Global Malmquist-Luenberger Research Articles

Evaluating the scientific and technological innovation efficiency of universities in China: evidence from the global Malmquist–Luenberger index model

ABSTRACT The evaluation of the scientific and technological (S&T) innovation efficiency of universities is important for the ‘double first-class’ construction in China. However, related research often only considers desirable outputs while ignoring undesirable ones, which tends to produce biased evaluation results. For this reason, this study takes the ratio of unemployed graduates as undesirable output, and uses the global Malmquist – Luenberger index method to evaluate the S&T innovation efficiency of 31 relevant universities in China during 2013–2017, then develops a panel Tobit model to analyse its influencing factors. The results reveal that: first, during the sample period, the S&T innovation efficiency of those universities improved overall, and technical progress was the main driver. Second, excluding undesirable output will result in an overestimate of the S&T innovation efficiency of universities. Third, the S&T innovation efficiency of universities features regional distribution, with high efficiency in the west and low efficiency in the east. Moreover, different regions have different drivers for the improvement of S&T innovation efficiency of universities, with technical progress for the east and technical efficiency changes for the central and west. Finally, the ‘double first-class’ construction proposal in China has significantly promoted the improvement of S&T innovation efficiency in universities.

Environmental Regulation, Urban-Rural Income Gap and Agricultural Green Total Factor Productivity

Environmental regulation is the basis for achieving green agricultural development, and urban-rural integration is the key to optimizing the allocation of agricultural elements and achieving sustainable agricultural development. This paper aims to investigate the spatial spillover effect of environmental regulation on China’s agricultural green total factor productivity (AGTFP) and examine the mediating effect of the urban-rural income gap. Both the Super-SBM-DEA model and the Global Malmquist–Luenberger productivity index are used to account for the AGTFP of China’s 30 provinces, and the spatial Durbin model and the mediating effect model are used to analyze the impact of environmental regulation. We found that firstly, during the sample period, China’s AGTFP has increased with an average annual growth rate of 3.27%, which is mainly promoted by agricultural green technology progress (AGTC). Secondly, both the direct and spatial effects of environmental regulation on AGTFP show a significant “U”-shaped feature and have regional heterogeneity based on differences in economic development levels and factor endowments. Thirdly, there is an “inverted U”-shaped relationship between environmental regulation and the urban-rural income gap, and the urban-rural income gap negatively affects AGTFP. Based on the empirical results, we propose that the Chinese government should pay attention to green technology innovation, break the market segmentation, promote urban-rural integration, and then promote the AGTFP.

Effects of subsidy and tax rebate policies on green firm research and development efficiency in China

This study attempts to fill important knowledge gaps to explore the distinct symbolic influences of subsidy and tax rebate policies on green firm research and development (R&D) efficiency using the global Malmquist–Luenberger (GML) index method, while focusing on green industries in China. Our empirical results demonstrate that the subsidies and tax rebates received from local and central governments have significant U-shaped impacts on green firm R&D efficiency at the 10% significance level, and subsidies and tax rebates have significant and positive interactions with green firm R&D efficiency under the U-shaped effects of subsidies and tax rebates at the 1% significance level. Furthermore, subsidies and tax rebates play distinct transmission roles in influencing green firm R&D efficiency by stimulating R&D investment, boosting technical efficiency change, and facilitating technical progress change. • This article explores the fiscal policy-R&D efficiency nexus in green firms. • Subsidies and tax rebates have a U-shape relationship with R&D efficiency. • Tax rebates and subsidies have a positive interaction with R&D efficiency. • 4.Subsidies and tax rebates produce effective transmission channels on R&D efficiency.

The driving effect of technological innovation on green development: dynamic efficiency spatial variation.

