Research

Published Papers

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Large Language Models have made remarkable progress in question-answering tasks, but challenges like hallucination and outdated information persist.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 www.chatclimate.ai, 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

Sustainable Finance Literacy and the Determinants of Sustainable Investing, R&R Journal of Banking and Finance

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

This paper introduces the concept of sustainable finance literacy. We survey a large sample of Swiss households and measure financial, sustainability, and sustainable finance literacy using two complementary approaches. First, we use traditional multiple-choice questions, and second, a novel approach based on open-ended questions that ask respondents to write a text response. We find that Swiss households, which are generally highly financially literate by international standards, exhibit low levels of sustainable financial literacy. Interestingly, multiple-choice questions lead to a gender gap, with women performing worse than men. However, this difference disappears when open-ended questions are used. Moreover, despite its low level, knowledge about sustainable finance turns out to be a highly significant factor for the ownership of sustainable products. Therefore, our results show that there is an urgent need to create transparent regulatory standards and to strengthen information campaigns about sustainable financial products.

This paper develops a novel topic model for text data by conditioning on observables, named the "Conditional Topic Allocation" (CTA). This data-driven method allows identification of latent topics that explain other observable variables. It is particularly suited for small-scale text data, such as open-ended survey responses. First, CTA is used to extract topics from open-ended text answers that explain single observable variables. Then, in empirical models where text is a control variable for unobservable characteristics, CTA's flexible scope of applications allows uncovering latent variables from the text. As a proof-of-concept, this approach is used to analyze the sentimental value homeowners place on their homes and how this relates to energy-efficiency valuation. Specifically, responses from open-ended survey questions are used as control variables in a hedonic regression, and CTA serves to identify latent preferences associated with nearly 50% of the valuation of energy efficiency for single-family houses.


Work in Progress

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

Sébastien Houde, Anna Spurlock, and Tobias Wekhof

Additional value from free text diagnoses in electronic health records: a proof of concept study  JMIR Medical Informatics (accepted)

Tarun Mehra, Tobias Wekhof, and Dagmar Iris Keller