Global Energy Efficiency Research Articles

Global Research Trend of Sustainable Transport in Response to Energy Efficiency: A Bibliometric Analysis

Numerous studies have been conducted on sustainable transportation and its impact on global energy efficiency. However, the evolution and performance patterns of this field remain relatively unknown and limited. A bibliometric analysis was conducted to explore the global research trends to determine the state-of-the-art sustainable transport linked to energy efficiency over the past 21 years. The Web of Science (WoS) database was searched to explore the research related to sustainable transport in response to energy efficiency from 2002 to 2022, resulting in the identification of 2,884 articles. In this study, VOSviewer was used to analyse collaboration networks between authors, countries, institutions and co-occurrence analysis of keywords in three defined periods. The results reveal that the majority of studies on sustainable transport and energy efficiency were primarily conducted in the USA, with limited research conducted in Asian countries. The study found that the subject categories of “transport planning,”“transport policy,”“climate change,”“electric vehicles,” and “public transport” are the most prolific research contributors. During Phase III (2016–2022), most studies were centred around electric vehicles in the field of sustainable transportation, as opposed to Phase II (2009–2015). These findings enhance our understanding of the research landscape in this field and offer valuable insights for future studies. Future studies could examine how the latest technology enhances energy efficiency in the transportation sector and focus on the usage and benefits of hydrogen fuel cell vehicles (HFCVs).

Global value chain embedding and enterprise energy efficiency: A worldwide firm-level analysis

Global value chain embedding brings growth opportunities for small and medium-sized enterprises, despite their continuously controversial environmental effects. This study examines the impact of global value chain embedding on the energy efficiency of small and medium-sized enterprises using a worldwide firm-level micro data. Results support that global value chain embedding significantly improves enterprise energy efficiency, and this conclusion holds even after self-selection and endogeneity are considered. Global value chain embedding promotes enterprise innovation and labor productivity through market scale and induced learning effects and improves the energy efficiency. Moreover, the evidence only supports the lock-in effect of low value-added, while the lock-in hypothesis of pollution link and foreign technology is invalid. The positive impact of global value chain embedding on energy efficiency is greater for non-high-tech industries, mainly coming from export learning effect and innovation effect. Furthermore, there is no industry heterogeneity in the lock-in effect. Country heterogeneity in the effect of global value chain embedding on energy efficiency characteristics of regional embedding and industrialization, but not to economic development and human capital.

The interconnectedness of energy consumption with economic growth: A granger causality analysis

In considering today's energy challenges, the link between the usage of renewable and non-renewable energy sources and economic growth has gained substantial policy attention. This research examines the complex relationship between these three variables to understand how non-renewable energy consumption and renewable energy consumption interact and what that means for economic growth. This study uses the Granger causality approach to explore the relationships between non-renewable energy consumption, renewable energy consumption, and economic development. It draws on a comprehensive dataset from the Word Bank database, including 152 nations from 1990 to 2019. The analysis is further disaggregated by four subgroups of countries; least developed, developed, transitional economies and developing countries. The result of this study provides valuable empirical evidence of uni-directional causality running from renewable energy consumption to economic growth and non-renewable energy consumption to economic growth in transitional economies. Furthermore, policymakers should focus on both variables when making decisions because the results show that energy consumption and economic growth are interconnected. Implementing global energy efficiency standards, reducing fossil fuel usage, and adopting regulatory measures are all viable policies for limiting adverse effects on the environment while encouraging economic development.

ENERGY EFFICIENCY AS A KEY FACTOR IN ENHANCING THE POTENTIAL AND COMPETITIVENESS OF ENTERPRISES IN THE CONSTRUCTION INDUSTRY THROUGH THE APPLICATION OF GLOBAL PRACTICES

The article substantiates the importance of energy efficiency as a key factor in enhancing the potential and competitiveness of enterprises in Ukraine's construction industry through the application of global practices. The article demonstrates how the implementation of advanced energy-saving technologies and renewable energy sources, which are widely used at the international level, allows enterprises to reduce energy costs, increase environmental responsibility, and strengthen market positions. It is proven that the integration of global practices in energy efficiency, including the modernization of production processes and the implementation of automated energy management systems, significantly enhances the economic efficiency of enterprises. The example of «DZBM» LLC, which reduces production costs and generates additional income from selling electricity at the «green tariff» through the construction of a mini-CHP plant running on biofuel, confirms the effectiveness of this approach. The relevance of the topic lies in the need to adapt global energy efficiency practices in Ukraine's construction industry, which is particularly important in the context of rising energy costs and economic instability. The article lays the foundation for further research in this area and proposes ways to optimize energy use to enhance the competitiveness of enterprises.

