Sustainable Development of New Urbanization from the Perspective of Coordination: A New Complex System of Urbanization‒Technology Innovation and the Atmospheric Environment

Driving impact and spatial effect of the digital economy development on carbon emissions in typical cities: a case study of Zhejiang, China

The digital economy (DE) not only drives economic innovation and development but also has significant environmental effects by promoting lower carbon emissions. To investigate the spatial effects of DE on urban carbon emissions, this study comprehensively measures the level of DE development based on the panel data from 11 typical cities in Zhejiang Province from 2011 to 2020, by comparing analysis using different regression models. The following conclusions are obtained: (1) The total carbon emissions (TC) of Zhejiang cities in general show a fluctuating change trend of first increasing and then slowly decreasing, while carbon emission intensity and carbon emission per capita in general show a fluctuating change trend of decreasing. Cities with high TC are primarily concentrated in the Hangzhou Bay city cluster, accounted for 62 ~ 65% of the province's carbon emissions. The development of the DE in Zhejiang cities shows steady growth, but there are large differences among cities, with Hangzhou and Ningbo standing out as particularly prominent. (2) There is a significant inverted U-shaped relationship between the DE and the level of carbon emissions in Zhejiang Province. The influence coefficient of the DE on the primary term of TC is 0.613, and the influence coefficient of the quadratic term of TC is - 1.008. (3) In terms of the spatial spillover effect of the DE on carbon emissions, the study finds that compared to the direct effect, the spatial spillover effect is not significant. However, the allocation of transport resources shows a positive spatial spillover effect (increasing carbon emissions, coefficient value is 0.138), while technological progress shows a somewhat negative spatial spillover effect (decreasing carbon emissions, coefficient value is - 0.035). (4) The study also finds that the smart city pilot policy significantly reduces urban carbon emissions. Moreover, the effect of the DE on carbon emissions is confirmed through the significance test of the quadratic term when replacing the geographical and economic distance weight matrices. This indicates that the empirical findings are robust to these tests. Finally, several countermeasures to reduce carbon emissions are proposed from the perspective of DE development.

Influencing factors and trend prediction of PM2.5 concentration based on STRIPAT-Scenario analysis in Zhejiang Province, China

The government's development of eco-environmental policies can have a scientific foundation thanks to the fine particulate matter (PM2.5) medium- and long-term change forecast. This study develops a STRIPAT-Scenario analysis framework employing panel data from 11 cities in Zhejiang Province between 2006 and 2020 to predict the changing trend of PM2.5 concentrations under five alternative scenarios. The results reveal that: (1) urbanization development (P), economic development (A), technological innovation investment (T) and environmental regulation intensity have a significant inhibitory effect on PM2.5 concentration in Zhejiang Province, while industrial structure, industrial energy consumption and the number of motor vehicles (TR) have a significant increase on PM2.5 concentration. (2) Under any scenario, the PM2.5 concentration of 11 cities in Zhejiang Province can reach the constraint target set in the 14th Five-Year plan. The improvement in urban PM2.5 quality is most obviously impacted by the high-quality development scenario (S4). (3) Toward 2035, PM2.5 concentrations of 11 cities in Zhejiang Province can reach the National Class I level standard in most scenario models, among which Hangzhou, Jiaxing and Shaoxing are under high pressure to reduce emissions and are the key areas for PM2.5 management in Zhejiang Province. However, most cities cannot reach the 10 μg/m3 limit of WHO's AQG2005 version. Finally, this study makes recommendations for reducing PM2.5 in terms of enhancing industrial structure and funding science and technology innovation.

The Impact of Coordinated Development of Ecological Environment and Technological Innovation on Green Economy: Evidence from China

Promoting the coordinated development of ecological environment and technological innovation is significant to the development of a green economy. In this study, we construct an index system of ecological environment, technological innovation, and green economy based on the panel data of 30 provinces and cities in China from 2005 to 2016, using the entropy weight method, the coupling coordination model, and the panel vector autoregressive model (PVAR) to calculate the comprehensive development levels of ecological environment, technological innovation, and green economy and the coordination degree between ecological environment and technological innovation, and then further explore the impact of the coordinated development level of ecological environment and technological innovation on the development of a green economy. The research results include: First, from 2005 to 2016, the comprehensive development levels of ecological environment, technological innovation, and green economy in China’s 30 provinces and cities achieved different degrees of improvement as a whole. Among them, the comprehensive development level of green economy was the highest, followed by the development level of technological innovation, and the comprehensive development level of ecological environment was the lowest. Second, from 2005 to 2016, the coordination degree between ecological environment and technological innovation in China’s provinces and cities increased year by year, but on the whole, the coordination degree between ecological environment and technological innovation in various regions was in a state of imbalance. Third, there was a long-term equilibrium relationship among the coordinated development levels of ecological environment, technological innovation, and green economy. Fourth, through pulse analysis and Monte Carlo simulation, we found that the coordinated development level of ecological environment and technological innovation had a lagging positive impact on green economy. Finally, we provide a summary of the results of this study.

