This article analyses the determinants of CO₂ emission for 15 post-Soviet Union independent (PSI) countries given their recent transition to market-based economies and their relatively high levels of corruption. The direct and indirect effects of economic growth on CO₂ emission for the PSI countries are derived using a multiple-equation generalized method of moment (GMM) approach to account for simultaneity among corruption, growth and CO₂ emission. A linear relationship between gross domestic product (GDP) and CO₂ emission was observed from the analysis. Furthermore, GDP influences CO₂ emission directly, but also indirectly through its impact on corruption. Similarly, corruption affects CO₂ emission directly, as well as indirectly through its impact on GDP. Political democracy and economic freedom increase CO₂ emission indirectly through their impact on economic growth. Improved energy efficiency and the EU climate policy reduce CO₂ emission, while inflows of foreign direct investment tend to increase CO₂ emission. Policy relevance First, PSI countries need to invest more in efficient energy technologies to mitigate CO₂ emission levels significantly. Second, PSI policies aimed at reducing deforestation (thereby increasing population density) may help mitigate carbon emission. Third, PSI countries would be well served to recognize the detrimental effects of foreign direct investment before embarking on a misguided policy path that attracts such inflows at any cost.

This paper examines whether green Official Development Aid (ODA) has a significant role in mitigating carbon emissions in recipient countries, and if institutional quality matters for the effectiveness of green ODA. For 86 green ODA recipient countries over the period 2003–2014, we explore the nexus between green ODA, institutions and carbon emissions. By using a two-step system generalized method of moment (GMM), we find that green ODA overall has no direct association with the mitigation of carbon emissions. However, when institutional quality indices are included, we found a significant effect of institutional quality on the effectiveness of green ODA. In general, green ODA is associated with higher carbon emissions in countries with poor institutions. In particular, green ODA is effective in mitigating carbon emissions when channeled to countries that enjoy higher economic freedom as well as more freedom from corruption. Results are mixed for the rule of law.

Currently, the pork industry is incorporating in-line automation with the aim of increasing the slaughtered pork carcass throughput while monitoring quality and safety. In Korea, 21 parameters (such as back-fat thickness and carcass weight) are used for quality grading of pork carcasses. Recently, the VCS2000 system—an automatic meat yield grading machine system—was introduced to enhance grading efficiency and therefore increase pork carcass production. The VCS2000 system is able to predict pork carcass yield based on image analysis. This study also conducted an economic analysis of the system using a cost—benefit analysis. The subsection items of the cost-benefit analysis considered were net present value (NPV), internal rate of return (IRR), and benefit/cost ratio (BC ratio), and each method was verified through sensitivity analysis. For our analysis, the benefits were grouped into three categories: the benefits of reducing labor costs, the benefits of improving meat yield production, and the benefits of reducing pig feed consumption through optimization. The cost-benefit analysis of the system resulted in an NPV of approximately 615.6 million Korean won, an IRR of 13.52%, and a B/C ratio of 1.65.

The Korean government is committed to advance the country’s energy transition to greener energy by increasing the share of renewable electricity to 20 percent by 2030 and to 30–35 percent by 2040. Achieving these goals will necessitate flexibility in energy markets as well as innovative business models and technological solutions. Peer-to-peer (P2P) electricity trading platforms (ETPs) are rapidly gaining traction, as they enable the integration of distributed energy sources into power systems. This study explores whether South Koreans are willing to participate in a hypothetical P2P ETP, which allows prosumers (who both consume and produce electricity) to trade electricity surpluses with their neighbours or with KEPCO (Korea Electric Power Corporation). We employ a choice experiment (CE) to examine respondent heterogeneous preferences and their willingness to pay (WTP) for a hypothetical P2P ETP in South Korea. Our findings indicate that two-thirds of total respondents in our CE are in favour of a P2P ETP if available. Cost savings and security play an essential role in respondent preferences for a P2P ETP business model. Respondents also prefer to trade renewable electricity with KEPCO rather than with other individuals. Per our statistical estimations, respondent WTP for a P2P ETP was estimated at USD 55.68/per month. Our analysis strongly recommends increasing consumer awareness of P2P ETPs to spur adoption. Energy trading platforms that are anchored in secure block chain technology can generate cost savings as well as support the country’s policy tilt toward green energy.

This paper demonstrates how artificial intelligence can be implemented in order to predict the energy needs of daily households using both multilinear regression (MLR) and single linear regression (SLR) methods. As a basic implementation, the SLR makes use of one input variable, which is the total amount of energy generated as an input. The MLR implementation involves multiple input variables being taken from various energy sources, including gas, coal, geothermal, wind, water, biomass, oil, etc. All of these variables are derived from detailed energy production data from the various energy sources. The purpose of this paper is to demonstrate that it is possible to analyze energy demand and supply directly together as a way to produce a more in-depth analysis. By analyzing energy production data from previous periods of time, a prediction of energy demand can be made. Compared to the SLR implementation, the MLR implementation is found to perform better because it is able to achieve a smaller error value. Furthermore, the forecasting pattern is carried out sequentially based on a periodic pattern, so this paper calls this method the recurrence multilinear regression (RMLR) method. This paper also creates a pre-clustering using the K-Means algorithm before the energy prediction to improve accuracy. Other models such as exponential GPR, sequential XGBoost, and seq2seq LSTM are used for comparison. The prediction results are evaluated by calculating the MAE, RMSE, MAPE, MAPA, and time execution for all models. The simulation results show that the fastest and best model that obtains the smallest error (3.4%) is the RMLR clustered using a weekly pattern period.

This study examined whether people who reside in different regions of South Korea exhibit different WTP for the mitigation of PM2.5 emissions from coal-fired power plants by restricting their operation during the winter and spring when air pollution becomes severe. The eastern portions of the country are relatively insulated from the harmful effects of PM2.5 emissions due to northwest winds which cause air pollutants generated from coal-fired power plants to be carried out to the East Sea (Sea of Japan). Consequently, our sample group was divided into central, western, eastern, and southern regions, and a contingent valuation method, combined with a double-bounded dichotomous choice, was used to derive people’s WTP for the mitigation of PM2.5 emissions. Our estimation results indicated that respondents who live in eastern regions showed a significantly lower WTP (about KRW 1280/month) than the mean WTP of other regions (above KRW 1337/month). Thus, we suggest that the current Seasonal Management System of PM2.5 emissions should be modified to consider regional differences. Statistical results from this study reinforce our suggestions—almost 78 percent of survey respondents support a revision of the current SMS policy and are in favor of a revised SMS policy—one that exempts eastern regions from a nationwide shutdown of coal-fired generation from December to March. Exempting coal-fired power plants in eastern regions from the Seasonal Management System might result in significant fiscal savings without a corresponding increase in nationwide PM2.5 concentrations.