Towards sustainability: Evaluating energy efficiency with a super-efficiency SBM-DEA model across 168 economies

Enhancing energy efficiency (EE) is a crucial strategy for fostering sustainable development through the alignment of economic growth, energy conservation, emission mitigation, and social welfare improvement. It is widely recognized as the most accessible, secure, and cost-effective means of advancing sustainable development. However, many developing economies still exhibit remarkably low levels of EE, resulting in significant disparities compared to their developed counterparts. These disparities pose challenges to the realization of the United Nations' sustainable development goals (SDGs). In response to the limitations of extant EE definitions, our study proposes a redefined concept of EE within the sustainable development framework. Subsequently, we utilize the super-efficiency SBM-DEA model to assess the EE of 168 economies globally from 2000 to 2017, offering a detailed examination centered on spatiotemporal patterns and inequality. Key findings include: (1) Global EE initially displayed an upward trajectory, followed by a decline, with a persistent decrease post-2009; the impact of global financial crisis on EE varies across income groups of economies. (2) Spatially, EE exhibits a notable geographical clustering effect, with regions displaying distinct concentrations of high and low EE throughout the study period. (3) While global EE inequality has generally diminished, disparities within income groups have become the dominant source of EE inequality. Our research elucidates the intricate dynamics of global EE and underscores its integral role in the pursuit of sustainable development.

Regional Differences, Dynamic Evolution, and Convergence of Global Agricultural Energy Efficiency

Understanding the regional disparities, dynamic evolution, and convergence–divergence characteristics of global agricultural energy efficiency is crucial for enhancing agricultural energy efficiency, ensuring food security, and responding to global green development trends. This paper utilizes 2002–2021 panel data from 144 countries globally, employing the epsilon-based measure–global Malmquist–Luenberger (EBM-GML) model to estimate agricultural energy efficiency, considering unexpected output. The Dagum Gini coefficient, kernel density estimation, spatial Markov matrix, and spatial convergence model are employed to explain the spatial patterns and evolving trends of global and regional agricultural energy efficiency at three levels: regional disparities, dynamic evolution, and convergence. The results indicate significant spatial heterogeneity in global agricultural energy efficiency, with Europe exhibiting the highest efficiency, followed by Asia and the Americas, while Oceania and Africa demonstrate the lowest efficiency. Agricultural energy efficiency globally and in each region continues to improve, with increasing regional disparities, and difficulties in grade transitions in agricultural energy efficiency across regions. Each region exhibits β-convergence characteristics, but the convergence rates vary, and various factors influence growth rates of agricultural energy efficiency differently across regions. Therefore, countries should tailor their strategies based on local conditions, considering their own resource endowments and developmental stages, and strengthen international exchanges and cooperation.

The complementary role of carbon dioxide removal: A catalyst for advancing the COP28 pledges towards the 1.5 °C Paris Agreement target

As the imperative to address climate change becomes more pressing, there is an increasing focus on limiting global temperature increase to 1.5 °C by the end of the century relative to pre-industrial levels. During the recent Conference of Parties (COP28), nations committed to tripling renewable energy generation to a minimum of 11,000 GW by 2030 and increasing the global annual energy efficiency from 2 % to 4 % annually until 2030. Additionally, the Food and Agricultural Organization (FAO) introduced a roadmap to transition the Agri-food system from a net emitter to a carbon sink. The role of carbon dioxide removal (CDR) is important; first to accelerate the near-term reduction in net emissions, counterbalance residual emissions at the point of net-zero by mid-century, and sustain large net negative emissions beyond mid-century to return warming to safe levels after decades of temporal overshoot. This paper assesses the impact of the COP 28 agreements, alongside the complementary role of CDR on emission levels, energy structure, land use, and global warming temperature. The findings indicate that implementing the COP28 pledges and FAO roadmap leads to a warming temperature of 2 °C, falling short of the ambitious 1.5 °C temperature limit. Likewise, more stringent actions on transitioning away from fossil plants is a high-priority mitigation action which drives significant emissions reduction. The modelled result shows that Agricultural soil carbon and biochar contribute 47–58 % share of the total CDR deployed in the stylized scenarios. In conclusion, CDR can expedite climate goals but must complement emission reduction efforts; hence, the transition away from fossil fuels should prompt the development of detailed roadmaps. Also, more global efforts should be placed on nature-based CDR methods, as they offer diverse co-benefits.

