Industrial Economic Growth Research Articles

Analysis of the Rebound Effect and Induced Effect of Non-residential Natural Gas Consumption: Empirical Evidence from China

Following the Paris Agreement, economic development demands energy services while reducing emissions. This study focuses on non-residential natural gas consumption, which plays a crucial role in transforming the energy sector; however, market-based mechanisms are still in the early stages and have not yet been explored. Based on the analysis of data collected from 2000 to 2020 in China, this study constructs a simultaneous equation model to analyze the rebound and induced effects of non-residential natural gas consumption. This paper finds that industrial economic growth has a significant promoting effect on non-residential gas consumption. In addition, implementing and promoting natural gas utilization policies and increases in natural gas prices can significantly reduce gas consumption intensity. This article innovatively calculates the rebound and induced effects of non-residential gas consumption, providing a decision-making reference for accelerating the realization of the dual-carbon goal.

Time management as the growth factor of small manufacturing enterprises

The article deals with small manufacturing enterprises as a point of an industry economic growth which potentially outpaces the average growth rate of the industry and the GDP growth rate that makes it possible to increase the share of gross production product in the general structure of small business of Russia. As one of the reserves of growth of such enterprises the author presents implementation of corporate time management technologies which allow the administration of the enterprises to increase the time for solving the tasks facilitating the economic growth: working with the major clients and contracts, searching for new financial sources, introduction of new capacities, development of new products, formation and development of new management teams. The author suggests the structure of a corporate time management standard which regulates the issues of managing meetings, calendars, operational tasks, work communications. The article contains description of the method of implementation of corporate time management at the example of a small manufacturing enterprise of the instrumentation industry.

The Correlation among Industrial Economic Growth, Renewable Energy Provision and CO2 Emissions in Saudi Arabia

The Correlation among Industrial Economic Growth, Renewable Energy Provision and CO2 Emissions in Saudi Arabia

Influence of Size and Fit on Malaysian Apparel Industry Sustainability: A Scoping Review

Mass production and global trade have fuelled apparel industry economic growth since the industrial revolution. Mass production and global trade have also contributed to sustainability issues, such as pollution and resource depletion. This scoping review explores how garment sizing and fit affect the sustainability of the Malaysian garment industry. The research objective was achieved using an in-depth analysis of the literature to identify the main sizing and fit issues and research gaps in the Malaysian apparel industry. Furthermore, the sustainability challenges resulting from sizing and fit issues were explored and the strategies to address these issues for apparel industry sustainability were proposed. Finally, the authors suggest that Malaysia should establish its own standard sizing system to contribute to the sustainable development of the local industry. This comprehensive analysis aims to provide an empirical basis and strategic guide for sustainable development by the Malaysian apparel industry.

The innovation-driven industrial agglomeration for the integrated market of China

From the perspective of investment-driven to innovation-driven for high-quality development, this study discussed an influence mechanism of investment structure optimization on the industrial agglomeration degree and its growth path of balanced regional economics. We put forward assumptions about their relationship with a mediator of the regional innovation capability and a moderator of the market-oriented business environment to solve problems of the regional economic gap to accelerate the construction of a unified national market in China. We used the regional industrial data of 31 provinces in China from 2004 to 2017 to verify these hypotheses. Finally, we found that regional innovation capability completely moderates the relationship between investment structure optimization and industrial agglomeration degree, and the market-oriented business environment adequately moderates the relationship. This result shows that regional innovation capability and market-oriented business environment restrict industrial agglomeration degrees and accelerate the integration of diversified capital. So, improving the investment of human capital and R&D capital is the key to solving the regional economic gap. It has enriched the research of high-quality development and provided necessary enlightenment for the practical application of regional industrial economic growth.

