Slacks-based Model Research Articles

Analysis of disequilibrium and driving factors of carbon emission efficiency: Evidence from five major urban agglomerations in China

Urban agglomerations (UAs) are not only highly integrated economic communities but also communities of shared destiny with integrated environmental protection and ecological construction. Although low-carbon development within UAs has garnered significant attention, a lack of comprehensive analysis remains regarding the imbalances and driving factors influencing carbon emission efficiency (CEE) in these regions. This study investigates the CEE of China's five national UAs from 2006 to 2020 by incorporating the meta-frontier framework into the super slack-based model. The global Malmquist index analyzes dynamic trends in CEE by decomposing the total factor productivity of carbon emissions to examine the catch-up, innovation, and technological leadership effects. In addition, this paper employs the Theil index to explore the disequilibrium of CEE among the five UAs. Outcomes reveal that, first, CEE has regional heterogeneity, with the Pearl River Delta UA (PRDUA) having the highest CEE and the Beijing–Tianjin–Hebei UA (BTHUA) having the lowest CEE. The gap between BTHUA's CEE and that of other UAs is widening. Second, the overall CEE holds improvement room, with its disequilibrium resulting from uneven city growth within the UA. Third, the innovation effect is the key to improving CEE. Fourth, PRDUA serves as the technical leader among all UAs, while the two UAs in the inland area possess the potential for technical leadership. Finally, drawing from the empirical results, this paper provides specific suggestions for advancing industrial transformation, boosting innovation capabilities, and minimizing internal imbalances within UAs. The goal is to steer UAs in China toward low-carbon development.

Governance and Efficiency in Brazilian Water Utilities: An Analysis Based on Revenue Collection Efficiency

This study analyzed the relevance of governance structure for efficient revenue collection by Brazilian water utilities (WUs). Data were collected from the National Information System on Sanitation (SNIS) for 127 Brazilian WUs, covering a balanced longitudinal panel from 2018 to 2022. The governance structures evaluated included ownership (public or private) and corporatization (publicly traded or not). We searched scientific databases and did not find any studies on the efficiency of specific WUs in collecting customer bills for services rendered and its relationship with this type of governance; thus, this is the main innovative contribution of this study to the literature. In the first stage, this study utilized the dynamic slack-based model (DSBM) to assess revenue collection efficiency. In the second stage, an econometric model with generalized estimating equation (GEE) was used to explain the efficiency. The findings revealed a global average inefficiency in revenue collection of 50.47%. Corporatization was linked to higher collection efficiencies, while ownership type was significantly linked with lower collection efficiency. Factors such as tariff accessibility, urbanization, and the COVID-19 pandemic also influenced efficiency. This study suggests that regulatory bodies should consider these insights to implement policies that prevent inefficiencies from affecting the tariff system for services.

Coupling and Coordinated Development of Carbon Emission Efficiency in Industrial Enterprises and the Digital Economy: Empirical Evidence from Anhui, China

To delve into the interrelationship between the green transformation of industry and the economy’s high-quality development, to promote the coordinated development of industrial carbon emission efficiency and digital economy, to expand the scope and research ideas related to economic and social sustainable development, and to provide scientific reference for the low-carbon sustainable development of regional economy, this article introduced a data-centric methodology for evaluating the collaborative advancement of both industrial enterprises’ carbon emission efficiency and the digital economy. To accurately gauge the carbon footprint of industrial enterprises, models focusing on carbon emissions as well as carbon emission intensity were employed. To enhance the precision of evaluation outcomes and mitigate biases stemming from subjective weighting factors, we employed the entropy weight method to objectively assign weights to each indicator. Furthermore, the super-efficient slack-based model (SBM) can solve the problem that the conclusions are biased, due to the different radial. Subsequently, a carbon-emission efficiency slack-based measure model, and models for coupling degree and coupling-coordination degree were formulated. Anhui, as a central province in China, is also an important province in the Yangtze River Delta integration development. Coordinated development of its carbon emission efficiency and digital economy has important implications for the sustainable economy advancements of other regions in China, and even other countries or regions in the world. Therefore, Anhui was selected to be the empirical research sample. The results showed that the comprehensive levels of these two systems followed an increasing trend, while the digital economy lagged. Their coupling degree fluctuated and reached its highest point in 2021, whereas their coupling-coordination degree increased, showing high coupling and low coordination overall. This study proposes specific countermeasures and suggestions for the relevant decision-makers.

