导入方法 EF 3.0 - 结果有问题
Importation of method EF 3.0 - trouble with results
我编写了一个脚本，用于在 Brightway 上导入 LCIA 方法 EF 3.0（已改编）的特征因子。我认为它工作正常，因为我在 Activity 浏览器上看到了正确的特征因素（例如气候变化方法： but when I run calculations with the method, the results are not the same as on Simapro (where I got the CSV Import File from) : 例如，气候变化方法的结果为 0。你知道吗可以是问题吗？
好像单位不一样，但是Brightway上的其他方法都是一样的。
另外，我在另一个问题上看到有实现导入EF 3.0方法的方法，现在可以使用了吗？
非常感谢您的帮助。
导入脚本代码：
import brightway2 as bw
import csv
import uuid
from bw2data import mapping
from bw2data.utils import recursive_str_to_unicode
class import_method_EF:
'''Class for importing the EF method from Simapro export CSV file to Brightway. '''
def __init__(
self,
project_name,
name_file,
):
self.project_name = project_name
self.name_file = name_file
self.db_biosphere = bw.Database('biosphere3')
#Definition of the dictionnary for the correspondance between the Simapro and the ecoinvent categories
self.dict_categories = {'high. pop.' : 'urban air close to ground',
'low. pop.' : 'low population density, long-term',
'river' : 'surface water',
'in water' : 'in water',
'(unspecified)' : '',
'ocean' : 'ocean',
'indoor' : 'indoor',
'stratosphere + troposphere' : 'lower stratosphere + upper troposphere',
'low. pop., long-term' : 'low population density, long-term',
'groundwater, long-term' : 'ground-, long-term',
'agricultural' : 'agricultural',
'industrial' : 'industrial',
}
#Definition of the dictionnary of the ecoinvent units abreviations
self.dict_units = {'kg' : 'kilogram',
'kWh' : 'kilowatt hour',
'MJ' : 'megajoule',
'p':'p',
'unit':'unit',
'km':'kilometer',
'my' : 'meter-year',
'tkm' : 'ton kilometer',
'm3' : 'cubic meter',
'm2' :'square meter',
'kBq' : 'kilo Becquerel',
'm2a' : 'm2a', #à modifier
}
def importation(self) :
"""
Makes the importation from the Simapro CSV file to Brightway.
"""
#Set the current project
bw.projects.set_current(self.project_name)
self.data = self.open_CSV(self.name_file, [])
list_methods = []
new_flows = []
for i in range(len(self.data)) :
#print(self.data[i])
if self.data[i] == ['Name'] :
name_method = self.data[i+1][0]
if self.data[i] == ['Impact category'] :
list_flows = []
j = 4
while len(self.data[i+j])>1 :
biosphere_code = self.get_biosphere_code(self.data[i+j][2],self.data[i+j][1],self.data[i+j][0].lower())
if biosphere_code == 0 :
if self.find_if_already_new_flow(i+j, new_flows)[0] :
code = self.find_if_already_new_flow(i+j, new_flows)[1]
list_flows.append((('biosphere3', code),float(self.data[i+j][4].replace(',','.'))))
else :
code = str(uuid.uuid4())
while (self.db_biosphere.name, code) in mapping:
code = str(uuid.uuid4())
new_flows.append({'amount' : float(self.data[i+j][4].replace(',','.')),
'CAS number' : self.data[i+j][3],
'categories' : (self.data[i+j][0].lower(), self.dict_categories[self.data[i+j][1]]),
'name' : self.data[i+j][2],
'unit' : self.dict_units[self.data[i+j][5]],
'type' : 'biosphere',
'code' : code})
list_flows.append((('biosphere3', code),float(self.data[i+j][4].replace(',','.'))))
else :
list_flows.append((('biosphere3', biosphere_code),float(self.data[i+j][4].replace(',','.'))))
