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Abstract Various electrocatalysts are extensively examined for their ability to selectively produce desired products by electrochemical CO2 reduction reaction (CO2RR). However, an efficient CO2RR electrocatalyst doesn't ensure an effective co‐catalyst on the semiconductor surface for photoelectrochemical CO2RR. Herein, Bi2S3 nanorods are synthesized and electrochemically reduced to Bi nanoplates that adhere to the substrates for application in the electrochemical and photoelectrochemical CO2RR. Compared with commercial‐Bi, the Bi2S3‐derived Bi (S‐Bi) nanoplates on carbon paper exhibit superior electrocatalytic activity and selectivity for formate (HCOO−) in the electrochemical CO2RR, achieving a Faradaic efficiency exceeding 93%, with minimal H2 production over a wide potential range. This highly selective S‐Bi catalyst is being employed on the Si photocathode to investigate the behavior of electrocatalysts during photoelectrochemical CO2RR. The strong adhesion of the S‐Bi nanoplates to the Si nanowire substrate and their unique catalytic properties afford exceptional activity and selectivity for HCOO− under simulated solar irradiation. The selectivity observed in electrochemical CO2RR using the S‐Bi catalyst correlates with that seen in the photoelectrochemical CO2RR system. Combined pulsed potential methods and theoretical analyses reveal stabilization of the OCHO* intermediate on the S‐Bi catalyst under specific conditions, which is critical for developing efficient catalysts for CO2‐to‐HCOO− conversion.

Expanding waste-to-energy (WtE) facilities is difficult, and with tightening incineration regulations, improvements in WtE facility operations are required to dispose of waste that is increasing by an average of 4.8% annually. To achieve this, an intelligent combustion control (ICC) system was studied using digital technologies such as the Internet of Things and artificial intelligence to improve the operation of WtE facilities. The ICC system in this study is composed of three modules: perception, decision, and control. Perception: collecting and visualizing digital data on the operating status of WtE facilities; Decision: using AI to propose optimal operation methods; Control: automatically controlling the WtE facility according to the AI-suggested optimization methods. The ICC system was applied to the “G” WtE facility, a solid waste WtE facility operating in Gyeonggi province, Republic of Korea, and the digital data collected over six months showed high quality, with low delay and a data loss rate of only 0.12%. Additionally, in January 2024, the ICC system was used to automatically control the second forced draft fan and induced draft fan over a four-day period. As a result, the incinerator flue gas temperature decreased by 0.66%, steam flow rate improved by 2.41%, power generation increased by 3.09%, CO emissions were reduced by 60.72%, and NOx emissions decreased by 7.33%. Future research will expand the ICC system to include the automatic control of the first forced draft fan and the operation time of the stoker.

This study develops a life-cycle model to predict an overlapping generations economy with heterogeneous agents. In our proposed model, ex-ante heterogeneity includes the work probability, survival rate, subjective discount rate, and intertemporal elasticity of substitution. We observe differences in financial decisions and asset accumulation among heterogeneous agents and calculate equilibrium values of aggregate economic variables, such as consumption and capital. For the numerical analyses, we classify the population into four groups based on labour productivity and gender, and investigate the life-cycle behaviours of heterogeneous agents. Retirees in the male group with low labour productivity and low remaining life expectancy have the highest propensity to consume and the highest consumption elasticity for retirement shock compared to other retirees. Workers with relatively high labour productivity have lower risk adjustment factors, indicating that they are less concerned about retirement risk than less productive workers. [ABSTRACT FROM AUTHOR]

Abstract The hydrologic connectivity of non-floodplain wetlands (NFWs) with downstream water (DW) has gained increased importance, but connectivity via groundwater (GW) is largely unknown owing to the high complexity of hydrological processes and climatic seasonality. In this study, a causal inference method, convergent cross mapping (CCM), was applied to detect the hydrologic causality between upland NFW and DW through GW. CCM is a nonlinear inference method for detecting causal relationships among environmental variables with weak or moderate coupling in nonlinear dynamical systems. We assumed that causation would exist when the following conditions were observed: (1) the presence of two direct causal (NFW → GW and GW → DW) and one indirect causal (NFW → DW) relationship; (2) a nonexistent opposite causal relationship (DW → NFW); (3) the two direct causations with shorter lag times relative to indirect causation; and (4) similar patterns not observed with pseudo DW. The water levels monitored by a well and piezometer represented NFW and GW measurements, respectively, and the DW was indicated by the baseflow at the outlet of the drainage area, including NFW. To elucidate causality, the DW taken at the adjacent drainage area with similar climatic seasonality was also tested as pseudo DW. The CCM results showed that the water flow from NFW to GW and then DW was only present, and any opposite flows did not exist. In addition, direct causations had shorter lag time than indirect causation, and 3-day lag time was shown between NFW and DW. Interestingly, the results with pseudo DW did not show any lagged interactions, indicating non-causation. These results provide the signals for the hydrologic connectivity of NFW and DW with GW. Therefore, this study would support the importance of NFW protection and management.