Research on AEE has predominantly focused on three key areas: first, the evaluation methods for AEE. Early techniques focused primarily on the ratio between economic value and environmental impact. Although the ratio method indicates the environmental impact of economic development to some extent, it overlooks the input side and fails to comprehensively represent the entire agricultural production process. As a result, this method gradually fell out of favor. Stochastic frontier analysis (Farrell, 1957) and the life-cycle approach (Pan et al., 2013) each have limitations in addressing multi-output problems or cross-regional comparisons, despite their distinctiveness. Currently, Data Envelopment Analysis is widely used for its objectivity, effectively avoiding the advantages of subjective setting of weight settings (Huang et al., 2018; Xiong et al., 2020). The integrated benefits of ecosystems and unexpected outputs in agricultural production, however, are not fully taken into account by the traditional DEA model. To overcome this shortcoming, Tone (2001) proposed the SBM model of undesired outputs, which significantly improves the scientific validity and objectivity of the assessment results by incorporating negative externality outputs (e.g., carbon emissions).

Second, the evaluation of constructing an AEE indicator system: a reasonable indicator system is the basis of AEE assessment. Several aspects, such as input indicators, output indicators, and externality indicators, contribute to the construction of the AEE indicator system (Andersen and Petersen, 1993; Quah, 1993). In choosing input indicators, researchers usually look at small inputs like fertilizers, pesticides, and agricultural films, while also taking into account larger factors, such as the number of machines used, average income, how much agriculture is commercialized, the rate of disasters, and the level of financial support for farming. In the selection of desired and non-desired output indicators, scholars differ in their research focus. Many studies utilize provincial panel data, identifying agricultural carbon emissions or agricultural surface pollution as non-desired outputs (Pishgar-Komleh et al., 2021; Ji and Hoti, 2022; Wu et al., 2022; Zhuang et al., 2021; Cecchini et al., 2018). In contrast, only a few studies have examined positive externalities of agricultural production beyond economic outputs (Rosano Peña et al., 2018; Wang et al., 2022), and even fewer focus specifically on agricultural carbon sinks. However, indicator systems that only consider negative environmental externalities reduce the comprehensiveness and accuracy of agricultural efficiency assessments (Cui et al., 2022).

Third, we investigate the factors that influence AEE. Analyzing the driving factors of AEE can effectively and scientifically explore pathways to improve agricultural economic efficiency (Xie et al., 2018). In analyzing the driving factors of AEE, panel data regression models are often employed, incorporating various factors, such as technological innovation, policy support, and environmental changes. People widely use the Tobit model due to its capacity to manage limited dependent variables. For example, Chen et al. (2023) used the Tobit model to analyze the impact of China’s urbanization composite index and indicators on AEE in 30 provinces and cities from 2009 to 2018, and proposed targeted measures to solve the problem.

The existing studies have achieved rich results, but there are still some shortcomings. (1) Most existing studies focus on the national (Gancone et al., 2017; Workneh and Kumar, 2023), provincial (Yan et al., 2020), or municipal (Rosano-Peña et al., 2020; Yang et al., 2021) levels, with fewer exploring the differential characteristics of AEE in specific regional contexts; (2) Agricultural production possesses dual attributes as both a carbon source and a carbon sink, yet most existing literature only considers the negative impact of agricultural carbon emissions while neglecting the significant value of positive ecological externalities in assessing AEE. (2) Agricultural production has the dual attributes of carbon source and sink, and most of the existing literature only considers the negative role of agricultural carbon emissions, ignoring the key value of positive ecological externality outputs in assessing AEE.

In view of this, this paper takes China’s YEB as the research object, and based on the panel data of each province and city from 2008 to 2023, based on the measurement of agricultural carbon sources and sinks and the revelation of the carbon balance characteristics of agricultural production, it adopts the Super-SBM model to accurately measure AEE and compare the traditional AEE, low-carbon AEE and green AEE, and to reveal the characteristics and change trends of low-carbon efficiency of green agriculture in each province in the domain. On this basis, the Tobit regression model was used to further examine the main drivers of the AEE in the region, with a view to providing data references and a decision-making basis for improving the AEE of the YEB, narrowing the gap between the AEE in the region, promoting the high-quality development of agriculture, and realizing the goal of “double-carbon.”

