Industrial Production Research Articles

Breathing in pollution, breathing out efficiency: how does air pollution affect labour productivity in China’s service sector?

ABSTRACT This paper examines the impact of air pollution on labour productivity in the service industry in China. Utilizing the Chinese National Tax Survey Database and the contemporaneous air pollution data for 2007–2016, we employ an instrumental variable method and find that the average labour productivity for service firms decreases by 2.11% for every 1ug/m3 increase in PM2.5 concentration. The influence is more pronounced among producer services firms, non-state-owned firms, small-scale firms, and firms located in less environmentally regulated regions, northern regions, central and western regions, and coastal regions. The detrimental effect of air pollution increases as air quality worsens. Additionally, a 1ug/m3 increase in PM2.5 concentration leads to a significant 2.83% increase in overall labour costs, which squeezes out firms’ capital investment and distorts their factor structure. This paper contributes to the literature by providing new evidence on adverse impact of air pollution on labour productivity in the service sector.

Macroeconomic Conditions and Private Equity in Morocco: A Bounds-Testing Cointegration Approach

Private equity plays a pivotal role in fostering entrepreneurship, innovation, and job creation across many economies. In Morocco, the private equity sector is in its developmental stage and faces significant macroeconomic challenges that hinder its growth. This study seeks to address the gap in the literature by examining how key macroeconomic factors—including GDP growth, exchange rates, government spending, inflation, gross domestic savings, interest rates, and industrial production—affect private equity fundraising in Morocco between 1990 and 2019. Employing the autoregressive distributed lag (ARDL) model, we investigate both short- and long-term relationships between these macroeconomic variables and private equity fundraising. The results reveal that GDP growth and interest rates significantly influence private equity fundraising in both the short and long run, while inflation and government spending have a more pronounced long-term effect. These findings highlight the need for policies focused on stabilizing macroeconomic conditions to attract private equity investments. Policymakers should prioritize fostering a stable economic environment characterized by sustainable growth, low inflation, and moderate interest rates to create a favorable investment climate.

Generative AI and Investigative Journalism—An Application of Baumol's Cost Disease Across Mass Communication Sectors

Generative artificial intelligence (AI) has transformed various industries, automating tasks and enhancing productivity. Yet, its impact varies across sectors. In mass communication, AI has notably benefited entertainment, public relations, and advertising. However, it poses a lesser advantage for investigative journalism, where labor-intensive research and other highly specialized activities depend on highly educated journalists. This situation parallels Baumol's concept of a cost disease, which applies to labor-intensive “stagnant” service sectors such as healthcare, education, and the performing arts. These sectors rely heavily on human labor and struggle to achieve significant productivity gains from technological advances. This means they may require decreasing wages or increasing subsidies to compete with more productive industries. In mass communication, this phenomenon could be especially pertinent to (investigative) journalism, which directly competes for attention with other communication forms. This could exacerbate pressures on wages and working conditions, hindering the attraction of talent to the field and necessitating higher public spending on this vital journalism form. This commentary presents a cross-industry analysis of the impact of generative AI on communication sectors and derives directions for further research that would enhance our understanding of journalism's economic viability in a digital communication landscape and inform policy makers to address the affiliated societal challenges. It closes with a discussion of Guzman and Lewis’s call for more cross-industry research on AI in communication industries.

The Impact of Carbon Emissions Trading Policy on Regional Economy and Pollution Reductions in Chinese Provinces

Carbon emissions trading policy is an important tool to achieve carbon peaking and carbon neutrality goals. In order to explore the effectiveness of carbon emissions trading policy, this paper adopts the difference-in-differences model to analyze the effects of China’s pilot policy on regional economic development and pollution reductions. The results show that carbon trading policy can significantly promote economic development and reduce total carbon emissions, industrial SO2 emissions and solid wastes production of pilot regions. Further research finds that there is significant regional difference in policy effectiveness, with the policy more effective in western areas. In addition to direct effects, carbon trading policy could exert an indirect effect on carbon emissions, air quality, industrial solid wastes and regional economic development by optimizing energy consumption structures and industrial structures and increasing technological investment. This result verifies the “Porter hypothesis”. China should improve the construction of national carbon trading markets, covering more industries, considering the regional differences and negative spillover effect.

