Carbon Footprint Calculator

Overview

Calculates organizational greenhouse gas emissions following the GHG Protocol Corporate Standard using the activity-based method (activity data x emission factor x global warming potential = CO2e). Applies EPA emission factors and IPCC AR5 global warming potentials, verifying time-sensitive values against live authoritative sources at runtime. Produces an interactive dashboard for executive communication and an audit-ready calculation workbook with full methodology documentation. Use it to build a Scope 1, 2, and 3 inventory, communicate results, and model reduction scenarios.

Workflow

<Workflow - Intake and Boundary Setting description="Verify reference data, establish the organizational boundary, and collect activity data." tools=[get_current_time, file_read, run_python, web_search, url_fetch] triggers=["User requests a carbon footprint calculation or uploads activity data"]

1. [Agent] Read references/emission-factors.md and references/scopes-and-categories.md. Identify which time-sensitive values the requested calculation needs. At minimum, fetch the current eGRID subregional emission rates from url_fetch("https://www.epa.gov/egrid/summary-data") and confirm the EPA GHG Emission Factors Hub edition year from url_fetch("https://www.epa.gov/climateleadership/ghg-emission-factors-hub"). Follow the full runtime verification procedure in references/emission-factors.md. Validate: Every time-sensitive value to be used has a verified source (URL fetched this session or user-provided). If fails: Per Rule 1, stop and ask the user to confirm or provide the value.

2. [Agent] Call get_current_time to determine the reporting year. Validate: Reporting year identified. If fails: Ask the user for the reporting year.

3. [Decide] Does the user have activity data files?

   • Yes: load with run_python (pandas, CSV or Excel) and identify available data categories.
   • No: switch to <Workflow - Guided Data Collection>. Validate: Data source determined. If fails: Ask whether they have data files or prefer guided input.

4. [Agent] If files were provided, analyze the structure with run_python: print columns, row count, date range, and unique fuel or source types. Map columns to emission sources (fuel_type, quantity, unit, location, department, date). Validate: Data loads and emission source categories are identifiable. If fails: Ask the user to describe the column meanings.

5. [Ask user] Confirm the organizational boundary and scope:

   • Boundary approach: operational control, financial control, or equity share?
   • Scopes to calculate: Scope 1 only, Scope 1+2, or Scope 1+2+3?
   • Organization name for the report, and reporting year if not already set.
   • Any excluded facilities or operations? Validate: Boundary and scope confirmed. If fails: Default to operational control and Scope 1+2+3, and note the assumption.

</Workflow - Intake and Boundary Setting>

<Workflow - Guided Data Collection description="Walk the user through activity data by emission source category when no data file is uploaded." tools=[] triggers=["User has no data file and prefers guided input"]

1. [Ask user] Scope 1 stationary combustion: fuel types used in facilities, plus annual consumption and unit for each. Validate: At least one fuel source with quantity and unit. If fails: Suggest checking utility bills for natural gas and fuel delivery receipts.

2. [Ask user] Scope 1 mobile combustion: fleet vehicle count, fuel type, and either total gallons consumed or total miles driven per year. Validate: Either gallons or miles provided. If fails: Estimate from fleet count times average miles (12,000 mi/yr passenger, 25,000 mi/yr truck), and flag it as an estimate.

3. [Ask user] Scope 1 fugitive emissions: refrigerant type, charge size (lbs), and either a known leak rate or purchase-versus-recovery records. SF6 equipment top-up if applicable. Validate: At least the refrigerant type identified. If fails: Note the gap and use default leak rates from references/emission-factors.md only with the user's acknowledgement.

4. [Ask user] Scope 2 purchased electricity: annual kWh or MWh, location (state or zip for eGRID mapping), and any renewable energy certificates or green power contracts. Validate: Consumption and location provided. If fails: If only a dollar amount is known, note that a rate assumption is required and flag the resulting uncertainty.

5. [Ask user] Scope 3 (selected categories): business travel (air miles by class, rental car miles, hotel nights), employee commuting (headcount, average one-way distance, mode split), and waste (tons per year by disposal method). Validate: At least one Scope 3 category has data. If fails: Note which categories are excluded due to data unavailability.

</Workflow - Guided Data Collection>

<Workflow - Emissions Calculation Engine description="Calculate emissions for each scope and category using verified emission factors." tools=[run_python, file_read] triggers=["After data collection (file or guided) completes"]

1. [Agent] Load the verified factors and GWP values (from step 1 of Intake and references/emission-factors.md) into the run_python session. Calculate Scope 1 emissions: stationary combustion, mobile combustion, and fugitive (refrigerant) emissions, using the formulas in references/emission-factors.md. Validate: All Scope 1 subtotals are non-negative and units are converted to tonnes. If fails: Check unit conversions, especially the kg-to-tonnes division by 1000.

