🏘️🌳🌲🌽🍀 Land cover LCA for the GOBLIN model (Ireland only)
Based on the GOBLIN (General Overview for a Backcasting approach of Livestock INtensification) land use tool module
The package takes outputs from the landcover_assignment module (land use data and transition matrix) and produces emissions inventory for cropland, grassland, forest and wetlands
Currently parameterised for Ireland, the historic land uses and areas of organic and mineral soil are taken from the National Invetory CRF Tables
Final result are pandas dataframes that can be read by numerous GOBLIN packages.
Installation
Install from git hub.
pip install "landcover_lca@git+https://github.com/GOBLIN-Proj/landcover_lca.git@main"
Install from PyPI
pip install landcover_lca
Usage
The baseline year represents the scenario baseline. The target year is the end year for each one of the scenarios.
Transition dataframe is the land use transition matrix, and the land use dataframe is the areas for each land use.
The land use dataframe includes proportions for area mineral, organic, rewetted, burnt, and peat extraction.
Annual trade data has been used for horticultural peat exports.
from landcover_lca.models import load_transition_matrix, load_land_use_data
import landcover_lca.lca_emission as lca
import pandas as pd
import os
def main():
data_dir = "./data"
ef_country = "ireland"
baseline = 2020
target = 2050
transition = pd.read_csv(os.path.join(data_dir, "transition.csv"), index_col = 0)
land_uses = pd.read_csv(os.path.join(data_dir, "land_uses.csv"), index_col = 0)
transition_matrix = load_transition_matrix(transition, ef_country, baseline, target)
land_use_data = load_land_use_data(land_uses, baseline)
baseline_index = -1
base = -baseline
emission_df = pd.DataFrame(
columns=["CO2", "CH4", "N2O", "CO2e"],
index=pd.MultiIndex.from_product(
[
# list(scenario_list),
[baseline_index],
["cropland", "grassland", "forest", "wetland", "total"],
[
baseline
],
],
names=["scenario", "land_use", "year"],
),
)
emission_df.index.levels[0].astype(int)
emission_df.loc[
(
baseline_index,
"total",
baseline,
),
"CH4",
] = (
lca.total_ch4_emission(
land_use_data[base],
land_use_data[base],
transition_matrix[base],
ef_country
)
)
emission_df.loc[
(
baseline_index,
"total",
baseline,
),
"CO2",
] = lca.total_co2_emission(
land_use_data[base],
land_use_data[base],
transition_matrix[base],
ef_country
)
emission_df.loc[
(
baseline_index,
"total",
baseline,
),
"N2O",
] = (
lca.total_n2o_emission(
land_use_data[base],
land_use_data[base],
transition_matrix[base],
ef_country
)
)
emission_df.loc[
(
baseline_index,
"cropland",
baseline
),
"CO2",
] = lca.total_co2_emission_cropland(
land_use_data[base],
land_use_data[base],
transition_matrix[base],
ef_country
)
emission_df.loc[
(
baseline_index,
"cropland",
baseline,
),
"CH4",
] = lca.total_ch4_emission_cropland(
ef_country,
transition_matrix[base],
land_use_data[base],
land_use_data[base]
)
emission_df.loc[
(
baseline_index,
"cropland",
baseline,
),
"N2O",
] = (
lca.total_n2o_emission_cropland(
ef_country,
transition_matrix[base],
land_use_data[base],
land_use_data[base],
)
)
emission_df.loc[
(
baseline_index,
"grassland",
baseline,
),
"CO2",
] = (
lca.total_co2_emission_grassland(
land_use_data[base],
land_use_data[base],
transition_matrix[base],
ef_country
)
)
emission_df.loc[
(
baseline_index,
"grassland",
baseline,
),
"CH4",
] = (
lca.total_ch4_emission_grassland(
land_use_data[base],
land_use_data[base],
transition_matrix[base],
ef_country
)
)
emission_df.loc[
(
baseline_index,
"grassland",
baseline
),
"N2O",
] = (
lca.total_n2o_emission_grassland(
land_use_data[base],
land_use_data[base],
transition_matrix[base],
ef_country
)
)
emission_df.loc[
(
baseline_index,
"wetland",
baseline,
),
"CO2",
] = (
lca.total_co2_emission_wetland(
land_use_data[base],
land_use_data[base],
transition_matrix[base],
ef_country,
) + lca.horticulture_co2_peat_export(ef_country, baseline, baseline)
)
emission_df.loc[
(
baseline_index,
"wetland",
baseline,
),
"CH4",
] = (
lca.total_ch4_emission_wetland(
land_use_data[base],
land_use_data[base],
transition_matrix[base],
ef_country
)
)
emission_df.loc[
(
baseline_index,
"wetland",
baseline,
),
"N2O",
] = (
lca.total_n2o_emission_wetland(
land_use_data[base],
land_use_data[base],
transition_matrix[base],
ef_country,
)
)
emission_df.loc[
(
baseline_index,
"forest",
baseline,
),
"CO2",
] = (
lca.total_co2_emission_forest(
land_use_data[base],
land_use_data[base],
transition_matrix[base],
ef_country,
)
)
emission_df.loc[
(
baseline_index,
"forest",
baseline
),
"CH4",
] = (
lca.total_ch4_emission_forest(
land_use_data[base],
land_use_data[base],
transition_matrix[base],
ef_country
)
)
emission_df.loc[
(
baseline_index,
"forest",
baseline
),
"N2O",
] = (
lca.total_n2o_emission_forest(
land_use_data[base],
land_use_data[base],
transition_matrix[base],
ef_country
)
)
emission_df["CO2e"] = (
emission_df["CO2"]
+ (emission_df["CH4"] * 28)
+ (emission_df["N2O"] * 295)
)
print(emission_df)
if __name__ == "__main__":
main()
License
This project is licensed under the terms of the MIT license.