The research of the impact of energy efficiency on mitigating greenhouse gas emissions at the national level

Improving energy efficiency is considered to be important and cost-effective strategies to reduce greenhouse gas emissions, which is crucial in the face of global climate change. This study hypothesizes that there are significant differences in the impact of energy efficiency on economic and energy systems. Using quantile regression method, the impact of economic and energy characteristics on energy-related greenhouse gas emissions in 120 countries over the period 2010–2020 is investigated. Detailed analyses are conducted by dividing the countries into 12 groups based on gross national income per capita (Atlas method) and the share of resource rents in gross domestic product. A macro-level system of indicators for assessing energy efficiency is proposed, which can be linked to the economic and energy characteristics of the systems: National Energy Efficiency, Energy Intensity Ratio, Primary Energy Production Efficiency, Industrial Sector Energy Efficiency, Share of Low Carbon Energy, Population Energy Efficiency. The findings show that (1) global energy efficiency has remained largely unchanged over the decade; (2) there are differences in energy efficiency between fossil fuel producing and non-fossil fuel producing countries; (3) the factors that have an inverse effect on emissions in fossil fuels dependent countries include industrial energy efficiency and population energy efficiency, which are far from exhausting their potential; (4) despite the recognized importance of energy efficiency, its impact on reducing greenhouse gas emissions globally has been unnoticeable. The study highlights that Sustainable Development Goals 7 and 13 on energy affordability and emission reductions are conflicting and require harmonization of mechanisms to achieve them, such as balancing climate change mitigation investments, identifying the potential for energy efficiency improvements across economy or improving energy saving practices. The overall conclusion suggests that there is an urgent need to review current decarbonization and energy efficiency initiatives, given the lack of significant global progress in reducing greenhouse gas emissions or improving energy efficiency between 2010 and 2020.

Is geopolitical oil price uncertainty forcing the world to use energy more efficiently? Evidence from advanced statistical methods

This paper argues that energy efficiency is a potent shield against oil price uncertainty in an increasingly interconnected world fraught with geopolitical tensions. By reducing dependence on oil, enhancing economic resilience, and improving energy security, energy efficiency measures offer multifaceted benefits for both national economies and global stability. Specifically, a wavelet coherence analysis is conducted to study the response of global energy efficiency to geopolitical oil price uncertainty. Quantile-on-quantile and quantile regressions are additionally employed to separate the impacts of various geopolitical oil price risk quantiles on the quantiles of energy efficiency. These methods are utilized in the examination of global time-series data covering the timeframe 2004:Q1–2020:Q4. The wavelet coherence outcomes indicate a positive correlation between geopolitical oil price uncertainty and energy efficiency, particularly during the energy crisis of the 2000s and the COVID-19 pandemic. The results also reveal that geopolitical oil price uncertainty leads to energy efficiency, indicating that an upsurge in geopolitical oil price uncertainty causes energy efficiency to increase. Moreover, the results from quantile-on-quantile and quantile regressions affirm the predominantly positive effects of geopolitical oil price uncertainty on global energy efficiency. Our conclusion therefore is that through strategic investments, innovative policies, and international collaborations relating to energy efficiency, nations can fortify themselves against the destabilizing effects of geopolitical conflicts on energy markets. This would ensure a more sustainable and secure energy future for all.

Energy-Efficiency Optimization With Model Convexification for Wireless Ad Hoc Networks With Multi-Packet Reception Capability

Energy efficiency is a significant requirement of resource management and design optimization in information networks. In this work, we propose an iterative fractional programming framework embedded with a distributed primal-dual extra-gradient projection algorithm, which addresses a wide class of the energy-efficiency optimization problems in wireless ad hoc networks with full-duplex radios and multi-packet reception capability. Specifically, we propose a model convexification mechanism by joining an affine transformation and an exponential transformation into the nonlinear fractional programming, which enables us to deal with the challenge arising from the complexity and non-convex structure of the original problem. With the model convexification, we can map the non-convex power control space into a convex space and equivalently derive a sequence of convex subproblems, which relaxes the convexity assumption widely adopted in the existing literature. We further propose a distributed primal-dual algorithm based on extra-gradient projection to solve the convex subproblem at each iteration of the fractional programming. The convergence of the proposed iterative fractional programming and the distributed optimization method is theoretically proven. Numerical results also verify the proposed method and demonstrate its superior performance over other representative distributed and centralized schemes in terms of achieving global energy efficiency.

