Can energy saving and emission reduction policies promote green transformation of industrial enterprises--The Case of China.

In the context of the continued advancement of the green economy transition, the proactive pursuit of carbon emissions reduction and the early attainment of carbon neutrality goals have emerged as essential components in promoting high-quality economic development. Not only does it contribute to the creation of a community of human destiny, but it is also vital to the realization of sustainable development for human civilization. A dynamic evolutionary game model, which encompasses the interactions among government, enterprises, and the public, was constructed to examine the inherent impact mechanisms of the behavior of three players on the development of a green economy under the context of energy saving and emission reduction subsidies. The results showed that the incentive and punishment mechanisms served as effective tools for harmonizing the interests of system members. Within the mechanisms, the public demonstrated a higher sensitivity to rewards, while enterprises exhibited greater responsiveness to fines. Consequently, the government could influence the behavior of enterprises by incentivizing the public to serve as a third-party inquiry and oversight body. Simultaneously, the government could encourage enterprises to expedite green technology innovation by employing a combination of incentive and punishment mechanisms.

Environmental regulation, industrial structure and energy efficiency: Evidence from 30 provinces in China.

The government's environmental protection policy can significantly contribute to alleviating resource shortages and curbing environmental pollution, but the impact of various policy instruments implemented by the government on energy efficiency is unclear. Based on the panel data of 30 provinces in China from 2005 to 2021, this paper analyses the impact of environmental regulation and the industrial structure on energy efficiency from the perspective of resource taxes. The U-shaped relationship between environmental regulation and energy efficiency and between the optimization of industrial structure can significantly improve energy efficiency, and the optimization of industrial structure is conducive to weakening the initial inhibitory effect of environmental regulation. In addition, the analysis of regional heterogeneity showed that the impact of environmental regulation was stronger in the central and western regions, while the impact of industrial structure was stronger in the eastern and western regions. The conclusions of this study can help to expand the understanding of the relationship between environmental regulation and industrial structure on energy efficiency, provide policy enlightenment for the realization of green development and high-quality development, and provide Chinese examples and experiences for developing countries to improve energy efficiency.

Nexus of green energy, financial inclusion, militarization, and environmental sustainability: A global perspective.

This article investigates the dynamic impact of green energy consumption (GE), financial inclusion (FI), and military spending (MS) on environmental sustainability (ES) by utilizing a sample of 121 countries from 2003 to 2022. The dataset is divided into high-income, upper-middle income and low and lower-middle-income countries. We employed a two-step system GMM approach, which was further robust through panel Quantile and Driscoll-Kraay (D-K) regressions. The findings divulged that green energy resources benefit ES at global and all income levels because of having a significant negative impact of 5.9% on ecological footprints. At the same time, FI and MS significantly enhance ecological footprints by 7% and 6.9%, respectively, proving these factors detrimental to ES. Moreover, conflicts (CON), terrorism (TM), institutional quality (IQ), and socioeconomic conditions (SEC) also have a significantly positive association with global ecological footprints and most of the income level groups. Dissimilarly, financial inclusion and armed conflicts have a non-significant influence on ecological footprints in low-income and high-income countries, respectively. Furthermore, institutional quality enhances ES in upper-middle and low and lower-middle-income countries by negatively affecting ecological footprints. At the same time, terrorism significantly reduces ecological footprints in high-income countries. This research also provides the imperative policy inferences to accomplish various SDGs.

Utilizing support vector machines to foster sustainable development and innovation in the clean energy sector via green finance.

