WO2023137484A1

WO2023137484A1 - Sustainability management systems and methods

Info

Publication number: WO2023137484A1
Application number: PCT/US2023/060736
Authority: WO
Inventors: Jan Nunney BELT; Robert E. Morgan
Original assignee: Vital Eco Technology, Llc
Priority date: 2022-01-17
Filing date: 2023-01-17
Publication date: 2023-07-20

Abstract

Disclosed are systems and methods for sustainability management, wherein the systems are connected to remote devices in a network and configured to determine status, benefits, costs or value created through the devices in the network. The disclosed systems and methods allow for creating, maintaining, and enriching a unique sustainability credit, such as a carbon credit, using distributed blockchain ledgers and smart contracts facilitates managing the relationship between many components that exist in the physical world and in an online environment.

Description

SUSTAINABILITY MANAGEMENT SYSTEMS AND METHODS CROSS-REFERENCE

[0001] This application claims the benefit of U.S. Provisional Application No.

17/577,137, filed January 17, 2022, entitled SUSTAINABILITY MANAGEMENT PLATFORM which application is incorporated herein in its entirety by reference.

BACKGROUND

[0002] As the climate continues to change, organizations are placed under increased regulatory requirements to monitor, measure, report and verify carbon unit credentials in their management systems. As a partial response to the current regulatory environment, a process for carbon credits or carbon offsets has been developed. A life cycle of a carbon offset is shown in FIG. 1. The offset lifecycle includes a plurality of offset projects 110 that avoid, sequester, or eliminate greenhouse gases. The projects include, for example, improved efficiency of operation for a facility, solar projects, wind projects, electric vehicles, planting trees and plugging of oil and gas wells. Emission reduction and/or elimination is verified 112, for a unit of removal (e.g., per ton), a carbon offset credit is issued 114. Organizations, companies and individuals can purchase carbon credits 116. The money that is earned in the sale of carbon credits funds new projects 118, and the lifecycle is complete. The life of a carbon offset goes through four general stages: development, validation and verification, registration and issuance, and retirement.

[0003] These increased requirements, as well as investor pressures, has also resulted in organizations adopting Environmental, Social and Governance (ESG) principles and associated strategies to adopt the ESG principles. The ESG comprises three scopes: Scope 1 is direct emissions from operations (e.g., facilities and vehicles); Scope 2 is direct emissions from energy purchases (e.g., electricity, heating and cooling); and Scope 3 is value chain emissions (e.g., goods and services, fuel, travel, distribution, waste, investments, leased assets, and employee activities). Additional requirements and reporting adherence to ESG principles requires access to environmental data and other data to determine the status of a particular process or asset.

[0004] Currently, data collection methods rely on different approaches. Many of the currently available approaches are outdated, and inefficient. Moreover, the currently available approaches are replete with inaccurate data which results in an inability to audit the chain of creation to retirement of carbon units and attributes supporting an organization’s ESG goals. Organizations may develop proprietary approaches to track the lifecycle of a carbon unit's credentials but the process can fail to perform a broad and deep review. The proprietary solutions can also be difficult to implement due to changing regulatory requirements as well as individual jurisdictional requirements that may exist for any jurisdiction where an organization operates. Another problem that occurs if ongoing monitoring and validation does not occur after the initial carbon credentialing or credit registration. Additionally, registration and/or retirement of carbon credits may be overlooked. Failure to register and/or retire a carbon credit can result in significant financial, reputational, and environmental damage to an organization. In some situations, the organizations look to outsource the validation and credentialing process but fail to understand that the organization can't outsource liability for the accuracy of the Scope 1, Scope 2, and Scope 3 information that is reported, or the accuracy of any reviews that are performed.

[0005] Additional problems encountered with current systems and processes include, for example: carbon unit credentialing systems rely on persistent, stored data with a lack transparency or ability to assess accuracy at a given point in time, requestors of carbon authentication data carry the burden of higher liability and business risk and accountability which cannot be delegated to third-party providers leading to higher underwriting costs and potentially reputational brand damage; systems utilize static information that does not fully or accurately exploit the context and lifecycle of the user, environment, and the interactions with the systems; requesters may be using information to credential carbon units based on stale, irrelevant, dated or compromised data sources; users interact with many different systems which have different approaches to credentialing carbon units, causing error in the selection and use of data as well as a potential for reuse of carbon credential information; existing carbon credentialing methods use technology approaches based on design and implementation patterns created before sophisticated encryption methods, inexpensive cloud computing and storage, quantum computing, and blockchain technologies; existing cryptographic systems are difficult to implement for non-sophisticated organizations and end users; data is maintained in silos that are difficult to share and, when shared, difficult to ensure data integrity; and different types of certificates are not interchangeable or tradeable.

[0006] What is needed are systems and methods for solving the identified problems and for managing and monitoring a complete lifecycle of carbon information for Scopes 1-3 of an ESG. Additionally, what is needed are methods and processes to collect and normalize data received from one or more sources, methods and processes for correlating data, and methods and processes for generating predictive information based on one or more of the normalized data and the correlated data.

SUMMARY

[0007] Disclosed are systems and methods for managing and monitoring a complete lifecycle of carbon information for Scopes 1-3 of an ESG. Additionally, disclosed is a process to collect, analyze, normalize, correlate and optimize data received from one or more sources. Moreover, the process of collecting, analyzing, correlating, normalizing and optimizing data is flexible to communication with different registries having different parameters and different measurement units (e.g., metric vs. imperial). Methods, systems, and computing products are described for processing a lifecycle for a user, leveraging data and events unique to a carbon unit. The methods, systems, and computing products described can also be used to develop predictive information and projections. The methods, systems and products allow for dynamic data to be presented in an authentication challenge - thereby making an assertion and credentialing process more robust and transparently verifiable. The systems and methods can use a non-transitory computer storage medium encoded with computer program instructions that, when executed, cause one or more computers to perform the identified operations.

[0008] The disclosed methods allow for creating, maintaining, and enriching a unique sustainability credit, such as a carbon credit, using distributed blockchain ledgers and smart contracts facilitates managing the relationship between many components that exist in the physical world (e.g., physical data) and in an online environment. The disclosed computer program products allow systems to leverage real-time and near-real time data from a variety of sources without the need to rely on static or persistent data. The use of real-time and near real-time data increases security and protection of data and sustainability credits. The disclosed systems and methods allow requesters and user to manage and control reporting of carbon attributes. The disclosed methods and systems can include credentialing or verification "as-a-service," as well as white labeling or OEM/licensing. White labelling and original equipment manufacturing (OEM) allows the platform to be deployed and used by a variety of entities. The systems are configurable to provide that data sources from external system partners is check-pointed and baselined for tracing and auditability.

[0009] Carbon token creation is also disclosed which can be hosted on any number of blockchains and distributed ledgers. The token creations can also be swapped, exchanged, bought, and sold on multiple exchanges based on data sources for users and requesters. Methods and processes are provided for updating, maintaining, retiring, and archiving carbon tokens over time and across channels. Carbon tokens can also be provided to requesters based on a pre-defined set of attributes and conditions that are met in smart contracts. Carbon attributes can also be recombined and distributed from one or more distributed blockchain ledgers based on an authorized request. The systems and methods also allow a requester (e.g., user, customer, company, etc.) to receive an affirmation/assertion of an existence and/or status of a carbon credit of another in order to set a goal and to generate a progress chart of a current status against a goal and trend lines. A requester can also be notified when the actual data is within range based on the goal. A historical view of the data of a carbon unit's change in status over time can also be provided. The historical view can be provided on demand or in response to a trigger, such as an event trigger. A carbon assertion result can be provided to a requester in a variety of electronic formats including but not limited to a carbon token defined by the system, a token format defined by the requester such as a white-list entry, a cryptocurrency wallet address, or some unique identifier to correlate back to a requester's user. A minimum level of regulations can be programmatically defined to be combined as part of an overall assertion. Additionally, the appropriateness of taking action can further be refined based on, for example, whether an orphan well is located on protected federal land. Thus, data captured and collected can be correlated to one or more regulatory requirements applicable to the geographic location of the well. Thus, if a requester did not provide correct or sufficient attributes, the system may augment the fields requested from the data partners. The programmatic definition of these rules can be implemented in a smart contract and be persisted on capable blockchain or distributed ledger.

[0010] Systems are configurable to allow data providers to self-integrate with the platform by providing an SDK and API to the providers. The system can also operate in a similar fashion in different domains such as augmented, mixed, and/or virtual reality environments where avatars, oracles, and nonhuman/synthetic entities can be the users and the partners are interchanged with environmental sources. Fraud signals can be detected through checks and other artificially intelligent heuristic techniques and technologies. An extensible set of properties in the smart contract that can be programmed to detect, identify, and act upon known fraudulent behavior or suspected irregularities. A business rules engine can also be provided that is meta-data driven, written into the form of a smart contract into a distributed blockchain ledger system and a validation process by the rules engine by providing several rules and populating a record. A recently complete carbon credential can then be transferred to another smart contract if and when the smart contract terms are met. Suitable terms for completion can, for example, be a “promise to buy” or “pre-buy” process. Gifting of carbon credentialed tokens can also be provided.

[0011] Both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosed embodiments, as claimed.

INCORPORATION BY REFERENCE

[0012] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference.

[0013] US Patent US 10,643,288 B2 issued May 5, 2020, for Use of Blockchain Based Distributed Consensus Control;

[0014] US Patent US 10,649,429 B2 issued May 12, 2020, for Use of Blockchain Based Distributed Consensus Control;

[0015] US Patent US 11,468,518 B2 issued October 11, 2022, for Use of Blockchain Based Distributed Consensus Control; [0016] US Patent US 11,474,488 B2 issued October 18, 2022 for Use of Blockchain Based Distributed Consensus Control;

[0017] US Pub US 2011/0208621 Al published August 25, 2011, for Carbon Neutrality Management;

[0018] US Pub US 2022/0101430 Al published March 31, 2022, for Blockchain-Based Carbon Credit Database;

[0019] US Pub US 2022/0358547 Al published November 10, 2022, for Carbon Credit Tokenization;

[0020] US Pub US 2022/0374912 Al published November 24, 2022, for Method and System for Wood Harvest and Storage, Carbon Sequestration and Carbon Management; [0021] PCT Publication WO 2011/106160 Al published September 1, 2011, for Carbon Neutrality Management; and

[0022] PCT Publication WO 2022/067045 Al published March 31, 2022.

BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The novel features of the invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention are utilized, and the accompanying drawings of which:

[0024] FIG. 1 illustrates a carbon offset lifecycle;

[0025] FIG. 2 illustrates an overview of the system and components contributing to the system;

[0026] FIG. 3 illustrates an overview of participants in the system;

[0027] FIG. 4 is a high level view of the system operation;

[0028] FIG. 5 illustrates components of the system;

[0029] FIG. 6 illustrates an application of the system for oil wells;

[0030] FIG. 7 illustrates a process applied to oil wells; and

[0031] FIGS. 8A-8E illustrate exemplar data sets. DETAILED DESCRIPTION

[0032] I. SUSTAINABILITY MANAGEMENT SYSTEMS

[0033] FIG. 2 illustrates an overview of the sustainability management system 200 and components contributing to the system. The sustainability management system 200 is dynamic, contextual, and event-driven. The sustainability management system 200 provides real-time and near-real-time processing of dynamic information and data that is configurable to span virtual and digital environments which can originate from the physical world. Moreover, the sustainability management system 200 uses physical, human, and device-based communication and collaboration networks to obtain information and/or data that is processed. The sustainability management system 200 performs several processes and has a plurality of phases from: enrollment, onboarding (users, entities and assets), registration, verification, validation, proof, utilization metrics, monitoring, reviewing retiring. Each process is integratable serially and/or in parallel throughout the lifecycle and operable to ensure data integrity across a lifespan of a certificate. The processes provide for managing the lifecycle on a system that provides sustainability management.

[0034] The core platform of the sustainability management system 200 comprises several different systems from different industries that can contribute interaction data from the environment to the sustainability management system 200. A ledger 210 is positioned between, and in communication with, entities that drive demand 220 and entities that provide supply 230. Entities that drive demand 220 include, but are not limited to companies, consumers, employees, institutional investors, subscribers, nongovernmental organizations (NGOs), governments and exchanges. The entities that provide supply 230 include, but are not limited to, projects for oil and gas clean-up, reforestation, water desalination, renewable energy (e.g., wind farms and solar farms), vehicle efficiency, carbon capture, sustainable farming, and biofuels. The ledger 210 is also in communication with a ledger and registry platform 240 which is configurable to include smart contracts 242, tokens 244, CO2 certificate creation 246, algorithm and data optimization 247, and verification and retirement 248 of the certificate. The ledger and registry platform 240 tracks credit originators and project developers; smart contracts, blockchain, certificate creation, verification of projects status and retirement of project assets. The ledger and registry platform 240 is also in communication with a compliance and monitoring platform 250. The compliance and monitoring platform 250 is configurable to engage in dynamic monitoring and analytics processes 252, partner ecosystem monitoring 254, subscriber tracking 256, and compliance reporting 258. The compliance platform is configurable to dynamically ingest the regulatory and compliance data feeds and/or signals and provide an output that is highly responsive to changes to regulations, economic trends and industry trends.

[0035] The smart contracts 242 are configurable to generate a self-executing contract for a transaction for the value from at least two nodes in a computing network, the computing network comprising a plurality of nodes, a subset of the plurality of nodes maintaining at least a predetermined number of tokens, the subset of the plurality of nodes comprising at least two nodes, each node of the subset of the plurality of nodes comprising at least one token as a requirement for participation on the computing network, the transaction comprising an exchange between at least two nodes. If and when the criteria of the smart contract 242 is completed, the certificate created 246 can be retired from further use and fulfillment information can be generated based on settlement information. The settlement information can be settlement information for an energy transaction or certificate.

[0036] One or more algorithms can be provided. Additionally, one algorithm can be provided that is configurable to perform a plurality of functions. As will be appreciated by those skilled in the art, an algorithm function is applied to the data to optimize the data 247. The algorithm function is configurable to receive data, e.g. after a shutdown of a well, normalize, analyze and process a large amount of data to provide a representative value over time for the data. Large data points (thousands to tens of thousands) are collected and sampled using a sliding window technique to avoid an inaccurate representative that might occur if only a few measurements were taken. Sampling and data smoothing of the raw data received eliminates outliers from the dataset to make patterns of the data more noticeable. Several mechanism can be used for data smoothing including, but not limited to, the random method, simple moving average, random walk, simple exponential and exponential moving average.

[0037] The algorithm also analyzes the data to look for patterns in the data to ensure that the collected data is within a target range set by the system. An algorithm function can also be applied to correlate data sets from the one or more devices and sensors, and the one or more data providers. Another algorithm function can provide for a prediction of future values, including performance events, based on an analysis of data sets from the one or more devices and sensors, the one or more data providers, and one or more monitored assets or events.

[0038] Following verification and retirement 248 of the certificate from further use (e.g., duplicate sale or use of the certificate), the monitoring and compliance platform 250 engages in dynamic monitoring and analytics 252. The dynamic monitoring and analytics 252 continues to obtain real-time or near real-time data from one or more sensors and environmental data. The information is analyzed and appended to the smart contract 242. The information, data and analytics maintained by the smart contract 242 can be used for verifiable compliance reporting 258. Compliance reporting 258 provides a mechanism for ensuring that work performed adheres to an identified standard as well we whether a failure has occurred which results in an increase in data monitored by one or more sensors.

[0039] FIG. 3 illustrates an overview of participants in the system 300. The sustainability management system 200 is in communication with a broad range of endpoints including devices and sensors 320, data providers 322, registries and verifiers 324, marketplaces and partner ecosystems 326, compliance reporting 326, subscriber tracking 334, dynamic monitoring and analytics 332, organizations 330, users 328 and one or more system oracles 329. Data providers 322 include data, received or obtainable, from one or more sensors, geologic data, environmental data, governmental data, and/or regulatory data. Additionally, persons of skill in the art will appreciate that the system oracle is software that activates, for example, a contract if a real world event occurs and triggers the activation of the software. The activation event could be, for example, an environmental event determined from measurement information or data received from a sensor reading, or the occurrence of an event, such as a press release for closing an oil well. Information available on the internet, such as key words, press releases, investor declarations, and governmental filings, can be retrieved using a software spider, e.g., an unmanned program operated by a search engine that searches the web for triggering information based on one or more provided criteria. Alternatively, a web crawler can be used with a data scraper to extract specific data points based on provided criteria. The extracted data points are then delivered to the oracle. The system oracle then delivers the real world event data as a translation into the online digital world for further storage and processing. [0040] FIG. 4 is a high level view of the system operation 400. A user 410 or organization interacts with a requester service 420. The requester service 420 establishes an account with the sustainability management system 200 so that the requester service 420 carbon credentials can be verified and asserted. The requester service 420 submits a set of rules and/or parameters 444 for the sustainability management system 200 to enforce in addition to specific data elements that are required to be gathered for the carbon verification/assertion from data sources and partners 426. The rules can be expressed as a set of business rules that can be implemented by smart contracts which in turn reside and execute on one or more distributed blockchain ledgers 428. The selfexecuting contract or smart contract maintains information on a public ledger. The public ledger can store information representing, for example, carbon credit certificates. Value associated with a carbon credit certificate or energy transaction can be any one or more of carbon offset, carbon credit, carbon impact, renewable energy certificates, energy efficiency credit, energy efficiency impact, demand response credit, or demand response impact. The value can be ascribed by, for example, complying with the rules provided by the requester service 420. The value can also represent one or more of energy loss due to transmission, constraint in operation conditions and/or asset utilization. Additionally, the value can be differentiated based on the quality and value of the data which results in a customizable credit which is not unit commoditized across a class of projects, e.g. a credit associated with one orphan well would not necessarily be the same as a credit associated with another well because each well will have a unique profile and unique sensor data attributes that are analyzed.

[0041] The requestor can also provide a set of constraints or parameters for fulfillment that are tied to the value of the credit. For example, an oil well may have different levels of toxicity of other greenhouse gases, the oil well may be in a highly populated area impacting social and health of surrounding populations. Thus, the closing of a well located in a highly populated area has a higher societal impact and benefit and receives a higher grading. Desired attributes are captured by the requester service 420 and encoded in the attributes contract 422 which is provided to the sustainability management system 200 a combination of human supplied data, environmental data, and data from the field via one or more data sources and partners 426.

[0042] The attributes defined by the requester service 420, are transmitted/submitted 432 to the sustainability management system 200 so that the attributes can be configured for when a user 410 begins to transact with the requester service 420.

[0043] The user 410 transacts 434 with the requester service 420 and the user 410 is informed that the user 410 first establishes an account with the sustainability management system 200 and go through the assertion/vetting process. User transaction can be subject to a login process, including a login process with dual authentication. A credentialing request of the user 410 may be submitted for the requester service 420. This step can be performed in a variety of ways including white-labeling to create a more seamless user experience or a message displayed on the requester service 420 website or app. User 410 interaction with the requester service 420 can be achieved via an API integration.

[0044] One or more system oracles 412 interacts 436 with the sustainability management system 200 by monitoring and computing real- word events to encode the methodology and measurements to trigger smart contract actions. The one or more system oracles 412 is software that can be located in the cloud and configurable to activate, for example, a contract if a real world event occurs and triggers the activation of the software. The activation event could be an environmental event determined from a sensor reading or an event, such as a press release for closing an oil well.

