scholarly journals Chemometrics in the assessment of the sustainable development rule implementation

2006 ◽  
Vol 4 (3) ◽  
pp. 543-564 ◽  
Author(s):  
Aleksander Astel ◽  
Grażyna Głosińska ◽  
Tadeusz Sobczyński ◽  
Leonard Boszke ◽  
Vasil Simeonov ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Sustainable Development ◽  
Principal Component ◽  
Anthropogenic Pollution ◽  
Anthropogenic Sources ◽  
Anthropogenic Source ◽  
Data Set ◽  
The Sustainable Development ◽  
Odra River ◽  
Rule Implementation

AbstractThe sustainable development rule implementation is tested by the application of chemometrics in the field of environmental pollution. A data set consisting of Cd, Pb, Cr, Zn, Cu, Mn, Ni, and Fe content in bottom sediment samples collected in the Odra River (Germany/Poland) is treated using cluster analysis (CA), principal component analysis (PCA), and source apportionment techniques. Cluster analysis clearly shows that pollution on the German bank is higher than on the Polish bank. Two latent factors extracted by PCA explain over 88 % of the total variance of the system, allowing identification of the dominant “semi-natural” and “anthropogenic” pollution sources in the river ecosystem. The complexity of the system is proved by MLR analysis of the absolute principal component scores (APCS). The apportioning clearly shows that Cd, Pb, Cr, Zn and Cu participate in an “anthropogenic” source profile, whereas Fe and Mn are “semi-natural”. Multiple regression analysis indicates that for particular elements not described by the model, the amounts vary from 4.2 % (Mn) to 13.1 % (Cr). The element Ni participates to some extent to each source and, in this way, is neither pure “semi-natural” nor pure “anthropogenic”. Apportioning indicates that the whole heavy metal pollution in the investigated river reach is 12510.45 mg·kg−1. The contribution of pollutants originating from “anthropogenic sources” is 9.04 % and from “semi-natural” sources is 86.53 %.

scholarly journals Factors influencing fluffy layer suspended matter (FLSM) properties in the Odra River - Pomeranian Bay - Arkona Deep System (Baltic Sea) as derived by principal components analysis (PCA), and cluster analysis (CA)

2005 ◽  
Vol 9 (1/2) ◽  
pp. 67-80 ◽  
Author(s):  
J. Pempkowiak ◽  
J. Beldowski ◽  
K. Pazdro ◽  
A. Staniszewski ◽  
A. Zaborska ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Organic Matter ◽  
Baltic Sea ◽  
Suspended Matter ◽  
River Mouth ◽  
Principal Component ◽  
Data Set ◽  
Sediment Fraction ◽  
Odra River ◽  
And Cluster Analysis

Abstract. Factors conditioning formation and properties of suspended matter resting on the sea floor (Fluffy Layer Suspended Matter - FLSM) in the Odra river mouth - Arkona Deep system (southern Baltic Sea) were investigated. Thirty FLSM samples were collected from four sampling stations, during nine cruises, in the period 1996-1998. Twenty six chemical properties of the fluffy material were measured (organic matter-total, humic substances, a variety of fatty acids fractions, P, N, δ13C, δ15N; Li; heavy metals- Co, Cd, Pb, Ni, Zn, Fe, Al, Mn, Cu, Cr). The so obtained data set was subjected to statistical evaluation. Comparison of mean values of the measured properties led to conclusion that both seasonal and spatial differences of the fluffy material collected at the stations occured. Application of Principal Component Analysis, and Cluster Analysis, to the data set amended with environmental characteristics (depth, salinity, chlorophyll a, distance from the river mouth), led to quantification of factors conditioning the FLSM formation. The five most important factors were: contribution of the lithogenic component (responsible for 25% of the data set variability), time dependent factors (including primary productivity, mass exchange with fine sediment fraction, atmospheric deposition, contribution of material originating from abrasion-altogether 21%), contribution of fresh autochtonous organic matter (9%), influence of microbial activity (8%), seasonality (8%).

Application of multivariable statistical techniques in plant-wide WWTP control strategies analysis

2007 ◽  
Vol 56 (6) ◽  
pp. 75-83 ◽  
Author(s):  
X. Flores ◽  
J. Comas ◽  
I.R. Roda ◽  
L. Jiménez ◽  
K.V. Gernaey
Keyword(s):  
Cluster Analysis ◽  
Control Strategies ◽  
Treatment Plant ◽  
Principal Component ◽  
Statistical Techniques ◽  
Data Sets ◽  
Data Set ◽  
Casual Relation ◽  
Evaluation Matrix ◽  
Natural Groups

The main objective of this paper is to present the application of selected multivariable statistical techniques in plant-wide wastewater treatment plant (WWTP) control strategies analysis. In this study, cluster analysis (CA), principal component analysis/factor analysis (PCA/FA) and discriminant analysis (DA) are applied to the evaluation matrix data set obtained by simulation of several control strategies applied to the plant-wide IWA Benchmark Simulation Model No 2 (BSM2). These techniques allow i) to determine natural groups or clusters of control strategies with a similar behaviour, ii) to find and interpret hidden, complex and casual relation features in the data set and iii) to identify important discriminant variables within the groups found by the cluster analysis. This study illustrates the usefulness of multivariable statistical techniques for both analysis and interpretation of the complex multicriteria data sets and allows an improved use of information for effective evaluation of control strategies.

