Induced innovation: evidence from China’s secondary industry

China is developing at relatively high speed, not only the regional development speed should be focused upon, but also the environmental impact of economic growth should be paid attention to, especially the level change of carbon dioxide emission. To some degree, quantity of carbon dioxide emission has become one of the most important indexes for measuring quality of a nations economic growth. Thus, this thesis is trying to analyze the driving relations between economic growth and carbon dioxide. Upon STIRPAT model, ridge regression method and elasticity theory are applied to analyze the influencing factors of carbon dioxide quantity such as the population quantity, Chinas urbanization process, per capita GDP, energy density and the percentage of the secondary industry. Correspondingly, based on the different influencing variables to carbon dioxide emission quantity, needy measures are brought out to control and decrease emissions. Feasible suggestions are trying to improve Chinas economic development quality.

Download Full-text

The Spatiotemporal Evolution of Urban Impervious Surface for Chengdu, China

Photogrammetric Engineering & Remote Sensing ◽

10.14358/pers.87.7.491 ◽

2021 ◽

Vol 87 (7) ◽

pp. 491-502

Author(s):

Mujie Li ◽

Zezhong Zheng ◽

Mingcang Zhu ◽

Yue He ◽

Jun Xia ◽

...

Keyword(s):

Neural Networks ◽

Growth Rate ◽

Urban Area ◽

Landsat Imagery ◽

Impervious Surface ◽

Kappa Coefficient ◽

Spatiotemporal Evolution ◽

Urban Impervious Surface ◽

The Past ◽

Secondary Industry

The spatiotemporal evolution of an impervious surface (IS) is significant for urban planning. In this paper, the IS was extracted and its spatiotemporal evolution for the Chengdu urban area was analyzed based on Landsat imagery. Our experimental results indicated that convolutional neural networks achieved the better performance with an overall accuracy of 98.32%, Kappa coefficient of 0.98, and Macro F1 of 98.28%, and the farmland was replaced by IS from 2001 to 2017, and the IS area (ISA) increased by 51.24 km2; that is, the growth rate was up to 13.8% in sixteen years. According to the landscape metrics, the IS expanded and agglomerated into large patches from small fragmented ones. In addition, the gross domestic product change of the secondary industry was similar to the change of ISA between 2001 and 2017. Thus, the spatiotemporal evolution of IS was associated with the economic development of the Chengdu urban area in the past sixteen years.

Download Full-text

Spatial Patterns of Urban Innovation and Their Evolution from Perspectives of Capacity and Structure: Taking Shenzhen as an Example

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi11010007 ◽

2021 ◽

Vol 11 (1) ◽

pp. 7

Author(s):

Erjie Hu ◽

Di Hu ◽

Handong He

Keyword(s):

Spatial Distribution ◽

Spatial Pattern ◽

Distribution Pattern ◽

Spatial Patterns ◽

Spillover Effect ◽

Tertiary Industry ◽

Innovation Capacity ◽

Structure Index ◽

Secondary Industry ◽

Spatial Spillover Effect

Innovation is a key factor for a country’s overall national strength and core competitiveness. The spatial pattern of innovation reflects the regional differences of innovation development, which can provide guidance for the regional allocation of innovation resources. Most studies on the spatial pattern of innovation are at urban and above spatial scale, but studies at urban internal scale are insufficient. The precision and index of the spatial pattern of innovation in the city needs to be improved. This study proposes to divide spatial units based on geographic coordinates of patents, designs the innovation capability and innovation structure index of a spatial unit and their calculation methods, and then reveals the spatial patterns of innovation and their evolutionary characteristics in Shenzhen during 2000–2018. The results show that: (1) The pattern of innovation capacity of secondary industry exhibited a pronounced spatial spillover effect with a positive spatial correlation. The innovation capacity and innovation structure index of the secondary industry evolved in a similar manner; i.e., they gradually extended from the southwest area to the north over time, forming a tree-like distribution pattern with the central part of the southwest area as the “root” and the northwest and northeast areas as the “canopy”. (2) The pattern of innovation capacity of tertiary industry also had a significant spatial spillover effect with a positive spatial correlation. There were differences between the evolutions of innovation capacity and innovation structure index of tertiary industry. Specifically, its innovation capacity presented a triangular spatial distribution pattern with three groups in the central and eastern parts of the southwest area and the south-eastern part of the northwest area as the vertices, while its innovative structure showed a radial spatial distribution pattern with the southwestern part of the southwest area as the source and a gradually sparse distribution toward the northeast. (3) There were differences between the evolution modes of secondary and tertiary industries. Areas with high innovation capacity in the secondary industry tended to be more balanced, while areas with high innovation capacity in the tertiary industry did not necessarily have a balanced innovation structure. Through the method designed in this paper, the spatial pattern of urban innovation can be more precise and comprehensive revealed, and provide useful references for the development of urban innovation.

Download Full-text