As the global demand for clean energy continues to grow, the sustainable development of clean energy projects has become an important topic of research. in order to optimize the performance and sustainability of clean energy projects, this work explores the environmental and economic benefits of the clean energy industry. through the use of Support Vector Machine (SVM) Multi-factor models and a bi-level multi-objective approach, this work conducts comprehensive assessment and optimization. with wind power base a as a case study, the work describes the material consumption of wind turbines, transportation energy consumption and carbon dioxide (CO2) emissions, and infrastructure material consumption through descriptive statistics. Moreover, this work analyzes the characteristics of different wind turbine models in depth. On one hand, the SVM multi-factor model is used to predict and assess the profitability of Wind Power Base A. On the other hand, a bi-level multi-objective approach is applied to optimize the number of units, internal rate of return within the project, and annual average equivalent utilization hours of the Wind Power Base A. The research results indicate that in March, the WilderHill New Energy Global Innovation Index (NEX) was 0.91053, while the predicted value of the SVM multi-factor model was 0.98596. The predicted value is slightly higher than the actual value, demonstrating the model's good grasp of future returns. The cumulative rate of return of Wind Power Base A is 18.83%, with an annualized return of 9.47%, exceeding the market performance by 1.68%. Under the optimization of the bi-level multi-objective approach, the number of units at Wind Power Base A decreases from the original 7004 to 5860, with total purchase and transportation costs remaining basically unchanged. The internal rate of return of the project increases from 8% to 9.3%, and the annual equivalent utilization hours increase to 2044 h, comprehensively improving the investment return and utilization efficiency of the wind power base. Through optimization, significant improvements are achieved in terAs the global demand for clean energy continues to grow, the sustainable development of clean energy projects has become an important topic of research. In order to optimize the performance and sustainability of clean energy projects, this work explores the environmental and economic benefits of the clean energy industry. Through the use of Support Vector Machine (SVM) multi-factor models and a bi-level multi-objective approach, this work conducts comprehensive assessment and optimization. With Wind Power Base A as a case study, the work describes the material consumption of wind turbines, transportation energy consumption and carbon dioxide (CO2) emissions, and infrastructure material consumption through descriptive statistics. Moreover, this work analyzes the characteristics of different wind turbine models in depth. On one hand, the SVM multi-factor model is used to predict and assess the profitability of Wind Power Base A. On the other hand, a bi-level multi-objective approach is applied to optimize the number of units, internal rate of return within the project, and annual average equivalent utilization hours of the Wind Power Base A. The research results indicate that in March, the WilderHill New Energy Global Innovation Index (NEX) was 0.91053, while the predicted value of the SVM multi-factor model was 0.98596. The predicted value is slightly higher than the actual value, demonstrating the model's good grasp of future returns. The cumulative rate of return of Wind Power Base A is 18.83%, with an annualized return of 9.47%, exceeding the market performance by 1.68%. Under the optimization of the bi-level multi-objective approach, the number of units at Wind Power Base A decreases from the original 7004 to 5860, with total purchase and transportation costs remaining basically unchanged. The internal rate of return of the project increases from 8% to 9.3%, and the annual equivalent utilization hours increase to 2044 h, comprehensively improving the investment return and utilization efficiency of the wind power base. Through optimization, significant improvements are achieved in terms of the number of units, internal rate of return within the project, and annual average equivalent utilization hours at Wind Power Base A. The number of units decreases to 5860, with total purchase and transportation costs remaining basically unchanged, the internal rate of return increases to 9.3%, and annual equivalent utilization hours increase to 2044 h. Energy consumption and CO2 emissions are significantly reduced, with energy consumption decreasing by 0.68 × 109 kgce and CO2 emissions decreasing by 1.29 × 109 kg. The optimization effects are mainly concentrated in the production and installation stages, with emission reductions achieved through the recycling and disposal of materials consumed in the early stages. In terms of investment benefits, environmental benefits are enhanced, with a 13.93% reduction in CO2 emissions. Moreover, there is improved energy efficiency, with the energy input-output ratio increasing from 7.73 to 9.31. This indicates that the Wind Power Base A project has significant environmental and energy efficiency advantages in the clean energy industry. This work innovatively provides a comprehensive assessment and optimization scheme for clean energy projects and predicts the profitability of Wind Power Base A using SVM multi-factor models. Besides, this work optimizes key parameters of the project using a bi-level multi-objective approach, thus comprehensively improving the investment return and utilization efficiency of the wind power base. This work provides innovative methods and strong data support for the development of the clean energy industry, which is of great significance for promoting sustainable development under the backdrop of green finance.

Research on total factor energy efficiency in western China based on the three-stage DEA-Tobit model.

As an essential material basis and power source for economic and social development, Western China's low energy use efficiency has hindered its sustainable economic development. This study aims to evaluate the total factor energy efficiency of the region and identify its influencing factors. A three-stage DEA model was used to measure the efficiency of 11 provinces from 2006 to 2021, and the Tobit model was employed to investigate internal factors. The findings show that (i) external environmental factors and stochastic perturbations have a significant impact on TFEE in the western region, overestimating integrated efficiency and scale efficiency and underestimating pure technical efficiency. (ii) the study of external influencing factors finds that the level of economic development increases input redundancy; the industrial structure increases capital input and labor input redundancy while decreasing energy input redundancy; and the energy consumption structure increases capital input and energy input redundancy while decreasing labor input redundancy. (iii) the study of internal influencing factors finds that the level of scientific and technological innovation, the level of openness to the outside world, and the TFEE have a positive correlation. In contrast, the intensity of environmental regulation has a negative correlation.

