The industrial sector, due to its significant share in national electricity consumption and its strategic role in sustaining production and economic growth, holds a prominent position in energy policymaking. However, the development of renewable electrical energy in this sector requires multidimensional alignment among economic, social, environmental, and technological dimensions, which in practice faces challenges related to policies, infrastructure, and implementation mechanisms. This study aims to propose a multidimensional alignment framework for the sustainability of renewable electrical energy in the industrial sector and to analyze the interactions among influential indicators using Fuzzy Cognitive Maps (FCM). First, key indicators were identified through a systematic review of the research literature. Subsequently, the causal relationships among these indicators were determined based on the opinions of 20 experts, including energy policymakers, industrial managers from both public and private sectors, and academic scholars. Data analysis were conducted using the Fuzzy Cognitive Maps approach with the FCMapper and UCINET software tools. The results indicate that factors such as electricity price and demand, self-sufficiency in electricity generation, energy conservation in industry, installation of solar photovoltaic systems, and climate change act as the main driving forces of the system. Additionally, variables such as industrial working capital, industrial self-reliance, reduction of fossil fuel consumption, and environmental protection play bidirectional roles, while the reduction of carbon dioxide (CO₂) emissions are identified as a response variable. The proposed framework provides a basis for strategic decision-making and for developing a roadmap toward the sustainable development of renewable electrical energy in the industrial sector.