农户数字能力对低碳农业采纳的影响——基于陕黔川10057户猕猴桃种植户的机器学习分析

1. Introduction

In light of the ongoing decline in atmospheric quality and the momentous deterioration of the ecological environment, the pursuit of low-carbon development has emerged as an imperative response to these challenges. The sustainable development model of low-carbon development, which is characterised by energy conservation, reduced consumption, and pollution reduction, has become a crucial avenue for addressing environmental issues [1,2,3,4]. In response to the specific conditions of its own development, China has put forth proposals for carbon peaking and carbon neutrality programmes. In 2020, China formally proposed a double carbon goal, committing to the international community to peak its carbon dioxide emissions by 2030 and achieve carbon neutrality by 2060 [5]. It is widely acknowledged that agriculture represents a significant global source of greenhouse gas emissions. Consequently, it has become evident that farmland has an integral part to play in achieving the double carbon goal [6,7].

Nevertheless, for an extended period, the direct cost of agricultural production has been the sole factor considered, with the cost of resource and environmental losses incurred during the production process remaining unaccounted for. The excessive and inefficient use of agricultural resources, such as pesticides and chemical fertilisers, in agricultural production has a direct impact on the quality and safety of agricultural products, the ecological environment, and human health. The negative externalities of this practice are becoming increasingly apparent [8,9,10].

Over the past few decades, China’s traditional agricultural production and cultivation patterns have undergone significant expansion, resulting in a considerable accumulation of high carbon emissions and a multitude of ecological damage issues [11,12,13]. In recent years, the central No. 1 document has consistently emphasised the necessity of promoting environmentally sustainable agricultural practices. This entails accelerating the promotion and application of agricultural input reduction and efficiency technology, integrating water and fertiliser management, and establishing and enhancing the collection and utilisation of straw, agricultural film, pesticide packaging waste, livestock and poultry manure, and other agricultural waste collection and treatment systems. Furthermore, General Secretary Xi Jinping emphasised the necessity of developing ecological and low-carbon agricultural practices, asserting that environmentally sustainable practices are the foundation of modern agriculture.

Green and low-carbon agriculture has the potential to save energy and reduce emissions, facilitate the sequestration of carbon in an ecological manner, enhance the utilisation of agricultural resources, produce safe and high-quality agricultural products, and provide robust support for the realisation of China’s double carbon target [14,15]. The foundation of low-carbon agricultural production is the utilisation of cutting-edge low-carbon agricultural technology [16,17]. It is for this reason that research and development, promotion, and application of low-carbon agricultural technology have become pivotal measures to achieve carbon sequestration and emission reduction in agricultural production. The contemporary understanding of low-carbon agricultural technology is primarily concerned with the minimisation of inputs, including energy, fertilisers, pesticides and water, in agricultural production. This approach ultimately aims to curtail greenhouse gas emissions from agricultural sources. In particular, low-carbon agricultural technology encompasses a range of techniques and practices, including formula fertilisation technology, commercial organic fertiliser application, farm fertiliser application, no-till technology, and water-saving irrigation technology.

