Technological Innovation Efficiency Research Articles

Value of dual-credit policy: Evidence from green technology innovation efficiency

The dual-credit policy (DCP), introduced by the Chinese government in 2017, aims to establish a market-based mechanism for fostering the coordinated development of energy-efficient and new energy vehicles in the automobile industry through a credit trading system. This policy plays a significant role in promoting green technology innovation, thereby contributing to the low-carbon transformation of the transportation sector in line with the dual-carbon target. However, challenges related to policy design and substantial variations among automakers during the implementation process have cast uncertainty on the effectiveness of the DCP. To address this issue, this study examines the impact and mechanisms of the DCP on the green technology innovation efficiency (GTIE) of automakers by utilizing a sample of 18 Chinese A-share listed automakers from 2012 to 2020. The analysis employs a combination of the propensity score matching (PSM) and difference-in-differences (DID) approaches. The empirical findings indicate that the introduction of the DCP significantly improves the GTIE of automakers. However, the intensity of this effect diminishes over time. The promotion of GTIE is primarily achieved by alleviating automakers’ financial constraints and increasing their management fund inputs. Furthermore, this effect is strengthened by customer concentration and supply chain concentration. Additionally, the study identifies certain enterprise characteristics that generate heterogeneity in the effects of the DCP, including enterprise ownership, region, and executive education background. The results reveal that the DCP has a more pronounced impact on private automakers, those located in eastern regions, and those with lower levels of executive education.

Environmental Regulation, Environmental Policy Complexity and Technological Innovation Efficiency

Abstract Previous studies on the relationship between environmental regulation (ER) and technological innovation efficiency of defence industry (TIE-DI) mainly focus on variability and complexity, while few empirical studies have incorporated environmental flexibility into models, and most of them are based on questionnaires. Therefore, this paper takes environmental regulation and environmental policy complexity (EPC) as the entry point, so as to discuss the feasibility of improving the technological innovation efficiency of China’s defence industry enterprises (CDI), which aims to empirically test the mechanism of ER and EPC on improving the efficiency of applied technological innovation of China’s defence industry enterprises. The research conclusion provides sufficient theoretical basis and empirical support for strengthening the technical innovation efficiency support, standardising the market order and the market leading, establishing the information disclosure mechanism and improving the internal control of industrial enterprises.

Analysis on the Influence of Government Subsidy on Foton Automobile Technology Innovation Efficiency under the Background of Dual Carbon

The new energy industry is an emerging industry; the rise of the emerging industry is inseparable from the national macro-control. In recent years, new energy automotive industry as the mainstay of carbon reduction, more and more to receive government subsidies. Based on the DEA-Malmquist model, this paper takes Foton Automobile as the research object to explore whether enterprises can rationally use government subsidies to promote technology innovation under the dual-carbon background. It is found that government subsidies have a certain incentive effect on Foton automobile technology innovation. The static efficiency score shows that after the two-carbon goal is proposed, the government subsidies continue to increase and the innovation efficiency of the enterprise reaches the best state. The dynamic efficiency score shows that the input-output of Foton Automobile has not reached the optimal scale, and Foton Automobile still needs to adjust the production scale of the enterprise and improve the innovation efficiency. Finally, the government should strengthen the supervision of the use of government subsidies, enterprises should speed up the transformation of technological achievements, pay attention to the rational distribution of input and output suggestions and countermeasures.

