High-Powered Computing: Energy Consumption and Its Environmental Impact

While High Performance Computing (HPC) is critical for scientific breakthroughs and AI development, its growing energy use (often dependent on fossil fuels) requires a detailed environmental impact analysis. This study addresses the ever-increasing ecological challenges posed by high-performance computing (HPC) data centers by developing a model to quantify and predict their energy consumption and carbon emissions. Our model estimates the annual energy consumption of HPC data centers globally using data on the size and capacity of small and massive data centers. It distinguishes between theoretical maximum and actual energy consumption based on average utilization. The results show an upward trend in carbon emissions associated with HPC, which are projected to reach 5.81 x 1017 grams of carbon dioxide by 2030. As a result, we propose a series of recommendations to reduce the environmental impact of HPC at both the technical and policy levels in favor of promoting a healthy and sustainable environment.

Purpose: This study evaluates the ecological footprint of Artificial Intelligence (AI)-driven healthcare solutions using Grey Relational Analysis (GRA), focusing on energy consumption, carbon emissions, electronic waste (e-waste), and natural resource depletion.Study design/methodology/approach: A multi-criteria decision-making approach integrating the Fuzzy Best Worst Method (FBWM) and the Decision-Making Trial and Evaluation Laboratory (DEMATEL) was employed to assess and prioritize the key environmental factors.Sample and data: Data for FBWM and DEMATEL were systematically collected from a panel of experts. These professionals, with extensive experience in the field, provided critical input on the relative importance of criteria and their interdependencies.Results: The findings highlight that electronic waste, natural resource depletion, and energy consumption are the most significant environmental concerns. At the same time, inefficiencies in AI training and carbon footprint emerge as key causal factors. Addressing these upstream drivers can significantly reduce AI’s overall environmental burden in healthcare.Originality/value: This research provides valuable insights for policymakers and healthcare institutions advocating sustainable AI adoption strategies that balance technological advancement with ecological responsibility.Research limitations/implications: AI development in healthcare requires a shift towards energy-efficient computing, optimized AI training methodologies, and responsible e-waste management. Healthcare institutions, policymakers, and AI developers must collaborate to ensure that medical AI innovations are both technologically transformative and environmentally responsible. Adopting green AI practices—such as hardware recycling, federated learning, and energy-efficient model deployment—is crucial to achieving long-term sustainability in AI-powered healthcare.

Energy consumption and carbon emissions forecasting for industrial processes: Status, challenges and perspectives

Transportation is an important part of social and economic development and is also a typical high-energy and high-emissions industry. Achieving low-carbon development in the transportation industry is a much-needed requirement and the only way to achieve high-quality development. Therefore, based on the relevant data of 30 provinces in China from 2010 to 2018, this research uses the static panel model, panel threshold model and spatial Durbin model to conduct an empirical study on the impact and mechanism of digital innovation on carbon emissions in the transportation industry, and draws the following conclusions. (1) Carbon emissions in the transportation industry have dynamic and continuous adjustment characteristics. (2) There is a significant inverted U-shape non-linear relationship between the level of digital innovation and carbon emissions in the industry. In regions with a low level of digital innovation, the application of digital technology increases carbon emissions in this industry, but as the level of digital innovation continues to increase its application suppresses carbon emissions, showing an effect of carbon emission reduction. (3) The impact of digital innovation on carbon emissions in the transportation industry has a spatial spillover effect, and its level in one province significantly impacts carbon emissions in other provinces’ transportation industry through the spatial spillover effect. Therefore, it is recommended to further strengthen the exchange and cooperation of digital innovation in the transportation industry between regions, improve the scale of digitalization in this industry, and accelerate its green transformation through digital innovation, thus promoting the green, low-carbon, and sustainable development of China’s economy.

