Super-efficiency Slacks-based Measure Model Research Articles

The threshold effect and driving path of the rapid transit system on tourism eco-efficiency in China

Rapid transit is important for the green development of China’s tourism industry because it profoundly affects the optimal structural layout and rational allocation of elements that impact regional tourism. Based on provincial panel data from 2009 to 2020, the entropy weight method, tourism carbon emission measurement model, Super efficiency slacks-based measure model and threshold regression model were used to determine the impact of the rapid transit system on tourism eco-efficiency in China. The findings showed that rapid transit system has increased steadily, while tourism eco-efficiency has fluctuated significantly, and the relative development characteristics of rapid transit system and tourism eco-efficiency gradually changed from “lagging rapid transit system” to “lagging tourism eco-efficiency.” In addition, the rapid transit system and the highway and civil aviation have a nonlinear relationship with tourism eco-efficiency, while the high-speed railway has not passed the threshold effect test, and at the regional level, the impacts of rapid transit system have heterogeneous characteristics. As the deepening construction of rapid transit system, its threshold effect on tourism eco-efficiency will change from positive “V” to positive “U” type, promoting the more efficient development of environmentally friendly tourism.

Network Centrality and Road Freight Efficiency: A Spatial Econometric Analysis of South Korea’s Transport Regions

Purpose - The purpose of this research is to investigate the road freight efficiency in 15 regions of South Korea using a super-efficiency SBM model and to understand the role of network centrality in influencing this efficiency. It aims to analyze how in-degree and out-degree centrality within the road freight network impacts the efficiency of road freight and how these effects manifest spatially across different regions. Design/Methodology – The study applied a super-efficiency Slacks-Based Measure (SBM) model to evaluate the efficiency of road freight across various regions. It incorporated two indices, indegree and out-degree centrality, to measure the network centrality of regions within the road freight network. Furthermore, it utilized the Spatial Durbin Model (SDM) to decompose and analyze the direct and indirect effects of network centrality on road freight efficiency, considering different spatial weight matrix. Findings – The study found that road freight efficiency in a region positively correlates with its centrality within the road transport network, indicating regions that are central to the network tend to utilize their inputs more effectively and improve transport efficiency. It also discovered positive spillover effects of network centrality, suggesting that a region’s central role in the network benefits not only itself but also adjacent regions. Originality/value - The research is valuable for its application of the super-efficiency SBM and SDM models to the context of road transport networks in South Korea, a relatively underexplored area in transportation studies. It contributes original insights into the spatial dynamics of road freight efficiency, emphasizing the role of network centrality and its variable effects across different regions.

Unveiling the Patterns and Drivers of Ecological Efficiency in Chinese Cities: A Comprehensive Study Using Super-Efficiency Slacks-Based Measure and Geographically Weighted Regression Approaches

Urban ecological efficiency stands as a pivotal indicator that mirrors the level of sustainable development within cities. To unravel the sustainable development status of Chinese cities and illuminate the factors impacting the diverse developments among them, this study leveraged the super-efficiency SBM (slacks-based measure) model to assess the ecological efficiency of 284 prefectural-level and above cities across China in 2019, divulging their spatial distribution. Furthermore, a GWR (geographically weighted regression) model was also employed to scrutinize the factors influencing the ecological efficiency of these cities. Key findings include: (1) The mean ecological efficiency of Chinese cities in 2019 stood at 0.555, signaling moderate urban sustainability, with southern cities outperforming their northern counterparts. (2) A pronounced spatial clustering of ecological efficiency was evident, featuring positive spillover effects around high-efficiency cities and conversely, negative spillover effects around low-efficiency cities. (3) Economic development and population density positively influenced urban ecological efficiency, while urbanization levels exhibited a negative impact. The influences of industrial structure, technological level, and opening-up level varied, showcasing both positive and negative impacts contingent upon the spatial disposition of the cities. Hence, policymakers are advised to recognize the spatial nuances in the impacts of distinct factors on urban ecological efficiency and tailor measures accordingly to fortify urban sustainability.

