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• Construct the green degree evaluation system of equipment manufacturing industry. • Verify the applicability of the particle swarm optimization algorithm method. • Evaluated the green degree of China's equipment manufacturing industry. • Proposing to improve the green degree of China's equipment manufacturing industry. The paper constructs the green degree evaluation system of equipment manufacturing industry, and then the improved particle swarm optimization algorithm is used to measure the green degree of China's equipment manufacturing industry. The results show that the improved particle swarm optimization algorithm is a better method to evaluate the green degree of China's equipment manufacturing industry. The paper measure the green degree of China's equipment manufacturing industry is 0.51, which is at a low level. So it is need to adopt policies to promote green degree to achieve sustainable development goals. In view of this, the paper proposes policy recommendations for the green degree of China's equipment manufacturing industry from the perspectives of improving relevant policies, technological upgrading and environmental protection input, applying legal policy systems, and increasing financial support. [ABSTRACT FROM AUTHOR]

• A novel composite forecasting framework is successfully developed. • The new-information based buffer operator is employed to process raw data with stochastic disturbances. • The optimized nonlinear grey Bernoulli model is put forward by designing the novel initial condition. • The proposed model with buffer operator strikingly outperforms any of other benchmarks. To accurately predict the time series having limited data with stochastic disturbances and nonlinearity, this paper proposed a composite forecasting model by adaptive data preprocessing and optimized nonlinear grey Bernoulli model. Specifically, improvements in the proposed model lie in the following aspects: firstly, the new-information-based buffer operator is utilized to eliminate stochastic disturbance effects and thereby enhance the smoothness of the system series, which can effectively extract the potential patterns of recent development. Secondly, based on the preprocessed data, the nonlinear grey Bernoulli model provided with the newly-designed initial condition that abides by the principle of new information priority without data lapses enables the forecasting precision and applicability enhancement. Thirdly, due to the nonlinear relationships between the prediction errors and the parameters introduced by the above optimization paths, the Particle Swarm Optimization algorithm is employed to ascertain the optimal parameters simultaneously, whose efficacy and robustness have been validated by experimental comparison and sensitivity analysis. On the foundation of the above functional improvements, the novel composite forecasting framework successfully overcomes the limitations of conventional grey models to obtain more accurate and robust forecasts, which are substantiated by the applications for predicting the sales of new energy vehicles in China and Norway. Experimental results and discussions demonstrate that the new-information-based buffer operator can be regarded as an excellent alternative tool for data preprocessing. Besides, the grey models with buffered preprocessing can deliver much more accurate forecasts than their corresponding versions without buffered preprocessing. Furthermore, the proposed model with buffered preprocessing outperforms any of the other competitors and can be considered as the most promising technique for prognosticating NEVs sales. [ABSTRACT FROM AUTHOR]