Spatial Correlation Network Analysis of Industrial Green Technology Innovation Efficiency in China

Exploring the spatial correlation network and its structural characteristics of China’s industrial green technology innovation efficiency is significant for promoting the coordinated development of inter-regional industrial green transformation. Based on the innovation value chain, this paper divides China’s industrial green technology innovation system into three interrelated sub-stages: technology research and development, achievement transformation, and commercialization. The NSBM model is used to measure the efficiency of industrial green technology innovation in 30 provinces and cities in mainland China from 2011 to 2020. The modified gravity model and social network analysis method are introduced to explore its spatial correlation network’s structural characteristics and evolution rules. The results show that the spatial network correlation intensity of the three stages of green technology innovation efficiency in regional industry has gradually strengthened. There is no strict hierarchical structure, and the spatial network tends to be stable. The network shows an apparent “core–edge” distribution in all three stages, with the eastern coastal and central more developed regions at the network’s core. Meanwhile, the northeastern and western remote areas are at the network’s edge and less connected with other regions’ provinces and cities. The distribution of network blocks in the three stages of green technology innovation efficiency is similar. The net benefit block mainly includes the eastern coastal and surrounding developed areas. The net spillover block mainly consists of the economically backward northwest region. The broker block is primarily distributed in the surrounding provinces and cities of the Bohai Rim. The bidirectional spillover block is mainly located in the southwest region. Finally, some suggestions are put forward to promote the coordinated improvement of regional industrial green technology innovation efficiency from the perspective of integrity, individuality, and agglomeration.

Research on the Measurement and Convergence of Technological Innovation Efficiency of New Energy Enterprises under the Target of “Double Carbon”

Research on the Measurement and Convergence of Technological Innovation Efficiency of New Energy Enterprises under the Target of “Double Carbon”

Path Optimization of Technological Innovation Efficiency Improvement in China’s High-Tech Industries Based on QCA and GA-PSO-BP Neural Network

Innovation is the main driving force to promote national technological progress. It is of great significance to explore the optimal path to improve innovation efficiency by using the qualitative method and neural network prediction model to promote the high-quality development of the national economy. This study focuses on high-tech industries in the eastern, central and western regions of China; a factor-dependent research framework for innovation efficiency improvement in high-tech industries is constructed in China. The fuzzy-set qualitative comparative analysis method (QCA) is used to explore multiple paths to enhance the innovation efficiency of China’s high-tech industries. Then, a GA-PSO-BP neural network is used to construct an optimization model for the enhancement path of technological innovation efficiency, which clarifies the optimal path for the enhancement of innovation efficiency of high-tech industries in the eastern, central and western regions of China. Finally, innovation management strategies for high-tech industries are presented with regional features. The study finds that none of the individual conditions are necessary to promote the innovation efficiency of China’s high-tech industries, and only the linkage effect of the factors can achieve the goal of improving the innovation efficiency level of China’s high-tech industries. There are four configuration paths to improve the innovation efficiency of China’s high-tech industries, which are: “Multinational company (MNC) innovation—economic development—government support”; “MNC innovation—government support”; “economic development—government support”; and “economic development”. The characteristics of regional heterogeneity make differences in the optimal paths of innovation efficiency improvement in high-tech industries in eastern, central and western regions of China.

The Impact of Manufacturing Intelligence on Green Development Efficiency: A Study Based on Chinese Data

This study used provincial panel data from 2011 to 2020 to empirically analyze the impact of improvements in the manufacturing intelligence level on the efficiency of green development. The results show the following: improvements in manufacturing intelligence significantly increased the efficiency of green development. Following the “Guidelines for the Construction of the National Intelligent Manufacturing Standard System” proposed by China as a quasi-natural experiment, the double-difference method was used to prove the following: the promotional effect is greater in eastern regions after the policy was implemented. An improvement in manufacturing intelligence increases green development efficiency by improving the efficiency of technological innovation and energy use. The impact of the manufacturing intelligence level on green development efficiency shows a threshold effect. The promotion effect is more pronounced in regions where the level of technological innovation and the strength of the government’s role cross this threshold. Therefore, the government should vigorously promote the intelligent transformation of the manufacturing industry and improve the efficiency of China’s green development.

