137 results about "Emission inventory" patented technology

The invention discloses a pollution space analysis method and system based on atmospheric pollution concentration data, and particularly relates to the technical field of data analysis. A high-precision pollutant concentration spatial distribution model is constructed by combining pollutant diffusion characteristics and geographic information, and regional pollution differences are revealed; identifying a pollution source emission mode and analyzing the evolution trends of pollutants in different time and space ranges by matching spatial and temporal change analysis with a pollution source emission list; the influence of real-time meteorological data on pollution diffusion is analyzed, and a pollutant spatial distribution model is adjusted, so that the prediction precision is improved; pollution concentration distribution, pollution source emission and meteorological influence are combined, pollution hot spot areas are accurately identified, future pollution evolution conditions are analyzed and predicted based on historical trends, scientific and reasonable pollution treatment suggestions are provided, the limitation of traditional interpolation and statistical regression models is broken through, dynamic and accurate modeling of pollutant concentration is achieved, and the method is suitable for large-scale popularization and application. And the scientificity of pollution source tracing and treatment decision making is improved.

The invention relates to the technical field of drainage basin water environment management, in particular to a construction method of a water pollution source emission list. The method comprises the following steps: performing classification and account subdivision on different types of pollution sources based on pollution source sub-categories, pollution production modes, treatment technologies and pollution discharge paths to obtain a pollution source classification and classification system; a multi-source data fusion strategy and a sparse data processing strategy are utilized to carry out parameter fine value processing on data in the pollution source grading and classification system, and calculation parameters of different types of pollution sources are determined; performing modeling characterization on the calculation parameters of the different types of pollution sources according to a pollutant generation and emission mechanism to obtain the river entering quantity of different types of pollutants of the different types of pollution sources, and constructing a water pollution source emission list based on the river entering quantity. Therefore, the problems of difficulty in full-aperture refined estimation of the pollution source emission list, relatively low space-time precision of the list and the like are solved, and refined calculation of pollution source emission is realized.

The embodiment of the invention discloses a causal relationship-based soil pollution tracing method, equipment and medium, and the method comprises the steps: obtaining the soil pollutant concentration detection data of a to-be-traced region, and the data of two emission sources of enterprises and traffic; performing positive matrix factorization on the detection data, and determining contribution of each hidden pollution source to each pollutant; determining at least one pollutant which is contributed by each hidden pollution source and is associated with the enterprise and / or traffic as at least one target pollutant to be traced; aiming at each type of target pollutants, through emission list spatialization and GCCM causal analysis, the causal relationship and the significance degree of the enterprise and the traffic and the target pollutants are verified respectively; and according to a verification result, carrying out auxiliary decision on the preferentially controlled blocks in the region and the types of pollutants and emission sources preferentially controlled by each block. According to the embodiment of the invention, accurate tracing of soil pollution in a complex environment can be realized.

The invention belongs to the field of air pollution prevention and control, and particularly discloses an urban air pollution transmission path and source contribution evaluation system and method based on machine learning and a numerical mode, and the evaluation system comprises a meteorological simulation module, a pollution emission list module, a numerical traceability module and a machine learning transmission path evaluation module. According to the method, machine learning and a numerical mode are combined, the automation degree is high, automatic downloading, automatic running and online data analysis can be achieved without manual data statistics and analysis, operation is convenient, data accuracy is high, the response speed is high, and important support is provided for work such as atmospheric pollutant cause tracing and atmospheric pollution control scheme making.

The present application relates to the technical field of heavy-metal pollution remediation, and relates in particular to a soil heavy‐metal accumulation predicting method employing an emission inventory and a receptor model. The method comprises: using atmospheric dust deposition, irrigation water, chemical fertilizers and pesticides as input flux sources of heavy metals to farmland soil, using surface runoff and crops as output fluxes, and constructing an input-output flux inventory of heavy-metal pollutants to clarify a dynamic balance relationship of soil heavy-metal accumulation; and continuously collecting and monitoring of soil samples. The invention provides crucial assistance in determining the transport balance of heavy metals in farmland areas impacted by non-ferrous metal smelting and beneficiation slag sites and in identifying sources of soil heavy-metal pollution, and offers theoretical guidance for subsequent soil heavy-metal remediation and targeted control.

