Driving Sustainable Agricultural Development in Hilly Areas: Interaction of Productive Services and Industrial Agglomeration

Abstract

Agricultural transformation is vital to sustainable development, allowing food security to be reconciled with environmental sustainability. However, the complex interplay between agricultural modernization and environmental systems, particularly the role of economic drivers, remains insufficiently understood. This study addresses this gap by analyzing the Fujian Province, a representative hilly region in southern China, during the period 2005–2021. We construct a comprehensive agricultural transformation evaluation index based on the “elements, structure, and function” framework and apply a modified coupling coordination model. Using random-effects and moderation-effect models, we assess the impact of agricultural productive services on this coordination and investigate the moderating role of agricultural industrial agglomeration. Our analysis identifies four distinct types of agricultural transformation in Fujian and shows that the overall coupling coordination degree improved steadily, rising from low to basic coordination over the study period. Agricultural productive services significantly enhance coordination, although their effects vary across transformation types. In addition, agricultural industrial agglomeration amplifies the positive influence of productive services, indicating a synergistic mechanism that supports sustainable agricultural development. These findings provide policy-relevant insights for East Asian economies with similar land endowments as well as for hilly regions worldwide.

1. Introduction

Agricultural transformation is a central component of national and regional modernization and plays a crucial role in achieving the Sustainable Development Goals, particularly SDG 2, which seeks to end hunger, ensure food security, improve nutrition, and promote sustainable agriculture [1]. This transformation typically progresses from rudimentary to advanced stages of agricultural development, driven by technological innovation, economies of scale, and market integration, fundamentally reshaping agricultural production systems [2,3]. It often involves the reallocation of surplus labor from agriculture to non-agricultural sectors and the consolidation of land among specialized farmers, resulting in significant social and economic impacts.

Historically, industrialization has dominated economic growth pathways, but agricultural modernization—epitomized by the Green Revolution—has reconfigured production systems through intensified reliance on machinery, chemical fertilizers, and pesticides [4,5,6]. While this high-input model substantially boosts yields, it concurrently amplifies environmental pressures, including agricultural carbon emissions, soil degradation, water pollution, and biodiversity loss [7,8,9,10]—challenges which have acutely manifested in China, where the sector contributes significantly to national greenhouse gas emissions amid world-leading fertilizer/pesticide usage [10,11], thereby complicating carbon peaking and neutrality targets. Critically, sluggish agricultural transformation may stall broader economic modernization, trapping economies in prolonged primary production [12,13], revealing an urgent trade-off between productivity gains and environmental sustainability that necessitates synergistic approaches that harmonize output with ecological protection [14].

As input patterns and land use patterns shift, agricultural production, ecology, and social functions are systematically impacted to varying degrees [15]. The impacts on the agricultural environment, human health, and rural society are particularly acute [16]. Addressing this complex challenge requires a deeper understanding of the intricate coupling and coordination mechanisms between agricultural transformation and environmental systems. Fortunately, scholars have already begun to explore a number of beneficial practices, such as strengthening sustainable and intensive utilization [17]; increasing farmers’ closeness to nature [18]; and addressing consumers’ concerns about the multifunctional values of agricultural production, culture, ecology, and landscape [19,20,21].

However, comprehensive and dynamic analyses of their integrated coupling coordination remain limited. Agricultural productive services, encompassing pre-harvest, in-harvest, and post-harvest services, have emerged as a crucial mechanism for improving food security, facilitating moderate-scale farming, and promoting green technologies [22,23,24]. APSs replace labor input [25], promoting standardized and sustainable agricultural development [26] and improving farmers’ welfare [27,28], potentially enhancing coupling coordination. Agricultural clusters enable farmers to share resources, obtain expert guidance, and exchange green technologies [29] through social and cultural unity [30], thus maintaining the competitiveness of agricultural development [31] and contributing to the sustainable development of the agricultural industry and the environment. Clearly, these studies offer insights into agricultural transformation and low-carbon agriculture, but the precise mechanisms through which APS influences this coupling coordination, and the ways in which these effects are modulated by industrial agglomeration, remain underexplored in the existing literature.

