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Against the background of the enormous economic transition China is undertaking, government intervention over corporate behavior is a frequent and, arguably, necessary measure. Among the most serious problems facing China, economically and reputationally, are environmental issues. So, how is the government intervening in the environmental performance of Chinese enterprises? And how are Chinese enterprises responding? These are the questions to be answered in this study.

This paper sampled listed companies on China’s Shanghai and Shenzhen Stock Exchanges. The data were collected from the HeXun corporate social responsibility report, CSMAR and WIND databases. A Tobit model was used to conduct the main 2SLS regression analysis, and the robustness tests followed the propensity score matching method.

The analysis shows that environmental performance is positively related to the government subsidies a company receives. The “Eight-point Regulation of the Centre” crack-down on social corruption introduced in 2012 has weakened rent-seeking overall, but rent-seeking behavior through the cloak of corporate environmental performance has become more serious. As a result, non-polluting and state-owned enterprises are significantly less concerned about their environmental performance, while polluting and private enterprises are more motivated to become good environmental citizens.

This research provides a greater understanding of the drivers behind environmentally-responsible behavior in Chinese companies. These insights can be used by policymakers and environmental regulators to incentivize a more widespread ground-swell of change across the gamut of Chinese business.

Environmental policy and practice informed by research-driven recommendations can not only make valuable contributions to the health and well-being of Chinese society but also, as a significant contributor to climate change, environmental reforms have global benefits.

This study explores the motivations behind rent-seeking associated with environmental investment. The findings expand the research horizon of relevant literature on corporate political rent-seeking and deepen the understandings of the economic consequences of corporate investment into environmental practice. The results provide empirical evidence for the Chinese government to implement environmental regulations based on incentives beyond simple profit-making.

This study aims to examine the perception of military people regarding the use of a network of various intelligent objects, the Internet of Things (IoT), in the future battlespace.

A research model that analyzes military acceptance of the IoT is proposed. The model is created by integrating the technology acceptance model and diffusion of innovation theory. Then an empirical study is conducted through a survey, and the proposed hypotheses are tested. The findings are obtained thanks to the structural equation model (SEM), which clearly reveals the overall dependency relationship among independent and dependent variables even when a dependent variable is an independent variable in another relationship.

The study clearly reveals the significant factors effecting the attitude toward the use of IoT in the military. It also uncovers potential barriers to the adoption of IoT in the military domain. Especially, risk factor seems to have no significant impact on the acceptance of IoT, and also, there seems to be a positive relationship between risk and trust contrary to an expected negative relationship.

To the best of the authors’ knowledge, this study is the first research analyzing the acceptance of IoT in the military domain through hypotheses in a SEM.

This study aims to reveal consumers' internal and external motivations to prefer online food shopping. The paper proposes an integrated model including aspirations, capabilities, subjective norms (divided into online resources and offline resources), perceived value and traditionalism to examine their effects on consumers' intention to do online food shopping.

A cross–sectional design was used to understand which factors affect consumers' intention to do online food shopping. The data were collected from a total of 400 people via an online survey. The conceptual model was tested using structural equational modeling to understand the relationships between the factors.

The results suggest that the conceptual framework can be used to have a better understanding of consumers' internal and external motivations to do online food shopping. The study proves that aspirations have a considerable direct effect on and a mediating role between capabilities, subjective norms from online resources, traditionalism and the effect of COVID-19 pandemics and the intention. Also, traditionalism was found to be an antecedent for consumers to prefer online food shopping.

This study reveals better insights for the sellers, marketers and system providers dealing with supplying food products through online channels. The findings suggest that the stakeholders take into consideration aspirations, capabilities, subjective norms, perceived value and traditionalism to organize their activities in food marketing in the online area.

In this study, aspirations–capabilities framework was adopted and confirmed within consumers' online food shopping domain. Also, it was proved that traditionalism was a driver of individuals' intention to do online shopping for food products.

This paper presents a comprehensive pallet-picking approach for forklift robots, comprising a pallet identification and localization algorithm (PILA) to detect and locate the pallet and a vehicle alignment algorithm (VAA) to align the vehicle fork arms with the targeted pallet.

Opposing vision-based methods or point cloud data strategies, we utilize a low-cost RGB-D camera, and thus PILA exploits both RGB and depth data to quickly and precisely recognize and localize the pallet. The developed method guarantees a high identification rate from RGB images and more precise 3D localization information than a depth camera. Additionally, a deep neural network (DNN) method is applied to detect and locate the pallet in the RGB images. Specifically, the point cloud data is correlated with the labeled region of interest (RoI) in the RGB images, and the pallet's front-face plane is extracted from the point cloud. Furthermore, PILA introduces a universal geometrical rule to identify the pallet's center as a “T-shape” without depending on specific pallet types. Finally, VAA is proposed to implement the vehicle approaching and pallet picking operations as a “proof-of-concept” to test PILA’s performance.

Experimentally, the orientation angle and centric location of the two kinds of pallets are investigated without any artificial marking. The results show that the pallet could be located with a three-dimensional localization accuracy of 1 cm and an angle resolution of 0.4 degrees at a distance of 3 m with the vehicle control algorithm.

PILA’s performance is limited by the current depth camera’s range (< = 3 m), and this is expected to be improved by using a better depth measurement device in the future.

The results demonstrate that the pallets can be located with an accuracy of 1cm along the x, y, and z directions and affording an angular resolution of 0.4 degrees at a distance of 3m in 700ms.

This study aims to enhance the resilience of foamed concrete (FC) against carbonation and water absorption (WA) by integrating microorganisms, specifically Aspergillus iizukae EAN605.

The focus was on understanding how variables such as microorganism concentration, concrete density and water-to-cement (w/c) ratio affect these properties. Optimal results were observed under specific conditions—FC density set at 1800 kg/m³, a w/c ratio of 0.5 and an Aspergillus iizukae EAN605 concentration of 0.5 g/L—resulting in significant reductions in carbonation and WA compared to standard FC.

It is observed that fungi not only fill pores with calcium oxalate but also limit carbonation by consuming CO₂ and block water penetration through their mycelial network. A central composite design within response surface methodology was employed for the experimental design, resulting in mathematical models that align closely with the empirical data. The models identified the most effective parameters for minimizing carbonation depth: FC density at 1970 kg/m³, fungal concentration at 0.585 g/L and w/c ratio at 0.470. Further regression analysis showed a high correlation between the experimental data and the predicted outcomes, with a coefficient of determination (R²) of 92.29 and a model F-value of 16.45.

Statistical analysis highlighted the significant roles of density and fungal concentration in these reductions. Besides, scanning electron microscopy provided visual evidence of fungal-mediated mineral formation in FC, supporting the empirical findings. Overall, the study demonstrated the effective use of Aspergillus iizukae EAN605 in enhancing the durability of FC, marking an innovative stride in sustainable construction materials.