Global Malmquist-Luenberger Index Research Articles

Provincial Coal Flow Efficiency of China Quantified by Three-Stage Data-Envelopment Analysis

The exploration of regional variations in coal flow efficiency (CFE) in China and the collaborative strategies for emission reduction are vital for accelerating the progress of ecological civilization within the coal industry and achieving an optimal allocation of coal resources. To unveil the evolutionary traits of actual CFE and its decomposition, this study employs a current technology based on a combined super-efficient measure (SBM), global SBM, the stochastic frontier approach (SFA), and the global Malmquist–Luenberger index (GML) model on panel data from 2010 to 2021 across 30 provinces in China. The research conclusions are as follows. First, significant efficiency gaps are observed among provinces, showcasing superior performance in the north and east regions. Moreover, the impact of environmental factors and random disruptions on individual slack variables varies, resulting in a decrease of 0.18 and 0.43 in the CFE of source-area and sink-area when these factors are not taken into account. Thirdly, a clear distinction emerges between the technical efficiency change index (EC) and the best-practice gap change index (BPC). Lastly, the CFE displays regional disparities marked by an upward trajectory and fluctuating patterns resembling a “W” shape.

Evaluation of China’s Marine Aquaculture Sector’s Green Development Level Using the Super-Efficiency Slacks-Based Measure and Global Malmquist–Luenberger Index Models

Given China’s rapidly expanding marine aquaculture industry, the associated ecological issues have garnered widespread attention. Therefore, it is crucial to speed up the green growth of marine aquaculture in order to save the environment and use resources sustainably. In order to statically assess and dynamically analyze the green development efficiency levels of marine aquaculture in nine coastal provinces of China from 2012 to 2021, this study uses the non-expected output super-efficiency Slacks-Based Measure model and the Global Malmquist–Luenberger index method. Additionally, it integrates input–output redundancy rates to analyze the causes of efficiency loss. Static efficiency primarily reflects whether a region’s inputs and outputs at a given point in time reach an effective efficiency level, while the level of dynamic efficiency mainly gauges the dynamic changes in the efficiency of green production. The results show that, from 2012 to 2021, China’s marine aquaculture industry’s average static efficiency of green output was 0.705. The southern marine economic zone exhibited the highest static efficiency value in the green development of marine aquaculture, displaying a stepped distribution pattern of “south–north–east” in decreasing order. The input–output redundancy analysis reveals that the primary causes of static efficiency loss in China’s marine aquaculture industry are attributed to varying degrees of redundant inputs and carbon emission outputs. Looking through the lens of the GML index, the annual average growth rate of the green total factor productivity in China’s marine aquaculture stands at 11.1%, with an annual average change in technical efficiency of 1.8%, while the annual average change in technological progress amounts to 9.1%, suggesting that technological advancement is the primary driver of the rise in green total factor productivity in China’s marine aquaculture sector. According to the study, in order to encourage China’s marine aquaculture industry to grow sustainably, efforts should be made not only to accelerate technological advancements but also to enhance technical efficiency. Policies that are specifically designed for the local environment should be developed to support the sustainable development of the marine aquaculture sector and to make resource allocation easier.

Clean air policy and green total factor productivity: Evidence from Chinese prefecture-level cities

Clean air policy is an important mechanism for improving the urban environment and promoting green economic development. Green total factor productivity (GTFP) is an important way to measure these impacts. We apply the super-efficiency slack-based measure model, considering undesirable output, and the Global Malmquist-Luenberger index to measure the GTFP of 278 prefecture-level cities in China from 2005 to 2020. We find that GTFP growth is driven by both green technology progress (GTC) and green technology efficiency change (GEC). The traditional total factor productivity is overestimated, because energy and environmental factors are not considered. GTFP growth is higher in the cities in the eastern coastal regions compared to the western regions. We analyze the effect of clean air policy on GTFP using a difference-in-differences model and spatial-difference-in-differences model, and further conduct a heterogeneity analysis and mechanism analysis. The results show that clean air policy promotes GTFP growth. In addition, there is a significant negative spatial spillover effect with respect to the impact of clean air policy on GTFP. The heterogeneity analysis results show that the impact of clean air policy on GTFP growth is lower in resource-based cities and cities in the northern region, compared with non-resource-based cities and cities in the southern region. Further, the impact of clean air policy on GTFP growth is mainly achieved through GTC, GEC, innovation, and financial development. In resource-based cities and cities in the northern region, clean air policy decreases GTFP by inhibiting GTC, innovation, and financial development.

