Published Papers

Sustainable Finance Literacy and the Determinants of Sustainable Investing, Journal of Banking and Finance (2024)

Massimo Filippini, Markus Leippold, and Tobias Wekhof; Press Coverage: Neue Zürcher Zeitung (link, German); MAIA Award 2023 (link)

In this paper, we survey a large sample of Swiss households to measure sustainable finance literacy, which we define as the knowledge and skill of identifying and assessing financial products according to their reported sustainability-related characteristics. To this end, we use multiple-choice questions. Furthermore, we measure Swiss private investors' level of awareness about sustainable financial products using open-ended questions. We find that Swiss households, which are generally highly financially literate by international standards, exhibit low levels of sustainable financial literacy compared to the current working definitions of sustainable finance. Moreover, despite its low level, knowledge about sustainable finance is a significant factor in the reported ownership of sustainable products. The empirical results also show a relatively low level of awareness. Generally, these empirical findings suggest a need to create transparent regulatory standards and strengthen information campaigns about sustainable financial products.

Using narratives to infer preferences in understanding the energy efficiency gap, Nature Energy (2023)

Tobias Wekhof  and Sébastien Houde (old title: "The narrative of the energy efficiency gap")

Review articles: Nature Energy, news&views (link); Nature Behind the Paper (link); ProClim Flash 77 (Swiss Academy of Sciences) (link)

Investing in energy efficiency is crucial for a low-carbon economy, particularly in the building sector. Despite various subsidy programs, meeting energy targets is challenging because households do not invest sufficiently. Here we study homeowners' low levels of energy efficiency retrofits. We use narratives, an emerging method based on open-ended survey responses, to identify the barriers and determinants behind renovation decisions. Using Natural Language Processing, we transform narratives into quantifiable metrics. While financial considerations are a major barrier, homeowners' main reasons for renovating are unrelated to energy savings. Most homeowners delay energy-saving investments until their buildings require renovations. Co-benefits like environmental concerns and comfort gains are equally or more important than financial motivations. Many homeowners are unaware of existing policies and would favor reducing the bureaucracy of retrofits. Subsidies, while popular, are likely to be mistargeted. Effective policies should also consider institutional factors such as bureaucratic burden and accessibility of information.

ChatClimate: Grounding Conversational AI in Climate Science, Communications Earth & Environment (2023)

Saeid Vaghefi, Veruska Muccione, Dominik Stammbach, Jingwei Ni, Mathias Kraus, Julia Bingler, Simon Allen, Chiara Colesanti-Senni, Tobias Wekhof, Tobias Schimanski, Glen Gostlow, Nicolas Webersinke, Christian Huggel, Qian Wang, Tingyu Yu, Markus Leippold

Review Article: Natue Behind the Paper (link)

Large Language Models have made remarkable progress in question-answering tasks, but challenges like hallucination and outdated information persist. These issues are especially critical in domains like climate change, where timely access to reliable information is vital. One solution is granting these models access to external, scientifically accurate sources to enhance their knowledge and reliability. Here, we enhance GPT-4 by providing access to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6), the most comprehensive, up-to-date, and reliable source in this domain (refer to the ’Data Availability’ section). We present our conversational AI prototype, available at, and demonstrate its ability to answer challenging questions in three different setups: (1) GPT-4, (2) ChatClimate, which relies exclusively on IPCC AR6 reports, and (3) Hybrid ChatClimate, which utilizes IPCC AR6 reports with in-house GPT-4 knowledge. The evaluation of answers by experts show that the hybrid ChatClimate AI assistant provide more accurate responses, highlighting the effectiveness of our solution. 

The effect of culture on energy efficient vehicle ownership, Journal of Environmental Economics and Management (2021)   

Massimo Filippini and Tobias Wekhof

We provide an empirical analysis on the relation between culture and revealed environmental preferences. Switzerland's citizens share the same set of institutions but belong to multiple population groups, which differ by culture and language across distinct geographical locations. This unique setting allows us to disentangle the effect of culture on individual consumer preferences from institutional characteristics. We analyze the effect of culture on energy efficient vehicle registration, using municipality level data and applying a spatial fuzzy Regression Discontinuity Design at the internal French/German language border. Our results indicate that French-speaking municipalities have a 3 to 6 percentage points higher share of energy efficient vehicles, compared to their German-speaking counterparts. These findings suggest that French-speakers place a higher value on the environment, which may be due to their higher sense of collectivism and altruism. 

Working Papers

Survey respondents often claim an interest in sustainable investment options but do not buy them -frequently called the intention-behavior gap. Here, we compare retail investors' sustainability intentions through open-ended questions requiring a written answer to closed-ended questions, where respondents chose several pre-defined topics. All respondents answered both question types in random order and then chose among hypothetical investments with different levels of sustainability. Both question types gave similar regression coefficients when explaining a hypothetical investment choice, but the open-ended responses showed a higher out-of-sample predictive power. Moreover, the open-ended responses contained fewer topics than closed-ended answers, with a clear ranking of the topics' frequency. In contrast, most closed-ended topics appeared with similar frequency. Many respondents would choose an option in the closed-ended question but not write about it, which led to inconsistent answers. We show that these inconsistencies can negatively bias the explanatory power of closed-ended responses for the investment choice. 

Researchers in social sciences are increasingly using surveys that require written responses from participants. Because of the small sample size and short answers, it is challenging to identify topics in the responses with Natural Language Processing (NLP). However, surveys allow collecting additional observable variables about respondents, which can help analyze the text. Here we introduce a data-driven method called "Conditional Topic Allocation" (CTA) for identifying latent topics in text data by conditioning on observables. Researchers can utilize CTA to extract topics from open-ended text answers that explain observable variables. CTA proves to be particularly valuable when analyzing small-scale text data, such as open-ended survey responses. We apply this new approach to two survey experiments and one classical survey and identify topics by conditioning the responses to either priming treatments or political affiliation.

Work in Progress

Do Energy Efficiency Standards Improve Quality? Evidence from a Revealed Preference Approach

Sébastien Houde, Anna Spurlock, and Tobias Wekhof

Other Work

Additional Value From Free-Text Diagnoses in Electronic Health Records: Hybrid Dictionary and Machine Learning Classification Study  JMIR Medical Informatics 12.1 (2024), e49007

Tarun Mehra, Tobias Wekhof, and Dagmar Iris Keller

CHATREPORT: Democratizing Sustainability Disclosure Analysis through LLM-based Tools Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations (2023)

Jingwei Ni, Julia Bingler, Chiara Colesanti-Senni, Mathias Kraus, Glen Gostlow, Tobias Schimanski, Dominik Stammbach, Saeid Ashraf Vaghefi, Qian Wang, Nicolas Webersinke, Tobias Wekhof, Tingyu Yu, and Markus Leippold