Resource-consuming

Describing any activity, process, or system that requires a significant amount of resources, including but not limited to time, energy, materials, labor, and financial investment. This term often implies a trade-off, where the benefits derived from the activity must be weighed against the resources expended to achieve them. Highly resource-consuming endeavors frequently necessitate careful planning, management, and optimization to maximize efficiency and minimize waste. The scale of 'significant' varies depending on the context. It always implies a cost to acquire or utilize.

Resource-consuming meaning with examples

• Developing a new electric vehicle battery technology is a highly resource-consuming project, requiring extensive research, specialized materials, and substantial financial backing. The potential benefits of increased range and reduced emissions must be weighed against the cost of research and development, testing, and manufacturing efforts. Careful resource allocation is critical for success.
• Large-scale construction projects, like building skyscrapers or bridges, are inherently resource-consuming. They demand massive quantities of raw materials, a skilled labor force, significant amounts of energy, and prolonged periods for completion. These endeavors require meticulous project management and detailed cost analysis.
• Running computationally intensive simulations, such as weather forecasting or scientific modeling, can be extremely resource-consuming. These tasks necessitate powerful computer hardware, considerable amounts of electricity, and specialized software. Optimization techniques are often employed to streamline processing and minimize resource demands.
• Modern agriculture, especially with intensive farming practices, is a resource-consuming industry. It necessitates significant investments in water, fertilizer, pesticides, and machinery, in addition to labor. Balancing food production with environmental impact and resource sustainability is a major challenge.
• Training large language models is demonstrably resource-consuming. It uses enormous datasets, massive computational power to train the neural networks, and substantial electrical requirements. This necessitates careful financial planning and a deep understanding of model optimization and computational performance.