The development of renewable electrical energy in the industrial sector requires multidimensional alignment across economic, social, environmental, and technological dimensions. This study aimed to present a multidimensional alignment framework for sustainability and to analyze indicator interactions using fuzzy cognitive maps. Key indicators were extracted from literature review and expert opinions of 20 energy policymakers, industrial managers, and faculty members, with causal relationships analyzed via specialized software. Findings identified electricity price and demand, production self sufficiency, energy saving, solar system installation, and climate change as main drivers; variables such as working capital and fossil fuel reduction played dual roles, while CO₂ reduction emerged as the response variable. The proposed framework supported strategic decision-making and roadmap development. Proposed solutions include smart monitoring and control systems based on IoT and digital twins, integrated energy management with employee self help, non governmental investment, public joint stock companies, participatory models for small and medium sized factories, and energy storage methods.
Introduction
The industrial sector occupies a strategic position in national energy policymaking because of its substantial share of electricity consumption and its essential contribution to production continuity, employment, and economic growth. A reliable electricity supply is therefore fundamental to industrial productivity, competitiveness, and resilience. Nevertheless, electricity imbalances during peak-demand periods often lead to supply restrictions for industrial units, causing reduced production capacity, lower efficiency and profitability, and, in some cases, temporary shutdowns. The existing electricity-generation portfolio remains highly dependent on fossil fuels, particularly natural gas. Constraints in fuel supply, increasing electricity demand, environmental concerns, and climate-change impacts demonstrate that the current energy system cannot adequately support long-term industrial growth. Renewable electrical energy (including solar, wind, and other clean sources) offers an important pathway for diversifying the electricity-generation mix, reducing fossil-fuel dependence, improving energy security, and mitigating environmental pollution and greenhouse-gas emissions.
However, renewable electricity development in the industrial sector is not solely a technological issue. Its sustainability depends on coherent alignment among economic, social, environmental, and technological dimensions. In practice, this alignment is challenged by inconsistent policies, inadequate infrastructure, limited financing mechanisms, insufficient operational coordination, and varying levels of industrial readiness. The absence of an integrated analytical framework for simultaneously examining these dimensions may lead to fragmented policies and ineffective investments.
Accordingly, this study aimed to develop a multidimensional alignment framework for the sustainability of renewable electrical energy in the industrial sector. It also investigated the causal interactions among the effective indicators through the Fuzzy Cognitive Map (FCM) approach. The study addressed four questions: identifying the key indicators, determining causal relationships and influence levels, classifying indicators according to their roles in the system, and explaining their priorities within the proposed multidimensional framework.
Methodology
This research employed a fuzzy cognitive mapping approach to model the complex causal relationships among the indicators affecting renewable electricity sustainability in the industrial sector. FCM was an appropriate method for analyzing systems characterized by uncertainty, interdependence, feedback mechanisms, and reliance on expert knowledge.
The research process was conducted in four stages. First, relevant indicators were identified through a review of theoretical foundations, previous studies, and energy-sustainability literature. The indicators were classified into four dimensions: economic, social, environmental, and technological. Second, the preliminary indicators were reviewed and validated by a panel of 20 experts, including energy policymakers, managers from public and private industrial organizations, and university faculty members. All participating experts had more than five years of relevant professional or academic experience.
Third, the experts assessed the existence, direction, and strength of causal relationships among the identified indicators. Data were collected through a structured questionnaire distributed via the Porsa platform of the Iranian Research Institute for Information Science and Technology. The questionnaire was designed to require responses to all items; therefore, no missing data were observed. Expert judgments were aggregated to generate the final weighted adjacency matrix of the fuzzy cognitive map.
Results and Discussions
The study identified a set of key indicators influencing the alignment and sustainability of renewable electrical energy in the industrial sector across the four economic, social, environmental, and technological dimensions. The FCM analysis revealed that electricity price and demand, self-sufficiency in electricity generation, industrial energy saving, installation of solar-energy systems, and climate change are the principal driving variables in the system. These variables have high outgoing influence and play a decisive role in shaping the development path of renewable electricity in industry.
Electricity price and demand affect investment incentives, consumption behavior, and the economic feasibility of renewable-energy projects. Industrial self-sufficiency in electricity generation reduces dependence on the national grid and improves resilience against supply restrictions. Energy-saving practices decrease industrial electricity demand and complement renewable-energy deployment. Solar-system installation contributes directly to decentralized clean-electricity production, while climate change acts as an important environmental driver that increases the urgency of energy transition and emission-reduction policies.
The findings also indicated that working capital in industries, industrial self-reliance, reduced fossil-fuel consumption, and environmental protection have bidirectional roles. These variables both influence and are influenced by other factors in the system. For example, access to working capital supports investment in renewable technologies, but it is itself affected by electricity costs, industrial performance, and policy incentives. Similarly, reduced fossil-fuel consumption contributes to environmental protection, while environmental policies and concerns can stimulate further reductions in fossil-fuel use.
The reduction of carbon dioxide emissions was identified as the main response variable. This indicated that emission reduction is primarily an outcome of coordinated improvements in electricity self-sufficiency, renewable-energy capacity, energy efficiency, financing, technology adoption, and environmental governance.
The proposed framework provided a structured basis for prioritizing policy interventions and industrial actions. It emphasizes smart consumption-monitoring and control systems, intelligent scheduling of industrial electricity use, Internet of Things (IoT)-based remote control, digital twins, integrated energy-management systems, employee participation in energy-saving initiatives, renewable-energy investment models, small-scale combined power plants for small and medium-sized enterprises, and electricity-storage solutions.
Conclusion
This study developed a multidimensional alignment framework for renewable electrical energy sustainability in the industrial sector using fuzzy cognitive maps. The findings demonstrated that sustainable renewable-energy development requires coordinated action across economic, social, environmental, and technological dimensions rather than isolated technological investments. The results identified electricity price and demand, electricity self-sufficiency, industrial energy saving, solar-system installation, and climate change as the system’s main drivers. Working capital, industrial self-reliance, fossil-fuel reduction, and environmental protection served as bidirectional variables, whereas carbon dioxide emission reduction is the main response variable. These results suggested that policymakers and industrial decision-makers should prioritize interventions that strengthen the system’s key drivers and improve coordination among interdependent variables.
The framework supported strategic decision-making and the preparation of a national or sectoral roadmap for renewable electricity development in industry. Practical measures include implementing smart energy-management systems, expanding decentralized renewable generation, promoting collective and public investment models, supporting small and medium-sized industries through participatory projects, deploying energy-storage technologies, and using advanced analytical tools for demand and generation forecasting. Future studies may extend the framework by incorporating additional stakeholders, comparing industrial subsectors, and conducting scenario-based simulations under alternative energy-policy conditions.