Resource management in agriculture is considered a pivotal issue because greenhouse farming and agriculture-related activities generate about 10–29% of all global greenhouse gas emissions. The problem of high greenhouse gas emissions is still unresolved due to the rapid expansion of arable land to meet global food demand. The purpose of this systematic literature review was to generate new perspectives and insights regarding the development of resource management and optimized environments in greenhouses, thereby lowering energy requirements and CO₂ emissions. This review sought to answer what technologies and inventions could be used to achieve zero greenhouse gas emissions through efficient energy-saving mechanisms while considering their technical and economic viability. The synthesis of the findings led to several themes which included energy-saving techniques for greenhouses, systems that reduced unfavorable external conditions and renewable energy systems. Other themes identified regarded energy storage systems, systems for managing conditions in greenhouses, carbon capture and storage, and factors influencing the performance of different technologies to enhance resource management and ensure zero carbon emissions. The findings also revealed various technologies used in the design of energy-saving techniques in greenhouses including proportional–integral–derivatives (PID), fuzzy, artificial neural networks, and other intelligent algorithms. Additionally, technologies that were a combination of these algorithms were also examined. The systems that reduced unfavorable external conditions included the use of insulation panels and intelligent shading systems. Greenhouse covers were also optimized by smart glass systems, sensors, Internet of Things (IoT), and Artificial Intelligence (AI) systems. Renewable energy systems included PV (solar) panels, wind turbines, and geothermal electricity. Some of the thermal energy storage systems widely studied in recent research included underground thermal energy storage (UTES) (for seasonal storage), phase-change materials (PCMs), and water tanks, which are used to address short-term shortages and peak loads. The adoption of the various technologies to achieve the above purposes was constrained by the fact that there was no isolated technology that could enable agricultural producers to achieve zero energy, zero emissions, and optimal resource utilization in the short term. Future research studies should establish whether it is economical for large agricultural companies to install smart glass systems and infrastructure for slow fertilizer release and carbon capture in greenhouse structures to offset the carbon footprint.

Energy management systems are essential and indispensable for the secure and optimal operation of autonomous polygeneration microgrids which include distributed energy technologies and multiple electrical loads. In this paper, a multi-agent decentralized energy management system was designed. In particular, the devices of the microgrid under study were controlled as interactive agents. The energy management problem was formulated here through the application of game theory, in order to model the set of strategies between two players/agents, as a non-cooperative power control game or a cooperative one, according to the level of the energy produced by the renewable energy sources and the energy stored in the battery bank, for the purpose of accomplishing optimal energy management and control of the microgrid operation. The Nash equilibrium was used to compromise the possible diverging goals of the agents by maximizing their preferences. The proposed energy management system was then compared with a multi-agent decentralized energy management system where all the agents were assumed to be cooperative and employed agent coordination through Fuzzy Cognitive Maps. The results obtained from this comparison, demonstrate that the application of game theory based control, in autonomous polygeneration microgrids, can ensure operational and financial benefits over known energy management approaches incorporating distributed intelligence.