Energy Resources Research Articles

Optimised Deep Learning for Time-Critical Load Forecasting Using LSTM and Modified Particle Swarm Optimisation

Short-term electric load forecasting is critical for power system planning and operations due to demand fluctuations driven by variable energy resources. While deep learning-based forecasting models have shown strong performance, time-sensitive applications require improvements in both accuracy and convergence speed. To address this, we propose a hybrid model that combines long short-term memory (LSTM) with a modified particle swarm optimisation (mPSO) algorithm. Although LSTM is effective for nonlinear time-series predictions, its computational complexity increases with parameter variations. To overcome this, mPSO is used for parameter tuning, ensuring accurate forecasting while avoiding local optima. Additionally, XGBoost and decision tree filtering algorithms are incorporated to reduce dimensionality and prevent overfitting. Unlike existing models that focus mainly on accuracy, our framework optimises accuracy, stability, and convergence rate simultaneously. The model was tested on real hourly load data from New South Wales and Victoria, significantly outperforming benchmark models such as ENN, LSTM, GA-LSTM, and PSO-LSTM. For NSW, the proposed model reduced MSE by 91.91%, RMSE by 94.89%, and MAPE by 74.29%. In VIC, MSE decreased by 91.33%, RMSE by 95.73%, and MAPE by 72.06%, showcasing superior performance across all metrics.

A Mathematically Optimized Design Solution for Structure of PEMFC Catalyst Layer Based on a Two-Phase Flow Model

Abstract Proton exchange membrane fuel cells (PEMFCs) have emerged as a promising solution as the world is moving toward sustainable energy resources. However, in order to compete economically with existing technologies, further improvements in performance are necessary. Mathematical modeling and optimization are viable tools for designing better PEMFCs. This study aims to provide a framework for topological optimization of the electrode structure, with the ultimate goal of enhancing cell performance. To achieve this, a two-phase flow model of PEMFC is developed to characterize the cell performance. The model is then coupled with a topology optimization technique, which is the main focus of the present work, to seek an optimized constituent distribution in the catalyst layer. Results indicate that an electrode with a heterogeneous structure can enhance the overall cell performance by balancing various transport and rate processes. The optimized designs are investigated for various key factors, including effective diffusivity, effective conductivity, and liquid water management, to demonstrate how an optimized design can be advantageous.

Patient perspectives on multidimensional learning and person-centred care: interviews with persons living with type 2 diabetes.

The objective of this study was to explore patients' learning and support needs within contemporary diabetes care to help them deal with daily life challenges. A qualitative descriptive design was used following the Consolidated Criteria for Reporting Qualitative Studies. The 15 individual face-to-face interviews were analysed using qualitative content analysis. Participants were drawn from three healthcare centres in rural and urban regions of West Sweden. The study involved 15 patients (8 men, 7 women) with T2DM who experienced contemporary diabetes care. Patients expressed a strong desire for access to person-centred, multidimensional learning, with a focus on genuine partnership, tailored education, and emotional engagement. Digital tools were seen as valuable aids in their self-care efforts. Sub-themes were 'Desiring genuine partnership and tailored patient education' and 'Needing support related to altered perspectives on life and awareness of care standards but with finite care resources'. The findings suggest that integrating person-centred, multidimensional learning strategies into diabetes care could be beneficial, particularly when addressing both practical and emotional needs. Encouraging active patient engagement through flexible digital solutions and providing support for emotional well-being may improve the overall patient experience. However, further research and practical application are needed to fully understand how these strategies could be effectively implemented to support patients with T2DM in managing their daily health challenges.

