Coupling coordination of digital finance, green fiscal support, and green technology adoption in China's agri-food systems

1 Introduction

Agri-food systems account for close to a third of anthropogenic greenhouse gas emissions once the full chain from input manufacture to final waste is counted (Crippa et al., 2021). Within that chain agriculture itself is a stubborn source, since emissions come from fertilizer use, machinery and irrigation energy, and the handling of land and livestock rather than from any single activity that could simply be switched off. China's agriculture contributes a sizeable share of national emissions, and the targets of carbon peaking and neutrality have raised the low-carbon transformation of the sector to a policy priority that runs alongside food security and several of the Sustainable Development Goals. Provincial accounting suggests that agricultural emissions have followed a long climb before cresting and beginning a slow decline, a turning point attributed in part to gains in input efficiency (Zhang and Shen, 2025). Part of that shift traces to changes in the production base itself, where the rise of new-quality productive forces reshapes how inputs translate into output and emissions (Xie et al., 2025). The integration of agriculture with other rural sectors has been read as one further route toward lower-carbon production (Wang and Pan, 2025). Progress, however, has been far from even across the country, and what actually propels it remains only partly understood. Lasting decarbonization is unlikely to come from isolated technical fixes; it rests instead on whether financial resources, public support, and the uptake of greener practices on the farm move forward together.

Three levers sit at the center of this discussion. Digital finance has broadened credit access and cut the cost of transactions for farmers and rural enterprises, reaching parts of the countryside that conventional banking long overlooked, and inclusive digital finance has been shown to steady food production when climate conditions turn adverse (Liu and Li, 2025). Green fiscal support plays a different part, steering public money into environmental protection and agricultural modernization and supplying resources that private lenders would not; its reach has been sharpened lately by targeted programs, with smart-agriculture policy lowering emissions where it has been rolled out (Zhang Z. et al., 2024). Green technology adoption, spanning water-saving irrigation, mechanization, and thriftier use of inputs, is where finance and policy finally register as lower emission intensity, because production is where fertilizer, fuel, and land are actually consumed. Yet these levers are usually examined one at a time. A study of digital finance may map its emission effect across provincial space and find that the influence reaches beyond the province where the finance originates (Bao et al., 2024), while a separate literature on green finance traces its bearing on emissions to the green innovation it helps fund (Huang et al., 2024). Each isolates one lever and in doing so sets aside a plain feature of the transition, which is that the three develop in relation to one another. Finance that outpaces the fiscal direction meant to steer it, or the on-farm capacity to absorb it, is unlikely to show up as abatement, and the same holds when any single lever runs ahead of the rest.

The coupling coordination framework offers a way to take this interdependence seriously. It began in the study of interacting physical systems, where coupling describes how strongly two systems act on each other, and has since been carried into economics to ask how closely two or more socioeconomic subsystems develop in a mutually reinforcing way. Its appeal lies in capturing two things at once, namely whether systems keep pace with each other and whether they do so at a respectable level. Provincial studies have applied it to the digital economy and green finance and reported coordination that sits at a primary stage while remaining regionally uneven (Liu et al., 2023). The same apparatus has since been extended to digitalization and green agriculture, where coordination improves over time but leaves the familiar regional gaps intact (Tang and Li, 2026). What is less settled is how the framework applies to the particular trio that matters most for agricultural decarbonization: digital finance as the source of financial access, green fiscal support as the channel for public resources, and green technology adoption as the point where these conditions turn into practice.

Finance, fiscal support, and technology adoption have rarely been examined together within a single coordination framework, and the structure of their joint development therefore remains poorly understood. Studies that do measure coordination have tended to emphasize its overall level and trajectory, giving comparatively little attention to the extent of regional disparities, the sources from which they arise, or whether provinces are converging or diverging over time. The question of what constrains coordination, examined at the level of individual indicators rather than entire subsystems, has seldom been addressed in the agricultural context, despite being the scale at which policy intervention actually takes place. Addressing these questions requires measurement to be combined with disparity decomposition, convergence testing, and obstacle diagnosis, allowing the analysis to move beyond describing coordination toward characterizing its distribution and its constraints.

This study measures and interprets the coupling coordination of digital finance, green fiscal support, and green technology adoption across 30 Chinese provinces over 2011 to 2023. It follows how coordination has evolved in time and space, decomposes regional disparities and locates their sources, tests for convergence nationally and by region, diagnoses the factors that most constrain coordination, and considers how coordination relates to agricultural carbon intensity. The theoretical rationale that links the three subsystems and the four hypotheses drawn from it are laid out in Section 2.4.

The study makes three modest contributions. It brings the three systems into one coordination framework suited to the agricultural setting, taking a step beyond the paired financial systems that earlier work has tended to examine. It joins disparity decomposition, convergence testing, and obstacle diagnosis to move from describing coordination toward accounting for its structure and limits. And by situating the analysis in the agri-food context and relating coordination to emission intensity, it links the measurement exercise to the wider question of low-carbon agricultural transition, pointing to where coordinated development might be strengthened rather than claiming to settle the matter.

