Search Results (34)

Renewable energy sources, such as solar thermal and photovoltaic, geothermal, biomass, hydropower, and wind, offer significant sustainability advantages. Yet the sector still faces difficulties in several areas that tend to reduce the efficiency of these new energy forms. Some of these challenges include inconsistent electricity supply, the diffuse nature of renewable energy sources, which makes them difficult to exploit, and the inconsistent and unpredictable nature of electricity supply, which has repercussions for renewable energy markets. Although Industry 4.0 is inherently energy-intensive, its positive contribution to renewable energy systems may outweigh its costs. Consequently, this study conducts a scoping review on the role of digital technologies in renewable energy systems. It focuses on open-access conference papers, journal articles, and book chapters published between 2020 and 2026, selected from scientific platforms and databases such as IEEE Xplore, ScienceDirect, SpringerLink, and Scopus. A multi-stage screening process and a summary sheet for a set of 89 selected articles were produced to extract the necessary information. The results show that Industry 4.0 influences renewable energy systems at the design and installation stage in predictive maintenance, efficient management, and energy security. Meanwhile, Industry 4.0 in renewable energy systems still faces negative externalities that can be categorised as political, financial, infrastructural, environmental, human, security, and technological. To address these challenges, which tend to become entangled in cycles of negative reinforcement, the paper suggests defining standardised, clear, strict, and stable frameworks at the political, legal, regulatory, and environmental levels to overcome most challenges associated with the digital transformation of renewable energy. The study also recommends flexible, inclusive strategic planning that accounts for the digital maturity of the renewable energy system. From these perspectives, the study contributes to the literature by addressing the role of Industry 4.0 technologies in renewable energy systems from a strategic and coordinated perspective, from both human and technological standpoints. It also offers managerial and policy implications by supporting innovation in renewable energy systems on the one hand and contributing to policy and regulatory decision-making that favour their growth on the other. Full article

Background: Interest in green hydrogen (GH) is increasing, as it can act both as an energy carrier and as an industrial feedstock to decarbonise applications that currently rely on fossil-based (grey) hydrogen. Hard-to-abate industries, such as steelmaking, face complex and multi-dimensional uncertainties when assessing conversion to GH and the associated supply chain redesign. Materials and Methods: We propose an enterprise-oriented decision-modelling framework that structures conversion drivers into six decision-relevant dimensions (socio-economic, infrastructure, technology, market, supply chain, and enterprise). The framework is refined through a two-round expert elicitation process and operationalised through a scenario planning workflow based on discrete key-factor projections and an elicited interdependency network. Building on this dependency structure, we propose a transparent consistency-based reduction approach that integrates pairwise projection compatibility and graph-guided screening to identify internally coherent and decision-relevant scenarios. The procedure is further demonstrated through an illustrative steelmaking conversion case. Results: The expert-supported workflow identifies 14 external key factors and their decision-relevant projections, together with an elicited interdependency structure among them. The illustrative application shows how an initial scenario space of 6561 configurations, based on eight selected key factors, can be screened to 1335 internally admissible configurations and consolidated into four representative scenarios. These scenarios capture distinct decision contexts, including coordinated acceleration, demand-led but infrastructure-constrained transition, technology and policy push with limited market pull, and fragmented, delayed transition. Conclusions: The approach enhances methodological transparency in scenario-based decision support and offers hard-to-abate industries a structured basis for evaluating green hydrogen conversion under systemic interdependencies and deep uncertainty. The illustrative application further demonstrates how the framework can transform combinatorial uncertainty into a compact and interpretable set of scenarios supporting stakeholder discussion and strategic decision-making. Full article

