Global Malmquist-Luenberger Index Research Articles

Measuring the urban land use efficiency of three urban agglomerations in China under carbon emissions.

On the basis of DMSP/OLS and NPP-VIIRS night light images, this study realized carbon emission estimations based on the municipal level from 1999 to 2017, compensating for the characteristics of incomplete statistical data and different statistical calibers. On this basis, the epsilon-based measure (EBM) super-efficiency model and the global Malmquist-Luenberger (GML) index are used to measure the urban land use efficiency (ULUE) and urban land total factor productivity (ULTFP) of the three urban agglomerations under the carbon emission constraints from 1999 to 2017. The following conclusions are drawn through research. (1) The correlation coefficient between the total value of night light pixels and energy consumption carbon emissions was relatively high in the three major urban agglomerations during 1999-2017, and they all passed the significance test of 1%. (2) The ULUE of the three major urban agglomerations generally shows a downward trend and then an upward trend, and spatial heterogeneity is obvious. The spatial distribution of the average level of ULUE is Pearl River Delta Urban Agglomeration (PRDUA) > Yangtze River Delta Urban agglomeration (YRDUA) > Beijing-Tianjin-Hebei Urban agglomeration (BTHUA). (3) The ULTFP of the three major urban agglomerations are all showing an increasing trend, but the geometric mean of URTFP in the PRDUA, BTHUA, and YRDUA decreases successively. Technological progress is the main driving force to promote the progress of ULTFP in each urban agglomeration. (4) The kernel density estimation shows a significant gap in ULUE between the three major urban agglomerations in China, and a phenomenon of polarization or multipolarization is observed. The main reason is the hysteresis of technology diffusion.

The Impact of Green Technology Innovation on Carbon Emissions in the Context of Carbon Neutrality in China: Evidence from Spatial Spillover and Nonlinear Effect Analysis.

The Paris agreement is a unified arrangement for the global response to climate change and entered into force on 4 November 2016. Its long-term goal is to hold the global average temperature rise well below 2 °C. China is committed to achieving carbon neutrality by 2060 through various measures, one of which is green technology innovation (GTI). This paper aims to analyze the levels of GTI in 30 provinces in mainland China between 2001 and 2019. It uses the spatial econometric models and panel threshold models along with the slack based measure (SBM) and Global Malmquist-Luenberger (GML) index to analyze the spatial spillover and nonlinear effects of GTI on regional carbon emissions. The results show that GTI achieves growth every year, but the innovation efficiency was low. China’s total carbon dioxide emissions were increasing at a marginal rate, but the carbon emission intensity was declining year by year. Carbon emissions were spatially correlated and show significant positive agglomeration characteristics. The spatial spillover of GTI plays an important role in reducing carbon dioxide emissions. In the underdeveloped regions in China, this emission reduction effect was even more significant.

Does trade in services improve carbon efficiency? —Analysis based on international panel data

• A combination of the slacks-based measure model under the variable return to scale assumption and the Global Malmquist–Luenberger index were used to measure carbon efficiency. • Panel data from 55 countries for 2001–2015 was used to empirically test the relationship between trade in services and carbon efficiency. • Trade in services significantly improved carbon efficiency. Specifically, an increase of 1% in total trade in services increased the carbon efficiency index by approximately 0.00194 units. • The improvement in carbon efficiency as a result of trade in services was greater in exports, and imported services had an inhibitory effect on carbon efficiency. • Following a change in national leader, the financial crisis in 2008, and the signing of the Kyoto Protocol, trade in services had a greater promotional effect on carbon efficiency. Trade in services has become an important driving force of high-quality and low-carbon growth in the global economy. Existing literature has largely focused on carbon efficiency from the perspective of trade in (final or intermediate) goods, but this study is one of the first to use data from multiple countries to investigate the theoretical and empirical impacts of trade in services on carbon efficiency. Taking into consideration the factors of market size, technological innovation and structural adjustments, this study discusses the impact of import and export trade in services on carbon efficiency. Using a carbon efficiency index calculated with the slacks-based measure model under the variable return to scale assumption in combination with the Global Malmquist–Luenberger index, panel data from 55 countries around the world from 2001 to 2015 was used to empirically test the relationship. The following results were found: trade in services significantly improved carbon efficiency; other conditions being equal, an increase of 1% in total trade in services increased the carbon efficiency index by approximately 0.00194 units; the improvement in carbon efficiency as a result of trade in services was greater in exports; and imported services had an inhibitory effect on carbon efficiency. These results are robust and remain after accounting for endogenous issues. Further tests showed that service exports had a positive effect on carbon efficiency in different countries and industries, and imported services had different effects in each direction in different countries and different industries. In addition, following a change in national leader, the financial crisis in 2008, and the signing of the Kyoto Protocol, trade in services had a greater promotional effect on carbon efficiency. This article can serve as a theoretical reference on optimizing global trade in services and improving carbon efficiency.

