Yan Xue

Urban inclusive green growth (UIGG) refers to the synergetic enhancement of the economy, the environment, and the society in a city. Achieving such enhancement requires addressing a series of problems in the development of urbanization, such as unemployment, lack of access to education, insufficient medical resources, inequity, and environmental pollution. As firms are critical to city development and urbanization, whether they practice corporate social responsibility (CSR) plays a crucial part in UIGG. In this study, we focus on Chinese cities as examples of UIGG and Chinese listed companies as CSR engagers, employing matched data on Chinese listed companies and cities to investigate how CSR affects UIGG. Specifically, the empirical results of a high-dimensional fixed effect model indicate that CSR significantly promotes UIGG. This finding remains valid after a set of robustness checks, including instrumental variable (IV) regression. CSR can promote UIGG by positively influencing enterprises’ economic performance, innovation, and employment. To promote firms’ substantive CSR actions, the government of Jiangsu Province, China, launched a pilot policy for CSR promotion by issuing an official guidance document, which can be considered a quasi-natural experiment to test the causality between CSR and UIGG. We find that the CSR pilot significantly promoted UIGG. The heterogeneity test results indicate that the influence of CSR on UIGG varies based on the audit company, the nature of the enterprise, and the structure of corporate leadership.

“Carbon neutrality, carbon peaking” is China’s national commitment to the whole world about its plans to manage global climate change. China faces many severe challenges in fulfilling its commitments to reduce emissions. China’s digital economy is currently booming, and whether it can provide opportunities for reducing regional carbon emissions is worth exploring. This study constructed a comprehensive system to evaluate the development of its digital economy based on China’s regional data and empirically tested the direct, indirect, and spatial effects of the comprehensive development of digital economy on regional carbon emissions. In addition, it examined the special stage characteristics using a Hansen threshold model. This study found the following: first, the digital economy significantly suppresses carbon emissions in general, notably with a spatial spillover effect to neighboring provinces. Secondly, an analysis of the mechanism shows that the comprehensive development of a digital economy can restrain regional carbon emissions through industrial progress and the optimization of energy consumption. Third, there are double thresholds, special driving trends and an “inverted N-type” relationship with development. Fourth, a spatial heterogeneity analysis revealed that significant “local” and “neighboring” impacts on the reduction of carbon emissions only exist in the central and eastern areas. This study has a reference value for releasing the dividend of digital economy development and reducing carbon emissions.

Human resource management risks are due to the failure of employer organization to use relevant human resources reasonably and can result in tangible or intangible waste of human resources and even risks; therefore, constructing a practical early warning model of human resource management risk is extremely important for early risk prediction. The back propagation neural network is an information analysis and processing system formed by using the error back propagation algorithm to simulate the neural function and structure of the human brain, which can handle complex and changeable things that do not have an obvious linear relationship between output results and input factors, so as to find the objective connection between the two. Based on the summary and analysis of previous research works, this article expounded the research status and significance of early warning for human resource management risks, elaborated the development background, current status, and future challenges of the BP neural network, introduced the method and principle of the BP neural network’s connection weight calculation and learning training, performed the risk inducement analysis, index system establishment, and network node selection of human resource management, constructed an early warning model of human resource management risk based on the BP neural network, conducted the risk warning model training and detection based on the BP neural network, and finally carried out a simulation and its result analysis. The study results show that the early warning model of human resource management risk based on the BP network is effective, and this trained and tested BP network risk warning model can be used to conduct early warning empirical research on human resource risks to prevent human resource risks, ensure enterprise’s benign operation, and at the same time play a role in supervision and promotion of market order rectification.

© Cambridge University Press, 2011.The Alpert–Stein Factor Separation Methodology method has been utilized in the study of the biophysical response to changes in the environment to assess the relative contribution of different atmospheric factors to the biological system. In this chapter we will discuss crop simulation and land surface model-based assessments of the sensitivity to past and future changes in climatic conditions: increasing CO 2, soil moisture, temperature and radiative conditions, and crop management procedures. FS is applied to discern specific contributions to plant responses by single variables or combinations of environmental conditions. Our FS analysis has shown that it is important to understand that biological responses are inherently dependent on multiple variables in the natural world and should not be limited to assessments of single specific parameters. Introduction In this chapter we demonstrate how the FS analysis technique is a useful tool for crop–climate change studies. Important interactions between the atmosphere and biophysical processes occur under land surface and atmospheric carbon dioxide level changes. We employ the Alpert–Stein FS Methodology to investigate the direct as well as the interactive effects of soil moisture, temperature, and radiative changes on the direct effects of CO 2 doubling for different land-use/vegetation types, including agricultural production.

The Ultimatum Game (UG) is one of the widely studied games in experimental economics. Past data shows a consistent deviation from the classical game theory prediction, which suggests a self-interested money maximizing rational agent would accept any nonzero offer as a responder. However, in reality, people often reject low offers that are less than 30% and above zero. Research from neuro-economics claims that such behavior is mostly emotion driven. However an important cross-cultural study shows that the results in these small-scaled societies deviate drastically from the industrialized societies. In addition, there is a high correlation between the deviations and the social systems. Economists call for a better understanding of how people apply social rules when they make economic decision. We believe that we need a framework that can show the following: 1. Preferences are constructed on demand. 2. Social rules are numerous and complex, and tend to trigger non-monotonic reasoning. Formally, we developed the Closed-World Reasoning in Games framework (CWRiG) adopted from Closed-World Reasoning framework (CWR) by van Lambalgen and Stenning. We defined what can be considered a monotone game process. More importantly we show how an agent can make a rational decision change through a non-monotonic reasoning process that brings social rules into the game process. Experimentally, we ran a modified ultimatum game. The results confirms that people's general, even ranked, attitudes towards some social matters are not necessarily equivalent to their preferences of the same kind of matter during the game. We also discover many instances of non-monotonic reasoning patterns used during the game