Search Results (558)

Mining (1) operations in remote areas (2) face significant challenges related to energy supply, high fuel costs, and limited infrastructure. This study investigates the potential for achieving energy independence (3) and resilience (4) in such environments through the integration of renewable energy sources (5) and battery–electric mining equipment. Using the “Studena Vrila” underground bauxite mine as a case study, a comprehensive techno-economic and environmental analysis was conducted across three development models. These models explore incremental scenarios of solar and wind energy adoption combined with electrification of mobile machinery. The methodology includes calculating levelized cost of energy (LCOE), return on investment (ROI), and greenhouse gas (GHG) reductions under each scenario. Results demonstrate that a full transition to RES and electric machinery can reduce diesel consumption by 100%, achieve annual savings of EUR 149,814, and cut GHG emissions by over 1.7 million kg CO₂-eq. While initial capital costs are high, all models yield a positive Net Present Value (NPV), confirming long-term economic viability. This research provides a replicable framework for decarbonizing mining operations in off-grid and infrastructure-limited regions. Full article

Sustainable investments in construction are essential for the development of communities and for reducing environmental impacts. This study analyzes two scenarios: rehabilitation of an existing building and construction of a new NZEB-compliant building, based on a life cycle cost–benefit analysis. The results show that both scenarios generate negative Net Present Values (NPVs) due to the social nature of the project, but the new NZEB building presents superior performance (NPV: USD –2.61 million vs. USD –3.05 million for rehabilitation) and lower operational costs (USD 1.49 million vs. USD 1.92 million over 30 years). Key financial indicators (IRR, CBR), sensitivity analysis, and discount rate variation support the conclusion that the NZEB scenario ensures greater economic resilience. This study highlights the relevance of extended LCCBA in guiding sustainable investment decisions in social infrastructure. Full article

In this study, we consider monthly load forecasting, which is an essential decision for energy infrastructure planning and investment. This study focuses on the Texas power grid, where electricity consumption has surged due to rising industrial activity and the increased construction of data centers driven by growing demand for AI. Based on an extensive exploratory data analysis, we identify key characteristics of monthly electricity demand in Texas, including an accelerating upward trend, strong seasonality, and temperature sensitivity. In response, we propose a regression-based forecasting model that incorporates a carefully designed set of input features, including a nonlinear trend, lagged demand variables, a seasonality-adjusted month variable, average temperature of a representative area, and calendar-based proxies for industrial activity. We adopt a rolling forecasting approach, generating 12-month-ahead forecasts for both 2023 and 2024 using monthly data from 2013 onward. Comparative experiments against benchmarks including Holt–Winters, SARIMA, Prophet, RNN, LSTM, Transformer, Random Forest, LightGBM, and XGBoost show that the proposed model achieves superior performance with a mean absolute percentage error of approximately 2%. The results indicate that a well-designed regression approach can effectively outperform even the latest machine learning methods in monthly load forecasting. Full article

In the context of global climate governance and the green transition, digital infrastructure serves as a critical enabler of resource allocation in the digital economy, offering strategic value in tackling synergistic pollution and carbon reduction challenges. Using panel data from 280 prefecture-level cities, this study employs a multiperiod difference-in-differences (DID) approach, leveraging smart city pilot policies as a quasinatural experiment, to assess how digital infrastructure affects urban synergistic pollution-carbon mitigation (SPCM). The empirical results show that digital infrastructure increases the urban SPCM index by 1.5%, indicating statistically significant effects. Compared with energy and income effects, digital infrastructure can influence this synergistic effect through indirect channels such as the energy effect, economic agglomeration effect, and income effect, with the economic agglomeration effect accounting for a larger share of the total effect. Additionally, fixed-asset investment has a nonlinear moderating effect on this relationship, with diminishing marginal returns on emission reduction when investment exceeds a threshold. Heterogeneity tests reveal greater impacts in eastern, nonresource-based, and environmentally regulated cities. This study expands the theory of collaborative environmental governance from the perspective of new infrastructure, providing a theoretical foundation for establishing a long-term digital technology-driven mechanism for SPCM. Full article

