To achieve China’s “dual carbon” and common prosperity goals, corporate green governance is crucial. A key tool for promoting green growth is environmental legislation, particularly market-based regulation. With China’s carbon emission trading as a natural experiment, we adopt the DID method to quantitatively compare the gap between ESG performance of pilot and non-pilot carbon trading enterprises before and after policy implementation, thereby examining the impact, mechanism and optimization conditions of market-based environmental policy on corporate green governance based on panel data of China’s A-share listed companies from 2007 to 2019. In addition, PSM-DID and other methods are employed for preventing estimation bias caused by sample self-selection bias. It is found that: (1) the green governance level of pilot firms can be considerably improved by a carbon emission trading scheme (ETS); (2) the ETS primarily encourages enterprises to uphold their ESG obligations through increasing regulatory pressure from the government and corporate involvement in clean innovation; (3) enhancing regional marketization can strengthen the impact of carbon trading policy, and enterprises that are large and non-state-owned exhibit better performance with regard to green governance as a result of carbon trading policy. This paper provides practical experience for promoting corporate green governance to achieve the “dual carbon” goal based on a market mechanism from a micro perspective.

The grey model, which is abbreviated as GM (1, 1), has been widely applied in the fields of decision and prediction, particularly in the prediction of time series with few observations, referred to as the poor information and small sample in the literature related to grey model. Previous studies focus on improving the accuracy of prediction but pay less attention to the robustness of the grey model to outliers, which often occur in practice due to an incorrect record by chance or an accidental failure in equipment. To fill that void, we develop a robust grey model, whose structural parameters are obtained from the least trim squares, to forecast Chinese electricity demand. Also, we use the last value in the first-order accumulative generating time series as the initial value, according to the new information priority criterion. We name the novel grey model, proposed in this paper, the novel robust grey model integrating the new information priority criterion, which could be abbreviated as NIPC-GM (1, 1). In addition, we introduce a novel approach, that is, the bootstrapping test, to investigate the robustness against outliers for the novel robust grey model and the classical grey model, respectively. Using the data on Chinese electricity demand from 2011 to 2021, we find that not only does the novel robust grey model integrating the new information priority criterion have a better robustness to outliers than the classical grey model, but it also has a higher accuracy of prediction than the classical grey model. Finally, we apply the novel robust grey model integrating the new information priority criterion to forecasting the future values in Chinese electricity demand during the period 2022 to 2025. We see that Chinese electricity demand would continue to rise in the next four years.

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Previous studies paid attention to improving the predicted capability of the classical grey model, but its robustness is still unclear and not explored, in particular when these exhibit outliers in the time series, which is due to measurement error, uncorrected record, and censored date. In this study, we proposed a novel robust grey model. The novel robust grey model adopts the median regression method to address these problems caused by outliers, which provides the robust parameters. The analytical expression for the time response function and the forecasting values is derived by the grey system technique and mathematical tool. With annual observational data of Chinese electricity demand, we examine the fitness capability of the novel robust grey model, by comparing it with the classical grey model. Also, we adopt the bootstrapping test to further illustrate the sensitivity for the new robust grey model when there are outliers in the time series. To our knowledge, it is the first to introduce the bootstrapping test to the literature related to the grey model and to focus on the robustness of the grey model. The computational results suggest that the new robust grey model has higher precision than the classical grey model, but it is also very robust to outliers, whose accuracy and robustness are better than the classical grey model. Finally, we apply the novel grey model to forecast the future values in Chinese electricity demand during the year 2022 to 2025. This new model proposed in this study estimates that the Chinese electricity demand would continue to increase after the year of 2022, arriving at 10.446×105 million kW·h in the year of 2025 approximately.