Multi-Criteria Assessment for City-Wide Rooftop Solar PV Deployment: A Case Study of Bandung, Indonesia

Machine learning based urban sprawl assessment using integrated multi-hazard and environmental-economic impact

The increasing demand for land development due to human activities has fueled urbanization. However, uncontrolled urban development in some regions has resulted in urban environmental problems arising from an imbalance between supply and demand. This study aims to develop an integrated model for evaluating and prioritizing the management of hazardous urban sprawl in the Bandung metropolitan region of Indonesia. The novelty of this study lies in its pioneering application of long-term remote sensing data-based and machine learning techniques to formulate an urban sprawl priority index. This index is unique in its consideration of the impacts stemming from human economic activity, environmental degradation, and multi-disaster levels as integral components. The analysis of hazardous urban sprawl across three distinct time periods (1985-1993, 1993-2008, and 2008-2018) revealed that the 1993-2008 period had the highest increase in human economic activity, reaching 172,776 ha. The 1985-1993 period experienced the highest level of environmental degradation in the study area. Meanwhile, the 1993-2008 period showed the highest concentration of multi-hazard locations. The combined model of hazardous urban sprawl, incorporating the three parameters, indicated that the highest priority for intervention was on the outskirts of urban areas, specifically in West Bandung Regency, Cimahi, Bandung Regency, and East Bandung Regency. Regions with high-priority indices require greater attention from the government to mitigate the negative impacts of hazardous urban sprawl. This model, driven by the urban sprawl priority index, is envisioned to regulate urban movement in a more sustainable manner. Through the efficient monitoring of urban environments, the study seeks to guarantee the preservation of valuable natural resources while promoting sustainable urban development practices.

Spatial integration framework of solar, wind, and hydropower energy potential in Southeast Asia

Amid its massive increase in energy demand, Southeast Asia has pledged to increase its use of renewable energy by up to 23% by 2025. Geospatial technology approaches that integrate statistical data, spatial models, earth observation satellite data, and climate modeling can be used to conduct strategic analyses for understanding the potential and efficiency of renewable energy development. This study aims to create the first spatial model of its kind in Southeast Asia to develop multi-renewable energy from solar, wind, and hydropower, further broken down into residential and agricultural areas. The novelty of this study is the development of a new priority model for renewable energy development resulting from the integration of area suitability analysis and the estimation of the amount of potential energy. Areas with high potential power estimations for the combination of the three types of energy are mostly located in northern Southeast Asia. Areas close to the equator, have a lower potential than the northern countries, except for southern regions. Solar photovoltaic (PV) plant construction is the most area-intensive type of energy generation among the considered energy sources, requiring 143,901,600 ha (61.71%), followed by wind (39,618,300 ha; 16.98%); a combination of solar PV and wind (37,302,500 ha; 16%); hydro (7,665,200 ha; 3.28%); a combination of hydro and solar PV (3,792,500 ha; 1.62%); and a combination of hydro and wind (582,700 ha; 0.25%). This study is timely and important because it will inform policies and regional strategies for transitioning to renewable energy, with consideration of the different characteristics present in Southeast Asia.