This study addresses these gaps by investigating the dynamic coupling coordination between agricultural transformation and environmental systems in county-level regions of Fujian Province, a representative hilly area in southern China, from 2005 to 2021. Located along China’s southeastern coast, Fujian is characterized by pronounced ecological concerns, active agricultural transformation, and distinctive hilly terrain. Over 80 percent of Fujian’s land area is covered by hills and mountains, which is well above the national average [32]. Despite its high forest coverage and role as a pilot region for low-carbon agricultural development, Fujian faces severe land constraints, with per capita arable land comprising less than 0.033 hectares, making it one of China’s major net importers of cultivated land [33]. This unique combination of ecological sensitivity, land scarcity, and ongoing agricultural transformation makes Fujian an ideal and representative case study for agricultural economists. It provides valuable insights not only for other hilly regions in China, but also for major East Asian economies and developing countries worldwide that face similar challenges in balancing food security, agricultural modernization, and environmental sustainability under resource constraints.

To facilitate our investigation, we construct a comprehensive agricultural transformation evaluation index system based on the “elements–structure–function” framework and employ a modified coupling coordination model. We utilize random effects and moderation effect models to analyze the mechanisms influencing agricultural productive services (APSs) and the novel moderating role of agricultural industrial agglomeration. Our research makes several significant contributions to the agricultural economics and development economics literature. First, we provide a systematic and dynamic assessment of the coupling coordination in a critical regional context, moving beyond fragmented analyses. Second, we empirically validate the direct positive role of agricultural productive services in enhancing this coordination, enriching the understanding of sustainable agricultural development drivers. Third, and most critically, we identify and quantify the significant moderating effect of agricultural industrial agglomeration, revealing a crucial synergistic mechanism that amplifies the benefits of productive services. This examination of the interaction between productive services and industrial agglomeration provides new insights into the complex economic mechanisms that drive sustainable agricultural development. Finally, by identifying four distinct agricultural transformation types and analyzing the heterogeneous impacts of APSs, this study offers differentiated policy implications for promoting coordinated agricultural and environmental development in diverse regional settings globally.

2. Theoretical Analysis: Unpacking the Mechanisms Driving Coupling Coordination

2.1. The Direct Effect of Agricultural Productive Services on Coupling Coordination: A Transaction Cost and Innovation Diffusion Perspective

Agricultural transformation in China is characterized by smallholder operations and fragmented land use, and thus faces inherent challenges in achieving economies of scale and adopting advanced technologies [34]. The development of factor markets and the increasing participation of farmers in the social division of labor have led to growing differentiation among agricultural households, with many prioritizing non-agricultural employment [35]. In this context, agricultural productive services (APSs) emerge as a crucial mechanism to bridge the gap between small-scale farming and modern agricultural development, and may significantly enhance the coupling coordination degree between agricultural transformation and environmental systems.

Firstly, APS plays a pivotal role in advancing agricultural transformation by addressing the transaction costs and information asymmetries faced by smallholder farmers. By outsourcing mechanized harvesting, crop protection, and other agronomic activities, farmers can achieve more standardized and professionalized production without incurring the high fixed costs of machinery or the learning costs associated with complex technologies [36,37,38]. In Ethiopia, the support services provided to farmers through agricultural cooperatives have effectively improved the technical efficiency of farmers [39]. This not only promotes land transfer and cooperative production, facilitating the transition towards moderate-scale farming, but also contributes to greater intensification and specialization in agriculture. Such specialization, driven by comparative advantages, can enhance overall agricultural productivity and economic welfare for rural households [40]. At the same time, the services provided by agricultural producer organizations can effectively improve market access and provide technology to enhance smallholder agricultural productivity [41,42]. Optimizing resource allocation and production methods improves agricultural productivity and efficiency, which in turn promotes the development of the agricultural sector towards sustainable modernization, strengthening its positive interaction with the environmental system and improving the overall coupling coordination.

