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Digitalization, carbon reduction costs and high-quality urban development with low-carbon

  • SHEN Hongliang ,
  • SONG Simeng ,
  • LI Jie
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  • School of Economics, Capital University of Economics and Business, Beijing 100070, China

Received date: 2023-09-14

  Revised date: 2024-02-29

  Online published: 2024-05-10

Abstract

The emerging digital economy can achieve low-carbon development at a lower cost, and it is an important engine for integrating carbon neutrality and economic development goals. This paper uses the matching data of the listed companies and the prefecture-level cities from 2011 to 2020 to discuss the mechanism and the effect of digitalization on the cost of carbon emission reduction and the changes brought about by urban low-carbon total factor productivity. The results show that the impact of digitalization on urban low-carbon total factor productivity through carbon emission reduction costs shows the characteristics of "first suppression, then promotion". The mechanism study found that the impact of digitalization on carbon emission reduction costs was nonlinear due to the combined effects of energy demand structure, factor technology efficiency and green technology innovation: in the early stage of digitalization, the surge in energy demand and the decline in factor technology efficiency offset the positive effects of green technology innovation, and the carbon emission reduction cost increased. In the middle and late stages of digitalization, with the optimization of energy demand structure and the improvement of factor technology efficiency, the cost of carbon emission reduction decreases. The heterogeneity analysis shows that compared with state-owned and high-energyconsuming enterprises, the digital transformation of non-state-owned enterprises and low-energy-consuming enterprises reduces the cost of carbon emission reduction earlier and improves the low-carbon total factor productivity of cities. Cities with low resource dependence, developed economy, large scale, and high coupling and coordination between digitalization and green technology innovation have a stronger inhibitory effect on the cost of carbon emission reduction, and the low-carbon total factor productivity of cities has been greatly improved.

Cite this article

SHEN Hongliang , SONG Simeng , LI Jie . Digitalization, carbon reduction costs and high-quality urban development with low-carbon[J]. Science & Technology Review, 2024 , 42(6) : 86 -102 . DOI: 10.3981/j.issn.1000-7857.2024.06.010

