Impact of Digital Transformation on Green Innovation in Manufacturing under Dual Carbon Targets
Abstract
:1. Introduction
2. Research Hypotheses
2.1. Digital Transformation (DT) and Green Innovation (GI)
2.2. Mediating Effect of Corporate Environmental Responsibility (CER)
2.3. Moderating Effect of Media Attention
3. Research Design
3.1. Data Source
- (1)
- Exclude listed companies with ST or *ST trading status for the year;
- (2)
- Exclude observational samples with significant missing data.
3.2. Variable Measurement
3.2.1. Dependent Variable
3.2.2. Explanatory Variable
3.2.3. Mediating Variable
3.2.4. Moderating Variable
3.2.5. Control Variables
3.3. Model Construction
3.3.1. Main Effects Model
3.3.2. Mediation Effects Model
3.3.3. Moderation Effects Model
4. Results
4.1. Descriptive Statistics and Correlation Analysis
4.1.1. Descriptive Statistics
4.1.2. Correlation Analysis
4.2. Regression Results Analysis
4.2.1. Main Effects Test Analysis
4.2.2. Mediation Effects Test Analysis
4.2.3. Moderation Analysis of Media Attention
4.3. Robustness Checks
4.3.1. Lagged Regression Analysis
4.3.2. Replacement of Dependent Variables
4.3.3. Exclusion of the Impact of the COVID-19 Pandemic
4.4. Heterogeneity Analysis
4.4.1. Analysis Based on Institutional Ownership
4.4.2. Heterogeneity Analysis Based on Analyst Attention
4.5. Incentive Effects of Dual Carbon Targets
5. Conclusions and Prospect
5.1. Conclusions
5.2. Management Insights
5.3. Research Limitations and Prospect
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Shi, B.; Li, N.; Gao, Q.; Li, G. Market incentives, carbon quota allocation and carbon emission reduction: Evidence from China’s carbon trading pilot policy. J. Environ. Manag. 2022, 319, 115650. [Google Scholar] [CrossRef] [PubMed]
- Pan, A.; Zhang, W.; Shi, X.; Dai, L. Climate policy and low-carbon innovation: Evidence from low-carbon city pilots in China. Energy Econ. 2022, 112, 106129. [Google Scholar] [CrossRef]
- Gao, J.; Xu, N.; Zhou, J. Does Digital Transformation Contribute to Corporate Carbon Emissions Reduction? Empirical Evidence from China. Sustainability 2023, 15, 13414. [Google Scholar] [CrossRef]
- Xiao, Y.; Yong, C.; Hu, W.; Wang, H. Factors Influencing Carbon Emissions in High Carbon Industries in the Zhejiang Province and Decoupling Effect Analysis. Sustainability 2023, 15, 15975. [Google Scholar] [CrossRef]
- Gao, P.; Yue, S.; Chen, H. Carbon Emission Efficiency of China’s Industry Sectors: From the Perspective of Embodied Carbon Emissions. J. Clean. Prod. 2021, 283, 124655. [Google Scholar] [CrossRef]
- Beraud, J.J.D.; Zhao, X.; Wu, J. Revitalization of Chinese’s manufacturing industry under the carbon neutral goal. Environ. Sci. Pollut. Res. 2022, 29, 66462–66478. [Google Scholar] [CrossRef]
- Deng, H.; Bai, G.; Shen, Z.; Xia, L. Digital economy and its spatial effect on green productivity gains in manufacturing: Evidence from China. J. Clean. Prod. 2022, 378, 134539. [Google Scholar] [CrossRef]