Secondly, APSs significantly reduce agricultural carbon intensity, which is vital for enhancing the coupling coordination between agricultural transformation and environmental systems. From an innovation diffusion theory perspective, productive services act as carriers of technical knowledge and modern management practices. They facilitate the adoption of green technologies such as conservation tillage, precision seeding, targeted fertilization, and efficient pesticide application [22,23]. By enabling larger-scale operations, APSs reduce per-unit input costs, encourage the use of advanced agricultural machinery, and improve input allocation efficiency. This leads to a reduction in the intensity of fertilizer and pesticide use, ultimately lowering the amount of carbon emissions produced by the agriculture sector [43,44]. Studies have shown that producer organizations are indispensable for providing technical support to small-scale producers, which is crucial for achieving Sustainable Development Goal 2 by 2030 [45]. Moreover, through a “learning by doing” dynamic, farmers can quickly master advanced knowledge and technologies, improving environmental technical efficiency and further reducing agricultural carbon emissions [46]. APSs reduce agricultural carbon intensity and enhance the promotion of green practices, improving the health and sustainability of environmental systems. The synergistic improvement of agricultural transformation and environmental systems leads to a higher degree of coupling and coordination.

Based on these mechanisms, we propose our first hypothesis:

H1. 

Agricultural productive services positively influence the coupling coordination degree between agricultural transformation and environmental systems.

2.2. The Moderating Effect of Industrial Agglomeration on Coupling Coordination: An Agglomeration Economies and Knowledge Spillover Perspective

Agricultural industrial agglomeration, defined as the geographical concentration of agricultural production and related industries, is critical for rural development and represents an effective pathway for smallholder-dominated countries to achieve agricultural modernization [47]. This study posits that industrial agglomeration plays a significant moderating role in the relationship between agricultural productive services and the coupling coordination degree between agricultural transformation and environmental systems.

Drawing on agglomeration economies theory, industrial agglomeration generates various benefits, including scale economies, specialized labor markets, and knowledge spillovers [48]. When agricultural productive services operate within an agglomerated industrial cluster, these benefits are amplified. Firstly, the concentration of producers and service providers reduces search costs and facilitates the matching of supply and demand for productive services, enhancing their accessibility and efficiency. Secondly, shared infrastructure and a denser network of related industries within the agglomeration accelerate technology spillovers and demonstration effects among producers [49]. This reduces both farmers’ operating costs and the costs of experimenting with new technologies, making green agricultural practices more economically viable and widely adopted. Such dynamics drive structural upgrades in the agricultural sector, improving agricultural productivity and supporting the multifunctionality of agriculture [50]. By fostering an environment where APSs can operate more efficiently and effectively, industrial agglomeration amplifies APSs’ positive effects on agricultural transformation and environmental sustainability. This synergistic interaction directly strengthens the positive influence of productive services, enhancing the overall coupling coordination degree between agricultural transformation and environmental systems.

However, it is also important to acknowledge that in the early or unregulated stages of rapid agricultural agglomeration, negative externalities may arise, potentially weakening the coupling coordination. These include congestion (e.g., increased competition for resources, rising land prices) and siphoning effects (e.g., key production factors like technology, talent, and capital flowing from surrounding areas to the agglomerated core, hindering endogenous development in peripheral regions) [51]. Farmers, influenced by the pursuit of short-term profits within a competitive environment, might overuse inputs if not properly regulated, leading to increased agricultural carbon intensity. These negative externalities can detrimentally impact the environmental system and, by extension, the overall coupling coordination degree, potentially offsetting some of the positive gains from agricultural transformation. Therefore, the net moderating effect of industrial agglomeration depends on its stage and quality of development. In a well-managed agglomeration, the positive effects of scale economies, technological demonstration, and diffusion are expected to outweigh the negative externalities, thereby strengthening the positive impact of productive services on both agricultural transformation and environmental systems, and ultimately enhancing the coupling coordination degree.

Based on these considerations, we propose our second hypothesis:

H2. 