2 Literature review

2.1 Digital finance, green fiscal support, and agricultural transition

The role of digital finance in agricultural decarbonization has drawn steady attention, and a fairly consistent picture has emerged. Digital financial inclusion tends to lower agricultural carbon emissions, and it appears to do so through channels that reach into production rather than through finance in the abstract. Evidence on the mechanism points to the easing of financing constraints and to changes on the farm, where the reduction of fertilizer use and the sharpening of input efficiency carry much of the effect (Yang and Wen, 2025). The reach of the relationship is not confined within provincial borders. When the emission effect is examined spatially, it turns out to extend into neighboring provinces, which suggests that the diffusion of financial services carries a geographic dimension worth taking seriously (Bao et al., 2024). There are also signs that digital finance narrows the gap between provinces, pulling the laggards in agricultural emission intensity toward the leaders (Tan et al., 2024). Beyond emissions, the same expansion has been tied to the resilience of food production under climate stress, working through technological progress and socialized services (Liu and Li, 2025). A recurring caveat is that the relationship bends rather than runs straight, since the marginal contribution of digital finance seems to depend on how developed a place already is (Li H. et al., 2024). Beyond the credit channel, another strand of work asks how public expenditure directed at environmental and agricultural priorities shapes green outcomes on the ground.

Green finance and green fiscal support form a companion strand. Resources directed toward environmental ends have been shown to lift green total factor productivity and to encourage the green innovation through which emission reductions are eventually realized (Lee and Lee, 2022). The path is indirect: capital shifts toward cleaner activities, production efficiency improves, and only then does the effect on emissions become apparent. In agriculture the point is sharper still, since green finance acts less as a standalone lever than as a funding condition for the diffusion of low-carbon technology, with green credit and investment together shaping what producers actually do (Li et al., 2023). Access to credit also governs the pace at which green technology reaches farmers, since cleaner equipment has to be afforded before it can be adopted (Li B. et al., 2024). Where finance gathers matters as well, as the concentration of financial activity has been found to influence agricultural emissions through its effect on industrial structure (Liu et al., 2024b). The emission effects of green finance likewise spill across regional lines, so that the geographic coordination of financial resources bears on outcomes well beyond any single province (Su et al., 2024). Read as a whole, this literature casts finance and fiscal support not as direct instruments of abatement but as enabling conditions whose influence comes through technology and production, which is what recommends treating them, alongside technology adoption, as interdependent systems.

2.2 Coupling coordination research

A distinct methodological tradition approaches interacting subsystems through coupling coordination, which registers both the strength of interaction and the balance of development among systems. Applications to China's provincial economy are by now numerous, and they share a recognizable vocabulary. A study framed directly around the dual-carbon goals examined the coupling of digital finance and green finance and linked their coordinated development to emission reduction (Shi and Yang, 2024). A related study extended the pairing to green finance, the digital economy, and the ecological environment, tracing their coordinated development across provinces (Zhang and Zhao, 2023). An assessment of the digital economy and green finance placed their coordination at a primary stage, rising over time but marked by clear regional imbalance (Liu et al., 2023). A parallel study pairing the digital economy with green innovation efficiency reached a similar reading, reporting an eastern lead and pronounced spatial heterogeneity (Zhang et al., 2023). The same has been found for green development efficiency across urban agglomerations, where coordination rises unevenly and concentrates in the more developed clusters of cities (Liu, 2025), and for the digital economy alongside high-quality energy development, where the two advance together but at an uneven regional pace (Wang et al., 2024). Coordination between the digital economy and high-quality development has been traced at the sectoral level too, across the industries of the Yangtze River Economic Belt, again finding an eastern advantage and a spatial linkage that weakens over time (Liu et al., 2024a). Green finance and the ecological environment have been coupled in the same manner, with regional disparities decomposed to locate their sources (Liu et al., 2025). The analytical routine is fairly stable across this work, combining the coordination model with entropy weighting to build system scores and, more and more often, adding disparity decomposition and spatial statistics to describe how coordination is distributed rather than only how high it stands.

2.3 Research gaps and contribution

Two features of this literature bear on the present study. Coordination analysis has started to move into agriculture, where a study of the digital economy and green agricultural development reports steady improvement shadowed by persistent regional gaps (Tang and Li, 2026). Work closer to the emissions question has coupled agricultural carbon-emission efficiency with food security and traced their joint evolution across provinces, finding that the two do not always advance together (Zheng and Liao, 2025). At the same time the toolkit has grown more capable, so that the entropy weight method, the coordination model, and obstacle-degree diagnosis are increasingly deployed together to turn an aggregate index into a diagnosis of specific weaknesses. This combined approach has proved workable in the study of new urbanization and rural revitalization (Zhang L. et al., 2024), and elsewhere in the coupling of eco-investment with tourism and logistics (Wang and Zhao, 2024). Two limitations nonetheless persist. Most studies weigh paired systems rather than the specific trio of finance, fiscal support, and technology adoption that bears on agricultural decarbonization, and few draw disparity decomposition, convergence testing, and obstacle diagnosis into a single agricultural application. The present study responds to both, applying an established coordination toolkit to a three-system framework built for the agri-food setting, without suggesting that the method is itself new.

