It has been established in 2030 sustainability objectives as per SDGs that highlight the critical importance of access to affordable, renewable energy, robust, long-term industrial progress, and digital financing in CO 2 emission. The intent of study is to test the trilemma nexus between digital finance, renewable energy consumption, and carbon emission reduction with nonlinear ARDL tests. The study acquired the data and applied the nonlinear ARDL test, split analysis tests, and vector-error correction model (VECM) tests. The results of the study highlighted that the increase of digital finance positively enhances the renewable energy and negatively reduces the CO 2 emissions which we calculate to be 11.4% of the digital finance funding on renewable energy goods. For this, a 39% increase in digital financing is noticed by the research findings during the COVID-19 crisis period. Such robust study findings present the latest insights that digital financing is an eminent and viable source of financing for the trilemma nexus with renewable energy consumption and the CO 2 emissions. Following these, multiple research implications are also presented for the key stakeholders.

The financial industry is developing rapidly, and risk management is an important part of the internal management of financial institutions. In order to accurately estimate international financial risks, improve the risk management performance of financial institutions, and ensure the sustainable development of the international financial market, an international financial risk estimation model based on improved genetic algorithms was designed, the value-at-risk model VAR model was selected to estimate the international financial risk by measuring the degree of economic loss, and the improved genetic algorithm was adopted to the seven parts of immature convergence to quickly obtain the VAR value of international financial risks, including initialize the population, real number coding, determine fitness function, selection operator, crossover operator, mutation operator and predict and process. Results show that the rapid estimation of international financial risks was realized, the designed model can achieve accurate estimation of international financial risks, and the time cost of financial risk estimation under different sample sizes is less than 500 ms.