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Journal of Resources and Ecology >

2024 , Vol. 15 >Issue 5: 1358 - 1367

DOI: https://doi.org/10.5814/j.issn.1674-764x.2024.05.022

Resource Economy

Agricultural Eco-efficiency: Progress, Challenges, and Prospects

  • CUI Xufeng ,
  • XIONG Jiaqi ,
  • LIU Yong , *
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  • School of Business Administration, Zhongnan University of Economics and Law, Wuhan 430073, China
* LIU Yong, E-mail:

CUI Xufeng, E-mail:

Received date: 2023-08-27

  Accepted date: 2024-03-10

  Online published: 2024-10-09

Supported by

The MOE (Ministry of Education of China) Project of Humanities and Social Sciences(23YJA790012)

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Abstract

Agricultural eco-efficiency is a vital indicator for assessing the sustainability of agriculture. Conducting evaluations of agricultural eco-efficiency can provide critical information for policymaking, resource allocation, and the advancement of agricultural sustainable development. While there exists a substantial body of research on the evaluation of agricultural eco-efficiency, its influencing factors, and improvement paths, there remains a paucity of systematic reviews, evaluations, and prospective analyses in this area. This study employed the literature search method to analyze relevant scholarly articles. Chinese literature was sourced from the CNKI (China National Knowledge Infrastructure) database, while international literature was obtained from the Web of Science database. The literature search was conducted from 1998 to 2023. The study reviewed the progress of research on agricultural eco-efficiency and outlined the challenges and prospects that lie ahead. The findings of this study indicate that the primary challenges in the field of agricultural eco-efficiency research include refining the evaluation system, integrating macro and micro influencing factors, and ensuring the suitability of models employed. In the future, the research could expand in areas such as deepening the application of artificial intelligence technology in evaluation methods, clarifying the driving factors of agricultural eco-efficiency, and promoting the green transformation of agriculture. This study provides a comprehensive systematic review and could provide critical information for related research expansion.

Key words： eco-efficiency; agriculture; sustainable development; agroecosystem; artificial intelligence

Cite this article

CUI Xufeng , XIONG Jiaqi , LIU Yong . Agricultural Eco-efficiency: Progress, Challenges, and Prospects[J]. Journal of Resources and Ecology, 2024 , 15(5) : 1358 -1367 . DOI: 10.5814/j.issn.1674-764x.2024.05.022

1 Introduction

With the development of the Chinese economy and the increase in population, the corresponding demand for food has continued to increase. Against this backdrop, the output level of agriculture has been continuously improving. Since 2015, grain production has exceeded 6.5×1011 kg for seven consecutive years, and the annual average growth rate of agricultural production value is about 10% (Hou and Yao, 2018). Although great achievements have been made in agricultural development, due to the long-term excessive development of agricultural resources, the resource and environmental constraints on agricultural development have become an important limiting factor for China’s future stable supply of agricultural products (Chi et al., 2022; Zhang et al., 2023).
According to the “National Plan for Agricultural Sustainable Development (2015-2030)”, China’s agricultural film recycling rate is less than 2/3, and both the utilization rates of pesticides and fertilizers are less than 1/3. The problem of extensive agricultural production is prominent. China, which only accounts for 7% of the world’s arable land, supports 22% of the world’s population and faces enormous agricultural and ecological pressures (Zhang and Xu, 2008; Liu et al., 2023). Furthermore, the trend of “Non-Agriculturalization” and “Non-Grainization” of arable land is significant, leading to a further decrease in the quantity of high-quality arable land. In addition, the problem of land desertification in China is becoming increasingly serious, with a desertification area of 2.674×108 ha and an annual expansion rate of 1.04×106 ha (Yang, 2004; Ma et al., 2020). At the same time, as China’s demographic dividend gradually disappears, the development model that relies solely on increasing traditional production factors such as labor and material resources is no longer sustainable (Robaina-Alves et al., 2015; Liu et al., 2020). In the context of the conflict between ecological crises and agricultural sustainable development, it is urgent to research agricultural eco-efficiency.
As an important indicator for evaluating agricultural sustainable development, agricultural eco-efficiency plays a role in accurately quantifying and evaluating the value of agroecosystems. At the same time, it provides practical significance for the efficient and sustainable uses of agroecological resources and can prevent the deterioration of agroecosystems, achieving the goal of protecting the safety of the agricultural ecological environment.
Currently, there has been some progress in the study of agricultural eco-efficiency (Casaejos et al., 2016; Soteriades et al., 2016; Coluccia et al., 2020; Hu et al., 2022). Specifically, domestic and international scholars have focused on analyzing agricultural eco-efficiency at the macro level. Firstly, they use different measurement methods to study agricultural eco-efficiency in different regions such as the country or province, and provide policy recommendations to improve efficiency. Secondly, they narrow the scope of the study to the city, county, or district level to study agricultural eco-efficiency from a temporal and spatial perspective.
Based on the background described above, this paper first reviews the progress of research on agricultural eco-efficincy. Secondly, it summarizes agricultural eco-efficiency from multiple perspectives and points out the challenges faced by current research on agricultural eco-efficiency. Finally, based on the above, the prospects of agricultural eco-efficiency are discussed.

