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Climate Change: Coastal Adaptation and Wave Power

Part IV of this thesis is dedicated to the impacts of climate change on coastal regions.

Anthropogenic climate change has caused global mean sea levels to rise substantially over the last century. As a result, local and regional sea level variations and the occurrence of sea level extremes increase climate change related risks for coastal regions. This is especially problematic for low-elevation coastal regions, as they are are not only the most attractive in which to live (Kron 2013), but are also characterized by significantly higher populations (Balk et al. 2009) and high asset and infrastructure values. Therefore, coastal flood damages are expected to increase significantly during the 21st century (Hinkel et al. 2014) and are projected to exceed $1 trillion a year by 2050 in major coastal cities if current adaptation measures are not upgraded Hallegatte et al. (2013). Thus, Chapters 6 and 7 will focus on two research questions related climate change’s effects on coastal regions.

Chapter 6 –Project Valuation for Coastal Adaptation – A Literature Review

This chapter presents a literature review of various valuation methods that can be used to evaluate coastal adaptation projects. Given the apparent increase in climate change related risks for coastal regions, the use of suitable adaptation strategies has grown in importance over the last decade. My colleagues Chi Truong, Stefan Trück, Supriya Mathew, and I presents a review of various valuation methods and provide a guide for policy makers on how to evaluate and choose between potential strategies for the management of coastal regions. We discuss cost benefit analysis (CBA), cost effective analysis (CEA), cost utility analysis (CUA) and multi-criteria analysis (MCA) as possible valuation methods and provides examples for their practical application. In addition, an overview of studies that have evaluated coastal adaptation projects using cost benefit and optimal timing approaches is provided to highlight methodological issues in this area of research.

Chapter 7 –Long-Term Trends in the Australian Wave Climate

This chapter examines how the wave climate and extreme values for wave power along Aus-tralia’s coast have changed since the 1970s. My colleague, Stefan Trück, and I contribute to the literature by using a comprehensive data set to investigate the wave climate and extreme values along Australia‘s east and southeast coasts at the regional level. We focus on long-term trends in wave power (and wave height) by using 18 wave rider buoys, ranging between 17 and 42 years of effective record years. We investigate and compare the distributions of wave power by using the Jensen-Shannon divergence and Laplacian embedding simultaneously, which allows us to draw conclusions about the temporal and spatial variations of the wave climate between and within different locations. Through this, we find that stations within a close proximity seem to share similar behavior in terms of their wave power distributions. In addition, we find potential decadal changes in the distribution, which we further address using bootstrapping to test for significant differences. We also illustrate an increase in the yearly mean wave power for a number of locations, including Brisbane, Tweed Head, Coffs Harbour, and Port Kembla.

More importantly, we also find an alarming increase in the 99th percentile, or the maximum, over the evaluation period for seven out of the 18 locations. This is of particular interest from a risk management perspective and underlines the importance of location-specific adaptation measures.

Part II

The Oil Market

Chapter 2

Forecasting the Real Price of Oil – Time-Variation and Forecast


The following chapter is based on the paper:

Title: Forecasting the Real Price of Oil - Time-Variation and Forecast Combination Authors: Christoph FUNK(contribution: 100%)

Status: Published:Energy Economics, 2018, vol. 76, pp. 288-302 Available from: https://doi.org/10.1016/j.eneco.2018.04.016

Earlier versions of this work were presented at the following scientific conferences with review process:

• 5th International Symposium on Environment and Energy Finance Issues (ISEFI), Paris, France, May 2017.

• 9thCFE-CMStatistics 2016, Seville, Spain, December 2016.

• 22th Young Researchers Workshops of the German Statistical Society, Augsburg, Germany, September 2016.

Forecasting the Real Price of Oil –

Time-Variation and Forecast Combination 1

Christoph FUNK2


This paper sheds light on the questions whether it is possible to generate an accurate forecast of the real price of oil and how it can be improved using forecast combinations. For this reason, the following paper will investigate the out-of-sample performance of seven individual forecasting models. The results show that it is possible to construct better forecasts compared to a no-change benchmark for horizons up to 24 months with gains in the MSPE ratio as high as 25%.

In addition, some of the existing models will be extended, e.g. the US inventories model by introducing more suitable real-time measures for the Brent crude oil price and the VAR model of the global oil market by using different measures for the economic activity. Furthermore, the time performance investigated by constructing recursively estimated MSPE ratios discovers potential weaknesses of the used models. Hence, several different combination approaches are tested with the goal of demonstrating that a combination of individual models is beneficial for the forecasting performance. A combination consisting of four models has proven to have a lower MSPE ratio than the best individual models over the medium run and, in addition, to be remarkably stable over time.

Keywords:Oil price, Forecasting, Combinations, Real-time data, Brent

JEL classification:C53, Q43

1 I am thankful to Peter Winker, Jana Brandt, Daniel Grabowski, Johannes Lips, Regina Ho and Sebastian Probst for their very helpful comments.

2 Faculty of Economics and Business Studies, Department of Statistics and Econometrics, Justus Liebig University Giessen, Licher Str. 64, 35394 Giessen , Germany.