Frankfurt School of Finance & Management

The aim of this study is to investigate the relationship between corporate emission reduction policies (ERPs), greenhouse gas (GHG) emissions, and the moderating role of corporate governance. Using a dataset of 18,559 firm-year observations from 28 developed and emerging countries from (Mollick, and Haidar, 2011) and 2022, the study finds that for firms with stronger corporate governance, higher ERPs are associated with more substantive emission intensity reductions. Findings remain robust across multiple specifications. Firm-level trend regression confirms that if a firm’s ERPs improve over time compared to the sector average, emission intensity decreases. Results underscore the importance of strong corporate governance in mitigating greenwashing risks and ensuring the credibility of corporate climate commitments. The study contributes to the growing literature on climate governance, and corporate environmental strategy by highlighting the interplay between corporate governance and ERPs in achieving emission intensity reductions. Research avenues, limitations, and recommendations are presented, emphasizing specifically the need for investors and regulators to not only focus on the level of ERP adoption, but scrutinize governance structures that determine ERP effectiveness in practice.

Effective regulation of information diffusion in complex social systems requires balancing containment and intervention cost, yet how network community structure interacts with targeted interventions remains unclear. We develop a community structure-regulation coupling framework (COSREF) that integrates community structure with process-level regulation of transmission and show how their interplay governs diffusion. Tuning two regulation parameters governing within- and cross-community transmission yields three regimes: no, localized, and global diffusion, separated by abrupt transitions. This structure-regulation perspective reveals a low-cost intervention region where small, targeted adjustments contain spread, unifies topology and regulation within a single theoretical setting, and provides general principles for efficiently and robustly regulating modular systems. Analyses of large cross-platform real-world social networks confirm our analytical predictions and simulation results, demonstrating COSREF's robustness across investigated topologies and its applicability to real information environments.

Prior studies on the relationship between decentralization of decision rights and variable compensation have largely focused on higher managerial levels and typically find a complementarity association. We extend this literature by examining this relationship at the production level, where managers oversee multidimensional tasks and where performance outcomes are often hard to contract upon. This makes the standard complementarity prediction less apparent and suggests a substitutive relationship instead. Using survey data from production managers in industrial firms, we find that the incentive intensity of variable compensation and decentralization of decision rights act as substitutes: production departments with greater decision rights have a lower level of variable compensation for their managers. Consistent with the role of performance contracting conditions, this negative association weakens with higher levels of performance contractibility—i.e., in more stable operational environments and when evaluation relies on more high-quality measures. Overall, the findings highlight that the incentive–decentralization relationship may differ systematically between production settings and higher managerial levels.

Value-based pricing (VBP) involves regulating the reimbursement or pricing of pharmaceuticals based on their therapeutic value. We adopt VBP as the policy context for this study. Unlike alternative approaches, VBP depends on an explicit relationship between price and incremental health outcomes and costs. This dependence makes VBP decisions especially sensitive to uncertainty about downstream costs and outcomes. A central question is whether—and how far—these components must be modeled beyond the trial period. A key gap is that the literature provides limited, generalizable conditions for when extrapolation is necessary for pricing conclusions versus when trial-period evidence is sufficient. This paper demonstrates the conditions under which it is possible to forgo modeling downstream costs beyond the clinical trial period for VBP purposes. It provides a series of specific mathematical proofs to support this approach. The proposed shortcuts reduce the burden and time required to produce evaluations. Furthermore, the shortcuts validate the robustness of decision models by allowing modelers to test whether applying these assumptions yields consistent results, enhancing the reliability of VBP methodologies.

Bounded rationality is typically understood as a concession to human cognitive limitations, a departure from an ideal coherent in principle if unattainable in practice. I argue this gets the relationship backwards. Unbounded rationality is a physical impossibility, and its attendant normative standards—Bayesian updating, sure loss avoidance, expected utility maximization, logical closure—are techniques for favorable circumstances when resources permit, not ideals from which mortals regrettably fall short. The argument rests on the physics of computation: any information-processing system incurs irreducible costs in energy and time. Three independent lines of support establish this conclusion. The first runs through Landauer’s principle. The second draws on Wolpert’s stochastic thermodynamics framework, extended by Kolchinsky and Wolpert to Turing machines, where thermodynamic costs track algorithmic complexity. The third draws on quantum-mechanical and relativistic bounds that fix finite ceilings on the operations any physical system can perform in a given region of space and time. These constraints bind all physical systems, natural or artificial. Coherence conditions like Savage’s axioms and Bayesian probability evaluate global states, not local procedures. No finite physical process can construct, verify, or maintain global coherence over a realistic state space. They can still function diagnostically, as devices for flagging departures worth explaining; what they cannot do is serve as action-guiding norms from which bounded reasoners fall short. Normative theories that presuppose unbounded rationality demand the physically impossible. Bounded rationality is not a departure from ideal rationality. It is the only kind there is.