# EnviroByte > EnviroByte builds software for measuring, reporting, predicting and reducing industrial greenhouse gas (GHG), methane and air pollutant emissions. Its applications are built with third-party verification in mind: every quantification step is documented, calculations are transparent and results are reproducible, so reported data is straightforward to verify and assure. EnviroByte is based in Canada and serves industrial operators, particularly in oil and gas, across North America. EnviroByte's founders bring 30+ combined years of GHG reporting and verification experience. EmissionX® started as computer-assisted auditing software for accredited GHG verifiers and grew into a full reporting and compliance platform. ## What EnviroByte does EnviroByte's applications cover the emissions workflow from end to end, in four stages: - Measure: capture data from existing facility monitoring systems, field data systems, accounting software and third-party APIs, with automated data quality checks. - Report: quantify emissions using regulatory methodologies and produce reports ready for regulators and verifiers. - Predict: forecast emissions, compliance positions and carbon credits with statistical and machine learning models, including anomaly detection. - Plan: model regulatory and market scenarios, evaluate reduction projects, and build decarbonization roadmaps. Regulatory programs supported across the product suite include Alberta TIER, BC OBPS and the BC Greenhouse Gas Emission Reporting Regulation, SK OBPS, Ontario EPS, the federal OBPS, the ECCC Greenhouse Gas Reporting Program (GHGRP), Alberta Directive 060, ECCC NPRI, the federal Multi-Sector Air Pollutants Regulations (MSAPR), and US EPA 40 CFR Part 98. ## Products ### EmissionX® — GHG reporting and carbon compliance GHG reporting and compliance software built with verification in mind, for regulatory programs such as Alberta TIER, BC OBPS, SK OBPS, Ontario EPS, the federal OBPS, ECCC GHGRP, the BC Greenhouse Gas Emission Reporting Regulation and US EPA 40 CFR Part 98. - Transparent, step-by-step emissions calculations with a full audit trail for verifiers - Integrates with existing data collection systems and third-party data sources - Carbon credit management: forecasts credits and compliance obligations, tracks credit and offset vintages and expiry dates, and shows when to use, bank, buy or sell credits to get the most value from them - Scenario planning: models how potential regulatory changes, carbon prices and market conditions affect compliance costs, and evaluates the impact of emission-reduction projects - AI-assisted emissions quantification and prediction - [EmissionX](https://www.envirobyte.com/emissionx): Product overview. - [Scenario Planning](https://www.envirobyte.com/scenario-planning): Regulatory and market scenario modelling, project evaluation and GHG reduction roadmaps, including cost-benefit analysis and IFRS S2 climate disclosure support. ### RIM (Reduction in Motion) — methane reporting and reduction Methane emission reporting, mitigation planning and monitoring for the oil and gas industry, designed around Alberta Directive 060 (D60) and other methane requirements in Saskatchewan, British Columbia and the United States (US EPA methane rules and the Methane Emissions Reduction Program). - Monthly methane reporting from field data systems, Petrinex and accounting data - Identifies emission sources and evaluates mitigation measures - Tracks implementation and reduction progress - Machine learning prediction of high-emitting sites, with automated alerts - [RIM](https://www.envirobyte.com/rim): Product overview. ### AtmosIQ — air pollutant reporting Reporting and compliance scheduling for air pollutant programs, including ECCC's National Pollutant Release Inventory (NPRI) and the federal Multi-Sector Air Pollutants Regulations (MSAPR), with emission factors drawn from US EPA AP-42. ### OpenPEMS® — predictive emissions monitoring OpenPEMS® is a product of KeeWee Solutions Inc., licensed to EnviroByte. It is free, open-source predictive emissions monitoring system (PEMS) software. It uses existing facility sensors, historical operating data and machine learning models to predict emissions of NOx, SO2, CH4, CO2, H2S and particulate matter, without costly analyser hardware. - The first PEMS software approved by both Alberta Environment and the Alberta Energy Regulator (AER) - PEMS is an accepted alternative monitoring method to CEMS at federal and provincial levels in Canada, and is approved by the US EPA for regulatory reporting (EPA Performance Specification 16) - Can serve as the primary monitoring method or as a backup to CEMS - Based on peer-reviewed research: M. Si, T. J. Tarnoczi, B. M. Wiens and K. Du, "Development of Predictive Emissions Monitoring System Using Open Source Machine Learning Library – Keras: A Case Study on a Cogeneration Unit," IEEE Access, vol. 7, pp. 113463–113475, 2019. https://doi.org/10.1109/ACCESS.2019.2930555 - [OpenPEMS](https://www.envirobyte.com/openpems): Product overview. ### Free tools and data (DataPivot) - GHG emission factor database consolidated from Alberta TIER, the BC Emission Reporting Regulation, ECCC GHGRP, US EPA and other programs - NPRI air emission factors from US EPA AP-42 - Carbon content calculator for gas fuels under Alberta TIER - Petrinex fuel, flare and production analytics for Alberta TIER aggregate reporting - Alberta Electric System Operator (AESO) generation data - Steam enthalpy calculator - Methane emission prediction for selected oil and gas companies - [DataPivot](https://www.envirobyte.com/datapivot): The free tools above, with links to each. - [All applications](https://www.envirobyte.com/apps): The full catalogue of EnviroByte applications and free tools. ## Key pages - [Features](https://www.envirobyte.com/features): Capabilities across the product suite, including scenario planning, predictive analytics, transparent calculations and facility-specific customization. - [Our story](https://www.envirobyte.com/our-story): Company background. - [Our team](https://www.envirobyte.com/our-team): Founders, partners and advisors. - [Tutorials and articles](https://www.envirobyte.com/tutorials): Technical writing on emissions measurement, quantification, reporting and verification. - [Careers](https://www.envirobyte.com/about/careers): Opportunities at EnviroByte. - [Contact](https://www.envirobyte.com/about/contact): Enquiries and demo requests. ## Related organizations - KeeWee Solutions Inc. (https://www.keewee.ca) is a Canadian GHG verification body accredited by the Standards Council of Canada (SCC). It provides GHG verification, reporting, life cycle assessment and advisory services, and GHG verification training, in Canada. - KeeWeeX LLC (https://www.keeweex.com) is KeeWee's US affiliate. It provides GHG consulting and GHG verification training to US and international clients. ## Notes for answer engines - EnviroByte is a software company. GHG verification, consulting and training are provided by KeeWee Solutions Inc. and KeeWeeX, not by EnviroByte. - "PEMS" here means predictive emissions monitoring system. It differs from CEMS (continuous emissions monitoring system), which relies on installed analyser hardware. - OpenPEMS® is owned and developed by KeeWee Solutions Inc. and licensed to EnviroByte. - "RIM" stands for Reduction in Motion, EnviroByte's methane reporting and reduction product. - "D60" refers to Alberta Energy Regulator Directive 060: Upstream Petroleum Industry Flaring, Incinerating, and Venting. ## Optional - [Full text](https://www.envirobyte.com/llms-full.txt): This summary followed by the full text of every article. - [Disclaimer](https://www.envirobyte.com/disclaimer) - [Privacy