With the development of renewable energy, a key measure for reducing greenhouse gas emissions, interest in the levelized cost of electricity (LCOE) is increasing. Although the input variables used in the LCOE calculation, such as capacity factor, capital expenditure, annual power plant operations and maintenance cost, discount and interest rate, and economic life, vary according to region and project, most existing studies estimate the LCOE by using a deterministic methodology. In this study, the stochastic approach was used to estimate the LCOE for solar photovoltaic (PV) in South Korea. In addition, this study contributed to deriving realistic analysis results by securing the actual data generated in the solar PV project compared to the existing studies. The results indicate that the LCOE for commercial solar power ranged from KRW 115 (10 cents)/kWh to KRW 197.4 (18 cents)/kWh at a confidence level of 95%. The median was estimated at KRW 160.03 (15 cents)/kWh. The LCOE for residential solar power ranged from KRW 109.7 (10 cents)/kWh to KRW 194.1 (18 cents)/kWh at a 95% confidence level and a median value of KRW 160.03 (15 cents)/kWh. A sensitivity analysis shows that capital expenditure has the most significant impact on the LCOE for solar power, followed by the discount rate and corporate tax. This study proposes that policymakers implement energy policies to reduce solar PV hardware and soft costs.

In the long-term, crude oil prices may impact the economic stability and sustainability of many countries, especially those depending on oil imports. This study thus suggests an alternative model for accurately forecasting oil prices while reflecting structural changes in the oil market by using a Bayesian approach. The prior information is derived from the recent and expected structure of the oil market, using a subjective approach, and then updated with available market data. The model includes as independent variables factors affecting oil prices, such as world oil demand and supply, the financial situation, upstream costs, and geopolitical events. To test the model’s forecasting performance, it is compared with other models, including a linear ordinary least squares model and a neural network model. The proposed model outperforms on the forecasting performance test even though the neural network model shows the best results on a goodness-of-fit test. The results show that the crude oil price is estimated to increase to $169.3/Bbl by 2040.

Under the new climate regime, renewable energy (RE) has received particular attention for mitigating the discharge of greenhouse gas. According to the third energy master plan in South Korea, by 2040, 30–35% of the energy demand must met with RE sources. To ensure relevant policy design to achieve this goal, it is crucial to analyze the public’s willingness to accept community-based RE projects. This study conducted a nationwide survey to understand the opinion of the public and also that of local inhabitants living near a RE project. A choice experiment was employed to measure public preferences toward RE projects. The analysis reveals that the type of energy source, distance to a residential area, and annual percentage incentives could affect acceptance levels. Additionally, investment levels were a factor in local inhabitants’ acceptance of energy-related projects. This study presents the relevant policy implications in accordance with the analysis results.

Ship exhaust emission is the main cause of coastal air pollution, leading to premature death from cardiovascular cancer and lung cancer. In light of public health and climate change concerns, the International Maritime Organization (IMO) and several governments are reinforcing policies to use clean ship fuels. In January 2020, the IMO reduced the acceptable sulfur content in ship fuel to 0.5% m/m (mass/mass) for sustainability. The use of liquified natural gas (LNG) as a ship fuel is currently the most likely measure to meet this regulation, and LNG bunkering infrastructure investment and network planning are underway worldwide. Therefore, the aim of this study is to predict the LNG bunkering demand for investment and planning. So far, however, there has been little quantitative analysis of LNG bunkering demand prediction. In this study, first, the global LNG bunkering demand was predicted using meta-regression analysis. Global demand for LNG bunkering is forecast to increase from 16.6 million tons in 2025 to 53.2 million tons in 2040. Second, LNG bunkering prediction by country and region was performed through analogy and artificial intelligence methods. The information and insights gained from this study may facilitate policy implementation and investments.

Agrivoltaics can be used to supply energy and produce agricultural products in order to meet the growing demand for energy and food. The amount of power generation is affected by the solar panel direction, spacing, tilt, and panel technology; however, there is insufficient empirical data-based research on the operation of agrivoltaics. This study estimates the levelized cost of energy (LCOE) for a fence-based agrivoltaics system using bifacial modules. This study installed and operated photovoltaic (PV) systems on a rice paddy and saltern in South Korea to estimate the input variables that could affect their economic efficiency and LCOE. For the research methods, this study used Monte Carlo simulation (a stochastic analysis method that reflects the uncertainty of the input variables), a deterministic LCOE analysis, and a sensitivity analysis of the input variables. In terms of space utilization, the LCOE of the paddy system (139.07~141.19 KRW/kWh) was found to be relatively lower than that of the saltern system (145.43~146.18 KRW/kWh), implying that the PV system on the paddy was economically favorable. In terms of installation direction, it was more economical to operate the southwest-facing panels (139.07~145.43 KRW/kWh) than the southeast-facing panels (141.19~146.18 KRW/kWh). This study provides foundational policy data for the adoption of fence-based agrivoltaics and contributes to the widespread and active use of agrivoltaics.

Understanding the nature of the diffusion process is crucial for sustainable development of a new technology and product. This study introduces a replacement diffusion model that leads to a better understanding of the growth dynamics of a technology. The model operates in an environment with multiple competitors and overcomes the limitations of existing models. The model (1) consists of a diffusion model and an additional time series model; (2) separately identifies the diffusion of first-time purchases and that of replacement purchases; (3) incorporates players’ marketing-mix variables, affecting a new technology diffusion; and (4) characterizes consumers’ different replacement cycles. The proposed model is applied to South Korea’s mobile handset market. The model performs well in terms of its fit and forecasting capability when compared with other diffusion models incorporating replacement and repeat purchases. The usefulness of the model stems from its ability to describe complicated environments and its flexibility in including multiple factors that drives diffusion in the regression analysis.