The future development of new-type urbanization has drawn great attention from both the government and public alike. In this context, the present study had three related research aims. Firstly, it sought to predict th...The future development of new-type urbanization has drawn great attention from both the government and public alike. In this context, the present study had three related research aims. Firstly, it sought to predict the urbanization and population dynamics in China at both national and provincial levels for the period of 2015 to 2030. Secondly, on this basis, it sought to examine the spatial variation of urbanization given the predicted national urbaniza- tion rate of 70.12%. Thirdly, it sought to estimate and evaluate the national and provincial demands of investment in the development of new-type urbanization. The main conclusions from this study were as follows： （1） The population size and urbanization rate will reach 1.445 billion and 70.12%, respectively, from 2015 to 2030. （2） The demographic dividend will vanish when the population pressure reaches its maximum. During this period, there will be 70.16 million urban population born. The suburban population that becomes urbanized will be 316.7 million, and thus the net increase in urban population will reach 386 million. （3） Although the urbanization rate of every Chinese province will increase during 2015-2030, it will do so un- equally, while differences in urbanization quality among provinces will also be substantial. In some provinces, moreover, the urbanization quality is not compatible with their eco-social development. （4）A total of 4,105,380 billion yuan is required to fund new-type urbanization and the investment demand for each province varies greatly; for example, Guangdong prov- ince requires the most funding, amounting to approximately 148 times that required by Tibet, the province in least need of funding. In the final part of this study, policy suggestions con- cerning the investment of the new-type urbanization are put forward and discussed.展开更多
The Sendai Framework for Disaster Risk Reduction 2015–2030 underlines the importance of Science and Technology(S&T) and S&T networks for effective disaster risk reduction(DRR). The knowledge of existing S&...The Sendai Framework for Disaster Risk Reduction 2015–2030 underlines the importance of Science and Technology(S&T) and S&T networks for effective disaster risk reduction(DRR). The knowledge of existing S&T networks and their exact role in DRR,however, is limited. This opinion piece initiates a discussion on the role of S&T networks in the implementation of the Sendai Framework. The article highlights that current practice is oriented towards a narrative that emphasizes the potential of S&T for DRR and stresses a collaborative approach delivered through networks. But a true understanding of whether and how S&T networks can mobilize and enable S&T for DRR is missing. We call for a review of existing S&T networks for DRR and the development of good practice guidelines on S&T networks for DRR. This review should include knowledge on how to overcome common challenges and maximize the benefits, along with a framework for successful evaluation of such networks.This knowledge would provide much needed guidance for existing and emerging networks.展开更多
基金National Natural Science Foundation of China, No.41501137, No.41530634, No.41271186
文摘The future development of new-type urbanization has drawn great attention from both the government and public alike. In this context, the present study had three related research aims. Firstly, it sought to predict the urbanization and population dynamics in China at both national and provincial levels for the period of 2015 to 2030. Secondly, on this basis, it sought to examine the spatial variation of urbanization given the predicted national urbaniza- tion rate of 70.12%. Thirdly, it sought to estimate and evaluate the national and provincial demands of investment in the development of new-type urbanization. The main conclusions from this study were as follows： （1） The population size and urbanization rate will reach 1.445 billion and 70.12%, respectively, from 2015 to 2030. （2） The demographic dividend will vanish when the population pressure reaches its maximum. During this period, there will be 70.16 million urban population born. The suburban population that becomes urbanized will be 316.7 million, and thus the net increase in urban population will reach 386 million. （3） Although the urbanization rate of every Chinese province will increase during 2015-2030, it will do so un- equally, while differences in urbanization quality among provinces will also be substantial. In some provinces, moreover, the urbanization quality is not compatible with their eco-social development. （4）A total of 4,105,380 billion yuan is required to fund new-type urbanization and the investment demand for each province varies greatly; for example, Guangdong prov- ince requires the most funding, amounting to approximately 148 times that required by Tibet, the province in least need of funding. In the final part of this study, policy suggestions con- cerning the investment of the new-type urbanization are put forward and discussed.
文摘The Sendai Framework for Disaster Risk Reduction 2015–2030 underlines the importance of Science and Technology(S&T) and S&T networks for effective disaster risk reduction(DRR). The knowledge of existing S&T networks and their exact role in DRR,however, is limited. This opinion piece initiates a discussion on the role of S&T networks in the implementation of the Sendai Framework. The article highlights that current practice is oriented towards a narrative that emphasizes the potential of S&T for DRR and stresses a collaborative approach delivered through networks. But a true understanding of whether and how S&T networks can mobilize and enable S&T for DRR is missing. We call for a review of existing S&T networks for DRR and the development of good practice guidelines on S&T networks for DRR. This review should include knowledge on how to overcome common challenges and maximize the benefits, along with a framework for successful evaluation of such networks.This knowledge would provide much needed guidance for existing and emerging networks.
