A combined input–output and sensitivity analysis of CO2 emissions in the high energy-consuming industries: A case study of China

2016 ◽  
Vol 7 (2) ◽  
pp. 315-325 ◽  
Author(s):  
Rong Yuan ◽  
Tao Zhao
Keyword(s):  
Sensitivity Analysis ◽  
Co2 Emissions ◽  
High Energy ◽  
Input Output ◽  
Energy Consuming

Assessing energy-related CO2 emissions with sensitivity analysis and input-output techniques

Energy ◽  
2012 ◽  
Vol 37 (1) ◽  
pp. 161-170 ◽  
Author(s):  
Miguel Angel Tarancon ◽  
Pablo Del Río
Keyword(s):  
Sensitivity Analysis ◽  
Co2 Emissions ◽  
Input Output

scholarly journals Database engines: evolution of greenness

2021 ◽  
Author(s):  
Zainab Al-Zanbouri
Keyword(s):  
Energy Consumption ◽  
Co2 Emissions ◽  
Execution Time ◽  
Software Metrics ◽  
Large Fraction ◽  
High Energy ◽  
Strongly Correlated ◽  
And Performance ◽  
Database Engine

Information Technology uses up to 10% of the world’s electricity generation, contributing to CO2 emissions and high energy costs. Data centers consume up to 23% of this energy, and a large fraction of this energy is consumed by databases. Therefore, building an energy efficient (green) database engine will reduce associated energy consumption and CO2 emissions. To understand the factors driving database energy consumption and execution time over the course of their evolution, we conducted an empirical case study of energy consumption of two MySQL database engines, InnoDB and MyISAM, across 12 releases. Moreover, we examined the relation between four software metrics and energy consumption & execution time, to determine the software metrics affecting the greenness and performance of a database. Our analysis shows that database engines energy consumption and execution time increase as databases evolve. Moreover, the Lines of Code metric is strongly correlated with energy consumption and execution time.

scholarly journals Database engines: evolution of greenness

2021 ◽  
Author(s):  
Zainab Al-Zanbouri
Keyword(s):  
Energy Consumption ◽  
Co2 Emissions ◽  
Execution Time ◽  
Software Metrics ◽  
Large Fraction ◽  
High Energy ◽  
Strongly Correlated ◽  
And Performance ◽  
Database Engine

Information Technology uses up to 10% of the world’s electricity generation, contributing to CO2 emissions and high energy costs. Data centers consume up to 23% of this energy, and a large fraction of this energy is consumed by databases. Therefore, building an energy efficient (green) database engine will reduce associated energy consumption and CO2 emissions. To understand the factors driving database energy consumption and execution time over the course of their evolution, we conducted an empirical case study of energy consumption of two MySQL database engines, InnoDB and MyISAM, across 12 releases. Moreover, we examined the relation between four software metrics and energy consumption & execution time, to determine the software metrics affecting the greenness and performance of a database. Our analysis shows that database engines energy consumption and execution time increase as databases evolve. Moreover, the Lines of Code metric is strongly correlated with energy consumption and execution time.

A combined input–output and sensitivity analysis approach to analyse sector linkages and CO2 emissions

2007 ◽  
Vol 29 (3) ◽  
pp. 578-597 ◽  
Author(s):  
Miguel Ángel Tarancón Morán ◽  
Pablo del Río González
Keyword(s):  
Sensitivity Analysis ◽  
Co2 Emissions ◽  
Analysis Approach ◽  
Input Output

scholarly journals Sensitivity analysis for interpretation of machine learning based segmentation models in cardiac MRI

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Markus J. Ankenbrand ◽  
Liliia Shainberg ◽  
Michael Hock ◽  
David Lohr ◽  
Laura M. Schreiber
Keyword(s):  
Neural Networks ◽  
Sensitivity Analysis ◽  
Cardiac Magnetic Resonance ◽  
Magnetic Resonance ◽  
Cardiac Mri ◽  
Magnetic Resonance Images ◽  
Model Interpretation ◽  
First Case ◽  
Segmentation Models

Abstract Background Image segmentation is a common task in medical imaging e.g., for volumetry analysis in cardiac MRI. Artificial neural networks are used to automate this task with performance similar to manual operators. However, this performance is only achieved in the narrow tasks networks are trained on. Performance drops dramatically when data characteristics differ from the training set properties. Moreover, neural networks are commonly considered black boxes, because it is hard to understand how they make decisions and why they fail. Therefore, it is also hard to predict whether they will generalize and work well with new data. Here we present a generic method for segmentation model interpretation. Sensitivity analysis is an approach where model input is modified in a controlled manner and the effect of these modifications on the model output is evaluated. This method yields insights into the sensitivity of the model to these alterations and therefore to the importance of certain features on segmentation performance. Results We present an open-source Python library (misas), that facilitates the use of sensitivity analysis with arbitrary data and models. We show that this method is a suitable approach to answer practical questions regarding use and functionality of segmentation models. We demonstrate this in two case studies on cardiac magnetic resonance imaging. The first case study explores the suitability of a published network for use on a public dataset the network has not been trained on. The second case study demonstrates how sensitivity analysis can be used to evaluate the robustness of a newly trained model. Conclusions Sensitivity analysis is a useful tool for deep learning developers as well as users such as clinicians. It extends their toolbox, enabling and improving interpretability of segmentation models. Enhancing our understanding of neural networks through sensitivity analysis also assists in decision making. Although demonstrated only on cardiac magnetic resonance images this approach and software are much more broadly applicable.

