The assessment of regional low carbon development level can fully reflect the current situation of low carbon development, and the carbon emission forecasting can reveal the future trend of carbon emission and pressure of emission reduction. Therefore, it is of great significance to carry out the assessment of low carbon development level and carbon emission forecasting in Gansu, Qinghai and Shaanxi provinces for regional green economy development. Therefore, this research constructs an index system to objectively reflect the low carbon development level of Gansu, Qinghai and Shaanxi provinces, and proposes IE-TOPSIS comprehensive assessment method to assess the regional low carbon development level from 2010 to 2020. It is showed that low carbon development level of the three provinces over the past decade has an increasing trend. Furthermore, this research establishes a GRA-DPC-MAPE combined forecasting model for carbon emission forecasting of Gansu, Qinghai and Shaanxi. GRA-DPC-MAPE model has higher forecast accuracy than individual model SVR, PSO-SVR, ELM and Elman. It is showed that carbon emission of Gansu and Shaanxi provinces will continue to increase from 2021 to 2025 under the current development.

Air pollution control is significant to promote the smooth implementation of air pollution treatment and reduce the related loss of life and property. Reliable and accurate prediction can provide data support and theoretical support for air pollution control. Therefore, it is of great significance to establish reliable air pollution prediction and early warning system. Moreover, air quality index (AQI) is a comprehensive environmental pollutant monitoring index. Therefore, this research proposes a nonlinear combination based on variational mode decomposition (VMD), generalized additive model (GAM) and neutral networks for AQI, namely VMD-GAM-NNCom model. The highlight of the proposed model is introducing three nonlinear neutral networks to efficiently combine the selected individual models, which makes up for the shortage of linear combinations. What’s more, the proposed model introduces GAM to efficiently integrate decomposition modes linearly or nonlinearly. From the prediction results, it can be concluded that the proposed model VMD-GAM-beetle antennae search (BAS)-Elman has higher prediction accuracy compared with the other five comparative models, and the corresponding prediction results of the proposed model have higher consistency with the original data. Taking Baoshan as an example, the mean of absolute relative percentage error (MAPE) value of VMD-GAM-BAS-Elman is less than 6%, and reduces by over 70% on average compared with other models. Therefore, the innovative model provides an efficient and reliable nonlinear combined technique for AQI forecasting.