example, the user device can display the user interface3100ofFIG.31which includes elements3102indicating various ranges of square footages. The ranges could be less than 1,000 square feet, 1,000-1,499 square feet, 1,500-1,999 square feet, 2,000-2,499 square feet, 2,500-2,999 square feet, 3,000 or more square feet.

In some embodiments, the user can be presented with options on the user device118to select or provide an input regarding one or more of the following prompts: “how many days per week do you typically work from home?”, “how many hours do you work on a typical work from home day?”, “how many desktop computers do you use?”, “how many laptops do you use?”, and/or “how many external monitors do you use?”. In some embodiments, a user may provide a numerical input, select a numerical input, or otherwise provide a quantity, quantifier, and/or qualifier regarding one or more of the aforementioned example prompts. For example, energy consumption for at-home workers may be calculated by determining the total kWh consumed by, for example, workstation electronic appliances, additional lighting usage, and additional home heating and cooling. The user can be presented with options to select or provide an input regarding measures relating to energy consumption for at-home workers may be calculated by determining the total kWh consumed by, for example, workstation electronic appliances, additional lighting usage, and additional home heating and cooling.

Referring now toFIG.32, a process3200of collecting high-level data for a shopping category for a corpus or group of entities is shown, according to an exemplary embodiment. The emissions system102can be configured to perform the process3200. For example, the process3200can be performed by components of the emissions system102. For example, user emissions questionnaire service110of the emissions system102can be configured to perform the process3200. Furthermore, any computing system described herein can be configured to perform the process3200.

In step3202, the process3200can include beginning a questionnaire for shopping. The service110can receive a command to start the questions from the user device118. A user, via the user device118, could open an application on the user device118for a first time causing the process3200to be executed. The user could select a start element displayed on the user device118to start the process3200.

In step3204, the process3200can include generating data that causes the user device118to display an element prompting a user to indicate how often they shop at a store. A user, via the user device118, can indicate a frequency at which they shop at a store, e.g., never, regularly, all the time, etc. In step3206, the process3200can include generating data the causes the user device118to display tips for reducing their carbon emissions from shopping. In step3208, the process3200can end.

Referring now toFIG.33, a process3300of collecting high-level data for a home category for a corpus or group of entities is shown, according to an exemplary embodiment. The emissions system102can be configured to perform the process3300. For example, the process3300can be performed by components of the emissions system102. For example, user emissions questionnaire service110of the emissions system102can be configured to perform the process3300. Furthermore, any computing system described herein can be configured to perform the process3300.

In step3302, the process3300can include beginning a questionnaire regarding utilities of a residence. In step3304, the process3300can include generating data that causes the user device118to display an element prompting a user to indicate how many people live in their home. For example, the data could cause the user device118to display the user interface3400ofFIG.34. The user interface3400includes an element3402allowing a user to enter the number of people that live at the residence. The user interface3400includes an element3404to confirm the number entered in the element3402.

In step3306, the process3300can include generating data that causes the user device118to display an element prompting a user to indicate a primary heating source for their home. For example, the data could cause the user device118to display the user interface3500ofFIG.35. The user interface3500includes elements3502that allow a user to indicate the heating source for their home. A user can select between a natural gas option, a propane option, an electricity option, a fuel oil option, etc. via the user interface3500.

In step3308, the process3300can include generating data that causes the user device118to display an element prompting a user to indicate a water heater energy source for the home of the user. The element can include various selectable options that a user can select from via the user device118. The options could be natural gas, propane, electricity or various other types of fuel sources. In step3310, the process3300can include generating data that causes the user device118to display an element prompting a user to indicate an energy source for their range or oven for the home of the user. The element can include various selectable options that a user can select from via the user device118. The options could be natural gas, propane, electricity or various other types of fuel sources.

In step3312, the process3300can include generating data that causes the user device118to display an element prompting a user to indicate a dwelling type for their home. The element can include various selectable options that a user can select via the user device118. The options could be single family with a detached garage, single family an attached garage, a mobile home, an apartment of various room numbers, etc.

In step3314, the process3300can include generating data that causes the user device118to display an element prompting a user to indicate whether their home includes air conditioning. In some embodiments, the user device118can display an interface with elements allowing a user to confirm whether or not that residence of the user includes air conditioning.

In step3316, the process3300can include generating data that causes the user device118to display an element providing tips for reducing carbon emissions. The tips can be recommendations for reducing the energy consumption for heating, cooling, or otherwise expending energy in the home of the user. The tips could be recommended heating or cooling setpoints that conserve energy and reduce carbon emissions. The recommendations could be suggestions to open windows or doors on hot days instead of running air conditioning. The recommendations could be suggestions to keep lights off in unused rooms or areas of a home to reduce electricity consumption. In step3318, the process3300can include ending the questionnaire. For example, a conclusion or summary could be displayed on the user device118summarizing the answers provided by the user in the process3300or summarizing predicted carbon emissions associated with the user based on the answers provided by the user.

Referring now toFIGS.36-38, a flow diagram of a process3600of collecting high-level entity data206for a transportation category for a corpus or group of entities is shown, according to an exemplary embodiment. The emissions system102can be configured to perform the process3600. For example, the process3600can be performed by components of the emissions system102. For example, user emissions questionnaire service110of the emissions system102can be configured to perform the process3600. Furthermore, any computing system described herein can be configured to perform the process3600.

In step3602, the process3600can include beginning a questionnaire regarding transportation. In step3604, the process3600can include determining whether a user selected a car as their transportation mode. The service110can determine whether the user selected the car via an element2802of the user interface2800ofFIG.28. Responsive to determining that the user selected the car, the process3600can proceed to steps3606-3614. Responsive to determining that the user did not select the car, the process3600can proceed to step3616.

In step3606, the process3600can include generating data that causes the user device118to display an element prompting the user to indicate the size of the vehicle that they drive. For example, the element can prompt the user to indicate the size of the vehicle that they drive most frequently to work. In some embodiments, the service110can cause the user device118to display the user interface3900ofFIG.39. The user interface3900can include a prompt asking a user what size vehicle they drive. The user interface3900can include elements3902that allow a user to respond to the prompt. Via the elements3902, the user can select between various vehicle sizes. The sizes could be sub-compact, compact, mid-size, or large.

In step3608, the process3600can include generating data that causes the user device118to display an element prompting the user to indicate how many days per week the user drives to work. For example, the element can prompt the user to indicate a number of work days that a user uses their car to drive to work. In some embodiments, the service110can cause the user device118to display the user interface4000ofFIG.40. The user interface4000can include a prompt asking a user how many days per week they drive to work. The user interface4000can include elements4002and4004that allow a user to respond to the prompt. Via the element4002, the user can enter a number of days that the user drives to work. Via the element4004, the user can confirm the input number of days and proceed to the next question.

In step3610, the process3600can include generating data that causes the user device118to display an element prompting the user to indicate how many miles it takes the user to drive to work. For example, the element can prompt the user to indicate the distance (e.g., in miles) that it takes to drive from home to work, from work to home, or for a round trip between work and home. In some embodiments, the service110can cause the user device118to display the user interface4100ofFIG.41. The user interface4100can include a prompt asking a user how many miles per day the user drives to work. The user interface4100can include elements4104,4106, and4108that allow a user to respond to the prompt. Via the element4104, the user can enter the distance. Via the element4106, the user can adjust the units input in the element4104, e.g., switch between miles, kilometers, feet, etc. Via the element4108, the user can confirm the input distance and proceed to the next question.

In step3612, the process3600can include generating data that causes the user device118to display an element prompting the user to indicate what type of fuel their vehicle uses. For example, the element can prompt the user to select between different fuel types for their vehicle. In some embodiments, the service110can cause the user device118to display the user interface4200ofFIG.42. The user interface4200can include a prompt asking a user what type of fuel their vehicle uses. The user interface4200can include elements4202that allow a user to respond to the prompt. Via the elements4202, the user can select the fuel type of their vehicle. The fuel type could be gas, diesel, electric, hybrid, hydrogen, etc. In step3614, the process3600can include generating data that causes the user device118to display an element providing a user with tips for reducing carbon emissions from commuting in their vehicle. The tips could be car-pooling suggestions, vehicle upgrades or replacements, suggestions to work remotely, etc.

In step3616, the process3600can include determining whether a user selected a bus as their transportation mode. The service110can determine whether the user selected the bus via an element2802of the user interface2800ofFIG.28. Responsive to determining that the user selected the bus, the process3600can proceed to the steps3618-3622. Responsive to determining that the user did not select the bus, the process3600can proceed to step3624.

In step3618, the process3600can include generating data that causes the user device118to display an element prompting the user to indicate how many days per week the user rides the bus. The element can prompt the user to indicate the number of work days that the user rides the bus. The element can prompt the user to indicate the number of weekend days that the user rides the bus. In step3620, the process3600can include generating data that causes the user device118to display an element prompting the user to indicate how far the user rides the bus on a typical day. The element can prompt the user to enter an approximate or average distance that the user rides the bus each day. The element can prompt the user to enter the distance in miles, kilometers, feet, etc. In step3622, the process3600can include generating data that causes the user device118to display tips. The tips can be emissions reduction tips, e.g., suggestions for reducing emissions reduction. The tips can recommend bus routes, suggest alternative transportation methods such as a sub-way, a train, walking, or cycling, suggest working remotely, etc.

In step3624, the process3600can include determining whether a user selected a subway as their transportation mode. The service110can determine whether the user selected the subway via an element2802of the user interface2800ofFIG.28. Responsive to determining that the user selected the subway, the process3600can proceed to the steps3626-3630. Responsive to determining that the user did not select the subway, the process3600can proceed to step3632.

In step3626, the process3600can include generating data that causes the user device118to display an element prompting the user to indicate how many days per week the user rides the subway. In step3628, the process3600can include generating data that causes the user device118to display an element prompting the user to indicate how far the user rides the subway on a typical day. In step3630, the process3600can include generating data that causes the user device118to display tips for reducing carbon emissions, e.g., making suggestions to walk to work certain days of the week instead of taking the sub-way, making suggestions to work remotely, etc.

In step3632, the process3600can include determining whether a user selected a bicycle as their transportation mode. The service110can determine whether the user selected the user via an element2802of the user interface2800ofFIG.28. Responsive to determining that the user selected the bicycle, the process3600can proceed to steps3634-3638. Responsive to determining that the user did not select the bicycle, the process3600can proceed to step3640. In step3634, the process3600can include generating data that causes the user device118to display an element prompting a user to indicate how many days per week they ride their bicycle to work. In step3636, the process3600can include generating data that causes the user device118to display an element prompting the user to indicate how far they ride a bicycle on a typical day. In step3638, the process3600can include generating data that causes the user device118to display tips for reducing carbon emissions, e.g., making suggestions to work remotely, etc.

In step3640, the process3600can include determining whether a user selected a motorcycle as their transportation mode. The service110can determine whether the user selected the motorcycle via an element2802of the user interface2800ofFIG.28. Responsive to determining that the user selected the motorcycle, the process can proceed to steps3642-3646. Responsive to determining that the user did not select the motorcycle, the process can proceed to step3648. In step3642, the process3600can include generating data that causes the user device118to display an element prompting the user to indicate how many days per week the user rides a motorcycle to work. In step3644, the process3600can include generating data that causes the user device118to display an element prompting the user to indicate how far they ride a motorcycle on a typical day. In step3646, the process3600can include generating data that causes the user device118to display tips for reducing carbon emissions, e.g., walking to work, cycling to work, making suggestions to work remotely, etc.

In step3648, the process3600can include determining whether a user selected a train as their transportation mode. The service110can determine whether the user selected the train via an element2802of the user interface2800ofFIG.28. Responsive to determining that the user selected the train, the process can proceed to steps3650-3654. Responsive to determining that the user did not select the train, the process can proceed to step3656. In step3650, the process3600can include generating data that causes the user device118to display an element prompting the user to indicate how many days per week the user rides the train to work. In step3652, the process3600can include generating data that causes the user device118to display an element prompting the user to indicate how far they ride the train to work a typical day. In step3654, the process3600can include generating data that causes the user device118to display tips for reducing carbon emissions, e.g., making suggestions to work remotely, ride a bicycle to work, etc.

