The development of information and communications technology has resulted in changes in electrical safety inspection. Electrical safety inspectors plan to introduce smart devices that detect causes of extensive electrical fires in real time without problems of power disconnections and on-site visits. However, smart inspection is expected to encounter acceptance issues because users are unaccustomed to it and obligated to pay additional costs for device installation and data communications. This study aims to investigate smart inspection acceptance using survey data. A hierarchical Bayesian model is employed to explore the effects of users' characteristics on its acceptance. The respondents prefer attributes of smart inspection to those of the prevailing method of on-site inspection, excluding monthly inspection costs. They prefer more frequent and extensive inspections, and prefer to avoid power disconnections and physical interaction. Accordingly, the government should inform users that smart inspection is convenient and accurate. It is also attractive to users who wish to avoid on-site visits because of privacy issues. The acceptance rate can increase if the government reduces inspection costs using the existing smart devices and communications infrastructure, and offers real-time electrical safety information using smart phones and in-home displays.

The importance of achieving carbon neutrality by 2050 to combat climate change is widely recognized, leading to the proposal of various net-zero scenarios using energy system models. In these scenarios, hydrogen energy is often considered as a primary mitigation option alongside renewables. However, uncertainties surrounding hydrogen technologies prevent a comprehensive analysis of its role in achieving carbon neutrality. This study focuses on examining the impact of hydrogen energy demand, supply methods, and technology levels on carbon neutrality in the Republic of Korea using an energy system model. Findings suggest that hydrogen energy could constitute a significant portion of energy supply and demand by 2050. Sensitivity analyses reveal that reducing hydrogen imports could increase domestic power demand, while improving electrolysis efficiency could lead to more efficient carbon neutrality. Additionally, employing carbon capture technologies in hydrogen production rather than power generation is deemed effective. In conclusion, while hydrogen is a main reduction option for carbon neutrality, the effective utilization of hydrogen requires an increased reliance on imported hydrogen, further technology development including improved electrolysis efficiency and increased dissemination of reforming technologies with carbon capture, and the establishment of appropriate utilization strategies.

Sector coupling emerges as a potentially efficient strategy for emission reduction, particularly when the power sector is sufficiently decarbonized. This study aims to explore the effects of sector coupling on the decarbonization of the manufacturing sector. This study also develops the hybrid energy system model by integrating bottom-up energy system models for the power and manufacturing sectors with the computable general equilibrium model. The hybrid model helps to explain the integration of the power, hydrogen, and manufacturing sectors. The manufacturing sector is electrified and integrated with other sectors based on its use of decarbonized electricity and electrolysis hydrogen. With a carbon tax rate of 200,000 Korean won (161 U S. dollars) per ton of carbon dioxide equivalent, the percentage of electricity in the manufacturing sector's energy mix is expected to increase from 30 % to 46 % by 2050 if electrolysis hydrogen is considered. Moreover, emission reduction from sector coupling accounts for more than 50 % of the total emission reduction in the manufacturing sector. If policymakers consider the overall benefit of decarbonizing the power sector and aid the development of power-to-X technologies, sector coupling can contribute the decarbonization of the manufacturing sector significantly.

Battery swapping services are attracting attention as an alternative method to charge electric vehicles due to their benefits in charging time. However, previous studies rarely addressed consumers’ preference of the services and how service providers can determine service prices. This study investigated consumers’ willingness to pay for the services using a contingent valuation method and showed the effects of individual characteristics on the willingness to pay. According to the results, consumers’ mean willingness to pay was KRW 107.4 thousand (USD 78.8). Potential electric vehicle consumers who felt more uncomfortable about long charging time had higher willingness to pay. Consumers’ psychological ownership and symbolic motives regarding their cars had negative and positive impacts on the willingness to pay, respectively. Moreover, service providers can encourage consumers to participate in the services and increase service revenue, applying price discrimination based on driving distance and the number of service uses.