The invention provides a multi-source constraint gridding atmospheric pollutant and greenhouse gas emission estimation method, and relates to the technical field of environmental information processing, and the method comprises the steps: constructing a hierarchical grid structure of an urban core domain and a peripheral background domain based on a unified data basic set, forming a gridding baseline emission list through unified activity data and emission factors, establishing a multi-source scale coordination mechanism, fusing ground monitoring and satellite observation information, and generating an observation data set; according to the method, an SMOKE sparse matrix processing chain is embedded to construct a joint estimation model, parameter inversion and consistency constraint optimization are implemented, boundary flux coordination is carried out between a core domain and a background domain, and finally an emission list which has cross-atmospheric pollutant and greenhouse gas consistency and can be used for refined emission inversion is formed. According to the method, the problem of unstable data fusion caused by scale mismatching between the multi-source observation data and the emission model in the prior art is solved.

The invention provides an environmental health risk judgment method based on an emission list dynamic management system, and the method comprises the following steps: S1, analyzing the pollution source emission characteristics of a target region, and building a region key risk source emission information base according to an emission list; s2, calculating the discharge amount of the key risk source specific pollutants; s3, simulating the regional characteristic pollutant exposure concentration by using an atmospheric diffusion model CALPUFF; s4, risk source grading judgment and regional atmospheric environment health risk grading judgment. According to the invention, necessary information for judging the environmental health risk is obtained based on the dynamic emission list database of the atmospheric pollution source, so that integration of management and control services of the atmospheric pollution source and the ecological environmental health risk can be realized; the method has very strong support to environmental health risk whole-process prevention and control policy guidance of heavy metals, volatile organic compounds, new pollutants and other toxic and harmful chemical substances.

The invention discloses a mining area methane emission inversion method based on satellite observation and a Gaussian plume model. The method comprises the following steps: S1, acquiring a satellite inversion XCH4 data set, meteorological data, emission list data and global energy monitoring data; s2, determining a methane emission source and a research area in a geographic map by using the emission list data and the global energy monitoring data; s3, obtaining methane background concentration and XCH4 enhancement in the geographic map by using an atmospheric background concentration calculation method; s4, combining XCH4 enhanced spatial distribution and meteorological data in a geographic map to identify a methane enhanced plume of a methane emission source in the research area; and S5, performing Gaussian plume flow field simulation inversion of the methane emission source through the Gaussian plume model, and outputting the methane emission rate of the methane emission source at the current time through inversion of the Gaussian plume model. According to the method, the methane emission and the time sequence data of the methane emission can be obtained through accurate inversion, the frequency of obtaining the methane emission is improved, and the methane emission can be obtained according to time scale accumulation.

The invention relates to the technical field of carbon emission detection, and discloses an urban near-real-time gridding carbon emission measuring and calculating method based on multi-source big data, which comprises the following steps: constructing a gridding carbon emission measuring and calculating database, historical carbon emission data of all departments and industries and multi-source big data capable of reflecting spatial and temporal characteristics of carbon emission intensity are collected and processed into activity level indication variables and spatial allocation weight variables; constructing a relationship between the activity level indication variable and carbon emissions of different departments or industries, and calculating a nearly real-time total daily carbon emission amount based on a grid carbon emission calculation database; and distributing the total day-by-day carbon emission to the 1km grid by using the spatial distribution weight variable, and generating near-real-time day-by-day carbon emission data of the 1km grid. The urban near-real-time gridding carbon emission measuring and calculating method based on the multi-source big data aims at overcoming the defects of an existing carbon emission list in the aspects of temporal-spatial resolution, data updating speed and urban scale applicability.

The invention discloses a carbon transaction-oriented biomass gas coupling coal-fired power generation full-life-cycle carbon emission accounting method. The method comprises the steps of determining accounting boundaries of collection and transportation, storage pretreatment, gas production and coupling power generation of covering raw materials; activity data are collected, space-time mapping of upstream batch data and unit side continuous operation data is established, and data quality grading and uncertainty measurement are carried out; when actually measured synthesis gas components or lower calorific values are missing, a soft measurement model is constructed based on operation data, batch characteristic feedforward coupling is introduced, and an estimated value and uncertainty are output; calculating an intermediate quantity based on a material-energy-carbon conservation mechanism model, performing dynamic correction by adopting a residual correction model containing physical consistency constraint, and adjusting a physical constraint weight according to uncertainty; and an emission list and an MRV data packet are generated in combination with the dynamic emission parameters, and are used for monitoring, reporting, checking and transaction settlement, so that the precision and the traceability are improved, and the physical consistency is ensured.