A new strategy for improving the energy efficiency of electro-Fenton: Using N-doped activated carbon cathode with strong Fe(III) adsorption capacity to promote Fe(II) regeneration.

Fe(II) regeneration plays a crucial role in the electro-Fenton process, significantly influencing the rate of ·OH formation. In this study, a method is proposed to improve Fe(II) regeneration through N-doping aimed at enhancing the adsorption capacity of the activated carbon cathode for Fe(III). N-doping not only enriched the pore structure on the surface of activated carbon, providing numerous adsorption sites, but also significantly increased the adsorption energy for Fe(III). Among the types of nitrogen introduced, pyridine-N exhibited the most substantial enhancement effect, followed by pyrrole-N, while graphite-N showed a certain degree of inhibition. Furthermore, N-doping facilitated the adsorption of all forms of Fe(III) by activated carbon. The adsorption and electrosorption rates of the NAC-900 electrode for Fe(III) were 30.33% and 42.36%, respectively. Such modification markedly enhanced the Fe3+/Fe2+ cycle within the electro-Fenton system. The NAC-900 system demonstrated an impressive phenol degradation efficiency of 93.67%, alongside the lowest electricity consumption attributed to the effective "adsorption-reduction" synergy for Fe(III) on the NAC-900 electrode. Compared to the AC cathode electro-Fenton system, the degradation efficiency of the NAC-900 cathode electro-Fenton system at pH = levels ranging from 3 to 5 exceeded 90%; thus, extending the pH applicability of the electro-Fenton process. The degradation efficiency of phenol using the NAC-900 cathode electro-Fenton system in various water matrices approached 90%, indicating robust performance in real wastewater treatment scenarios. This research elucidates the impact of cathodic Fe(III) adsorption on Fe(II) regeneration within the electro-Fenton system, and clarifies the influence of different N- doping types on the cathodic adsorption of Fe(III).

Green energy innovation initiatives for environmental sustainability: current state and future research directions.

Worldwide, all countries have been facing the crisis of climate change problem. They have been addressing this issue by focusing on implementing green energy innovation initiatives and promoting a sustainable future through environmental sustainability. In this research study, we focus on examining the role of green finance through green energy innovations, which are taking place in several sectors across different regions to promote environmental sustainability. The study has analysed 152 articles on this research domain through a systematic literature review to understand the present state of existing knowledge. The current study examines the Scopus-indexed research articles from the time period 2002 to 2023. Six emerging themes have been examined to understand their development and the potential impact of green initiatives for environmental sustainability. Various institutional theories have been explored to understand their association with the investigated research area. The paper has discussed multiple challenges that need to be addressed for the speedy implementation of green innovations. Finally, future research questions have been proposed based on the findings from the extant literature and the existing research gaps.

An energy efficiency assessment of Yttrium-aluminum-garnet laser in vitro.

To study the effect range of the Nd:YAG laser through various levels of cloudy medium for targets with varying grayscale values in vitro. The coated paper cards with grayscale values of 0, 50, 100, and 150 were used as the laser's targets, which were struck straightly with varying energies using three burst modes (single pulse, double pulse, and triple pulse). Six filters (transmittances of 40, 50, 60, 70, 80, and 90) were applied to simulate various levels of cloudy refractive medium. Image J software was used to measure the diameters and regions of the laser spots. The ranges of the Nd:YAG laser spots increased with energy in the same burst mode (P < 0.05). Under the same amount of energy, the ranges of the Nd:YAG laser spot increased with the grayscale value of the targets (P < 0.05). The greater the transmittance of the filters employed, the larger the range of the Nd: YAG laser spots produced. Assuming that the total pulse energy is identical, the effect ranges of multi-pulse burst modes were significantly larger than those of single-pulse burst mode (P < 0.05). The effect range of a Nd:YAG laser grows with increasing energy and the target's grayscale value. A cloudy refractive medium has a negative impact on the effect range of the Nd: YAG laser. The single pulse mode has the narrowest and safest efficiency range.