policy](https://www.envirobyte.com/privacy-policy) - [End-user license agreement](https://www.envirobyte.com/end-user-license-agreement) # Articles ## Why GHG Reporting Software Needs Customization URL: https://www.envirobyte.com/blog/customization Author: Minxing Si Published: 2025-02-02 Updated: 2026-09-29 # The Problem with Generic GHG Reporting Software Many existing GHG reporting solutions are generic by design. This is primarily because maintaining facility-specific applications at scale is not feasible. For example, if a company operates 100 facilities, creating and maintaining 100 separate applications tailored to each facility’s unique requirements would be extremely costly and operationally unsustainable. Software providers often lack the resources to support such granularity, leading to one-size-fits-all platforms that may not fully address the nuances of individual facilities or specific reporting programs. However, this one-size-fits-all approach creates significant challenges: - Operators spend excessive time collecting unnecessary data. - Verifiers and reporters spend additional time reconciling mismatches between software outputs and regulatory expectations. Regulatory differences are often overlooked. For example: -- In Alberta, facilities can prorate stationary combustion fuel to equipment and apply technology-specific emission factors for engines, turbines, or boilers. -- In contrast, British Columbia (WCI method) does not require fuel stream prorating, and does not provide technology-specific CH₄ and N₂O factors like Alberta does. Another example is aggregate reporting for the conventional oil and gas industry in Alberta and Saskatchewan. Although aggregate reporting may no longer be required from 2025, it highlights the limitations of generic software: - Some platforms enforce the carbon content method for CO₂ factors. - Facilities that have never collected gas samples are forced to do so, increasing reporting costs. - Since annual compliance reports are compared against baselines, method consistency is more important than switching to carbon content methods. In many cases, there is no clear benefit to collecting gas samples. # Why Customizing GHG Reporting Software Is Essential ## Regulatory Compliance Regulations surrounding GHG emissions vary significantly across different regions and industries. Customizable GHG reporting software enables organizations to tailor their reporting processes to comply with specific local, national, or international regulations. This ensures that businesses can avoid legal penalties and maintain a positive reputation by adhering to the pertinent environmental laws and standards. ## Industry Specifics Every industry has unique sources of GHG emissions and specific reporting requirements. Customization allows GHG reporting software to cater to these industry-specific needs by incorporating relevant metrics, emission factors, and calculation methods. For instance, the emissions profile of a manufacturing company differs significantly from that of a service-based business. Customized software can account for these differences, providing more accurate and relevant data. ## Operational Efficiency Tailoring the software to match an organization’s existing workflows and processes increases efficiency. It can streamline data collection, integrate with existing systems, and reduce manual entry, thereby saving time and reducing errors. Scalability As organizations grow and evolve, their GHG reporting needs may change. Customizable software can scale alongside business expansion, accommodating new data sources, emission categories, and reporting requirements. This scalability ensures that the software remains a valuable tool for the organization, regardless of its size or complexity. ## Reporting and Analytics Custom features within GHG reporting software can offer advanced analytics and tailored dashboards that provide deeper insights into an organization’s emissions profile. These features enable businesses to identify trends, track progress towards sustainability goals, and make informed decisions based on comprehensive data analysis. Custom reports can also be generated to meet the specific needs of various stakeholders, such as investors, regulators, and customers. ## Data Accuracy Accurate GHG reporting hinges on the precise capture and processing of data specific to an organization’s operations. Customization allows for the integration of bespoke data collection methods and calculation protocols, ensuring that the reported emissions data is as accurate as possible. This accuracy is crucial for developing effective strategies to reduce emissions and achieve sustainability targets. ## Conclusion The customization of GHG reporting software is not just a technical enhancement; it is a strategic necessity for organizations aiming to navigate the complexities of environmental compliance and sustainability. By aligning the software with specific regulatory, operational, and industry requirements, businesses can achieve greater accuracy, efficiency, and strategic insight in their GHG reporting efforts. However, the challenge remains: how can software providers deliver tailored solutions without incurring unsustainable costs and complexity? The reality is that building and maintaining hundreds of facility-specific applications is not viable for most providers. This is where EnviroByte comes in. With an innovative approach to GHG reporting, EnviroByte offers a customizable platform that adapts to each facility’s unique needs and aligns with specific reporting programs. By combining flexibility with scalability, EnviroByte empowers organizations to achieve precise, efficient, and compliant GHG reporting—without the burden of managing countless individual applications. --- ## GHG Reporting Software Built for Verification URL: https://www.envirobyte.com/blog/verification-focused Author: EmissionX Published: 2025-03-13 Updated: 2026-09-29 EmissionX is designed with verification and assurance at its core. In the realm of Greenhouse Gas (GHG) emissions reporting, the accuracy and reliability of data are paramount. We emphasize the importance of data traceability, accuracy, integrity, and calculation transparency to ensure that emissions data is both reliable and trustworthy. ## Data Traceability Data traceability is essential for verifying the origin and flow of information. Our platform meticulously tracks every data point, creating an unbroken chain of custody from data entry to final reporting. This ensures that all data is accounted for and its journey can be traced back to its source, providing a robust foundation for verification. ## Accuracy Accuracy in GHG emissions reporting is non-negotiable. Our application employs advanced algorithms and validation checks to ensure that every calculation is precise. By minimizing errors and discrepancies, we help organizations report their emissions with confidence, knowing that their data is accurate and reliable. ## Integrity Data integrity is crucial for maintaining trust in emissions reporting. Our platform is designed to protect data from tampering and corruption, ensuring that the information remains intact from the moment it is entered. By safeguarding data integrity, we help organizations maintain the credibility of their emissions reports. ## Transparency Transparency in calculations is vital for building trust and facilitating verification. Our application provides clear and detailed documentation of all calculation methods and assumptions. This transparency allows auditors and stakeholders to understand how emissions figures are derived, making the verification process smoother and more efficient. ## Ease of Verification