Measurement of efficiency and its drivers in the Chilean banking industry.

This paper estimates efficiency measures for the banking system in Chile for the period 2000-2019. In contrast to previous studies, we use input-distance functions, introduce the nonparametric slack-based model, and choose the intermediate inputs approach in determining inputs and outputs. Our results suggest that the Chilean system has achieved relatively high levels of efficiency, although with no significant variation over the sample period. Ownership (government, foreign and public) and size had a positive impact on efficiency. On average, mergers and acquisitions seem to have targeted highly efficient banks in order to improve the overall efficiency of the controlling institution in the short run. Other sources of efficiency gains could be an increase in bond funding or a reduction in expenses and capital holdings. The latter could be induced by deepening the local derivatives market.

Spatial spillover effects of conversion of new and old driving forces on high-quality development: Evidence from 283 cities in China

Converting the forces driving is essential for breaking away from the traditional unsustainable development model and achieving High-Quality Development (HQD). Understanding how the conversion from old to new forces influences HQD is crucial for achieving sustainable development. Therefore, the assessment framework for Conversion of New and Old Driving Forces (CNODF) and HQD are established. From an efficiency perspective, the Super-Efficiency slacks-based model (SBM) model, considering undesirable outputs, is used to measure HQD levels in Chinese cities. The entropy weight method is applied to measure CNODF levels. By integrating coupling coordination degree and relative development degree, the matching and synchronization between HQD and CNODF are categorized. Further, the study introduces the Spatial Durbin Model to quantitatively identify the spatial relationships between HQD and CNODF at both national and urban agglomerations scales. The research findings indicate that from 2006 to 2019, the national average HQD level increased by 50.5 %, and the average CNODF level increased by 11.5 %. The association between the two has strong spatial heterogeneity. At the national scale, CNODF has significant nonlinear spatial spillover effects on HQD. These findings provide theoretical insights for advancing the conversion of driving forces, and can help inform regional high-quality development strategies.

Spatiotemporal evolution and influencing factors of urban industrial carbon emission efficiency in the Mid-Yangtze River urban agglomeration of China

Carbon emissions from energy consumption caused by industrial production have become an important factor in global warming, and China now has the highest global carbon emissions, with enormous pressure to reduce emissions. The Mid-Yangtze River urban agglomeration (MYRUA), which is an important region for China, also faces the contradiction of balancing environmental protection and economic production. Currently, there is little discussion on low-carbon pollution research in the MYRUA in China. Therefore, this study takes the MYRUA as the research object, uses a slack-based model (SBM) and exploratory spatial data analysis (ESDA) to explore the spatiotemporal evolution of the industrial carbon emission efficiency (ICEE) of the MYRUA in China from 2006 to 2019, and empirically analyses the factors influencing the ICEE based on a spatial econometric model. The results show that (1) in the period 2006–2019, the ICEE in the MYRUA region generally exhibited a “jagged” trend of decreasing, then increasing until finally decreasing again. Spatially, ICEE generally exhibited a dynamic trend of being low throughout, with high-efficiency areas tending to move to the center and low-efficiency areas being scattered. (2) The global Moran's I coefficient for ICEE ranged between 0.002 and 0.213, with the highest Moran's I value and significant spatial correlation occurring in 2006, after which the value gradually decreased. On the local autocorrelation, the ICEE in 2006 exhibited an agglomeration effect and weakened after 2010. (3) In 2019, the level of industrialization (LI) and the level of external openness (LEO) in the MYRUA changed from nonsignificant to significant positive correlations, and the LEO had the largest impact. The energy consumption structure (ECS) was always significantly negatively correlated. The levels of social development (LSD) and environmental regulation (ER) changed from showing a significant positive correlation to showing a nonsignificant correlation. In conclusion, ICEE is affected by interactions of multiple factors. In the future, the ECS of the MYRUA should be continuously optimized and replaced by clean energy. In addition, foreign investment compensates for the lack of production and technology research funds, which can increase investment and promote the digitalization of manufacturing enterprises.