How does digital infrastructure affect industrial eco-efficiency? Considering the threshold effect of regional collaborative innovation

The implementation of Digital China and Made in China 2025 strategies has brought guiding the rapid development of digital infrastructure (DI) and industrial ecology into focus. Based on Beijing–Tianjin–Hebei (BTH) city data from 2011 to 2020, this study empirically investigates the influence mechanism and spatial effect of DI and regional collaborative innovation (RCI) on industrial eco-efficiency (IEE) using the improved gravity model and super-efficiency slacks-based measure model. The relevant findings are as follows: (1) Static and dynamic analyses show that DI significantly improves IEE. (2) The moderating effect test shows that RCI strengthens the effect of DI on IEE. (3) The threshold effect test captures a U-shaped nonlinear relationship between DI and IEE at different RCI intervals. In the positive stage, the improvement effect of DI on IEE presents marginal increasing characteristics. (4) For the spatial perspective test, the BTH city's DI and IEE exhibit a positive spatial correlation, and gradually form H–H and L–L cluster states. Furthermore, the city's DI improves the local city's IEE and benefits neighboring city's IEE, revealing spatial spillover effect. This study has valuable reference significance for other regions to simultaneously solve industrial development and environmental problems by advancing DI.

Construction and Practice of Livelihood Efficiency Index System for Herders in Typical Steppe Area of Inner Mongolia Based on Super-Efficiency Slacks-Based Measure Model

Inner Mongolia is one of the main animal husbandry production bases in China, with herders being the main animal husbandry producers. A systematic analysis of the efficiency of herding households’ livelihoods and the influencing factors is of great importance to formulate effective policies to support herding households’ livelihoods, enhance their social adaptability, and alleviate the vulnerability of poor people in herding areas. This study used a typical steppe of Inner Mongolia as the research area. It used the interview data of herding households from 2021, constructed the evaluation index system of herding households’ livelihood efficiency, analyzed the redundancy of the inputs and outputs of herding households’ livelihoods, and examined the key factors affecting herding households’ livelihood efficiency. The results indicate that (1) the pure technical effectiveness of the livelihood efficiency of typical grassland herding households in Inner Mongolia is the highest; the comprehensive technical efficiency and scale efficiency are low. The scale return of most herders’ livelihoods shows a decreasing state. (2) According to the results of the model, under the premise of the output not being reduced, reducing the amount of social capital input can effectively save resources. Without increasing the input, the room for improvement in the living level is the most obvious. (3) The pasture area, the communication network, and the access to information have significant negative effects on the efficiency of herders’ livelihoods; infrastructure and water supply have significant positive impacts. In summary, we built a model for evaluating the livelihood efficiency of herders in typical grassland areas of Inner Mongolia, which can provide a reference for the revitalization work of pastoral areas and related research in the future.

Does industrial upgrading improve eco-efficiency? Evidence from China's industrial sector

Promoting industrial upgrading is the fundamental path for China to realize the coordination of industrial development and ecological protection. This paper firstly measures the industrial upgrading index (IUI) and eco-efficiency of 37 sub-industries in China's industrial sector based on the comprehensive weighting method and super-efficiency slacks-based measure model, and then for the first time, examines the impact of industrial upgrading on China's industrial eco-efficiency and its differences among industries and influential mechanisms using the panel data models. The following main conclusions are obtained. First, both the IUI and eco-efficiency of China's whole industrial sector showed an upward trend from 2005 to 2019. Second, there are significant gaps in the IUI and eco-efficiency among industries, and these gaps gradually increase in 2005–2019. Third, industrial upgrading significantly improves China's industrial eco-efficiency, and the results of robustness test also support this conclusion. Fourth, industrial upgrading positively affects the eco-efficiency of both high-pollution and low-pollution industries, and the positive effect on low-pollution industries is slightly greater than that on high-pollution industries. Fifth, industrial upgrading improves industrial eco-efficiency by enhancing production efficiency and energy utilization efficiency and optimizing energy consumption structure.

Identifying the evolution of ecological poverty alleviation efficiency and its influencing factors: evidence from counties in Northeast China.

Ecological poverty alleviation (EPA) is an effective strategy to address the vicious circle of poverty and environmental destruction in poor areas. However, it remains controversial whether this strategy has really succeeded in this respect. Previous research investigated the impact of a certain factor on EPA, and only few studies integrated them to explore their differential effects, thereby overlooking the complexity of EPA. Therefore, this study quantified the overall efficiency of the EPA strategies of 28 poor counties in three provinces of Northeast China from 2005 to 2018 by using a super-efficiency slacks-based measure model. This model can take into account undesirable outputs; as such, it has significant advantages in measuring the coordination among economic and social development and environmental protection. The Tobit model was used to explore the factors influencing EPA efficiency. The results show that, first, the majority of counties investigated had an EPA efficiency below the overall national average. Second, as for the factors influencing EPA efficiency, it was found that (1) GDP per capita and investment in environmental governance favored EPA efficiency, as they are conducive to stimulating regional consumption dynamics and achieving green economic development; (2) science and technology expenditure and urbanization were not conducive to EPA efficiency; and (3) industrial structure and trade had insignificant effects on EPA efficiency, due to the small scale of industry and the inadequacy of the policy system. This study assessed EPA efficiency from a holistic perspective, and addressed the controversies over EPA's influencing factors, thereby providing an effective method to conduct regional EPA assessment and improve EPA performance in poor regions of China.

