We observe the link between Artificial Intelligence (AI) and Sustainable Development Goals (SDGs). We use automated methodologies to find insights and overlaps between AI and the SDGs. AI-Ethics frameworks need to give more attention to Society and Environment areas. Inclusive action is needed to balance the efforts for solving SDGs by using AI.SDGs 13, 14, and 15 (all related to the Environment area) are not sufficiently addressed.
The article investigates the role of environmental pollutants, especially endocrine disrupting chemical, and their negative effect on fertility.
This is an Opinion article by two highly accomplished synthetic biologists that explains how synthetic biology tools can benefit oceans.
Indigenous agricultural knowledge is observed as an important national human capital to improve crop productivity and enhance sustainable agricultural development.
This article supports SDG's 3, 6, and 11 by illustrating the reduction of pollutants using various microbial techniques. Strategy and recommendations are summarized, along with the future prospects.
This article supports SDGs 11, 6, and 13 by analyzing the impact of Denver Water's annual energy use and water use alongside local precipitation over a 20-year period from 1995 to 2014 and highlighting the implications for altered energy footprints as water utilities respond to new precipitation patterns in a changing climate.
This paper highlights the alarming rate of which levels of chemicals are being found in seawater.
Ultrafiltration (UF) of rainwater with chlorination can produce water for potable purposes supporting SDG 6. Long-term tests demonstrate that the produced water meets the WHO standards. PV powered rainwater ultrafiltration cost ranges from 0.04 to 0.13 US$ m3. The developed technique can be implemented with economic viability even for small buildings. The technique can be a reliable alternative to traditional water supply approaches in remote areas across the world.
This study attempts to investigate the suitability of the Wabe river water for Agricultural and Industrial purpose.
Proposal for a new machine learning-based smart irrigation system, designed to aid the development of more sustainable agricultural practices, saving water and contributing towards better food security.
