K. Jung, J. Yoo*, S. Shin, J. Kim, and I. Min. 2025 “Forward-looking Flood Defense Based on High-resolution Risk Assessment: Lessons from the Nakdong River Basin” Journal of Environmental Management. (SCIE, Q1, IF: 8.0)
J. Yoo*, E. Jiyong, and Y. Zhou. 2024 "Thermal Comfort and Retail Sales: A Big Data Analysis of Extreme Temperature's Impact on Brick-and-Mortar Stores" Journal of Retailing and Consumer Services. (SSCI, Q1, IF:11.0)
S. Park et al. 2024 “Exploring the Potential Application of a Custom Deep Learning Model for Camera Trap Analysis of Local Urban Species” Landscape and Ecological Engineering. (SCIE, Q2, IF: 1.7)
K. Jung, H. An, S. Hwang, S. Seo, H. Park, C. Park, and J. Yoo*. 2023 “Assessing the suitability of the flood defense policy of South Korea for risk reduction in local rivers” Water.(SCIE, Q2, IF: 3.0)
A. Favero, A. Daigneault, J. Baker, and J. Yoo*. 2023. "Temperature and energy security: will forest biomass help in the future?" Climate Change Economics. (SSCI, Q1, IF: 2.3)
K. Seo, S. Seo*, K. Kim, C. Park, H. Park, and J. Yoo. 2023. “Genetic Algorithm-based Allocation of LID Practices to Mitigate Urban Flooding” Natural Hazards. (SCIE, Q2, IF: 3.3)
J. Yoo, V. Sinha, and R. Mendelsohn*. 2022. “Can Seawalls Help American Cities Adapt to Coastal Flooding?” International Journal of Climate Change Strategies and Management. (SSCI, Q2, IF: 3.6)
H. Park, S. Seo, C. Park, and J. Yoo*. 2022. “Biodiversity Agenda Congruent with ‘One Health’: Focusing on CBD, FAO, and WHO” Sustainability. (SSCI, Q3, IF: 3.9)
J. Yoo and R. Mendelsohn*. 2021. “What Role Do the Impacts from High Emissions and High Temperatures Have on Choosing Optimal Mitigation Targets?” Environmental Science and Technology. (SCIE, Q1, IF: 11.4)
W. Oh and J. Yoo*. 2020. “Long-term Increases and Recent Slowdowns of CO2 Emissions in Korea” Sustainability. (SSCI, Q3, IF: 3.9)
R. Mendelsohn*, A. Rajaoberison, and J. Yoo. 2020. “A Coastal Resilience Analysis of a Heterogeneous Landscape” Journal of Environmental Protection.
J. Yoo* and R. Mendelsohn. 2018. “Sensitivity of Mitigation to the Optimal Global Temperature: An Experiment with DICE” Climate Change Economics. (SSCI, Q3, IF: 2.3)
Y.G. Kim, J. Yoo and W. Oh*. 2015. “Driving Forces of Rapid CO2 Emissions Growth: A Case of Korea” Energy Policy. (SSCI, Q1, IF: 9.3)
Y. Bae and J. Yoo*. 2025. “South Korea's Long-term Greenhouse Gas Emissions Reduction Pathways: Lessons from Domestic and International Studies” Journal of Climate Change Research.
J. Yang and J. Yoo*. 2025 “Integrated Flood Risk Assessment: Object-Level Analysis in Korea's Nakdong River Basin” Journal of Korea Planning Association.
S. Han, H. Ham, and J. Yoo*. 2024 “Estimating Willingness to Pay for Distance from Ground Railroads: A Case Study of Southwestern Seoul” Journal of Korea Planning Association.
J. Yoo*, Y. Choi, and J. Oh. 2024 “Economic Impacts of Climate Change in Korea: Accounting for Climate Damage and Carbon Abatement Costs” The Korean Journal of Economic Studies.
S. Lee and J. Yoo*. 2023. "Driving Factors of Residential Building Energy Consumption in Seoul: A Big Data Analysis" Journal of Climate Change Research.
S. Hwang and J. Yoo*. 2022. "Analysis of Short-term and Long-term Effects of Sea Level Rise on Coastal Cities: Case of Myeongji City, Busan" Journal of Korea Planning Association.