The Effect of Place-Based Policy on Urban Land Green Use Efficiency: Evidence from the Pilot Free-Trade Zone Establishment in China

The improvement of urban land green use efficiency is significant in promoting sustainable development. As an important place-based policy in China, the pilot free-trade zone establishment has exerted a profound impact on urban economy and society. Meanwhile, the issue of whether the pilot free-trade zone can improve urban land green use efficiency or not is missing from prior literature, which is the concern of this study. Specifically, based on the super-efficiency slacks-based measure model, we first measure urban land green use efficiency of 279 cities in China from 2011 to 2019 and then use the multi-period difference-in-differences method to investigate the causal effect of the pilot free-trade zone establishment on urban land green use efficiency. The results show that the pilot free-trade zone significantly improved urban land green use efficiency, especially in the eastern cities, the coastal cities, and the cities with a high economic development level. Further, there exists a spatial spillover effect of the pilot free-trade zone on urban land green use efficiency. This study has implications for optimal allocation of urban land in the deployment of place-based policies, serving economic structural upgrading and a carbon neutrality vision.

The Effect of Environmental Regulation on the Efficiency of the Green Economy: The Intermediary Role of the Aggregation of Innovative Elements.

Green development is the only way to realize harmonious coexistence between people and nature, so it is of great significance to create a benchmark for high-quality development. Based on the panel data of 30 provinces (except Tibet, Hong Kong, Macao, and Taiwan) in China from 2009 to 2020, the super-efficiency slacks-based measure model was used to calculate the green economic efficiency of various regions in China, and a related statistical model was used to verify the influence of different types of environmental regulation policies on green economic efficiency and the intermediary effect of innovation factor agglomeration. The results show that: (1) during the inspection period, the influence of public-participation environmental regulation on the efficiency of the green economy presents an "inverted U" trend, while command-control and market-incentive environmental regulation policies inhibit the improvement of green economic efficiency; (2) the agglomeration of innovative elements plays a significant intermediary role in the transmission path of environmental regulation affecting green economic efficiency, but the intermediary effects of different types of environmental regulation are slightly different. Finally, we discuss environmental regulation and innovative elements, and some corresponding suggestions are put forward.

Green Growth or Gray Growth: Measuring Green Growth Efficiency of the Manufacturing Industry in China

The manufacturing industry has created a rapid evolution of the economy, but it has also negatively impacted the ecosystem. A better understanding of the manufacturing industry in green growth is crucial to achieving the sustainability goals in China’s high-quality development stage and is better for identifying the impact of scale effect or technological effect in EKC. In this research, a super-efficiency slacks-based measure model is proposed to evaluate the green growth efficiency of 27 manufacturing industries, and a Luenberger index method is adopted to interpret the driving forces of efficiency. The results demonstrate that green growth efficiency in the manufacturing industry shows a fluctuating upward trend, and more than 60% of the industries are in a gray growth state. The growth of green growth efficiency mainly depends on the pulling effect of technological dividends brought by technological progress, rather than the improvement of technical efficiency. As the industry heterogeneity is analyzed, technology-intensive industries still dominate in the process of manufacturing industry and have shown a significant upward trend. Finally, some suggestions are proposed from the perspective of the government and enterprises.

The Role of Foreign Technology Transfer in Improving Environmental Efficiency: Empirical Evidence From China’s High-Tech Industry