This study systematically examines the policy and technological pathways for the sustainable development of China’s 3D printing industry under the “Dual Carbon” goals. A three-dimensional sustainability framework is developed, integrating resource efficiency, environmental performance, and socio-economic value. Based on this framework, the study conducts a full-process analysis covering design, material preparation, manufacturing, post-processing, use, and recycling stages. The analysis identifies key carbon-reduction mechanisms of 3D printing, including material savings, reduced energy consumption, lightweight-enabled emission reduction, and distributed manufacturing. A comparative analysis of China, the European Union, and the United States reveals major constraints in China’s 3D printing sector, particularly in top-level policy design, standardization systems, legal frameworks, industrial coordination, and low-carbon core technologies. Based on these findings, the study proposes a dual-driven development pathway integrating policy optimization and technological innovation. From an institutional perspective, this pathway emphasizes green policy incentives, including strategic planning, standard setting, green finance, and collaborative governance. From a technological perspective, it highlights the importance of low-carbon material development, refined energy-efficiency management, life-cycle carbon accounting platforms, and value creation across the product life cycle. Overall, the study demonstrates that effective policy–technology synergy is essential for transforming theoretical carbon-reduction potential into scalable and practical outcomes, providing a systematic analytical framework for academic research and actionable guidance for policymakers and industry stakeholders. Full article

Cross-impact analysis is frequently used in scenario-analogous studies to identify critical factors influencing ecological change, strategic planning, technology foresight, resource allocation, risk mitigation, cost optimization, and decision support. Scenarios enable different organizations to comprehend prevailing situations, prepare for probable futures, and mitigate conceivable risks. Unfortunately, cross-impact analysis methods are often criticized for their difficulty in handling complex interactions, cognitive bias, time-intensiveness, heavy reliance on a limited pool of experts, and inconsistency in assigning judgment, which can affect the expected outcomes. This paper introduces a novel method for constructing consistent scenarios that addresses these criticisms and those associated with scenario methods. The method is based on cross-impact analysis and crowdsourcing for constructing consistent scenarios. The cross-impact analysis component of the method is based on advanced impact analysis and cross-impact balance analysis to, respectively, provide a time-efficient reduction in complex interdependent factors and construct consistent scenarios from a set of reduced factors. The crowdsourcing element leverages the cumulative intelligence of a group of experts to help mitigate cognitive bias and transparently give a more inclusive analysis. The method was implemented and validated with a practical case of renewable energy adoption, a vital challenge for socioeconomic progress and climate change resilience. While the method provides a sturdy foundation for writing scenario narratives, the result confirms its robustness for constructing consistent scenarios and suggests that the future of renewable energy adoption can be enhanced through careful cogitation of best-case, base-case, and worst-case scenarios, which include varying states of perceived value, awareness, and perceived support. These findings contribute to a more nuanced understanding of how socio-cognitive and institutional factors interact to influence the pace and direction of sustainable energy transitions. Full article

The increasing emphasis on environmental protection, climate change mitigation, and the transition to a circular economy requires industries, including the wood-processing sector, to integrate sustainability into strategic and operational management. Green growth indicators represent essential tools for evaluating the environmental, economic, and social impacts of business activities, while also contributing to the sustainable economics and responsible management of forest resources and products. This study applies a qualitative research design using structured interviews with 10 executives from medium and large woodworking enterprises in Slovakia. The interviews examined company strategies, practices, and challenges in sustainable development and forest resource utilization. The findings reveal that while many companies actively manage waste, invest in green technologies, and conduct internal audits, the broader implementation of environmental management systems and the uptake of public sustainability funding remain limited. Notably, 90% of respondents emphasized waste volume and recovery rates as critical indicators. Based on the results, a set of green growth indicators was developed and categorized across key thematic areas including waste management, energy efficiency, stakeholder communication, certification, and strategic planning. These indicators not only support the assessment of corporate sustainability but also strengthen efficient forest resource management, responsible use of raw materials, and the long-term economic viability of the sector. The study highlights the importance of systematically designed and practically applicable indicators for guiding companies toward sustainable competitiveness and emphasizes the need for stronger institutional support, improved access to reliable data, and integration of sustainability metrics into core business decision-making. Full article