Agricultural industrial agglomeration positively moderates the effect of agricultural productive services on the coupling coordination degree between agricultural transformation and environmental systems.

3. Methods and Data

3.1. Methods

3.1.1. Construction of an Agricultural Transformation Indicator System

Huttunen [52] argues that the agricultural transformation system is formed through the interaction between elements and structure. External input factors are influenced by socioeconomic development, which in turn shapes the structure of agricultural production. Changes in agricultural production structure also affect the allocation and use of land, labor, and inputs. However, shifts in factor inputs may either improve or degrade agricultural productivity and environmental quality, with corresponding changes in the multifunctionality of agriculture. Based on county-level locational characteristics, resource endowments, and agricultural foundations, and drawing on the theoretical framework presented by Long et al. [53], rural areas are not only key vehicles for ensuring agricultural product supply, maintaining cultural heritage, and providing ecological services; they are also crucial spaces for absorbing urban resource overflow and easing rural population displacement. Therefore, building on the three major functions of production, society, and ecology, this article further incorporates the function of urban–rural transformation to improve the analytical framework, constructing an agricultural transformation evaluation index system along three dimensions: factors, structure, and functions. The specific indicators used are presented in

Table 1. Evaluation index system for agricultural production transformation.

Framework		Indicator	Unit	Calculation Method
Elements	Input	Fertilizer application per mu	kg	Fertilizer application amount (in pure active ingredients) per sown crop area
		Pesticide application per mu	kg	Pesticide application amount per sown crop area
		Agricultural film usage per mu	kg	Agricultural film usage per sown crop area
		Total agricultural machinery power per mu	kw	(Total agricultural machinery power (arable land area/(arable land area + forest land area)))/sown crop area
Structure	Production structure	Specialization index	—	${\displaystyle \sum _{j=1}^n{(S_{ij})}^2}$ where Sij represents the proportion of the sown area of crop j in county I; n denotes the number of crop types.
		Proportion of grain crop sown area	%	Proportion of grain crop sowing area to total crop sowing area
Functions	Production function	Cultivated land area per laborer	ha	Cultivated land area per agricultural, forestry, animal husbandry, and fishery workforce
		Cropping intensity index	—	Crop sowing area per unit of cultivated land area
	Social function	Income equity index	%	Per capita disposable income of rural residents compared to that of urban residents
	Ecological function	Biomass index	—	Calculated according to the technical specification for ecological environment status evaluation
		Total ecosystem service value	CNY/hm	Accounted according to land use type classification
		Net primary productivity (NPP)	g C/m	Data product
	Urban–rural transformation function	Urbanization rate of population	%	Urban population as a proportion of total population

.

The biomass is calculated based on the Technical Specification for Ecological Environment Status Assessment (HJ 192–2015) [54], and the formula is as follows:

$$BI=A_{bio}\times (0.35\times S_1+0.21\times S_2+0.28\times S_3+0.11\times S_4+0.04\times S_5+0.01\times S_6)/{\displaystyle \sum _{i=1}^6{S}_i}$$

(1)

where A_bio is the normalized coefficient of the habitat quality index, with a reference value of 511.2642131067. $S_1$, $S_2$, $S_3$, $S_4$, $S_5$, $S_6$ represent the areas (in hectares) of forest land, grassland, water area and wetland, cultivated land, construction land, and unused land, respectively.

The calculation formula for ecosystem service value is

$$ESV={\displaystyle \sum \left(V{C}_i\times S_i\right)}$$

(2)

where $V{C}_i$ is the ecological service value coefficient (CNY/hectare) for land type $i$. The ecological service values of land use types are determined based on [55,56], with particular focus on Fujian Province and the southern hilly mountainous regions.

To eliminate the impact of the original data’s units on the evaluation results, the extreme value method is used to standardize both positive and negative indicators. Following standardization, the entropy weight method is used to determine the weight of each indicator. The agricultural transformation is then evaluated using the following formula:

$$F_m={\displaystyle \sum _{j=1}^n{W}_j{T}_{mj}}$$

(3)

where $F_m$ represents the agricultural transformation index of county $m$; $W_j$ represents the weight of the j-th evaluation indicator; and $T_{mj}$ represents the standardized value of the j-th indicator in county $m$.