The contributions of this study are: First, it expands the assessment framework of AEE, incorporates agricultural carbon sinks into the evaluation system, and constructs a multi-dimensional analysis framework covering resource utilization, environmental impact and economic benefits, which is more in line with the nature of agro-ecological system’s balance of “carbon source-sink.” Secondly, the study reveals the spatial and temporal characteristics of AEE in the YEB and identifies the strengths and weaknesses of its upstream, midstream, and downstream regions, providing data support for optimizing agricultural resource allocation. Third, the research results can provide a reference for other regions and agricultural agglomerations to explore the path of low-carbon agriculture enhancement and achieve the carbon-neutral goal.

The following section outlines the structure of the remainder of this paper. Chapter 2: Constructing the research methodology and clarifying the data sources. Chapter 3: reports and analyzes the empirical results, including study of agricultural net carbon, AEE, and influencing factors. Chapter 4: Provides an in-depth discussion of the empirical results. Chapter 5: Contains conclusions, recommendations, and directions for further research.

The YEB is a major economic region and an essential ecological security barrier in China (Zhang et al., 2024). It serves as a key platform for sustainable development while playing a crucial role in ensuring national ecological security. It fulfills multiple functions, including food production, water conservation, soil preservation, and carbon sequestration. The YEB consists of three main development areas: the upstream region (Chongqing, Sichuan, Yunnan, and Guizhou), the midstream region (Hubei, Hunan, and Jiangxi), and the downstream region (Shanghai, Jiangsu, Zhejiang, and Anhui). To create a safe ecological barrier for the YEB, the State issued the Outline of the Development Plan for the YEB in 2016, which clearly stated the need to advance agricultural modernization and improve the ecological environment of the Yangtze River Basin. In 2018, the central government further proposed that the YEB should adhere to the principles of ecological priority and green development, with the transformation of agricultural production to low-carbon models being a key initiative to implement these principles. Against this background, the provinces along the Yangtze River are actively promoting the transformation of agricultural production toward green development and implementing several initiatives to optimize the structure of agricultural production. The study aims to analyze the current situation and factors affecting the agroecological development of the YEB, which is important for further improving the overall competitiveness of agriculture in the region and maintaining regional ecological security. It will also provide replicable experiences for other major agricultural production areas and help achieve the ambitious goals of carbon peaking and carbon neutrality (Figure 1).

This study employs panel data from provinces and cities in the YEB from 2008 to 2023 to conduct a rigorous and effective evaluation of the region’s AEE. Additionally, the study analyzes the underlying causes of efficiency disparities across provinces and cities and offers insights into the future trajectory of low-carbon agriculture in the region. The data presented in this research are derived from the China Statistical Yearbook (2007–2023) and the statistical yearbooks of provinces and cities within the YEB. Some of the missing values were filled in using interpolation. Missing data for some years are made up by linear interpolation.

Table 3 presents the total agricultural carbon sources, carbon sinks, and net carbon sink in the YEB from 2008 to 2023. Overall, the net carbon sink in the YEB remained positive during this period, indicating that agriculture in the region contributed to carbon sequestration. The development trend can be broadly divided into three stages:

First, the period from 2008 to 2015 marks a phase of sustained growth. The net agricultural carbon sink in the Yangtze River Economic Zone increased from 108.16 to 132.46 million tons, with an average annual increase of 2.29%. While agricultural carbon emissions remained constant during the study period, the net carbon sink increased primarily because of the growth in carbon sinks. The reason for this may be the large-scale use of agricultural mechanization, which improves the efficiency of agricultural production and increases the output of agricultural products, which in turn leads to an increase in carbon sinks.

Second, the period from 2016 to 2019 is characterized by fluctuating and gradual growth. During this period, the net agricultural carbon sink in the YEB showed a first decline and then a rise. Carbon emissions were cumulatively reduced by nearly 8 million tons during this phase, but the carbon sink declined in 2018 and 2019. This phenomenon is attributed to the country’s increasing emphasis on green development strategies and the introduction of stricter fertilizer reduction actions to promote agricultural cost savings and efficiency. The implementation of these policies has reduced carbon emissions on the one hand, but on the other hand, the reduction in the use of agricultural materials has led to a reduction in the level of crop yields, thus affecting the carbon sink.

