M3 - AWARE in LCA software

1. CFs_country scale

1.1 CFs in Simapro

1.2 CFs in openLCA

2. CFs_watershed scale for regionalized LCA

2.1 geo-CFs in openLCA

2.2 AWARE_Brightway2 (BW2)

AWARE in openLCA

3 generic CFs and geo-CFs in openLCA

AWARE factors can be downloaded from:

https://wulca-waterlca.org/aware/download-aware-factors/

Below is an example, fully regionalized native scale CF, at watershed ID 10492, monthly CF

SAME CF in the corresponding excel file:

1. Country-level CFs in SP(v8_4) & openLCA

1.1 SP_CFs

source: https://wulca-waterlca.org/aware/download-aware-factors/

NOTE: This set of AWARE CFs is downloaded directly from WULCA, as indicated on its website for SP version 8.4, not for most updated SP v9

Raw CF data

all data entries with "non-agri" CFs: N=1584

"non-agri" & "agri" CFs: N = 1584*2 = 3168

# deleted Intro headers (row1-45), read-in & cleanup
SP_df = pd.read_csv('Aware102_SimaPro8_4_NOheader.csv', header=None)
print("Total number of CFs:", len(SP_df))
#SP_df.head()
#first 28 rows for world-average (not-country level) CFs:  SP_df[0:40] 

Total number of CFs: 5449

#all data entries with "non-agri" CFs:  N=1584, to print out:
#SP_df[SP_df[1].str.contains("non-agri", na=False) | SP_df[2].str.contains("non-agri", na=False) | SP_df[3].str.contains("non-agri", na=False)]
# "non-agri" & "agri" CFs:   N = 1584*2 = 3168
SP_df[SP_df[1].str.contains("agri", na=False) | SP_df[2].str.contains("agri", na=False) | SP_df[3].str.contains("agri", na=False)]

	0	1	2	3	4
10	Water;(unspecified);Water	agri;007732-18-5;-0.045736878;kg	NaN	NaN	NaN
11	Water;ocean;Water	agri;007732-18-5;0;kg	NaN	NaN	NaN
12	Raw;(unspecified);Water	lake	agri;007732-18-5;45.73687803;m3	NaN	NaN
13	Raw;(unspecified);Water	river	agri;007732-18-5;45.73687803;m3	NaN	NaN
14	Raw;(unspecified);Water	unspecified natural origin/m3	agri;007732-18-5;45.73687803;m3	NaN	NaN
...	...	...	...	...	...
5442	Water;(unspecified);Water/m3	non-agri	OECD+BRIC;007732-18-5;-17.45632;m3	NaN	NaN
5443	Water;(unspecified);Water/m3	non-agri	OECD+BRICS;007732-18-5;-17.48948;m3	NaN	NaN
5444	Water;(unspecified);Water/m3	non-agri	CS;007732-18-5;-3.29108;m3	NaN	NaN
5445	Water;(unspecified);Water/m3	non-agri	PS;007732-18-5;-54.0157;m3	NaN	NaN
5446	Water;(unspecified);Water/m3	non-agri	OCE;007732-18-5;-19.47827;m3	NaN	NaN

3168 rows × 5 columns

# the raw CSV file with messed up data structure, define a function to extract whatever you want, rawdf has total 0-4 cols
def select_df(rawdf, containstring):
    sliced_df =  rawdf[rawdf[0].str.contains(containstring, na=False)].append(
                 rawdf[rawdf[1].str.contains(containstring, na=False)]).append( 
                 rawdf[rawdf[2].str.contains(containstring, na=False)]).append( 
                 rawdf[rawdf[3].str.contains(containstring, na=False)]).append(
                 rawdf[rawdf[4].str.contains(containstring, na=False)])
    return sliced_df

Take a closer look for CN

CN_aware = select_df(SP_df, 'CN')
print("Total number of CFs (agri. + non-agri + generic) for CN:", len(CN_aware)) 
#incl. both agri and non-agri
CN_allagri = select_df(CN_aware, 'agri')
print("Total number of agri and non-agri CFs for CN:",len(CN_allagri))

Total number of CFs (agri. + non-agri + generic) for CN: 24
Total number of agri and non-agri CFs for CN: 14

