Can Industrial Restructuring Improve Urban Air Quality?—A Quasi-Experiment in Beijing during the COVID-19 Pandemic

The conflict between economic growth and environmental pollution has become a considerable bottleneck to future development throughout the world. The industrial structure may become the possible key factor in resolving the contradiction. Using the daily data of air quality from January to April in 2019 and 2020, we used the DID model to identify the effects of industrial structure on air quality by taking the COVID-19 pandemic as a quasi-experiment. The results show that, first, the impact of profit of the secondary industry on air quality is ten times higher than that of the tertiary industry. Therefore, the secondary industry is the main factor causing air pollution. Second, the effect of the reduction in the secondary industry on the improvement of air quality is better than that of the tertiary industry in Beijing. Therefore, the implementation of Beijing’s non-capital function relief policy is timely and reasonable, and the adjustment of the industrial structure is effective in the improvement of air quality. Third, PM2.5, NO2, and CO are affected by the secondary and tertiary industries, where PM2.5 is affected most seriously by the second industry. Therefore, the transformation from the secondary industry to the tertiary industry can not only solve the problem of unemployment but also relieve the haze. Fourth, the result of O3 is in opposition to other pollutants. The probable reason is that the decrease of PM2.5 would lead to an increase in the O3 concentration. Therefore, it is difficult to reduce O3 concentrationby production limitation and it is urgent to formulate scientific methods to deal with O3 pollution. Fifth, the air quality in the surrounding areas can also influence Beijing. As Hebei is a key area to undertake Beijing’s industry, the deterioration of its air quality would also bring pressure to Beijing’s atmospheric environment. Therefore, in the process of industrial adjustment, the selection of appropriate regions for undertaking industries is very essential, which is worth our further discussion.

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

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.

Social and Economic Value Evaluation of Water Resource Based on Fuzzy Mathematics and Emergy Theory

Water resources is one of the important drivers of socio-economic development, but the value of water resources in society is not clear. In order to accurately describe the impact of water resources on socio-economic value, a socio-economic value evaluation index system for water resources is established. This paper is based on the theory of utility value of water resources. Discussed how to use fuzzy mathematics and benefit sharing coefficient method to calculate the socio-economic value of water resources in different industries. Take the Golog Zang A.P in the source region of the Yellow River as an example. Calculated the socio-economic value of water resources for residential life, irrigation planting, industry, construction and tertiary industry. Finally, analysis results show that the value of comprehensive water resources in the study area is between 9.4-40CNY, tertiary industry highest, lowest value for domestic water. The calculation results provide a reference for the rational and efficient use of regional water resources and the scientific formulation of water resources policies.

The regional differences and influencing factors of tourism development on Hainan Island, China

Exploring the spatial pattern of tourism resources and tourism economy is vital to improve the utilization efficiency of tourism resources and promote sustainable tourism development. This research investigated the quantity and types of tourism resources and analyzed the spatial patterns of tourism resources on Hainan Island from the perspectives of spatial variation and spatial association. The spatial and temporal pattern of the number of tourists and tourism revenue during 2010–2019 were further analyzed. The influencing factors of tourism development were explored based on the geographic detector. The results showed that 10425 tourism resources exist on Hainan Island, and the type of buildings and facilities had the largest number of tourism resources. The geological landscape, astronomical phenomena and meteorological landscapes, buildings and facilities, ruins and remains, tourism commodities, and human activities showed significant spatial agglomeration. Domestic tourism was far more developed than inbound tourism in terms of the number of tourists and tourism revenue. However, the spatial difference of tourism resources and tourism economy was apparent on Hainan Island. Factor analysis showed that the quantity of hotels, the proportion of tertiary industry in the GDP, and the regional population were the most influential factors for the distribution of tourism resources, while the density of the road network, the quantity of hotels, the per capita GDP, the proportion of tertiary industry in GDP, the regional population, and the quantity of tourism resources showed obvious influences on the tourism economy of Hainan Island. Interactions of the factors mainly fell into three types: synergistic increases, single factor weakening, and nonlinear weakening. It is suggested that the local government should fully exploit diversity types of tourism resources on Hainan Island to attract more tourists and improve the tourism revenue; improving the inbound tourism, and to strengthen the construction of road network on Hainan Island.