In order to further explore the internal transmission mechanism between technological innovation and green development in manufacturing industry under the background of obvious development characteristics in the new era, this paper constructed an integrated methodology system to evaluate the internal impact mechanism of technological innovation value chain efficiency on green development efficiency based on spatial perspective. First, the Network Slack-based model and Global Malmquist-Luenberger model are constructed to reveal the internal development law of technological innovation and green development of manufacturing industry. Secondly, the spatial Dubin model is employed to analyze the impact of current development characteristics and technological innovation on green development. The results show that innovation value chain efficiency is higher than technological innovation efficiency, and economic transformation efficiency is lower than that of technological innovation value chain. During the study period, the efficiency of technological innovation value chain in the four economic regions present fluctuant growth trend, and the eastern region has the highest value. The green development efficiency in the east, central, west, and northeast regions of manufacturing industry is higher than 1, and it shows an obvious spatial agglomeration effect. Besides, the efficiency of technological innovation, information and communication technology, urbanization, and the advanced industrial structure are all conducive to the improvement of green development in manufacturing industry. This paper studies the influence mechanism of technological innovation value chain efficiency on green development based on spatial perspective and puts forward relevant countermeasures and suggestions to effectively promote green development of manufacturing industry, providing relevant theoretical research for green and high-quality development.

Does the Application of ICTs Improve the Efficiency of Agricultural Carbon Reduction? Evidence from Broadband Adoption in Rural China.

Based on the Environmental Kuznets Curve (EKC) hypothesis, this paper examines whether rural broadband adoption affects agricultural carbon reduction efficiency (ACRE), using panel data from 30 Chinese provinces from 2011 to 2019. This paper achieves a measurement of ACRE by taking the carbon sink of agricultural as one of the desired outputs and using a Slacks-Based Measure (SBM) model and the global Malmquist–Luenberger (GML) index. The results show that: (1) Rural broadband adoption has a positive effect on ACRE. The relationship between the income of rural residents and ACRE was an inverted U-shaped, which confirms the EKC hypothesis. (2) Land transfer has a significant promoting effect on the relationship between rural broadband adoption and ACRE. When the land transfer rate is high, the positive effect of broadband adoption is obvious. (3) The positive effect of broadband adoption on ACRE was more obvious when farmers invested more in production equipment, that is to say, it has a significant positive moderating effect. As farmers in many developing countries suffer from increasingly frequent and severe extreme weather events, we believe that the results of this study also have implications for the implementation of agricultural carbon reduction and smart agricultural equipment roll-out in many countries.

Paris climate agreement and global environmental efficiency: New evidence from fuzzy regression discontinuity design

The most remarkable global climate agreement to date, the Paris Agreement of 2015 requires all countries to set emissions-reduction commitments. However, experts say that the pledges are not enough to prevent the global average temperature from rising 1.5 °C. This study adopts the Paris Agreement as a quasi-natural experiment and evaluates its policy intervention on environmental efficiency as well as GHG emissions in 162 countries from 1990 to 2020. We applied a global Malmquist-luenberger productivity (GML) index to measure global environmental efficiency. Moreover, we used seven major sources of GHG emissions to develop a comprehensive indicator of environmental degradation using the Principle Component Analysis (PCA). Results showed that developed countries experienced an increase in environmental efficiency, whereas developing and least developing countries (LDCs) showed decline. Technological progress was the key component of environmental efficiency in developed countries, while technical efficiency was the main indicator in developing countries and LDCs. Results of fuzzy regression discontinuity analysis documented that the policy intervention of the Paris Agreement was conducive to the global environmental efficiency by reducing the level of GHG emissions. However, the implementation of the Paris Agreement exerted heterogeneous effects on environmental efficiency with developed countries relatively experienced more profound effect.