In step3656, the process3600can include determining whether a user selected a ferry as their transportation mode. The service110can determine whether the user selected the ferry via an element2802of the user interface2800ofFIG.28. Responsive to determining that the user selected the ferry, the process can proceed to steps3658-3662. Responsive to determining that the user did not select the ferry, the process can proceed to step3664. In step3658, the process3600can include generating data that causes the user device118to display an element prompting the user to indicate how many days per week the user takes the ferry to work. In step3660, the process3600can include generating data that causes the user device118to display an element prompting the user to indicate how they ride the ferry on a typical day. In step3662, the process3600can include generating data that causes the user device118to display tips for reducing carbon emissions, e.g., making suggestions to work remotely, etc.

In step3664, the process3600can include determining whether a user selected a walking as their transportation mode. The service110can determine whether the user selected walking via an element2802of the user interface2800ofFIG.28. Responsive to determining that the user selected walking, the process can proceed to steps3666-3670. Responsive to determining that the user did not select walking, the process can proceed to step3672. In step3666, the process3600can include generating data that causes the user device118to display an element prompting the user to indicate how many days per week the user walks to work. In step3668, the process3600can include generating data that causes the user device118to display an element prompting the user to indicate far they walk to work on a typical day. In step3670, the process3600can include generating data that causes the user device118to display tips for reducing carbon emissions, e.g., making suggestions to work remotely, etc.

In step3672, the process3600can include determining whether a user selected carpooling as their transportation mode. The service110can determine whether the user selected carpooling via an element2802of the user interface2800ofFIG.28. Responsive to determining that the user selected carpooling, the process can proceed to steps3674-3680. Responsive to determining that the user did not select carpooling, the process can proceed to step3682. In step3674, the process3600can include generating data that causes the user device118to display an element prompting the user to indicate how many days per week the user carpools to work. In step3676, the process3600can include generating data that causes the user device118to display an element prompting the user to indicate how far they carpool to work on a typical day. In step3678, the process3600can include generating data that causes the user device118to display an element prompting the user to indicate how many other individual the user carpools with. In step3680, the process3600can include generating data that causes the user device118to display tips for reducing carbon emissions, e.g., making suggestions to work remotely, etc. In some embodiments, the service110determine whether a user selected ferry travel, and whether the user indicated that they bring a car on the ferry, the frequency they use a ferry, the distance they ride the ferry, whether they take their transportation device on the ferry. In some embodiments, responsive to a determination that the user indicated that they bring a car on the ferry, the service110may determine whether the ferry is propelled along a route via a biodiesel system, electric system, gasoline system, coal system, steam system, diesel tugboat, cable system, etc.

In some embodiments, a user may selectively modify or adjust the determination made by the service110. For example, the service110may determine a transportation type, diet type, etc., based on the data, and a user may temporarily or permanently adjust the value to reflect a temporary or permanent adjustment in the behavior corresponding to the determination made by the service110. In other words, a user may manually override one or more determinations made by the service110.

In step3682, the process3600can include determining whether a user selected vanpooling as their transportation mode. The service110can determine whether the user selected the vanpooling via an element2802of the user interface2800ofFIG.28. Responsive to determining that the user selected vanpooling, the process can proceed to steps3684-3690. Responsive to determining that the user did not select the vanpooling, the process can proceed to step3692. In step3684, the process3600can include generating data that causes the user device118to display an element prompting the user to indicate how many days per week the user vanpools to work. In step3686, the process3600can include generating data that causes the user device118to display an element prompting the user to indicate how far they vanpool to work on a typical day. In step3688, the process3600can include generating data that causes the user device118to display an element prompting the user to indicate how many other individual the user vanpools with. In step3690, the process3600can include generating data that causes the user device118to display tips for reducing carbon emissions, e.g., making suggestions to work remotely, etc.

In step3692, the process3600can include generating data that causes the user device118to display tips for reducing carbon emissions. For example, the tips can be an aggregation of tips of the steps3614,3622,3630, and3638. The tips displayed via the user device118can be suggestions to switch from one selected mode of transportation to another, e.g., from driving a car to car-pooling, from riding a motorcycle to riding a bicycle, etc. In step3694, the process3600can end.

Referring now toFIG.43, a user interface4300of the providing a user with their carbon footprint is shown, according to an exemplary embodiment. The user interface4300includes a graphic representation4302of the emissions indicators228for a user. The user interface4300can be displayed via the user device118responsive to a user completing a questionnaire. The user interface4300can provide the user with an indication of their carbon emissions in tons per year. In some embodiments, the emissions system102can compare the carbon emissions in tons per year to an average. The average can indicate average carbon emissions for an individual. The user interface4300can include the result of the comparing. For example, the user interface4300could indicate that the emissions of the user is higher than the average, that the emissions of the user is lower than the average, that the emissions is approximately the same as the average.

Referring now toFIG.44, a user interface4400providing a user with their carbon footprint is shown, according to an exemplary embodiment. The user interface4400includes a graphic representation4402of the emissions indicators228for a user. The user interface4300can provide the user with an indication of their carbon emissions in tons per year. A user can interact with element4404to view recommendations for reducing their carbon emissions.

Referring now toFIG.45, a user interface4500prompting a user to provide input for a habit is shown, according to an exemplary embodiment. The user interface4500can be a home screen of a mobile application for a user. The user interface4500can include an element4502that includes a question. The question can prompt a user to confirm whether they followed a habit. The habit can be selected by a user for the emissions system102to track. The user interface4500further includes an element4504prompting the user to complete a profile. The profile can include answer questions of a questionnaire by the user emissions questionnaire service110. The profile can include indicating topics of interest for carbon offsets.

Referring now toFIG.46, a user interface4600prompting a user to add a description of a trip is shown, according to an exemplary embodiment. The trip can be logged and saved in the low-level consumption data226by the emissions system102. A user can interact with an element4602to create a segment for their route. Responsive to interacting with element4602one or multiple times, elements4604and4606representing different segments of a trip can be displayed. A user can indicate a vehicle for each segment via the user interface4700ofFIG.47. The user can select between a car, a bus, a subway, a bicycle, a flight, a train, a ferry, a carpool, or a motorcycle via elements4702of the user interface4700ofFIG.47via elements4702. In the element4604, the user selected a car and the user can enter the distance that they drove in the car. In the element4606the user can select a flight. A user can enter a flight number of their flight and the emissions system102can retrieve a distance of the flight, an origin location, and a destination location. A user can interact with element4608to save the trip constructed in user interface4600.

Referring now toFIG.48is a user interface4800of a summary of the trip constructed in the user interface4700ofFIG.46is shown, according to an exemplary embodiment. The user interface4800can include a total distance traveled in the trip. The user interface4800can include an emissions indicator for the trip. The user interface4800can summarize each segment of the trip, e.g., car ride, flight, train ride, etc. and provide a distance traveled for each segment and a carbon emissions production associated with each segment.

Referring now toFIG.49, a user interface4900including a trip log of trips taken by the user is shown, according to an exemplary embodiment. The trip log of user interface4900can include all trips taken on a certain day (e.g., the day that the user is viewing the user interface4900) or all trips recorded by the user. The user interface4900can allow a user to switch between viewing trips on a certain day or all trips recorded for the user. The user interface4900can include a total distance traveled across all of the trips. The user interface4900can include a representation of each trip. The representation can include a tag indicating whether the trip was a personal trip or a business trip. The representation can include a distance traveled in the trip. The representation can include an indication of the carbon emissions created by the trip.

Referring now toFIG.50, a user interface5000providing topics of interest and a lifestyle of a user is shown, according to an exemplary embodiment. The user interface5000can summarize topics for carbon emissions credits that the user is interested in. In the user interface5000, the user has selected pipeline emissions and biomass emissions as topics of interest. For example, in the user interface5100ofFIG.51, a user can select various topics of interest via elements5102.

Referring now toFIG.52, a user interface5200including emissions indicators a trend of a carbon footprint of a user is shown, according to an exemplary embodiment. The user interface5200includes analytics data for a company. The user interface5200can represent a carbon footprint of a company via element5202and offsets of the company via element5204. The user interface5200includes a trend5206. The trend5206can trend an average employee carbon emissions footprint over time. The user interface5200includes tiles5208that describe various goals for the company. Furthermore, the interface5200includes a section5210that indicates the habits that the employees of the company are participating in. For example, the emissions system102can track what habits employees of the company participate in and present a number of the habits that the employees participate in frequently, e.g., the most frequently. The user interface5200can indicate carbon offset categories that employees are interested in via element5212.

Referring now toFIG.53, a user interface5300including an emissions indicator broken down into multiple categories for a user, according to an exemplary embodiment. The user interface5300includes a footprint5302that indicates a carbon footprint of the user. Furthermore, the user interface5300includes an offsets element5304that indicates offsets associated with the user. The offsets element5304can indicate offsets allocated to the user or offsets purchased by the user. The user interface5300includes a footprint breakdown5306. The footprint breakdown5306can indicate the carbon emissions resulting of the user resulting from various categories. For example, the categories can include transportation, household, commuting, food and diet, and shopping.

Referring now toFIG.54, a user interface5400including a trend element5402of an emissions indicator for a user is shown, according to an exemplary embodiment. In some embodiments, the emissions system102(e.g., the user interface portal218or the mobile application222) can retrieve emissions indicators228for a particular user that is logged into the user interface5400. The emissions indicators228can be retrieved from the data storage216. The emissions system102can plot the emissions indicators228for the user over time and generate a trend line across the plotted points. The emissions system102can plot the emissions indicators228or the trend line in the trend element5402.

Referring now toFIG.55, a user interface5500prompting a user to enter a target goal for an emissions indicator is shown, according to an exemplary embodiment. A user can enter a user level goal to reduce their own emissions production via the user interface5500. The user can enter a company level goal to reduce the emissions production associated with a company via the user interface5500. The user interface5600includes an element5502. The element5502allows for a value to be entered. The value may be entered in units of metric tons of CO2e. Responsive to a user interacting with an element5504of the user interface5500, the target goal can be saved. The emissions system102can plot the target goal as a horizontal line in various user interfaces, e.g., the user interfaces ofFIG.8-17orFIG.54. Responsive to a user interacting with the element5504, the use interface5600ofFIG.56can be displayed. The user interface5600can confirm the entered target goal and include an element5602confirming the entered target goal.

Referring now toFIG.57, a user interface5700including articles regarding habits that a user can select from is shown, according to an exemplary embodiment. In the user interface5700, various articles can be displayed via elements5702. The user can select from various articles via the elements5702to read the article on their user device118. For example, the articles could be articles about a daily meditation, exercising, cating healthy, kayaking, hiking, camping, walking to work, etc.

Referring now toFIG.58, a user interface5800including habits that a user can select from, according to an exemplary embodiment. The user interface5800can display the habits via selectable elements5802allowing a user to select a habit for tracking. Responsive to a user interacting with a habit, the user device118can track the habit, e.g., ask the user questions to determine whether the user is performing activities for the habit or is not performing the activities for the habit. The selectable elements5802can include washing clothes in cold water, showering in under five minutes, bicycling to work, walking to work, using a reusable water bottle, sorting trash, etc.

Referring now toFIG.59, a user interface5900including a button5902for a user to add a habit, according to an exemplary embodiment. The user interface5900can be displayed responsive to a user selecting the shower under five minutes habit via element5802. The user interface5900can provide a description of the habit and tips for adopting and succeeding with the habit. A user can interact with the button5902to begin tracking the habit.