2 Methodology

The specific research method employed in this paper is the literature search method, which is utilized to collect literature and conduct in-depth interpretations of textual contents, based on which the research progress, challenges, and prospects of agricultural eco-efficiency are distilled and summarized. The databases used for the literature search are CNKI (China National Knowledge Infrastructure), and WoS (Web of Science), and the time range of the literature search is 1981-2023.
Specifically, Chinese literature was mainly based on the CNKI database, the literature search mode was “Advanced Search”, the keywords of “agriculture” and “eco-efficiency” were searched, and a total of 870 pieces of related literature were searched, because this paper mainly explored the progress, challenges, and prospects of agricultural eco-efficiency in agricultural ecology through academic journals, so 381 non-academic journals were excluded and 489 academic articles were retained.
On this basis, we further screened 253 relevant academic articles belonging to the Overview of Chinese Core Journal Directory (i.e., Peking University core journals), CSSCI (Chinese Social Sciences Citation Index), CSCD (Chinese Science Citation Database), and excluded 16 articles that were not related to the research topic of this paper, and retained 237 articles. Then we first selected articles with high citation frequency and recent times, and read the titles and abstracts to grasp the core contents of these articles. Based on this, we made relevant notes to summarize the progress, challenges, and prospects of agricultural eco-efficiency.
Web of Science database was used as the main international literature, and the search mode was “Advanced Search”, the database selected was Web of Science Core Collection, the citation index was “All”, and the keywords for the search were “agriculture” and “eco-efficiency” (or “ecological efficiency”). 235 papers were obtained through keyword search, and then through manual screening, 32 papers that were not related to the research topic of this paper were excluded, and 203 papers were retained. For this literature, similar criteria were applied as for Chinese literature: selecting those with high citation frequency and recent publication dates, and capturing the core contents of the articles by reading the titles and abstracts. Relevant notes were then taken to summarize the progress, challenges, and prospects of agricultural eco-efficiency were summarized.
In summary, this study employed a literature review approach to examine global agricultural eco-efficiency. Drawing from 440 academic papers, it outlines the current research status and offers prospects for addressing the associated challenges. Through comprehensive analysis, we delved into various aspects of agricultural eco-efficiency, including evaluation frameworks, evaluation methods, spatiotemporal variations, and influencing factors. Furthermore, we deeply pondered on strategies to enhance agricultural eco-efficiency, address climate change, and promote ecological conservation, offering valuable insights and references for future research and practical applications in related fields.

3 Results

This paper provides a comprehensive overview of the research progress on agricultural eco-efficiency from aspects such as conceptual connotations, evaluation frameworks, evaluation methods, evaluation contents, and influencing factors. It clarifies the research context and identifies the expandable research space.