Linking natural resource abundance and green growth: The role of energy transition

A better understanding of the impact of natural resource abundance on green growth in the context of the energy transition could serve to break the resource curse and avoid pollution transfer, thereby promoting green and sustainable development. To this end, this study combines the Slack-Based Measure (SBM) method and the Global Malmquist-Luenberger (GML) index to measure the green growth in 77 countries from 2002 to 2019, and examines the impact of natural resource abundance on green growth. This study also investigates the moderating role of energy transition, heterogeneity analysis, and asymmetry analysis. The results show that (i) the average green growth levels for most countries surpassed 1, indicating green growth is the trend. (ii) The impact of natural resource abundance on green growth is negative, indicating that the natural resource abundance hinders the improvement of green growth. Moreover, this negative impact is more prominent in countries with lower natural resource abundance and lower green growth. (iii) Energy transition can effectively alleviate the negative impact of natural resource abundance on green growth. Besides, this mitigation effect is greater in high-resource-abundance countries than in low-resource-abundance countries. Finally, this study highlights several policy implications to break the resource curse and achieve green growth.

Study on the impact of green finance on green total factor productivity in forestry—evidence from China

China's forestry industry's ecological, economic, and social benefits have improved significantly. However, practical problems remain, such as irrational industrial structure, serious resource consumption, and low environmental transformation efficiency. The green total factor productivity is an important index to measure the transformation of economic development mode, and improving the green total factor productivity in forestry is of great significance in guiding the transformation of forestry economy, improving the structure of agricultural economy, and realizing the green development of forestry economy. Based on the panel data of 30 provinces and cities in China from 2006 to 2021, this article adopts the entropy value method and the Global Malmquist–Luenberger index based on the SBM directional distance function to construct the index system of green finance and forestry green total factor productivity and empirically analyzes the impact and mechanism of green finance on forestry green total factor productivity. It was found that 1) green finance can improve local forestry green total factor productivity and influence the neighboring regions to improve forestry green total factor productivity through economic spillover effect, and the coefficient of spatial spillover effect was 0.1041. 2) There were regional differences in the impact of green finance on forestry green total factor productivity, which was explicitly presented as a gradual decrease from east to west “ladder-like”. 3) There was a 30.46% intermediary effect of forestry industry structure in the influence of green finance on forestry green total factor productivity. 4) There was a double-threshold effect of human capital in the influence of green finance on forestry green total factor productivity, and there was a non-linear effect of marginal increment in the influence of green finance on forestry green total factor productivity after the threshold of 5.87 human capital. Based on China's ecological and economic development background, it was significant to utilize green finance to solve forestry economic development problems and promote China's green and sustainable development, and therefore put forward policy recommendations.

Urban land use efficiency and its driving factors in ecologically fragile areas: the case of northwest China

ABSTRACT Urban land serves as the medium for urban economic and residential activity. The degree of intensive and efficient use of urban land and the coordinated growth of the economy and society are significantly impacted by utilization efficiency. Northwest China’s temperature, geography, water, and soil characteristics make less of the region suited for urban development. Thus, it is imperative to investigate Northwest China’s urban land use efficiency (ULUE). This study applies the efficiency loss model to highlight the discrepancy between the optimal and actual values of each input-output variable of the ULUE, and uses the Super-SBM model to compute the ULUE of 39 cities. Additionally, the internal driving elements of the ULUE are examined using the Global Malmquist-Luenberger (GML) index. This study discovered that Northwest China had a low ULUE, with a modest upward trend seen between 2004 and 2018. The overall shape of the region was similar to an ‘N’ curve, with high efficiency values concentrated in the southeast of the region in 2018. When considering the agglomeration impact of urban agglomeration, these results are dependable. Moreover, the efficiency loss model discovered that pollution and wasteful resource usage were the main reasons behind the drop in ULUE in Northwest China. Finally, the GML index and its constituents revealed that the main force behind ULUE’s growth in Northwest China is technological developments. This research provides important policy implications for reducing the gap between eastern and western regions, supporting high-quality development in northwest China, and protecting and intensively using urban land.

