scholarly journals A Framework for Assessing Green Capacity Utilization Considering CO2 Emissions in China’s High-Tech Manufacturing Industry

2020 ◽  
Vol 12 (11) ◽  
pp. 4424 ◽  
Author(s):  
Ya Wang ◽  
Jiaofeng Pan ◽  
Ruimin Pei ◽  
Guoliang Yang ◽  
Bowen Yi
Keyword(s):  
Industrial Development ◽  
Manufacturing Industry ◽  
Production Capacity ◽  
Capacity Utilization ◽  
High Tech ◽  
Eastern Region ◽  
Low Carbon ◽  
Assessment Framework ◽  
Industrial Transformation ◽  
Green Capacity

China’s high-tech manufacturing industry has become the mainstay of the country’s domestic industrial transformation and upgrading. However, in recent years, the industry has experienced huge blind expansion under policy stimulus, which is not good for long-term industrial development. Therefore, this article attempts to explore the extent to which such an important and critical industry in China utilizes its production capacity and provides a basis for future policymaking. Coupled with the country’s increasing emphasis on the green and low-carbon development of the industry, this article extends the green and low-carbon thinking based on capacity utilization, namely green capacity utilization (CU). On this basis, the study empirically investigates the green CU of the high-tech manufacturing industry in 28 provinces in mainland China from 2010 to 2015. In performing the investigation, the inputs were divided into (quasi-)fixed and variable inputs, and an assessment framework was established based on the data envelopment analysis (DEA) method. Moreover, optimal variable inputs are also available as by-products within the assessment framework. The results were as follows: First, China’s high-tech manufacturing industry showed an excellent overall performance in green CU. Moreover, half of the provinces were at fully utilized capacity, and half were under-utilized. On average, there was a slight deterioration in green CU. Second, the results showed regional differences. The western region had the highest green CU followed by the middle and northeastern regions, and the eastern region had the lowest green CU. Third, regarding the optimal variables inputs, the total amount of labor in China’s high-tech manufacturing industry met the demand, but the distribution was uneven. Fourth, the scale of traditional energy consumption needs to be reduced both in individual provinces and in general. These conclusions have implications for the formulation of policies to promote the green development of China’s high-tech manufacturing industry.

scholarly journals Untapped export opportunities of Serbian economy after a decade of investment and export based growth model

2021 ◽  
Vol 69 (3-4) ◽  
pp. 273-288
Author(s):  
Jasna Atanasijević ◽  
Duško Vasiljević ◽  
Zoran Nikolić ◽  
Olivera Pavlović
Keyword(s):  
Manufacturing Industry ◽  
Production Capacity ◽  
Electrical Equipment ◽  
Low Complexity ◽  
High Tech ◽  
Embedded Devices ◽  
Financial Reports ◽  
Economic Complexity ◽  
Transfer Of Technology ◽  
Limited Scale

Relying on the economic complexity and product space approach developed by Hidalgo and Haussmann [21], and using trade data, exporters' financial reports and available macroeconomic statistics, we try to assess the degree of transformation of structure and production potential of the Serbian economy over the last decade. We argue that although the overall economic complexity, as a decent predictor of higher economic growth, did slightly improve over the observed period, there is still large untapped potential in local knowledge and know-how. FDI inflow into manufacturing industry, as the most important factor of the transformation of the production structure and size of the economy, has contributed to growth in employment and export, improving the macro stability. On the other side, its contribution to the higher growth outlook by improving the production capacity was limited as FDI inflow has been directed mostly into low and medium-low technology industries with low complexity products. Moreover, it seems that the vertical spillover through linkages with local suppliers and transfer of technology, knowledge and practices could also be larger. In the same period, some positive developments of limited scale yet are reflected in emergence of a certain number of high-tech industries' products with high complexity, most likely produced by SMEs, such as electrical equipment, lighting, various software embedded devices, etc.

scholarly journals National industrial competence and global added value chains: methodological instrumentarium of macroeconomic analysis

2021 ◽  
pp. 40-52
Author(s):  
Sergey A. Tolkachev ◽  
◽  
Artyom Y. Teplyakov ◽  
Keyword(s):  
Industrial Development ◽  
Manufacturing Industry ◽  
Global Value Chains ◽  
Added Value ◽  
Value Chains ◽  
High Tech ◽  
Macroeconomic Analysis ◽  
The World ◽  
Manufacturing Activity ◽  
Production Value