3.1 Concepts of agricultural eco-efficiency

The concept of “eco-efficiency” was first proposed by Schaltehher and Sturm in 1990 and later became well-known to the public through the promotion of World Business Council for Sustainable Development (WBCSD) at the end of the 20th century (Yin et al., 2012). Schaltehher first defined “eco-efficiency” as the ratio of products, services, and value-added to environmental burdens (Schaltegger and Sturm, 1990). Later, the WBCSD published “Changing Course: A Global Business Perspective on Development and Environment,” defining eco-efficiency as “the delivery of competitively priced goods and services that satisfy human needs and bring quality of life, while progressively reducing ecological impacts and resource intensity throughout the life cycle, to a level at least in line with the earth’s estimated carrying capacity, and at the same time achieving social and environmental objectives” (Desimone and Popoff, 1999). In 1995, the concept of eco-efficiency emerged in China for the first time. After nearly 30 years of development, Chinese scholars have also proposed various definitions related to the concept of eco-efficiency. Some scholars define eco-efficiency as “the production of products and the provision of services that not only meet human needs but also have an impact on the environment that is lower than the environmental carrying capacity during the process” (Liao et al., 2021; Huang et al., 2022; Zhang et al., 2023). In summary, eco-efficiency can be seen as a balance between the carrying capacity input of the earth in the ecological environment and the output caused by human social production activities, and its essence lies in achieving the best coordination between environmental carrying capacity and economic output.
The concept of agricultural eco-efficiency is based on the essence of eco-efficiency and specifically applies to agricultural production. From different perspectives, scholars have made definitions of agricultural eco-efficiency. For example, some scholars define agricultural eco-efficiency as “the maximum output of agricultural products obtained with minimal resource consumption and environmental pollution while ensuring product quality” (Pan and Ying, 2013). This definition emphasizes output maximization but negative impacts on the environment have been neglected. In addition, scholars have defined agricultural eco-efficiency from the perspectives of crop cultivation (Chen, 2012), agricultural development models (Wu et al., 2009), expected output (Chen et al., 2013), and other aspects, but these definitions are relatively one-sided and do not fully consider other agricultural production activities, agricultural eco-efficiency itself, and unexpected outputs.
Based on the aforementioned research, it is apparent that the definition of eco-efficiency proposed by the WBCSD garners extensive recognition within the global academic community. Therefore, this paper defines eco-efficiency according to the WBCSD standards (WBCSD, 2000). Furthermore, this paper reviews the relevant concepts of agricultural eco-efficiency, pointing out both the contributions and shortcomings of current research. Based on the characteristics and comprehensive benefits of agricultural production activities, this paper defines agricultural eco-efficiency as “in the agricultural production process, minimizing natural resource consumption and maximizing environmental protection within the carrying capacity of the natural ecosystem, while achieving maximum output efficiency of agricultural products and services, and minimizing negative impacts on the agricultural ecological environment”.