The invention provides a fossil source CO2 emission accounting method for a closed sewage treatment system, and the method comprises the steps: dividing a plurality of stages of a sewage treatment process into a pretreatment stage, a biochemical treatment stage and an advanced treatment stage; according to the actual working condition of each stage, greenhouse gas emission is monitored and checked at the tail gas total emission outlet, and the greenhouse gas emission amount is obtained; according to the actual working condition of each stage, a gas sample is collected at a tail gas total discharge outlet, radioactive carbon isotopes are measured, and the fossil carbon proportion of the sample is obtained; and calculating the emission amount of CO2 of the fossil source and the contribution of the CO2 to a direct greenhouse gas emission list by combining the fossil carbon proportion and the greenhouse gas emission amount which are obtained previously. Based on the actual situation of the closed sewage treatment system, the system boundary of the CO2 emission accounting of the fossil source is perfected, the comprehensive CO2 emission accounting method of the fossil source of the closed sewage treatment system is established, and the content of the greenhouse gas emission list of the sewage treatment system is enriched.

The invention discloses a carbon emission checking method and system based on a multi-mode inversion model, and relates to the technical field of carbon emission checking. Air-based monitoring, foundation monitoring, mobile monitoring and maneuvering monitoring are adopted to form a three-dimensional monitoring network; the intelligent inversion calculation layer fuses global background concentration and regional real-time monitoring data, and adopts a GSI 4DVar algorithm to optimize a three-dimensional concentration field; in the first inversion stage, random disturbance is applied to the prior emission list, multiple emission sets are generated, each emission set corresponds to a grid area of a three-dimensional concentration field, and concentration distribution is simulated through a WRF-CMAQ model; in the second inversion stage, an EnSRF algorithm is adopted to assimilate measured data, and emission parameters are iteratively optimized; according to the invention, various monitoring means, advanced algorithms and data analysis methods are organically integrated, and automation and intelligentization of the whole process from data acquisition, processing and analysis to abnormity determination are realized. The method provides efficient and scientific technical support for carbon emission supervision, and is helpful for promoting the realization of carbon emission fine management and energy conservation and emission reduction targets.

The present application relates to the technical field of the treatment of heavy metal pollution, and in particular, to a method for predicting heavy metal accumulation in soil based on an emission inventory and a receptor model. Atmospheric dust fall, irrigation water, a fertilizer, and a pesticide are used as heavy metal input flux sources of farmland soil, and surface runoff and a crop are used as output fluxes. A heavy metal pollutant input-output flux inventory is established to specify a dynamic equilibrium relationship of heavy metal accumulation in soil, and soil samples are collected and monitored continuously, which provides important help for determining a migration equilibrium of heavy metals in a farmland region affected by a nonferrous metal dressing and smelting slag yard and a source of soil heavy metal pollution, thereby providing theoretical guidance for subsequent prevention and accurate control of heavy metals in soil.

The invention provides a # imgabs0 # and # imgabs1 # concentration estimation method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining environment data of a target region, the environment data comprising satellite data, meteorological data, free radical data, normalized vegetation index data, emission list data and topographic data; inputting environmental data into the estimation model to obtain the concentrations of # imgabs2 # and # imgabs3 # output by the estimation model; wherein the estimation model is an integrated model which is obtained by training an environmental data sample set with actual measurement # imgabs4 # and # imgabs5 # concentration labels and is integrated with M sub-models, and the value of M is an integer greater than 1. By the adoption of the technical scheme, the problem that in the prior art, the concentration estimation effect on the # imgabs6 # and the # imgabs7 # is poor is solved.

This invention discloses an automatic identification method, system, device, and medium for greenhouse gas point source emissions, relating to the field of environmental monitoring technology. The method includes: acquiring a greenhouse gas emission inventory and satellite remote sensing data of the area to be monitored; performing initial screening of emission sources based on the greenhouse gas emission inventory to determine preliminary greenhouse gas sources; retrieving a greenhouse gas concentration distribution map using the satellite remote sensing data, and employing a Mask R-CNN model to perform target detection and pixel segmentation on the greenhouse gas concentration distribution map to determine greenhouse gas plumes; the greenhouse gas plumes include specific locations and shapes; performing quantitative analysis of the greenhouse gas plumes using the IME algorithm, and combining this with the preliminary screening of greenhouse gas sources to determine the final emission sources. This invention can improve the accuracy and real-time performance of greenhouse gas emission source identification.

