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This paper aims to construct evaluation index system of industrial economy-ecology-coordinated development based on “driving-force-pressure-state-impact-response” conceptual model. Grey target theory is introduced to evaluate industrial economy-ecology-coordinated development level and identify its key influencing factors. On that basis, the countermeasures are proposed to improve industrial economy-ecology-coordinated development in China.

Bull's-eye degree of grey target theory is introduced to evaluate industrial economy-ecology-coordinated development level of 31 provinces, municipalities and autonomous regions in China. The contribution degree of influence factors is analyzed by contribution degree theory.

The results show that first, the overall level of industrial economy-ecology-coordinated development in China is not high, there is a big gap of coordinated development level between provinces, municipalities and autonomous region, and there is still a large room to improve the status quo. Second, the major factors affecting industrial economy-ecology-coordinated development are gross industrial output value (GIOV) share of investment completed in the treatment of industrial pollution, common industrial solid wastes produced per GIOV, sulphur dioxide emission per GIOV, energy consumption per 10,000 yuan of gross regional product.

This paper constructs evaluation index system of industrial economy-ecology-coordinated development and applies grey target theory to evaluate industrial economy-ecology-coordinated development level and identify its key influencing factors.

This chapter models industrial, tech, and financial cooperation between Russia and the countries of the Asia-Pacific region. We use several complex methods of economic and mathematical modeling to analyze specific features of such cooperation and determine critical factors in industrial, technological, and financial development. The preferable choice for the Asia-Pacific region is cooperation with Russia, which is ready for an increase in imports of industrial and high-tech products as well as joint industrial innovational entrepreneurship. Investments would lead to synergetic effects, ensuring simultaneous industrial, technological, and financial development.

The purpose of this study is to explore the impact of environmental pollution and industrial structure upgrading on environmental pollution in different stages based on the temporal and spatial heterogeneity of economic development level and industrial structure upgrading level in eastern, central and western regions of China and discuss whether there is adjustment effect and threshold effect in the process of economic growth affecting environmental pollution, and finally realizes sustainable economic development.

Based on panel data from 30 provincial-level administrative regions of China (excluding Tibet and Hong Kong, Macao and Taiwan) from 2000 to 2019, this paper uses the environmental Kuznets curve, regulating effect model and panel threshold model to analyze the impact of economic growth and industrial structure upgrading on environmental pollution.

The results present that the uneven distribution of natural resources leads to different levels of economic development and industrial structure upgrading in eastern and western regions, and its impact on environmental pollution is also different. Economic growth and industrial structure upgrading have a positive effect on environmental pollution, and the relationship between economic growth and environmental pollution is inverted U-shaped. At present, the eastern, central and western regions of China are at the right end of the inverted U-shaped relationship. In general, industrial structure upgrading in eastern, central and western regions has a significant inhibitory effect on environmental pollution. Industrial structure upgrading has a negative moderating effect on the relationship between economic growth and environmental pollution, and the regulating effect is most significant in the central region, followed by the eastern region, and not significant in the western region. The results of panel threshold model show that the industrial structure upgrading can slow down the positive impact of economic growth on environmental pollution and strengthen the negative moderating effect of industrial structure upgrading on economic growth and environmental pollution.

The innovation of this study is to bring economic growth, industrial structure upgrading and environmental pollution into a unified analytical framework, analyze the impact of economic development and industrial structure upgrading levels in different periods on environmental pollution, and select industrial structure upgrading as the moderating variable and threshold variable. It provides a thought for the influence mechanism of different levels of industrial structure upgrading on economic growth and environmental pollution. Based on the panel data in China, this study emphasizes the concept of sustainable development, adheres to green development and proposes relevant policies to improve environmental pollution. And this paper proposes relevant policies to improve environmental pollution from the perspective of transforming economic growth mode and optimizing industrial structure in China, which also has reference significance for developing countries to realize sustainable economic development.

Currently, China’s economy is in the critical phase of transforming economic development patterns and replacing old growth drivers with new ones. Whether it can successfully overcome the “middle-income trap” has become a significant issue attracting wide attention.

Driven by underlying digital technologies such as artificial intelligence, blockchain, cloud computing and big data, the fourth industrial revolution featuring the booming digital economy has provided significant opportunities for China’s economy to “overtake” and overcome the “middle-income trap”. The transformation of economic development pattern, the optimization of industrial structure, and the change of growth drivers, brought by the deep integration of digital and real economies are the keys to leaping over the “middle-income trap”.

From the supply side, the digital economy can improve the quality and efficiency of the supply side and promote the supply-side structural reform and economic growth from the following three aspects: First, promote the quality, efficiency and diversification of the supply system; second, promote networking, opening-up and synergy in the innovation system and third, promote the socialization, modularization and flexibility of production pattern. From the demand side, the digital economy can boost the new drivers of the “troika” of economic growth consisting of consumption, exports and investment by changing the market investment direction, promoting consumption upgrade and fostering export strengths. However, once these two attributes interact with each other, especially when data is combined with capital, the most adhesive factor in the market economy, a series of new social relations will then be produced based on the technical attribute, resulting in significant adjustments in social relations, involving both positive and negative externalities.

To overcome the “middle-income trap”, it is necessary to adapt to the laws of economic evolution and promote a fundamental change in economic growth drivers; boost the high-quality development of the digital economy by strengthening the support role of data in the digital economy; and accelerate digital industrialization and industrial digitalization to realize the integration of digital and real economies.

Why is the “education to industrial innovation” equation not working in China? Why has education development contributed to South Korea’s success but not promoted technology development and industrial upgrading in China? The purpose of this paper is to compare South Korea and China and try to address that puzzle. The author will also identify which mediating factors are crucial in linking education development to industrial innovation and industrial upgrading.