Can cross-regional environmental protection promote urban green development: Zero-sum game or win-win choice?

Forming an effective mechanism of cross-regional environmental protection (CEP) is a feasible way to break the administrative boundary of pollution control and build an ecological community. Using China's prefecture-level city data and patent data from 2003 to 2018, this paper takes the implementation of China's 12th Five-year Plan for the Prevention and Control of Air Pollution in Key Regions as a quasi-natural experiment of CEP and adopts Global Malmquist-Luenberger index method to measure China's urban green total factor productivity (GTFP). Then the Fuzzy Regression Discontinuity Design, DID model, PSM-DID model and Spatial Lag of X model are used to evaluate the impact of CEP on urban GTFP. Furthermore, this study explains the impact mechanism of CEP on urban GTFP from two aspects, the environmental protection effect and the Potter effect. Finally, what kind of cities are more likely to get the policy dividends from CEP is investigated. It shows that, first, CEP helps to improve urban GTFP, which can increase urban GTFP by about 0.05. In addition, the CEP can not only reduce urban energy intensity and carbon emissions, but also promotes urban innovation and industrial structure upgrading. Furthermore, there are obvious local effects and cross-regional conduction phenomena in the above two mechanisms. Third, for the cities with low resource density, perfect infrastructure and strong government intervention, the promoting effect of CEP on urban GTFP is more obvious. The conclusions of this paper not only help to deepen the understandings about the green economy effect of joint pollution prevention and control, but also provides inspiration in forming an effective cross-regional or multinational environmental protection mechanism.

Impacts of heterogenous technological innovations on green productivity: An empirical study from 261 cities in China

As the world second largest economy, China aims at achieving high-quality and innovation-driven development. This paper investigates the effects of different technological innovations on green productivity in 261 Chinese cities from 2004 to 2017. We employ the super-efficiency Epsilon-based measure (EBM) model with undesirable outputs under meta frontier and a global Malmquist-Luenberger (GML) index. We decompose the GML index into efficiency change (GEC) index and technology change (GTC) index to measure the dynamic changes of China's urban green development. Results showed that the average GML index is 0.924 thereby, indicating China's urban green development is at a decreasing trend. The eastern region had the highest GML index among three regions followed by the western region. Moreover, we explored the determinants of urban green productivity through panel quantile regression and found the heterogeneous impacts of different technological innovations on green productivity. Invention patents and design patents positively affected the urban green productivity while utility patents exerted a significantly negative influence. Invention patents promoted the GTC and GEC index while utility patents impeded at some quantiles. FDI yielded both “Pollution Halo Hypothesis” and “Pollution Heaven Hypothesis” effects. Invention, utility and design patents all have threshold effects on China's green productivity.

Evaluating Green Innovation Efficiency and Its Socioeconomic Factors Using a Slack-Based Measure with Environmental Undesirable Outputs.