Classifying flexible measures in two-stage network DEA and DEA-RA using novel slacks-based models

In conventional DEA models it has been assumed that each measure status is considered input or output. However, a performance measure in some cases can have input role for some DMUs and output role for others and is known as flexible measure. In this paper new slacks-based FNSBM models are proposed in general two-stage network DEA to determine the relative efficiency of units and the role of flexible measures. Then new radial FNDEA-R models and new slacks-based FNSBM-DEA-R models are developed in the presence of flexible measures based on the ratio of input components to output components or vice versa in the input and output orientation under constant returns to scale in general two-stage network. In our proposed models, flexible measures are determined as input or output to improve performance to maximize the relative efficiency of the DMU under evaluation. The FNDEA-R and FNSBM-DEA-R models versus FNSBM models prevent efficiency underestimation and pseudo inefficiency issues. The status of one flexible measure in the input-oriented and output-oriented FNDEA-R and FNSBM-DEA-R models may have different conclusions. The radial FNDEA and FNDEA-R models have unitsinvariant and the objective function of the FNSBM and FNSBM-DEA-R models are invariant with respect to the units of data. A numerical example is used to illustrate the procedures.

An Application of the DEA-Cross Efficiency Approach in Turkish Dry-Bulk and General Cargo Terminals

Dry bulk and general cargo terminals are the facilities that should quickly adapt to global supply chain dynamics. Each loading/unloading, conveying, horizontal carriage and temporary storage process involves complex organizational structures and procedures. Planned physical investments may lead to inefficiency under dynamic environmental conditions and may also result in a waste of resources. This study aims to examine the technical efficiency of dry-bulk and general cargo terminals in Türkiye with DEA cross-efficiency and DEA Slacks-based models. The findings imply that the terminals handling iron and steel are more efficient than the others. Besides, on average, the dry bulk and general cargo terminals can achieve higher output levels with fewer infrastructures and handling equipment. Therefore, it may be appropriate for the terminals examined to revise their resource utilization rates and short-term investment strategies. Moreover, since it allows pair-wise comparisons of terminals handling similarly featured cargo, DEA cross-efficiency can play a crucial role in dry-bulk performance measurement. Input slacks of relatively inefficient terminals are also calculated.

How does green finance influence industrial green total factor productivity? Empirical research from China

Industry is the pillar of the real economy, and its green development contributes to achieving the green and low-carbon development goals proposed by the United Nations. This paper constructs an analytical framework of "green finance-green technological innovation-industrial green total factor productivity" to study how green finance (GF) influences industrial green total factor productivity (IGTFP). Based on the panel data from 30 provinces and 281 cities in China between 2011–2019，this study measures the GF levels and the IGTFP index by using the entropy method and super-efficiency slacks-based model with Malmquist-Luenberger index method, to conduct a series of empirical tests. Also, we focus on the mechanism effect of green technology innovation (GTI) in the process of GF affecting IGTFP. Additionally, we have discussed the supporting role of digital financial inclusion (DFI) in green finance development at the micro level. The main findings include: (1) The development of green finance (GF) can significantly improve IGTFP, and it’s still credible after a series of robustness tests. (2) The heterogeneity analysis indicates that the influence of GF on the IGTFP varies from different cities, and this positive effect tends to increase with the expansion of city size. (3) Mechanism tests show that green technological innovation (GTI) has a positive mechanism effect in the process of GF influencing IGTFP. (4) The discussion indicates that digital financial inclusion can support green finance development, so as to promote industrial green development in China. This study has momentous theoretical value and policy implications for China and other developing countries in exploring green finance and industrial green development.

Efficiency evaluation and productivity analysis of complex electric power systems in China: A directional slacks-based network data envelopment analysis approach

The paper introduces a robust methodology for evaluating complex electricity system efficiency, making several contributions. It systematically decomposes the electric power system into various processes, including power generation, transmission, distribution, and power consumption, with parallel sub-processes for production and domestic power consumption. Employing data envelopment analysis (DEA), the paper develops a network directional slacks-based (DSB) model to evaluate the whole system and the internal processes. The model is theoretically proven to be feasible, providing efficiency scores and identifying inefficiencies in the system and its processes. A noteworthy addition is the introduction of the DSB green Luenberger productivity index and its decomposition, offering an analysis of productivity changes in electricity systems. Empirical analysis covering 30 Chinese provinces from 2013 to 2019 uncovers the performance variations among provinces and processes, pointing out inefficiencies and changes in technology and efficiency over time. The empirical findings aim to support efforts in enhancing the overall efficiency of China’s interprovincial power system.