Referring now toFIG.60, a user interface6000including a trend of points earned by a user is shown, according to an exemplary embodiment. The emissions system102can track whether the user performs the activities associated with a habit. The emissions system102can award the user points responsive to the user performing the activities associated with the habit. For example, the user could confirm that they took a shower in under five minutes. The emissions system102could award the user ten points responsive to receiving the confirmation. The emissions system102can store a record of points awards to the user and plot the total points for the user over time. The emissions system102can cause the plot to be shown in element6002. The user interface6000can include a record of points earned by the user in elements6004of the user interface6000. The elements6004can indicate the type of habit, the number of days in a row that the user has performed the habit, and the total number of points earned for each habit by the user. In some embodiments, a user can redeem the points for an award, e.g., a free drink, a certificate, a free lunch, etc.

Referring now toFIG.61, a user interface6100indicating badges earned by a user is shown, according to an exemplary embodiment. The user interface6100includes badges earned by the user. The user can perform various activities via the user device118and the emissions system102can award the user badges based on the activities. General badges can be displayed in the element6102of the user interface6100. The general badges could indicate improvements, number of times logged in, purchase of an offset, completion of a profile, etc. The user interface6100can further include category specific badges, e.g., transportation badges displayed in the element6104of the user interface6100.

Referring now toFIG.62, a user interface6200indicating challenges that a user can select and participate in is shown, according to an exemplary embodiment. The challenges can be a challenge to perform a particular activity over a period of time. For example, the challenge could be walking to work one day per week. The challenge could be carpooling two times a day. The challenge could be eating a salad for lunch every day.

Referring now toFIG.63, a user interface6300allowing a user to purchase and allocate offsets is shown, according to an exemplary embodiment. The user interface6300can allow for the purchase and allocation of offsets on a user level, e.g., for an individual user. The user interface6300can allow a user to purchase offsets for themselves and offset their own carbon emissions footprint for a specific period of time. The user interface6300includes an indication of a carbon footprint for the user that is logged in to the user interface6300in element6302. The user interface6300includes an indication of offsets for the user that is logged in to the user interface6300in the element6304. The user interface6300includes a feed of carbon offset purchases, e.g., element6308and element6310. The carbon offset purchases can indicate an amount of metric tons of carbon offset purchased and can allow a user to view additional details for the carbon offset purchase.

Referring generally toFIGS.64-66, a sustainability data (e.g., ecological sustainability data, green data, eco-friendly data, etc.) management is shown, according to various exemplary embodiments. The system and methods can manage sustainability data for sustainability tracking and enhancement, such as emissions tracking and reduction, water use tracking and reduction, single-use plastic use tracking and reduction, and sustainability activity tracking and incentivizing. A sustainability system can, in some embodiments, collect activity data of a corpus or group of entities (e.g., of a user or group of users, a family, a company, a city, a state, a country, etc.). The activity data can be used to identify sustainability metrics (e.g., emissions production, water-utilization, single-use plastic utilization, etc.) resulting from the activities of the activity data. The sustainability metrics information can be used by the sustainability system to establish a sustainability metric based on, for example: (i) an emissions footprint, e.g., carbon footprint, indicating emissions associated with a particular user or group of users; (ii) water waste, e.g., water footprint, indicating water use associated with a particular user or group of users; (iii) single-use plastics footprint, e.g., plastic footprint, indicating plastic use associated with a particular user or group of users; and/or (iv) offset activities, e.g., footprint credit, indicating participation in or knowledge of sustainable activities, practices, and habits. The data collected can be high-level data. For example, the data can represent general activities, behaviors, or preferences of the entities of the corpus or group of entities.

A sustainability system that collects granular low-level data for every entity of a corpus or group of entities and determines sustainability indicators for multiple sustainability categories based on the granular low-level data (e.g., a sustainability system that collects granular low-level data for every entity of a corpus or group of entities and determines emissions indicators for multiple emissions categories based on the granular low-level data) may encounter various problems. The granular low-level data may directly describe a particular factor, e.g., consumption (e.g., energy consumption, fuel consumption, food consumption, water consumption, etc.). For example, the corpus or group of entities may be very large, e.g., hundreds, thousands, millions, or even billions of entities. Furthermore, the granular data points or data features that could be collected for each entity of the corpus or group of entities to determine sustainability indicators (e.g., emissions indicators, water use indicators, plastic use indicators, etc.) may be even larger. These granular data points can indicate real-time or historical activities of users, specific granular descriptions of commuting routes of the users, granular descriptions of vehicle engine types or fuel efficiencies, etc. The amount of data storage needed to store the granular data points for the corpus or group of entities may be very large. Furthermore, processing and managing this large volume of data can require significant amounts of computational resources (e.g., processor and memory resources) and require significantly long processing times. These long processing times can cause computational resources to be in an operational state causing significant amounts of power to be drawn from a power source. Furthermore, entities of the corpus or group of entities may not wish to provide granular data to the sustainability system for security reasons and therefore collecting the granular data from entities may have challenges.

To solve these, and other technical challenges, the systems and methods discussed herein can manage the large volume of data for the corpus or group of entities in a manner that reduces data storage resources used, reduce processor and memory resources used, reduce an amount of power consumption needed by the computing systems that implement the systems and methods, and allow for sustainability indicators to be generated faster than conventional methods. For example, the sustainability system can collect high-level data for the corpus or group of entities instead of, or in addition to, low-level data. The high-level data can indicate general behaviors, habits, or activities of the corpus or group of entities. The sustainability system can generate sustainability indicators based on the high-level data. However, because the high-level data is less granular, an accuracy of the sustainability indicators could be reduced. In this regard, the sustainability system can implement modeling assumptions that model low-level data based on the collected high-level data. This allows the sustainability system to quickly and efficiently determine sustainability indicators (e.g., emissions indicators, water use indicators, plastic use indicators, etc.) while maintaining a high accuracy for the sustainability indicators.

The sustainability system can further solve technical challenges in the display of sustainability indicators for a large corpus or group of entities. Displaying the causes of, for example, emissions production for a corpus or group of entities may be difficult to summarize since there are a significant amount of possible emissions causes. Likewise, displaying the causes of, for example, water use and/or plastic use may be difficult to summarize because there are a significant amount of possible water use causes and plastic use causes. To solve these, and other technical problems, the sustainability system can generate sustainability indicators based on the collected high-level data and modeling assumptions in multiple categories. The sustainability system can, based on the modeling assumptions and the high-level data for each entity of the corpus or group of entities, generate a sustainability indicator for each entity in each category. The sustainability system can sort the sustainability indicators into buckets of data such that the sustainability data is organized by categorizations corresponding to, for example, sustainability data type (e.g., emissions data type, water use data type, plastic use data type, sustainable practices data type, etc.). The sustainability system can aggregate the sustainability indicators of each bucket into a single sustainability indicator for each category, and aggregate the sustainability indicators of the categories into a single sustainability indicator (e.g., a chief sustainability indicator). The sustainability indicators can, in some embodiments, be timeseries of sustainability indicators, e.g., sustainability indicators, such as emissions indicators, for multiple points in time. In this regard, the sustainability system can generate a set of sustainability indicators for each point in time for a set of points in time for each category, and for each data type. The sustainability system can generate a total sustainability indicator for the corpus or group. The total sustainability indicator can be an aggregate for sustainability indicators of each sustainability data type.

The sustainability system can generate a user interface that displays the total sustainability indicator for the corpus or group of entities. The user interface could be a trend or bar graph, a pie chart (e.g., fractional diagram), a percentage, a numerical value, or other visual representation. The sustainability system can cause the user interface to include a selectable element that allows a user to select between the sustainability data types and/or between the categories (e.g., the sub-categories of each sustainability data type). The user interface can update based on a selection of the user and drill down from the total sustainability indicator to type level sustainability indicators, down to category level emissions indicators, down to entity level emissions indicators. This user interface can allow a user to grasp, within a single interface, the breakdown of sustainability indicators for the large corpus or group of entities which would normally require multiple different types of presentation formats.

Furthermore, the sustainability system can aid a user or group of users to reduce their emissions footprint, water footprint, plastic footprint, and track the performance of the corresponding emissions reduction, water use reduction, or plastic use reduction. The sustainability system can help a user set sustainability goals, for example, carbon footprint goals, e.g., zero emissions goals or near zero emissions goals (e.g., net zero emissions goals, including offsets/investments). The sustainability system can provide projects or carbon offsets (e.g., correction factors, correction units, behavioral credits, indirect ecological behavioral benefit units, sustainable practice rewards) that allow the user or group of users to reduce their carbon footprint, water footprint, plastic footprint, and meet the carbon footprint goals, water footprint goals, and plastic footprint goals, that they have set.

Advantageously, the single sustainability score and adjusted sustainability score can facilitate an improved user interface and data management for a sustainability data for an entity or corpus or group of entities and provides an efficient, effective, and succinct high-level display of a very large quantity of low-level data that is otherwise impossible to display, fit, or otherwise present on a display device having a limited display area. For example, a screen (e.g., liquid crystal display, light emitting diode display, electrophoretic display, backlight display, etc.) having limited dimensions, for example, a screen of a wearable smart watch or other small screen device may not have a resolution, pixel density, available screen area, and/or computing resource supportive of a display of sustainability data. Advantageously, the sustainability system configured to generate sustainability indicators, a single sustainability indicator, and an adjusted sustainability indicator facilitates presentation, management, and control for a very large and very complex volume of sustainability data of an entity or a corpus or group of entities. For example, the sustainability indicators, single sustainability indicator, and adjusted sustainability indicator are configured to be presented to a user in a variety of user interface devices such as displays, wearable devices, residential computing systems, transport devices, and other devices. Unexpectedly, the sustainability indicators, single sustainability indicator, and adjusted sustainability indicator effectuate upstream impacts on the suitability data. For example, the adjusted sustainability indicator can effectuate implementation of sustainability data collection, sustainability data accuracy, and sustainability data standardization.

The systems and methods described with reference toFIGS.64-66relate to managing sustainability data, such as emissions data, water consumption and conservation data, single-use/disposable plastics utilization data, sustainable practices participation data, and other ecological sustainability data. The behavior of individuals and groups of individuals can increase or decrease overall carbon production, water consumption, utilization of single-use/disposable plastics, participation in sustainable practices, and the availability of ecological sustainability data. However, the volume of data points or data features that describe carbon production, water consumption, plastic utilization, and sustainable practices implementation is extremely large and difficult to manage.

Referring now toFIG.64, a block diagram of a system6400including an sustainability system6402tracking and reducing emissions, water use, and plastic use, and increasing participation in sustainable practices, of a user or company is shown, according to an exemplary embodiment. The sustainability system6402can be a computer system (e.g., desktop computer, database system, server system, a cloud computing platform, etc.) that is configured to communicate with a wearable device6414(e.g., smart watch, fitness device, health bracelet, fitness ring, etc.), a user device6418(e.g., personal computing device, portable computing device, cellular telephone, pocketable computing device, etc.), a transport device6420(e.g., automobile computing system, motorcycle computing system, train computing system, ferry computing system, bicycle computing system, etc.), a residential computing system6422(e.g., work from home equipment, desktop computing system, home management system, smart home management system, HVAC system, thermostat, environmental controller, etc.), and/or a cloud platform6426(e.g., a network based computing system, a cloud-based computing service or system, a server system, a data processing system, a remote computing platform, a data processing platform, etc.). The user interfaces and interface elements ofFIGS.4-63can be generated by the sustainability system6402and displayed on display devices (e.g., touch screen displays, light emitting diode (LED) displays, organic LED (OLED) displays, capacitive touch screens, etc.) of the wearable device6414, user device6418, transport device6420, residential computing system6422, and/or the cloud platform6426. Furthermore, the sustainability system6402can receive user input from the user interfaces ofFIGS.4-63via the wearable device6414, user device6418, transport device6420, residential computing system6422, and/or the cloud platform6426.

The wearable device6414can be a smartwatch, a smart ring, smart glasses, a smart necklace, a pacemaker, etc. The wearable device6414can collect data associated with a user's travel, a user's heart rate, a user's blood pressure, etc. The user device6418can be a smartphone, a tablet, a laptop, a desktop computer, a mobile device, etc. The user device6418can include a display device for displaying user interfaces to a user (e.g., a LED screen, an OLED screen, etc.). The user device6418can include input devices for receiving user input. For example, a touch screen, a mouse, a keyboard, etc. The wearable device6414, transport device6420, residential computing system6422, and cloud platform6426can include a similar display device and/or an input device.

