Human Resources
21 columns
45 rows
8 formats

Remote Workforce Carbon Footprint Log

This dataset provides a detailed log of the daily carbon footprint generated by remote employees, capturing work activities, energy usage, commute avoidance, and sustainability actions. It enables organizations to monitor, compare, and optimize carbon emissions across departments and locations, supporting data-driven sustainability reporting and targeted reduction strategies for remote operations.

last updated 19 Dec 2025
by GoMask

The brief that made it

Track and benchmark remote workforce carbon emissions by department or region

Sample rows

preview · 8 of 45 rows · all 21 columns

log_idstring	commute_mode_avoidedstring	commute_avoided_kmfloat	renewable_energy_usedboolean	countrystring	employee_idstring	employee_namestring	departmentstring	job_titlestring	manager_idstring	citystring	log_datedate	work_hoursfloat	device_typestring	device_energy_consumption_kwhfloat	home_energy_consumption_kwhfloat	internet_usage_gbfloat	video_calls_hoursfloat	total_carbon_emissions_kgco2efloat	emissions_reduction_actionsstring	notesstring
LOG001	car	18	false	United States	EMP1001	Sarah Johnson	Engineering	Software Engineer	MGR2001	San Francisco	2024-05-28	8.2	laptop	0.85	2.2	5.1	2.5	3.7	Worked in natural light	Typical day, no unusual activities
LOG002	train	9	true	United Kingdom	EMP1002	James Lee	Marketing	Content Strategist	MGR2002	London	2024-05-28	7.6	desktop	1.2	3.5	3.7	1.1	2.6	Used energy-saving mode	blank
LOG003	bus	7.5	false	India	EMP1003	Priya Patel	Finance	Financial Analyst	MGR2003	Mumbai	2024-05-28	9	laptop	0.95	1.8	2.4	1.8	3.1	Worked in natural light	blank
LOG004	train	10	true	Singapore	EMP1004	Linda Chen	Human Resources	HR Manager	MGR2004	Singapore	2024-05-28	7.2	laptop	0.82	1.3	2.9	1.9	2	Used energy-saving mode	Short meetings today
LOG005	car	15	false	Egypt	EMP1005	Mohamed El-Sayed	Engineering	DevOps Engineer	MGR2001	Cairo	2024-05-28	8.5	desktop	1.4	2.6	4.5	2	4.2	Used energy-saving mode	blank
LOG006	bus	5	false	Spain	EMP1006	Elena Garcia	Customer Support	Support Specialist	MGR2005	Madrid	2024-05-28	7.8	laptop	0.88	1.6	2.2	3	2.9	Worked in natural light	Handled customer calls
LOG007	train	12	true	Canada	EMP1007	John Smith	Engineering	QA Engineer	MGR2001	Toronto	2024-05-28	7.5	desktop	1.3	2.1	2.8	1.3	2.2	Used energy-saving mode	blank
LOG008	bike	8	true	Germany	EMP1008	Anna Müller	Finance	Accountant	MGR2003	Berlin	2024-05-28	6.9	laptop	0.78	1.7	1.9	1	1.6	Worked in natural light	blank
LOG009	car	13	false	Brazil	EMP1009	Lucas Rossi	Marketing	Digital Marketing Manager	MGR2002	São Paulo	2024-05-28	8	laptop	0.92	2.5	3.2	2.8	3.9	Used energy-saving mode	Campaign review meeting
LOG010	car	16	false	United States	EMP1010	Emily Davis	Engineering	Frontend Developer	MGR2001	Seattle	2024-05-28	8.6	laptop	0.91	1.9	4.1	2.6	3.3	Worked in natural light	blank
LOG011	bus	6	true	Australia	EMP1011	Oliver Nguyen	Human Resources	Recruiter	MGR2004	Sydney	2024-05-28	7	desktop	1	2	2.7	2.3	2.1	Used energy-saving mode	blank
LOG012	bus	3	false	Bulgaria	EMP1012	Nina Popov	Finance	Payroll Specialist	MGR2003	Sofia	2024-05-28	8.4	laptop	0.83	1.5	2.1	1.7	2.6	Worked in natural light	blank
LOG013	train	7	false	United Kingdom	EMP1013	Tom Baker	Customer Support	Customer Success Manager	MGR2005	Manchester	2024-05-28	7.2	desktop	1.2	2.2	3.5	3.5	3.1	Used energy-saving mode	High call volume
LOG014	car	11	false	UAE	EMP1014	Amira Haddad	Engineering	Backend Developer	MGR2001	Dubai	2024-05-28	7.5	desktop	1.3	2.3	2.8	2.4	3.4	Worked in natural light	blank
LOG015	train	5	true	Portugal	EMP1015	Miguel Santos	Finance	Finance Manager	MGR2003	Lisbon	2024-05-28	8.9	laptop	0.97	2	2.5	2	2.8	Used energy-saving mode	blank
LOG016	bus	4	false	South Korea	EMP1016	Jessica Kim	Marketing	Social Media Specialist	MGR2002	Seoul	2024-05-28	6.7	tablet	0.52	1.2	2	0.9	1.3	Used energy-saving mode	blank
LOG017	car	16	false	United States	EMP1017	David Brown	Engineering	Full Stack Developer	MGR2001	Boston	2024-05-28	9.5	desktop	1.5	2.7	3.9	3.1	4.7	Worked in natural light	Long day
LOG018	bike	4	true	Sweden	EMP1018	Clara Svensson	Human Resources	HR Coordinator	MGR2004	Stockholm	2024-05-28	7.3	laptop	0.81	1.4	2.4	1.6	1.7	Worked in natural light	blank
LOG019	car	14	false	United States	EMP1019	Ethan Miller	Engineering	Cloud Architect	MGR2001	Austin	2024-05-28	8.3	laptop	0.89	2.3	4.7	2.2	3.5	Used energy-saving mode	blank
LOG020	bus	11	false	Brazil	EMP1020	Sofia Almeida	Marketing	Brand Manager	MGR2002	Rio de Janeiro	2024-05-28	8	laptop	0.93	2.4	3.6	2.5	3.6	Worked in natural light	blank

