#Carbon Emissions Reduction

[pebblegalaxy]

The Inflation Reduction Act: A Green Facade Masking Protectionism and Global Inequality

The Inflation Reduction Act (IRA), signed into law by President Biden on August 16, 2022, has been lauded by its proponents as a monumental step in addressing economic and environmental issues in the United States. But let’s peel back the polished rhetoric, and what do we find? A web of contradictions, protectionism disguised as progress, and a policy framework that, despite its lofty…

[nnctales]

Exploring the Types and Properties of Fly Ash

Introduction: Fly ash, a byproduct of coal combustion in power plants has gained significant importance in various industries due to its versatile properties and environmental benefits. This fine powder, composed of mineral particles, is collected from the flue gas during the combustion process. In this article, we will delve into the different types of fly ash and explore their unique properties…

[laylachloe29]

Carbon Emissions Reduction

It may interest you to know that the carbon emissions reduction is a detailed process encompassing multiple elements of how carbon can lead you towards the best of vision and sustainability for the forthcoming years. This whole process revolves around three different segments of emission reduction in general. 

[reasonsforhope]

"Proving that change is possible if the will to create it is present, Chinese megacities like Beijing that were once famous for their apocalyptic grey skies are enjoying the lowest levels of air pollution they’ve experienced in the 21st century.

Falling 42% from an average high in 2013 when Chinese air pollution was higher than 50 particles per cubic centimeters of city air, the change has increased the lifespan of Chinese urbanites by 2.2 years.

The news comes from a report published by the University of Chicago called the Air Quality Life Index which listed some of the actions taken by the Chinese government to reduce air pollution, described by the CCP as a “war on pollution.”

This has included reducing the presence of heavy industry like steel production in city centers, as well as restricting coal power plants from being built inside cities while shuttering those that were already there.

Some cities like Beijing have reduced the number of cars allowed on the roads during peak hours, similar to London’s congestion charge. Lastly, China’s mass urban tree-planting campaigns have been well documented.

While the life expectancy has risen on average 2.2 years, some cities have seen far more drastic increases. Citizens living under the new “Beijing Blue,” are predicted to live 4 additional years, while those 11 million in the north-central city of Baoding are predicted to gain 6.

“At the foundation of those actions were common elements: political will and resources, both human and financial, that reinforced each other,” the report said. “When the public and policymakers have these tools, action becomes much more likely.”

In fact, the decline in China’s pollution levels has been so drastic that it lowered the world average, which the report says would have increased if not for the Middle Kingdom’s war on pollution.

Although Chinese city air is still several times higher than the WHO’s recommended minimum, it shows what’s accomplishable with political and civic effort—particularly to its neighbors in South Asia where the report warns air quality is worsening."

-via Good News Network, September 1, 2023

[cognitivejustice]

Emissions fell by a steep 15.5% in 2023, largely driven by reductions in carbon from electricity generation and industry. EU countries added 17 gigawatts (GW)-worth of windmills and covered roofs and fields with 56GW of new solar panels. (For comparison, nuclear-power capacity in the EU was roughly 100GW, though it can run 24 hours a day.) Officials reckon 2024 will be another record year for renewables.

The commission’s modelling suggests that current policies should get the bloc to an 88% reduction of overall emissions by 2040, compared with 1990 levels. With the 2030 target of a 55% reduction within reach, the EU should be able to agree to a target for 2040 of 90%. The main target, to get to net zero by 2050, is unchanged.

[head-post]

COP28 countries reach landmark deal to “transition” away from fossil fuels

The COP28 climate talks in Dubai have culminated in a historic agreement that will see the world phase out all fossil fuels for the first time.

The president of this year’s UN-organised summit, Sultan Al Jaber of the UAE, brokered an agreement that was strong enough for the US and the EU on the need to sharply curb the use of fossil fuels while keeping Saudi Arabia and other oil producers on board.

The final agreement calls for countries to phase out fossil fuels from their energy systems in a swift and orderly fashion, which helped convince sceptics. The agreement also calls for countries to contribute to the global transition effort – rather than explicitly forcing the transition on their own.

The so-called “UAE Consensus” ends the hottest year on record, which led to droughts and devastating wildfires. Al Jaber, who’s also chief executive officer of Abu Dhabi National Oil Co, noted:

 “Together we have confronted the realities and sent the world in the right direction.”

Read more HERE

[this-user-is-sus]

Just a reminder: to limit global temperature rise to 1.5C, humans need to halve our carbon dioxide emissions from fossil fuels by 2030.

