Bing Chen

With the deterioration of environmental quality caused by fossil energy use, the research on energy internet and energy misallocation is of critical relevance to achieve low-carbon sustainable development. However, we find that the relevant research that analyzes energy internet and energy misallocation on carbon emissions under the same framework is ignored. For this purpose, the generalized method of moments (GMM), panel threshold model, and spatial analysis (deviation ellipse, hotspot analysis, and geographically and temporally weighted regression (GTWR)) model were applied to investigate the impact of energy internet and energy misallocation on carbon emissions using panel data of 30 provinces in China from 2004 to 2018. The major statistical results include the following: (1) energy misallocation significantly contributes to carbon emissions, while energy internet inhibits carbon emissions. Energy internet can negatively moderate the positive effect of energy misallocation on carbon emissions. (2) The effect of energy misallocation on carbon emissions reveals an inverted “U-shaped” characteristic of first promoting and later inhibiting, but the inhibiting effect is insignificant. Moreover, the marginal effect of energy misallocation on carbon emissions decreases when the energy internet crosses the second thresholds consecutively, while the marginal effect of the energy internet on carbon emissions shows an inverted “N” shape. (3) Compared with the under-allocated regions, the promotion effect of energy misallocation on carbon emissions and the inhibitory effect of energy internet on carbon emissions are stronger in the over-allocated regions, while the energy internet has a more significant negative moderating effect on energy misallocation. (4) The gravity center of China’s carbon emissions gradually shifts to the northwest with time. The longitude of the gravity center (east–west direction) changes greatly, while the latitude of the gravity center (north–south direction) changes less. Besides, the carbon emission hotspot regions centered on Shanxi spread to the neighboring provinces, which form a high-high agglomeration region, and the cold spot region dominated by Qinghai, Guangxi, and Guangdong forms low-low agglomeration characteristics. Finally, the GTWR model shows that the impact of energy internet and energy misallocation on carbon emissions shows significant hierarchical, banded, or block-like characteristics in spatial distribution.

This study measured the economic resilience of 269 prefecture-level cities in China by constructing an indicator evaluation system for the resilience, adjustment, and responsiveness of the economic system under external shocks. A dynamic spatial Durbin model and a moderating mediation model were employed to analyze empirically the impact of economic policy uncertainty and innovative entrepreneurial vitality on economic resilience using prefecture-level panel data from 2004 to 2018. The statistical results revealed that there were significant “snowball” effects and spatial spillover characteristics of economic resilience. Under the moderating effect of economic policy uncertainty, innovative entrepreneurial vitality was found to have a significant positive effect on economic resilience. Furthermore, innovative entrepreneurial vitality was found to enhance economic resilience significantly by upgrading the industrial structure, alleviating the income gap, and guiding economic agglomeration in the context of economic policy uncertainty. Moreover, the impacts of innovative entrepreneurial vitality and economic policy uncertainty on economic resilience, respectively, showed significant heterogeneities in terms of the cities’ regions and economic sizes. The above-mentioned results were found to be valid even after a series of robustness tests were carried out.