Socially and environmentally responsible investing is becoming the benchmark in financial markets. Promoting emerging industries’ environmental performance, social responsibility, and corporate governance (ESG) ratings are increasingly becoming the consensus of multinational green financial institutions, investors, and governments. This study employs 3100 panel data from 2014 to 2019 to conduct empirical research on green innovation, ESG indicators, and the financial performance of China’s Growth Enterprise Market (GEM) listed companies. Based on the “causal steps approach”, we adopt the Sobel–goodman and Bootstrap test to explore the partial mediation effect of ESG indicators. Moreover, when testing the interactive effect of endogeneity, instrumental variables combined with two-stage least squares (2SLS) and a general method of moments (GMM) system are applied in the dynamic panel for robustness. Combing with the approach of ESG factors-integrated and ESG factors-embedded regression models, we find that: (1) Green innovation can significantly improve the ESG scores of GEM listed companies. (2) Both green innovation and ESG performance can improve the financial performances of GEM listed companies, and ESG performance plays an indirect mediating role in the promotion of green innovation on financial performance. (3) Both political connection strength and regional innovation capabilities can negatively moderate the promotion of green innovation on financial performance, and moderating the effect of corporate political connections is more significant than the regional innovation. This study expands the research on the effectiveness of ESG indices and green innovation from the view of micro-GEM companies, providing policy enlightenment for the sustainable development of emerging industries. Our findings provide noteworthy implications for regulators, academicians and practitioners interested in exploring green innovation, ESG rating and financial performance. In addition, providing regulators and the board of directors with insights into the company’s and country’s future growth prospects.

A green supply chain economy considering environmental, social, and governance (ESG) factors improves the chances of functional growth through minimal risk factors. The implication of sophisticated technologies such as the Industrial Internet of Things (IIoT) and the blockchain improves the optimization and evaluation of ESG performance. An IIoT-Blockchain-based Supply Chain Economy Evaluation (IB-SCEE) model is introduced to identify and reduce functional growth risk factors. The proposed model uses green blockchain technology to identify distinct transactions’ economic demands and supply distribution. The flaws and demands in the circular economy process are validated using the IIoT forecast systems relying on ESG convenience. The minimal and maximum risks are identified based on economic and distribution outcomes. The present investigation highlights the significance of ongoing ESG-conceptualized research into blockchain-based supply chain economics. Companies who recognize the blockchain’s potential can improve corporate governance, environmental impact, and social good by increasing transparency, traceability, and accountability. A more sustainable and responsible future for global supply chains can be shaped through further research and development in this field, which will make a substantial contribution to the scientific world. This information is individually held in the green blockchain for individual risk factor analysis. The proposed model improves the recommendation and evaluation rate and reduces the risk factors with controlled evaluation time.

In order to solve the problems of inefficient allocation of teaching resources and inaccurate recommendation of learning paths in higher education, this paper proposes a smart education optimization model (SEOM) by combining the improved random forest algorithm (RFA) based on adaptive enhancement mechanism and the Graph Neural Network (GNN) algorithm. The public data and information such as the national higher education intelligent education platform are collected, and SEOM is trained and verified. The results show that SEOM has high accuracy and generalization ability in three different teaching scenes: online mixed teaching, personalized teaching and project-based teaching. The Root Mean Square Error (RMSE) value in cross-validation is between 0.2 and 0.5, and the Mean Absolute Error (MAE) value is between 0.1 and 0.5. SEOM shows strong stability when dealing with multidimensional educational resources and complex teaching modes. The accuracy rate remains at 85-97%, indicating its reliability in personalized learning path recommendation. Further analysis shows that the chi-square freedom ratio is between 1.0 and 2.5, the fitting index and the adjusted fitting index are both above 0.85, and the comparative fitting index is close to 0.95, which shows that SEOM has high accuracy and rationality in capturing the dependence of knowledge points in different teaching modes. The Root Mean Square Residual (RMR) and Root Mean Square Error of Approximation (RMSEA) are both below 0.05, which indicates that SEOM has small residual and strong scene adaptability. In addition, in the abnormal network environment, the resource allocation efficiency of SEOM is above 60%, and the Shapley value is between 0.1 and 0.4, which shows that SEOM can adapt to the change of network environment and the resource allocation effect is still obvious. Generally speaking, SEOM can optimize the allocation of educational resources and recommend learning paths in a complex environment, and effectively improve the intelligence and efficiency of teaching decision-making, especially for university administrators and educational technology developers.

With the development of sustainable theory, environmental and resource issues have become one of the major challenges facing human society. As an important part of social economy, enterprises are also an important source of carbon emissions and environmental pollution. With the growth of the digital economy, digital technology has played an important role in improving economic efficiency. Digital transformation can not only enable enterprises to obtain and allocate resources more efficiently and reasonably, but can also provide a powerful driving force for the healthy development of the environment. This can improve the positive role and impact of enterprises on environmental development. Based on the overview of social carbon neutrality and green sustainable development goals, this paper made an in-depth study of digital transformation to help carbon neutrality and green sustainable development. In order to verify its effect, this paper took a medium-sized enterprise as the object, analyzed the growth of its economic and environmental benefits in the process of digital transformation, and compared it with the traditional development strategy. The empirical results showed that in the perspective of the Metaverse, the highest growth rate of environmental benefits of the enterprise would reach 19.6% every month in 2021. From this data, digital transformation based on the perspective of the Metaverse was more able to help carbon neutrality and green sustainable development.

The last couple of decades have witnessed growing interest in the academic literature in the conciliation of finance and sustainable development. Foreign direct investment (FDI) faces increasing pressure from both host and home country towards adoptinsg sustainable approaches. Such solutions can be green innovation (GI) for climate change, environmental risks, green processes and products that allow tracking the carbon footprint, as well as many other green technologies. Based on the macro-level data of 31 provinces in China from 2003 to 2020, this paper employed policy environment (PE) and marketization level (ML) as moderating variables to further investigate the impact of FDI on GI. Our results show the following: (1) FDI has a significant positive and dynamic evolution feature of diminishing marginal efficiency on GI. (2) The heterogeneity analysis of regional regression shows that FDI significantly increases GI in the eastern and western regions. In contrast, FDI in the central region inhibits GI but not significantly. (3) Both PE and ML can positively moderate the impact of FDI on GI. Furthermore, our empirical results of the robustness test of 2SLS and GMM are highly consistent with the main test. The conclusions of this paper provide policy implications for local governments to fully and effectively utilize foreign capital for green innovation activities.