scholarly journals The stochastic determinants of happiness in South Africa: A micro-economic modelling approach

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Carel J. van Aardt ◽  
Bernadene de Clercq ◽  
Jacolize Meiring
Keyword(s):  
South Africa ◽  
Economic Modelling ◽  
Modelling Approach

The biophysical and socio-economic dimension of yield gaps in the southern Amazon – A bio-economic modelling approach

2018 ◽  
Vol 165 ◽  
pp. 1-13 ◽  
Author(s):  
Anna C. Hampf ◽  
Marcelo Carauta ◽  
Evgeny Latynskiy ◽  
Affonso A.D. Libera ◽  
Leonardo Monteiro ◽  
...  
Keyword(s):  
Economic Modelling ◽  
Economic Dimension ◽  
Yield Gaps ◽  
Modelling Approach

scholarly journals Forecasting COVID-19 Cases Using Alpha-Sutte Indicator: A Comparison with Autoregressive Integrated Moving Average (ARIMA) Method

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
A. M. C. H. Attanayake ◽  
S. S. N. Perera
Keyword(s):  
South Africa ◽  
New Zealand ◽  
Transmission Rate ◽  
Moving Average ◽  
Specific Treatment ◽  
Study Data ◽  
Percentage Error ◽  
The Usa ◽  
Short Term Forecasting ◽  
Modelling Approach

COVID-19 is a pandemic which has spread to more than 200 countries. Its high transmission rate makes it difficult to control. To date, no specific treatment has been found as a cure for the disease. Therefore, prediction of COVID-19 cases provides a useful insight to mitigate the disease. This study aims to model and predict COVID-19 cases. Eight countries: Italy, New Zealand, the USA, Brazil, India, Pakistan, Spain, and South Africa which are in different phases of COVID-19 distribution as well as in different socioeconomic and geographical characteristics were selected as test cases. The Alpha-Sutte Indicator approach was utilized as the modelling strategy. The capability of the approach in modelling COVID-19 cases over the ARIMA method was tested in the study. Data consist of accumulated COVID-19 cases present in the selected countries from the first day of the presence of cases to September 26, 2020. Ten percent of the data were used to validate the modelling approach. The analysis disclosed that the Alpha-Sutte modelling approach is appropriate in modelling cumulative COVID-19 cases over ARIMA by reporting 0.11%, 0.33%, 0.08%, 0.72%, 0.12%, 0.03%, 1.28%, and 0.08% of the mean absolute percentage error (MAPE) for the USA, Brazil, Italy, India, New Zealand, Pakistan, Spain, and South Africa, respectively. Differences between forecasted and real cases of COVID-19 in the validation set were tested using the paired t -test. The differences were not statistically significant, revealing the effectiveness of the modelling approach. Thus, predictions were generated using the Alpha-Sutte approach for each country. Therefore, the Alpha-Sutte method can be recommended for short-term forecasting of cumulative COVID-19 incidences. The authorities in the health care sector and other administrators may use the predictions to control and manage the COVID-19 cases.

scholarly journals Cost-effective water quality improvement in linked terrestrial and marine ecosystems: a spatial environmental - economic modelling approach

2009 ◽  
Vol 60 (11) ◽  
pp. 1150 ◽  
Author(s):  
P. C. Roebeling ◽  
M. E. van Grieken ◽  
A. J. Webster ◽  
J. Biggs ◽  
P. Thorburn
Keyword(s):  
Water Pollution ◽  
Water Quality ◽  
Land Use ◽  
Environmental Economic ◽  
Management Practice ◽  
Economic Modelling ◽  
Agricultural Industry ◽  
Water Quality Improvement ◽  
Land Use And Management ◽  
Modelling Approach

Worldwide, coastal and marine ecosystems are affected by water pollution originating from coastal river catchments, even though ecosystems such as the Great Barrier Reef are vital from an environmental as well as an economic perspective. Improved management of coastal catchment resources is needed to remediate this serious and growing problem through, e.g. agricultural land use and management practice change. This may, however, be very costly and, consequently, there is a need to explore how water quality improvement can be achieved at least cost. In the present paper, we develop an environmental–economic modelling approach that integrates an agricultural production system simulation model and a catchment water quality model into a spatial environmental–economic land-use model to explore patterns of land use and management practice that most cost-effectively achieve specified water quality targets and, in turn, estimate corresponding water pollution abatement cost functions. In a case study of sediment and nutrient water pollution by the sugarcane and grazing industries in the Tully–Murray catchment (Queensland, Australia), it is shown that considerable improvements in water quality can be obtained at no additional cost, or even benefit, to the agricultural industry, whereas larger water quality improvements come at a significant cost to the agricultural industry.

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