Carbon emission efficiency of China's logistics industry: Measurement, evolution mechanism, and promotion countermeasures

Improving carbon emission efficiency of the logistics industry (LCEE) is important in achieving carbon neutrality. We examine the carbon emissions of 31 provinces and cities in China that aim to reduce emissions and increase efficiency in the logistics industry. We construct the evaluation index system of LCEE, using the Super-EBM-unexpected model to measure it. Moreover, the global Malmquist–Luenberger index and the spatial autocorrelation model are used to analyze its temporal and spatial evolution mechanisms and identify its influencing factors via the spatial Durbin model. The results show a very low average LCEE value and a decreasing gradient distribution in space for China. The average pure technical efficiency is the main driving force for improving LCEE. Environmental regulation and the residents' consumption level positively affect LCEE. However, economic level, industrial structure, government input, and energy intensity have a negative impact. Countermeasures and recommendations are proposed to improve LCEE and provide theoretical and methodological support for its research and management. The study provides implications for policymaking related to ecosystem protection and sustainable development in the logistics industry.

The green productivity of broiler production in China: Considering the resource utilization of manure

Resource constraints and environmental challenges have emerged as serious impediments to the sustainable development of China's broiler industry, with potentially adverse consequences. The pursuit of sustainable development in China's broiler industry is predicated on significant reductions in manure and pollutant emissions from broiler farming. This study utilizes the slacks-based model and the global Malmquist-Luenberger index to calculate the green total factor productivity of broiler breeding across various provinces and scales from 2005 to 2020 within a joint production framework of considering undesirable outputs and desirable outputs. Fluctuations in economic distribution of broiler breeding are characterized using the kernel density estimation, and a convergence analysis is performed via absolute and conditional β convergence methods. The results revealed an overall upward trend in China's broiler farming green total factor productivity from 2005 to 2020, corresponding to green total factor productivity in small-, medium-, and large-scale broiler breeding were 1.015, 1.017, and 1.009, respectively. The kernel density curve implies a narrowing trend in the discrepancy of green total factor productivity levels among provinces in broiler breeding of varying scales. For all scales, broiler breeding's green total factor productivity demonstrates considerable conditional and absolute β convergence. Therefore, improving the efficiency of broiler breeding while addressing externalities requires the cultivation of broilers at different scales across diverse regions, coupled with an increased focus on improving the utility efficiency of broiler waste fertilization.

Green total factor productivity growth and its driving forces in China’s fisheries sector

The rapid development of China’s fisheries sector has brought about significant environmental problems, which are detrimental to the sustainable development of the sector. Balancing environmental protection while promoting fisheries development has become an urgent issue in China. Based on data from 30 provincial-level administrative regions in China from 2004 to 2019, this study utilizes the Epsilon-based Measure (EBM) model considering undesirable outputs and the global Malmquist-Luenberger (GML) index to measure the green total factor productivity (GTFP) growth in China’s fisheries sector. Furthermore, it explores the spatiotemporal evolution and driving forces of fisheries GTFP growth using spatial Durbin model (SDM). The results indicate that ignoring the resource and environmental costs in fisheries production would overestimate the growth of total factor productivity (TFP) by 1.3%. The growth of fishery output primarily comes from the increase in input factors, exhibiting extensive characteristics that have been gradually diminishing over time. During the sample period, the provinces with the fastest growth in GTFP shifted from being mainly concentrated in the central and western regions to the eastern region. The local driving forces behind the improvement of fisheries GTFP include internet penetration rate, transportation convenience, education level of rural residents. The driving forces from economically similar provinces include the positive spatial interplay between provinces, fishery disaster rate, fisherman training, fishery trade openness, and urbanization rate. Overall, these finds offer a novel approach to reexamine the growth of China’s fisheries and provide valuable insights for the future fisheries development.

