Abstract
This study investigates the influence of digital financial inclusion on green total factor productivity of grain in China, using data from 30 provinces (excluding Tibet) from 2011 to 2020. Our findings reveal a dual role of digital finance: it significantly enhances green productivity of grain in central and eastern China but has a contrasting effect in the western regions. The research also uncovers a unique pattern where the benefits of digital finance in one area can negatively affect nearby regions. This research contributes significantly to the discourse on finance and agriculture, providing nuanced perspectives on the regional implications of digital financial inclusion for grain productivity, and thereby enriching the understanding of its role in agrarian economies.
1. Introduction
The rising challenge of global climate change, driven by carbon emissions, has become a central issue affecting food security, sustainable agricultural development, and overall economic and social progress worldwide (Asumadu-Sarkodie and Owusu, 2017; Huffman, 2019; Shen and Sun, 2016; Xiong et al., 2016; Zou et al., 2015). Within this context, Digital Financial Inclusion (DFI) has emerged as an instrumental factor in improving sustainable food production practices in China, thereby addressing critical global food security issues (H. Huang et al., 2022; Ozili, 2018). In this scenario, China stands out for its ability to feed 22% of the world’s population with just 7% of all arable land. However, this rapid agricultural growth in China is marred by challenges such as excessive resource consumption, pollution, and ecological deterioration (Jun and Xiang, 2011; Rozelle and Rosegrant, 1997). To address these issues, the adoption of Green Total Factor Productivity (GTFP) for grain production becomes essential, focusing on increasing efficiency in grain production while minimizing environmental impact through reduced carbon emissions and pollution, marking a step towards eco-friendly agriculture. Concurrently, the emergence of transformative digital technologies—including advancements in data analytics, mobile connectivity, and cloud-based services—has reshaped the agricultural landscape. These advancements in digital technology have significantly influenced green food production by introducing both novel challenges and opportunities through the deployment of digital financial inclusion (DFI) services (Yu et al., 2014).
Extensive studies have explored various determinants of agricultural GTFP, focusing on elements like the disparity in incomes between rural and urban settings, green trade barriers, stages of economic growth, technological advancements, education, the provision of credit in agriculture, governmental agricultural support, green low-carbon initiatives, and the transfer of farmland (Chen et al., 2021; Fang et al., 2021; X. Huang et al., 2022; Liu et al., 2023; Song et al., 2022; Yu et al., 2022). As per China’s 49th Internet Development Statistical Report, rural areas now have 284 million internet users, achieving a 57.6% penetration rate. DFI is instrumental in enhancing the allocation of financial resources and promoting both rural revitalization and agricultural production. In light of DFI’s significant role, Pan et al. (2015) demonstrate that DFI improves agricultural GTFP by optimizing agricultural industrial structures. Complementing this, Xiao et al. (2023) found that DFI’s support of green agricultural technologies significantly contributes to the growth of agricultural GTFP in China. Further adding to this discourse, Gao et al. (2022) detail how various aspects of DFI, including the extent of financial coverage, the depth of its utilization, and the degree of digital integration, are collectively propelling the advancement of agricultural GTFP. Given the growing relevance of DFI, Zhai et al. (2023) analyzed panel data to examine its impact on agricultural GTFP, using the dynamic panel fixed effect model.
Current research predominantly analyzes the overall influence of DFI on agricultural GTFP, with less emphasis on its specific impacts on sectors like food production. This leaves significant aspects of DFI’s effects on targeted agricultural productivity largely unexplored. In addition, the majority of studies use panel regression to analyze this impact but often overlook the variability in DFI’s coverage breadth, usage depth, and digitization level, as well as regional disparities across China. This limitation is noteworthy as it disregards the potential spatial spillover effects of DFI, key for strategic decision-making in the food and agriculture sectors.