Read together, this body of work has left several matters comparatively less developed. Coordination between digital finance and green finance under the dual-carbon goals, as in Shi and Yang (2024), keeps the analysis within the financial system and does not carry it into the on-farm side where finance is meant to change practice. Broader assessments of the digital economy alongside green finance track the aggregate level and its regional spread but stop short of convergence testing and obstacle diagnosis (Liu et al., 2023), so the shape of the disparity and the location of its binding constraint remain less visible. The work closest in orientation to the present study, which pairs the digital economy with green agricultural development (Tang and Li, 2026), takes in the production side but leaves aside the public-resource channel through which fiscal effort steers both finance and practice toward greener directions. This study adds to that literature by looking at the three levers together in a single framework built for the agri-food setting, and by pairing coordination measurement with disparity decomposition, convergence testing, and obstacle diagnosis in the same application. The methodological components themselves are not new, and the aim is to bring them to bear on a question earlier work has approached from narrower angles.

2.4 Theoretical framework and hypotheses

Coordinated advancement of the three subsystems in the agri-food low-carbon transition rests on their functional complementarity, since each operates on a distinct side of the same transformation. Digital finance eases information asymmetry and transaction costs, and inclusive digital services have reached parts of the countryside long underserved by conventional banking, broadening the range of producers who can afford greener inputs and equipment. A parallel line of firm-level evidence traces the same easing of financing constraints in agricultural enterprises, where digital financial inclusion has been shown to lift innovation input by relaxing the credit-side barrier (Li et al., 2025). Its impact on agricultural emissions has been traced through both eased financing constraints and changes on the farm, and the reach extends across provincial borders (Bao et al., 2024), so the credit channel does not sit only where finance originates. Where digital finance meets the resilience of food production, the working channel appears to run through technological progress and socialized services (Liu and Li, 2025). Green fiscal support plays a different part, channeling public money into environmental protection and agricultural modernization and shaping the direction in which private lenders and producers move. The pass-through has been traced through gains in green total factor productivity and the innovation that finance helps fund (Lee and Lee, 2022), with green credit and investment together shaping producer behavior on the ground (Li et al., 2023). Green technology adoption is where these forces register as physical outcomes, since inputs and land are consumed on the farm rather than in the loan office or at the fiscal desk. The pace at which cleaner equipment reaches farmers is itself governed by finance (Li B. et al., 2024), and recent provincial accounting attributes much of the observed decline in agricultural emissions to gains in input efficiency of the sort that on-farm adoption underwrites (Zhang and Shen, 2025).

The interdependence among the three is complementary rather than additive, and the slowest-moving component tends to set the effective pace of coordinated transition. When digital finance runs ahead of fiscal direction and of the on-farm capacity meant to absorb it, credit tends to be reallocated toward higher-return non-agricultural uses, and the environmental payoff stays latent. Where fiscal support outpaces the financial system meant to leverage it, the public purse ends up carrying the transition on its own, and scale suffers accordingly. Where technology adoption lacks the financial and public backing to spread beyond the producers who can absorb the initial cost, diffusion stalls. Similar reasoning has underpinned earlier applications of the coordination framework to paired financial and environmental systems (Zhang and Zhao, 2023), and to ternary systems studied under the dual-carbon goals (Yu and Yang, 2025). Reading the three subsystems together in one framework is therefore a way to capture the level, the distribution, and the binding constraints of coordinated development that separate analyses of individual levers are less able to bring out.

Four hypotheses follow from the framework. The strengthening of the three subsystems over the sample period should raise the coupling coordination degree, while structural differences in factor endowment, industrial structure, and agricultural conditions across the eastern, central, and western belts leave the east-central-west gradient largely in place (H1). Because inter-belt gaps in these underlying conditions are slow-moving while provinces within a belt tend to share broadly similar circumstances, regional disparities in coordination should arise mainly from differences between belts rather than from variation among provinces within a belt (H2). Latecomer catch-up, familiar from convergence analyses of agricultural green productivity (Zhu et al., 2022) and from parallel evidence on green productivity in the Yangtze River Delta (Bao et al., 2023), gives the western region more room to improve, so provincial coordination levels should converge over time, with the west closing its gap faster than the eastern and central leaders (H3). Because digital financial infrastructure expanded most rapidly in the early years while public expenditure and on-farm adoption advanced more slowly, the binding constraint on coordination should migrate over the period from digital finance toward green fiscal support and green technology adoption, in a pattern that obstacle-degree diagnostics have proved able to capture (Wei et al., 2023; Che et al., 2024) (H4). The analysis that follows is set up as a structural diagnosis of these hypotheses rather than as a causal identification exercise, and identification of causal effects is reserved for research designs equipped for that purpose, as noted in the Limitations.

