Search Results (2)

Improving green total factor productivity (GTFP) is the key for China’s mineral resources industry to get out of the dilemma of resource depletion and environmental degradation. The Super Slacks-Based Measure (Super-SBM) model with undesirable output is used to calculate the GTFP of China’s mineral resources industry between 2004 and 2019, and the space–time correlation threshold is quantitatively determined by the space–time semivariogram. On this basis, the spatial weight matrix is constructed, and the spatial panel vector autoregression (SPVAR) model is used to quantitatively estimate the space–time impact response among GTFP, import dependence, and R&D investment. The results show that: (1) The maximum range of mineral resources industry GTFP in time and space are 12.28 years and 635.28 km, respectively. Taking the space range as the correlation distance threshold to construct spatial weight matrix improves the accuracy of spatial analysis. (2) The increase in import dependence and R&D investment can effectively improve the GTFP of local and its neighboring provinces. In the long term, an increase in import dependence has a positive impact on R&D investment, and an increase in R&D investment can reduce the import dependence. (3) In the response to impact, the eastern region is greater than the western region, the coastal provinces are greater than the inland provinces, and the provinces close to the impact source are greater than the provinces far away. Therefore, policies to limit resource and energy consumption, pollution, and carbon emissions should be strengthened. The incentive policies should be emphasized differently and adopted for the impact sources and response areas. The R&D investment in the full mineral industry process should be increased to improve the GTFP. Full article

Sound navigating and ranging (SONAR) detection systems can provide valuable information for navigation and security, especially in shallow coastal areas. The last few years have seen an important increase in the volume of bathymetric data produced by Multi-Beam Echo-sounder Systems (MBES). Recently, the General Bathymetric Chart of the Oceans (GEBCO) released these MBES dataset preprocessed and processed with Computer Aided Resource Information System (CARIS) for public domain use. For the first time, this research focuses on the validation of these released MBES-CARIS dataset performance and robustness for bathymetric mapping of shallow water at the regional scale in the Kingdom of Bahrain (Arabian Gulf). The data were imported, converted and processed in a GIS environment. Only area that covers the Bahrain national water boundary was extracted, avoiding the land surfaces. As the released dataset were stored in a node-grid points uniformly spaced with approximately 923 m and 834 m in north and west directions, respectively, simple kriging was used for densification and bathymetric continuous surface map derivation with a 30 by 30 m pixel size. In addition to dataset cross-validation, 1200 bathymetric points representing different water depths between 0 and −30 m were selected randomly and extracted from a medium scale (1:100,000) nautical map, and they were used for validation purposes. The cross-validation results showed that the modeled semi-variogram was adjusted appropriately assuring satisfactory results. Moreover, the validation results by reference to the nautical map showed that when we consider the total validation points with different water depths, linear statistical regression analysis at a 95% confidence level (p < 0.05) provide a good coefficient of correlation (R² = 0.95), a good index of agreement (D = 0.82), and a root mean square error (RMSE) of 1.34 m. However, when we consider only the validation points (~800) with depth lower than −10 m, both R² and D decreased to 0.79 and 0.52, respectively, while the RMSE increased to 1.92 m. Otherwise, when we consider exclusively shallow water points (~400) with a depth higher than −10 m, the results showed a very significant R² (0.97), a good D (0.84) and a low RMSE (0.51 m). Certainly, the released MBES-CARIS data are more appropriate for shallow water bathymetric mapping. However, for the relatively deeper areas the obtained results are relatively less accurate because probably the MBSE did not cover the bottom in several deeper pockmarks as the rapid change in depth. Possibly the steep slopes and the rough seafloor affect the integrity of the acquired raw data. Moreover, the interpolation of the missed areas’ values between MBSE acquisition data points may not reflect the true depths of these areas. It is possible also that the nautical map used for validation was not established with a good accuracy in the deeper regions. Full article