How Does Digital Transformation Improve Corporate Carbon Emission Reduction Performance? An Empirical Study on Data from Listed Companies in China
Abstract
:1. Introduction
- Can digital transformation positively enhance corporate carbon emission reduction performance? Are there heterogeneous results under different conditions?
- How does digital transformation influence corporate carbon emission reduction performance? Is there mediation or moderation?
- If present, what are the mechanisms of mediation or moderation? Are they positive or negative?
2. Literature Review
2.1. Corporate Carbon Emission Reduction Performance
2.2. Digital Transformation
2.3. Green Technology Innovation
3. Theoretical Framework and Research Hypotheses
3.1. Impact of Digital Transformation on Corporate Carbon Emission Reduction Performance
3.2. The Mediating Role of Green Technology Innovation
3.3. Moderating Effect of Tax Reduction Incentives
3.4. Moderating Effect of Environmental Subsidies on Green Technology Innovation
4. Empirical Analysis and Results
4.1. Sample Selection and Data Sources
4.2. Definition of Variables
4.2.1. Carbon Emission Reduction Performance
- (1)
- Identifying and collecting data on fossil energy consumption, electricity consumption, and heat consumption for corporations that do not disclose direct carbon emissions.
- (2)
- Applying the appropriate conversion factors from Table 1 to standardize these different forms of consumption into a common unit, typically carbon emissions equivalent.
- (3)
- Summing up the standardized values to estimate the total carbon emissions for each corporation.
4.2.2. Digital Transformation
4.2.3. Green Technology Innovation (EnvrPat)
4.2.4. Tax Reduction Incentives
4.2.5. Environmental Subsidy (Subsidy)
4.2.6. Control Variables
4.3. Regression Models
4.3.1. Benchmark Modeling
4.3.2. Mediating Effects Model
4.3.3. Moderating Effects Model
4.3.4. Moderated Mediation Effects Model
5. Results
5.1. Descriptive Statistics
5.2. Multicollinearity Test
5.2.1. Correlation Analysis
5.2.2. Variance Inflation Factor Analysis
5.3. Regression Analysis
- (1)
- The F-test suggests that the fixed effects (FE) model is superior to the mixed cross-sectional effects model, with a significant probability value (Prob > F = 0.000).
- (2)
- The LM test indicates that the random effects (RE) model is preferable to the mixed cross-sectional effects model, also with a significant probability value (Prob > chibar2 = 0.0000).
- (3)
- Hausman’s test shows that the fixed effects model is superior to the random effects model, with a significant probability value (Prob > chi2 = 0.0000).
5.3.1. Baseline Regression
5.3.2. Robustness Test
5.3.3. Mediating Effects
5.3.4. Moderating Effect
5.3.5. Moderated Mediation Effects
6. Further Analysis
6.1. Fixed Effects Panel Quantile Regression
6.2. Tests for Regional Heterogeneity
6.3. Tests for Heterogeneity in Property Rights
6.4. Tests for Heterogeneity in Industry Type
7. Conclusions
7.1. Research Findings
- (1)
- Digital transformation can positively contribute to corporate carbon emission reduction performance improvement and shows heterogeneity when there are differences in the level of carbon emission reduction performance of corporations, the region where corporations are located, the nature of property rights, and the type of industry.
- (2)
- Green technology innovation mediates the relationship between digital transformation and corporate carbon emission reduction performance.
- (3)
- Tax reduction incentives play a positive moderating role in the relationship between digital transformation and corporate carbon emission reduction performance.
- (4)
- Environmental subsidies play a positive moderating role in the relationship between digital transformation and green technology innovation.
7.2. Policy Recommendations
- (1)
- Internally, promoting the research, development, and application of digitization technologies within corporations is crucial for accelerating digital transformation. This can be achieved by establishing a corporate digital resource-sharing platform and increasing subsidies for digital transformation. These measures can lower the information threshold and financial pressure associated with digital transformation, thus expediting the process and enhancing corporations’ performance in reducing carbon emissions.
- (2)
- Externally, it is vital to acknowledge the influence of various factors and implement diverse measures to enhance corporations’ carbon emission reduction performance. Supporting green technology innovation and leveraging tax reduction incentives can be effective. Strengthening existing subsidies for green technology innovation while emphasizing environmental subsidies can enhance corporate carbon emission reduction performance through refined regulatory mechanisms.
