As demand response (DR) strategies such as time-of-use (TOU) pricing gain renewed attention amid rising integration of variable renewable energy, evaluating their effectiveness requires careful consideration of existing incentives or penalties embedded in current tariff schemes. This study provides an early assessment of the behavioral effects of the TOU program that began rolling out across Jeju Island, Korea, in 2021. Implemented in parallel with a highly progressive increasing block pricing (IBP) scheme, the opt-in TOU is corrective in nature—offering peak rates below top-tier IBP rates to address cross-subsidies inherent in the current IBP structure. Using a Difference-in-Differences approach and a two-level electricity demand model following Hausman et al., (1979), we analyze both within-day and overall daily consumption effects of TOU adoption. Results show that households that opted into the TOU shifted electricity usage toward off-peak hours, reducing their mid- and peak-period consumption shares. The estimated price responsiveness of usage ratios during the peak period and mid-peak period is -5.8% and -11.8%, respectively. However, total daily electricity consumption increased, offsetting much of the intended peak-load reduction. This counterintuitive outcome stems from selective TOU adoption by high-usage households who, once exempt from IBP’s cross-subsidy penalties, increased their baseline usage. These findings suggest that TOU designs with steeper peak-to-off-peak price differentials and lower fixed charges, thereby increasing average rates, can achieve peak reduction goals without undermining revenue neutrality or consumer welfare. Our study provides policy-relevant insights for integrating opt-in DR mechanisms within existing electricity tariff regimes.

In this study, we examine how a firm's carbon risk influences its decision to divest carbonintensive assets. We classify corporate divestiture deals as carbon-intensive or non-carbonintensive based on textual analysis of deal synopsis. Using firm-year panel data, we find that firms with higher carbon exposure are more likely to undertake carbon-intensive asset divestitures. This relationship does not appear to be driven by traditional divestiture motivessuch as financial constraints, operational efficiency, or past negative ESG incidents-suggesting that carbon risk plays a distinct role in asset divestiture decisions. We also find that public attention to climate change moderates this relationship: during periods of heightened attention, high-emission firms become less likely to divest, consistent with concerns about making irreversible mistakes in divestiture decision-making. Taken together, these findings suggest that firm-level carbon exposure, along with public attention to climate change, significantly shapes divestiture decisions in the climate finance context.

Global warming is reshaping energy demand in buildings, one of the largest sectors for energy consumption and CO2 emissions. Yet widely used mitigation scenarios, such as those in the Shared Socioeconomic Pathways and Representative Concentration Pathways (SSP-RCP) framework, often assume fixed historical climate baselines when projecting building energy use. This modeling convention, originally intended to isolate anthropogenic effects, now increasingly overlooks how rising temperatures systematically alter heating and cooling needs. Here we integrate updated Heating and Cooling Degree Days, derived from the latest Coupled Model Intercomparison Project Phase 6 climate projections, into the Global Change Analysis Model to assess the consequences of incorporating future warming into global mitigation pathways. We find that ignoring warming trends introduces substantial biases: cooling demand is underestimated by up to 23% in scenarios with low warming and 79% in scenarios with high warming, while heating demand is overestimated by up to 14% and 40%, respectively, by 2100. Importantly, these misestimates reshape global energy and emissions trajectories, reducing CO2 emissions by 83-1600 Mt CO2 year-1 in 2100 when warming trends are included across different SSP-RCP scenarios, but also underestimating the rise in F-gas emissions related to cooling, especially in warmer developing regions.

At COP28, the international community, including Korea, committed to tripling renewable energy capacity by 2030 and nuclear capacity by 2050. These pledges are embedded within Korea's carbon neutrality framework and the 11th Basic Plan for Long-Term Electricity Supply and Demand. This study examines the consistency of Korea's clean energy policies with Paris-compliant trajectories derived from the IPCC AR6 scenario database. Our analysis demonstrates that Korea's current electricity plan exhibits a slower phase-out of fossil fuel capacity relative to Paris-aligned pathways. Moreover, while the renewable energy tripling target demonstrates strong alignment with global decarbonization scenarios, the majority of IPCC AR6 models project constrained nuclear expansion, rendering the nuclear tripling pledge inconsistent with prevalent long-term pathways. As Korea advances its energy transformation, enhanced science-based modeling and rigorous scenario analysis are imperative to ensure policy coherence with global decarbonization objectives and support evidence-informed decision-making.