This paper aims to investigate the influence of DFI on grain GTFP and its underlying mechanisms. Improving the green total factor productivity of grain is critical for achieving sustainable grain production. Achieving green grain production necessitates a strategic approach emphasizing sustainable practices like crop rotation and Integrated Pest Management (IPM) to conserve resources and minimize environmental harm. Precision agriculture and green technologies further aid this transition by optimizing resource use and reducing emissions. To this end, and based on data availability, we analyze panel data from 30 provinces in China (excluding Tibet) from 2011 to 2020, using the DFI index developed by Peking University. Our approach includes examining grain GTFP and its components, such as the Green Total Productivity Index (GTPI) and Green Technical Efficiency Index (GTEI). We employ benchmark regression, two-stage least squares estimation (2SLS) to address endogeneity concerns, and spatial econometric modeling to explore the spatial spillover effects of DFI on grain GTFP. These methods are strategically selected to guide decision-making in the food and agriculture sectors.
Our study makes three significant contributions: First, it evaluates the growth in grain GTFP and constructs a digital economy index to illustrate how DFI affects the grain sector and identifies key influencing factors. Second, it examines the differential impacts of DFI on grain GTFP across its various dimensions and regions, essential for effective management in agribusiness. Third, it examines the potential spatial spillover effect of DFI on grain GTFP, offering valuable insights for policy and strategy development in China’s food industry.
The structure of the paper is as follows: Section 2 provides a detailed description of the materials and methods employed. Section 3 presents and discusses the empirical findings and their implications. Section 4 concludes the study with significant insights and policy recommendations for firms in the food and agricultural industries.
2. Materials and methods
2.1 Measurement of grain GTFP
Directional distance function (EBM model)
In this study, we used a modified EBM direction distance function and data envelope analysis (DEA) with the help of MATLAB software. These methods allow us to measure the relative efficiency of various decision-making units in scenarios involving multiple inputs and outputs (Cvetkoska et al., 2023; Vaseei et al., in press). The EBM model is formulated as follows:
Citation: International Food and Agribusiness Management Review 27, 3 (2024) ; 10.22434/ifamr1055
In equation (1), γ denotes the optimal efficiency score ranging from 0 to 1. The variables , , , , and represent the weights and slack variables for inputs, desirable outputs, and undesirable outputs, respectively. The parameter ε is core parameter of the EBM model, ranging from 0 to 1, integrating both radial and non-radial slack variable components.
GML index model
The GML index model circumvents the limitations of linear programming’s non-transmissibility (Zhu et al., 2023). The calculation of the GML index model is as follows:
Citation: International Food and Agribusiness Management Review 27, 3 (2024) ; 10.22434/ifamr1055
In this model GMLt, t+1, GTPIt, t+1 and GTEIt, t+1 represent the grain GTFP Index, Green Technical Progress Index of grain, and Green Technical Efficiency Change Index of grain from period 1 to 2, respectively. Values above 1 indicate optimization in green total factor productivity, green technical efficiency, and green technical progress; values below 1 signify deterioration; and a value of 1 indicates no change.
Carbon emissions from food production primarily originate from six sources, as detailed in Table 1. The total carbon emissions in food production are calculated using the formula:
Citation: International Food and Agribusiness Management Review 27, 3 (2024) ; 10.22434/ifamr1055
Here, E is the total carbon emissions from food production, Ei represents emissions from each carbon source, Ti is the quantity of each carbon source, and
2.2 Model specification
Fixed effects model
Based on the theoretical analysis, this study constructs the following benchmark regression model to analyze the data (Lu et al., 2023):
Citation: International Food and Agribusiness Management Review 27, 3 (2024) ; 10.22434/ifamr1055
In equation (4), GTFPit is the logarithmic representation of GTFP of grain in region i during year t, DFIit is the DFI index in region i in year t, ln GTPI and ln GTEI represent the logarithms of green technological progress and green technological efficiency of grain, respectively, in region i during year t. Zit includes control variables, including human capital (HC), economic development level (PGDP), food cultivation structure (CPS), agricultural disaster rate (ADR), and level of financial support to agriculture (lnFSA). The model also accounts for regional fixed effects (F) and a random error term (G).
Factors and sources of carbon emissions in food production.
