arXiv subjects

Yongyang Cai

Publications and source records attributed to Yongyang Cai.

9 recordsLinked to original sources

arXiv2026

Concentrated siting of AI data centers drives regional power-system stress under rising global compute demand

The rapid rise of generative artificial intelligence (AI) is driving unprecedented growth in global computational demand, placing increasing pressure on electricity systems. This study introduces an AI-energy coupling framework that combines large language models (LLMs)-based analysis of corporate, policy, and media data with quantitative energy-system modeling to forecast the electricity footprint of AI-driven data centers from 2025 to 2030. Results show that the new AI infrastructure is highly concentrated in North America, Western Europe, and the Asia-Pacific, which together account for more than 90% of projected compute capacity. Aggregate electricity consumption by the six leading firms is projected to increase from roughly 118 TWh in 2024 to between 239 TWh and 295 TWh by 2030, equivalent to about 1% of global power demand. Regions such as Oregon, Virginia, and Ireland may experience high Power Stress Index (PSI) values exceeding 0.25, indicating local grid vulnerability, whereas diversified systems such as those in Texas and Japan can absorb new loads more effectively. These findings demonstrate that AI infrastructure is evolving from a marginal digital service into a structural component of power-system dynamics, underscoring the need for anticipatory planning that aligns computational growth with renewable expansion and grid resilience.

cs.CY↗

arXiv2025

Modeling Uncertainty in Integrated Assessment Models

Integrated Assessment Models (IAMs) are pivotal tools that synthesize knowledge from climate science, economics, and policy to evaluate the interactions between human activities and the climate system. They serve as essential instruments for policymakers, providing insights into the potential outcomes of various climate policies and strategies. Given the complexity and inherent uncertainties in both the climate system and socio-economic processes, understanding and effectively managing uncertainty within IAMs is crucial for robust climate policy development. This review aims to provide a comprehensive overview of how IAMs handle uncertainty, highlighting recent methodological advancements and their implications for climate policy. I examine the types of uncertainties present in IAMs, discuss various modeling approaches to address these uncertainties, and explore recent developments in the field, including the incorporation of advanced computational methods.

econ.GN↗

arXiv2025

Long Coalition Leads to Shrink? The Roles of Tipping and Technology-Sharing in Climate Clubs

Global cooperation is posited as a pivotal solution to address climate change, yet significant barriers, like free-riding, hinder its realization. This paper develops a dynamic game-theoretic model to analyze the stability of coalitions under multiple stochastic climate tippings, and a technology-sharing mechanism is designed in the model to combat free-ridings. Our results reveal that coalitions tend to shrink over time as temperatures rise, owing to potential free-ridings, despite a large size of initial coalition. The threat of climate tipping reduces the size of stable coalitions compared to the case where tipping is ignored. However, at post-tipping period, coalitions temporarily expand as regions respond to the shock, though this cooperation is short-lived and followed by further shrink. Notably, technology-sharing generates greater collective benefits than sanctions, suggesting that the proposed dynamic technology-sharing pathway bolsters coalition resilience against free-riding while limiting the global warming. This framework highlights the critical role of technology-sharing in fostering long-term climate cooperation under climate tipping uncertainties.

econ.GN↗

arXiv2025

Solving Nash Equilibria in Nonlinear Differential Games for Common-Pool Resources

Many resources are provided by an ecological system that is vulnerable to tipping when exceeding a certain level of pollution, with a sudden big loss of ecosystem services. An ecological system is usually also a common-pool resource and therefore vulnerable to suboptimal use resulting from non-cooperative behavior. An analysis requires methods to derive cooperative and non-cooperative solutions for managing a dynamical system with tipping points. Such a game is a differential game which has two well-defined non-cooperative solutions, the open-loop and feedback Nash equilibria. This paper provides new numerical methods for deriving open-loop and feedback Nash equilibria, for one-dimensional and two-dimensional dynamical systems. The methods are applied to the lake game, which is the classical example for these types of problems. Especially, two-dimensional feedback Nash equilibria are a novelty of this paper. This Nash equilibrium is close to the cooperative solution which has important policy implications.

econ.GN↗

arXiv2023

Dynamics of Global Emission Permit Prices and Regional Social Cost of Carbon under Noncooperation