National climate mitigation scenarios play a significant role in the national climate policymaking. They tend to include heterogeneous contexts due to individual socio-economic circumstances, resource availability, national interests, and emission levels. However, this hinders the collective evaluation of national pathways across countries and their coherence with global goals. Here, we assess national net-zero emissions scenarios for 23 countries from 38 national models developed under a common scenario protocol. We show that, despite heterogeneity in national circumstances, consistent mitigation patterns emerge across countries, including declining fossil fuel supply, increasing electrification, and non-fossil energy supply. Meanwhile, the contribution of individual non-fossil sources, such as wind and solar, varies across countries. We also find that national scenarios show greater divergence, which implies a greater advantage over global scenarios for national policy. This study underlines the importance of country-specific scenarios and routine evaluations to align collective efforts toward global temperature goals.

Current plant-level industrial decarbonization studies evaluating onsite renewables and storage often overlook broader supply chain and economy-wide impacts. This study proposes an integrated decision framework coupling a multi objective mixed integer linear programming model for onsite photovoltaic, electrolysis, hydrogen storage, and fuel cell with environmentally extended input output (EEIO) accounting. The optimization minimizes life cycle system cost, reduces grid CO2 emissions, and generates a Pareto frontier across increasing levels of grid displacement for a representative small and medium sized manufacturer. Results reveal strongly nonlinear tradeoffs: photovoltaic dominant designs deliver substantial emissions reductions at relatively low incremental cost, whereas deeper decarbonization requires hydrogen production and large storage capacity, leading to diminishing marginal abatement returns and sharply rising capital intensity. To quantify broader consequences, selected Pareto solutions are evaluated in a national domestic EEIO model to estimate sectoral output, value added, and embodied greenhouse gas emissions attributable to the required equipment and balance of plant components. Embodied emissions increase disproportionately as storage scales, but cumulative avoided operational CO2 over the project life remains larger even in near zero grid use scenarios, indicating net climate benefits alongside upstream impact shifts. By linking operationally feasible design and dispatch decisions with supply chain and economy wide metrics, the framework provides a case-based decision support approach for industrial decarbonization planning. The quantitative results are specific to a representative Ohio SMM under the adopted U.S. modeling boundary, while the broader value of the study lies in revealing the directional trade-offs associated with deeper electrification.

Households, as primary agents of consumption, play a major role in electricity use and greenhouse gas emissions through the production of the goods and services that satisfy their needs. Understanding how household consumption patterns influence electricity use is therefore increasingly important. This study investigates the indirect electricity consumption driven by household demands across Korean regions from 2005 to 2020, using an energy-extended input-output framework based on inter-regional input-output tables. Structural decomposition analysis is then applied to identify the main factors driving changes in indirect electricity use over the study period. The results highlight four key findings. First, indirect electricity consumption accounts for a substantial share of total electricity use attributable to households, ranging from 66.3% to 68.1%. Second, the service sector is the largest contributor, responsible for nearly half of indirect electricity use, with a larger share in non-metropolitan regions due to higher electricity intensity. Third, the inter-regional structure reveals substantial spillovers and spatial decoupling: electricity embodied in metropolitan household consumption is realized disproportionately in electricity-intensive producer provinces. Fourth, although electricity intensity decreased, these improvements were insufficient to offset rising consumption, and household downsizing further increased indirect electricity demand. Overall, these findings underscore the need for policies that explicitly address indirect household electricity consumption and its inter-regional spillover pathways while accounting for regional and demographic differences.

Electric vehicles (EVs) offer a promising solution for mitigating the intermittency of renewable energy through flexible charging. Demand Response (DR) has been tested as one of the key demand-side solutions to capture the flexibility potential of EVs in Korea. This study evaluates the effects of DR interventions focusing on temporal factors and station-level characteristics that are often overlooked in existing literature. Using panel data from 558 EV charging stations (EVCSs) in Korea that participated in the DR program (November 9, 2022-April 30, 2023), we develop a CatBoost-based predictive model to estimate counterfactual consumption and isolate DR impacts at the station level. Results show that EVCSs with automatic controls achieve an average reduction of 11.8 % during event hours, while manual adjustments in charging patterns yield only a 0.4 % reduction, underscoring the limitations of voluntary user compliance. Moderately visited EVCSs exhibit the largest reductions in load, suggesting that station-level characteristics such as occupancy rate play a crucial role in DR effectiveness. Analysis reveals that stations with occupancy rates between 25 % and 63 % demonstrate the most substantial consumption reductions, indicating an optimal operational range for DR program effectiveness. DR interventions were the most effective during evening hours for EVCSs with automatic controls, whereas manual adjustments showed no significant variation by time. In addition, intervention effects during the evening hours differ across seasons. These findings provide insights for the development of DR programs that consider temporal variations and imply the need for automation of EVCSs to enhance grid flexibility.