The impact of government support and market competition on China's high-tech industry innovation efficiency as an emerging market

With the building of an innovative country in an emerging market, the role of government support (GS) and market competition (MC) in the innovation efficiency of China's high-tech industry (HTI) has become increasingly prominent. Considering innovation value chain (IVC) theory, based on 2004–2016 panel data covering 30 regional HTIs in mainland China, the spatial autoregressive (SAR) Tobit model, stepwise regression method with spatial factors, and threshold panel model are applied to examine the main effects, moderating effects, mediating effects, and nonlinear effects of GS and MC on the innovation efficiency of China's HTI. GS significantly inhibits the industry's technological development efficiency but promotes its innovation achievement transformation efficiency. However, the impact of MC on the industry's technological innovation efficiency has stage homogeneity, significantly promoting both stages of innovation efficiency. Economic policy uncertainty (EPU) exerts a significant moderating effect on GS and technological development efficiency. The transmission mechanism test reveals that technology market development (TMD) has a partial mediating effect between GS and technological development efficiency, while a masking effect exists between MC and technological development efficiency. A complete mediating effect exists between GS and innovation achievement transformation efficiency, while a partial mediating effect exists between MC and innovation achievement transformation efficiency. Regional heterogeneity is apparent. Of note, the impact of MC on the industry's technological development and innovation achievement transformation efficiency has a nonlinear effect based on GS.

The Technological Innovation Efficiency of China’s Renewable Energy Enterprises: An Estimation Based on a Three-Stage DEA Model

The advantages of clean, ecologically friendly, and renewable energy have drawn considerable attention from all nations in the world. The growth of the renewable energy industry has frequently been elevated to the status of national policy. By evaluating the technical innovation effectiveness of China’s renewable energy sector, the energy crisis may be alleviated, and the innovation potential of renewable energy can be boosted. At present, the research content of domestic renewable energy enterprises mainly adopts DEA and Cobb–Douglas production functions. Moreover, there is limited literature on the factors impacting efficiency, and most research results center on efficiency assessment. This study employs a three-step DEA method to determine the technological innovation efficiency for China’s A-share renewable energy firms from 2016 to 2020. To investigate the factors influencing technological innovation’s effectiveness, the panel Tobit model is then developed. In light of the empirical data, the main conclusions of this paper are as follows: First, despite a slow but steady improvement, Chinese renewable energy companies still need to increase their technological innovation efficiency. Pure technical efficiency is the main factor contributing to low innovation efficiency. Second, environmental laws such as reliance on global commerce, industrial structure, and local science and technology affect the innovation effectiveness of listed renewable energy enterprises. After excluding environmental factors, the comprehensive technical efficiency of listed renewable energy companies has decreased. Finally, the innovation and technological efficiency of renewable energy firms are positively impacted by government subsidies, top operational revenue, and enterprise scale.

Crowding effect of institutional openness based on the big data algorithm on the efficiency of new energy technology innovation.

In recent years, new energy vehicles, as a high-tech industry, have developed rapidly. This paper uses "number of new energy project personnel" and "hours of R&D (research and development) personnel" as design indicators to evaluate the investment of innovative talents in enterprises. This paper first introduces the supporting factors of the innovation environment in the input of innovation resources, and conducts research from four perspectives: human resources, innovation R&D, technology acquisition, and environmental support. In the construction of the innovation output index system, this paper outlines that the technological innovation (TI) achievements of enterprises are related to factors such as technological capabilities, profits, and market competitiveness of enterprises. Finally, this paper evaluates it from three aspects: the research and development achievements, the economic benefits obtained and the competitive benefits of the enterprise. The results show that from 2018 to 2022, the average technological innovation efficiency of new energy enterprises is 1.06; TI's efficiency indicators in the past five years are all above 1, and the overall improvement trend of TI is relatively stable. The new energy vehicle collaborative innovation system constructed in this paper will promote the overall development of the new energy vehicle industry.

