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Gas Engine Market worth $6.3 billion by 2029 – Exclusive Report by MarketsandMarkets™

Published

2 months ago

March 8, 2024

gas-engine-market-worth-$6.3-billion-by-2029-–-exclusive-report-by-marketsandmarkets™

Gas Engine Market is expected to reach USD 6.3 billion by 2029 from USD 5.1 billion in 2024 at a CAGR of 4.5% during the 2024–2029 period according to a new report by MarketsandMarkets. The growth in this market can be attributed to various factors stimulating demand. A prominent driver is the escalating focus on cleaner energy sources and sustainability initiatives, prompting a surge in the adoption of gas-powered engines as a viable alternative to conventional fossil fuel-based options. Furthermore, technological advancements, including enhanced efficiency and reliability of gas engines, are significantly contributing to market expansion. These advancements not only improve the performance of gas engines but also make them more competitive and appealing to consumers seeking reliable and environmentally friendly power solutions.
Browse in-depth TOC on “Gas Engine Market“
291 – Tables58 – Figures282 – Pages
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Gas Engine Market Scope:

Report Coverage

Details

Market Revenue in 2024

$5.1 billion

Estimated Value by 2029

$6.3 billion

Growth Rate

Poised to Grow at a CAGR of 4.5%

Largest Market

Asia Pacific

Market Size Available for

2020-2029

Forecast Period

2024-2029

Forecast Units

Value (USD Billion)

Report Coverage

Revenue Forecast, Competitive Landscape, Growth Factors, and Trends

Segments Covered

By Fuel Type, By Power Output, By Application, and By End User

Geographies Covered

Asia Pacific, North America, Europe, Middle East and Africa, and South America

Key Market Opportunities

Navigating the Evolving Landscape of Coal, Gas, and Nuclear.

