International trade levels can change the relationship between resource endowments and green economic growth. Therefore, this study tested the resource curse hypothesis from the perspective of green growth in China using provincial-level panel data for 2005–2017. Energy conservation and environmental improvement were considered under green growth to further analyze the regional mechanism of the resource curse. A panel threshold model was used to identify the impact of import and export threshold effects on the transformation of this mechanism. The resource curse hypothesis was found to be valid nationwide; it hindered green economic growth mainly by impeding energy conservation and curbing environmental improvement. In terms of regional differences in green growth, resource endowment had a positive impact on the eastern region, a negative impact on the central region, and no effect on the western region. When the levels of import and export trade exceeded the threshold values, the resource curse effect was enhanced by reducing energy conservation and weakened by promoting environmental improvement, respectively. Therefore, the Chinese government should establish a more reasonable import and export trade structure, promote changes to the energy structure and green technological innovation, and reduce the negative impact of resource endowment on green growth.

With increasing environmental pollution, China has instituted corresponding environmental regulations to address environmental challenges. Estimating the costs of such environmental regulations can help governments to formulate rational environmental policies. This review estimates the costs of environmental regulations based on a novel perspective of energy consumption. Using panel data for Chinese provincial regions in 2006–2015, we developed a non-parametric directional distance function and estimated different optimal energy inputs based on data envelopment analysis under two scenarios, namely, those with and without emission reduction constraints. The gap between the two groups of optimal energy inputs facilitated the estimation of the energy costs associated with reducing SO₂ (sulfur dioxide) emissions in China’s industrial sectors. The results suggest that approximately 13.40 tons of standard coal were required to reduce SO₂ emissions by 1 ton, highlighting the discrepancy between energy savings and emission reduction. The energy costs of SO₂ emission reduction were the highest in West China (18.63), followed by those in Central and Northeast China; meanwhile, those in East China were the lowest (9.91). The large differences between the energy costs of emission reduction in different regions indicated that economically underdeveloped areas have scope for improvement with respect to energy structures and innovation in the green technology field.

Global soil moisture is crucial for multiple disciplines such as earth science and agricultural science. Therefore, there are many studies on how to improve the prediction accuracy of soil moisture, especially the rapid development of deep learning in recent years, which has greatly improved data-driven models. This paper proposes an adaptive weight long short-term memory (AW-LSTM) model based on dynamic weight allocation, which dynamically optimizes the model by calculating the correlation coefficient \(p\) between tasks. The experimental verification demonstrated that the AW-LSTM model exhibited the most accurate soil moisture prediction, with \(R\) and \(R^2\) values of 0.9456 and 0.8489, respectively. These values were 0.019 and 0.072 higher than the single-task prediction values observed in the benchmark dataset.