The invention relates to a multi-model fusion method for analyzing PM2.5 source contribution and disease burden thereof, which comprises the following steps of: building and verifying an air quality simulation system of a corresponding region, simulating the concentration of air pollutants and PM2.5 components of the corresponding region in recent 10 years by using a WRF-CMAQ model, and discussing the spatial and temporal distribution characteristics of the pollutant concentration; step 2, analyzing a PM2.5 pollution source of a corresponding region, and based on an HYSPLIT model and a high-resolution global emission list; step 3, estimating PM2.5 related disease burden and source contribution thereof, and quantifying premature death and health economic loss thereof caused by PM2.5 in recent ten years in a region by utilizing PM2.5 concentration data simulated by a WRF-CMAQ model and combining a comprehensive exposure-response model and a statistical life value method; the method can help to formulate accurate and effective PM2.5 control countermeasures, improve air quality and protect people's health, and provides a theoretical basis for realizing joint prevention and joint control and sustainable development of polluted areas.

The invention discloses a carbon emission accounting method based on an urban sewage treatment plant, and relates to the technical field of sewage treatment carbon emission, the carbon emission accounting method based on the urban sewage treatment plant comprises the following steps: determining an accounting boundary, clarifying a carbon emission activity greenhouse gas generation principle and a carbon emission source, and formulating a greenhouse gas emission list; carbon accounting activities are analyzed, and corresponding accounting methods and accounting principles are confirmed according to different types of carbon emissions; according to an accounting method and principle, an accounting model is built, an accounting system is established, emission factors corresponding to all carbon emission activities are selected, a data monitoring plan is made according to the accounting model / system, carbon emission accounting of all levels is carried out, and the total carbon emission amount is calculated. According to the method, an analysis framework is provided, four carbon reduction types of carbon reduction, carbon substitution, carbon sequestration and endogenous negative carbon are clarified, and a subsequent path scheme is convenient to propose.

The invention provides a real-time emission list calculation method and device and electronic equipment, and belongs to the field of data processing.The method comprises the steps that motor vehicle factor libraries corresponding to various energy types are constructed and stored, and the motor vehicle factor libraries comprise a gasoline vehicle environmental inspection factor library with different fine granularities, a gasoline vehicle brand model factor library and a gasoline vehicle correction factor library; when basic information of a target vehicle is obtained, under the condition that the energy type is gasoline, an emission factor library with the highest matching degree with the basic information is selected from emission factor libraries of gasoline vehicles to serve as a target factor library; and according to the basic information, obtaining a matched emission factor from the target factor library, and calculating a real-time emission list of the target vehicle. Therefore, a plurality of emission factor libraries with different fine granularities are constructed, and the emission list is calculated by using the emission factor with the highest matching degree with the basic information of the target vehicle, so that the precision and the overall accuracy of the emission list are greatly improved.

The invention relates to a method for estimating high-resolution atmosphere carbon dioxide concentration in a land area of China, which comprises the following steps of: (1) calculating a vegetation index, a surface water index and a vegetation phenology index by using satellite data, and generating a vegetation data file; (2) generating a carbon dioxide background field file and a meteorological background field file by using a CAMS global greenhouse gas concentration data set and a fifth-generation atmosphere reanalysis data set of the European mid-term weather forecast center; (3) generating a human-derived carbon dioxide emission data file by using a multi-scale emission list of the Xinghua University; (4) constructing an optimized atmospheric carbon dioxide simulation system; and (5) inputting the vegetation data file, the carbon dioxide background field and meteorological background field file and the man-made source carbon dioxide emission data file into the optimized atmospheric carbon dioxide simulation system, and operating to obtain a high-resolution atmospheric carbon dioxide concentration estimation result in the land area of China. The method can significantly improve the ability to estimate the carbon dioxide concentration of the atmosphere in the land area of China.

