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.

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.

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. 

This paper studies the impact of an educational program on Sustainable Finance Literacy (SFL) and the impact of this program on sustainable investment decisions. For this purpose, we conducted a randomized controlled trial (RCT) and an incentivized choice experiment. Our findings demonstrate that the SFL educational treatment significantly improves literacy while considering the influence of priming. Participants exposed to the SFL program were more likely to invest in highly sustainable funds by 6 percentage points and less likely to choose less sustainable options with magnitudes between 3 and 2.5 percentage points. The treatment effects increased by up to one half among investors with pre-existing green attitudes. In addition, we provide suggestive evidence that a higher SFL leads to more accurate sustainability perceptions and reduces the tendency to chase high past returns.

Survey respondents often express interest in sustainability and low management fees regarding investment products but do not align these attitudes with their purchases. We compare open-ended and closed-ended survey questions to elicit investor attitudes about mutual fund characteristics and then analyze their choices in a hypothetical investment scenario. Our results confirm a gap between attitudes and choices, but this disparity is smaller when using open-ended questions. We document that the gap's magnitude is sensitive to the type of survey question. The gap for management fees declines from 51% to 42% when using open-ended instead of closed-ended questions. However, we do not find such an effect for sustainability, with a gap between 10% and 15%. The effect between the question types can be partly explained by priming from pre-defined answer choices. Many respondents would choose a closed option but not write about it, suggesting that closed-ended questions may overestimate non-salient attitudes and could bias results.

We introduce the "Conditional Topic Allocation" (CTA), a data-driven text analysis method that identifies topics by conditioning them on observable variables. This approach is particularly valuable when analyzing small-scale text data combined with numerical variables, as in surveys. Researchers can use CTA to extract topics from open-ended text answers that explain respondent characteristics. We have applied this new method to two survey experiments and one classical survey, identifying topics by conditioning the responses on priming treatments or political affiliation. CTA is available as a software package for Python, CTApy. 

We examine Swiss retail investors’ willingness to pay (WTP) for sustainable finance products and the influence of bank advisor certification. In a hypothetical choice experiment with a randomized controlled trial (RCT), we assigned partic ipants to either a priming treatment with a bank advisor certified in sustainable investing or a control group. We found a WTP between 1 and 1.5 percentage points of annual return for sustainable mutual funds, especially among financially literate investors. The presence of a certified green finance advisor further increased WTP between 0.2 and 0.8 percentage points, particularly for investors with low financial literacy. 

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.