The South China Sea is a major shipping hub between the West Pacific and Indian Oceans. In this region, the demand for energy is enormous, both for residents’ daily lives and for economic development. Wave energy and wind energy are two major clean and low-cost ocean sources of renewable energy. The reasonable development and utilization of these energy sources can provide a stable energy supply for coastal cities and remote islands of China. Before wave energy and wind energy development, however, we must assess the potential of each of these sources. Based on high-resolution and high-accuracy wave field data and wind field data obtained by ERA-Interim reanalysis for the recent 38-year period from 1979–2016, the joint development potential of wave energy and wind energy was assessed in detail for offshore and nearshore areas in the South China Sea. Based on potential installed capacity, the results revealed three promising areas for the joint development of nearshore wave energy and wind energy, including the Taiwan Strait, Luzon Strait and the sea southeast of the Indo-China Peninsula. For these three dominant areas (key stations), the directionality of wave energy and wind energy propagation were good in various seasons; the dominant wave conditions and the dominant wind conditions were the same, which is advantageous for the joint development of wave and wind energy. Existing well-known wave energy converters (WECs) are not suitable for wave energy development in the areas of interest. Therefore, we must consider the distributions of wave conditions and develop more suitable WECs for these areas. The economic and environmental benefits of the joint development of wave and wind energy are high in these promising areas. The results described in this paper can provide references for the joint development of wave and wind energy in the South China Sea.

This study corroborates the asymmetric and heterogeneous associations between clean energy intensity (CEI) and carbon dioxide (CO₂) emissions among the world's top ten renewable energy consumer countries using quarterly data from 1970Q1 to 2018Q4. We quantify the complete dependence structure between CEI and CO₂ at quantile distributions using a novel quantile-on-quantile (QQ) method proposed by Sim and Zhou (2015). Compared to classic approaches such as quantile regression and ordinary least squares, the QQ technique can provide more information on the overall relationship between CEI and CO₂. Furthermore, we also seek to determine causal relationships between CEI and CO₂ using a quantile Granger causality approach suggested by Troster. According to our empirical evidence, the link between the two variables is predominantly negative. Moreover, there are significant disparities across countries in the quantile ranges of CEI and CO₂. In particular, there is a weak positive link between CEI and CO₂ in the case of Sweden, Italy, Japan, and Australia, which may be because CEI has a minimal direct influence on CO₂ in these countries. The empirical findings clarify that policymakers should fund renewable energy industries to minimize carbon dioxide (CO₂) emissions and achieve Sustainable Development Goals (SDGs).

Since the Industrial Revolution, methane has become the second most important greenhouse gas component after CO₂ and the second most important culprit of global warming, leading to serious climate change problems such as droughts, fires, floods, and glacial melting. While most of the methane in the atmosphere comes from emissions from energy activities such as petroleum refining, storage tanks are an important source of methane emissions during the extraction and processing of crude oil and natural gas. Therefore, the use of high-resolution remote sensing image data for oil and gas production sites to achieve efficient and accurate statistics for storage tanks is important to promote the strategic goals of “carbon neutrality and carbon peaking”. Compared with traditional statistical methods for studying oil storage tanks, deep learning-based target detection algorithms are more powerful for multi-scale targets and complex background conditions. In this paper, five deep learning detection algorithms, Faster RCNN, YOLOv5, YOLOv7, RetinaNet and SSD, were selected to conduct experiments on 3568 remote sensing images from five different datasets. The results show that the average accuracy of the Faster RCNN, YOLOv5, YOLOv7 and SSD algorithms is above 0.84, and the F1 scores of YOLOv5, YOLOv7 and SSD algorithms are above 0.80, among which the highest detection accuracy is shown by the SSD algorithm at 0.897 with a high F1 score, while the lowest average accuracy is shown by RetinaNet at only 0.639. The training results of the five algorithms were validated on three images containing differently sized oil storage tanks in complex backgrounds, and the validation results obtained were better, providing more accurate references for practical detection applications in remote sensing of oil storage tank targets in the future.

Based on ERA-Interim reanalysis wave field data for the 36 years from 1979 to 2014, the temporal and spatial distributions and development potential of wave energy are studied in detail in the offshore and relatively nearshore waters adjacent to the Zhoushan Islands. The results show that areas of relatively high wave energy are located in the offshore waters to the east and southeast of the Zhoushan Islands. The potential wave energy in relatively nearshore waters (water depths from 10 m to 65 m) is relatively higher and especially in the nearshore waters to the southeast of the city of Taizhou, Zhejiang Province, which are suitable locations for wave energy development. The conclusions provide scientific guidance for wave energy development in the sea areas adjacent to the Zhoushan Islands.

Synthetic aperture radar (SAR) is an important method for observing ocean waves, and inversion of the ocean wave spectrum is an important part of SAR ocean wave detection. For most wave spectrum inversion methods, the first guess spectrum is crucial to the accuracy of SAR wave spectrum inversion. The first guess spectrum can be provided by some existing fixed wave spectrum models, but the applicability of these wave spectrum models as the first guess spectrum has not been systematically studied. This paper focused on the applicability of the Elfouhaily spectrum (E spectrum) as a full wavenumber spectrum and the Pierson-Moscowitz spectrum (PM spectrum) as a wind wave spectrum as the first guess spectrum for inverting the wave spectrum. Based on the SENTINEL-1A and RADARSAT-2 SAR satellite data of the Maritime Silk Road, using the E spectrum and PM spectrum as the first guess spectrum by the Max Planck Institute (MPI) method, the ability of the E spectrum and PM spectrum as the first guess spectrum to retrieve the wave spectrum and wave parameters was systematically evaluated. The significant wave height (HS) and zero-crossing period (TZ) wave parameters obtained from SAR inversion were compared with the European Center for Medium-Range Weather Forecasts (ECMWF) wave data. By the comparison results, we analyzed the best wind speed and significant wave height ranges for the inversion of the PM spectrum and E spectrum as the first guess spectrum. The results showed that the E spectrum as the first guess spectrum had a good effect on the inversion at medium sea situation. It was suitable as the first guess spectrum of the SAR wave spectrum inversion.