Carbon dioxide reduction incentive for eco-industrial parks using bilevel fuzzy programming

A potential approach in improving the sustainability of microalgae biofuels is through eco-industrial parks. Eco-industrial parks implement industrial symbiosis to generate economic and environmental benefits. The operation of an eco-industrial park involves the interaction of the park authority and...

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Main Authors: Aguilar, Kyle Darryl T., Ubando, Aristotle T., Culaba, Alvin B., Aviso, Kathleen B., Tan, Raymond Girard R., Chiu, Anthony Shun Fung
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Published: Animo Repository 2017
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Online Access:https://animorepository.dlsu.edu.ph/faculty_research/2192
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spelling oai:animorepository.dlsu.edu.ph:faculty_research-31912021-08-19T02:20:19Z Carbon dioxide reduction incentive for eco-industrial parks using bilevel fuzzy programming Aguilar, Kyle Darryl T. Ubando, Aristotle T. Culaba, Alvin B. Aviso, Kathleen B. Tan, Raymond Girard R. Chiu, Anthony Shun Fung A potential approach in improving the sustainability of microalgae biofuels is through eco-industrial parks. Eco-industrial parks implement industrial symbiosis to generate economic and environmental benefits. The operation of an eco-industrial park involves the interaction of the park authority and the industry plants. Their conflicting objectives lead to the determination of a compromise solution between them. However, the industrial plants may not cooperate since the compromise solution is generally suboptimal for their respective objectives. The situation results to increased emissions of carbon dioxide which has the highest contribution to climate change among the greenhouse gases. Thus, a bi-level fuzzy optimization model for algae-based eco-industrial parks under carbon dioxide reduction incentives was proposed to address these. The results of the case study showed that the use of carbon dioxide reduction incentives improved both the environmental and economic aspects of the compromise solution obtained between the park authority and the industrial plants. The obtained cost savings per industrial plant were increased beyond their respective optimal values. It was noteworthy that the incentive used did not exceed its upper limit. Overall, the incentives enhanced the cost savings and carbon dioxide reduction of the algae-based eco-industrial park. In addition, the developed model can aid the decision-makers in designing algae-based eco-industrial parks and determining the appropriate regulatory frameworks to enhance the industrial symbiosis. Measures aside from carbon dioxide can also be used. © 2017 IEEE. 2017-07-02T07:00:00Z text text/html https://animorepository.dlsu.edu.ph/faculty_research/2192 https://animorepository.dlsu.edu.ph/context/faculty_research/article/3191/type/native/viewcontent Faculty Research Work Animo Repository Industrial ecology Industrial districts Microalgae Algal biofuels Greenhouse gas mitigation
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
topic Industrial ecology
Industrial districts
Microalgae
Algal biofuels
Greenhouse gas mitigation
spellingShingle Industrial ecology
Industrial districts
Microalgae
Algal biofuels
Greenhouse gas mitigation
Aguilar, Kyle Darryl T.
Ubando, Aristotle T.
Culaba, Alvin B.
Aviso, Kathleen B.
Tan, Raymond Girard R.
Chiu, Anthony Shun Fung
Carbon dioxide reduction incentive for eco-industrial parks using bilevel fuzzy programming
description A potential approach in improving the sustainability of microalgae biofuels is through eco-industrial parks. Eco-industrial parks implement industrial symbiosis to generate economic and environmental benefits. The operation of an eco-industrial park involves the interaction of the park authority and the industry plants. Their conflicting objectives lead to the determination of a compromise solution between them. However, the industrial plants may not cooperate since the compromise solution is generally suboptimal for their respective objectives. The situation results to increased emissions of carbon dioxide which has the highest contribution to climate change among the greenhouse gases. Thus, a bi-level fuzzy optimization model for algae-based eco-industrial parks under carbon dioxide reduction incentives was proposed to address these. The results of the case study showed that the use of carbon dioxide reduction incentives improved both the environmental and economic aspects of the compromise solution obtained between the park authority and the industrial plants. The obtained cost savings per industrial plant were increased beyond their respective optimal values. It was noteworthy that the incentive used did not exceed its upper limit. Overall, the incentives enhanced the cost savings and carbon dioxide reduction of the algae-based eco-industrial park. In addition, the developed model can aid the decision-makers in designing algae-based eco-industrial parks and determining the appropriate regulatory frameworks to enhance the industrial symbiosis. Measures aside from carbon dioxide can also be used. © 2017 IEEE.
format text
author Aguilar, Kyle Darryl T.
Ubando, Aristotle T.
Culaba, Alvin B.
Aviso, Kathleen B.
Tan, Raymond Girard R.
Chiu, Anthony Shun Fung
author_facet Aguilar, Kyle Darryl T.
Ubando, Aristotle T.
Culaba, Alvin B.
Aviso, Kathleen B.
Tan, Raymond Girard R.
Chiu, Anthony Shun Fung
author_sort Aguilar, Kyle Darryl T.
title Carbon dioxide reduction incentive for eco-industrial parks using bilevel fuzzy programming
title_short Carbon dioxide reduction incentive for eco-industrial parks using bilevel fuzzy programming
title_full Carbon dioxide reduction incentive for eco-industrial parks using bilevel fuzzy programming
title_fullStr Carbon dioxide reduction incentive for eco-industrial parks using bilevel fuzzy programming
title_full_unstemmed Carbon dioxide reduction incentive for eco-industrial parks using bilevel fuzzy programming
title_sort carbon dioxide reduction incentive for eco-industrial parks using bilevel fuzzy programming
publisher Animo Repository
publishDate 2017
url https://animorepository.dlsu.edu.ph/faculty_research/2192
https://animorepository.dlsu.edu.ph/context/faculty_research/article/3191/type/native/viewcontent
_version_ 1709757411098624000