- Rupasinghe, L.R.; Pushpakumari, M.D.; Perera, G.D.N. Mapping the knowledge of green innovation: A systematic literature review. J. Humanit. Appl. Soc. Sci. 2024, 6, 357–376. [Google Scholar] [CrossRef]
- Xu, J.; Yu, Y.; Zhang, M.; Zhang, J.Z. Impacts of digital transformation on eco-innovation and sustainable performance: Evidence from Chinese manufacturing companies. J. Clean. Prod. 2023, 393, 136278. [Google Scholar] [CrossRef]
- Liu, X.; Liu, F.; Ren, X. Firms’ digitalization in manufacturing and the structure and direction of green innovation. J. Environ. Manag. 2023, 335, 117525. [Google Scholar] [CrossRef]
- Ji, K.; Liu, X.; Xu, J. Digital Economy and the Sustainable Development of China’s Manufacturing Industry: From the Perspective of Industry Performance and Green Development. Sustainability 2023, 15, 5121. [Google Scholar] [CrossRef]
- Abdullah, S.; Nawi, M.N.M.; Halim, M.A.S.A.; Omar, K.; Rosli, N.A.; Harun, A.N. Digital transformation for sustainable business performance: A systematic literature review and the way forward. UCJC Bus. Soc. Rev. 2024, 21. [Google Scholar]
- Feroz, A.K.; Zo, H.; Chiravuri, A. Digital transformation and environmental sustainability: A review and research agenda. Sustainability 2021, 13, 1530. [Google Scholar] [CrossRef]
- Li, H. Green Innovation, Green Dynamic Capability, and Enterprise Performance: Evidence from Heavy Polluting Manufacturing Enterprises in China. Complexity 2022, 2022, 7755964. [Google Scholar] [CrossRef]
- Gong, C.; Ribiere, V. Developing a unified definition of digital transformation. Technovation 2021, 102, 102217. [Google Scholar] [CrossRef]
- Yongchang, S.; Yunyun, F.; Malin, S. Does digital transformation make enterprises greener? Evidence from China. Econ. Anal. Policy 2023, 80, 1642–1654. [Google Scholar]
- Wang, Y.; Yang, Y.; Fu, C.; Fan, Z.; Zhou, X. Environmental regulation, environmental responsibility, and green technology innovation: Empirical research from China. PLoS ONE 2021, 16, e0257670. [Google Scholar] [CrossRef]
- Mubarak, M.F.; Tiwari, S.; Petraite, M.; Mubarik, M.; Raja Mohd Rasi, R.Z. How Industry 4.0 technologies and open innovation can improve green innovation performance? Manag. Environ. Qual. Int. J. 2021, 32, 1007–1022. [Google Scholar] [CrossRef]
- Kong, T.; Sun, R.; Sun, G.; Song, Y. Effects of Digital Finance on Green Innovation considering Information Asymmetry: An Empirical Study Based on Chinese Listed Firms. Emerg. Mark. Financ. Trade 2022, 58, 4399–4411. [Google Scholar] [CrossRef]
- Deyong, S.; Wenbo, Z.; Hai, D. Can Firm Digitalization Promote Green Technological Innovation? An Examination Based on Listed Companies in Heavy Pollution Industries. J. Financ. Econ. 2022, 48, 34–48. [Google Scholar]
- Xin, L.; Qiong, X.; Hecheng, W. Enterprise Digital Transformation and Green Technology Innovation. Stat. Res. 2023, 40, 107–119. [Google Scholar]
- El-Kassar, A.; Singh, K.S. Green innovation and organizational performance: The influence of big data and the moderating role of management commitment and HR practices. Technol. Forecast. Soc. Chang. 2019, 144, 483–498. [Google Scholar] [CrossRef]