Synergistic consideration of co-treatment of sewage sludge, low-rank coal, and straw for sustainable resource utilization and enhanced energy efficiency: a review.

This article provides a comprehensive exploration of the imperative necessity for coupling the utilization of low-rank coal, sewage sludge, and straw. It studies the challenges and limitations of individual utilization methods, addressing the unique hurdles associated with feedstocks. It focused on achieving integrated and sustainable resource management, emphasizing efficient resource utilization, waste minimization, and environmental impact reduction. The investigation extends to the intricate details of reaction processes in co-processing, with a specific emphasis on the drying of raw materials to enhance combustion characteristics. The molding and preparation of feedstock are dissected, encompassing raw material selection, mixing, and the crucial addition of additives and binders. The proportions and homogenization of these feedstocks are intricately examined for uniformity and effectiveness. Furthermore, it presents theoretical approaches for investigating the co-combustion of these diverse feedstocks, contributing a solid foundation for future studies in this dynamic field. The findings presented in it offer valuable insights for researchers, practitioners, and policymakers seeking sustainable solutions in the co-disposal technology of these feedstocks. Therefore, it provides a holistic understanding of the challenges and opportunities in coupling the utilization of these selected feedstocks. By addressing individual limitations and emphasizing integrated resource management, the article establishes the groundwork for sustainable and efficient co-processing practices. The exploration of reaction processes gives a comprehensive framework for future research and application in the field of co-combustion technology. The insights gleaned from this study contribute significantly to advancing knowledge in the sustainable utilization of diverse feedstocks, guiding efforts towards environmentally responsible and resource-efficient practices.

Impacts of digital finance on energy efficiency: does environmental regulation matter?

The paper examines how digital finance affects energy efficiency in China using a dynamic panel model and data from 282 cities between 2011 and 2019. The study is based on the hypothesis which is related with digital finance, environmental regulation, and energy efficiency. The results indicate that: (1) Digital finance significantly improves energy efficiency, and this finding is consistent after several tests; (2) Digital finance has a positive effect on energy efficiency in non-resource-based cities, recession and regeneration resource-based cities, and old industrial base cities, but no significant effect on energy efficiency in growth and maturity resource-based cities and non-old industrial base cities; (3) Environmental regulation positively influences how digital finance affects energy efficiency; (4) The impact of digital finance on energy efficiency depends on the degree and tools of environmental regulation. This research offers valuable insights to local governments in China for promoting financial digitization and enhancing energy efficiency.

Dynamic role of clean energy and sustainable economic growth in coastal region: Novel observations from China.

China's coastal region is the major geographical unit for the future development of China's industrial sector. The transformation of basic structure to high-class development in China's coastal places is a significant tool for promoting the changes related to quality, power and efficiency in regional economic development. In the 21st century, environmental and energy issues have increased worldwide, and challenges related to environmental pollution, energy crises, and ecological imbalances have emerged. To climate change and energy utilization, the sustainable progress of clean energy is the new route of future energy development. Based on China's non-polluting energy growth process in the last ten years, this article explores China's clean/green energy policies and economic growth development plans. Clean energy utilization is crucial for sustainable development in the context of high-quality economic growth and climate change. However, the monetary evolution and carbon emission are not investigated whole from the clean energy aspects. Using Wind energy sources as the acceptable variable, this paper employs threshold regression and impulse functions to assess the energy consumption and economic growth on carbon emission in 30 Chinese provinces over the 2000 to 2020 period. The Deep Belief Network (DBN) model predicts wind energy utilization and efficiency. The results show that economic development and carbon emissions are connected. Further, growth influences promote the offset of carbon emissions. Green innovation alters the nexus of carbon emissions, and China's economy reduces carbon usage. It provides the decision-making policies for clean energy development.

The impact of green innovation resilience on energy efficiency: A perspective based on the development of the digital economy.

