Intensive Manufacturing Industries Research Articles

Trade-off between carbon emissions and employment embodied in global value chains: A study based on enterprise heterogeneity in China

Comprehensively measuring embodied carbon emissions and employment generation from global value chain (GVC) production activities is essential for GVC upgrades and sustainable development. Previous studies have ignored embodied carbon emissions and employment generation from GVC production activities involving cross-border direct investment, which may underestimate the impact of GVC participation. Using an intercountry input-output model distinguishing domestic and foreign-invested enterprises, this study measured embodied carbon emissions and employment generation in China's GVC production activities from 2005 to 2018 and explored their trade-offs based on the aggregate-embodied carbon employment intensity (ACEI). The major findings were as follows. (1) Trade-related GVC contributes more to embodied carbon emissions than to that of embodied employment. However, investment-related GVC had the opposite effect. Mixed trade and investment-related GVC contribute approximately equally to carbon emissions and employment. (2) China experienced increasing environmental losses relative to employment generation from GVC participation increased between 2005 and 2018. The environmental costs of creating employment units through investment-related GVC activities were lower than those for the other two GVC routes. (3) High R&D-intensive manufacturing industries suffered a notable decline in ACEI. This finding suggests that a double dividend for the environment and employment is more likely to be achieved in high-tech manufacturing industries. (4) The ACEI of foreign-invested enterprises in China was lower than that of domestic enterprises, particularly in the production-sharing activities between foreign-invested enterprises, which exhibited a better trade-off between socioeconomic benefits and environmental damages. These findings provide useful insights into the impact assessment of GVCs, which aid in achieving sustainable development in China and other emerging economies.

The impact of digital economy on green total factor productivity considering the labor-technology-pollution factors

The digital economy provides new impetus for the high-quality development of manufacturing industry. Through the DEA-Malmquist model and panel regression model, this paper confirmed that there is a positive and significant relationship between the development of digital economy and the green total factor productivity (GTFP) of manufacturing industry. The research result is as follows: (1) the development of digital economy can enhance the overall GTFP of manufacturing industry. (2) The green technology progress brought by the development of digital economy is the main path to promote the GTFP of manufacturing industry. (3) The heterogeneity analysis shows that the impact of digital economy on GTFP of high pollution manufacturing industry is significantly positive, the impact of labor-intensive manufacturing industry is significantly negative, and the impact of technology intensive manufacturing industry is not obvious. The contributions of this study are as follow. In terms of theory, this study theoretically continues Solow’s classical theory, demonstrating the scientific nature of digital technology progress in promoting GTFP growth. In empirical analysis, this study build a new digital economy development level evaluation index system based on the perspective of manufacturing industry. In addition, this study also add a labor-technology-pollution perspective for the development of relevant policies.

Suppliers' GHG mitigation strategies (Scope 3): the case of a steelmaking company

PurposeThe emission of greenhouse gases has become an increasingly relevant topic in supply chain management. The steel industry is a highly intensive manufacturing industry with significant emission levels, particularly Scope 3 emissions, which are the indirect emissions from suppliers. Since a supply chain is seen as a non-mandatory measurement item within GHG measurement protocols, this article contributes to the literature on assessing the suppliers of a focal company relative to their emissions for complying with Scope 3 (indirect emissions). It adds to the evolving literature on low-carbon supply chains.Design/methodology/approachThis study first conducted a survey with 110 suppliers from a focal transnational buyer company. A cluster analysis was performed, and ANOVA compared constructs relating to public or private ownership and country of origin. Finally, regression tested the relationship between the motivators and governance in the mitigation strategies.FindingsUsing cluster analysis, two groups of companies were found that have statistically significant differences. The influence of the country of origin was also found in relation to governance and mitigation strategies, as was the influence of the type of company on governance. Furthermore, the more motivated the suppliers and the more governance measures they adopt, the more companies adopt their own GHG mitigation strategies. These findings are summarized by way of an analytical framework that integrates the constructs with empirical evidence.Originality/valueThe steel industry is a sector that is particularly energy-intensive and produces millions of tons of CO2 per year. Emissions from its SC (Scope 3) are relevant but still seen as a non-mandatory item for measurement purposes within the GHG measurement protocols, which leads to less attention being paid to the subject. This study contributes by way of its analytical framework that is validated by empirical data that can be tested in further studies.