- Chang, L.; Huifeng, P.; Pei, L.; Feng, Y. Impact and mechanism of digital transformation on the green innovation efficiency of manufacturing enterprises in China. China Soft Sci. 2023, 2023, 121–129. [Google Scholar]
- Yan, Z.; Niannian, Q. Centrality, Technological Influence and Firm’s Sustainable Innovation--The Moderating Role of Rnvironmental Responsibility and Corporate Resilience. Manag. Rev. 2023, 35, 105–117. [Google Scholar]
- Zhao, X.; Cai, L. Digital transformation and corporate ESG: Evidence from China. Financ. Res. Lett. 2023, 58, 104310. [Google Scholar] [CrossRef]
- Jin, W.; Zihan, H.; Qingqing, D.; Jian, X. The Impact and Mechanism of Digital Transformation of Heavily Polluting Enterprises on ESG Performance. Sci. Technol. Manag. Res. 2024, 44, 192–205. [Google Scholar]
- Ruan, R.; Chen, W.; Zhu, Z. Linking Environmental Corporate Social Responsibility with Green Innovation Performance: The Mediating Role of Shared Vision Capability and the Moderating Role of Resource Slack. Sustainability 2022, 14, 16943. [Google Scholar] [CrossRef]
- Jiang, R.; Chen, G. Governmental Allowance, Enteprise ESG and Green Innovation. Resour. Ind. 2022, 24, 90–102. [Google Scholar]
- Qingjiang, J.; Chuanhong, W.; Yiping, F. Research on enterprise green technology innovation strategy based on environmental responsibility. Sci. Technol. Manag. Res. 2008, 28, 9–12. [Google Scholar]
- Wang, J.; Xue, Y.; Yang, J. Boundary-spanning search and firms’ green innovation: The moderating role of resource orchestration capability. Bus. Strategy Environ. 2020, 29, 361–374. [Google Scholar] [CrossRef]
- Dai, J.; Zhu, Q. ESG performance and green innovation in a digital transformation perspective. Am. J. Econ. Sociol. 2023, 83, 263–282. [Google Scholar] [CrossRef]
- Mengmeng, M.; Xiangyu, T.; Sirui, L.; Jiasu, L. Research on the Impact of ESG Performance on Green Innovation. J. Technol. Econ. 2023, 42, 13–24. [Google Scholar]
- Zhongbo, Y.; Gaoliang, T.; Baolei, Q.; Hao, Z. Corporate governance mechanism concerned by media—An investigation based on the perspective of earnings management. J. Manag. World 2011, 2011, 127–140. [Google Scholar]
- He, Z.; Cao, C.; Feng, C. Media Attention, Environmental Information Disclosure and Corporate Green Technology Innovations in China’s Heavily Polluting Industries. Emerg. Mark. Financ. Trade 2022, 58, 3939–3952. [Google Scholar] [CrossRef]
- Savastano, M.; Amendola, C.; Bellini, F.; D’Ascenzo, F. Contextual impacts on industrial processes brought by the digital transformation of manufacturing: A systematic review. Sustainability 2019, 11, 891. [Google Scholar] [CrossRef]
- Haifeng, T.; Huajun, L. “Dual synergy”of the digital transformation and green innovation by enterprises in China. Ind. Econ. Res. 2023, 2023, 29–41+72. [Google Scholar]
- Fei, W.; Huizhi, H.; Huiyan, L.; Xiaoyi, R. Enterprise Digital Transformation and Capital Market Performance: Empirical Evidence from Stock Liquidity. J. Manag. World 2021, 37, 130–144+10. [Google Scholar]
