InvestESG: A multi-agent reinforcement learning benchmark for studying climate investment as a social dilemma
This work addresses the challenge of informing climate investment policies for policymakers and researchers by providing a scalable simulation tool, though it is incremental as it applies existing MARL methods to a new socio-economic domain.
The authors tackled the problem of studying how ESG disclosure mandates affect corporate climate investments by developing InvestESG, a multi-agent reinforcement learning benchmark that models this as a social dilemma. Their experiments showed that without sufficient ESG-conscious investors, corporate mitigation efforts are limited, but with a critical mass, cooperation increases, reducing climate risks and enhancing financial stability.
InvestESG is a novel multi-agent reinforcement learning (MARL) benchmark designed to study the impact of Environmental, Social, and Governance (ESG) disclosure mandates on corporate climate investments. The benchmark models an intertemporal social dilemma where companies balance short-term profit losses from climate mitigation efforts and long-term benefits from reducing climate risk, while ESG-conscious investors attempt to influence corporate behavior through their investment decisions. Companies allocate capital across mitigation, greenwashing, and resilience, with varying strategies influencing climate outcomes and investor preferences. We are releasing open-source versions of InvestESG in both PyTorch and JAX, which enable scalable and hardware-accelerated simulations for investigating competing incentives in mitigate climate change. Our experiments show that without ESG-conscious investors with sufficient capital, corporate mitigation efforts remain limited under the disclosure mandate. However, when a critical mass of investors prioritizes ESG, corporate cooperation increases, which in turn reduces climate risks and enhances long-term financial stability. Additionally, providing more information about global climate risks encourages companies to invest more in mitigation, even without investor involvement. Our findings align with empirical research using real-world data, highlighting MARL's potential to inform policy by providing insights into large-scale socio-economic challenges through efficient testing of alternative policy and market designs.