Integration of risk assessment and economic dynamics models in the system analysis of energy transition scenarios with the study of policy and technology feedback effects

Abstract

The work is devoted to the development and testing of a comprehensive methodology for the system analysis of energy transition scenarios, integrating models of dynamic risk assessment and macroeconomic dynamics in a single hybrid simulation complex with an agent-oriented model (ABM), stochastic DSGE block, modified diffusion model of Bass and Monte Carlo for probabilistic calibration of outcomes.; The study is based on an array of data from 2000-2024 and a horizon up to 2050. Three scenarios are considered: inertia, a regulatory push with a carbon tax, and technological incentives with a focus on subsidies and R&D. Materials and methods include parameterization of agents' behavioral rules, endogenous cost of capital assessment (WACC) as a function of cash flow volatility and regulatory risk, as well as 10,000 runs per scenario to build distributions of key metrics (GDP, Inflation, Employment, energy balance structure, Value-at-Risk, and the likelihood of exceeding the carbon budget). The results show that the static estimate has negative effects on GDP, while the full dynamic feedback model demonstrates a long-term positive result: the deviation of GDP by 2050. It is +1.34% with the carbon tax and +0.67% with subsidies; the probability of not meeting the emissions target is reduced to 22.71% with the tax regime and 35.86% with subsidies; the economic VaR (95%) is minimal for the tax scenario (2.49% vs. 3.82%), reflecting a more robust risk profile. In the technological dimension, the diffusion of renewable energy is accelerating: the share of solar generation reaches 29.61% (tax) and 34.19% (subsidies) by 2045, while the largest reduction in WACC for wind energy is achieved in the tax case (-92.7 bp versus -78.3 bp). The discussion proves that a universal and predictable carbon tax price signal reduces the risk premium more effectively and builds adaptive investment trajectories, while policy incentives are vulnerable to political cycles and inefficient capital allocation. The conclusion highlights the need to design a long-term, consistent carbon pricing framework, complemented by targeted social and R&D measures, to trigger positive feedbacks between politics, technology, and economics and minimize tail-end energy transition risks.