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Research on the Economic Transmission Mechanism and Dynamic Optimization of Computing Power Networks Based on a Multi-Sectoral Input–Output Model and a Hybrid Algorithm Solution.

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Title: Research on the Economic Transmission Mechanism and Dynamic Optimization of Computing Power Networks Based on a Multi-Sectoral Input–Output Model and a Hybrid Algorithm Solution.
Authors: Du, Chunxiang1 (AUTHOR), Li, Shuangjie1,2 (AUTHOR), Wang, Huijuan1 (AUTHOR), Shi, Wenhua2 (AUTHOR), Feng, Lu2 (AUTHOR) fenglu92@163.com, Zhang, Xinyu1 (AUTHOR), Zhang, Xiaojuan1 (AUTHOR), Jia, Nan2 (AUTHOR)
Source: Energies (19961073). Jun2026, Vol. 19 Issue 11, p2709. 25p.
Subject Terms: *Input-output analysis, *Algorithms, *High technology industries, *Dynamic programming, *Energy consumption, *Distributed computing, *Carbon emissions
Geographic Terms: China
Abstract: In the digital economy era, computing power, as a novel factor of production, serves as a vital engine for driving high-quality economic development. Building upon China's traditional 42-sector input–output table, this paper incorporates computing power networks as a new sector to construct a 43-sector dynamic input–output (IO) model. Based on this framework, a Dynamic Stochastic General Equilibrium (DSGE) analysis framework is constructed to systematically reveal the dynamic transmission mechanism of computing power within industrial linkages and capital accumulation. From an energy perspective, energy consumption is implicitly captured through carbon emissions and energy structure, which together reflect the scale, efficiency, and composition of energy use in computing power networks. The findings show that the optimal computing power allocation follows a temporal evolution pattern from the service sector to the manufacturing sector, with ICT manufacturing's computing power quota reaching 31% by 2030. An investment inflection point occurs in 2026, aligning with the digital infrastructure cycle of China's 14th Five-Year Plan. The "Eastern Data, Western Computing" strategy reduces unit carbon emissions from computing power by 41%. Policy simulations demonstrate that R&D tax credits generate a 2.9-fold multiplier effect through industrial linkages, boosting GDP by 2.3%. The integrated IO-DSGE framework developed in this study provides a quantitative tool for the full-cycle management of "construction–application–regulation" in computing power networks. It holds significant theoretical value and practical implications for enhancing resource allocation efficiency and promoting green, climate-friendly development. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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  Availability: 1
Header DbId: enr
DbLabel: Energy & Power Source
An: 194588097
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PubType: Academic Journal
PubTypeId: academicJournal
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  Label: Title
  Group: Ti
  Data: Research on the Economic Transmission Mechanism and Dynamic Optimization of Computing Power Networks Based on a Multi-Sectoral Input–Output Model and a Hybrid Algorithm Solution.
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  Data: <searchLink fieldCode="AR" term="%22Du%2C+Chunxiang%22">Du, Chunxiang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Shuangjie%22">Li, Shuangjie</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Huijuan%22">Wang, Huijuan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shi%2C+Wenhua%22">Shi, Wenhua</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Feng%2C+Lu%22">Feng, Lu</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> fenglu92@163.com</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Xinyu%22">Zhang, Xinyu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Xiaojuan%22">Zhang, Xiaojuan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jia%2C+Nan%22">Jia, Nan</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Jun2026, Vol. 19 Issue 11, p2709. 25p.
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  Data: *<searchLink fieldCode="DE" term="%22Input-output+analysis%22">Input-output analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22High+technology+industries%22">High technology industries</searchLink><br />*<searchLink fieldCode="DE" term="%22Dynamic+programming%22">Dynamic programming</searchLink><br />*<searchLink fieldCode="DE" term="%22Energy+consumption%22">Energy consumption</searchLink><br />*<searchLink fieldCode="DE" term="%22Distributed+computing%22">Distributed computing</searchLink><br />*<searchLink fieldCode="DE" term="%22Carbon+emissions%22">Carbon emissions</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In the digital economy era, computing power, as a novel factor of production, serves as a vital engine for driving high-quality economic development. Building upon China's traditional 42-sector input–output table, this paper incorporates computing power networks as a new sector to construct a 43-sector dynamic input–output (IO) model. Based on this framework, a Dynamic Stochastic General Equilibrium (DSGE) analysis framework is constructed to systematically reveal the dynamic transmission mechanism of computing power within industrial linkages and capital accumulation. From an energy perspective, energy consumption is implicitly captured through carbon emissions and energy structure, which together reflect the scale, efficiency, and composition of energy use in computing power networks. The findings show that the optimal computing power allocation follows a temporal evolution pattern from the service sector to the manufacturing sector, with ICT manufacturing's computing power quota reaching 31% by 2030. An investment inflection point occurs in 2026, aligning with the digital infrastructure cycle of China's 14th Five-Year Plan. The "Eastern Data, Western Computing" strategy reduces unit carbon emissions from computing power by 41%. Policy simulations demonstrate that R&D tax credits generate a 2.9-fold multiplier effect through industrial linkages, boosting GDP by 2.3%. The integrated IO-DSGE framework developed in this study provides a quantitative tool for the full-cycle management of "construction–application–regulation" in computing power networks. It holds significant theoretical value and practical implications for enhancing resource allocation efficiency and promoting green, climate-friendly development. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=194588097
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/en19112709
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 25
        StartPage: 2709
    Subjects:
      – SubjectFull: Input-output analysis
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: High technology industries
        Type: general
      – SubjectFull: Dynamic programming
        Type: general
      – SubjectFull: Energy consumption
        Type: general
      – SubjectFull: Distributed computing
        Type: general
      – SubjectFull: Carbon emissions
        Type: general
      – SubjectFull: China
        Type: general
    Titles:
      – TitleFull: Research on the Economic Transmission Mechanism and Dynamic Optimization of Computing Power Networks Based on a Multi-Sectoral Input–Output Model and a Hybrid Algorithm Solution.
        Type: main
  BibRelationships:
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      – PersonEntity:
          Name:
            NameFull: Du, Chunxiang
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          Name:
            NameFull: Li, Shuangjie
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            NameFull: Wang, Huijuan
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            NameFull: Shi, Wenhua
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            NameFull: Feng, Lu
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            NameFull: Zhang, Xinyu
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            NameFull: Zhang, Xiaojuan
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            NameFull: Jia, Nan
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          Dates:
            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 19961073
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            – Type: volume
              Value: 19
            – Type: issue
              Value: 11
          Titles:
            – TitleFull: Energies (19961073)
              Type: main
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