3.2 Evaluations of agricultural eco-efficiency

3.2.1 Evaluation framework

The evaluation framework of agricultural eco-efficiency mainly includes “Agricultural Economic Value-Positive and Negative Consumption of Environmental Resources”, “Actual Environmental Load Emissions-Ideal Environmental Load Emissions”, “Production Function-Combination of Input Factors-Optimal Output”, “Population, Social and Economic Activities-Ecological Environment Carrying Capacity”, “Multiple Input-Multiple Output-Best Efficiency Value-Comparison of Ideal Differences”, etc., with evaluation areas involving state, provincial, municipal, and county levels. The methods used in the application of the framework can be mainly classified into three categories: single ratio method, index system method, and model method. Based on these three evaluation methods, various subdivision research methods can be extended, such as stochastic frontier analysis, ecological footprint analysis, data envelopment analysis, etc., each subdivision method corresponding to different evaluation frameworks. The evaluation framework of agricultural eco-efficiency is shown in Table 1.
Table 1 Evaluation framework
Evaluation framework Framework application Evaluation areas Literature sources
Agricultural economic value—Positive and negative consumption of environmental resources Single ratio method Cities of Jiangxi Province, etc. Wang et al. (2016);
Huang et al. (2018)
Actual environmental load emissions—Ideal environmental load emissions Life cycle assessment Cities of Hubei Province, etc. Wang et al. (2018);
Huang et al. (2022)
Production function—Combination of input factors—Optimal output Stochastic
frontier analysis		 Cities of Jiangxi Province, etc. Gu and Zhu (2020);
Ji and Shang (2021)
Population, social and economic activities—Ecological environment carrying capacity Ecological footprint analysis Guizhou Province, etc. Hong and Quan (2012);
Wang and Lin (2021)
Multiple input—Multiple output—Best efficiency value—Comparison of ideal differences Data envelopment analysis Wuxi City, the whole of China, etc. Wu et al. (2009);
Liao et al. (2021);
Cui et al. (2022)
The evaluation framework of the single ratio method measures agricultural eco-efficiency based on the positive and negative consumption of the agricultural economy and agricultural environment. In addition, scholars have derived from this framework the measurement of eco-efficiency changes by combining the life cycle principle, GDP, and the total environmental pressure value (Zhao et al., 2016). The essence of this evaluation framework is to use various agricultural economic values and the degree of consumption of the agricultural environment for ratio analysis. While this framework possesses a certain level of scientific validity, in practical implementation, the allocation of indicator weights is contingent upon scholars’ subjective interpretations, potentially resulting in inaccuracies.
The framework of the life cycle assessment method is to measure the environmental carrying capacity by using the material energy output and input generated in agricultural production activities, aiming to measure agricultural eco- efficiency by using the difference between actual environmental load consumption and ideal environmental load consumption (Lin et al., 2019). The agricultural life cycle assessment framework mainly includes four steps: goal definition and scope determination, inventory analysis, impact assessment, and result interpretation (Huang et al., 2022). Currently, this evaluation framework has been widely studied, and the current mainstream is to combine the life cycle assessment framework with data envelopment analysis, which is suitable for micro-level research on agricultural eco-efficiency.
The evaluation framework of the stochastic frontier analysis method is generally based on a parametric model. By establishing a production function model and input element combination, the method compares the differences between the actual reference values and the model’s ideal values to scientifically evaluate agricultural eco-efficiency.
The ecological footprint analysis framework mainly discusses the impacts of human activities on the carrying capacity of ecosystems. However, the shortcomings of this framework are that the indicators have not fully considered the differences in regional and temporal scales.
The utilization of the Data Envelopment Analysis (DEA) framework is quite widespread. The essence of this framework is to use multiple input and output indicators and apply linear methods to find the best efficiency value and effectively evaluate units of the same type.
In summary, contemporary mainstream research predominantly utilizes the DEA framework incorporating unexpected output, complemented by other efficiency models like Stochastic Frontier Analysis (SFA) and SBM to gauge agricultural eco-efficiency. Presently, the research framework of agricultural eco-efficiency is diverse, yielding a wealth of findings that furnish scholars with robust framework support. Nonetheless, opportunities for further innovation persist.

