@article{VoegeleJosyabhatlaBalletal.2023,
  author    = {V{\"o}gele, Stefan and Josyabhatla, Vishnu Teja and Ball, Christopher and Rhoden, Imke and Grajewski, Matthias and R{\"u}bbelke, Dirk and Kuckshinrichs, Wilhelm},
  title     = {Robust assessment of energy scenarios from stakeholders' perspectives},
  series = {Energy},
  journal   = {Energy},
  number    = {In Press, Article 128326},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {1873-6785 (Online)},
  doi       = {10.1016/j.energy.2023.128326},
  year      = {2023},
  abstract  = {Using scenarios is vital in identifying and specifying measures for successfully transforming the energy system. Such transformations can be particularly challenging and require the support of a broader set of stakeholders. Otherwise, there will be opposition in the form of reluctance to adopt the necessary technologies. Usually, processes for considering stakeholders' perspectives are very time-consuming and costly. In particular, there are uncertainties about how to deal with modifications in the scenarios. In principle, new consulting processes will be required. In our study, we show how multi-criteria decision analysis can be used to analyze stakeholders' attitudes toward transition paths. Since stakeholders differ regarding their preferences and time horizons, we employ a multi-criteria decision analysis approach to identify which stakeholders will support or oppose a transition path. We provide a flexible template for analyzing stakeholder preferences toward transition paths. This flexibility comes from the fact that our multi-criteria decision aid-based approach does not involve intensive empirical work with stakeholders. Instead, it involves subjecting assumptions to robustness analysis, which can help identify options to influence stakeholders' attitudes toward transitions.},
  language  = {en}
}
@article{RuebbelkeVoegeleGrajewskietal.2023,
  author    = {R{\"u}bbelke, Dirk and V{\"o}gele, Stefan and Grajewski, Matthias and Zobel, Luzy},
  title     = {Cross border adjustment mechanism: Initial data for the assessment of hydrogen-based steel production},
  series = {Data in Brief},
  volume    = {47},
  journal   = {Data in Brief},
  number    = {Article 108907},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {2352-3409},
  doi       = {10.1016/j.dib.2023.108907},
  pages     = {1 -- 5},
  year      = {2023},
  abstract  = {Ambitious climate targets affect the competitiveness of industries in the international market. To prevent such industries from moving to other countries in the wake of increased climate protection efforts, cost adjustments may become necessary. Their design requires knowledge of country-specific production costs. Here, we present country-specific cost figures for different production routes of steel, paying particular attention to transportation costs. The data can be used in floor price models aiming to assess the competitiveness of different steel production routes in different countries (R{\"u}bbelke, 2022).},
  language  = {en}
}
@article{RhodenBallGrajewskietal.2023,
  author    = {Rhoden, Imke and Ball, Christopher Stephen and Grajewski, Matthias and Kuckshinrich, Wilhelm},
  title     = {Reverse engineering of stakeholder preferences - A multi-criteria assessment of the German passenger car sector},
  series = {Renewable and Sustainable Energy Reviews},
  volume    = {181},
  journal   = {Renewable and Sustainable Energy Reviews},
  number    = {July 2023},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {1364-0321},
  doi       = {10.1016/j.rser.2023.113352},
  pages     = {Article number: 113352},
  year      = {2023},
  abstract  = {Germany is a frontrunner in setting frameworks for the transition to a low-carbon system. The mobility sector plays a significant role in this shift, affecting different people and groups on multiple levels. Without acceptance from these stakeholders, emission targets are out of reach. This research analyzes how the heterogeneous preferences of various stakeholders align with the transformation of the mobility sector, looking at the extent to which the German transformation paths are supported and where stakeholders are located. Under the research objective of comparing stakeholders' preferences to identify which car segments require additional support for a successful climate transition, a status quo of stakeholders and car performance criteria is the foundation for the analysis. Stakeholders' hidden preferences hinder the derivation of criteria weightings from stakeholders; therefore, a ranking from observed preferences is used. This study's inverse multi-criteria decision analysis means that weightings can be predicted and used together with a recalibrated performance matrix to explore future preferences toward car segments. Results show that stakeholders prefer medium-sized cars, with the trend pointing towards the increased potential for alternative propulsion technologies and electrified vehicles. These insights can guide the improved targeting of policy supporting the energy and mobility transformation. Additionally, the method proposed in this work can fully handle subjective approaches while incorporating a priori information. A software implementation of the proposed method completes this work and is made publicly available.},
  language  = {en}
}
@article{GrajewskiKleefeld2023,
  author    = {Grajewski, Matthias and Kleefeld, Andreas},
  title     = {Detecting and approximating decision boundaries in low-dimensional spaces},
  series = {Numerical Algorithms},
  volume    = {93},
  journal   = {Numerical Algorithms},
  number    = {4},
  publisher = {Springer Science+Business Media},
  address   = {Dordrecht},
  issn      = {1572-9265},
  pages     = {35 Seiten},
  year      = {2023},
  abstract  = {A method for detecting and approximating fault lines or surfaces, respectively, or decision curves in two and three dimensions with guaranteed accuracy is presented. Reformulated as a classification problem, our method starts from a set of scattered points along with the corresponding classification algorithm to construct a representation of a decision curve by points with prescribed maximal distance to the true decision curve. Hereby, our algorithm ensures that the representing point set covers the decision curve in its entire extent and features local refinement based on the geometric properties of the decision curve. We demonstrate applications of our method to problems related to the detection of faults, to multi-criteria decision aid and, in combination with Kirsch's factorization method, to solving an inverse acoustic scattering problem. In all applications we considered in this work, our method requires significantly less pointwise classifications than previously employed algorithms.},
  language  = {en}
}