Forecast of regional water demand based on NSGAII-FORAGM

Regional water demand is an important basic data for regional engineering planning, design and management. Making full use of multi-source data and prior knowledge to quickly and economically obtain high-precision regional water demand is of great significance to the optimal allocation of regional water resources. In order to accurately predict the regional water demand, this study took Yulin City as a research area to predict the water demand of the city from 2017 to 2019. Aiming at the oscillating characteristics of the regional water demand sequence and the over-fitting problem of traditional prediction models, this study proposed the non-dominated sorting genetic algorithm II-fractional order reverse accumulative grey model (NSGAII-FORAGM). The regional water demand oscillation sequence was transformed into a monotonically decreasing non-negative sequence. Based on the transformation sequence, an optimization model was constructed according to the two objective functions of ‘maximum (or minimum) order’ and ‘best fit to historical data’, and the NSGAII method were adopted to solve the model. The three model structures of ‘fractional order’, ‘reverse accumulation’ and ‘obtaining order through multi-objective optimization model ‘ were tested based on the water use sequence of the three sectors (industry, tertiary industry and domestic) in Yulin City, and the performance of the method is compared with NSGAII-IORAGM, NSGAII-FOFAGM and SOGA-FORAGM. The results showed that the average relative error of the model established in this study for the simulation of industry, tertiary industry (The tertiary industry is a technical name for the service sector of the economy, which encompasses a wide range of businesses), and domestic was 15.54%, 11.20%, 9.98% respectively. The average relative error of the model established in this study for the prediction of industry, tertiary industry and domestic was 9.46%, 7.9%, 1.8% respectively. For the simulation of water demand sequences in three sections, the simulation average relative errors of the other three models were not absolutely dominant except for the SOGA-FORAGM model. The average relative predicted error by the model in this study was the smallest (The relative errors of the three sequence predictions for industry, tertiary industry and domestic were lower than the relative errors of the optimal results of the comparison model, which were 0.97%, 0.72% and 4.5%, respectively), indicating that the model had certain applicability for the water demand prediction of various sectors (industry, tertiary industry and domestic) in the region compared with other models, and can improve the accuracy of the prediction results.

The impact of consumption on economic growth in Chongqing based on the decomposition of input output table

This paper investigates the effects of Chongqing’s rural and urban residents and total resident population on economic development based on the residents’ consumption structure and analyses of economic development theories concerned by using the input-output table of Chongqing during 2002–2017 and SDA (Structure Decomposition Analysis) model. The study found that, compared with the previous years, the direct consumption of the primary industry’s unit output to the industrial products has decreased significantly in 2017, while the direct consumption to the tertiary industry has increased significantly; The direct consumption per unit output of the second industry is basically equal to that of the products of the industry, while the direct consumption of the products of the third industry has increased; The direct consumption per unit output value of the tertiary industry is basically equal to that of the primary industry. In the long run, the changes in consumption structure of rural and urban residents and total resident population and the increase in proportion of tertiary industry accelerate the transformation and upgrading of industrial structure. However, the effect of consumption structure on GDP (Gross Domestic Product) varies from year to year. On the whole, the changes of residents’ consumption have a positive effect on GDP (Gross Domestic Product).

Analysis on Transformation and Upgrading Levels of Three Industries in 11 Prefectures of Zhejiang Based on Shift-Share Method

This paper attempts to evaluate the transformation and upgrading (T&U) levels of the three industries in 11 prefectures of Zhejiang Province, China, since 2016. Taking the provincial T&U levels of the three industries as the benchmark, the three industries in each prefecture were analyzed by shift-share method (SSM). The main results are as follows: In terms of primary industry, none of the 11 prefectures had structural advantage (structural shifts < 0), but 3 had regional competitiveness (competitiveness shifts > 0); in terms of secondary industry, none of the 11 prefectures had structural advantage (structural shifts < 0), but 5 had regional competitiveness (competitiveness shifts > 0); in terms of tertiary industry, all of the 11 prefectures had structural advantage (structural shifts > 0), and 6 had regional competitiveness (competitiveness shifts > 0); Shaoxing was competitive in all three industries, ranking the first in the competitiveness of every industry; Huzhou, Quzhou, and Jinhua were not competitive in tertiary industry. The research provides a new yardstick of industrial T&U level and lays the decision-making basis for local governments in Zhejiang to formulate industrial T&U policies.

Threshold Effect of Economic Growth on Energy Intensity—Evidence from 21 Developed Countries

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.