scholarly journals Assessing The Job Creation Potential of Energy Conservation Investments

2011 ◽  
Vol 6 (2) ◽  
pp. 156-169
Author(s):  
Charles J Kibert ◽  
Richard Fobair ◽  
James Sullivan
Keyword(s):  
Energy Conservation ◽  
Job Creation ◽  
Geographic Region ◽  
Skill Level ◽  
Fine Tuning ◽  
Regional Effects ◽  
Worker Skill ◽  
Input Variables ◽  
The Cost ◽  
Conservation Investment

This paper presents a model for assessing the job creation potential of energy conservation investments resulting from construction related installation activities. It also addresses indirect job creation in manufacturing resulting from the purchase of energy conservation related equipment and materials. The model is based on construction estimating techniques and is designed to be flexible for the purpose of addressing job creation due to a wide variety of energy conservation investments. It uses a reverse estimating technique that begins with the scale of the energy conservation investment and then backs out the contractor's profit and overhead and then the cost of equipment/materials. Next, the model allocates the labor portion of the investment based on loaded labor rates and typical crew make-up. The input variables, including worker skill level, allocation of worker time per skill level, regional effects on job creation, rate of pay, and worker benefits, can be modified for the purpose of fine-tuning the model for geographic region and specific energy conservation programs. Outputs from the model include construction job creation and manufacturing job creation.

Optimized Mooring Line Simulation Using a Hybrid Method Time Domain Scheme

2014 ◽  
Author(s):  
Niels Hørbye Christiansen ◽  
Per Erlend Torbergsen Voie ◽  
Jan Høgsberg ◽  
Nils Sødahl
Keyword(s):  
Hybrid Method ◽  
Time Domain ◽  
Computation Time ◽  
Mooring Line ◽  
Mooring Lines ◽  
Fem Model ◽  
Input Variables ◽  
The Time Domain ◽  
The Cost ◽  
Short Time

Dynamic analyses of slender marine structures are computationally expensive. Recently it has been shown how a hybrid method which combines FEM models and artificial neural networks (ANN) can be used to reduce the computation time spend on the time domain simulations associated with fatigue analysis of mooring lines by two orders of magnitude. The present study shows how an ANN trained to perform nonlinear dynamic response simulation can be optimized using a method known as optimal brain damage (OBD) and thereby be used to rank the importance of all analysis input. Both the training and the optimization of the ANN are based on one short time domain simulation sequence generated by a FEM model of the structure. This means that it is possible to evaluate the importance of input parameters based on this single simulation only. The method is tested on a numerical model of mooring lines on a floating off-shore installation. It is shown that it is possible to estimate the cost of ignoring one or more input variables in an analysis.

scholarly journals Hiring, Churn, and the Business Cycle

2012 ◽  
Vol 102 (3) ◽  
pp. 575-579 ◽  
Author(s):  
Edward P Lazear ◽  
James R Spletzer
Keyword(s):  
Business Cycle ◽  
Job Creation ◽  
Labor Turnover ◽  
Present Evidence ◽  
Employment Dynamics ◽  
Job Openings ◽  
The Business Cycle ◽  
The Cost

Hires occur for two reasons - to grow a business and to replace those who have left (churn). Churn is an important part of employment dynamics, allowing workers to move to their most productive use. We present evidence on churn from the Job Openings and Labor Turnover Survey (JOLTS). Churn is procyclical. During the 2007-09 recession, four-fifths of hiring reductions are associated with reduced churn, not with reductions in job creation. We estimate that the cost of reduced churn is about two-fifths of a percentage point of GDP annually throughout the three-and-one-half year period since the beginning of the recession.

scholarly journals Fine-Tuning by Curriculum Learning for Non-Autoregressive Neural Machine Translation

2020 ◽  
Vol 34 (05) ◽  
pp. 7839-7846
Author(s):  
Junliang Guo ◽  
Xu Tan ◽  
Linli Xu ◽  
Tao Qin ◽  
Enhong Chen ◽  
...  
Keyword(s):  
Machine Translation ◽  
Fine Tuning ◽  
Inference Process ◽  
Neural Machine Translation ◽  
Training Strategy ◽  
Speed Up ◽  
Good Improvement ◽  
Tuning Process ◽  
Translation Accuracy ◽  
The Cost

Non-autoregressive translation (NAT) models remove the dependence on previous target tokens and generate all target tokens in parallel, resulting in significant inference speedup but at the cost of inferior translation accuracy compared to autoregressive translation (AT) models. Considering that AT models have higher accuracy and are easier to train than NAT models, and both of them share the same model configurations, a natural idea to improve the accuracy of NAT models is to transfer a well-trained AT model to an NAT model through fine-tuning. However, since AT and NAT models differ greatly in training strategy, straightforward fine-tuning does not work well. In this work, we introduce curriculum learning into fine-tuning for NAT. Specifically, we design a curriculum in the fine-tuning process to progressively switch the training from autoregressive generation to non-autoregressive generation. Experiments on four benchmark translation datasets show that the proposed method achieves good improvement (more than 1 BLEU score) over previous NAT baselines in terms of translation accuracy, and greatly speed up (more than 10 times) the inference process over AT baselines.

scholarly journals Principles of energy saving in water supply and sewage systems

2018 ◽  
Vol 144 ◽  
pp. 04013 ◽  
Author(s):  
Nikolay Makisha ◽  
Tatiana Kazimirova
Keyword(s):  
Energy Efficiency ◽  
Energy Conservation ◽  
Water Supply ◽  
Energy Saving ◽  
The Cost ◽  
Sewage Systems ◽  
Regions Of Russia ◽  
Water Supply And Sanitation

In this paper, we consider energy efficiency in the system of water supply and sanitation. Particular attention is paid to energy conservation techniques, stages of implementation. The review of energy conservation methods, a table of classification, taking into account the cost. The work is aimed at further more detailed study of energy efficiency techniques in the water supply and sanitation and their application in the regions of Russia.

African economies face rising risks from reshoring

2021 ◽  
Keyword(s):  
Supply Chains ◽  
Multinational Firms ◽  
Wage Differentials ◽  
Job Creation ◽  
Youth Unemployment ◽  
Foreign Investors ◽  
Content Type ◽  
Skilled Labour ◽  
Near Shore ◽  
The Cost

Significance As the need for cheap and low-skilled labour falls, wage differentials between Africa and other regions will cease to be a major draw for multinational firms and foreign investors, who may seek to re-shore or near-shore operations, triggering job losses and dampening new job creation. Impacts Job losses threaten to exacerbate youth unemployment and discontent in a continent where 70% of the population is under 30. The pandemic has increased the cost and risks of international supply chains. Africa’s tech sector will expand but its spread is regionally uneven and its impact on job creation will be limited for now.

White House plans a broad approach to infrastructure

2021 ◽  
Keyword(s):  
Economic Development ◽  
Local Economic Development ◽  
Job Creation ◽  
Green Energy ◽  
Infrastructure Investment ◽  
Climate Mitigation ◽  
Content Type ◽  
White House ◽  
The Cost ◽  
Broad Approach

Significance Democrats see infrastructure spending as driving both the post-pandemic recovery through job creation and the transition to green energy needed to meet climate mitigation goals. Given broad acknowledgement that US competitiveness is damaged by ageing and poorly maintained infrastructure, Biden hopes for bipartisan support in Congress. Impacts Biden will use infrastructure investment to increase domestic procurement and so generate US industrial jobs. Additional infrastructure investment could give a renewed boost to regional and local economic development programmes. US voters remain reluctant to meet the cost of using, maintaining and improving infrastructure through user-based fees or taxes.

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