Innovation efficiency analysis of capital intensive manufacturing industry based on DEA and Malmquist index--Take Shaanxi Province as an example

The innovation development of capital intensive manufacturing industry is an important driving force to promote the transformation of manufacturing industry in Shaanxi Province. In this paper, DEAP2.1 software and DEA-Malmquist model are used to make static analysis of innovation efficiency, pure technology efficiency and scale efficiency of five major capital-intensive sub-industries in Shaanxi Province during 2015-2020 from static and dynamic perspectives. And the dynamic analysis of efficiency change, technology change and their product total factor productivity of five major sub-industries is carried out to get the decomposition of capital intensive manufacturing industry in different industries and time in Shaanxi Province. Therefore, the research conclusion is drawn: the comprehensive innovation efficiency of capital intensive manufacturing industry in Shaanxi province is low, the innovation level is not high, and the scale efficiency value is low. The change of pure technical efficiency has a significant impact on the change of total factor productivity. Therefore, in order to improve the value of total factor productivity, theoretically speaking, we should always pay attention to the fluctuation of pure technical efficiency, and put forward suggestions for the development of capital intensive manufacturing industry in Shaanxi Province from the aspects of talent, technological innovation and resource allocation.

Have the construction of China’s software industry bases improved the level of manufacturing servitization?

This paper takes the software industry base of national torch plan as the research object, describes its construction process and distribution characteristics for more than 20 years, and uses the cross-sectional data of 2007, 2012 and 2017, Empirically test the impact of software industry base on the service level of manufacturing industry in the region, and further analyze the regulatory effect of regional intellectual property protection intensity. It is found that the overall scale of the national software industry base has expanded steadily in recent 20 years, but the spatial distribution is extremely unbalanced. The software industry base in the eastern region is significantly stronger than that in the central and western regions in terms of base number and base income. The construction and development of software industry base can significantly improve the service level of manufacturing industry in the region. After the robustness test, the conclusion is still valid. The results of regional heterogeneity test show that with the deepening of industrialization, the construction of software industry base plays a more obvious role in improving the service-oriented manufacturing industry in the eastern region. The results of industry heterogeneity test show that the construction of software industry base can more effectively improve the service-oriented level of technology intensive manufacturing industry. The results of regulation effect test show that the strength of local intellectual property protection and strengthening the construction of software industry base have a positive regulation effect on the service level of manufacturing industry.

Intellectual Property Information Service in Colleges and Universities Under the Background of Innovation Driven

This paper studies the intellectual property information service in Colleges and Universities under the background of innovation driven. Firstly, this paper expounds the concept and characteristics of intellectual property management in knowledge intensive manufacturing industry. On this basis, this paper compares and analyzes the current situation of intellectual property in knowledge intensive manufacturing industry at home and abroad from the aspects of intellectual property development, operation, protection and coordination, and finds out the achievements and shortcomings of intellectual property management in knowledge intensive manufacturing industry in China. This paper analyzes the hypercycle model of knowledge transfer and the collaborative evolution mechanism of knowledge transfer in knowledge intensive manufacturing industry by using the relevant theories of linkage and hypercycle. On this basis, the linkage relationship between knowledge transfer and intellectual property management in knowledge intensive manufacturing industry is analyzed. Based on the specific domestic intellectual property environment, this paper constructs the intellectual property management system of China's knowledge intensive manufacturing industry from three dimensions: intellectual property development, intellectual property protection and intellectual property operation.