Assessing the Green Technology Innovation Efficiency in Yangtze River Delta Region under Dual Carbon Background

With the promotion of the dual carbon policy and the process of transforming and upgrading manufacturing, vigorously developing high-tech manufacturing with high-technology content and significant added value has become a powerful driving force to improve the development of the regional economy. In this regard, this research employs the two-stage network SBM-DEA method with undesirable outputs and multi-stage activities involved in the green production process. We examine the green technology innovation efficiency of high-tech manufacturing in the Yangtze River Delta (YRD) region from 2010 to 2020. We divide the activities of the high-tech manufacturing industry into two sub-stages, namely, the research and development (R&D) stage and the result transformation stage. The results are as follows: (1) The efficiency level of green technology innovation in Shanghai is at an outstanding level, having the most significant performance both in the R&D stage at 0.833 as well as in the result transformation stage at 1.006. (2) Anhui’s green technology innovation efficiency is mainly driven by the R&D stage. Green technology innovation efficiency in Jiangsu province depends primarily on the stage of result transformation, but there is still room for improvement in resource allocation. (3) Zhejiang needs to catch up in both the R&D stage and the result transformation stage, as well as to optimize the resource allocation of the green technology innovation process.

How does two-way FDI affect China’s green technology innovation?

ABSTRACT As China enters a new stage of opening up, the two-way FDI will affect the efficiency of technological innovation, which is significant to alleviate the current dilemma of resource exhaustion. In this paper, we analyze the effects of two-way FDI and other variables on green technology innovation based on the Granger test, coupling coordination model and fixed effect model, and further study the nonlinear effects with the financial development level as the threshold variable. The obtained results indicated that: (1) There is an interactive relationship between IFDI and OFDI, and two-way FDI can produce a greater positive synergy effect, with regional heterogeneity. (2) Urbanization level, property protection and government support have a positive effect on the promotion of green technology innovation. (3) The advanced industrial structure plays a suppressive role. (4) The impact of two-way FDI on China’s green technology innovation level has a double threshold effect.

The evaluation of innovation efficiency of China’s high-tech manufacturing industry based on the analysis of the three-stage network DEA-Malmquist model

The paper applies the three-stage network DEA and DEA-Malmquist method to measure the innovation efficiency of the high-tech Manufacturing industry for the period 2009–2017 in China. The analysis is divided into two parts to study. In the first analysis, we analyse the innovation efficiency of the high-tech manufacturing industry by using a three-stage network DEA, to open the black box of high technology innovation process in Chia. In the second stage, we use the DEA-Malmquist index method to evaluate the efficiency of technological innovation dynamically. We first found that the average innovation efficiency in the three stages from 2009 to 2017 shows a ‘Λ’ trend, there is room for improvement, especially in the basic and applied innovation stages. Moreover, the innovation is mainly driven by the east. Through the years 2009–2017, the development of high technology is unbalanced as well as insufficient. Our analyses show that the efficiency of technological innovation has been affected by the SEC and national policies. Therefore, we should adopt strategic measures to improve the innovation efficiency.

Analysis on Stochastic Change Characteristics of Technological Innovation Efficiency under Endogenous Change in Technological Information and Investment of Knowledge Capital

The allocation of innovation-related elements is influenced by intangible elements such as technological information, which affects knowledge capital, human capital, and material element investment, resulting in the stochastic change of technological innovation efficiency. The endogenous change in technological information in element investment reduces friction, lowers costs, improves efficiency, and gradually reduces the deviation between knowledge capital investment and technological innovation efficiency. In terms of small knowledge capital investment characterized by the low demand for technological information and a single source, it is easier to make more-accurate predictions, so the stochastic change in technological innovation efficiency tends to be gentle. The endogenous change in technological information continuously increases the proportion of replacing other elements with knowledge capital investment, and the efficiency of technological innovation improves steadily. Under the condition of unchanged investment in other elements, there is a great difference in the duration of technological information between the simultaneous selection and the successive selection of knowledge capital investment. When one-time instantaneous information and continuous complete information are respectively acquired, the stochastic variation of technological innovation efficiency is obvious. On the basis of the technological innovation data of 287 listed companies in eight industries, this paper compares and analyzes the measurement results of GMM and OLS to verify the above findings.