3.2.2 Evaluation methods

The evaluation methods for agricultural eco-efficiency mainly include single ratio method, life cycle assessment, stochastic frontier, ecological footprint, data envelopment analysis, etc. Specific evaluation methods are shown in Table 2.
Table 2 Evaluation methods of agricultural eco-efficiency
Evaluation methods Application areas Formulae and explanation
Single ratio method Agricultural economic development A E = E V C + N E − P E
where, AE: agricultural eco-efficiency; EV: economic value of agricultural products; C: consumption of agricultural products; NE: negative environmental effects of agricultural products; PE: positive environmental effects of agricultural products.
Life cycle assessment Agricultural energy consumption and waste discharge The life cycle assessment (LCA) method evaluates the use of resources and materials, as well as the emissions of environmental impacts, by identifying and quantifying them. It assesses the impacts of these inputs and emissions, and evaluates and explains opportunities for environmental improvement.
The LCA process consists of four specific steps: 1) Setting evaluation goals and scope; 2) Inventory analysis; 3) Impact assessment and analysis; 4) Interpretation of life cycle results.
Stochastic frontier
analysis		 Calculation of agricultural eco-efficiency y i t = f p i t , t × exp r i t , o i t
where, yit: the output of i in period t; pit: the production factors invested by i in period t (t is the time); rit: the random error term of the equation; oit: another error term of the equation.
Ecological footprint
analysis		 The impact of human agricultural activities on the environment e f = ∑ ​ a i × r j = ∑ ​ c i p i × r j
where, ef: per capita ecological footprint; ai: per capita biological production area occupied by i substance; rj: equivalence factor; ci: per capita consumption of substance i; pi: world average production capacity of substance i; i: types of substances consumed; j: type of biological production area.
Data envelopment
analysis		 Calculation of agricultural eco-efficiency max μ T y 0 v T x 0 = h 0 ; μ T y j v T x j ≤ 1 , j = 1 , 2 , ⋯ , n ; μ ≥ 0 , v ≥ 0
where, h0: efficiency value of evaluation unit; μT: weight of a certain investment; vT: weight of a certain output; xj: the input vector of j; yj: the output vector of j.
(1) Single ratio method
The ratio method is used to calculate the impact of economic development on the environment, and it can also be used to calculate agricultural eco-efficiency. The ratio method can not only calculate the negative environmental impacts of agricultural production, but also the positive effects. Based on this, some scholars have conducted research on agricultural production efficiency based on green GDP (Huang et al., 2018) and the ecological cost ratio model (Yin et al., 2012). The use of the ratio method is to consider the impact of the economy on the environment. This measurement method is relatively single and lacks comprehensiveness. In measuring agricultural eco-efficiency, it only considers the impact of economic development on the agricultural environment. Therefore, the method of measuring agricultural eco-efficiency by the ratio method has gradually been replaced by other methods.
(2) Life cycle assessment
Life cycle assessment (LCA) was first proposed by SETAC in 1990, which aimed to evaluate the environmental impacts associated with the entire life cycle of a product (Yang and Wang, 1998). With the advancement of modernization and the promotion of sustainable development, International Organization for Standardization (ISO) officially defined the LCA in 1997 and developed a theoretical framework for it (ISO, 2006). The formal definition of LCA is “the assessment of the environmental impacts of a product throughout its life cycle, including energy consumption and waste emissions, and the development of suggestions and measures for improvement” (Pan et al., 2013). Meanwhile, Swiss researchers were the first to use LCA for quantitative analysis and further developed the Ecological Scarcity Method (ESM) based on it (Lv and Yang, 2006). The current research on life cycle assessment in China faces many challenges, including limited application areas and difficulties in controlling source reduction (Zhai et al., 2021). In addition, the current life cycle assessment method mainly focuses on micro-level issues, which may lead to regional and temporal differences, affecting the accuracy and reliability of the results.
(3) Stochastic frontier analysis
Stochastic frontier analysis (SFA) is a parameter analysis method, which was proposed by Farrell (1957) and widely used for efficiency measurement. The first step in SFA is to construct a production function, and then calculate the optimal output by optimizing the given input factors to achieve Pareto optimality. Finally, the actual output level is compared with the maximum expected output level to calculate the stochastic disturbance term in the function, which is then separated into the inefficiency term and the random error term.
(4) Ecological footprint analysis
The ecological footprint analysis method was proposed by Rees (1992), which reflects the extent of human activities in a country or region that exert pressure on and impact the natural ecological environment. The emergence of the ecological footprint method has provided a direction for ecological construction research (Pan and Ying, 2013). Some scholars have also pointed out that for humans to have sufficient natural resources in the future, they must protect the current and future ecological systems, otherwise nature will no longer ensure basic ecological functions (Wackernagel et al., 2006). Some scholars have used this method to study the sustainability of planting rapeseed and sunflower crops (Forled et al., 2018), as well as the ecological carrying capacity of Guizhou (Hong and Quan, 2012). Although the ecological footprint method is relatively intuitive and can reflect the interaction between a regional economy and nature, there are still shortcomings in theory and practical measurement methods, such as the lack of actual consumption data for bioproducts, which can lead to measurement errors (Rees and Wackernagel, 1996).
(5) Data envelopment analysis
Data envelopment analysis (DEA) was proposed by Charnes based on probability (Zhang and Wei, 2007), which is a non-parametric analysis method. DEA replaces the original production function with an envelopment curve and combines linear programming theory to find the DMU (Decision-Making Unit) with the minimum inputs and maximum outputs. Eventually, it obtains the optimal efficiency value for the combination of inputs and outputs and sets its efficiency value to 1, while the remaining inefficient DMUs are given a relative efficiency value between 0 and 1 (Jiang and Zhao, 2021). In the application of data envelopment analysis, some scholars have constructed an agricultural eco-efficiency evaluation system and improvement system based on DEA, and have researched on scale efficiency, influencing factors, and improvement directions (Mai et al., 2014; Ren, 2019; Dong et al., 2021; Liu et al., 2021). In the field of data envelopment analysis, some scholars have constructed an evaluation system and improvement system for agricultural eco-efficiency based on DEA, and have studied scale efficiency, influencing factors, and improvement directions.
In summary, methods for calculating agricultural eco- efficiency can be classified into three categories: single economic or environmental ratio methods, index system methods, and model methods (Yin et al., 2012). Existing literature mainly uses ratio analysis, LCA, SFA, ecological footprint analysis, and DEA for research. Every method has its advantages when considering different research objects and output indicators. Currently, the most widely used method for measuring agricultural eco-efficiency is DEA, which is easy to use and is a non-parametric method that can solve the multiple input and output decision units. Nonetheless, DEA exhibits certain limitations, including oversight of statistical testing considerations, susceptibility to sample variability and outliers, and reliance on data quality.