Abstract. Accurate building energy demand modeling is critical to decarbonizing regional energy systems. The cooling and heating degree-day models are widely used due to their simplicity and low data requirements; however, the lack of accurate base temperature data limits their performance. In particular, the scarcity of high temporal resolution building energy demand data constrains regional-scale base temperature estimation through conventional methods such as the energy signature method and the performance line method. To address this limitation, this study develops a global regional-scale base temperature dataset based on the BiLSTM neural network framework with an attention mechanism. The dataset includes both cooling base temperature (Tcool) and heating base temperature (Theat) for each region, defined at a spatial scale equivalent to a U.S. state or a Chinese province. The BiLSTM framework demonstrates strong performance, with RMSE values of 1.39°C for training and 1.33°C for testing, and Pearson correlation coefficients of 0.84 for Tcool and 0.70 for Theat. Predicted results show that global Tcool ranges from 19–25°C and Theat from 14–18°C, consistent with physical principles. External validations using 16 independent datasets demonstrate that the predicted base temperatures significantly improve the accuracy of building energy demand modeling, reducing RMSE by 10.01% for cooling and 10.02% for heating, compared to official or empirical base temperatures. This dataset supplements sparse observational base temperature data and enhances the accuracy of building energy demand modeling, contributing to low-carbon energy system planning, broader climate impact assessment and weather-related financial applications. The proposed global Tbase dataset can be acquired from https://doi.org/10.6084/m9.figshare.30646376.v2 (He et al., 2025).

In October 2021, Korea announced its mid-century carbon mitigation target of achieving carbon neutrality by 2050, reaffirming its commitment by enhancing its 2030 Nationally Determined Contribution (NDC). This study employs six energy-economic and integrated assessment models to explore net-zero emission pathways and strategies for Korea’s power sector, while assessing the associated costs and challenges. The findings underscore the complexity and urgency of this transition, with the power sector playing a pivotal role in balancing the dual challenges of rapidly growing electricity demand and full decarbonization. A shift toward a renewable-dominated power sector emerges as a robust strategy, though it poses unprecedented technological and economic challenges. Large-scale low-carbon technologies, such as carbon capture and storage (CCS) and nuclear power, are identified as crucial solutions to reduce reliance on variable renewable energy sources and mitigate associated costs. Additionally, the study finds that current energy and climate policies are insufficient to meet the mid-century mitigation target, highlighting the urgent need for policy enhancements to bridge the gap and ensure the feasibility of Korea’s carbon neutrality goal.

High-yield green bonds are corporate bonds rated BB+ or below, specifically designated to finance environmentally friendly projects. The market for these bonds is approximately one-fifth the size of the investment-grade green bond market and has been steadily growing. However, this segment has received little attention in the literature. In this study, we make the first attempt to examine the pricing of high-yield green bonds, both theoretically and empirically. We propose a novel economic model in which the credit spread of high-yield green bonds depends not only on the probability of financial success, but also on the environmental success probability of the projects financed by the bond and the investor's willingness to trade financial return for environmental return. The credit spread of a high-yield green bond can be lower than that of a conventional (or brown) bond only if some investors have confidence in the issuer's ability to deliver environmental value by successfully implementing green projects. Empirically, we investigate whether such a negative wedge between green and brown credit spreads exists in the high-yield bond market, using a standard matching methodology. We find that the mean credit spread of high-yield green bonds is lower than that of their brown counterparts, although the difference is not statistically significant.

Evolving environmental conditions due to climate change have brought about changes in agriculture, which is required for human life as both a source of food and income. International trade can act as a buffer against potential negative impacts of climate change on crop yields, but recent years have seen breakdowns in global trade, including export bans to improve domestic food security. For countries that rely heavily on imported food, governments may institute policies to protect their agricultural industry from changes in climate-induced crop yield changes and other countries’ potential trade restrictions. This study assesses the individual and combined effects of climate impacts and food self-sufficiency policies in Korea, which is highly dependent on imports. We use the Global Change Analysis Model (GCAM), a global integrated assessment model, to explore (1) the direct impact of climate change on Korea’s agricultural yields, (2) the full impacts of global climate change on agricultural production, including trade-induced changes due to yield changes in other regions, (3) the impacts of food self-sufficiency policy, and (4) the interactive impact of climate change and self-sufficiency policies. We find that, in Korea, the direct impact of climate change on agricultural yields would be overshadowed by the impact of global climate change due to changing trade patterns. Second, global climate change leads to a rise (rice and wheat) or a decline (soybeans) in Korean producer revenues, while simultaneously raising consumer expenditures on both staples and non-staples. Third, implementing self-sufficiency policies for wheat and soybeans in Korea boosts the nation’s producer revenues, in conjunction with the effects of climate change, at the cost of additional increases in consumer expenditures for both staples and non-staples.