Citation: International Food and Agribusiness Management Review 27, 3 (2024) ; 10.22434/ifamr1055
Spatial econometric model
To investigate the spatial spillover effects of the influence of DFI on GTFP of grain, this study employs general spatial econometric models (Shen et al., 2023; Jiang et al., 2022):
Citation: International Food and Agribusiness Management Review 27, 3 (2024) ; 10.22434/ifamr1055
Equation 5 explicates the logarithmic value of GTFP of grain (ln GTFPit) in region i for year t. DFIit represents the DFI index region i for year t, while Zit encapsulates a range of control variables. Wit is the spatial weight matrix denoting neighborhood influences. The equation also includes time fixed effects (ϑt) and spatial fixed effects (μi), with εit as the random error term. The model transitions to a spatial lag model if
2.3 Sample and data sources
The dataset includes information on inputs and outputs for assessing grain Green Total Factor Productivity (GTFP), as well as data on carbon emissions and their influencing factors. These datasets were compiled from a variety of sources, including the “2012–2021 Compilation of Costs and Benefits of Agricultural Products in the Country”, “2012–2021 China Rural Statistics Yearbook”, “2012–2021 China Statistics Yearbook,” regional annual publications, several online resources, and the EPS database. The dataset was rigorously processed to rectify any anomalies and to impute missing values using linear interpolation techniques. Table 2 presents a descriptive statistical analysis of the data used in this study.
Summary of descriptive statistics for research variables.
Citation: International Food and Agribusiness Management Review 27, 3 (2024) ; 10.22434/ifamr1055
3. Results and discussion
3.1 Statistical overview of grain GTFP and its constituent elements
Figure 1 illustrates the growth of GTFP of grain and its constituent elements, the Green Technical Progress Index (GTPI) and Green Technical Efficiency Index (GTEI), from 2011 to 2020. GTFP of grain exhibited an overall upward trajectory, with an average annual growth of 2.06%. This growth primarily resulted from an annual increase of 1.92% in GTPI and a smaller yet significant rise of 0.23% in GTEI. These trends indicate that advancements in China’s grain production are largely attributable to technological innovation and efficiency improvements, which is consistent with the findings of Han et al. (2018). One possible explanation is that a strong focus on green technology allows for better use of resources and more effective production processes.
Evolution of grain GTFP and its constituent indices, 2011–2020.
Citation: International Food and Agribusiness Management Review 27, 3 (2024) ; 10.22434/ifamr1055
Comprehensive overview of regional and provincial variations in grain GTFP growth.
Citation: International Food and Agribusiness Management Review 27, 3 (2024) ; 10.22434/ifamr1055
The analysis of Table 3, which explores the progression of grain GTFP across various Chinese regions, reveals a comprehensive picture of agricultural progress. Each province shows an upward trend in GTFP of grain, indicating nationwide improvements in grain production. The Eastern provinces, such as Beijing and Tianjin, demonstrate balanced growth in both the GTPI and GTEI, indicating well-rounded development in these areas.
In the Central provinces like Henan and Hubei, there is a noticeable elevation in GTFP, primarily driven by advances in GTPI. This suggests that in these agriculturally intensive regions, technological progress plays a pivotal role in enhancing productivity. The GTPI figures in these provinces are consistently higher than their GTEI counterparts.
The Western provinces, including Shaanxi and Gansu, exhibit the highest rates of GTFP growth. This significant increase is largely due to advancements in technology, as evidenced by high GTPI growth coupled with modest gains in GTEI. This pattern suggests a focused approach on technology-driven productivity enhancements.
Certain provinces, such as Guizhou, stand out with exceptional increases in both GTPI and GTEI, signaling the successful adoption of innovative agricultural practices. On the other hand, regions like Zhejiang, where GTEI growth is lower, point to areas where efficiency improvements are necessary.
The overall trend at the national level indicates a predominant influence of technological progress on grain GTFP growth, with less contribution from efficiency improvements. This trend underscores the necessity of ongoing technological innovation to improve grain production in China and highlights areas where efficiency enhancements are needed. In addition, the diverse patterns observed in GTEI growth underscore the geographical heterogeneity and distinct regional characteristics in agricultural development, as highlighted by Li et al. (2023) and Sun et al. (2022).