How does green technology innovation affect urbanization? An empirical study from provinces of China

The construction of new-type urbanization with the theme of innovation, green, and smart development is becoming the endogenous driving force of China's economic transformation and upgrading, and green technological innovation is a key factor in cracking the problems of development motivation and environmental constraints in urbanization construction. This paper investigates the impact of green technology innovation on urbanization based on a panel dataset covering 30 provinces in China from 2005 to 2019. First, we use the entropy method and the super-efficiency DEA method to measure the level of urbanization and green technology innovation, respectively. Moreover, on this basis, we use panel regression model and FGLS model to estimate the direct impact of green technological innovation on urbanization and its three dimensions-population urbanization, industrial urbanization, and ecological urbanization. Then, the mediating effect model is used to further study the indirect impact of green technological innovation on urbanization. The results indicate that green technological innovation is the most effective way to promote the development of new urbanization currently. In addition, green technology innovation can indirectly affect urbanization through the effects of foreign capital, energy consumption and information development, while the effect of industrial structure optimization effects is not significant. Finally, some policy suggestions are discussed to better promote the development of urbanization in China.

Socioeconomic factors of industrial air pollutants in Zhejiang Province, China: Decoupling and Decomposition analysis

In order to analyze and control air pollutant emissions effectively, on the basis of comprehensive consideration of three different pollution sources of industrial sulfur dioxide, industrial nitrogen oxides, and industrial smoke and dust, the Tapio decoupling model and LMDI decomposition model with six decomposition variables are constructed to compare the effects of socioeconomic factors on industrial air pollutant emissions in 11 cities in Zhejiang Province during 2006-2017. Then, a decoupling effort model is developed to analyze the effectiveness of the decoupling efforts taken at city level. This study found that (1) during the period of 2006-2017, the air pollutant emission reduction work in Zhejiang Province achieved remarkable results. More specifically, economic scale effect and population effect are the main factors for the increase of air pollutant emissions. And, the energy emission intensity effect and technological progress are the main driving forces for the reduction of three atmospheric pollutants, followed by the reduction effect of industrial structure and energy structure. (2) The environmental pollution problems of different air pollution sources in different cities are heterogeneous. (3) Eleven cities in Zhejiang Province have made significant decoupling efforts on the emission of three kinds of air pollutants, but there are some differences in the trend of the decoupling effort index of different pollution sources in different cities. In the future, illustrating by the example of Zhejiang, we should implement a "common but different" emission reduction strategy and emphasize pollutant emissions control during energy use in the efforts of further promoting the reduction of air pollutants.

Efficiency Measurement of Green Regional Development and Its Influencing Factors: An Improved Data Envelopment Analysis Framework

Reasonably assessing the efficiency of green regional development is a key to improving environmental management and implementing sustainable development strategies. From the perspectives of environmental pollutant emissions, energy consumption, and production factor cost, the non-radial data envelopment analysis model based on the Malmquist index was applied to measure the green development efficiency and regional differences of 11 cities in Zhejiang from 2007 to 2016 from both static and dynamic aspects. This paper further analyzes the inherent influencing factors through the panel data model. The result shows: (1) The average static efficiency of green development in Zhejiang Province is 0.844. There is still 15.6% of improvement space from the frontier of production. Pollution emission management has the greatest improvement potential. Pure technical efficiency is the main factor restricting the static efficiency. (2) The dynamic efficiency of Zhejiang’s green development achieves an average annual rate of 0.26%, with a cumulative growth of 2.33%. The improvement of green development efficiency mainly depends on scale efficiency change. (3) The inherent factors affecting the efficiency of green development in the 11 cities mainly include three factors: the industrial structure, environmental regulation, and the urbanization level. The industrial structure has a positive effect, while environmental regulation and the urbanization level have negative effects. (4) The 11 cities are relatively evenly distributed in the four “static–dynamic efficiency” classification quadrants, and there is no "Matthew effect" of high–high, low–low polarization.

Investigating the Relationship between the Industrial Structure and Atmospheric Environment by an Integrated System: A Case Study of Zhejiang, China

Under the dual pressure of industrial structure upgrade and atmospheric environment improvement, China, in a transition period, is facing the challenge of coordinating the relationship between the industry and the environment system to promote the construction of a beautiful China. Based on system theory and coupling coordination model, the interaction analysis framework between industrial structure (IS) and atmospheric environment (AE) was constructed. An integrated system with 24 indicators was established by the pressure–state–response (PSR) model of IS and level–quality–innovation (LQI) model of AE. Then, we analyzed trends observed in coupling coordination degree (CCD) and dynamic coupling coordination degree (DCCD) for 11 cities in Zhejiang Province, China, using statistical panel data collected from 2006 to 2017. Conclusions were as follows: (1) the 11 cities’ comprehensive level of the IS system shows a trend of stable increase, yet the comprehensive level of AE demonstrated a trend of fluctuation and transition. There are significant spatial variations among cities; (2) The CCD analysis results found that Hangzhou, Ningbo, and Wenzhou take the lead in realizing the transformation from barely coordinated development to superior coordinated pattern, while other cities were still in the stage of barely coordinated development; (3) the DCCD phase of 11 cities can be roughly divided into three types: upgraded—utmost development type (only Hangzhou), stable—harmonious development type (Wenzhou, Lishui, and Zhoushan) and transitional—harmonious development type (the remaining seven cities). This means, for most cities, the contradiction between the transformation process of IS and the AE has become increasingly prominent and intensified. Finally, three necessary and sustainable strategies were proposed to environmental policy makers.