An inductive and qualitative research approach was desirable for the present study considering the novelty of the research area. Merriam (1998) suggested a step-by-step design for qualitative research, beginning with a literature review, then constructing a theoretical framework, problem identification, definition of the research questions, and sample selection by purposive sampling. We have utilised three qualitative techniques for our study- a systematic literature review followed by intensive case study research and sentiment analysis.
In academic research, literature reviews aid in the mastery of the breadth and depth of an existing body of knowledge and identify research gaps (Xiao & Watson, 2017). Where a review heeds specific research questions rather than broad descriptive ones, it is known as a systematic literature review (SLR) or a systematic review. Such quests for the answers to clearly formulated research questions encompass the identification, selection, and critical evaluation of the previous studies (Dewey & Drahota, 2016). Accordingly, we conducted a systematic review for an intensive study of the state of the art in the topic. Before the review, a clearly defined protocol is usually identified, specifying the rationale for the review and its method, including the inclusion and exclusion criteria we have followed. An attempt is made to answer a research question, usually about the status quo of a field of research through SLR (Kraus et al., 2020). It helps condense the knowledge from multiple studies on the research area. We identify that this characteristic could help find answers to our research objectives. The literature for review was retrieved using Web of Science, Scopus and Google Scholar database. The review considered studies associated with identified keywords and research questions. Figure 1 provides an overview of the search process.
We then conducted an exploratory case study with multiple cases of climate fintech operations in India, intending to draw insights into the concept of climate fintech and, most importantly, to know its progress in India. The case study method involves a study in depth rather than breadth. As per Yin (1994), a case study could be utilised first to explain complex causal links in real-life interventions, subsequently to describe the real-life context in which the intervention has occurred, thirdly to describe the intervention itself and finally to explore those situations in which the intervention being evaluated has no clear set of outcomes. Accordingly, case study research at this juncture could be beneficial as the area of climate fintechs are at its initiatory stages, and any novel models would certainly involve associated uncertainties. Besides, it is widely used in social entrepreneurship and impact investing research, closely related to the climate fintech model. A case study typically adopts a triangulation approach using multiple sources of evidence (Yin, 2009). It deserves a significant role in business research, theory building, and testing (Dul & Hak, 2007). The case study design widely differs (Yazan, 2015) and is typically arrived at as per the research questions and objectives. However, it usually comprises an intensive study of the nature, background, physical setting, and other institutional and political factors. It should be presented as an invitation to the reader to join in case discovery (Ebneyamini& Sadeghi Moghadam, 2018).
Yin (2009) suggests that case study research can be exploratory, explanatory, or descriptive. We have centred on an explorative method with a focus on theory building. Based on the findings of the SLR, the sample case selection criteria were framed, which include: the entity should have three fundamental elements; first, it must be a financial sector entity based on technology working for climate action; it must be an Indian entity operational in India, and it must be a green fintech as per the green fintech categories in the final ‘green fintech classification’ released by the Green Digital Finance Alliance. The lack of a formal dataset on climate fintech motivated us to resort to the purposive sampling technique. Those entities fulfilling the criteria and assessable under the study were included in the sample. Accordingly, three climate fintechs Pyse, Climate Angels and ESGDS have been studied, their features detailed below.
Pyse Fintech Private Limited is a for-profit, private company working for enhancing quality of life with the development of tech-driven solutions. The idea behind the start-up has been to bridge the gap between green projects in need of financing and investors looking to deliver impact with returns. They aid in climate action, by providing a platform for investors to engage in sustainable digital investments, through a simple, hassle-free mechanism. It provides access to verified deals, allows smaller investment sizes, assures returns and liquidity, and helps deliver a positive environment. It promotes inclusive sustainable investing, as against the notion that it is the responsibility of the governments or philanthropists.
Climate Angels, on the other hand, is a VC fund investing in promising climate tech start-ups at their pre-revenue stage, needing access to finance and not charging a fee for startups. The platform enables investors to invest along with experienced and angel investors, offering friction-free investment as the fund will manage legal compliance. Climate Angels vet the deals before they are listed and provide quarterly updates to all investors. Both are green digital investment solutions. We also analyse a start-up called ESGDS, a digital ESG data and analytics solution. It provides tools to create custom assessments aligned with organisational use cases. It offers ESG data and analytical tools, assessment models and SaaS platform for interpreting and integrating ESG in risk assessments, scrip selection, performance monitoring and issuer engagement. Their platform, purpose-built for ESG analytics, is created using a glass box model, enabling customisation of all aspects of the model using the front end. It acts as an ESG database for institutional investors, asset managers, and investment banks and helps in custom corporate and portfolio assessments.
We utilised secondary data for case analysis. Data were accessed from the official documentation of the climate fintech firms, other information and resources provided on their websites, their newsletters, social media and related news articles. A mix of varied approaches has been used for data analysis, including data reduction, data display, explanation building, content analysis, cross-case analysis etc. Yin (1994) has prescribed the tactics that could be employed to secure reliability and validity in case study research. Our study has resorted to some of them that could be adopted to the best of our ability. First, we followed a case study protocol with multiple cases. We also examined all possible data sources and chains of evidence at the data collection point and utilised an explanation-building approach in the data analysis phase.
To analyse the public sentiments on climate finance and climate fintech, sentiment analysis was also employed, which was helpful in the validation of the outcomes of the SLR and the case study. Sentiment analysis is a text mining technique whereby subjective knowledge is extracted from text using natural language processing and text analytics (Medhat et al., 2014; Hussein, 2018). Social media has great significance in unveiling public sentiments (Drus & Khalid, 2019). This is usually not able to be fetched from conventional quantitative data. Among the many platforms, we have taken the data from Twitter, which allows for analysing sentiments across time and space as geotagged tweets include timestamps and geographic coordinates. The tool used is NVivo, primarily due to its simplicity. The degree of sentiment was retrieved using the keywords ‘climate finance’ and ‘climate fintech’.