A network can be used by the sustainability system6402to communicate with the wearable device6414and/or the user device6418. The network can be a Local Area Network (LAN), a Wide Area Network (WAN), a wireless network, the Internet, a cellular network (e.g., 3G, 4G, 5G), a Bluetooth connection, a Wi-Fi network, and any other type of wired or wireless form of communication. The sustainability system6402can include one or more processors6404and one or more memory devices6406.

The processor(s)6404can be a general purpose or specific purpose processor, an application specific integrated circuit (ASIC), one or more field programmable gate arrays (FPGAs), a group of processing components, or other suitable processing components. The processor(s)6404may be configured to execute computer code and/or instructions stored in the memories or received from other computer readable media (e.g., CDROM, network storage, a remote server, etc.).

The memory device(s)6406can include one or more devices (e.g., memory units, memory devices, storage devices, etc.) for storing data and/or computer code for completing and/or facilitating the various processes described in the present disclosure. The memory device(s)6406can include random access memory (RAM), read-only memory (ROM), hard drive storage, temporary storage, non-volatile memory, flash memory, optical memory, or any other suitable memory for storing software objects and/or computer instructions. The memory device(s)6406can include database components, object code components, script components, or any other type of information structure for supporting the various activities and information structures described in the present disclosure. The memory device(s)6406can be communicably connected to the processor(s)6404and can include computer code for executing (e.g., by the processors) one or more processes described herein.

The sustainability system6402can include a water manager6408, a plastics manager6410, an emissions manager6412, and an engagement manager6416. The water manager6408, the plastics manager6410, the emissions manager6412, and the engagement manager6416can be stored as instructions on the memory devices6406and run by the processors6404. The water manager6408, the plastics manager6410, the emissions manager6412, and the engagement manager6416can be computer code, computing instructions, executables, functions, modules, software applications, etc. The water manager6408, the plastics manager6410, the emissions manager6412, and the engagement manager6416can provide information to the wearable device6414, transport device6420, residential computing system6422, and/or cloud platform6426. Similarly, the wearable device6414, transport device6420, residential computing system6422, and/or cloud platform6426can provide information to the water manager6408, the plastics manager6410, the emissions manager6412, and the engagement manager6416. The water manager6408, the plastics manager6410, the emissions manager6412, and the engagement manager6416, can receive information recorded and/or input by users of the wearable device6414, transport device6420, residential computing system6422, and/or cloud platform6426.

The water manager6408(e.g., water service) can be configured to record water usage information of a company, generate water usage indicators, and generate user interfaces including the water usage indicators for the company. In some embodiments, the water manager6408can generate user interfaces for any other type of group of individuals, e.g., a company, a school, a college, a university, a family, a state, a city, a country, etc.

The water manager6408can include a user water usage questionnaire service configured to provide a user with a series of questions to determine a water footprint of a user, e.g., via the wearable device6414and/or the user device6418. The water questionnaire service can generate the water footprint based on the responses received from the user. The user water usage questionnaire service of the water manager6408can generate user interfaces regarding the water usage information. The user interfaces can be displayed on, for example, the wearable device6414and/or the user device6418. The water manager6408can be configured to record water usage information specific to a user and generate water usage user interfaces for the user. The water manager6408can receive tracking data (e.g., global positioning system data) and/or user input from the wearable device6414, transport device6420, residential computing system6422, and/or cloud platform6426and generate water usage user interfaces based on the recorded data.

The plastics manager6410(e.g., plastics service) can be configured to record plastic usage information of a company, generate plastic usage indicators, and generate user interfaces including the plastic usage indicators for the company. In some embodiments, the plastics manager6410can generate user interfaces for any other type of group of individuals, e.g., a company, a school, a college, a university, a family, a state, a city, a country, etc.

The plastics manager6410can include a user plastic usage questionnaire service configured to provide a user with a series of questions to determine a plastic footprint of a user, e.g., via the wearable device6414and/or the user device6418. The emissions questionnaire service can generate the plastic footprint based on the responses received from the user. The user plastic usage questionnaire service of the plastics manager6410can generate user interfaces regarding the plastic usage information. The user interfaces can be displayed on the wearable device6414and/or the user device6418. The plastics manager6410can be configured to record plastic usage information specific to a user and generate plastic usage user interfaces for the user. The plastics manager6410can receive tracking data (e.g., global positioning system data) and/or user input from the wearable device6414, transport device6420, residential computing system6422, and/or cloud platform6426and generate plastic usage user interfaces based on the recorded data.

The emissions manager6412can be configured to record emissions information of a company, generate emissions indicators, and generate user interfaces including the emissions indicators for the company. In some embodiments, the emissions manager6412can generate user interfaces for any other type of group of individuals, e.g., a company, a school, a college, a university, a family, a state, a city, a country, etc. In some embodiments, the emissions manager6412includes some or all of the features of the emissions service108, user emissions questionnaire service110, and user emissions service112. In some embodiments, the emissions service108includes some or all of the features described with respect to the emissions manager6412.

The emissions manager6412can include a user emissions questionnaire service configured to provide a user with a series of questions to determine a carbon footprint of a user, e.g., via the wearable device6414and/or the user device6418. The emissions questionnaire service can generate the carbon footprint based on the responses received from the user. The user emissions questionnaire service of the emissions manager6412can generate the user interfaces ofFIGS.27-42. The user interfaces ofFIGS.27-42can be displayed on the wearable device6414and/or the user device6418. The emissions manager6412can be configured to record emission information specific to a user and generate emissions user interfaces for the user. The emissions manager6412can receive tracking data (e.g., global positioning system data) and/or user input from the wearable device6414, transport device6420, residential computing system6422, and/or cloud platform6426and generate the emissions user interfaces based on the recorded data.

The engagement manager6416can be configured to record sustainable practices engagement information of a company, generate engagement indicators, and generate user interfaces including the engagement indicators for the company. In some embodiments, the engagement manager6416can generate user interfaces for any other type of group of individuals, e.g., a company, a school, a college, a university, a family, a state, a city, a country, etc.

The engagement manager6416can include a user engagement questionnaire service configured to provide a user with a series of questions to determine engagement in one or more sustainable practices that counteract, lessen, or mitigate one or more factors contributing to at least one of a carbon footprint of a user, a water footprint of a user, or a plastics footprint of a user, e.g., via the wearable device6414and/or the user device6418. The engagement questionnaire service can generate an engagement metric based on the responses received from the user and/or based on a measure of user interaction with the sustainability system6402(e.g., inputs, data source connections, frequency of use, quality of inputs, consistency of inputs, time logged into a profile or portal, quantity or quality of attempted sustainable activities, number of views of one or more user interfaces, etc.). The user engagement questionnaire service of the engagement manager6416can generate the user interfaces. The user interfaces can be displayed on, for example, the wearable device6414and/or the user device6418. The engagement manager6416can be configured to record engagement information specific to a user and generate engagement user interfaces for the user. The engagement manager6416can receive tracking data (e.g., global positioning system data) and/or user input from the wearable device6414, transport device6420, residential computing system6422, and/or cloud platform6426and generate the emissions user interfaces based on the recorded data.

In some embodiments, the sustainability system6402includes a habits manager6430, a habits database6432, a home improvement manager6434, a home improvement database6436, a residential data manager6438, a residential data database6440, a footprint manager6442, a footprint database6444, a team events manager6446, a team events database6448, a drought data manager6450, a drought database6452, and a location manager6454. The habits manager6430, the habits database6432, the home improvement manager6434, the home improvement database6436, the residential data manager6438, the residential data database6440, the footprint manager6442, the footprint database6444, the team events manager6446, the team events database6448, the drought data manager6450, the drought database6452, and/or a location manager6454can be stored as instructions on the memory devices6406and run by the processors6404. The habits manager6430, the habits database6432, the home improvement manager6434, the home improvement database6436, the residential data manager6438, the residential data database6440, the footprint manager6442, the footprint database6444, the team events manager6446, the team events database6448, the drought data manager6450, the drought database6452, and/or a location manager6454can provide information to the wearable device6414, transport device6420, residential computing system6422, and/or cloud platform6426. Similarly, the wearable device6414, transport device6420, residential computing system6422, and/or cloud platform6426can provide information to the habits manager6430, the habits database6432, the home improvement manager6434, the home improvement database6436, the residential data manager6438, the residential data database6440, the footprint manager6442, the footprint database6444, the team events manager6446, the team events database6448, the drought data manager6450, the drought database6452, and/or a location manager6454. The habits manager6430, the habits database6432, the home improvement manager6434, the home improvement database6436, the residential data manager6438, the residential data database6440, the footprint manager6442, the footprint database6444, the team events manager6446, the team events database6448, the drought data manager6450, the drought database6452, and/or a location manager6454, can receive information recorded and/or input by users of the wearable device6414, transport device6420, residential computing system6422, and/or cloud platform6426.

In some embodiments, the habits database6432may store and/or maintain data regarding activities of a company, user(s), group of users, etc. The habits manager6430may collect, distribute, adjust, categorize, modify, and/or convert some or all of the data stored in the habits database6432. For example, the habits manager6430may generate the user interface shown inFIG.45and data corresponding to an input received based on the user interface may be stored in the habits database6432. For example, the habits database may include data regarding habits corresponding to prompts such as “Wash your clothes with cold water,” “Turn off the water when brushing your teeth,” “Take a 5 minute shower,” “Grow a vegetable garden or join a local community garden!”, “Compost your food waste,” “Use a Reusable water bottle,” “Use reusable shopping bags,” “Use reusable storage containers for leftover food,” “Use a reusable coffee cup when buying coffee from a café,” “Use a reusable straw,” “Sort your trash (i.e. recycle),” “Buy Local Produce from a farmers market or local choices at a grocery store),” “Buy Produce from Local Farms,” “Check Pantry Before Shopping (consumer food waste),” “Air dry your clothes,” “Close/open shades,” “Eat Less Beef,” “Eat No Beef,” “Eat Less Meat,” “Eat No Meat/seafood,” “Eat Less Dairy,” “Eat No Dairy,” “Bike to Work,” “Walk to Work,” “Take bus to work,” “Take train to work,” and/or “Take subway/light rail to work.” Each habit within the habits database6432may have one or more data attributes such as a static, dynamic, calculated, or predetermined, offset value for one or more of the sustainability data types for satisfying a criteria indicative of doing a habit (e.g., based on an input that indicates the user exhibits the habit). For example, each habit may be associated with metrics such as frequency, emissions reduction per habit occurrence, plastic reduction per habit occurrence, water reduction per habit occurrence, and a metric of the relative difficulty of the habit (e.g., habit points, a nonzero integer value, etc.).

In some embodiments, the home improvement database6436may store and/or maintain home improvement data regarding activities of a company, user(s), group of users, etc. The home improvement manager6434may collect, distribute, adjust, categorize, modify, and/or convert some or all of the data stored in the home improvement database6436. For example, the home improvement database6436may include data regarding home improvements corresponding to prompts such as “Install a smart thermostat,” “Improve insulation and air-scaling in attic, crawl spaces, walls, and windows,” “Upgrade your windows to energy star certified windows,” “Upgrade to Energy Star Appliances,” “Implement Solar Energy Methods,” and/or “Implement a new renewable energy method.” Each improvement within the home improvement database6436may have one or more data attributes such as a static, dynamic, calculated, or predetermined, offset value for one or more of the sustainability data types for satisfying a criteria indicative of an improvement (e.g., based on an input indicating their residence exhibits the improvement). For example, each improvement may be associated with a metric such an emissions reduction per improvement occurrence.