Electrospun Micro/Nanofiber-Based Electrocatalysts for Hydrogen Evolution Reaction: A Review

Hydrogen is regarded as an ideal energy carrier to cope with the energy crisis and environmental problems due to its high energy density, cleanliness, and renewability. Although there are several primary methods of industrial hydrogen production, hydrogen evolution reaction (HER) is an efficient, eco-friendly, and sustainably green method for the preparation of hydrogen which has attracted considerable attention. However, this technique is characterized by slow reaction kinetics and high energy potential owing to lack of electrocatalysts with cost-effective and high performance which impedes its scale-up. To address this issue, various studies have focused on electrospun micro/nanofiber-based electrocatalysts for HER due to their excellent electron and mass transport, high specific surface area, as well as high porosity and flexibility. To further advance their development, recent progress of highly efficient HER electrospun electrocatalysts is reviewed. Initially, the characteristics of potential high-performance electrocatalysts for HER are elucidated. Subsequently, the advantages of utilizing electrospinning technology for the preparation of electrocatalysts are summarized. Then, the classification of electrospun micro/nanofiber-based electrocatalysts for HER are analyzed, including metal-based electrospun electrocatalyst (noble metals and alloys, transition metals, and alloys), metal–non-metal electrocatalysts (metal sulfide-based electrocatalysts, metal oxide-based electrocatalysts, metal phosphide-based electrocatalysts, metal nitride-based electrocatalysts, and metal carbide-based electrocatalysts), metal-free electrospun micro/nanofiber-based electrocatalysts, and hybrid electrospun micro/nanofiber-based electrocatalysts. Following this, enhancement strategies for electrospun micro/nanofiber-based electrocatalysts are discussed. Finally, current challenges and the future research directions of electrospun micro/nanofiber-based electrocatalysts for HER are concluded.

CONTRIBUTION TO IMPROVING OF MACHINE PARTS MECHANICAL PROPERTIES BY THERMOMECHANICAL HARDENING

Current industrial production is characterized by requirements for improving the physical and mechanical properties of the used material. This causes a workload creating a rather complex load on all types of machines and mechanisms during operation. The design of such objects is currently based on a wide range of computational and experimental methods that allow modelling their state and behaviour even in the case of complicated non-stationary loading conditions. In this paper, the authors focus on the statistical evaluation of selected manufacturing operations related to mechanical and thermomechanical processing of products [Kuric 2011]. For example, in the case of long pipe billets with a thickness coefficient of 2-4, practically the only way of their production in the metallurgical cycle is cross-roll piercing followed by reduction. This process has high productivity but at the same time certain disadvantages. These disadvantages limit the efficiency and the range of use of the pipe blanks obtained by piercing, which leads to a consequent shortening of the machine production cycle. The presented approaches allow changing the structure of technological processes of production of axisymmetric metal products, by modifying the thermomechanical processing at the beginning of the production cycle.

CREATION, MANAGEMENT, AND USE OF A DATABASE FOR THE MAINTENANCE PROCESS OF PRODUCTION EQUIPMENT IN AN INDUSTRIAL COMPANY

The paper presents the creation of a database of motion norms to support the maintenance management system in an industrial production company, implemented by a team from the Faculty of Mechanical Engineering, VSB - Technical University of Ostrava. This database is intended for filling and optimal use of maintenance modules of ERP systems and for use in the processes of preparation of maintenance and service orders. The article focuses on the methodology of creation, characteristics, use and availability of this database and its application in industrial practice.

Effects of regional network economies on industrial productivity in Japan: dynamic total factor productivity function approach

AbstractImproving productivity performance is essential for achieving regional economic sustainability. With the advent of the network society, regional economic agents have benefited not only from local within-region interactions but also from external out-of-region economies. However, it remains unclear whether localized external economies or spillovers of externalities through inter-regional networks are more significant in improving productivity performance. Therefore, this study examined the relationships between productivity performance and geographical externalities from an industry perspective using regional economic data for Japan and employed a dynamic total factor productivity function approach. It also assessed the impact of high-quality transportation infrastructure improvements on productivity catch-up. The results revealed the long-run effects of inter-regional networks on the productivity performance of industries. Furthermore, it revealed that, as a geographical externality, the “borrowed size” effect contributed significantly to productivity improvements in both manufacturing and non-manufacturing industries. These findings suggest that enhancing both high-quality transportation infrastructure and inter-regional transportation networks is likely to improve industry productivity performance.