A Study of the Spatial Structure and Regional Interaction of Agricultural Green Total Factor Productivity in China Based on SNA and VAR Methods

As regional interaction increases in an open economy, a region’s green total factor productivity in agriculture must be considered alongside relationships with other regions. In this study, the slack-based model (SBM) global Malmquist–Luenberger (GML) index is used to measure the green total factor productivity of agriculture in each province of China, and the social network analysis (SNA) and vector autoregressive model (VAR) impulse response function (IRF) are used to examine the spatial network structure and regional interactivity. The research confirms that the absolute value and concentration of agricultural green total factor productivity are generally higher in the south than in the north of China, but the peak is lower in the south than in the north. The network density of agricultural green total factor productivity in China from 2008 to 2019 shows an increase, with the cut-off values of mean, 10, 50, and 100 treated as 4.97%, 2.57%, 3.30%, and 2.43%, respectively. From 2008 to 2019, the central potentials of network entry and network exit of green total factor productivity in China’s agriculture show a “V”-shaped and inverted “V”-shaped evolution path, respectively, with the density of cohesive subgroups growing, which demonstrates that the spatial structure of green total factor productivity in Chinese agriculture has experienced an evolutionary path from polycentric to monocentric to polycentric conditions. The spatial interaction of different cohesive subgroups is intensifying and has a certain degree of self-stability. In terms of regional interaction, the siphon effect of the east on the green development of agriculture in the central and western regions is significant, but the trickle-down effect is not obvious, and the interaction between the central and western regions has a catalytic effect on the efficiency of the green economy of agriculture in both regions. It is recommended that targeted policies be introduced to support the flow of agricultural factors and industrial division of labour between the central and western regions and the south and north, taking into account the actual situation. The novelty of this paper is that it focuses on the green total factor productivity of Chinese agriculture and combines the innovative use of the social network analysis paradigm to analyse the green development of agriculture in a country from a spatial dynamic evolutionary perspective. A limitation of the research methodology in this paper is its poor applicability to closed economy analysis.

Convergence of green total factor productivity in China's service industry.

With the rising global concern over environment and energy issues, there are many scholars incorporating them into total factor productivity (TFP) analytical framework. This paper aims to outline the dynamic change trend and convergence characteristics of green total factor productivity (GTFP) of 14 sub-sectors in China's service industry. This is of great significance to the coordinated development and green transformation of sub-sectors of China's service industry. This paper commences to gauge and decompose GTFP of China's service industry by cutting-edge epsilon-based measure (EBM) generalizing slacks-based measure (SBM) and radial measure as well as Global Malmquist-Luenberger (GML) productivity index, and to examine the existence of convergence hypothesis in China's service industry. The core conclusions are as follows: (1) the average annual growth rate of GTFP in China's service industry is 1.00%, and there exists heterogeneity for GTFP in different sub-sectors; (2) there are phenomena of σ-convergence, absolute β-convergence, and conditional β-convergence in GTFP of China's overall service industry, producer service industry, and consumer-oriented service industry. The main policy suggestions include: increasing investment in green technology innovation of the service industry, formulating more effective industrial development planning and energy policies, and optimizing energy structure and industry structure of the service industry.

Efficiency of China's urban development under carbon emission constraints: A city-level analysis

This study explores the efficiency of China's urban development under carbon emission constraint from 1996 to 2017. Firstly, it uses the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) and Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nightlight imaging to evaluate the carbon dioxide emission levels of 249 cities in China. Then, it utilises the epsilon-based measure (EBM) superefficiency model and Global Malmquist–Luenberger (GML) index to evaluate the urbanization efficiency and changes in the total factor productivity (TFP) of urban development of the 249 cities under carbon emission constraints. Results show that from 1996 to 2017, the total carbon emissions of the 249 cities grew during a rapid development stage, while the efficiency of urban development under carbon emission constraints showed fluctuation and did not increase significantly. The redundant improvement values of government fiscal expenditure, urban employees, urban fixed asset investment, foreign direct investment, and carbon emission increased in fluctuations. From the regional spatial perspective, the urbanization efficiency of western China was the highest, followed by the northeast region, the central region, and finally the eastern region. In addition, the driving factors of TFP of urbanization in China have improved as a whole. The application of nightlight imaging data and EBM superefficiency model improves the accuracy of urban development efficiency measurement. The findings of this study can provide a scientific basis for different regions and cities of different sizes to formulate carbon emission reduction strategies, and have important policy value for improving urban development efficiency in China. In addition, the research of index system should be deepened to further improve the measurement accuracy.