- Yunpeng, F.; Yajing, M.; Bin, H. Corporate ESG Performance and Cost of Debt: Theoretical Mechanism and Empirical Evidence. Bus. Manag. J. 2023, 45, 123–144. [Google Scholar]
- Yun, W.; Yanxi, L.; Zhuang, M.; Jinbo, S. Media Coverage, Environmental Regulation and Corporate Environment Behavior. Nankai Bus. Rev. 2017, 20, 83–94. [Google Scholar]
- Zhihong, M.; Li, L. The impact of ESG performance on M&A performance commitment. Jiangxi Soc. Sci. 2024, 44, 139–148. [Google Scholar]
- Wen, Z.; Ye, B. Analyses of Mediating Effects: The Development of Methods and Model. Adv. Psychol. Sci. 2014, 22, 731–745. [Google Scholar] [CrossRef]
Research Hypothesis | Contents |
---|---|
Hypothesis 1 | Under the dual carbon targets, digital transformation positively promotes green innovation in manufacturing enterprises in China. |
Hypothesis 2 | Under the dual carbon targets, corporate environmental responsibility (CER) mediates between enterprise digital transformation and green innovation in China. |
Hypothesis 3 | Hypothesis 3: Under the dual carbon targets, media attention media attention positively moderates digital transformation and green innovation in China. |
Primary Indicators | Secondary Indicators | Tertiary Indicators |
---|---|---|
Environment | Climate Change | Green finance, sponge cities, addressing climate change, carbon reduction pathways, greenhouse gas emissions |
Resource utilization | Water resource consumption, land use, biodiversity, material consumption | |
Environmental pollution | E-waste, hazardous waste, industrial emissions | |
Environmentally friendly | Renewable energy, green factories, green buildings | |
Environmental management | Medical Digitalization, Mobile Wallets, Barcode Payments, Supply Chain Management—E, Sustainable Certification, Environmental Penalties |
Variable | Type | High-Frequency Keywords |
---|---|---|
Digital Transformation (DIG) | Artificial Intelligence (AI) | Artificial Intelligence (AI), Intelligent Warehousing, Semantic Search, Kingdee (EAS), Business Intelligence (BI), Financial Systems, Intelligent Robots, Fine Management, Intelligent Workshops, Expert Systems, Identity Verification, Numerical Control (NC), Facial Recognition, Electronic Design Automation (EDA), Customer Relationship Management (CRM), Enterprise Resource Planning (ERP), Manufacturing Resource Planning System (MRP), Investment Decision Support Systems, Enterprise Resource Management (ERP), Manufacturing Execution Systems (MES), Office Automation (OA), Product Lifecycle Management (PLM), Robotic Process Automation (RPA), Enterprise Resource Planning Software Systems (SAP S/4HANA), Financial Management Systems, Biometric Technology, UFIDA (U9), Machine Learning, Intelligence, Smart Office, Smart Technology, Intelligent Identification, Intelligent Manufacturing, Smart Terminals, Autonomous Driving, Robots, Automation Control, Smart Equipment, Smart Factory, Intelligent Decision-making, Flexibility, Integration, Numerical Control, Computer Vision (CV), Industrial Intelligence, Intelligent Systems, Driverless, Intelligent Operations and Maintenance, Natural Language Processing (NLP) |