The implementation of the household contract responsibility system in China has resulted in the farming household becoming the primary entity engaged in production and management activities within rural areas, and is thus the most fundamental decision-making unit. The uptake of low-carbon agricultural technology by farmers is contingent upon the comprehensive promotion and implementation of ecological low-carbon agriculture in China [18]. The decision to adopt technology is a behavioural choice made by an individual, influenced by the opinions of others and based on an evaluation of the advantages offered by the technology and their own suitability for it [19]. The dissemination and uptake of low-carbon agricultural technology can be conceptualised as a process of technology diffusion [20]. The process can be divided into two distinct stages: the initial stage concerns farmers making the decision to adopt low-carbon agricultural technology; the subsequent stage pertains to farmers deciding to continue the behaviour after adopting low-carbon agricultural technology [21]. In accordance with the conventional perspective on economics, a considerable number of scholars have examined the internal and external elements influencing the initial adoption of low-carbon agricultural technology by farmers at both the theoretical and empirical levels. Internal factors include gender, age, health, education, peer effects, ecological awareness, household income, non-farm labour migration, time preference, and risk perception [22,23,24,25,26,27,28]. The adoption of low-carbon agricultural technology by farmers is influenced by several external factors, including training, social capital, market incentives, government regulation, subsidies, agricultural insurance, participation in cooperatives, and machinery outsourcing services [29,30,31,32,33,34]. A review of the literature reveals a paucity of studies investigating the sustained adoption of low-carbon agricultural technology by farmers. Many scholars have employed sustained use modelling to examine the influence of various factors, including technology cost and efficacy, technology complexity, government technology promotion, and policy subsidies, on the sustained adoption of low-carbon agricultural technology by farmers [35,36,37]. A consensus was reached on the fundamental premise that the continued adoption of low-carbon agricultural technology by farmers represents an extension of the technology adoption process. This extension is based on the evaluation of the actual results of technology adoption in relation to individual expectations of the effects of low-carbon agricultural technology. It is frequently observed that the performance of a technology is at its lowest during the initial adoption phase. It is only through continued utilisation of the technology that its intrinsic value can be fully appreciated. Despite the ascendance of green agriculture in China in the context of production and development, a discrepancy persists between the initial and sustained adoption of green and low-carbon agricultural technologies by farmers. Nevertheless, the majority of existing studies focus on the initial decision-making process surrounding the adoption of low-carbon technology, neglecting to consider the dynamic characteristics of technology adoption behaviour across different stages. Furthermore, there is a tendency to conflate initial adoption with sustained adoption.

The digital economy is particularly adept at optimising the low-carbon agricultural industry landscape [38]. The advent of the digital economy has given rise to a plethora of novel technologies, industries, and business models. Moreover, the digitisation of knowledge and information has become a primary factor in the production process [39]. The integration of modern information technology with agriculture, including the internet, the Internet of Things, cloud computing, big data, and artificial intelligence, enables real-time collection of agricultural data, real-time monitoring of the agricultural ecological environment, precise input of agricultural production factors, quantitative decision-making in the agricultural production process, and intelligent regulation and control of agricultural production. These functions collectively constitute a new low-carbon agricultural production model. It has been demonstrated that the digital economy plays a notable role in the implementation of low-carbon agricultural technology by farmers [40]. Concurrently, the digital economy has considerable expectations regarding the digital capability of farmers. Those with superior digital abilities are more adept at navigating digital technology and are more likely to receive a premium for their skills in the context of the advancement of the digital economy.

The disparate digital capability of farmers gives rise to variations in the digital divide and the digital dividend. This raises the question of whether digital capability has a significant positive effect on the initial adoption of low-carbon agricultural technology by farmers. Does digital capability also have a significant positive effect on the sustained adoption of low-carbon agricultural technology? Given the established link between initial and sustained adoption of technology, it is pertinent to inquire whether the factors influencing the initial behaviour of farmers in the adoption of low-carbon agricultural technology also exert an influence on their sustained adoption behaviour. This study proposes an innovative perspective to study the gap between the initial and sustained adoption of existing low-carbon agricultural technology based on a comprehensive analysis of the preceding findings. The objective of this study is to achieve an accurate match between low-carbon agricultural technology and farmers.

Furthermore, it is worthwhile to mention that the conventional methodology utilised in agricultural policy research typically entails the gathering of data, the application of analytical models to elucidate the interrelationships between variables, and the formulation of policy recommendations. A review of the existing literature reveals that the majority of studies have formulated hypotheses and conducted regression analyses, primarily utilising econometric methods such as binary probit, propensity score matching, and double difference methods. Nevertheless, the inherent bias in establishing linear relationships between indicators in explanatory modelling results in insufficient discriminatory precision, which may lead to a wide range of policy recommendations that are disparate. The conventional techniques employed in statistical analysis are gradually being shown to have certain inherent limitations.

In recent years, machine learning methods have been employed extensively in agri-environmental and economic research due to their ability to effectively address complex non-linear problems. The field of machine learning is predicated on the analysis and learning of the mapping of existing data to expert classification models. Techniques for processing data and information are developed through algorithms that facilitate the stable and reliable classification of unknown categorical data. In the context of artificial intelligence and big data technology, the computational analysis method of machine learning can be applied to discover data laws or implicit models from multi-dimensional data that are difficult to perceive and discover. The computational analysis method offers several advantages, including high accuracy, rapid training speed, and straightforward implementation and interpretation. The method enhances the existing research paradigms and is extensively employed in diverse classification and regression tasks [41].