Research on Industrial Economics Models in Rapid Development of Digital Economy

Abstract Amidst the rapid evolution of the digital economy, this research positions fuzzy rough set theory at the forefront of industrial economic modeling, acknowledging its profound capability to manage uncertain information. This theory’s analytical precision facilitates a deep dive into the dynamics of industrial economic growth, enabling the refinement of industrial structures for sustainable development. Our empirical investigation into region A’s grain industry showcases a remarkable output growth of 272.88% from 2008 to 2018, with a model accuracy exceeding 95%. Analysis on cost inputs further demonstrates how innovation in science and technology plays a crucial role in cost reduction. By leveraging fuzzy rough set theory, this work contributes significantly to the strategic development of industrial economics, offering a robust methodology for data-driven analysis and forecasting in industrial sectors.

Spatiotemporal trends in lead pollution worldwide over the past millennium

Reconstructing spatiotemporal trends in Pb pollution history is important for understanding global Pb cycling, controlling Pb pollution, and allowing assessment of the extent and timing of human impacts on environments. In this study, the high-resolution heavy-metals (Pb, Zn, Cd, Cu, Co, and Ti) pollution history over the past millennium was reconstructed based on sedimentary records from Lake Chenghai in Southwestern China, and the possible origins of Pb and its relationship with global Pb records were revealed. The marked high Pb concentrations occurred during the Medieval period (1200s–1400s), and enrichment factor analysis suggested that the high pollution degrees during the Medieval period were derived primarily from the anthropogenic sources of mining activity. Furthermore, our compilation records worldwide showed that Pb pollution level in most areas of China during the Medieval period is the same as or even higher than present, while in the other regions such as America and Europe, modern-day Pb pollution level is significantly higher than that in the Medieval period. In the past two centuries, Pb concentration rapidly increased again since the 1980s in China, coinciding with China's reform and opening up that spawned social economic transformation and rapid industrial economic growth, while in America and Europe, it rapidly increased in the 1950s and then declined in the 1980s, coinciding with the impacts of industrial revolution on developed regions. Those records imply Pb could be regards as the regional human activity intensity index during the Anthropocene. This study could provide insight into the onset timing of anthropogenic impacts in different areas and also provide scientific supports for Pb pollution control.

Bitcoin network-based anonymity and privacy model for metaverse implementation in Industry 5.0 using linear Diophantine fuzzy sets

AbstractMetaverse is a new technology expected to generate economic growth in Industry 5.0. Numerous studies have shown that current bitcoin networks offer remarkable prospects for future developments involving metaverse with anonymity and privacy. Hence, modelling effective Industry 5.0 platforms for the bitcoin network is crucial for the future metaverse environment. This modelling process can be classified as multiple-attribute decision-making given three issues: the existence of multiple anonymity and privacy attributes, the uncertainty related to the relative importance of these attributes and the variability of data. The present study endeavours to combine the fuzzy weighted with zero inconsistency method and Diophantine linear fuzzy sets with multiobjective optimisation based on ratio analysis plus the multiplicative form (MULTIMOORA) to determine the ideal approach for metaverse implementation in Industry 5.0. The decision matrix for the study is built by intersecting 22 bitcoin networks to support Industry 5.0's metaverse environment with 24 anonymity and privacy evaluation attributes. The proposed method is further developed to ascertain the importance level of the anonymity and privacy evaluation attributes. These data are used in MULTIMOORA. A sensitivity analysis, correlation coefficient test and comparative analysis are performed to assess the robustness of the proposed method.

Research on the influence factors of ship resistance and navigation drag reduction

With economic growth, ship transportation plays a major role in global logistics. The optimization of the ship form based on minimum resistance has been the research focus of ship builders for hundreds of years, but the actual process is quite complex, which also brings huge benefits. Ship drag reduction is not only conducive to industrial economic growth, but also helpful to improve the global energy consumption and environmental problems. In this paper, the Reynolds number and the Froude number are obtained by a dimensional analysis, and then the data are obtained from the model ship experiment with the same Froude number by the law of similarity. The total resistance is subdivided into four kinds with different causes, namely the friction resistance, wave-making resistance, form resistance, and additional resistance. In this way, the ship resistance can be reduced more scientifically and efficiently.