Third, the period from 2020 to 2023 represents a phase of stable growth. During this period, the total agricultural carbon emissions and carbon sinks within the YEB show a steady increase, with a cumulative increase in net agricultural carbon sinks of 5.699 million tons. Looking specifically at carbon emissions, emissions from agricultural inputs and rice paddy cultivation have decreased, while carbon emissions from livestock farming have returned to the level before the outbreak of African swine fever. The reason for this shift is that the development of green agriculture has taken root in people’s hearts, and the transformation and development of low-carbon agriculture have achieved certain results, which, while reducing inputs of agricultural materials, have been able to promote the double enhancement of the yield and quality of agricultural products, further improving the carbon sink capacity of agriculture.

Overall, the net agricultural carbon sinks in the YEB exhibit a distribution pattern of high values on both ends and low values in the middle. From an interprovincial perspective, the total net agricultural carbon sinks in each province and city have shown an upward trend compared to the baseline period. Specifically, Jiangsu Province has the most significant growth in carbon sinks, from 17.395 million tons in 2008 to 26.038 million tons in 2023, with an increment of 8.463 million tons. The reason for this is that agricultural production in Jiangsu Province has shifted from “high inputs and high outputs” to “green and low carbon” and has improved soil carbon storage capacity through the promotion of green technologies such as returning straw to the fields, replacing chemical fertilizers with organic fertilizers, and no-tillage and minimum-tillage. With the support of large-scale planting and mechanization, agricultural output in Jiangsu Province has been rising, leading to an increase in the carbon sink from agricultural production. Meanwhile, the net carbon sinks of Anhui, Sichuan, Hubei, and Hunan are also at the forefront of the increase, with an increase of 8.445, 7.034, 5.838, and 3.669 million tons, respectively. By contrast, Shanghai and Zhejiang recorded the smallest growth in agricultural net carbon sinks. Shanghai, as one of China’s highly developed and modernized cities, has a relatively weak agricultural function, with limited farmland and smaller scales of both carbon emissions and sequestration. Particular attention should be paid to Zhejiang Province, which is regarded as a pilot area for China’s new era of common prosperity. Its agriculture has long been dominated by high value-added cash crops (e.g., tea, flowers, and vegetables in facilities) and large-scale aquaculture, and this type of production mode is significantly more dependent on chemical fertilizers, pesticides, and agricultural energy than traditional food crops. In addition, urbanization and industrialization have led to the encroachment of high-quality farmland on construction land, and agricultural production has been forced to shift to the ecologically fragile hills and mountains, resulting in high agricultural carbon emissions and low carbon sinks (Figure 2).

From a temporal standpoint, the top three provinces in the YEB in 2023 concerning net carbon sequestration were Anhui, Jiangsu, and Sichuan, with net carbon sequestration values of 27.861, 26.038, and 24.398 million tons, respectively. The higher levels of agricultural carbon sequestration in these three provinces are largely due to their geographical characteristics, with most regions being in plains or basins that feature flat topography and extensive arable land. Additionally, the high level of mechanization and favorable crop yields in these regions enable effective absorption of CO₂ generated during agricultural production. In contrast, Shanghai, Zhejiang, and Chongqing provinces had relatively lower agricultural net carbon sinks, with values of 0.399, 0.219, and 6.002 million tons, respectively. The cases of Shanghai and Zhejiang have already been discussed earlier, so further elaboration is unnecessary here. As for Chongqing, the agricultural net carbon sink is limited by topographical constraints. The arable land area in Jiangxi is relatively small and fragmented, making large-scale mechanized operations difficult. This results in higher agricultural input materials but lower crop yields, affecting the region’s overall agricultural net carbon sink. This analysis highlights the significant regional differences in agricultural net carbon sinks across the YEB, with clear variations in the rate of increase in net carbon sinks among different provinces and municipalities.

In this section, the driving factors that influence AEE across provinces in the YEB were analyzed using Stata software. The results of this analysis are presented in Table 5, showcasing the factors and their impact on AEE within the region. Conversely, the living standard of farmers, the level of financial support for agriculture, the level of agricultural marketization, and the level of urbanization will have a significant positive impact on the value of AEE; the level of mechanization and the disaster rate will have a significant negative impact on the value of AEE. The level of farmland water conservancy facilities and the level of fertilizer application do not affect the value of AEE (Table 6).

