Achieving sustainable energy goals (SDG 7) in the European Union requires a deep understanding of the factors driving energy management and conservation. This study investigates the macroeconomic, institutional, and policy drivers of energy management and conservation outcomes across the 27 EU member states from 2014 to 2023. Using multilevel two-way fixed-effects models on a panel dataset, we analyze primary energy consumption, final energy consumption, and energy productivity as complementary indicators of energy conservation and energy management. Panel results show that higher energy prices are key drivers of improved energy productivity and conservation, supporting the effectiveness of price-based instruments. Furthermore, cross-sectional analysis indicates that stronger institutional quality is positively associated with these outcomes, highlighting the relevance of robust governance. Moderation results relying on the 2023 cross-section should be interpreted cautiously, as they cannot support causal inference or persistence over time. Coherent policy mixes significantly enhance energy management, particularly in the industry and services sectors; notably, the policy mix acts as a suppressor rather than a mediator in these relationships. Our findings underscore that a successful and just energy transition, aligned with the Sustainable Development Goals, necessitates an integrated strategy that combines effective policies, strong institutions, and targeted measures to address distributional impacts.
Pyrolysis is one of the most promising methods for transforming metal-organic frameworks (MOFs) as precursors into metal-based products with tailored properties by decomposing their organic linkers into smaller molecules and carbonaceous. This research aims to explore the fundamental pyrolysis behavior of MOFs for potential upscaling. The experiments were performed on the laboratory-prepared nickel MOFs doped on magnetic graphene oxide composite (NiMOF@MGO) and its elemental and proximate analysis were investigated. The main active pyrolysis zones of MOFs were identified by thermogravimetric (TG) anlyzer, while TG-Fourier Transform Infrared (FTIR) spectroscopy and TG-gas chromatography-mass spectrometry (TG-GC-MS) were used to characterize the composition of the vapors emitted from each zone. The kinetic and thermodynamic parameters of the pyrolyzed MOFs were determined using different linear methods (Kissinger-Akihira-Sonoze, Flynn-Wall-Ozawa, and Friedman) and nonlinear Vyazovkin methods to determine the activation energy (Ea) required to terminate the reaction. In addition, an artificial neural network (ANN) algorithm was constructed and trained to predict the thermal degradation of MOFs under ambiguous heating rate parameters. Physical analysis showed that MOFs contain high content of volatile matter (72.04 wt%) and carbon element (21.30 wt%). The TG results showed three active pyrolysis zones (at 160-178 degrees C, 290-306 degrees C, 410-433 degrees C) with an overall weight loss of 73 wt%. Based on TG-FTIR analysis, CO bond (carbonyl) was the main group of the vapor emitted from the first active zone, while asymmetric stretching vibration of CO2 was the main group of the other zones. While GC-MS showed that ethyl cyanoacetate (reactive core of linkers) was the dominant compound in all active regions with a abundance ranging from 60.94 % to 70.31 % at 5 degrees C/min. The kinetic analysis showed that MOFs linkers consume low Ea for decomposition in the range of 164-202 kJ/mol (Ea), while the thermodynamic parameters were estimated in the ranges of 158755-196,288 J/mol.K (enthalpy), -11,059 to -18,184 J/mol.K (Gibbs free energy), and 232-287 J/mol.K (entropy). The constructed ANN algorithm also showed high performance in predicting the degradation of MOFs with R >= 0.999 and MSE in the range of 0.0427-0.0683. Accordingly, pyrolysis can be considered a promising technology not only for producing metal-based products from MOFs, but also for valorisation of their organic linkers into value-added chemicals.
