Community public safety is facing great challenges in the context of the COVID-19 pandemic. While helping communities identify crises and prevent risks, and improving community public safety governance, collaborative governance is changing the trends of a complex, uncertain, and ever-changing environment, and helping to drive communities toward higher levels of development and improved community sustainability. The purpose of this paper was to design a model of collaborative governance regime (CGR) that better fits the particular context of COVID-19 to enhance community safety and achieve sustainability of collaborative governance. This paper investigated and empirically analyzed the COVID-19 response in Chinese and Korean communities. It is found that collaborative dynamics can positively contribute to collaborative performance; collaborative dynamics can positively contribute to collaborative actions; collaborative actions can positively contribute to collaborative performance; collaborative actions play a partially mediating role between collaborative dynamics and collaborative performance; and there is heterogeneity in collaborative governance regimes in different system contexts. The paper suggests several insights: collaborative governance can deal with uncertainty and unpredictable turbulence; enhancing the capacity for joint action is more conducive to collaborative performance; and the role of government in collaborative governance is valued. Our study provides data support for validating the operating principles and internal logical relationships of collaborative governance and provides an empirical basis for responding to large-scale public crises in different contexts.

This paper emphasizes the key role of policy in the development of agricultural services outsourcing. In this paper, a theoretical framework is constructed to analyze the role of government policies on agricultural service outsourcing under the assumption of the separability of agricultural production processes. The article constructs a quasi-natural experiment using the China-targeted poverty alleviation program and nationally representative microdata, and the PSM-DID model is chosen to estimate the policy effects. We also discuss regional heterogeneity, aiming to identify the ways in which policy affects agricultural service outsourcing. Based on a comprehensive household-level dataset and econometric analysis, we find that targeted poverty alleviation programs significantly promote the use of agricultural service outsourcing by low-income farmers, and the effects of the policies are more pronounced for the central and western regions. These findings suggest that targeted poverty alleviation programs improve the income of farm households in poor areas and encourage the use of agricultural service outsourcing, which can save agricultural labor, reduce the opportunity cost of agricultural production, and contribute to the sustainable development of the poor.

In the context of continuous improvement in China’s land system, the development of the rural economy is insufficient, and the growth of farmers’ income lacks sustainable momentum. The development of the internet and agricultural socialization services has had a huge impact on farmers’ land-scale management. In particular, the proliferation of internet technology in rural areas could affect farmers’ use of agricultural socialization services and increase farmers’ willingness to operate their land on a large scale. However, there is a lack of empirical evidence on the impact of the internet and agricultural socialization services on farmers’ land-scale management decisions. This study constructs a probit model using the cross-sectional data of the nationally representative CFPS2018, and empirically tests the influencing factors of farmers’ land-scale management decisions and the mechanism of heterogeneity. The research results show that, first, the popularity of the internet significantly promoted farmers’ decisions towards land rented-out, but has no significant impact on land rented-in; second, agricultural socialization services are significantly negatively correlated with farmers’ decisions towards land rented-out, but the internet may moderate this inhibitory effect and has an incentive effect on farmers’ land rented-in; third, the results of heterogeneity analysis show that the impact of the internet and agricultural socialization services on farmers’ land-scale management decisions vary with income levels and regions. Therefore, the policy direction should focus on making “internet + agriculture” and agricultural socialization services benefit all farmers, to more effectively improve the efficiency of rural land use and promote the optimal allocation of rural resources.

Since the twenty‐first century, the Internet of Things technology has developed rapidly, and its impact on China’s economy and society is far‐reaching, and its significance is also far‐reaching. This makes more and more scholars start to pay attention to the related issues of the Internet of Things and economic development. The Internet of Things provides a superior information platform for various economic entities, enabling economic exchanges to transcend the boundaries of time and space. The IoT generates a lot of big data, and it is not easy to get valuable insights from the huge amount of data in various formats, ensuring that sensors are working properly and data is transmitted securely and processed efficiently. In order to study the role of the Internet of Things in promoting economic growth, clarify the status of the Internet of Things in the national economy and explore public management innovation as a change in actual social relations. This paper analyzes the application of the Internet of Things technology in public management. In order to deal with the massive multisource heterogeneous data involved in public management, this paper proposes a resource optimization scheduling algorithm for differentiated distributed storage systems and focuses on the analysis of China’s economic growth in recent years. Through experiments, it is found that from 2014 to 2020, China’s economy will continue to maintain rapid growth, with a GDP of 101,5986 billion yuan. Since 2014, the number and quality of R&D personnel in China have increased significantly. In 2020, due to the resumption of growth in corporate profits in the second half of the year, the cumulative decline in revenue will result in a decline in domestic value‐added tax by 8.9% and corporate income tax by 2.4%.

KSII Transactions on Internet and Information Systems TIIS Paper Details

The enhancement of the carbon trading mechanism signifies a gradual transition in China’s environmental regulatory framework, shifting from a command and control approach to a market-based incentive model. Despite the significance of this shift, existing research has insufficiently explored the impact of market-oriented environmental policies on consumption-based emissions. This study leverages the carbon trading policies implemented in 2013 as a quasi-natural experiment, combined with a precise measurement of urban and rural household carbon emissions (HCE) during 2005–2021. Employing a difference-in-differences method, we evaluate the heterogeneous impact of these policies on urban and rural HCE. The results demonstrate a significantly negative effect of the policies on indirect HCE, a conclusion that remains robust across various placebo and robustness tests. Furthermore, we identify the transmission mechanisms through which carbon trading policies affect the reduction in HCE. The results indicate that the policy has a significant negative impact on indirect HCE, with a notable urban–rural difference. The effect of the policy is −0.829 for urban areas and −0.365 for rural areas, a conclusion that remains robust across various placebo and robustness checks. Additionally, we identified two transmission mechanisms through which carbon trading policies operate: financial deepening and employment effects. Lastly, we found that carbon trading policies can reduce carbon inequality between urban and rural areas by 46.8%.