3 Materials and methods

3.1 Data and sample

The analysis covers 30 provinces of mainland China from 2011 to 2023, setting aside Tibet and the special administrative regions because of data limitations. The choice of 2011 as the starting point is dictated by the digital financial inclusion index, which does not run earlier at the provincial level. Following common practice in regional work, the provinces are sorted into eastern, central, and western belts so that spatial structure can be read off directly, a grouping that convergence studies of agricultural green development have likewise adopted (Zhu et al., 2022; Huang et al., 2022). The division follows the standard administrative classification and reflects broad differences in development, factor endowment, and agricultural conditions among the three belts. Data are drawn from the China Statistical Yearbook, the China Rural Statistical Yearbook, the Peking University digital financial inclusion index, and provincial fiscal statistics. Continuous variables enter after their units are reconciled, and monetary values are deflated to 2011 constant prices so that price change over the period does not distort comparisons. A handful of missing entries in the source statistics were filled by linear interpolation between adjacent years. Descriptive statistics for the full variable set appear in Table 1. The variables fall into three functional groups. The coupling coordination degree and its three subsystems enter the coordination measurement, disparity decomposition, convergence testing, and obstacle diagnosis reported below. The five control variables enter only the conditional beta-convergence specification, where they absorb differences in starting economic and structural conditions so that catch-up in coordination can be told apart from mechanical variation in initial levels. The carbon-related variables enter the association analysis that relates coordination to emission outcomes. Logarithmic transformations are applied to skewed level variables to stabilize variance and to permit interpretation in elasticity terms. None of these variables enters a causal regression, and the table is presented as a descriptive inventory rather than as a specification of a structural model.

Table 1. Descriptive statistics.

N = 390 (30 provinces × 13 years).

3.2 Agricultural carbon emissions

Agricultural carbon emissions are measured on the input side, summing the contributions of six sources: chemical fertilizer, pesticides, agricultural film, diesel, irrigation, and tillage. This inventory captures the main carbon-relevant inputs of crop production and rests on a long-standing accounting approach whose coefficients have been applied widely in provincial emission studies (West and Marland, 2002). The same input-side inventory underlies recent provincial accounting of agricultural emissions and their drivers, which lends the choice further support (Zhang and Shen, 2025). The input-side route is preferred here because the underlying quantities are reported consistently across provinces and years and the coefficients are well established, which keeps the resulting series comparable and easy to reproduce. Total emissions E for each province-year are calculated as shown in Equation 1:

E = ∑ i = 1 6 T i , δ i (1)

Where T i is the quantity of the i-th input and δ i its emission coefficient. The coefficients are listed in Table 2. Agricultural carbon intensity is then defined as emissions per unit of real agricultural output, a scale-adjusted measure that makes provinces of very different size comparable. This intensity measure enters the association analysis later, while total emissions and their logarithm serve as alternative expressions of the same quantity. The inventory omits livestock sources such as enteric fermentation and manure management, so the measure speaks to the crop-input side of agricultural emissions rather than to the sector in full.

Table 2. Agricultural carbon emission sources and coefficients.

Agricultural carbon emission efficiency provides a companion measure alongside carbon intensity and is estimated for each province-year through a slacks-based measure of efficiency in the presence of undesirable outputs, following the approach set out by Tone (2001) and its later refinement for non-radial handling of undesirables (Tone, 2004). Four inputs enter the model, namely sown area, primary-sector labor, agricultural machinery power, and chemical fertilizer use, with real agricultural output as the desirable output and total agricultural carbon emissions as the undesirable output. The frontier is estimated year by year so that cross-sectional efficiency in a given year is not mixed with movement of the frontier itself over time. The resulting index sits within the unit interval, with values closer to one flagging provinces that combine higher output with lower emissions per unit of input. Because efficiency scores are computed relative to an annual frontier, the analysis draws on cross-sectional patterns and quartile-group comparisons rather than on movement of the frontier across years.

Carbon intensity and carbon emission efficiency read the same low-carbon performance from complementary angles. The former summarizes single-factor emission per unit of output, while the latter evaluates multi-factor production performance conditional on emissions treated as an undesirable output, so parallel associations reported by the two indicators offer mutual corroboration rather than duplicate confirmation. The three subsystems bear on these outcomes through the mechanisms outlined earlier, and pairwise correlations reported in the association analysis are broadly consistent with the direction the framework anticipates for each subsystem.

3.3 Indicator systems

Each subsystem is represented by indicators tied to its function, and weights are set objectively through the entropy weight method to sidestep the subjectivity of expert scoring (Chen et al., 2023). The method assigns more weight to indicators that vary more across observations, on the reasoning that greater variation carries more information, which suits a panel that stretches across many provinces and years. Digital finance is captured by the provincial total of the Peking University digital financial inclusion index, whose construction and spatial properties are documented in the source that introduced it (Guo et al., 2020). Green fiscal support combines two indicators, the intensity of energy-conservation-and-environmental spending and of agriculture-forestry-water spending relative to the general public budget, both entering positively, since stronger fiscal effort signals firmer public backing for green agriculture. In a few provinces the agriculture-forestry-water measure exceeds unity, which reflects the concentration of central transfers and earmarked agricultural funds in provinces where such spending is prioritized, rather than a share bounded by the local budget. Green technology adoption is built from four indicators that track the spread of green practice in the field: the effective irrigation rate, the mechanization level, fertilizer-use efficiency, and the film-reduction level. The effective irrigation rate is read as an index of irrigation provision rather than a strict areal share, so in a few province-years with dense irrigation infrastructure or high multiple-cropping, such as Beijing and Xinjiang, it may exceed unity. These indicators were chosen because each corresponds to a concrete on-farm practice and because all are reported consistently over the sample. The full system, with directions and weights, appears in Table 3. Indicators are range-standardized within each subsystem before weighting, and subsystem scores follow as weighted sums.