- (3)
- Adapting digital transformation policies to suit the diverse landscape of corporations globally is paramount. These policies must account for variables such as carbon emission reduction efficacy, geographical distribution, intellectual property rights, and industry classifications. This necessitates the development of incentive structures tailored to varying degrees and extents. Facilitating collaboration across regions, industries, and enterprises, and enacting a comprehensive strategy for industrial spatial planning, can help alleviate discrepancies in digital transformation efforts. This, in turn, bolsters the efficacy of carbon emission reduction initiatives on a global scale.
7.3. Limitations and Future Research
- (1)
- In terms of research samples and methods, this paper selects Chinese listed companies as research objects, and in the future, it can focus on specific industries (e.g., chemical industry, iron and steel industry, etc.) to carry out empirical evidence or case studies, to obtain more typical and targeted research results.
- (2)
- In terms of internal mechanism, this paper focuses on the mediating role of green technology innovation and the moderating role of tax reduction incentives, and in the future, we can further analyze the internal mechanism of carbon emission reduction performance driven by corporate digital transformation from other perspectives.
- (3)
- In the selection of explanatory and interpreted variables, future research can further change the variables based on this study and conduct more specific research. For example, by considering the impact of individual technologies in digitalization, such as artificial intelligence technology itself, on corporate carbon emission reduction performance, or by considering changing carbon emission reduction performance to other energy-related variables, the impact of digital transformation on fossil fuel prices can be explored.
- (4)
- In the existing research on digital transformation and carbon emission reduction, there has been abundant progress in macro-level research, with scholars mainly focusing on regional economic development, technological-level policy influence, etc. This paper enriches the research at the corporate level, and in the future, we can consider further research at the corporate level by taking into account the above factors at the regional level.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
AI | artificial intelligence |
IPCC | Intergovernmental Panel on Climate Change |
ST | special treatment |
PT | particular transfer |
VIF | variance inflation factor |
LM | Lagrange multiplier |
FE | fixed effects |
RE | random effects |
R&D | research and development |
Nomenclature | |
i | firm |
t | year |
α | the parameter to be estimated in Equation (1) |
γi | the firm fixed effect |
σt | the year fixed effect |
CV | control variable |
ε | the random perturbation term |
β | the parameter to be estimated in Equation (2) |
θ | the parameter to be estimated in Equation (3) |
δ | the parameter to be estimated in Equation (4) |
μ | the parameter to be estimated in Equation (5) |
References
- Guo, Y.; Gou, X.; Xu, Z.; Skare, M. Carbon Pricing Mechanism for the Energy Industry: A Bibliometric Study of Optimal Pricing Policies. Acta Montan. Slovaca 2022, 27, 49–69. [Google Scholar]
- Biglan, A. The Role of Advocacy Organizations in Reducing Negative Externalities. J. Organ. Behav. Manag. 2009, 29, 215–230. [Google Scholar] [CrossRef] [PubMed]