7 CFs for non-agri:

CF = 27.08 (lake, river, well, unspecified input) | -27.08 (discharge)

# 7 CFs for non-agri, to print:
select_df(CN_aware, 'non-agri')

	0	1	2	3	4
3910	Water;(unspecified);Water	non-agri	CN;007732-18-5;-27.08065257;m3	NaN	NaN
5260	Water;(unspecified);Water/m3	non-agri	CN;007732-18-5;-27.08065257;m3	NaN	NaN
4135	Raw;(unspecified);Water	lake	non-agri	CN;007732-18-5;27.08065257;m3	NaN
4360	Raw;(unspecified);Water	river	non-agri	CN;007732-18-5;27.08065257;m3	NaN
4585	Raw;(unspecified);Water	unspecified natural origin	non-agri	CN;007732-18-5;27.08065257;m3	NaN
4810	Raw;(unspecified);Water	unspecified natural origin/m3	non-agri	CN;007732-18-5;27.08065257;m3	NaN
5035	Raw;(unspecified);Water	well	in ground	non-agri	CN;007732-18-5;27.08065257;m3

7 CFs for agri:

CF = 45.847 (lake, river, well, unspecified input) | -45.847 (discharge)

CN_allagri.drop(select_df(CN_aware, 'non-agri').index)

	0	1	2	3	4
2335	Water;(unspecified);Water	agri	CN;007732-18-5;-45.8472784;m3	NaN	NaN
3685	Water;(unspecified);Water/m3	agri	CN;007732-18-5;-45.8472784;m3	NaN	NaN
2560	Raw;(unspecified);Water	lake	agri	CN;007732-18-5;45.8472784;m3	NaN
2785	Raw;(unspecified);Water	river	agri	CN;007732-18-5;45.8472784;m3	NaN
3010	Raw;(unspecified);Water	unspecified natural origin	agri	CN;007732-18-5;45.8472784;m3	NaN
3235	Raw;(unspecified);Water	unspecified natural origin/m3	agri	CN;007732-18-5;45.8472784;m3	NaN
3460	Raw;(unspecified);Water	well	in ground	agri	CN;007732-18-5;45.8472784;m3

10 CFs for generic:

CF = 42.47 (lake, river, well, unspecified input) | -42.47 (discharge)

CF = 27.08 (cooling | turbine use, unspecified natural origin) == CFs (non-agri)

CN_aware.drop(CN_allagri.index)

	0	1	2	3	4
67	Water;(unspecified);Water	CN;007732-18-5;-42.47130357;m3	NaN	NaN	NaN
2110	Water;(unspecified);Water/m3	CN;007732-18-5;-42.47130357;m3	NaN	NaN	NaN
751	Raw;(unspecified);Water	lake	CN;007732-18-5;42.47130357;m3	NaN	NaN
976	Raw;(unspecified);Water	river	CN;007732-18-5;42.47130357;m3	NaN	NaN
1426	Raw;(unspecified);Water	unspecified natural origin	CN;007732-18-5;42.47130357;m3	NaN	NaN
1660	Raw;(unspecified);Water	unspecified natural origin/m3	CN;007732-18-5;42.47130357;m3	NaN	NaN
301	Raw;(unspecified);Water	cooling	unspecified natural origin	CN;007732-18-5;27.08065257;m3	NaN
526	Raw;(unspecified);Water	cooling	unspecified natural origin/m3	CN;007732-18-5;27.08065257;m3	NaN
1201	Raw;(unspecified);Water	turbine use	unspecified natural origin	CN;007732-18-5;27.08065257;m3	NaN
1885	Raw;(unspecified);Water	well	in ground	CN;007732-18-5;42.47130357;m3	NaN

CFs for any selected country

US_aware = select_df(SP_df, 'US')
US_aware_allagri = select_df(US_aware, 'agri')

take the US for example:

• agri CFs = 35.71
• non-agri CFs = 9.08
• generic = 33.12

# US agri CFs = 35.71
US_aware_allagri.drop(select_df(US_aware, 'non-agri').index)

# US non-agri CFs = 9.08, to print:
#select_df(US_aware, 'non-agri') 

# US generic = 33.12, except for cooling | turbine use: CF = 9.08 -> same as CF(US-non-agri), to print:
#US_aware.drop(US_aware_allagri.index)

