Hasil untuk "Industrial productivity"

Yun Wang, Xiaohua Sun, Xu Guo

Abstract Green growth has become an important development strategy for OECD countries and governments have correspondingly implemented various environmental regulation policies, whereas few studies have discussed the impacts of environmental regulation on green productivity growth in OECD countries. Based on a panel data of OECD countries' industrial sectors, this study analyzes the stringency of environmental regulation policies and measures green productivity growth using an extended SBM-DDF approach. The dynamic panel regression investigates the impacts and mechanism of environmental policy stringency on green productivity growth in OECD countries’ industrial sectors. The main results are: (i) Porter hypothesis is validated that the environmental policy has a positive impact on green productivity growth within a certain level of stringency (lower than 3.08); (ii) The impact turns to be adverse when the environmental regulation policy is stringent over a certain level, as the compliance cost effect is higher than innovation offset effect. The findings provide new empirical evidence for the strong version of the Porter Hypothesis and some implications for OECD countries to further promote green growth.

Xiping Liu, Xiaoling Zhang

Abstract Industrial agglomeration and technological innovation are considered significant mechanisms to affect carbon productivity. This paper investigates the relationship between heterogeneous industrial agglomeration, technological innovation and carbon productivity using the Dynamic Spatial Durbin Model based on panel data of the industrial sector of 30 Chinese provinces from 1998 to 2017. The results show that: overall, there is an inverted “U” relationship between industrial agglomeration and carbon productivity, and technological innovation plays an important role in determining the “inflection point”. The impact of technological innovation upon industrial agglomeration as well as on carbon productivity is different under different types of industrial agglomerations and different regions. Compared with the general technological innovation, low-carbon technological innovation has a larger impact on carbon productivity, whilst its potential to improve the situation has not been fully realized. Carbon productivity has significant path-dependent characteristics and, together with industrial agglomeration and technological innovation, they all have significant spatial spillover effects. Both theoretical and practical significance have drawn from this paper, in particular for China, to optimize the efficiency through spatial reforming of industrial agglomerations to maintain economic growth and increase the total carbon productivity from the long run.

Shilei Qiu, Zilong Wang, Shuaishuai Geng

This study investigates the impact of environmental regulations (ERs) and foreign direct investment (FDI) on the green total factor productivity (GTFP) of the industrial sectors in 30 provinces in China by controlling human capital, technological innovation, energy structure, degree of opening up, and ownership structure for the period of 2004-2017. This not only helps to explain the influence path of ERs and FDI on green economic growth, but also effectively measures the moderating effect of ERs on technology spillover from FDI. The purpose of this paper is to explore the relationship between ERs, FDI and industrial GTFP from the perspective of regional heterogeneity, focusing on studying how ERs regulate the impact of FDI on GTFP. By constructing an influence mechanism of ERs and FDI on industrial GTFP, this study employs the feasible generalized least squares (FGLS) model and dynamic generalized method of moments (GMM) model to analyze the effects of ERs, FDI and their cross-terms on GTFP. The empirical results show that (1) the relationship between ERs and GTFP is not linear, but "U"-shaped and China is still in the left half of the "U"-shaped curve; (2) FDI flowing into China has a "pollution heaven" effect on the GTFP in the eastern and central regions while a "pollution halo" effect on the GTFP in the western region; (3) the strengthening of ERs weakens the negative effect of FDI on GTFP and plays a role in "screening" foreign investment; and (4) the spatial heterogeneity could affect the synergistic effect between ERs and FDI. Therefore, it is necessary for China to consider a series of environmental policies to "screen" inward FDI to ensure its move to a green economy benefits its own sustainable development by contributing to the increase in GTFP.

Lequn Fu, Yijun Zhong, Xiao Li et al.

Humanoid robots deployed in industrial environments are required to perform load-carrying transportation tasks that tightly couple locomotion and manipulation. However, achieving stable and robust locomotion under varying payloads and upper-body motions is challenging due to dynamic coupling and partial observability. This paper presents a load-aware locomotion framework for industrial humanoids based on a decoupled yet coordinated loco-manipulation architecture. Lower-body locomotion is controlled via a reinforcement learning policy producing residual joint actions on kinematically derived nominal configurations. A kinematics-based locomotion reference with a height-conditioned joint-space offset guides learning, while a history-based state estimator infers base linear velocity and height and encodes residual load- and manipulation-induced disturbances in a compact latent representation. The framework is trained entirely in simulation and deployed on a full-size humanoid robot without fine-tuning. Simulation and real-world experiments demonstrate faster training, accurate height tracking, and stable loco-manipulation. Project page: https://lequn-f.github.io/LALO/

Dingyun Duan, Shaojian Chen, Zongxian Feng et al.