- Zhou, Z.; Li, J.; Zeng, H. Research on the influence of political and economic stakeholders on corporate carbon performance—An empirical analysis based on Chinese listed companies. J. Yunnan Univ. Financ. Econ. 2020, 36, 72–88. [Google Scholar]
- Chen, H.; Dong, Z. Research on factors influencing corporate carbon performance—An analytical perspective based on legitimacy theory. Sci. Technol. Dev. 2020, 16, 284–292. [Google Scholar]
- Wang, P.; Huang, S.; Wang, Y.; Li, W. The impact of carbon emissions trading on corporate carbon performance. Res. Manag. 2023, 44, 158–169. [Google Scholar]
- Zhang, C.-P.; He, T.; Liu, M. Evaluation of carbon performance of paper corporates based on the perspective of the carbon value stream. J. Dalian Univ. Technol. (Soc. Sci. Ed.) 2021, 42, 50–60. [Google Scholar]
- Ghassan, H.M.; Fathia, E.L. Impact of foreign directors on carbon emissions performance and disclosure: Empirical evidence from France. Sustain. Account. Manag. Policy J. 2021, 13, 221–246. [Google Scholar]
- Alsaifi, K. Carbon disclosure and carbon performance: Evidence from the UK’s listed companies. Manag. Sci. Lett. 2021, 11, 117–128. [Google Scholar] [CrossRef]
- Zhao, C.; Wang, W.; Li, X. How digital transformation affects corporate total factor productivity. Financ. Trade Econ. 2021, 42, 114–129. [Google Scholar]
- Qi, Y.; Xiao, X. Corporate management change in the era of digital economy. Manag. World 2020, 36, 135–152+250. [Google Scholar]
- Yi, L.; Wu, F.; Xu, S. Research on the performance-driven effect of corporate digital transformation. Secur. Mark. Her. 2021, 349, 15–25+69. [Google Scholar]
- Guo, F.; Yang, S.; Chai, Z. Does Digital Transformation of Corporates Promote “Increase in Quantity and Improve in Quality” of Green Technology Innovation?—A textual analysis based on annual reports of Chinese-listed companies. South. Econ. 2023, 2, 146–162. [Google Scholar]
- Zhao, C. Research on the Impact of Digital Transformation on Corporate Social Responsibility. Contemp. Econ. Sci. 2022, 44, 109–116. [Google Scholar]
- Hu, J.; Han, Y.; Zhong, Y. How Corporate Digital Transformation Affects Corporate ESG Performance-Evidence from Listed Companies in China. Ind. Econ. Rev. 2023, 54, 105–123. [Google Scholar]
- Xu, C.; Chen, X.; Dai, W. Effects of Digital Transformation on Environmental Governance of Mining Corporates: Evidence from China. Int. J. Environ. Res. Public Health 2022, 19, 16474. [Google Scholar] [CrossRef] [PubMed]
- Zhu, Q.; Liu, Y.; Zhang, W. Party Organization Participation in Governance, Environmental Regulation and Green Technology Innovation-Empirical Evidence from China’s A-share Listed Companies in Heavy Pollution Industries. Corp. Econ. 2023, 42, 68–78. [Google Scholar]
- Zheng, J.; Han, M. Environmental Regulation, Technological Capability, and Green Innovation-Moderating Effects Based on Managers’ Environmental Awareness. Sci. Econ. 2023, 36, 31–35. [Google Scholar]
- Wang, M.; Li, Y.; Wang, Z.; Cao, X.; Shi, W. Research on the impact of corporates’ common technology innovation participation on green technology innovation under the perspective of innovation chain. J. Manag. 2023, 20, 856–866. [Google Scholar]
- Zou, G.; Yuan, Y.; Xu, Q. Environmental costs, financial subsidies and corporate green innovation. China Soft Sci. 2023, 386, 169–180. [Google Scholar]
- Bae, J.; Cho, C. Green technology innovation and economic performance: Evidence from the Korean manufacturing industry. J. Clean. Prod. 2018, 196, 1315–1324. [Google Scholar]
- Wu, L.; Chen, W.; Lin, L.; Feng, X. Research on the impact of innovation and green technology innovation on corporate total factor productivity. Math. Stat. Manag. 2021, 40, 319–333. [Google Scholar]
