Contributions in Merlin

This paper investigates the divergence of environmental, social, and governance (ESG) ratings based on data from six prominent ESG rating agencies: KLD, Sustainalytics, Moody’s ESG (Vigeo-Eiris), S&P Global (RobecoSAM), Refinitiv (Asset4), and MSCI. We document the rating divergence and map the different methodologies onto a common taxonomy of categories. Using this taxonomy, we decompose the divergence into contributions of scope, measurement, and weight. Measurement contributes 56% of the divergence, scope 38%, and weight 6%. Further analyzing the reasons for measurement divergence, we detect a rater effect where a rater’s overall view of a firm influences the measurement of specific categories. The results call for greater attention to how the data underlying ESG ratings are generated.

Rund um den Globus sind die Kurse an den Finanzmärkten ins Minus gerutscht: Der Krieg in der Ukraine, hohe Teuerungsraten und Lieferengpässe drücken auf die Stimmung an den Börsen. Der Finanzexperte Thorsten Hens sagt: «Wir erleben gerade eine Zeitenwende an den Finanzmärkten.» «An den Börsen geht die Angst um» oder «Weltwirtschaft droht perfekter Sturm.» So lauten die Schlagzeilen aus der Finanzwelt in den letzten Tagen. Die Schweizer Börse steht mit einem Minus von elf Prozent seit Anfang Jahr noch vergleichsweise gut da. Andere Finanzplätze hat es noch schlimmer erwischt. Hohe Tagesverluste wecken bereits Erinnerungen an die Finanzkrise 2008 oder an die geplatzte Dotcom-Blase zu Beginn des Jahrtausends. Thorsten Hens forscht zu den Ursachen der aktuell ruppigen Börsenzeiten und möglichen Folgen für die Finanzwelt. Am Institut für Banking und Finance der Universität Zürich untersucht der Finanzprofessor die Psychologie des Investierens. Hens ist zudem außerordentlicher Professor für Finanzen an der Norwegischen Handelshochschule in Bergen, Norwegen.

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Climate change will impact current and future generations in different regions very differently. This paper develops the first large-scale, annually calibrated, multi-region, overlapping generations model of climate change and carbon policy. It features region-specific temperature and damage functions with the phased impact of emissions on global and regional temperature calibrated to the latest scientific evidence. Absent policy, calibrated worst-case damages in the next 200 years reach and remain near 20 percent of GDP for most regions, with India, Brazil, and the South Asian Pacific suffering roughly 40 percent of GDP losses. Russia and Canada benefit somewhat from global warming. Carbon taxation, coupled with region- and generation-specific transfers, can both correct the carbon externality and raise the welfare of all current and future agents across all regions by 4.3 percent. The impact on the use and duration of fossil fuels is dramatic, as is the reduction in the path of global emissions. However, achieving completely uniform welfare gains leaves future generations in particular regions with exceptionally high net taxes. Fortunately, a carbon tax-cum redistribution policy that limits the consumption-equivalent net tax burden on any generation in any region to less than 10 percent can deliver a 4.0 percent or higher welfare gain for all peoplekind – present and future. However, carbon taxes set through time, at carbon’s marginal intertemporal social cost do far too little to mitigate climate change unless all major emitters, particularly China, adopt them and do so immediately.

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What is the role market- and bank-based debt play in the climate transition process? We present evidence that bond markets price the risk that assets held by fossil fuel firms strand, while banks in the syndicated loan market seemingly do not price this risk much. Consequently, to fulfill their financing needs fossil fuel firms increasingly rely less on bonds and more on loans. We can interpret the within-firm bond-to-loan substitution along stranding risk as a contraction in the supply of bond versus bank funding. Within the banking sector especially the big banks are willing to provide cheaper and more financing to fossil fuel firms

Automatic recognition of chemical literature facilitates expanding new areas of research, boosting the overall and detailed chemistry-related knowledge. Chemical formulas and tables, widely used in chemistry literature, can be easily extracted. Recently, machine-learning methods have successfully been applied to obtain textual representations of structure depictions of molecules. However, the successful extraction of machine-readable representation of chemical reactions from graphical depictions has not yet been demonstrated. Here we present a twofold approach based on a visual recognition system to detect high-interest elements of depictions of chemical reactions and apply various digitalisation techniques to translate the detections into machine-readable representations. We provide a Resnet50 backbone and an encoder-decoder transformer (DETR) to locate and classify graphical elements of chemical reactions such as molecules, arrows, textual information, and symbols. Given the scarcity of annotated chemical reaction depictions, we introduced a synthetic training data set with sufficient intra-variability following real-world depictions distribution. Detected elements are then combined and brought into a machine-readable format using existing tools. The open-source library Molvec translates detected depictions of molecules into machine-readable molecular representations. An Optical Character Recognition model is trained with chemical-related data to extract valuable textual information. This project aims to provide digital tools that aid in building on-demand data sets for areas with insufficient freely available chemical data.