Impacts of changing urban land-use structure on sustainable city growth in China: A population-density dynamics perspective

Song, Xianfeng; Feng, Qinglai; Xia, Fangzhou; Li, Xinyi; Scheffran, Jürgen

doi:10.1016/j.habitatint.2020.102296

Cited by 72 publications

(20 citation statements)

References 80 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From the perspective of opportunity cost, if the land resource is used as a green belt, its green economic benefit will be higher than that of idle commercial housing, so it can be concluded that the land marketization will inhibit the development of urban GTFP. Land marketization will inhibit urban GTFP growth whether it is from the perspective of resource mismatch, industrial structure or financial development [7]. Therefore, the first hypothesis of this paper is proposed.…”

Section: Theoretical Analysis and Research Hypothesismentioning

confidence: 99%

“…FAN et al [2] found that different types of urban land marketization level play different roles in urban economic development by the spatial lag model with the data of land transfer in China from 2007 to 2016. Song et al [7] studied 331 cities in China and found that the economic benefits caused by different degrees of land market development are biased, and these effects are significant for promoting sustainable urban growth. Other scholars believe that the development of the land market has a negative impact on urban economy.…”

Section: Literature Review and Contributionmentioning

confidence: 99%

See 1 more Smart Citation

Does the Land Market Have an Impact on Green Total Factor Productivity? A Case Study on China

2021

Land

View full text Add to dashboard Cite

The influence process of the land market on urban green total factor productivity (GTFP) is characterized by complexity and region. Based on the panel data of 271 cities in China from 2004 to 2016, this paper analyzes the impact of the land market on urban GTFP and explores the regulatory effect of the innovation investment level and the infrastructure investment level on the land market. The following conclusions are drawn: First, the land market restrains the improvement of urban GTFP, whether analyzed from the dimension of land transfer price or land transfer scale, and the influence degree varies in different dimensions. Second, there is regional heterogeneity in the inhibition effect of the land market on urban GTFP. Third, the level of innovation investment and the level of infrastructure investment have significantly different regulatory effects on the impact of the land market on urban GTFP; the level of innovation investment aggravates the inhibition effect of urban GTFP by the land market, while the infrastructure investment level weakens this inhibition effect.

show abstract

Section: Theoretical Analysis and Research Hypothesismentioning

confidence: 99%

Section: Literature Review and Contributionmentioning

confidence: 99%

Does the Land Market Have an Impact on Green Total Factor Productivity? A Case Study on China

2021

Land

View full text Add to dashboard Cite

show abstract

“…Urban areas are more attractive to citizens since they usually provide high-quality opportunities in terms of economic development, knowledge transfer, innovation, and social interaction, among other dimensions [2] . But the overcrowding of many cities has made their management more complex and critical [3] . Furthermore, current societies frequently face threats from a wide variety of sources, such as terrorism, climate change, economic crisis, pandemics, among other hazards [4] that make cities increasingly vulnerable, not to mention when some of them happen linked in series, as the recent Covid crisis and its consequences at the economic and social dimensions have demonstrated [5] .…”

Section: Introductionmentioning

confidence: 99%

Towards Digital Transformation of a City Resilience Framework

Canós¹,

Penadés²,

Borges³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

Improving city resilience is among the most challenging strategic goals for city administrators worldwide. To support their work, frameworks providing technical support and methodological guidance have been developed. Such frameworks define resilience improvement processes based on multidimensional resilience models to assess one city's resilience level, plus a collection of policies to increase such level in different dimensions. Although some frameworks include software tools to support the process, their scope is limited to a particular step of the process, and global management is still done manually, hindering agility in the process. In this paper, we present our work towards the digital transformation of a city resilience framework. The use of process technology to specify and enact the process is combined with the application of model-based development techniques to provide interoperability of the different framework tools. Our work is framed within a threeyear project started in mid 2020. Here, we report about the results of the first year of the project. Specifically, we describe the architecture of the solution proposed, as well as the major features of our approach.

show abstract

“…Rapid urbanization brings great economic, cultural, and societal benefits but also challenges to city and community sustainable development (Klopp and Petretta, 2017). Understanding population dynamics, therefore, plays a crucial role in human-centered urban studies, typically for disaster management (Smith et al, 2019), infectious diseases prevention (Tatem et al, 2012), and urban planning (Song et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Evaluating spatial statistical and machine learning models in urban dynamic population mapping

Cheng

Wang

Zhu

et al. 2022

Transactions in Urban Data, Science, and Technology

View full text Add to dashboard Cite

Understanding population dynamics at fine spatiotemporal granularities are valuable to human-centered studies. With the increasing availability of high-frequency human digital footprint data, the past decades have witnessed numerous efforts in mapping populations at fine spatiotemporal scales. However, such research still lacks a unified standard in modeling strategy and auxiliary data selection, especially a systematic comparison between newly developed machine learning techniques and traditional spatial statistical methods under different covariates provisions. Here, we compared two spatial statistical models, the Bayesian space-time model and geographically and temporally weighted regression, with two machine learning techniques, random forest and eXtreme gradient boosting, in a case study of hourly population mapping at 100 m resolution in Beijing. We evaluated the model performance with varied covariates combinations and found that the Bayesian space-time model achieved the best in conjunction with urban function data. Leveraging the optimal model constructed, we mapped dynamic population distribution and concluded human activity patterns on diverse city amenities. This paper emphasizes the importance of spatiotemporal dependency information in fine temporal scale population mapping and the urban function covariates in urban population mapping.

show abstract

Impacts of changing urban land-use structure on sustainable city growth in China: A population-density dynamics perspective

Cited by 72 publications

References 80 publications

Does the Land Market Have an Impact on Green Total Factor Productivity? A Case Study on China

Does the Land Market Have an Impact on Green Total Factor Productivity? A Case Study on China

Towards Digital Transformation of a City Resilience Framework

Evaluating spatial statistical and machine learning models in urban dynamic population mapping

Contact Info

Product

Resources

About