- Fan, B.; Wang, W. Synergistic effects of environmental protection investment and green technology innovation on financial performance of coal corporates. Chongqing Soc. Sci. 2019, 295, 70–82. [Google Scholar]
- Liu, Z.; Wang, F. Impact of green science and technology innovation and financial investment on industrial structure upgrading—An empirical analysis based on 2003–2019 data of the Yangtze River Economic Belt. Sci. Technol. Prog. Countermeas. 2021, 38, 53–61. [Google Scholar]
- Zhu, W. How Environmental Protection Taxes Affect Corporate Carbon Emission Reduction Performance: Internal Mechanism and Empirical Evidence. Mod. Manag. Sci. 2023, 4, 127–135. [Google Scholar]
- Wang, H.; Liu, J.; Zhang, L. Carbon Emissions and Asset Pricing: Evidence from Chinese Listed Companies. J. Econ. 2022, 9, 28–75. [Google Scholar]
- Yan, H.; Jiang, J.; Wu, Q. Research on the impact of carbon performance on financial performance based on the analysis of property rights nature. Math. Stat. Manag. 2019, 38, 94–104. [Google Scholar]
- Wu, F.; Hu, H.; Lin, H.; Ren, X. Corporate digital transformation and capital market performance-Empirical evidence from stock liquidity. Manag. World 2021, 37, 130–144. [Google Scholar]
- Wang, X.; Wang, Y. Research on green credit policy to enhance green innovation. Manag. World 2021, 37, 173–188+11. [Google Scholar]
- Liu, G. Analysis of incentive effect of tax preference and financial subsidy policy—An empirical study based on the perspective of information asymmetry theory. Manag. World 2016, 8, 62–71. [Google Scholar]
- Cheng, B.; Fang, Y. Environmental Regulation “Combination Punch” and Performance of Environmental Subsidies. Financ. Econ. Mon. 2021, 22, 28–37. [Google Scholar]
- Wen, Z.; Zhang, L.; Hou, J.; Liu, H. The mediation effect test program and its application. J. Psychol. 2004, 5, 614–620. [Google Scholar]
- Zhang, Y.; Li, X.; Xing, M. Corporate digital transformation and audit pricing. Audit. Res. 2021, 3, 62–71. [Google Scholar]
- Shi, Y.; Wang, Y.; Zhang, W. Digital transformation of corporates in China: Status quo, problems and prospects. Econ. 2021, 12, 90–97. [Google Scholar]
- Shen, X.; Chen, Y.; Lin, B. The impact of technological progress and industrial structure distortion on energy intensity in China. Econ. Res. 2021, 56, 157–173. [Google Scholar]
- Zhang, Z.; Ma, Y. Crisis or opportunity: Corporate customer relationship and digital transformation. Econ. Manag. 2022, 44, 67–88. [Google Scholar]
- Zhang, D. How does environmental policy affect firm upgrading in China?—A quasi-natural experiment from the “two control zones” policy. Ind. Econ. Res. 2020, 5, 73–85. [Google Scholar]
- Lu, T.; Dang, Y. Corporate governance and technological innovation: A comparison by industry. Econ. Res. 2014, 49, 115–128. [Google Scholar]
Type of Energy | Standard Coal Conversion Factor (kg Standard Coal/kg) | Carbon Emission Factor (t/t Standard Coal) |
---|---|---|
Raw coal | 0.7143 | 0.7559 |
Coke (processed coal used in blast furnaces) | 0.9714 | 0.8550 |
Crude oil | 1.4286 | 0.5857 |
Diesel | 1.4714 | 0.5538 |
Gasoline | 1.4571 | 0.5714 |
Diesel fuel | 1.4571 | 0.5921 |
Fuel oil | 1.4286 | 0.6185 |
Petroleum | 13.3000 tons of standard coal per 10,000 m3 | 0.4483 |
Digital Dimension | Characteristic Word |
---|---|
Artificial intelligence technology | Artificial intelligence seminar, business intelligence, image understanding, investment decision aids, data analytics only, intelligent robotics, machine learning, deep learning, semantic search, biometrics, face recognition, speech recognition, identity verification, autonomous driving, natural language processing |