In recent years, China’s high-tech industry has made remarkable technological progress, but it has also brought serious environmental pollution, which has aroused great concern about its environmental efficiency. Although foreign technology transfer is considered as important ways for technological progress of the high-tech industry, the existing research on what role foreign technology transfer plays in improving the environmental efficiency of the high-tech industry is still lacking. Based on China’s interprovincial panel data from 2008 to 2017, we evaluated the environmental efficiency of the high-tech industry using the super-efficiency slacks-based measure (SBM) model with undesirable outputs. We then used the Tobit model to analyze the impact of technology introduction (TI) and foreign direct investment (FDI)—two major types of foreign technology transfer—on the environmental efficiency of the high-tech industry. The results of the super-efficiency SBM model show that the average environmental efficiency of China’s high-tech industry is only 0.4375. Except for Guangdong, Shanghai, and Beijing, most of the provinces in China have low environmental efficiency. The provinces with high environmental efficiency are in the eastern region, whereas the provinces with low environmental efficiency are concentrated in the central and western regions. Tobit regression results confirm the difference in the role of technology import and foreign direct investment in the improvement of environmental efficiency in China’s high-tech industry. Technology introduction has a significant positive impact on environmental efficiency. FDI also promotes environmental efficiency, but it is not statistically significant. These findings were confirmed by a series of robust tests. This study not only deepens our understanding of the environmental efficiency of China’s high-tech industry but also expands the theoretical research on the relationship between technology transfer and environmental efficiency.

Ecological well-being performance and influencing factors in China: from the perspective of income inequality

PurposeChina's increasing income inequality might cause a series of problems, such as the slowdown of economic growth, social and economic tension, the decline of the ecological environment quality and the threat to citizens' health. Consequently, income inequality will inevitably affect the ecological well-being performance (EWP) level of China's provinces through the above aspects. Analyzing the impact of income inequality on EWP and its spatial spillover effects are conducive to improving the level of EWP in China. Therefore, the research purpose of this paper is to use China's provincial data from 2001 to 2017 to analyze the impact of income inequality on EWP and the spatial spillover effect based on the evaluation of the EWP value of each province.Design/methodology/approachAt first, this study utilizes the super efficiency slacks-based measure model (Super-SBM model) to calculate the EWP values of 30 provinces in China, which can evaluate and rank the effective decision units in the SBM model and make up for the defect that the effective decision units cannot be distinguished. Then this study applies the spatial Durbin model and Tobit regression model (SDM-Tobit model) to explore the impact of income inequality and other influencing factors on EWP and the spatial spillover effects in adjacent areas.FindingsFirstly, the average EWP in China fluctuated slightly and showed a downward trend from 2001 to 2017. In addition, the EWP values of the provinces in the western region are usually weaker than those in the eastern and central regions. Moreover, income inequality is negatively correlated with EWP, and the EWP has a spatial spillover effect, which means the EWP level in a region is affected by EWP values in the adjacent regions. Furthermore, the industrial structure and urbanization level are both negatively related to EWP, while technology level, investment openness, trade openness and education level are positively related to EWP.Originality/valueCompared with the existing research, the possible contribution of this research is that it takes income inequality as one of the important influencing factors of EWP and adopts the SDM-Tobit model to analyze the impact mechanism of income inequality on EWP from the perspective of time and space, providing new ideas for improving the EWP of various provinces in China.

The Influencing Effects of Industrial Eco-Efficiency on Carbon Emissions in the Yangtze River Delta

A low-carbon economy is the most important requirement to realize high-quality integrated development of the Yangtze River Delta. Utilizing the following models: a super-efficiency slacks-based measure model, a spatio-temporal correlation model, a bivariate LISA model, a spatial econometric model, and a geographically weighted random forest model, this study measured urban industrial eco-efficiency (IEE) and then analyzed its influencing effects on carbon emission in the Yangtze River Delta from 2000 to 2017. The influencing factors included spatio-temporal correlation intensity, spatio-temporal association type, direct and indirect impacts, and local importance impacts. Findings showed that: (1) The temporal correlation intensity between IEE and scale efficiency (SE) and carbon emissions exhibited an inverted V-shaped variation trend, while the temporal correlation intensity between pure technical efficiency (PTE) and carbon emissions exhibited a W-shaped fluctuation trend. The negative spatial correlation between IEE and carbon emissions was mainly distributed in the developed cities of the delta, while the positive correlation was mainly distributed in central Anhui Province and Yancheng and Taizhou cities. The spatial correlation between PTE and carbon emissions exhibited a spatial pattern of being higher in the central part of the delta and lower in the northern and southern parts. The negative spatial correlation between SE and carbon emissions was mainly clustered in Zhejiang Province and scattered in Jiangsu and Anhui provinces, with the cities with positive correlations being concentrated around two locations: the junction of Anhui and Jiangsu provinces, and within central Jiangsu Province. (2) The direct and indirect effects of IEE on carbon emissions were significantly negative, indicating that IEE contributed to reducing carbon emissions. The direct impact of PTE on carbon emissions was also significantly negative, while its indirect effect was insignificant. Both the direct and indirect effects of SE on carbon emissions were significantly negative. (3) It was found that the positive effect of IEE was more likely to alleviate the increase in carbon emissions in northern Anhui City. Further, PTE was more conducive to reducing the increase in carbon emissions in northwestern Anhui City, southern Zhejiang City, and in other cities including Changzhou and Wuxi. Finally, it was found that SE played a relatively important role in reducing the increase in carbon emissions only in four cities: Changzhou, Suqian, Lu’an, and Wenzhou.

