Tertiary industry under the COVID-19 pandemic

2021 ◽  
pp. 118-140
Author(s):  
Xia Jiechang
Keyword(s):  
Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4199
Author(s):  
Jinjin Zhou ◽  
Zenglin Ma ◽  
Taoyuan Wei ◽  
Chang Li

Based on threshold regression models, this paper analyzes the effect of economic growth on energy intensity by using panel data from 21 developed countries from 1996 to 2015. Results show that a 1% increase in GDP per capita can lead to a 0.62–0.78% reduction in energy intensity, implying economic growth can significantly reduce energy intensity. The extent of the reduction in energy intensity varies depending on the economic development stages represented by key influencing factors including energy mix in consumption, urbanization, industrial structure, and technological progress. Specifically, the reduction in energy intensity due to economic growth can be enhanced with relatively more renewable energy consumption and more urban population until a threshold point, where the enhancement disappears. On the other hand, the extent of the energy intensity reduction due to economic growth can be weakened with relatively more tertiary industry activities and more research and development (R&D) investment in an economy until a threshold point, where the weakening cannot continue. However, compared to the early stages represented by the low ends of renewable energy consumption, urban population, tertiary industry activities, and R&D investment, the later stages represented by the high ends of these key factors after a threshold show the weakened effect of economic growth on the decline of energy intensity. Hence, when an economy is well-developed, policy makers are advised to put fewer expectations on the role of economic growth to reduce energy intensity, while pursuing relatively cleaner energy, greater urbanization, more tertiary industry activities, and advanced technologies.


2017 ◽  
Vol 53 (4) ◽  
pp. 9-26
Author(s):  
Hailun Zhang ◽  
Sheng Xu

AbstractThe research measures the driving force of innovation in economic structure transition. In order to change the pattern of economic development, China is implementing a strategy of innovation-driven development. China’s capacity of innovation has been increasing, especially since 2012, and China’s innovations have taken a leap-forward development. Nowadays, innovation has become a main driving force in China’s economic development and hi-tech industries particularly make a great contribution. Although China’s tertiary industry has been dominant and its share in three industrial sectors has been exceeding 50% since 2015, a problem still exists in China’s economy that the proportions of primary and secondary industries are relatively higher compared with developed countries. In this paper we use PLSR model to measure the impact of innovation on China’s economic structure transition. It is found that innovation can expand the tertiary industry through shrinking the proportions of primary and secondary industries, transforming China’s economic structure into a more advanced pattern. Additionally, China is also devoting itself to the “Belt and Road Initiative”, which should be combined with China’s domestic innovation-driven development and realize sustainable development of economy worldwide.


2021 ◽  
Vol 11 (1) ◽  
pp. 7
Author(s):  
Erjie Hu ◽  
Di Hu ◽  
Handong He

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.


2020 ◽  
pp. 0958305X2092159
Author(s):  
Xiongfeng Pan ◽  
Mengna Li ◽  
Chenxi Pu ◽  
Haitao Xu

This study establishes a multi-sector dynamic computable general equilibrium framework that integrates energy intensity module to explore the reverse feedback effect of energy intensity control on industry structure. The results indicate that (1) the tightening effect of energy intensity constrains on the Industrial sector is most significant, followed by the Tertiary Industry, with the least impact on Agriculture; (2) when there is no technological progress in the departments, the change of industrial structure is mainly reflected in the sharp decline in the proportion of Industry and the significant increase in the proportion of Tertiary Industry. When technological progress exists in high energy-consumption departments, the tightening effect of energy intensity constraints on the industrial sector will be reduced; when there is technological progress in all departments, the industrial structure will have a smaller change, and the technology progress can alleviate the tightening effect of the energy intensity target on various sectors; (3) under the constraint of energy intensity, the high energy-consuming industry shifts to the Equipment Manufacturing with low energy-consumption and high-added value. The increasing proportion of Tertiary Industry mainly comes from two industries including Wholesale, Retail, Hoteling and Catering, and Transportation, Storage, and Post.


1989 ◽  
pp. 189-201
Author(s):  
P. N. Harper
Keyword(s):  

2014 ◽  
Vol 962-965 ◽  
pp. 2165-2169
Author(s):  
Feng Yun Wang ◽  
Bing Hua Chen ◽  
Jin Ru Fan

This paper studies the causal relationship between energy consumption and economic growth in Beijing by using econometric method from 1980 to 2012, and then establishes the model between the energy consumption of three industries and economic growth to study the influence of the secondary industry and tertiary industry energy consumption on economic growth in Beijing. Finally, we put forward counter measures and suggestions on sustainable development of Beijing's economy and energy industry.


2014 ◽  
Vol 522-524 ◽  
pp. 117-121
Author(s):  
Li Yun Yang ◽  
Ying He

The tertiary industry in Beijing has won rapid development. In 2010, GDP proportion of the tertiary industry has reached 75%, while the energy consumption of the tertiary industry accounted for 55.4% of the total consumption in Beijing. With the economy development of Beijing, the GDP proportion of the tertiary industry will increase further, thus, the energy saving of the tertiary industry is rather imperative. Based on the corresponding data of economy and energy consumption of Beijing from 2005 to 2010, in this study the system dynamics model was proposed to analyze and predict the energy consumption of the tertiary industry of Beijing in 2015.


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