Conducting research into the multifaceted aspects of environmental regulation and the pivotal role played by digitalization in promoting sustainable innovation represents an invaluable compass guiding the path toward green development. However, it receives little attention in Small and Medium-Sized Enterprises (SMEs). This study examines the impact of environmental regulations on sustainable innovation in SMEs and the moderating role of digitalization. Findings from a panel of 871 SMEs over the period 2012-2020 reveal that command control and public-participative regulations have a U-shaped relationship with green innovation in SMEs, with digital tools accelerating their effects. Market-incentive regulations exhibit an inverted U-shaped effect, with digitalization enhancing the benefits of pursuing green incentives. These insights emphasize the importance of tailored approaches that consider regulatory diversity and the transformative potential of digitalization in promoting green development within SMEs.

The resource and environmental constraints on China’s economic development have become more prominent; thus there is an urgent need for enterprises to achieve green innovation transformation to promote high-quality economic development. We obtained data on 655 on Chinese A-share companies listed on the Shanghai and Shenzhen Stock Exchanges from 2010 to 2020, a total of 7205 samples, and explored the influencing mechanism of environmental regulation on corporate green innovation and the moderating mechanism of CSR disclosure by constructing a nonlinear fixed-effect regression model. The results showed: (1) the overall level of green innovation of listed companies is low, and the relationship between environmental regulation and enterprise green innovation presents the U-shaped characteristic of changing from cost effect to innovation compensation effect; (2) non-state-owned enterprises have less tolerance and more sensitive response to environmental regulation than state-owned enterprises; (3) social responsibility information disclosure has a positive regulatory effect on environmental regulation and enterprise green innovation, and non-state-owned enterprises are more significant. It provides references for the government to adjust the intensity of environmental regulation, and, meanwhile, for enterprises to improve the level of environmental protection and the CSR disclosure, and enhance the green innovation ability of enterprises in emerging market.

The combination and convergence of energy-intensive industries developed by ecological factors based on energy clusters is discussed in this paper. Here, a few models for the prediction of greenhouse effects are used as a single type of modeling. In this model, the solar panel system is included as a measure of the greenhouse effect; Commitment Unit (CU) formulations are changed with flouted logic because solar integrations and other unknown variables are intermittent. In general, the greenhouse model with natural ventilation temperature prediction is incomplete, in which the resulting fluid logical CU problem can be solved with an evolutionary algorithm based on the definition and the theory of quantum calculation. This paper proposes a Fuzzy Model-Based Quantum Greenhouse Evolutionary Ventilation Algorithm (FM-BSQGEVA) which helps to minimize the CU problem. The QGEVA is updated to include a hierarchy-group-oriented scheme to tackle the non-linear nature of the issue and its multifaceted nature. The QGEVA is further developed to support a new binary differential operator and several genetic algorithm operators with a redefined rotational angle look-up. The chances that such operators are used on separate solutions are affected by stating the membership function based on their related fitness. The fitness function is calculated through a combination of the penalty function, objective function and the added fluid function. The models built can be used to regulate and control natural ventilation in greenhouse effects. This finding shows that an energy-intensive industrial cluster’s environmental chain of the industry has improved eco-efficiency.

Based on the 2018 China Family Panel Studies data, this paper uses the OLS model, threshold regression model, and mediation effect model to analyze the influencing mechanism and degree of data capital on farmers’ income. The study found that data capital has a significant positive impact on farmers’ income, which can effectively promote farmers’ income. There is a nonlinear relationship between data capital and farmers’ income, and its marginal return increases with farmers’ human capital increasing. Social network plays a partial intermediary role between data capital and farmers’ income, that is, social network is an important channel for data capital to affect farmers’ income improvement. Based on the above conclusions, some reference suggestions are put forward.

Promoting farmers’ adoption of low-carbon technologies constitutes a vital pathway for achieving agricultural sustainability and a key measure for implementing the national dual-carbon strategy. Drawing upon field survey data from 602 wheat farmers in Heze City, Shandong Province, this study employs an ordered probit model to empirically examine the influence mechanism of data capital on farmers’ low-carbon technology adoption behaviour. The results show that (1) data capital significantly promotes farmers’ adoption of low-carbon technologies; (2) ecological cognition plays a significant mediating role, while economic incentives and human capital serve as positive moderators; (3) heterogeneity analysis indicates that the promoting effect of data capital is more pronounced among large-scale farmers. Based on these findings, recommendations are proposed to consolidate rural digital infrastructure, establish agricultural data service platforms, and enhance farmers’ ecological cognition, thereby fully unleashing the potential of data capital in driving agricultural sustainability.

The journal retracts the article titled “An Empirical Analysis of Green Technology Innovation and Ecological Efficiency Based on a Greenhouse Evolutionary Ventilation Algorithm Fuzzy-Model”