Assessing urban atmospheric environmental efficiency and factors influencing it in China.

With rapid urbanization and industrialization in developing countries, cities have become the major sources of air pollution. Studying urban atmospheric environmental efficiency has an important reference value for the prevention and control of air pollution. This study used data from 267 cities in China between 2001 and 2016 to assess the urban atmospheric environmental efficiency using the super-efficiency slacks-based measure model, to test the spatial characteristics of urban atmospheric environmental efficiency using the spatial autocorrelation method, and to identify factors influencing it using the Geodetector. The results are as follows: (1) The atmospheric environmental efficiency of most cities in China is increasing. The average efficiency in the entire country exhibits an upward "wavy" trend. The average urban atmospheric environmental efficiency in Eastern China is the highest, and that in Western China is the lowest. (2) The urban atmospheric environmental efficiency exhibits the characteristic of global spatial autocorrelation, and high-high and low-low are the main types of efficiency in local spatial autocorrelation. (3) Population density, industrialization, and science and technology are the main factors influencing urban atmospheric environmental efficiency.

A study on the effects of regional differences on agricultural water resource utilization efficiency using super-efficiency SBM model

This study evaluated the water resource utilization efficiency and resource consumption for planting, forestry, animal husbandry, and fishery in various regions of China. Using the super-efficiency Slacks-Based Measure (SBM) analysis method, the main agricultural pollution emissions (Chemical Oxygen Demand, ammonia nitrogen, and agricultural carbon emissions) were proposed as environmental constraints for the first time. The threshold regression model was used to measure the impact of agricultural water use efficiency on agricultural water consumption by constructing seven different explanatory variables. The results show that the overall utilization efficiency of agricultural water resources in China presents a fluctuating downward trend, and the regional differences are significant. A single threshold effect on agricultural water consumption was found in five variables: per capita water resources, disposable income, dependence on foreign trade, industrial structure, and Gross Domestic Product. The increase in each parameter will have a positive effect on agricultural water consumption. The relationship between agricultural water use efficiency and agricultural water use was non-linear when the government's attention and the rural labor force were used as threshold variables.

Evaluation of Forestry Ecological Efficiency: A Spatiotemporal Empirical Study Based on China’s Provinces

Forests play a very important role in carbon dioxide emissions and climate change, and the development of China’s forestry is of great significance to our citizens. However, it is an arduous task for us to improve forestry output at a high and good level while taking environmental factors into account. In this paper, the non-expected super-efficiency SBM (slacks-based measure) model was used to measure the forestry ecological efficiency (FEE) of 31 provinces in China from 2004 to 2018, and the spatial and temporal evolution of FEE in different regions of China was analysed by using spatial econometric method. Tobit regression and random forest algorithm were selected to analyze the influence on FEE. The results showed that, firstly, the average annual increase of the national total factor productivity change of China’s forestry was 1.2%, and that the average annual increase of the national total factor productivity change in the eastern region was lower than that in the central and western regions. Secondly, the distribution of China’s FEE of the northeast and the south was higher, and FEE of China’s middle regions was relatively lower in 2004, but then the FEE in Northeast China has decreased, and the FEE has increased gradually from north to south in 2018. The agglomeration of high-tech industries in most regions of China had obvious positive spatial correlation characteristics in 2018. Thirdly, there was a negative correlation between forestry fixed assets investment and FEE, environmental regulation was an important factor affecting the ecological efficiency of forestry in China, and the level of economic development and industrial structure also had a certain impact on FEE.