[0045] The sustainability management system 200 contacts 438 the requester service 420 programmatically to identify the user 410 and/or system oracle 412 and the range of services being requested. Based on the range of services and the previously established attributes contract 422 with the requester service 420, the user 410 and/or system oracle 412 is presented with an interface (or an API message payload) to collect the data, such as carbon data, that the requester service 420 requires. In one example, this step can be optional for a user 410 but not a system oracle 412 depending on the nature of integration between the requester service 420 and the sustainability management system 200. For example, a federated login process can be set up to seamlessly transfer the user 410 or

-Il system oracle 412 account and carbon information to the sustainability management system 200.

[0046] The sustainability management system 200 intelligently determines 440 which data source and partner 426, of one or more data sources and one or more partners. The data sources and/or data measurement information from, for example, one or more sensors providing one or more environmental data can be received synchronously or asynchronously in real-time or received in batch mode depending on the complexity, urgency, size of the transaction to name a few possible parameters. The data received can also stream continuously into the sustainability management system 200.

[0047] Once the responses are received from the sources that the sustainability management system 200 determined were relevant, the sustainability management system 200 dynamically determines in real-time optimal data encoding attributes initially captured using data streaming methods and an encoding algorithm. The sustainability management system 200 is in bilateral communication 442 with one or more distributed blockchain ledgers 428 and one or more data storage platforms 450. The data storage platforms 450 are provided, as needed, to store large amounts of data collected for, for example, time- series data received from one or more sensors associated with a measurement system. The large volumes of data can be buffered and stored locally and transmitted at a later time. The large volumes of, for example, measurement data can also be provided to the data storage platforms 450 for processing prior to submission to the sustainability management system 200. Databases used can be any one of, or a mix of, relational or non-relational data storage systems, such as those available from Oracle®, PostgreSQL, mySQL, and mongoDB).

[0048] An encoding algorithm ensures that the data is cryptographically protected and then the sustainability management system 200 will determine how best to persist the protected data on one or more distributed blockchain ledgers 428 that may be using one or more different consensus methods. The consensus methods used depends on the nature of the transactions, timeliness, cost, speed to name a few constraints. The consensus process step can be used to ensure that the sustainability management system 200 persists locally for a specific user 410 or carbon data. When the acknowledgement 442 of the successful write to the distributed blockchain ledgers is received, the system creates a cross-reference of the protected records including but not limited to items such as where the data was written to, when, and how it was distributed. The-cross-reference table and entries of the distributed blockchain ledgers 428 are further protected by using current industry standard cryptographic processes that can be upgraded and modified as the start of the technology evolves. This cross-reference is what permits the sustainability management system 200 to reconstruct the data.

[0049] The sustainability management system 200 returns a response to the requester service 420 in a format that has been previously agreed upon. For example, the response to the requester service 420 can be a virtual token or currency wallet address or some other identifier that allows the requester service 420 to continue with the transaction of the service/product to the user. As will be appreciated by those skilled in the art, the sequence of events does not need to occur synchronously or in real-time. The sequence of events can occur asynchronously and/or at selected time intervals.

[0050] FIG. 5 illustrates components of the system 500. Components of the system 500 include, but are not limited to: one or more data providers 510, wherein each of the one or more data providers has a provider software development kit 512, one or more data collectors 520, de-identified analytics 522, carbon intelligence API 534, a core engine 524, an attribute encoder / decoder 532, one or more attribute references 528, one or more rules engine 530, and one or more distributed blockchain ledgers 428. The data provider 510 is also in communication network 511 which in turn is in communication with the distributed ledgers 428. One or more algorithm(s) 521is provided that receives data from the data collector 520 and the attributed encoder/decoder 532. A function of the one or more algorithm(s) 521 is to analyze and process the received data from the plurality of data sources to generate one or more submissions for the verifiers. The algorithm is also configurable to receive many readings (hundreds to tens of thousands) and looks for a stable behavior pattern, for example, of an oil well. Using a high number of data measurements ensures that the decisional data produced is not the result of a one-off event or measurement that skews the system or does not really contribute to the real world material gain for the measurement objective. The algorithm is configurable to analyze large data sets and builds a pattern corpus from the analyzed data that is then used within the system, e.g., for wildcatting, exploration, identification, and refinement or leaming for the algorithm. This software can be implemented by a variety of machine learning tools in the marketplace. The nature of the algorithm is to be trained and continuously learn from the large amount of data it has analyzed and reviewed. The output of the algorithm 521 is provided to one or both of the distributed ledgers 428 and one or more databases 542.

[0051] The distributed platform leverages blockchain technology of a distributed blockchain ledger 428 to record the attributes of the carbon data in part and in whole. Smart contracts are employed in the distributed blockchain ledger 428 to enable decision making of the incoming data received by the data collector(s) 520 from one or more data providers 510 against the pre-defined business rules and contracts. The use of smart contracts allow for efficient and unattended execution. The distributed ledgers 428 also include attributes contract 422.

[0052] The system 500 determines using the attribute encoder/decoder 532 how to interact with the distributed blockchain ledger(s) 428, which ones, the timing, protection, and granularity of the carbon data that ends up persisted in the distributed blockchain ledger 428. The system is configurable to interface and write to the distributed ledgers 428, thus generated certificates do not need to be stored centrally. The attribute references 528 is the part of the system 500 that accepts the data attributes that are collected so that the system 500 operates correctly and accurately. The core engine 524 also defines a language and structure that allows the requester to set-up constraints on the attributes that can include the collecting of a certain number, variety of attribute, acceptable ranges/values for the answers. Many other rules or conditions can be set to tailor the requester offering. The rules engine 530, is the part of the system 500 that operates on the attribute references 528 and ensures that the checks and validations are performed before being allowed to return a credential or assertion answer to the requester 550. The rules engine 530 is implemented with machine learning technologies and algorithms that continuously learn from the data being gathered from one or more data providers 510 in communication with the system 500.

[0053] The data collector 520 interfaces with the one or more data providers 510 that supply data to the system 500. The data collector 520 checks and validates the data submitted from the one or more data providers 510 to the system 500. The check and validation processes include a range of services including accessing and evaluating data from: one or more environmental and industrial sensors (the range of loT devices), data aggregators, government and industry sources, financial/commodity/trading records, as well as a range of other data sources that can be considered more localized or varying in their rigor/trust. The data collector 520 has a set of application programming interfaces (APIs) that data providers can easily integrate with to be a part of the system 500. The data collector 520 may also make use of a blockchain oracle to obtain data from the external systems and bring it in for processing on the signal message processor (SMP). The SMP has a traits function that captures the personality of the asset under measurement, for example, the target well under measurement. The “personality” is a combination of a plurality of data sources that provides a unique fingerprint for the asset based on any of, for example, location, ecological information, geological information, environmental information, sensor measurement information, and equipment information. The personality of the asset can be assessed at any time against a baseline for continued uniqueness and/or retirement of an associated asset. The SMP and algorithm may, in at least some configurations, perform the same or similar functions.

[0054] De-identified analytics 522 is part of the system 500 that tracks the transactions and develops trends of the types of data being submitted and the credentials. The metadata describing the carbon attribute types is used to develop trends to facilitate the types of services to offer, and to improves the SMP in ensuring that minimum system partners are in place. A carbon intelligence API 534 allows the system 500 to communicate with the requester 550. The carbon intelligence API 534 can be a website, mobile app, upstream/downstream sensor or system, smart contract, API, as an example, that interacts with the SMP and requester SDK 552. Provider SDK 512 and requester SDK 552 are offered to facilitate the integration with the system. The core engine 524 allows the system to orchestrate the subsystems of the system. The core engine 524 and rules engine 530 determines the validity, integrity, and uniqueness of the token and the carbon certificate - makes sure it has not been double counted.

[0055] II. SUSTAINABILITY MANAGEMENT SYSTEMS

[0056] Referring back to FIGS. 4 and 5, schematic diagrams of an exemplary system used to implement or practice one or more embodiments of the present disclosure, at the point of registration, identification, and/or authentication is the point at which the system subscribes users to the systems. The subscribing interaction begins a credentialing or assertion event occurs is captured by the system. The system contains a registration function for users, systems, and interconnected devices.

[0057] Third party providers, such as device manufacturers, sensor manufacturers, agencies and/or entities providing data, government data sources at different levels (e.g., Federal, state, local, etc.) and, monitoring services, can connect to and extend functionality of the system by registering through the third-party connection system provided. These “apps” and applications are configurable to make use of technologies such as OAuth, SAML, OpenlD (or any new emerging de-facto or industry standards) for identification, authentication, and authorizing applications and devices to perform functions such as read and write carbon data on behalf of users and systems into and out of the system. An event or notification mechanism (such as, but not limited to, web hooks, callbacks, synchronous or asynchronous) are provided by the system for third party developers and third-party applications to register for events from the system. [0058] The system is configurable to monitor incoming events that are part of the system interactions with one or more of a user, the system, and/or devices irrespective of the length of the cycle. In some instances, the cycle is a finite short duration of a specified interaction with a system and in other cases an ongoing or streaming interaction with the system. The system is configurable to analyze and track the carbon unit lifecycle assigned to any of a user, portfolio, organization, portfolio, exchange irrespective of system or operator. The system provides a holistic and user centric view of the carbon assigned to an individual or organization - thereby reducing the risk that the carbon unit is inaccurate and dramatically improving the probability that the carbon unit is verifiable. [0059] The system is configurable to continuously monitor streaming data received from a plurality of data sources across third party systems, and a plurality of monitoring devices. The system correlates the monitored carbon data against rules and thresholds that are set by the requester which can be in one or more smart contracts. The use of a blockchain oracle is employed to transfer the physical world data into the smart contract residing on the distributed blockchain ledger. The system third-party registration process allows one or more devices to subscribe to carbon data feeds from the system for a variety of functions including different levels of carbon interactions.

