This empirical study investigates industry awareness about the adoption of sustainable or green practices across five modern software architectures: Microservices, Event-Driven, Serverless, Machine Learning, and Generative AI. Using an approach that combined surveys (109 responses) and interviews (19 professionals) from the industry, we gathered data to understand awareness of green practices throughout the design, development and maintenance of software architectures. Our findings reveal that while software professionals understand green software concepts, practical implementation lags behind due to higher priority given to performance, cost, accuracy, and reliability. Each architecture has specific energy challenges: Microservices struggles with resource allocation and excessive communication; Serverless faces over-invocation and cold-start issues; Event-Driven systems struggle with excessive triggering; Machine Learning systems have challenges with inadequate maintenance of model health and lack of awareness in compute requirements between training and inference phases; and Generative AI consumes high energy due to model size and algorithmic complexity. We identify both architecture-specific and common operational strategies for reducing energy consumption, establishing a foundation for future work in sustainable development in resource-intensive architectures.