DBAM: Making Virtual Metrology/Soft sensing with time series data scalable through Deep Learning

Machine Learning (ML) based technologies, like Virtual Metrology (VM)/Soft Sensing, Predictive Maintenance and Fault Detection, have been successfully applied in the past recent years in data intensive manufacturing industries, like semiconductor manufacturing, to improve process monitoring and related operations. Standardization and alignment over multiple equipment is a key element to ensure industry-wide adoption and scalability for ML-based technologies in complex production environment. In this work we address the topic of VM/Soft Sensing – a particular ML-based technology for process control – in the context of equipment matching and scalability. We present a Deep Learning-based domain adaptation approach, called DANN-Based Model Alignment (DBAM), that provides a common VM model for two identical-in-design systems whose data are following different distributions. The proposed approach has the merit of (i) exploiting directly raw sensor data (that typically present themselves in the form of time series) and (ii) offering interpretability of the features. The proposed approach is compared against other approaches in the literature for VM/Soft Sensing on a real-world case study from semiconductor manufacturing.

Research On The Impact Of Producer Services Import On China's Manufacturing Industry's Climbing Global Value Chain

Intermediate investment in producer services is an effective way to promote the manufacturing industry to climb the global value chain. This paper uses the export added value data of OECD-TiVA database to calculate the export technical complexity of 17 types of manufacturing industries in China, so as to measure the position of various industries in the value chain, and empirically test the impact of producer services import on the promotion of the position of China's manufacturing value chain. The results show that producer services import has a significant positive impact on the promotion of China's manufacturing value chain, and the promotion effect has industry heterogeneity. In the labor-intensive manufacturing industry, the import of producer services plays the strongest role in promoting the rise of its value chain, followed by the capital intensive manufacturing industry.

A Review on Big Data Analytics for Energy Efficiency, Conservation and Management in Energy Intensive Manufacturing Industries

A Review on Big Data Analytics for Energy Efficiency, Conservation and Management in Energy Intensive Manufacturing Industries

When labour market rigidities are useful for innovation. Evidence from German IAB firm-level data

Supply-side economists made numerous pleas for removing labour market rigidities through ‘structural reforms’. From a Schumpeterian viewpoint, however, one can argue that labour market rigidities create useful incentives for innovation. Our estimates on German panel data (2007–2015) support the Schumpeterian view. Using the taxonomy of industries by Peneder (2010) we find that evidence of the Schumpeterian view is particularly strong in R&D intensive manufacturing industries, as well as in service industries with high degrees of cumulativeness of knowledge. Evidence of the Schumpeterian view is weaker in low-technology industries and in start-ups when firms rely more on generally available knowledge rather than on firm-specific and tacit knowledge accumulated in the past.

Labor-Saving Agricultural Technical Change and Industrial Development

The introduction of labor-saving technologies in agriculture can release workers who find occupation in the manufacturing sector. The traditional view is that this structural transformation process leads to economic growth. However, if workers leaving agriculture are unskilled, the labor reallocation process reinforces comparative advantage in the least skill-intensive manufacturing industries. We embed this mechanism in a multi-sector endogenous growth model where only skill-intensive manufacturing industries innovate and generate knowledge spillovers. In this setup, the increase in the relative size of the unskilled-labor intensive industries reduces the incentives to innovate and slows down growth. We test the predictions of the model in the context of a large and exogenous increase in agricultural productivity in Brazil. We use social security data to develop a new measure of the labor input in innovation which is representative at any level of spatial aggregation. We find that regions adopting the new agricultural technology experienced a reallocation of unskilled workers away from agriculture into the least R&D-intensive manufacturing industries. The expansion of low-R&D industries attracted workers away from innovative occupations in high-R&D industries, slowing down local aggregate manufacturing productivity growth.