j+=1
list_methods.append({'name' : self.data[i+1][0],
'unit' : self.data[i+1][1],
'flows' : list_flows})
new_flows = recursive_str_to_unicode(dict([self._format_flow(flow) for flow in new_flows]))
if new_flows :
print('new flows :',len(new_flows))
self.new_flows = new_flows
biosphere = bw.Database(self.db_biosphere.name)
biosphere_data = biosphere.load()
biosphere_data.update(new_flows)
biosphere.write(biosphere_data)
print('biosphere_data :',len(biosphere_data))
for i in range(len(list_methods)) :
method = bw.Method((name_method,list_methods[i]['name']))
method.register(**{'unit':list_methods[i]['unit'],
'description':''})
method.write(list_methods[i]['flows'])
print(method.metadata)
method.load()
def open_CSV(self, CSV_file_name, list_rows):
'''
Opens a CSV file and gets a list of the rows.
: param : CSV_file_name = str, name of the CSV file (must be in the working directory)
: param : list_rows = list, list to get the rows
: return : list_rows = list, list of the rows
'''
#Open the CSV file and read it
with open(CSV_file_name, 'rt') as csvfile:
data = csv.reader(csvfile, delimiter = ';')
#Write every row in the list
for row in data:
list_rows.append(row)
return list_rows
def get_biosphere_code(self, simapro_name, simapro_cat, type_biosphere):
"""
Gets the Brightway code of a biosphere process given in a Simapro format.
: param : simapro_name = str, name of the biosphere process in a Simapro format.
: param : simapro_cat = str, category of the biosphere process (ex : high. pop., river, etc)
: param : type_biosphere = str, type of the biosphere process (ex : Emissions to water, etc)
: return : 0 if the process is not found in biosphere, the code otherwise
"""
if 'GLO' in simapro_name or 'RER' in simapro_name :
simapro_name = simapro_name[:-5]
if '/m3' in simapro_name :
simapro_name = simapro_name[:-3]
#Search in the biosphere database, depending on the category
if simapro_cat == '' :
act_biosphere = self.db_biosphere.search(simapro_name, filter={'categories' : (type_biosphere,)})
else :
act_biosphere = self.db_biosphere.search(simapro_name, filter={'categories' : (type_biosphere, self.dict_categories[simapro_cat])})
#Pourquoi j'ai fait ça ? ...
for act in act_biosphere :
if simapro_cat == '' :
if act['categories'] == (type_biosphere, ):
return act['code']
else :
if act['categories'] == (type_biosphere, self.dict_categories[simapro_cat]):
return act['code']
return 0
def _format_flow(self, cf):
# TODO
return (self.db_biosphere.name, cf['code']), {
'exchanges': [],
'categories': cf['categories'],
'name': cf['name'],
'type': ("resource" if cf["categories"][0] == "resource"
else "emission"),
'unit': cf['unit'],
}
def find_if_already_new_flow(self, n, new_flows) :
"""
"""
for k in range(len(new_flows)) :
if new_flows[k]['name'] == self.data[n][2] :
return True, new_flows[k]['code']
return False, 0
Edit ：我对 get_biosphere_code 方法进行了修改，效果更好（它没有找到一些生物圈流），但我仍然有重要的区别我在 Brightway 上获得的结果和我在 Simapro 上获得的结果。我的调查使我得出以下观察结果：
- ecoinvent 活动存在一些差异，尤其是在生物圈流量列表中（应该是结果差异的汇），Brightway 和用于进口的 ecoSpold 数据中缺少一些Simapro 中的数据
- 似乎 LCA 计算在子类别方面的工作方式不同：例如，生物圈流量二氧化碳、化石（空气）在气候变化方法的特征化因素列表中，并且当查看 Simapro LCA 结果中的清单时，似乎所有流向空气的二氧化碳、化石都参与了气候变化影响，无论它们的子类别是什么。但 Brightway 不是这样工作的，它只考虑完全相同的流量，因此导致结果存在重大差异。
在 LCA 中，没有就基本流程和原型排放场景/背景达成一致 (https://doi.org/10.1007/s11367-017-1354-3), and implementations of the impact assessment methods differ (https://www.lifecycleinitiative.org/portfolio_category/lcia/)。
相同的activity和相同的影响评估方法returns在不同的软件中产生不同的结果并不罕见。有一些改进当前实践的尝试（参见例如 https://github.com/USEPA/LCIAformatter）。
我编写了一个脚本，用于在 Brightway 上导入 LCIA 方法 EF 3.0（已改编）的特征因子。我认为它工作正常，因为我在 Activity 浏览器上看到了正确的特征因素（例如气候变化方法：