Blockchain technology | Blockchain, digital currency, distributed computing, differential privacy technology, smart financial contracts |
Cloud computing technology | Cloud computing, stream computing, graph computing, in-memory computing, multi-party secure computing, brain-like computing, green computing, cognitive computing, convergence frameworks, billion-dollar development, Excess Burst -scale storage, Internet of Things, information-physical systems |
Big data technology | Big data, text mining, data visualization, heterogeneous data, credit, augmented reality, mixed reality, virtual reality |
Digital technology applications | Mobile internet, industrial internet, mobile internet, internet healthcare, E-commerce, mobile payment, third-party payment, near field communication payment, smart energy, business-to-business, business to customer, customer to business, Netflix, smart wearable, intelligent agriculture, intelligent transportation, intelligent healthcare, intelligent customer service, intelligent home, intelligent investment, intelligent literature and tourism, intelligent environmental protection, intelligent grid, intelligent marketing, digital marketing, unmanned retail, Internet finance, digital finance, Fintech, fintech, quantitative finance, open banking |
Variable Type | Variable Name | Variable Codes | Variable Description |
---|---|---|---|
Explained variable | Carbon reduction performance | CP | Ln (corporate revenue/corporate carbon emissions) |
Explanatory variable | Digital transformation | DCG | Natural logarithm of digitized word frequencies |
Mediating variable | Green technology innovation | EnvrPat | Ln (number of green patent applications) |
Moderating variables | Tax reduction incentives | TRI | Tax refunds received/(tax refunds received + taxes paid) |
Environmental subsidies | Subsidy | Amount of government environmental subsidies as a percentage of total corporate assets × 100 | |
Control variables | Corporation size | Size | Natural logarithm of total annual corporation assets |
Debt asset ratio | Lev | Total liabilities to total assets at the end of the period | |
Return on assets | ROA | The ratio of net profit to the average balance of total assets at the end of the period | |
Number of years listed | ListAge | Ln (difference between the current year and the year the firm went public + 1) | |
Return on equity | ROE | The ratio of net profit to the average balance of owners’ equity of the corporation |
Variable | Sample | Average | Standard Deviation | Minimum | Maximum |
---|---|---|---|---|---|
CP | 22,943 | 11.72675 | 1.934288 | 5.887388 | 21.09832 |
DCG | 22,943 | 1.25785 | 1.385933 | 0 | 6.300786 |
EnvrPat | 22,943 | 0.2616111 | 0.688472 | 0 | 7.158514 |
TRI | 22,943 | 0.1444793 | 0.2048263 | 0 | 0.838963 |
Subsidy | 22,943 | 0.0234161 | 0.0925327 | 0 | 0.7047926 |
Size | 22,943 | 21.93576 | 1.531538 | 10.8422 | 31.31009 |
Lev | 22,943 | 0.5056053 | 3.973375 | −0.194698 | 877.2559 |
ROA | 22,943 | 0.0325165 | 0.449198 | −64.81915 | 64.75456 |
ListAge | 22,943 | 1.98381 | 0.9130565 | 0 | 3.496508 |
ROE | 22,943 | 0.0816877 | 6.31867 | −174.8947 | 1389.551 |
CP | DCG | EnvrPat | TRI | Subsidy | Size | Lev | ROA | ListAge | ROE | |
---|---|---|---|---|---|---|---|---|---|---|
CP | 1.0000 | |||||||||
DCG | 0.6032 *** | 1.0000 | ||||||||
EnvrPat | 0.3239 *** | 0.1581 *** | 1.0000 | |||||||
TRI | 0.3237 *** | 0.0887 *** | 0.0919 *** | 1.0000 | ||||||
Subsidy | −0.1574 *** | −0.0890 *** | 0.0164 *** | −0.0081 | 1.0000 | |||||