Exploring the thermal conductivity of green filamentous algae natural fibre: an experimental investigation

Abstract The use of non-renewable resources in thermal insulation has significant environmental impacts. Another major problem faced by the ecosystem is the invasive growth of water weeds. Making sustainable products from waterweeds helps to prevent its overgrowth that disrupts the balance of the ecosystem. This study explores the viability of Green Filamentous Algae (GFA) as an eco-friendly thermal insulation material. GFA was collected from water bodies, cleaned, and processed into two forms: untreated (UTD) and alkali-treated (TD). Experimental investigations were conducted to evaluate the physical properties of GFA, including density, self-ignition temperature, water absorption, moisture absorption, flammability, and thermal conductivity. Results show that the untreated GFA had a density of 402.52 kg/m³, while the treated GFA exhibited a slightly lower density of 392.32 kg/m³. The self-ignition temperature for both untreated and treated GFA was measured between 275°C and 280°C. The water absorption capacity was higher in treated GFA (654.7%) compared to untreated (493.83%). Moisture absorption capacity was 15.14% for untreated GFA and 17.47% for treated GFA. Flammability tests revealed a burning rate of 20.026 mm/min, placing GFA in the combustibility classification 1 (CC1). Thermal conductivity values were found to be 0.308 W/mK (UTD) and 0.273 W/mK (TD), making treated GFA a promising candidate for sustainable insulation applications. This study demonstrates the potential of GFA as a bio-based insulation material and highlights future improvements to enhance its moisture resistance and thermal performance.

Organised abandonment in Lebanon's Litani River Basin

This paper is a historical political ecology of the organised abandonment of Lebanon's rural margins. It argues that the present ecological catastrophe in the Litani River basin derives from the historical constitution of Lebanon's environmental regulatory apparatus as mediators of flows of capital that generate ecological fragility, or “capitalogenic flows.” In 2021, a massive fish kill brought the Litani River basin's spectacular ecological degradation to global attention. In response, the Litani River Authority (LRA), the Lebanese agency that manages the basin, launched a campaign against “environmental crimes” that concentrated on displacing thousands of Syrian refugees and demolishing their homes. This reflects a rebranding of the LRA, which was founded in 1955 to coordinate a World Bank loan for hydroelectric infrastructure on the Litani. Rather than commodify the water itself for irrigation or drinking—which might entail pollution controls—the Bank monopolised the basin landscape as an energy resource. Prevented from accessing the river, Litani communities have repeatedly reasserted popular sovereignty. The paper situates that history in the context of the institutional formation of Lebanon's environmental regulatory apparatus. Unlike postcolonial countries subjected to neoliberal dispossession after 1980, decades of contested institution-building from Lebanon's origins arrayed the state and its regulatory capacities around capitalogenic flows and the organised abandonment of the country's rural surplus population.

Nonlinear Control of Photovoltaic (PV) Solar-Powered Centrifugal Pump for Irrigation System: A Case Study of Fadak Farm in Karbala

Abstract In both the agricultural and industrial sectors, pumping water is essential. Due to its arid climate, our Saharan area has a substantial solar energy resource, making constructing a PV solar irrigation system possible. Given solar resources' unpredictable and intermittent nature, it is imperative to set up a system that permits optimal usage. This study aims to design and simulate a solar pumping system's effective nonlinear direct torque command. A solar generator provides an asynchronous three-phase machine coupled to a centrifugal pump as part of the system. MPPT monitors the boost converter via duty cycle. The solar generator's functioning at full power will be ensured. The first uses PSO in the MPPT system to supply the motor pump with three phases of power. The second one employs direct torque command (DTC) to regulate the operation of a centrifugal pump coupled with an induction machine. More advancements will be made to the DTC scenario. The proposed mathematical model of the solar irrigation system was simulated using a MATLAB simulation environment, adopting accurate data provided by Fadak Farm. The maximum power extracted from PV tends to peak at 280 Wp with the assistance of particle swarm optimization that energizes the use of the centrifugal pump for the proposed irrigation system. It is evident from numerical simulation results based on accurate input data that the power extracted from PV solar is positively affected by solar radiation and surface temperature variations. It is clear from simulation results that the speed of three–phase I.M motor converges to 22.5 rad/sec, and water flow pumping by centrifugal is decreased and increased, converging to 7.5 *10-8 m/s with the variation of solar irradiance.