Green economic recovery in central Asia by utilizing natural resources

In this research, the impacts of efficiency in natural resource utilization on green economic recovery are measured for the case of five Central Asian countries from 2000 to 2021 through the Panel ARDL (Autoregressive Distributed Lag) approach. The primary findings confirmed that for the industrial and agricultural sectors of the region, the efficiency of utilization of natural resources has a positive effect on green economic growth. In addition, the panel causality test confirmed a bi-directional causal linkage between green economic growth in industry and resource efficiency. In contrast, the efficiency in natural resource deployment causes green economic growth in the agricultural sector in Central Asia. The governments of Central Asia can follow practical policies like supporting green technologies and innovations, transferring green technologies from advanced countries, imposing a carbon tax, and greening mechanization of the agricultural sector.

The dynamic relationship between trade openness, foreign direct investment, capital formation, and industrial economic growth in China: new evidence from ARDL bounds testing approach

The objective of this research is to use annual data from 1990 to 2021 to examine the long- and short-run dynamic relationships among China’s trade openness (TRO), foreign direct investment (FDI), capital formation (K), and industrial economic growth (IEG) using the Autoregressive Distribution Lag (ARDL) method. Firstly, the results of the ARDL co-integration tests show that there is a long-run co-integration relationship among TRO, FDI, K, and IEG. Secondly, from a path of influence perspective, both the long- and short-run relationships are almost the same. Specifically, TRO, FDI, and K all have positive effects on IEG and vice versa, which supports the feedback hypothesis. However, contrary to the short-run relationship, TRO and K have a small negative effect on IEG, but this is not statistically significant. Finally, K and TRO positively affect FDI, while FDI negatively affects K, although the effect is minimal and negligible at the 10% significance level. On the contrary, they are not statistically significant in the long run. These results support the theory that technological innovation in the trade, investment and capital system based on economic and market capital can stimulate the development of China’s industrial economy.

Spatial Effect Analysis of Forestry Technology Innovation on Forestry Industry Economic Growth

The forestry industry is a low-carbon green industry with great potential for development, but its current development model is facing multiple dilemmas that urgently require a shift to high-quality forestry development. Neoclassical theory and endogenous growth theory consider technology innovation as the foundation of economic growth. In order to explore the impact of forestry technology innovation (FTI) on the economic growth of forestry industry, this paper uses the entropy method, spatial Durbin model, and threshold model to explore the spatial effect of forestry technology innovation on forestry industry economy for analysis and exploration of the non-linear relationship between the two, and the panel data from 31 provinces in China from 2002 to 2020 are used as the sample for empirical study. Results show that: (1) Forestry technology innovation has a significant role in promoting the economic growth of the forestry industry and has obvious spatial spillover effects, which also promotes the economic growth of forestry in neighboring regions. (2) There is a threshold effect of forestry technology innovation on the economic growth of the forestry industry. When the forestry technology innovation exceeds its optimal interval, its effect on the economic growth of forestry industry shows diminishing marginal benefits. (3) Forestry technology innovation has industrial heterogeneity on the economic growth of the forestry industry. Therefore, managers should take advantage of the growth effect of forestry technology innovation in the forestry industry at the level of sustainability, and further make full use of the spatial effect resulting from the flow of technology to establish a system of communication and learning to form a virtuous coordination of the forestry economic environment for the high-quality development of the forestry industry.

Role of green finance in resource efficiency and green economic growth

The issue of resource efficiency and achieving green economic growth has become a controversial issue in recent years. This research has studied the green financing tool as a reliable and effective solution to achieve green economic growth and increase resource efficiency. Using the method of Panel Vector Autoregressive (PVAR), the relationships between green bonds, resource efficiency index, and green economic growth rates in the industrial and agricultural sectors in 29 countries with green financing markets during 2015–2021 were analyzed. The major results confirmed that issuing green bonds and green economic growth in the industry could accelerate agriculture's green economic growth rate. At the same time, resource efficiency has not significantly impacted the green economic growth rate in the agriculture sector. In addition, the feedback hypothesis exists in the relationship between issued green bonds and green economic growth in industry and agriculture. The main recommended policy implications are strengthening and reforming the financial sector, redefining resource efficiency indicators in countries, and developing of digital green financing market.