Recent shifts toward demand-side electricity management have brought increased attention to Peak Time Rebate (PTR) initiatives, which aim to reduce household electricity use during peak hours by offering financial incentives. However, previous studies often overlook the heterogeneity in household responses-that is, differences in how individual households react to these incentives-and the long-term effects of behavioral changes triggered by PTR programs. To address this research gap, this study employs machine learning techniques to analyze hourly electricity consumption for 125 households participating in the People Demand Response (DR) program, a PTR initiative in Korea. First, households are clustered based on their hourly electricity consumption patterns. Machine learning is then used to learn consumption patterns, and a predictive model is applied to evaluate the impact of DR events by estimating the counterfactual condition. The findings indicate varying effects of DR interventions across these clusters. Moreover, learning effects emerged over time within specific clusters, highlighting the need for personalized targeting strategies. This study disputes the universality of PTR impacts and offers guidance for designing more effective and enduring PTR programs by service providers and policymakers.

In this study, a high-energy-density electrode was fabricated by combining cobalt-free layered oxide (NM) with olivine LiFePO4 (LFP) nanoparticles. The resulting mixed all-cobalt-free cathode electrode effectively minimized electrode porosity by filling the interstitial spaces between the micron-sized layered-oxide particles with nanoscale LFP particles, significantly improving electrode density, and exhibiting excellent electrode conductivity. Furthermore, the composite electrode composed of NM and LFP achieved a volumetric capacity exceeding 600 mAh/cm− 3, comparable to that of typical layered oxide cathode materials, while also demonstrating enhanced cycle-life performance relative to electrodes composed solely of layered oxide or LFP. The enhanced electrochemical performance is attributed to the efficient lithium-ion and electron conduction facilitated by the intimate contact between NM and LFP particles, the suppression of NM particle degradation due to the relatively stable LFP particles on the NM surface, and the reduced particle fracture during roll-pressing. These improvements have been confirmed through electrochemical analyses and electrode observations.

The effect of weather on retail sales has long been of great interest to both the business and academic fields. This study investigated the impact of extreme temperatures on brick -and -mortar retail stores in Seoul, Korea. Using a comprehensive credit card transaction dataset, high -resolution weather data, and a semiparametric model, we found a significant increase in sales during extreme temperature events: 4% during heatwaves exceeding 35 degrees C and 11% during cold spells below -15 degrees C. This finding is supported by the thermal comfort hypothesis in retail sales, which suggests that consumers are driven to temperature -controlled indoor environments and are inclined to purchase products that provide thermal comfort, such as hot or cold beverages. As extreme weather events become more frequent owing to climate change, accurate sales forecasting during such conditions becomes crucial for retailers. Insights from our research enable retailers to better predict sales under extreme temperature conditions and to strategize accordingly, such as by highlighting thermal comfort products or ensuring optimal indoor temperatures with efficient air conditioning or heating systems.

Lithium metal is often regarded as the ultimate anode for lithium-ion batteries (LIBs) due to its superior specific capacity (3,860 mAh g-1) and density (0.534 g cm-3). However, the real-world application of Li-metal electrodes is presently hindered by unregulated Li-plating and stripping, leading to unwanted dendritic growth and significant volume change during cycles. To overcome these challenges, we suggest a three-dimensional (3D) Li-metal anode, which incorporates 3D Ni foam as a current collector through a molten Li impregnation process. Our theoretical simulations and experimental results show that this 3D Li-metal electrode establishes an extensive contact area between the active Li metal and the current collector. This is advantageous in enhancing the reversibility by lowering the overpotential for Li-plating and stripping and suppressing the dendritic growth of Li during charge/discharge cycles. Moreover, the 3D Ni foam framework effectively mitigates dimensional change of the Li-metal electrode. For practical applications, we evaluate the viability of the 3D Li-metal electrode in a full-cell in comparison to a traditional 2D Li-metal electrode. We anticipate that our findings will contribute to the development of advanced Li-metal batteries.