Microorganisms of solid waste as an opportunity for waste disposal and increasing environmental sustainability in the south of Kazakhstan

The study is devoted to the problem of processing the organic waste that is generated as a result of paper, textiles and other industries production as well as food waste. The growth of economic activity in Kazakhstan has resulted in a significant challenge with regard to industrial waste management. The accumulation of waste on the territory of the country has reached 31.72 billion tonnes, comprising approximately 2.5 billion tonnes of hazardous waste, 50 million tonnes of phosphorus-containing waste, over 2.5 million tonnes of lead-zinc waste and more than 120 million tonnes of solid domestic waste. The study object was the Shymkent-Kokys polygons. According to the research carried out, it was determined that the titer of microorganisms of the studied groups is 1–10 CFU/g in the soils selected around the garbage in the area of the Shymkent landfill. The titer of microorganisms in the soil horizons was high at a depth of 20–30 cm and the titer were 109 cells/mL. The structure of the soil microbiome obtained around the Shymkent Waste Landfill consists of actinomycetes, micromycetes, heterotrophic bacteria, nitrifying, nitrogen-fixing bacteria, enterobacteria, as well as algae and protozoa. It was found that strains KPA1, KPA2 Pseudomonas sp. strains KPA3, KPA4, KPA5 Bacillus sp. isolated from the soils of the Shymkent-Kokys landfill are able to recycle domestic waste with a high content of cellulose and organic substances up to 95%–97%. The findings can be used to develop more effective organic cellulosic waste management strategies and improve the environmental sustainability of various industries.

Enhancement of heat tolerance by salt stress in Tetraselmis striata CTP4: impacts on HSP gene expression, pigments, and proximal composition

AbstractAs the world average temperature is on the rise and heat waves are becoming more prevalent, microalgal producers have been facing significant challenges regarding the time periods during which they are able to grow less thermotolerant microalgae in outdoor production facilities. Therefore, it is urgent to explore how microalgae cope with thermal stress and under which growth conditions tolerance to temperatures close to or higher than their maximum thermal threshold is induced. For this purpose, Tetraselmis striata CTP4, a euryhaline microalga known for its thermotolerance, was selected. Tetraselmis striata belongs to the Chlorodendrophyceae, a clade that branched off early from other "core chlorophyte" clades, usually comprised of microalgae able to colonise freshwater habitats. Here, we present compelling evidence that the ability of this microalga to withstand otherwise lethal thermal upshifts to 40 °C is induced by exposure to higher salinity (35 ppt). In contrast, this response is abrogated at lower salinities. Concomitantly, the expression of genes encoding HSP70 and HSP100, two heat shock proteins known to mediate thermotolerance and tolerance to other stresses (e.g., salt stress) in fungi, animals and plants, was enhanced when exposed to both heat stress and higher salinities. This suggests that cross-protective mechanisms against abiotic stress appeared early during the evolution of the core chlorophytes and of Archaeplastida in general. This knowledge can be used to select novel strains and growth conditions that promote thermotolerance in microalgae that are grown in outdoor industrial production facilities in environments where heat waves are expected.

Study on the Pilot-Scale Technology of Ginkgolide B Synthesis by Coprinus comatus

Ginkgo biloba extract (EGB) has been approved by the Food and Drug Administration in the United States for clinical studies on memory disorders. Ginkgolide B (GB) is the major terpene lactone component of EGB and is a specific and potent antagonist of platelet-activating factor (PAF). In a previous study, we reported the medium composition for the conversion of ginkgolides to GB by Coprinus comatus. In the present study, we applied the response surface methodology (RSM) to optimize the conversion conditions in a 20 L fermenter and applied HPLC-MS/MS, circular dichroism (CD) and infrared (IR) spectroscopy analyses, and nuclear magnetic resonance (NMR) to further confirm the sample structure. The optimal conversion conditions consisted of 12.7 L/min of ventilation, a 156 h conversion time, a 132 rpm rotating speed, a 0.04 MPa fermenter pressure, and a 27.8 °C conversion temperature. Under the optimal conditions, the GB conversion rate was 98.62%, and the GB content of the sample was higher than 98%. HPLC-MS/MS, CD, IR, and NMR analyses showed that the molecular formula of the sample was C20H24O10 and the chemical structure of the sample was in good agreement with the standard GB. Our current study lays the groundwork for the industrial production of GB.

