Strategies for resilient climate in smart cities mediated by Nash equilibrium and autonomous decision-makers.

Recently, smart cities are facing pressing challenges due to global warming. Rising greenhouse gas emissions driven by population growth, increased mobility, and the increasing industrial demand for production and maintenance of technological infrastructure have intensified the global warming effects in cities. To enable global warming mitigation (GWM) tailored to the specific needs of individual cities, a decision-making framework was developed by integrating Nash equilibrium, autonomous multi-agent systems, and machine learning methods. Each city was considered an autonomous decision-making agent that formulated policies regarding its levels of pollution, climate warming risk management, and its own technical capabilities. Nash equilibrium guided the process for the cities' cooperation, adaptation, or minor scales, identifying the previous gains in pollution control, climate change risk management, and technical preparedness of each city. By resolving policy conflicts and guiding cooperative mitigation strategies, the equilibrium was helped in attaining balanced solutions with no biased gain for any party and avoiding selfish strategy changes. The autonomous decision-making was data-driven from each city's information. Machine learning-stylized simulation preceded the scenarios outlined with real data. The use of Nash equilibrium along with machine learning offered an efficient basis to cope with the dynamic interaction of cities for GWM. The hybrid setup was useful as an analytical tool for evaluating urban climate policies based on the relationship between strategy decisions and measurable results such as emission distribution, risk reduction, and increased resilience. Urban climate cooperation planning based on certain sustainable development goals (SDG), namely (SDG 11) sustainable cities and communities, (SDG 13) climate action, and (SDG 17) partnerships for the goals, were also modeled.