Sustainable finance literacy and the determinants of sustainable investing

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

Paradigm Shift in Sustainability Disclosure Analysis: Empowering Stakeholders with CHATREPORT, a Language Model-Based Tool

This paper introduces a novel approach to enhance Large Language Models (LLMs) with expert knowledge to automate the analysis of corporate sustainability reports by benchmarking them against the Task Force for Climate-Related Financial Disclosures (TCFD) recommendations. Corporate sustainability reports are crucial in assessing organizations' environmental and social risks and impacts. However, analyzing these reports' vast amounts of information makes human analysis often too costly. As a result, only a few entities worldwide have the resources to analyze these reports, which could lead to a lack of transparency. While AI-powered tools can automatically analyze the data, they are prone to inaccuracies as they lack domain-specific expertise. This paper introduces a novel approach to enhance LLMs with expert knowledge to automate the analysis of corporate sustainability reports. We christen our tool CHATREPORT, and apply it in a first use case to assess corporate climate risk disclosures following the TCFD recommendations. CHATREPORT results from collaborating with experts in climate science, finance, economic policy, and computer science, demonstrating how domain experts can be involved in developing AI tools. We make our prompt templates, generated data, and scores available to the public to encourage transparency.Comment: This is a working pape

ChatClimate: Grounding conversational AI in climate science

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

Using Narratives to Understand Sustainable Investment Decisions

This dissertation is composed of three essays analyzing the underlying factors of environmentally friendly investment decisions. Methodologically, the essays use open-ended survey responses and develop new text-analysis methods based on artificial intelligence that allows for quantitative analysis of this type of data. The results of these studies provide new insights into designing energy and climate policy instruments. To mitigate climate change, promoting energy efficiency and increasing investments in the green transition constitute two essential pillars for policymakers. In order to attain this objective, individuals’ preferences are crucial to success. For policymakers and researchers alike, it is essential to understand the underlying reasoning behind people’s support, or lack, of environmentally friendly decision-making. However, the complex nature of climate change elicits reactions that exceed purely monetary concerns. For this reason, existing economic analysis can be extended with new research methods based on narratives that aim to collect information on the underlying factors behind individual preferences. From a method point of view, this dissertation aims to develop a new set of tools for analyzing open-ended survey responses. Unstructured text answers allow respondents to express their narratives, consisting of individual thoughts. These narratives, in turn, provide deeper insights into the underlying factors of environmental preferences in economics. In contrast to answers to closed-ended questions, open-ended responses allow respondents to speak their minds, which provides additional insights. Based on this approach, the three essays analyze investment decisions in the following areas: investments in energy-saving houses, energy-efficient retrofits, and sustainable financial products. Essay I proposes a novel topic model for text data by conditioning on observables, named the “Conditional Topic Allocation” (CTA). This datadriven method allows the identification of latent topics in the text that explain other observable variables. It is particularly suited for open-ended survey responses that allow collecting text data from respondents in combination with other variables from standard closed-ended questions. I apply this method to the context of homeowners’ valuation of energyefficient housing in Switzerland. Housing decisions involve numerous factors, only some of which are readily observable. Text data can capture subtle variations related to emotions important in the housing decision and a potential source of bias if left unaccounted for. In this context, open-ended survey questions provide a new opportunity to capture the complex individual narratives associated with the sentimental value of a home. Results show that nearly 50% of the monthly premium associated with the Minergie certification for energy efficiency decreases can be associated to the latent variables captured in the text. Further, CTA allows identifying topics extracted from the text data that are correlated with the missing latent variables. These topics can be associated with either a negative or a positive bias on the green premium. A negative bias is associated with emphasizing renovations, a rural location, and childhood memories. A positive bias, and hence overvaluation, is associated with building characteristics such as the home’s light and general appeal. From a general perspective, the results provide an encouraging use case to employ CTA for analyzing open-ended survey responses in social sciences. Essay II analyzes the determinants and barriers of energy-efficient home renovations. The analysis of this essay is based on a household survey that included open-ended questions on homeowners’ decisionmaking concerning retrofits. Methodologically, this essay introduces a lexicon-based hybrid approach to text classification that allows for identifying topics in open-ended survey responses. The narratives obtained from open-ended survey responses offer a powerful way to elicit and rank important barriers and determinants of households’ retrofit decisions. The results suggest energy-efficiency investments are highly opportunistic. Non-takers believe, rightfully or not, few opportunities for energy efficiency exist in their home. Takers primarily invest in energy efficiency out of necessity to replace old building parts or out of financial opportunity when they perceive the investment as profitable. The monetary aspect also constitutes a major barrier because many respondents stated they face financial constraints concerning renovation plans. However, several co-benefits of energy efficiency, mainly increased comfort, also emerged as important determinants. Finally, environmental concerns showed to be a significant determinant for energy-efficiency retrofits. Essay III studies the role of knowledge on sustainable finance for investment decisions by introducing the concept of sustainable finance literacy. The analysis builds on a survey of Swiss households and measures financial, sustainability, and sustainable finance literacy, using two complementary approaches. The first approach consists of traditional multiple-choice questions, and the second is a novel approach based on open-ended questions asking respondents to write a text response. Results indicate that Swiss households, which are generally highly financially literate, exhibit low levels of sustainable financial literacy. Moreover, multiple-choice questions lead to a gender gap, with women performing worse than men. However, this difference disappears with open-ended questions. Further, despite its low level, knowledge about sustainable finance is a significant factor in the ownership of sustainable products. Therefore, the results show an urgent need to create transparent regulatory standards and strengthen information campaigns about sustainable financial products

The Narrative of the Energy Efficiency Gap

For more than forty years analysts have pointed out that society might be too slow in adopting energy efficiency technologies, a phenomenon known as the Energy Efficiency Gap. There are persistent market barriers that impede these efforts. Eliciting these barriers and their heterogeneity is key for policy design. In this paper, we use narratives, a novel approach based on unstructured text answers in surveys, to elicit the barriers and determinants of energy efficiency investments. Using recent advances in Natural Language Processing (NLP), we turn narratives into quantifiable metrics to rank households’ barriers and determinants. We find that financial motives are not the primary barriers or determinants of energy efficiency investments. Instead, we find that such investments are highly opportunistic and co-benefits, such as ecological concerns and comfort, also play an important role. Although there is substantial heterogeneity across the population in the type of barriers and determinants, demographics and building characteristics poorly predict heterogeneity patterns. This has important implications for the targeting of policies. Narratives could be a novel and effective way to implement policy targeting

The effect of culture on energy efficient vehicle ownership

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