Carbon capture, utilisation, and storage (CCUS) technologies are essential for achieving the 1.5 °C target. Predicting the emission reduction potential of CCUS technology is particularly important for countries to pursue carbon neutrality. However, the existing literature assessing the potential lacks consideration of the structural changes in industrial product demand and the trade-offs companies face between CCUS and traditional emission reduction technologies. This study used agent-based modelling (ABM) to simulate the emission reduction potential of CCUS in China’s thermal power, steel, cement, and chemical industries from 2022 to 2060 under scenarios of different carbon prices, subsidies, and technology progress rates. The possible biases of the traditional prediction model were corrected incorporating the structural changes in industrial product demand and the marginal abatement cost curves of traditional emission reduction technologies for the four major industries into the ABM model. The simulation results indicate that under each of the ten possible scenarios, China’s CCUS technologies will reach 100% penetration in the four mentioned industries by 2060, with the emission reduction potential fluctuating between 2222 and 1568 Mt of CO₂ (corresponding to 40% and 10% share of thermal power, respectively). The difference comes in the scaled-up threshold time point and the growth trend. Sensitivity analyses show that the carbon price affects changes in the emission reduction potential of CCUS technologies the most, while the impact of subsidies, rates of technological progress and oil prices were not significant. The stepped carbon price policy can effectively regulate and promote the expansion of CCUS emission reduction potential, which is worth considering for policymakers.

Foreign exchange is a significant financial market that attracts investors and countries seeking profitable investments. Despite the numerous techniques available for exchange rate forecasting and trend analysis, there is still a need for an automated, intelligent model to understand patterns and predict future trends. The creation of such prediction models can provide assistance for investors, financial institutions, and policymakers in governments. To overcome these issues, the proposed study has developed a novel hybrid deep learning model that encompasses a Bidirectional Long Short-Term Memory, an additive attention approach, and a random forest regressor (for long-horizon historical data), attempting to provide a prediction model for the following year’s official exchange rates (LCU per USD). The random forest regressor models the nonlinear interaction of features and assists with generalization, the attention layer focuses on the most influential time steps, and the Bidirectional Long Short-Term Memory (Bi-LSTM) captures all historical data for exchange rate series and temporal dependencies (or dependencies of a sequence of historical data). The use of a time partition (1960–2018 training data + 2019–2023 validation data + 2024 testing data) to train and evaluate the model provides realistic forecasting and prevents temporal leakage. The global panel dataset for more than 250 and 60+ year countries and regions demonstrate that all of the proposed models are better than all classical machine learning models, stand-alone deep learning models, and naive persistence models. The hybrid model shows the most significant prediction error reduction with R² as 0.98, proving long-horizon currency forecasting is extremely robust.

Nitrogen oxides (NOx), pivotal atmospheric pollutants, significantly imperil the environment and human health. The CH4-SCR process, leveraging the abundant and accessible methane (CH4), emerges as a promising avenue for NOx abatement. Previous studies have demonstrated that zeolite support with twelve-membered ring (12-MR) and five-membered ring (5-MR) structures are susceptible to framework collapse in the presence of water, leading to catalyst deactivation. Consequently, there is a necessity to explore novel zeolites with enhanced hydrothermal stability for application in CH4-SCR processes. This research introduced for the first time an investigation into a novel In-H-SSZ-39 catalyst, synthesized via ion exchange and meticulously optimized for preparation conditions, including calcination temperature, In ions concentraiton, and reaction conditions, including CH4/NO ratio, O2 concentration, water vapor content, and Gas hourly space velocity (GHSV). Notably, the In/H-SSZ-39 catalyst achieved superior performance of 42.1% at 605 °C, with a calcination temperature of 500 °C and an In ion concentration of 0.066 M, under CH4/NO ratio of 1.5:1, 10% O2, and GHSV of 21000 h-1. In addition, the catalytic performance was influenced by the concentration of water vapor and the GHSV value.