Tinjauan Literatur Sistematis Penerapan DMAIC dan DMADV di Industri Manufaktur

Manufacturing company activities consist of several activities, one of which is management. The Process Management Framework includes activities to develop, improve and manage business processes in manufacturing companies. Some companies use some of the best methodologies to improve or develop existing processes and develop new ones. This literature aims to analyze the application of the Define, Measure, Analyze, Improve, Control (DMAIC) and Define, Measure, Analyze, Design, Validate (DMADV) methods in the Manufacturing Industry. The method used is Systematic Literature Review. These results indicate that the DMAIC and DMADV methods are methods that drive the pace of industrial economic growth, because they reduce process variability and improve product quality and prevent errors from occurring. However, this process requires trained and experienced personnel to meet the demand for product defect reduction. This method is the choice of industries that want to improve product quality and avoid mistakes, even though it takes a long time to implement, but almost all problems can be solved and improve processes in the manufacturing industry.

Driving Effect of Decoupling Provincial Industrial Economic Growth and Industrial Carbon Emissions in China.

China is facing the dual challenges of fostering economic growth and mounting an effective response to climate change, so it is vital to continue promoting industrial carbon emission reduction. This paper uses panel data from 1998 to 2019 to measure the industrial carbon emissions of 30 provinces in China. The Tapio decoupling and IPAT (Impact = Population × Affluence × Technology)-based decoupling models are used to analyze each province's velocity and quantity decoupling index for industrial carbon emissions. The fixed effect model analyzes the influencing factors for carbon decoupling. The results show that the industrial carbon emissions of various provinces in China are increasing yearly, but there are significant differences among provinces. The carbon decoupling of the industrial economy in most provinces is weak, and the quantitative decoupling index is better than the velocity decoupling index. The cleanliness of energy, balance, and labor productivity significantly affect the velocity decoupling index. The cleanliness of energy, the industry's structure, and the population significantly affect the quantity decoupling index. Based on empirical results, the study puts forward some policies to promote the efficient carbon decoupling of the industrial economy.

On the dynamics of expectations, uncertainty and economic growth: an empirical analysis for the case of Uruguay

PurposeFrom these data, the authors construct an uncertainty index through the use of a vector autoregressive (VAR) model to measure the impact of uncertainty on GDP, controlling for inflation, which may affect macroeconomic performance. Results indicate that uncertainty is negatively correlated with the economic cycle and the inter-annual variation of the biannual average product.Design/methodology/approachThis study empirically explores the dynamics of expectations of the Uruguayan manufacturing firms about industrial economic growth. This study explores the dynamics of the industrial economic growth expectations of Uruguayan manufacturing firms. The empirical research is based on firms' expectations data collected through a monthly survey carried out by the Chamber of Industries of Uruguay (CIU) in 2003–2018.FindingsGranger causality tests show that uncertainty Granger-causes industrial production growth and a one standard deviation shock on uncertainty generates a contraction in the industrial production growth rate. Finally, the authors use statistical and network tools to identify groups of firms with similar performance on expectations. Results show that higher uncertainty is associated with smaller, more interconnected groups of firms, and that the number of homogeneous groups and the distance between groups increases with uncertainty. These findings suggest that policies focused on the coordination of expectations can lead to the development of stable opinion groups.Originality/valueThe paper introduces new data and new methodologies to analyze the dynamics of expectations of manufacturing firms about industrial economic growth.HighlightsAn empirical approach to compare expectations of firms is introduced.The occurrence of groups of opinion is tested.Central companies in the network of expectations are detected.More uncertainty implies a higher degree of discrepancy between the overall firm’s opinions and more compact opinion groups.