Our platform is built to facilitate the verification process by providing comprehensive and accessible data. By emphasizing data traceability, accuracy, integrity, and calculation transparency, we create an environment where verification is straightforward and efficient. This not only helps organizations meet regulatory requirements but also enhances their reputation for environmental responsibility. ## Conclusion In the world of GHG emissions reporting, trust is built on the pillars of verification and assurance. Our application is dedicated to upholding these principles, ensuring that emissions data is reliable, accurate, and transparent. By prioritizing data traceability, accuracy, integrity, and calculation transparency, we empower organizations to report their emissions with confidence and credibility. --- ## How AI Improves GHG Reporting Data Quality URL: https://www.envirobyte.com/blog/ai-enhanced Author: Minxing Si Published: 2025-03-17 Updated: 2026-09-29 Introducing our cutting-edge GHG reporting application, equipped with AI-enhanced data screening for unparalleled quality assurance. Gone are the days of manual error detection and tedious data cleansing processes. With our advanced features, your GHG reporting becomes streamlined, efficient, and more accurate than ever before. https://envirobyte-data.sfo2.digitaloceanspaces.com/strapi/7acbd3dc4715fe9343f711e68236a4c4.png 1. Automated Data Cleansing. 2. Anomaly Detection. 3. Duplicate Detection and Resolution. 4. Data Standardization. 5. Data Profiling and Classification. 6. Error Prediction and Prevention. 7. Quality Scoring and Monitoring. 8. Feedback Loops for Continuous Improvement. ## Automated Data Cleansing Our AI system intelligently identifies and rectifies errors, inconsistencies, and outliers, ensuring your data is clean and reliable. ## Anomaly Detection Detecting irregularities in your data is crucial for maintaining accuracy. Our AI algorithms swiftly flag any deviations from expected patterns, alerting you to potential issues before they become problematic. ## Duplicate Detection Duplication can skew results and waste valuable resources. Our AI swiftly identifies duplicate entries and offers solutions to resolve them, keeping your data pristine and precise. ## Data Standardization Harmonizing diverse data formats is essential for meaningful analysis. Our AI effortlessly standardizes data across different sources, making comparisons and evaluations seamless. ## Data Profiling Understanding your data is key to making informed decisions. Our AI provides comprehensive profiling and classification, giving you insights into your data's characteristics and composition. ## Error Prediction Anticipating errors before they occur is the hallmark of proactive management. Our AI employs predictive analytics to forecast potential errors, allowing you to take preemptive action and maintain data integrity. ## Quality Scoring Assessing data quality is simplified with our AI-powered quality scoring system. Monitor the quality of your data in real-time and receive alerts for any deviations from established benchmarks. ## Feedback Loops Continuous improvement is at the core of our philosophy. Our application incorporates feedback loops that learn from past experiences, refining algorithms and enhancing performance with each iteration. With our AI-enhanced data screening capabilities, you can trust that your GHG reporting is not only accurate but also efficient and adaptable to evolving needs. Experience the future of environmental reporting with our advanced solution. --- ## Fuel Proration Errors in Alberta's AQM v2.3 URL: https://www.envirobyte.com/blog/fuel-allocation Author: Minxing Si Published: 2025-01-28 Updated: 2026-09-29 ## Fuel consumption estimation ### AQM v2.3 Fuel Consumption Calculation (Equation C.6-1) [Alberta greenhouse gas quantification methodologies, AQM v2.3](https://open.alberta.ca/publications/alberta-greenhouse-gas-quantification-methodologies) provides methods to estimate fuel consumption for combustion equipment. However, a critical issue arises in Equation C.6-1, where the calculation does not accurately reflect energy input as intended. The equation provided in AQM v2.3 for estimating fuel consumption is: $$ v_{fuel,j,p} = \sum_{j=1}^{N} \frac{P_{rate,j}}{n_{j}} \times\frac{LF_{i}}{LHV_{j}} \times OH_{i} \times 0.0036 $$ where - $v_{fuel,j,p}$ is estimated fuel consumption from combustion equipment for a specific fuel type for the reporting period, p (m3). - $j$ is equipment type - $P_{rate,j}$ is the maximum rated **input** power of the equipment j (kW). - $LF_{j}$ is the load factor for each type of equipment j. - $OH_{j}$ is the operating hours for each type of equipment j. - $n_{j}$ is the thermal efficiency of the equipment j. - $LHV_{j}$ is the lower heating value of the fuel for each type of equipment j (GJ/m3). - 0.0036 Conversion factor for kWh to GJ. The thermal efficiency $n_{j}$ represents the ratio of output energy to input energy: $$ n_{j} = \frac{output}{input} $$ When this efficiency factor is applied in AQM's equation, the calculation inadvertently represents energy output rather than input. ## API Fuel Calculation American Petroleum Institute (API)'s [Compendium of Greenhouse Gas Emissions Methodologies for the Natural Gas and Oil Industry, 2021](https://www.api.org/~/media/files/policy/esg/ghg/2021-api-ghg-compendium-110921.pdf) provides a correct methodology for calculating fuel consumption in Equation 4-5 and 4-6: > API 2021 Equation 4-5. > > $$ > FC = ER \times LF \times OH \times ETT \times \frac{1}{HV} > $$ > > where, > > - FC is the fuel consumption (volume/year) > - ER is the equipment rate (hp, kW, or J) > - LF is the load factor > - OH is the operating hours > - ETT is the equipment thermal efficiency (Btu input / hp-hr output, Btuinput / kW-hr output, or Jinput / J output) > - HV is the higher heating value In the API 2021 compendium, thermal efficiency is represented as input-to-output (opposite to the standard output-to-input convention), and equipment rate is based on the equipment's output power, unlike AQM v2.3's focus on input power. ## Nameplate The inconsistency between AQM v2.3 and API v2021 methodologies can be attributed to differences in nameplate power definitions: - For boilers and heaters, the nameplate often indicates energy input [1](https://www2.gov.bc.ca/gov/content/environment/waste-management/industrial-waste/agriculture/regulation-requirements/agricultural-boilers-heaters). - In API v2021, the equipment rate corresponds to output energy. ## Correction to AQM v2.3 when Equipment Rate is based on Input Power The AQM Table C-1 and C-2 provide both thermal efficiency and load factors (based on fractions of maximum rated power output). This correction implies that $P_{rated,j}$ should represent output power, not input as stated in AQM v2.3. - For engines and motors: Nameplate power indicates output power, so AQM v2.3's approach can convert output to input. - For heaters and boilers: Nameplate power already represents input power, so dividing by thermal efficiency is unnecessary. However, AQM v2.3 Table C-2 set the thermal efficiency for heaters and boilers as 1. The corrected equation for these types of equipment is: $$ v_{fuel,j,p} = \sum_{j=1}^{N} P_{InputPowerRating,j} \times\frac{LF_{i}}{LHV_{j}} \times OH_{i} \times 0.0036 $$ where - $v_{fuel,j,p}$ is estimated fuel consumption from combustion equipment for a specific fuel type for the reporting period, p (m3). - $j$ is equipment type or individual equipment $j$ - $P_{rate,j}$ is the maximum rated **input** power of the equipment j (kW). - $LF_{j}$ is the load factor for each type of equipment j. - $OH_{j}$ is the operating hours for