Today's supply chains are laden with inefficiencies, as most companies rely on antiquated practices to oversee and manage how goods get from place to place. The supply chain is delicate -- even one disruption among suppliers, buyers, and logistics providers can have a trickle-down effect that causes waste, time loss, and increased carbon emissions. With the supply chain still managed manually, logistics managers are operating under intense pressure, with the sheer amount of data about material supply, demand, and transportation routes overwhelming. Even with machine learning providing managers with intelligent analysis, logistics managers can only react so quickly to the thousands of changes along a single supply chain. As managers are overburdened, their slow reactions to real-time problems and disruptions cause the supply chain inefficiencies that create higher costs, waste, and even greater environmental impact.

The names of many of the new companies and technologies created to combat the effects of climate change on marine ecosystems can evoke thrilling acts of derring-do on the high seas. WaveKiller uses compressed air systems to create "walls" of bubbles up to 50 feet thick, to guard against erosion and contain waste and oil spills. The Inceptor is a solar-powered barge deployed by the Dutch nongovernmental organization Ocean Cleanup along rivers in Southeast Asia to gather tons of waste before it hits the sea. Saildrone and WasteShark build and deploy fleets of autonomous drones to ply the oceans, gathering meteorological and marine data in the former case and trash in the latter. This sample of (often menacingly-named) technologies represents the increasingly diverse approaches to combat marine degradation--diversity which is desperately needed, as climate change wages war on the health of the world's oceans on many different fronts.

For all the advances enabled by artificial intelligence, from speech recognition to self-driving cars, AI systems consume a lot of power and can generate high volumes of climate-changing carbon emissions. A study last year found that training an off-the-shelf AI language-processing system produced 1,400 pounds of emissions -- about the amount produced by flying one person roundtrip between New York and San Francisco. The full suite of experiments needed to build and train that AI language system from scratch can generate even more: up to 78,000 pounds, depending on the source of power. But there are ways to make machine learning cleaner and greener, a movement that has been called "Green AI." Some algorithms are less power-hungry than others, for example, and many training sessions can be moved to remote locations that get most of their power from renewable sources.

Tackling carbon emissions is one of the biggest challenges faced by the world today. For big business, this means making a strategic and managed move towards increasing the use of renewable energy sources, as well as creating efficiencies across all aspects of their operations. It's a difficult task to manage alone, even for an enterprise on the scale of tech giant Microsoft or energy titan Shell. But working together creates new possibilities that go further than what it is likely they could accomplish individually. Beyond meeting their own zero-carbon commitments, there's the opportunity to help other companies within their vast ecosystems of customers and suppliers to meet their environmental and safety goals, too.

Tackling carbon emissions is one of the biggest challenges faced by the world today. For big business, this means making a strategic and managed move towards increasing the use of renewable energy sources, as well as creating efficiencies across all aspects of their operations. It's a difficult task to manage alone, even for an enterprise on the scale of tech giant Microsoft or energy titan Shell. But working together creates new possibilities that go further than what it is likely they could accomplish individually. Beyond meeting their own zero-carbon commitments, there's the opportunity to help other companies within their vast ecosystems of customers and suppliers to meet their environmental and safety goals, too.

The UK's HS2 has trialled a new artificial intelligence-based carbon and cost estimating solution to decrease carbon emissions. The technology was trialled at several HS2 locations managed by the Skanska Costain STRABAG joint venture. The solution helps in automating building information model (BIM) processes. It enables simulating multiple design options using different combinations and types of construction materials. The process helps in measuring and comparing the environmental impacts and carbon emissions for each simulation and accordingly design a cost-effective and environmentally friendly construction model.

Artificial intelligence has a terrible carbon footprint. Researchers at Stanford University, Facebook AI Research, and Canada's McGill University have developed a tool to measure the hidden cost of machine learning. The "experiment impact tracker" quantifies how much electricity a machine learning project will consume, and its cost in carbon emissions. The team first measured the energy cost of a specific artificial intelligence (AI) model--a challenge because a single machine often trains several models concurrently, while each session also draws power for shared overhead functions like data storage and cooling. The researchers then translated power consumption into carbon emissions, whose blend of renewable and fossil fuels varies by location and time of day, by tapping into public sources about this energy mix.

For all the advances enabled by artificial intelligence, from speech recognition to self-driving cars, AI systems consume a lot of power and can generate high volumes of climate-changing carbon emissions. A study last year found that training an off-the-shelf AI language-processing system produced 1,400 pounds of emissions – about the amount produced by flying one person roundtrip between New York and San Francisco. The full suite of experiments needed to build and train that AI language system from scratch can generate even more: up to 78,000 pounds, depending on the source of power. But there are ways to make machine learning cleaner and greener, a movement that has been called "Green AI." Some algorithms are less power-hungry than others, for example, and many training sessions can be moved to remote locations that get most of their power from renewable sources.

For all the advances enabled by artificial intelligence, from speech recognition to self-driving cars, AI systems consume a lot of power and can generate high volumes of climate-changing carbon emissions. A study last year found that training an off-the-shelf AI language-processing system produced 1,400 pounds of emissions--about the amount produced by flying one person roundtrip between New York and San Francisco. The full suite of experiments needed to build and train that AI language system from scratch can generate even more: up to 78,000 pounds, depending on the source of power. But there are ways to make machine learning cleaner and greener, a movement that has been called "Green AI." Some algorithms are less power-hungry than others, for example, and many training sessions can be moved to remote locations that get most of their power from renewable sources.

The construction industry contributes to 39% of global carbon emissions while aviation contributes to only 2% which means we need to look for alternative building materials if we are to make a big impact on the climate crisis soon. We've seen buildings being made using mushrooms, bricks made from recycled plastic and sand waste, organic concrete, and now are seeing another innovative solution – a floating 3D printed house! Prvok is the name of this project and it will be the first 3D printed house in the Czech Republic built by Michal Trpak, a sculptor, and Stavebni Sporitelna Ceske Sporitelny who is a notable member of the Erste building society. The house is designed to float and only takes 48 hours to build! Not only is that seven times faster than traditional houses, but it also reduces construction costs by 50%.