	0	1	2	3	4
2494	Water;(unspecified);Water	agri	US;007732-18-5;-35.71546543;m3	NaN	NaN
3844	Water;(unspecified);Water/m3	agri	US;007732-18-5;-35.71546543;m3	NaN	NaN
2719	Raw;(unspecified);Water	lake	agri	US;007732-18-5;35.71546543;m3	NaN
2944	Raw;(unspecified);Water	river	agri	US;007732-18-5;35.71546543;m3	NaN
3169	Raw;(unspecified);Water	unspecified natural origin	agri	US;007732-18-5;35.71546543;m3	NaN
3394	Raw;(unspecified);Water	unspecified natural origin/m3	agri	US;007732-18-5;35.71546543;m3	NaN
3619	Raw;(unspecified);Water	well	in ground	agri	US;007732-18-5;35.71546543;m3

Below CFs for SP v9, different from AWARE CFs downloaded directly from WULCA (SP v8.04)

only generic CFs, not seperating agri./non-agri

total number of CFs (N = 1807) is about 1/3 of that of CFs presented in WULCA

consistent with openLCA AWARE CFs

SPv9_df = pd.read_excel('SP_9_AWARE.xlsx')
print("Total number of CFs_in SP v9", len(SPv9_df))
SPv9_df.head()

Total number of CFs_in SP v9 1807

	Compartment	Subcompartment	Substance	CAS	Factor	Unit
0	Raw	(unspecified)	Water, cooling, drinking	007732-18-5	0.04295	m3 / kg
1	Raw	(unspecified)	Water, cooling, surface	007732-18-5	0.04295	m3 / kg
2	Raw	(unspecified)	Water, cooling, unspecified natural origin, AD	007732-18-5	74.67000	m3 / m3
3	Raw	(unspecified)	Water, cooling, unspecified natural origin, AE	007732-18-5	18.56000	m3 / m3
4	Raw	(unspecified)	Water, cooling, unspecified natural origin, AF	007732-18-5	57.20000	m3 / m3

In SP v9, total number of AWARE CFs for China = 8, all generic CF

SPv9_df[SPv9_df["Substance"].str.contains("CN", na=False)]

	Compartment	Subcompartment	Substance	CAS	Factor	Unit
41	Raw	(unspecified)	Water, cooling, unspecified natural origin, CN	007732-18-5	42.43	m3 / m3
271	Raw	(unspecified)	Water, lake, CN	007732-18-5	42.43	m3 / m3
495	Raw	(unspecified)	Water, river, CN	007732-18-5	42.43	m3 / m3
715	Raw	(unspecified)	Water, turbine use, unspecified natural origin...	007732-18-5	42.43	m3 / m3
932	Raw	(unspecified)	Water, unspecified natural origin, CN	007732-18-5	42.43	m3 / m3
1161	Raw	(unspecified)	Water, well, CN	007732-18-5	42.43	m3 / m3
1384	Water	(unspecified)	Water, CN	007732-18-5	-42.43	m3 / m3
1617	Water	ocean	Water, CN	007732-18-5	0.00	m3 / m3

1.2 openLCA_CFs

https://github.com/GreenDelta/data/blob/master/LCIA%20method/categories/Water%20use%20%2B%20AWARE.csv

AWARE (Available WAter REmaining) is the recommended method from WULCA to assess water consumption impact assessment in LCA. In May 2016, the method was also endorsed by the EU Joint Research Center. AWARE is to be used as a water use midpoint indicator representing the relative Available WAter REmaining per area in a watershed after the demand of humans and aquatic ecosystems has been met. It assesses the potential of water deprivation, to either humans or ecosystems, building on the assumption that the less water remaining available per area, the more likely another user will be deprived. It is first calculated as the water Availability Minus the Demand (AMD) of humans and aquatic ecosystems and is relative to the area (m3 m-2 month-1). In a second step, the value is normalized with the world average result (AMD = 0.0136 m3 m-2 month-1) and inverted. The result represents the relative value in comparison with the average m3 consumed in the world (the world average is calculated as a consumption-weighted average). The indicator is limited to a range from 0.1 to 100, with a value of 1 corresponding to the world average, and a value of 10, for example, representing a region where there is 10 times less available water remaining per area than the world average. Documentation is available from: http://www.wulca-waterlca.org.

