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This paper comments on a paper provided by Joseph Carens for the 2018 ZiF Workshop "Studying Migration Policies at the Interface Between Empirical Research and Normative Analysis", September 2018, in Bielefeld. Carens's paper is available under doi:10.17879/15199614880.

Industries such as the oil, mining and chemical industry have been under a lot of pressure from governments and certain organizations worldwide to reduce their carbon footprint. The United Nations (UN), the International Council on Mining and Metals (ICMM) and other organizations, have mapped out policies and recommendations that can be used to achieve this. Mining companies all over the world have adopted sustainability commitments based on recommendations by the United Nations Intergovernmental Panel on Climate Change and have set targets for managing their energy use and GHG emissions. This research assessed the energy management culture of twenty (20) leading mining companies worldwide, using the UN's Sustainable Development Goals (SDGs) 7, Affordable and Clean Energy and 13, Climate Action as a performance metric, and established a trend of adaptation to these sustainability goals. Results showed that the mining industry is so far on an average, committed to achieving 80% of these goals. An investigation into the activities of these mining companies revealed what renewable technologies and energy management structures are currently being used. This research also reviewed how renewable technologies are a product of mining, which goes to prove that mining is essential in the combat of climate change. Future work will focus on assessing the impact of these management goals on the economic model of the companies.

The introduction of Karmarkar's polynomial algorithm for linear programming (LP) in 1984 has influenced wide areas in the field of optimization. While in the 1980s emphasis was on developing and implementing efficient variants of interior point methods for LP, the 1990s have shown applicability to certain structured nonlinear programming and combinatorial problems. We will give a historical account of the developments and illustrate the typical results by analyzing a new method for computing the smallest eigenvalue of a matrix. We formulate this latter problem as a so‐called semidefinite optimization problem. Semidefinite optimization has recently gained much attention since it has a lot of applications in various fields (like control and system theory, combinatorial optimization, algebra, statistics, structural design) and semidefinite problems can be efficiently solved with interior point methods.