Blockchain Technology (BC) | Blockchain, Digital Currency, Virtual Currency, Differential Privacy Technology, Distributed Communication, Consensus Mechanism, Distributed Control Systems (DCS), Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Distributed Computing, Peer-to-Peer Distributed Technology (P2P), Intelligent Financial Contracts | |
Cloud Computing Technology (CC) | Cloud Computing, Edge Infrastructure, Neuromorphic Computing, Server Virtualization, Cognitive Computing, Fusion Architecture, Service-Oriented Architecture (SOA), EB-level Storage, Fog Computing, Haze Computing, Edge Computing, Multi-party Secure Computing, Memory Computing, Internet of Things (IoT), Cloud Storage, Cloud Networking, Cloud Platforms, Cloud Migration, Cloud Computing Technologies, Cloud Light, Billion-level Concurrent Systems, Programming Models | |
Big Data Technology (BD) | Big Data (BD), Industrial Data, Augmented Reality, Relational Databases, Heterogeneous Data, Credit Reporting, Data Exchange, Virtual Reality, Multi-level Data Compression, Geographic Information Systems (GIS), Cyber-Physical Systems, Mixed Reality, Databases (Oracle), Data Empowerment, Data Visualization, Data Mining, Data Acquisition, Data Cleaning, Data-Driven, Data Storage, Data Industry, Digital Transformation, Data Technology | |
Digital Technology Application (DTA) | Medical Digitalization, Mobile Wallets, Barcode Payments, NFC Payments, Internet Finance, Smart Devices, Digital Control, Intelligent Workshops, Smart Terminal Products, Financial Technology, Smart Energy, Network Connection, Quantitative Finance, Smart Energy Conservation and Environmental Protection, Smart Healthcare, Unmanned Retail, Smart Home, Smart Investment Consulting, Smart Cultural Tourism, Smart Power Systems, Digital Retail, Digital Finance, Smart Logistics, Open Banking, Electronic Medical Records (EMR), New Retail, Intelligent Customer Service, B2B, B2C, C2B, C2C, C2M, Online-to-Offline (O2O), Online Retail, E-commerce, Official Accounts, WeChat Mini Programs, Applications (APP), Live Streaming, Microblogs, Mobile E-commerce (M2M), Presale, Online Office, Online Education, Telemedicine, Unmanned Delivery |
Type | Name | Symbol | Definition |
---|---|---|---|
Dependent Variable | Enterprise Green Innovation | GI | Natural logarithm of the total number of patents applied for by the company in the current year |
Independent Variable | Enterprise Digital Transformation | DT | Natural logarithm of the sum of frequencies of DIG-related keywords, plus 1 |
Mediating Variable | Corporate Environmental Responsibility | CER | Environmental responsibility sub-rating in the Sino-securities ESG assessment system |
Moderating Variable | Media Attention | Media | Logarithm of the quantity of media coverage received by the company |
Mediating Variable | Company Size | Size | Natural logarithm of total assets |
Financial Leverage | Lev | Debt-to-asset ratio (total liabilities divided by total assets) | |