This paper introduces a new method to promote energy efficiency in computing systems. The rapidly growing demand for computational power in high-performance computing (HPC) systems is accompanied by a significant increase in energy consumption. This research investigates the many ways to minimise energy consumption in HPC systems without sacrificing computational performance. Building upon previous research we combined and refined ideas to develop an optimised approach. Our method utilises a tri-modular framework incorporating an Energy-Efficient Hardware Design, Resource Management, and Optimisation, and Server Virtualisation. The first module employs a method of designing smart energy-efficient hardware. This method incorporates leakage reduction techniques such as clock gating and sleep states. The second module uses a technique focused on optimising server software. This method is based on Dynamic Voltage and frequency scaling (DVFS) for power management in data centres. The third module is based on server virtualisation and employs software to create multiple virtual machines on a single physical server allowing for a significant reduction in energy and hardware costs. The methods used in these three modules are systematically integrated to produce a more efficient consumption of electricity. This will allow for the computing system to minimise energy consumption without compromising computational power.

Urban water resources management with energy constraint and carbon emission intensity under uncertainty: A dual objective optimization model

Usually, scientific applications outlive the lifespan of the High Performance Computing (HPC) systems for which they are initially developed. The innovations in the HPC systems’ hardware and parallel programming standards drive the modernization of HPC applications so that they continue being performant. While such code modernization efforts may not be challenging for HPC experts and well-funded research groups, many domain-experts and students may find it challenging to adapt their applications for the latest HPC systems due to lack of expertise, time, and funds. The challenges of such domain-experts and students can be mitigated by providing them high-level tools for code modernization and migration. A brief overview of two such high-level tools is presented in this chapter. These tools support the code modernization and migration efforts by assisting users in parallelizing their applications and porting them to HPC systems with high-bandwidth memory. The tools are named as: Interactive Parallelization Tool (IPT) and Interactive Code Adaptation Tool (ICAT). Such high-level tools not only improve the productivity of their users and the performance of the applications but they also improve the utilization of HPC resources.

Climate change calls for worldwide public concern and effort to cut down carbon dioxide (CO2) emissions and globally realize sustainable development. As one of the largest energy consumers, the construction industry plays a crucial role in achieving the national carbon emission reduction goal. This paper, by employing an improved Kaya model, explored the relationships and trends among carbon emission, energy consumption, and GDP growth rate and carbon emission intensity of the Chinese construction industry. Results showed that the carbon emission of the construction industry is mainly affected by GDP growth of construction scale. The energy consumption was the main driver to the increase of carbon emissions. The carbon emissions of unit area and carbon intensity showed a decreased trend with the development of economy and the increase of construction scale. The dependence of construction activities on the fossil fuel remained despite noticeable decrease. Energy intensity helps to reduce construction carbon emissions. Low carbon economy development of construction industry calls for technological innovation, alternative energy and new technical support for further breakthrough. These findings provide scientific evidence of carbon emissions in the Chinese construction industry and useful inputs for industry-specific emission regulations.

The implications of China’s investment-driven economy on its energy consumption and carbon emissions

Energy consumption is the major source of industrial carbon emissions. Energy consumption carbon emission factor method and LMDI (Logarithmic Mean Divisia Index) method was used to analyze the carbon emission evolution of industrial economy energy consumption in Jiangsu Province with collected data on industrial energy consumption in 1995-2012. Results showed that Jiangsu province economic industrial carbon emissions keep increasing in 1995-2012 years. The results of carbon emission increase analysis of energy consumption structure effects, industrial energy consumption intensity effects and output scale effects in 1999-2012 showed that energy consumption intensity effect has the maximum contribution to carbon emissions in industrial carbon emissions Jiangsu Province. Therefore, the main way to control carbon emissions of industrial energy consumption in Jiangsu Province is reasonably control the growth of energy consumption.

