Shingled technology is the latest cell interconnection technology developed in the photovoltaic (PV) industry due to its reduced resistance loss, low-cost, and innovative electrically conductive adhesive (ECA). There are several advantages associated with shingled technology to develop cell to module (CTM) such as the module area enlargement, low processing temperature, and interconnection; these advantages further improves the energy yield capacity. This review paper provides valuable insight into CTM loss when cells are interconnected by shingled technology to form modules. The fill factor (FF) had improved, further reducing electrical power loss compared to the conventional module interconnection technology. The commercial PV module technology was mainly focused on different performance parameters; the module maximum power point (Pmpp), and module efficiency. The module was then subjected to anti-reflection (AR) coating and encapsulant material to absorb infrared (IR) and ultraviolet (UV) light, which can increase the overall efficiency of the shingled module by up to 24.4%. Module fabrication by shingled interconnection technology uses EGaIn paste; this enables further increases in output power under standard test conditions. Previous research has demonstrated that a total module output power of approximately 400 Wp may be achieved using shingled technology and CTM loss may be reduced to 0.03%, alongside the low cost of fabrication.

This study analyzed whether the diffusion of new and renewable energy contributed to mitigating emissions of various air pollutants, including particulate matter, using panel econometric models. The theoretical foundation of such econometric models is based on the Environmental Kuznets Curve (EKC) hypothesis, which assumes an inverted U-shaped relation between national income and environmental pollution, as originally proposed by Grossman and Krueger. We examined whether there are inverted U-, U-shaped, or N-shaped relations between national income and air pollution. We demonstrate that increases in new and renewable energy significantly mitigated emissions of CO, NOX, and PM2.5. Additionally, we included NOX, SOX, PM10, and VOCs as secondary emission sources of PM2.5 and found that emission of PM10 resulted in the highest PM2.5 emissions, followed by NOX and SOX emissions. The impact of new and renewable energy on air pollution varied across regions. Increase of new and renewable energy in the Honam region significantly mitigated CO, NOX, and TSP emissions, while that in the Youngnam and metropolitan areas did not significantly mitigate air pollution overall. There was a U-shaped relationship between air pollution and national income for CO, NOX, PM2.5, and SOX, while an inverted N-shape was observed for PM10.

This study involved a total budget analysis on the greenhouse gas (GHGs) emissions of landfills, focusing on surface emissions and the effect on emissions reductions of generating landfill gas (LFG) electricity from March 7, 2007 to December 31, 2018. The GHGs reduction effect from the electricity generation using 536.6 × 103 tCO2 of CH4 was only 5.8% of the GHGs from surface emissions of 9191×103 tCO2. In the total budget, the collection ratio should be over 95% if the reduction effect is greater than the surface emissions. The correlation coefficient for the relationship between the LFG collection ratio and GHGs reduction was -0.89. An additional effect of lowering CH4 content may occur if the surface emitting flux of LFG decreased with an increase in the collection ratio. The unit reduction effect of GHGs by suppressing surface emissions was 4174 tCO2/TJ. This was far greater than that of LFG power generated (54.3 tCO2/TJ), demonstrating that surface emission control is the most important measure by which to mitigate GHGs emission.

In this study, a floating LiDAR system (FLS) is investigated through a field test involving two steps. First, correlations among wind speeds, measured using the met mast and two LiDARs, are computed to analyze the acceptance criteria of LiDAR for measuring wind speed. The results of the analysis show that the slopes of single variant regression between mean wind speeds are below 1.03 and the coefficient of determination is above 0.97. Next, correlations among wind speeds measured using the FLS and a fixed LiDAR are determined through a field test carried out in Doomi-doo, Tong-young, Gyeongsangnam-do. The FLS is installed 300 m away from the fixed LiDAR on the ground. The results show that the slope of single variant regression is approximately 1.0275 and the coefficient of determination is above 0.971. According to the IEA/wind 18 recommendation, it is found that the developed FLS measures valid wind speeds to assess wind resources for the development of offshore wind farms.