scholarly journals Energy and Environment Performance of Resource-Based Cities in China: A Non-Parametric Approach for Estimating Hyperbolic Distance Function

2020 ◽  
Vol 17 (13) ◽  
pp. 4795
Author(s):  
Yao Hu ◽  
Tai-Hua Yan ◽  
Feng-Wen Chen
Keyword(s):  
Distance Function ◽  
Resource Dependence ◽  
Environmental Efficiency ◽  
Hyperbolic Distance ◽  
Annual Growth Rate ◽  
Undesirable Output ◽  
Green Development ◽  
Technological Efficiency ◽  
Efficiency And Productivity

Scientific determination of energy and environmental efficiency and productivity is the key foundation of green development policy-making. The hyperbolic distance function (HDF) model can deal with both desirable output and undesirable output asymmetrically, and measure efficiency from the perspective of “increasing production and reducing pollution”. In this paper, a nonparametric linear estimation method of an HDF model including uncontrollable index and undesirable output is proposed. Under the framework of global reference, the changes of energy environmental efficiency and productivity and their factorization of 107 resource-based cities in China from 2003 to 2018 are calculated and analyzed. With the classification of resource-based cities by resource dependence (RD) and region, we discuss the feature in green development quality of those cities. The results show that: (1) On the whole, the average annual growth rate of energy and environmental productivity of resource-based cities in China is 2.6%, which is mainly due to technological changes. The backward of relative technological efficiency hinders the further growth of productivity, while the scale diseconomy is the main reason for the backward of relative technological efficiency. (2) For the classification of RD, the energy and environmental efficiency of the high-dependent group are significantly lower than the other two, and the growth of productivity of the medium-dependent group is the highest. (3) In terms of classification by region, the energy and environmental efficiency of the eastern region is the highest, and that of the middle and western regions is not as good as that of the eastern and northeastern regions. The middle region shows the situation of “middle collapse” in both static efficiency and dynamic productivity change, and the main reason for its low productivity growth is the retreat of relatively pure technical efficiency. This conclusion provides practical reference for the classification and implementation of regional energy and environmental policies.

scholarly journals Environmental Efficiency of Organic and Conventional Cotton in Benin

2019 ◽  
Vol 11 (11) ◽  
pp. 3044
Author(s):  
Régina D.C. Bonou-zin ◽  
Khalil Allali ◽  
Aziz Fadlaoui
Keyword(s):  
Shadow Price ◽  
Environmental Efficiency ◽  
Joint Production ◽  
Relative Advantage ◽  
Hyperbolic Distance ◽  
Undesirable Output ◽  
Cotton Production ◽  
Conventional Agriculture ◽  
Organic Cotton ◽  
Cotton Belt

Recent years have seen an increasing awareness of the relative advantage of organic and conventional agriculture. This study aims to analyze the environmental efficiency of organic and conventional cotton in Benin. A Translog hyperbolic distance function which allows us to consider the joint production of desirable and undesirable output is used to analyze the environmental efficiency among organic and conventional cotton production farmers. The model includes factors that affect environmental efficiency. Greenhouse gas (GHG) was used as an indicator of undesirable output. Data were collected from 355 cotton producers (180 organics and 175 conventional) randomly selected in the cotton belt of Northern Benin. The results show that although organic cotton producers contribute less to GHG emission, they are environmentally inefficient compared to their conventional counterparts. Producers could improve the quantity of cotton produced by 27% and 17% while reducing emissions by 21% and 14% respectively for both organic and conventional cotton to achieve better environmental performance. However, the analysis of the shadow price revealed that organic cotton producers face lower opportunity cost than conventional producers. These results suggest that there is a need for more technical support and environmental education to improve the environmental efficiency of organic cotton in Benin.