scholarly journals Hydrochemical assessment of Semarang area using multivariate statistics: A sample based dataset

2016 ◽  
Author(s):  
Dasapta Erwin Irawan ◽  
Thomas Triadi Putranto
Keyword(s):  
Cluster Analysis ◽  
Statistical Treatment ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Data Set ◽  
Anal Ysis ◽  
R Packages ◽  
Point Data ◽  
Depth Water ◽  
Water Elevation

Abstract. The following paper describes in brief the data set related to our project "Hydrochemical assessment of Semarang Groundwater Quality". All of 58 samples were taken in 1992, 1993, 2003, 2006, and 2007 using well point data from several reports from Ministry of Energy and Min- eral Resources and independent consultants. We provided 20 parameters in each samples (sample id, coord X, coord Y, well depth, water level, water elevation, TDS, pH, EC, K, Ca, Na, Mg, Cl, SO4, HCO3, year, ion balance, screen location, and chemical facies). The chemical composi- tion were tested in the Water Quality Laboratory, Universitas Diponegoro using mas spectrofotometer method. The statistical treatment for the dataset (available on Zenodo doi:10.5281/zenodo.57293) were described as follows: (1) data preparation in to csv file format, load it in to R environment; (2) data treatment, including: correlation matrix, cluster analysis using kmeans and hierarchical cluster analysis, and principal component analysis. For anal- ysis and visualizations, We used the following R packages: ggplot2, dplyr, factomineR, factoExtra, cluster, ggcorrplot, and ape.

scholarly journals A Comparative Analysis of Countries' Performance According to SDG Indicators based on Machine Learning

2021 ◽  
Author(s):  
Guilherme Souza ◽  
Julian Santos ◽  
Gabriel SantClair ◽  
Janaina Gomide ◽  
Luan Santos
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Economic Growth ◽  
Principal Component Analysis ◽  
Sustainable Development ◽  
Sustainable Development Goals ◽  
Principal Component ◽  
The Sustainable Development ◽  
Development Goals ◽  
Impacts Of Climate Change

The Sustainable Development Goals (SDGs) are part of a global effort to reduce the impacts of climate change, promoting social justice and economic growth. The United Nations provides a database with hundreds of indicators to track the SDGs since 2016 for a total of 302 regions. This work aims to assess which countries are in a similar situation regarding sustainable development. Principal Component Analysis was used to reduce the dimension of the dataset and k-means algorithm was used to cluster countries according to their SDGs indicators. For the years of 2016, 2017 and 2018 were obtained 11, 13 and 11 groups, respectively. This paper also analyses clusters changes throughout the years.

Tax risks control and sustainable development: evidence from China

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Wanyi Chen
Keyword(s):  
Sustainable Development ◽  
Influencing Factors ◽  
Institutional Environment ◽  
Stock Exchanges ◽  
Data Set ◽  
Content Type ◽  
The Sustainable Development ◽  
Factors Affecting ◽  
The Government ◽  
New Perspective

Purpose Tax risks are common in China but often ignored by enterprises. Determining how to measure tax risks and effectively identify and control influencing factors is the key to the sustainable development of enterprises. This study aims to explore the key factors affecting corporate tax risks and analyze influencing factors from external and internal perspectives. Design/methodology/approach After selecting a data set comprising 11,503 firm-year observations of Chinese firms in the Shanghai and Shenzhen Stock Exchanges from 2008–2017, this study applied a panel regression model to identify the factors’ impact. Findings The results indicate that the more standardized the institutional environment and stronger the tax supervision, the lower the tax risks. Taking into account the internal factors of a firm, private companies with political connections have lower tax risks than those without. Originality/value This study enriches the literature on the factors affecting tax risks. The conclusion provides significant insights for enterprises to effectively control tax risks and maintain sustainability. The research findings also provide a new perspective for the government to guard against corporate risks and maintain the stable development of the economy.

Research on the Sustainable Development Based on PREES Model of Beijing

2013 ◽  
Vol 291-294 ◽  
pp. 1605-1609
Author(s):  
Wen Yan Guo ◽  
Li Ma ◽  
Qiong Wang ◽  
Xiao Liu Shen
Keyword(s):  
Principal Component Analysis ◽  
Sustainable Development ◽  
Empirical Analysis ◽  
Evaluation System ◽  
Rapid Development ◽  
Principal Component ◽  
Coordinated Development ◽  
The Sustainable Development ◽  
The City ◽  
Coordination Degree

This paper studies the coordinated development of Beijing's population, resources, environment, economic and society. With the rapid development of the city, Beijing’s population, society and economic is getting more and more contradictive against its resources and environment, under which circumstance a scientific study on the coordinated development is urgently required. This essay is based on the data of last 10 years of Beijing, and formulates a PREES model of the PREES model in Beijing. This study uses the method of principal component analysis via SPSS, establishes the coordination degree evaluation system of Beijing’s population, resources, environment, economic and society, and runs an empirical analysis afterward. This essay calculates the coordination degree of Beijing’s population, resources, environment, economic and society, analyzes and studies the main issues in Beijing’s coordinated development, and gives relative suggestions.

