Biomass, one of the renewable resources, is expected to play an important role in the world’s energy future. In Asia, rice straw is an abundant agricultural surplus because rice is one of the leading staple food crops in the region. Often, rice straw is burned directly in the field via uncontrolled combustion methods that emit large amounts of short-lived air pollutants, greenhouse gases, and other pollutants. In Vietnam, the energy and environment protection sectors are facing great challenges because of rapid urbanisation and industrialisation. A national strategic choice is to exploit renewable energy, including biomass-derived energy, to achieve energy security and CO₂ emission reduction. This study investigates the potential of rice straw as an energy source for power plants at a local scale in Vietnam using data derived from satellite Sentinel-1 images. The results show that Vietnam can produce 2,565 MW from rice straw, for which 24 out of 63 provinces have a potential capacity higher than 30 MW, and the Kien Giang province has the highest capacity (245 MW). The study also analyses limitations and obstacles overcoming which can promote the biomass energy sector in the country.

Artificial Neural Networks (ANNs), the most popular and widely used Artificial Intelligence (AI) technology due to their proven accuracy and efficiency in control, estimation, optimization, decision making, forecasting, and many other applications, can be employed to achieve faster sustainable development of construction industry. The study presents state-of-the-art applications of ANNs to promote sustainability in the construction industry under three aspects of sustainable development, namely, environmental, economic, and social. The environmental aspect surveys ANNs’ applications in sustainable construction materials, energy management, material testing and control, infrastructure analysis and design, sustainable construction management, infrastructure functional performance, and sustainable maintenance management. The economic aspect covers financial management and construction productivity through ANN applications. The social aspect reviews society and human values and health and safety issues in the construction industry. The study demonstrates the wide range of interdisciplinary applications of ANN methods to support the sustainable development of the construction industry. It can be concluded that a holistic research approach with comprehensive input data from various phases of construction and segments of the construction industry is needed for the sustainable development of the construction industry. Further research is certainly needed to reduce the dependency of ANN applications on the input dataset. Research is also needed to apply ANNs in construction management, life cycle assessment of construction projects, and social aspects in relation to sustainability concerns of the construction industry.