Setting Attribute Weights for Nearest Neighbor Learning Algorithms Using C4.5

1997 ◽  
Vol 11 (03) ◽  
pp. 405-415 ◽  
Author(s):  
Charles X. Ling ◽  
John J. Parry ◽  
Handong Wang
Keyword(s):  
Machine Learning ◽  
Decision Trees ◽  
Distance Function ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
Simple Approach ◽  
Nearest Neighbour ◽  
Attribute Weights ◽  
New Methods

Nearest Neighbour (NN) learning algorithms utilize a distance function to determine the classification of testing examples. The attribute weights in the distance function should be set appropriately. We study situations where a simple approach of setting attribute weights using decision trees does not work well, and design three improvements. We test these new methods thoroughly using artificially generated datasets and datasets from the machine learning repository.

scholarly journals Classifying the Level of Energy-Environmental Efficiency Rating of Brazilian Ethanol

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 2067
Author(s):  
Nilsa Duarte da Silva Lima ◽  
Irenilza de Alencar Nääs ◽  
João Gilberto Mendes dos Reis ◽  
Raquel Baracat Tosi Rodrigues da Silva
Keyword(s):  
Decision Tree ◽  
High Efficiency ◽  
Rating Scale ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Environmental Efficiency ◽  
Classification Model ◽  
Bayes Algorithm ◽  
J48 Decision Tree

The present study aimed to assess and classify energy-environmental efficiency levels to reduce greenhouse gas emissions in the production, commercialization, and use of biofuels certified by the Brazilian National Biofuel Policy (RenovaBio). The parameters of the level of energy-environmental efficiency were standardized and categorized according to the Energy-Environmental Efficiency Rating (E-EER). The rating scale varied between lower efficiency (D) and high efficiency + (highest efficiency A+). The classification method with the J48 decision tree and naive Bayes algorithms was used to predict the models. The classification of the E-EER scores using a decision tree using the J48 algorithm and Bayesian classifiers using the naive Bayes algorithm produced decision tree models efficient at estimating the efficiency level of Brazilian ethanol producers and importers certified by the RenovaBio. The rules generated by the models can assess the level classes (efficiency scores) according to the scale discretized into high efficiency (Classification A), average efficiency (Classification B), and standard efficiency (Classification C). These results might generate an ethanol energy-environmental efficiency label for the end consumers and resellers of the product, to assist in making a purchase decision concerning its performance. The best classification model was naive Bayes, compared to the J48 decision tree. The classification of the Energy Efficiency Note levels using the naive Bayes algorithm produced a model capable of estimating the efficiency level of Brazilian ethanol to create labels.

Estimating the hyperbolic distance function: A directional distance function approach

2016 ◽  
Vol 254 (1) ◽  
pp. 312-319 ◽  
Author(s):  
Rolf Färe ◽  
Dimitris Margaritis ◽  
Paul Rouse ◽  
Israfil Roshdi
Keyword(s):  
Distance Function ◽  
Directional Distance Function ◽  
Function Approach ◽  
Hyperbolic Distance

scholarly journals Holistic Impact and Environmental Efficiency of Retrofitting Interventions on Buildings in the Mediterranean Area: A Directional Distance Function Approach

2021 ◽  
Vol 11 (22) ◽  
pp. 10794
Author(s):  
Monica Cariola ◽  
Greta Falavigna ◽  
Francesca Picenni
Keyword(s):  
Distance Function ◽  
Life Cycle Cost ◽  
Optimal Solution ◽  
Mediterranean Area ◽  
Directional Distance Function ◽  
Environmental Efficiency ◽  
Function Approach ◽  
Building Stock ◽  
Simple Method ◽  
The Mediterranean

the study focuses on the application of a nonparametric methodology for evaluating the sustainability of retrofitting interventions to be applied on different typologies of buildings and different climate zones of the Mediterranean area. The paper starts from the analysis of data collected through the HAPPEN project, that is a H2020 European project which proposes a holistic approach for a deep and sustainable renovation of the Mediterranean residential Building stock. Even if the European Commission allocated considerable funds for retrofitting interventions, the choice of the optimal solution is not always that easy because several variables have to be considered. The present manuscript proposes a methodology to compare different retrofitting solutions combining Life-Cycle Cost (i.e., LCC) estimations with the nonparametric Directional Distance Function approach (i.e., DDF). In detail, the literature suggests that the DDF can be effectively used for comparing different observations through efficiency scores. The main result of the paper is the definition of a hybrid methodology that, starting from estimates of LCC and applying a DDF technique, represents a simple method for evaluating the best retrofitting intervention. Results are represented by two scores where the former represents a holistic efficiency measure, while the latter shows an environmental efficiency score.

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