First, the living standard of farmers is significant at the 1% level, and the coefficient of the variable is positive. Economic development and the continuous improvement of the industrial base have largely contributed to the transformation and development of the agricultural economy in the YEB, and the continuous expansion of the scale of planting and breeding by farmers has made them very profitable. At the same time, the strong growth of market demand further promotes the prosperity of the agricultural sector; therefore, farmers will continue to improve green production technologies (such as drip irrigation and organic fertilizer substitution) to expand yields and enhance the quality of agricultural products, which will increase their business income and facilitate the transition of agricultural production from a “production-oriented” approach to one focused on “quality and efficiency.” Increase efficiency “transformation of agricultural production from” production-oriented “to” quality-enhancing.”

Second, the level of financial support to agriculture has a significant positive impact on the AEE of the YEB at the 1% significance level. The increase of government financial investment directly promotes the upgrading of water conservation systems for farmland, agricultural surface pollution control, and the smooth implementation of ecological compensation programs. Through the construction of ecological ditches, the promotion of resource utilization of livestock and poultry waste and the return of farmland to forests and grasslands, the YEB has gradually formed a virtuous cycle of “policy inputs-ecological improvement,” which has strongly promoted the improvement of AEE.

Third, the level of agricultural marketization is significant at the 1% level with a positive coefficient of the variable. It indicates that as the level of agricultural marketization increases, it has a promoting effect on AEE. In the context of the new era, the improvement of people’s living standards greatly stimulates the demand for green agricultural products. At the same time, the high value-addedness of green agricultural products also promotes the transformation of energy-intensive agriculture to a green and low-carbon direction (e.g., soil fertilization to maintain the quality of arable land), forming a self-driven mechanism of “economic dependence-ecological protection,” which promotes the AEE to be significantly improved.

Fourth, the level of water conservation facilities for farmland is significant at 1% level, and the coefficient of the variable is negative. This result reveals that there is an inverse relationship between the level of farmland water conservancy facilities and AEE, which is contrary to traditional research. It is generally believed that effective water conservation systems for farmland can improve water resource utilization and enhance agriculture’s ability to withstand natural disasters. However, in-depth analysis reveals that the current construction of on-farm water conservancy facilities is still mainly aimed at ensuring stable and increased agricultural production, especially in the middle reaches of the rice-growing provinces, which overly rely on the big water irrigation system, resulting in the waste of water resources and the aggravation of the problem of soil salinization. In addition, the construction of some old water conservancy facilities has not fully taken into account the need for ecological protection, resulting in a weakening of the ecosystem’s ability to repair itself.

Fifth, the damage rate has a negative impact on the AEE of the YEB at the 1% significance level. The reason for these findings is that agricultural production is greatly affected by natural climatic conditions, such as floods, droughts and other natural disasters that directly damage farmland ecosystems, leading to soil erosion and loss of organic matter from arable land, which in turn have a huge impact on crop growth and yield. In addition, post-disaster replanting and pesticide misuse exacerbate the ecological load, limiting the function of agricultural carbon sinks and thus reducing AEE.

Sixth, the level of urbanization is significant at the 5% level with negative coefficients of the variables. This result indicates that the increase of urbanization level has a certain counter effect on AEE. The increase in urbanization level is often accompanied by the transfer of rural labor to cities and towns, which leads to a structural shortage of labor in agricultural production and the phenomenon of abandonment of arable land or rough management in some areas, which in turn affects the effective implementation of agroecological management measures. At the same time, the expansion of cities and towns may encroach on high-quality arable land, forcing agricultural production to move to ecologically fragile areas and increasing the difficulty of ecological protection. In addition, industrial pollution and domestic sewage generated during urbanization, if not properly handled, may spread to the surrounding agricultural areas, exacerbating agricultural surface pollution and further restricting the improvement of AEE.

Seventh, the level of mechanization did not pass the significance test, indicating that there is no stable relationship between it and AEE. This outcome stems from the dual nature of mechanization in agricultural production. On the one hand, increasing the level of mechanization has significant economic and ecological benefits. That is to say, it can effectively reduce labor costs and significantly increase labor productivity, thus promoting the green production efficiency of agriculture. However, on the other hand, agricultural mechanization relies on fossil energy and generates a large amount of carbon emissions, which has a negative impact on AEE. In addition, regional differences further blur the association between mechanization and AEE, e.g., Jiangsu has popularized low-carbon farm machinery and precision operation technology, while the middle and upper provinces are still dominated by traditional fuel-fired farm machinery. This regional heterogeneity leads to insignificant overall regression results.