A Study on the Coupling Coordination between Forestry Production Efficiency and Regional Economic Development in China

Abstract Forestry production is one of the fundamental industries of the national economy, and enhancing production efficiency can aid in balancing the contradiction between the economic growth of forestry and forest resource conservation. Therefore, studying the coupling coordination between forestry production efficiency and regional economic development is of great practical significance for obtaining the development philosophy of green and circular economies and implementing supply-side structural reform in forestry. In this study, the forestry production efficiencies and economic development levels of 31 provincial-level administrative regions of China during 2009 to 2018 are estimated using the super efficiency slacks-based measure model and the entropy weight method. Additionally, the coupling coordination and development trends of forestry production efficiencies and regional economic development levels are investigated by means of the coupling coordination model and GM (1,1) model. Research findings suggest that forestry production efficiencies and economic development levels of 31 provincial-level administrative regions in China maintained steadily growing trends during 2009 to 2018; however, there was middle to low-level coupling coordination between regions. Such coupling coordination remains to be improved because it indicated a spatial pattern of high in the east and south and low in the west and north. To conclude, some suggestions for promoting sound and coordinated interactions between forestry production efficiency and regional economic development are offered.

An integrated model for SBM and Super-SBM DEA models

Tone proposed a super-efficiency slacks-based measure (SBM) model, known as Super-SBM, which can’t identify the efficiency of the inefficient DMU. However, the super-efficiency model for radial measure can identify both the efficiency for the inefficient DMUs and the super-efficiency for the efficient DMUs with one model. Tran et al. proposed a binary linear programming model that integrates the SBM and Super-SBM. In this paper, we propose a linear programming model that integrates SBM and Super-SBM. With the proposed model, we can identify the efficiency of the inefficient DMUs as the SBM model and the super-efficiency of the efficient DMUs as the Super-SBM model at one stage. Results show that our model is faster than that of Tran et al. Our computation time is about only two-third of that of Tran et al.

Efficiency Evaluation and Influencing Factor Analysis of China’s Public Cultural Services Based on a Super-Efficiency Slacks-Based Measure Model

It has been established that culture can contribute to the sustainable development of a country and the world. In China, cultural demands are increasing while cultural resources are relatively scarce. Therefore, this paper evaluates the efficiency of public cultural services in China from 2013 to 2017 and analyzes the major factors affecting this efficiency based on the panel data of 31 provinces, municipalities, and autonomous regions (hereafter referred to as “provinces”) in mainland China. The super-efficiency slacks-based measure (SBM) model in Data Envelopment Analysis (DEA) was adopted. The results show that the efficiency of the public cultural services in each province is significantly different, and the overall efficiency shows a downward trend from 2013 to 2017. The gross domestic product per capita, the education level of the residents, fiscal decentralization, and population density significantly impact public culture service efficiency. Based on these results, the following policy recommendations are proposed: (1) Optimize the input structure of public cultural services and adjust the service direction to satisfy the emerging needs for a more diversified and personalized public with economic development and the improvement of education level; (2) adjust the allocation of public cultural resources nationwide and facilitate the flow of public cultural resources from developed to underdeveloped areas; and (3) provide local governments with higher fiscal autonomy and appropriately introduce the Public-Private Partnership (PPP) model to utilize private capital.

Temporal-spatial differences in and influencing factors of agricultural eco-efficiency in Shandong Province, China

ABSTRACT: Based on the panel data of 134 counties (cities and districts) from 1998 to 2017, the temporal-spatial variation characteristics and influencing factors of agricultural eco-efficiency in Shandong Province were analyzed by using various methods, such as the super-efficiency SBM (slacks-based measure) model considering undesired output and the STIRPAT (stochastic impacts by regression on population, affluence, and technology) model, which helps clarify the improvements needed for agricultural eco-efficiency and provides a basis for the development of ecological agriculture in Shandong Province. Results showed the following: (1) During 1998-2017, the agricultural eco-efficiency of Shandong Province showed a fluctuating increasing tendency, but the overall efficiency value wasrelatively low. (2) The agricultural eco-efficiency of Shandong Province had a significant regional disparity, and its spatial agglomeration gradually weakened. The spatial distribution had a sporadic distribution of high value areas at first and then gradually formed the “low-high-low-high” zonal distribution from west to east. (3) The net income per capita of farmers and the added value of the primary industry had a significantly positive correlation with the agricultural eco-efficiency of Shandong Province, while the mechanization level, the planting area per capita, the level of financial support to agriculture and the planting structure exhibited a mainly negative correlation with the agricultural eco-efficiency of Shandong Province. Moreover, the added value of the primary industry and the financial support to agriculture in the 0.75 quantile had no significant influence on the agricultural eco-efficiency of Shandong Province, and the planting structure in the 0.25 and 0.75 quantiles also had no significant influence.