Understanding green innovation efficiency (GIE) is crucial in assessing achievements of the current development strategy scientifically. Existing literature on measuring green innovation efficiency with considering environmental undesirable outputs at the city level is limited. Consulting existing studies, this paper constructs an evaluation index system to measure green innovation efficiency and its socioeconomic impact factors. Employing a super slacks-based measure (Super-SBM) model, which takes into account undesirable outputs (industrial wastewater emissions, industrial exhaust emissions and CO2 emissions), and a Global Malmquist–Luenberger index (GML), we calculate the green innovation efficiency of 15 cities in the Pearl River Delta (PRD) urban agglomeration from 2009 to 2017, exploring the impact factors behind green innovation efficiency using a Tobit panel regression model. The empirical results are as follows: Due to the heterogeneity of urban functional division and economic development in the Pearl River Delta, more than half of the region’s cities were found to be in ineffective or transitional states with respect to their green innovation efficiency. A GML decomposition index shows that technological efficiency and technological progress are out of step with one another in the Pearl River Delta, an asymmetry which is restricting regional green innovation growth. The influencing factors of industrial structure, the level of economic openness, and the urban informationization level are shown to have promoted green innovation efficiency in the Pearl River Delta’s cities, while government R&D expenditure and education expenditure exerted negative effects. This paper concludes by highlighting the importance of cooperation between the government and enterprises in achieving green innovation.

Green TFP Heterogeneity in the Ports of China's Pilot Free Trade Zone under Environmental Constraints.

In the context of China’s Pilot Free Trade Zone (FTZ), ports have a new opportunity to realize high-quality development. Based on the analysis of the current situation of pollutant emissions from ports in China’s Pilot Free Trade Zones (FTZs), this paper introduces environmental factors into the analysis framework of the total factor productivity (TFP) of ports in China’s FTZs, and uses the Global Malmquist–Luenberger index method to analyze the evolution trend and heterogeneity of green TFP in 28 ports of China’s 19 FTZs from 2011 to 2017. The results show that firstly, the emissions of sulfur dioxide (), nitrogen oxides () and other pollutants in China’s FTZs have been decreasing year by year. Secondly, both the green TFP and the traditional TFP of the ports in FTZs are on the rise. The absence of environmental factors leads to the underestimation of the TFP of ports. For the green TFP, the main source of its growth is technological progress. Thirdly, there is obvious port heterogeneity in the green TFP of FTZ ports. Nanjing Port has the highest green TFP growth rate, with an average annual growth rate of 21.95%. Ningbo Port, which ranks 14th, has an average annual growth rate of 5.46%. Fuzhou Port, which is rated last, has negative growth. Fourthly, there is also obvious types and regional heterogeneity in the green TFP of FTZ ports. When categorized by type, the average annual growth rate of green TFP in inland ports is significantly higher than that of coastal ports. When categorized by region, the descending order of the average annual growth rate of green TFP is the western region, the eastern region and the central region. Fifthly, the green TFP differences among the eastern, central, and western regions, as well as between inland ports and coastal ports, are shrinking. Moreover, the green TFP differences within inland ports and coastal ports and within central ports and eastern ports are also shrinking, implying there may be σ convergence. The conclusions of this paper have important implications for the scientific understanding of the heterogeneity of green TFP growth in ports in China’s FTZs, and how to promote the green development of ports in China’s FTZs under environmental constraints.

Research on Environmental Regulation and Green Total Factor Productivity in Yangtze River Delta: From the Perspective of Financial Development.