What the 45 rows show

from the 45-row sample

Car (commute mode avoided) stands out: mean commute_avoided_km is 14.3, against 6.9 for the rest.

42%renewable_energy_used = true
8.0median commute_avoided_km
2emissions reduction actionses
3device types
5departments
5managers

Mean commute_avoided_km by commute_mode_avoided45 rows

commute_avoided_km45 rows, in bands of 2

Median 8.0, from 3.0 to 19.0.

country45 rows · top 10 of 22 values

United States10
United Kingdom3
Canada3
Brazil3
India2
Egypt2
Spain2
Germany2
Portugal2
South Korea2

21 columns by typefrom the column list below

string 12
float 7
date 1
boolean 1

Columns

21 columns in four groups

blueprint · 21 columns
column	type	description	example
Text 12 columns
`log_id`	string	Unique identifier for each carbon footprint log entryunique	LOG001
`employee_id`	string	Unique identifier for the remote employee	EMP1001
`employee_name`	string	Full name of the employee	Sarah Johnson
`department`	string	Department to which the employee belongs5 departments	Engineering
`job_title`	string	Employee's job title or roleoptional	Software Engineer
`manager_id`	string	Unique identifier for the employee's manager5 managers · optional	MGR2001
`country`	string	Country where the employee is located	United States
`city`	string	City where the employee is locatedoptional	San Francisco
`device_type`	string	Primary device used for remote work (e.g., laptop, desktop, tablet)laptop · desktop · tablet · other	laptop
`commute_mode_avoided`	string	Primary mode of transportation avoided (e.g., car, bus, train, bike, walk, none)6 values · optional	car
`emissions_reduction_actions`	string	Short description of any actions taken by the employee to reduce emissions (e.g., used energy-saving mode, worked in natural light)2 actionses · optional	Worked in natural light
`notes`	string	Additional notes or comments regarding the log entryoptional	Short meetings today
Numbers 7 columns
`work_hours`	float	Number of hours worked remotely on the log date0 to 24	8.2
`device_energy_consumption_kwh`	float	Estimated energy consumption (in kWh) of the primary device during work hours0 or more	0.85
`home_energy_consumption_kwh`	float	Estimated additional home energy consumption (in kWh) due to remote work (lighting, heating/cooling, etc.)0 or more · optional	2.2
`internet_usage_gb`	float	Estimated internet data usage (in GB) for work activities on the log date0 or more · optional	5.1
`video_calls_hours`	float	Total hours spent on video calls during the log date0 to 24 · optional	2.5
`commute_avoided_km`	float	Estimated kilometers of commute avoided due to remote work on the log date0 or more · optional	18
`total_carbon_emissions_kgco2e`	float	Total estimated carbon emissions (in kg CO2e) for the log date0 or more	3.7
Dates and times 1 column
`log_date`	date	Date for which the carbon footprint is logged	2024-05-28
True or false 1 column
`renewable_energy_used`	boolean	Indicates if the employee's home uses renewable energy sourcesoptional	false

Use it for

A human resources dashboard
The renewable_energy_used rate, commute_avoided_km by commute_mode_avoided and a breakdown of country. Excel, Power BI or Tableau.
Why do the 11 car rows have a mean commute_avoided_km of 14.3?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Logs45
LOG00118car
LOG0029train
LOG00410train
A software demo
Believable logs with employee_id, employee_name and department to fill a screen in front of a buyer.

This dataset45 rows21 columns

Yours1,000 rows21 columnscountry: UK only

blueprint · remote-workforce-carbon-footprint-log

Behind this dataset

Same schema. As many rows as you need.

These 45 rows came out of a blueprint — 21 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with

Each entry must correspond to one remote employee.
Carbon emissions are calculated based on home energy usage and work-related digital activities.
Department and role must be specified for every record.
Monthly emission totals are required; incomplete months are excluded.
Include estimated commuting emissions if hybrid work reported.

Rows1,000 rows10,000 rows100,000 rows1,000,000 rows

Open the blueprint in Data Factory

1 credit per row. Preview 20 rows free. Buy rows for the full run.

Exports: CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence: yours to use, including commercially
API slug: remote-workforce-carbon-footprint-log

What should your data show?

No signup. No card.

Sample rows

What the 45 rows show

Columns

Use it for

A human resources dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Human Resources

What should your data show?