I am running a Montecarlo simulation on ecoinvent v 3.8 consequential system model and when randomly sampling the activity market for waste paper, sorted' (kilogram, GLO, None) I get very unrealistic and overestimated results.
myact = bw.Database('ecoinvent 3.8_conseq').get('aae12a8b0ba521d60af5341c75cc9d3c') # waste paper sorted
mymethod = ('IPCC 2013', 'climate change', 'GWP 100a')
lca = bw.LCA({myact : 1}, mymethod)
lca.lci()
lca.lcia()
lca.score
Returns a value of -2.768 kg CO2-eq while
mc = bw.MonteCarloLCA({myact: 1}, mymethod)
mc_results = [next(mc) for x in range(20)]
Returns values with a median over 100 kg CO2-eq. Which not only seems absurd but also skews all results in when sampling any foreground or background activity downstream, i.e. having this activity as input (e.g. cellulose fibre production '48506ab8ea444c5e826cc079ff0d4c11')
I have tried removing all uncertainties for the exchanges in the activity but the result did not change.
for exc in list(myact.exchanges()):
exc['uncertainty type'] = 0
exc['loc'], exc['scale'] = np.log(1), np.log(1)
exc.save()
My question is: how can I figure out if this is an ecoinvent problem or a brightway problem and how to fix it?