That means, halve the usage by buildings you live and work in. Halve the emissions from your gasoline/petrol car (hello public transit). Halve the emissions from the food you eat (reduce food waste, eat more plants). Halve the electricity generated from non-renewable sources. Halve the things you buy (reduce, reuse) or make sure that what you do buy has half the carbon footprint (recycle recycle recycle).

[olivegaea]

Custom Emission Reduction Plans

Carbon Offset Program for Climate Objectives

Olive Gaea aims to provide custom emission reduction plans to help you achieve your climate objectives.

Calculate your carbon footprint to see which activities have the most influence on the environment.

We can continue to restore this globally recognised biodiversity hotspot with your kind support.

[bidmyasset]

carbon emissions usually have a disastrous effect on our environment. Going for carbon emission reduction can help restore a balance to ice caps, slow the melting of ice caps, and prevent the possibility of ocean acidification.

[poojagblog-blog]

The global Carbon Offset/Carbon Credit Market is expected to grow from USD 414.8 billion in 2023 to USD 1,602.7 billion by 2028, at a CAGR of 31.0% according to a new report by MarketsandMarkets™. The voluntary carbon market continues to play a critical role in that transition by helping to channel funding into projects that reduce carbon emissions or remove carbon from the atmosphere. Since, the need to curb global warming has significantly increased, the carbon offsetting has become fundamental to achieving net-zero greenhouse-gas emissions. Increasing investments in carbon capture technologies and solutions along with the rise in projects that are helping communities and creating social impact, is expected to drive the market.

[artisticdivasworld]

Navigating the Financial Impact of EPA Emission Standards: Strategic Insights for Trucking Companies

Renee Williams, CEO & PresidentFreightRevCon, a Freight Revenue Consultants, LLC. company The U.S. Environmental Protection Agency’s (EPA) new emission standards for medium- and heavy-duty trucks represent a landmark move toward reducing greenhouse gas emissions and improving public health. With these standards set to roll out from 2027 through 2032, they are projected to cut greenhouse gas…

[reasonsforhope]

“The number of carbon capture and storage projects in the pipeline is exploding, thanks to worldwide efforts to cut emissions.

A new report from the Global CCS Institute, which studies carbon capture and storage-(CCS), shows an impressive growth of 44 percent over the past 12 months.

The CEO of the climate change think tank, Jarad Daniels, believes the outlook for climate action “has never been more positive.”

The record-high total comes from 196 commercial CCS facilities in the project pipeline, including 30 in operation, 11 under construction, and 153 in development.

With 61 new facilities added to the project pipeline in 2022 alone, the CO2 capture capacity of all facilities under development has grown to 244 million tons per annum (Mtpa)—an increase from 169 last year.

Carbon capture and storage is used to filter emissions from power generators, steel mills, cement plants, and other industrial sites, and then bury the sequestered carbon underground. [note: there are a couple extant/in progress carbon capture facilities that convert CO2 to sodium bicarbonate, aka baking soda, so not harmful, for industrial purposes.]

Daniels believes that CCS is essential for reaching national climate goals—and is noticing that as CCS continues to scale-up, prices are going down while efficiency is going up...

The Inflation Reduction Act legislation passed by the US Congress provides tax credits for CCS, and early analysis suggests it could increase the growth by 13-fold, or well over 110 Mtpa, by 2030.

CCS projects also offer economic and social benefits because they can bring local jobs to communities that once relied on carbon-intensive industries, like coal mining.

In Europe, the Danish government has committed €5 billion for CCS over 10 years and the Dutch government has more than doubled its pledge to €13 billion. Australia saw new project announcements in Victoria and Western Australia, and notable progress in the Northern Territory.

“Government policy must be met with private capital to unlock the full potential of CCS and limit global warming,” says Daniels, who see the next decade as an “absolutely critical time to move from ambition to action.”” -via Good News Network, 10/30/22

[jcmarchi]

Distilled Giants: Why We Must Rethink Small AI Development

New Post has been published on https://thedigitalinsider.com/distilled-giants-why-we-must-rethink-small-ai-development/

Distilled Giants: Why We Must Rethink Small AI Development

In recent years, the race to develop increasingly larger AI models has captivated the tech industry. These models, with their billions of parameters, promise groundbreaking advancements in various fields, from natural language processing to image recognition. However, this relentless pursuit of size comes with significant drawbacks in the form of high costs and significant environmental impact. While small AI offers a promising alternative, providing efficiency and lower energy use, the current approach to building it still requires substantial resources. As we pursue small and more sustainable AI, exploring new strategies that address these limitations effectively is crucial.

