HubPublic
PackagePublished package

@pasar6987/sf-net-zero-travel-freight

Emission factors for business travel by air, ground, hotel, and rental car, plus freight hauling calculations.

Published package · Latest published 1.0.1 Mar 4, 2026 · 10 datasets / 82 measures in the latest review · Updated Mar 11, 2026

Release path

1.0.1Published Mar 4, 2026

Publisher

@pasar6987Published Mar 4, 2026

Structure snapshot

10 datasets82 measures · 150 dimensions

Semantic Graph

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Loading graph

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Package relationships will appear when the summary is ready.

Reference context

Secondary package facts stay compact

Updated
Mar 11, 2026
Visibility
Public hub listing
License
MIT
Created
Mar 4, 2026

Reference facts

Secondary package facts after the usage path is clear

Licensing, categorization, ownership, and linked metadata stay below the runnable path so the page reads in the right order.

Format

OSI

Upstream

developer.salesforce.com/docs/atlas.en-us.object_reference.meta/object_reference

Repository

Not linked

Owner

@pasar6987

Organization

Independent

License

MIT

Visibility

Public hub listing

Publisher

@pasar6987Published Mar 4, 2026

Latest published version

1.0.1Published Mar 4, 2026

Tags

salesforcecrmnet-zerotravel-freight

Schema preview

Schema 0.1.1

SDK handoff

Use this package in code and AI

After structure review, move straight into typed reads with load() or compact LLM context with to_prompt().

Python SDK

Python example for @pasar6987/sf-net-zero-travel-freight

This example uses the current package ref and, when preview data is available, fills in real dataset names from the published summary.

import rawctx

model = rawctx.load("@pasar6987/sf-net-zero-travel-freight")
prompt = rawctx.to_prompt(
    "@pasar6987/sf-net-zero-travel-freight",
    datasets=["AirTravelEmssnFctr", "AirTravelEnrgyUse"],
    max_tokens=2000,
)

print(model.datasets)        # ["AirTravelEmssnFctr", "AirTravelEnrgyUse", "FrgtHaulingEmssnFctr"]
print(model.measures)        # [Measure(name="AirTravelEmssnFctr.Ch4PsgrKmLongHaulInKgCo2e", ...), Measure(name="AirTravelEmssnFctr.Ch4PsgrKmMediumHaulInKgCo2e", ...), Measure(name="AirTravelEmssnFctr.Ch4PsgrKmShortHaulInKgCo2e", ...)]
print(model.dimensions)      # [Dimension(name="DistanceUnit", ...), Dimension(name="EmissionFactorDataSource", ...), Dimension(name="EmissionFactorUpdateYear", ...)]
print(model.relationships)   # [Relationship(name='...', ...)]
print(prompt)

README

Package narrative and examples

Use documentation after the package clears provenance, ownership, and runnable-path checks.

@pasar6987/sf-net-zero-travel-freight

Emission factors for business travel by air, ground, hotel, and rental car, plus freight hauling calculations.

Overview

Count
Objects (Datasets)10
Dimensions150
Measures82
Relationships0

Objects

  • AirTravelEmssnFctr — Represents the emissions factors for scope 3 commercial air travel.
  • AirTravelEnrgyUse — Represents the energy consumption related to air travel.
  • FrgtHaulingEmssnFctr — Represents the emissions factors for scope 3 freight hauling.
  • FrgtHaulingEnrgyUse — Represents the energy consumption related to freight hauling.
  • GroundTravelEmssnFctr — Represents the emissions factors for scope 3 ground travel.
  • GroundTravelEnrgyUse — Represents the energy consumption related to ground travel.
  • HotelStayEmssnFctr — Represents the emissions factors for scope 3 hotel stay.
  • HotelStayEnrgyUse — Represents the energy consumption related to hotel stay.
  • RentalCarEmssnFctr — Represents the emissions factors for scope 3 rental car usage.
  • RentalCarEnrgyUse — Represents the energy consumption related to rental cars.

Install

rawctx snapshot-download @pasar6987/sf-net-zero-travel-freight

Structure review

Inspect package structure after the usage path is clear

Use the structural review when you need the package footprint, field counts, and model paths before a deeper explorer pass.

Models1
datasets10
measures82
dimensions150
relationships0
AI context1
models/sf-net-zero-travel-freight.osi.yamlAI context included
10 datasets82 measures150 dimensions0 relationships