def METHOD_NAME(dest_dir: str) -> None:
    #
    # Load data.
    #
    # Load BP statistical review dataset.
    ds_bp: Dataset = paths.load_dependency("statistical_review")
    # Read main table from dataset.
    tb_bp = ds_bp["statistical_review"]
    # Load Shift data.
    ds_shift: Dataset = paths.load_dependency("fossil_fuel_production")
    # Read main table from dataset.
    tb_shift = ds_shift["fossil_fuel_production"]
    # Load population dataset from garden.
    ds_population: Dataset = paths.load_dependency("population")
    # Get table from dataset.
    tb_population = ds_population["population"]
    # Make a dataframe out of the data in the table, with the required columns.
    df_population = pd.DataFrame(tb_population)
    #
    # Process data.
    #
    # Prepare BP data.
    tb_bp = prepare_bp_data(tb_bp=tb_bp)
    # Prepare Shift data on fossil fuel production.
    tb_shift = prepare_shift_data(tb_shift=tb_shift)
    # Combine BP and Shift data.
    df = combine_bp_and_shift_data(tb_bp=tb_bp, tb_shift=tb_shift)
    # Add annual change.
    df = add_annual_change(df=df)
    # Add per-capita variables.
    df = add_per_capita_variables(df=df, population=df_population)
    # Remove spurious values and rows that only have nans.
    df = remove_spurious_values(df=df)
    # Create an appropriate index and sort conveniently.
    df = df.set_index(["country", "year"], verify_integrity=True).sort_index()
    # Create new table.
    table = Table(df, short_name="fossil_fuel_production")
    #
    # Save outputs.
    #
    # Create a new dataset with the same metadata as in Meadow.
    ds_garden = create_dataset(dest_dir, tables=[table])
    ds_garden.save()