To achieve the energy transition and carbon neutrality targets, governments have implemented multiple policies to incentivize electricity suppliers to invest in renewable energy. Considering different government policies, we construct a renewable energy supply chain consisting of electricity suppliers and electricity retailers. We then explore the impact of four policies on electricity suppliers’ renewable energy investments, environmental impacts, and social welfare. We validated the results based on data from Wuxi, Jiangsu Province, China. The results show that government subsidy policies are more effective in promoting electricity suppliers to invest in renewable energy as consumer preferences increase, while no-government policies are the least effective. We also show that electricity suppliers are most profitable under the government subsidy policy and least profitable under the carbon cap-and-trade policy. Besides, our results indicate that social welfare is the worst under the carbon cap-and-trade policy. With the increase in carbon intensity and renewable energy quota, social welfare is the highest under the subsidy policy. However, the social welfare under the renewable energy portfolio standard is optimal when the renewable energy quota is low.

Credit policies for clean and renewable energy businesses play a crucial role in supporting carbon neutrality efforts to combat climate change. Clustering the credit capacity of these companies to prioritize lending is essential given the limited capital available. Support Vector Machine (SVM) and Artificial Neural Network (ANN) are two robust machine learning algorithms for addressing complex clustering problems. Additionally, hyperparameter selection within these models is effectively enhanced through the support of a robust heuristic optimization algorithm, Particle Swarm Optimization (PSO). To leverage the strength of these advanced machine learning techniques, this paper aims to develop SVM and ANN models, optimized with the PSO, for the clustering problem of green credit capacity in the renewable energy industry. The results show low Mean Square Error (MSE) values for both models, indicating high clustering accuracy. The credit capabilities of wind energy, clean fuel, and biomass pellet companies are illustrated in quadrant charts, providing stakeholders with a clear view to adjust their credit strategies. This helps ensure the efficient operation of banking green credit policies.

Global CO₂ emissions pose a serious threat of climate change for high-growth countries, requiring increased efforts to preserve the environment and meet growing economic needs through the use of renewable energies. This research significantly enhances the current literature by filling a void and differentiating between short-term and long-term impacts across economic growth, renewable energy consumption, energy intensity, and CO₂ emissions in BRIC countries from 2002 to 2019. In contrast to approaches that analyze global effects, this study’s focus on short and long-term effects offers a more dependable insight into energy and environmental research. The empirical results confirmed that the effect of economic growth on CO₂ emissions is positive both in the short and long term. Moreover, the effect of energy consumption is negative in the short term and positive in the long term. The effect of energy intensity is positive in the short term and negative in the long term. Accordingly, policy recommendations must be adopted to ensure that these economies respond to the notion of sustainable development and the relationship with the environment. BRIC countries must strengthen their industries in the long term in favor of the use of renewable energies by introducing innovation and technology. These economies face the challenge of a transition to renewable energy sources by creating a new energy and industrial sector environment that is more environmentally friendly atmosphere.