3.2.3 Evaluation contents

After defining the concept and measurement methods of agricultural eco-efficiency, experts and scholars in the field of agricultural eco-efficiency research have conducted in-depth research from different perspectives, and their research characteristics can be divided into two research starting points.
First, the evaluation of agricultural eco-efficiency. The so-called evaluation of agricultural eco-efficiency refers to using economic efficiency and environmental efficiency of agricultural production activities as evaluation indicators to measure the economic value of agricultural products, while taking natural resource consumption as evaluation premises. Various economic models are used to analyze agricultural eco-efficiency, and policy recommendations are made based on the results of agricultural eco-efficiency measurement. Currently, most scholars analyze and evaluate agricultural eco-efficiency by constructing an agricultural eco-efficiency evaluation system, selecting expected and unexpected outputs to establish an efficiency model, and putting forward relevant suggestions for improving agricultural eco-efficiency based on the calculation index (Hong and Quan, 2012; Wu et al., 2014; Yu and Hao, 2018; Ren, 2019; Cao et al., 2021; Huang and Zhang, 2021; Jiang and Zhao, 2021).
The second focus is on the spatiotemporal differences in agricultural eco-efficiency. Studies in this category explore the spatiotemporal distribution characteristics of the research area using relevant economic models, measure the agricultural eco-efficiency, analyze the main driving factors that cause changes, and propose relevant policy recommendations (Liu and Shi, 2020; Wu et al., 2020; Liang and Wang, 2021; Wang and Lin, 2021).
Therefore, existing studies mainly focus on the evaluation of agricultural eco-efficiency and its spatiotemporal differences. Based on these perspectives, some suggestions for further research can be made: Firstly, refining the evaluation framework of agricultural eco-efficiency; Secondly, enhancing the precision of measuring agricultural eco-efficiency by refining the anticipated output indicator system; Lastly, scrutinizing the factors constraining the enhancement of agricultural eco-efficiency and devising strategies for its further augmentation.