Size | −0.1270 *** | 0.1362 *** | 0.2302 *** | −0.0482 *** | 0.0012 *** | 1.0000 | ||||
Lev | −0.0493 *** | −0.0233 *** | −0.0032 | −0.0199 *** | 0.0014 | −0.0400 *** | 1.0000 | |||
ROA | 0.0489 *** | −0.0082 * | 0.0090 ** | −0.0106 ** | −0.0122 ** | 0.0191 *** | −0.0770 *** | 1.0000 | ||
ListAge | −0.1953 *** | −0.0317 *** | −0.0233 *** | −0.0787 *** | 0.0343 *** | 0.2959 *** | 0.0298 *** | −0.0297 *** | 1.0000 | |
ROE | −0.0031 | −0.0001 | −0.0001 | −0.0052 | −0.0081 | −0.0026 | −0.0600 | −0.0022 | −0.0800 * | 1.0000 |
ROA | Lev | Size | ListAge | EnvrPat | DCG | TRI | Subsidy | ROE | MEAN | |
---|---|---|---|---|---|---|---|---|---|---|
VIF | 1.53 | 1.52 | 1.29 | 1.20 | 1.15 | 1.06 | 1.04 | 1.01 | 1.00 | 1.00 |
1/VIF | 0.6533 | 0.6596 | 0.7741 | 0.8336 | 0.8729 | 0.9391 | 0.9590 | 0.9869 | 0.0067 | 0.8336 |
Model 1 | Model 2 | Model 3 | Model 4 | |
---|---|---|---|---|
Variable | CP | CP | CP | CP |
DCG | 0.638 (8.43) *** | 0.603 (7.98) *** | 0.356 (3.05) *** | 0.425 (4.57) *** |
Size | 0.505 (5.79) *** | 0.073 (0.51) | 0.329 (3.48) *** | |
Lev | −0.005 (−0.15) | 0.234 (1.18) | −0.014 (−1.16) | |
ROA | 0.653 (3.40) *** | 0.521 (7.32) *** | 0.746 (3.26) *** | |
ListAge | 0.112 (0.63) | 0.106 (0.86) | 0.225 (1.42) | |
ROE | −0.021 (−0.91) | −0.035 (−1.16) | −0.02 (−0.77) | |
L.CP | 0.584 (68.64) *** | |||
Constant | 13.041 *** | 11.091 *** | 10.066 *** | 8.113 *** |
Observation | 22,943 | 22,943 | 22,943 | 22,943 |
R-squared | 0.4053 | 0.5055 | 0.3301 | 0.3399 |
Company FE | Yes | Yes | Yes | Yes |
Year FE | Yes | Yes | Yes | Yes |
F | 80.70 | 67.25 | 56.05 | 63.03 |
Model 1 | Model 2 | Model 3 | |
---|---|---|---|
Variable | CP | EnvrPat | CP |
DCG | 0.603 (7.98) *** | 0.354 (8.25) *** | 0.448 (7.53) *** |
EnvrPat | 0.186 (2.00) ** | ||
Size | 0.505 (5.79) *** | 0.253 (5.27) *** | 0.477 (5.08) *** |
Lev | −0.005 (−0.15) | 0.002 (1.54) | −0.006 (−0.16) |
ROA | 0.653 (3.40) *** | 0.022 (0.52) | 0.833 (3.03) *** |
ListAge | 0.112 (0.63) | 0.167 (1.86) | 0.113 (0.64) |
ROE | −0.021 (−0.91) | 0.009 (0.62) | −0.022 (−0.92) |
Constant | 11.091 *** | 1.867 *** | 11.902 *** |
Observation | 22,943 | 22,943 | 22,943 |
R-squared | 0.5055 | 0.2119 | 0.5124 |
Company FE | Yes | Yes | Yes |
Year FE | Yes | Yes | Yes |
F | 67.25 | 14.47 | 68.79 |
Model 1 | Model 2 | |
---|---|---|
Variable | CP | CP |
c_DCG | 0.603 (7.98) *** | 0.457 (5.75) *** |
c_TRI | 0.362 (3.15) *** | |
inter | 0.284 (2.97) *** | |
Size | 0.505 (5.79) *** | 0.463 (4.72) *** |
Lev | −0.005 (−0.15) | −0.014 (−0.30) |
ROA | 0.653 (3.40) *** | 0.070 (2.53) ** |
ListAge | 0.112 (0.63) | 0.141 (0.79) |
ROE | −0.021 (−0.91) | −0.002 (−0.86) |
Constant | 11.091 *** | 10.112 *** |
Observation | 22,943 | 22,943 |
R-squared | 0.5055 | 0.3526 |
Company FE | Yes | Yes |
Year FE | Yes | Yes |
F | 67.25 | 65.87 |
Model 1 | Model 2 | |
---|---|---|
Variable | EnvrPat | EnvrPat |
c_DCG | 0.354 (8.25) *** | 0.310 (5.73) *** |
c_Subsidy | 0.312 (0.65) ** | |
inter | 0.411 (1.12) ** | |
Size | 0.253 (5.27) *** | 0.245 (4.84) *** |
Lev | 0.002 (1.54) | 0.018 (1.04) |
ROA | 0.022 (0.52) | 0.012 (0.11) |
ListAge | 0.167 (1.86) | 0.290 (2.77) *** |
ROE | 0.009 (0.62) | 0.002 (0.10) |
Constant | 1.867 *** | −0.382 *** |
Observation | 22,943 | 22,943 |
R-squared | 0.2119 | 0.2510 |
Company FE | Yes | Yes |
Year FE | Yes | Yes |
F | 14.47 | 15.77 |
Variable | P25 | P50 | P75 |
---|---|---|---|
DCG | 0.825 (47.69) *** | 0.672 (63.17) *** | 0.523 (46.06) *** |
Size | −0.356 (−21.63) *** | −0.266 (−27.18) *** | −0.215 (−18.78) *** |
Lev | −0.017 (−0.68) | −0.009 (−0.60) | −0.004 (−0.26) |
ROA | 0.303 (2.44) ** | 0.319 (4.31) *** | 0.327 (3.74) *** |