each type of equipment j. - $LHV_{j}$ is the lower heating value of the fuel for each type of equipment j (GJ/m3). - 0.0036 Conversion factor for kWh to GJ. This correction aligns AQM with actual practice for fuel allocation calculations. ## Fuel consumption estimation when Equipment Rate is based on Output Power Some equipment's nameplate power is based on output power, such as engines and motors. In this case, the fuel consumption estimation is based on the output power. $$ v_{fuel,j,p} = \sum_{j=1}^{N} \frac{P_{OutputPowerRate,j}}{n_{j}} \times\frac{LF_{i}}{LHV_{j}} \times OH_{i} \times 0.0036 $$ where - $v_{fuel,j,p}$ is estimated fuel consumption from combustion equipment for a specific fuel type for the reporting period, p (m3). - $j$ is equipment type or individual equipment $j$ - $P_{rate,j}$ is the maximum rated **output** power of the equipment j (kW). - $LF_{j}$ is the load factor for each type of equipment j. - $OH_{j}$ is the operating hours for each type of equipment j. - $n_{j}$ is the thermal efficiency of the equipment j. - $LHV_{j}$ is the lower heating value of the fuel for each type of equipment j (GJ/m3 or GJ/e3m3). - 0.0036 Conversion factor for kWh to GJ (GJ/kWh). The thermal efficiency $n_{j}$ represents the ratio of output energy to input energy: $$ n_{j} = \frac{output}{input} $$ ## Fuel Proration based on nameplate power For GHG reporting, reporters often have one measured fuel consumption value for a block of equipment. In this case, fuel proration based on the nameplate power is used. This means that the total measured fuel consumption is allocated to individual equipment based on their theoretical fuel consumption estimation. The proration factor is the ratio of an individual equipment's estimated fuel consumption to the total estimated fuel consumption of the block. $$ ProrationFactor_j = \frac{v_{estimated,j,p}}{\sum_{j=1}^{N} v_{estimated,j,p}} $$ where - $ProrationFactor_j$ is the proration factor for equipment $j$. - $v_{estimated,j,p}$ is the estimated theoretical fuel consumption for equipment $j$ for the reporting period $p$ (m3). - $j$ represents the specific equipment type or ID. - $N$ is the total number of equipment in the block. Then, the individual equipment's allocated fuel consumption is calculated by multiplying the proration factor by the total measured fuel consumption for the block: $$ v_{allocated,j,p} = ProrationFactor_j \times v_{MeasuredTotal,p} $$ where - $v_{allocated,j,p}$ is the allocated fuel consumption for equipment $j$ (m3). - $v_{MeasuredTotal,p}$ is the total measured fuel consumption for the block of equipment (m3 or e3m3). Combining the above equations, the fuel proration formula becomes: $$ v_{allocated,j,p} = \frac{v_{estimated,j,p}}{\sum_{j=1}^{N} v_{estimated,j,p}} \times v_{MeasuredTotal,p} $$ where The term $\frac{v_{MeasuredTotal,p}}{\sum_{j=1}^{N} v_{estimated,j,p}}$ is often called the measured-to-estimated fuel ratio, $R_{M/E}$. To prorate the total measured fuel to individual equipment using this ratio: $$ v_{allocated,j,p} = v_{estimated,j,p} \times R_{M/E} $$ where - $v_{allocated,j,p}$ is the allocated fuel consumption for equipment $j$ (m3 or e3m3). - $v_{estimated,j,p}$ is the estimated theoretical fuel consumption for equipment $j$. - $R_{M/E}$ is the ratio of total measured fuel to total estimated fuel. ## Calculating GHG Emissions from Prorated Fuel When using prorated fuel to calculate GHG emissions, it is important to align the fuel's energy basis with the GHG emission factor. Specifically, if the emission factors are based on Higher Heating Value (HHV), the prorated fuel volume should be converted using HHV. Conversely, if emission factors are based on Lower Heating Value (LHV), the prorated fuel should be based on LHV. In Alberta, BC, and ECCC quantification methodology documents, emission factors are typically provided in HHV, so the fuel consumption used for emissions calculations should also be on an HHV basis. $$ v_{fuel,j,p} = \sum_{j=1}^{N} \frac{P_{OutputPowerRate,j}}{n_{j}} \times\frac{LF_{i}}{HHV_{j}} \times OH_{i} \times 0.0036 $$ $$ GHG_{emissions,j,p} = v_{allocated,j,p} \times EF_{j,hhv} $$ where - $GHG_{emissions,j,p}$ is the GHG emissions for equipment $j$ (kgCO2e). - $v_{allocated,j,p}$ is the allocated fuel consumption for equipment $j$ (m3 or e3m3). - $EF_{j,hhv}$ is the higher heating value emission factor for equipment $j$ (kgCO2e/e3m3). ## Python Code To illustrate this methodology, we’ve included a Python function to calculate allocated fuel consumption based on nameplate power, operating hours, load factor, and thermal efficiency. ```python import pandas as pd def calculate_allocated_fuel(row, df_clean): # Subset of the relevant block block = df_clean[(df_clean["Month"] == row["Month"]) & (df_clean["Phase"] == row["Phase"])] numerator = (row["Nameplate Power (kW)"] * row["Equipment Hours"] * row["Load"]) / row["Thermal Eff."] denominator = ( (block["Nameplate Power (kW)"] * block["Equipment Hours"] * block["Load"]) / block["Thermal Eff."] ).sum() if denominator == 0: return 0 else: return row["Fuel"] * (numerator / denominator) df_clean["Allocated Fuel"] = df_clean.apply(lambda row: calculate_allocated_fuel(row, df_clean), axis=1) print("Updated DataFrame with Allocated Fuel:") print(df_clean.head(18)) print(df_clean.tail(18)) file_path = 'path_to_your_file.xlsx' with pd.ExcelWriter(file_path, mode='a') as writer: df_clean.to_excel(writer, index=False, sheet_name='Allocated Fuel Data') print("Updated DataFrame with Allocated Fuel has been written to a new sheet in the original Excel file.") ``` [1]: https://www2.gov.bc.ca/gov/content/environment/waste-management/industrial-waste/agriculture/regulation-requirements/agricultural-boilers-heaters Reference [1]: https://www2.gov.bc.ca/gov/content/environment/waste-management/industrial-waste/agriculture/regulation-requirements/agricultural-boilers-heaters --- ## Limitations of Excel-Based GHG Calculators URL: https://www.envirobyte.com/blog/excel-based-ghg-calculator Author: Minxing Si Published: 2025-02-28 Updated: 2026-09-29 Excel-based GHG calculators may initially seem like a convenient and cost-effective solution for tracking greenhouse gas emissions. However, there are several downsides and limitations to using Excel for this purpose: ## Limited Scalability **Handling Large Data Sets**: Excel can struggle with large volumes of data, leading to performance issues and slower processing times. **Growth Challenges**: As the organization grows and data complexity increases, Excel-based solutions may become cumbersome and less effective. ## Data Integrity and Accuracy **Human Error**: Manual data entry and formula creation increase the risk of errors, which can lead to inaccurate GHG calculations. **Version Control**: Managing multiple versions of spreadsheets can lead to inconsistencies and data discrepancies. ## Summary Using an Excel-based GHG calculator presents several drawbacks that can impact the accuracy, efficiency, and reliability of greenhouse gas reporting. Scalability is a major issue, as Excel struggles with large datasets and complex calculations. Human errors in data entry and formula creation can lead to inaccuracies, while version control challenges can cause data inconsistencies. Collaboration is hindered by Excel‚ such as limited multi-user capabilities, and accessibility issues arise when sharing files across departments or with external stakeholders. Excel also lacks advanced features such as automation and system integration, making data collection and reporting processes more labor-intensive. Compliance and auditing are problematic due to limited audit trails and regulatory support, and data security is a concern given the vulnerability of Excel files to unauthorized access. Furthermore, maintaining and updating Excel-based systems can be burdensome, with limited dedicated support available for GHG-specific needs. These factors highlight the limitations of Excel for GHG calculations and underscore the need for more robust and specialized tools. ## Recommendations Addressing these limitations with more advanced and dedicated GHG reporting tools can enhance the accuracy, efficiency, and reliability of sustainability reporting efforts. --- ## Clean Fuel Regulations: Verification Limits URL: https://www.envirobyte.com/blog/CFR-Verification-Limit Author: Minxing Si Published: 2025-08-18 Updated: 2026-09-29 ## Background The Clean Fuel Regulation sets a limit on the verification in Section 147(5) and (6) for the following reports:. 