[0060] The system can also assign one or more risk levels to any of the carbon units, certificates, and data sources. The risk can be calculated from one or more incoming data sources from surrounding environmental, both geologic and environmental. The risk can be assigned either by answers returned from data providers and/or by rules established by the requester(s). The assigned risk can be used to determine how often and to what extent to re-validate received carbon attributes. The system may also determine these rules because of trend analysis and machine learning from the platform algorithms. For example, the platform receives data from the field but based on the risk level, surrounding environment, the quality or integrity of the data being captured may be questioned. The platform would then take that analysis into account and could request additional readings until the data analysis performed by the platform demonstrates that the asset meets a pre-defined threshold of quality. For example, a straightforward noneventful oil well under measurement may be subjected to a first measurement protocol where the measurements are taken at a first time interval over a period of a day or two and then subjected to a second measurement protocol over a subsequent period of weeks where the second time interval is different than the first time interval, e.g. greater. An eventful (charismatic) oil well on the other hand with a different personality may require multiple and more frequent readings spread out over time and/or for longer duration in order to meet the measurement stability requirements before the data is submitted for the credentialing process.

[0061] The registration process (not shown) is used to collect carbon data for the members and/or users who wish to participate and receive the benefits of the requester. Registration can be performed proactively by the member and/or user or be linked automatically to an interaction engagement and initiated from that event provided the sponsoring organization and/or requestor is already integrated into the system. API's can be used to allow for programmatic access to the functions including registration. A preference and profile system is shown at this time and is used to present options and collect preferences of members on how they want to be notified, of what event, and in what time frame. This process is available to members and/or hierarchies of the system. [0062] Location-based services can be used to correlate carbon events to stakeholders such as confirmations and notification that data was collected or used at a location at a specific time. Real world external events and/or data including, for example, weather (current and projected), seismic information, pressure, temperature, humidity, etc., that aid and augment the correlation of the carbon events. Contextual events can also be included. Suitable contextual events can include, altitude, direction, speed are examples of when the data collecting devices may be collecting and storing or transmitting/relaying data from a device not permanently installed at a data collection site. Additional data may be added to the recorded event to augment the data points, examples of this may include video and photo images of the location where the data is being collected.

[0063] Additional retail commerce services can be provided including marketing and informational services from any number of approved requesters relevant to the system. Examples of retail commerce services includes, but is not limited to, manufacturer and/or retail rebates, rewards, offers, etc. Analytics are provided to stakeholders of the system on key carbon events and key carbon metrics such as: time to credential or assert, locations and systems or requesters, device or channel interacted with, data provided to gain access, etc. Key carbon metrics can then be dimensionally analyzed according to many business and technical needs. The analytics results can be subscribed to and delivered in real-time or triggered by a predefined event which could be the outcome of a smart contract having executed.

[0064] III. REPORTING

[0065] The systems and methods are configurable to generate a variety of reports on demand. Reports include reports affirming status of carbon credits, performance of orphan wells, performance of orphan wells compared to a goal or target, a historical view of the carbon unit’s change in status, event trigger statuses, compliance reporting including compliance reporting tailored to requirements of a specific jurisdiction, and irregularity or fraud reports.

[0066] IV. EXEMPLAR IMPLEMENTATIONS

[0067] The system can be interfaced to any number of external systems, including television, a variety of displays, wearables or embeddable devices, and/or tactile systems like sensor-based watches or other wearable devices where a message comes in notifying the user of past and/or present interactions of their carbon. The system may also be connected to virtual environments such as augmented reality, virtual reality, mixed reality and digital, virtual environments/worlds known as meta verses. The system in this implementation could send carbon current state data from the physical world to the metaverse world for consumption in the virtual world as a form of currency or points or some other asset that could be leveraged in the environment. The virtual world could be the point of transaction from which a company/person/entity purchases their carbon tokens/credits. Additionally, credits or tokens can be redistributed in response to an authorized request.

[0068] FIG. 6 illustrates an implementation of the system applied to orphan wells 600. An orphan well is an oil or gas well that has been abandoned by the fossil fuel extraction industries. Orphan wells are a contributor to greenhouse gas emissions and can be a source of methane emission through plug leakage or failure to plug the well properly. It is estimated that there are 29 million abandoned wells internationally. In addition to greenhouse gas emissions, orphan wells may also be a source of other noxious and harmful gases. Each emission of an orphan well can be monitored as part of the disclosed process and may contribute to, for example, prioritizing the order in which a series of wells is plugged.

[0069] The process starts with identification of the orphan well. Qualification and predictive modeling can be used to provide visualization and analytics for the orphan well. Prior to plugging the orphan well, pre-plugging measurements are taken. Suitable pre-plugging measurements include a determination of methane concentration and methane flow. The pre-plugging measurements are subjected to visualization and analytics. The pre-plugging measurements can also take into account environmental factors to establish a personality of the well. The pre-plugging measurements can also provide a data baseline to support tracing and auditability of any carbon credit that is created for the orphan well. An algorithm is applied to the collected data, for example, after a shutdown of an oil well. The collected data is sampled to determine an initial amount of greenhouse gas emissions. The algorithm can then analyze the collected data to identify patterns and to ensure that the collected data remains continuously within range set of carbon credit authorities. [0070] System oracles can be used to take into account seismic data, weather, etc. when establishing the well profile or personality. The well profile is used to make a predictive model of what the well might yield under expected conditions. Once the orphan well is plugged, post-plugging measurements are taken to ensure that the well remains plugged. Raw data is collectable across a variety of vendors and OEM measurement devices by communicating with an API associated with each device. Suitable post-plugging measurements include methane concentration and methane flow. The post-plugging measurements are subjected to visualization and analytics. The pre -plugging and postplugging measurements can be obtained from, for example, a portable gas chromatograph, a thermal mass flow, a remote terminal unit (RTU), and/or a data capture platform.

[0071] The identification step 610, for example, begins with identifying an orphaned oil well. Background research is performed and a well profile is created. The well profile can be used by a field team. The well profile includes, for example, a well history, well characteristics, well location, surface ownerships (i.e., owners of the real property associated with the well), and existing oil and gas leases. All of the data points or attributes are encodable by attributes contract 422.

[0072] Following the identification step 610, a qualification step 612 occurs. The qualification step 612 involves an on-site field verification. The algorithm takes into account, for example, the sputtering, burping and spikes that might occur at the oil well, to seek the best signal amongst the dozens-hundreds-thousands of measurements, samples and/or recordings. The on-site field verification drives a generation of a detailed orphaned oil well report to determine if the oil well qualifies for further analysis and work with the one or more surface owners for an access agreement to perform further testing. Further testing can include, for example, greenhouse gas (GHG) emissions, surface conditions, and accessibility. Measurement data is collected from the abandoned well using one or more measurement devices and environmental data is collected. The environmental data includes, for example, real world external events, data, and contextual events. The real-world external events and data can include, for example, weather (current and projected), seismic information, pressure, temperature, humidity, etc., that aid and augment the correlation of the carbon events. Contextual events can include, altitude, direction, speed are examples of when the data collecting devices may be collecting and storing or transmitting/relaying data from a device not permanently installed at a data collection site. The collected data is analyzed to provide insights into the orphan well and to provide an orphan well profile. By, for example, taking methane concentration and flow measurements continuously or at regular intervals over time, a more accurate assessment of the amount of methane to be eliminated can be achieved. The analysis of measurements over time, allows the data to be normalized for a well or for a plurality of wells. Thus, any spikes in activity, such as caused by sputtering, can be factored into the overall well performance. Additionally, analysis of the measurements in combination with environmental factors can further refine the measurements to allow for projections of projected methane emission over time which is eliminated once the orphan well is capped. These projected measurements can also be dynamically refined in response to changing environmental conditions even when a well has been capped.

[0073] Following the qualification step 612, an adoption process 614 takes place. In the United States, orphaned wells are monitored and analyzed in accordance with the American Carbon Registry (ACR) standards. With adoption, an ACR project is initiated and a third-party verifier is engaged to begin the validation of the GHG emissions. A bond may be posted, and the orphaned well is adopted from, for example, the State. A budget is created to satisfy funding needs for plugging the well and surface restoration. [0074] After successfully adopting the orphan well, measurement analytics are performed 616 and the data is optimized 618. A process to retire the oil well, e.g., plug, measure and analyze, can be performed in partnership with a governmental entity (e.g., State). Additionally, surrounding conditions can be restored. A plan for plugging the orphan well is developed and approved. Activities necessary to plug the orphan well are planned and coordinated with surface owners, state and local agencies. Local and regional service companies may be engaged to perform the work needed to plug the orphan well.

[0075] Once the orphan well is plugged, the carbon offset for the orphan well is verified and sold 620 by using optimized and stable behavior data for the well. The information is then used to document and describe the well by providing a detailed analysis for verifiers who are responsible for reviewing and vetting the data. Once the ACR issues a serial number for each carbon credit associated with the orphan well, the carbon credit is sold and the credit is retired to prevent reuse of the credit.

[0076] Data monitoring after the well closure can be continued to ensure adherence to the output expected from a standard methodology. Thus, for example, where there are no measurable greenhouse gas emissions, confirmation that the oil well has in fact been properly shutdown can be provided as well as an identification if the well closure components have failed, resulting in a leakage and measurement.

[0077] Turning to FIG. 7, an exemplar oil well 702 process 700 is in communication with a plurality of oil well sensors 710, e.g., sensor 1 712, sensor 2 714, to sensor n 716. The sensors can be one or more of temperature, flow, humidity, pressure, gas concentration, infrared, etc. Additionally, a plurality of environmental data sources 720 can also be provided, e.g. environmental data 1 722, environmental data 2 724, to environmental data n 726. Environmental data includes, for example, ambient temperature, ambient humidity, seismic activity, soil type, soil acidity/basicity, wind speed, wind direction, light levels, air particulates (e.g., particulate matter in the air or air position information), soil geology of location (e.g., type of rock or soil), etc. The sensors 710 can be in communication with the environmental data 720 or directly with a distributed network 730. The distributed network 730 provides information to the cloud 740 which can then provide information to a data service 750. The data service 750 is in communication with the platform 760 having a process engine 762 and a data collector 764. Large amounts of data can be buffered and stored locally, then transferred later. [0078] FIGS. 8A-8B illustrate data samples by value 802 (y-axis) over time (x-axis) for measurements taken by an exemplar sensor. As illustrated the data samples are numerous. However, as can be appreciated by those skilled in the art, failure to take a large number of measurements can result in a skew in the data if, for example, the representative samples only represent the extreme measurements or the measurements of little variation. By taking a large number of samples over time and, for example, correlating the samples with associated environmental data, a personality of the well can be obtained which can then be adjusted in the future in response to changing environmental conditions. FIG. 8A shows sample readings over multiple days prior to processing via the algorithm (i.e., the raw data); FIG. 8B illustrates the sample readings after processing the data via the algorithm processing. Once the data is processed, as shown in FIG. 8B, the data is sampled and all constraints accounted for. The algorithm selects the best possible set of readings from the raw data separated by a registry methodology specified time frame. The difference in time and multiple measurements are used to take into account varying behaviors of the natural phenomenon of the well.

