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Internet technology has developed rapidly, especially in the field of cloud computing. With the gradual growth of cloud computing capabilities, power consumption in data centres has become a very important issue. The development of cloud computing has made data centres the cornerstone of today's global economic development, so data centres have also developed rapidly both in terms of construction scale and growth speed. However, large numbers of data centres consume huge amounts of power while also increasing the economic cost of cloud computing. They have led to soaring carbon dioxide emissions, which will have an unimaginable impact on the global climate. Therefore, the energy-consumption problem has become an important topic in current cloud computing research. How to save energy and reduce power consumption is a key issue, and this paper proposes an energy-saving job-scheduling method, which considers task dependency in a cloud computing environment. The proposed method considers the heterogeneous characteristics of data centres, models energy consumption based on the frequency and kernel number of the virtual machine CPU and provides new solutions to the problem of energy-consumption monitoring of cloud computing data centres. The main task is to divide each job into several tasks and then assign the tasks to virtual machines. Comparison of the simulation results, i.e. total execution time with job cutting and without job cutting, using the virtual machine (VM) (with the number of jobs set to 1000 and 2000), indicated that the total execution time and total energy consumption are better with job cutting than when the job is not cut, and this was not affected by the dependency of tasks. Moreover, job cutting also effectively reduces energy consumption and job discard rate. [ABSTRACT FROM AUTHOR]

The construction industry is one of the most energy-intensive industries in China, which contributes to high carbon emissions and hinders efforts to reduce the emissions. The high energy consumption and emission have seriously constrained the growth of low carbon economy in China's construction industry. And reducing the carbon emission from construction industry (CECI) has become the key to sustainable development and the achievement of "double carbon target". The paper firstly decomposes the main influencing factors and calculates their effects by LMDI model. Then, the Tapio decoupling index is used to explore the decoupling relationship between carbon emissions and economic output. Finally, the spatial-temporal evolution of carbon emissions is analyzed through spatial autocorrelation theory. The results show that the carbon emissions from construction industry are highly increased by the construction employment rate (EM), and may be reduced by the energy intensity (EI) and carbon emission efficiency (CI). And the regions with better decoupling status are the eastern and central regions, which have relatively developed construction industry. Moreover, the spatial-temporal characteristic of carbon emissions is "high in the east and low in the west, high in the south and low in the north," showing obviously regional differences. [Display omitted] • Factors about production technology are taken into account, reflecting the characteristics of the construction industry. • Developed regions have better decoupling potential and more responsibility for CO 2. • The spatial distribution is "high in the east and low in the west, high in the south and low in the north". • The provinces in a region have similar decoupling status and carbon emission distribution. [ABSTRACT FROM AUTHOR]

While the deployment of technological innovation was able to avert a devastating global supply chain fallout arising from the impact of ravaging COronaVIrus Disease 19 (COVID-19) pandemic, little is known about potential environmental cost of such achievement. The aim of this paper is to identify the determinants of logistics performance and investigate its empirical linkages with economic and environmental indicators. We built a macro-level dataset for the top 25 ranked logistics countries from 2007 to 2018, conducting a set of panel data tests on cross-sectional dependence, stationarity and cointegration, to provide preliminary insights. Empirical estimates from Fully Modified Ordinary Least Squares (FMOLS), Generalized Method of Moments (GMM), and Quantile Regression (QR) model suggest that technological innovation, Human Development Index (HDI), urbanization, and trade openness significantly boost logistic performance, whereas employment and Gross Fixed Capital Formation (GFCF) fail to respond in such a desirable path. In turn, an increase in the Logistic Performance Index (LPI) is found to worsen economic growth. Finally, LPI exhibits a large positive effect on carbon emissions, which is congruent with a strand of the literature highlighting that the modern supply chain is far from being decarbonized. Thus, this evidence further suggest that more global efforts should be geared to attain a sustainable logistics. • Examining 25 topmost Logistics Countries on many fronts. • Technological innovation, HDI, urbanization, and trade openness spur logistic performance. • Logistic performance does not improve with employment rate and Gross Fixed Capital Formation. • Logistic performance increases carbon dioxide emissions. [ABSTRACT FROM AUTHOR]