This paper employs the global Malmquist Luenberger (GML) index and the System Generalized method of moments (GMM) estimation method to investigate the influence of both environmental regulation and financial development on green total factor productivity in 41 cities of the Yangtze River Delta (YRD) in China from 2003–2019. We select the relevant input-output data to measure the green total factor productivity (GTFP) and its decomposition index including undesirable output. The results show that the GTFP and its decomposition index in the YRD have a slow fluctuating upward trend. The YRD mainly depends on improving the level of technological progress and environmental governance to promote the improvement of regional economic green development level. The empirical research results show that there is an inverted U relationship between environmental regulation and GTFP in the YRD, too strict environmental regulation will inhibit the growth of green total factor productivity. By adding control variables, the inflection point of environmental regulation is 0.5034, which is lower than that without control variables. There is a strong interaction and superposition effect between financial development and environmental regulation, which is closely related to the established financial cooperation mechanism, perfect financial system arrangement and cross-regional financial cooperation platform in the YRD. Government intervention should be reduced, the introduction of foreign capital should be controlled appropriately, foreign capital should be guided to green industries, and the use efficiency of foreign capital should be improved. This paper holds that we should pay attention to the strength of environmental regulation, prevent overcorrection, increase the guidance of credit funds, deepen the reform of the financial system, appropriately intervene in the market by the government, strengthen the guidance of foreign capital, and promote the development and transformation of the green economy in the YRD region with the help of several policies.

Environmental regulation, governance transformation and the green development of Chinese iron and steel enterprises

The green development of Chinese iron and steel industry with high pollution and energy consumption has become an urgent issue. Under the economic transition of China, this paper first establishes a theoretical mechanism for the differential effects of governance transformation models on the green process innovation (GPI) of Chinese iron and steel enterprises (CISEs). Based on the environmental protection data of 86 key CISEs from 2005 to 2018, the global Malmquist-Luenberger (GML) index is used to measure the total factor environmental governance efficiency (TFEGE) which represents the green development. Then the compound effects of environmental regulation (ER) and governance transformation on the TFEGE of 86 key CISEs are empirically studied. The results show that: (1) there is a U-shaped relationship between ER and the TFEGE. (2) Governance transformation and GPI can significantly improve TFEGE, and governance transformation will advance the inflection point of the U-shaped curve and accelerate the realization of the Porter hypothesis (PH), producing a preliminary compound effect. (3) The interaction of governance transformation and GPI has a positive and significant effect on improving TFEGE, but the heterogeneity analysis of regional economic and manufacturing process shows that it only promotes the inflection point of ER and accelerates the realization of the PH in middle- and short-process CISEs, producing a profound lagging compound effect.

Factor market distortion, technology change, and green growth in the Chinese civil airline industry

PurposeThis study evaluates the growth in the green performance of 35 Chinese airlines from 2010 to 2018. This study analyzes whether factor market distortion affects technology change and green growth in China's aviation industry and proposes corresponding policy suggestions. Design/methodologyA Global Malmquist-Luenberger (GML) productivity index and its decomposition are used to evaluate the green growth and technology change of thirty-five Chinese airlines from 2010 to 2018. Additionally, to overcome the endogeneity problems, the system generalized method of moments is used to test the effect of factor market distortions on the green growth of Chinese airlines. FindingsThe GML index reveals that although total CO2 emissions continue to increase, the growth in the green performance of Chinese airlines is positive. The regression results show that factor distortion has a negative impact on airlines’ technology change and impedes airlines’ green performance. The scale of airlines, ownership, and R&D intensity of airlines could have heterogeneous effects on the relationship between factor distortion and airlines’ green growth. Originality/valueFactor market distortion is the most important factor influencing the green performance of airlines. Rectifying factor market distortion can be regarded as another impetus to promote the green growth of airlines.

Research on the improvement of total factor productivity in China's power industry: a perspective of technological heterogeneity.

An improvement in total factor productivity of the power industry (TFPPI) is crucial for achieving China's green transformation and high-quality development. Considering the technological heterogeneity of different regions, the Meta-frontier Global Malmquist-Luenberger(MGML) index was constructed to measure TFPPI in China's 30 provinces from 2003 to 2017, and then we investigated the dynamic evolution and regional differences of TFPPI. On this basis, the two-step system GMM method was used to explore the influencing factors of TFPPI. The results showed that: (1) During the survey period, the average annual growth rate of China's TFPPI was 4.2%, and average values of TFPPI in all provinces were greater than 1. The innovation effect was the key to TFPPI growth, while the catch-up effect and leading effect were not significant. (2) TFPPI showed a decreasing trend from the western to eastern and central regions, with average annual growth rates of 5.69%, 3.66%, and 2.89%, respectively, and the driving factors of each region were different. Moreover, the technology gap was narrowing. (3) Both the economic development level and R&D level had a positive impact on TFPPI, whereas the power consumption intensity was negatively correlated with TFPPI. Based on the conclusions of this paper, some effective recommendations were put forward to improve TFPPI.

