AI can support the sustainable future2 min read
How Artificial Intelligence (AI) can enable a sustainable future?
This is a question Microsoft, in association with PricewaterhouseCoopers (PwC), tries to respond to, providing all the insights into a specific report.
Artificial Intelligence and Sustainability
Using AI for environmental applications has the potential to boost global GDP by 3.1-4.4% while also reducing global greenhouse gas emissions by around 1.5-4.0% by 2030 relative to Business as Usual (BAU).
Economic benefits could be predominantly captured by Europe, East Asia, and North America regions as they each achieve GDP gains in excess of US$1 trillion.
AI applications in energy (up to -2.2%) and transport (up to -1.7%) have the largest impact on GHG emissions reduction of our sectors covered, but water and agriculture still have an important role to play for the environment more broadly.
These projections rely not just on AI, but on the adoption of a wider complementary technology infrastructure
AI applications can offer environmental benefits beyond GHG emissions, including impacts on water quality, air pollution, deforestation, land degradation, and biodiversity.
For example, there are several impacts of agricultural AI levers on the economy and environment such as:
AI robotics that is programmed to carry out agricultural tasks autonomously with optimal timing (es. an autonomous tractor picking fruit only when ripe).
Precision monitoring of environmental conditions for agriculture and forestry
Utilizing field sensors to precisely measure the impact of environmental factors and inputs on agricultural and forestry activities, and providing agri-advisory services (i.e. monitoring local weather conditions to predict the impact on yield and tailor required inputs).
Land-use planning and management
Using AI for mapping agricultural and forestry activities over time for better farm management and better enforcement of regulation.
Monitoring of crop, soil, and livestock health
Monitoring conditions of agriculture (i.e crop health, prevalence of pests, disease among livestock) to inform better management of crop habitats, and of livestock. For example, monitoring and identification of pests in real-time to inform the use of pesticides, including volume needed, specific locations on a farm that pesticides are needed, etc.
The same positive impacts are evaluated for energy AI levers (i.e. smart monitoring and management of energy consumption, energy supply, and demand prediction, coordination of decentralized energy networks, predictive maintenance, increased operational efficiency of renewable assets, increased operational efficiency of fossil fuel assets); transport AI levers (i.e autonomous vehicles, autonomous deliveries, traffic optimization of connected vehicles, demand prediction and logistics planning, predictive maintenance for vehicles), water AI levers (i.e. predictive maintenance of water infrastructure, monitoring and predicting water demand, monitoring wastewater sources).