Tianli Wang’s research while affiliated with Capital University of Economics and Business and other places

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Publications (3)


Common support process test
Notes: The color blocks from deep to light depict the common range of propensity scores for the control group, matched experimental group, and unmatched experimental group, respectively.
Common support process, balance test(Before)
Common support process, balance test(After)
1.Notes:1. The left image shows the K-density estimation before matching, while the right image shows the K-density estimation after matching. 2.The treat group refers to listed companies that have undergone digital transformation, while the control group refers to listed companies that have not undergone digital transformation. 3.The solid line depicts the distribution of propensity scores in the treatment group, while the dashed line depicts the distribution of propensity scores in the control group.
Parallel trend testing
Notes: 1. The parallel trend test results describe the changes of the coefficients and confidence intervals of the dependent variables in the reduce-form DID model before and after the digital transformation. 2. Take the year before the digital transformation of the company as the benchmark group. 3. The coefficient of the independent variables is not significantly different from 0 before the implementation of the policy but is significantly different from 0 after the implementation of the policy, which means that the parallel trend test has been passed.
Heterogeneity analysis
Notes: 1. The above figure conducts heterogeneity analysis on whether the company is a manufacturing, high-tech, state-owned, heavily polluting, or manufacturing enterprise. 2. On the left side of the vertical dashed line are heterogeneity variables defined as 1, and on the right side are heterogeneity variables defined as 0. 3. Above the horizontal zero baseline means that the digital transformation has a positive impact on this heterogeneous variable, and below it means that the sigital transformation has a negative impact on this heterogeneous variable.

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Unveiling the social and environmental benefits of digital transformation in corporations
  • Article
  • Full-text available

March 2025

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20 Reads

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1 Citation

Biru Cao

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Tianli Wang

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Ang Li

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[...]

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Jinghao Zhu

In the context of the “dual-carbon” goal and the digital economy, exploring the impact of digital transformation on enterprises’ social and environmental responsibility is a key issue for achieving sustainable enterprise development and promoting high-quality economic development. This study empirically examines the impact of digital transformation on enterprises’ social and environmental responsibility and its mechanism. We achieved this through using instrumental variables and DID (Differences-in-Differences)models and selecting the data of Chinese A-share listed enterprises from 2008 to 2021. The study concludes that an enterprise’s digital transformation positively contributes to the fulfilment of corporate social responsibility. However, the digital transformation of the enterprise has a dampening effect on the fulfilment of the enterprise’s environmental responsibility. Additionally, this effect holds after a series of robustness tests. Further investigation shows that financial constraints have a positive moderating effect on enterprise digital transformation and corporate social responsibility(CSR) and a negative moderating effect on enterprise environmental responsibility. In addition, we found that the impact of enterprise digital transformation on CSR and environmental responsibility varies by firm type. The above studies provide valuable practical experiences for enterprises to achieve green and low-carbon development, reduce environmental pollution, and realize high-quality economic development as well as insight for enterprises and policy implementers.

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Green Economic Efficiency Indicator System.
Spatial Durbin Model.
Spatial Durbin Decomposition.
SDM Effect Decomposition Results at Different Periods.
Economic Policy Uncertainty’s Spatial Spillover Impacts on Green Economy Efficiency

January 2024

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67 Reads

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1 Citation

Green economy has emerged as a significant pillar of sustainable development in the current global context, and the unpredictability of economic policy has progressively garnered the interest of businesses and government officials as one of the obstacles they must confront. This paper examines how uncertainty in the green economy impacts the green economy's efficacy. The research concentrates on how changes in a particular region can result in geospatial diffusion (spatial spillover), which is a form of spatial spillover wherein the uncertainty of the green economy can impact its efficacy. This paper examines the policy formulation and current state of the green economy across various regions, as well as the diffusion of economic policy uncertainty across geographical areas and the subsequent impact on the green economy's status quo. There is a negative correlation between economic policy uncertainty and green economy efficacy, according to research. There is a spatial spillover effect between economic policy uncertainty and the efficacy of the green economy. Regional variations exist in the impact of economic policy uncertainty on the efficacy of the green economy. The findings indicate that the implementation of AI has a more substantial beneficial effect on carbon emissions. An increase in carbon dioxide emissions poses a growing challenge for the industrial sector in its pursuit of carbon neutrality.