In the context of the developing global economic crisis, it is important to have an adequate methodological toolkit for the global positioning of the manufacturing industry in different countries of the world in the production value chains. In this work, the authors made an attempt to further develop their own concept that solves this problem. A methodology for calculating indices reflecting the dynamics of national industrial competence “in the context” of the integration of the country’s manufacturing industries into global value chains is presented. The calculations and conclusions are based on the OECD TiVA statistical database (2018). The tendencies of industrial development of thirty economies of the world, including the Russian one, were identified, taking into account their “embedding” in global value chains. So, if the manufacturing industry of Russia, participating in the international division of labor, manages to maintain an average level of general national industrial competence, then its strategic positions associated with the development of high-tech industries can be qualified as “outsider”. The author’s methodology seems promising in terms of assessing the global economic positioning of countries and formulating recommendations for national regulators of manufacturing activity.

scholarly journals Industrial Policy Strategy: A Case of Changing National Priorities in Russia

2020 ◽  
Vol 17 ◽  
Keyword(s):  
Industrial Policy ◽  
Manufacturing Industry ◽  
Developing Economies ◽  
Oil And Gas Industry ◽  
Developed Countries ◽  
Digital Economy ◽  
High Tech ◽  
Industrial Complex ◽  
Industrial Transformation ◽  
Industrial Enterprises

New technology trends, mainly related to the development of Industry 4.0 and the digital economy, have created significant prerequisites for changing the priorities of industrial policy. This topic is particularly relevant for countries with economies in transition or developing economies, including Russia. The accumulated structural gap, expressed in the level of industries' digitalization, indicates a low willingness of industrial enterprises to introduce digital and related advanced technologies. The data obtained show that this gap is especially pronounced (more than 50% of the average for the EU countries) in the manufacturing industry, oil and gas industry, and transport. In mining, this gap approaches 70%. These circumstances predetermined the need to identify the strategic vector of Russian industrial policy against the background of the developing modern technologies that predetermine the adjustment of industrial policy priorities. To assess the potential of industrial transformation, the authors conducted a comparative analysis of changed targets for the formation of industrial policy in the developed countries and Russia. The analysis showed a sharp evolution in the priorities of industrial policy in Russia – those changed six times during the period from 2014 through 2019. The strategic policy focus has shifted from supporting projects in the production of high-tech civilian and/or dual-use products by enterprises of the military-industrial complex and the transition of enterprises to the best available technologies to supporting the digital economy and artificial intelligence technologies. Based on the results, the researchers suggested the development of industrial policy instruments adapted to the new priorities.

scholarly journals Monitoring Spatial Changes in Manufacturing Firms in Seoul Metropolitan Area Using Firm Life Cycle and Locational Factors

2019 ◽  
Vol 11 (14) ◽  
pp. 3808
Author(s):  
Youngsoo An ◽  
Li Wan
Keyword(s):  
Life Cycle ◽  
Metropolitan Area ◽  
Industrial Development ◽  
Manufacturing Industry ◽  
Firm Growth ◽  
High Tech ◽  
Location Quotient ◽  
Seoul Metropolitan Area ◽  
Spatial Changes ◽  
High Level

This paper diagnoses the development of the manufacturing industry in the Seoul Metropolitan Area (SMA) using portfolio and regression analyses. Following the life-cycle perspective, four types of spatial changes of firms have been identified, namely firm formation, inflow, outflow, and dissolution, which are applied to analyze the manufacturing development in SMA. For portfolio analysis, we propose the Net Formation Index and Net Inflow Index to measure the spatial changes of firms at the city level. The two indices facilitate horizontal comparison among cities in SMA in terms of firm growth from new opening and relocation. Using spatial regression analysis, we identify significant location factors that contribute to firm change patterns. Our tests show that a high level of industrial specialization (measured by location quotient) has a dual effect. On the one hand, high level specialization attracts new or inflow firms, particularly in the light and high-tech manufacturing industries. On the other hand, it leads to an increased number of closed or outflow firms, plausibly due to increased competition among local firms. The proposed methods can be applied to diagnose industrial development in clusters of inter-connected cities and design policy tools to boost the local industry.