Investigating and improving the ecological efficiency of urban agglomeration based on Super-SBM model.

The management of ecological efficiency (eco-efficiency) is of great significance to promote the sustainable development of urban agglomerations. The objective of this research is to evaluate the eco-efficiency of urban agglomerations. Taking the Yangtze River Delta Urban Agglomeration (YRDUA) as a case study, this study evaluated the spatio-temporal evolution characteristics of the eco-efficiency by using the Super-SBM model. Additionally, the factors that affect the efficiency fluctuations are analyzed by decomposing the Global Malmquist-Luenberger (GML) index and the input-output redundancy rates are measured. The study additionally put forth a conceptual framework aimed at evaluating eco-efficiency within the context of urban agglomerations. The findings indicated that the eco-efficiency of the YRDUA had not reached the effective level with noticeable variations across different regions. The resource redundancy of the YRDUA was severe and negatively correlated with eco-efficiency. Environmental pollution was the primary cause of efficiency loss. Technological progress was the main driving factor of regional total factor productivity (TFP), but technical efficiency still needed to be improved. Therefore, it is urgent for the YRDUA to improve the efficiency of its industrial structure and foster the harmonious growth of various sectors, in order to achieve the sustainable utilization of natural resources and the preservation of the ecological environment.

The efficiency evaluation and the spatiotemporal differences of CO2 emissions in China's construction industry

Under the promotion of China's double carbon policy, China's construction industry (CI) is facing the pressure for CO2 emissions (CE) reduction. In order to promote China's high-quality green development, CO2 emissions efficiency (CEE) evaluation in China's CI was crucial. Based on the super-efficiency slacks-based measure–data envelopment analysis (SBM-DEA) model, the Global Malmquist-Luenberger (GML) index, the global and local Moran index in spatial autocorrelation, 11 indicators of CI were selected to evaluate CEE and the spatiotemporal differences of CEE in China's CI 31 provinces from 2000 to 2020 were analyzed. The results indicated the CEE was on the rise on the whole. The average annual CEE of Hainan, Heilongjiang, Tianjin, Shanghai and Xizang was bigger than 1. The CEE of Sichuan, Gansu and Guizhou was around 0.5. The order of CE of CI in China from high to low Was South China, North China, Northeast China, Central China, East China, Northwest China and Southwest China. The GML index increased by 6.25% annually, the average annual TC increased by 11.68%, and the average annual EC increased by 5.21%. The overall CEE of China's CI showed a positive correlation between 2010 and 2015, but the spatial agglomeration was weak.

Balancing crop security and sustainable cropland use: Policy lessons from the Watershed Ecosystem Service Payments in Xin’an River, China

This study investigates the impact of Payments for Watershed Ecosystem Services (PWS) on the sustainable utilisation of cropland and crop security in Huangshan City, which is the primary implementation area for the Payments for Xin’an River Watershed Ecosystem Services pilot in China. Using a two-stage analytical framework along with a quasi-natural experiment, the research examines the policy’s effectiveness in Huangshan City. In the initial stage, the directional distance function and global Malmquist-Luenberger index (DDF-GML) model are employed to calculate the agricultural green total factor productivity (AGTFP), representing sustainable management of cultivated land. Concurrently, the study constructs unobservable counterfactual paths using the synthetic control method (SCM) to evaluate impacts on land use practices. The findings show an increase in Huangshan City’s AGTFP from 1.118 in 2010 to 1.823 in 2020. Quantitative assessments indicate that the policy contributed to a 15.3% rise in AGTFP in the PWS cohort compared to the control group. Though initial decreases were detected during three pilot intervals, a consistent upward trajectory in AGTFP was identified, suggesting that PWS encourages crop stability and the sustainable development of arable land. Further support from permutation, regional, and temporal placebo examinations affirm these conclusions. This work implies that PWS may be extended to other agriculturally vulnerable regions to foster a balanced alignment between sustainable growth and the enhancement of crop security The assessment focuses on policy lessons that could inform broader sustainability strategies.

