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<!DOCTYPE html>
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<title>Ashish kushwaha</title>
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<body>
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<div class="brand">
<a href="#" class="fav-icon">Global Climate Change - Vital Signs Of The Planet</a>
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<a href="#home" class="nav-link">Home</a>
<a href="#contact" class="nav-link">About</a>
<a href="#about" class="nav-link">FAQs</a>
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<h2 class="page-title">Climate Change</h2>
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<h4>Climate Change: How Do We Know?</h4>
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<img src="assets\image\graph.jpg" alt="Temp. graph" style="width:100%;height:auto;">
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<p>Earth's climate has changed throughout history. Just in the last 650,000 years there have been seven cycles of glacial advance and retreat, with the abrupt end of the last ice age about 11,700 years ago marking the beginning of the modern climate era — and of human civilization. Most of these climate changes are attributed to very small variations in Earth’s orbit that change the amount of solar energy our planet receives.</p>
<p>The current warming trend is of particular significance because it is unequivocally the result of human activity since the mid-20th century and proceeding at a rate that is unprecedented over millennia.1 It is undeniable that human activities have warmed the atmosphere, ocean, and land and that widespread and rapid changes in the atmosphere, ocean, cryosphere, and biosphere have occurred. Earth-orbiting satellites and other technological advances have enabled scientists to see the big picture, collecting many different types of information about our planet and its climate on a global scale. This body of data, collected over many years, reveals the signals of a changing climate.
The heat-trapping nature of carbon dioxide and other gases was demonstrated in the mid-19th century.2 Their ability to affect the transfer of infrared energy through the atmosphere is the scientific basis of many instruments flown by NASA. There is no question that increased levels of greenhouse gases must cause Earth to warm in response.
Ice cores drawn from Greenland, Antarctica, and tropical mountain glaciers show that Earth’s climate responds to changes in greenhouse gas levels. Ancient evidence can also be found in tree rings, ocean sediments, coral reefs, and layers of sedimentary rocks. This ancient, or paleoclimate, evidence reveals that current warming is occurring roughly ten times faster than the average rate of ice-age-recovery warming. Carbon dioxide from human activity is increasing more than 250 times faster than it did from natural sources after the last Ice Age.3</p>
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<h1>The evidence for rapid climate change is compelling:</h1>
<h2>Global Temperature Rise </h2>
<p>The planet's average surface temperature has risen about 2.12 degrees Fahrenheit (1.18 degrees Celsius) since the late 19th century, a change driven largely by increased carbon dioxide emissions into the atmosphere and other human activities.4 Most of the warming occurred in the past 40 years, with the seven most recent years being the warmest. The years 2016 and 2020 are tied for the warmest year on record.</p>
<h2>Warming Ocean</h2>
<p>The ocean has absorbed much of this increased heat, with the top 100 meters (about 328 feet) of ocean showing warming of more than 0.6 degrees Fahrenheit (0.33 degrees Celsius) since 1969.6 Earth stores 90% of the extra energy in the ocean.</p>
<h2>Shrinking Ice Sheets</h2>
<p>The Greenland and Antarctic ice sheets have decreased in mass. Data from NASA's Gravity Recovery and Climate Experiment show Greenland lost an average of 279 billion tons of ice per year between 1993 and 2019, while Antarctica lost about 148 billion tons of ice per year</p>
<h2>Glacial Retreat</h2>
<p>Glaciers are retreating almost everywhere around the world — including in the Alps, Himalayas, Andes, Rockies, Alaska, and Africa.</p>
<h2>Decreased Snow Cover</h2>
<p>Satellite observations reveal that the amount of spring snow cover in the Northern Hemisphere has decreased over the past five decades and the snow is melting earlier.</p>
<h2>Sea Level Rise</h2>
<p>Global sea level rose about 8 inches (20 centimeters) in the last century. The rate in the last two decades, however, is nearly double that of the last century and accelerating slightly every year.</p>
<h2>Extreme Events</h2>
<p>The number of record high temperature events in the United States has been increasing, while the number of record low temperature events has been decreasing, since 1950. The U.S. has also witnessed increasing numbers of intense rainfall events.</p>
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<hr>
<h1>How technologies are helping us save the environment</h1>
<p>Saving environment with technology is helping us build better ecosystems or remedy the damages done to the environment by employing both organic and inorganic techniques for a cleaner planet.</p>
<p>Technology has helped us simplify our lives. It has made the world a smaller place, assisted in fighting off the deadliest diseases, and solved the most complicated problems for us. The next problem that stands before us is that of the ecological changes we face. Keeping in mind the potential technology has, environmental scientists are now saving the environment with technology.</p>
<h2>SAVING ENVIRONMENT WITH TECHNOLOGY</h2>
<p>Though technology and environment are thought to be on the opposite ends of the spectrum, humans are looking for ways to save the environment with technology. So, be it generating renewable, green energy or using sensors to monitor endangered species, technologies like AI and IoT are helping create a sustainable future for us.</p>
<div class="data-example" id="contact">
<h4>A grid of the different sub-disciplines in machine learning and how they can help fight climate change.</h4>
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<img src="assets\image\ml.jpg" alt="Green energy" style="width: 100%;height:auto;">
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<h2>Here are some ways AI & ML could help fight climate change :</h2>
<p>AI applications could also help design more energy-efficient buildings, improve power storage and optimise renewable energy deployment by feeding solar and wind power into the electricity grid as needed.