UAVs Revolutionizing Efficiency: Integrating DRL and Random Walrus for Enhanced Energy and Spectral Resource Management

The improvement of energy efficiency and spectral efficiency in networks is achieved by seamlessly integrating energy harvesting, cognitive radio technologies, and Non-Orthogonal Multiple Access (NOMA) techniques. These complementary strategies optimize resource usage and address challenges related to energy consumption. Additionally, the adaptability and versatility of Unmanned Aerial Vehicles (UAVs) offer innovative solutions for enhancing coverage performance, thereby improving connectivity, efficiency, and reliability. We introduce a novel approach called the Deep Reinforcement Learning-Random Walrus (DRL-RW) algorithm, which combines Deep Reinforcement Learning (DRL) with the Random Walrus optimization (RWO) technique for efficient spectrum resource allocation and energy harvesting management in dynamic environments. The DRL-RW algorithm enables UAVs to learn optimal spectrum-sharing strategies and energy harvesting policies, while the RWO enhances the algorithm's adaptability and speed in exploring diverse solutions. Simulation results demonstrate the effectiveness of the DRL-RW algorithm, showing significant improvements in several performance metrics, including reduced energy consumption, enhanced computation time, improved convergence, increased signal-to-noise ratio, higher throughput, extended network lifetime, and increased harvested energy. These findings underscore the efficacy of the DRL-RW approach in addressing challenges associated with energy management in cognitive radio networks. The integration of UAVs, NOMA networks, and this novel algorithm represents a promising direction for developing energy-efficient communication systems.

Research on the Hierarchical Separation of Rare Earths and Lithium from Rare Earth Molten Salt Slag

Lithium is an important non-renewable resource, and the study of the tiered separation of rare earths and lithium from rare earth molten salt slag is an important approach to solving the current global resource shortage. This article adopts a sulfuric acid leaching process and a lithium-containing solution iron lithium extraction separation process to recover lithium from rare earth molten salt slag. The method systematically investigates the impact of sulfuric acid concentration, liquid-to-solid ratio, leaching temperature, leaching time, pH, P507 concentration, phase ratio, extraction temperature, and extraction time on the lithium extraction effect and iron lithium separation effect in rare earth molten salt slag. The results show that under the conditions of a sulfuric acid concentration of 0.9 mol/L, a liquid-to-solid ratio of 8:1 mL/g, a leaching temperature of 70 °C, a leaching time of 90 min, a pH of 0.9, a P507 concentration of 50%, a phase ratio of 5:3, an extraction temperature of 30 °C, and an extraction time of 20 min, the lithium leaching rate reaches 97.8%, and the separation of iron and lithium is fully achieved. This method can efficiently recover the valuable element lithium from rare earth molten salt slag, which is of great significance for the subsequent preparation of lithium products and the realization of a closed-loop production of rare earth alloys by molten salt electrolysis.

Research on Hybrid Heating System in Cold Oilfield Regions

Efficient and clean treatment of wastewater and energy recovery and utilization are important links to realize low-carbon development of oilfields. Therefore, this paper innovatively proposes a multi-energy complementary co-production heating system which fully and efficiently utilizes solar energy resources, oilfield waste heat resources, and biomass resources. At the same time, a typical dormitory building in the oil region was selected as the research object, the system equipment selection was calculated according to the relevant design specifications. On this basis, the simulation system model is established, and the evaluation index and operation control strategy suitable for the system are proposed. The energy utilization rate of the system and the economic, energy-saving, and environmental benefits of the system are simulated. The results show that, under the simulated conditions of two typical days and a heating season, the main heat load of the system is borne by the sewage source heat pump, the energy efficiency is relatively low in the cold period, and the energy-saving characteristics are not obvious. With the increase in heating temperature and anaerobic reactor volume, the energy consumption of the system also increases, and the energy efficiency ratio of each subsystem and the comprehensive energy efficiency ratio of the system gradually decrease. In addition, although the initial investment in cogeneration heating systems is high, the operating costs and environmental benefits are huge. Under the condition of maintaining 35 °C, the anaerobic reactor in the system can reduce carbon emissions by 12.15 t per year, reduce sulfur dioxide emissions by 98.4 kg, reduce dust emissions by 49.2 kg, and treat up to 2700 t of sewage per year, which has broad application prospects.