Xinhai Lu, Xu-song Jiang, Mengyuan Gong

Abstract Market-oriented allocation of land resources is one of the main economic reforms in China. However, the influence of land transfer marketization (LTM) on green total factor productivity (green TFP) and its mechanisms remain unknown. Based on panel data of 30 provincial administrative regions in China from 2004 to 2016, this study attempts to establish mechanism among LTM, industrial structure and green TFP, which is measured by the Slack Based Measure DEA (SBM-DEA) model containing undesirable output, and empirically estimates the impact of LTM, the rationalization and optimization of industrial structure and their interactions on green TFP further. The results show that: 1) LTM has a significant promoting effect on the improvement of green TFP in China, and the effect is also significant in the eastern, central and western regions as well, indicating that the application of land transfer policy to regulate regional economic development is widespread in China. 2) The rationalization of industrial structure has significantly promoted the development of green TFP in China, as well as at the regional levels, and the effect decreases from the western region to the central and eastern regions. The optimization of industrial structure also has a promoting effect on green TFP in China and all the regions, but the regional differences of the effect are contrary to that of the rationalization of industrial structure, with a decreasing trend from the eastern region to the central and western regions. 3) The interaction between LTM and the rationalization of industrial structure has significantly inhibited the improvement of green TFP in China, and the regional variations of the restraining effect are higher in the western region, followed by the central and eastern regions. The interaction between LTM and the optimization of industrial structure also has a negative effect on green TFP in China, and the regional variations of the effect showing the decreasing trend from the eastern region to the western and central regions. Therefore, China should continue to adhere to the market-oriented urban land transfer system reform, actively play the role of the government in the industrial upgrading and industrial transfer, and formulate a differentiated land transfer system and industrial development policies based on the economic development and industrial characteristics of various regions, so as to achieve the sustainable development of China’s economy and society.

Binbin Yu

Elsadig Musa Ahmed, Khalid Eltayeb Elfaki, Eimad Abusham

This study introduces a novel Hybrid Deep Ensemble (HDE) model, which can maximize the accuracy of the prediction by combining the benefits of multiple learning architectures to examine the predictive performance of Machine Learning (ML) Models. The proposed model consists of three models: Multiple Linear Regression (MLR), Random Forest Regression (RF), and Gradient Boosting Regression (GBR) as the base learners. Meta-learners combine these models' outputs to make final predictions using a regression model. The HDE model was applied to forecast Gross Domestic Product (GDP) and Carbon Dioxide (CO2) emissions by examining the influence of labor, capital, energy efficiency, and renewable energy in selected Asian Economies. The proposed HDE model performance is evaluated against two ensemble benchmark models: RF, which is based on bagging, and GBR, which is based on boosting; and MLR, which is a non-ensemble baseline algorithm. Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) measures were employed to evaluate the accuracy of the models using a dataset of economic and environmental indicators. With the lowest MAE and RMSE values for both GDP and CO2 emissions estimates, the results show that HDE is always lower than the MAE and RMSE values of both GDP and CO2, revealing the better suitability to predict complex and nonlinear patterns. This study highlights the importance of selecting the appropriate modelling approaches based on the properties of the data and the feasibility of ensemble learning. Overall, three models demonstrate that CO2 emissions are the primary factor influencing economic development, revealing a strong correlation between industrial or energy-related activities and economic performance. Renewable energy may also facilitate sustainable growth, while labour and capital have limited or adverse effects, exhibiting complex dynamics that vary by environment. The findings show that HDE and GBR are the best models for predicting economic growth and pollutant emissions and accurately capturing intricate non-linear interactions. Additionally, HDE, RF, and GBR offer greater insights into nonlinear interactions than MLR, revealing how these factors affect GDP. Green Total Factor Productivity (GTFP) indicates the influx of capital and labour in Asian countries, facilitating rapid development and industrialisation progress through technological innovation and the development of human capital skills. CO2 emissions and renewable energy influence economic growth, ensuring green technological progress through a productivity-driven approach to maximise the significant effects of energy efficiency and renewable energy, and to support GDP growth. An efficient strategy for utilising these factors is essential, leveraging the combined contributions of their qualities. These results underscore the significance of renewable energy in promoting sustainable development and the complex interplay between economic and environmental factors via implementing Sustainable Development Goals 7 and 13, affordable and clean energy (SDG7), and Climate Action (SDG13) to achieve SDG 8 Decent Work and economic growth and other SDGs of the United Nations (UN) agenda 2030.

