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, output; end; end; run; title1 'Monte Carlo Simulation of ODE'

, procedure model data=drive1 noprint

, y = a -a * y; fit y / outest=est; by iter; run; proc univariate data=est noprint

, var a; output out=monte mean=mean p5=p5 p95=p95; run; proc print data=monte; run

, Source: the program created by authors based on statistics database (central bureau of statistics

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, stream economic theory of capital accumulation, technological progress and long-run economic growth") based on many assumptions

, Further Information on Literature Review Energy efficiency can be defined as the ratio of useful outputs to energy inputs for a system. The system in question may be an individual energy conversion device (i.e. a boiler), a building, an industrial process, a firm, a sector or an entire economy. In all cases, the measure of energy efficiency will depend upon how useful is defined and how inputs and outputs are measured, The options include: ?, 1996.

, the outputs (and sometimes also the inputs) are defined in economic terms, such as value-added or GDP. When outputs are measured in thermodynamic or physical terms, the term energy efficiency tends to be used, but when outputs are measured in economic terms it is more common to use the term energy productivity. The inverse of both measures is termed energy intensity. The choice of measures for inputs and outputs, the appropriate system boundaries and the time frame under consideration can vary widely from one study to another. However, physical and economic measures of energy efficiency tend to be influenced by a greater range of variables than thermodynamic measures, as do measures appropriate to wider system boundaries. Hence, the indicator that is furthest from a thermodynamic measure of energy efficiency is the ratio of GDP to total primary energy consumption within a national economy. According to economists are primarily interested in energy efficiency improvements that are consistent with the best use of all economic resources. These are conventionally divided into two categories: those that are associated with improvements in overall, or "total factor productivity" (in economics so called; technical change), and those that are not (in case of the existent of "substitution"). Further-more, its assumed by changes of energy price to other inputs. However, 2004.

. Capehart, These include the role of value judgments in the construction of energy efficiency indicators, the energy quality problem, the boundary problem, the joint production problem and the question of isolating the underlying technical energy efficiency trend from the aggregate indicator, 2001.

, Waste minimization and disposal also presents many energy management opportunities. A whole systems viewpoint to energy management is required to ensure that many important activities will be examined and optimized. Presently, industries are adopting total quality management (TQM) strategy for improving their operations. Total quality management approach should include an energy management component to reduce energy costs. The primary objective of energy manage-ment is to maximize profits or minimize costs. To distinguish sub objectives of energy management programs: 1. improving energy efficiency and reducing energy use (cost minimization), 2. decreasing greenhouse gas emissions and improve air quality, 3. improving better communications on energy matters, 4. sustainable energy management, 5. effective monitoring, reporting, and management strategies, 6. energy investments through research and development, "the efficient and effective use of energy to maximize profits (minimize costs) and enhance competitive positions, vol.7

, Combining services such as chartering, project freight forwarding, and specialized cargo shipping together with world-class project management to remove complexity from energy, mining and projects supply chains. The project freight at anywhere in the world for example; by land, sea and air, using trucks, rail, cranes, ships, barges, and planes. Providing high-quality, configured logistics solutions for projects of any size. For examples areas: ? oil and gas, ? refineries, pipelines LNG plants on and off-shore drilling upstream developments, ? power stations;gas, steam, thermal, hydro, coal sub-stations, power distribution, transmission lines engines and turbines, ? "green field" mining developments, ? airports, bridges harbors, rail road networks, and pipelines industrial, ? industrial factories for the toughest supply chain challenges, Mining and projects supply chain team combines the experience, expertise and longstanding relationships of our mining, energy, infrastructure, oil and gas, and industrial clients

, Source : ES France data base, it was compiled, formulated, and organized from the original of International business transaction, 2011.

, Food and Beverages Products: SITC Code: 313 LOG(Y) = 6.611+ 0.016*LOG (K), p.82

, Garment and Textiles Products: SITC Code: 321 LOG(Y) = -12

P. Paper and . Products, SITC Code: 341 LOG(Y) = 6

, Other Chemical Products: SITC Code: 352 LOG(Y) = -4.286 + 0.100*LOG (K)

. Non-metallic, Mineral Products: SITC Code: 369 LOG(Y) = 1.7639 + 0.195*LOG (K) + 1.093*LOG (TW) + 0.201*LOG (M

, Basic Metallic Products and Other: SITC Code: 381 LOG(Y) = 5.909 + 0.150*LOG(K)

, Machinery except Electrical Products: SITC Code: 382 LOG(Y) = -4

, Electrical Machinery Apparatus: SITC Code: 383 LOG(Y) = 2.938 + 0.184*LOG (K) + 1.102*LOG (TW) -0.010*LOG(M

, Transport Equipment Products: SITC Code: 384 LOG(Y) = -2.698 + 0.047*LOG (K) + 1.744*LOG (TW) +0.063*LOG(M

, Other Industrial Products: SITC Code: 390 LOG(Y) = 2.798 + 0.056*LOG (K)

, The M in M-estimation stands for "maximum likelihood type". The method is robust to outliers in the response variable, but turned out not to be resistant to outliers in the explanatory variables (leverage points). In fact, when there are outliers in the explanatory variables, the method has no advantage over least squares. In the 1980s, several alternatives to M-estimation were proposed as attempts to overcome the lack of resistance. See the book by Rousseeuw and Leroy for a very practical review. The most common general method of robust regression is M-estimation, introduced by Huber, 1964. In The SAS procedure, here we used robust regression (procedure in robust regression model in SAS software) and the model construction on it, Why we applied robust procedure? Because, in order to know well the value added level or output level as a function of total paid and unpaid workers, 1973.

, Total paid and unpaid workers, -LTL: Average supervisory and professional workers, -ZPDVCU: Total payment to production workers, -ZNDVCU: Total payment to non-production workers, -YT1VCU: Total profit, -IADVCU: Advertising and promotion Estimation result for value added: Variable of VTLVCU =, All List of data used and definition: -VTLVCU: Value added