There is a growing global emphasis on reducing environmental pollution through innovative clean energy technologies, with photovoltaic systems gaining prominence as a sustainable solution. This study presents an integrated approach, combining advanced architectural modeling and dynamic energy simulation to evaluate the utilization of rooftop photovoltaic panels on a high-density higher educational building in Saudi Arabia. Utilizing detailed modeling involving Autodesk Revit and energy simulation through DesignBuilder to Level of Detail 3, the research provides unprecedented accuracy, validated against actual energy consumption data with a remarkable 92.28% precision. Notably, approximately 60% of the rooftop area is identified as suitable for photovoltaic installation, demonstrating a significant capacity to generate 1,028,494.50 kWh annually, covering 61.7% of the building’s energy needs. Financial analysis reveals robust economic benefits, including annual savings of USD 74,938.84, a payback period of under 7 years, and lifetime savings exceeding USD 1.87 million over 25 years. Seasonal variations and surplus energy during winter months are also detailed, highlighting the system’s resilience. Importantly, this study aligns with Saudi Arabia’s “Vision 2030” by showcasing the feasibility and strategic importance of rooftop photovoltaic solutions in urban educational settings within hot-climate regions, offering a pioneering contribution to sustainable urban energy planning. Full article

The evolution of electrical power systems into smart grids has brought about significant advancements in electricity generation, transmission, and utilization. These cutting-edge grids have shown potential as an effective way to maximize energy efficiency, manage resources effectively, and enhance overall reliability and sustainability. However, with the integration of complex technologies and interconnected systems inherent to smart grids comes a new set of safety and security challenges that must be addressed. First, this paper provides an in-depth review of the key considerations surrounding safety and security in smart grid environments, identifying potential risks, vulnerabilities, and challenges associated with deploying smart grid infrastructure within the context of the Internet of Things (IoT). In response, we explore both cryptographic and non-cryptographic countermeasures, emphasizing the need for adaptive, lightweight, and proactive security mechanisms. As a key contribution, we introduce a layered classification framework that maps smart grid attacks to affected components and defense types, providing a clearer structure for analyzing the impact of threats and responses. In addition, we identify current gaps in the literature, particularly in real-time anomaly detection, interoperability, and post-quantum cryptographic protocols, thus offering forward-looking recommendations to guide future research. Finally, we present the Multi-Layer Threat-Defense Alignment Framework, a unique addition that provides a methodical and strategic approach to cybersecurity planning by aligning smart grid threats and defenses across architectural layers. Full article

As cities worldwide pursue sustainability, integrating renewable energy has emerged as a strategic priority in urban planning. This research provides a case study investigation into how Dubai South, a distinctive aerotropolis combining aviation, logistics, and residential sectors, can implement a comprehensive renewable energy strategy aligned with the UAE’s clean energy goals. Grounded in the theoretical frameworks of Sustainable Strategic Management (SSM) and Energy Management Systems (EMSs), and informed by global best practices and advanced technological innovations, this study proposes a strategic roadmap tailored to the complex energy demands and urban dynamics of Dubai South. Using the Dubai South HQ solar deployment as a baseline, this research explores technical, regulatory, and economic barriers alongside key enabling factors. Its core contribution is the development of a scalable strategy for renewable energy integration in aerotropolis settings, offering practical insights for policymakers, urban planners, and developers aiming to advance sustainability in rapidly evolving, logistics-based cities. Full article

The study examines the role of heat pumps (HPs) in achieving the targets set by the Italian National Integrated Energy and Climate Plan (PNIEC) for 2030, using official data and European-recognized calculation methodologies to quantify the renewable energy produced. Starting from the current stock analysis—21 million HPs installed in 2022, providing 39 GW_th of thermal capacity—the research outlines potential growth scenarios based on installation trends from the past three years: Scenario A assumes 2.5 million HPs/year, (b) 2.2 million/year, and (c) 1.6 million/year. Only Scenario A, the most ambitious, achieves full compliance with 2030 targets by ensuring over 4723 ktoe of renewable energy produced. An additional Scenario D is analyzed, based on the lowest annual installed capacity observed in the past three years but with a modified technology mix emphasizing air-to-water (A/W) and ground-source water-to-water (W/W) HPs. This scenario still achieves the 2030 goals, reaching 66.04 GW_th and 4859 ktoe of renewable energy. The results confirm that technology choices will be strategic to meet the targets. The study also highlights the importance of stable incentive policies, proper development of the industrial supply chain, and a plan for the technological upgrading of the existing systems stock. Full article

