Code Examples

This page provides a small set of concrete, copy-pasteable examples showing how to use MOCA’s MonteCarloLCA in practice.

All examples assume that:

• a Brightway / ActivityBrowser project already exists

• uncertainty information is already defined on exchanges

• the package moca_uncertainty_lca is installed

---

Minimal example: serial Monte Carlo

This is the most basic example of how to run a Monte Carlo simulation with MOCA. It runs in serial and uses the default settings.

import brightway2 as bw
import moca_uncertainty_lca as moca

bw.projects.set_current("your_project")  # change to your project

demand = {
    bw.Database("your_database") # database of your demand activity
    .get("492e35473j74034b6c49b0240ff7800"): 1 # key of your demand activity
}

mc_lca = moca.MonteCarloLCA(
    demand=demand,
    lcia_method_name="EF v3.1 no LT", # change to your preferred LCIA method
    run_parallel=False
)

# for a quick test, use a small number of iterations
# for a more thorough analysis, use 2000 or more iterations
mc_lca.execute_monte_carlo(iterations=100)

# these lines will save the results and statistics to JSON files
mc_lca.results_to_json()
mc_lca.stats_to_json()

---

Basic parallel execution

To speed up larger Monte Carlo runs, enable parallel execution. By default, all available CPU cores (up to 60) are used. On shared machines or laptops, it is often useful to limit CPU usage explicitly using the num_cores option.

import brightway2 as bw
import moca_uncertainty_lca as moca

bw.projects.set_current("your_project")  # change to your project

demand = {
    bw.Database("your_database") # database of your demand activity
    .get("492e35473j74034b6c49b0240ff7800"): 1 # key of your demand activity
}

mc_lca = moca.MonteCarloLCA(
    demand=demand,
    lcia_method_name="EF v3.1 no LT",
    run_parallel=True,  # this flag enables parallel execution
    num_cores=4  # this is an optional parameter - by default up to 60 cores are used
)

mc_lca.execute_monte_carlo(iterations=100)

mc_lca.results_to_json()
mc_lca.stats_to_json()

---

Looking at your uncertainty data

Before running a Monte Carlo simulation, it can be helpful to get an overview of the uncertainty information in your model. MOCA provides a convenient method to print a summary of the uncertainty types and distributions that you have defined.

import brightway2 as bw
import moca_uncertainty_lca as moca

bw.projects.set_current("your_project")  # change to your project

demand = {
    bw.Database("your_database") # database of your demand activity
    .get("492e35473j74034b6c49b0240ff7800"): 1 # key of your demand activity
}

mc_lca = moca.MonteCarloLCA(
    demand=demand,
    lcia_method_name="EF v3.1 no LT",
)

mc_lca.print_uncertainty_info()

This will print a summary of the uncertainty information in your model, looking something like this:

Total exchanges: x
Exchanges with uncertainty: x (x.x%)

Uncertainty type distribution:
    Type 4 (Uniform): x (x.x%)
    Type 0 (Undefined): x (x.x%)
    Type 2 (Lognormal): x (x.x%)
    Type 5 (Triangular): x (x.x%)
    Type 3 (Normal): x (x.x%)

Foreground exchanges: x (x.x%)
Background exchanges: x (x.x%)

Foreground exchanges with uncertainty:
    Type 4 (Uniform): x (x.x%)

---

Using explicit LCIA methods

Instead of matching LCIA methods by name, you can pass explicit method tuples (like in standard Brightway2).

import brightway2 as bw
import moca_uncertainty_lca as moca

bw.projects.set_current("your_project")  # change to your project

demand = {
    bw.Database("your_database") # database of your demand activity
    .get("492e35473j74034b6c49b0240ff7800"): 1 # key of your demand activity
}

lcia_methods = [
    ('EF v3.1 no LT', 'climate change', 'global warming potential (GWP100)'),
    ('EF v3.1 no LT', 'acidification', 'accumulated exceedance (AE)')
]

mc_lca = moca.MonteCarloLCA(
    demand=demand,
    lcia_methods=lcia_methods,
    run_parallel=True,
    num_cores=6
)

mc_lca.execute_monte_carlo(iterations=250)

mc_lca.results_to_json()
mc_lca.stats_to_json()

---

Custom output filenames

For larger studies or multiple runs, custom filenames and folders help keep results organised.

import brightway2 as bw
import moca_uncertainty_lca as moca

bw.projects.set_current("your_project")  # change to your project

demand = {
    bw.Database("your_database") # database of your demand activity
    .get("492e35473j74034b6c49b0240ff7800"): 1 # key of your demand activity
}

mc_lca = moca.MonteCarloLCA(
    demand=demand,
    lcia_method_name="EF v3.1 no LT",
    run_parallel=True,
    num_cores=8
)

mc_lca.execute_monte_carlo(iterations=500)

mc_lca.results_to_json(
    filename="lh2_tank_mc_results.json",
    folder_path="results/lh2_tank"
)

mc_lca.stats_to_json(
    filename="lh2_tank_mc_stats.json",
    folder_path="results/lh2_tank"
)

---

Accessing results as a DataFrame

Monte Carlo results can be returned as a Pandas DataFrame, which is useful for plotting or integration with the Activity Browser.

import brightway2 as bw
import moca_uncertainty_lca as moca

bw.projects.set_current("your_project")  # change to your project

method = (
    'EF v3.1 no LT',
    'climate change',
    'global warming potential (GWP100)'
)

demand = {
    bw.Database("your_database") # database of your demand activity
    .get("492e35473j74034b6c49b0240ff7800"): 1 # key of your demand activity
}

mc_lca = moca.MonteCarloLCA(
    demand=demand,
    lcia_methods=[method],
    run_parallel=False
)

mc_lca.execute_monte_carlo(iterations=100)

df = mc_lca.get_results_dataframe(method=method)
print(df.head())