I would like to attack
this a bit more systematically:
Querying the core database
alone (noting that the core
observations/datapoints are “flows”):
(BW_Q1) What is
the direct input flow
of flow-object
F to activity A
measured by
flow-property P in location L in the time period T under
macro-economic
scenario S?
(example: What is the direct input
of coal to steel production measured by dry mass in Germany in
year 2020 under
the Business-as-Usual scenario?)
(BW_Q2) What is the direct
output flow of flow-object F from
activity A measured by flow-property P in location L in the time
period T under
macro-economic scenario S?
(example: What is the direct output
of steel from steel production measured by EUR2011 nominal value
in Germany in
year 2020 under the Business-as-Usual scenario?)
(BW_Q3) What is the determining flow of activity A in location L in
the time period T
under macro-economic scenario S?
(example: What is the determining
flow of soybean mills in Brazil in year 2020 under the
Business-as-Usual scenario?)
(BW_Q4) What is the
difference between input
flows and output flows of flow-property P for activity A in
location L in the
time period T under macro-economic scenario S?
(example:
What is the difference between input flows
and output flows of
dry mass for all
activities globally in year 2020 under the Business-as-Usual
scenario?)
Querying the core database
together with a system
model algorithm (for creating product footprints):
(BW_Q5) What is the
additional input flow of flow-object F to
activity A measured by flow-property P in location M in the time
period T
resulting from a demand of flow-object G in location M in the
time period U,
all under macro-economic scenario S?
(example: What is the additional input
of surface water to steel production measured by wet mass in
Germany in year
2020 resulting from a demand of 100 square meter of office
building in Spain in
year 2019 under the Business-as-Usual scenario?)
(BW_Q6) What is the
additional output flow of flow-object F from
activity A measured by flow-property P in location M in the time
period T
resulting from a demand of flow-object G in location M in the
time period U,
all under macro-economic scenario S?
(example: What is the additional radiative
forcing from the atmospheric energy transfer measured by power
per area globally
in year 2020 resulting from a demand of 100 square meter of
office building in
Spain in year 2019 under the Business-as-Usual scenario?)
I will break the rule of
not commenting, by pointing
out that:
(ML_Q1) is an instance of
(BW_Q3)
(ML_Q2) is an instance of (BW_Q5)
(ML_Q3) is an instance of (BW_Q2)
(ML_Q4) and (ML_Q5) are queries for the location of specific
agents or
activities, and thus possible and relevant queries, but not
related to the
flows that is the core observations in the database
(AG_Q1) can be obtained as
an instance of either of
(BW_Q1), (BW_Q2), (BW_Q5) and (BW_Q6) (AG_Q2) is similar to
(BW_Q4)
(AG_Q3) is an instance of (BW_Q6)