Global Value Chain Linkages and Employment: Empirical Assessment for India’s Manufacturing Sector

We explore the connection between global value chain linkages and employment outcomes for 17 manufacturing sectors in India utilizing panel long run estimator and short run dynamics for the period 1995–2017. We model this relationship within the production function framework and disentangle two significant outcomes. In the first place, we find that in four varieties of the advancement model, employment and value chain linkages exhibit a long run relationship; that is, they are cointegrated. Second, we find that over the long haul, while forward linkages (DVX), compensation to labourers and industrial economic growth have contributed decidedly to employment in manufacturing sector, while the backward linkages (FVA) have had an unimportant impact. Another striking result in the short run shows that in an emerging economy like India, a rise in forward value chain linkage is directly associated with an increase in relative employment of skilled labour force. We draw significant arrangement suggestions from these findings.

A data-driven approach for the measurement and improvement of regional industrial ecological efficiency for carbon peaking and carbon neutralization.

Industrial ecological efficiency is regarded as an urgent challenge that affects the development of ecological civilization and environmental governance. Here, we propose a data-driven approach to measure and promote regional industrial ecological efficiency. We collected data related to regional industrial development and used the Data Envelopment Analysis-Banker Charnes and Cooper (DEA-BCC) model to measure regional industrial ecological efficiency from a static perspective. The Malmquist index model was then used to measure regional industrial ecological efficiency from a dynamic perspective. In addition, we used a Tobit regression model to identify the factors affecting regional industrial ecological efficiency. Through a case study of regional industrial ecological efficiency, we demonstrate the specific application of the proposed data-driven approach. This study provides a new and effective tool for improving industrial ecological efficiency at a regional scale. This method can help enterprises and local governments improve industrial ecological efficiency, coordinate the relationship between industrial economic growth and the ecological environment, and boost regional efforts to achieve carbon peaking and carbon neutralization goals.

How Did Work-Related Depression, Anxiety, and Stress Hamper Healthcare Employee Performance during COVID-19? The Mediating Role of Job Burnout and Mental Health.

The study objective was to examine the psychological impact of the COVID-19 pandemic on the performance of healthcare employees. The study was informed by a theoretical framework that incorporates different psychological issues (i.e., stress, depression, and anxiety) that influence healthcare workers’ performance through the mediating roles of job burnout and mental health. The study data was gathered through structured questionnaires from 669 participants working in the healthcare sector in Pakistan. A structured equation modeling (SEM) technique was used for data analysis and hypothesis development. It was found that stress, depression, and anxiety positively affected healthcare employees’ job performance during COVID-19. Psychological factors had a positive and significant impact on job burnout and mental health. Job burnout and mental health mediated the relationship between stress, anxiety, depression, and employee performance. The ongoing repercussions of COVID-19 include their impact on employee performance in the healthcare sector. Healthcare worker performance is critical to fostering industrial economic growth. Elevated levels of stress, depression, and anxiety have profoundly exacerbated employee mental health issues. COVID-19 has created challenging working conditions in organizations requiring that they address the growing psychological issues which impact negatively on worker performance.

The Spatial Economic Effect of Industrial Credit Misallocation in China

The cross-industry allocation of bank credit plays an important role in China’s economic development and optimizing and upgrading industrial structures. This article collects the panel data of 19 industries in China from 2007 to 2017, constructs dynamic spatial panel models, and deeply analyzes the impact of credit misallocation on industry economy. The results show that structural misallocation of bank credit exists in various industries in China. Particularly, credit deviation degree of China’s industries has gradually declined, but structural volatility remains large. A large amount of capital flows to the tertiary industry, while the capital of the primary industry and the secondary industry is relatively scarce. What is more, credit misallocation has a negative impact on industry economic growth and shows a weak acceleration effect. Therefore, in formulating economic policies, the government should actively guide the rational flow of inter industry credit funds and improve the efficiency of capital allocation. The government should continue to increase investment in manufacturing and promote independent innovation. Additionally, reasonable financial policies should be formulated to ensure the effective operation of the market and reduce the degree of credit misallocation.