[0079] For an oil well, data is collected (i.e. measurements before the well is plugged), the pre-plug data is an output of the algorithm after the data has been sampled and the optimal data sets have been selected to establish the most stable behavior of the well. The large emission provide an indication of the highest flow rate applicable for determining credits while adhering to the methodology and constraints required by registries. Postplug data is also collected showing that the methane gas emissions have been eliminated or are negligible and are stable.

[0080] FIGS. 8C-D illustrate selection portions of data 810, 812 where the maximum emissions were recorded in the time intervals.

[0081] Post algorithm selected subsequence time series accounted for the algorithm selects the best possible sets of readings separated by a by a registry methodology specified time frame. The difference in time and multiple measurements is used to take into account the varying behaviors of the natural phenomenon of the well. The two charts below show the selected portion of the data where the maximum emissions were recorded in the time interval. Calculations are then performed on the best samples (from hundreds or thousands of measurements taken) by the methodology described above and submitted to verifiers and for registration of credits. Selected data 810, 812 from the entire data set are stored in the ledger if the sequence of recorded and processed data meets a defined methodology for stability. The selected data and charts are then stored in a database for a generation of the documentation for the verifiers and registries. Information is included to support calculations and determination of credit values.

[0082] FIG. 8E is a post-close example of a data recording which illustrates continual recordings and monitoring of the data, e.g., elimination of emissions.

[0083] IV. COMPUTING AND NETWORK ENVIRONMENTS

[0084] Many example implementations have been described in part in the above sections of this disclosure. The system operates on computer systems that can be a combination of on-premises, in the cloud (hosted externally), mobile devices, loT sensors attached to equipment stationary or mobile such as to UAV (Unmanned Aerial Vehicles or drones) and an extensible set of third party supplied applications and devices that extend the functionality of the system. Distributed network architecture ensures network stability, redundancy and resilience built into network. A distributed computing network built using the distributed network architecture described above can run distributed applications, for example, autonomous distributed building or device control systems, web services, secure peer to peer networking, distributed data management services, cloud storage, distributed databases, decentralized groups or companies, blockchain based distributed trading platforms, cryptographic tokens, document processing, blockchain based Turing complete virtual machines, graphics rendering, distributed blockchain based accounting systems, etc.

[0085] A plurality of computing devices can be deployed in implementing the disclosed systems and methods. Computing devices include one or more: processors, memories, storage devices, high-speed interfaces connecting to memory and high-speed expansion ports, and low speed interfaces connecting to low speed bus and storage device. Each of component of the one or more computing devices can also be interconnected using various busses, and can be mounted on a common motherboard or in other manners as appropriate. Processor can process instructions for execution within computing device, including instructions stored in memory or on storage device to display graphical data for a GUI on an external input/output device, including, e.g., each computing device can include a display coupled to high speed interface. In other implementations, multiple processors and/or multiple busses can be used, as appropriate, along with multiple memories and types of memory. Also, multiple computing devices can be connected, with each device providing portions of the operations (e.g., as a server bank, a group of blade servers, or a multi-processor system).

[0086] Memories are configurable to store data within computing devices. In one implementation, memory is a volatile memory unit or units. In another implementation, memory is a non-volatile memory unit or units. Memory can also be another form of computer-readable medium (e.g., a magnetic disk, optical disk or solid state disk). Memory can also be non-transitory. [0087] Storage devices are capable of providing mass storage for computing device. In one implementation, storage device can be or contain a computer-readable medium (e.g., a floppy disk device, a hard disk device, an optical disk device, or a tape device, a flash memory or other similar solid state memory device, or an array of devices, such as devices in a storage area network or other configurations). A computer program product can be tangibly embodied in a data carrier. The computer program product also can contain instructions that, when executed, perform one or more methods (e.g., those described above.) The data carrier is a computer- or machine-readable medium, (e.g., memory, storage device, memory on processor, and the like).

[0088] High-speed controllers manage bandwidth-intensive operations for computing device, while low speed controllers manage lower bandwidth-intensive operations. Such allocation of functions is an example only. In one implementation, high-speed controller is coupled to memory, display (e.g., through a graphics processor or accelerator), and to high-speed expansion ports, which can accept various expansion cards. In the implementation, low-speed controllers are coupled to storage devices and low-speed expansion port. The low-speed expansion port, which can include various communication ports (e.g., USB, Bluetooth®, Ethernet, wireless Ethernet), can be coupled to one or more input/output devices (e.g., a keyboard, a pointing device, a scanner, or a networking device including a switch or router, e.g., through a network adapter). Computing devices can be implemented in a number of different forms, as shown in the figure. For example, computing devices can be implemented as standard server, or multiple times in a group of such servers. Computing devices can be implemented as part of rack server system. In addition or as an alternative, it can be implemented in a personal computer (e.g., laptop computer). In some examples, components from computing devices can be combined with other components in a mobile device (not shown), e.g., device. Each of such devices can contain one or more of computing devices and an entire system can be made up of multiple computing devices communicating with each other.

[0089] Computing device includes processor, memory, an input/output device (e.g., display, communication interface, and transceiver) among other components. Device also can be provided with a storage device, (e.g., a microdrive or other device) to provide additional storage. Each of the devices, processor, display, memory, communication interfaces, and transceiver, are interconnected using various buses, and several of the components can be mounted on a common motherboard or in other manners as appropriate.

[0090] A processor can execute instructions within computing device, including instructions stored in memory. The processor can be implemented as a chipset of chips that include separate and multiple analog and digital processors. The processor can provide, for example, for coordination of the other components of device, e.g., control of user interfaces, applications run by device, and wireless communication by device. [0091] Processor can communicate with a user through control interface and display interface coupled to display. Display can be, for example, a TFT LCD (Thin-Film- Transistor Liquid Crystal Display) or an OLED (Organic Light Emitting Diode) display, or other appropriate display technology. Display interface can comprise appropriate circuitry for driving display to present graphical and other data to a user. Control interface can receive commands from a user and convert them for submission to processor. In addition, external interface can communicate with processor, so as to enable near area communication of device with other devices. External interface can provide, for example, for wired communication in some implementations, or for wireless communication in other implementations, and multiple interfaces also can be used.

[0092] Memory stores data within computing device. Memory can be implemented as one or more of a computer-readable medium or media, a volatile memory unit or units, or a non-volatile memory unit or units. Expansion memory also can be provided and connected to device through expansion interface, which can include, for example, a SIMM (Single In Line Memory Module) card interface. Such expansion memory can provide extra storage space for device, or also can store applications or other data for device. Specifically, expansion memory can include instructions to carry out or supplement the processes described above, and can include secure data also. Thus, for example, expansion memory can be provided as a security module for device, and can be programmed with instructions that permit secure use of device. In addition, secure applications can be provided through the SIMM cards, along with additional data, (e.g., placing identifying data on the SIMM card in a non-hackable manner). [0093] The memory can include, for example, flash memory and/or NVRAM memory, as discussed below. In one implementation, a computer program product is tangibly embodied in a data carrier. The computer program product contains instructions that, when executed, perform one or more methods, e.g., those described above. The data carrier is a computer- or machine-readable medium (e.g., memory, expansion memory, and/or memory on processor), which can be received, for example, over transceiver or external interface.

[0094] Device can communicate wirelessly through communication interface, which can include digital signal processing circuitry where necessary. Communication interface can provide for communications under various modes or protocols (e.g., GSM voice calls, SMS, EMS, or MMS messaging, CDMA, TDMA, PDC, LTE, WCDMA, CDMA2000, or GPRS, among others or any newly developed communication protocols) Such communication can occur, for example, through radio-frequency transceiver. In addition, short-range communication can occur, e.g., using a Bluetooth®, WiFi, or other such transceiver (not shown). In addition, GPS (Global Positioning System) receiver module can provide additional navigation- and location-related wireless data to device, which can be used as appropriate by applications running on a device. Sensors and modules such as cameras, microphones, compasses, accelerators (for orientation sensing), etc. may be included in the device. It will be appreciated by those skilled in the art, that the devices and systems described can communicate using many of the common and emerging intemet-of-things (loT) protocols depending on the situation and the environment.

Examples of protocols include Zigbee, LoRa (wide area long range protocol), NB-IoT (narrow band loT), WiFi, BLE (blue tooth low energy).

[0095] Device also can communicate audibly using audio codec, which can receive spoken data from a user and convert it to usable digital data. Audio codec can likewise generate audible sound for a user, (e.g., through a speaker in a handset of device). Such sound can include sound from voice telephone calls, can include recorded sound (e.g., voice messages, music files, and the like) and also can include sound generated by applications operating on device.

[0096] Computing device can be implemented in a number of different forms, as shown in the figure. For example, it can be implemented as cellular telephone. It also can be implemented as part of smartphone, tablet, a personal digital assistant, or other similar mobile device.

[0097] Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor. The programmable processor can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0098] These computer programs (also known as programs, software, software applications or code) include machine instructions for a programmable processor, and can be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. The programs can use one or more algorithms. As used herein, the terms machine-readable medium and computer-readable medium refer to a computer program product, apparatus and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions.