Simulation methodology and numerical comparison of boxer and opposed free piston linear generator configuration in 0D/1D environment

Free Piston Linear Generators (FPLGs) are thermal machines suitable for both stationary power generation unit (PU) and vehicle propulsion. They convert chemical energy from the fuel into electric energy to charge a battery using a linear electric generator. Due to the reduction of friction losses and the ability to work with different compression ratios, FPLGs are potentially characterized by high overall global efficiency with respect to a conventional alternative internal combustion engine. Like conventional internal combustion engines, FPLGs have several configurations: single piston, dual piston, opposed piston and boxer are the configurations considered in this work. Two configurations are compared in this paper from a functional and energetic perspective: opposed piston (Opposed) and Boxer. This is done with the aim to highlights pro and cons of these configurations. They are modelled using the 0D/1D modelling coupling GT-Power® and Simulink®. The comparison between the two configurations was done modelling the architecture with the same size and in two-strokes operating mode. The comparison of the two configurations highlights differences between them in terms of scavenging process, thermodynamic efficiency and global energy efficiency, putting in evidence what aspects need to be improved for each configuration and what are their advantageous features. Boxer configuration can provide less power due to lower volumetric efficiency compared to the Opposed configuration. Boxer configuration can provide 102.2 kW while Opposed can provide 110.0 kW at full load with a frequency of 20 Hz (equivalent to 1200 rpm). The Opposed configuration results in lower heat loss but higher energy lost at the exhaust. The final balance shows that Boxer configuration has a slightly higher brake efficiency (equal to 28.9 %) compared to the value found for Opposed (27.6 %).

Transfer of building retrofitting evaluations for data-scarce conditions: An empirical study for Sweden to China

Evaluating and comparing the performances of different strategies is critical for energy-efficient building retrofitting. Data-driven modelling based on large building performance datasets is an effective method for such evaluations. However, it could be challenging to apply this approach to buildings from data-scarce areas where local building performance datasets have not been well-established, which means the data falls short of the high demand for building retrofitting on a global level. To address this, a transfer learning approach is proposed in this study that can evaluate the performance of buildings without local well-established building performance datasets. The proposed approach is applied in the Swedish-Chinese empirical study that relies on the Swedish dataset to transfer and predict the building performance in China without well-established datasets. It was achieved by applying fuzzy C-means clustering and a neural network (FCM-BRBNN) to pre-train the evaluation model based on the Swedish dataset. Then, the proposed approach collects a small sample of Chinese buildings in the data-scarce area and transfers the model to local building performance prediction. The results show that the transfer learning approach can reliably predict the performance of building retrofitting in data-scarce areas with only hundreds of local building samples. As such, this study provides a novel methodology that can support the evaluation and comparison of retrofitting strategies in data-scarce regions and countries with only limited local data. It could efficiently assist designers in optimizing energy-efficient designs in the pre-retrofit stage. Crucially, the methodology enables the transfer of knowledge regarding building performance across different countries and regions, being pivotal for the international collaboration required to stimulate the global energy-efficiency transformation.

Solar Wall Technology and Its Impact on Building Performance

Solar walls provide transformative solutions by harnessing solar energy to generate electricity, improve thermal comfort, and reduce energy consumption and emissions, contributing to zero-energy buildings and mitigating climate change. In hot and humid regions, solar walls can reduce indoor temperatures by 30% to 50%, significantly improving energy efficiency. Optimizing the performance of solar walls includes factors such as glazing, shading, solar orientation, ventilation, and catalytic techniques, allowing them to be adapted to different climates. Innovative solar wall variants that include photovoltaic panels, water storage, and phase-change materials offer multifunctionality and sustainability in building design and are in line with global energy efficiency and environmentally conscious goals. In addition, innovative solar wall variants that combine photovoltaic panels, water storage, and phase-change materials promise even more sustainability in building design. These multifunctional solar wall systems can efficiently heat, cool, and generate energy, further reducing a building’s environmental impact. Solar walls have the potential to significantly reduce heating energy consumption; align with global goals for energy-efficient, environmentally conscious, and climate-responsive building design; and offer dynamic and adaptable solutions for sustainable architecture.