Return on Total Assets | ROA | Net profit/total assets | |
Growth | Growth | Revenue growth rate | |
Operating Cash Flow | OCF | Operating cash flow/total assets | |
Company Age | Age | Difference between the current year and the year of establishment | |
Proportion of Independent Directors | BInd | Number of independent directors/total board members | |
Ownership Concentration | Top1 | Largest shareholder/total shares outstanding | |
Executive Compensation | Salary | Natural logarithm of the total executive compensation |
Variables | N | Mean | Median | Std. Dev | Minimum | Maximum |
---|---|---|---|---|---|---|
GI | 21,842 | 0.823 | 0 | 1.103 | 0 | 4.29 |
DT | 21,842 | 1.345 | 1.099 | 1.271 | 0 | 4.963 |
Size | 21,842 | 22.022 | 21.863 | 1.19 | 19.303 | 27.145 |
Lev | 21,842 | 0.388 | 0.374 | 0.198 | 0.051 | 1.034 |
ROA | 21,842 | 0.04 | 0.042 | 0.071 | −0.36 | 0.211 |
Growth | 21,842 | 0.175 | 0.108 | 0.436 | −0.653 | 3.261 |
OCF | 21,842 | 0.05 | 0.049 | 0.069 | −0.204 | 0.255 |
Age | 21,842 | 2.929 | 2.944 | 0.31 | 1.386 | 4.174 |
BInd | 21,842 | 0.377 | 0.364 | 0.054 | 0.3 | 0.571 |
Top1 | 21,842 | 0.33 | 0.308 | 0.14 | 0.084 | 0.743 |
Salary | 21,842 | 15.354 | 15.328 | 0.728 | 13.005 | 17.403 |
Variables | GI | DT | Size | Lev | ROA | Growth | OCF | Age | BInd | Top1 | Salary |
---|---|---|---|---|---|---|---|---|---|---|---|
GI | 1 | ||||||||||
DT | 0.234 *** | 1 | |||||||||
Size | 0.490 *** | 0.117 *** | 1 | ||||||||
Lev | 0.273 *** | 0.014 ** | 0.432 *** | 1 | |||||||
ROA | −0.014 ** | −0.00500 | 0.026 *** | −0.410 *** | 1 | ||||||
Growth | 0.039 *** | 0.016 ** | 0.066 *** | 0.040 *** | 0.229 *** | 1 | |||||
OCF | −0.00600 | −0.0100 | 0.099 *** | −0.190 *** | 0.434 *** | 0.015 ** | 1 | ||||
Age | 0.040 *** | 0.019 *** | 0.155 *** | 0.131 *** | −0.062 *** | −0.057 *** | 0.022 *** | 1 | |||
BInd | −0.00600 | 0.061 *** | −0.020 *** | −0.00100 | −0.029 *** | −0.00900 | −0.00400 | −0.0110 | 1 | ||
Top1 | −0.00600 | −0.046 *** | 0.088 *** | −0.044 *** | 0.160 *** | −0.00600 | 0.117 *** | −0.070 *** | 0.047 *** | 1 | |
Salary | 0.286 *** | 0.209 *** | 0.507 *** | 0.050 *** | 0.215 *** | 0.046 *** | 0.213 *** | 0.104 *** | −0.075 *** | −0.013 * | 1 |
Variables | (1) | (2) |
---|---|---|
GI | GI | |
DT | 0.166 *** | 0.118 *** |
(28.21) | (22.77) | |
Size | 0.398 *** | |
(57.45) | ||
Lev | 0.396 *** | |
(10.03) | ||
ROA | 0.391 *** | |
(3.57) | ||
Growth | −0.039 *** | |
(−2.65) | ||
OCF | −0.349 *** | |
(−3.45) | ||
Age | −0.099 *** | |
(−4.67) | ||
BInd | −0.0530 | |
(−0.47) | ||
Top1 | −0.199 *** | |
(−4.44) | ||
Salary | 0.055 *** | |
(5.05) | ||
Cons | −0.116 *** | −9.351 *** |
(−3.35) | (−54.40) | |
Ind | YES | YES |
Year | YES | YES |
N | 21,842 | 21,842 |
0.121 | 0.340 |
Variables | (1) | (2) | (3) |
---|---|---|---|
GI | CER | GI | |
DT | 0.121 *** | 0.057 *** | 0.116 *** |
(22.68) | (8.57) | (21.90) | |
CER | 0.081 *** | ||
(14.56) | |||
Size | 0.401 *** | 0.224 *** | 0.383 *** |
(56.00) | (25.19) | (52.96) | |
Lev | 0.407 *** | 0.288 *** | 0.384 *** |