PLEASE NOTE! Implementation of AWARE includes only the generic factors for unknown water usage and not the factors specific for agricultural and non-agricultural use of water (irrigation/non-irrigation, these are currently not supported the inventory data). The second set can be used to reduce uncertainty when specific watershed or month is not known, based on the activity's water use pattern. The full version of the method, including those additional flows, both in the flow-based and in the parametrized version can be downloaded from http://www.openlca.org/download/ and imported as a JSON-LD in openLCA. Note that this is only useful if you have data available in your model that support the additional flows.

Adaptations by PRé Consultants: 7 aluminium producing regions were included in order to fit the inventories. They were given the factors of the following regions: IAI Area, Africa -> Africa IAI Area; Asia, without China and GCC -> Asia; IAI Area, EU27 & EFTA -> Europe; IAI Area, Gulf Cooperation Council -> Middle East; IAI Area, North America, without Quebec -> North America; IAI Area, Russia & RER w/o EU27 & EFTA -> Europe; IAI Area, South America -> South America. Other adaptations (September 2018, version 1.02): (i) Added characterisation factors for the following geographies: NORDEL, UCTE, UCTE without Germany, WEU; (ii) Corrected all characterisation factors to include four significant digits. Other adaptation (March 2017, version 1.01): (i) Changed the subcompartment for the Raw materials - from 'in water' to '(unspecified)'. (ii) Added the Waterborne emissions flows for subcompartment 'ocean' with factor 0.

Method checked against SimaPro 9.0.0.30.

openLCA AWARE CFs: source https://raw.githubusercontent.com/GreenDelta/data/master/LCIA%20method/categories/Water%20use%20%2B%20AWARE.csv

Full transparency of all LCIA methods implemented in openLCA: https://github.com/GreenDelta/data

olca_CF = pd.read_csv('https://raw.githubusercontent.com/GreenDelta/data/master/LCIA%20method/categories/Water%20use%20%2B%20AWARE.csv')
print("Total number of CFs in openLCA:", len(olca_CF))

Total number of CFs in openLCA: 4568

Raw CFs, how it look like

olca_CF.head()

	IMPACT_METHOD	IMPACT_CATEGORY_UUID	IMPACT_CATEGORY	REFERENCE_UNIT	FLOW_UUID	FLOW	CAS_NUMBER	SUBCATEGORY	CATEGORY	UNIT	VALUE
0	AWARE	2e0e479e-6ea7-3221-88ea-41aa6530c5d2	Water use	m3	2ec98519-0441-3879-bc20-0483a724fab1	Water, unspecified natural origin, RoW	NaN	in ground	Resource	m3	44.040
1	AWARE	2e0e479e-6ea7-3221-88ea-41aa6530c5d2	Water use	m3	abc005bc-18ef-4134-a532-215fe609b66e	Water, unspecified natural origin, GA	NaN	in ground	Resource	m3	1.088
2	AWARE	2e0e479e-6ea7-3221-88ea-41aa6530c5d2	Water use	m3	c3b30b05-89b9-4429-afdd-f2555ba9cb14	Water, unspecified natural origin, SM	NaN	in ground	Resource	m3	12.190
3	AWARE	2e0e479e-6ea7-3221-88ea-41aa6530c5d2	Water use	m3	39a69e8b-8315-4cbd-95b7-7b38a4570683	Water, unspecified natural origin, PG	NaN	in ground	Resource	m3	1.427
4	AWARE	2e0e479e-6ea7-3221-88ea-41aa6530c5d2	Water use	m3	7a576055-2843-4b8c-8d52-cdf2b4c5af3a	Water, unspecified natural origin, LT	NaN	in ground	Resource	m3	1.232

CATEGORY/SUBCATEGORY:

• Emissions (discharges, - CF, to ocean = 0)
• Resource/in water
• Resource/unspecified
• Resource/in ground
• compared to SP (compartment), only "unspecified", no further seperation

Flow: lake | river | well | (cooling / turbine use) unspecified, all same CF

CN:

• CF = 42.43 (cooling | turbine use, unspecified natural origin) == CFs (all other flows)</b>
• as compared to SP_CFs (WULCA) v8_04 (w/h agri & non-agri CFs) CF = 27.08 (cooling | turbine use, unspecified natural origin) == CFs (non-agri)</b>
• as compared to SP_CFs v9 (w/h generic CFs only) same CF = 42.43</b>

print("Total number of CFs for China:", len(olca_CF[olca_CF['FLOW'].str.contains("CN")]))
olca_CF[olca_CF['FLOW'].str.contains("CN")]

Total number of CFs for China: 19

	IMPACT_METHOD	IMPACT_CATEGORY_UUID	IMPACT_CATEGORY	REFERENCE_UNIT	FLOW_UUID	FLOW	CAS_NUMBER	SUBCATEGORY	CATEGORY	UNIT	VALUE
15	AWARE	2e0e479e-6ea7-3221-88ea-41aa6530c5d2	Water use	m3	3415f4a8-db36-3c69-b844-26281514fa51	Water, unspecified natural origin, CN	NaN	in ground	Resource	m3	42.43
428	AWARE	2e0e479e-6ea7-3221-88ea-41aa6530c5d2	Water use	m3	d3079d00-615f-3804-b658-d7c3f073f09d	Water, turbine use, unspecified natural origin...	NaN	unspecified	Resource	m3	42.43
781	AWARE	2e0e479e-6ea7-3221-88ea-41aa6530c5d2	Water use	m3	25ee87eb-a72e-33b6-a4e5-32319607b905	Water, lake, CN	NaN	unspecified	Resource	m3	42.43
798	AWARE	2e0e479e-6ea7-3221-88ea-41aa6530c5d2	Water use	m3	a9ef7558-2b99-3635-bcdf-e769625b036d	Water, river, CN	NaN	unspecified	Resource	m3	42.43
808	AWARE	2e0e479e-6ea7-3221-88ea-41aa6530c5d2	Water use	m3	b6ea128a-d262-33b6-9d09-d81aec9f0bc0	Water, well, CN	NaN	unspecified	Resource	m3	42.43
1273	AWARE	2e0e479e-6ea7-3221-88ea-41aa6530c5d2	Water use	m3	bdc7d16a-dd5a-3bea-9afa-ca982715107e	Water, unspecified natural origin, CN	NaN	unspecified	Resource	m3	42.43
1467	AWARE	2e0e479e-6ea7-3221-88ea-41aa6530c5d2	Water use	m3	91a8a7ab-2278-3f30-a335-e60d4fbaf8b8	Water, cooling, unspecified natural origin, CN	NaN	unspecified	Resource	m3	42.43
1817	AWARE	2e0e479e-6ea7-3221-88ea-41aa6530c5d2	Water use	m3	be16cf07-da08-3ff5-856d-0e1f2aa3e806	Water, unspecified natural origin, CN	NaN	in water	Resource	m3	42.43
2172	AWARE	2e0e479e-6ea7-3221-88ea-41aa6530c5d2	Water use	m3	e6bf7393-ca27-36b6-89d0-0c155afe959a	Water, turbine use, unspecified natural origin...	NaN	in water	Resource	m3	42.43
2576	AWARE	2e0e479e-6ea7-3221-88ea-41aa6530c5d2	Water use	m3	a4c9c845-737c-3304-a2de-9c3e09f320cf	Water, river, CN	NaN	in water	Resource	m3	42.43
3062	AWARE	2e0e479e-6ea7-3221-88ea-41aa6530c5d2	Water use	m3	84bfc229-0279-3203-b773-65f263df050c	Water, lake, CN	NaN	in water	Resource	m3	42.43
3135	AWARE	2e0e479e-6ea7-3221-88ea-41aa6530c5d2	Water use	m3	c0ec2e25-fb9e-3e87-8138-7e049cb5ab18	Water, well, CN	NaN	in water	Resource	m3	42.43
3300	AWARE	2e0e479e-6ea7-3221-88ea-41aa6530c5d2	Water use	m3	121b0f77-c14f-393e-8b68-30b46e1a426c	Water, cooling, unspecified natural origin, CN	NaN	in water	Resource	m3	42.43
3479	AWARE	2e0e479e-6ea7-3221-88ea-41aa6530c5d2	Water use	m3	315bad13-6e3d-3a70-9163-d9502cc768db	Water, CN	NaN	unspecified	Emission to water	m3	-42.43
3791	AWARE	2e0e479e-6ea7-3221-88ea-41aa6530c5d2	Water use	m3	558c271a-fb4d-3dc7-a7e0-f1e7ec9336b0	Water, CN	NaN	ocean	Emission to water	m3	0.00
3920	AWARE	2e0e479e-6ea7-3221-88ea-41aa6530c5d2	Water use	m3	2f94f7a8-d996-4d16-bbc8-5c11b2b13418	Water, CN	NaN	surface water	Emission to water	m3	-42.43
4120	AWARE	2e0e479e-6ea7-3221-88ea-41aa6530c5d2	Water use	m3	5c701a7b-d757-3e17-a9d5-c317dccdb8c3	Water, CN	007732-18-5	river	Emission to water	m3	-42.43
4271	AWARE	2e0e479e-6ea7-3221-88ea-41aa6530c5d2	Water use	m3	b1dec0e9-3843-352d-9358-cb41be2c9f33	Water, CN	007732-18-5	lake	Emission to water	m3	-42.43
4415	AWARE	2e0e479e-6ea7-3221-88ea-41aa6530c5d2	Water use	m3	4f2c6805-4c00-347c-8554-9e1e474e9b99	Water, CN	NaN	ground water	Emission to water	m3	-42.43