China and Sustainable Development Performance in Economic Transition

2019 ◽  
Vol 11 (3) ◽  
pp. 149-164
Author(s):  
Soo Jung Kim ◽  
Sung Jin Kang ◽  
Tae Yong Jung ◽  
Shijun Cao
Keyword(s):  
Cluster Analysis ◽  
Sustainable Development ◽  
Economic Transition ◽  
Environmental Indicators ◽  
Economic Sector ◽  
Transition Country ◽  
The Sustainable Development ◽  
Remarkable Improvement ◽  
The Social ◽  
Countries In Transition

By conducting a cluster analysis for the period 1990–2014, this study compares the sustainable development performance of China’s economic transition with 41 other countries in transition. While the previous studies mainly used economic indicators as a comparison factor, this study uses economic, social, and environmental indicators, which are the three main pillars of sustainable development. The cluster analysis results indicate that China shows the most remarkable improvement in terms of sustainable development. The improvement was the largest among the other transition countries. In particular, the social and environmental sectors have improved significantly. Through further improvement in the economic sector, China would be the most successful transition country in sustainable development performance.

Urban Air Quality Grade Evaluation Based on Cluster Analysis

2014 ◽  
Vol 522-524 ◽  
pp. 231-234
Author(s):  
Xin Yan Li ◽  
Yan Wang ◽  
Hui Zhang ◽  
Hong Jing Chen ◽  
Jing Tian Zhang
Keyword(s):  
Cluster Analysis ◽  
Sustainable Development ◽  
Air Quality ◽  
Urban Air ◽  
Human Environment ◽  
The Sustainable Development ◽  
Quality Grade ◽  
Grade Difference ◽  
The City

Understanding the air quality grade difference among cities in our country plays a role in promoting the improvement of the city air quality and the sustainable development of human environment. This paper analyzes the air quality of the main 31 cities in China with the methods of cluster analysis and correspondence analysis, and according to the index related to air quality, cluster analysis classifies these areas air quality into 5 kinds. The result shows that Haikou and Lhasa rank top 2 in air quality, but Lanzhou and Urumqis air has been polluted most heavily.

scholarly journals Synergies and Trade-Offs in Reaching the Sustainable Development Goals

2020 ◽  
Vol 12 (20) ◽  
pp. 8729 ◽  
Author(s):  
Håvard Hegre ◽  
Kristina Petrova ◽  
Nina von Uexkull
Keyword(s):  
Sustainable Development ◽  
Goal Attainment ◽  
Sustainable Development Goals ◽  
Principal Component ◽  
Clean Energy ◽  
The Sustainable Development ◽  
Trade Offs ◽  
Novel Approach ◽  
Development Goals ◽  
Over Time

The Sustainable Development Goals (SDGs) adopted in 2015 integrate diverse issues such as addressing hunger, gender equality and clean energy and set a common agenda for all United Nations member states until 2030. The 17 SDGs interact and by working towards achieving one goal countries may further—or jeopardise—progress on others. However, the direction and strength of these interactions are still poorly understood and it remains an analytical challenge to capture the relationships between the multi-dimensional goals, comprising 169 targets and over 200 indicators. Here, we use principal component analysis (PCA), an in this context novel approach, to summarise each goal and interactions in the global SDG agenda. Applying PCA allows us to map trends, synergies and trade-offs at the level of goals for all SDGs while using all available information on indicators. While our approach does not allow us to investigate causal relationships, it provides important evidence of the degree of compatibility of goal attainment over time. Based on global data 2000–2016, our results indicate that synergies between and within the SDGs prevail, both in terms of levels and over time change. An exception is SDG 10 ‘Reducing inequalities’ which has not progressed in tandem with other goals.

Design and Application of Comprehensive Development Level of the Construction Industry Model Based on SPSS in Shandong, China

2011 ◽  
Vol 368-373 ◽  
pp. 3059-3062
Author(s):  
Xiu Hai Song ◽  
Yu Ting Gu ◽  
Jing Jing Sun ◽  
Chao Wang ◽  
Xiao Jie Cao
Keyword(s):  
Sustainable Development ◽  
Principal Component ◽  
Analytic Hierarchy ◽  
The Sustainable Development ◽  
Development Level ◽  
Comprehensive Development ◽  
The Index System ◽  
Spss Software ◽  
Set Up ◽  
Hierarchy Process

To evaluate the sustainable development level of construction in Shandong, China, the model was built by principal component analysis method. Meanwhile, Analytic Hierarchy Process (AHP) and basic rules of statistics were fully used to set up the index system. The data from 2007 to 2009 were processed by SPSS software, through calculation, the degree of sustainable development were shown intuitively.

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