RETRACTED: Efficiency Measurement and Spatial Spillover Effect of Green Agricultural Development in China

Green agriculture is mainstream for the sustainable development of agriculture. Based on the Chinese provincial agriculture panel data from 2010 to 2019, we adopted the slack-based measure (SBM) super-efficiency model, sales force automation (SFA) model, and global malmquist–luenberger (GML) production index to measure the efficiency of agricultural green development (AGD). Moreover, Moran’s I and spatial econometric model were applied to analyze factors influencing AGD. The threshold model was used to analyze the relationship between the scale of AGD and gross domestic product (GDP). The results show that 1) Chinese green agricultural development efficiency is on a rising trend, reducing the impact of environmental factors and random interference on the AGD. 2) The analysis of AGD in the spatial effect showed a direct positive effect from agricultural mechanization, science and technology innovation, industrial agglomeration, income level, and environmental rule and a direct negative effect from agricultural yield structure, farmland pollution, and agricultural disasters. Furthermore, industrial structure optimization and environmental rule evoke a demonstration effect, but technical innovation, income level, and agricultural industrial agglomeration triggered a siphonic effect. 3) The threshold model was used to analyze the scale of AGD to realize sustainable development between agriculture and economy.

Green total factor productivity measurement of industrial enterprises in Zhejiang Province, China: A DEA model with undesirable output approach

The green production efficiency of industrial enterprises in Zhejiang Province is not only related to the level of green economy in Zhejiang Province, but also provides an example for the green development of national industrial enterprises. This paper used the panel data of industrial enterprises by sector and city in Zhejiang Province from 2012 to 2017. Firstly, the entropy weight method is employed to calculation the Comprehensive Environment Pollution Index (CEPI) of various sectors and cities including sulfur dioxide; nitrogen oxide; smoke and dust; solid waste and hazardous solid waste. The method of output-oriented Data Envelopment Analysis (DEA) model with undesirable output was employed to measure Global Malmquist-Luenberger Productivity Index (GMLPI). The conclusion shows that: (1) Zhejiang industrial enterprises have made slight progress (0.738% per year on average) in comprehensive efficiency between 2012 and 2017. (2) In sectoral efficiency analysis, the fluctuation of TC and SEC is the main factor affecting the comprehensive efficiency of the sector. (3) In city efficiency analysis, Hangzhou; Ningbo; Wenzhou; Jiaxing and Shaoxing have made progress in comprehensive efficiency in the research period, while Jinhua; Quzhou and Zhoushan have made decline. This paper provides support for the green development of Zhejiang Province.

Does the Environmental Information Disclosure Promote the High-Quality Development of China’s Resource-Based Cities?

The high-quality development (HQD) of resource-based cities (RBCs) is the premise on which to ensure the healthy, stable, and sustainable development of China’s economy. In this study, we use the global Malmquist–Luenberger index based on the slacks-based measure of directional distance function (SBM-DDF-GML index), which is an improved data envelopment analysis (DEA) model of the non-radial and non-oriented, to calculate the HQD level of 102 RBCs in China from 2003 to 2019. Then, we empirically evaluate the effect of environmental information disclosure (EID) on HQD improvement in RBCs by adopting the method of time-varying difference-in-difference with propensity score matching (PSM-DID) and investigate the heterogeneous effects of EID. Additionally, the mediating effect model is employed to explore the impact mechanisms of EID on the HQD. The results show that: (1) EID has a significant and positive effect on the HQD of RBCs, and this conclusion is still valid after a series of robustness tests. (2) EID plays a more effective role in the promotion of HQD in central RBCs, resource strong-dependent RBCs, growth RBCs, and regenerative RBCs than in other types of cities. (3) EID promotes the HQD of RBCs through the environmental pollution reduction effect and the industrial structure upgrading effect. These findings enrich the content of the relationship between EID and the HQD and present a feasible path for RBCs in China to achieve the HQD through environmental governance.

