10 Serious Games in Sustainable Land Management
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data available on demography, employment patterns, transport infrastructure, residential locations, taxes, household incomes, education and other important aspects
(for further information about the model, see Gertz et al. 2015).
The second step of the project attempted to communicate this data via different
channels in an effort to make simulation as realistic as possible for the participants.
For the professional level, for example, the project created a regional planning report
with common facts and figures about commuter data, demographic developments,
etc. typically used in such media. Participants were also addressed on a more personal
level by media clips and short video clips designed as news reports. The articles for
those media clips were produced (based on scenario data) by a professional journalist
and imitated the styles and characteristics of layouts of current print media products.
The goal was to sensitise the participants to the topic and to enable them to relate to
the scenario in different ways on a broad professional and personal basis.
These visualisations were used in the third step to prompt the participants to
identify the effects of rising energy prices for their respective municipality and to
develop measures, ideas and strategies to respond to such cost developments as
decision-makers for their specialisation and/or municipality (Fig. 10.3, right-hand
side).
During the fourth step, the participants’ ideas were reformulated as far as possible
into numeric values according to previously identified parameters, such as the time
frame for implementation and financial effort.
The output generated by the serious game was translated and fed back into the
model (Fig. 10.3, bottom) to generate a scenario for 2025. The new scenario was
then translated into different media and presented to the decision-makers. Confronted
with these outcomes, they were asked to respond once again. This created an iterative
cycle between the model and decision-makers, and the project simulated a span of
20 years (2010–2030).
10.7 Investigating Energy-Price Effects in the Hamburg
Metropolitan Region—How to Integrate Regional
Decision-Makers
The study area of the eLAN project is the—still growing—Hamburg Metropolitan
Region (HMR). Currently, it is made up of 1177 municipalities in four federal states
in Northern Germany,
3 with about 5 million inhabitants. It comprises an area of
around 26,000 km
2 . The city of Hamburg, with around 1.7 million inhabitants, is the
dominant centre in the region and is a hub for key economic activities. The region
also contains sub-centres with universities, entertainment facilities, hospitals and
3 In eLAN, the Hamburg Metropolitan Region encompasses the city-state of Hamburg, the western
part of Mecklenburg-West Pomerania and the southern part of Schleswig–Holstein. The Hamburg
Metropolitan Region also includes parts of Lower Saxony, but this area was omitted from the study.
193
data available on demography, employment patterns, transport infrastructure, residential locations, taxes, household incomes, education and other important aspects
(for further information about the model, see Gertz et al. 2015).
The second step of the project attempted to communicate this data via different
channels in an effort to make simulation as realistic as possible for the participants.
For the professional level, for example, the project created a regional planning report
with common facts and figures about commuter data, demographic developments,
etc. typically used in such media. Participants were also addressed on a more personal
level by media clips and short video clips designed as news reports. The articles for
those media clips were produced (based on scenario data) by a professional journalist
and imitated the styles and characteristics of layouts of current print media products.
The goal was to sensitise the participants to the topic and to enable them to relate to
the scenario in different ways on a broad professional and personal basis.
These visualisations were used in the third step to prompt the participants to
identify the effects of rising energy prices for their respective municipality and to
develop measures, ideas and strategies to respond to such cost developments as
decision-makers for their specialisation and/or municipality (Fig. 10.3, right-hand
side).
During the fourth step, the participants’ ideas were reformulated as far as possible
into numeric values according to previously identified parameters, such as the time
frame for implementation and financial effort.
The output generated by the serious game was translated and fed back into the
model (Fig. 10.3, bottom) to generate a scenario for 2025. The new scenario was
then translated into different media and presented to the decision-makers. Confronted
with these outcomes, they were asked to respond once again. This created an iterative
cycle between the model and decision-makers, and the project simulated a span of
20 years (2010–2030).
10.7 Investigating Energy-Price Effects in the Hamburg
Metropolitan Region—How to Integrate Regional
Decision-Makers
The study area of the eLAN project is the—still growing—Hamburg Metropolitan
Region (HMR). Currently, it is made up of 1177 municipalities in four federal states
in Northern Germany,
3 with about 5 million inhabitants. It comprises an area of
around 26,000 km
2 . The city of Hamburg, with around 1.7 million inhabitants, is the
dominant centre in the region and is a hub for key economic activities. The region
also contains sub-centres with universities, entertainment facilities, hospitals and
3 In eLAN, the Hamburg Metropolitan Region encompasses the city-state of Hamburg, the western
part of Mecklenburg-West Pomerania and the southern part of Schleswig–Holstein. The Hamburg
Metropolitan Region also includes parts of Lower Saxony, but this area was omitted from the study.