数实融合是数字技术赋能实体经济的重要体现,也是推动我国经济社会全面绿色转型的重要抓手。基于我国2013—2022年县域面板数据,从技术驱动的产业融合视角构造数实融合程度指标,实证检验数实融合对县域绿色低碳发展的影响及作用机制。研究发现,数实融合显著降低了县域碳排放强度,产业结构升级和绿色低碳技术创新是数实融合推动县域绿色低碳发展的重要路径。产业融合类型对县域碳排放强度具有异质性影响,在政府环境关注度、经济集聚程度、老工业基地振兴等维度,数实融合对县域绿色低碳发展的影响呈现非均衡特征,数实融合有助于促进减污降碳协同治理与经济增长。基于研究结论提出政策建议,以期为推动县域绿色低碳转型提供经验证据和实践借鉴。
Digital-real integration is an important embodiment of digital technology empowering the real economy, and also an important starting point to promote the comprehensive green transformation of China’s economy and society. Based on China’s county panel data from 2013 to 2022, this paper constructs an index of digital-real integration degree from the perspective of technology-driven industrial integration, and empirically tests its impact and mechanism on county green and low-carbon development. We find that the improvement of data-real integration significantly reduces the county carbon emission intensity, and this finding is still valid after a series of robustness tests. The mechanism analysis shows that industrial structure upgrading and green and low-carbon technology innovation are important paths to promote the green and low-carbon development of counties through the integration of data and reality; Third, heterogeneous industrial integration types have different impacts on carbon emission intensity. In the dimensions of government environmental attention degree, economic agglomeration degree and old industrial base, the integration of data and real has heterogeneous effects on county green and low-carbon development. Fourthly, the extended analysis finds that the data-real integration is helpful to realize the coordinated development of pollution reduction and carbon reduction and economic growth. The research conclusions of this paper not only expand the relevant research on data-real integration, but also provide empirical evidence and policy reference for promoting green and low-carbon transformation at the county level.
[1] 宋祺佼,王宇飞,王晓. 新型城镇化低碳发展的实践、问题及对策[J].当代经济管理, 2015(8): 47-52
[2] 魏滨辉,罗明忠. 劳动力返乡创业与县域绿色低碳发展[J].广东财经大学学报, 2024(1): 70-84
[3] 王睿,张赫,强文丽,等. 基于城镇化的中国县级城市碳排放空间分布特征及影响因素[J].地理科学进展, 2021(12): 1999-2010
[4] 张荣博,钟昌标. 智慧城市试点、污染就近转移与绿色低碳发展——来自中国县域的新证据[J].中国人口·资源与环境, 2022(4): 91-104
[5] 张国胜,严鹏,李欣珏,等. 大数据要素集聚、技术能力缺口与生产率区域差距[J].中国工业经济, 2024(10): 118-136
[6] 韩兆安,赵景峰,吴海珍. 中国省际数字经济规模测算、非均衡性与地区差异研究[J].数量经济技术经济研究, 2021(8): 164-181
[7] 钞小静,薛志欣,王宸威. 中国新经济的逻辑、综合测度及区域差异研究[J].数量经济技术经济研究, 2021(10): 3-23
[8] 周密,王雷,郭佳宏. 新质生产力背景下数实融合的测算与时空比较——基于专利共分类方法的研究[J].数量经济技术经济研究, 2024(7): 5-27
[9] 陈雨露. 数字经济与实体经济融合发展的理论探索[J].经济研究, 2023(9): 22-30
[10] 刘尚希,邢丽,樊轶侠,等. 以数字化引领数实融合的内在机理与现实思考[J].财政研究, 2023(12): 3-15
[11] 史丹,孙光林. 数字经济和实体经济融合对绿色创新的影响[J].改革, 2023(2): 1-13
[12] 任保平,李婧瑜. 以数实融合推动新型工业化的阶段性特征、战略定位与路径选择[J].经济与管理评论, 2024(2): 5-16
[13] 朱俏俏,靳春丽,李征帛. 数实融合能否助推工业减污降碳协同增效？——基于信息产业与工业的前后向关联考察[J].南方经济, 2025(2): 68-96
[14] 张帅,吴珍玮,陆朝阳,等. 中国省域数字经济与实体经济融合的演变特征及驱动因素[J].经济地理, 2022(7): 22-32
[15] 蒋为,倪诗程,彭淼. 数实融合与企业出口产品策略优化:基于柔性生产视角[J].世界经济, 2024(5): 3-33
[16] 温科,李常洪.数实技术融合对企业新质生产力的影响研究[J/OL].科研管理,1-12[2025-08-30].https://kns.cnki.net/kcms2/article/abstract?v=MmR6bzzH81F6xU9mkMFuCUzfZOabVTU-Gg3OF8foMicXGVpbgFdG5XBxfKB3n9QsxyfqjVJDU1Q2eXX6oD3waWIwJ4bd7nxmFm82e_IiVNy6Ig6jmwCb4p0o6W2XlrmW0CtPUAnP6_eZAK7-42BzHHz_EEdgur_eoSoLpcnp49j8Q7sJnlyraQ==&uniplatform=NZKPT&language=CHS
[17] 黄先海,高亚兴. 数实产业技术融合与企业全要素生产率——基于中国企业专利信息的研究[J].中国工业经济, 2023(11): 118-136
[18] Sun G, Fang J, Li J, et al. Research on the impact of the integration of digital economy and real economy on enterprise green innovation[J].Technological Forecasting and Social Change, 2024, 200: 123097
[19] Heo P S, Lee D H. Evolution of the linkage structure of ICT industry and its role in the economic system: The case of Korea[J].Information Technology for Development, 2019(3): 424-454
[20] Goldfarb A, Tucker C. Digital Economics[J].Journal of Economic Literature, 2019(1): 3-43
[21] Guo D, Li L, Pang G. Does the integration of digital and real economies promote urban green total factor productivity? Evidence from China[J].Journal of Environmental Management, 2024, 370: 122934
[22] 辛冲冲,徐斯旸. 中国CO2排放量的动态演进、地区差异及空间集聚特征——来自中国2000—2019年2898个区县的经验证据[J].软科学, 2024(6): 105-112
[23] 周克清,吴近平. 税收分成激励促进了县域经济低碳发展吗——来自增值税“五五分成”改革的证据[J].财经科学, 2024(10): 53-68
[24] 王守坤,范文诚. 金融可得性与碳排放——来自金融地理结构的宏观与微观证据[J].数量经济技术经济研究, 2024(1): 67-89
[25] 夏杰长. 中国式现代化视域下实体经济的高质量发展[J].改革, 2022(10): 1-11
[26] 陈晓红,张静辉,汪阳洁,等. 数字赋能、技术创新与空气污染治理——来自专利文本挖掘的证据[J].经济研究, 2024(12): 21-39
[27] Luan B, Zou H, Chen S, et al. The effect of industrial structure adjustment on China’s energy intensity: Evidence from linear and nonlinear analysis[J].Energy, 2021, 218: 119517
[28] 王俊豪,谢倩莹,史丹. 绿色技术创新对能源效率的影响研究[J].财贸经济, 2024(6):143-159
[29] 张龙鹏,张双志,胡燕娟. 企业价值链智能化对全要素生产率的影响[J].南方经济, 2023(10): 94-111