Coupling coordination degree and influencing factors of green science and technology innovation efficiency and digital economy level: Evidence from provincial panel data in China

Green innovation and the digital economy are the new engine and driving force for Chinese high-quality development and will become the mainstream of China’s high-quality development. Therefore, it is of great significance to explore the interaction between the two for the formulation of economic development policies. This paper constructed an evaluation system of green science and technology innovation efficiency (GSTIE) and digital economy level (DEL) based on 30 provinces in China. Through the corrected coupling coordination degree (CCD) model, this paper measured the coupling coordination degree of green science and technology innovation efficiency and DEL and analyzed its provincial differences and spatial effects. By employing the fuzzy set Qualitative Comparative Analysis (fsQCA) method, this paper further explored the influencing factors configuration affecting the coupling coordination degree of GSTIE and DEL. The research results are as follows. Compared with the development of green science and technology innovation, the development of the digital economy was relatively backward. The coupling coordination degree between China’s provincial GSTIE and DEL showed an overall increasing trend year by year, and there was obvious spatial heterogeneity in which the eastern region was the highest, followed by the western and central regions. A single influencing factor does not constitute a necessary condition for a high coupling coordination degree. There were four paths that improve the coordinated development level between GSTIE and DEL: HC + RD + OP-jointly driven, RD + OP-dual driven, HC + GS-dual driven, and GS-oriented. Finally, based on the research conclusions, this paper proposed corresponding policy suggestions.

Carbon neutrality and green technology innovation efficiency in Chinese textile industry

The present trend of rapid expansion in the textile industry is highly polluting and energy intensive, and the government intervention is necessitated. This study develops a Data Envelopment Analysis (DEA) model, examines the green technology innovation of listed textile enterprises, and explores its determining factors in order to address the issue of carbon emission reduction in the textile sector. The research sample for this study is obtained from 42 listed textile enterprises from 2017 to 2020. The research demonstrates: (1) the green technology innovation efficiency of domestic listed textile enterprises remains marginal; (2) from 2017 to 2020, the mean value of total factor productivity (TFP) and its decomposition index of listed enterprises in China's textile industry exhibit a downward trend, primarily as a consequence of the slowdown of technological progress; (3) the green technology of the entire industry is in recession from 2017 to 2020; (4) green technology innovation efficiency is inversely correlated with government subsidies, but positively correlated with net profit and asset size of enterprises. In light of the above information, enterprises should boost their ability to innovate with green technologies and implement a research and development (R&D) management development strategy that gives equal consideration to both economic and environmental benefits.

Research on the Improvement Path of Regional Green Technology Innovation Efficiency in China Based on fsQCA Method

Improvements in green technology innovation efficiency is the core factor to promote to shape new advantages in resource-saving and environmental friendliness under the new pattern of double-cycle development. It is also the main driving force needed to establish a high-quality development model of an efficient and sustainable economy. Taking 30 provinces of China as case samples, this paper establishes the appraisal system of green technology innovation efficiency. The first step is using the three-stage DEA model to measure green technology innovation efficiency. Then, according to the configuration perspective, the paper uses fuzzy set qualitative comparative analysis (fsQCA) to explore multiple paths for promoting green technology innovation efficiency. The findings are as follows: (1) A single factor of environmental support or technology supply cannot effectively stimulate the improvement of green technology innovation efficiency. Therefore, the impacting factors must be matched to jointly improve green technology innovation efficiency. (2) There are three configuration paths for high green technology innovation efficiency. Namely, they are the driven by economic environment and environmental regulation type; the driven by industrial structure and supply of finance type; and the driven by industrial structure, supply of finance, and supply of manpower type. (3) The paths to generate non-high green technology innovation efficiency can be summarized as one. The shortage of human resources and a poor economic environment are the main reasons for the inhibition of improvements in green technology innovation efficiency; additionally, the configuration of high and non-high green technology innovation efficiency is asymmetrical. On the one hand, our results are helpful for the study of the efficiency of regional green technology innovation at the provincial level. On the other hand, the results also provide practical solutions and a theoretical basis for provinces to promote regional green technology innovation efficiency under the new economic normal.

