The decarbonization of residential domestic hot water systems is a key component of reducing greenhouse gas emissions in fossil-fuel-dependent energy systems. Although Azerbaijan possesses considerable solar potential, the long-term environmental benefits of solar water heater (SWH) deployment under local climatic conditions remain insufficiently quantified. This study evaluates the technical performance and decarbonization impact of an evacuated tube SWH system for a representative residential building in the Aghdam district using dynamic simulation modeling. The results indicate that the proposed system achieves a solar fraction of 0.47 and reduces annual auxiliary energy consumption by 2863 kWh. Over an assumed 20-year operational lifetime, cumulative energy savings reach approximately 57 MWh. Depending on the displaced energy carrier, total lifetime CO₂ emission reductions range between approximately 11.6 and 25.2 tons. These findings demonstrate that solar water heating provides a technically feasible and environmentally effective pathway for residential decarbonization, offering measurable long-term emission mitigation potential in support of Azerbaijan’s renewable energy transition and postconflict reconstruction strategies.

The use of fossil fuels leads to greenhouse gas emissions and climate change. At the same time, the finite reserves of crude oil, natural gas, and coal necessitate a shift to alternative energy sources. Apart from the typical non-conventional energy sources, waste-to-energy routes are gaining popularity. Non-biodegradable plastic waste, which possesses a high amount of energy, can be thermo-chemically treated (pyrolysis or gasification) to generate fuel. On the other hand, biomass (BM) thermochemical conversion has the potential to emerge as a green energy source with proper forest management. Co-pyrolysis and co-gasification of plastic and BM show the potential for further improvement in fuel quality and quantity. The available research works involve a range of BM and plastic types, making it difficult to conclude a generalised trend of product generation. The current work systematically reviews the recent research data by categorising the results as per the type of feedstock used and the conversion processes. A general trend of fuel yield for various feedstock types and relative contents is summarised. The effects of various parameters – operating temperature, gasifying agent, blending ratio, reactor type, and use of catalysts are also discussed, along with an insight into the catalytic conversion mechanism. The review will be beneficial to get a broad picture of the recent progress in BM-plastic co-pyrolysis and co-gasification, associated challenges, and potential applications.

Underground coal gasification (UCG) is a crucial innovation for clean coal development and large-scale hydrogen production. Water influx affects gasification and gas quality, but managing it remains challenging due to the complex interactions of various processes. This paper presents a UCG water influx management framework that integrates water influx prediction, gasification safety assessment, and optimal water injection design. A prediction model combining convolutional neural networks (CNN), bidirectional long short-term memory networks (BiLSTM), the attention mechanism, and support vector machines (SVM) is proposed for water influx prediction. New safety assessment criteria for gasification site selection and an optimization model for water injection volume are established, considering the dynamic water influx effect. The results show that the CNN-BiLSTM-Attention-SVM model achieves the highest prediction accuracy compared with CNN-BiLSTM-Attention, CNN-BiLSTM-SVM, CNN-SVM, and SVM models. Taking the X experimental area in Xinjiang's Santanghu Basin, China, as an example, the dynamic water influx during gasification is below the upper limit, indicating its suitability for gasification. When the gasification water demand is 0.8 m³ per ton of coal, the optimal strategy is no injection in the initial stage and 121.68 m³/d in the steady stage. Hydrodynamics indicates that a gradual and slow pressure-drop scheme could benefit the hydrogen production in the initial stage of gasification. This study provides theoretical support for the selection of UCG sites and the optimization of injection schemes. • A water influx management framework that integrates prediction, evaluation, and optimization is developed. • The proposed CNN-BILSTM-Attention-SVM model achieves long-term water influx predictions. • Safety assessment criteria for gasification site selection are established based on static and dynamic parameters. • An optimization model for water injection volume is developed based on the water injection-production balance equation.