Conference Agenda

Green or sustainable finance seeks to reduce greenhouse gas emissions by reallocating capital from the most to the least emissions-intensive economic activities. We examine the effectiveness and efficiency of the financial approach to a production externality using the standard microeconomic analysis apparatus traditionally used to assess externality pricing. We model green finance as a capital (or capital cost) intervention, which can be voluntary or induced by public regulation. First-best optimal green finance requires either a specific intervention for each economic project or complex firm-level interventions contingent on each firm's actions, instead of a single carbon pricing instrument. In practice, green finance interventions are imperfect. They target capital in a group of activities, such as, firms, sectors, and, most often, green or brown aggregates, changing the group's cost of capital in a non-contingent fashion, thus, not directly incentivizing emissions reduction. Moreover, green finance is an indirect approach seeking to change emissions through capital or its cost. We examine the grip of capital over carbon emissions at various levels of intervention. Our analysis highlights the difficulty of simultaneously exploiting the "between-groups" reallocation effect of green finance, and its "within-group" effect on emissions intensity. This limits the potential effectiveness of green finance and has the potential of making green finance interventions counterproductive. Our formulas have implications for the design of an effective taxonomy maximizing impact. We also derive (second-best) optimal investment rules balancing, on the one hand, within- and between-groups effects on emissions and, on the other hand, the production inefficiency due to the reallocation of capital. Finally, we illustrate how sector-level costs of capital should be adjusted to align finance with decarbonization objectives and the deadweight-loss of green finance.

The ongoing transition to a low-carbon economy creates transition risks and opportunities for firms. We show that emission intensity is an insufficient predictor of a firm’s transition risk; other factors are also critical, including market structure, where emissions are released in the value chain, and the drivers of decarbonization—whether driven by costly regulations or productivity-enhancing innovation. Modelling a supply chain with Melitz-type industries, we show that high emissions do not necessarily translate to high transition risks. Moreover, commonly used risk metrics can misclassify some firms as vulnerable when they might actually benefit from economy-wide decarbonization. We outline conditions under which a firm’s Scope 1, 2, and 3 emissions accurately reflect its risk. We also identify scenarios in which aggregating these different emissions scopes enhances (or obscures) the true picture of a firm’s transition risk. Our paper lays out a framework for evaluating and improving the wide variety of transition-risk metrics currently used to guide investor decisions, inform stakeholders, and comply with climate-risk disclosure mandates.

An extensive climate policy literature provides various recommendations for mitigating climate change, but these recommendations are not supported democratically, since the models employed consider either infinitely-lived individuals or normative social objectives (or both). In contrast, the present paper provides policy recommendations capable of incorporating democratic processes. I develop an overlapping generation model with political process micro-foundations and show how democratic climate policies are interconnected with other democratic policies. Time inconsistent social objectives combined with commitment issues lead to an inefficient tax on capital accumulation and a climate policy below the efficient level; while suppressing the tax on capital accumulation generates a climate policy even further below the efficient level. I derive a novel politico-economic Keynes– Ramsey rule for the market interest rate, which is useful for calculating the climate policy level. I show that individual pure time preference, individual altruism toward descendants, and young generation political power are key determinants of democratic climate policy ambition.

General financial theory predicts a carbon premium as brown stocks bear greater uncertainty under the energy transition. However, a contrary green premium has been identified in China, as evidenced by the return spread between the green and brown sectors. The aggregated climate transition sentiment, measured from news data using a large language model, explains 12%-33% of the variability in the anomalous alpha. This factor intensified after China announced its national commitments. The sentiment-driven green premium arises from speculative trading by retail investors in green sector stocks, as evidenced by abnormal increases in turnover rates and elevated investor attention. In addition, our discussion highlights the advantages of large language models over lexicon-based sentiment analysis.