Technological Innovation Efficiency of Listed Carbon Capture Companies in China: Based on the Dual Dimensions of Legal Policy and Technology

To achieve carbon neutrality and improve emission reduction efficiency, capturing carbon dioxide from the air on a large scale and promoting the application and innovation of carbon capture technology (CCUS) are the most important goals. This study undertakes an annual and comprehensive evaluation of the policy and the technological innovation efficiency (TIE) of 10 listed companies in China using the DEA model and the Malmquist index analysis method. The number of relevant laws and policies is significant, but they are not well coordinated. The static evaluation results indicate that the complete factor production rate is low, generally lower than 0.9, and the technical innovation efficiency is weak, mainly because of technological backwardness. The dynamic evaluation results indicate that the changes in total factor productivity (TFP) each year are primarily affected by changes in technological progress. This suggests that most domestic enterprises are still exploring technological innovation (TI) and operational business models. Finally, this study proposes measures to improve the TIE of carbon capture technology enterprises in China, including giving full play to the role of the government, expanding effective investment, and improving innovational ability.

IMPACT OF INNOVATION ORGANIZATION NETWORK ON THE SYNERGY OF CROSS-ORGANIZATIONAL TECHNOLOGICAL INNOVATION: EVIDENCE FROM MEGAPROJECT PRACTICES IN CHINA

The construction industry has made an indispensable contribution to China’s environmental and economic development. With the advent of the volatile, uncertain, complex, and ambiguous (VUCA) era, cross-organizational collaboration has enabled megaproject participants to engage significantly in problem-solving and technological innovation. The impact of innovation organization network on the synergy of cross-organizational technological innovation (COTI synergy) in megaprojects is imperative for theoretical researchers and engineering practitioners. Therefore, this study aims to develop a hypothetical model of innovation organization network and COTI synergy, focusing on the mediating role of interorganizational relationships and the moderating effect of the technological innovation environment. The results of 211 questionnaires from the Chinese construction industry show that innovation organization network improves COTI synergy. Trust and communication play a vital mediating role between innovation organization network and COTI synergy. Moreover, innovative culture has a significant positive moderating effect on innovation organization network and interorganizational relationships. From the perspective of organization network, this study provides new insights into the development of technological innovation management research on megaprojects, theoretical references, and practical suggestions for project teams in developing countries to improve collaborative technological innovation efficiency.

Research on Evaluation of Scientific and Technological Innovation Efficiency in Chinese Universities-An Analysis Based on Sample Data of 31 Provinces

2011~2020 is a golden decade of innovation-driven development for China. During this period, scientific and technological innovation (STI) of Chinese universities has achieved historic development. This paper uses DEA-BCC model and Malmquist index to calculate the STI efficiency of Chinese universities in 31 provinces and municipalities from 2011 to 2020. The findings illustrate that: (1) the barrier effect of pure technical efficiency is greater than that of scale efficiency; (2)although the STI efficiency of Chinese universities increased by 0.6% on average, the contribution rate of technological progress growth was high, and the scale efficiency showed negative growth; (3) in terms of regional distribution, DEA efficient provinces and cities are concentrated in northern China, while non DEA efficient provinces and cities are mostly concentrated in southern China. Based on the findings, countermeasures and suggestions are made to improve the STI efficiency for Chinese universities from the aspects of scientific innovation alliance, evaluation mechanism and innovation management ability. It provides a theoretical and practical reference for innovation-driven development of Chinese universities.