3.2 Analysis of the baseline relationship between DFI and grain GTFP
Utilizing the F-test and Hausman test to ascertain the most suitable econometric model, the study adopted the fixed effect model to evaluate the impact of DFI on GTFP of grain. The results from the baseline model estimations are presented in Table 4. Initial results without control variables are shown in columns (1) to (3), indicating a significant influence of DFI on grain GTFP of grain. After the inclusion of control variables, DFI continues to exhibit a significant positive effect on grain GTFP, suggesting its enduring impact on agricultural productivity. In addition, DFI shows a significant positive influence on the GTPI, irrespective of the presence of control variables. However, the effect of DFI on the GTEI was found to be non-significant. This result may be attributed to the limited scope and depth of DFI’s involvement in agricultural development. In addition, factors like regional human capital, grain planting structure, and government support positively affect GTFP, while economic development level and agricultural disaster rates negatively influence it (Ma et al., 2023).
Baseline model estimation results for DFI and grain GTFP relationship.
Citation: International Food and Agribusiness Management Review 27, 3 (2024) ; 10.22434/ifamr1055
3.3 Testing for heterogeneity in DFI’s effect on GTFP of grain
Investigating DFI’s dimensional influence
The three principal dimensions of DFI — coverage breadth (DFI_CB), usage depth (DFI_UD), and digitization level (DFI_DL) — have varying impacts on GTFP of grain. To delve deeper into the effects of these DFI dimensions, individual regression analyses were performed. As depicted in Table 5, the initial results without control variables indicate significant positive impacts of these dimensions on grain GTFP at the 1% statistical level. This denotes that each dimension of DFI, in its capacity, substantially enhances grain GTFP. Incorporating control variables modifies the results: coverage breadth and usage depth retain their positive impact on grain GTFP, whereas the digitization level shows a negative effect. One possible explanation is that digital agriculture technologies are still developing, and this might currently limit their ability to improve grain GTFP (Yang et al., 2022).
Regression analysis of DFI’s dimensional effects on grain GTFP.
Citation: International Food and Agribusiness Management Review 27, 3 (2024) ; 10.22434/ifamr1055
Regional heterogeneity analysis
The investigation into the impact of DFI on grain GTFP considers the regional variability in DFI development. Table 6 presents findings that illuminate these regional differences. Notably, DFI significantly enhances grain GTFP in the Eastern region (coefficient = 0.000480), with a 5% significance level, underscoring the region’s advanced DFI infrastructure and a workforce in the food and agriculture sectors that is more adept at integrating new technologies (Yu et al., 2022). DFI also benefits the Central region, but to a lesser extent. Interestingly, the impact of DFI on grain GTFP is negative in the Western region. This could be because farmers in the Western region have less scientific knowledge and are less familiar with digital technology, making it harder to benefit from DFI (Yu et al., 2022). These findings highlight the varied regional capabilities and environments for effectively using DFI to improve agricultural productivity.
Regional differential effects of DFI on grain GTFP.
Citation: International Food and Agribusiness Management Review 27, 3 (2024) ; 10.22434/ifamr1055
3.4 Validation of results through robustness testing
In our robustness checks, Internet penetration (INT) was chosen as the instrumental variable for DFI in a two-stage least squares (2SLS) estimation, given its critical role in shaping digital financial infrastructure and consequently, DFI’s progression (Table 7). The primary estimation phase revealed a significantly positive coefficient for INT, confirming the instrument’s strength. Following this, the Wald test’s rejection of the null hypothesis in the second stage serves to validate the study’s fixed effects model estimations. Accounting for endogeneity, the enduring significant positive effect of DFI on the GTFP of grain firmly establishes the robustness of regression results (Ye et al., 2023).
Regression results for endogeneity test in DFI-GTFP relationship.
Citation: International Food and Agribusiness Management Review 27, 3 (2024) ; 10.22434/ifamr1055
3.5 Analysis of spatial correlations and spillover effects
Assessing spatial autocorrelation of DFI
The global Moran indices for DFI, as shown in Table 8, affirm a positive spatial effect of DFI over the years 2011–2020. Each year, the Moran index is significantly above 0.25, indicating consistent and significant spatial autocorrelation at 1% significance threshold (Zhang et al., 2023). These persistent Moran’s I indices across the years suggest that regions are not isolated in their financial inclusion advancements; rather, there is significant regional interdependence. This interconnectivity implies that the advancement of DFI in one region may influence or be influenced by the DFI levels in neighboring areas. The high Moran index values over a decade underscore a robust and widespread diffusion of digital financial practices across China’s various provinces.
