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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Collaborative Emission Reduction Model Based on Multi-Objective Optimization for Greenhouse Gases and Air Pollutants.
Qing-chun Meng1, Xiao-xia Rong2, Yi-min Zhang3
1School of Management, Shandong University, Jinan, Shandong 250100, China.
Plos One
|March 25, 2016
Summary
This study introduces a mathematical model for optimizing energy distribution to reduce greenhouse gas emissions and air pollutants like sulfur dioxide (SO2) and nitrogen oxides (NOx). It proposes an efficient, clean, and economic energy scheme for collaborative emission reduction.
Area of Science:
- Environmental Science
- Energy Policy
- Mathematical Optimization
Background:
- Carbon dioxide (CO2) emissions impact global climate, economics, and politics.
- Reducing greenhouse gases is a critical global environmental issue.
- Fossil fuel consumption drives emissions of CO2, SO2, and NOx in China.
Purpose of the Study:
- To develop an economic, clean, and efficient energy distribution scheme.
- To analyze collaborative emission reduction of multiple gases using multi-objective optimization.
- To address common restraints in energy consumption for emission control.
Main Methods:
- Multi-objective optimization mathematical method.
- Granger causality test for air quality-pollutant emission relationships.
- Function analysis for energy consumption and pollutant emission quantification.
- Optimality condition analysis for designing an optimal-pole algorithm.
Main Results:
- Verified the model's effectiveness using Tianjin's 1996-2012 data.
- Analyzed efficient solutions and dominant sets for collaborative reduction.
- Developed an efficient collaborative reduction scheme for multiple gases.
Conclusions:
- The study provides a framework for optimizing energy distribution for emission reduction.
- Empirical analysis validates the proposed mathematical model.
- Recommendations for collaborative reduction strategies are presented.
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