Table 3. Indicator system and entropy weights.

Weights are within-subsystem entropy weights; each subsystem's indicator weights sum to one.

The two components of green fiscal support are the ratio of energy-conservation-and-environmental expenditure to the general public budget and the ratio of agriculture-forestry-water expenditure to the general public budget. Both are standardized within the sample and combined by their entropy weights of 0.543 and 0.457 into the subsystem score. The two ratios stand as broad proxies for the strength of public effort directed at green and agricultural priorities rather than as narrow measures of purely green agricultural spending, since finer categories are not consistently reported at the provincial level across the sample period. This coverage limit is picked up again in the Discussion. Green technology adoption combines the effective irrigation rate, the mechanization level, fertilizer-use efficiency, and the film-reduction level, standardized within the sample and weighted by entropy at 0.207, 0.174, 0.307, and 0.312. Mechanization is included because larger and more efficient machinery replaces lower-efficiency human and draft-animal work, supports conservation tillage and more precise input application, and lowers the energy and emission intensity of a given output, so it enters as one dimension of on-farm greening rather than as a complete marker of green production. The four indicators together speak to crop-input-side greening, and categories such as livestock waste treatment, straw resource utilization, conservation tillage in its own right, and precision agriculture are left out, since their provincial coverage across 2011 to 2023 is fragmentary. The omission is picked up in the Discussion.

3.4 Coupling coordination model

Coordination among the three subsystems is gauged with the coupling coordination degree model, which keeps the intensity of interaction distinct from the overall level of development. The distinction matters, because two systems can be tightly linked while both remain weak, a case a coupling measure on its own would misread as healthy. The coupling degree is given by Equation 2:

C = 3 × ( U 1 U 2 U 3 ) 1 / 3 U 1 + U 2 + U 3 (2)

Where U 1, U 2, and U 3 are the standardized scores of digital finance, green fiscal support, and green technology adoption. Because a high coupling degree can arise even when the systems are jointly underdeveloped, a comprehensive index T = ( U 1 + U 2 + U 3 ) / 3 is added, the three systems weighted equally in the absence of any theoretical reason to do otherwise. The coupling coordination degree is then obtained from Equation 3:

D = C × T (3)

The index runs from zero to one and rises only when the systems are both closely linked and jointly well developed. This three-system form follows the way the model has been applied to other ternary systems, such as the economy, transport, and ecology under the dual-carbon goals (Yu and Yang, 2025). To make it easier to read, the index is sorted into ten grades from extreme imbalance to high-quality coordination, following the grading common to coordination studies (Zhang L. et al., 2024). The grade intervals appear in Table 4 and underpin the descriptive account that follows.

Table 4. Coupling coordination degree grades.

Read in the present setting, the coefficient carries a substantive rather than a purely algebraic meaning. A high value calls for the three subsystems to advance together at broadly comparable levels rather than for one lever to run far ahead of the others, so an increase over time reflects that finance, public support, and on-farm adoption have moved into closer step in the agri-food system.

3.5 Dagum Gini, convergence, and obstacle models

Three further tools look past the average level of coordination to its structure. Regional disparities are measured and decomposed with the Dagum Gini coefficient, which separates total inequality into within-region, between-region, and transvariation parts and so reveals whether the gaps sit mainly inside the belts or between them (Dagum, 1997). The decomposition has become a standard instrument in studies of regional imbalance in green development, valued for distinguishing sources of inequality that a single summary figure would blur (He et al., 2025). Convergence is examined two ways. The σ-convergence test tracks the coefficient of variation of coordination over time to see whether provinces are drawing closer, and the β-convergence test asks whether provinces that began lower grew faster, in both absolute and conditional forms drawn from convergence analysis of agricultural green productivity (Zhu et al., 2022; Huang et al., 2022). The conditional form adds economic and structural controls so that genuine catch-up can be distinguished from differences in starting conditions. An obstacle-degree model then diagnoses what most constrains coordination, ranking indicators by the product of their weight and their distance from the ideal, a device used elsewhere to pinpoint the restrictive factors in coupling systems, as in assessments that pair entropy weighting with an obstacle model to locate binding constraints on resource carrying capacity (Wei et al., 2023; Che et al., 2024). Together these carry the analysis from the level of coordination to its distribution, its motion over time, and its binding constraints.