[0099] To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a device for displaying data to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor), and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be a form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in a form, including acoustic, speech, or tactile input.

[00100] The systems and techniques described here can be implemented in a computing system that includes a backend component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a frontend component (e.g., a client computer having a user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or a combination of such back end, middleware, or frontend components. The components of the system can be interconnected by a form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet. [00101] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client- server relationship to each other. [00102] In some implementations, the engines described herein can be separated, combined or incorporated into a single or combined engine. The engines depicted in the figures are not intended to limit the systems described here to the software architectures shown in the figures. Components of the system can be distributed by short, medium, and long distances depending on the location of the target under measurement. In some configurations the devices, such as measurement devices, operate asynchronously and capture data locally and then transit/retransmit when a signal is detected.

[00103] The present teachings may also extend to one or more of the following numbered clauses:

[00104] Item 1. A computer-implemented method comprising: receiving one or more measurement information from one or more sensors and one or more environmental data; analyzing the one or more measurement information from the one or more sensors and the one or more environmental data; creating a profile based on the one or more measurement information and one or more environmental data; assigning a value based on the profile; creating a self-executing contract for a transaction for the value from at least two nodes in a network, the network comprising a plurality of nodes, a subset of the plurality of nodes maintaining at least a predetermined number of tokens, the subset comprising the at least two nodes, each node of the subset comprising at least one token as a requirement for participation on the network, the transaction comprising an exchange between the at least two nodes; and validating a current state of the self-executing contract on a public ledger. [00105] Item 2. The computer-implemented method of item 1, wherein the selfexecuting contract is for at least one of buying, selling, or bartering based on the value. [00106] Item 3. The computer-implemented method of item 2, further comprising verifying at least one of buying, selling, or bartering based on the value.

[00107] Item 4. The computer-implemented method of item 1, wherein the public ledger stores information representing carbon credit certificates.

[00108] Item 5. The computer-implemented method of item 1, wherein the value represents at least one of carbon offset, carbon credit, carbon impact, renewable energy certificates, energy efficiency credit, energy efficiency impact, demand response credit, or demand response impact.

[00109] Item 6. The computer-implemented method of item 1, wherein the value represents at least one of energy loss due to transmission, constraint in operation condition, or asset utilization.

[00110] Item 7. The computer-implemented method of item 1, wherein the one or more measurement information from the one or more sensors and the one or more environmental data is received real-time.

[00111] Item 8. The computer-implemented method of item 7, wherein the one or more measurement information and the one or more environmental data is appended to the self-executing contract.

[00112] Item 9. The computer-implemented method of item 7, wherein the one or more measurement information is received for an orphan well.

[00113] Item 10. The computer-implemented method of item 9, wherein the orphan well is plugged.

[00114] Item 11. The computer-implemented method of item 10, further comprising receiving one or more measurement information from one or more sensors and the one or more environmental data from the plugged orphan well.

[00115] Item 12. The computer-implemented method of item 11, further comprising verifying a compliance with a requirement for the at least one of a carbon offset, a carbon credit, a carbon impact, a renewable energy certificate, an energy efficiency credit, an energy efficiency impact, a demand response credit, or a demand response impact. [00116] Item 13. A system comprising: a network comprising a plurality of nodes, a subset of the plurality of nodes maintaining at least a predetermined number of tokens, the subset comprising at least two nodes of the plurality of nodes, each node of the subset comprising at least one token as a requirement for participation on the network, each token representing a value; wherein a node of the plurality of nodes generates a selfexecuting contract, the self- executing contract configured to: receive one or more measurement information from one or more sensors and one or more environmental data, analyze the one or more measurement information from the one or more sensors and the one or more environmental data, create a profile based on the one or more measurement information and one or more environmental data, and assign a value based on the profile; validating a current state of a public ledger; and contributing to an updated state of the public ledger.

[00117] Item 14. The system of item 13, wherein the self-executing contract is for at least one of buying, selling, or bartering based on the value.

[00118] Item 15. The system of item 14, further comprising verifying at least one of buying, selling, or bartering based on the value.

[00119] Item 16. The system of item 13, wherein the public ledger stores information representing carbon credit certificates.

[00120] Item 17. The system of item 13, wherein the value represents at least one of carbon offset, carbon credit, carbon impact, renewable energy certificates, energy efficiency credit, energy efficiency impact, demand response credit, or demand response impact.

[00121] Item 18. The system of item 13, wherein the value represents at least one of energy loss due to transmission, constraint in operation condition, or asset utilization. [00122] Item 19. The system of item 13, wherein the one or more measurement information from the one or more sensors and the one or more environmental data is received real-time.

[00123] Item 20. The system of item 19, wherein the one or more measurement information and the one or more environmental data is appended to the self-executing contract. [00124] Item 21. The system of item 19, wherein the one or more measurement information is received for an orphan well.

[00125] Item 22. The system of item 21, wherein the orphan well is plugged.

[00126] Item 23. The system of item 22, further comprising receiving one or more measurement information from one or more sensors and the one or more environmental data from the plugged orphan well.

[00127] Item 24. The system of item 23, further comprising verifying a compliance with a requirement for the at least one of a carbon offset, a carbon credit, a carbon impact, a renewable energy certificate, an energy efficiency credit, an energy efficiency impact, an demand response credit, or an demand response impact.

[00128] Item 25. A non-transitory computer storage medium encoded with computer program instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: receiving, by a self-executing contract, settlement information of an energy transaction from at least two nodes in a network, the network comprising a plurality of nodes, a subset of the plurality of nodes maintaining at least a predetermined number of tokens, the subset comprising the at least two nodes, each node of the subset comprising at least one token as a requirement for participation on the network, the energy transaction comprising an exchange between the at least two nodes for one or more tokens, each token representing a value; wherein a node of the plurality of nodes generates a self-executing contract, the self- executing contract configured to: receive one or more measurement information from one or more sensors and one or more environmental data, analyze the one or more measurement information from the one or more sensors and the one or more environmental data, create a profile based on the one or more measurement information and one or more environmental data, and assign a value based on the profile; validating a current state of a public ledger; and contributing to an updated state of the public ledger using.

[00129] Item 26. The non-transitory computer storage medium of item 25, wherein the self-executing contract is for at least one of buying, selling, or bartering based on the value.

[00130] Item 27. The non-transitory computer storage medium of item 26, further comprising verifying at least one of buying, selling, or bartering based on the value. [00131] Item 28. The non-transitory computer storage medium of item 25, wherein the public ledger stores information representing carbon credit certificates. [00132] Item 29. The non-transitory computer storage medium of item 25, wherein the value represents at least one of carbon offset, carbon credit, carbon impact, renewable energy certificates, energy efficiency credit, energy efficiency impact, demand response credit, or demand response impact.

[00133] Item 30. The non-transitory computer storage medium of item 25, wherein the value represents at least one of energy loss due to transmission, constraint in operation condition, or asset utilization.

[00134] Item 31. The non-transitory computer storage medium of item 25, wherein the one or more measurement information from the one or more sensors and the one or more environmental data is received real-time.

[00135] Item 32. The non-transitory computer storage medium of item 31, wherein the one or more measurement information and the one or more environmental data is appended to the self-executing contract.

[00136] Item 33. The non-transitory computer storage medium of item 31, wherein the one or more measurement information is received for an orphan well. [00137] Item 34. The non-transitory computer storage medium of item 33, wherein the orphan well is plugged.

[00138] Item 35. The non-transitory computer storage medium of item 33, further comprising receiving one or more measurement information from one or more sensors and the one or more environmental data from the plugged orphan well.

[00139] Item 36. The non-transitory computer storage medium of item 35, further comprising verifying a compliance with a requirement for the at least one of a carbon offset, a carbon credit, a carbon impact, a renewable energy certificate, an energy efficiency credit, an energy efficiency impact, a demand response credit, or a demand response impact.

[00140] Item 37. A computer-implemented method comprising: receiving one or more measurement information from one or more sensors and one or more environmental data, analyze the one or more measurement information from the one or more sensors and the one or more environmental data, creating a profile based on the one or more measurement information and one or more environmental data, and forecasting a future measurement value based on an analyzed data pattern used to correlate at least two of the plurality of data points.

[00141] Item 38. The computer-implemented method of item 37, wherein the forecast represents at least one of energy loss due to transmission, constraint in operation condition, or asset utilization.

[00142] Item 39. The computer-implemented method of item 37, wherein the one or more measurement information from the one or more sensors and the one or more environmental data is received real-time.

[00143] Item 40. The computer-implemented method of item 37, wherein the one or more measurement information is received for an orphan well.

[00144] Item 41. The computer-implemented method of item 40, wherein the orphan well is plugged.

[00145] Item 42. The computer-implemented method of item 41, further comprising receiving one or more measurement information from one or more sensors and the one or more environmental data from the plugged orphan well.

[00146] Item 43. The system of item 40, further comprising predicting a compliance with a requirement for the at least one of a carbon offset, a carbon credit, a carbon impact, a renewable energy certificate, an energy efficiency credit, an energy efficiency impact, an demand response credit, or an demand response impact.

[00147] Item 44. A non-transitory computer storage medium encoded with computer program instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: receiving, by a self-executing contract, settlement information of an energy transaction from at least two nodes in a network, the network comprising a plurality of nodes, a subset of the plurality of nodes maintaining at least a predetermined number of tokens, the subset comprising the at least two nodes, each node of the subset comprising at least one token as a requirement for participation on the network, the energy transaction comprising an exchange between the at least two nodes for one or more tokens, each token representing a value; wherein a node of the plurality of nodes generates a self-executing contract, the self- executing contract configured to: receive one or more measurement information from one or more sensors and one or more environmental data, analyze the one or more measurement information from the one or more sensors and the one or more environmental data, create a profile based on the one or more measurement information and one or more environmental data, and forecast a future measurement value based on an analyzed data pattern used to correlate at least two of the plurality of data points.

