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The main purpose of this study is to investigate the impact of state capital participation (SCP) on the corporate environmental engagement (CEE) of privately controlled listed firms in China.

We use a sample of 20,133 firm-year observations from 2009 to 2021. We use three different measures to proxy corporate environmental engagement and two different measures to proxy for state capital participation. We employ a difference-in-difference regression model to estimate the effect of state capital participation on corporate environmental engagement.

Using a sample of 20,133 firm-year observations from 2009 to 2021, we find that SCP significantly increases corporate expenditure on environmental protection, corporate environmental performance and ESG ratings. Specifically, SCP increases environmental investment capacity and attracts more media coverage, online attention and analysts’ following, which leads to better environmental engagement. Further analyses show that after state shareholders exit privately controlled firms, CEE deteriorates, while private capital injection in state-owned firms has no significant impact on CEE. The positive effect of SCP is stronger in privately controlled firms with local government ownership, a larger number of state shareholders, longer state shareholder holding periods, those without politically connected managers and firms operating in heavy pollution industries. Lastly, we show that minority government ownership reduces firm-level toxic emissions and enhances financial performance.

We enrich the literature on the role of minority state ownership in corporate financial and environmental performance.

We enrich the literature on the role of minority state ownership in corporate financial and environmental performance. In light of the escalating environmental concerns and the growing emphasis on corporate environmental responsibility, this study highlights the beneficial role of minority government ownership in driving environmental performance. By providing resources and attracting external scrutiny, the government, as a minority shareholder, can significantly enhance the environmental engagement of privately controlled firms.

Building a smart city is a necessary path to achieve sustainable urban development. Smart city public–private partnership (PPP) project is a necessary measure to build a smart city. Since there are many participants in smart city PPP projects, there are problems such as uneven distribution of risks; therefore, in order to ensure the normal construction and operation of the project, the reasonable sharing of risks among the participants becomes an urgent problem to be solved. In order to make each participant clearly understand the risk sharing of smart city PPP projects, this paper aims to establish a scientific and practical risk sharing model.

This paper uses the literature review method and the Delphi method to construct a risk index system for smart city PPP projects and then calculates the objective and subjective weights of each risk index through the Entropy Weight (EW) and G1 methods, respectively, and uses the combined assignment method to find the comprehensive weights. Considering the nature of the risk sharing problem, this paper constructs a risk sharing model for smart city PPP projects by initially sharing the risks of smart city PPP projects through Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to determine the independently borne risks and the jointly borne risks and then determines the sharing ratio of the jointly borne risks based on utility theory.

Finally, this paper verifies the applicability and feasibility of the risk-sharing model through empirical analysis, using the smart city of Suzhou Industrial Park as a research case. It is hoped that this study can provide a useful reference for the risk sharing of PPP projects in smart cities.

In this paper, the authors calculate the portfolio assignment by EW-G1 and construct a risk-sharing model by TOPSIS-Utility Theory (UT), which is applied for the first time in the study of risk sharing in smart cities.