In some embodiments, the residential data database6440may store and/or maintain data regarding activities of a company, user(s), group of users, etc. The residential data manager6438may collect, distribute, adjust, categorize, modify, and/or convert some or all of the data stored in the residential data database6440. For example, the residential data database6440may include residential data objects (e.g., containers, classes, buckets, etc.) regarding information such as, a location-based climate categorization, a dwelling type, residential occupants (e.g., number of occupants living in a home or building), size of the residence (e.g., square footage, number of floors, etc.), heating system (e.g., electric heating system, natural gas heating system, etc.), and/or appliance information (e.g., water heater type, stove fuel type, gas stove, electric stove, etc.).

In some embodiments, the footprint database6444may store and/or maintain data regarding activities of a company, user(s), group of users, etc. The footprint manager6442may collect, distribute, adjust, categorize, modify, and/or convert some or all of the data stored in the footprint database6444. For example, the footprint database6444may include a conversion table for converting between, for example: a diet type (e.g., omnivore, vegetarian, etc.) and an emissions footprint quantity; a car size and/or fuel type (e.g., Motorcycle Gas, Sub-Compact Gas, Sub-Compact Diesel, Sub-Compact Hybrid, Sub-Compact Electric, Compact Gas, Compact Diesel, Compact Hybrid, Compact Electric, Mid-Size Gas, Mid-Size Diesel, Mid-Size Hybrid, Mid-Size Electric, Large Gas, Large Diesel, Large Hybrid, Large Electric, Large Van Gas, etc.) and a corresponding emissions footprint quantity (e.g., on a per unit fuel basis, on a per mile traveled basis); a transportation method (e.g., bus, train, subway, walk, bicycle, etc.) and a corresponding emission footprint value, a shopping metric and a corresponding emission footprint quantity; and/or an appliance type (e.g., desktop, laptop, monitor, etc.) and a corresponding emission footprint quantity.

In some embodiments, the team events database6448may store and/or maintain data regarding activities of a company, user(s), group of users, etc. The team events manager6446may collect, distribute, adjust, categorize, modify, and/or convert some or all of the data stored in the team events database6448. For example, the team events database6448may include multiple team events available to the company or a subset of the company. For example, the team events stored in the team events database6448may include one or more criteria for selection, team event rules (e.g., contest rules, qualifiers, etc.) and team event durations (e.g., contest durations), and team event rewards (e.g., points awarded to the winner(s), etc.). For example, a team event may include an event having a team event duration of 5 weeks, a team event reward of 500 units, and is based on satisfying one or more criteria (e.g., achieve a threshold number of habit occurrences in the shortest duration of time, or, have the highest number of habit occurrences during the team event duration, etc.).

In some embodiments, the drought database6452may store and/or maintain data regarding droughts (e.g., droughts attributed to a lack of rainfall or other causes). The drought data manager6450may collect, distribute, adjust, categorize, modify, and/or convert some or all of the data stored in the drought database6452. For example, the drought data stored in the drought database6452can include a plurality of locations and an associated drought metric (e.g., a drought index value). In some embodiments, the drought data manager6450is configured to convert a drought index value into a value or correction factor for adjusting the water footprint and/or related offsets (e.g., offset emission quantities associated with a habit stored within the habits database6432).

In some embodiments, the habits database6432, home improvement database6436, residential data database6440, footprint database6444, team events database6448, or drought database6452are at least partially network based or updated (e.g., periodically or continuously) based on an updated dataset. For example, the drought data manager6450may communicate with a network-based (e.g., internet-based) public or proprietary data management system (e.g., via interaction with an application programing interface) or other interface of a system maintaining a source dataset in order to refresh, update, merge, or sync the data stored in the drought database6452with the dataset.

In some embodiments, the habits database6432, home improvement database6436, residential data database6440, footprint database6444, team events database6448, and/or drought database6452are at least partially network based and/or updated (e.g., periodically or continuously) based on an updated dataset over a network.

In some embodiments, sustainability system6402includes a scoring engine6460, a scoring database6462, and/or a ranking engine6464. The scoring engine6460, scoring database6462, and ranking engine6464can be stored as instructions on the memory devices6406and run by the processors6404. The scoring engine6460, scoring database6462, and ranking engine6464can provide information to the wearable device6414, transport device6420, residential computing system6422, and/or cloud platform6426. Similarly, the wearable device6414, transport device6420, residential computing system6422, and/or cloud platform6426can provide information to the scoring engine6460, scoring database6462, and ranking engine6464. The scoring engine6460, scoring database6462, and ranking engine6464can receive information recorded and/or input by the users of the wearable device6414, transport device6420, residential computing system6422, and/or cloud platform6426.

In some embodiments, the scoring engine6460is configured receive one or more sustainability indicators and output an aggregated and/or adjusted (e.g., standardized) sustainability indicator (e.g., a standardized sustainability score). For example, the scoring engine6460may aggregate sustainability indications from the water manager6408, plastics manager6410, emissions manager6412, engagement manager6416, and/or location information from the location manager6454and determine a sustainability indication (e.g., a sustainability score). In some embodiments, the sustainability score is a function of a water use footprint indication, a plastics use footprint indication, an emissions footprint indication, an engagement indication, and a location indication. In some embodiments, the range of sustainability scores output by the scoring engine6460is bounded (e.g., kept within one or more threshold values). In some embodiments, the range of sustainability scores varies by location (e.g., by state, by region, by county, etc.). In some embodiments, the scoring engine6460is configured to query the scoring database6462for a sustainability score limit by using location data as a query key. In some embodiments, the scoring engine6460may present the sustainability score via a user interface of one or more of the wearable device6414, user device6418, transport device6420, residential computing system6422, and/or the cloud platform6426.

In some embodiments, the ranking engine6466is configured receive one or more sustainability scores, generate ranking information for each of the one or more sustainability scores, and output ranking information regarding the one or more sustainability scores. For example, the ranking engine6466may be configured to categorize, list, or rank the one or more sustainability scores output by the scoring engine6460. In some embodiments, the ranking engine6466is configured to rank and/or categorize the sustainability score. In some embodiments, the ranking engine6466is configured to categorize the sustainability score and one or more factors of the sustainability score. For example, the ranking engine6466may generate ranking information for the water use footprint indication (e.g., water footprint score), the plastics footprint indication (e.g., plastics footprint score), and engagement indication (e.g., engagement score). For example, the ranking engine6466may categorize the sustainability score by comparing the sustainability score to a set of sustainability scores. For example, the ranking engine6466may categorize the sustainability score by comparing the sustainability score to one or more statistical measures of the set of sustainability scores. As another example, the ranking engine6466may categorize the sustainability score by comparing the sustainability score to the average score of the set of sustainability scores, and/or by determining standard deviation, and/or a percentile. In some embodiments, the ranking engine6466may present the ranking data via a user interface of one or more of the wearable device6414, user device6418, transport device6420, residential computing system6422, and/or the cloud platform6426.

In some embodiments, the sustainability system6402, or various components of the sustainability system6402, can generate data that causes the wearable device6414, transport device6420, residential computing system6422, and/or cloud platform6426to display the interfaces described with references atFIGS.4-63.

Referring now toFIG.65, the sustainability system6402generating sustainability indicators6550from high-level entity data6506based on modeling assumptions6504is shown, according to an exemplary embodiment. The sustainability system6402includes a modeler6510. The modeler6510can model high-level entity data6506with modeling assumptions6504to generate low-level sustainability data6514via an engine6512. In some embodiments, the low-level sustainability data6514includes low-level consumption data6516, low-level engagement data6518, low-level plastics data6520, and low-level water data6522. The modeling assumptions6504can indicate low-level sustainability data6514that results from certain high-level entity data6506. The modeling assumptions6504can be global modeling assumptions or customer specific assumptions. For example, the high-level entity data6506could describe general characteristics or behaviors of a an entity (e.g., a user, a group of users, a family, etc.), for example, commuting characteristics, residential heating, cooling, or electrical consumption, eating tendencies, etc. In some embodiments, one or more of the characteristics or behaviors of an entity can have a first characteristic corresponding to working from home, and a second characteristic corresponding to not working from home (e.g., commuting to an office). For example, the commuting characteristics, residential heating, cooling, electrical consumption, cating tendencies, etc., can have a first a work from home commuting characteristic may be different than a non-work from home commuting characteristic. The high-level entity data6506can be non-specific, e.g., the high-level entity data6506could indicate an cating preference (e.g., meat, vegan, vegetarian, pescatarian, etc.). Similarly, the high-level entity data6506could indicate characteristics of a vehicle of the user or commute of the user, e.g., a size (e.g., small, medium, or large) of the vehicle and a fuel type of the vehicle (e.g., gas, electric, hydrogen, etc.).

The modeling assumptions6504can model the low-level consumption data6516with the high-level entity data6506. For example, the modeling assumptions6504can indicate expected consumption levels for a vehicle of a particular size (e.g., small, medium, or large). The modeling assumptions6504can indicate expected food consumption levels of eating habits (e.g., meat, vegan, vegetarian, pescatarian, etc.). The modeling assumptions6504can indicate expected low-level consumption data6516of shopping habits, e.g., amount of merchandise purchased that result from in-person shopping, online shopping, etc. The modeling assumptions6504can indicate expected low-level consumption data6516that results from certain types of HVAC equipment for certain sizes of a home, e.g., certain run times, energy consumptions, fuel consumptions, etc. The modeling assumptions6504can be region specific, in some embodiments. For example, different geographic regions may have different weather patterns and residential homes in different geographic regions can consume various amounts of energy based on their location, e.g., extreme hot or cold climates can cause HVAC equipment to consume more energy than mild or temperate climates. In some embodiments, the modeling assumptions204can indicate low-level consumption data226of a work from home setup. For example, the modeling assumptions204may indicate an expected low-level consumption data226, based on an indication regarding the frequency of working from home (e.g., days/week, days/month, etc.), a work from home duration (e.g., hours/day, etc.), the number of computers (e.g., desktop computers, laptop computers) utilized while working from home, the number of external monitors (e.g., external displays, screens, graphical display devices) utilized while working from home, and other remote work quantifiers. In some embodiments, the modeling assumptions204may indicate an expected low-level consumption data226of additional home heating and cooling emissions, workstation electronics that are added onto existing residential footprint, etc. In some embodiments, the modeling assumptions204may be based on temporary or lasting statuses of societal or regional health emergencies and/or government orders (e.g., stay-at-home orders). For example, different geographic regions may have different restrictions on commuting and social engagements, which can cause the amount of energy consumed due to working from home or by a building to vary. For example, additional heating and cooling emissions may be based on increases or decreases in residential energy consumption. For example, the modeling assumptions204may produce low-level consumption data226based on data associated with pre-pandemic circumstances, ongoing-pandemic circumstances, or post-pandemic circumstances.

In some embodiments, shopping habits are based on an indication regarding the size of a household, a household income, an average household spending, an average household spending per category (e.g., online shopping, groceries, etc.), household spending on recycled or pre-owned items. In some embodiments, a user can specify their annual average expenditure in a number of shopping categories, which are then multiplied by emission conversion factors. In some embodiments, the user is asked to estimate how many items in their estimates are thrifted or reused from previous owners. In some embodiments, their emissions in the corresponding category will be reduced by the percentage/option they select. In some embodiments, the emissions corresponding to the shopping category is offset based on a determination that one or more purchases correspond to thrifted goods.

The modeler6510can receive telemetry data from a telemetry data source6508. The telemetry data source6508can be data storage element (e.g., a database) that collects data of devices. The devices could be a vehicle telematics system, airline flight data, a wearable, a smart thermostat, etc. The telemetry data source6508could be an Internet of Things (IoT) event hub. The telemetry data source6508could establish a communication connection with an edge device and collect telemetry data from the edge device via the communication connection. The communication connection could be established via a network, e.g., the Internet, a cellular network, MQ Telemetry Transport (MQTT), etc. The network could be the network described with reference toFIG.64. The devices could be smartphones (e.g., the user device6418), vehicle systems (e.g., the transport device6420), health tracking devices (e.g., the wearable device6414), a smart home device (e.g., a residential computing system6422), etc. In addition to the high-level entity data6506, the modeler6510can model the low-level sustainability data6514based on the telemetry data of the telemetry data source6508. While the high-level entity data6506might indicate that a user commutes on average five times a week in a compact vehicle, the telemetry data could be telemetry data received from a telematics system of the vehicle that indicates specific fuel efficiencies, distances traveled, etc. In some embodiments, the telemetry data is telemetry data received from an expense report, a travel itinerary, a records from a customer loyalty platform such as a retailers loyalty membership or a grocery store membership.