How industrial convergence affects regional green development efficiency: A spatial conditional process analysis

Industrial convergence is a key means to transform the economic mode. Taking the convergence of manufacturing and producer services in China as the research object, this study explored how industrial convergence affects regional green development efficiency (GDE). First, a coupling evaluation system was established to measure industrial convergence degree, and the directional distance function-based slacks-based measure was combined with the global Malmquist-Luenberger index to measure GDE. Second, we employed spatial econometric models to analyze the relationship between industrial convergence and GDE. Then, using the spatial conditional process analysis, a unified framework of green innovation, investment structure, and energy intensity was constructed to investigate the transmission mechanism involved. The results showed that: (1) Regional GDE and green innovation had a spatial dependence. (2) Considering the spatial correlation, industrial convergence is conductive to regional GDE. (3) Green innovation is an effective path by which industrial convergence improves regional GDE. (4) In this mediating process, the investment structure and energy intensity play a moderating role. The investment bias in high-tech industries increases the role of industrial convergence in promoting regional GDE and green innovation, while the moderating direction of energy intensity is opposite. In addition, there is a crowding-out effect in energy dependence, which hinders the effectiveness of green innovation.

Efficiency of tourism development in China's major cities under the constraint of PM2.5.

Climate / weather factors are important factors for tourists to choose tourist destinations. With the public’s attention to the influence of haze, air quality will have a profound impact on the development of tourism in tourist destinations. Based on the Epsilon-based Measure (EBM) super-efficiency model and Global Malmquist–Luenberger index analysis method, this paper aims to study the tourism development efficiency of 58 major cities in China from 2001 to 2016 and analyse the total factor productivity in the development of urban tourism and the changing driving factors in consideration of the undesirable output of haze characterised by PM2.5 emission concentration. The study findings show that the overall efficiency of tourism development of 58 cities is not high in 2001–2016, but the tourism development efficiency of all cities is increasing year by year. Under the constraint of haze, the efficiency of urban tourism development is not directly proportional to the degree of urban development. The overall redundancy rate of each input index is slightly high, and the redundancy of PM2.5 emission concentration has a considerable effect on the efficiency of urban tourism development. The overall change trend in total factor productivity in the development of urban tourism is improved, mainly due to the improvement of technological progress factors. On this basis, the corresponding policy implications are concluded according to high-efficiency and high-quality development of tourism in 58 major cities.

Spatial-temporal evolution and influencing factors of total factor productivity in China's logistics industry under low-carbon constraints.

Behind the rapid development of China's logistics industry, there are problems of high energy consumption and high pollution. Under the dual constraints of resources and environment, promoting the low-carbon transformation of the logistics industry is the key to achieving sustainable development of the logistics industry. This paper applies the epsilon-based measure (EBM) model which considers undesirable output and global Malmquist-Luenberger (GML) index to measure the logistics efficiency under the low-carbon constraints of 30 provinces in China from 2005 to 2017, that is, the green total factor productivity (GTFP), and characterizes its temporal and spatial evolution characteristics through visualization and spatial analysis methods. Then, this paper uses the geographically weighted regression (GWR) model to analyze the influence of industrial agglomeration level, informatization level, foreign direct investment, logistics energy intensity, traffic network density, and technological innovation capability on the GTFP of the logistics industry. The findings of this paper show that (1) during the inspection period, the overall average GTFP of the logistics industry was 0.992, which did not reach the effective level, and the spatial differentiation showed that the average GTFP of eastern was greater than that of in central, and that of in central was greater than that of in western. (2) The GTFP of the logistics industry has experienced an alternating process of rising and falling in time, with large fluctuations. Also, in terms of spatial dimension, there is a trend that high-level areas gradually gather to the southeast, and there is significant spatial autocorrelation. (3) For the logistics industry, high-efficiency areas and high-output areas show significant spatial homogeneity. (4) The estimation results of the GWR show that the direction and intensity of the multi-dimensional driving factors on the GTFP of the logistics industry are different in different regions, showing obvious spatial non-stationary characteristics.