On a smaller scale, it could help households minimize their energy use, automatically switching off lights not in use or sending power from electric vehicles back into the grid to meet anticipated demand.</p>
<h3>1. Improve predictions of how much electricity we need</h3>
<p>If we’re going to rely on more renewable energy sources, utilities will need better ways of predicting how much energy is needed, in real time and over the long term. Algorithms already exist that can forecast energy demand, but they could be improved by taking into account finer local weather and climate patterns or household behavior. Efforts to make the algorithms more explainable could also help utility operators interpret their outputs and use them in scheduling when to bring renewable sources online.</p>
<h3>2. Discover new materials</h3>
<p>Scientists need to develop materials that store, harvest, and use energy more efficiently, but the process of discovering new materials is typically slow and imprecise. Machine learning can accelerate things by finding, designing, and evaluating new chemical structures with the desired properties. This could, for example, help create solar fuels, which can store energy from sunlight, or identify more efficient carbon dioxide absorbents or structural materials that take a lot less carbon to create. The latter materials could one day replace steel and cement—the production of which accounts for nearly 10% of all global greenhouse-gas emissions.</p>
<h3>3. Optimize how freight is routed</h3>
<p>Shipping goods around the world is a complex and often highly inefficient process that involves the interplay of different shipment sizes, different types of transportation, and a changing web of origins and destinations. Machine learning could help find ways to bundle together as many shipments as possible and minimize the total number of trips. Such a system would also be more resilient to transportation disruptions.</p>
<h3>4. Lower barriers to electric-vehicle adoption</h3>
<p>Electric vehicles, a key strategy for decarbonizing transportation, face several adoption challenges where machine learning could help. Algorithms can improve battery energy management to increase the mileage of each charge and reduce “range anxiety,” for example. They can also model and predict aggregate charging behavior to help grid operators meet and manage their load.</p>
<h3>5. Help make buildings more efficient</h3>
<p>Intelligent control systems can dramatically reduce a building’s energy consumption by taking weather forecasts, building occupancy, and other environmental conditions into account to adjust the heating, cooling, ventilation, and lighting needs in an indoor space. A smart building could also communicate directly with the grid to reduce how much power it is using if there’s a scarcity of low-carbon electricity supply at any given time.</p>
<h3>6. Create better estimates of how much energy we are consuming</h3>
<p>Many regions of the world have little to no data on their energy consumption and greenhouse-gas emissions, which can be a major obstacle to designing and implementing effective mitigation strategies. Computer vision techniques can extract building footprints and characteristics from satellite imagery to feed machine-learning algorithms that can estimate city-level energy consumption. The same techniques could also identify which buildings should be retrofitted to maximize their efficiency.</p>
<h3>7. Optimize supply chains</h3>
<p>In the same way that machine learning can optimize shipping routes, it can also minimize inefficiencies and carbon emissions in the supply chains of the food, fashion, and consumer goods industries. Better predictions of supply and demand should significantly reduce production and transportation waste, while targeted recommendations for low-carbon products could encourage more environmentally friendly consumption.</p>
<h3>8. Make precision agriculture possible at scale</h3>
<p>Much of modern-day agriculture is dominated by monoculture, the practice of producing a single crop on a large swath of land. This approach makes it easier for farmers to manage their fields with tractors and other basic automated tools, but it also strips the soil of nutrients and reduces its productivity. As a result, many farmers rely heavily on nitrogen-based fertilizers, which can convert into nitrous oxide, a greenhouse gas 300 times more potent than carbon dioxide. Robots run on machine-learning software could help farmers manage a mix of crops more effectively at scale, while algorithms could help farmers predict what crops to plant when, regenerating the health of their land and reducing the need for fertilizers.</p>
<h3>9. Improve deforestation tracking</h3>
<p>Deforestation contributes to roughly 10% of global greenhouse-gas emissions, but tracking and preventing it is usually a tedious manual process that takes place on the ground. Satellite imagery and computer vision can automatically analyze the loss of tree cover at a much greater scale, and sensors on the ground, combined with algorithms for detecting chainsaw sounds, can help local law enforcement stop illegal activity.</p>
<h3>10. Nudge consumers to change how we shop</h3>
<p>Techniques that advertisers have successfully used to target consumers can be used to help us behave in more environmentally aware ways. Consumers could receive tailored interventions to promote their enrollment in energy saving programs.</p>
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<h1>Pillars of Action</h1>
<p>AI brings lots of opportunities, but there are also tradeoffs and concerns. Shaping a positive scenario for our future will require collective action at multiple levels:
Integrating technology regulation with Green New Deal policies, developing new standards to mitigate environmental impacts, adopting green AI industry guidelines and training the next generation of responsible AI technologists.