(9.95) | (5.67) | (9.42) | |
ROA | 0.331 *** | 0.589 *** | 0.283 ** |
(2.84) | (4.08) | (2.44) | |
Growth | −0.042 *** | −0.188 *** | −0.027 * |
(−2.74) | (−9.89) | (−1.76) | |
OCF | −0.379 *** | 0.324 ** | −0.405 *** |
(−3.60) | (2.48) | (−3.87) | |
Age | −0.111 *** | −0.063 ** | −0.106 *** |
(−5.05) | (−2.30) | (−4.84) | |
BInd | −0.041 | −0.244 * | −0.0210 |
(−0.35) | (−1.69) | (−0.18) | |
Top1 | −0.224 *** | 0.00300 | −0.225 *** |
(−4.90) | (0.06) | (−4.93) | |
Salary | 0.054 *** | 0.048 *** | 0.050 *** |
(4.80) | (3.45) | (4.47) | |
Cons | −9.372 *** | −3.702 *** | −9.073 *** |
(−52.65) | (−16.79) | (−50.89) | |
Ind | YES | YES | YES |
Year | YES | YES | YES |
N | 21,015 | 21,015 | 21,015 |
0.341 | 0.0920 | 0.348 |
Variables | (1) | (2) |
---|---|---|
GI | GI | |
DT | −0.0160 | 0.00700 |
(−0.54) | (0.26) | |
Media | 0.255 *** | 0.000 |
(22.67) | (−0.01) | |
DT × Media | 0.030 *** | 0.022 *** |
(5.18) | (4.35) | |
Size | 0.391 *** | |
(52.84) | ||
Lev | 0.389 *** | |
(9.78) | ||
ROA | 0.362 *** | |
(3.27) | ||
Growth | −0.040 *** | |
(−2.68) | ||
OCF | −0.395 *** | |
(−3.87) | ||
Age | −0.095 *** | |
(−4.41) | ||
BInd | −0.0690 | |
(−0.60) | ||
Top1 | −0.200 *** | |
(−4.43) | ||
Salary | 0.051 *** | |
(4.61) | ||
Cons | −1.504 *** | −9.135 *** |
(−22.62) | (−50.45) | |
Ind | YES | YES |
Year | YES | YES |
N | 28,388 | 28,388 |
0.179 | 0.341 |
Variables | (1) | (2) |
---|---|---|
GI | GI | |
DTit−1 | 0.173 *** | 0.123 *** |
(26.18) | (21.07) | |
Size | 0.411 *** | |
(53.59) | ||
Lev | 0.394 *** | |
(8.98) | ||
ROA | 0.390 *** | |
(3.26) | ||
Growth | −0.030 * | |
(−1.80) | ||
OCF | −0.432 *** | |
(−3.80) | ||
Age | −0.117 *** | |
(−4.65) | ||
BInd | −0.0550 | |
(−0.44) | ||
Top1 | −0.209 *** | |
(−4.14) | ||
Salary | 0.062 *** | |
(5.11) | ||
Cons | −0.0240 | −9.646 *** |
(−0.64) | (−49.44) | |
Ind | YES | YES |
Year | YES | YES |
N | 18,326 | 18,326 |
0.125 | 0.345 |
Variables | (1) | (2) |
---|---|---|
GI1 | GI1 | |
DT | 0.122 *** | 0.079 *** |
(23.00) | (16.83) | |
Size | 0.345 *** | |
(55.01) | ||
Lev | 0.382 *** | |
(10.70) | ||
ROA | 0.127 | |
(1.28) | ||
Growth | −0.064 *** | |
(−4.83) | ||
OCF | −0.207 ** | |
(−2.27) | ||
Age | −0.095 *** | |
(−4.94) | ||
BInd | 0.0770 | |
(0.75) | ||
Top1 | −0.198 *** | |
(−4.91) | ||
Salary | 0.058 *** | |
(5.92) | ||
Cons | −0.072 ** | −8.239 *** |
(−2.32) | (−53.05) | |
Ind | YES | YES |
Year | YES | YES |
N | 21,842 | 21842 |
0.108 | 0.321 |
Variables | (1) | (2) |
---|---|---|
GI | GI | |
DT | 0.156 *** | 0.113 *** |
(23.14) | (18.73) | |
Size | 0.375 *** | |
(47.25) | ||
Lev | 0.382 *** | |
(8.53) | ||
ROA | 0.442 *** | |
(3.52) | ||
Growth | −0.0230 | |
(−1.34) | ||
OCF | −0.358 *** | |
(−3.12) | ||
Age | −0.111 *** | |
(−4.61) | ||
BInd | −0.00900 | |
(−0.07) | ||
Top1 | −0.181 *** | |
(−3.55) | ||
Salary | 0.058 *** | |
(4.70) | ||
Cons | −0.086 ** | −8.841 *** |
(−2.39) | (−45.24) | |
Ind | YES | YES |
Year | YES | YES |
N | 16,324 | 16,324 |
0.112 | 0.320 |
Variables | (1) | (2) |
---|---|---|
GI | GI | |