Digital technologies could facilitate green development by enhancing energy efficiency. However, existing research on coupling coordination between digital infrastructure and green development remains scarce. To fill this research gap, this study analyzes the spatio-temporal variations and barriers of coupling coordination. An evaluation index system is established and then the coupling relationship and the barrier factors between digital infrastructure and green development are analyzed. A provincial analysis is conducted by using data from China. The results in the study indicate (1) coupling coordination between digital infrastructure and green development exhibits a relatively low state, characterized by an overall upward trend; (2) noteworthy disparities are observed in the spatio-temporal pattern of the coupling coordination degree, reflecting the overall evolutionary trend from low to high coupling coordination, along with the characteristics of positive spatial correlation and high spatial concentration; and (3) obstacle factors are analyzed from the aspects of digital infrastructure and green development, emphasizing the construction of mobile phone base stations and investment in pollution control, among other aspects. This study contributes valuable insights for improvement paths for digital infrastructure and green development, offering recommendations for optimizing strategies to promote their coupled development. Full article

Direct electrification and hydrogen utilization represent two key pathways for decarbonizing the glass industry, with their effectiveness subject to adequate furnace design and renewable energy availability. This study presents a techno-economic assessment for optimal solar energy integration in a representative 300 t/d oxyfuel container glass furnace with a specific energy consumption of 4.35 GJ/t. A mixed-integer linear programming formulation is developed to evaluate specific melting costs, carbon emissions, and renewable energy self-consumption and self-production rates across three scenarios: direct solar coupling, battery storage, and a hydrogen-based infrastructure. Battery storage achieves the greatest reductions in specific melting costs and emissions, whereas hydrogen integration minimizes electricity export to the grid. By incorporating capital investment considerations, the study quantifies the cost premiums and capacity requirements under varying decarbonization targets. A combination of 30 MW of solar plant and 9 MW of electric boosting enables the realization of around 30% carbon reduction while increasing total costs by 25%. Deeper decarbonization targets require more advanced systems, with batteries emerging as a cost-effective solution. These findings offer critical insights into the economic and environmental trade-offs, as well as the technical constraints associated with renewable energy adoption in the glass industry, providing a foundation for strategic energy and decarbonization planning. Full article

As the global energy transition accelerates, the integration of solar power and artificial intelligence (AI) technologies offers new pathways for sustainable local development. This study examines four Serbian municipalities—Šabac, Sombor, Pirot, and Čačak—to assess how AI-enabled solar power systems can enhance energy resilience, reduce emissions, and support community-level sustainability goals. Using a mixed-method approach combining spatial analysis, predictive modeling, and stakeholder interviews, this research study evaluates the performance and institutional readiness of local governments in terms of implementing intelligent solar infrastructure. Key AI applications included solar potential mapping, demand-side management, and predictive maintenance of photovoltaic (PV) systems. Quantitative results show an improvement >60% in forecasting accuracy, a 64% reduction in system downtime, and a 9.7% increase in energy cost savings. These technical gains were accompanied by positive trends in SDG-aligned indicators, such as improved electricity access and local job creation in the green economy. Despite challenges related to data infrastructure, regulatory gaps, and limited AI literacy, this study finds that institutional coordination and leadership commitment are decisive for successful implementation. The proposed AI–Solar Integration for Local Sustainability (AISILS) framework offers a replicable model for emerging economies. Policy recommendations include investing in foundational digital infrastructure, promoting low-code AI platforms, and aligning AI–solar projects with SDG targets to attract EU and national funding. This study contributes new empirical evidence on the digital–renewable energy nexus in Southeast Europe and underscores the strategic role of AI in accelerating inclusive, data-driven energy transitions at the municipal level. Full article

The widespread adoption of cloud computing has amplified the demand for electric power. It is strategically important to address the limitations of reliable sources and sustainability of power. Research and investment in data centres and power infrastructure are therefore critically important for our digital economy. A novel heuristic for the minimisation of energy consumption in cloud computing is presented. It draws similarities to the concept of “network slices”, in which an orchestrator enables multiplexing to reduce the network “churn” often associated with significant losses of energy consumption. The novel network slicing–ring fencing ratio is a heuristic calculated through an iterative procedure for the reduction in cloud energy consumption. Simulation results show how the non-convex equation optimises power by reducing energy from 10,680 kJ to 912 kJ, which is a 91.46% efficiency gain. In comparison, the Heuristic AUGMENT Non-Convex algorithm (HA-NC, by Hossain and Ansari) reported a 312.74% increase in energy consumption from 2464 kJ to 10,168 kJ, while the Priority Selection Offloading algorithm (PSO, by Anajemba et al.) also reported a 150% increase in energy consumption, from 10,738 kJ to 26,845 kJ. The proposed network slicing–ring fencing ratio is seen to successfully balance energy consumption and computing performance. We therefore think the novel approach could be of interest to network architects and cloud operators. Full article