Eighth, the level of fertilizer application did not pass the significance test, implying that it did not show a stable association with AEE. On the one hand, rational application of fertilizer can provide sufficient nutrients for crop growth and significantly increase crop yield. As an important agricultural production area in China, the YEB’s, chemical fertilizer application has increased the yield per unit area to a certain extent and guaranteed China’s food security; however, excessive application of chemical fertilizer will lead to soil crusting and acidification, reduce soil fertility, and affect the balance of the soil ecosystem. At the same time, fertilizers not absorbed by crops will be washed into water bodies with rainwater, triggering environmental problems such as eutrophication of water bodies. The combined effect of the two ultimately makes the overall regression results insignificant.

In the measurement of agricultural carbon sources and sinks, it was found that the net agricultural carbon sink in the Yangtze River Economic Zone showed a three-stage evolution of “continuous increase, fluctuating increase, and stable increase” during the study period, which echoes and expands on the findings of existing studies. For example, Zhang et al. (2022) pointed out that China’s AEE is driven by technological progress, and in this study, the popularization of mechanization promoted the increase of yields and hence carbon sinks, which confirms the positive role of technological factors. In addition, the present study further verifies that Jiangsu Province achieved a significant reduction in carbon emissions through the development of low-carbon production technologies, resulting in a significant increase in carbon sinks (Xiong et al., 2020), while Zhejiang and Shanghai lagged behind due to the contradiction between resource scarcity and economic development (Jing et al., 2022).

In the measurement of AEE, the classification of traditional, low-carbon and green AEE adopted in this study shows that the green AEE is significantly higher than the low-carbon AEE and the traditional AEE, which echoes Zhu and Cao (2024) conclusions about the efficiency of carbon sinks. In terms of temporal evolution trend, the AEE is basically consistent with Cui et al. (2022) study on the green development of agriculture in the YEB, which indicates that policy-driven (e.g., the post-2016 strategy of “grasping the maximum protection”) is the core driving force for the improvement of efficiency. In terms of spatial characteristics, AEE in upstream provinces such as Guizhou and Yunnan was at a high level in 2008, which is at variance with the finding that efficiency in downstream areas generally led in previous studies (Jin B. et al., 2024; Jin G. et al., 2024). This variance may stem from the inclusion of the carbon sink factor in this study—the upstream provinces rely on rich forest and grassland resources to develop eco-agriculture, and their carbon sink capacity significantly supports AEE (Wang and Liu, 2023). The downstream provinces, on the other hand, have high carbon emission intensity mainly due to high value-added agricultural patterns, which complements Cai and Li (2024) suggestion that industrial structure affects AEE. In addition, this study divides the provinces by the “carbon sink-source ratio” and finds that all the provinces are out of the “low-surplus, low-efficiency zone” in 2023, which indicates that the synergistic management policy of the YEB is effective in achieving balanced development in the region. This study indicates that the policy of coordinated governance of the YEB is effective in balanced regional development.

In terms of influencing factors, this study found that the living standard of farmers, the level of financial support for agriculture, and the level of agricultural marketization have a significant positive effect on the improvement of AEE, a result similar to the findings of Li et al. (2022), which suggests that governmental inputs and the market mechanism are the key to promoting the coordinated development of agricultural economy and ecology. The level of farmland water conservancy facilities, the disaster rate, and the level of fertilizer application will have a significant negative impact on the value of AEE. It is noteworthy that the increase in the level of urbanization has a significant negative impact on AEE, which is inconsistent with existing studies (Jin B. et al., 2024; Jin G. et al., 2024). This discrepancy may stem from the superimposed effect of fragmentation of arable land and loss of agricultural labor in the urbanization process in the middle and upper reaches of the YEB.

Although this study provides a useful reference for the study of AEE in the YEB, certain limitations still exist. (1) Due to the availability of data collection, this study only accounts for carbon sinks from crop photosynthesis and temporarily does not include soil carbon pools, forestry carbon sinks, and other carbon sequestration contributions of agroecosystems, which may underestimate the overall carbon sink capacity of agriculture. (2) This study is mainly based on provincial panel data and fails to capture the spatial heterogeneity fully at the city and county scales. (3) The model does not incorporate the impacts of exogenous shocks such as climate change and international agricultural market fluctuations, whose short-term perturbations and long-term impacts on AEE need to be specifically assessed.