Hydraulic legged robots, with advantages such as high load capacity and power density, have become a strategic driving force in advancing intelligent mobile platform technologies. However, their high energy consumption significantly limits long-duration endurance and efficient operational performance. In this paper, inspired by the excellent autonomous energy-efficient consciousness of mammals endowed by natural evolution, a bionic energy-efficient inverse kinematics method based on neural networks (EIKNN) is proposed for the energy-efficient motion planning of hydraulic legged robots with redundant degrees of freedom (RDOFs). Firstly, the dynamic programming (DP) algorithm is used to solve the optimal joint configuration with minimum energy loss as the goal, and the training data set is generated. Subsequently, the inverse kinematic model of the leg with minimum energy loss is learned based on neural network (NN) simulation of the autonomous energy-efficient consciousness endowed to mammals by natural evolution. Finally, extensive comparative experiments validate the effectiveness and superiority of the proposed method. This method not only significantly reduces energy dissipation in hydraulic legged robots but also lays a crucial foundation for advancing hydraulic legged robot technology toward high efficiency, environmental sustainability, and long-term developmental viability. Full article

This study aimed to develop a strategic management model for the agricultural sector to effectively reduce greenhouse gas emissions in the future, primarily focusing on increasing agricultural waste. This study was built upon a model known as the Path Analysis with Simultaneous Equation System based on Full Information Maximum-Likelihood (Path-SFIML) Model, which has been thoroughly validated for its validity, measurement of model fit, and absence of spurious results. The findings revealed that the environmental sector is with the has low capacity to readjust to equilibrium, requiring thousands of years to recover. Therefore, this study proposes a new policy scenario for urgent national management through scenario planning. Based on the research results, the key indicators identified for scenario planning include clean technology, waste biomass, organic waste treatments, and renewable energy. These indicators must be prioritized to effectively manage the increase in agricultural waste. This study demonstrates that implementing these measures would reduce the growth rate of agricultural waste to 30.38% (2037/2018) and decrease the growth rate of greenhouse gas emissions to 36.20% (2037/2018). These rates remain within the national safety threshold, which is set at 1302 Gg CO₂e. This study also derived strategic guidelines from stakeholders to enhance the dissemination of research findings and address gaps in quantitative research, enabling more appropriate strategy formulation. It was found that the key approach to defining the new scenario policy in this research is suitable but requires improvements in criminal law, administrative law, and environmental law to ensure they are relevant and enforceable in the present context. Hence, the 20 Year National Strategy must urgently adopt this critical tool for decision-making to achieve sustainable green environmental goals. Full article

This research aims to identify appropriate strategies for reducing CO₂ emissions under the carbon neutrality framework within Smart City Thailand. The Path Solow model based on vector moving average–GARCH in mean with environmental pollution (PS–VMA–GARCHM–EnPoll model) has been developed, and it is a highly suitable tool for environmental protection. This model can also be applied to other sectors and stands out from previous models by effectively prioritizing key factors for long-term strategic planning in a concrete and efficient manner. Additionally, the model illustrates the direction of causal relationships, both positive and negative, which is highly beneficial for more concrete policy formulation. This allows the government to determine which factors should be reduced or receive less support and which factors should be promoted for greater growth compared to the past. The findings suggest two strategic approaches to reducing CO₂ emissions: (1) New-scenario policy based on high-sensitivity indicators—By selecting indicators with a sensitivity analysis value above 90%, including clean technology, renewable energy rate, biomass energy, electric vehicles, and green material rate, CO₂ emissions can be reduced by 43.06%, resulting in a total CO₂ gas emission of 398.01 Mt CO₂ Eq. by 2050, which is below Thailand’s carrying capacity threshold of 450.07 Mt CO₂ Eq.; and (2) Expanded-scenario policy using all indicators with sensitivity above 80%—By incorporating additional indicators, such as waste biomass, gasohol use rate, fatty acid methyl ester rate, and solar cell rate, along with those in the first scenario, CO₂ emissions can be reduced by 60.65%, leading to a projected CO₂ gas emission of 275.90 Mt CO₂ Eq. by 2050, which aligns with Thailand’s national strategy goal of reducing CO₂ gas emissions by at least 40% by 2050 and sets the country on the right path toward achieving net-zero greenhouse gas emissions by 2065. Thus, implementing the PS–VMA–GARCHM–EnPoll model can effectively contribute to the long-term national strategy for greenhouse gas reduction, ensuring sustainable environmental management for the future. Full article