The spatial spillover effect of low-carbon city pilot scheme on green efficiency in China's cities: Evidence from a quasi-natural experiment

To test the direct effect (impact on local) and indirect effect (spatial spillover) of low-carbon city pilot scheme (LCCPS) on China's cities' green efficiency, this paper first calculates and decomposes the green efficiency constrained with CO2 emissions and pollutants by using a range directional model (RDM) based on data envelopment analysis (DEA), then the global Malmquist-Luenberger (GML) index of 285 China's cities' green efficiency is obtained. Through the GML index, it is found that the green efficiency of sample cities has improved by 29.56% evenly in 2003–2017, and the main driving force of green efficiency progress is technological innovation. The green efficiency progress of LCCPS pilot cities is significantly greater than that of non-pilot cities, and there is a significant positive spatial autocorrelation in the green efficiency of 285 sample cities. According to the test results of the spatial difference in differences (Spatial-DID) model, LCCPS can positively promote the green efficiency of local and neighboring cities, and this result can pass the placebo test, and remain robust when using an alternative green efficiency index (without CO2 emissions) or a spatial-economic weight matrix. The use of propensity score matching (PSM) and instrumental variable method further shows that the above results will not change substantially even if the selection bias and endogeneity are eliminated. The mechanism analysis shows that: First, LCCPS improves the local green efficiency by promoting the aggregation of innovation resources, but the resulting siphon effect inhibits the aggregation of innovation resources and the green efficiency progress in neighboring cities. Second, LCCPS improves the local green efficiency of pilot cities by inhibiting the industrial activities and industrial energy consumption, but it can inhibit the green efficiency progress by increasing industrial activities and industrial energy consumption of neighboring cities. Although the above mechanism cannot completely reverse the beneficial space spillover effect of LCCPS, it can also effectively point out the future improvement direction of the policies in LCCPS.

Total factor productivity of high coal-consuming industries and provincial coal consumption: Based on the dynamic spatial Durbin model

To promote the coordinated governance of atmospheric pollutants and greenhouse gases (GHGs), and to meet the emission goals of carbon peak and carbon neutrality, China should optimize the spatial pattern of its coal consumption. The present study employs the global Malmquist–Luenberger index to calculate the total factor productivity (TFP) of high coal-consuming industries in different provinces in China. Taking TFP as the independent variable, provincial coal consumption (PCC) as the dependent variable, and urbanization rate (UR) and regional gross domestic product (GDP) as the control variables, we establish a dynamic spatial Durbin model of TFP and PCC. We also use spatial effects decomposition to analyze the spatial influence relation between TFP and PCC. The results show a fluctuating upward trend of TFP in China overall, but the TFP in the eastern region is higher than that in the central and western provinces. The direct effects show a significant U-shaped relation between TFP and PCC, where PCC trends down with an increase in TFP. The indirect effects show an inverted U-shaped relation between local TFP and PCC in neighboring provinces. Finally, according to the estimated results, we propose some policy recommendations for China's government to control coal consumption.

Evaluation of Industrial Green Transformation in the Process of Urbanization: Regional Difference Analysis in China

Population mobility and the expansion of construction land in industrial development promote urbanization, and the sustainable development of cities creates requirements for the green transformation of industry. This paper uses the directional distance function (DDF) and the global Malmquist–Luenberger (GML) index method—including urbanization factors—to calculate the industrial green transformation (IGT) index in China, and to analyze its evolution and spatial distribution characteristics. The results show that ignoring the urbanization factor will lead to the overestimation of the IGT. The growth of the index has multiple stages, and it shows a decreasing order in the middle, east and west regions. Overall, the distribution of the index spreads out over time, and the gap widens. In terms of spatial correlation, high values are mostly concentrated in the eastern region and low values are mostly concentrated in the western region, and the gap in the eastern region is larger than those in the central and western regions. Therefore, in addition to the transformation of industrial production into a circular economy model, it is also necessary to promote the circulation of technical talent between regions in the development of urbanization, reducing the unbalanced development between regions and comprehensively promoting the green transformation of industry.