Output, employment and productivity: contribution of different technology intensive manufacturing industries of India

Output, employment and productivity: contribution of different technology intensive manufacturing industries of India

Output, Employment and Productivity: Contribution of different technology intensive manufacturing industries of India

Structural shift in manufacturing sector from low-technology intensive industries towards high-technology intensive industries is seen as critical for the development process. This is based on the argument that high-tech industries are highly innovative and have potential of high growth and productivity. The study has examined the same argument by analysing the real contribution of different technology intensive manufacturing sub-sectors in terms of output, employment and productivity by using firm level data drawn from CMIE Prowess for the period 1999–2000 to 2015–2016. Results show that high-tech oriented structural change is present in Indian manufacturing sector but it is very slow and small. There is not much variation in growth rates of high-tech industries and its counterparts. The finding casts doubt on the wisdom of targeting high-tech industries only because high-tech industries are overrepresented as high growth industries but in Indian context other sub-sectors (medium low-tech and low-tech industries) are also contributing to substantial portion of aggregated growth of Indian manufacturing sector in terms of output, employment and productivity.

Comment on “Geographic Concentration in Indian Manufacturing and Service Industries: Evidence from 1998 to 2013”

Comment on “Geographic Concentration in Indian Manufacturing and Service Industries: Evidence from 1998 to 2013”

The Imitator's Dilemma: Why Imitators Should Break Out of Imitation

Imitation and innovation are two primary R&D approaches that firms follow in technology development, especially in R&D‐intensive industries. That imitation and innovation share R&D resources and investments gives rise to what is coined in this article as the imitator's dilemma. The imitator's dilemma tells a story of why firms should break out of imitation‐oriented R&D and move toward innovation‐oriented R&D in order to sustain their innovation output and profit performance. This article contributes to the technology and innovation management literature by illuminating the imitator's dilemma both theoretically and empirically. To this end, this study develops and tests hypotheses to investigate the influence of a firm's imitation activity on its innovation output and profit performance, which represent a gap in the current literature. A longitudinal research design is followed on an unbalanced panel dataset between 1991 and 2010 from a sample of 227 firms in three R&D‐intensive manufacturing industries in the United States, including computer, semiconductor, and pharmaceutical. The results of this research reveal a dilemma for imitators. Imitation activity can generate positive returns in terms of a firm's innovation output and return on assets ROA (a measure of short‐term profits). However, these returns are unsustainable. Excessive levels of imitation activity within the firm results in negative returns in terms of its innovation output and ROA. Additionally, any level of imitation activity, low or high, negatively impacts a firm's Tobin's Q (a measure of long‐term corporate valuation). Accordingly, this article makes novel contributions to the technology and innovation management literature by explaining the imitator's dilemma and how firms may effectively manage it.

Carbon footprint stock analysis of US manufacturing: a time series input-output LCA

PurposeThe purpose of this paper is to provide an input-output life cycle assessment model to estimate the carbon footprint of US manufacturing sectors. To achieve this, the paper sets out the following objectives: develop a time series carbon footprint estimation model for US manufacturing sectors; analyze the annual and cumulative carbon footprint; analyze and identify the most carbon emitting and carbon intensive manufacturing industries in the last four decades; and analyze the supply chains of US manufacturing industries to help identify the most critical carbon emitting industries.Design/methodology/approachInitially, the economic input-output tables of US economy and carbon footprint multipliers were collected from EORA database (Lenzen et al., 2012). Then, economic input-output life cycle assessment models were developed to quantify the carbon footprint extents of the US manufacturing sectors between 1970 and 2011. The carbon footprint is assessed in metric tons of CO2-equivalent, whereas the economic outputs were measured in million dollar economic activity.FindingsThe salient finding of this paper is that the carbon footprint stock has been increasing substantially over the last four decades. The steep growth in economic output unfortunately over-shadowed the potential benefits that were obtained from lower CO2 intensities. Analysis of specific industry results indicate that the top five manufacturing sectors based on total carbon footprint share are “petroleum refineries,” “Animal (except poultry) slaughtering, rendering, and processing,” “Other basic organic chemical manufacturing,” “Motor vehicle parts manufacturing,” and “Iron and steel mills and ferroalloy manufacturing.”Originality/valueThis paper proposes a state-of-art time series input-output-based carbon footprint assessment for the US manufacturing industries considering direct (onsite) and indirect (supply chain) impacts. In addition, the paper provides carbon intensity and carbon stock variables that are assessed over time for each of the US manufacturing industries from a supply chain footprint perspective.