In the future, the research team will deepen the field of agricultural low-carbon research and further expand the research content. (1) In the future, the research team will collect more information and data from all aspects and further construct an ideal system for assessing carbon emissions. (2) In the future, the research team will construct a “region-province-city-county” analysis framework and explore the AEE of different regions by combining more detailed regional data and spatial measurement models. (3) In the future, climate change indicators and international shock data will be introduced to further analyze the short-term disturbances and long-term impacts of exogenous shocks on AEE. It is expected that, through further research and policy implementation, the AEE of the YEB will achieve a higher level of improvement, providing a model for the green and low-carbon transformation of agriculture in China and around the world.

This study conducted an in-depth assessment of the level of Agricultural Energy Efficiency (AEE) in the YEB from 2008 to 2023, utilizing panel data from provinces and cities, along with the super-efficiency SBM model and the Tobit model to measure agricultural carbon sources and sinks. The conclusions of the study are as follows:

(1) The net agricultural carbon sink of the YEB in general presents a three-stage evolution characteristic of continuous rise, fluctuating rise, and stable rise, increasing from 108.160 million tons in 2008 to 147.656 million tons in 2023. Spatially, Jiangsu has the largest increase in net carbon sinks (8.463 million tons) during the study period, while Shanghai (0.399 million tons) and Zhejiang (2.190 million tons) have low net carbon sinks due to the scarcity of arable land and the high-carbon agricultural model.

Firstly, optimize the structure of agricultural production factors and promote low-carbon inputs and technological upgrading. The reason for the slow increase in the net carbon sink of agriculture lies in the irrationality of agricultural input factors in the agricultural production process. For this reason, it is necessary to continue to promote the reduction plan of pesticides, fertilizers, and other chemicals, as well as the structural optimization of agricultural inputs. For example, Yunnan, Guizhou and other upstream provinces have implemented actions to replace chemical fertilizers with organic fertilizers, Hubei, Hunan, Jiangxi and other rice-producing areas have promoted side-deep fertilization + biological control technology, and Jiangsu, Zhejiang and other downstream provinces have promoted the innovative use of agricultural machinery in a low-carbon manner, guiding them to reasonably control the use of agricultural inputs to achieve low-carbon development of agriculture; at the same time, we have promoted the enhancement of the digital-enabled carbon sinks by setting up an agricultural intelligent monitoring platform, promoting intelligent irrigation systems, and improving the structure of agricultural inputs. Additionally, the initiative promotes digital empowerment to enhance carbon sinks by establishing an agricultural intelligent monitoring platform and implementing intelligent irrigation systems. This approach aims to improve water resource utilization in key rice production areas while effectively preventing pests and diseases, thereby further advancing the development of low-carbon agriculture.

Secondly, differentiated low-carbon agricultural development strategies should be implemented according to local conditions. According to the natural conditions and market demand of different regions, develop specialized agriculture. For example, the Yangtze River Delta region develops modern urban agriculture and increases scientific and technological investment in research and development of new machinery for emission reduction agriculture so as to do a good job of demonstrating low-carbon agriculture. Jiangsu, Anhui, Sichuan Basin, the Dongting Lake Basin in Hunan Province, and the Poyang Lake Basin in Jiangxi Province have excellent natural resource endowments, so they should make more efforts to promote the construction of high-standard farmland and popularize the low-carbon model of integrated rice farming so as to enhance the carbon storage in the soil and improve the yield and quality of agricultural products at the same time. In the hilly and mountainous areas of the Yangtze River Basin, forestry and orchards can be developed to increase forestry carbon sinks.

Thirdly, the Government should strengthen collaborative governance and build a low-carbon agricultural policy support system. Financial expenditures on agriculture should be increased, with a focus on supporting the research and development of carbon sink enhancement technologies, low-carbon model pilots, and technical training for farmers. At the same time, the government should innovate market-based mechanisms, promote pilot trading of agricultural carbon sinks, and establish and improve cross-regional compensation mechanisms. In addition, in order to more effectively assess and monitor the low-carbon development of agroecology in the YEB, a dynamic assessment system of the basin’s AEE should be set up so as to provide a scientific basis and data support for regional cooperation and policy formulation.