# if only see "subcategory" == "unspecified" (as in SP compartment, only "unspecified"), to print:
#olca_CF[olca_CF['FLOW'].str.contains("CN") & olca_CF['SUBCATEGORY'].str.contains("unspecified") ]
# to see CFs for the US, 33.84
#olca_CF[olca_CF['FLOW'].str.contains("US")]

2. Regionalized CFs in openLCA & BW2

2.1 openLCA_(geospatial) CFs

Version 03 August 2017

The present implementation allows the user to calculate the Water Footprint of a process/system for any chosen polygon, it works by aggregating the anual CFs for each of the watersheds intersected following the guidelines provided by the AWARE authors. Some of the results may differ from the pre-defined factors published on WULCA due to a mistake on the pre-defined factors' calculations. CFs for irrigation activities, non-irrigation activities and unknown differ as determined on AWARE method. To get the correct results make sure "#Arithmetic mean" is selected at the "Shape files (beta)" tab below

Limitations: 1 - Monthly aggregated CFs are not supported by this implementation of the method.

Info: Further information about the method can be found on the publicly available study, please check the referece below. Boulay, A.-M., J. Bare, L. Benini, M. Berger, M. J. Lathuillière, A. Manzardo, M. Margni, M. Motoshita, M. Núñez, A. V. Pastor, B. Ridoutt, T. Oki, S. Worbe and S. Pfister (2017). "The WULCA consensus characterization model for water scarcity footprints: assessing impacts of water consumption based on available water remaining (AWARE)." The International Journal of Life Cycle Assessment: 1-11.

2.2 AWARE CFs in BW2

BW2 source code hided for clean presentation, only output is shown

import brightway2 as bw
import os               # to use "operating system dependent functionality"
import numpy as np      # "the fundamental package for scientific computing with Python"
import pandas as pd     # "high-performance, easy-to-use data structures and data analysis tools" for Python

bw.projects.set_current("EIDB38AWARE")

my_bio = bw.Database('biosphere3')

[m for m in bw.methods if 'AWARE' in str(m)]

[('AWARE', '1.2 (April 2017)', 'Annual', 'Agricultural'),
 ('AWARE', '1.2 (April 2017)', 'Annual', 'Non-agricultural')]

AWARE_agri = [m for m in bw.methods if 'AWARE' in str(m) and "Agricultural" in str(m)][0]
AWARE_nonagri = [m for m in bw.methods if 'AWARE' in str(m) and "Non-agricultural" in str(m)][0]

AWARE_agri_method = bw.Method(AWARE_agri)
AWARE_nonagri_method = bw.Method(AWARE_nonagri)

