Abstract
The clean-tech industry has experienced remarkable growth, bringing forth groundbreaking technologies and sustainable solutions. This research article delves into the examination of factors that shape the evaluation of net-zero assets in various sectors and themes. Through observational analysis utilizing key financial indicators, it becomes apparent that companies exclusively involved in the clean-tech industry, known as pure players, generally outperform those that have less focus in this area, referred to as non-pure players in terms of financial performance [50]. The transition towards a sustainable energy system is greatly facilitated by comprehensive policies and regulations. For instance, in the United States, the Inflation Reduction Act (IRA) and in Europe, the Net-Zero Industry Act (NZIA) play significant roles in shaping the dynamics of asset valuation. These regulatory frameworks contribute to the valuation dynamics and help drive the growth of clean-tech investments [26]. Additionally, the physical and transitional climate risk exert a substantial influence on the valuation of net-zero assets. To gain a deeper understanding of the drivers behind clean technologies and their causal relationships, our study employs a specific branch of Bayesian probabilistic approach introduced by Judea Pearl, the Ladder of Causation, explained in The Book of Why. This approach enables us to model the dependency structure among these influential factors and evaluate their direct and indirect impacts on cleantech stock returns by manipulating the explanatory variables. By creating coherent scenarios through interventions on these variables, we can address essential what-if questions, aiding investors and policymakers in making more informed decisions in this ever-evolving and dynamic industry. Within the framework of Bayesian analysis, the do-calculus and the counterfactual concept play a pivotal role and make it possible to calculate the probability distribution of a random variable under a hypothetical scenario on the explanatory variables different from the observed data. We not only explore the direct effects of interventions on explanatory variables but also reveal sensitivity groups among clean-tech companies. These sensitivity groups consist of companies that exhibit a similar sensitivity to a specific causal factor. This insight is valuable for pinpointing which clean-tech subsectors or companies are particularly affected by certain changes or interventions, offering a more detailed understanding of the industry’s dynamics.
Authors