Fault Classification in Power System with Inverter-Interfaced Renewable Energy Resources Using Machine Learning

Fault Classification in Power System with Inverter-Interfaced Renewable Energy Resources Using Machine Learning

Influence of Model Resolution on Wind Energy Simulations over Tibetan Plateau Using CMIP6 HighResMIP

The assessment of wind energy resources is critical for the transition from fossil fuel to renewable energy sources. Using the outputs from high-resolution global climate models (GCMs), such as the High Resolution Model Intercomparison Project (HighResMIP) of the Coupled Model Intercomparison Project Phase 6 (CMIP6), has become one of the most important tools in wind energy research. This study evaluated the reliability of the 22 GCMs available in the HighResMIP-PRIMAVERA project by simulating the wind energy climatology and variability over the Tibetan Plateau (TP) with reference to observations and investigated the differences in performance of the GCMs between high-resolution (HR) and low-resolution (LR) simulations. The results show that most models performed relatively well in simulating the probability distribution of the observed wind speed over the TP, but nearly half of the models generally underestimated the wind speed, whereas the others tended to overestimated the wind speed. Compared with the wind speed, the GCMs showed larger biases in reproducing the wind power density (WPD) and other wind energy resources, whereas the biases in multi-model ensembles were relatively smaller than those in most individual models. With respect to interannual variability, both the HR and LR models failed to capture interannual variations in WPD over the TP. Furthermore, more than half of the HR GCMs had a reduced bias relative to the corresponding LR GCMs, indicating the good performance of most HR models in simulating wind energy resources over the TP in terms of spatial pattern and temporal variability. However, the overall performance of HR GCMs varied among models, which suggests that solely improving the horizontal resolution is not sufficient to completely solve the uncertainties and deficiencies in the simulation of wind energy over complex terrain.

High-performance floating thermoelectric generator for all-day power supply

Thermoelectric generators (TEGs) offer notable benefits in harnessing diverse low-grade waste heat from the environment. However, the uninterrupted capture of these energy resources still remains a huge challenge. Herein, we have developed a temperature-adaptive floating thermoelectric generator (TAFTEG) by integrating a temperature-adaptive absorber/emitter (TAA/E) to synergistically exploit renewable energy from the sun, outer space, and the water bodies by leveraging diurnal spectrally selective absorption and nocturnal radiative cooling. The WxV1-xO2-based TAA/E demonstrates remarkably solar absorption of ∼96 % and a superior switching ability of dynamic thermal radiation modulation from the low emission of ∼45 % to high emission of ∼81 %. Assembled with 8 pairs of Bi2Te3-based TEG legs, the TAFTEG enables a stable temperature difference of 34°C under indoor solar irradiation (1 kW m−2), yielding a peak power output of ∼1.0 mW cm−2, and continuous all-day power supply under both clear and overcast conditions. This strategy paves a new avenue toward the round-the-clock power supply from ambient environments.

Is Clonal Integration a Buffer for the Stress of Resource Acquisition Depletion in Eichhornia crassipes (Pontederiaceae) Ramets?