Global Moran Index for DFI.
Citation: International Food and Agribusiness Management Review 27, 3 (2024) ; 10.22434/ifamr1055
Spatial econometric model results
The application of spatial econometric techniques — SEM, SLM, and SDM — as presented in Table 9, and utilizing the spatial collinear weight matrix as suggested by Hu et al. (2022), yields significant positive regression coefficients. These results indicate that DFI contributes positively to grain GTFP, reinforcing the notion that digital financial inclusion facilitates improvements in agricultural productivity. The Log-likelihood values for the SEM, SLM, and SDM are 411.821, 410.725, and 419.482, respectively.
Results from spatial econometric regression analysis.
Citation: International Food and Agribusiness Management Review 27, 3 (2024) ; 10.22434/ifamr1055
However, the spatial Durbin model (SDM) reveals a critical nuance — a negative spatial lag effect of DFI. This suggests that while DFI positively impacts grain GTFP within a region, its proliferation in neighboring regions might introduce competitive or resource allocation challenges, thereby potentially reducing the efficiency of local grain production.
Interpretation of spatial spillovers
The analysis in Table 10, focusing on the spatial spillover effects of DFI on grain GTFP, suggests complex regional interactions. While the positive direct effect of DFI on regional grain GTFP highlights its potential in enhancing agricultural efficiency and productivity, the negative indirect effect reflects a competitive or resource diversion dynamic in neighboring regions. This dichotomy indicates that while DFI fosters growth in regions where it is directly implemented, it may inadvertently create imbalances or resource competition in adjacent areas. This phenomenon could be attributed to a redistribution of financial and human resources, where areas with more advanced DFI infrastructures attract resources from less developed neighboring regions.
Spatial spillover effects of DFI on grain GTFP.
Citation: International Food and Agribusiness Management Review 27, 3 (2024) ; 10.22434/ifamr1055
4. Summary and conclusions
This study measures grain GTFP across 30 provinces (excluding Tibet) from 2011–2020, integrating carbon emission considerations, and further analyzes the impact of digital financial inclusion (DFI) on grain GTFP. The results provide detailed and unbiased data, aiding decision-making for a sustainable Chinese food security system. Our analysis confirms that DFI positively impacts grain GTFP, mainly by enhancing green technological progress rather than technical efficiency. A nuanced view emerged, highlighting heterogeneity in DFI’s impact across its dimensions and regions. Financial breadth and depth positively affected grain GTFP, while digitalization showed a negative influence. Regionally, DFI boosted productivity in Eastern and Central China but was less effective in the Western provinces. Spatial econometric analysis revealed positive local effects of DFI but negative spillovers to adjacent areas. We acknowledge that limitations in data collection prevent the direct measurement of the carbon emission index for grain production in individual provinces, potentially leading to some discrepancies between our results and the actual situation.
For the food and agricultural industries, these findings are of paramount importance. They suggest that adopting digital financial tools should be a strategic and regionally tailored decision, emphasizing the need for enhancing financial literacy in tandem with digital tool deployment. To guide policy-making effectively, it’s important for policymakers to not just expand the use of digital financial services but also to create an environment that encourages new green technologies. Supporting the growth and sharing of these technologies can enhance the beneficial impacts of digital finance on farming productivity. In addition, it is critical to develop policies that reduce the negative side effects of digital finance in less developed areas (Ogutu et al., 2014). This strategic balance is key for firms to optimize efficiency and sustain a competitive edge in the agricultural sector.
Acknowledgements
Wenjiang Ma and Qing Zhang contributed equally to this work and are to be considered co-first authors. This study was supported by the Ministry of Education Executive Committee Project (Project No.: NJX22141) and the Tarim University Graduate Student Innovation and Entrepreneurship Project (Project No: TDGRI202267 and TDETR202218).
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