Mattia Merlin, Lorenzo Antonioli, Federico Bin et al.

Foundry aluminum-silicon (Al-Si) alloys, especially those containing Cu and/or Mg, are widely used in casting processes for fabricating lightweight parts. This study focuses on the optimization of the solution heat treatment parameters within the T6 heat treatment of an innovative AlSi7Cu0.5Mg0.3 secondary alloy, aiming at achieving energy savings and reducing the environmental impact related to the production of foundry components for the automotive industry. Different combinations of solution times and temperatures lower than those typically adopted in industrial practice were evaluated, and their effects on tensile properties were investigated on samples machined from as-cast and T6-treated castings produced by pouring the alloy into a steel permanent mold. Thermal analysis (TA) and differential thermal analysis (DTA) were performed to monitor the solidification sequence of microstructural phases as well as their dissolution on heating according to the proposed solution heat treatments. Microstructural analysis by light microscopy (LM) and scanning electron microscopy (SEM), together with Brinell hardness testing, was also carried out to assess the effects of heat treatment parameters. The results suggested that a shorter solution heat treatment set at a temperature lower than that currently adopted for the heat treatment of the studied alloy can still ensure the required mechanical properties while improving productivity and reducing energy consumption.

T. Bitkova, T. Chen

As one of the world's largest producers and exporters of textiles and clothing, China plays a key role in the reconstruction of the global supply chain and in the changes in the international trade situation. China's textile industry, as a traditionally profitable one, is facing unprecedented challenges and opportunities, which confirms the relevance of this study. The development of cross-border trade in textile products significantly depends on the business environment, which includes many factors, such as government support, prime cost, tariffs and external demand. Analyzing the impact of these factors on Chinese textile exports is a key focus of this study. Jiangsu Province is a home to numerous textile factories specializing in various products – from raw materials to finished garments. The province has a well-developed infrastructure, including ports, highways and railways, which facilitates the efficient movement of goods. Analyzing the impact of both domestic and international trade policies on textile industry may provide insights into the dynamics of cross-border supply chains. The paper focuses on sustainable practices in China’s textile industry that can improve the productivity and efficiency of supply chains. The study focuses on the impact of the international business environment on cross-border trade of Jiangsu Province. Through qualitative and quantitative data analysis on export demand, production costs, tariffs, exchange rates and policy support, the impact of these factors on the volume of cross-border trade in textiles and apparel is assessed. The paper proposes such strategies as improving the resilience of Jiangsu’s textile industry, optimizing production cost control, promoting innovation and upgrading of the industry with government support and enhancing the ability of enterprises to withstand trade risks. This can provide a sound basis for decision-making on industrial upgrading and expanding the global market for the textile industry of Jiangsu and other provinces in China.

Cong Cen, Xiaoyan Lin

Abstract As a crucial digital general-purposed technology (DGPT) in the current digital economy era, the rapid development of mobile Internet technology not only promotes the digital upgrading of the economy, but has a profound impact on firm labor demands. Based on microdata from China’s listed companies and macrodata from prefecture-level cities, the paper evaluates the effect of mobile Internet development on employment at the firm level. Research shows that there is a roust causal relationship between mobile Internet development and the growth of firm labor demands. Influencing path tests found that mobile Internet can change firm labor demands by affecting its productivity, digital process, production scale and business scope. In terms of changes in employment structure, the paper found that mobile Internet is conducive to promoting the flow of labor to the advanced industrial structure; mobile Internet development significantly increased firm’s demands for high-skilled labor and low-level labor, so the technical polarization effect of mobile Internet applications on employment has not been observed. mobile Internet development improved firm labor demands for positions that are highly related to digital applications, but has reduced the labor demands for positions of routine tasks, which shows the bias of mobile Internet technology from the side. The above research provides theoretical and practical reference for us to promote mobile Internet technology innovation and innovative applications to achieve fuller employment and optimize employment structure in the era of digital economy.