As digital economy develops, its impact on green innovation and energy efficiency has become the focus of current research. To explore the impact of the current development of the digital economy on the energy industry, this paper selects the parameter of green innovation resilience, analyzes the impact mechanism of green innovation resilience on energy efficiency under the shock of digital economic development, and uses relevant data from 284 cities in China from 2011 to 2019 for empirical testing. It is found that: green innovation resilience promotes energy efficiency; low level of green innovation resilience inhibits the improvement of energy efficiency, while high level of resilience promotes energy efficiency; the initial stage of digital economic development generates resource grabbing and the effect of technological constraints, which weakens the role of green innovation resilience in promoting energy efficiency. The results indicate that the resilience of the green innovation system should be strengthened in order to fully tap the potential for promoting energy efficiency; the policy orientation of "digital greening-energy efficient" should be pursued in the development of digital economy; the rational allocation of resources and the implementation of green standards should be strengthened in the process of digital economic development; and the constraints on energy efficiency improvement in the early stage of digital economic development should be broken through by accelerating the digitalization process.

Can digitalization effectively promote green energy efficiency? The linear and nonlinear relationship analysis.

In light of the integration of digitalization and the energy revolution, digitalization can be integrated into the energy industry to develop energy-saving technologies and improve resource allocation efficiency. On the basis of 2013-2019 Chinese provincial panel data, this paper measures the level of green energy efficiency based on the super-EBM-DEA model and analyzes the linear relationship, nonlinear relationship, and potential mechanism between digitalization and green energy efficiency. The findings indicate that (1) overall, both China's digitalization and green energy efficiency formed a steady upward trajectory during the sample period. Digitalization showed a spatial characteristic of extending and spreading from the eastern region to the central and western regions. Green energy efficiency was characterized by obvious regional heterogeneity. (2) Progress in digitalization has a significant driving effect on green energy efficiency. Subdimensional analysis shows that this driving effect mainly comes from digital development and digital transactions. (3) The impact of digitalization on green energy efficiency presents a threshold effect of economic agglomeration (with a threshold of 0.0257 and a marginally increasing, positive driving trend) and population agglomeration (with a threshold of 4.2750 and a marginally decreasing, positive driving trend). (4) Decomposing changes in green energy efficiency into scale efficiency and pure technical efficiency, this study shows that pure technical efficiency gains due to digitalization are the main driver of green energy efficiency improvements. Finally, some specific policy recommendations are proposed.

Renewable energy technology innovation and urban green economy efficiency.

Green economy efficiency is the core-factor of urban economic and environmental development. As a sustainable instruments, renewable energy technology innovation (RETI) not only reflects the low energy-consumption, but also promotes the reasonable and balanced relationship between resources utilization and urban economy. In this regard, this paper selects China's cities to investigate the effect of RETI on urban green economy efficiency from 2004 to 2020 based on theoretical analyses and previous studies. The paper finds that RETI can promote urban green economy efficiency significantly, passing a series of robustness test, and its effect has connected differently with the factor of regional factor, cleaner production level and environment pollution. Meanwhile, RETI promotes urban green economy efficiency by reducing CO2 emission and polluting manufacturing agglomeration. To date, this study has discovered the green economy efficiency improvement effects of RETI, providing theoretical basis and practical recommendations for government, technological agency and urban industries.

Optimizing energy efficiency in heterogeneous networks: An integrated stochastic geometry approach with novel sleep mode strategies and QoS framework.

The quest for energy efficiency (EE) in multi-tier Heterogeneous Networks (HetNets) is observed within the context of surging high-speed data demands and the rapid proliferation of wireless devices. The analysis of existing literature underscores the need for more comprehensive strategies to realize genuinely energy-efficient HetNets. This research work contributes significantly by employing a systematic methodology, utilizing This model facilitates the assessment of network performance by considering the spatial distribution of network elements. The stochastic nature of the PPP allows for a realistic representation of the random spatial deployment of base stations and users in multi-tier HetNets. Additionally, an analytical framework for Quality of Service (QoS) provision based on D-DOSS simplifies the understanding of user-base station relationships and offers essential performance metrics. Moreover, an optimization problem formulation, considering coverage, energy maximization, and delay minimization constraints, aims to strike a balance between key network attributes. This research not only addresses crucial challenges in creating EE HetNets but also lays a foundation for future advancements in wireless network design, operation, and management, ultimately benefiting network operators and end-users alike amidst the growing demand for high-speed data and the increasing prevalence of wireless devices. The proposed D-DOSS approach not only offers insights for the systematic design and analysis of EE HetNets but also systematically outperforms other state-of-the-art techniques presented. The improvement in energy efficiency systematically ranges from 67% (min side) to 98% (max side), systematically demonstrating the effectiveness of the proposed strategy in achieving higher energy efficiency compared to existing strategies. This systematic research work establishes a strong foundation for the systematic evolution of energy-efficient HetNets. The systematic methodology employed ensures a comprehensive understanding of the complex interplay of network dynamics and user requirements in a multi-tiered environment.