Any application of AI in climate change mitigation and adaptation will need to ensure that environmental impacts are not externalised onto the most marginalised populations</p>
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<h4>What can AI do ?</h4>
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<img src="assets\image\ai-for-tackling-climate-change-13-638.jpg" alt="ai-for-tackling-climate-change" style="width:100%;height:auto;">
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<h2>Moving forward, I want to suggest four pillars of action for technologists, data scientists, designers, engineers and technology activists:</h2>
<h3>DEVELOP ENABLING TECHNICAL ENVIRONMENTS FOR THE GREEN TRANSITION </h3>
<p>I invite technologists to apply their skills to climate change mitigation and work towards transforming how data-driven solutions are being developed and commercialised at scale. Industries like energy, food, manufacturing and finance need to transition within the next five years. A trustworthy data and AI environment will require, among others: open standards, shared frameworks for data sharing and robust data discovery and publishing practices between transition industries. These emerging data markets can give us a systemic picture of supply and demand at national and regional levels. Moreover, the integration of various forms of public, private, and citizen science data will require guidelines for public-private data collaborations that can be materialised through data commons and other novel data institutions. </p>
<h3>DEVELOP A CLIMATE AWARE DATA SCIENCE PRACTICE</h3>
<p>AI and data science communities will need to follow the steps of computer scientists who have a long history of investigating sustainable computing. Researchers may advocate for making efficiency an evaluation criterion for research, use more computationally efficient hardware and algorithms and report the “price tag” of their models. Alternatively, Energy Usage Reports have been proposed as part of greener algorithmic accountability practices and tools like Machine Learning Emissions Calculator can help estimate the amount of carbon emissions produced by the training of AI models. Similarly, practitioners may start reporting the time to retrain models, share local infrastructure instead of relying on cloud computing and choose cloud providers who are offsetting their emissions.</p>
<h3>FOCUS ON CLIMATE JUSTICE</h3>
<p>A just transition requires that we pay attention to the struggles of various communities who are already defending their land, air, water, and livelihoods from extractive activities such as mining, fracking, gas flaring, etc. Any application of AI in climate change mitigation and adaptation will need to ensure that environmental impacts are not externalised onto the most marginalised populations, and that the gains are not only captured by digitally mature countries in the global north. This requires centring front-line communities and enabling them to take ownership of their data and bottom-up climate action plans. </p>
<h3>ORGANISE IN THE WORKPLACE </h3>
<p>In 2019, thousands of employees from Amazon, Google, Microsoft, Facebook and Twitter organised as the Tech Workers Coalition. They marched to demand from their employers to bring their emissions to zero by 2030, stop exploiting climate refugees and cancel contracts with fossil fuel companies. It will be of paramount importance for technology workers to raise awareness in their work about the climate impacts of technology. Technology companies need to be more transparent about their emissions and be pressured to provide this information to customers, regulators, and the public. This transparency will be the first step towards informing regulation and public discourse as well as incentivising practitioners to make more sustainable decisions.</p>
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<hr>
<h1>FAQs</h1>
<p>Got more question About "How AI can help in Climate Change or Global warming?". Ask Here. </p>
<div class="data-example" id="about">
<ul>You can Start With:
<li>Is the ozone hole causing climate change?</li>
<li>What’s the difference between climate change and global warming?</li>
<li>How do we know what greenhouse gas and temperature levels were in the distant past?</li>
<li>Is it too late to prevent climate change?</li>
<li>Has Earth continued to warm since 1998?</li>
<li>How do scientists deal with these changes?</li>
<li>Who crated this bot?</li>
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<p>Answers are provided by - NASA</p>
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