The invention relates to the field of air quality monitoring, and discloses a regional air quality prediction method based on a convolutional network, and the method comprises the steps: carrying out the conjoint analysis of the topographic distribution data of a target region and the real-time air quality monitoring data collected by a plurality of automatic monitoring stations, and dividing the data into a plurality of monitoring sub-regions with homogeneous internal parts and heterogeneous external parts, independently constructing a corresponding interpolation variation model for each monitoring sub-region; for each monitoring sub-region, constructing a corresponding interpolation variation model and an air quality grid map based on the real-time air quality monitoring data of the k automatic monitoring stations in each monitoring sub-region; rasterizing the pollution source emission list to construct a pollution source distribution map; generating a thermodynamic diagram by using satellite remote sensing data; and re-sampling the air quality grid map, the pollution source distribution map and the thermodynamic diagram to a target resolution grid, and inputting the target resolution grid into the air quality prediction model to obtain regional air quality prediction data. Therefore, the prediction precision of the local area is improved.

The invention discloses a multi-source data fusion high-resolution carbon emission list construction method based on a GWR model, and the method comprises the steps: collecting the carbon emission statistical data of electric power, industrial, traffic and civil departments, combining the night light, road density, population density, point source emission position and other space agent data, and improving a conventional downscaling method. The spatial heterogeneity of carbon emission is dynamically captured by using a GWR model, and an enhanced spatial proxy index is generated. Experimental results show that the method is remarkably superior to a traditional method in spatial resolution and prediction precision, and technical support is provided for precise carbon emission management and policy making.

The application discloses a kind of static water pressure simulation device and pressure sampling method with pressure, it is related to sediment-water interface biogeochemical process simulation technical field, the application includes container cylinder, top sealing installation has sealing cylinder cover;Pressure gauge and temperature table are located on sealing cylinder cover;Air inlet assembly and air release assembly are located on sealing cylinder cover;Discharge pipe is located in the lower part of container cylinder, and first control valve, sampling tube, second control valve are sequentially connected in series.The application has broken through to realize the sampling without pressure relief under the condition of in-situ static water pressure, effectively avoids systematic error caused by dissolved gas due to decompression escape;It is suitable for a variety of sediment types and gas types, and provides a high-fidelity experimental platform for greenhouse gas emission research under extreme conditions of deep water reservoir, thermal stratification area;Provide key data support and methodological basis for constructing pressure-corrected methane flux model and deep water carbon emission inventory, serve the national "double carbon" strategy and global change research needs.

The invention discloses a drainage basin multi-element pollutant quantitative traceability method and device based on a multi-scene water quality model, and relates to the field of pollutant quantitative traceability, and the method comprises the steps: constructing a multi-element pollutant input load emission list in a research region, and setting a plurality of simulation scenes; based on the pollutant input load of each simulation scene, a multi-scene water quality model is used for simulating the migration and transformation process of the drainage basin pollutants, and the output load of each pollutant in each simulation scene is obtained; and calculating the contribution ratio of each pollutant from each emission source and the input load ratio of each pollutant from each pollution source. According to the method, different simulation scenes are set by constructing the multi-element pollutant input load emission list, the emission load capacity of the pollutants is simulated by utilizing the multi-scene water quality model, the emission proportions of the pollutants from all emission sources and pollution sources are accurately and quantitatively analyzed, and the quantitative tracing accuracy of the multi-element pollutants is improved.

The application discloses a ship carbon emission feature prediction method based on a dynamic method and an attention mechanism, adopts an AIS data-based dynamic method and a TFT time sequence prediction model based on the attention mechanism, utilizes ship AIS data, ship Lloyd's file data, pollutant emission factor parameters and geographic information, establishes a high-resolution ship carbon emission list, and realizes multi-period pollutant emission feature prediction of the ship. Through data cleaning, AIS data trajectory integration, data fusion and data completion and other operations, the AIS data is preprocessed, and the accuracy of the AIS data is ensured. According to the ship type and the ship running state, the dynamic method is used to estimate and analyze the pollutant emission amount, and the dynamic ship behavior is considered in real time when analyzing the carbon emission law and predicting the carbon emission feature.

The application discloses a method for ozone and particulate matter prediction and early warning based on a multi-mode air quality model, and the method comprises the following steps: obtaining meteorological prediction data, satellite remote sensing data, pollution source emission inventory and air quality monitoring data of a target region; inputting the obtained data into a CMAQ air quality model, a CAMx air quality model, a particulate matter ozone neural network prediction model and a satellite remote sensing aerosol inversion model to obtain model prediction values; adopting a neural network model architecture, and on the basis of long-term historical related data, cyclically training the model prediction values and actual monitoring values to establish a multi-mode air quality model; inputting the latest related data of the target region into the multi-mode air quality model, and predicting and early warning the ozone and particulate matter of the target region according to the output results of the multi-mode air quality model. The method has good effects on particulate matter and ozone prediction and early warning.