This study critically examines the sectoral dynamics of renewable energy (RE) adoption across the EU-27 from 1990 to 2023, addressing the persistent gap between electricity generation and end-use sectors. Utilizing Eurostat energy balance data, the research employs a robust multi-methodological framework. We apply the Logarithmic Mean Divisia Index (LMDI) decomposition to isolate driving factors, and the Self-Organizing Maps (SOM) of Kohonen to cluster countries with similar transition structures. Furthermore, the Method of Moments Quantile Regression (MMQR) is used to estimate heterogeneous drivers across the distribution of RE shares. The empirical findings reveal a sharp dichotomy: while the share of renewables in the electricity generation mix (RES-E-Renewable Energy Share in Electricity) reached approximately 53.8% in leading member states, the aggregated share in the transport sector (RES-T) remains significantly lower at 9.1%. This distinction highlights that while power generation is decarbonizing rapidly, end-use electrification lags behind. The MMQR analysis indicates that economic growth drives renewable adoption more effectively in countries with already high renewable shares (upper quantiles) due to established market mechanisms and grid flexibility. Conversely, in lower-quantile countries, regulatory stability and direct infrastructure investment prove more critical than market-based incentives, highlighting the need for differentiated policy instruments. While EU policy milestones (RED I–III-) align with progress in power generation, they have failed to accelerate transitions in lagging sectors. This study concludes that achieving climate neutrality requires moving beyond aggregate targets to implement distinct, sector-specific interventions that address the unique structural barriers in transport and thermal applications.
Recently, the co-pyrolysis of biomass and various types of plastic waste (PW) has shown great potential in improving H/Ceff ratio of biomass, making it a sustainable and competitive source of bioenergy, especially in the presence of catalysts. However, this strategy is limited by high contamination and the difficulty of sorting PW, requiring the development of another clean and uniform source of PW. In this context, this research presents polyester and nylon buttons (major part of non-textile components) as a new type of clean and sortable PW for this purpose. The experiments at this stage was focused on studying the catalytic pyrolysis of plastic buttons only by thermogravimetric analysis (TGA) coupled with Fourier transform infrared (TG-FTIR) and gas chromatography-mass spectrometry (GC/MS) to provide the basic data needed for future co-pyrolysis with biomass. The energy consumed during the reaction (Ea) and other catalytic pyrolysis characteristics over ZSM-5 zeolite catalyst were evaluated using kinetic models along with determination of their thermodynamic parameters. Also, an artificial neural network (ANN) algorithm was proposed to expect TGA properties of buttons at ambiguous heating parameters. The TGA results revealed that polyester sample can be decomposed in two stages up to 360 degrees C and 460 degrees C, while nylon sample decomposed in a single stage up to 490 degrees C. The TGA-FTIR analysis highlighted that carbonyl groups (polyester) and aliphatic hydrocarbons (nylon) are the main functional groups of polyester and nylon vapors. Meanwhile, benzoic acid (72.94 % at 20 min/degrees C) the main compound of nylon sample and 1,2-Benzenedicarboxylic acid (plasticizers) the main compound of polyester and its toxic styrene compound was completely removed. Finally, the Ea used in decomposition of buttons was estimated at 241.6-262.7 kJ/mol (polyester) and 165.6-173.4 kJ/mol (nylon). The suggested ANN model showed high potential in predicting the catalytic pyrolysis characteristics with R > 0.98. Based on these findings, plastic buttons can be used as a co-feeding hydrogen-rich source to biomass to enhance its H/Ceff ratio and aromatic compounds.
Computational analysis of river flow remains a cornerstone of mathematical hydrological research, particularly in ecologically sensitive and morphologically complex environments. This study introduces an integrated workflow that combines aerial image-based obstacle detection in the riverbed with two-dimensional (2D) depth-averaged simulations to assess meso-scale flow structures in morphologically complex lowland rivers. It presents a novel framework for modelling shallow rivers that is able to capture meso- to micro-scale flow dynamics influenced by instream boulders, spatially heterogenous vegetation types, and bottom roughness. In situ measurements from four lowland river reaches in Lithuania were used to parameterize and calibrate flow resistance based on two boulder types and three distinct vegetation cover classes. A finite element (FE) approach was applied to solve the shallow water equations (SWE) with site-specific roughness coefficients. The model simulations were validated against observed velocity data, with relative errors of less than 20% at most observation points. The proposed approach demonstrated strong potential for accurate reproduction of fine-scale hydrodynamic behaviour in shallow lowland rivers, offering a flexible environment and scalable platform for future integration with automated AI-based remote sensing data. The precision and adaptability of the model make it a valuable tool for ecological assessments, flood risk studies, and restoration planning.