This study explores the complex interaction between the Internet of Things (IoT) and the new energy sector and analyzes how their integration can catalyze a transition toward a sustainable low-carbon economy. Through the full-sample and rolling sub-sample methods, we empirically examine the dynamic interrelationship between China’s IoT index (IoT) and the New Energy Index (NEI). Quantitative analysis reveals significant time-varying characteristics and bidirectional causal complexity in the interaction between the IoT and new energy. The IoT has a dual-edged impact on the development of new sources of energy. In the long run, the IoT plays a dominant role in incentivizing new energy, helping to enhance its stability and economic value. However, during stages characterized by technological bottlenecks or resource competition, the high energy consumption of IoT infrastructure may suppress the investment returns of new energy. Simultaneously, new energy has both positive and negative impacts on the IoT. On the one hand, new energy provides low-cost, sustainable power to support the IoT, driving the construction of the IoT ecosystem. On the other hand, it may threaten the continuity of IoT power supply, and the complexity of standardization and regulation in the sector may constrain the development of the IoT. This study provides a fresh perspective on promoting the integration of digital technology and green energy, uncovering nonlinear trade-offs between innovation-driven growth and carbon reduction goals, and offering policy insights for cross-sectoral collaboration to achieve sustainability. Full article

Green hydrogen, produced via renewable-powered electrolysis, offers a promising path toward deep decarbonisation in energy systems. This study investigates the major technological, infrastructural, and economic challenges facing green hydrogen production in Jordan—a resource-constrained yet renewable-rich country. Key barriers were identified through a structured survey of 52 national stakeholders, including water scarcity, low electrolysis efficiency, limited grid compatibility, and underdeveloped transport infrastructure. Respondents emphasised that overcoming these challenges requires investment in smart grid technologies, seawater desalination, advanced electrolysers, and policy instruments such as subsidies and public–private partnerships. These findings are consistent with global assessments, which recognise similar structural and financial obstacles in scaling up green hydrogen across emerging economies. Despite the constraints, over 50% of surveyed stakeholders expressed optimism about Jordan’s potential to develop a competitive green hydrogen sector, especially for industrial and power generation uses. This paper provides empirical, context-specific insights into the conditions required to scale green hydrogen in developing economies. It proposes an integrated roadmap focusing on infrastructure modernisation, targeted financial mechanisms, and enabling policy frameworks. Full article

As global climate change intensifies and the transition to clean energy accelerates, lithium-ion batteries—critical components of electric vehicles—are becoming increasingly vital in international trade networks. This study investigates the structural evolution and determinants of the electric vehicle lithium-ion battery trade network among European Union (EU) member states from 2012 to 2023, employing social network analysis and the multiple regression quadratic assignment procedure method. The findings demonstrate the transformation of the network from a centralized and loosely connected structure, with Germany as the dominant hub, to a more interconnected and decentralized system in which Poland and Hungary emerge as the leading players. Key network metrics, such as the density, clustering coefficients, and average path lengths, reveal increased regional trade connectivity and enhanced supply chain efficiency. The analysis identifies geographic and economic proximity, logistics performance, labor cost differentials, energy resource availability, and venture capital investment as significant drivers of trade flows, highlighting the interaction among spatial, economic, and infrastructural factors in shaping the network. Based on these findings, this study underscores the need for targeted policy measures to support Central and Eastern European countries, including investment in logistics infrastructure, technological innovation, and regional cooperation initiatives, to strengthen their integration into the supply chain and bolster their export capacity. Furthermore, fostering balanced inter-regional collaborations is essential in building a resilient trade network. Continued investment in transportation infrastructure and innovation is recommended to sustain the EU’s competitive advantage in the global electric vehicle lithium-ion battery supply chain. Full article

This study conducts a comprehensive systematic review of the economic impact of Digital Twin (DT) technology within the Architecture, Engineering, and Construction (AEC) industry, following the PRISMA methodology. While DT adoption has been accelerated by advancements in Building Information Modelling (BIM), the Internet of Things (IoT), and data analytics, significant challenges persist—most notably, high initial investment costs and integration complexities. Synthesising the literature from 2016 onwards, this review identifies sector-specific barriers, regulatory burdens, and a lack of standardisation as key factors constituting DT implementation costs. Despite these hurdles, DTs demonstrate strong potential for enhancing construction productivity, optimising lifecycle asset management, and enabling predictive maintenance, ultimately reducing operational expenditures and improving long-term financial performance. Case studies reveal cost efficiencies achieved through DTs in modular construction, energy optimisation, and infrastructure management. However, limited financial resources and digital skills continue to constrain the uptake across the sector, with various extents of impact. This paper calls for the development of unified standards, innovative public–private funding mechanisms, and strategic collaborations to unlock and utilise DTs’ full economic value. It also recommends that future research explore theoretical frameworks addressing governance, data infrastructure, and digital equity—particularly through conceptualising DT-related data as public assets or collective goods in the context of smart cities and networked infrastructure systems. Full article