scholarly journals Analysis of CO2 Emissions from Industrial Parks: A Case Study in Ho Chi Minh City, Vietnam

2021 ◽  
Vol 1092 (1) ◽  
pp. 012084
Author(s):  
T LVan ◽  
V T T Ho ◽  
H D T Thanh ◽  
N T Thong ◽  
Q Huynh ◽  
...  
Keyword(s):  
Co2 Emissions ◽  
Ho Chi Minh City ◽  
Industrial Parks

scholarly journals What Is the Environmental Impact of Wine Entering Global Value Chains? Studying the Evolution of CO2 Emissions from the Export of Spanish Denomination of Origin Wines

Foods ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 1664
Author(s):  
Juan Sebastián Castillo-Valero ◽  
Inmaculada Carrasco ◽  
Marcos Carchano ◽  
Carmen Córcoles
Keyword(s):  
Environmental Impact ◽  
Co2 Emissions ◽  
Carbon Footprint ◽  
Environmental Performance ◽  
Service Provision ◽  
International Markets ◽  
Value Chains ◽  
Input Output ◽  
Continuous Growth ◽  
Wine Trade

The continuous growth of the international wine trade and the expansion of international markets is having significant commercial, but also environmental, impacts. The benefits of vineyards in terms of ecosystem service provision are offset by the increase in CO2 emissions generated by transportation. Denominations of Origin, as quality labels, emphasise a wine’s links to the terroir, where specific elements of culture and environment merge together. However, Denominations of Origin can also have differentiating elements as regards environmental performance. Drawing on an extended multiregional input–output model applied to the Spanish Denominations of Origin with the largest presence in the international wine trade, this study shows that wines with the greatest exporting tradition are those that most reduced their carbon footprint per litre of exported wine in the period 2005–2018, thus being the most environmentally efficient.

scholarly journals Sensitivity Analysis of Fuzzy Activity-Based Life Cycle Costing

2018 ◽  
Vol 225 ◽  
pp. 05002
Author(s):  
Freselam Mulubrhan ◽  
Ainul Akmar Mokhtar ◽  
Masdi Muhammad
Keyword(s):  
Sensitivity Analysis ◽  
Life Cycle ◽  
Interest Rate ◽  
Life Cycle Costing ◽  
High Impact ◽  
Final Output ◽  
Left And Right ◽  
The Interest Rate ◽  
Operation Cost

A sensitivity analysis is typically conducted to identify how sensitive the output is to changes in the input. In this paper, the use of sensitivity analysis in the fuzzy activity based life cycle costing (LCC) is shown. LCC is the most frequently used economic model for decision making that considers all costs in the life of a system or equipment. The sensitivity analysis is done by varying the interest rate and time 15% and 45%, respectively, to the left and right, and varying 25% of the maintenance and operation cost. It is found that the operation cost and the interest rate give a high impact on the final output of the LCC. A case study of pumps is used in this study.

Rheed Studies of a-Axis Oriented DyBa2Cu3O7 Films Grown by All-MBE

1997 ◽  
Vol 502 ◽  
Author(s):  
Ivan Bozovic ◽  
J. N. Eckstein ◽  
Natasha Bozovic ◽  
J. O'Donnell
Keyword(s):  
Phase Transitions ◽  
Secondary Phase ◽  
Atomic Layer ◽  
High Energy ◽  
Layer By Layer ◽  
Growth Modes ◽  
Flow Surface ◽  
Surface Phase Transitions

ABSTRACTReal-time, in-situ surface monitoring by reflection high-energy electron diffraction (RHEED) has been the key enabling component of atomic-layer-by-layer molecular beam epitaxy (ALL-MBE) of complex oxides. RHEED patterns contain information on crystallographic arrangements and long range order on the surface; this can be made quantitative with help of numerical simulations. The dynamics of RHEED patterns and intensities reveal a variety of phenomena such as nucleation and dissolution of secondary-phase precipitates, switching between growth modes (layer-by-layer, step-flow), surface phase transitions (surface reconstruction, roughening, and even phase transitions induced by the electron beam itself), etc. Some of these phenomena are illustrated here, using as a case study our recent growth of atomically smooth a-axis oriented DyBa2Cu3O7 films.

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