Small AI: A Sustainable Solution to High Costs and Energy Demands

Developing and maintaining large AI models is an expensive endeavor. Estimates suggest that training GPT-3 costs over $4 million, with more advanced models potentially reaching high-single-digit millions. These costs, including necessary hardware, storage, computational power, and human resources, are prohibitive for many organizations, particularly smaller enterprises and research institutions. This financial barrier creates an uneven playing field, limiting access to cutting-edge AI technology and hindering innovation.

Moreover, the energy demands associated with training large AI models are staggering. For example, training a large language model like GPT-3 is estimated to consume nearly 1,300 megawatt hours (MWh) of electricity—equivalent to the annual power consumption of 130 U.S. homes. Despite this substantial training cost, each ChatGPT request incurs an inference cost of 2.9 watt-hours. The IEA estimates that the collective energy demand of AI, data centers, and cryptocurrency accounted for nearly 2 percent of global energy demand. This demand is projected to double by 2026, approaching the total electricity consumption of Japan. The high energy consumption not only increases operational costs but also contributes to the carbon footprint, worsening the environmental crisis. To put it in perspective, researchers estimate that training a single large AI model can emit over 626,000 pounds of CO2, equivalent to the emissions of five cars over their lifetimes.

Amid these challenges, Small AI provides a practical solution. It is designed to be more efficient and scalable, requiring much less data and computational power. This reduces the overall costs and makes advanced AI technology more accessible to smaller organizations and research teams. Moreover, small AI models have lower energy demands, which helps cut operational costs and reduces their environmental impact. By utilizing optimized algorithms and methods such as transfer learning, small AI can achieve high performance with fewer resources. This approach not only makes AI more affordable but also supports sustainability by minimizing both energy consumption and carbon emissions.

How Small AI Models Are Built Today

Recognizing the advantages of small AI, major tech companies like Google, OpenAI, and Meta have increasingly focused on developing compact models. This shift has led to the evolution of models such as Gemini Flash, GPT-4o Mini, and Llama 7B. These smaller models are primarily developed using a technique called knowledge distillation.

At its core, distillation involves transferring the knowledge of a large, complex model into a smaller, more efficient version. In this process, a “teacher” model—large AI model—is trained on extensive datasets to learn intricate patterns and nuances. This model then generates predictions or “soft labels” that encapsulate its deep understanding.

The “student” model, which is small AI model, is trained to replicate these soft labels. By mimicking the teacher’s behavior, the student model captures much of its knowledge and performance while operating with significantly fewer parameters.

Why We Need to Go Beyond Distilling Large AI

While the distillation of large AI into small, more manageable versions has become a popular approach for building small AI, there are several compelling reasons why this approach might not be a solution for all challenges in large AI development.

Continued Dependency on Large Models: While distillation creates smaller, more efficient AI models and improves computational and energy efficiency at inference time, it still heavily relies on training large AI models initially. This means building small AI models still requires significant computational resources and energy, leading to high costs and environmental impact even before distillation occurs. The need to repeatedly train large models for distillation shifts the resource burden rather than eliminating it. Although distillation aims to reduce the size and expense of AI models, it doesn’t eliminate the substantial initial costs associated with training the large “teacher” models. These upfront expenses can be especially challenging for smaller organizations and research groups. Furthermore, the environmental impact of training these large models can negate some of the benefits of using smaller, more efficient models, as the carbon footprint from the initial training phase remains considerable.

Limited Innovation Scope: Relying on distillation may limit innovation by focusing on replicating existing large models rather than exploring new approaches. This can slow down the development of novel AI architectures or methods that could provide better solutions for specific problems. The reliance on large AI restricts small AI development in the hands of a few resource-rich companies. As a result, the benefits of small AI are not evenly distributed, which can hinder broader technological advancement and limit opportunities for innovation.

Generalization and Adaptation Challenges: Small AI models created through distillation often struggle with new, unseen data. This happens because the distillation process may not fully capture the larger model’s ability to generalize. As a result, while these smaller models may perform well on familiar tasks, they often encounter difficulties when facing new situations. Moreover, adapting distilled models to new modalities or datasets often involves retraining or fine-tuning the larger model first. This iterative process can be complex and resource-intensive, making it challenging to quickly adapt small AI models to rapidly evolving technological needs or novel applications.

The Bottom Line

While distilling large AI models into smaller ones might seem like a practical solution, it continues to rely on the high costs of training large models. To genuinely progress in small AI, we need to explore more innovative and sustainable practices. This means creating models designed for specific applications, improving training methods to be more cost- and energy-efficient, and focusing on environmental sustainability. By pursuing these strategies, we can advance AI development in a way that is both responsible and beneficial for industry and the planet.

[delicatelysublimeforester]

Earth Overshoot Day: Facing the Future with Hope