References

[1] 项目综合报告编写组.《中国长期低碳发展战略与转型路径研究》综合报告[J]. 中国人口·资源与环境, 2020, 30(11):1-25.
[2] Zhang C G, Liu C. The impact of ICT industry on CO2 emissions:A regional analysis in China[J]. Renewable and Sustainable Energy Reviews, 2015, 44:12-19.
[3] 张三峰, 魏下海. 信息与通信技术是否降低了企业能源消耗——来自中国制造业企业调查数据的证据[J]. 中国工业经济, 2019, 37(2):155-173.
[4] 白雪洁, 孙献贞. 互联网发展影响全要素碳生产率:成本、创新还是需求引致[J]. 中国人口·资源与环境, 2021, 31(10):105-117.
[5] 史丹, 李鹏."双碳" 目标下工业碳排放结构模拟与政策冲击[J]. 改革, 2021, 334(12):30-44.
[6] Asongu S A, Le Roux S, Biekpe N. Enhancing ICT for en-vironmental sustainability in Sub-Saharan Africa[J]. Tech-nological Forecasting and Social Change, 2018, 127:209-216.
[7] Park Y, Meng F C, Baloch M A. The effect of ICT, finan-cial development, growth, and trade openness on CO2 emissions:An empirical analysis[J]. Environmental Sci-ence and Pollution Research, 2018, 25(30):30708-30719.
[8] Higón D A, Gholami R, Shirazi F. ICT and environmental sustainability:A global perspective[J]. Telematics and In-formatics, 2017, 34(4):85-95.
[9] 缪陆军, 陈静, 范天正, 等. 数字经济发展对碳排放的影响——基于278个地级市的面板数据分析[J]. 南方金融, 2022, 546(2):45-57.
[10] 郭家堂, 骆品亮. 互联网对中国全要素生产率有促进作用吗[J]. 管理世界, 2016, 277(10):34-49.
[11] 谢莉娟, 陈锦然, 王诗桪. ICT投资、互联网普及和全要素生产率[J]. 统计研究, 2020, 37(9):56-67.
[12] Popp D. Entice-br:The effects of backstop technology R&D on climate policy models[J]. Energy Economics, 2006, 28(2):188-222.
[13] Richels R G, Blanford G J. The value of technological advance in decarbonizing the U. S. economy[J]. Energy Economics, 2008, 30(6):2930-2946.
[14] 王兵, 杜敏哲. 低碳技术下边际减排成本与工业经济的双赢[J]. 南方经济, 2015, 305(2):17-36.
[15] 周丽, 陈文颖. 建筑部门典型节能减排技术的成本效益分析[J]. 生态经济, 2015, 31(8):84-87.
[16] Lewis N S. Research opportunities to advance solar ener-gy utilization[J]. Science, 2016, 351(6271):aad1920.
[17] Bauman Y, Lee M, Seeley K. Does technological innova-tion really reduce marginal abatement costs? Some theo-ry, algebraic evidence, and policy implications[J]. Envi-ronmental and Resource Economics, 2008, 40:507-527.
[18] Baker E, Clarke L, Shittu E. Technical change and the marginal cost of abatement[J]. Energy Economics, 2008, 30(6):2799-2816.
[19] 茹雪, 雷鹏飞, 刘培. 二氧化碳动态边际减排成本及其影响机制[J]. 中国人口·资源与环境, 2022, 32(11):43-57.
[20] 邵帅, 尹俊雅, 范美婷, 等. 僵尸企业与低碳转型发展:基于碳排放绩效的视角[J]. 数量经济技术经济研究, 2022, 39(10):89-108.
[21] 张宁. 碳全要素生产率、低碳技术创新和节能减排效率追赶——来自中国火力发电企业的证据[J]. 经济研究, 2022, 57(2):158-174.
[22] 渠慎宁, 史丹, 杨丹辉. 中国数字经济碳排放:总量测算与趋势展望[J]. 中国人口·资源与环境, 2022(9):11-21.
[23] 王山, 余东华. 数字经济的降碳效应与作用路径研究——基于中国制造业碳排放效率的经验考察[J]. 科学学研究, 2024, 42(2):310-321.
[24] 时大红, 蒋伏心. 我国企业数字化转型如何促进居民消费升级[J]. 产业经济研究, 2022, 119(4):87-100.
[25] 卢东, 刘懿德, Ivan K, 等. 分享经济下的协同消费:占有还是使用?[J]. 外国经济与管理, 2018, 40(8):125-140.