Regional differences and evolution trends of China’s industrial green transformation

Green and low-carbon development is the direction of the current technological revolution and industrial transformation, while China is still in the historical stage of deep industrialization and has yet to completely break away from the high-input, high-consumption, and high-emission development method, and is still facing serious challenges in terms of improving the efficiency of resource utilization and reducing pollution emissions. To effectively promote China’s industrial green transformation, it is necessary to accurately grasp its development connotations and scientifically realize the measurement of industrial green transformation. Therefore, this paper measures the efficiency of China’s industrial green transformation, based on the directional distance function and the Global Malmquist-Luenberger (GML) index, to portray its distribution dynamics, regional differences and further identify its growth drivers. The results found that the overall efficiency of China’s industrial green transformation has been steadily increasing, and that the regional pattern is characterized by northwestern, northeastern, central, eastern and southwestern regions, in that order. The Markov chain estimates show that industrial green transformation efficiency is most likely to remain in its original state, with probabilities of 88.31%, 63.54%, 42.86%, and 75.61% for low, medium-low, medium-high, and high levels respectively, but also has a jump shift characteristic, with a certain possibility of falling back from the high-efficiency state to the low state. Dagum Gini coefficient estimation results show that differences between groups in the five major regions are the main source of the widening differences in the overall industrial green transformation, with the contribution remaining at around 60%. Further research suggests that economic growth, technological progress, foreign trade, and foreign direct investment (FDI) may lead to a widening of the efficiency gap in industrial green transformation, while the industrial structure and outward foreign direct investment (OFDI) help to reduce spatial differences to some extent. Based on the above conclusions, this paper proposes some countermeasures to promote the overall improvement and coordinated development of China’s industrial green transformation.

The Evaluation of Energy Efficiency and its Decoupling from Economic Growth under the Carbon Peak Target

Abstract The Global Malmquist-Luenberger index was used to measure the energy efficiency of 11 provinces and regions in eastern China from 2010 to 2019, the Tapio decoupling model was constructed to analyze the decoupling relationship between economic growth and energy consumption and energy use efficiency, and the prediction results of the carbon peak time of each province in the existing literature were combined to put forward suggestions for the provinces that were not ideal for the decoupling. The results show that: (1) The total average energy efficiency of the 11 eastern regions of China during the period from 2010 to 2019 was 0.911, showing an overall upward trend, while the energy efficiency changes in each region were different; (2) The decoupling of economic growth, energy consumption and energy efficiency in Hebei and Hainan provinces was not ideal, and the decoupling in other regions was getting better; (3) There will be a large gap in the time of carbon peak in 11 regions in eastern China. Beijing, Tianjin, Shanghai, Jiangsu, Zhejiang, Guangdong and Liaoning will achieve carbon peak in 2030 and before; There will be no peaks in Hebei and Hainan regions, while the peaks in Shandong and Fujian appear after 2030. JEL classification numbers: F06. Keywords: Carbon peak, Eastern region, Energy efficiency, Tapio decoupling model, Directional distance function, Global Malmquist-Luenberger index (GML).

The impact of China's information infrastructure construction policy on green total factor productivity: moving towards a green world.

China's economic growth over the past two decades has led to severe environmental pollution in most of its cities. To address this issue, China has developed and implemented several strategies to increase its resource utilization efficiency and move towards a green production economy, such as the information infrastructure construction policy (IICP). To assess the extent to which green production is being achieved, this study first measured the green total factor productivity (GTFP) in Chinese prefecture-level cities using a combined super slacks-based measure and the global Malmquist-Luenberger index (SBM-GML) model. Then, to assess the effectiveness of the IICP, the paths affecting the GTFP were explored using a time-varying difference-in-difference (time-varying DID) model. It was found that the IICP had significantly contributed to urban GTFP through its green technology diffusion effects and improved employment structures, with its implementation resulting in an average increase of 0.201 in the pilot cities compared to the non-pilot cities. This research attempted to explain how IICP plays a role in improving green production, and policy makers should attach importance to the power of IICP.

