supplies, gives the Faculty of Natural Science the highest intensity of
their purchases made. However, the small variations of intensities
indicate that it is the volume, and not the type, of purchases that is
the main reason for the difference in CF with other normalization
factors across different faculties.
4. Discussions
4.1. Normalization and comparing results
Normalization of the CF per study programme (faculty) identifies
large variations. The results clearly show that to either educate
a student or produce a publication on average causes much less GHG
emissions in the Faculty of Humanities and the Faculty of Social
Science, in comparison to themore engineering and science oriented
study programmes. A low contribution of transport in the Faculty of
Humanities and equipment in the Faculty of Social Science is especially
apparent. Inmost cases, differences in normalized CFs are likely
due to of the inherent nature of the field of study; studying medicine
requires specific high quality equipment. Other differences could be
due to more cultural factors in the different departments, e.g. related
to the eagerness of traveling to conferences. In some cases, even
single influential individuals can make important decisions that can
heavily influence the CF. This is especially likely to be the case in the
finances and property department, where a few people manage
a substantial part of the CF. Nevertheless, normalized CFs per
department/faculty will identify the largest contributions to the CF
and thereby allowdepartment/faculty-specific mitigation strategies.
Per-faculty calculations are also available in the EEIO modeling
for the university of Sydney (Baboulet and Lenzen, 2010). Using the
total material requirements (TMR) indicator, it identifies experimental
research such as science, veterinary science, and agriculture
to have much higher material intensities in contrast to economics,
law, art and education that have a low material intensity. In Baboulet
(2009), we also find GHG emission inventory of the different faculties.
In both cases, results are normalized per $ only. However, perfaculty
enrollment data are easily available3 and enable us to do
supplies, gives the Faculty of Natural Science the highest intensity of
their purchases made. However, the small variations of intensities
indicate that it is the volume, and not the type, of purchases that is
the main reason for the difference in CF with other normalization
factors across different faculties.
4. Discussions
4.1. Normalization and comparing results
Normalization of the CF per study programme (faculty) identifies
large variations. The results clearly show that to either educate
a student or produce a publication on average causes much less GHG
emissions in the Faculty of Humanities and the Faculty of Social
Science, in comparison to themore engineering and science oriented
study programmes. A low contribution of transport in the Faculty of
Humanities and equipment in the Faculty of Social Science is especially
apparent. Inmost cases, differences in normalized CFs are likely
due to of the inherent nature of the field of study; studying medicine
requires specific high quality equipment. Other differences could be
due to more cultural factors in the different departments, e.g. related
to the eagerness of traveling to conferences. In some cases, even
single influential individuals can make important decisions that can
heavily influence the CF. This is especially likely to be the case in the
finances and property department, where a few people manage
a substantial part of the CF. Nevertheless, normalized CFs per
department/faculty will identify the largest contributions to the CF
and thereby allowdepartment/faculty-specific mitigation strategies.
Per-faculty calculations are also available in the EEIO modeling
for the university of Sydney (Baboulet and Lenzen, 2010). Using the
total material requirements (TMR) indicator, it identifies experimental
research such as science, veterinary science, and agriculture
to have much higher material intensities in contrast to economics,
law, art and education that have a low material intensity. In Baboulet
(2009), we also find GHG emission inventory of the different faculties.
In both cases, results are normalized per $ only. However, perfaculty
enrollment data are easily available3 and enable us to do
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