The three tools are read together rather than in isolation. The Dagum share attributed to between-region inequality is taken as evidence on where the gap in coordination actually sits. The beta-coefficient is read as the speed at which a province with a given initial gap closes half of it, so that convergence figures translate into recognisable time scales rather than being left as bare parameters. The obstacle-degree ranking is treated as a list of the specific indicators whose current values keep the coupling coordination degree from rising further, so that structural bottlenecks can be named as well as measured. The three readings tie the analysis back to the agri-food coordination question rather than to the algebra of the underlying models.

4 Results

4.1 Spatiotemporal evolution

The three subsystems and their coordination traced distinct paths over the period, shown in Figure 1. Digital finance rose most steeply. It began from the lowest base of the three in 2011 and, through the rapid national build-out of digital financial services across the decade, ended as the strongest. Green fiscal support advanced more gently, in line with the gradual growth of public environmental and agricultural spending. Green technology adoption grew slowest of all, and although it was not the lowest at the outset, its sluggish rise left it the weakest subsystem by the end of the period, a sign that greener practice on the farm has trailed the expansion of financial and fiscal capacity. The coupling coordination degree climbed from 0.37 in 2011 to 0.71 in 2023, moving from mild imbalance up to intermediate coordination, and it did so without a down year, which points to broad rather than episodic progress.

The trajectory of green technology adoption stayed comparatively flat across the sample, moving from a national mean near 0.30 in 2011 to close to 0.31 in 2023. Digital finance, by contrast, climbed from about 0.05 to about 0.83 and green fiscal support from about 0.30 to about 0.54 over the same interval. The flatter path of adoption reflects a structural lag in crop-input-side greening, since effective irrigation and mechanization have approached levels shaped by resource endowment and farm structure, and improvement in fertilizer-use efficiency and film reduction has been slow. The mismatch between the fast-moving financial and fiscal subsystems and the slower-moving production side sits behind the later shift in the binding obstacle from digital finance toward fiscal support and on-farm efficiency, and it gives some idea of why coordination among the three levers reveals a bottleneck that studies focused on one lever at a time have found harder to locate.

Over this general rise the spatial pattern held a steady east–central–west gradient, as Figure 2 shows. In 2011 coordination was low almost everywhere, with only a scatter of eastern provinces reaching higher grades. By 2023 the whole country had shifted upward, and much of the map had entered the primary and intermediate bands, yet the order of regions did not change, with the east ahead, the center in the middle, and the west behind. The regional means confirm the ranking, standing at 0.78, 0.72, and 0.62 in the final year. This mix of general improvement and a fixed hierarchy fits H1, that coordination has risen everywhere while the gradient endures. That the gradient survives the convergence documented below suggests its roots are structural rather than temporary.

4.2 Regional disparities

The Dagum decomposition brings out both a sharp narrowing of overall disparity and a clear account of what remains, reported in Table 5 and Figure 3. The overall Gini coefficient fell from 0.176 in 2011 to 0.062 in 2023, with the steepest drop in the first two years and a slower decline after. Provincial coordination levels thus grew considerably more even, and most of the equalization came early, as the laggards closed the largest part of the initial gap. The regional order nonetheless held, which shows that a shrinking overall inequality can sit alongside a stable ranking of regions. By 2023 the regional means stand at 0.780 in the east, 0.723 in the center, and 0.621 in the west, so the three belts remain in the same order even as the overall spread narrows.

Table 5. Dagum Gini decomposition of the coupling coordination degree.

The decomposition is the more instructive part. Across the whole period the between-region share stayed near 85% of total inequality, the within-region share hovered around 15%, and transvariation counted for almost nothing. Within-region disparity here refers to variation among provinces that belong to the same eastern, central, or western belt, and between-region disparity to variation across the three belts. The decomposition is carried out at the provincial level and does not descend to prefectural or city units. What separates provinces in coordinated development, then, is overwhelmingly the gap between the eastern, central, and western belts rather than differences among provinces within a belt. The pattern holds in every one of the thirteen years, which gives H2 firm support. Its policy reading is direct: any further equalization would have to work on inter-regional gaps, since intra-regional differences are already small and contribute little. The dominance of between-region disparity also helps explain why the spatial gradient has proved so durable, because systematic gaps between belts close more slowly than the scattered differences within them.

4.3 Convergence analysis

The convergence tests show provinces moving toward shared levels of coordination, though at speeds that differ by region, as Table 6 reports. The σ-convergence result has the national coefficient of variation falling from 0.316 in 2011 to 0.111 in 2023, a sustained decline confirming that dispersion has eased over time, plotted in Figure 4. Each belt follows the same downward course, and the west shows the steepest early reduction from a wide starting spread, reflecting how far apart its provinces stood at the outset and how much they tightened afterward.

Table 6. Convergence tests.

The conditional national model excludes lagged subsystem scores to avoid mechanical collinearity.