A novel outlier calendrical heterogeneity reconstruction deep learning model for electricity demand forecasting

The development of an accurate electricity demand forecasting model is of paramount importance for promoting global energy efficiency and sustainability. Nonetheless, the presence of outliers and inappropriate model training can result in suboptimal performance. To tackle these challenges, this study explores the potential of Convolutional Neural Network (CNN) and active learning theory as forecasting solutions, offering high efficiency and advantages for long time series. In this study, a hybrid model that combines Isolation Forest (IF), Outlier Reconstruction (OR), CNN and Random Forest (RF) is conducted to mitigate computational complexity and enhance the accuracy of electricity demand forecasting in the presence of outliers. IF is employed to detect outliers in electricity demand time series, while OR is used to reconstruct subsequences based on calendrical heterogeneity for training. CNN is applied for both training and forecasting, and the final output is combined using RF. The effectiveness of the proposed IF-OR-CNN-RF model is validated using electricity data collected from recent sources in Australia at different sampling frequency. The experimental results demonstrate that, in comparison with other popular CNN-based electricity demand forecasting models, IF-OR-CNN-RF model outperforms with significantly improved performance metrics. Specifically, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and R-squared values are 77.92, 179.18 and 0.9769 in 5-minute frequency; 162.67, 353.96 and 0.9775 in 10-minute frequency; 841.27, 1374.79 and 0.9622 in 30-minute frequency; 2746.01, 3824.00 and 0.9262 in 60-minute frequency; 9106.08, 12269.04 and 0.8044 in 120-minute frequency. IF-OR-CNN-RF model represents a valuable framework for future electricity demand forecasting, particularly in scenarios involving outliers.

Development of Patented Autonomous Quantum Gravitational Electric Energy Generator Prototypes

After quantum physics modelling of Gravitational interaction from hypotheses, we performed experimental confirmation experiments of predicted increased Faraday’s induction. Brief and low energy electric discharges were made, at room temperature, into partially superconducting Graphite based devices, patented and named “emitters”, inserted in series with the primary low inductance of a transformer. The high voltage secondary inductance of that transformer was connected to a capacitor, so the secondary current oscillated during several milliseconds.
 
 We Measured the global energy efficiency of Faraday’s induction, during each electric discharge into the emitter of the primary circuit. We also measured evolution of the peak primary discharge current, and of its derivative, versus the initial charge voltage. We observed systematically a much larger than 100% energy efficiency. That energy efficiency increases with the primary discharge energy. The peak discharge current is observed to be much larger than predicted by Ohm’s Law, and the discharge current derivative is also observed to be much larger than classically predicted.
 
 Same experiments, performed with “normal conductive devices (control)”, gave energy efficiencies much lower than 100 %, independent of the stored energy. And their peak discharge currents and derivatives followed Ohm’s Law.
 
 From these confirmations of predicted results, we proposed and tested successfully concepts of Autonomous electric Generators prototypes, extracting their energy from the cosmological Gravitational quantum field. Their industrial use into electric vehicles should preserve Earth fossil energy resources, as well as reduce the detrimental climate effects of greenhouse gases emissions. Hypotheses are suggested to explain the observed experimental facts.

SnSe: The rise of the ultrahigh thermoelectric performance material

AbstractThermoelectric materials can generate electric power from dissipating heat without releasing any undesirable chemicals. They thus can increase global energy efficiency and reduce the use of fossil fuels that are a major resource for generating electric energy, thereby concurrently addressing energy and environmental crises seriously threatening humanity. Increasing a thermoelectric figure of merit, ZT, of materials has been a prime goal in thermoelectrics because an efficiency of thermoelectric power generation has been low until very recently. The recent development of ultrahigh thermoelectric performance in polycrystalline SnSe‐based materials is one of the most prominent breakthroughs in the history of thermoelectrics. They show an exceptionally high ZT of ~3.1 at 783 K and average ZT of ~2.0 from 400 to 783 K, which are the highest for any bulk thermoelectric systems. Here we review the recent advances in SnSe thermoelectrics, greatly changing the paradigm of studies and applications of thermoelectric technology.