A Study on Tourism Carbon Emission Efficiency in Guangxi Region Through Spatiotemporal Evolution

Currently, there are high emissions and high pollution in the tourism industry around the world, particularly due to carbon emissions from transportation. At the same time, attention is being paid to improving tourism carbon emission efficiency as tourist accommodations and activities consume significant amounts of energy. This article used the slack-based model (SBM) to calculate the tourism carbon emission efficiency in different years and explored its spatiotemporal evolution in the Guangxi region. The factors that affected tourism carbon emission efficiency in Guangxi were selected and examined through Tobit model regression. The results showed that the correlation coefficient of the tourism scale was the largest, reaching 0.6572, making it the variable factor with the greatest impact. The second factor was the tourism industry structure coefficient, which had a correlation coefficient of 0.5688. The impact of tourism industry size and transportation facilities on tourism carbon emission efficiency in Guangxi Province was relatively small, with coefficients of 0.0451 and 0.0357, respectively, i.e., both significance levels were below 0.1, indicating that spatiotemporal evolution can be applied to analyze Guangxi’s tourism carbon emission efficiency.

Impact of artificial intelligence on carbon emission efficiency: evidence from China.

Artificial intelligence (AI) has been extensively used as a revolutionary and versatile technology in various fields. However, scholars have not given substantial consideration to the impact of AI on the environment, particularly carbon emission efficiency (CEE). This study adopts the global super-efficiency slacks-based model to evaluate CEE of 30 provinces in China from 2006 to 2019. Thereafter, the current study investigates the impact mechanism of AI on CEE using the stochastic impact of population, affluence, and technology (STIRPAT) model. The empirical analysis provides the following valuable research findings. First, AI, represented by industrial robots, can significantly improve CEE. Second, AI can enhance CEE by promoting technological innovation and upgrading industrial structures. Lastly, the relationship between AI and CEE is influenced by marketization and government intervention.

Research on the spatiotemporal evolution and driving mechanism of coupling coordinating between green transition of urban land use and urban land use efficiency: a case study of the Yangtze River Delta Region in China.

Analyzing the spatiotemporal characteristics and driving mechanisms of the coupling coordination between the Green Transition of Urban Land Use and urban land use efficiency can help explore the future development direction of sustainable land use in cities. This paper constructs a theoretical framework for the coupling coordination between Green Transition of Urban Land Use and urban land use efficiency. We use several models, including the super-efficiency slack-based model, the coupling coordination degree model, the non-parametric kernel density estimation method, exploratory spatial data analysis, and the geographically and temporally weighted regression model to examine the real level of Green Transition of Urban Land Use and urban land use efficiency in the Yangtze River Delta region from 2003 to 2020. Based on this, we investigate the spatiotemporal evolution characteristics and driving mechanisms of the two coupling coordination processes. The study found that (1) from 2003 to 2020, the overall trend of the coupling coordination between Green Transition of Urban Land Use and urban land use efficiency in the Yangtze River Delta region tended to be coordinated and developed, but still at a primary coordination level, with sufficient room for improvement in the future. (2) The coupling coordination level of each city in the Yangtze River Delta region from 2003 to 2020 showed obvious spatial non-equilibrium and correlation characteristics, and overall dynamic polarization effects were exhibited during the study period; the spatial pattern of high-value areas showed a regularity of prioritizing Shanghai and Zhejiang Province, gradually penetrating into Jiangsu Province and Anhui Province. (3) Economic and social factors have a positive influence on the degree of coupling coordination; natural factors and policy factors have a predominantly negative influence on the degree of coupling coordination. Research conclusions include establishing a regional collaborative development mechanism, utilizing the spatial spillover effect of leading cities; emphasizing science, education, and culture, strengthening the introduction of scientific and technological talents, increasing fiscal inputs, raising the level of economic development, and further expanding the driving effect of economic and social factors; and optimizing the layout of urban and rural construction land, developing urban land in an orderly manner, appropriately strengthening environmental regulation, thereby suppressing the negative effects caused by natural and policy factors.