The β-convergence tests reinforce the reading and add a detail. Absolute β-convergence holds nationally, the significantly negative coefficient telling that provinces starting lower grew faster than those starting higher. Conditional β-convergence, allowing for economic and structural differences, holds as well and remains robust once subsystem covariates that would mechanically absorb variation are dropped from the specification. The regional estimates are the telling part. The west converges fastest, with a half-life of about 0.53 years, while the east and center move more slowly, with half-lives of 4.79 and 4.89 years. The contrast is substantive rather than merely numerical. A western province halves its coordination gap in roughly half a year, while an eastern or central province takes close to five years to cover the same ground, so the catch-up dynamic sits mainly in the west and pulls the national profile toward a narrower spread. That the region which lagged catches up most quickly is a familiar mark of latecomer advantage, where a low starting point leaves more room to improve, and it supports H3. Read together, the two tests establish that the narrowing documented above is a systematic catch-up rather than a passing swing, and that it is sharpest where coordination began lowest.

4.4 Obstacle diagnosis

The obstacle-degree diagnosis names the factors that most constrain coordination and shows how they have shifted, reported in Table 7 and Figure 5. Early on, digital finance was the leading obstacle in nearly every province, a reflection of how thin digital financial services still were in 2011, when their rural reach remained slight. As those services spread, the obstacle they posed fell year on year, and by the close of the period the binding constraints lay elsewhere.

Table 7. Obstacle diagnosis (latest year).

In the latest year the foremost obstacle, ranking first in fourteen provinces, was the agriculture-forestry-water expenditure share, trailed by the energy-conservation-and-environmental expenditure share and by fertilizer-use efficiency. The structure of constraints thus migrated from digital finance toward green fiscal support and green technology adoption, which supports H4. In plainer terms, the earlier bottleneck of financial access has largely eased, and the frontier of coordinated development now runs through the strength of green public spending and the efficiency of input use on the farm. The shift is visible across the full period, as the digital-finance band lightens while the fiscal-support and fertilizer-efficiency bands deepen. The diagnosis converts the aggregate index into a more concrete account of where the remaining constraints sit, and it marks public spending and input efficiency as the places where further effort would tell most.

4.5 Association with agricultural carbon emissions

To connect the coordination analysis to the emissions question behind it, the relationship between coordination and agricultural carbon intensity is examined in Table 8. Over the full sample the two are strongly and negatively related, the Pearson coefficient sitting at −0.75 and the Spearman coefficient close by, and the year-by-year correlations stay within a narrow band across all thirteen years, so the relationship is steady rather than the product of a single year. Sorting provinces by coordination quartile yields a monotone fall in carbon intensity from the lowest to the highest group, and the gap between the extreme groups is large and significant under both parametric and non-parametric tests. The reporting is kept consistent with the diagnostic orientation of the study, so the correlation is read as a co-movement between coordinated development and lower emission intensity, and it is deliberately kept apart from causal estimation. Separating the two would call for an identification strategy that the province-year panel used here does not, on its own, deliver.

Table 8. Association between coupling coordination and agricultural carbon intensity.

Q4 vs Q1 difference is significant under both Welch t-test and Mann–Whitney U test, p < 0.001.

The reading calls for care. Provinces with higher coordination among finance, fiscal support, and technology adoption do tend to show lower agricultural carbon intensity, a pattern in line with the expectation that coordinated development travels with lower-emission production. It does not follow that coordination causes the lower emissions. Both coordination and emission intensity respond to shared underlying forces such as the level of economic development, and more developed provinces tend to rank high on coordination and low on intensity at once, so part of the observed link reflects that common background. The province-and-year structure of the data cannot separate these strands. The result is therefore presented as descriptive evidence that coordinated development and lower emission intensity move together, with the causal question left to designs equipped to address it.

5 Discussion

The evidence gathered here describes a transition that advances broadly but stays uneven in shape. Coordination rose steadily from mild imbalance to intermediate coordination on the back of a national expansion in digital finance and green public spending, yet the three subsystems moved at different speeds, and that difference is itself the point. Digital finance climbed from the lowest starting point to the front, while green technology adoption advanced most slowly and slipped to the rear, which is why the main obstacle to coordination drifted over time from financial access toward the spread of green practice in production. The sequence fits an argument already present in the literature, that the emission-relevant contribution of digital finance shows up only once finance turns into altered behavior on the farm, with fertilizer reduction and efficiency gains doing much of the work (Yang and Wen, 2025). The finding that fertilizer-use efficiency now sits among the leading constraints puts empirical weight behind that claim, since the financial and fiscal conditions appear to have outrun the on-farm efficiency they were meant to unlock. Coordinated development, on this reading, is held back less by the supply of finance than by the slower diffusion of the practices that would let finance and public support register as lower emissions.

