RESUMO
As China's economy advances into a new stage of high-quality development driven by scientific and technological innovation, it is of great practical importance to probe what effects land disposition, which underpinned the previous round of rapid economic growth, and may have an exertion on developing innovation. Based on a deep exploration of the potential positive and negative influences of land disposition in relation to the effects of land finance on urban innovation, we employed a dynamic spatial Durbin model, along with panel data from 266 Chinese prefecture-level cities over the period 2004-2017. The empirical results show that the development of China's urban innovation has had significant path dependence, spatial agglomeration, and inhibiting effects on neighboring cities, and these effects are attributed to inter-governmental competition and the Matthew effect. Overall, the combined impacts of land disposition modes on urban innovation have changed, from facilitative in the early stage to inhibitory at present. In the developed cities of east China, the facilitative effect of land disposition has weakened gradually, and tends to disappear entirely, while the change in impact over time in less developed mid-western cities is consistent with the national sample. This study broadens our understanding of the role of land disposition in China's urban innovative development and has meaningful direct implications for policymakers.
Assuntos
Desenvolvimento Econômico , Invenções , China , CidadesRESUMO
Enhancing urban vitality is a key goal for both the government and ordinary urban residents, and creating this vitality is emphasized in China's urban development strategy. Enhancing urban vitality through the rational design of urban forms is a leading topic of Western urban research. An urban growth pattern (UGP) reflects the dual characteristics of a static pattern and the dynamic evolution of the external urban form. It affects urban vitality by influencing the spatial allocation of internal structural elements and patterns in the adjacent location. The cellular automata (CA) mode can effectively simulate the aggregation process of urban growth (infilling expansion or edge expansion). However, it does not simulate the diffusion of urban growth, specifically the evolution of outlying expansion. In addition, CA focuses on learning, simulating, and building knowledge about geographic processes, but does not spatially optimize collaborative land use against multiple objectives or model multi-scale land use. As such, this paper applies a coupling model called the "promoting urban vitality model," based on cellular automata (CA) and genetic algorithm (GA) (abbreviated as UV-CAGA). UV-CAGA optimally allocates cells with different UGPs, creating a city form that promotes urban vitality. Wuhan, the largest city in Central China, was selected as a case study to simulate and optimize its urban morphology for 2025. The main findings were as follows. (1) The urban vitality of the optimized urban form scheme was 4.8% higher than the simulated natural expansion scheme. (2) Compared to 2015, after optimization, the simulated sizes of the newly increased outlying, edge, and infilling areas in 2025 were 6.51 km2, 102.69 km2, and 23.48 km2, respectively; these increases accounted for 4.90%, 77.32%, and 17.68%, respectively, of the newly increased construction land area. This indicated that Wuhan is expected to have a very compact urban form. (3) The infilling expansion type resulted in the highest average urban vitality level (0.215); the edge expansion type had the second highest level (0.206); outlying growth achieved the lowest vitality level (0.199). The UV-CAGA model proposed in this paper improves on existing geographical process simulation and spatial optimization models. The study successfully couples the "bottom-up" CA model and "top-down" genetic algorithm to generate dynamic urban form optimization simulations. This significantly improves upon traditional CA models, which do not simulate the "diffusion" process. At the same time, the spatial optimization framework of the genetic algorithm in the model also provides insights related to other effects related to urban form optimization, such as urban environmental security, commuting, and air pollution. The integration of related research is expected to enrich and improve urban planning tools and improve the topic's scientific foundation.