“How Learning Helps Mitigate the Worst When the Downside of Climate Change is Extreme” (with W. Chang)
There is growing appreciation for the significance of low-probability, high-impact climate outcomes given the speed and scale at which greenhouse gas (GHG) emissions are increasing. Deep uncertainties in the climate system, such as uncertainties in temperature dynamics and the damage function however, make it challenging to hedge against or even assess the risk of catastrophic climate outcomes. In this paper, we examine whether learning efforts that reduce uncertainty in equilibrium climate sensitivity (ECS), the eventual temperature increase in response to changes in GHG concentrations, can enable meaningful midcourse corrections in optimal policy when catastrophic impacts are imminent. For this we simulate learning under three catastrophe-inducing assumptions: the ECS is uncertain and its true value exceeds the range of current scientific beliefs; climate damages are catastrophic at high temperatures; and negative emissions technologies do not materialize at scale for deployment. We find that with learning, the optimal carbon price increases from $130/tCO2 in 2020 to $660/tCO2 in 2050, and further to $3,070/tCO2 in 2100. Learning primarily contributes to welfare improvements through corrections in short-run emission abatement and long-run capital investment decisions. In the short-run, a more stringent carbon abatement policy accelerates the transition to a zero-carbon economy. In the long-run, after global decarbonization is achieved, increased capital investment in anticipation of higher climate damages increases economic output and smoothens consumption.
“Does the Uncertainty about Long-run Temperature Changes Alter Carbon Mitigation Policy?” (with R. Mendelsohn)
The economic literature presents mixed results on whether uncertainty surrounding the long-term temperature response to greenhouse gases justifies more or less near-term mitigation efforts. Using a simple qualitative approach, we explain this debate. Uncertainty about the transient temperature response calls for more immediate mitigation, as it increases the expected welfare loss in the near-term. However, uncertainty about ECS (which pertains to long-term temperatures) calls for less near-term mitigation. Higher ECS values lead to higher temperatures, but with a greater time lag, as it takes a long time to heat up the entire ocean. The present value of welfare is a concave function of ECS. Thus, ECS uncertainty reduces the expected welfare loss and leads to less immediate mitigation.
“The Heterogeneous Effects of COVID-19 on Building Energy Consumption: Evidence from South Korea” (with D. Kim and M. Kim)
This paper examines how major shocks reshape infrastructure utilization patterns using the COVID-19 pandemic as a unique empirical setting. Drawing on comprehensive electricity consumption data from over one million buildings in South Korea, we highlight critical heterogeneity in pandemic responses and disentangle the underlying mechanisms behind the well-documented initial decline and subsequent rebound in aggregate energy consumption. We find four key results. First, building types exhibit divergent outcomes: a doubling of local COVID-19 deaths reduces electricity consumption by 5.7–11.8% in industrial and business facilities yet increases residential consumption by 5.8%, indicating significant substitution effects. Second, these impacts attenuate over time, but at varying rates: industrial facilities recover more rapidly (0.2% monthly) relative to more persistent changes in residential buildings (0.06% monthly). Third, spatial patterns indicate that local pandemic severity shapes regional energy consumption trajectories. Fourth, anti-contagion policies and public health risks generate distinct—and sometimes opposing—effects across sectors. While stricter policies reduce consumption in residential and retail buildings, they increase it in industrial facilities, often due to enhanced ventilation requirements. These findings illustrate how behavioral responses, policy interventions, and risk perceptions interact to drive infrastructure utilization during crises, offering important lessons for future epidemic preparedness.