Ecological footprint in the OECD countries: do energy efficiency and renewable energy matter?

Ecological footprint (EFP) measures the amount of area, that is land or sea, which is required to absorb the waste generated through human activities or to support the production of resources consumed by populations. EFP index therefore includes six dimensions that are cropland, forestland, carbon, fishing grounds, grazing land, and built-up area. Human activities have impacted the environment, leading to global warming, widespread droughts, and diseases. The present study aims to investigate the role of renewable energy (RE) and energy efficiency on the EFP index. Past researchers have widely used carbon emission (CE) to represent environmental impact, and recent studies have shown that EFP index is a better proxy of environmental degradation. Therefore, the present research differs from past studies in that it compares on how the determinants of environmental degradation affects EFP index and CE. Panel dataset of the OECD countries from 1990 to 2020 is employed. The CS-ARDL, DCCEMG, and AMG techniques, which overcome dynamics, heterogeneity, and cross-sectional dependence, are employed. The main findings depict that RE significantly reduces EFP and CE, while economic growth significantly exacerbates them. Energy efficiency reduces CE, but does not significantly affect EFP. Non-renewable energy and research & development significantly increase CE, while an insignificant positive effect is observed with EFP. This paper shows that factors that significantly influence CE may not always significantly affect the EFP index. Thus, to reduce environmental degradation it is fundamental to understand on how each dimension of EFP is influenced.

Viscoelastic materials are most energy efficient when loaded and unloaded at equal rates.

Biological springs can be used in nature for energy conservation and ultra-fast motion. The loading and unloading rates of elastic materials can play an important role in determining how the properties of these springs affect movements. We investigate the mechanical energy efficiency of biological springs (American bullfrog plantaris tendons and guinea fowl lateral gastrocnemius tendons) and synthetic elastomers. We measure these materials under symmetric rates (equal loading and unloading durations) and asymmetric rates (unequal loading and unloading durations) using novel dynamic mechanical analysis measurements. We find that mechanical efficiency is highest at symmetric rates and significantly decreases with a larger degree of asymmetry. A generalized one-dimensional Maxwell model with no fitting parameters captures the experimental results based on the independently characterized linear viscoelastic properties of the materials. The model further shows that a broader viscoelastic relaxation spectrum enhances the effect of rate-asymmetry on efficiency. Overall, our study provides valuable insights into the interplay between material properties and unloading dynamics in both biological and synthetic elastic systems.

Eco-friendly hospital architecture.

Following a reminder on the quantities of carbon emitted in the healthcare sector, and casting a spotlight on those directly related to architecture, the authors of this article will develop three large-scale themes, the objective being to render hospital construction sustainable. 1. Energy consumption and how to reduce it. 2. "Low-carbon" construction and how building designers can limit emissions by the choice of construction materials. 3. The "resilience" of some constructions, their capacity to stave off obsolescence. As a conclusion, the authors present one of the most recent projects of the Brunet Saunier & Associates architecture agency: the Saint-Ouen university hospital, Grand Paris Nord. This project is illustrative of these preoccupations and demonstrates the possibility of meeting the challenges of sustainable development by means of simple and durable architecture.

Energy efficient virtual machines placement in cloud datacenters using genetic algorithm and adaptive thresholds.

Cloud computing platform provides on-demand IT services to users and advanced the technology. The purpose of virtualization is to improve the utilization of resources and reduce power consumption. Energy consumption is a major issue faced by data centers management. Virtual machine placement is an effective technique used for this purpose. Different algorithms have been proposed for virtual machine placement in cloud environments. These algorithms have considered different parameters. It is obvious that improving one parameter affects other parameters. There is still a need to reduce energy consumption in cloud data centers. Data centers need solutions that reduce energy consumption without affecting other parameters. There is a need to device solutions to effectively utilize cloud resources and reduce energy consumption. In this article, we present an algorithm for Virtual Machines (VMs) placement in cloud computing. The algorithm uses adaptive thresholding to identify over utilized and underutilized hosts to reduce energy consumption and Service Level Agreement (SLA) violations. The algorithm is validated with simulations and comparative results are presented.