3.1.2. Coupling Coordination Model

The coupling coordination degree model is used to assess the interaction and coordination levels between systems as well as among the elements within those systems [57]. It mainly consists of two parts: the coupling degree and the coordination degree. The coupling degree reflects the extent of interaction between systems or among elements within a system, while the coordination degree measures how well these systems coordinate and cooperate during their development, indicating the quality of their coordinated state. Since the traditional coupling coordination degree values are unevenly distributed within the [0, 1] interval, often skewed toward the upper bound of 1, this study adopts a modified coupling coordination degree model following the approach of Wang et al. [58] to calculate the coupling coordination degree between agricultural transformation and the environmental system. When the number of subsystems n = 2, the modified coupling coordination degree model is shown in Equations (4)–(6).

$$C=\sqrt{[1-(U_2-U_1)]\times {\displaystyle \frac{U_1}{U_2}}}$$

(4)

$$T={\alpha }_1{U}_1+{\alpha }_2{U}_2,{\alpha }_1+{\alpha }_2=1$$

(5)

$$D=\sqrt{C\times T}$$

(6)

where $U_i(i=1,2)$ represents the comprehensive index of agricultural transformation and agricultural environment system, and it is assumed that $\max U_i$ is $U_2$; $C$ denotes the coupling degree between the agricultural transformation and environmental systems; $T$ represents the coordination degree between these two systems; $D$ indicates the overall coupling coordination degree of the agricultural transformation and environmental systems; and ${\alpha }_1$ and ${\alpha }_2$ correspond to the relative importance weights of the agricultural transformation and environmental systems.

The coupling degree reflects the extent of mutual influence between the agricultural transformation system and the agricultural environmental system. It can be categorized into four stages: the low coupling stage (0.0 to 0.3), the antagonistic stage (0.3 to 0.6), the adjustment stage (0.6 to 0.8), and the high coupling stage (0.8 to 1.0). In this study, ${\alpha }_1$ = ${\alpha }_2$ = 0.5. The coupling coordination degree is divided into five categories. This study divides the coupling coordination degree into five levels. When the value falls within the following ranges, the coupling coordination level is classified as:

(0.0, 0.2]: Severe disorder.

(0.2, 0.4]: Disorder.

(0.4, 0.6]: Low-level coordination.

(0.6, 0.8]: Basic coordination.

(0.8, 1.0]: High-level coordination.

3.1.3. Econometric Model

To ensure the accuracy of the regression results, some variables are transformed using their logarithmic forms. The regression model between agricultural productive services and the coupling coordination degree of agricultural transformation and environmental systems is constructed as follows:

$$C{c}_{it}={\alpha }_0+{\alpha }_{1}Ap{s}_{it}+{\displaystyle \sum _{k=1}^i{\beta }_k}Control{s}_{kit}+{\mu }_i+{\nu }_t+{\epsilon }_{it}$$

(7)

where the subscript $i$ represents different regions and $t$ represents different years; $Cc$ denotes the coupling coordination degree of the system; $Aps$ represents the level of agricultural productive services; $Controls$ stands for control variables; $\mu$, $\nu$ represent individual and time fixed effects, respectively; $\epsilon$ is the random error term; and $\alpha$ and $\beta$ are the coefficients to be estimated.

To further explore the mechanism by which agricultural productive services and industrial agglomeration affect the system coupling coordination degree, a moderation effect model is constructed as follows:

$$C{c}_{it}={\alpha }_0+{\alpha }_{1}Ap{s}_{it}+{\alpha }_{2}L{q}_{it}+{\displaystyle \sum _{k=1}^n{\beta }_k}Control{s}_{kit}+{\mu }_i+{\nu }_t+{\epsilon }_{it}$$

(8)

$$C{c}_{it}={\alpha }_0+{\alpha }_{1}Ap{s}_{it}+{\alpha }_{2}L{q}_{it}+{\alpha }_{3}Ap{s}_{it}\times L{q}_{it}+{\displaystyle \sum _{k=1}^n{\beta }_k}Control{s}_{kit}+{\mu }_i+{\nu }_t+{\epsilon }_{it}$$

(9)

where $Lq$ represents the moderating variable and $Aps\times Lq$ denotes the interaction between the core explanatory variable agricultural productive services and the moderating variable.