In light of the aforementioned evidence, this study proceeded to devise and administer a questionnaire survey comprising 10,057 kiwifruit farmers situated in the provinces of Shaanxi, Guizhou, and Sichuan, which collectively represent the three leading kiwifruit-producing regions in China. The current status and challenges associated with the initial and sustained adoption of low-carbon agricultural technology by kiwifruit growers were further elucidated. The ordered logit model, which has been demonstrated to offer a robust explanatory framework for behavioural decisions, was selected as a reference for the analysis of the results obtained from other machine learning methods. The dataset was divided into training and testing sets based on the results of the experimental analysis, which employed a range of machine learning techniques. The optimal model is selected on the basis of the expected goodness of fit. Subsequently, the SHAP value interpretive method is implemented to ascertain the contribution rate of each digital capability dimension to the initial and sustained adoption of low-carbon agricultural technology by farmers, thereby facilitating the effective identification of the most significant influencing factors. The influence of digital capability and its constituent elements on the initial and sustained adoption of low-carbon agricultural technology by farmers is subjected to rigorous analysis, and recommendations for pertinent policy measures are put forth.

The contributions of this study are mainly reflected in the following three aspects: (1) Research Theme: This study investigates the characteristics of a sample of farmers and their initial and sustained adoption of low-carbon agricultural technology through micro-field research. In accordance with the findings of static technology adoption studies, further consideration is given to the continued adoption of technology. Accordingly, the objective of this study is to address the existing gap in the literature on the sustained adoption of low-carbon agricultural technology by farmers. (2) Research Content: This study provides a comprehensive examination of the composition of the digital capability of farmers, with the utilisation of factor analysis to construct and measure the digital capability index from four dimensions: digital access capability, digital acquisition capability, digital processing capability, and digital sharing capability. This offers a reference paradigm for the construction of indicators for subsequent related studies. Concurrently, it furnishes novel evidence to elucidate the influence of the digital capability of farmers and their multifaceted dimensions on the initial and sustained adoption of low-carbon agricultural technology by farmers. (3) Research Method: This study offers a comprehensive integration of the strengths inherent to both traditional econometric research methods and machine learning methods. In order to more accurately capture the impact of digital capability on the initial and sustained adoption of low-carbon agricultural technology by farmers, and to provide more scientific policy recommendations for the decision-making community, an ordered logit model, a Lasso regression method, a random forest method, an XGBoost model, and a BP neural network model were used.

2. Theoretical Analysis and Research Hypotheses

2.1. Theoretical Analysis Framework

The behaviours of farmers in the adoption of green and low-carbon agricultural technologies can be divided into two progressive stages: initial trial and long-term sustained application. However, there exist significant discrepancies in the behavioural constraint mechanisms between the two stages [42]. The former stage is primarily restricted by decision-making uncertainty arising from insufficient information, whereas the latter stage hinges on perceived technical benefits and long-term production preferences. As a core endowment enabling farmers to adapt to the digital transformation of modern agriculture, digital capability can systematically resolve practical dilemmas hindering the adoption of low-carbon technology by farmers through two pathways: alleviating endogenous decision-making constraints and optimising exogenous production mechanisms. Digital capability effectively drives the behavioural transition of farmers from passive wait-and-see to proactive trials, and from one-off usage to regular, in-depth implementation. On the one hand, grounded in the bounded rationality decision-making theory and farmer behaviour theory, comprehensive digital capability integrated by multi-dimensional capability including digital access, information acquisition, data processing, and information sharing can break the ingrained cognitive habits shaped by the traditional farming experiences of farmers, expand supply channels of agricultural technical information, correct farmers’ biased risk perceptions of low-carbon technology, and reshape their long-term income expectations. From the endogenous cognitive dimension, it thereby facilitates farmers’ acceptance of innovative low-carbon production modes. On the other hand, drawing upon the sustained technology application theory, a sound d