The invention discloses a hectometer-level greenhouse gas emission list generation method fused with vehicle-mounted underway observation, which comprises the following steps of: acquiring data through vehicle-mounted underway observation, eliminating background influence in observation data, performing quality control and preprocessing, determining an optimal space average scale and characteristic variables, dividing data subsets based on a spatial clustering analysis method, and generating a hectometer-level greenhouse gas emission list. A model is constructed and evaluated, and the rationality of characteristic values is checked; and constructing a model to simulate regional concentration, and carrying out emission list downscaling on the basis of a concentration quantification weight coefficient. According to the method, the problems of light population misallocation, heterogeneous interpolation extrapolation distortion and lack of high-resolution input of machine learning in a hectometer method in the prior art are solved; a set of greenhouse gas emission list with high spatial resolution is generated by fusing the emission list with kilometer-level or even higher resolution with vehicle-mounted underway observation data, and hectometer-level greenhouse gas emission data can be provided for cities.

The invention relates to the technical field of water pollution treatment, and discloses a multi-scale coupled drainage basin high-resolution pollution source emission list construction method, which comprises the following steps: acquiring multi-scale administrative division statistical data, and preprocessing the multi-scale administrative division statistical data to obtain multi-scale pollution source basic data; converting the basic data space of the multi-scale pollution source into gridding space distribution data of each pollution source in a target drainage basin range by utilizing a space mapping relation between an administrative scale and a gridding drainage basin scale; calculating multi-source pollution emission data based on the gridding spatial distribution data of each pollution source in the target drainage basin range; and superposing the multi-source pollution emission data on the watershed grid scale, and constructing a watershed high-resolution pollution source emission list. According to the method, accurate description of drainage basin pollution source emission and identification of key pollution units are realized, and the scientization, refinement and datamation levels of regional water environment treatment can be effectively improved.

This application provides a method, apparatus, device, and medium for optimizing greenhouse gas emission inventory data. The method acquires emission inventory data, multi-source observation concentration data, and auxiliary data for a target greenhouse gas in a target area. The emission inventory data includes the emission amount of the target greenhouse gas in the spatiotemporal dimensions within the target area. Using an atmospheric transport model, gas concentration simulation is performed based on the emission inventory data to obtain simulated gas concentration data in the spatiotemporal dimensions. The emission inventory data, multi-source observation concentration data, and auxiliary data are spatiotemporally aligned and correlated with the simulated gas concentration data to obtain correlated data. The correlated data is input into a trained physical-guided neural network to obtain an optimization coefficient sequence. The emission inventory data is then optimized based on the optimization coefficient sequence to obtain optimized target emission inventory data. Embodiments of this application can improve the accuracy of the emission inventory.

The present invention discloses a method, system, and device for inverting atmospheric pollutant emissions based on machine learning. The method comprises: obtaining pollutant concentration observation data within a regional grid; obtaining an influence function and simulated concentration values ​​within the regional grid; establishing an emissions inversion model within the regional grid based on the pollutant concentration observation data, the simulated concentration values ​​within the regional grid, the influence function of the regional grid, and prior emission information; and iteratively calculating the inverted atmospheric pollutant emissions within the regional grid based on the loss function of the emissions inversion model. The technical solution provided by the present invention solves the current problems of low accuracy and resolution of atmospheric pollutant emission inventory data. Furthermore, the atmospheric pollutant emission inversion equipment used is inexpensive, solving the problem of high construction costs for current inversion systems and equipment used.

A system for enabling enterprise decarbonization is disclosed. The system includes three platforms that can operate on a standalone basis, or in conjunction with the other platforms as Climate Suite. The three platforms include a Sustain platform configured to streamline emissions inventory, analytics and reporting and enable creation of emissions reductions that become deals in the PPA or Carbon marketplaces; a PPA platform configured to provide digital transaction services for clean power purchases and sales; and a Carbon platform configured to provide digital transaction services for purchases and sale of carbon credits and renewable energy certificates.