“The Economics of Coastal Resilience” (with R. Mendelsohn and J. Li)
Economists have made considerable progress examining how sea level rise would affect coastal resilience planning through 2100 (Yohe et al. 1995; 1996; Diaz 2016). The literature suggests protecting urban coastlines and retreating from rural areas globally. However, comparatively little attention has been paid to how best to manage the probability distribution of storm surges. In this paper, we address this gap by introducing a novel integrated assessment model—the Coastal REsilience to Storm Surge (CRESS) simulation model—that determines optimal seawall designs under both sea level rise and storm probability distributions. Building on van Dantzig’s (1956) framework to minimize the sum of expected residual flood damage and seawall costs, we unite natural science, geography, and economics to strike a cost-effective balance with robust risk mitigation. Calibrated to six U.S. Atlantic coast cities, our results show that relatively low seawalls (averaging 1.3 meters above ground) are optimal in these locations, focusing on segments with high expected damage per kilometer. This approach prevents a large share of storm-surge damage while remaining more economical than building tall walls designed for rare but intense storms. Such findings offer a practical and economically efficient alternative to the more precautionary strategies favored by countries like Holland or recommended by agencies such as FEMA. In this sense, CRESS provides a new lens to inform coastal resilience planning around the globe.
“Adapting to Sea Level Rise and Storms” (with R. Mendelsohn and J. Li)
It is well known that cities need to protect themselves from the increasing flood damage by storms as future sea levels rise (SLR). This paper models the dynamics of that adaptation depending on the speed at which SLR increases through 2150. The faster that SLR rises, the more dramatic the increase in flood damage to coastal cities. The optimal adaptation to this dynamic problem is a gradual increase in the extent of urban seawalls. These seawalls average about 1-2 meters in height but they must also gradually move to higher ground as the ocean rises. Flood insurance needs to recognize the protection provided by relatively low seawalls so that this adaptation can take hold in America.
“The Macroeconomic Effect of Temperature Shocks.” (with R. Mendelsohn)
This study reproduces the reported effect of temperature on the growth rate of poor countries (Dell, Jones, Olken 2012) using the same data and method (panel analysis). DJO classify countries as rich or poor based on when they first report real income. Simply classifying whether a country is poor versus rich based on their income each year, leads to the result that temperature shocks do not affect the instantaneous growth rate of either poor or rich countries.
“Integrated Assessment of Renewable Hydrocarbons” (with E. Massetti and H. Naghash)
“Country-level Social Cost of Carbon: An Inter-model Comparison Study” (with Richard Tol, Massimo Tavoni, James Rising, In Chang Hwang et al.)
“Extreme Temperature Impacts on Retail Sales: A Big Data Analysis” (with Yuyu Zhou et al.)
“Economic and Climate Potential of Cross-Laminated Timber” (with Yuan Yao, Alice Favero, Kai Lan, Robert Mendelsohn et al.)
"Optimal National-level Carbon Abatement Path with the RICE-FUND-GCAM Soft-linking" (with McJeon Haewon, Jiyong Eom, In Chang Hwang)
"Estimating the Social Cost of Carbon in Korea Using Integrated Assessment Models", Ministry of Environment, Korea (PI, $2,000,000, 2023-2027)
"Developing Climate-Economic Scenarios for Korea Using the RICE Model", Bank of Korea (PI, $20,000, 2023-2024)
"Regional Climate Change Damage in Korea Using Regional Socio-Economic Long-Term Projection Data", Korea Data Agency (PI, $27,000, 2024)
"Optimal planning of coastal city to deal with sea level rise and coastal storm surges", National Research Foundation of Korea (PI, $100,000, 2021-2024)
"Economic Analysis to Establish a National Flood Defense Standard under Climate Change", Korea Environment Institute (PI, $20,000 2022-2023)
"Big Data Analysis for Pathways of Seoul towards Carbon Neutral City", Seoul Metropolitan Government (PI, $10,000, 2022)
"Creating Prosperous City Centres Post-Pandemic Through Repurposing Retail Space", UK Research and Innovation (ESRC) (Co-I, $60,000, 2022-2023)
"Development of a global climate change integrated assessment model", Ministry of Environment, Korea ($14,000,000, 2022-2023)
"Industrial Competitiveness under the Transition to 2050 Net-Zero", Ministry of Economy and Finance, Korea ($100,000, 2022)
"Climate Technologies to Decarbonize the Korean Economy", Ministry of Science and ICT, Korea ($50,000, 2022)
"Carbon Neutral and Climate Adaptation Path Based on Spatial Big Data", University of Seoul (Co-PI, $100,000, 2021-2022)
"The 2050 Net Zero Strategy of Jeju Island", Jeju Regional Government ($170,000, 2021-2022)