Research on a carbon reduction optimization model for a megalopolis based on land - use planning and ICCLP method

• Autorzy: Wang Z.-s. , Liu L. , Xu Z. , Li Z. , Li Y.
• Języki publikacji: EN

Abstrakty

EN

For the primary purpose of minimizing carbon dioxide emissions in a megalopolis, an optimization model that remarkably reduces carbon emissions for the megalopolis, which is based on the inexact chanceconstrained linear programming (ICCLP) method and incorporates interval linear programming (ILP), and chance constrained programming (CCP), has been constructed. The corresponding net emissions of carbon dioxide results in probability levels of default equalling pᵢ =0.01, 0.05, 0.1 are [1,383.379, 1,825.311]×10⁴, [1,357.728, 1,800.841]×10⁴, [1,338.671, 1,780.060]×10⁴ tons in the megalopolis in 2015. Besides, the areas of different types of carbon-sinkable land of various cities within planned regions are obtained. The volume of energy consumption of dominating energy consumption industries in planned regions equals [965.52, 1,136.79]×10⁴ tons, which is reduced by [14.97, 22.09]%, while the intensity of energy consumption is decreased by [18.00, 20.00]% compared with that in 2010. Meanwhile, the intensities of carbon emissions are reduced by 20.00%, 19.00%, and 18.08%, respectively, under the conditions of pᵢ =0.01, 0.05, 0.1. It meets the requirements that carbon intensity shall be cut down by 17.00% in 2015 compared with that in 2010, which was proposed by “The 12th Five-Year Initiative of Controlling Greenhouse Gas Emissions.” The annual average GDP growth rate is 12.20%, reaching 9.79×10¹¹ yuan in total, higher than the expected annual growth rate of 10% in accordance with the development objective of “12th Five-Year” plan.

• Wydawca: -
• Czasopismo: Polish Journal of Environmental Studies
• Rocznik: 2015
• Tom: 24
• Numer: 1
• Opis fizyczny: p.347-354,fig.,ref.

Twórcy

autor

Wang Z.-s.

• MOE Key Laboratory of regional Energy Systems Optimization, Resources and Environmental Research Academy, North China Electric Power University, Beijing 102206, China

autor

Liu L.

• MOE Key Laboratory of regional Energy Systems Optimization, Resources and Environmental Research Academy, North China Electric Power University, Beijing 102206, China

autor

Xu Z.

• School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 100012, China

autor

Li Z.

• Changsha Vocational College of Environmental Protection, Changsha 410004, China

autor

Li Y.

• MOE Key Laboratory of regional Energy Systems Optimization, Resources and Environmental Research Academy, North China Electric Power University, Beijing 102206, China

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Identyfikatory

DOI

10.15244/pjoes/28544