The disparity and convergence results together sharpen the spatial picture. That gaps between belts dominate inequality while provinces within a belt differ little implies that the relevant divisions are structural features of regional development rather than provincial happenstance, the sort of divisions rooted in durable differences of economic capacity and farming conditions. Coordination studies in other domains report the same persistent eastern lead and marked spatial spread, which lends the reading some external footing (Liu et al., 2023). Parallel evidence on regional green development, where disparities between economic growth and environmental protection are decomposed across provinces, points the same way (Su and Li, 2026), as does work coordinating new infrastructure with inclusive green growth, which finds the widest gaps between rather than within regions (Gao et al., 2025). The convergence evidence tilts the picture in a more hopeful direction. Both σ- and β-convergence hold, the west closing its gap fastest, so the gradient, durable as it is, narrows through the quicker progress of the places that started behind. Analyses of agricultural green productivity describe a similar latecomer dynamic, with western provinces converging rapidly from low bases (Zhu et al., 2022). A study of the Yangtze River Delta reaches the same conclusion at the city scale, where lagging areas close in on the leaders as green technology diffuses (Bao et al., 2023), and a broader account of urban green transition documents convergence alongside persistent spatial disparity (Huo and Jiao, 2026). Convergence has likewise been found in narrower agricultural settings, such as green productivity in pig breeding, where regional gaps close over time (Wang et al., 2023). Set side by side, the fixed gradient and the active convergence suggest that regional gaps close slowly enough to remain visible in any given year yet steadily enough over the full span to reward sustained, differentiated support.

The negative link between coordination and carbon intensity ties these structural findings back to the emissions concern that opened the study, while demanding restraint in interpretation. A correlation this strong and this stable is consistent with the wider literature that connects financial and technological development to lower agricultural emissions (Xu et al., 2024). It cannot, though, be taken as a causal effect. Coordination and emission intensity draw on common determinants, and more developed provinces tend to score high on one and low on the other, so the observed relationship partly restates that shared background. A further note of caution comes from the spatial structure of emissions themselves, since digital finance and green technology have been found to shape carbon efficiency in ways that spill across provincial borders (Zhang and Liu, 2022), and provincial emissions display the kind of spatial correlation that a province-level association cannot register (Wang and Guo, 2022). What the present design can support is the narrower but still useful claim that coordinated development and lower emission intensity tend to travel together, not the stronger claim that the first brings about the second.

Several limits bound these conclusions. The provincial scale aggregates a great deal of heterogeneity and restricts any identification of mechanism, which is why the coordination–emissions link is kept descriptive. Green technology adoption is measured through the consistently available production indicators, so it stands as a reasonable proxy rather than a complete accounting of low-carbon practice. The emission inventory covers input-side sources and leaves out livestock methane, so it speaks more to crop production than to the sector entire. And the study stops short of spatial econometrics, which leaves the spillovers that related work hints at unexplored. These boundaries sketch a fairly clear agenda. Finer data at the county or farm level would tighten both measurement and identification and would let the coordination–emissions link be tested rather than merely observed. Admitting spatial dependence would show whether coordinated development in one province reaches into its neighbors. And identification built on the staggered rollout of relevant reforms would move the analysis from association toward cause. Each of these would build on the descriptive groundwork set down here. One practical route would combine a staggered difference-in-differences design keyed to the phased introduction of green-agriculture pilots and rural digital-finance reforms with a spatial-econometric specification that admits cross-provincial dependence. This would allow the emission-related consequences of coordinated development to be distinguished from common determinants that drive both coordination and intensity at the province level.

6 Conclusion

This study measured the coupling coordination of digital finance, green fiscal support, and green technology adoption across 30 Chinese provinces from 2011 to 2023 and examined its spatiotemporal patterns, regional disparities, convergence, and constraints. Taken together, the results bear out the four hypotheses that guided the analysis. Coordination rose steadily from mild imbalance to intermediate coordination, carried mainly by the rapid expansion of digital finance, while green technology adoption lagged and became, with green fiscal support, the principal constraint that remains. Regional disparities narrowed markedly, yet the differences that persist come overwhelmingly from gaps between the eastern, central, and western belts rather than from variation inside them. Both σ- and β-convergence held, the western provinces catching up fastest, so the gradient is closing through latecomer advantage. Coordination was negatively related to agricultural carbon intensity, an alignment best read as descriptive rather than as an identified causal effect.

A few implications for strengthening coordinated agricultural transition follow from the results. As the binding obstacle has migrated from digital finance toward green fiscal support and on-farm input efficiency, in line with the obstacle diagnosis behind H4, further progress seems to depend less on expanding digital finance and more on channeling fiscal resources into low-carbon agriculture and on lifting the efficiency of fertilizer and film use. Because inequality is largely between belts rather than within them, as reflected in the Dagum decomposition supporting H2, strategies that accelerate coordination in central and western provinces are likely to address the main source of disparity more effectively than uniform national measures. The fast western convergence returned by the beta-tests, in the direction anticipated by H3, suggests that targeted support directed at the lagging western provinces can yield relatively quick gains where coordination began lowest.

The work is bounded by its provincial scale, its descriptive handling of the coordination–emissions relationship, and its silence on spatial dependence and livestock emissions. Studies drawing on finer data, spatial models, and policy-based identification could test the associations reported here for cause, and could carry the analysis from measuring coordinated development toward accounting for what it produces. Laying out where coordination lags and what constrains it, as this study has done, offers a place to begin rather than the last word.

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Footnotes

Edited by: Vijay Singh Meena, ICAR - Mahatma Gandhi Integrated Farming Research Institute, India

Reviewed by: Qian Li, Beijing Technology and Business University, China

Yunxian Yan, Jilin Agriculture University, China