Key Market Drivers

Advance Gas Engine Technology

Mechanical Drive, by application, to occupy majority of Gas Engine Market share.
Mechanical Drive applications are poised to dominate the majority of the Gas Engine Market share due to several compelling reasons. Firstly, in industries such as oil and gas, mechanical drive applications are vital for powering compressors, pumps, and other essential equipment used in extraction, processing, and transportation operations. Gas engines offer a reliable and cost-effective solution for driving these critical machinery, providing the necessary power while minimizing operational costs. Additionally, the inherent flexibility of gas engines allows for efficient operation in diverse mechanical drive applications, accommodating varying load requirements and operating conditions. Moreover, the growing demand for natural gas as a cleaner alternative to traditional fuels in industrial processes further drives the adoption of gas engines for mechanical drive applications. With their proven performance, versatility, and environmental benefits, gas engines are poised to remain the preferred choice for mechanical drive applications across a wide range of industries, consolidating their position as a dominant force in the Gas Engine Market.
By Power Output, the 1.1-2 MW segment is expected to be the largest market.
The 1.1-2 MW segment is anticipated to emerge as the largest market segment by power output in the Gas Engine Market due to several key factors driving its prominence. Firstly, this power range aligns well with the requirements of a wide range of applications across various industries, including industrial, commercial, and utility sectors. Gas engines in the 1.1-2 MW range offer sufficient power capacity to meet the needs of medium-sized facilities and operations, such as manufacturing plants, data centers, and district heating systems, among others. Additionally, advancements in gas engine technology have enhanced the efficiency, reliability, and performance of engines within this power range, making them increasingly attractive to end-users seeking cost-effective and sustainable power solutions. Furthermore, favorable economics, including lower installation and operational costs compared to larger capacity engines, contribute to the popularity of 1.1-2 MW gas engines among end-users. Moreover, the versatility of gas engines in this power range allows for flexible deployment in both standalone and combined heat and power (CHP) applications, further expanding their market appeal. Overall, the 1.1-2 MW segment is poised to lead the Gas Engine Market by power output, driven by its suitability for a diverse range of applications and its ability to deliver reliable and efficient power solutions to meet evolving customer needs.
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Asia Pacific to emerge as the largest Gas Engine Industry.
Asia Pacific is poised to emerge as the largest Gas Engine Market due to several compelling factors propelling its growth and dominance in the industry. Firstly, the region’s rapid industrialization, urbanization, and population growth are driving significant demand for energy across various sectors such as manufacturing, construction, and transportation. Gas engines offer a reliable and efficient solution to meet this escalating energy demand while addressing environmental concerns, as they produce lower emissions compared to conventional fossil fuel-based alternatives. Furthermore, supportive government policies and initiatives aimed at promoting cleaner energy sources and reducing greenhouse gas emissions are accelerating the adoption of gas engines in the Asia Pacific region. These policies create a conducive environment for investment in gas engine infrastructure and deployment, further stimulating market growth. Additionally, the abundant availability of natural gas resources in countries like China, India, and Australia enhances the attractiveness of gas engines as a cost-effective and sustainable power generation solution. Moreover, ongoing investments in infrastructure development, including power plants, cogeneration facilities, and distributed energy systems, contribute to the widespread deployment of gas engines across the region. With favorable market dynamics, supportive regulatory frameworks, and increasing environmental awareness, Asia Pacific is poised to lead the global Gas Engine Market, offering lucrative opportunities for industry players and driving innovation and sustainable development in the energy sector.
Key Players
Key players in the global Gas Engine Companies include Caterpillar (US), Rolls-royce Holdings (UK), Siemens Energy (Germany), Wartsila (Finland), and Cummins (US).
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About MarketsandMarkets
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MarketsandMarkets is a blue ocean alternative in growth consulting and program management, leveraging a man-machine offering to drive supernormal growth for progressive organizations in the B2B space. We have the widest lens on emerging technologies, making us proficient in co-creating supernormal growth for clients.
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The B2B economy is witnessing the emergence of $25 trillion of new revenue streams that are substituting existing revenue streams in this decade alone. We work with clients on growth programs, helping them monetize this $25 trillion opportunity through our service lines – TAM Expansion, Go-to-Market (GTM) Strategy to Execution, Market Share Gain, Account Enablement, and Thought Leadership Marketing.
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Unveiling the Complex Psychological Implications of Artificial Intelligence

Published

2 days ago

May 2, 2024

Peter Tolan

In today’s world, the realm of artificial intelligence (AI) presents us with fascinating possibilities and unsettling dilemmas. From engaging in nuanced conversations with humanoid robots to grappling with the consequences of deepfake technology, the advancements in AI have far-reaching implications that extend into the realm of human psychology, as noted by Joel Pearson, a cognitive neuroscientist at the University of New South Wales.
While AI holds the promise of simplifying our lives, Pearson emphasizes that these developments can also have profound effects on our mental well-being, challenging our perceptions and emotional responses in ways we may not fully comprehend. Despite our fears of killer robots and rogue self-driving cars, Pearson suggests that the psychological impacts of AI are equally if not more significant, albeit less tangible.
One area of concern highlighted by Pearson is the tendency for humans to anthropomorphize AI entities, attributing human-like qualities to non-human agents such as chatbots. This phenomenon can lead to emotional attachments and vulnerabilities, as evidenced by individuals who develop romantic feelings for AI companions like Replika. Pearson underscores the need for further research into the implications of these human-AI relationships, particularly regarding their impact on interpersonal dynamics and emotional health.
Furthermore, Pearson raises alarm about the proliferation of deepfake technology, which has the potential to distort our perception of reality and erode trust in media. Deepfake images and videos, often used for nefarious purposes like non-consensual pornography, can leave lasting impressions on our psyche, even after their falsity is exposed. Pearson warns of the long-term effects of exposure to such content, particularly on vulnerable populations like teenagers whose developing brains may be more susceptible to manipulation.
In response to these challenges, Pearson calls for a nuanced understanding of AI’s psychological impact and advocates for a proactive approach to addressing its potential harms. He stresses the importance of prioritizing human connection and well-being in the face of technological uncertainty, urging individuals to reflect on their values and embrace activities that foster genuine human interaction.
Ultimately, Pearson’s message serves as a reminder that while AI offers immense potential, we must remain vigilant about its unintended consequences and prioritize our mental and emotional resilience in navigating an increasingly AI-driven world. By acknowledging the psychological implications of AI and engaging in thoughtful dialogue, we can work towards harnessing its benefits while mitigating its risks.
Source: abc.net.au

