HESTIA Engine Models
HESTIA's set of models for running calculations or retrieving data using external datasets and internal lookups.
Documentation
Documentation for every model can be found in the HESTIA Guide.
Install
- Install python
3.12minimum - Install the module:
pip install hestia_earth.models
Usage
from hestia_earth.models.pooreNemecek2018 import run
cycle_data = {"@type": "Cycle", ...}
# cycle is a JSONLD node Cycle
result = run('no3ToGroundwaterSoilFlux', cycle_data)
print(result)
This will display only the result of the no3ToGroundwaterSoilFlux model (Emission).
Additionally, to reduce the number of queries to the HESTIA API and run the models faster, prefetching can be enabled:
from hestia_earth.models.preload_requests import enable_preload
enable_preload()
Using the orchestrator
The models come with an "orchestrator", which allows you to run a pre-configured set of models instead of a single one.
The configuration for each Node (Cycle, Site or ImpactAssessment) can be found in the config folder.
Usage:
from hestia_earth.orchestrator import run
from hestia_earth.models.config import load_config
cycle = {"@type": "Cycle", ...}
result = run(cycle, load_config(cycle))
print(result)
This will display the Cycle recalculated with all HESTIA default models running.
Using Spatial Models
We have models that can gap-fill geographical information on a Site. If you want to use these models:
- Install the library:
pip install hestia-earth-earth-engine - Follow the Getting Started instructions.
Disabling the standardised drought indices
The SPEI and SPI models run by default. Set the env variable ENABLE_DROUGHT_INDEX to false to turn them off.
They are fitted over a fixed climate reference period (1979-2020), so they also fetch
precipitationMonthly and potentialEvapotranspirationMonthly over that period rather than over the
Site's own years only - four decades of monthly values per Site. Turning them off restores the
narrower window, which is the reason to do it on a self-hosted run.
Collecting logs in JSON format
:info: This is an experimental feature and is not yet integrated in all the HESTIA tools.
To collect logs in JSON format:
- Set the env variable
LOG_JSON_ENABLEDtotrue - Use the following code to collect contribution logs:
from hestia_earth.orchestrator import run
from hestia_earth.models.config import load_config
from hestia_earth.models.jlog import reset_collected_logs, get_collected_logs
# make sure the logs are reset before each run
reset_collected_logs()
# run the models as usual
impact = {"@type": "ImpactAssessment", ...}
result = run(impact, load_config(impact))
print(result)
# write the logs to a JSON file
logs = get_collected_logs()
with open("logs.json", "w") as f:
f.write(to_string(logs, indent=2))
When a node is recalculated in separate passes (e.g. stage 1 then stage 2 in different processes), seed the later pass with the earlier one's logs so stage-1-only placements are not lost:
from hestia_earth.models.jlog import load_collected_logs, get_collected_logs
# stage 2: load the logs collected during stage 1, then run stage 2 as usual
load_collected_logs(stage1_logs)
result = run(impact, load_config(impact), stage=2)
logs = get_collected_logs() # contains both stage 1 and stage 2
Collecting contribution data
:info: This is an experimental feature and is not yet integrated in all the HESTIA tools.
To collect contribution data of impacts/endpoints on ImpactAssessment:
- Set the env variable
LOG_CONTRIBUTION_ENABLEDtotrue - Use the following code to collect contribution logs:
from hestia_earth.orchestrator import run
from hestia_earth.models.config import load_config
from hestia_earth.models.log_contribution import reset_contributions, get_contributions
# make sure the contributions are reset before each run
reset_contributions()
# run the models as usual
impact = {"@type": "ImpactAssessment", ...}
result = run(impact, load_config(impact))
print(result)
# write the contributions to a JSON file
contributions = get_contributions()
with open("contributions.json", "w") as f:
f.write(to_string(contributions, indent=2))
The contribution data will store the factors used for each emissionsResourceUse that contributes to specific impacts, and likewise impacts that contribute to endpoints. The index in the data represents the index of the blank node in the list of contributing blank nodes.
Using the ecoinventV3 model
⚠️ This model has been deprecated and no further support will be provided.
ecoinvent is a consistent, transparent, and well validated life cycle inventory database. We use ecoinvent data to ascertain the environmental impacts of activities that occur outside of our system boundary, for example data on the environmental impacts of extracting oil and producing diesel, or the impacts of manufacturing plastics.
The ecoinventV3 model requires a valid license to run. We are currently working on a way to enable users of this code with a valid ecoinvent licence to run these models themselves, but for now, these models are only available on the public platform.
Amortising Land Use Change Emissions Using Linear Discounting
In compliance with Land Sector and Removals Standard & Guidance set by the Greenhouse Gas Protocol (2026), HESTIA amortises LUC emissions using linear discounting by default.
On the main HESTIA platform, we have chosen to amortise emissions using linear discounting, and this cannot be changed. The amortisation method can only be changed on the Community Edition.
For more information, see our guide page on linear discounting.
To opt-out of linear discounting on the Community Edition, and revert to amortising emissions using equal apportioning:
- Set the env variable
USE_LINEAR_DISCOUNTINGtofalse
TypeScript / JavaScript package
The model metadata is also published to npm as @hestia-earth/engine-models:
npm install @hestia-earth/engine-models
It exposes the links between models, terms and their documentation (model-links.json), and the KaTeX formulas extracted from each model, with bindings that map every symbol to a value logged at runtime (the "jlog"):
import { getFormulas } from '@hestia-earth/engine-models';
getFormulas({ model: 'ipcc2019', term: 'ch4ToAirEntericFermentation' });
// => [ { formula: 'E_{CH4} = (GE \\times Y_m) / 55.65', bindings: [...] } ]
See formulas/README.md for the full guide on getting a model's formulas, rendering them with KaTeX, and substituting the symbols with the values from a given execution.
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