This study will use the historical comparative method to compare South Korea and China. The author will try to explore the differences in education and industrial upgrading in the two countries, and identify which factors are producing different educational development effects, mainly by narrative comparison. Data will mainly come from online databases such as Statistics Korea, the Center on International Education Benchmarking, the UNESCO Institute for Statistics, China Education Statistics and the World Bank, as well as from second-hand resources.

In summary, this research has revealed that education itself or the production of human capital may not be sufficient conditions for technology innovation or industry upgrading. For human capital to affect industrial upgrading positively, it is not enough for the Chinese government just to invest in education. Other intermediating market and social contexts are crucial too, especially the allocation of resources between the private and the public sectors, and the existence of a proper employment structure.

The role of education in economic development for the developing world is debated a lot. However, there is little development study research which directly explores the relationship between education and industrial upgrading via macroeconomic analysis. In a globalized world, the situation of international industrial value chains is an important element for sustainable long-term development. Industrial structures and their transformation are becoming more and more important for developing countries. While most past research has treated the absorbing economy’s structure as a condition that determines education’s contribution to development, this paper will treat the industrial structure as the dependent variable, and analyze how education would contribute to the upgrading of industrial structure and, in turn, be of benefit to sustainable economic development.

Researchers continue to seek understanding of industrialization as a state managed process. How to create and implement new industries based on advanced knowledge is on the policy agenda of many advanced nations. Measures that promote these developments include national capacity building in science and technology, the formation of technology transfer systems, and the establishment of industrial clusters. What these templates often overlook is an analysis of use. This chapter aims to increase the understanding of the processes that embed new solutions in structures from an industrial network perspective. The chapter describes an empirical study of high-technology industrialization in Taiwan that the researcher conducts to this end. The study shows that the Taiwanese industrial model is oversimplified and omits several important factors in the development of new industries. This study bases its findings on the notions that resource combination occurs in different time and space, the new always builds on existing resource structures, and the users are important as active participants in development processes.

The chapter is devoted to the analysis of industrial development in Russia since 2005. Characteristic features of the Russian industry are considered. It is shown that the Russian industry is specific both by its structure and its high heterogeneity. The mining and quarrying sector and related manufacturing industries play a significant role in the Russian economy and major role in industrial production. In the foreseeable future, these sectors can strengthen their leading positions.

Considerable attention is paid to the analysis of the industrial policy of the Russian government, which has traditionally been one of the leading actors of industrial development. The main directions of industrial policy are singled out: compensation of negative changes, catching-up, and advance development. It is noted that the role of the government is ambivalent: on the one hand, it seeks to develop high-performing companies, including new and fast-growing ones, and on the other hand, it often supports large, low-performing enterprises. With the declared continuous emphasis of industrial policy on innovation and modernization, low innovativeness of Russian companies remains. Based on the analysis of modern conditions, as well as new objects and instruments of industrial policy, some prospects for future industrial development have been revealed.

Building industrial clusters is getting much more political attention and strategic orientation in all developing countries. This study started by revising the conceptual and theoretical frameworks for industrial clusters, followed by some insights and contributions about empirical bases for clusters' dynamics and processes. The study focused on the case of Agadir Agreement between four Arab countries (Egypt, Jordon, Morocco, and Tunisia), which was initiated after the Euro-Mediterranean partnerships, and the rationale of the agreement was based on the concept of cumulative value-added origin. The study based its methodology on analyzing the international and bilateral trade flows of six industrial goods from the automotive sector among the four countries and with the EU countries to detect the degree of industrial collaboration and the achieved success of each country in this sector. The study indicated that the four countries used the concept of industrial clusters for economic development, but the results of the analysis showed that till now Agadir Agreement only achieved a shallow integration, while failed to deeply integrate as one big collaborative industrial cluster.

This paper aims to build an improved grey panel clustering evaluation model and evaluate the comprehensive development potential of industrial economy, society and ecological environment in the Yangtze River Economic Belt of China. The purpose of this study is to provide some theoretical basis and tool support for management departments and relevant researchers engaged in industrial sustainable development.

This study uses the driving force pressure state impact response analysis framework to build a comprehensive evaluation index system. Based on the center point triangle whitening weight function, it classifies the panel grey clustering of improvement time and index weight.

The results show that there are great differences in the level of industrial ecological development in different regions of the Yangtze River Economic Belt, which further illustrates the scientificity and rationality of the evaluation method proposed in this paper.

Due to the industrial ecological development is in a constantly changing state, and the information is uncertain. Whitening weight function is introduced to represent the complete information of relevant data. The industrial ecological evaluation involves a comprehensive complex system, which belongs to the panel data analysis problem. The improved grey panel clustering evaluation model is applied to grade the industrial ecological development level of the Yangtze River Economic Belt. The results have important guiding significance for the balanced development of industrial ecology in the region.

Due to the industrial ecological development is in a constantly changing state, and the information is uncertain. Whitening weight function is introduced to represent the complete information of relevant data. The industrial ecological evaluation involves a comprehensive complex system, which belongs to the panel data analysis problem. In order to improve the effectiveness of industrial ecological evaluation, the improved grey panel clustering evaluation model is applied to grade the industrial ecological development level of the Yangtze River Economic Belt. The results have important guiding significance for the balanced development of industrial ecology in the region.

the new model proposed in this paper complements and improves the grey clustering analysis theory of panel data, that is, aiming at the subjective limitation of using time degree to determine time weight in panel grey clustering, a comprehensive theoretical method for determining time weight is creatively proposed. Combining the DPSIR (Driving force-Pressure-State-Influence-Response) model model with ecological development, a comprehensive evaluation model is constructed to make the evaluation results more authentic and comprehensive.