M. Sychov, M. Mandryha

The study investigated the effect of organic acids and their salts in broiler diets on feed intake, digestibility, and utilization efficiency. The experiment was carried out on Cobb-500 broilers during a 42-day growing period. Four groups of 100 birds were formed: a control group fed a basal diet and three experimental groups supplemented with 0.5, 1.0, and 2.0 kg/t of a blend of organic acids and their salts. Birds were reared on litter under identical microclimatic and lighting conditions according to the «Cobb Broiler Management Guide». Feed intake, body-weight gain, and feed conversion ratio (FCR) were monitored weekly. It was found that inclusion of 1.0–2.0 kg/t of organic acids significantly increased both daily and total feed intake by 3.4–3.5 % compared to the control, most notably during the 8–14- and 29–35-day periods corresponding to maximal growth intensity. Feed conversion efficiency improved in the 15–28-day phase, with FCR reduced by 5.1–5.6 % and averaged 1.52 kg/kg in experimental groups versus 1.54 kg/kg in control. Improved utilization efficiency is attributed to a better gastrointestinal environment, lower pH enhancing digestive enzyme activity (pepsin, trypsin, lipase), and a favorable balance of intestinal microbiota with stimulated Lactobacillus and Bifidobacterium populations and suppressed pathogenic bacteria (E. coli, Salmonella spp.). Organic acids also celebrate minerals, improve bioavailability of Ca and P, and supply energy to enterocytes. In conclusion, dietary supplementation with 1.0–2.0 kg/t of organic acids and their salts improve feed digestibility, nutrient absorption, and growth performance of broilers without increasing feed costs. These additives can be considered an effective and safe alternative to antibiotic growth promoters in industrial poultry production, contributing to enhanced productivity, animal welfare, and ecological safety.

Qiyu Chen, Huiyuan Luo, Haiming Yao et al.

Anomaly detection plays a vital role in the inspection of industrial images. Most existing methods require separate models for each category, resulting in multiplied deployment costs. This highlights the challenge of developing a unified model for multi-class anomaly detection. However, the significant increase in inter-class interference leads to severe missed detections. Furthermore, the intra-class overlap between normal and abnormal samples, particularly in synthesis-based methods, cannot be ignored and may lead to over-detection. To tackle these issues, we propose a novel Center-aware Residual Anomaly Synthesis (CRAS) method for multi-class anomaly detection. CRAS leverages center-aware residual learning to couple samples from different categories into a unified center, mitigating the effects of inter-class interference. To further reduce intra-class overlap, CRAS introduces distance-guided anomaly synthesis that adaptively adjusts noise variance based on normal data distribution. Experimental results on diverse datasets and real-world industrial applications demonstrate the superior detection accuracy and competitive inference speed of CRAS. The source code and the newly constructed dataset are publicly available at https://github.com/cqylunlun/CRAS.

Shuai Li, Chen Yizhe, Li Dong et al.

The application of artificial intelligence (AI) in industry is accelerating the shift from traditional automation to intelligent systems with perception and cognition. Vision language-action (VLA) models have been a key paradigm in AI to unify perception, reasoning, and control. Has the performance of the VLA models met the industrial requirements? In this paper, from the perspective of industrial deployment, we compare the performance of existing state-of-the-art VLA models in industrial scenarios and analyze the limitations of VLA models for real-world industrial deployment from the perspectives of data collection and model architecture. The results show that the VLA models retain their ability to perform simple grasping tasks even in industrial settings after fine-tuning. However, there is much room for performance improvement in complex industrial environments, diverse object categories, and high precision placing tasks. Our findings provide practical insight into the adaptability of VLA models for industrial use and highlight the need for task-specific enhancements to improve their robustness, generalization, and precision.

