Communicative roots of complex sociality and cognition

Mammals living in more complex social groups typically have large brains for their body size and many researchers have proposed that the primary driver of the increase in brain size through primate and hominin evolution was the selection pressures associated with sociality. Many mammals, and especially primates, use flexible signals that show a high degree of voluntary control and these signals may play an important role in forming and maintaining social relationships between group members. However, the specific role that cognitive skills play in this complex communication, and how in turn this relates to sociality, is still unclear. The hypothesis for the communicative roots of complex sociality and cognition posits that cognitive demands behind the communication needed to form and maintain bonded social relationships in complex social settings drives the link between brain size and sociality. We review the evidence in support of this hypothesis and why key features of cognitively complex communication such as intentionality and referentiality should be more effective in forming and maintaining bonded relationships as compared with less cognitively complex communication. Exploring the link between cognition, communication and sociality provides insights into how increasing flexibility in communication can facilitate the emergence of social systems characterised by bonded social relationships, such as those found in non‐human primates and humans. To move the field forward and carry out both within‐ and among‐species comparisons, we advocate the use of social network analysis, which provides a novel way to describe and compare social structure. Using this approach can lead to a new, systematic way of examining social and communicative complexity across species, something that is lacking in current comparative studies of social structure.     

A cost–benefit analysis of the environmental taxation policy in China: A frontier analysis‐based environmentally extended input–output optimization method

China's high‐speed economic development and reliance on overconsumption of natural resources have led to serious environmental pollution. Environmental taxation is seen as an effective economic tool to help mitigate air pollution. In order to assess the effects of different scenarios of environmental taxation policies, we propose a frontier‐based environmentally extended input–output optimization model with explicit emission abatement sectors to reflect the inputs and benefits of abatement. Frontier analysis ensures policy scenarios are assessed under the same technical efficiency benchmark, while input–output analysis depicts the wide range of economic transactions among sectors of an economy. Four scenarios are considered in this study, which are increasing specific tax rates of SO₂, NO_x, and soot and dust separately and increasing all three tax rates simultaneously. Our estimation results show that: raising tax rates of SO₂, NO_x, and soot and dust simultaneously would have the highest emission reduction effects, with the SO₂ tax rate making the greatest contribution to emission reduction. Raising the soot and dust tax rate is the most environmentally friendly strategy due to its highest abatement to welfare through avoided health costs. The combination of frontier analysis and input–output analysis provides policy makers a comprehensive and sectoral approach to assess costs and benefits of environmental taxation.