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US official calls on China and Russia to affirm human, not AI, control over nuclear weapons

Published

2 days ago

May 2, 2024

Peter Tolan

Senior U.S. Official Urges China and Russia to Affirm Human Control Over Nuclear Weapons
In a recent online briefing, Paul Dean, an arms control official from the State Department, called on China and Russia to align their declarations with those of the United States and other nations. He stressed the importance of ensuring that only humans, not artificial intelligence, are responsible for decisions regarding the deployment of nuclear weapons.
Dean highlighted Washington’s firm commitment to maintaining human control over nuclear weapons, a commitment echoed by France and Britain. He expressed the hope that China and Russia would issue similar statements, emphasizing the significance of this norm of responsible behavior, especially within the context of the five permanent members of the United Nations Security Council.
These remarks coincide with efforts by the administration of U.S. President Joe Biden to engage in separate discussions with China on nuclear weapons policy and the development of artificial intelligence.
While the Chinese defense ministry has yet to respond to these comments, discussions on artificial intelligence emerged during recent talks between U.S. Secretary of State Antony Blinken and China’s Foreign Minister Wang Yi in Beijing. Both parties agreed to hold their first bilateral talks on artificial intelligence in the coming weeks, aiming to address concerns about the technology’s risks and safety.
Although U.S. and Chinese officials resumed nuclear weapons discussions in January as part of efforts to normalize military communications, formal arms control negotiations are not expected in the near future. Meanwhile, China, amid its expansion of nuclear capabilities, previously suggested that the largest nuclear powers should prioritize negotiating a no-first-use treaty between each other.
Source: reuters.com

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Enterprise AI Faces Looming Energy Crisis

Published

2 days ago

May 2, 2024

Peter Tolan

The widespread adoption of artificial intelligence (AI) has been remarkable, but it has come at a significant cost.
R K Anand, co-founder and chief product officer at Recogni, highlighted the exponential growth in data and compute power required to train modern AI systems. He emphasized that firms must invest substantial resources, both in terms of time and money, to train some of today’s largest foundational models.
Moreover, the expenditure doesn’t end once the models are trained. Meta, for instance, anticipates spending between $35 billion and $40 billion on AI and metaverse development this fiscal year. This substantial investment underscores the ongoing financial commitment necessary for AI development.
Given these challenges, Anand stressed the importance of developing next-generation AI inference solutions that prioritize performance and power efficiency while minimizing total ownership costs. He emphasized that inference is where the scale and demand of AI will be realized, making efficient technology essential from both a power cost and total cost of operations perspective.
AI inference, which follows AI training, is crucial for real-world applications of AI. Anand explained that while training builds the model, inference involves the AI system producing predictions or conclusions based on existing knowledge.
However, inference also represents a significant ongoing cost in terms of power and computing. To mitigate these expenses, Anand suggested methods such as weight pruning and precision reduction through quantization to design more efficient models.
Since a large portion of an AI model’s lifespan is spent in inference mode, optimizing inference efficiency becomes crucial for lowering the overall cost of AI operations.
Anand highlighted the importance of efficient inference for enterprises, noting that it enables higher productivity and returns on investment. However, he cautioned that without favorable unit economics, the AI industry could face challenges, especially considering the increasing volume of data.
Ultimately, Anand emphasized the need for AI solutions that increase productivity without significantly increasing operating costs. He predicted a shift towards allocating a larger portion of computing resources to inference as AI becomes more integrated into day-to-day work.
Source: pymnts.com

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