Alex Farach, Alexia Cambon, Jared Spataro

As the digital economy grows increasingly intangible, traditional productivity measures struggle to capture the true economic impact of artificial intelligence (AI). AI systems capable of cognitive work significantly enhance productivity, yet their contributions remain obscured within the residual category of Total Factor Productivity (TFP). This paper explores whether it is time for a conceptual shift to explicitly recognize "digital labor," the autonomous cognitive capability of AI, as a distinct factor of production alongside capital and human labor. We outline the unique economic properties of digital labor, including scalability, intangibility, self-improvement, rapid obsolescence, and elastic substitutability. By integrating digital labor into growth models (such as those by Solow and Romer), we demonstrate strategic implications for business leaders, including new approaches to productivity tracking, resource allocation, investment strategy, and organizational design. Ultimately, treating digital labor as an independent factor offers a clearer view of economic growth and helps organizations manage AI's transformative potential.

Dan Parii, Evelyne Janssen, Guangzhi Tang et al.

Accurately predicting the lifespan of critical device components is essential for maintenance planning and production optimization, making it a topic of significant interest in both academia and industry. In this work, we investigate the use of survival analysis for predicting the lifespan of production printheads developed by Canon Production Printing. Specifically, we focus on the application of five techniques to estimate survival probabilities and failure rates: the Kaplan-Meier estimator, Cox proportional hazard model, Weibull accelerated failure time model, random survival forest, and gradient boosting. The resulting estimates are further refined using isotonic regression and subsequently aggregated to determine the expected number of failures. The predictions are then validated against real-world ground truth data across multiple time windows to assess model reliability. Our quantitative evaluation using three performance metrics demonstrates that survival analysis outperforms industry-standard baseline methods for printhead lifespan prediction.

Jaxson Brown, Duc-Son Pham, Sie-Teng Soh et al.

Industrial Control Systems (ICSs) are complex interconnected systems used to manage process control within industrial environments, such as chemical processing plants and water treatment facilities. As the modern industrial environment moves towards Internet-facing services, ICSs face an increased risk of attacks that necessitates ICS-specific Intrusion Detection Systems (IDS). The development of such IDS relies significantly on a simulated testbed as it is unrealistic and sometimes hazardous to utilize an operational control system. Whilst some testbeds have been proposed, they often use a limited selection of virtual ICS simulations to test and verify cyber security solutions. There is a lack of investigation done on developing systems that can efficiently simulate multiple ICS architectures. Currently, the trend within research involves developing security solutions on just one ICS simulation, which can result in bias to its specific architecture. We present ICS-SimLab, an end-to-end software suite that utilizes Docker containerization technology to create a highly configurable ICS simulation environment. This software framework enables researchers to rapidly build and customize different ICS environments, facilitating the development of security solutions across different systems that adhere to the Purdue Enterprise Reference Architecture. To demonstrate its capability, we present three virtual ICS simulations: a solar panel smart grid, a water bottle filling facility, and a system of intelligent electronic devices. Furthermore, we run cyber-attacks on these simulations and construct a dataset of recorded malicious and benign network traffic to be used for IDS development.

F. Venturini

Keliang Wang, Su-Qin Pang, Lili Ding et al.

Improving the green total-factor productivity (GTFP) is a key measure to coordinate industrial development and environmental protection in China. This study adopts the biennial Malmquist-Luenberger (BML) productivity index to estimate the GTFP change of China's 34 industrial subsectors covering the period 2005-2015. Subsequently, fixed-effect panel quantile regression is applied to analyze the heterogeneous effects of eight selected influencing factors on China's industrial GTFP change. The results show that China's overall industrial GTFP exhibited an increasing trend during the study period and varied greatly in different sub-sectors. Moreover, technological innovation rather than efficiency promotion was the main contributor to the improvement of industrial GTFP in China. The impact of the scale structure (SS) was significantly positive across the quantiles and maintained a slightly downward trend. The impact of the property rights structure (PTS) was significantly negative and showed an increasing trend across the quantiles. The impact of the energy intensity (EI) slightly increased and was significantly negative at most quantiles. The energy consumption structure (ECS) exhibited an increasing trend and had a significantly negative effect at the middle quantiles. Technological innovation (TI) exerted a significantly positive effect and displayed a downward trend across the quantiles, and it was the most important factor to drive industrial GTFP growth. The "pollution halo" hypothesis and the Porter hypothesis were both verified with a certain range from the analysis of foreign direct investment (FDI) and environmental regulation (ER), as well as the interaction between ER and TI. Our results stress the importance of the heterogeneous effects of these influencing factors on different quantile subsectors when formulating the related measures and policies.

Shujie Yao, Xiaoqian Zhang, Weiwei Zheng et al.