China’s Port Carbon Emission Reduction: A Study of Emission-Driven Factors

Ports offer an effective way to facilitate the global economy. However, massive carbon emission during port operating aggravates the atmospheric pollution in port cities. Capturing characteristics of port carbon emission is vital to reduce GHG (greenhouse gas) in the maritime realm as well as to achieve China’s carbon neutral objective. In this work, an integrated framework is proposed for exploring the driving factors of China ports’ emissions combined with stochastic effects on population, affluence and technology regression (STIRPAT), Global Malmquist-Luenberger (GML) and multiple linear regression (MLR). The port efficiency is estimated for each port and the potential driving factors of carbon emission are explored. The results indicate that port carbon emissions have a strong connection with port throughput, productivity, containerization and intermodal transshipment. It is worth noting that the containerization ratio and port physical facility with fossil-free energy improvement have positively correlated with carbon emissions. However, the specific value of waterborne transshipment shows a complex impact on carbon dioxide emission as the ratio increases. The findings reveal that China port authorities need to improve containerization ratio and develop intermodal transportation; meanwhile, it is responsible for port authorities to update energy use and improve energy efficiency in ways to minimize the proportion of non-green energy consumption in accordance with optimizing port operation management including peak shaving and intelligent management systems under a new horizon of clean energy and automatic equipment.

Spatial-Temporal Evolution and Influencing Factors of Urban Green and Smart Development Level in China: Evidence from 232 Prefecture-Level Cities.

Green and smart city is an optimal choice for cities to realize their modernization of governance capacity and sustainable development. As such, it is necessary to clarify the evolutionary characteristics and driving mechanism of urban green and smart development level (GSDL) systematically. From the perspective of green total factor productivity (GTFP), this study adopted the SBM-GML (slack-based model & global Malmquist–Luenberger) method to measure the urban GSDL considering smart input-output elements. Based on the panel data of China’s 232 prefecture-level cities from 2005 to 2018, the spatial and temporal evolution characteristics of urban GSDL were explored, and the factors and structural mutation points affecting urban GSDL were analyzed with quantile regression tests and threshold regression tests. The findings of this paper showed that (1) there is an upward trend in the volatility of urban GSDL from 2005 to 2018, in which the eastern region was highest, followed by the central and western regions, and the differentiation showed no converge among regions; (2) the effect of technical progress and technical efficiency improvement on the urban GSDL was demonstrated with a fluctuating “Two-Wheel-Drive” trend on the whole; (3) the urban GSDL was promoted by the opening-up level and urban scale significantly, while inhibited by the level of economic development and government size. Additionally, the effects of industrial structure, financial development level, and human capital level on the urban GSDL were distinctive at different loci; (4) the threshold effects of economic and financial development level on improving the positive effects of industrial structure and opening-up level on urban GSDL were significant. These findings may enrich the research literature on the evolutionary heterogeneity of green and smart cities and provide theoretical and practical exploration for the construction of green and smart cities.

Impacts of land finance on green land use efficiency in the Yangtze River Economic Belt: a spatial econometrics analysis.