[26] 胡珺, 方祺, 龙文滨. 碳排放规制、企业减排激励与全要素生产率——基于中国碳排放权交易机制的自然实验[J]. 经济研究, 2023, 58(4):77-94.
[27] 张庆宇, 张雨龙, 潘斌斌. 改革开放40年中国经济增长与碳排放影响因素分析[J]. 干旱区资源与环境, 2019, 33(10):9-13.
[28] 巴曙松, 吴大义. 能源消费、二氧化碳排放与经济增长——基于二氧化碳减排成本视角的实证分析[J]. 经济与管理研究, 2010, 211(6):5-11.
[29] 吴施美, 郑新业, 安子栋. 气候治理与短期经济波动:气候变化奥肯定律[J]. 经济学动态, 2022, 734(4):49-66.
[30] 周鹏, 周迅, 周德群. 二氧化碳减排成本研究述评[J]. 管理评论, 2014, 26(11):20-27.
[31] 王勇, 李雅楠, 俞海. 环境规制影响加总生产率的机制和效应分析[J]. 世界经济, 2019(2):97-121.
[32] Minihan E S, Wu Z. Economic structure and strategies for greenhouse gas mitigation[J]. Energy Economics, 2012, 34(1):350-357.
[33] 林美顺. 中国城市化阶段的碳减排:经济成本与减排策略[J]. 数量经济技术经济研究, 2016, 33(3):59-77.
[34] 佘群芝, 吴柳. 数字经济发展的碳减排效应[J]. 经济经纬, 2022, 39(5):14-24.
[35] 王芳, 董战峰. 数字经济对我国碳排放的影响——基于省级面板数据的实证检验[J]. 改革, 2023, 349(3):76-90.
[36] Färe R, Grifell-Tatjé E, Grosskopf S, et al. Biased tech-nical change and the malmquist productivity index[J]. Scandinavian Journal of Economics, 1997, 99(1):119-127.
[37] 张伟, 朱启贵, 李汉文. 能源使用、碳排放与我国全要素碳减排效率[J]. 经济研究, 2013, 48(10):138-150.
[38] 何小钢, 王善骝. 信息技术生产率悖论:理论演进与跨越路径[J]. 经济学家, 2020, 259(7):42-52.
[39] 杜传忠, 郭美晨. 信息技术生产率悖论评析[J]. 经济学动态, 2016, 662(4):140-148.
[40] 邵帅, 范美婷, 杨莉莉. 经济结构调整、绿色技术进步与中国低碳转型发展——基于总体技术前沿和空间溢出效应视角的经验考察[J]. 管理世界, 2022, 38(2):46-69.
[41] 韩宝国, 朱平芳. 宽带对中国经济增长影响的实证分析[J]. 统计研究, 2014, 31(10):49-54.
[42] 宋德勇, 朱文博, 丁海. 企业数字化能否促进绿色技术创新——基于重污染行业上市公司的考察[J]. 财经研究, 2022, 48(4):34-48.
[43] Li F, Nucciarelli A, Roden S, et al. How smart cities transform operations models:A new research agenda for operations management in the digital economy[J]. Pro-duction Planning & Control, 2016, 27(6):514-528.
[44] 周青, 王燕灵, 杨伟. 数字化水平对创新绩效影响的实证研究——基于浙江省73个县(区、市)的面板数据[J]. 科研管理, 2020, 41(7):120-129.
[45] 孙国锋, 潘珊珊, 徐瑾. 制造业投入数字化对绿色技术创新的影响——基于静态和动态的空间杜宾模型研究[J]. 中国软科学, 2022, 382(10):30-40.
[46] 张永珅, 李小波, 邢铭强. 企业数字化转型与审计定价[J]. 审计研究, 2021, 221(3):62-71.
[47] 邓荣荣, 张翱祥. 中国城市数字经济发展对环境污染的影响及机理研究[J]. 南方经济, 2022, 389(2):18-37.
[48] 陈诗一. 工业二氧化碳的影子价格:参数化和非参数化方法[J]. 世界经济, 2010, 33(8):93-111.
[49] Zhang N, Kong F, Kung C C. On modeling environmen-tal production characteristics:A slacks-based measure for China's Poyang Lake ecological economics zone[J]. Computational Economics, 2015, 46(3):389-404.
[50] 丁一兵, 付林. 中美大型企业社会责任对其企业效率的影响机制研究——基于DEA-Tobit两步法的分析[J]. 产业经济研究, 2015, 79(6):21-31.
[51] Ball R T, Gallo L, Ghysels E. Tilting the evidence:The role of firm-level earnings attributes in the relation be-tween aggregated earnings and gross domestic product[J]. Review of Accounting Studies, 2019, 24:570-592.
[52] 李治国, 王杰, 王叶薇. 经济集聚扩大绿色经济效率差距了吗——来自黄河流域城市群的经验证据[J]. 产业经济研究, 2022, 116(1):29-42.
[53] 赵卉心, 孟煜杰. 中国城市数字经济与绿色技术创新耦合协调测度与评价[J]. 中国软科学, 2022, 381(9):97-107.
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