Healthcare services efficiency and its intrinsic drivers in China: based on the three-stage super-efficiency SBM model

BackgroundThe purpose of this study is to examine the development of healthcare services efficiency in China since the reform of the healthcare system. By examining the development environment of healthcare services in China and examining the driving factors affecting the efficiency of healthcare services, we provide a reference for the future high-quality development of healthcare services in China.MethodsA three-stage super-efficient slack-based measure (SBM) model with undesirable outputs was used to measure the efficiency of healthcae services in 31 Chinese provinces from 2009 to 2021, and a global Malmquist-Luenberger (GML) index was used to assess their spatiotemporal evolution characteristics and internal influencing mechanisms of healthcare services efficiency.ResultsThe empirical results showed that the efficiency of China's healthcare services changed significantly from 2009–2014 and then remained stable. During the study period, the efficiency of healthcare services in the eastern region was higher than the national level, while it was lower in the western region. The results of the analysis of environmental factors indicated that an increase in population density reduced the redundancy of healthcare input resources and that economic development as well as an increase in government subsidies, contributed to an increase in the redundancy of healthcare input resources. The main contribution to the growth of healthcare sercices efficiency in China came from the technological innovation effect, and the growth was most significant in the western region.ConclusionFrom 2009 to 2021, the efficiency of national healthcare services generally showed a slow upward trend, and the efficiency of healthcare services varied widely among regions. Under the existing environmental constraints, relevant departments in each region should strengthen technological innovation in healthcare services, completely focus on the regional catch-up effect, and promote the balanced development of regional health.

The impact of the digital economy on green total factor productivity in Belt and Road countries: the mediating role of energy transition

Introduction: The prospective Belt and Road (B&R) Initiative by China must be thoroughly examined by the participating nations in all respects. It is now essential to investigate whether the digital economy of the B&R countries can support green total factor productivity (GTFP). This study examines the connection between green total factor productivity (GTFP) and the digital economy in B&R countries with the aim of providing China with practical recommendations for advancing the initiative.Methods: This study explores 40 B&R countries from 2006 to 2021, calculates the GTFP using the unexpected super-efficient SBM model and the Global Malmquist-Luenberger index method, and constructs the digital economy index using the principal component analysis method. OLS, FMOLS methods, and spatial panel regressions are used to examine the digital economy-GTFP nexus.Results and Discussion: In the selected 40 B&R countries, there is a non-linear relationship between the digital economy and GTFP, and the overall effect of the digital economy on GTFP is negative, implying that the growth of the digital economy will cause a decline in GTFP. Energy transition has mediation effects that can mitigate the negative impact of digital economic growth on GTFP. The spatial spillover effects of the digital economy on the GTFP of neighboring countries are evident. There is also heterogeneity; the digital economy will reduce GTFP in high- and middle-income countries, but the negative effects are not evident in low-income countries. This paper adds to the discussion of the digital economy and green development by drawing different conclusions from previous studies using a variety of regression models, providing a fresh foundation for policy-making.

The role of renewable energy investment and energy resource endowment in the evolution of carbon emission efficiency: spatial effect and the mediating effect.

