How does digital technology innovation drive synergies for reducing pollution and carbon emissions?

IF 10.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Sustainable Cities and Society Pub Date : 2024-10-22 DOI:10.1016/j.scs.2024.105932

Yiheng Zhu , Yingqi Xu , Shanggang Yin

{"title":"How does digital technology innovation drive synergies for reducing pollution and carbon emissions?","authors":"Yiheng Zhu , Yingqi Xu , Shanggang Yin","doi":"10.1016/j.scs.2024.105932","DOIUrl":null,"url":null,"abstract":"<div><div>Digital technological innovation is a key force in reshaping production and achieving green, low-carbon development, provides new impetus to reducing pollution emissions (PE) and carbon emissions (CE). This study employed the coupled coordination model, panel regression model and spatial Durbin model to examine how the digital technology innovation level (DTIL) and digital technology transfer scale (DTTS) affected synergies for reducing pollution and carbon emissions (PCRS) in the Yangtze River Delta region from 2015 to 2021. The results showed that: The evolution of PCRS is characterized by high synergy cities are increasing, low synergy cities are decreasing, and excellent coordination cities are becoming more concentrated. In the synergistic type migration evolution, the core area primarily ascends; the central and peripheral areas remain mostly stable. The effects of DTIL and DTTS on PCRS follow a non-linear inverted U-shaped pattern. DTIL has a stronger effect on reducing PE, while DTTS tends to increase CE. In terms of spatial spillover effects, DTIL has an inverted U-shaped relationship with PCRS in local regions and a positive spillover effect on neighboring regions; DTTS has a negative impact on PCRS in local regions, but shows an inverted U-shaped relationship in neighboring regions. Both of them also affect PCRS through industrial structure and energy efficiency.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"116 ","pages":"Article 105932"},"PeriodicalIF":10.5000,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sustainable Cities and Society","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S221067072400756X","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}

引用次数: 0

Abstract

Digital technological innovation is a key force in reshaping production and achieving green, low-carbon development, provides new impetus to reducing pollution emissions (PE) and carbon emissions (CE). This study employed the coupled coordination model, panel regression model and spatial Durbin model to examine how the digital technology innovation level (DTIL) and digital technology transfer scale (DTTS) affected synergies for reducing pollution and carbon emissions (PCRS) in the Yangtze River Delta region from 2015 to 2021. The results showed that: The evolution of PCRS is characterized by high synergy cities are increasing, low synergy cities are decreasing, and excellent coordination cities are becoming more concentrated. In the synergistic type migration evolution, the core area primarily ascends; the central and peripheral areas remain mostly stable. The effects of DTIL and DTTS on PCRS follow a non-linear inverted U-shaped pattern. DTIL has a stronger effect on reducing PE, while DTTS tends to increase CE. In terms of spatial spillover effects, DTIL has an inverted U-shaped relationship with PCRS in local regions and a positive spillover effect on neighboring regions; DTTS has a negative impact on PCRS in local regions, but shows an inverted U-shaped relationship in neighboring regions. Both of them also affect PCRS through industrial structure and energy efficiency.

查看原文本刊更多论文

数字技术创新如何推动减少污染和碳排放的协同效应？

数字技术创新是重塑生产方式、实现绿色低碳发展的关键力量，为减少污染排放（PE）和碳排放（CE）提供了新动力。本研究采用耦合协调模型、面板回归模型和空间杜宾模型，考察了2015-2021年数字技术创新水平（DTIL）和数字技术转移规模（DTTS）对长三角地区污染减排和碳减排协同效应（PCRS）的影响。研究结果表明PCRS的演化特点是高协同城市增加，低协同城市减少，优秀协同城市更加集中。在协同型迁移演化过程中，核心区主要呈上升趋势，中心区和外围区基本保持稳定。DTIL 和 DTTS 对 PCRS 的影响呈非线性倒 U 型。DTIL 对减少 PE 的影响更大，而 DTTS 则倾向于增加 CE。在空间溢出效应方面，DTIL 与本地区域的 PCRS 呈倒 U 型关系，对邻近区域有正向溢出效应；DTTS 对本地区域的 PCRS 有负向影响，但对邻近区域呈倒 U 型关系。二者还通过产业结构和能源效率影响 PCRS。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Sustainable Cities and Society Social Sciences-Geography, Planning and Development

CiteScore

22.00

自引率

13.70%

发文量

810

审稿时长

27 days

期刊介绍： Sustainable Cities and Society (SCS) is an international journal that focuses on fundamental and applied research to promote environmentally sustainable and socially resilient cities. The journal welcomes cross-cutting, multi-disciplinary research in various areas, including: 1. Smart cities and resilient environments; 2. Alternative/clean energy sources, energy distribution, distributed energy generation, and energy demand reduction/management; 3. Monitoring and improving air quality in built environment and cities (e.g., healthy built environment and air quality management); 4. Energy efficient, low/zero carbon, and green buildings/communities; 5. Climate change mitigation and adaptation in urban environments; 6. Green infrastructure and BMPs; 7. Environmental Footprint accounting and management; 8. Urban agriculture and forestry; 9. ICT, smart grid and intelligent infrastructure; 10. Urban design/planning, regulations, legislation, certification, economics, and policy; 11. Social aspects, impacts and resiliency of cities; 12. Behavior monitoring, analysis and change within urban communities; 13. Health monitoring and improvement; 14. Nexus issues related to sustainable cities and societies; 15. Smart city governance; 16. Decision Support Systems for trade-off and uncertainty analysis for improved management of cities and society; 17. Big data, machine learning, and artificial intelligence applications and case studies; 18. Critical infrastructure protection, including security, privacy, forensics, and reliability issues of cyber-physical systems. 19. Water footprint reduction and urban water distribution, harvesting, treatment, reuse and management; 20. Waste reduction and recycling; 21. Wastewater collection, treatment and recycling; 22. Smart, clean and healthy transportation systems and infrastructure;