[00148] Item 45. The non-transitory computer storage medium of item 44, wherein the forecast represents at least one of energy loss due to transmission, constraint in operation condition, or asset utilization.

[00149] Item 46. The non-transitory computer storage medium of item 44, wherein the one or more measurement information from the one or more sensors and the one or more environmental data is received real-time.

[00150] Item 47. The non-transitory computer storage medium of item 44, wherein the one or more measurement information is received for an orphan well. [00151] Item 48. The non-transitory computer storage medium of item 47, wherein the orphan well is plugged.

[00152] Item 49. The non-transitory computer storage medium of item 44, further comprising receiving one or more measurement information from one or more sensors and the one or more environmental data from the plugged orphan well.

[00153] Item 50. The non-transitory computer storage medium of item 44, further comprising predicting a compliance with a requirement for the at least one of a carbon offset, a carbon credit, a carbon impact, a renewable energy certificate, an energy efficiency credit, an energy efficiency impact, an demand response credit, or an demand response impact.

[00154] Item 51. A system for predicting an event in a computing environment, comprising: one or more computers with executable instructions that when executed cause the system to: receive one or more measurement information from one or more sensors and one or more environmental data, analyze the one or more measurement information from the one or more sensors and the one or more environmental data, create a profile based on the one or more measurement information and one or more environmental data, and forecast a future measurement value based on an analyzed data pattern used to correlate at least two of the plurality of data points. [00155] Item 52. The system of item 51, wherein the forecast represents at least one of energy loss due to transmission, constraint in operation condition, or asset utilization.

[00156] Item 53. The system of item 51, wherein the one or more measurement information from the one or more sensors and the one or more environmental data is received real-time.

[00157] Item 54. The system of item 51, wherein the one or more measurement information is received for an orphan well.

[00158] Item 55. The system of item 54, wherein the orphan well is plugged.

[00159] Item 56. The system of item 55, further comprising receiving one or more measurement information from one or more sensors and the one or more environmental data from the plugged orphan well.

[00160] Item 57. The system of item 51, further comprising predicting a compliance with a requirement for the at least one of a carbon offset, a carbon credit, a carbon impact, a renewable energy certificate, an energy efficiency credit, an energy efficiency impact, an demand response credit, or an demand response impact.

[00161] Item 58. A computer-implemented method comprising: receiving one or more measurement information from one or more sensors and one or more environmental data; analyzing the one or more measurement information from the one or more sensors and the one or more environmental data; creating a profile based on the one or more measurement information and one receive one or more measurement information from one or more sensors and one or more environmental data, analyze the one or more measurement information from the one or more sensors and the one or more environmental data, correlate the one or more sensor data and the one or more environmental data, create a correlated data set of the one or more sensor data and the one or more environmental data, forecast a future measurement value based on an analyzed data pattern used to correlate at least two of the plurality of data points.

[00162] Item 59. The computer-implemented method of item 58, wherein the forecast represents at least one of energy loss due to transmission, constraint in operation condition, or asset utilization. [00163] Item 60. The computer-implemented method of item 58, wherein the one or more measurement information from the one or more sensors and the one or more environmental data is received real-time.

[00164] Item 61. The computer-implemented method of item 58, wherein the one or more measurement information is received for an orphan well.

[00165] Item 62. The computer-implemented method of item 61, wherein the orphan well is plugged.

[00166] Item 65. The computer-implemented method of item 62, further comprising receiving one or more measurement information from one or more sensors and the one or more environmental data from the plugged orphan well.

[00167] Item 66. The computer-implemented method of item 65, further comprising predicting a compliance with a requirement for the at least one of a carbon offset, a carbon credit, a carbon impact, a renewable energy certificate, an energy efficiency credit, an energy efficiency impact, an demand response credit, or an demand response impact.

[00168] Item 67. A non-transitory computer storage medium encoded with computer program instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: receive one or more measurement information from one or more sensors and one or more environmental data, analyze the one or more measurement information from the one or more sensors and the one or more environmental data, correlate the one or more sensor data and the one or more environmental data, create a correlated data set of the one or more sensor data and the one or more environmental data, forecast a future measurement value based on an analyzed data pattern used to correlate at least two of the plurality of data points.

[00169] Item 68. The non-transitory computer storage medium of item 67, wherein the forecast represents at least one of energy loss due to transmission, constraint in operation condition, or asset utilization.

[00170] Item 69. The non-transitory computer storage medium of item 67, wherein the one or more measurement information from the one or more sensors and the one or more environmental data is received real-time. [00171] Item 70. The non-transitory computer storage medium of item 67, wherein the one or more measurement information is received for an orphan well. [00172] Item 71. The non-transitory computer storage medium of item 70, wherein the orphan well is plugged.

[00173] Item 72. The non-transitory computer storage medium of item 71, further comprising receiving one or more measurement information from one or more sensors and the one or more environmental data from the plugged orphan well.

[00174] Item 73. The non-transitory computer storage medium of item 70, further comprising predicting a compliance with a requirement for the at least one of a carbon offset, a carbon credit, a carbon impact, a renewable energy certificate, an energy efficiency credit, an energy efficiency impact, an demand response credit, or an demand response impact.

[00175] Item 74. A system for correlating data in a computing environment, comprising: one or more computers with executable instructions that when executed cause the system to: receive one or more measurement information from one or more sensors and one or more environmental data, analyze the one or more measurement information from the one or more sensors and the one or more environmental data, correlate the one or more sensor data and the one or more environmental data, create a correlated data set of the one or more sensor data and the one or more environmental data, forecast a future measurement value based on an analyzed data pattern used to correlate at least two of the plurality of data points.

[00176] Item 75. The system of item 74, wherein the forecast represents at least one of energy loss due to transmission, constraint in operation condition, or asset utilization.

[00177] Item 76. The system of item 74, wherein the one or more measurement information from the one or more sensors and the one or more environmental data is received real-time.

[00178] Item 77. The system of item 74, wherein the one or more measurement information is received for an orphan well.

[00179] Item 78. The system of item 77, wherein the orphan well is plugged. [00180] Item 79. The system of item 78, further comprising receiving one or more measurement information from one or more sensors and the one or more environmental data from the plugged orphan well.

[00181] Item 80. The system of item 77, further comprising predicting a compliance with a requirement for the at least one of a carbon offset, a carbon credit, a carbon impact, a renewable energy certificate, an energy efficiency credit, an energy efficiency impact, an demand response credit, or an demand response impact.

[00182] Item 81. A computer-implemented method comprising: receiving one or more measurement information from one or more sensors; analyzing the one or more measurement information from the one or more sensors at a first time, sample the one or more measurement information from the one or more sensors, establishing a behavior of the sampled one or more measurement information; and determining a credit associated with the sampled measurement information.

[00183] Item 82. The computer-implemented method of item 81, wherein the sampled measurement is at least one of energy loss due to transmission, constraint in operation condition, or asset utilization.

[00184] Item 83. The computer-implemented method of item 81, wherein the one or more measurement information from the one or more sensors is received real-time.

[00185] Item 84. The computer-implemented method of item 81, wherein the one or more measurement information is received for an orphan well.

[00186] Item 85. The computer-implemented method of item 84, wherein the orphan well is plugged.

[00187] Item 86. The computer-implemented method of item 85, further comprising receiving one or more measurement information from one or more sensors from the plugged orphan well.

[00188] Item 87. The computer-implemented method of item 81 further comprising sampling the one or more measurement information from the one or more sensors at a second time, wherein the second time is later than the first time.

[00189] Item 88. The computer-implemented method of item 81, further comprising predicting a compliance with a requirement for the at least one of a carbon offset, a carbon credit, a carbon impact, a renewable energy certificate, an energy efficiency credit, an energy efficiency impact, an demand response credit, or an demand response impact.

[00190] Item 89. A non-transitory computer storage medium encoded with computer program instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: receive one or more measurement information from one or more sensors; analyze the one or more measurement information from the one or more sensors at a first time, sample the one or more measurement information from the one or more sensors, establishing a behavior of the sampled one or more measurement information; and determine a credit associated with the sampled measurement information.

[00191] Item 90. The non-transitory computer storage medium of item 89, wherein the forecast represents at least one of energy loss due to transmission, constraint in operation condition, or asset utilization.

[00192] Item 91. The non-transitory computer storage medium of item 89, wherein the one or more measurement information from the one or more sensors and the one or more environmental data is received real-time.

[00193] Item 92. The non-transitory computer storage medium of item 89, wherein the one or more measurement information is received for an orphan well. [00194] Item 93. The non-transitory computer storage medium of item 92, wherein the orphan well is plugged.

[00195] Item 94. The non-transitory computer storage medium of item 93, further comprising receiving one or more measurement information from one or more sensors and the one or more environmental data from the plugged orphan well.

[00196] Item 95. The non-transitory computer storage medium of item 89, further comprising predicting a compliance with a requirement for the at least one of a carbon offset, a carbon credit, a carbon impact, a renewable energy certificate, an energy efficiency credit, an energy efficiency impact, an demand response credit, or an demand response impact.

[00197] Item 96. The computer-implemented method of item 89 further comprising sampling the one or more measurement information from the one or more sensors at a second time, wherein the second time is later than the first time. [00198] Item 97. A system for correlating data in a computing environment, comprising: one or more computers with executable instructions that when executed cause the system to: receiving one or more measurement information from one or more sensors; analyzing the one or more measurement information from the one or more sensors at a first time, sample the one or more measurement information from the one or more sensors, establishing a behavior of the sampled one or more measurement information; and determining a credit associated with the sampled measurement information.

[00199] Item 98. The system of item 97, wherein the forecast represents at least one of energy loss due to transmission, constraint in operation condition, or asset utilization.

[00200] Item 99. The system of item 97, wherein the one or more measurement information from the one or more sensors and the one or more environmental data is received real-time.

[00201] Item 100. The system of item 97, wherein the one or more measurement information is received for an orphan well.

[00202] Item 101. The system of item 100, wherein the orphan well is plugged.