Climate change is leading modern society to seek innovative solutions for sustainable development and a zero-carbon economy. Nevertheless, new technologies strongly rely on precious raw materials and might suffer from supply chain risks. The European Union has identified a set of raw materials deemed to be critical or strategic because they appear essential for energy transition technologies. Consequently, long-term energy system planning must factor in the availability of these critical raw materials when selecting specific technologies, as their supply could be affected by global policies or conflicts. This paper provides a literature review on the assessment of critical raw materials in energy technologies comparing the main approaches on critical raw materials content assessment in technologies, long-term planning studies considering critical raw materials, and the development of indicators for critical raw materials content in energy technologies. The main findings of this review suggest that existing reliable databases with the bill of materials, such as life cycle inventories, should be exploited and that proper indicators to rank the criticality of materials and the importance of a specific technology should be developed. These findings are discussed and organized proposing a method for the optimal planning of an energy technologies mix in regional or national energy systems considering the availability and future supply of critical raw materials. Full article

In response to the urgent threat of climate change and the drivers of high greenhouse gas emissions, countries worldwide are adopting policies to reduce their carbon emissions, with net-zero emissions targets. These targets vary by region, with Canada aiming to achieve net-zero emissions by 2050. In response to the Independent Electricity System Operator’s (IESO’s) “Pathways to Decarbonization” report, which evaluates a proposed moratorium on new natural gas generating stations, this study presents a methodology to support energy transitions in Ontario by using a modified Dynamic Integrated Climate-Economy (DICE) model, which focuses on replacing fossil fuel power plants (FFPPs) with clean energy sources, including nuclear, solar, wind, and hydro. This research expands on our prior work that used the DICE model to evaluate the potential for replacing FFPPs with Small Modular Reactors (SMRs) on a global scale. This study includes solar, wind, hydro, and SMRs to provide a diversified clean energy portfolio and integrates fuzzy logic to optimize construction rates and address uncertainties. The study uses Ontario as a case study, aligning with IESO’s objectives for Ontario’s energy transition. The IESO’s projections for net zero by 2050 are applied. The study is extended to 2100 to assess the longer-term implications of sustained energy transition efforts beyond the immediate goals set by the IESO. This approach is scalable to other regions and countries with similar energy transition challenges. The study results indicate that to meet Ontario’s 2050 net-zero target, approximately 183 SMR units, 1527 solar units, 289 wind units, and 449 hydro units need to be constructed. For the 2100 target, the required number of units is slightly higher due to the longer time frame, reflecting a gradual ramp-up in construction. The optimization of construction rates using fuzzy logic shows that the pace of deployment is influenced by critical factors such as resource availability, policy support, and public acceptance. This underscores the need for accelerated clean energy deployment to meet long-term emissions reduction goals. The findings highlight the complexities of transitioning to a low-carbon energy system and the importance of addressing uncertainties in planning. Policymakers are urged to integrate these insights into strategic energy planning to ensure the successful deployment of clean energy technologies. This study provides valuable recommendations for optimizing energy transitions through a robust, flexible framework that accounts for both technological and socio-economic challenges. Full article

The proliferation of information and communication technologies (ICTs) within smart cities has not only enhanced the capabilities and efficiencies of urban energy systems but has also introduced significant cyber threats that can compromise these systems. To mitigate the financial risks associated with cyber intrusions in smart city infrastructures, this study introduces a two-stage hierarchical planning model for ICT-integrated multi-energy systems, emphasizing the economic role of cyber insurance. By adopting cyber insurance, smart city operators can mitigate the financial impact of unforeseen cyber incidents, transferring these economic risks to the insurance provider. The proposed two-stage optimization model strategically balances the economic implications of urban energy system operations with cyber insurance coverage. This approach allows city managers to make economically informed decisions about insurance procurement in the first stage and implement cost-effective defense strategies against potential cyberattacks in the second stage. Utilizing a distributionally robust approach, the study captures the emergent and uncertain nature of cyberattacks through a moment-based ambiguity set and resolves the reformulated linear problem using a dynamic cutting plane method. This work offers a distinct perspective on managing the economic risks of cyber incidents in smart cities and provides a valuable framework for decision making regarding cyber insurance procurement, ultimately aiming to enhance the financial stability of smart city energy operations. Full article