Impact of energy efficiency gains on output and energy use with Cobb–Douglas production function

Energy Policy ◽  
2007 ◽  
Vol 35 (4) ◽  
pp. 2023-2030 ◽  
Author(s):  
Taoyuan Wei
Keyword(s):  
Energy Efficiency ◽  
Production Function ◽  
Energy Use ◽  
Efficiency Gains ◽  
Douglas Production Function

scholarly journals The Rebound Effect of Energy Efficiency Policy in the Presence of Energy Theft

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3379
Author(s):  
Tugba Somuncu ◽  
Christopher Hannum
Keyword(s):  
Energy Efficiency ◽  
Service Sector ◽  
Energy Use ◽  
Developing Economies ◽  
Rebound Effect ◽  
Accurate Estimation ◽  
Efficiency Gains ◽  
Rebound Effects ◽  
Energy Efficiency Policy ◽  
Energy Theft

Introduction: Estimating the effectiveness of energy efficiency policy in reducing energy use requires a full understanding of the energy efficiency rebound effect, where energy use reductions differ from engineering expectations. Prior models that estimate the size of the total rebound effect ignore energy theft, which is a common feature in developing economies. Objectives: The primary objective of this study was to evaluate the role that energy theft plays in determination of the size of the rebound effect of energy efficiency policy in developing countries, using the Turkish economy and the specific Turkish regulation regarding compensation for energy theft as an example. Methods: We construct two energy-economy computable general equilibrium (CGE) models for Turkey that do and do not incorporate energy theft. Costs of energy theft are passed on to consumers through a recovery surcharge. Two energy efficiency policies are modeled; one leading to a 42% energy efficiency increment for the service sector and another leading to a 48% energy efficiency increment for households. Results: Without energy theft, rebound effects for both policies are small: between −1.4% and 3.1% for the service sector and between 0.4% and 2.1% for households. With energy theft, we see a −7.9% to −19.7% rebound for the service sector and a 10.4% to 40.7% rebound for households. The recovery surcharge on energy sales rises when energy efficiency gains affect the service sector but fall when they affect households. Conclusions: The interaction between energy efficiency and energy theft may be critical in accurate estimation of rebound effects where energy theft is prevalent. Where energy efficiency gains disproportionately reduce electricity sales rather than theft, the rising recovery surcharge leads to a negative rebound or super-conservation. However, where theft is disproportionately reduced rebound will be higher.

Significance of Experience, Farm Size, Quantity of Propagules and Location f or Seaweed Farmers in the Divided Island of Sebatik

2018 ◽  
Vol 9 (9) ◽  
pp. 825-832
Author(s):  
James M. Alin ◽  
◽  
Datu Razali Datu Eranza ◽  
Arsiah Bahron ◽  
◽  
...  
Keyword(s):  
Regression Analysis ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Production Function ◽  
Weather Conditions ◽  
Education Level ◽  
Farm Size ◽  
Total Production ◽  
Explanatory Factors ◽  
Douglas Production Function

Seaweed-Kappaphycus-Euchema Cottonii and Denticulum species was first cultivated at Sabah side of Sebatik in 2009. By November 2014, sixty one Sabahan seaweed farmers cultivated 122 ha or 3,050 long lines. Thirty Sabahan seaweed farmers in Kampung Pendekar (3.2 m.t dried) and 31 in Burst Point (12.5 m.t dried) produced 16 metric tonnes of dried seaweed contributed 31% to Tawau’s total production (51 m.t). The remaining 69% were from farmers in Cowie Bay that separates Sebatik from municipality of Tawau. Indonesian in Desa Setabu, Sebatik started in 2008. However, the number of Indonesian seaweed farmers, their cultivated areas and production (as well as quality) in Sebatik increased many times higher and faster than the Sabah side of Sebatik. In 2009 more than 1,401 households in Kabupaten Nunukan (including Sebatik) cultivated over 700 ha and have produced 55,098.95 and 116, 73 m.t dried seaweed in 2010 and 2011 respectively. There is a divergence in productions from farming the sea off the same island under similar weather conditions. Which of the eight explanatory factors were affecting production of seaweeds in Sebatik? Using Cobb Douglas production function, Multiple Regression analysis was conducted on 100 samples (50 Sabahan and 50 Indonesian). Results; Variable significant at α = 0.05% are Experience in farming whereas Farm size; Quantity of propagules and Location — Dummy are the variables significant at α 0.01%. Not significant are variables Fuel; Age; Number of family members involved in farming and Education level.