Natural selection favors the allocation of finite resources to different functions maximizing fitness. In this sense, some functions may decrease whereas others increase when resources are limited in a process called a trade-off. However, a great variety of situations may obscure trade-off detection in clonal plants, such as the ability to generate offspring by clonal growth that represents opportunities for resource uptake. The aim of this work was to evaluate if clonal integration and resource availability mediate biomass allocation patterns in E. crassipes through a greenhouse experiment. We set ramets in clonal and isolated conditions, and with and without leaf blades, and compared the relationship of biomass proportion allocated to each vegetative organ. We found that biomass allocation to vegetative structures in E. crassipes is primarily shaped by resource pools and is enhanced by clonal integration as attached ramets invest more in growth and vegetative structures. In this sense, regarding trade-off patterns in biomass allocation among vegetative organs and under resource depletion, clonal integration may represent a way to stabilize biomass allocation patterns and may decrease trade-off importance. We discuss trade-offs and clonal integration as evolutionary strategies that allow plant persistence and improve plants fitness. These findings may support aquatic plant management and control efforts while highlighting the evolutionary significance of clonal integration for plant life strategies.

Research on the Correlation between the Movement of the Dollar and the Price of Gold

Abstract. Gold acts as a hedge to protect investors' assets. The U.S. dollar is a global currency and has an important place in international trade. Oil, on the other hand, is a non-renewable resource and an important international resource with unstable prices. This study used price data for the U.S. dollar, gold and oil from 2000 to 2023 to analyze the movement of gold, U.S. dollar and oil prices. The experiment uses a regression model to determine the effect of the dollar on the price of oil and gold. The study found a significant negative correlation between the U.S. dollar and the price of gold and an insignificant negative correlation with oil between 2000 and 2014. Between 2015 and 2023, there is a change in U.S. monetary policy, which leads to a weakening of the negative price correlation between gold and the U.S. dollar, but U.S. dollar still with the negative correlation with oil remaining weak. These results suggest that buying gold is the best way to protect your assets in times of financial crisis or market instability, but the US dollar and oil can change in price due to macroeconomic and political factors. To some extent these data provide a reference value for investment in the coming year.

Modular hydrogel selectively adsorbs phosphates and hexavalent chromium while enabling phosphate recovery

Electroplating wastewater containing high concentrations of phosphates and hexavalent chromium Cr(VI) poses serious environmental pollution. Moreover, phosphorus, as a non-renewable resource, necessitates its recovery to meet sustainable development goals. To address this issue, this study used sodium alginate as the scaffold module, synthesized lanthanum carbonate in situ within a chitosan module to serve as the phosphate adsorption module, and employed polyethyleneimine (PEI) modules to enhance the adsorption capacity for Cr(VI), successfully fabricating a modular hydrogel (LC-CSP). LC-CSP exhibits a complex porous structure and surface morphology, forming an ultra-low-density fiber network with good strength and elasticity, ensuring uniform distribution and exposure of active sites. Under optimal conditions for single-component adsorption, LC-CSP achieved adsorption capacities of 232.02 mg/g for phosphates and 474.61 mg/g for Cr(VI). Additionally, LC-CSP demonstrated excellent reusability, retaining over 83 % of its performance after five cycles. In simulated electroplating wastewater experiments with various interfering substances, LC-CSP maintained high removal efficiencies (>90.72 %) for phosphates and Cr(VI). Post-experiment, enriched water after phosphate desorption was further treated to recover phosphorus resources in complex water environments. Multiple characterization techniques elucidated the adsorption mechanisms of LC-CSP: phosphate adsorption primarily involved ligand exchange, electrostatic interactions, and hydrogen bonding, while Cr(VI) adsorption included electrostatic interactions, hydrogen bonding, and reduction reactions. Finally, fixed-bed simulated wastewater adsorption experiments validated the technical potential of LC-CSP for practical electroplating wastewater management.