2. [Agent] Calculate Scope 2 emissions both ways:

   • Location-based: electricity_mwh * grid_factor using the fetched eGRID subregion factor.
   • Market-based: apply RECs or PPA coverage (instrument factor of 0) to covered MWh, grid factor to the residual; if no instruments, market equals location. Use the CO2e total output emission rate (lb CO2e/MWh) from eGRID, which already combines CO2, CH4, and N2O. Convert to tCO2e/MWh with the conversion factor in references/emission-factors.md. If Scope 3 Category 3 (T&D losses) is in scope, also fetch the grid gross loss percentage from the 2025 EPA GHG Emission Factors Hub Table 6. Validate: Location-based >= market-based. If fails: Verify the REC quantity does not exceed total consumption.

3. [Agent] Calculate Scope 3 for each category with data (business travel, employee commuting, waste, and any others available), citing the source factor for each. Validate: Each category result is non-negative and its factor is documented. If fails: Mark any category that cannot be calculated as "excluded, data unavailable."

4. [Agent] Compile inventory totals: Scope 1, Scope 2 (location and market), Scope 3, and the two grand totals (location-based and market-based). Keep CO2, CH4, and N2O disaggregated. Write intermediate results to a file in the confirmed output location so the run is resumable. Validate: Totals sum correctly with no double-counting across scopes. If fails: Trace category assignments to find overlap.

</Workflow - Emissions Calculation Engine>

<Workflow - Dashboard and Report Generation description="Create a self-contained HTML dashboard, an Excel workbook, and a markdown summary." tools=[run_python, file_write, open_in_session_tab] triggers=["After emissions calculations complete"]

1. [Agent] Load the html_design and highcharts built-in skills. Build a self-contained HTML dashboard using the vendored Highcharts, containing: summary cards (total tCO2e location-based, Scope 1, Scope 2 location/market, Scope 3, and an intensity metric if revenue or headcount is available); a scope-breakdown donut; a source-category waterfall colored by scope; a stacked bar by organizational unit if available; and a time-trend line if temporal data is available. Include the Highcharts exporting module for PNG/PDF download. Validate: HTML renders without errors and every data point matches the calculated values. If fails: Generate a simplified single-chart HTML or a matplotlib PNG fallback.

2. [Agent] Write the dashboard to the user-confirmed output location, then open it with open_in_session_tab. Validate: File saved (reasonable size) and the tab renders. If fails: Offer to open externally with open_file.

3. [Agent] Generate an audit-ready Excel workbook via the canvas_xlsx built-in skill with sheets: Summary, Scope 1 Detail, Scope 2 Detail (location and market), Scope 3 Detail, Emission Factors (every factor with source and year), Methodology (principles, boundary, exclusions), and Data Quality. Validate: All sheets are populated and factor sources are cited. If fails: Generate CSV files as a fallback.

4. [Agent] Generate a markdown summary report (organization, reporting year, boundary, totals by scope with percentages, grid subregion, categories included and excluded, intensity metrics if available, data gaps, and methodology notes). Save it to the confirmed location and open it with open_in_session_tab. Validate: The report is complete and every number is traceable to the workbook. If fails: Output the summary in chat.

5. [Ask user] Offer next steps: run a reduction scenario analysis, compare against industry benchmarks, or generate a climate disclosure summary. Validate: User responds. If fails: Conclude with the delivered outputs.

</Workflow - Dashboard and Report Generation>

<Workflow - Reduction Scenario Analysis description="Model emissions reduction scenarios to support target-setting and decarbonization planning." tools=[run_python, file_write, open_in_session_tab] triggers=["User requests reduction scenarios after the inventory"]

1. [Ask user] Which reduction levers to model: 100% renewable electricity, fleet electrification, energy efficiency, refrigerant transition, remote-work increase, or waste diversion. Validate: User selects at least one scenario. If fails: Model all available scenarios at default assumptions and label them clearly.

2. [Agent] Calculate the reduction for each selected scenario against the baseline inventory, documenting every assumption. For fleet electrification, subtract the residual grid emissions of EV charging (using the verified grid factor) from the displaced fuel emissions. Validate: Each modeled reduction is positive and percentages are computed against the correct baseline. If fails: Re-check that the baseline totals were calculated correctly.

3. [Agent] Generate a scenario comparison visualization and update the summary report with the scenarios and their assumptions. Save to the confirmed location and open with open_in_session_tab. Validate: Scenarios are displayed clearly with assumptions documented. If fails: Present the scenarios as a table in chat.

</Workflow - Reduction Scenario Analysis>