The steel industry remains difficult to decarbonize because of its high dependence on coal. This industry plans to use hydrogen instead of coal via hydrogen direct reduction. Although this technology reduces steel emissions significantly, considerable uncertainty remains. This study aimed to explore how internal and external uncertainties in hydrogen direct reduction affect uncertainties in steel decarbonization using an energy system model and Monte Carlo simulation. This study assumed that the investment cost of hydrogen direct reduction, hydrogen price, and emission coefficient of hydrogen are uncertain. The uncertainty in hydrogen prices was the most important factor affecting hydrogen use, emissions, and emission reduction costs. Moreover, the probability of steel emissions deviating from their target level was highest when hydrogen price was uncertain. This study contributes to the development of strategies for steel decarbonization and assists policymakers in probabilistic decision making with uncertainty regarding hydrogen direct reduction. Policymakers should understand the relationship between the steel, power, and hydrogen sectors and manage external uncertainties in the power and hydrogen sectors because steel decarbonization largely depends on renewable electricity and electrolyzer costs.

Ammonia can help decarbonize energy systems as a hydrogen energy carrier or a carbon-free fuel. However, consumers' acceptance of ammonia will be low because ammonia has the potential for explosion, toxicity, and odor. This study analyzed consumers' ammonia acceptance based on a survey method by assuming hydrogen refueling stations with six attributes, including explosion, toxicity, and odor potential of gases for hydrogen production. Respondents avoided the negative attributes of ammonia in the order of the explosion (-1.788), toxicity (-1.025), and odor (-0.222) potential. A carbon tax (USD 205.338/ton CO2 eq) increased ammonia acceptance, but its effects on the acceptance rate were only 5 %p. Additionally, respondents who had knowledge of ammonia safety had a lower reluctance toward ammonia. The utility coefficients of the explosion and toxicity potential of ammonia were -1.630 and -0.907 for respondents with the knowledge, and -1.949 and -1.136 for those without the knowledge. Therefore, it is important to improve the perception of ammonia safety rather than depend on the greenness of ammonia and monetary incentives. It is also important to develop technological measures to control the explosion potential of ammonia, the most significant factor in determining ammonia acceptance.

Sustainable energy transition is garnering attention as a method to achieve emission reduction targets. In this transition, technology learning and spillovers, which are representative sources of technological change, have significant implications. Most previous studies focused on inter-country and inter-region spillovers, although spillovers can be observed at the various levels. This study develops the industrial energy system model, which explores learning and inter-industry spillovers at the technology level, and investigates learning and spillover effects on climate policy performance. The model reflects spillovers between industrial common technologies based on an iterative approach and describes the effects of learning and spillovers on technology characteristics, such as technology efficiency. The results describe that not only the speed of learning but also the existence of spillovers is important for sustainable industrial energy systems. Spillovers also help to mitigate industrial emissions with less subsidies for efficient technologies and affects the preference of industries for a carbon tax. The government should create an appropriate environment for efficiency improvements and their diffusion in industries. Moreover, a wider range of policy options is available if industries are more cooperative in diffusing their technology innovation.

Carbon capture and storage (CCS) is necessary to reduce greenhouse gas emissions that cannot be mitigated using other reduction options. However, the high cost of CCS raises doubts about its economic feasibility. This study analyzes CCS cost reduction and its macroeconomic effects and shows that CCS can be economically feasible in the long term. This study incorporates technology learning into a hybrid energy system model and investigates its impact on the economic feasibility of CCS in the Korean steel industry. The hybrid model integrates a bottom-up energy system model and a computable general equilibrium model and overcomes the limitations of employing independent models in exploring technology learning. According to the model, in 2050, the CCS unit cost decreases by 64% when the learning rate is 20%. Due to this cost reduction, the steel industry’s additional capital and labor costs resulting from CCS adoption decrease by 60%. Moreover, although CCS adoption and diffusion reduce steel production, 60% of this production loss can be mitigated by CCS cost reduction. The cost reduction also helps to reduce the GDP loss resulting from CCS adoption by 0.3 %p.