We develop a dynamic multi-region climate-economy model with emissions trading and solve for the dynamic Nash equilibrium under noncooperation, where each region follows Paris Agreement-based emissions caps. The permit price reaches $923 per ton of carbon by 2050, and global temperature rises to 1.7 degrees Celsius above pre-industrial levels by 2100. The regional social cost of carbon equals the difference between regional marginal abatement cost and the permit price, highlighting complementarity between carbon taxes and trading. We find substantial heterogeneity in regional social costs of carbon, show that lax caps can raise emissions, and demonstrate strong free-rider incentives under partial participation.

econ.GN↗

arXiv2020

The Role of Uncertainty in Controlling Climate Change

Integrated Assessment Models (IAMs) of the climate and economy aim to analyze the impact and efficacy of policies that aim to control climate change, such as carbon taxes and subsidies. A major characteristic of IAMs is that their geophysical sector determines the mean surface temperature increase over the preindustrial level, which in turn determines the damage function. Most of the existing IAMs are perfect-foresight forward-looking models, assuming that we know all of the future information. However, there are significant uncertainties in the climate and economic system, including parameter uncertainty, model uncertainty, climate tipping risks, economic risks, and ambiguity. For example, climate damages are uncertain: some researchers assume that climate damages are proportional to instantaneous output, while others assume that climate damages have a more persistent impact on economic growth. Climate tipping risks represent (nearly) irreversible climate events that may lead to significant changes in the climate system, such as the Greenland ice sheet collapse, while the conditions, probability of tipping, duration, and associated damage are also uncertain. Technological progress in carbon capture and storage, adaptation, renewable energy, and energy efficiency are uncertain too. In the face of these uncertainties, policymakers have to provide a decision that considers important factors such as risk aversion, inequality aversion, and sustainability of the economy and ecosystem. Solving this problem may require richer and more realistic models than standard IAMs, and advanced computational methods. The recent literature has shown that these uncertainties can be incorporated into IAMs and may change optimal climate policies significantly.

econ.GN↗

arXiv2020

Numerical Solution of Dynamic Portfolio Optimization with Transaction Costs

We apply numerical dynamic programming techniques to solve discrete-time multi-asset dynamic portfolio optimization problems with proportional transaction costs and shorting/borrowing constraints. Examples include problems with multiple assets, and many trading periods in a finite horizon problem. We also solve dynamic stochastic problems, with a portfolio including one risk-free asset, an option, and its underlying risky asset, under the existence of transaction costs and constraints. These examples show that it is now tractable to solve such problems.

q-fin.PM↗

arXiv2019

Climate Policy under Spatial Heat Transport: Cooperative and Noncooperative Regional Outcomes

We build a novel stochastic dynamic regional integrated assessment model (IAM) of the climate and economic system including a number of important climate science elements that are missing in most IAMs. These elements are spatial heat transport from the Equator to the Poles, sea level rise, permafrost thaw and tipping points. We study optimal policies under cooperation and noncooperation between two regions (the North and the Tropic-South) in the face of risks and recursive utility. We introduce a new general computational algorithm to find feedback Nash equilibrium. Our results suggest that when the elements of climate science are ignored, important policy variables such as the optimal regional carbon tax and adaptation could be seriously biased. We also find the regional carbon tax is significantly smaller in the feedback Nash equilibrium than in the social planner's problem in each region, and the North has higher carbon taxes than the Tropic-South.

econ.GN↗

arXiv2015

The Social Cost of Carbon with Economic and Climate Risks

There is great uncertainty about future climate conditions and the appropriate policies for managing interactions between the climate and the economy. We develop a multidimensional computational model to examine how uncertainties and risks in the economic and climate systems affect the social cost of carbon (SCC)---that is, the present value of the marginal damage to economic output caused by carbon emissions. The SCC is substantially increased by economic and climate risks at both current and future times. Furthermore, the SCC is itself a stochastic process with significant variation; for example, the basic elements of risk incorporated into our model cause the SCC in 2100 to be, with significant probability, ten times what it would be without those risks. We have only imprecise information about what parameter values are best for approximating reality. To deal with this parametric uncertainty we perform extensive uncertainty quantification and show that these findings are robust for a wide range of alternative specifications. More generally, this work shows that large-scale computing can enable economists to examine substantially more complex and realistic models for the purposes of policy analysis.

econ.GN↗