Development Model of Engineering Manufacturing and its Application

2014 ◽  
Vol 1006-1007 ◽  
pp. 386-389
Author(s):  
Ying Hong Yu
Keyword(s):  
Manufacturing Industry ◽  
Time Series Data ◽  
Production Capacity ◽  
Economic Modeling ◽  
Series Data ◽  
High Tech ◽  
Network Training ◽  
Modeling And Forecasting ◽  
Practical Engineering ◽  
Forecasting System

In recent years, with the continuous improvement of production capacity, manufacturing industry restructuring and achieved great results, significantly increased the proportion of high-tech industries, some traditional industries has continued to decline. Manufacturing is the material basis of our national economy and the main industry, which largely determines the level of development of comprehensive national strength. This has brought great difficulties to the economic modeling and forecasting system. This paper presents an improved modeling and forecasting methods, recombinant methods by introducing chain data and add data growth economic indicators in an artificial neural network training, the time series data input window to solve practical engineering problems forecasts.

scholarly journals Analyzing the regional economic changes in a high-tech industrial development zone using machine learning algorithms

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0250802
Author(s):  
ErLe Du ◽  
Meng Ji
Keyword(s):  
Machine Learning ◽  
Economic Efficiency ◽  
Capital Investment ◽  
Industrial Development ◽  
Economic Structure ◽  
Machine Learning Algorithms ◽  
High Tech ◽  
Eastern Region ◽  
Regional Economic ◽  
Economic Changes

The aims are to improve the efficiency in analyzing the regional economic changes in China’s high-tech industrial development zones (IDZs), ensure the industrial structural integrity, and comprehensively understand the roles of capital, technology, and talents in regional economic structural changes. According to previous works, the economic efficiency and impact mechanism of China’s high-tech IDZ are analyzed profoundly. The machine learning (ML)-based Data Envelopment Analysis (DEA) and Malmquist index measurement algorithms are adopted to analyze the dynamic and static characteristics of high-tech IDZ’s economic data from 2009 to 2019. Furthermore, a high-tech IDZ economic efficiency influencing factor model is built. Based on the detailed data of a high-tech IDZ, the regional economic changes are analyzed from the following dimensions: economic environment, economic structure, number of talents, capital investment, and high-tech IDZ’s regional scale, which verifies the effectiveness of the proposed model further. Results demonstrate that the comprehensive economic efficiency of all national high-tech IDZs in China is relatively high. However, there are huge differences among different regions. The economic efficiency of the eastern region is significantly lower than the national average. The economic structure, number of talents, capital investment, and economic efficiency of the high-tech IDZs show a significant positive correlation. The economic changes in high-tech IDZs can be improved through the secondary industry, employee value, and funding input. The ML technology applied can make data processing more efficient, providing proper suggestions for developing China’s high-tech industrial parks.

The Provincial Carbon Footprint and Trade

2012 ◽  
Vol 524-527 ◽  
pp. 3514-3518 ◽  
Author(s):  
Yan Wang ◽  
Na Li
Keyword(s):  
Carbon Footprint ◽  
Carbon Emissions ◽  
Industrial Development ◽  
Manufacturing Industry ◽  
High Energy ◽  
Manufacturing Industries ◽  
Total Carbon ◽  
Low Carbon ◽  
Demand Structure ◽  
High Energy Consumption

Based on the data of provincial input-output model and the carbon footprint model, the analysis is focused on provincial carbon footprint and the space transfer of carbon emissions. The results have shown that: (1) There are significant differences of provincial total carbon footprint amounts: resource-rich provinces have high total carbon footprint amounts, followed by processing and manufacturing provinces and municipalities; Regions with high energy efficiency have low carbon footprint amounts, so does southwestern region where economic and industrial development level is relatively low. (2) The provincial differences of carbon footprint per capita are related to demand structure: the amounts of carbon footprint are high in provinces with higher demand of consumption and investment, especially those provinces with strong demand for construction and processing industries. The amounts of carbon footprint are low in provinces which are non-resource-based, have limited investment and construction, and its economic structure is not dominated by processing and manufacturing. (3) Interprovincial trades have a significant impact on carbon footprint and carbon emissions. Provinces with well developed infrastructure have net CO2 emissions flow-in that are directly induced by high energy consumption products; southwestern region, where processing and manufacturing industry is relatively less-developed, has main CO2 emission flow-in, which are induced by the demand of processing and manufacturing industries; resource-intensive provinces and provinces with well-developed processing and manufacturing industries have net CO2 emission flow-out, which are induced by interprovincial trades.