Low Institutional Ownership | High Institutional Ownership | |
DT | 0.114 *** | 0.125 *** |
(17.02) | (15.58) | |
Size | 0.335 *** | 0.427 *** |
(30.57) | (44.13) | |
Lev | 0.433 *** | 0.361 *** |
(8.51) | (5.90) | |
ROA | 0.366 *** | 0.494 *** |
(2.71) | (2.75) | |
Growth | −0.042 ** | −0.0320 |
(−2.03) | (−1.53) | |
OCF | −0.437 *** | −0.310 ** |
(−3.27) | (−2.02) | |
Age | −0.114 *** | −0.099 *** |
(−4.01) | (−3.13) | |
BInd | −0.122 | 0.00100 |
(−0.81) | (0.01) | |
Top1 | −0.416 *** | −0.0920 |
(−5.92) | (−1.36) | |
Salary | 0.098 *** | 0.027 * |
(6.38) | (1.77) | |
Cons | −8.422 *** | −9.711 *** |
(−31.93) | (−39.37) | |
Ind | YES | YES |
Year | YES | YES |
N | 10,910 | 10,932 |
0.269 | 0.369 |
Variables | (1) | (2) |
---|---|---|
GI | GI | |
Low Analyst Attention | High Analyst Attention | |
DT | 0.111 *** | 0.131 *** |
(12.87) | (12.40) | |
Size | 0.375 *** | 0.421 *** |
(30.23) | (28.22) | |
Lev | 0.551 *** | 0.762 *** |
(7.91) | (7.62) | |
ROA | 0.395 * | 0.658 * |
(1.91) | (1.89) | |
Growth | −0.058 ** | −0.087 ** |
(−2.45) | (−2.39) | |
OCF | −0.248 | −0.825 *** |
(−1.43) | (−3.46) | |
Age | −0.095 *** | −0.0730 |
(−2.71) | (−1.62) | |
BInd | −0.087 | 0.0130 |
(−0.44) | (0.06) | |
Top1 | −0.165 ** | −0.245 *** |
(−2.23) | (−2.64) | |
Salary | 0.038 ** | 0.058 *** |
(2.04) | (2.60) | |
Cons | −8.651 *** | −10.352 *** |
(−26.78) | (−28.52) | |
Ind | YES | YES |
Year | YES | YES |
N | 8126 | 6124 |
0.299 | 0.396 |
Variables | (1) | (2) | (3) | (4) |
---|---|---|---|---|
GIx (2018–2019) | GIy (2021–2022) | |||
DT | 0.185 *** | 0.123 *** | 0.154 *** | 0.100 *** |
(14.13) | (10.71) | (14.44) | (10.86) | |
Size | 0.435 *** | 0.432 *** | ||
(27.35) | (34.55) | |||
Lev | 0.447 *** | 0.501 *** | ||
(4.82) | (6.81) | |||
ROA | 0.337 | 0.121 | ||
(1.63) | (0.58) | |||
Growth | 0.0170 | −0.089 *** | ||
(0.43) | (−3.23) | |||
OCF | −0.576 ** | −0.0680 | ||
(−2.42) | (−0.37) | |||
Age | −0.109 ** | −0.0500 | ||
(−2.09) | (−1.30) | |||
BInd | −0.229 | −0.260 | ||
(−0.88) | (−1.27) | |||
Top1 | −0.285 *** | 0.0150 | ||
(−2.70) | (0.18) | |||
Salary | 0.048 * | 0.000 | ||
(1.88) | (−0.01) | |||
Cons | 0.0120 | −9.857 *** | 0.173 *** | −9.223 *** |
(0.20) | (−24.01) | (3.54) | (−28.77) | |
Ind | YES | YES | YES | YES |
Year | YES | YES | YES | YES |
N | 4434 | 4434 | 6224 | 6224 |
0.110 | 0.344 | 0.115 | 0.372 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Yang, J.; Shan, H.; Xian, P.; Xu, X.; Li, N. Impact of Digital Transformation on Green Innovation in Manufacturing under Dual Carbon Targets. Sustainability 2024, 16, 7652. https://doi.org/10.3390/su16177652
Yang J, Shan H, Xian P, Xu X, Li N. Impact of Digital Transformation on Green Innovation in Manufacturing under Dual Carbon Targets. Sustainability. 2024; 16(17):7652. https://doi.org/10.3390/su16177652
Chicago/Turabian StyleYang, Jianliang, Hongbo Shan, Penglong Xian, Xiaomeng Xu, and Na Li. 2024. "Impact of Digital Transformation on Green Innovation in Manufacturing under Dual Carbon Targets" Sustainability 16, no. 17: 7652. https://doi.org/10.3390/su16177652