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The rise of robotics has brought great uncertainty to the labor market. Based on the sectoral data from 22 economies during 2008–2019, this paper explores the impact of robot application on employment. The results show that, on the whole, robot application will have complementary effects on lab or force employment, and the grouped regression by economic development level and demographic characteristics supports this conclusion, while the effect of robot application on labor force employment is significantly different by industry. Further research shows that the degree of robot use is the key factor that determines the effect of robots on employment, and the complementary effect is dominant in economies with low degree of robot application, and the subtitution effect is dominant in economies with high degree of robot application. In addition, obvious spillover effects are observed in robotic application. On the one hand, robot application will have a forward crowding– out effect and a reverse siphon effect, which drive the labor force moving from the primary industry to the secondary and tertiary industries. On the other hand, robot application will also have heterogeneous effects on the labor force employment of economies in the upstream and downstream position along the value chain through the transmission effect of the Global Value Chains (GVC). The conclusions of this paper provide some practical implications for the rational formulation of artificial intelligence plans in the context of "stabilizing employment". [ABSTRACT FROM AUTHOR]

High-quality development is the inevitable choice for overcoming structural problems and resource bottlenecks and achieving sustainable development after more than three decades of rapid growth in China. Finance-driven technological innovation is an effective way to realise high-quality development. Working with a data set that includes 41 cities in China's Yangtze River Delta, we used a panel threshold model and the spatial Durbin model to empirically analyse the impact of financial agglomeration on technological innovation. Our results indicate that the promoting effect of financial agglomeration on technological innovation increases with an improvement in the financial agglomeration level. Financial agglomeration has an inverted U-shaped spatial spillover effect on technological innovation. That is, moderate financial agglomeration will promote technological innovation in their neighbourhood, while excessive financial agglomeration will inhibit it. The government should fully utilise the radiating and dispersing functions of financial and innovation centres to rationally allocate resources and narrow gaps between regions. [ABSTRACT FROM AUTHOR]

The lockdown of cities in the Yangtze River Delta (YRD) during COVID-19 has provided many natural and typical test sites for estimating the potential of air pollution control and reduction. To evaluate the reduction of PM2.5 concentration in the YRD region by the epidemic lockdown policy, this study employs big data, including PM2.5 observations and 29 independent variables regarding Aerosol Optical Depth (AOD), climate, terrain, population, road density, and Gaode map Point of interesting (POI) data, to build regression models and retrieve spatially continuous distributions of PM2.5 during COVID-19. Simulation accuracy of multiple machine learning regression models, i.e., random forest (RF), support vector regression (SVR), and artificial neural network (ANN) were compared. The results showed that the RF model outperformed the SVR and ANN models in the inversion of PM2.5 in the YRD region, with the model-fitting and cross-validation coefficients of determination R2 reached 0.917 and 0.691, mean absolute error (MAE) values were 1.026 μg m−3 and 2.353 μg m−3, and root mean square error (RMSE) values were 1.413 μg m−3, and 3.144 μg m−3, respectively. PM2.5 concentrations during COVID-19 in 2020 have decreased by 3.61 μg m−3 compared to that during the same period of 2019 in the YRD region. The results of this study provide a cost-effective method of air pollution exposure assessment and help provide insight into the atmospheric changes under strong government controlling strategies. [ABSTRACT FROM AUTHOR]