With ever increasing usage of IT infrastructure around the globe, the increase in energy consumption and carbon emission is a worrisome situation for everyone because of its direct effect on environment. The environment is being dangerously impacted by Information Technology infrastructure extensive use and its waste. Cloud computing has emerged as one step further solution to deploy all virtualized IT resources as per need on self-service basis as a rental method for the users. Different cloud deployment methods have emerged according to the need of users, institutions and communities. In last decade virtualization has been a milestone in enabling optimal usage of information technology resources. Cloud computing has provided a platform for deploying everything as a Service in IT, which has changed the economics of IT based business and organizations from Capital Expenditure to Operational Expenditure. For enabling Cloud services we first need to understand our existing infrastructure. In second step we should be able to virtualize it for optimal usage. Then with the help of existing cloud applications we can deliver and deploy information technology as a resource at various levels such as infrastructure, platform, and software. Keywords; cloud computing, carbon emission, energy consumption, IT waste, virtualization. The Advancement in the field of information technology has enabled us to optimize the use of physical infrastructure with the help of virtualization, most of which is only used in specific applications like vitalizing a local area network on switch or vitalizing a compute resource to run more than one operating system on a desktop. The benefits of same has not been completely implemented and utilized in industrial environments where we require a large amount of IT resources. The advantage of using cloud is to enable complete utilization of Information technology resources, where most of the resources are underutilized. The cost involved in maintenance and management of those resources is huge which can be saved due to centralized management of private cloud infrastructure. We create private cloud keeping in mind. • Availability and performance requirements for the cloud • Infrastructure sizing, performance and continual availability requirements • The need for data security or isolation • The appropriate user interface With advent of IT as a service in cloud computing technology energy consumption and related carbon emissions and waste are considerably lesser than in-house implementation. Technology in Cloud Computing creates an able structure for sharing resources with the help of virtualization among many; it greatly decreases or abolishes the need for most products, equipment, utilities and facilities. The results are more sustainable IT infrastructures and processes which help in keeping environment safe. • Highest resource consumption and effectiveness • Reduced energy consumption by up to 95 % with the help of virtualization which optimizes the use of physical infrastructure and thereby helps IT to be delivered as a service. • Displaced utility costs Workstation utility costs are cut by 90% due to change of capital expenditure into operational expenditure in industries or organizations across the world. • Reduced electronic waste as No more batteries and minimal software/hardware waste. • Reduced waste overall due to consumption of computational power as service, no more as infrastructure. • Reduced ecological impact of workstations as compared to thin clients as anything related to computation can be provided as a service on a client connected with internet to the cloud provider.

This work aims to analyze the impact of population aging on the relationship between economic growth and carbon emissions, and the relationship between energy consumption and carbon emissions. To this end, based on the panel data of 36 OECD countries from 1996 to 2016, the panel threshold regression model was developed. In the model, per capita carbon emissions are the explained variables, economic development (per capita GDP) and per capita energy consumption are the core explanatory variables of the two models respectively, population aging is the threshold variable, population size, technological innovation level, the degree of urbanization, industrial structure and energy intensity are control variables. The empirical results show that there is a population aging threshold effect between per capita GDP and per capita carbon emissions, and between per capita energy consumption and per capita carbon emissions. This means that population aging is an important factor that affects the relationship between economic growth and carbon emissions, as well as energy consumption and carbon emissions. Although economic growth and carbon emissions, energy consumption and carbon emissions were coupled, the coupling state decline when the population aging crosses the threshold value. This indicates that the population aging contributes to the decoupling of the economic growth and carbon emissions, and the decoupling of energy consumption and carbon emissions. Finally, on the basis of the proposed model, a robust analysis of the variable of trade openness was conducted to prove the validity of this research.

Environment-conscious scheduling of HPC applications on distributed Cloud-oriented data centers

Sustainability-based performance evaluation of hybrid nanofluid assisted machining

Energy consumption carbon emission factor method was used to analyze the carbon emission evolution of industrial energy consumption in Hunan Province with collected data on industrial energy consumption in 2000-2012. Results had shown that Hunan province industry’s carbon emission keep increasing in 2000-2012. There is a highly correlation between the total coal consumption and carbon emission of industrial energy carbon emission. Industrial energy consumption structure plays a decisive role in carbon emission. Industrial economic growth at the expense of high energy consumption in 2000-2005 has changed. Industrial carbon intensity has a fluctuated downward trend from 2005 to 2012. From the perspective of carbon emission per industrial output and industrial energy consumption structure, there is a large potential for carbon emission control in Hunan industrial energy consumption. Therefore, the main way to control carbon emission of industrial energy consumption in Hunan Province is to optimize the energy structure, reasonable adjustment of industry structure, improve energy technical level, proper control the growth of energy consumption.