Soil application potential of post-sorbents produced by co-sorption of humic substances and nutrients from sludge anaerobic digestion reject water

This study introduces a novel soil conditioning approach using humic substances (HSs) and nutrients co-recovered from reject water from sewage sludge anaerobic digestion. For the first time, HSs and nutrients were simultaneously recovered through sorption on low-cost, environmentally inert materials: natural rock opoka (OP) and waste autoclaved aerated concrete (WAAC). This innovative application of OP and WAAC as carriers and delivery agents for soil-relevant substances offers potential for resource recovery and soil conditioning. Results indicate that the post-sorption opoka (PS-OP) and post-sorption waste autoclaved aerated concrete (PS-WAAC) effectively release retained HSs at 350–480 μg g⁻1 d⁻1, respectively. These materials also show potential as NPK fertilizers, releasing 280–430 μg g⁻1 d⁻1 N-NH₄⁺, 80–150 μg g⁻1 d⁻1 P-PO₄³⁻, and 270–350 μg g⁻1 d⁻1 K⁺. Additionally, PS-OP demonstrated promising fungicide properties, reducing P. diachenii growth by 31% at a concentration of 1 g L⁻1. A two-way ANOVA indicated that the effects of PS-OP and PS-WAAC on soil physicochemical and biological parameters varied with plant species. Both post-sorbents improved the quality of soil collected from sand mining area, increasing cation exchange capacity by 7%–85% and organic matter content by 10%–58%. They also enhanced the functional potential of soil microbial communities, increasing their metabolic activities by 23%–36% in soils sown with clover and by 33%–39% in soils sown with rapeseed. An opposite effect was observed in soils sown with sorghum, suggesting these amendments may not universally act as plant biostimulants. The effectiveness of these post-sorbents in enhancing plant growth varied depending on plant species and the mineral base of the post-sorbent. PS-OP increased the total length of clover and sorghum by 41% and 36%, and their fresh biomass by 82% and 80%, respectively. In turn, PS-WAAC increased the total length of clover and sorghum by 76% and 17%, and their fresh biomass by 29% and 15%, respectively. It was notably more effective than PS-OP for rapeseed. This study proposes a strategy to decrease reliance on non-renewable resources and costly sorbents while minimizing environmental impact. It shows that PS-OP and PS-WAAC can enhance soil quality, microbial activity, and plant growth. Given their origins, these amendments are recommended for soil remediation, particularly in degraded areas. Future research should focus on optimizing their application across various plant species to maximize effectiveness.

The benefits and burdens of wind power systems in reaching China’s renewable energy goals: Implications from resource and environment assessment

Recognizing the economy’s growing reliance on global energy landscape transformation on wind power deployment, as well as the general reality that renewable facilities require lower operational but higher up-front inputs than fossil-based power systems, this paper focuses on the life-cycle burdens of wind power systems and their substitution benefits compared with coal-fired power systems. The estimation considers the consumption of nonrenewable sources of energy, emissions of greenhouse gases, and other environmental resource factors that have received significantly less research attention, such as industrial land use, water use, PM2.5, SO2, NOX, and Hg emissions. The scope of this study differs from earlier ones in that it includes a comprehensive description of all the equipment, materials, and services used, as opposed to earlier studies that either omit crucial supporting infrastructure or just focus on the plant’s physical structure as its primary materials. Results based on a typical plant in China show that the state-of-the-art onshore wind power systems can provide significant reductions in nonrenewable energy use (9.2 MJ/kWh) and GHG emissions (782.8 GtCO2/kWh). To produce an equivalent amount of electricity, wind power systems require more than three times industrial land use and almost one-third of industrial water use of that for traditional coal-fired power systems. The avoided SO2, Hg, PM2.5, and NOX emissions per unit of electricity generation account for 60.6%-89.3% of the total air pollution emissions induced by supercritical coal-fired power systems. By integrating 3797 operating plant-specific data and fixed-point wind energy information, this study scales up results of the single plant to a country level. The macro picture implies different opportunities born by wind power systems in easing multiple resource and environmental pressures, highlighting the significance of designing hierarchical strategies to improve penetration levels of wind power. By 2050, cumulative climate benefits obtained from onshore wind power technology are predicted to reach 74.2 Gt CO2, achieving around 17.2%-45.5% of the national carbon-neutral goal.