另外，我在另一个问题上看到有实现导入EF 3.0方法的方法，现在可以使用了吗？ 非常感谢您的帮助。
导入脚本代码：
import brightway2 as bw
import csv
import uuid
from bw2data import mapping
from bw2data.utils import recursive_str_to_unicode
class import_method_EF:
'''Class for importing the EF method from Simapro export CSV file to Brightway. '''
def __init__(
self,
project_name,
name_file,
):
self.project_name = project_name
self.name_file = name_file
self.db_biosphere = bw.Database('biosphere3')
#Definition of the dictionnary for the correspondance between the Simapro and the ecoinvent categories
self.dict_categories = {'high. pop.' : 'urban air close to ground',
'low. pop.' : 'low population density, long-term',
'river' : 'surface water',
'in water' : 'in water',
'(unspecified)' : '',
'ocean' : 'ocean',
'indoor' : 'indoor',
'stratosphere + troposphere' : 'lower stratosphere + upper troposphere',
'low. pop., long-term' : 'low population density, long-term',
'groundwater, long-term' : 'ground-, long-term',
'agricultural' : 'agricultural',
'industrial' : 'industrial',
}
#Definition of the dictionnary of the ecoinvent units abreviations
self.dict_units = {'kg' : 'kilogram',
'kWh' : 'kilowatt hour',
'MJ' : 'megajoule',
'p':'p',
'unit':'unit',
'km':'kilometer',
'my' : 'meter-year',
'tkm' : 'ton kilometer',
'm3' : 'cubic meter',
'm2' :'square meter',
'kBq' : 'kilo Becquerel',
'm2a' : 'm2a', #à modifier
}
def importation(self) :
"""
Makes the importation from the Simapro CSV file to Brightway.
"""
#Set the current project
bw.projects.set_current(self.project_name)
self.data = self.open_CSV(self.name_file, [])
list_methods = []
new_flows = []
for i in range(len(self.data)) :
#print(self.data[i])
if self.data[i] == ['Name'] :
name_method = self.data[i+1][0]
if self.data[i] == ['Impact category'] :
list_flows = []
j = 4
while len(self.data[i+j])>1 :
biosphere_code = self.get_biosphere_code(self.data[i+j][2],self.data[i+j][1],self.data[i+j][0].lower())
if biosphere_code == 0 :
if self.find_if_already_new_flow(i+j, new_flows)[0] :
code = self.find_if_already_new_flow(i+j, new_flows)[1]
list_flows.append((('biosphere3', code),float(self.data[i+j][4].replace(',','.'))))
else :
code = str(uuid.uuid4())
while (self.db_biosphere.name, code) in mapping:
code = str(uuid.uuid4())
new_flows.append({'amount' : float(self.data[i+j][4].replace(',','.')),
'CAS number' : self.data[i+j][3],
'categories' : (self.data[i+j][0].lower(), self.dict_categories[self.data[i+j][1]]),
'name' : self.data[i+j][2],
'unit' : self.dict_units[self.data[i+j][5]],
'type' : 'biosphere',
'code' : code})
list_flows.append((('biosphere3', code),float(self.data[i+j][4].replace(',','.'))))
else :
list_flows.append((('biosphere3', biosphere_code),float(self.data[i+j][4].replace(',','.'))))
j+=1
list_methods.append({'name' : self.data[i+1][0],
'unit' : self.data[i+1][1],
'flows' : list_flows})
new_flows = recursive_str_to_unicode(dict([self._format_flow(flow) for flow in new_flows]))
if new_flows :
print('new flows :',len(new_flows))
self.new_flows = new_flows
biosphere = bw.Database(self.db_biosphere.name)
biosphere_data = biosphere.load()
biosphere_data.update(new_flows)
biosphere.write(biosphere_data)
print('biosphere_data :',len(biosphere_data))
for i in range(len(list_methods)) :
method = bw.Method((name_method,list_methods[i]['name']))
method.register(**{'unit':list_methods[i]['unit'],
'description':''})
method.write(list_methods[i]['flows'])
print(method.metadata)
method.load()
def open_CSV(self, CSV_file_name, list_rows):
'''
Opens a CSV file and gets a list of the rows.