[00203] Item 102. The system of item 101, further comprising receiving one or more measurement information from one or more sensors and the one or more environmental data from the plugged orphan well.

[00204] Item 103. The system of item 102, further comprising predicting a compliance with a requirement for the at least one of a carbon offset, a carbon credit, a carbon impact, a renewable energy certificate, an energy efficiency credit, an energy efficiency impact, an demand response credit, or an demand response impact.

[00205] Item 104. The computer- implemented method of item 97 further comprising sampling the one or more measurement information from the one or more sensors at a second time, wherein the second time is later than the first time.

[00206] While preferred embodiments of the present invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the invention. In addition, the logic flows depicted in the figures do not require the particular order shown, or sequential order, to achieve desirable results. In addition, other steps can be provided, or steps can be eliminated, from the described flows, and other components can be added to, or removed from, the described systems. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in practicing the invention. It is intended that any claims presented define the scope of the invention and that methods and structures within the scope of these claims and their equivalents be covered thereby.

Claims

CLAIMS WHAT IS CLAIMED:

1. A computer-implemented method comprising: receiving one or more measurement information from one or more sensors and one or more environmental data; analyzing the one or more measurement information from the one or more sensors and the one or more environmental data; creating a profile based on the one or more measurement information and one or more environmental data; assigning a value based on the profile; creating a self-executing contract for a transaction for the value from at least two nodes in a network, the network comprising a plurality of nodes, a subset of the plurality of nodes maintaining at least a predetermined number of tokens, the subset comprising the at least two nodes, each node of the subset comprising at least one token as a requirement for participation on the network, the transaction comprising an exchange between the at least two nodes; and validating a current state of the self-executing contract on a public ledger.

2. The computer- implemented method of claim 1, wherein the self-executing contract is for at least one of buying, selling, or bartering based on the value.

3. The computer- implemented method of claim 2, further comprising verifying at least one of buying, selling, or bartering based on the value.

4. The computer- implemented method of claim 1, wherein the public ledger stores information representing carbon credit certificates.

5. The computer- implemented method of claim 1, wherein the value represents at least one of carbon offset, carbon credit, carbon impact, renewable energy certificates, energy efficiency credit, energy efficiency impact, demand response credit, or demand response impact.

6. The computer- implemented method of claim 1, wherein the value represents at least one of energy loss due to transmission, constraint in operation condition, or asset utilization.

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7. The computer- implemented method of claim 1, wherein the one or more measurement information from the one or more sensors and the one or more environmental data is received real-time.

8. The computer- implemented method of claim 7, wherein the one or more measurement information and the one or more environmental data is appended to the self-executing contract.

9. The computer- implemented method of claim 7, wherein the one or more measurement information is received for an orphan well.

10. The computer-implemented method of claim 9, wherein the orphan well is plugged.

11. The computer- implemented method of claim 10, further comprising receiving one or more measurement information from one or more sensors and the one or more environmental data from the plugged orphan well.

12. The computer-implemented method of claim 11, further comprising verifying a compliance with a requirement for the at least one of a carbon offset, a carbon credit, a carbon impact, a renewable energy certificate, an energy efficiency credit, an energy efficiency impact, a demand response credit, or a demand response impact.

13. A system comprising: a network comprising a plurality of nodes, a subset of the plurality of nodes maintaining at least a predetermined number of tokens, the subset comprising at least two nodes of the plurality of nodes, each node of the subset comprising at least one token as a requirement for participation on the network, each token representing a value; wherein a node of the plurality of nodes generates a self-executing contract, the self- executing contract configured to: receive one or more measurement information from one or more sensors and one or more environmental data, analyze the one or more measurement information from the one or more sensors and the one or more environmental data, create a profile based on the one or more measurement information and one or more environmental data, and assign a value based on the profile;

-44- validating a current state of a public ledger; and contributing to an updated state of the public ledger.

14. The system of claim 13, wherein the self-executing contract is for at least one of buying, selling, or bartering based on the value.

15. The system of claim 14, further comprising verifying at least one of buying, selling, or bartering based on the value.

16. The system of claim 13, wherein the public ledger stores information representing carbon credit certificates.

17. The system of claim 13, wherein the value represents at least one of carbon offset, carbon credit, carbon impact, renewable energy certificates, energy efficiency credit, energy efficiency impact, demand response credit, or demand response impact.

18. The system of claim 13, wherein the value represents at least one of energy loss due to transmission, constraint in operation condition, or asset utilization.

19. The system of claim 13, wherein the one or more measurement information from the one or more sensors and the one or more environmental data is received realtime.

20. The system of claim 19, wherein the one or more measurement information and the one or more environmental data is appended to the self-executing contract.

21. The system of claim 19, wherein the one or more measurement information is received for an orphan well.

22. The system of claim 21, wherein the orphan well is plugged.

23. The system of claim 22, further comprising receiving one or more measurement information from one or more sensors and the one or more environmental data from the plugged orphan well.

24. The system of claim 23, further comprising verifying a compliance with a requirement for the at least one of a carbon offset, a carbon credit, a carbon impact, a renewable energy certificate, an energy efficiency credit, an energy efficiency impact, an demand response credit, or an demand response impact.

25. A non-transitory computer storage medium encoded with computer program instructions that when executed by one or more computers cause the one or more computers to perform operations comprising:

-45- receiving, by a self-executing contract, settlement information of an energy transaction from at least two nodes in a network, the network comprising a plurality of nodes, a subset of the plurality of nodes maintaining at least a predetermined number of tokens, the subset comprising the at least two nodes, each node of the subset comprising at least one token as a requirement for participation on the network, the energy transaction comprising an exchange between the at least two nodes for one or more tokens, each token representing a value; wherein a node of the plurality of nodes generates a self-executing contract, the self- executing contract configured to: receive one or more measurement information from one or more sensors and one or more environmental data, analyze the one or more measurement information from the one or more sensors and the one or more environmental data, create a profile based on the one or more measurement information and one or more environmental data, and assign a value based on the profile; validating a current state of a public ledger; and contributing to an updated state of the public ledger using.

26. The non-transitory computer storage medium of claim 25, wherein the selfexecuting contract is for at least one of buying, selling, or bartering based on the value.

27. The non-transitory computer storage medium of claim 26, further comprising verifying at least one of buying, selling, or bartering based on the value.

28. The non-transitory computer storage medium of claim 25, wherein the public ledger stores information representing carbon credit certificates.

29. The non-transitory computer storage medium of claim 25, wherein the value represents at least one of carbon offset, carbon credit, carbon impact, renewable energy certificates, energy efficiency credit, energy efficiency impact, demand response credit, or demand response impact.

30. The non-transitory computer storage medium of claim 25, wherein the value represents at least one of energy loss due to transmission, constraint in operation condition, or asset utilization.

31. The non-transitory computer storage medium of claim 25, wherein the one or more measurement information from the one or more sensors and the one or more environmental data is received real-time.

32. The non-transitory computer storage medium of claim 31, wherein the one or more measurement information and the one or more environmental data is appended to the self-executing contract.

33. The non-transitory computer storage medium of claim 31, wherein the one or more measurement information is received for an orphan well.

34. The non-transitory computer storage medium of claim 33, wherein the orphan well is plugged.

35. The non-transitory computer storage medium of claim 33, further comprising receiving one or more measurement information from one or more sensors and the one or more environmental data from the plugged orphan well.

36. The non-transitory computer storage medium of claim 35, further comprising verifying a compliance with a requirement for the at least one of a carbon offset, a carbon credit, a carbon impact, a renewable energy certificate, an energy efficiency credit, an energy efficiency impact, a demand response credit, or a demand response impact.

37. A system for predicting an event in a computing environment, comprising: one or more computers with executable instructions that when executed cause the system to: receive one or more measurement information from one or more sensors and one or more environmental data, analyze the one or more measurement information from the one or more sensors and the one or more environmental data, create a profile based on the one or more measurement information and one or more environmental data, and forecast a future measurement value based on an analyzed data pattern used to correlate at least two of the plurality of data points.

38. The system of claim 37, wherein the forecast represents at least one of energy loss due to transmission, constraint in operation condition, or asset utilization.

39. The system of claim 37, wherein the one or more measurement information from the one or more sensors and the one or more environmental data is received realtime.

40. The system of claim 37, wherein the one or more measurement information is received for an orphan well.

41. The system of claim 40, wherein the orphan well is plugged.

42. The system of claim 41, further comprising receiving one or more measurement information from one or more sensors and the one or more environmental data from the plugged orphan well.

43. The system of claim 40, further comprising predicting a compliance with a requirement for the at least one of a carbon offset, a carbon credit, a carbon impact, a renewable energy certificate, an energy efficiency credit, an energy efficiency impact, an demand response credit, or an demand response impact.

44. A system for correlating data in a computing environment, comprising: one or more computers with executable instructions that when executed cause the system to: receive one or more measurement information from one or more sensors and one or more environmental data, analyze the one or more measurement information from the one or more sensors and the one or more environmental data, correlate the one or more sensor data and the one or more environmental data, create a correlated data set of the one or more sensor data and the one or more environmental data, forecast a future measurement value based on an analyzed data pattern used to correlate at least two of the plurality of data points.

45. The system of claim 44, wherein the forecast represents at least one of energy loss due to transmission, constraint in operation condition, or asset utilization.

46. The system of claim 44, wherein the one or more measurement information from the one or more sensors and the one or more environmental data is received realtime.

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47. The system of claim 44, wherein the one or more measurement information is received for an orphan well.

48. The system of claim 47, wherein the orphan well is plugged.

49. The system of claim 48, further comprising receiving one or more measurement information from one or more sensors and the one or more environmental data from the plugged orphan well.

50. The system of claim 47, further comprising predicting a compliance with a requirement for the at least one of a carbon offset, a carbon credit, a carbon impact, a renewable energy certificate, an energy efficiency credit, an energy efficiency impact, an demand response credit, or an demand response impact.

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