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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Adjusting the global industrial structure for minimizing global carbon emissions: A network-based multi-objective

Meihui Jiang1, Haizhong An2, Xiangyun Gao2

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A new network model reveals that optimizing supply chains can reduce carbon emissions while boosting GDP. Key sectors like materials and energy in developing nations need output reduction for global climate goals.

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Area of Science:

  • Environmental Science
  • Economics
  • Industrial Ecology

Background:

  • Traditional studies focus on industrial structure for carbon reduction and economic growth.
  • The comprehensive influence of supply chain structure on carbon emissions is understudied.

Purpose of the Study:

  • To propose a novel network-based optimization model for balancing carbon emission reduction and economic growth.
  • To analyze the impact of supply chain structure on carbon emissions and industrial optimization.

Main Methods:

  • Development of a novel network-based optimization model.
  • Scenario analysis comparing network-based and non-network-based models.
  • Identification of principal sectors influencing global industrial structure and carbon emissions.

Main Results:

  • The network-based model demonstrated improved performance in reducing carbon emissions with GDP growth compared to non-network models.
  • Specific sectors (e.g., materials, energy in China) require output reduction, while others (e.g., machinery, services) can increase output.
  • Carbon emission reduction costs influence national responsibilities, with countries like China, India, and Russia facing greater burdens.

Conclusions:

  • Supply chain structure is a critical factor in achieving optimal industrial structure for carbon emission reduction.
  • Developing countries, constrained by technology, should reduce energy and material production, while developed nations can aid global reduction through technology export.