This study examines cost-effective strategies for reducing greenhouse gas (GHG) emissions in Korea's industry sector to achieve the nation's 2050 carbon neutrality goal, using the Global Change Analysis Model (GCAM) integrated assessment modeling (IAM) framework. We find that the iron & steel, cement, and chemical industry segments contribute about 70% of the sector's GHG mitigation efforts to align with 2050 carbon neutrality. Key decarbonization options include electrification of industrial processes, utilization of hydrogen and bioenergy, and adoption of carbon capture and storage (CCS) technologies, although the relative significance of these options varies across the segments. We also demonstrate the dependency of sectoral decarbonization pathways on the availability of CCS and hydrogen technologies, as well as their influence on the development of upstream energy infrastructures, such as electricity and hydrogen production. Our results indicate that limited CCS deployment would necessitate a faster phase-out of fossil fuel-based industrial technologies and quicker decarbonization of the electricity and hydrogen production sectors. Conversely, constrained hydrogen deployment would increase reliance on CCS, placing a greater emission reduction burden on other sectors like electricity generation and buildings. Within the industry sector, limited CCS utilization would force the chemical segment to achieve disproportionately larger negative emissions compared to the constrained hydrogen deployment scenario, as the latter allows for wider adoption of CCS, particularly in the iron & steel segment. Overall, the study emphasizes the need for rapid and extensive transformation of the three key industrial segments to achieve Korea's 2050 carbon neutrality target, with CCS technologies and, to a lesser extent, hydrogen technologies playing a significant role in shaping the industry's decarbonization pathways and the nation's energy systems.

As demand for electric vehicles increases, interest in cathode materials with high energy density continues to increase. Ni-rich layered NCM811 is a cathode material attracting attention because it can realize high energy density. However, currently available NCM cathode materials use secondary particles. In the case of commercial NCM811, which has a secondary particle shape, the cycle-life characteristics rapidly decrease due to the cracks of the primary particles constituting the secondary particles. Single crystallization of NCM cathode materials is used to minimize these particle cracks. Single-crystal cathodes without intergranular cracks have been shown to improve cycling and thermal stability by minimizing surface degradation. In this study, A high-performance single crystal cathode material was synthesized by directly converting commercial NCM811 polycrystal using a crystal growth promoter (Ce0.5Zr0.5O2) during heat treatment. The CeZrO2 additive used in the synthesis was able to grow primary particles significantly at the same temperature compared to when no additive was used or when CeO2 or ZrO2 was used. The performance of single crystal particles synthesized using this method was further improved through cobalt coating. Single crystals synthesized using additives showed very excellent cycle-life performance compared to existing commercial secondary particles or active materials that were heat-treated without additives at the same temperature. The enhanced electrochemical performance is attributed to decreased crack generation, reduced surface reactivity by cobalt coating, which was confirmed through various electrochemical analyses and electrode observations.

Digital transformation has become a top priority for organizations. As more and more organizations undergo digital transformation, sustainability alignment is gaining attention as it is considered the next big challenge. However, little is currently known about the nexus between digital transformation and environmental sustain-ability. This study is designed to fill this gap by employing mixed methods to develop a framework for envi-ronmentally sustainable digital transformation and explore dynamic capabilities for organizations. To begin with, we review extant literature and adopt the grounded theory approach to define and develop a framework for environmentally sustainable digital transformation (SDT). The framework identifies sustainable digital trans-formation as a reconfiguration process of an organization's core strategy to align novel digital technologies with sustainability goals. Using a qualitative meta-synthesis procedure (195 full-length articles screened) and a survey questionnaire (n = 63), we identify dynamic capabilities for SDT. Finally, we employ the modified Delphi method to build a consensus of experts (n = 52) on the identified capabilities. This study initially identified 28 capabilities based on the mixed method. However, consensus (>70% agreement) was achieved for 19 capabil-ities, classified into sustainable seizing, sensing, and transforming capabilities for organizations. The dynamic capabilities outlined in this paper provide insights for organizations seeking sustainable digital transformation. This study contributes to the literature by defining the novel concept of SDT through a conceptual framework and has many practical and theoretical implications.

Decarbonising the power sector requires feasible strategies for the rapid phase-out of fossil fuels and the expansion of low-carbon sources. This study assesses the feasibility of plausible decarbonisation scenarios for the power sector in the Republic of Korea through 2050 and 2060. Our power plant stock accounting model results show that achieving zero emissions from the power sector by the mid-century requires either an ambitious expansion of renewables backed by gas-fired generation equipped with carbon capture and storage or a significant increase of nuclear power. The first strategy implies replicating and maintaining for decades the maximum growth rates of solar power achieved in leading countries and becoming an early and ambitious adopter of the carbon capture and storage technology. The alternative expansion of nuclear power has historical precedents in Korea and other countries but may not be acceptable in the current political and regulatory environment. Hence, our analysis shows that the potential hurdles for decarbonisation in the power sector in Korea are formidable but manageable and should be overcome over the coming years, which gives hope to other similar countries.