The equitable allocation of clean energy access across towns–rural divides is a critical benchmark of modernization in developing economies. This is because it is intricately linked to the realization of strategic goals such as shared prosperity, ecological civilization advancement, and national energy security reinforcement. This research examines the impact of China’s digital village (DV) construction in reducing the urban–rural disparity in household clean energy access, evaluates the effect on towns–rural clean energy consumption inequality (CEI), explores the mediating mechanisms, and considers regional heterogeneity. It is an innovative approach to test the influence of digital village construction on clean energy consumption inequality between urban and rural areas, beyond which conventional research is limited to infrastructure investment and policy considerations. We can reach the following three results: (1) With the continuous improvement of digital village construction, CEI between towns and rural areas shows an “inverted U-shaped” change. (2) From the perspective of the intermediary mechanism, agricultural technological progress (ATP) and industrial structure upgrading (IND) can facilitate digital village construction and reduce the disparity in clean energy consumption between towns and rural regions. (3) From the perspective of heterogeneity analysis, digital village construction in areas with low urbanization levels, high terrain undulation, and non-clean energy demonstration provinces can significantly alleviate CEI. It is on this basis that the present paper proposes a policy recommendation for the Chinese government to effectively reduce the gap between towns and rural clean energy consumption in the process of digital village construction. Full article

The integration of high-penetration renewable energy sources (RESs) into transmission networks introduces profound uncertainty that challenges traditional infrastructure planning approaches. Existing transmission expansion planning (TEP) models either rely on static scenario sets or over-conservative worst-case assumptions, failing to capture the operational stress triggered by rare but structurally impactful renewable behaviors. This paper proposes a novel bi-level optimization framework for transmission planning under adversarial uncertainty, coupling a distributionally robust upper-level investment model with a lower-level operational response embedded with physics and market constraints. The uncertainty space was not exogenously fixed, but instead dynamically generated through a physics-informed spatiotemporal generative adversarial network (PI-ST-GAN), which synthesizes high-risk renewable and load scenarios designed to maximally challenge the system’s resilience. The generator was co-trained using a composite stress index—combining expected energy not served, loss-of-load probability, and marginal congestion cost—ensuring that each scenario reflects both physical plausibility and operational extremity. The resulting bi-level model was reformulated using strong duality, and it was decomposed into a tractable mixed-integer structure with embedded adversarial learning loops. The proposed framework was validated on a modified IEEE 118-bus system with high wind and solar penetration. Results demonstrate that the GAN-enhanced planner consistently outperforms deterministic and stochastic baselines, reducing renewable curtailment by up to 48.7% and load shedding by 62.4% under worst-case realization. Moreover, the stress investment frontier exhibits clear convexity, enabling planners to identify cost-efficient resilience strategies. Spatial congestion maps and scenario risk-density plots further illustrate the ability of adversarial learning to reveal latent structural bottlenecks not captured by conventional methods. This work offers a new methodological paradigm, in which optimization and generative AI co-evolve to produce robust, data-aware, and stress-responsive transmission infrastructure designs. Full article

As many renewable energy sources have been waiting to be interconnected with distribution systems due to the lack of power system infrastructure in Korea, studies to solve the delayed problem for renewable energy sources required. In order to overcome these problems, this paper presents an introduction model and optimal capacity algorithm of a VPL (virtual power line) device, which is a virtual power line operation technology to manage the power system by operating an ESS installed at the coupling point of renewable energy source without additionally expanding the power system infrastructure in a conventional way; this paper also proposes an economic evaluation method to assess the feasibility of the VPL device. The optimal capacity of the VPL device is determined by solving the over-voltage problem for the customer, and the economic evaluation method for the VPL device is considered by cost and benefit elements to evaluate the feasibility of introduction model for VPL device. From the simulation result of the proposed optimal capacity algorithm and economic evaluation method based on the introduction model in the VPL device, and it was confirmed that the optimal kW capacity of VPL device was selected as the maximum value in power control values, and the optimal kWh capacity was also determined by accumulating the power control values over the time intervals; also, the proper capacity of the VPL can be more economical than the investment cost of power system infrastructure expansion in the conventional method. Full article