Inter‐tow toughness modification of carbon fiber reinforced cyanate resin matrix composites

AbstractCarbon fiber‐reinforced cyanate ester resin matrix composites (CF/CE) are widely used in aerospace for their high‐temperature resistance and wave‐absorbing properties. However, CF/CE is prone to interlaminar delamination during the curing molding process or practical applications due to the brittleness of CE resin. In this study, we use polyether sulfone (PES) as a toughening layer through the inter‐tow toughening method to prepare PES inter‐tow toughened CF/CE (CF/CE‐P) and to investigate their thermal, mechanical, and microscopic morphology. We find that CF/CE‐P has a higher glass transition temperature (Tg) and a lower coefficient of thermal expansion (CTE) than those of CF/CE composites. The flexural strength, flexural modulus, interlaminar shear strength (ILSS), and impact toughness of CF/CE‐P with 15 wt% PES is increased by 34%, 15%, 34%, and 45%, respectively, compared to CF/CE. In addition, the mode I interlaminar fracture toughness (GIC) of CF/CE‐P with 15 wt% PES is 1.86 times that of CF/CE.Highlights The inter‐tow toughening technique on a three‐dimensional scale is proposed. Model I interlaminar fracture toughness increased by 86% after inter‐tow toughness modification carbon fiber‐reinforced cyanate ester resin matrix composites. The composite material exhibits improved toughness and thermal performance. The prepreg preparation process is suitable for continuous industrial production.

Patterns of surface water dynamics and storage changes in a basin of Bundelkhand Region, India: Implications for water management

AbstractSurface water is essential for agricultural, domestic and industrial production worldwide. Monitoring surface dynamics is crucial for sustainable ecosystems and global water resources. Importance of monitoring surface water dynamics is even more pronounced in the semi‐arid regions worldwide. An analysis of surface water extent and volume change patterns has been conducted, comparing these dynamics with alterations in precipitation patterns within a basin in Central Bundelkhand, a semi‐arid region in the Central India prone to droughts. To map the waterbodies, we leveraged Sentinel‐1 SAR data using an automated mapping framework and utilised DEM dataset to extract bathymetry using interpolation with modifications using water persistence. Analysis revealed a lag in surface water peak water level with respect to accumulated rainfall by 2–3 months. Furthermore, we have categorised the water bodies into small, medium and large by surface area and found that smaller water bodies show a higher intra‐annual variance, while medium and large water bodies show a lower intra‐annual variance. The findings suggest that smaller communities reliant on smaller water bodies are at a higher risk from climate variability in the region and a delay in attaining peak surface storage across the basin causes further challenges to water management.

Process Modeling and Convective Drying Optimization of Raspberry Pomace as a Fiber-Rich Functional Ingredient: Effect on Techno-Functional and Bioactive Properties

This study aimed to transform raspberry pomace, a by-product of the berry industry, into a sustainable, fiber-rich functional ingredient using convective drying. Drying experiments were conducted at temperatures of 50, 60, 70, 80, and 90 °C to identify the optimal conditions that balance process efficiency and preservation of functional and bioactive properties. The best results were achieved at 70 °C, where a high drying rate (DR) of 0.46 kg H2O·kg−1 db·min−1, effective moisture diffusivity (Deff) of 1.53 × 10−10 m2·s−1, and activation energy (Ea) of 34.90 kJ·mol−1 were observed. The Page model accurately represented the drying behavior (R2 = 0.9965−0.9997). Total dietary fiber (TDF) content remained stable across temperatures (52.52–64.76 g·100 g−1 db), while soluble dietary fiber (SDF) increased by 43.40%, resulting in a solubility (SOL) of 71.8%, water-holding capacity (WHC) of 8.2 mL·g−1 db, and oil-holding capacity (OHC) of 3.0 mL·g−1 db. High retention of bioactive compounds was achieved at 70 °C, including phenolics (32.10 mg GAE·g−1 db) and anthocyanins (25.84 mg C3G·g−1 db), resulting in significant antioxidant activities (DPPH: 33.29 mg AAE·g−1 db, IC50 0.016 mg·mL−1; ABTS: 35.85 mg AAE·g−1 db, IC50 0.029 mg·mL−1). These findings demonstrated the potential of convective drying at 70 °C to efficiently transform raspberry pomace into a high-quality functional ingredient. This process promotes sustainable production and waste reduction in the berry industry.