In some embodiments, a user can provide low-level sustainability data6514to the sustainability system6402via the user device6418via a questionnaire. The questionnaire can collect one, tens, or hundreds of attributes that describe a sustainability profile (e.g., a consumption profile, a water use profile, a plastic use profile, an engagement profile, etc.) of the user. Via the modeling assumptions6504, the modeler6510and the engine6512can generate the low-level sustainability data6514for the user based on the profile built for the user. The modeler6510can identify attribute values that are appropriate for each user. The attribute values can be modeling assumptions6504. The modeler6510can analyze the profile built for an individual and select the modeling assumptions6504most appropriate for the individual. For example, if the individual rides the bus to work and cats meat, the modeler6510can select modeling assumptions6504that model consumption for riding the bus for a commuting category and eating meat for an eating category.

In some embodiments, the modeling assumptions6504can be modified for individual entities of a corpus or group of entities. For example, if a user knows the average annual temperature, the average summer temperature, or the average winter temperature for their geographic region, the user could provide this information to the sustainability system6402via the user device6418. The sustainability system6402could set the modeling assumptions6504for energy consumption to heat or cool the home of the user based on the average temperatures provided by the user. These specific details entered by one user could be used by the modeler6510for another user. For example, if a first user provides temperature data for a geographic region of the user, the modeler6510could identify that a second user is in the same geographic region. The modeler6510could select the modeling assumptions6504for the second user to be the same as the modeling assumptions6504determined to be used for the first user based on the temperature data provided by the first user for the geographic region. Similarly, the user could provide an average monthly bill for energy for their home to the sustainability system6402.

In some embodiments, for a corpus or group of entities, the telemetry data source6508can sort and organize telemetry data for various entities of the corpus or group of entities. For example, the telemetry data source6508can store an indication of each entity of the corpus or group of entities and store relationships between each entity and the telemetry devices of each entity. In this regard, the telemetry data source6508can sort, filter, or tag data based on the relationships between the entities and the edge devices.

The modeler6510can, in some embodiments, execute machine learning and/or an artificial intelligence algorithm to tune the modeling assumptions6504. For example, because the telemetry data of the telemetry data source6508is granular and specific to the activities of an entity, the low-level sustainability data6514that is generated from the telemetry data can be highly accurate. The modeler6510can execute the machine learning and/or artificial intelligence algorithm based on the telemetry data to learn modeling assumptions6504. This allows the sustainability system6402to collect a small amount of telemetry data for a small portion of entities of the corpus or group of entities and utilize the learned modeling assumptions6504to make accurate determinations of the low-level sustainability data6514for entities of the corpus or group of entities that do not have telemetry data. In this regard, data storage reductions, processing resource reductions, processing speed improvements can be realized. For example, instead of storing and processing telemetry data for an entire corpus or group of entities, the sustainability system6402may only store and process telemetry data for a small portion of the corpus or group of entities. Instead of storing telemetry data for the other entities of the corpus or group of entities (which would require a large amount of storage resources) or perform a lengthy and resource intensive processing of the telemetry for the other entities, the modeler6510can model the low-level sustainability data6514with the high-level entity data6506and the learned modeling assumptions6504.

For example, the telemetry can be collected from sensors within equipment that gather actual consumption data related to a carbon emissions calculation or score. For example, the system can receive the communication from an embedded sensor within a device and act upon the information received to determine if the reading is outside of the normal range for that user. For example, telemetry data can be collected for trips or roundtrips of users to determine actual carbon emissions data.

The engine6512can generate the low-level sustainability data6514based on the output of the modeler6510. The low-level consumption data6516can be consumption values, the low-level engagement data6518can be engagement values, the low-level plastics data6520can be plastic usage values, the low-level water data6522can be water usage values, all of which having values in one or multiple categories (e.g., as sub-categories of each data type). For example, for the low-level consumption data6516, the categories could be commuting to work, commuting home from work, residential heating, residential cooling, residential electric consumption, food consumption, additional residential energy consumption attributable to working remotely (e.g., computing devices energy consumption, communications network system energy consumption, desktop computer energy consumption, computer monitor energy consumption, heating, ventilating and air conditioning energy consumption, lighting energy consumption, etc. The low-level sustainability data6514could be generated by the engine6512for one or multiple times. For example, the low-level sustainability data6514could be generated to indicate the consumption value of each entity of a corpus or group of entities in each category on a daily, weekly, bi-weekly, monthly, or yearly basis. For example, the low-level consumption data6516could indicate an amount of fuel consumed to commute to work on a particular day, a number of bus rides taken, a number of train rides taken, a length of time that a vehicle charged, an amount of energy consumed to heat or cool a building, an amount of meat, fish, vegetables, or grains consumed, etc.

The emissions identifier6524can generate the emission indicator6552based on the low-level consumption data6516. The emissions identifier6524can generate the emissions indicator6552by determining an amount of emissions, e.g., carbon dioxide (CO2) or carbon dioxide equivalent (CO2e) that results from each particular consumption value of the low-level consumption data6516. The emissions identifier6524can generate an emissions indicator6552for each entity of a corpus or group of entities. The emissions identifier6524can sort the emissions indicators6552into buckets based on the category of the emissions indicators6552. For example, a commuting related emissions indicators6552for the corpus or group of entities could be sorted into a commuting bucket. All shopping related emission indicators6552can be sorted into a shopping bucket. In some embodiments, the emissions identifier6524is configured to sort the emissions indicators6552into a residential bucket, a dietary bucket, a transportation bucket, and a shopping bucket. In some embodiments, the residential bucket includes emissions attributed to working from home. For example, the residential bucket can include emissions attributed to workstation electronics (e.g., computer monitors, laptops, and desktop computers), heating, cooling, ventilating, and air conditioning energy use, lighting energy use, and creature comfort energy use (e.g., speaker systems, massage chairs, etc.).

In some embodiments, the emissions manager6412determines a value regarding work from home energy usage attributed to a workstation. For example, energy consumption for at-home workers can be calculated by determining the total kWh consumed by the at-home workers. For example, energy consumption for at-home workers can include energy consumption of workstation electronic appliances, additional lighting usage, and additional home heating and cooling. In some embodiments, work for home energy consumption is a function of the workstation utilized implemented in a work-from-home setup. For example, energy consumption for at-home workers can be calculated according to the equation illustrated below:

where “Ews” is the energy consumed by a workstation (e.g., in kilowatt-hours), “Emonitor” is the energy consumed by a workstation (Newton's notation, i.e., dot notation, indicating differentiation with respect to time), “nmonitor” is the quantity of monitors, “Elaptop” is the energy consumed by a laptop, “Edesktop” is the energy consumed by a desktop, and “ntime” is a duration of time during which the workstation is utilized. In some embodiments, time-varying values of energy consumption of the workstation is retrieved from the workstation. In some embodiments, a predetermined value and/or static value is used to estimate the workstation's energy usage.

In some embodiments, the emissions manager6412determines a value regarding work from home energy usage attributed to lighting. For example, when people work from home, there is generally an increase in home lighting energy consumption. In some embodiments, the lumens required to light an average home office space is calculated or retrieved and then matched to an LED kWh value using a linear regression equation for LED watts vs lumens. In some embodiments, the work from home energy value can be calculated according to the equation illustrated below:

where “Elighting” is the energy consumed by lighting (e.g., in kilowatt-hours), “CWFM” is a scalar representing energy consumption per unit time, and “ntime” is a duration of time during which the lighting is utilized (e.g., the work-from-home duration). In some embodiments, time-varying values of energy consumption of the lighting is retrieved from the residential computing system6422. In some embodiments, a predetermined value and/or static value is used to estimate the lighting energy usage. For example, the scalar, “CWFM” may be or be based on an average energy use per unit time of a large set of similar or different entities.

In some embodiments, the emissions manager6412determines a weather-dependent value regarding work from home energy usage attributed to a workstation and/or lighting. For example, the weather dependent energy consumption can be calculated by subtracting the baseline non-weather dependent consumption values from the daily average totals in each month. These values can then be standardized for differences in weather using heating and cooling degree days. The average percent change between these the two periods can be calculated for winter months and summer months in order to calculate the percent change in electricity consumption for home heating and cooling, respectively. Across the US, summer months usually have a low number of heating degree days, and winter months have a low number of cooling degree days, which means the total weather-based consumption in those months can be attributed to either heating or cooling. Summer and winter months can be selected on a state-by-state basis to incorporate differences in climate. Several states had significant decreases in residential electricity sales data during covid as a result of increased vacancy, decreased tourism, or other covid related factors. In some embodiments, the weather dependent energy consumption for work from home can be calculated according to the equation illustrated below:

where “EWFM” is the heating and cooling energy consumption, “Ecooling” is a baseline energy consumed for cooling (e.g., an average energy consumed), “%cooling” is a weather-based cooling energy adjustment, “Eheating” is a baseline energy consumed for heating (e.g., an average energy consumed), “%heating” is a weather based heating energy adjustment.

In some embodiments, the emissions manager6412determines a value regarding a total energy use for working from home. For example, the total energy use may be according to the equation illustrated below:

where “EWFM,Tot” is the total work from home energy consumption, “EWFM” is the heating and cooling energy consumption, “Elighting” is the lighting energy consumption, and “Eworkstation” is the workstation energy consumption.

In some embodiments, the emissions identifier6524aggregates the emissions indicators6552in each of the buckets to generate an emissions indicator for each category. For example, for a corpus or group of entities, the emission identifier6524could aggregate (e.g., sum, average, weight, etc.) the emissions indicators of each category into a single category emissions indicator6552. Furthermore, the emissions identifier6524can aggregate (e.g., sum, average, weight, etc.) the emissions indicators6552of each category into a total emissions indicator6552for the corpus or group of entities. The individual emissions indicators6552for each category, the category level emissions indicators6552, and the total emissions indicator can be time correlated data (e.g., timeseries data). For example, each emissions indicator6552could be a series of emissions values for points in time, e.g., for days, weeks, months, years. The emissions identifier6524can store trends of the emissions indicator6552and update each trend as new emissions indicators6552are generated.

In some embodiments, the emissions identifier6524determines whether a user selected ferry travel, and whether the user indicated that they bring a car on the ferry, the frequency they use a ferry, the distance they ride the ferry, whether they take their transportation device on the ferry. In some embodiments, responsive to a determination that the user indicated that they bring a car on the ferry, the emissions identifier6524may determine whether the ferry is propelled along a route via a biodiesel system, electric system, gasoline system, coal system, steam system, diesel tugboat, cable system, etc.

In some embodiments, a user may selectively modify or adjust the determination made by the emissions identifier6524. For example, the emissions identifier6524may determine a transportation type, transportation distance, commute distance, etc., based on low-level data or telemetry data, and a user may temporarily or permanently adjust the value to reflect a temporary or permanent adjustment corresponding to the determination made by emissions identifier6524. In other words, a user may manually override one or more determinations made by the emissions identifier6524.

The emissions identifier6524can determine lifecycle emissions, in some embodiments. The emissions indicators6552can include lifecycle emissions indicators. Lifecycle emissions can attribute carbon emissions back to the source of the original energy that is being consumed in a downstream activity. For example, the emissions identifier6524can determine carbon emission from the generation of electric power at a plant flowing into a residential home. If the power plant sources energy from 50% nuclear and 50% coal, the emissions identifier6524can determine emissions indicators that accurately reflect not only the emission from the use of appliances in a home, but the emission associated with the actual generation of power via coal and nuclear production.