Efficient Cobb-Douglas Production Function

2019 ◽  
Vol 1 (1) ◽  
pp. 16-23
Author(s):  
Farhad Savabi ◽  
Keyword(s):  
Production Function ◽  
Douglas Production Function

PENERAPAN PENDUGAAN EFISIENSI ALOKATIF PADA USAHATANI BUNCIS (Phaseolus vulgaris, L)

2020 ◽  
Vol 21 (1) ◽  
pp. 81-90
Author(s):  
Marthen Robinson Pellokila
Keyword(s):  
Production Function ◽  
Allocative Efficiency ◽  
Maximum Output ◽  
Phaseolus Vulgaris L ◽  
Land Area ◽  
Price Efficiency ◽  
Efficiency Estimation ◽  
Area Use ◽  
A Company ◽  
Douglas Production Function

ABTRACT Efficiency is one of the important indicators to assess the performance of a company or farm. Efficiency guarantees the use of certain inputs to achieve maximum output levels (technical efficiency) and also efficiency ensures the use of certain inputs that maximize profits (price efficiency or allocative efficiency). This article discusses the application of the estimation of price efficiency / allocative efficiency of the use of production inputs in bean farming using the linearized Cobb-Douglas Production function. The results of the analysis shows that the application of price efficiency estimation for production inputs using the Cobb-Douglas production function is satisfactory as long as the classical assumptions required by the multiple regression are fulfilled. Of the five production inputs included in the model, only one production input provides a significant value to production, namely the production input for the land area use. Thus, only the production input for land area use is estimated at the value of its price efficiency. Based on the results of the analysis, it is found that the use of production inputs for land area use has not yet reached its price efficiency.

scholarly journals Statistical and machine learning models for optimizing energy in parallel applications

2019 ◽  
Vol 33 (6) ◽  
pp. 1079-1097 ◽  
Author(s):  
Mark Endrei ◽  
Chao Jin ◽  
Minh Ngoc Dinh ◽  
David Abramson ◽  
Heidi Poxon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Efficiency ◽  
High Performance ◽  
Large Scale ◽  
Energy Use ◽  
Parallel Applications ◽  
Learning Models ◽  
Trade Off ◽  
Time Required ◽  
Machine Learning Models

Rising power costs and constraints are driving a growing focus on the energy efficiency of high performance computing systems. The unique characteristics of a particular system and workload and their effect on performance and energy efficiency are typically difficult for application users to assess and to control. Settings for optimum performance and energy efficiency can also diverge, so we need to identify trade-off options that guide a suitable balance between energy use and performance. We present statistical and machine learning models that only require a small number of runs to make accurate Pareto-optimal trade-off predictions using parameters that users can control. We study model training and validation using several parallel kernels and more complex workloads, including Algebraic Multigrid (AMG), Large-scale Atomic Molecular Massively Parallel Simulator, and Livermore Unstructured Lagrangian Explicit Shock Hydrodynamics. We demonstrate that we can train the models using as few as 12 runs, with prediction error of less than 10%. Our AMG results identify trade-off options that provide up to 45% improvement in energy efficiency for around 10% performance loss. We reduce the sample measurement time required for AMG by 90%, from 13 h to 74 min.

scholarly journals Evaluation of CO2 emissions and energy use with different container terminal layouts

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Muhammad Arif Budiyanto ◽  
Muhammad Hanzalah Huzaifi ◽  
Simon Juanda Sirait ◽  
Putu Hangga Nan Prayoga
Keyword(s):  
Energy Efficiency ◽  
Sustainable Development ◽  
Energy Consumption ◽  
Statistical Analysis ◽  
Energy Use ◽  
Container Terminal ◽  
Container Terminals ◽  
Future Planning ◽  
Container Handling

AbstractSustainable development of container terminals is based on energy efficiency and reduction in CO2 emissions. This study estimated the energy consumption and CO2 emissions in container terminals according to their layouts. Energy consumption was calculated based on utility data as well as fuel and electricity consumptions for each container-handling equipment in the container terminal. CO2 emissions were estimated using movement modality based on the number of movements of and distance travelled by each container-handling equipment. A case study involving two types of container terminal layouts i.e. parallel and perpendicular layouts, was conducted. The contributions of each container-handling equipment to the energy consumption and CO2 emissions were estimated and evaluated using statistical analysis. The results of the case study indicated that on the CO2 emissions in parallel and perpendicular layouts were relatively similar (within the range of 16–19 kg/TEUs). These results indicate that both parallel and perpendicular layouts are suitable for future ports based on sustainable development. The results can also be used for future planning of operating patterns and layout selection in container terminals.

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