Ethanol Production From Sugarcane Molasses: Effects of Ph, Supplementation, and Refrigeration in Simulated Industrial Conditions at a Microdistillary

Objective: This study aims to evaluate the effects of pH, ammonium sulfate supplementation, and refrigeration on fermentation performance in sugarcane molasses, with the goal of optimizing ethanol production under simulated industrial conditions. Theoretical Framework: The research builds on key concepts in biofuel production, emphasizing the role of pH, nutrient supplementation, and temperature control in influencing yeast metabolism and fermentation efficiency. Theories on enzymatic activity and nutrient absorption (Vidal et al., 2013; Gutierrez, 1993) provide the foundation for understanding the interactions between these factors. Method: A full 2³ factorial design was employed, evaluating the effects of pH (3.5 and 5.0), supplementation (0.0 and 1.0 g/L), and refrigeration (with or without) on fermentation efficiency, process efficiency, ethanol productivity, and substrate-to-cell conversion in a microdistillery. Analytical methods included spectrophotometry, refractometry, and chromatography. Results and Discussion: The results indicate that fermentation conditions with pH 3.5, without supplementation, and with refrigeration yielded the best performance, with significant increases in ethanol productivity and fermentation efficiency. The interaction between variables suggests that these factors should be considered jointly to optimize the process. Research Implications: The findings offer practical insights for improving fermentation in industrial biofuel production, particularly in optimizing conditions for ethanol yield and process efficiency under near-industrial conditions. Originality/Value: This study contributes by using a microdistillery to simulate industrial conditions, providing more accurate data for scaling ethanol production. It adds value by highlighting how specific combinations of pH, supplementation, and refrigeration can improve biofuel sustainability.

On Efficient Training of Large-Scale Deep Learning Models

The field of deep learning has witnessed significant progress in recent times, particularly in areas such as computer vision (CV), natural language processing (NLP), and speech. The use of large-scale models trained on vast amounts of data holds immense promise for practical applications, enhancing industrial productivity and facilitating social development. However, it suffers extremely from the unstable training process and stringent requirements of computational resources. With the increasing demands on the adaption of computational capacity, though numerous studies have explored the efficient training field to a certain extent, a comprehensive summarization/guideline on those general acceleration techniques of training large-scale deep learning models is still much anticipated. In this survey, we present a detailed review of the general techniques for training acceleration. We consider the fundamental update formulation and split its basic components into five main perspectives: (1) “data-centric,” including dataset regularization, data sampling, and data-centric curriculum learning techniques, which can significantly reduce the computational complexity of the data samples; (2) “model-centric,” including acceleration of basic modules, compression training, model initialization, and model-centric curriculum learning techniques, which focus on accelerating the training via reducing the calculations on parameters and providing better initialization; (3) “optimization-centric,” including the selection of learning rate, the employment of large batch size, the designs of efficient objectives, and model average techniques, which pay attention to the training policy and improving the generality for the large-scale models; (4) “budgeted training,” including some distinctive acceleration methods on source-constrained situations, e.g., for limitation on the total iterations; and (5) “system-centric,” including some efficient distributed frameworks and open source libraries that provide adequate hardware support for the implementation of the above-mentioned acceleration algorithms. By presenting this comprehensive taxonomy, our survey presents a comprehensive review to understand the general mechanisms within each component and their joint interaction. Meanwhile, we further provide a detailed analysis and discussion of future works on the development of general acceleration techniques, which could inspire us to re-think and design novel efficient paradigms. Overall, we hope that this survey will serve as a valuable guideline for general efficient training.

Efficient Cross-Domain Fault Diagnosis via Distributed Multi-Source Domain Deep Transfer Learning

Abstract In the actual industrial production, the working conditions of rotating machinery are complex and changeable, and the health-state monitoring data are increasingly large and difficult to be labeled, which will seriously restrict the accuracy and efficiency of cross-domain fault diagnosis (CDFD) of rotating machinery. Therefore, an efficient multi-source domain deep transfer learning (MDDTL) method for CDFD of rotating machinery is proposed. Firstly, a MDDTL model is constructed to improve the accuracy of CDFD. In the model, a dual-phase domain alignment strategy is designed, which considers the alignment of feature distributions between each source and target domain pair in the feature space and that of prediction probabilities between domain-specific fault classifiers in the output space. The fault prediction results from multiple different fault classifiers are merged dynamically by the proposed imbalanced adaptive prediction strategy. Secondly, a data-parallel distributed training scheme for MDDTL model is proposed. Based on the idea of data parallelism, the distributed parallel training of MDDTL model is performed with Horovod-GPU platform, and the parameters are synchronously updated with the bandwidth-optimal Ring-AllReduce architecture. Under the premise of ensuring the accuracy of fault diagnosis (FD), the training time of MDDTL model is significantly reduced. Finally, extensive experiments are conducted to verify the effectiveness of the proposed MDDTL method. The results demonstrate that the proposed method not only effectively improves the accuracy of CDFD of rotating machinery, but also significantly improves the training efficiency of MDDTL model. After adopting the proposed method, the diagnosis accuracies achieved under two different cross-working condition scenarios reach 97.09% and 97.87% respectively, and the model training time is reduced by 73.62% when facing a large-scale rotating machinery training set.

