Computational sustainability
In the realm of artificial intelligence, particularly within machine learning, the 1990s saw research efforts addressing ecological modeling and wastewater management, among other sustainability issues.
This interest extended beyond the immediate environmental effects of computing to consider second-order and higher-order impacts, such as the potential of ICT to reduce the carbon footprint of air travel through online conferencing or to optimize delivery routes to lower CO2 emissions.
Before the OECD's 2008 conference, mathematicians proposed using their expertise to combat climate change, signaling a growing recognition of the research community's role in sustainability.
The establishment of sustainability-related tracks and awards at various conferences, along with targeted funding by organizations like the NSF, underscores the growing importance of computing in addressing sustainability challenges.
In recent years there has been significant research on wildlife monitoring strategies to better understand patterns and enhance security to combat poaching (Gomes).
While technology has historically favored profitable sectors, its potential to revolutionize environmental sustainability, particularly in wildlife conservation, remains largely untapped.
The emphasis on two key variables, vapour pressure deficit (VPD) and spruce fraction, reflects the paper's commitment to practical and actionable computational approaches in environmental assessment.
[16] 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.
If using renewable energies, scientists need to seek different sources for compensation, which usually links back to fossil fuels that are considered unsustainable.
Scientists have turned this scenario into an optimization problem that involves the three "broad sustainability themes"—simulation, machine learning, and citizen science (Gomes et al., 2019).
Simultaneously, machine learning techniques, including normalizing flows, can infer long-term patterns and behaviors from data from a short period.
When predicting and establishing the climate model, AI cannot consider different factors in physics, including gravity and temperature gradient, for efficiency.
It encompasses a broad spectrum of activities related to the use and management of land and public spaces, aiming to ensure sustainable development and to improve the built and natural environments.
It aims to coordinate the various aspects of policy and regulation over land use, housing, public amenities, and transport infrastructure, ensuring that these elements work together to promote economic development, environmental sustainability, and quality of life for communities in all types of areas.
This term is often used in a European context and can be seen as an integrated approach that looks beyond traditional urban planning to address the needs and development strategies of a wider range of environments.
Data collection can be achieved with video cameras over busy areas, sensors that detect various pieces from location of certain vehicles to infrastructure that is breaking down, and even drivers who notice an accident and use a mobile app, like Waze, to report its whereabouts.
Electronic highway signs relay information regarding travel times, detours, and accidents that may affect drivers ability to reach their destination.
By harnessing the power of these technologies, researchers and practitioners are able to analyze vast amounts of data, extract meaningful patterns, and develop sustainable strategies for managing natural resources and ecosystems.
For example, camera traps equipped with computer vision algorithms can automatically detect and identify species, allowing researchers to study their behaviors without disturbing them.
Machine learning algorithms can analyze these data to understand animal behavior, habitat preferences, and population dynamics, aiding in conservation efforts.
Remote sensing technologies combined with machine learning can monitor air and water quality, detecting pollutants and assessing environmental health.
For example, drones equipped with multispectral cameras can capture images of crops, which are then analyzed using machine learning algorithms to identify health issues.
By providing insights into soil health, moisture levels, and crop growth, these algorithms help farmers make informed decisions to improve productivity and sustainability.
This information is crucial for developing strategies to mitigate the impacts of climate change, such as planning for extreme weather events and sea level rise.
By analyzing large datasets, researchers can identify trends, predict outcomes, and make informed choices to conserve natural resources and protect the environment.
By enabling more precise monitoring and analysis, computer vision and machine learning enhance conservation efforts, helping to protect endangered species, preserve biodiversity, and mitigate the effects of climate change.
These apps allow volunteers to easily record and share species observations, photos and other ecological data directly from the field using their smartphones.
By harnessing the power of mobile technology and an active citizen community, these projects can gather large amounts of valuable biodiversity data across a variety of settings in a cost-effective way, compared to traditional survey methods conducted by professional scientists alone.
iNaturalist allows users to record observations, share them with fellow naturalists, and contribute to biodiversity science by sharing findings with scientific data repositories.eBird, managed by the Cornell Lab of Ornithology, enables birdwatchers to enter their sightings and access tools that make birding more rewarding, such as managing lists, photos, and audio recordings, and seeing real-time species distribution maps.
Merlin, also from the Cornell Lab, helps users identify bird species through AI-powered visual recognition and question-based filtering, and contributes sightings to the eBird database.