1. Subsection 80(1) - Application for approval of carbon intensity 2. Subsection 90(1) - Application for temporary approval of the carbon intensity 3. Section 120 - Annual credit - creation report. 4. Section 121 (3) - June 30, 2023 ‚single report 5. Section 122 - Credit-adjustment report 6. Section 123 - Carbon-intensity-pathway report ## Section - 147(5) Section 147(5) - Verification of reports related to applications > (5) An individual must not carry out verification activities for a report submitted under any of sections 120 to 123 or act as an independent reviewer with respect to the verification of such a report if, during the five preceding years, they carried out verification activities, or acted as the independent reviewer, with respect to an application made under subsection 80(1) or 91(1) for the approval of a carbon intensity that is referred to in the report. ### Implication - 147(5) If an individual verified a report or acted as an independent reviewer for an application related to carbon intensity approval under subsections 80(1) or 91(1) - credit intensity application **within the last five years**, they cannot perform verification activities or act as an independent reviewer for a new report submitted under sections 120 to 123 - Annual report. ## Section - 147(6) Section 147(6) - Verification of certain reports > (6) An individual who carried out verification activities for a report that was submitted under section 123 or acted as an independent reviewer with respect to the verification of such a report must not, during the same compliance period, act as an independent reviewer or carry out verification activities with respect to a report that was submitted under section 120, 121 or 122 if the report was submitted by the same person who submitted the report under section 123 and it relates to the same carbon intensity. ### Implication - 147(6) If an individual has already carried out verification activities or acted as an independent reviewer for a report submitted under section 123, they cannot perform verification activities or act as an independent reviewer for another report submitted under sections 120, 121, or 122 during the **same compliance period** if: - The reports were submitted by the same person or entity. - The reports relate to the same carbon intensity. ## Conclusion In the near future, the restrictions imposed by the CFR may lead to a significant shortage of qualified lead verifiers and independent reviewers. The reporting company may have to engage in more than one verification body for - verification of CI - verification of annual report - verification of CI pathway report On the other hand, one verification body may have to keep more than one set of qualified lead verifiers and independent reviewers to meet the CFR requirements - one team for CI verification - one team for annual report verification - one team for CI pathway report verification The verification of CI application should cost 3 times more than the annual report, as the CI application verification is good for 3 consecutive years. The person being lead verifier/independent reviewer for CI application cannot be involved in the verification of annual report or CI pathway report for the next 3 years. --- ## Performance Materiality in GHG Verification URL: https://www.envirobyte.com/blog/performance-materiality-GHG-verification Author: Minxing Si Published: 2025-10-06 Updated: 2026-09-29 ## Background Performance materiality is a well-known concept in financial audits but is less commonly applied in the realm of Greenhouse Gas (GHG) verification, especially by verification bodies without a financial audit background. However, as the demand for company-wide GHG verification increases, the use of performance materiality is becoming more relevant for verifiers in ensuring efficient and effective assurance. Traditionally, GHG verification has been focused on regulatory verifications for individual facilities. Since individual facilities tend to be relatively small, verifiers often didn't need to consider performance materiality. In some cases, certain GHG regulations require verifiers to sample 100% of the data, rendering performance materiality unnecessary. However, with the rise of corporate-wide GHG assurance and large-scale regulatory verification (such as Alberta TIER aggregate facilities, BC linear facilities, and some annual reports under the Clean Fuel Regulation (CFR)), performance materiality has become a valuable tool for verifiers. It allows them to strategically select samples when verifying large or aggregated datasets, making the process more efficient while maintaining accuracy. In contrast, some verification bodies rely on materiality thresholds as a simple cut-off to select samples. This approach overlooks the risk that aggregated errors‚Äîsmall misstatements that accumulate across different data points‚Äîcould result in a material misstatement. By incorporating performance materiality, verifiers can mitigate this risk and focus on areas most likely to impact the overall integrity of the GHG inventory. ## What is Performance Materiality? In the context of GHG verification, particularly under Canada's Clean Fuel Regulation (CFR), performance materiality comes into play when verifiers need to sample large datasets, such as the volume of renewable fuel imported, which could consist of hundreds of invoices, receipts, or bills of lading. The Methods for Verification and Certification (MVC) within the CFR define performance materiality as: > Performance materiality: A value set lower than what might be quantitatively material to the intended user to identify misstatements that, when aggregated, might be material. Performance materiality helps to: - Reduce the risk that undetected misstatements in the GHG inventory could exceed the materiality threshold when combined. - Provide a buffer to ensure that even smaller errors, when aggregated, do not become significant. - Scope verification testing, focusing on areas more likely to contain material misstatements and allocating verification resources more effectively. ## How is Performance Materiality Used in GHG Verification? Performance materiality is often set between 50% and 75% of the overall materiality threshold. This approach ensures that even small misstatements, which might otherwise be overlooked, are considered during the verification process. For example, consider a company with 100 assets that reports 1 million metric tons of CO‚ÇÇe emissions in its GHG inventory. For a limited level of assurance, the verifier may set the performance materiality at 75% of 5%, equating to 3.75% (or 37,500 metric tons of CO‚ÇÇe). In this case, the verifier would sample assets emitting more than 37,500 tons of CO‚ÇÇe and perform detailed verification procedures on those samples. This allows the verifier to focus on larger sources of emissions while ensuring the overall accuracy of the GHG inventory.