With climate change caused by massive greenhouse gas emissions emerging as an issue of global concern, it is urgent to improve carbon emission efficiency (CEE) for countries along the Belt and Road (BRI). Considering the resource endowment characteristics of green development in BRI countries, the super-efficiency slacks-based measure (SBM) model is adopted to evaluate the current status and tendency of CEE in 60 BRI countries, while the Global Malmquist-Luenberger (GML) index to quantify the spatial and temporal variation and dynamic evolution of CEE. Subsequently, from the perspective of energy development and utilization, the spatial Durbin model (SDM) and the mediating effect model are employed to empirically examine the spillover effects and driving mechanisms of renewable energy investment and energy resource endowment on CEE. Empirical results reveal that (1) from a static perspective, the CEE of BRI countries is generally poor and unevenly distributed in terms of temporal and spatial dimensions, with significant room for enhancement. (2) Referring to the dynamic level, the GML index featured a U-shaped fluctuation, with technological progress contributing to the improvement of CEE. (3) There is a significant positive effect of renewable energy investment on CEE in the home country and neighboring countries, while energy resource endowment presents a remarkable adverse correlation. Consequently, it is suggested that inter-regional cooperation among BRI countries should be strengthened to reinforce renewable energy investment, exert the technology and knowledge spillover effect sufficiently, and break the resource curse in the environmental field. (4) The mediating effect model confirms the significant mediating mechanism of technological innovation. Renewable energy investment can enhance the CEE of BRI members by promoting the positive mediating effect of technological innovation, while energy resource endowment can inhibit the local level of technological innovation and indirectly inhibit the CEE of BRI members. The findings provide new ideas on the green development and ecological sustainability of the energy industry in BRI members and other economies.

The relationship between rural finance development and food ecological total factor productivity: Moderating effects of food science and technology progress

Achieving increased food production while reducing pollution emissions is an effective way to ensure food security and sustainable development. However, China's increased food production has been accompanied by resource depletion, ecological degradation and environmental pollution. The purpose of our study is to explore effective strategies to enhance food ecological total factor productivity (FETFP) in China. We considered the ecological value of food cultivation and used Global Malmquist-Luenberger (GML) index method to measure the FETFP. Besides, the entropy weight TOPSIS method is utilized to calculate the composite index of rural finance development. This study improves the precision of the measurement of rural financial development and FETFP variables. We innovatively examined the relationship between rural financial development and FETFP using Tobit model. And we used hierarchical regression analysis to test the moderating role of food science and technology progress in it. The results are as follows. (i)China's ecological value of food cultivation per hectare by region shows an upward trend. Rural finance development and FETFP tend to fluctuate upward in each region of China and the rate of increase is faster in the major food-producing regions (MFPR). (ii)Rural finance development does contribute to the improvement of FETFP. Interestingly, rural finance development in non food-producing regions (NFPR) has a greater impact on FETFP than in MFPR. (iii)Food science and technology progress moderates the relationship between rural finance development and FETFP. The regression results of group regulation show that food science and technology progress can play a stronger positive moderating role in lower FETFP provinces rather than higher FETFP provinces. This study is both a further exploration of total factor productivity improvement in food ecology and an extension of the research area of rural finance development planning.

Spatial and temporal evolution characteristics of urban green innovation efficiency in Northeast China

In the strategic context of promoting the comprehensive revitalization of Northeast China, green innovation, as the integration of the two development concepts of innovation-driven and green development, has become an essential means to promote sustainable and high-quality development in Northeast China. By constructing the Super-SBM model with undesired output and the Global Malmquist-Luenberger index model, the green innovation efficiency and green innovation total factor productivity of 34 prefecture-level cities in Northeast China from 2011 to 2020 are measured. Then the natural break point classification method, standard deviation ellipse and Dagum Gini coefficient decomposition method are used to explore their spatial and temporal evolution characteristics. Based on the measured results of green innovation efficiency and green innovation total factor productivity in Northeast China, all the cities are classified into four types: high-high, high-low, low-high and low-low. The study results show that the green innovation efficiency (GIE) of cities in the northeast region shows an excellent upward trend, and the distribution pattern of “strong in the North and weak in the South” is more prominent. The green innovation total factor productivity shows a fluctuating decline, and technical efficiency and technological progress inhibit the improvement of green innovation efficiency. The centre of gravity of efficiency has generally migrated towards the southwest, green innovation efficiency among cities has gradually tended to develop in a balanced manner, and inter-provincial differences are the primary source of spatial differences in green innovation efficiency. The study concludes that the northeast region should continuously improve its green innovation resource allocation capacity, accelerate the green transformation and upgrading of industrial structure, clarify green innovation subjects, optimize the green innovation environment and formulate differentiated development strategies to promote the synergistic development of green innovation among cities.