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. 

Survey respondents often express interest in sustainable investments but do not purchase green products, known as the attitude-behavior gap. Here, we capture retail investors' sustainability attitudes with open- and closed-ended questions and conduct a hypothetical investment experiment. Both question types yielded similar regression coefficients when explaining a hypothetical investment choice, but the open-ended responses showed higher predictive power. Open-ended responses provided clearer frequency rankings of topics compared to closed-ended questions. Respondents answered both types of questions in random sequential order, revealing that respondents would select an option in the closed but not in the open-ended response, leading to biased closed-ended answers.

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. 

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.