Computational sustainability is an emerging field that attempts to balance societal, economic, and environmental resources for the future well-being of humanity using methods from mathematics, computer science, and information science fields. Sustainability in this context refers to the world's ability to sustain biological, social, and environmental systems in the long term. Using the power of computers to process large quantities of information, decision making algorithms allocate resources based on real-time information. Applications advanced by this field are widespread across various areas. For example, artificial intelligence and machine learning techniques are created to promote long-term biodiversity conservation and species protection. Smart grids implement renewable resources and storage capabilities to control the production and expenditure of energy. Intelligent transportation system technologies can analyze road conditions and relay information to drivers so they can make smarter, more environmentally-beneficial decisions based on real-time traffic information. Origins and motivations The field of computational sustainability has been motivated by Our Common Future, a 1987 report from the World Commission on Environment and Development about the future of humanity. More recently, computational sustainability research has also been driven by the United Nation's sustainable development goals, a set of 17 goals for the sustainability of human economic, social, and environmental well-being world-wide. Researchers in computational sustainability have primarily focused on addressing problems in areas related to the environment (e.g., biodiversity conservation), sustainable energy infrastructure and natural resources, and societal aspects (e.g., global hunger crises). The computational aspects of computational sustainability leverage techniques from mathematics and computer science, in the areas of artificial intelligence, machine learning, algorithms, game theory, mechanism design, information science, optimization (including combinatorial optimization), dynamical systems, and multi-agent systems. Biodiversity and conservation Computational sustainability researchers have advanced techniques to combat the biodiversity loss facing the world during the current sixth extinction. Researchers have created computational methods for geospatially mapping the distribution, migration patterns, and wildlife corridors of species, which enable scientists to quantify conservation efforts and recommend effective policies. In addition to scientific research contributions, the computational sustainability community has also contributed technologies that support citizen science conservation initiatives. An example is the creation of eBird, which enables citizens to share sightings of birds and crowd-source the creation of a global bird distribution database for researchers. An example of successful application of eBird database is the Nature Conservancy's Bird Returns program (2013), where farmers are compensated for their effort of maintaining suitable habitats for birds during