While renewable energy is an eco-friendly source of electricity, it can be difficult to garner local acceptance when securing sites for renewable energy projects. Local acceptance is an important factor in determining the success of these projects. To elicit local acceptance, governments can consider profit-sharing, in which project developers pay a subsidy to the locals to compensate for the inconvenience of a renewable energy facility. The purpose of this study is to analyze local acceptance levels for photovoltaic and wind power projects in Korea and to evaluate the impact of profit-sharing on the profitability of such projects. Contingent valuation is used to estimate willingness to accept for a baseline renewable energy project. A choice experiment is conducted to estimate a marginal willingness to accept for project attributes. These are combined to simulate the total willingness to accept needed to gain local acceptance. Total willingness to accept for baseline photovoltaic and wind power projects are, respectively, USD 13,815 and USD 27,587 per household for 5 years. Internal rate of return is employed to assess the profitability of such projects. This rate without profit-sharing ranges from 4.49% to 9.50%. Meanwhile, the rate with profit-sharing ranges from −1.67% to 7.64%.

Appropriate treatment of technological changes has been an important issue in the field of energy system modeling. Although some mixed integer programming-based formulations have been introduced to incorporate learning-by-doing endogenously, they require high computational effort. Therefore, many practitioners have not considered the technological changes endogenously. Recently, some studies have suggested iterative approaches to incorporate learning-by-doing indirectly. This study provides a comparative analysis among the most famous mixed integer programming-based formulation and iterative approaches. We also propose a revised iterative approach that can overcome the cons partially. Lastly, as a numerical study, we apply the previously suggested methods and our proposed method to analyze two renewable energy policies, carbon taxation and subsidy, in the Korean electricity sector. This numerical study illustrates the results of our comparative analysis. The iterative approaches can be approximately 5–23 times more computationally efficient compared to the revised formulation. In addition, the required total carbon tax or total subsidy in scenarios using different iterative approaches are 10%–50% lower than no learning scenarios. The practical implications of this study are the correct approaches that would aid in determining the accurate futuristic scenarios. This will lead to the timely and effective implementation of relevant policies.

Improving efficiency is an important option for reducing manufacturing sector emissions. More efficient technologies reduce energy use and emissions. However, efficiency improvements can induce unexpected effects known as rebound effects. Although previous studies have analyzed these effects, these studies fail to precisely evaluate the rebound effects due to improvements in technology efficiency. Bottom-up models are appropriate for analyzing efficiency improvements at the technology level, but they face limitations in exploring output changes because they usually assume that demand is given. In contrast, top-down models are appropriate for observing output changes in an efficiency-improving sector and in the rest of the economy, but they face limitations in explicitly describing technological changes. This study therefore constructs a hybrid model to overcome the limitations of both types of models and evaluates the impacts of climate policy in Korea’s manufacturing sector when rebound effects are considered. The expected emissions reduction due to new technology adoption in the manufacturing sector is 23.8 million tons CO2eq without rebound effects, but when rebound effects are included, the actual emission reduction (12.7 million tons CO2eq) is about 50% of the expected amount.

The bottom-up model of the industrial energy system has hitherto been analyzed using linear programming. However, it has limitations in describing practical technology selection and reproducing base-year technology selection. Positive mathematical programming, which provides an interior solution without any subjective constraints, can be considered as an alternative method that overcomes the limitations of linear programming when constructing a bottom-up model of the industry sector. The purpose of this study is to apply positive mathematical programming and identify the plausibility of using it in a forward-looking optimization model of the industry sector. A bottom-up model based on positive mathematical programming has the advantages of avoiding impractical technology selection in the industry sector, describing more flexible reactions to external changes, and calibrating base-year technology selection without subjective constraints. Although optimal solutions and simulation responses are dependent on parameter identification, the dependence of positive mathematical programming on the identification method can be lower than that of linear programming on the subjective constraints.