Temporal-Spatial Evolution and Driving Factors of the Green Total Factor Productivity of China’s Central Plains Urban Agglomeration

As China’s largest urban agglomeration with rapid growth of economy and population, the development of the Central Plains Urban Agglomeration (CPUA) has been severely restricted by environmental problems. Thus, the green development performance of the CPUA is worth studying. This study used the panel data of 29 cities in the CPUA from 2003 to 2018 based on the Slacks-based Measure and Global Malmquist–Luenberger index to measure the green total factor productivity (GTFP) and its decomposition index of each city. A spatial econometric model was developed to explore the factors affecting the GTFP of the CPUA. The results show that the GTFP of the CPUA had an upward trend in 2003–2018, but the productivity level was still low. There were significant spillover effect in the GTFP among different cities of the CPUA. The results of the spatial measurement model show that technological progress, industrial structure and solid waste environmental regulationhave a significant positive spillover effect on the GTFP. The, fiscal expenditure, and informatization level also have positive impacts on the GTFP. In the future, local government should provide technical and financial support for the development of green industries in the CPUA, and accelerate the construction of environmental protection infrastructures.

The co-integration identification of green innovation efficiency in Yangtze River Delta region

The research innovatively identifies the co-integration of green innovation efficiency and its related determinants for 26 cities in Yangtze River Delta region (YRD) in China during 2011–2017. Specifically, the research firstly measures the green innovation efficiency with super slacks based measure and global Malmquist-Luenberger index. The regional green innovation efficiency experienced a steady growth in YRD during 2011–2017, showing YRD is an ecologically pioneer in China. Secondly, the spatial econometric estimation validated that the green innovation efficiency among YRD cities finally realized regional co-integration in 2017 with spatial evolution. Finally, the expenditures on technology and education are positive determinants of green innovation efficiency in a city directly. GDP per capita is the positive determinant for co-integration of green innovation efficiency; while foreign direct investment, the proportion of expenditure on technology, and education proportion in local fiscal budget are negative determinants for co-integration of green innovation efficiency in YRD.

Can China's Environmental Regulations Effectively Reduce Pollution Emissions?

Based on the provincial panel data of China during 2006–2017, this study uses the panel smooth transition (PSTR) model to study the dynamic transformation mechanism of pollution emission under environmental regulation. We focus on technological progress, economic growth, and foreign direct investment (FDI) as threshold variables, and analyses the non-linear effects of environmental regulation on pollution emissions under those threshold variables, attempting to explore the effectiveness of existing environmental regulations. The structure of biased technological progress is based on the slacks-based measure (SBM) and Global-Malmquist–Luenberger index, which is divided into pollution-biased technology progress and clean-biased technology progress. Finally, we use the panel vector auto regressive (PVAR) algorithm to further verify the relationship. The findings are as follows: (1) Environmental regulation has a significant nonlinear effect on pollution emissions, and technological progress is the optimal threshold variable of this study. (2) Under the influence of these three factors, environmental regulation has a substitution effect on pollution discharge, and a stronger substitution effect on emission reduction in areas with advanced technology and high FDI. It also has a lower emission reduction effect in the high-system areas of economic development than in the low-system areas. (3) The PVAR results show that the impact on environmental regulation of technological progress and FDI has gradually turned from positive to negative; the impact of economic growth on environmental regulation has always been positive but is gradually decreasing. This study points out the direction for governments and companies to implement effective environmental regulations.