This paper uses the Global Malmquist-Luenberger Index (GMLI) based on directional distance function (DDF) super efficiency model to measure the urban green land use efficiency (UGLUE) of 108 cities in the Yangtze River Economic Belt (YREB) from 2007 to 2018, and it utilizes the spatial economic model to analyze the impact of land finance on the UGLUE and its mechanism of action. The results show that, firstly, the UGLUE in the YREB shows a steady development trend, the overall efficiency level is high, and there are spatial agglomeration characteristics. Secondly, the impact of land finance on the UGLUE presents "inverted U-shaped." With the continuous expansion of the scale of land finance, the impact of land finance on the UGLUE in the city has changed from positive to negative. Thirdly, land finance has a spatial spillover effect. Land finance will inhibit the improvement of UGLUE in surrounding areas through the "peer effect." With the continuous expansion of land finance scale, land finance will promote the improvement of UGLUE in surrounding cities through the "warning effect."

Influence Mechanism of Industrial Agglomeration and Technological Innovation on Land Granting on Green Total Factor Productivity

The influence of land granting on green total factor productivity (GTFP) and its mechanisms remain under-explored. Based on dynamic spatial panel data of 30 provincial administrative regions in China from 2007 to 2017, this study attempts to establish the framework of a transmission mechanism among land granting, manufacturing agglomeration and technological innovation, and green total factor productivity, and measures their interactions by the mixed directional distance function and Global Malmquist–Luenberger index model containing undesirable output. Then, this paper empirically estimates the impacts of land granting, industrial agglomeration and technological innovation on green total factor productivity in China with a dynamic spatial mediating effect model. The results show that the development paradigm of China’s industrial green economy is characterized by path dependence, and industrial GTFP has a significant spatial effect. Local governments rely on low-price competition to obtain cost advantages and facilitate the agglomeration of local manufacturing industries, and promote the impact of negotiation on industrial GTFP through the mediating effect of manufacturing agglomeration. There exists a non-linear relationship between manufacturing agglomeration and industrial GTFP. The land acquired through negotiation has a promoting effect on green technology innovation, and will foster industrial GTFP through the intermediary effect of technological innovation. No obvious non-linear relationship is observed between technological innovation and industrial GTFP. The grant of “Tender, Auction and Listing” has no significant influence on the industrial GTFP, and the mediating conduction effect on the GTFP of industry is not established. Industrial structure, government management and infrastructure will significantly promote the improvement of industrial GTFP, while the level of transportation will inhibit the improvement of industrial GTFP. Through administrative intervention in low-production capacity departments, local governments can integrate regional resource endowments, give play to the comparative advantages of industries, and achieve industrial structure upgrades and core competitiveness, which will be conducive to the improvement of industrial GTFP. High-level transportation conditions increase energy consumption and greenhouse gas emissions in transportation operation, which makes no contribution to the enhancement of industrial GTFP.

Research on carbon productivity and its spatial convergence of steel industry in China.

The Global-Malmquist-Luenberger (GML) index was applied to analyze the carbon productivity in steel industry (SICP) of 29 provinces in China from 2006 to 2019, and then, the SICP was decomposed into technical efficiency change index (TC) and technical progress index (EC). On this basis, the spatial effect is introduced into the traditional convergence model to investigate the spatial convergence of SICP. The empirical results show that: (1) the overall carbon productivity of China's steel industry is at a relatively low level, showing a slow growth trend. (2) The average value of the GML index of SICP is higher than 1, showing obvious inter-provincial and regional heterogeneity. Compared with TC, EC is the leading factor that promotes the increase of SICP. (3) The spatial absolute and condition β convergence of SICP exist in the whole country and the three major regions, but the σ convergence feature is not significant. The addition of spatial factors speeds up the convergence trend, and the speed of spatial absolute β convergence is about 3 times that of the classical convergence model. At the same time, the conditional convergence rate is significantly faster than the absolute convergence, which is closely related to the differences in influencing factors such as the industrial structure, economic development level, human capital, energy consumption intensity, and R&D investment among regions. There is still much room for improvement in carbon productivity in China's steel industry, and investment in scientific research must be increased in order to achieve the upgrading of the industrial structure and technological innovation. The existence of spatial convergence requires strengthening the joint reorganization of steel enterprises between provinces and regions, making full use of the spatial spillover effects of production technology, and realizing regional green and coordinated development.