The engagement identifier6526can generate the engagement indicator6554based on the low-level engagement data6518. The engagement identifier6526can generate the engagement indicator6554by determining an amount of engagement, e.g., user onboarding, user participation, user interaction with the sustainability system interfaces, etc. that results from each particular engagement value of the low-level engagement data6518. The engagement identifier6526can generate an engagement indicator6554for each entity of a corpus or group of entities. The engagement identifier6526can sort the engagement indicators6554into buckets based on the category of the engagement indicators6554. For example, an article-reading related indicator6554for the corpus or group of entities could be sorted into an article-reading bucket. All login related engagement indicators6554can be sorted into a logins bucket.

In some embodiments, the engagement indicator6554accounts for a user's engagement with the sustainability system, and determines a value representing their willingness to learn more about sustainability and environmentally friendly habits/practices (e.g., by viewing sustainability system content such as articles, events, games, etc.). In some embodiments, the user receives a point logging in to an account. In some embodiments, there is a limit on the number of points a user can receive for logging into an account over a period of time. In some embodiments, the engagement indicator6554accounts for a time, duration, frequency, communication, and or promotional activity (e.g., sharing an internet link with an entity having a smaller engagement indicator than another entity).

In some embodiments, the engagement identifier6526aggregates the engagement indicators6554in each of the buckets to generate an engagement indicator for each category. For example, for a corpus or group of entities, the engagement identifier6526could aggregate (e.g., sum, average, weight, etc.) the engagement indicators6554of each category into a single category engagement indicator6554. Furthermore, the engagement identifier6526can aggregate (e.g., sum, average, weight, etc.) the engagement indicators6554of each category into a total engagement indicator6554for the corpus or group of entities. The individual engagement indicators6554for each category, the category level engagement indicators6554, and the total engagement indicator can be time correlated data (e.g., timeseries data). For example, each engagement indicator6554could be a series of engagement values for points in time, e.g., for days, weeks, months, years. The engagement identifier6526can store trends of the engagement and update each trend as new engagement indicators6554are generated.

The plastics identifier6528can generate the plastic usage indicator6556based on the low-level plastics data6520. The plastics identifier6528can generate the plastic usage indicator6556by determining an amount of plastic usage, e.g., single-use plastics usage, that results from each particular value of the low-level plastics data6520. The plastics identifier6528can generate a plastic usage indicator6556for each entity of a corpus or group of entities. The plastics identifier6528can sort the plastic usage indicators6556into buckets based on the category of the plastic usage indicators6556. For example, a food related plastic usage indicators6556for the corpus or group of entities could be sorted into a food plastics bucket. All shopping related plastic usage indicators6556can be sorted into a shopping plastics bucket. In some embodiments, the plastics identifier6528is configured to output the plastic usage indicator6556as indicator based on a total weight of single-use plastics based on the low-level plastics data6520.

For example, the plastic usage indicator6556can account for the amount of plastic, paper, or other material saved by switching single-use products to reusable products (i.e., plastic water bottles to reusable water bottle) and recycling. For example, for each unit or a quantity of material saved, e.g., by following a habit stored in the habits database6432, the quantity of material is converted to a normalized unit or assigned a score (e.g., a point).

In some embodiments, the plastics identifier6528aggregates the plastic usage indicators6556in each of the buckets to generate a plastic usage indicator for each category. For example, for a corpus or group of entities, the plastics identifier6528could aggregate (e.g., sum, average, weight, etc.) the plastic usage indicators of each category into a single category plastic usage indicator6556. Furthermore, the plastics identifier6528can aggregate (e.g., sum, average, weight, etc.) the plastic usage indicators6556of each category into a total plastic usage indicator6556for the corpus or group of entities (e.g., a plastic footprint score). The individual plastic usage indicators6556for each category, the category level plastic usage indicators6556, and the total plastic usage indicator can be time correlated data (e.g., timeseries data). For example, each plastic usage indicator6556could be a series of plastic usage values for points in time, e.g., for days, weeks, months, years. The plastics identifier6528can store trends of plastic usage indicators6556and update each trend as new plastic usage indicators6556are generated.

The water identifier6530can generate the water usage indicator6558based on the low-level water usage data6522. The water identifier6530can generate the water usage indicator6558by determining an amount of water usage, e.g., direct water usage and/or indirect water usage, that results from each particular value of the low-level water usage data6522. The water identifier6530can generate a water usage indicator6558for each entity of a corpus or group of entities. The water identifier6530can sort the water usage indicators6558into buckets based on the category of the water usage indicators6558. For example, food related water usage indicators6558for the corpus or group of entities could be sorted into a food water bucket. All cleaning related water usage indicators6558can be sorted into a cleaning water bucket. All hygiene related water usage indicators6558can be sorted into a hygiene water bucket. In some embodiments, the water identifier6530is configured to output the water usage indicator6558as an indicator based on a total volume of water saved based on the low-level water usage data6522. In some embodiments, the water usage indicators6558is based one or more habits stored in the habits database6432.

The water usage indicator6558accounts for the amount of water saved by the user based on the habits they follow related to water conservation. However, the total weekly point value will be weighted based on a regional drought factor, such as the average daily drought severity and coverage index (DSCI) for the region.

In some embodiments, the water identifier6530aggregates the water usage indicators6558in each of the buckets to generate a water usage indicator for each category. For example, for a corpus or group of entities, the water identifier6530could aggregate (e.g., sum, average, weight, etc.) the water usage indicators of each category into a single category water usage indicator6558. Furthermore, the water identifier6530can aggregate (e.g., sum, average, weight, etc.) the water usage indicators6558of each category into a total water usage indicator6558for the corpus or group of entities (e.g., a water footprint score). The individual water usage indicators6558for each category, the category level water usage indicators6558, and the total water usage indicator can be time correlated data (e.g., timeseries data). For example, each water usage indicator6558could be a series of water usage values for points in time, e.g., for days, weeks, months, years. The water identifier6530can store trends of water usage indicators6558and update each trend as new water usage indicators6558are generated.

In some embodiments, the sustainability system6402aggregates the emissions indicators6552, engagement indicators6554, plastic usage indicators6556, and water usage indicators6558to generate a sustainability indicator. For example, for a corpus or group of entities, the sustainability system6402could aggregate (e.g., sum, average, weight, etc.) the emissions indicators6552, engagement indicators6554, plastic usage indicators6556, and water usage indicators6558into a single sustainability indicator6550(e.g., a sustainability score). The individual sustainability indicators6550for each category of each indicator type (e.g., the emissions indicators6552, engagement indicators6554, plastic usage indicators6556, and water usage indicators6558), the category level sustainability indicators6550, and the total sustainability indicators, can be time correlated data (e.g., timeseries data). For example, each sustainability indicator could be a series of sustainability values for points in time, e.g., for days, weeks, months, years. The sustainability system6402can store trends of sustainability indicators6550(e.g., in data storage6560, scoring database6462) and update each trend as new sustainability indicators6550are generated.

A user interface portal6562can allow a user to access and view the sustainability indicators6550. The user interface portal6562can generate user interfaces, e.g., the user interfaces ofFIGS.4-64. The user interface portal6562can populate various user interface elements of the user interfaces ofFIGS.4-64based on the sustainability indicators6550(e.g., emissions indicators6552) stored in the data storage6560. Furthermore, recommendations generated by the recommendation engine6564can be displayed in the user interfaces of theFIGS.4-64. The user interface portal6562can retrieve the recommendations from the data storage6560stored in the data storage6560via the recommendation engine6564. The user interface portal6562can populate user interface elements (e.g., the user interfaces ofFIGS.4-64) with the recommendations.

The sustainability system6402can include a recommendation engine6564. The recommendation engine6564can generate recommendations for improving the sustainability indicators6550. For example, the recommendation engine6564can generate recommendations on a company level. The recommendation engine6564can generate the recommendations on the company level based on category level sustainability indicators6550or total sustainability indicators6550for the corpus or group of entities. The recommendation engine6564can generate recommendations for individual entities of the corpus or group of entities. For example, the recommendation engine6564can generate a recommendation for a particular user based on one or more of the sustainability indicators6550for each user. The recommendation engine6564can generate category-based recommendations for the entire corpus or group of entities e.g., based on category level sustainability indicators6550. The recommendation engine6564can generate category-based recommendations for particular entities based on the sustainability indicators6550for the particular entities in particular categories.

The recommendations can be recommendations to adjust commuting, e.g., a suggestion to take a bus more frequently, invest in a more fuel-efficient vehicle, work from home more frequently, etc. The recommendations could be recommendations to change water usage, e.g., take shorter showers. The recommendations could be recommendations to change eating habits, e.g., cat less meat, cat more vegetables, etc.

The sustainability system6402includes an offset manager6570. The offset manager6570can acquire offset items that offset the sustainability indicators6550. The offset manager6570can receive a section, by a user, to acquire a particular offset and communicate with an external system that manages the offset to acquire the offset. In some embodiments, the offset manager6570receives votes or indications of interest of various types of offsets from a user via a mobile application6572. The offset manager6570can aggregate the votes or indications of interest to determine which offsets have the most votes or indications of interest. The offset manager6570could identify which categories have a number of votes or indications of interest greater than a particular amount. The offset manager6570can acquire an offset responsive to determining that the offset has the most votes or indications of interest. The offset manager6570can acquire the offset responsive to determining that the offset has a number of votes or indications of interest greater than a particular amount.

The mobile application6572can be a mobile application run on a user device such as the user device6418or the wearable device6414. The mobile application can include user interfaces, for example, the user interfaces ofFIGS.4-63. The mobile application6572can generate user interfaces, e.g., the user interfaces ofFIGS.4-63. The mobile application6572can populate various user interface elements of the user interfaces ofFIGS.4-63based on the sustainability indicators6550stored in the data storage6560. Furthermore, recommendations generated by the recommendation engine6564can be displayed in the user interfaces of theFIGS.4-63. The mobile application6572can retrieve the recommendations from the data storage6560stored in the data storage6560via the recommendation engine6564. The mobile application6572can populate the user interface elements ofFIGS.4-63with the recommendations.

The recommendations can gamify emissions reduction for individuals. The individuals can compete to reduce their emissions levels and increase sustainable behaviors and usage. The recommendations can be part of an engagement platform.

Referring now toFIG.66, a process6600of generating the sustainability indicators from the high-level data based on the modeling assumptions is shown, according to an embodiment. The sustainability system6402can be configured to perform the process6600. For example, the process6600can be performed by components of the sustainability system6402. For example, the modeler6510, the engine6512, the emissions identifier6524, etc. of the sustainability system6402can be configured to perform the process6600. Furthermore, any computing system described herein can be configured to perform the process6600.

In step6602, the process6600can include receiving, by one or more processing circuits, high-level data for multiple categories for a corpus or group of entities. For example, the sustainability system6402can receive the high-level entity data6506for the corpus or group of entities. The corpus or group of entities could be users of a group, e.g., employees of a company, members of a family, citizens of a city, state, or country, occupants of a building, etc. The high-level data can indicate high-level behaviors, characteristics, preferences, or a profile of consumption for the entities of the corpus or group of entities in various categories (e.g., commuting, food, shopping, business travel, additional consumption from remote work, remote work behaviors, work from home setups, work from home frequency, work from home durations, etc.). For example, the high-level data could indicate typical commute distance, typical commute day per week, average size of a vehicle driven, utilization of busses, trains, shopping habits, food habits, residential information, plastic usage information, user engagement with sustainable practices, user engagement with recommendations regarding sustainability, etc. As another example, the high-level data could indicate typical work from home frequency (e.g., days per week, days per month, days per year, work from home occurrences per unit time, etc.), work from home duration (e.g., hours per day, hours per week, hours per month, etc.), work from home hardware and energy consumption (e.g., energy consumptions attributed to desktop computer use, laptop computer use, external monitor use, home office lighting usage, home office air conditioning usage, home office internet usage, etc.).