Renewable energy development efficiency: Spatial dynamic evolution and influencing factors

AbstractThis paper aims to estimate the spatial dynamic evolution of renewable energy development efficiency and justify the dimensions that impact renewable energy development efficiency. The study applies the following methods: the ultraefficient slack‐based model (SBM) (to measure the efficiency of renewable energy development); the Dagum‐Gini coefficient decomposition process (to measure the interregional differences in the development of renewable energy efficiency); nuclear density estimation (to measure the dynamic distribution); the Markov model (to forecast renewable energy development efficiency); and the Tobit model (to justify the influencing factors of renewable energy development efficiency). The empirical findings confirm that the overall regional gaps in renewable energy development efficiency in China are widening year by year. The average value of renewable energy development efficiency increased from 0.932 in 2006 to 1.078 in 2020. The mean Gini coefficient increased gradually from 0.028 in 2006 to 0.174, with mean differences exceeding the average growth trend after 2011 and slowly decreasing post‐2016. There is polarization in the eastern region, while there is no polarization in the northeast. The overall level of renewable energy development efficiency in the middle and western areas is improving and showing a trend of absolute difference narrowing. In addition, economic development, green finance, technological progress, urbanization rate, and economic openness are conducive to renewable energy development efficiency, and renewable energy development efficiency is in a rapid development trend. Considering the findings, China should implement targeted regional development strategies, enhance green finance mechanisms, promote technological innovation, and align urbanization policies with renewable energy goals to reduce regional disparities and accelerate sustainable renewable energy development.

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.

The effect of synergistic pollution and carbon reduction in the digital economy: Quasi-experimental evidence from Chinese cities

Introduction: The digital economy plays a crucial role in achieving synergistic reduction in air pollutants and carbon emissions.Methods: A super-efficiency slack-based model with undesirable outputs was applied to systematically calculate the efficiency of synergistic air pollutants and carbon emissions governance (ESACG). This study used the difference-in-differences (DID), propensity score matching-DID, quantile DID methods and generalized random forest model to empirically test the impact and its heterogeneity of the digital economy on the ESACG, with the establishment of the National Big Data Comprehensive Pilot Zone as a quasi-natural experiment.Results: 1) The digital economy significantly improved the ESACG by optimizing industrial structure in source management, improving energy utilization efficiency in process control, and promoting green technological innovation in end blocking. The digital literacy of talent and digital financial support strengthened its enhancing effect, but the digital infrastructure was insignificant. 2) The digital economy significantly enhanced the ESACG in the cross-regional and regional demonstration zones but inhibited it in the pioneering zones. Its impact on the ESACG in big data infrastructure-integrated development zones was insignificant. 3) Between the 25th and 90th quantiles, there was an asymmetric inverted U-shaped influence of the digital economy on the ESACG, with no discernible impact at the 10th quantile. In cities with better economic development and technological innovation, the contribution of the digital economy to the ESACG was more significant.Discussion: It is necessary to continuously advance the construction of existing pilot zones, steadily expand their coverage, and differentiate between harnessing the experiences of reducing pollution and carbon emissions to formulate strategies for synergistic regional governance.

Does Fiscal Mechanism Affect the Promotion of Outward Foreign Direct Investment in Green Innovation?

This study aims to investigate the driving mechanisms behind outward foreign direct investment (OFDI) on regional green total factor innovation efficiency (GTFIE), focusing specifically on the external fiscal factor perspective. We measured the GTFIE using the super slacks-based model and established a threshold model using financial agglomeration and fiscal decentralization as moderating variables for empirical analysis. The findings indicate that the level of GTFIE in China is relatively low, with an average value of only 0.588 in the sample provinces. It is worth noting that there is a serious imbalance in China’s regional green innovation development. The eastern region has an average GTFIE of 0.886 and is home to nine of the top ten GTFIE provinces. Moreover, regional financial factors exert a substantial moderating impact on the spillover of OFDI. OFDI exhibits a significant dual threshold effect on GTFIE under external fiscal moderation. Specifically, with an increase in the degree of financial agglomeration, the relationship between OFDI and GTFIE changes from being insignificant to a significant positive correlation, with a gradient enhancement. Meanwhile, the relationship between them exhibits an “inverted U-shaped” nonlinear dynamic pattern under fiscal decentralization moderation.

Spatiotemporal evolution and determinants of urban land use efficiency under green development orientation: Insights from 284 cities and eight economic zones in China, 2005–2019

