We provide evidence that unexpected negative environmental and social (E&S) news significantly disrupts global supply chains, with downstream firms cutting ties with affected suppliers. These terminations are primarily driven by input elasticity of substitution. Firms are more likely to end supplier relationships when this elasticity is high. This is especially notable in sectors characterized by low input specificity and low upstream industry concentration, and so where alternative suppliers are more easily found. Firms with higher visibility are more likely to drop suppliers due to reputational risks, but this effect is evident only when input specificity is low. Additionally, we find that EU firms and those with more international suppliers are more likely to terminate relationships following E&S shocks than American firms and those with more domestic suppliers. We also demonstrate that severing supplier relationships imposes short-term costs on downstream firms, leading to lower markups and reduced total factor productivity, and so reflecting an inability to adjust prices in the short run.

This paper investigates business executives' subjective uncertainty regarding their firms' sales growth, using the Survey of Business Uncertainty. The analysis reveals that subjective uncertainty fluctuates over time, rises with forecast errors, and exhibits a diminishing sensitivity to the forecast errors as time progresses. Based on these findings, the paper introduces a Bayesian learning framework, proposing that the variance of posterior beliefs more accurately predicts subjective uncertainty than traditional GARCH models. To explore the implications of this learning mechanism, a partial equilibrium model is constructed, illustrating that learning-induced uncertainty reduces firms' investment responsiveness to idiosyncratic productivity shocks, particularly for firms with a history of volatile productivity, and produces asymmetric responses to positive and negative shocks. Empirical analysis using Compustat data supports these patterns, suggesting that firms act as Bayesian learners, with the variance of their posterior beliefs significantly shaping investment decisions. These findings highlight learning-induced uncertainty as a pivotal factor in shaping firm-level uncertainty and consequently investment behavior.

Life Cycle Assessment (LCA) is essential for evaluating the environmental impact of sustainable activities in industry. Despite its importance, there exist challenges negatively impacting its deployment, particularly the time-consuming process of gathering inventory data. This research introduces a novel framework that leverages advanced text-based algorithms from Natural Language Processing (NLP), significantly enhancing the efficiency of data collection in LCA studies. Focusing on the inventory phase, the novelty of this research lies in its ability to reduce data collection time by an estimated 80%–90% compared to conventional methods and improve accuracy by directly extracting materials from bills of quantities (BoQs), which usually list all the construction materials. While our methodology shows promise, it faces challenges due to project complexity, particularly the need for consistent terminology between BoQ and reference databases, though future advancements in matching algorithms may enhance our approach’s efficiency. Real-world case studies demonstrate the framework’s effectiveness, offering flexibility across industries and system complexities.

I present a theoretical framework that features contractionary productivity dispersion shock which is a result of the interaction between substitutability of supplied labor and demanded goods. I introduce information friction as a source of nominal rigidity to study the impact of the productivity dispersion shock on the conduct of monetary policy. In particular, I assume firms have incomplete information about the productivity dispersion when they set the price. I show that in the environment with nominal rigidity, replicating full-information flexible price equilibrium is always feasible and optimal, however, the optimal policy is not an inflation targeting policy. The optimal monetary policy is the policy which eliminates the dependence of the idiosyncratic nominal variables on the unknown productivity dispersion and as a result makes the information friction irrelevant.