Load AWARE_agri_CF introduction

Total number of CFs = 49940 becaz it's fully regionalized with monthly/watershed CFs at native scale

# load AWARE_agri_CF introduction
AWARE_agri_method.metadata

{'unit': 'Availability Minus Demand (AMD), m**3/m**2/month',
 'description': 'AWARE is a water use midpoint indicator representing the relative Available WAter REmaining per area in a watershed, after the demand of humans and aquatic ecosystems has been met. It assesses the potential of water deprivation, to either humans or ecosystems, building on the assumption that the less water remaining available per area, the more likely another user will be deprived.\n\n    It is first calculated as the water Availability Minus the Demand (AMD) of humans and aquatic ecosystems and is relative to the area (m3 m-2 month-1). In a second step, the value is normalized with the world average result (AMD = 0.0136m3m-2 month-1) and inverted, and hence represents the relative value in comparison with the average m3 consumed in the world (the world average is calculated as a consumption-weighted average). Once inverted, 1/AMD  can be interpreted as a surface-time equivalent to generate unused water in this region. The indicator is limited to a range from 0.1 to 100, with a value of 1 corresponding to the world average, and a value of 10, for example, representing a region where there is 10 times less available water remaining per area than the world average.',
 'url': 'http://www.wulca-waterlca.org/aware.html',
 'geocollections': ['watersheds-aware'],
 'abbreviation': 'aware12-April-2017aa.7c809839dc850d0740950ffb10c7bf5a',
 'num_cfs': 49940}

#AWARE_agri_method.load()
len(AWARE_agri_method.load())

49940

Select some activity (from ecoinvent v3.6) for LCIA

ei36 = bw.Database('ecoinvent 3.6 cutoff')

xx = [act for act in ei36 if 'electricity production, photovoltaic, 3kWp slanted-roof installation, single-Si, panel, mounted' in act['name'] and act['location'].startswith('CN-BJ')][0]
xx

'electricity production, photovoltaic, 3kWp slanted-roof installation, single-Si, panel, mounted' (kilowatt hour, CN-BJ, None)

xxAware_nonagri = bw.LCA({xx:1}, ('AWARE', '1.2 (April 2017)', 'Annual', 'Non-agricultural'))   

xxAware_nonagri.lci()
xxAware_nonagri.lcia()
xxAware_nonagri.score

0.044134310408349364

xxAware_agri = bw.LCA({xx:1}, ('AWARE', '1.2 (April 2017)', 'Annual', 'Agricultural'))

xxAware_agri.lci()
xxAware_agri.lcia()
xxAware_agri.score

0.09944352256275835

BW2 LCIA score (w/h Agri CF) is similar (but slightly larger) to the LCIA results in openLCA - AWARE (generic CF)

another example: cow milk

# cow milk 
mm = [act for act in ei36 if 'milk production, from cow' in act['name'] and 'RoW' in act['location'] and 'milk' in act['reference product']][0]

mmAware_nonagri = bw.LCA({mm:1}, ('AWARE', '1.2 (April 2017)', 'Annual', 'Non-agricultural'))   

mmAware_nonagri.lci()
mmAware_nonagri.lcia()
mmAware_nonagri.score

0.837412093064728

mmAware_agri = bw.LCA({mm:1}, ('AWARE', '1.2 (April 2017)', 'Annual', 'Agricultural'))

mmAware_agri.lci()
mmAware_agri.lcia()
mmAware_agri.score
# openLCA result = 1.77

1.886858718320724

in openLCA, you can choose:

AWARE generic CFs, or

regionalized LCIA (geospatial CFs)

Example: 1kg water input, region = China

Example: 1kg water input, region = Zhejiang, China

Choosing AWARE (generic CFs), same result when region = China

Choosing AWARE (geospatial CFs)

Results: different from region = China

End of Module 3

TAKE HOME Messages

AWARE CFs:

ratio scale -> AMD

native scale -> aggregated CFs (typically used in LCIA)

Regionalized LCIA:

openLCA AWARE (watershed/annual)

BW2 (watershed/monthly)

DO NOT compare LCA arbitrarily:

look at the underlying CFs (also highlighted in the below article)

Thank you

For questions, please contact us:

info@susdatability.com