: param : CSV_file_name = str, name of the CSV file (must be in the working directory)
: param : list_rows = list, list to get the rows
: return : list_rows = list, list of the rows
'''
#Open the CSV file and read it
with open(CSV_file_name, 'rt') as csvfile:
data = csv.reader(csvfile, delimiter = ';')
#Write every row in the list
for row in data:
list_rows.append(row)
return list_rows
def get_biosphere_code(self, simapro_name, simapro_cat, type_biosphere):
"""
Gets the Brightway code of a biosphere process given in a Simapro format.
: param : simapro_name = str, name of the biosphere process in a Simapro format.
: param : simapro_cat = str, category of the biosphere process (ex : high. pop., river, etc)
: param : type_biosphere = str, type of the biosphere process (ex : Emissions to water, etc)
: return : 0 if the process is not found in biosphere, the code otherwise
"""
if 'GLO' in simapro_name or 'RER' in simapro_name :
simapro_name = simapro_name[:-5]
if '/m3' in simapro_name :
simapro_name = simapro_name[:-3]
#Search in the biosphere database, depending on the category
if simapro_cat == '' :
act_biosphere = self.db_biosphere.search(simapro_name, filter={'categories' : (type_biosphere,)})
else :
act_biosphere = self.db_biosphere.search(simapro_name, filter={'categories' : (type_biosphere, self.dict_categories[simapro_cat])})
#Pourquoi j'ai fait ça ? ...
for act in act_biosphere :
if simapro_cat == '' :
if act['categories'] == (type_biosphere, ):
return act['code']
else :
if act['categories'] == (type_biosphere, self.dict_categories[simapro_cat]):
return act['code']
return 0
def _format_flow(self, cf):
# TODO
return (self.db_biosphere.name, cf['code']), {
'exchanges': [],
'categories': cf['categories'],
'name': cf['name'],
'type': ("resource" if cf["categories"][0] == "resource"
else "emission"),
'unit': cf['unit'],
}
def find_if_already_new_flow(self, n, new_flows) :
"""
"""
for k in range(len(new_flows)) :
if new_flows[k]['name'] == self.data[n][2] :
return True, new_flows[k]['code']
return False, 0
Edit ：我对 get_biosphere_code 方法进行了修改，效果更好（它没有找到一些生物圈流），但我仍然有重要的区别我在 Brightway 上获得的结果和我在 Simapro 上获得的结果。我的调查使我得出以下观察结果：
- ecoinvent 活动存在一些差异，尤其是在生物圈流量列表中（应该是结果差异的汇），Brightway 和用于进口的 ecoSpold 数据中缺少一些Simapro 中的数据
- 似乎 LCA 计算在子类别方面的工作方式不同：例如，生物圈流量二氧化碳、化石（空气）在气候变化方法的特征化因素列表中，并且当查看 Simapro LCA 结果中的清单时，似乎所有流向空气的二氧化碳、化石都参与了气候变化影响，无论它们的子类别是什么。但 Brightway 不是这样工作的，它只考虑完全相同的流量，因此导致结果存在重大差异。
在 LCA 中，没有就基本流程和原型排放场景/背景达成一致 (https://doi.org/10.1007/s11367-017-1354-3), and implementations of the impact assessment methods differ (https://www.lifecycleinitiative.org/portfolio_category/lcia/)。
相同的activity和相同的影响评估方法returns在不同的软件中产生不同的结果并不罕见。有一些改进当前实践的尝试（参见例如 https://github.com/USEPA/LCIAformatter）。