Resource Endowment, Industrial Structure, and Green Development of the Yellow River Basin

The Yellow River Basin is an important energy base of China, and its green development is crucial to Chinese economic transformation. In this paper, we calculate the green total factor productivity (GTFP) to measure the green development level of the Yellow River Basin by using an Slack Based Model- Global Malmquist-Luenberger (SBM-GML) index model. On this basis, we use a Generalized Method of Moments (GMM) model to further analyze the impact of resource endowment and industrial structure on the green development of cities. The results show that resource endowment inhibits the green development of cities and that the resource curse is observed in the Yellow River Basin. The industrial structure advancement significantly promotes the green development of cities. The impact of industrial structure rationalization on green development varies significantly on the type of city. Specifically, it has an inhibiting effect on key environmental protection cities but a promoting effect on non-key environmental protection cities.

Heterogeneity of Green TFP in China’s Logistics Industry under Environmental Constraints

In the context of China’s supply-side structural reform and the concept of green development, we introduce the energy and environmental factors into the analytical framework of the total factor productivity (TFP) of the logistics industry and use the Global Malmquist–Luenberger index method to analyze the evolution trend and heterogeneity of green TFP in the logistics industry of 30 provinces (cities and districts) in China from 2003 to 2017. The results show that, firstly, the green TFP and traditional TFP of China’s logistics industry are both on the rise and the absence of energy and environmental factors will lead to the overestimation of TFP of the logistics industry. Whether it is green TFP or traditional TFP, the main source of its growth is technical progress. Secondly, there is obvious regional heterogeneity in green TFP of logistics industry. Under the three regional division standards, the average annual growth rate of green TFP is from high to low in the order of eastern, western, and central regions. Under the eight regional classification standards, the eastern coastal economic zone, the southern coastal zone, the northern coastal zone, the northeast region, the middle reaches of the Yellow River, the southwest region, the middle reaches of the Yangtze River, and the northwest region are in order from high to low. Thirdly, there is obvious interprovincial heterogeneity in green TFP of the logistics industry. The highest growth rate of green TFP is in Zhejiang, followed by Jiangsu and Guangdong, and the slowest growth rate is in Chongqing. The technical progress of logistics industry in most provinces contributes more to the growth of green TFP. Fourthly, the differences of green TFP in the three regions of the east, center, and west are shrinking, which may be σ convergence, but the differences among the three regions are expanding. Compared with the existing literature, this paper applies the measurement framework of green TFP to China’s logistics industry and investigates the regional and provincial heterogeneity of green TFP in logistics industry. The conclusions are significant to understand and grasp the heterogeneity of green TFP growth in China’s logistics industry under environmental constraints and how to promote the development of green logistics in China.

Green productivity growth and competition analysis of road transportation at the provincial level employing Global Malmquist-Luenberger Index approach

Road transportation has been playing an irreplaceable role in the process of pursuing sustainable development. This study employs a Global Malmquist-Luenberger Index approach to evaluate the green productivity growth of this industry at the provincial level based on the Data Envelopment Analysis and Directional Distance Function. Further, it decomposes green productivity growth into changes in various types of efficiency and technological progress. Finally, this study structures a novel quadrant matrix analysis framework based on the green productivity growth rate and stability, using the matrix to analyze the performance of provincial road transportation industries. This analysis results show that: (1) a fluctuating and slowly upward trend of green productivity over time exists; (2) at the regional level, the road transportation industries in Western and Central China achieved green productivity growth because of the catch-up effect and the economies of scale, respectively; (3) the green productivity in China’s Eastern area (the most developed area) declined, giving opportunities for improvement in both technology and efficiency; and (4) the southeast coast and border provinces showed large fluctuations in terms of green productivity of road transportation because of the impact of foreign trade environment. Implications for transportation system planning are provided based on the results.