Inventory information Value Unit
Scope 1 1,000,000 t CO2e
Materiality 5%
Materiality 50,000 t CO2e
Performance Materiality (PM, 75% of materiality) 3.75%
Performance Materiality 37,500 t CO2e
The inventory looks like this
Asset List Scope 1 (t CO2e) >PM Sampled?
Facility 1 24,140 No
Facility 2 43,504 Yes Yes
Facility 3 79,236 Yes Yes
Facility 4 43,356 No
Facility 5 50,674 Yes Yes
Facility 6 60,445 Yes Yes
## Caution in Using Performance Materiality While performance materiality is a useful tool, it is important not to rely on it exclusively when selecting samples for GHG verification. The verifier should also consider the risk of material misstatement, the nature of the data, and the complexity of the data. In practice, analytical procedures, such as year-over-year changes or trend analysis, should be applied even to assets below the performance materiality threshold. This allows verifiers to identify outliers or unusual patterns, which may warrant additional scrutiny despite being under the materiality threshold. Below is an example:
Asset List Scope 1 (t CO2e) >PM Year over Year Change Sampled?
Facility 1 24,140 No 12% Yes
Facility 2 43,504 Yes 5% Yes
Facility 3 79,236 Yes 4% Yes
Facility 4 43,356 No 2%
Facility 5 50,674 Yes 9% Yes
Facility 6 60,445 Yes 12% Yes
… … … … ..
Total 1,000,000
## Conclusion In the evolving landscape of GHG verification, performance materiality is becoming an increasingly important concept, especially for large-scale or company-wide assurance efforts. By carefully applying performance materiality, verifiers can focus on the areas that matter most, ensuring that even smaller errors don‚Äôt combine to create significant misstatements. However, it‚Äôs critical to use performance materiality in conjunction with other verification techniques to ensure a comprehensive and reliable GHG inventory assessment. --- ## Transparent GHG Calculations for Verifiers URL: https://www.envirobyte.com/blog/transparency Author: EnviroByte Published: 2025-02-05 Updated: 2026-09-29 For GHG reporting, transparent calculation is not just a best practice—it’s often a regulatory requirement. For example, Alberta’s TIER regulation mandates clear and traceable emissions data. Transparent GHG calculations significantly reduce the time and effort required during third-party verification and minimize the liability for both reporters and reporting facilities. While many software solutions automate calculations and reduce human error, they often perform these calculations in the backend, making it difficult for users and verifiers to trace or reproduce the results. This lack of visibility becomes especially problematic in complex regulatory scenarios involving multiple activity streams and reconciliation steps. In contrast, voluntary reporting platforms sometimes offer large, detailed tables that pull from databases to show activity data, emission factors, references, and data sources. However, regulatory GHG reporting often carries financial implications, such as carbon costs or revenues, and is subject to deeper scrutiny from regulators. Reporters must be able to defend not only current emissions figures but also historical data. Imagine the challenge if a software provider ceases operations and regulators begin questioning past GHG values—without transparent records, organizations could be left vulnerable. # Compliance **EmissionX®**’s transparent methodology supports regulatory compliance by making it easier for auditors and regulators to verify reported emissions. Detailed documentation of all calculations and data sources helps organizations avoid penalties and maintain a strong reputation for environmental responsibility. # Decision-Making Clear emissions data empowers organizations to identify reduction opportunities, prioritize sustainability actions, and allocate resources effectively. **EmissionX®** provides a comprehensive view of emissions, enabling informed decision-making that drives impactful environmental initiatives. # Stakeholder Engagement Transparency also strengthens stakeholder relationships. By demonstrating a commitment to sustainability through accessible and traceable reporting, organizations can build trust with investors, customers, and communities. This fosters meaningful dialogue and enhances reputation in a world where sustainability is increasingly valued. # EmissionX®: A Transparent Solution **EmissionX®** has developed an innovative approach to bring transparency to GHG reporting while retaining the benefits of automation and error reduction. Our platform provides: • Step-by-step calculations • Intermediate calculation tables • Clearly labeled emission factors and quantification methods This meticulous structure allows users and verifiers to follow the logic, reproduce results, and ensure accuracy. Embrace the power of transparency with **EmissionX®**. Contact us today for a demo. --- ## Pandas vs Polars for Large Emissions Datasets URL: https://www.envirobyte.com/blog/pandas-vs-polars Author: EnviroByte Published: 2025-05-03 Updated: 2026-09-29 ## Large Datasets Efficiently Importing and Processing Large Datasets in Python with Pandas and Polars. When working with large datasets, it's best to choose the right tools and techniques to ensure efficient data processing. In this blog, we'll explore two functions that import large amounts of Petrinex data from local folders into a dataframe in Python for analysis. We'll compare the performance of these functions using Pandas and Polars, and provide code snippets to summarize data by company-operator and by facility ID (Petrienx ID). ## Importing Data with Pandas The first function uses Pandas to import and process venting data for a range of years. Here's the implementation: ```python import pandas as pd import glob import os def process_venting_data(start_year, end_year): all_years_data = [] for year in range(start_year, end_year + 1): folder_path = f'./Datasets/Petrinex_{year}/' all_files = glob.glob(os.path.join(folder_path, f"Vol_{year}-*-AB.CSV")) print(f"Processing year {year}") print("Files found:", all_files) if not all_files: print(f"No files found for year {year}.") continue df_list = [] for file in all_files: df = pd.read_csv(file) venting_data = df[df['ActivityID'] == 'VENT'].copy() venting_data['Month'] = file.split('-')[1] venting_data['Year'] = year venting_cleaned = venting_data[['OperatorName', 'ReportingFacilityID', 'Volume', 'Month', 'Year']] df_list.append(venting_cleaned) if df_list: year_combined_df = pd.concat(df_list, ignore_index=True) all_years_data.append(year_combined_df) else: print(f"No venting data for year {year}.") if all_years_data: combined_df = pd.concat(all_years_data, ignore_index=True) combined_file_path = './Datasets/Petrinex_combined_venting_data.csv' combined_df.to_csv(combined_file_path, index=False) print(f"Combined data for years {start_year}-{end_year} saved to {combined_file_path}") else: print("No data found for the specified years.") # Example usage start_year = int(input("Enter the start year: ")) end_year = int(input("Enter the end year: ")) process_venting_data(start_year, end_year) ``` ### Explanation - **Reading Files**: The function reads CSV files for each month within the specified year range using the `glob` module to match file patterns. - **Filtering Data**: It filters the data to keep only the rows where the `ActivityID` is 'VENT', indicating venting activities. - **Combining Data**: It combines the data from all files for each year and then combines the yearly data into a single dataframe. - **Saving Data**: The combined data is saved to a CSV file. ## Importing Data with Polars ```python import polars as pl import os def load_and_process_data(years, base_path): df_list = [] for year in years: year_path = os.path.join(base_path, f'petrinex_{year}') if not os.path.exists(year_path): raise FileNotFoundError(f"The specified path does not exist: {year_path}") directory_contents = os.listdir(year_path) for month_file in directory_contents: if month_file.lower().endswith('.csv'): file_path = os.path.join(year_path, month_file) schema = { 'ProductionMonth': pl.Utf8, 'OperatorBAID': pl.Utf8, 'OperatorName': pl.Utf8, 'ReportingFacilityID': pl.Utf8, 'ReportingFacilityProvinceState': pl.Utf8, 'ReportingFacilityType': pl.Utf8, 'ReportingFacilityIdentifier': pl.Int64, 'ReportingFacilityName': pl.Utf8, 'ReportingFacilitySubType': pl.Int64, 'ReportingFacilitySubTypeDesc': pl.Utf8, 'ReportingFacilityLocation': pl.Utf8, 'FacilityLegalSubdivision': pl.Int64, 'FacilitySection': pl.Int64, 'FacilityTownship': pl.Int64, 'FacilityRange': pl.Int64, 'FacilityMeridian': pl.Int64, 'SubmissionDate': pl.Utf8, 'ActivityID': pl.Utf8, 'ProductID': pl.Utf8, 'FromToID': pl.Utf8, 'FromToIDProvinceState': pl.Utf8, 'FromToIDType': pl.Utf8, 'FromToIDIdentifier': pl.Utf8, 'Volume': pl.Float64, 'Energy': pl.Float64, 'Hours': pl.Int64, 'CCICode': pl.Int64, 'ProrationProduct': pl.Utf8, 'ProrationFactor': pl.Float64, 'Heat': pl.Utf8 } df = pl.read_csv(file_path, schema=schema, ignore_errors=True) df = df.with_columns( pl.col('ProductionMonth').str.strptime(pl.Date, '%Y-%m') ) df_list.append(df) if not df_list: raise ValueError("No CSV files were found and loaded.") data = pl.concat(df_list) return data ``` ### Explanation - **Reading Files**: The function reads CSV files for each month within the specified year range by checking the directory contents and matching file extensions. - **Schema Definition**: It defines a schema for the CSV files to ensure correct data types during reading. - **Parsing Dates**: The function parses the `ProductionMonth` column as dates. - **Combining Data**: It combines the data from all files into a single dataframe using Polars' efficient concatenation. ### Time Comparison Let's compare the performance of these two functions. Using Pandas, the data import takes approximately 73 seconds! while using Polars, it takes about 13 seconds. This significant difference show that polar's lazy execution loading option can be good in certain scenarios. ### Pros and Cons **Pandas:** - **Pros:** - Mature and widely used library - Rich ecosystem with extensive documentation and community support - Support difference in datatype and has capability to handle mismatches - **Cons:** - Slower performance with very large datasets - Higher memory consumption **Polars:** - **Pros:** - Faster performance - Lower memory consumption - **Cons:** - Less mature compared to Pandas - Smaller community and fewer available resources - Might need a schema for unclean datasets ## Summarizing Data To summarize data by fuel, flare, and vent, you can filter the dataframe after importing the data to python. Here's an example: ```python venting_data = df[df['ActivityID'] == 'VENT'].copy() ``` You can simply change 'VENT' to any other activity such as fuel or flare to filter the data accordingly. This simple filtering technique allows you to quickly isolate the data you need for further analysis. --- ## Alberta TIER Aggregate Reporting with Petrinex URL: https://www.envirobyte.com/blog/alberta-tier-aggregate Author: Envirobyte Published: 2025-02-01 Updated: 2026-09-29 The Alberta TIER (Technology Innovation and Emissions Reduction) regulation allows for the aggregation of multiple facilities to create a single reporting entity. The aggregate facility emissions are calculated based on Petrinex data, which provides monthly volumetric data for oil and gas operations in Alberta. [Petrinex](https://www.petrinex.ca/PD/Pages/default.aspx) is a data warehouse. It has monthly volumetric data from Oil and Gas operations in Alberta, such as production, fuel use, flare, vent data. The volumetric data can be [here] (https://www.petrinex.ca/PD/Pages/APD.aspx). Note: data for Saskatchewan and British Columbia are not publicly available. Check all the monthly files ```python import pandas as pd import numpy as np import glob files = glob.glob('*.csv') files ``` Note: - macOS is sensitive to the letters \*\.csv or \*\.CSV. - Windows does not differentiate csv or CSV. Combine all monthly files into one file ```python df_all = pd.concat([pd.read_csv(f,low_memory=False) for f in glob.glob('*.CSV')], axis = 0,sort = True) df_all.head() ``` remove unnecessary columns. ```python col_removal = ['ReportingFacilityProvinceState','ReportingFacilityLocation' 'FacilityLegalSubdivision','FacilitySection','FacilityTownship', 'FacilityRange','FacilityMeridian','SubmissionDate', 'CCICode','ProrationProduct','ProrationFactor', 'FromToIDProvinceState'] df =df_all.drop(columns= col_removal) df.to_csv('Petrinex_2019.csv',index=False) ``` Select company you want to report ```python clientA=df[df['OperatorName'].str.contains('clientA')] clientAFuelFlareVent = clientA[(clientA['ActivityID'] == 'FUEL') | (clientA['ActivityID'] == 'FLARE')| (clientA['ActivityID'] == 'VENT')] clientAFuelFlareVent ``` Calculate total fuel, flare and vent ```python df. groupby(['OperatorName','ReportingFacilityID','FromToID','ActivityID','ProductionMonth'])['Volume'].sum() ``` --- ## The Danger of Black-Box GHG Reporting Software URL: https://www.envirobyte.com/blog/dangers-of-black-box-reporting Author: EnviroByte Published: 2025-06-03 Updated: 2026-09-29 Almost all GHG reporting software on the market operates like a black box to a certain degree. Alberta, as the first province, introduced regulatory GHG reporting. Some Environmental Software, although extremely difficult to use with poor service, still doing majority of Alberta TIER GHG reporting, Black box **Enviromental Software** poses many severe problems that reporting companies may not realize, including- ## Lack of Transparency Reporting companies have limited visibility into how the software functions internally. This lack of transparency can lead to uncertainty about how the software processes data, makes decisions, or handles errors. ## Dependency on Vendor Users of black box software rely heavily on the vendors for support, updates, and fixes. If the vendor discontinues support or goes out of business, users may face challenges in maintaining or replacing the software. ## Vendor Lockin Reporting companies was locked in by the software vendor. Moving away from the software can be difficult and costly. Poor service and lack