Design standards for environmentally friendly and energy-efficient buildings

The relevance of the work. In modern society, the problem of reducing the consumption of primary energy is becoming more acute. Scientists around the world are solving problems related to global energy efficiency. At the same time, issues of environmental protection, reducing energy costs, ensuring energy security and many others are considered. Construction, as one of the largest energy consumers on the planet, cannot stay away from this problem. The search for energy-efficient solutions in construction has been going on for a long time. However, at the same time, the main attention is paid to the engineering equipment of construction objects and the creation of shells impermeable from a heat engineering point of view. In the context of the gradual exhaustion of opportunities for further improving energy efficiency in construction using traditional methods, this work aims to draw attention to the influence of the constructive solution of buildings on their energy consumption. It can be assumed that the optimization of the supporting structures of buildings will become a new driver for the development of the energy efficiency of buildings and structures.

SE Analysis and GEE Optimization of Hardware-Impaired Multi-Cell Massive MIMO Systems With Rician Fading and Phase Shifts

We consider a practical hardware-impaired multi-cell massive multi-input multi-output (mMIMO) system with spatially-correlated Rician fading channels, whose line-of-sight component experiences a random phase shift. The multi-cell mMIMO system employs two-layer large-scale fading decoding (LSFD) to mitigate the interference due to pilot contamination. We consider a dynamic analog-to-digital converter (ADC) architecture at the base stations (BS), which enables us to independently vary the resolution of ADC connected to its each antenna, and thus properly choose them to achieve a high spectral efficiency (SE). Both BS and user equipments (UEs) are also equipped with low cost radio frequency chains, which introduce additional hardware impairments. We first derive a closed-form SE expression for this system, and then optimize the UEs uplink data power and LSFD vectors to maximize the global energy efficiency metric. The proposed optimization, which is practically implementable due to its closed-form updates, is derived by combining the quadratic and Dinkelbach transforms, and generalized Rayleigh quotient. We investigate the ADC resolution and hardware impairments values for which LSFD is highly effective, and the values for which its effectiveness reduces.

Intelligent Cloud-Edge Collaborations Assisted Energy-Efficient Power Control in Heterogeneous Networks

We consider a typical heterogeneous network (HetNet), which consists of a macro base station (BS) and multiple small BSs sharing the same spectrum band. Since the spectrum sharing among different BS-user links may cause severe mutual interference and degrades the global energy efficiency (GEE), it is important to optimize the transmit power of each BS and enhance the GEE. Conventional methods first collect the global instantaneous channel state information (CSI) and then optimize the transmit power in a centralized manner. Nevertheless, it is demanding to obtain the global instantaneous CSI in practical situations and the centralized optimization may easily overwhelm the coherence time of wireless channels. To tackle these issues, we leverage the strong computing capability of the (cloud) core network and the fast configuration capability of (edge) BSs and propose an intelligent cloud-edge collaboration framework. By properly designing the cloud-edge collaboration, we develop a deep reinforcement learning (DRL) based energy efficient power control algorithm. With the proposed algorithm, each BS can configure its transmit power independently and enhance the GEE. Simulation results reveal that, in both static-user and mobile-user scenarios, the proposed algorithm can provide comparable GEE performance with the conventional methods while requiring a much lower time complexity.

Is the digital economy an effective tool for decreasing energy vulnerability? A global case

Energy vulnerability is gaining attention in light of global economic, social, and political conflicts. The digital economy has the potential to become a vital tool in addressing current issues. This paper explores the relationship, mechanisms, and heterogeneity between the digital economy and energy vulnerability for 110 economies by using a generalized method of moments (GMM) model. Our key findings are as follows: (1) The digital economy effectively reduces energy vulnerability, with digital infrastructure and social impact as the main contributors to this effect. (2) The digital economy helps upgrade the industrial structure and the level of financial development, thereby reducing energy vulnerability. (3) Heterogeneity results show that the negative impact of the digital economy on energy vulnerability is more significant in regions with higher income levels, particularly Europe and North America. (4) The level of economic development acts as a practical threshold variable, with the digital economy having a more pronounced negative impact on energy vulnerability at higher threshold levels. These findings provide new development ideas for improving global energy security and energy efficiency, and reducing energy vulnerability.