The literature on the impact of policy uncertainty on climate change has grown rapidly in recent years as policymakers and researchers have become increasingly concerned about the potential adverse effects of policy uncertainty on environmental sustainability. This study aims to investigate the impact of economic policy uncertainty (EPU), GDP per capita, renewable energy consumption (REC), and foreign direct investment (FDI) on environmental sustainability from the perspectives of the environmental Kuznets curve (EKC) and pollution halo/haven hypotheses. The research employs panel data analysis techniques, including panel corrected standard errors (PCSE) and generalized least squares (GLS), to analyze the data from a panel of 19 developed and developing countries from 2001 to 2019. The results reveal that EPU, GDP per capita, REC, and FDI significantly impact GHG emissions, contributing to climate change. The results of the study confirm a U-shaped EKC and pollution haven hypothesis in the selected economies. The findings of this study provide valuable insights for policymakers, as they highlight the need to consider the interplay between economic growth, foreign investment, and environmental policy in addressing climate change. The results also suggest that reducing policy uncertainty and promoting sustainable economic growth can mitigate the effects of climate change and ensure environmental sustainability.

This study aimed to respond to the national “carbon peak” mid-and long-term policy plan, comprehensively promote energy conservation and emission reduction, and accurately manage and predict carbon emissions. Firstly, the proposed method analyzes the Yangtze River Economic Belt as well as its “carbon peak” and carbon emissions. Secondly, a support vector regression (SVR) machine prediction model is proposed for the carbon emission information prediction of the Yangtze River Economic Zone. This experiment uses a long short-term memory neural network (LSTM) to train the model and realize the experiment’s prediction of carbon emissions. Finally, this study obtained the fitting results of the prediction model and the training model, as well as the prediction results of the prediction model. Information indicators such as the scale of industry investment, labor efficiency output, and carbon emission intensity that affect carbon emissions in the “Yangtze River Economic Belt” basin can be used to accurately predict the carbon emissions information under this model. Therefore, the experiment shows that the SVR model for solving complex nonlinear problems can achieve a relatively excellent prediction effect under the training of LSTM. The deep learning model adopted herein realized the accurate prediction of carbon emission information in the Yangtze River Economic Zone and expanded the application space of deep learning. It provides a reference for the model in related fields of carbon emission information prediction, which has certain reference significance.

In this study, we aim at investigating factors significantly affecting innovation behavior of SMEs in developing country context. To realize the research objective, a thorough literature view on SME innovativeness has been realized and initial model for the study has been constructed. Data collected by self-administered questionnaire from SME CEOs and high level managers of Uzbekistan and China sample SMEs has been analyzed by Multivariate Logistic Model to make inference about significant determinants of SME innovativeness. The findings of the study suggest that CEOs with higher education, their innovation goal-oriented determination, and competition pressure from home market and abroad due to high export orientation of the SME and government incentives for innovations are being main forces making SMEs realize innovations. Internally, innovative SMEs are being led by CEOs with higher education degree and who well fits the great importance of innovations in their company strategy.