Optimum Throughput Rate under Buyer’s Market in Capital Intensive Manufacturing Industry

Optimum Throughput Rate under Buyer’s Market in Capital Intensive Manufacturing Industry

Analysing energy intensity trends and decoupling of growth from energy use in Indian manufacturing industries during 1973–1974 to 2011–2012

This paper has two research objectives: first, it derives and analyses energy intensity trends for seven energy intensive manufacturing industries and the aggregate manufacturing sector in India for the period 1973–1974 to 2011–2012 and compares the same with best practice benchmarks. Second, based on Index Decomposition Analysis, it studies the extent to which the energy efficiency has contributed in the decoupling of industrial activity growth from growth in energy use. The study finds faster decline in energy intensity in all the seven industries during the recent years (1998–1999 to 2011–2012). Aluminium, cement and fertilizer industries are found to operate close to the global best-practice energy intensities with transformational changes in process technology. Iron and steel and pulp and paper are found to be lagging behind with only incremental transformation in technology in place. The decomposition results show that activity growth is the major driver of growth in energy demand with marginal impact coming from structural change. However, declining energy intensity has been able to neutralize a major portion of the growing energy demand resulting in decoupling trends, especially in recent years, with more energy efficiency-related voluntary and mandatory policies in place).

Indian Steel Industry: Current Business Trends and HR Interventions

Background Objective: A typical matured and labour intensive manufacturing industry like Indian steel Industry is on a transformation path for improving operational efficiency and creating value for the customer through leveraging its major strength i.e. Human Resource. The objective is to identify the HR interventions required at this juncture keeping in view the key business drivers. Methods/Statistical Analysis: Indian Steel Industry today is at a cross road. Global market and free trade agreements with other steel producing Nations, unbelievable pace of change in technology have impacted the Industry as never before. At this juncture it is imperative first to identify and analyze the Key Business Drivers and Current Business Trends. Based on the above two parameters as well as Strength and weakness of the industry, HR Challenges for the Industry can be identified. The HR challenges can lead to focused HR interventions required for the Industry to have competitive advantage in Global Market. Findings: Since the future and nature of work in the Industry is under rapid transformation, Indian Steel organization have to realize that the traditional approach of HR interventions so far being implemented in the Industry are loosing their relevance. The HR challenges being faced by the Industry is different than the challenges in the past and hence have to be approached through new set of HR interventions. Application/Improvement: In this paper, a systematic approach has been attempted to identify HR Interventions required today by the Indian Steel Industry through focus on Key Business Drivers, Current Business Trends, SWOT analysis of the Industry, HR Challenges and HR levers.

Productivity and efficiency of labour intensive manufacturing industries in India

Purpose This paper aims to make an attempt to identify labour intensity of organized manufacturing industries in India using the Annual Survey of Industry (ASI) data at three-digit level. It estimates total factor productivity growth (TFPG) and technical efficiency for both labour intensive and all manufacturing industries during the pre- and post-reforms periods. Design/methodology/approach The study uses three approaches to estimate TFPG. They are growth accounting (GA) (non-parametric), production function with correction for endogeneity – Levinsohn-Petrin (LP) (semi-parametric) and stochastic production frontier (SPF) analysis (parametric). The study uses ASI data published by Central Statistical Organization, Government of India for the period 1980-1981 to 2007-2008 for the analysis. Findings The study finds that the rate of decline of the labour intensity is more pronounced in the case of labour-intensive industries than all the manufacturing industries. The results of GA method suggest that the TFPG of labour-intensive industries has declined continuously from the pre-reforms period to the post-reforms period. Similarly, LP method indicates a continuous decline in TFPG of labour-intensive manufacturing industries during the post-reforms period. Interestingly, the results of SPF method also corroborate the findings of earlier two methods at the aggregate level but vary at a certain degree at the disaggregated level. Originality/value This paper is useful in the context of India considering the importance given to labour-intensive industries by the present government in terms of reviving the sector and improving the productivity and output.