Selling green first or not? A Bayesian analysis with service levels and environmental impact considerations in the Big Data Era

Application of Big Data in agriculture could both contribute to economic gain and to reduction of environmental impact. Especially at the farming level, the high cost of measuring actual operations as they occurred historically restrained decision making in the sector. Currently several sensing technologies associated with Big Data are being evaluated and adopted within the sector. Their adoption offers the opportunity to alter that historic benefit/cost relationship. Combined with advanced analytics, measurement and analysis of diverse sources of data promise to create value for sector decision makers and society. While consumers likely will continue to be the ultimate beneficiaries of such advances, the pattern by which value is captured by entities in the sector remains uncertain. Factors such as organizational collaboration and the application of rules associated with intellectual property will have significant impact upon the evolution of Big Data’s implementation within agriculture.

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Mapping the Process to Improve the Operative and Environmental Performance of Feeder Ports

10.4018/978-1-7998-8709-6.ch004 ◽

2022 ◽

pp. 67-99

Author(s):

Daniel Londono-Bernal ◽

Adil Hammadi ◽

Torsten Reiners

Keyword(s):

Environmental Impact ◽

Co2 Emissions ◽

Simulation Software ◽

Quantitative Approach ◽

Container Terminals ◽

Service Levels ◽

Port Authorities ◽

Competitive Prices ◽

Increasing Demand ◽

Operative Costs

Container terminals play an important role in linking regional and continental areas for the exchange of goods. Port authorities have to provide their services under competitive prices and service levels to customers. This increasing competition pushes feeder ports to improve their processes. The goal is to increase the port capacity to deal with the increasing demand for containers and, at the same time, to reduce the environmental impact and operative costs. The authors address the gap in the literature regarding alternatives for feeder ports. They analyse best practices adopted in international terminals and evaluate the implementation in feeder ports. They apply a quantitative approach using the simulation software AnyLogic. The model uses market data to analyse the vessel unloading process at the berth. Moreover, an alternative to reduce the CO2 emissions for diesel equipment is presented. A flowchart for the vessel unloading and loading operations is proposed that includes the strategies to increase capacity and efficiency of operations and the utilisation of equipment.

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Web-Based Environmental Impact Assessment EIA Application for Big Data Management

10.2118/195411-ms ◽

2019 ◽

Author(s):

Ortip Jeerapornchai ◽

Surasak Chonchirdsin ◽

Lawan Pornsakulsakdi

Keyword(s):

Big Data ◽

Environmental Impact ◽

Data Management ◽

Impact Assessment ◽

Environmental Impact Assessment ◽

Web Based

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A Trend Analysis of Development Projects in South Korea during 2007–2016 Using a Multi-Layer Perceptron Based Artificial Neural Network

Applied Sciences ◽

10.3390/app11157133 ◽

2021 ◽

Vol 11 (15) ◽

pp. 7133

Author(s):

Sung-Hwan Park ◽

Hyung-Sup Jung ◽

Sunmin Lee ◽

Heon-Seok Yoo ◽

Nam-Wook Cho ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Big Data ◽

Environmental Impact ◽

South Korea ◽

Trend Analysis ◽

Development Project ◽

Development Projects ◽

Multi Layer Perceptron ◽

The Impact

In Korea, the Ministry of Environment and regional environment management agencies conduct environmental impact assessments (EIA) to mitigate and assess the impact of major development projects on the environment. EIA Big Data are used in conjunction with a geographical information system (GIS), and consist of indicators related to air, soil, and water that are measured before and after the development project. The impact of the development project on the environment can be evaluated through the variations of each indicator. This study analyzed trends in the environmental impacts of development projects during 2007–2016 using 21 types of EIA Big Data. A model was developed to estimate the Korean Environment Institute’s Environmental Impact Assessment Index for Development Projects (KEIDP) using a multi-layer perceptron-based artificial neural network (MLP-ANN) approach. A trend analysis of development projects in South Korea revealed that the mean value of KEIDP gradually increased over the study period. The rate of increase was 0.007 per year, with an R2 value of 0.8. In the future, it will be necessary for all management agencies to apply the KEDIP calculation model to minimize the impact of development projects on the environment and reduce deviations among development projects through continuous monitoring.

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