ABSTRACT
China's rapid urbanization has produced problems of excessive resource use and environmental pollution, threatening the country's sustainable development. Previous studies mainly focused on empirical observation of the interactions between urbanization and the eco-environment, mainly using econometric models which lacked detailed explanations of the coupling mechanisms between various elements. No quantitative models have been developed to describe the complex nonlinear relationships between various elements, so our understanding of urbanization and eco-environment coupling is vague, and therefore not conducive to coordinating the relationship between them. Coupling urbanization with the eco-environment allows us to simulate interactions between them and enables us to explore the most suitable scenarios for sustainable development. We designed and developed the Urbanization and Eco-environment Coupler (UEC) using system dynamics to simulate regional urbanization and eco-environment coupling and to compare different sustainable development scenarios. UEC integrates human and natural elements. It includes four urbanization submodels (the economy, society, population and construction land) and five eco-environment submodels (water, arable land, ecology, pollution and energy). UEC can fully represent the nonlinear interactions between these submodels by identifying feedback linkages. This allows us to identify an optimal sustainable regional development pattern. We chose the Beijing-Tianjin-Hebei urban agglomeration as a case study research area and obtained the following results: (1) prioritizing urbanization will accelerate economic growth and increase pollution emissions whereas prioritizing the eco-environment will negatively affect both total population and arable land; (2) when sufficient policy and technical support is directed to a particular area, urbanization may not further degrade the eco-environment; and (3) simulation results for various scenarios show that the key to guaranteeing sustainable development is improving technical and political support rather than further restricting urbanization. The UEC model is a significant aid to improving sustainable regional planning.
Subject(s)
Conservation of Natural Resources , Economic Development , Environmental Pollution/analysis , Sustainable Development , Urbanization , Beijing , China , Models, TheoreticalABSTRACT
Individuals show marked variability in determining to be honest or deceptive in daily life. A large number of studies have investigated the neural substrates of deception; however, the brain networks contributing to the individual differences in deception remain unclear. In this study, we sought to address this issue by employing a machine-learning approach to predict individuals' deceptive propensity based on the topological properties of whole-brain resting-state functional connectivity (RSFC). Participants finished the resting-state functional MRI (fMRI) data acquisition, and then, one week later, participated as proposers in a modified ultimatum game in which they spontaneously chose to be honest or deceptive. A linear relevance vector regression (RVR) model was trained and validated to examine the relationship between topological properties of networks of RSFC and actual deceptive behaviors. The machine-learning model sufficiently decoded individual differences in deception using three brain networks based on RSFC, including the executive controlling network (dorsolateral prefrontal cortex, middle frontal cortex, and orbitofrontal cortex), the social and mentalizing network (the temporal lobe, temporo-parietal junction, and inferior parietal lobule), and the reward network (putamen and thalamus). These networks have been found to form a signaling cognitive framework of deception by coding the mental states of others and the reward or values of deception or honesty, and integrating this information to make a final decision about being deceptive or honest. These findings suggest the potential of using RSFC as a task-independent neural trait for predicting deceptive propensity, and shed light on using machine-learning approaches in deception detection.