In some embodiments, the user can be presented with options to select or provide an input regarding one or more of the following prompts: “how many days per week do you typically work from home?”, “how many hours do you work on a typical work from home day?”, “how many desktop computers do you use?”, “how many laptops do you use?”, and/or “how many external monitors do you use?”. In some embodiments, a user may provide a numerical input, select a numerical input, or otherwise provide a quantity, quantifier, and/or qualifier regarding one or more of the aforementioned example prompts. For example, energy consumption for at-home workers may be calculated by determining the total kWh consumed by, for example, workstation electronic appliances, additional lighting usage, and additional home heating and cooling. The user can be presented with options to select or provide an input regarding measures relating to energy consumption for at-home workers may be calculated by determining the total kWh consumed by, for example, workstation electronic appliances, additional lighting usage, and additional home heating and cooling.

In step6604, the process6600can include selecting, by one or more processing circuits, one or more modeling assumptions for the multiple categories that model low-level data based on the high-level data. The high-level data can be the high-level data received in the step6602. The sustainability system6402can select one or multiple of the modeling assumptions6504. The modeling assumptions6504can model the low-level sustainability data6514based on the high-level entity data6506. For example, the modeling assumptions6504could indicate energy consumption for heating or cooling a building for certain ranges of square feet, geographic locations, equipment types, etc. The high-level entity data6506could indicate an approximate residence size, geographic location of the residence (e.g., state, city, region, etc.), and/or an indication of a type of equipment (e.g., air conditioning unit and furnace, heat pump system, etc.). Furthermore, the modeling assumptions6504could indicate the amount of meat, vegetables, or dairy products consumed based on different food consumption behaviors (e.g., meat eater, vegan, vegetarian, pescatarian, etc.) of an entity indicated by the high-level entity data6506.

In step6606, the process6600can include generating, by one or more processing circuits, sustainability indicators for the multiple sustainability data types for the multiple categories for multiple points in time based on the one or more modeling assumptions and the high-level data. The engine6512can generate the low-level sustainability data6514based on the modeling assumptions6504and the high-level entity data6506. The engine6512can further generate the low-level sustainability data6514based on telemetry data of the telemetry data source6508. The engine6512can provide the low-level consumption data6516to the emissions identifier6524and the emissions identifier6524can generate the emissions indicators6552based on the low-level consumption data6516. The engine6512can provide the low-level engagement data6518to the engagement identifier6526and the engagement identifier6526can generate the engagement indicators6554based on the low-level engagement data6518. The engine6512can provide the low-level plastic usage data6520to the plastics identifier6528and the plastics identifier6528can generate the plastic usage indicators6556based on the low-level plastics usage data6520. The engine6512can provide the low-level water usage data6522to the water identifier6530and the water identifier6530can generate the water usage indicators6558based on the low-level water usage data6522.

In step6608, the process6600can include sorting the sustainability indicators into buckets based on the categories. For example, the emissions identifier6524can generate an emissions indicator6552for each entity for a corpus or group of entities in each category. The emissions identifier6524can sort the emissions indicators6552into buckets. The buckets can be data groupings or regions of the data storage6560for storing emissions indicators6552of each category. The emissions identifier6524can sort the emissions indicators based on category such that each bucket includes all of the emissions indicators of the corpus or group of entities for each category for the emissions data type of the sustainability data type. The emissions identifier6524can store the sorted data in the data storage6560.

In step6610, the process6600can include generating data causing a computing device to display the sustainability indicators sorted into the buckets for one or more sustainability data types. The sustainability system6402can generate data that causes user interfaces to be displayed on computing devices such as the wearable device6414or the user device6418. The user interfaces can be the user interfaces ofFIGS.4-63.

In step6612, the process6600can include obtaining a single sustainability indicator representing the sustainability indicators of the sustainability data types. For example, the scoring engine6460can obtain a single sustainability indicator6550by aggregating the emissions indicators6552, the engagement indicators6554, the plastic usage indicators6556, and the water usage indicators6558. The single sustainability indicator6550may be the superposition of the emissions indicator6552, engagement indicator6554, plastic usage indicator6556, and water usage indicator6558. For example, the single sustainability indicator may be a function as illustrated in the equation below:

where, “S” is the single sustainability score, “F” is an average or standard value of an emissions indicator for a standard (e.g., average) user, “E” is a user's emissions indicator quantity (e.g., the value of emissions indicator6552), “O” is an offset value based on the user's habit data (e.g., the habit data stored in the habits database6432), the user's home improvement data (e.g., the home improvement data stored in the home improvement database6436), and the user's offset purchases. “C” is a predictive indicator (e.g., a future climate impact indicator) that is based on a predictive model using the trajectory of the sustainability indicator6550, the low-level engagement data, the modeling assumptions6504, high-level entity data6506, and/or the telemetry data of the telemetry data source6508.

In step6614, the process6600can include obtaining an adjusted data value based on a comparison of the at least one data value to an expected value corresponding to the one or more data values. In some embodiments, the single sustainability indicator is adjusted (e.g., by the scoring engine6460) based on large-scale factors such as weather conditions, ambient climate, political attitudes toward resource conservation, and societal changes. The adjusted single sustainability indicator, e.g., the standardized sustainability score, can aid in ensuring equality among users from a variety of locales and socioeconomic backgrounds. For example, a user in a dry or desert region may experience water shortages or droughts more frequently than a user in a tropical region, and accordingly, the sustainability system6402is configured to account for the relatively higher emphasis on water conservation in areas experiencing a water shortage or drought and reflects the disparity via a standardized sustainability score. In some embodiments, the standardized sustainability score is a function as illustrated in the equation below:

where “S*” is the standardized sustainability score, “P” is a value corresponding to the plastic usage indicator6556, “S” is the single sustainability score, and “I” is a value corresponding to the engagement indicator6554. In some embodiments, the sustainability system6402may rank the single sustainability indicator and provide recommendations (e.g., via the recommendation engine6564) on ways a user can improve their individual standardized sustainability score and/or their group(s) standardized sustainability score.

In step6616, the process6600can include presenting an adjusted data in a user interface associated with at least one entity of the corpus or group of entities. The sustainability system6402can generate data that causes user interfaces to be displayed on computing devices such as the wearable device6414or the user device6418. The user interfaces can be the user interfaces ofFIGS.4-63. In some embodiments, the recommendation engine6564may, based on a determination that one or more sustainability scores are above or below one or more threshold values, provide an alert via at least one of the wearable device6414, user device6418, transport device6420, and/or the residential computing system6422. For example, if the recommendation engine6564detects a running faucet, running toilet, water line rupture, etc., that, for example, may have been inadvertent due to inattentiveness, a medical emergency, a broken plumbing system, etc., the recommendation engine6564may cause the sustainability system6402to display a suggestion for taking an action to prevent, troubleshoot, or diagnose the change in the water usage indication.

For example, the recommendation engine6564may suggest actuating a valve to pause the water flow. In some embodiments, the recommendation engine6564can automatically take an action (e.g., actuate a valve, etc.) to work around the detected change. In some embodiments, the recommendation engine6564can implement, control, or configure equipment of buildings, cars, or other devices to improve a sustainability indicator of a user. For example, the recommendation engine6564can generate a control parameter, a control schedule, a control signal, a data packet, a data message, or a command and transmit the information via a network to an actuator to cause the actuator to control a condition. For example, the command may be a command to update a temperature setpoint of a thermostat that controls a temperature of a building (e.g., a building corresponding to the residential computing system6422). The command can update the temperature setpoint to a value that reduces energy consumption. The command can be a command to configure a transit system of a vehicle (e.g., a car or bus) to take a route that consumes less energy.

The recommendation engine6564can generate notifications, e.g., smart notifications, that notify a user when their actual consumption (e.g., the single emissions indicator) deviates by a predefined amount. For example, if the single emissions indicator deviates outside a predefined range for a user, a notification can be generated and pushed to a user device of the user.

Accordingly, as described herein, the sustainability system6402is configured to cause a variety of devices to perform various actions aimed at improving various sustainability indicators of a user. For example, the recommendation engine6564may cause one or more devices (e.g., wearable device114, user device118, wearable device6414, user device6418, transport device6420, residential computing system6422) to provide a variety of information and/or options to a user (e.g., via the user interface portal6562) to allow the user to improve the sustainability indicators. The information and/or options can include, for example, the various sustainability indicators, options for carbon offsets, changes that user can make (e.g., in habits or manual updates to device setpoints or functionalities) to improve the sustainability indicators, and/or any other information, options, or functionalities described herein. In some instances, the recommendation engine6565may cause one or more devices (e.g., equipment of buildings, cars, or other devices) to take various actions (e.g., automatically via one or more command signals), such as, for example, adjusting or modifying various optional parameters of the devices (e.g., set points, operational schedules) and/or performing any other actions discussed herein configured to improve the sustainability indicators.

Advantageously, the single sustainability score and adjusted sustainability score can facilitate an improved user interface and data management for a sustainability data for an entity or corpus or group of entities and provides an efficient, effective, and succinct high-level display of a very large quantity of low-level data that is otherwise impossible to display, fit, or otherwise present on a display device having a limited display arca. For example, a screen (e.g., liquid crystal display, light emitting diode display, electrophoretic display, backlight display, etc.) having limited dimensions, for example, a screen of a wearable smart watch or other small screen device may not have a resolution, pixel density, available screen area, and/or computing resource supportive of a display of sustainability data. Advantageously, the sustainability system configured to generate sustainability indicators, a single sustainability indicator, and an adjusted sustainability indicator facilitates presentation, management, and control for a very large and very complex volume of sustainability data of an entity or a corpus or group of entities. For example, the sustainability indicators, single sustainability indicator, and adjusted sustainability indicator are configured to be presented to a user in a variety of user interface devices such as displays, wearable devices, residential computing systems, transport devices, and other devices. Unexpectedly, the sustainability indicators, single sustainability indicator, and adjusted sustainability indicator effectuate upstream impacts on the suitability data. For example, the adjusted sustainability indicator can effectuate implementation of sustainability data collection, sustainability data accuracy, and sustainability data standardization.

The shopping data can be sorted into multiple categories of items purchased and an emissions indicator can be generated for each category. The categories can include a home maintenance, home improvements, or home services category. The categories can include a cleaning and house keepings category. The categories can include a major appliances category. The categories can include a small kitchen appliances, cookware, dinnerware, or heating/cooling equipment category. The categories can include an electronic devices and miscellaneous home equipment and supplies category. The categories can include a clothing and jewelry category. The categories can include a personal care products and medical supplies category. The categories can include an entertainment category. The categories can include a pets and pet care category. The categories can include a reading (e.g., paper books, newspapers, etc.) category.

A user can provide their annual spend on different shopping categories for a period of time (e.g., quarter, month, year). A user can adjust a slider to estimate their annual spend. A system can allow a user to provide their annual average expenditure the shopping categories, which can multiplied by emission conversion factors that convert the dollar amounts into kg CO2e. In order to help the user accurately determine their expenditure estimations, the system can use average expenditure values for shoppers.

The system can include an application programming interface (API) that integrates with human resources, financial management systems, expense systems, etc. other platforms supporting emissions calculations to facilitate system to system communication of inputs to calculations and the return of emission values for each consumption record.

In various implementations, the steps and operations described herein may be performed on one processor or in a combination of two or more processors. For example, in some implementations, the various operations could be performed in a central server or set of central servers configured to receive data from one or more devices (e.g., edge computing devices/controllers) and perform the operations. In some implementations, the operations may be performed by one or more local controllers or computing devices (e.g., edge devices), such as controllers dedicated to and/or located within a particular building or portion of a building. In some implementations, the operations may be performed by a combination of one or more central or offsite computing devices/servers and one or more local controllers/computing devices. All such implementations are contemplated within the scope of the present disclosure. Further, unless otherwise indicated, when the present disclosure refers to one or more computer-readable storage media and/or one or more controllers, such computer-readable storage media and/or one or more controllers may be implemented as one or more central servers, one or more local controllers or computing devices (e.g., edge devices), any combination thereof, or any other combination of storage media and/or controllers regardless of the location of such devices.