The modern Anthropocene would not exist without electricity. It is the
ticking clockwork that guarantees and dictates the rhythm of modernity
in all of its beneficial and challenging extents. Electricity generation
is a complex process with various inter-dependencies. It is
thus important to survey, maintain and continuously adapt all contributors to
guarantee a stable electricity generation.
This is all the more valid as humanity became aware of the negative repercussions
of its constant striving for growth and wealth that provoked various
threats, ranging from the climate change over poverty to health issues. In this
light, the Sustainable Development Goals were created that should help to assess
and guide humanity to a more sustainable way of thriving. The Sustainable
Development Goals address several targets, one of them being sustainable (electric)
energy supply.
The electricity generation’s dependencies are manifold and certainly dependent
on the type of generation, but one constant prerequisite is (almost) inevitable
for nearly every type of electric generation facility: water. There exists
a vast energy-water context with various threads of dependency. It is safe to
say that electricity generation without continuous water supply is not secure.
And though water is also a special target within the Sustainable Development
Goals, the analysis and evaluation of those inter-dependencies between energy
and water are scant. Few are the tools that exist to survey, assess and remedy
energy-water context challenges.
What is more, electricity as pillar of our Anthropocene is already a globally
implemented infrastructure that demands constant management action. Yet,
the research of the sustainable and secure energy-water context management on
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its various levels - ranging from macro-scale analysis and strategy development
over meso measures of transference to the mindset behind the micro actions
that maintain the electricity generation - is not too advanced, though the topic
is of utmost importance.
This dissertation investigates on various levels to develop and survey methodologies
that reveal and remedy the energy-water context challenge. It does so
with five studies. Three of them investigate the special issue of reservoir sedimentation
as a prime example of threatened energy-water infrastructure, whose
management needs to be surveyed. Out of the five articles, one is already published,
one is in press and three are under review. The research will be presented
in five chapters.
Chapter 1 prepares the ground of the thesis as an introduction. The nature
of energy-water dependency is demonstrated and the lack of energy-water sustainability
research outlined. On various levels, central research questions for
sustainable and secure energy-water management are developed for the thesis.
As reservoir sedimentation as a special case and artificial neural networks as a
research methodology are of key importance, their principles and backgrounds
are illustrated.
Chapter 2 surveys the possibilities to evaluate, analyze and assess the multifaceted
nature of water-dependent electricity generation. It lights on an essential
gap of holistic energy-water security assessment and fills this gap with a broad
methodological approach for holistic energy-water security assessment.
In Chapter 3 the transference of developed energy strategies to the level of
application for an energy-water-(food)-context is investigated. A gap between
public professionals and other stakeholder groups as major inhibitors is identified.
Within the chapter, an approach to overcome this gap is developed and
investigated in a case study in Ouarzazate, Morocco.
Subsequently, the degree of security and sustainability thinking of the mindset
behind applied energy-water management action is subject to investigation
in Chapter 4. This is executed using the example of reservoir sedimentation in
Japanese reservoirs. The optimism bias, an influential and non-sustainability
mindset in infrastructure management, is used as a proxy to do so. Artificial
neural networks serve as a prime tool to derive evidence.
Management action is bound to have (expected or unexpected) effects. In
the case of reservoir sedimentation in Japan, a mass data methodology based
on artificial neural networks is developed in Chapter 5 to extract traces for such
effect. It is based on a thorough data set of 1225 Japanese reservoirs with
(among others) individual 18 year sedimentation and precipitation time series
as well as continuous management action notations. The key element is a Gated
Recurrent Unit (GRU) core of the neural networks that allows a memory function.
The extensive research reveals evidence of concrete management action on
a meso scale.
The conditions of the energy-water context are globally quite different. Chapter
6 is another case to survey the effect of management action, investigating
again via artificial neural networks on reservoir sedimentation. This time, the
study is settled in the state of Ceará in Brazil and the focus object is a certain,
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presumably sustainable, management directive of the state governance.
The results are discussed in Chapter 7, where a conclusion and outlook of
the dissertation is given.
The dissertation reveals the Gordian web of multi-leveled governance and
management of the complex energy-water context. It is emphasized that the
presented findings are not the only way to respond to the established research
questions, since the results are by no means of panacea character. Rather, the
outcomes of the dissertation are very worthwhile tools that bear the flexibility
of being applied to the highly variable challenges of the energy-water context.
The dissertation is thus a valuable contribution to establish a secure and sustainable
utilization of (water) resources and (electricity) infrastructure within
the modern Anthropocene.
| Identifer | oai:union.ndltd.org:uni-osnabrueck.de/oai:osnadocs.ub.uni-osnabrueck.de:ds-202112175704 |
| Date | 17 December 2021 |
| Creators | Landwehr, Tobias |
| Contributors | Prof. Dr. Claudia Pahl-Wostl, Prof. Dr. José Carlos de Araújo |
| Source Sets | Universität Osnabrück |
| Language | English |
| Detected Language | English |
| Type | doc-type:doctoralThesis |
| Format | application/zip, application/pdf |
| Rights | Attribution-NonCommercial-NoDerivs 3.0 Germany, http://creativecommons.org/licenses/by-nc-nd/3.0/de/ |
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