Digital transformation and carbon performance: evidence from firm-level data

被引:24
作者
He, Ling-Yun [1 ]
Chen, Kun-Xian [1 ]
机构
[1] Jinan Univ, Sch Econ, Guangzhou 510632, Peoples R China
关键词
Digital transformation; Carbon emissions; Carbon intensity; Direct and indirect effects; COMMUNICATION TECHNOLOGY; BIG DATA; FINANCIAL CONSTRAINTS; ENVIRONMENTAL-REGULATION; ELECTRICITY CONSUMPTION; TEXTUAL ANALYSIS; ECONOMIC-GROWTH; INFORMATION; PRODUCTIVITY; MANAGEMENT;
D O I
10.1007/s10668-023-03143-x
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The relationship between digital transformation and carbon emissions at the firm level remains unclear. In this paper, we explore the impact of digital transformation on corporate carbon performance using data from the pollution emissions and taxation survey data of Chinese listed companies from 2009-2015 and find that digital transformation can significantly reduce carbon emissions by 8% and carbon intensity by 10%. To address the endogeneity, we employ propensity score matching method and "Broadband China" as a quasi-natural experiment. After replacing emissions and digitization measures, excluding confounding policy and potential omitted variables, the conclusions still hold. Digitalization has a greater impact on firms in high-carbon emitting industries and on non-state-owned enterprises. Mechanistic studies show that the direct effect of digital transformation increases firms' electricity consumption and electricity intensity, thus increasing carbon emissions and carbon intensity; the indirect effect reduces carbon emissions and carbon intensity by increasing firms' productivity, alleviating their financing constraints, saving energy consumption, and reducing energy intensity. Finally, using expenditures related to digital transformation, we find that digital transformation imposes a cost burden and validate that using annual report information to measure corporate digitalization has veracity.
引用
收藏
页数:26
相关论文
共 69 条
[1]   IDENTIFICATION PROPERTIES OF RECENT PRODUCTION FUNCTION ESTIMATORS [J].
Ackerberg, Daniel A. ;
Caves, Kevin ;
Frazer, Garth .
ECONOMETRICA, 2015, 83 (06) :2411-2451
[2]   Big data applications in operations/supply-chain management: A literature review [J].
Addo-Tenkorang, Richard ;
Helo, Petri T. .
COMPUTERS & INDUSTRIAL ENGINEERING, 2016, 101 :528-543
[3]   The influence of economic growth, urbanization, trade openness, financial development, and renewable energy on pollution in Europe [J].
Al-Mulali, Usama ;
Ozturk, Ilhan ;
Lean, Hooi Hooi .
NATURAL HAZARDS, 2015, 79 (01) :621-644
[4]  
Andrae Anders S. G., 2015, Challenges, V6, P117, DOI [DOI 10.3390/CHALLE6010117, 10.3390/CHALLE6010117]
[5]   Improving green flexibility through advanced manufacturing technology investment: Modeling the decision process [J].
Bai, Chunguang ;
Sarkis, Joseph .
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS, 2017, 188 :86-104
[6]   Information and communications technology as a general-purpose technology: Evidence from US industry data [J].
Basu, Susanto ;
Fernald, John .
GERMAN ECONOMIC REVIEW, 2007, 8 (02) :146-173
[7]   Internet entrepreneurship: Social capital, human capital, and performance of Internet ventures in China [J].
Batjargal, Bat .
RESEARCH POLICY, 2007, 36 (05) :605-618
[8]   More Sustainability in Industry through Industrial Internet of Things? [J].
Beier, Grischa ;
Niehoff, Silke ;
Xue, Bing .
APPLIED SCIENCES-BASEL, 2018, 8 (02)
[9]   Changing the channel: Digitization and the rise of "middle tail" strategies [J].
Benner, Mary J. ;
Waldfogel, Joel .
STRATEGIC MANAGEMENT JOURNAL, 2023, 44 (01) :264-287
[10]  
Berghaus S., 2016, MCIS 2016 P