3.3 Influencing factors of agricultural eco-efficiency

Based on previous research, the study of factors affecting agricultural eco-efficiency can be elaborated from two aspects: 1) The micro level, which mainly focuses on the redundancy of agricultural production factors, including redundancy in expected and unexpected output; and 2) The macro level, which includes factors such as agricultural policies, adjustment of agricultural industry structure, improvement of agricultural financial systems, agricultural machinery density, per capita industrial value-added, disaster incidence, agricultural scale, and average years of education. Overall, the main factors can be specifically divided into economic level factors, natural disaster factors, and social characteristic factors (Pan and Ying, 2013; Cheng et al., 2014; Wang and Zhang, 2018; Wang, 2020). The specific influencing factors are shown in Table 3.
Table 3 Influencing factors of agricultural eco-efficiency
Levels of influencing factors Specific influencing factors Literature sources
The micro level Excessive use of fertilizers Fischer et al. (2010)
Reducing fertilizer application Wu et al. (2012)
Excessive redundancy of non-expected outputs Zhang et al. (2014)
Spillover effect of agricultural production Hou et al. (2021)
Fertilizer efficiency promotion policies Liu et al. (2019)
The macro level The transfer of rural labor Zhang and Song (2003); Wang et al. (2013)
Industrial structure Pang et al. (2016)
The scale of agricultural technology input Wang and Yao (2021)
The level of integration development of the tertiary industry Wang and Zhou (2021)
Agricultural planting structure Zeng et al. (2021)
The level of sustainable development of agricultural economy Huang and Liu (2018)
At the micro level, the influencing factors are mainly divided into redundancy of expected and non-expected outputs. Many scholars have found that the use of chemicals such as fertilizers, pesticides, and plastic film is the reason for the low agricultural eco-efficiency. Excessive use of fertilizers is one of the important factors causing agricultural pollution (Fischer et al., 2010). Scholars have conducted research from various aspects, such as reducing fertilizer application (Wu et al., 2012), excessive redundancy of non-expected outputs (Zhang et al., 2014), spatial spillover effects (Hou et al., 2021), and fertilizer efficiency promotion policies (Liu et al., 2019). Research indicates that the intensity of fertilizer application exerts a noteworthy influence on agricultural eco-efficiency. Therefore, the implementation of targeted policies aimed at nurturing and revitalizing agricultural ecology is imperative for enhancing agricultural eco-efficiency.
At the macro level, research on factors covers many aspects. With the gradual elimination of the dual urban-rural structure, rural labor has gradually migrated to urban areas, becoming the main driving force behind urbanization (Zhang and Song, 2003). Some scholars have pointed out that the transfer of rural labor has promoted the recombination of production factors, thereby affecting agricultural input-output efficiency and eco-efficiency (Wang et al., 2013). Other scholars have found that industrial structure, total population, and fixed assets investment in agriculture have a positive effect on agricultural eco-efficiency, while per capita net income of farmers, planting structure, labor culture level, urban-rural income ratio, effective irrigation area, and per capita arable land have a negative effect (Pang et al., 2016). In addition, scholars have studied the factors from the perspectives of the agricultural technology input (Wang and Yao, 2021), the level of integration development of the tertiary industry (Wang and Zhou, 2021), agricultural planting structure (Zeng et al., 2021), and the level of sustainable development of agricultural economy (Huang and Liu, 2018).
Current research on agricultural eco-efficiency can be delineated into two levels: macro and micro. At the micro level, a more nuanced exploration of factors is warranted, necessitating a departure from prevailing research perspectives to unearth micro and specific driving forces. Concurrently, at the macro level, an enhanced focus on the selection breadth of variables and the refinement of economic model theory precision is imperative when scrutinizing factors impacting agricultural eco-efficiency.

