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Assessing potential reduction in greenhouse gas: An integrated approach

Author:
Olanrewaju, Oludolapo Akanni, Mbohwa, Charles
Source:
Journal of cleaner production 2017 v.141 pp. 891-899
ISSN:
0959-6526
Subject:
carbon dioxide, greenhouse gas emissions, greenhouse gases, greenhouses, industry, neural networks
Abstract:
Greenhouse gases remain as threat to the environment. Various models employed in greenhouse gases are either to determine the causative factors responsible for emission, forecast emission or to optimize. Integrating these models would reduce the limitations of individual models to better assess possible greenhouse mitigation. This paper addresses the management technique for analyzing, assessing and mitigating industry's carbon dioxide (CO2) emission. The current work offers a different technique based on an integrated model utilizing the functions of Index Decomposition Analysis (IDA), Artificial Neural Network (ANN) and Data Envelopment Analysis (DEA) composed of activity, structure, intensity and energy-mix as inputs responsible for CO2 emission. By considering how the three different models are integrated into one system, it will be demonstrated how much percentage of an industry's CO2 can be reduced. The Canadian industrial sector was analyzed using the integrated model and it was discovered that 3.13% of emitted CO2 from year 1991 to year 2035 could be mitigated.
Agid:
5573539