Green development is essential for improving urban land use efficiency (ULUE) as it seeks to optimize resource utilization and minimize waste and pollution. However, a long-term evolution of ULUE and its determinants under the context of green development are less discussed in existing studies. Drawing on remote sensing and statistical data and utilizing the super efficiency slack-based model (SBM) and the geographically and temporally weighted regression (GTWR) model, this study evaluates green development-oriented ULUE and its spatiotemporal associations with determinants across 284 cities and eight economic zones in China from 2005 to 2019. We found that the green development-oriented ULUE in China generally increased in 15 years (from 0.404 to 0.55), with higher levels in coastal and northwestern regions than in central regions. Besides, per capita GDP, investment in technology and science, and degree of openness generally contributed to ULUE (over 75% observations showed positive coefficients), while investment in real estate had a negative impact on ULUE. The impact of industrial structure on ULUE experienced a transition from positive to negative in northern and eastern cities, with the highest coefficient decreasing from 0.194 in 2005 to −0.032 in 2019. Population density contributed to ULUE in southern and northwestern cities during 2005–2010 (coefficients ranging from 0.008 to 0.198), while it negatively influenced ULUE in most cities since 2015 (coefficients ranging from −0.009 to −0.283). The correlation between nighttime light, per capita road, and ULUE showed noticeable south-north differentiation. Our study provides valuable guidelines for Tailor-made strategies of efficient urban management towards sustainable urbanization.

Understanding the efficiency and evolution of China's Green Economy: A province-level analysis

The efficiency level, evolution characteristics, and factors driving the green economy in all provinces and regions should be clarified to achieve high-quality economic development and meet China's “double carbon” target. This study conducted the Super-Effective Slack-Based Model considering unexpected outputs to evaluate province-level Green Economic Efficiency (GEE) analysis (including 30 provinces, autonomous regions, and municipalities directly under the Central Government) in China from 2005 to 2020. Moreover, the distribution and dynamic evolution trend of GEE development was estimated through Kernel density estimation. Besides, GEE and its factors (i.e., industrial structure rationalization [ISR], industrial structure advancement [ISA], and urbanization level [UL]) were examined using a Panel vector autoregressive model that was built in this study. As indicated by the result of this study, China's GEE level generally displayed a “U-shaped” development trend of declining, stabilizing, and then rising, whereas the overall efficiency level is low, where the national GEE average reached 0.6934. The regional GEE level exhibited a significant “ladder” distribution, with the highest level, the second level, and the lowest level in the east, the middle, and the west, respectively. The GEE level varied significantly with the province, and most of the levels were at a medium efficiency level. Notably, 60% of regions had medium efficiency in 2020. The levels of ISR, ISA, and UL play significant roles in boosting green economic growth. This study provides valuable insights into the drivers of green economic growth in China guiding policy decisions on achieving a sustainable and low-carbon economy. As China strives to fulfill its ambitious carbon reduction goals, the findings of this study highlight the significance of continuing to prioritize green economic development at the provincial level.

Evaluation of Agricultural Water Resources Allocation Efficiency and Its Influencing Factors in the Yellow River Basin

Improving the agricultural water resources allocation efficiency (AWRAE) and promoting the efficient and intensive utilization of agricultural water resources and high-quality development is an effective path to alleviate the water scarcity in one basin. In this study, the AWRAE and its influencing factors were measured and evaluated by constructing the evaluation system of the AWRAE in nine provinces (autonomous regions) of the Yellow River Basin, which consists of the super-efficiency slacks-based model (SBM), standard deviation ellipse (SDE), spatial autocorrelation analysis, Malmquist index and Tobit regression model. The results show that the value of AWRAE is 0.768 and it is at the medium level in the whole Yellow River Basin. The AWRAE values in the nine provinces (autonomous regions) ranking from large to small are Sichuan > Shaanxi > Ningxia > Henan > Inner Mongolia > Shanxi > Qinghai > Shandong > Gansu, and the AWRAE values show a significant increasing trend in Shanxi, Henan, Inner Mongolia and Shandong. The gravity center of the AWRAE keeps wandering along the provincial boundaries of Gansu and Shaanxi, which presents a counterclockwise rotation trend; the AWRAE of Shaanxi exhibits significant H-H aggregation in 2000, 2005, 2010, and 2015 (p < 0.05) while the agglomeration is not significant in 2020. The AWRAE has been continuously improved in which the technological progress change (Techch) and technical efficiency change (Effch) play an important positive role while the pure technical efficiency change (Pech) acts as the negative role in the Yellow River Basin. Moreover, the key influencing factors on the AWRAE in different provinces and autonomous regions are significantly different; for example, the total power of agricultural machinery (AMTP) has a significant positive effect on AWRAE in most provinces, but the annual average precipitation (AAP), agricultural water (AW), water saving irrigated area (WIA) and water saving irrigation machinery (WIM) have significant negative effects on the efficiency of AWRAE in Qinghai. The research results can provide quantitative support for agricultural water-saving and stable grain yield increase in the Yellow River Basin.