Unveiling an asymmetric relationship between global crude oil and local food prices in an oil-importing economy

AbstractRecent swift comovements of local food and global crude oil prices have attracted the attention of policymakers and researchers. To evaluate this relationship, many studies have used time series models to explore global crude oil and local food prices. However, robust research based on advanced nonlinear time series models that incorporate control variables for their formation is lacking. In this paper, nonlinear techniques are applied to assess the asymmetric nexus between Brent oil prices and local retail food prices in Slovakia. To estimate this value, we extend the single-threshold NARDL approach to the MTNARDL model. The nominal exchange rate and industrial production index are used as the control variables. Compared with conventional NARDL models, the MTNARDL model provides a more detailed representation of global oil‒local food price linkages and detects the asymmetric effect of global oil prices on food prices from both long- and short-term perspectives. Interestingly, with respect to long- and short-term food price volatility, changes in response to oil price fluctuations are greatest under a regime with rather a small number of positive and moderate changes.

Advancements in the Engineering Modification of Sucrose Phosphorylase

Sucrose phosphorylase (SPase) is a member of the glycoside hydrolase family 13, catalyzing the reversible phosphorolysis of sucrose to produce α–glucose–1–phosphate and exhibiting transglycosylation activity toward multiple substrates. Its wide substrate specificity enables the synthesis of various glycosides, which are broadly applied in food, cosmetics, and pharmaceuticals. However, the industrial application of SPase is constrained by its poor thermostability and limited transglycosylation activity. Therefore, current research focuses on enhancing the thermostability and transglycosylation activity of SPase through efficient engineering strategies based on its crystal structure and catalytic mechanism. This paper systematically reviews the crystal structure and catalytic mechanism of SPase, outlines the application of protein engineering and immobilization strategies in improving the thermostability of SPase, and analyzes how modifications at key amino acid sites affect the synthesis of typical glycosylation products. It also summarizes the limitations of SPase engineering modification strategies and explores the potential of diversified approaches for SPase modification, highlighting its broad application prospects in industrial production and laying a solid foundation for further advancements in SPase engineering modification and its industrial application.

Reductive Transformation of CO2 to Organic Compounds

AbstractCarbon dioxide is a major greenhouse gas and a safe, abundant, easily accessible, and renewable C1 resource that can be chemically converted into high value‐added chemicals, fuels and materials. The preparation of urea, organic carbonates, salicylic acid, etc. from CO2 through non‐reduction conversion has been used in industrial production, while CO2 reduction transformation has become a research hotspot in recent years due to its involvement in energy storage and product diversification. Designing suitable catalysts to achieve efficient and selective conversion of CO2 is crucial due to its thermodynamic stability and kinetic inertness. From this perspective, the redistribution of charges within CO2 molecules through the interaction of Lewis acid/base or metal complexes with CO2, or the forced transfer of electrons to CO2 through photo‐ or electrocatalysis, is a commonly used effective way to activate CO2. Based on understanding of the activation/reaction mechanism on a molecular level, we have developed metal complexes, metal salts, inorganic/organic salts, ionic liquids, as well as nitrogen rich and porous materials as efficient catalysts for CO2 reductive conversions. The goal of this personal account is to summarize the catalytic processes of CO2 reductive conversion that have been developed in the past 7 years: 1) For the reductive functionalization of CO2, the major challenge lies in accurately adjusting reaction parameters (such as pressure) to achieve high catalytic efficiency and the product selectivity; 2) For photocatalytic or electrocatalytic reduction of CO2, how to suppress competitive hydrogen evolution reactions and improve catalyst stability are key points that requires continuous attention.