Optimization of a Bayesian Game for Peer-to-Peer Trading among Prosumers under Incomplete Information via a CNN-LSTM-ATT

In modern low-carbon industrial parks, various distributed renewable energy resources are employed to fulfill production needs. Despite the growing capacity of renewable energy generation, a significant portion of the power produced by these renewable resources remains unconsumed, resulting in a waste of resources. Within an industrial park, microgrids that both generate and consume energy resources act as energy prosumers. Peer-to-peer (P2P) trading provides an efficient means of utilizing renewable energy among these energy prosumers, who possess both power generation and consumption capabilities. However, within the current market mechanism, each prosumer retains private information that is not disclosed on the network. To address the issue of incomplete information among multiple prosumers during the decision-making process, we develop a Bayesian game model based on the CNN-LSTM-ATT prediction method for P2P electricity transactions among multiple prosumers. The energy prosumers in each industrial park aim to minimize their energy consumption costs by adjusting strategies that include P2P energy trading and managing thermal loads. Prosumers make decisions on the basis of their own characteristics and estimates of other prosumer characteristics, which are obtained from the joint probability distribution predicted by the CNN-LSTM-ATT method. These decisions are aimed at minimizing each prosumer's electricity costs. The simulation results demonstrate the effectiveness of the Bayesian game model proposed in this study.

Achieving Carbon Neutrality in Emerging Markets: The Dual Impact of Energy Transition Investments on Economic Growth and Carbon Emissions.

This study investigates the effectiveness of energy transition investments (ETIs) in achieving net-zero emissions. Specifically, it revaluates the environmental Kuznets curve (EKC) by analysing whether ETIs can simultaneously stimulate economic activity and reduce carbon emissions (CE) using the Autoregressive Distributed Lag (ARDL) model. The analysis focuses on a sample of emerging countries. In contrast to previous research, the study expands the EKC model by incorporating the ETIs variable, which encompasses a broader and more comprehensive range of investments that contribute to climate change mitigation, beyond just renewable energy. Additionally, the study employs total factor productivity (TFP) as a measure of economic activity, instead of GDP, considering the efficiency of technology, energy, and other resources. Key findings indicate that the TFP coefficient is higher in the short term compared to the long-term supporting the validity of the EKC hypothesis. This suggest that emerging countries have reached a TFP level of that helps reduce their CE, aiding climate change adaptation and mitigation. The study also reveals a negative effect of ETIs on CE and shows that an increase in TFP significantly enhances ETIs, suggesting that higher TFP levels attract more investment in energy transitions. These findings provide insights for policymakers on the impact of ETIs on CE and aid in formulating effective policies to achieve net-zero emissions.

Enhancing smart city assessment: an advanced MCDM approach for urban performance evaluation

Urban centers in the 21st century face significant challenges impacting future urban living. The numerous indicators and inherent complexity involved in assessing urban performance in smart cities necessitate the application of multi-criteria evaluation methods, which are frequently associated with fuzziness and uncertainties. Thus, this study introduces an innovative decision support approach that integrates complex spherical fuzzy set to enhance decision-making for smart city evaluation. By incorporating a consensus measurement mechanism for judgments, the reliability of results is improved. The methodology assesses smart city performance by calculating a level index using criteria of resilience, sustainability, and urban livability, along with eighteen sub-criteria for comprehensive benchmarking. Results highlight pollution control, energy and environmental resources, community well-being, eco-friendly transport, and social cohesion as critical factors for smart city performance. Singapore demonstrates high-level performance due to its integration of advanced technologies and community-focused strategies. The evaluated results obtained via the developed approach offer valuable insights for urban planners aiming to promote sustainable, resilient, and livable urban development.