ListAge | −0.417 (−17.28) *** | −0.297 (−20.70) *** | −0.229 (−13.60) *** |
ROE | −0.004 (−0.33) | −0.004 (−0.63) | −0.005 (−0.57) |
Observation | 22,943 | 22,943 | 22.943 |
Company FE | Yes | Yes | Yes |
Year FE | Yes | Yes | Yes |
West | Central | East | |
---|---|---|---|
Variable | CP | CP | CP |
DCG | 0.564 (3.25) *** | 0.926 (5.28) *** | 0.324 (4.28) *** |
Size | 0.498 (3.48) ** | 0.598 (6.60) *** | 0.496 (5.10) *** |
Lev | −0.019 (−0.32) | 0.357 (1.22) | −0.051 (−0.26) |
ROA | 0.277 (2.74) *** | 0.177 (1.58) | 0.100 (2.57) *** |
ListAge | −0.338 (−0.78) | −0.176 (−0.32) | 0.122 (0.57) |
ROE | −0.042 (−0.54) | −0.010 (−0.31) | −0.005 (−1.24) |
Constant | 10.697 *** | 10.151 *** | 12.015 *** |
Observation | 22,943 | 22,943 | 22,943 |
R-squared | 0.4869 | 0.4521 | 0.4605 |
Company FE | Yes | Yes | Yes |
Year FE | Yes | Yes | Yes |
F | 86.55 | 63.45 | 59.63 |
State-Owned | Non-State-Owned | |
---|---|---|
Variable | CP | CP |
DCG | 0.742 (6.84) *** | 0.246 (3.05) *** |
Size | 0.148 (0.89) | 0.787 (6.35) *** |
Lev | −0.478 (−0.78) | −0.010 (−0.29) |
ROA | 0.404 (3.78) *** | 0.748 (2.48) ** |
ListAge | 0.040 (0.09) | −0.441(−2.04) *** |
ROE | 0.144 (1.84) * | −0.023 (−0.98) |
Constant | 12.100 *** | 10.863 *** |
Observation | 22,943 | 22,943 |
R-squared | 0.5067 | 0.4893 |
Company FE | Yes | Yes |
Year FE | Yes | Yes |
F | 85.56 | 53.62 |
Labor-Intensive | Technology-Intensive | Capital-Intensive | |
---|---|---|---|
A. Agriculture, forestry, animal husbandry, and fisheries. | E Construction. | C3 Paper and printing. | C5 Electronics. |
B Extractive industries. | F Transportation and warehousing. | C4 Petroleum, chemical, and plastic. | C7 Machinery, equipment, and instrumentation. |
C0 Food and beverages. | H Wholesale and retail trade. | C6 Metals and non-metals. | C8 Pharmaceuticals and biologics. |
C1 Textiles, clothing, hides, and skins. | Communication and Cultural Industries. | J Real estate. | C9 Other manufacturing. |
C2 Wood and furniture. | M General. | K Social services. | G Information technology industry. |
D Electricity, gas, and water production and supply industries. |
Labor-Intensive | Technology-Intensive | Asset-Intensive | |
---|---|---|---|
Variable | CP | CP | CP |
DCG | 0.724 (7.68) *** | −0.062 (−0.74) | 0.136 (2.12) ** |
Size | 0.547 (3.95) *** | 0.406 (3.28) *** | 0.192 (2.08) ** |
Lev | −0.208 (−0.74) | 0.073 (0.42) | 0.999 (7.21) *** |
ROA | 0.595 (8.76) *** | 0.713 (2.06) | 0.434 (8.27) *** |
ListAge | 0.160 (0.65) | −0.066 (−3.13) *** | −0.247 (−1.24) |
ROE | −0.001 (−0.09) | −0.080 (−2.37) ** | 0.111 (2.84) *** |
Constant | 9.876 *** | 11.292 *** | 8.275 *** |
Observation | 20,945 | 20,945 | 20,945 |
R-squared | 0.2331 | 0.2756 | 0.2589 |
Company FE | Yes | Yes | Yes |
Year FE | Yes | Yes | Yes |
F | 61.62 | 21.56 | 60.24 |
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Guo, L.; Tang, M. How Does Digital Transformation Improve Corporate Carbon Emission Reduction Performance? An Empirical Study on Data from Listed Companies in China. Sustainability 2024, 16, 3499. https://doi.org/10.3390/su16083499
Guo L, Tang M. How Does Digital Transformation Improve Corporate Carbon Emission Reduction Performance? An Empirical Study on Data from Listed Companies in China. Sustainability. 2024; 16(8):3499. https://doi.org/10.3390/su16083499
Chicago/Turabian StyleGuo, Li, and Min Tang. 2024. "How Does Digital Transformation Improve Corporate Carbon Emission Reduction Performance? An Empirical Study on Data from Listed Companies in China" Sustainability 16, no. 8: 3499. https://doi.org/10.3390/su16083499