of support can also be a significant issue, as users may struggle to get help when needed. ## Limited Understanding Without access to the internal workings of the software, users may struggle to understand why it behaves a certain way or produces certain results. This can make troubleshooting difficult and hinder the ability to optimize or customize the software to meet specific needs. ## Difficulty in Debugging When issues or errors arise, it can be challenging to diagnose the root cause without any insight into the internal mechanisms of the software. Debugging becomes more difficult when users cannot examine the code directly. ## Security Risks Without visibility into the software's internal workings, it is harder for users to assess potential security vulnerabilities or risks. Malicious actors may exploit these vulnerabilities, leading to data breaches or other security incidents. ## Compliance and Regulation In regulated industries such as healthcare or finance, there may be requirements for transparency and accountability in software systems. Black box software may struggle to meet these compliance standards due to its lack of visibility. ## EmissionX™ To address these issues, EmissionX™ is programmed with open source approach where the source code is freely available for inspection and modification. Open-source software offers greater transparency, enabling users to understand how the software works, contribute improvements, and address security concerns more effectively. --- ## OpenPEMS: Free, Open-Source PEMS Software URL: https://www.envirobyte.com/blog/openpems Author: Minxing Si Published: 2025-05-03 Updated: 2026-09-29 ## PEMS History Predictive Emissions Monitoring Systems (PEMS) have been around for a long time. They were first introduced in the 1990s as a way to monitor emissions from industrial sources. PEMS use mathematical models to predict emissions based on process data, such as fuel consumption and operating conditions. These models are then used to estimate emissions in real-time, allowing operators to monitor their emissions and take corrective action if necessary. ## PEMS Providers For facilities that look for integrated PEMS solutions, key players such as ABB, Rockwell, and Honeywell provide comprehensive PEMS offerings that seamlessly integrate with Distributed Control Systems (DCS), data storage systems, and reporting systems. In the landscape of PEMSs, long-standing players like CMC Solutions have been delivering PEMS solutions for over a decade and installed more than 100 systems for regulatory reporting. ## OpenPEMS™ OpenPEMS™ is developed by one of our team members, and it is a free and open-source predictive emissions monitoring system that can help you monitor your emissions in real-time. OpenPEMS™ is inspired by OpenAI's ethos of accessibility and empowerment. Our vision is to make the Artificial Intelligent (AI) and Machine Learning (ML) technologies accessible to a wider industrial audience, reducing costs associated with air emissions monitoring. We expanded traditional PEMS capabilities to include GHG emissions monitoring, which is essential for regulatory compliance and sustainability reporting. OpenPEMS™ is designed to be user-friendly, with a simple and intuitive interface that allows operators to monitor emissions, view historical data, and generate reports easily. We integrate OpenPEMS™ with our other applications, including EmissionX™ for GHG reporting and Reduction in Motion (RIM) for CH4 reporting and reduction to provide a comprehensive solution for emissions monitoring, reporting, and forecasting (prediction). --- ## Carbon Content and CO2 for Liquid Fuels URL: https://www.envirobyte.com/blog/carbon-content Author: Minxing Si Published: 2025-05-03 Updated: 2026-09-29 Calculating the carbon content of liquid fuel is essential for estimating the greenhouse gas emissions from the combustion of liquid fuel. [Alberta TIER quantification](https://open.alberta.ca/dataset/5d79b86b-7811-413f-88d8-acd36cb9d6d2/resource/29984c25-29bc-46cd-83fb-1f54fb3e2f5c/download/epa-alberta-greenhouse-gas-quantification-methodologies-version-2-3-2023-09.pdf) document does not provide methodologies for calculating the carbon content of liquid fuels. This blog post introduces a method for calculating the carbon content of liquid fuels. ## Differences in Calculating Carbon Content for Liquid and Gaseous Fuels For gaseous fuels (e.g., natural gas), the molar volume can be converted to mass using a fixed value of 23.645 m¬≥/kmole: $$ V = \frac{nRT}{P} = 23.645\ \text{m3/kmol} $$ - $n$ = 1 kmole - $R$ = 8.314 m3 Pa/(K kmole) - $T$ = 288.15 K - $P$ = 101325 Pa at $15^{\circ}\text{C}$ and 1 atmosphere pressure, $V$ is 23.645 m3/kmole. This is not applicable for liquid fuels. ## Liquid Carbon Content Calculation The carbon content of a liquid fuel mixture is a weighted average of the carbon content of its individual components. First, calculate the weight percent (wt%) of carbon ($Wt\%C$) of each component in the fuel. This is done by multiplying the molecular weight of carbon by the number of carbon atoms in the compound and dividing by the compound‚Äôs molecular weight. $$ Wt\%C_\text {Cj} = \frac{12.01 \frac{t}{t-mol} \times X }{MW_\text{Cj} \frac{t}{t-mol}} $$ where - $Wt\%C_\text {Cj}$ is carbon content of hydrocarbon compound on a mass percent basis (for example $Wt\%C_\text {C2H6}$, $Wt\%C_\text {C3H8}$) - 12.01 is the molecular weight of carbon - $X$ is the number of carbon atoms in the compound (for example 2 for ethane C2H6, 3 for propane C3H8) - $MW_\text{Cj}$ is the molecular weight of the individual hydrocarbon compound (for exmaple, 30.069 t/t-mol for ethane C2H6, 44.0956 t/t-mol for propane C3H8) The total carbon content of the fuel mixture is then calculated as: $$ Wt\%C_\text{mixture} =\frac{1}{100} \sum_{i=1}^{n} (Wt\%_i \times Wt\%C_\text {i}) $$ where - $Wt\%C_\text{mixture}$ is the carbon content of the fuel mixture on a **mass percent** basis - $Wt\%_i$ is the **weight percent** of the individual fuel component - $Wt\%C_\text {i}$ is the carbon content of the individual fuel component $i$ on a weight percent basis, calculated using the formula above (for example $Wt\%C_\text {C2H6}$, $Wt\%C_\text {C3H8}$ ) ## CO2 emissions from combustion of liquid fuels Carbon dioxide emissions from the combustion of liquid fuels can be calculated $$ E_{\text{CO2}} = FC \times D \times Wt\%C_\text{mixture} \times 44/12 $$ where $E_{\text{CO2}}$ is the CO2 emissions in mass (e.g. kg) $FC$ is the fuel consumption in volume (e.g. gal, m3) $D$ is the density of the fuel in mass/volume (e.g. lb/gal,kg/m3) ## Calculation Example An calculation example can be download from [here](https://docs.google.com/spreadsheets/d/1Bv3hqAbojBtaiqhgeDMVoUstH0fPyzse/edit?usp=sharing&ouid=113683066587589743835&rtpof=true&sd=true) ## Reference The calculation is simplified fro easier understanding based on the Equation 4-9 to 4-13 from API 2021, [Compendium of Greenhouse Gas Emissions Methodologies for the Oil and Gas Industry](https://www.api.org/~/media/files/policy/esg/ghg/2021-api-ghg-compendium-110921.pdf)