4 Discussion

4.1 The challenges of agricultural eco-efficiency research

Presently, scholarly attention towards research on agricultural eco-efficiency has intensified, yielding substantive outcomes. Its practical applications and scholarly significance have garnered extensive acknowledgment within academia. Nonetheless, concurrent with this recognition, persisting challenges hinder the advancement of future research on agricultural eco-efficiency.
Firstly, the evaluation system for agricultural eco-efficiency. Currently, many scholars have constructed an evaluation system for agricultural eco-efficiency in their research, but the selection of indicators is controversial. On the one hand, it is difficult to obtain regional data, and the relevant data in some areas are scattered, making it difficult to calculate indicators. On the other hand, there is controversy over the selection of indicators based on the principle of maximizing comprehensive benefits. For example, when selecting non-expected output indicators, the waste and pollutants generated in the development of an agricultural ecological environment are often considered. Nevertheless, the composition of waste and pollutants is intricate, and the process of singling out specific indicators from a plethora of options risks overlooking valuable information. Consequently, meticulous deliberation is warranted in the selection of indicators when constructing an assessment framework for agricultural eco-efficiency.
Secondly, the combination of macro and micro factors in influencing agricultural eco-efficiency. Although scholars have selected macro and micro factors to explore the driving mechanism of agricultural eco-efficiency in current research, at the micro level, the exploration of driving factors can seek more micro factors beyond the general perspectives of expected output redundancy and non-expected output redundancy. At the macro level, the theoretical derivation, model construction, and selection are challenging. In the process of studying the driving factors of agricultural eco-efficiency, in addition to single macro or micro factors, there is still some research space on how to combine both to conduct a comprehensive analysis. Previous studies tended to choose one macro or micro factor for research, while future research can further integrate diverse influencing factors, such as exploring the mediating effects of implementing agricultural ecological compensation policies on agricultural eco-efficiency.
Thirdly, the suitability of the research model for agricultural eco-efficiency. In the process of research, it is difficult to explore the influencing factors due to differences in resource endowment among different regions, which leads to a large deviation in the fitting degree of the model. Presently, scholars commonly employ the super-efficiency SBM-Undesirable model to investigate agricultural eco-efficiency, founded on the fundamental assumption of decision-making units to formulate the model for analysis. However, in the research results, the root causes of the crisis in agricultural eco-efficiency and environment are not explained in most studies. In future research, how to improve the innovation of the model and consider research problems from a broader perspective systematically poses significant challenges.

4.2 The prospects of agricultural eco-efficiency

In summary, research on agricultural eco-efficiency should be advanced in the following aspects in future exploration. Firstly, innovation in research perspectives. Currently, agricultural production in various regions still faces significant ecological pressures. The promotion of agricultural green transformation and sustainable development models is imminent, and there is still room for improvement in China’s agricultural eco-efficiency. Consequently, there is a pressing imperative to undertake research on agricultural eco-efficiency from novel perspectives. Secondly, research evaluation methods need innovation. As an important indicator for measuring agricultural sustainable development, agricultural eco-efficiency can further improve the measurement accuracy by using emerging machine learning and artificial intelligence methods, such as ChatGPT, to improve the evaluation system. Thirdly, evaluation analysis in the spatiotemporal dimension needs to be strengthened. While current research on the spatiotemporal differences of agricultural eco-efficiency has made some progress, the evaluation of future agricultural eco-efficiency should be more comprehensive at the micro level and consider the evaluation in each stage. Fourthly, research should be strengthened in agricultural policy innovation, agricultural sustainable development models, and reducing the redundancy of production factors.

5 Conclusions

This paper analyzed the research progress, challenges, and prospects of agricultural eco-efficiency based on the literature analysis method, from which the following conclusions are drawn:
(1) Current research on agricultural eco-efficiency mainly focuses on assessing the levels, spatiotemporal variations, and influencing factors based on specific patterns and regions. There is limited innovative and expansive research, and more novel exploration will be needed in the future.
(2) In terms of the research challenges of agricultural eco-efficiency, the current eco-efficiency is mainly based on agricultural models such as crop rotation systems, green agriculture, microbial technology, and agricultural circular economy. There is a need for innovation in agricultural ecological models. In addition, research on influencing factors mainly focuses on urban-rural structure, differences in industrial structure, and economic levels. There is a lack of diversity and variability in factors, and the selection of research variables still needs further discussion.
(3) In terms of the research prospects for agricultural eco- efficiency, the current wave of machine learning and artificial intelligence technologies is surging and is an important direction for method development. Confronted with the pressing realities of global climate change and the escalating challenges posed to environmental resources, the paramount emphasis for enhancing agricultural eco-efficiency rests on the innovation of ecological agricultural models, the advancement of ecological agricultural technologies, and the formulation of progressive agricultural policies aimed at fostering best practices within ecological agriculture.

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