Improving the system of measures to support industrial development in Russia

2021 ◽  
pp. 53-62
Author(s):  
M. V. Dubovik ◽  
N. Sh. Salakhova ◽  
D. A. Sizova
Keyword(s):  
Industrial Development ◽  
Manufacturing Industry ◽  
Business Processes ◽  
Banking Sector ◽  
Main Tool ◽  
Global Market ◽  
Value Chains ◽  
High Tech ◽  
The Sustainable Development ◽  
Support Measures

Currently, it is not individual enterprises that compete in the global market of industrial goods, but value chains. Within their framework, technological and production interfaces, diversification of demand markets, and optimization of business processes contribute to a sharp increase in the competitiveness of enterprises. The existing measures to support the development of industry do not confirm their effectiveness based on the results of work. To improve the system of support measures, the following steps are proposed: value chains should become the main tool for implementing the state’s industrial policy, measures for technological re-equipment based on intersectoral interaction of enterprises of high-tech and medium-tech industries should be implemented, as well as development institutions alternative to the banking sector should be formed. When implementing the proposed state support measures, the conditions for the sustainable development of the manufacturing industry in Russia are formed.

Production capacity of Russian industry: Potential of import Substitution and economic Growth

2015 ◽  
pp. 5-24 ◽  
Author(s):  
B. Zamaraev ◽  
T. Marshova
Keyword(s):  
Economic Growth ◽  
Import Substitution ◽  
Production Capacity ◽  
Current Level ◽  
High Tech ◽  
Industrial Growth ◽  
Russian Industry ◽  
Technological Modernization ◽  
Capacity Structure ◽  
Idle Capacity

The article examines the state of production capacity of Russian industry. It is shown that in spite of certain positive shifts, the rate of technological modernization in recent years has been insufficient for marked progressive changes in the capacity structure and quality. In contrast to the industrial growth after the crisis of 1998 that took place in the presence of significant reserves of capacity, the current level of idle capacity is much lower. The lack of mass input of modern and high-tech industries objectively limits the possibilities of import substitution and economic growth.

Artificial Intelligence as a Technological Innovation to Accelerate Economic Development

2019 ◽  
Vol 12 (3) ◽  
pp. 125-133
Author(s):  
S. V. Shchurina ◽  
A. S. Danilov
Keyword(s):  
Artificial Intelligence ◽  
Economic Development ◽  
Technological Innovation ◽  
Manufacturing Industry ◽  
New Technologies ◽  
Retail Trade ◽  
High Tech ◽  
Industrial Complex ◽  
Technological Gap ◽  
The Government

The subject of the research is the introduction of artificial intelligence as a technological innovation into the Russian economic development. The relevance of the problem is due to the fact that the Russian market of artificial intelligence is still in the infancy and the necessity to bridge the current technological gap between Russia and the leading economies of the world is coming to the forefront. The financial sector, the manufacturing industry and the retail trade are the drivers of the artificial intelligence development. However, company managers in Russia are not prepared for the practical application of expensive artificial intelligence technologies. Under these circumstances, the challenge is to develop measures to support high-tech projects of small and medium-sized businesses, given that the technological innovation considered can accelerate the development of the Russian economy in the energy sector fully or partially controlled by the government as well as in the military-industrial complex and the judicial system.The purposes of the research were to examine the current state of technological innovations in the field of artificial intelligence in the leading countries and Russia and develop proposals for improving the AI application in the Russian practices.The paper concludes that the artificial intelligence is a breakthrough technology with a great application potential. Active promotion of the artificial intelligence in companies significantly increases their efficiency, competitiveness, develops industry markets, stimulates introduction of new technologies, improves product quality and scales up manufacturing. In general, the artificial intelligence gives a new impetus to the development of Russia and facilitates its entry into the five largest world’s economies.

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