3.2. Variables

3.2.1. Variable Selection

(1) Dependent variables

Coupling Coordination Degree between Agricultural Transformation and Environmental Systems (Cc). The agricultural transformation index is calculated using the indicators in Table 1 and weighted through the entropy method. The environmental index is measured by agricultural carbon intensity, defined as the amount of carbon emissions per CNY 10,000 of agricultural GDP. Agricultural carbon emissions include emissions from crop-related greenhouse gases (such as methane emissions from rice paddies and nitrous oxide emissions from croplands), input-related emissions, agricultural energy consumption, and emissions from cultivation and land management practices. The crop category includes rice, tubers, oil crops, vegetables, and other dryland crops. Inputs refer to the usage of chemical fertilizers, pesticides, and agricultural plastic films. Energy consumption includes agricultural diesel fuel and electricity used for irrigation. Tillage is used to represent land management and is measured by the total sown area of crops. Carbon emission factors for each source are derived from the Intergovernmental Panel on Climate Change guidelines and estimates provided by Oak Ridge National Laboratory and their studies [59,60]. The coupling coordination degree between agricultural transformation and environmental systems is calculated using a coupling coordination model.

(2) Independent variables

The level of agricultural productive services is measured by the ratio of the value of productive services to the total agricultural output. A higher ratio indicates a more advanced development of agricultural productive services in the region.

(3) Mediating variables

The location quotient index is used to measure the level of agricultural industrial agglomeration. This index evaluates the degree of agglomeration by comparing the industrial structure of a specific region with the average structure of a higher-level region. It is commonly used to assess the extent to which a particular industry is concentrated in different regions and to identify the dominant industries within a given area.

$$L{q}_{it}=(a_{it}/gd{p}_{it})/(A_t/GD{P}_t)$$

(10)

where $L{q}_{it}$ represents the location quotient of the agricultural industry in region $i$ in year $t$; meanwhile, $a_{it}$ and $gd{p}_{it}$ indicate the agricultural output value and total regional GDP in region $i$ in year $t$, respectively. $A_t$ represents the agricultural output value and total GDP of the higher-level region in year i, respectively. A higher $L{q}_{it}$ value indicates a greater degree of agricultural industrial clustering.

(4) Control variables

Agricultural transformation and environmental systems do not evolve in isolation within the agricultural sector. Rather, they are embedded within a broader framework involving institutional arrangements, productivity levels, production modes, and inter-agent relationships, and are influenced by socioeconomic conditions, land endowments, and climatic factors. Accordingly, this study incorporates the following control variables.

Socioeconomic development variables: Agricultural transformation is deeply intertwined with the broader economic transition of a country or region [1] (Jayne et al., 2018). Three variables are used to capture socioeconomic conditions: the level of economic development (Ecd), the level of non-agricultural employment (Nfe), and public financial support for agriculture (Agf). Public financial support is measured by the share of government expenditure allocated to agriculture. Non-agricultural employment is represented by the share of employment in agriculture, forestry, animal husbandry, and fishery in total employment, and economic development is captured by regional per capita GDP.

Agricultural land conditions: Two variables are included: cultivated land production potential (Clpt) and farmland cultivation conditions (Flcs). Land production potential is measured by the maximum attainable yield per unit of land area per year. Cultivation conditions are measured by the ratio of effectively irrigated area to the total sown area.

Climate change variables. Two climate variables are selected: temperature (Temp) and precipitation (Perc). To better assess their effects on crop growth, accumulated annual temperature is used to represent temperature. Climate change is measured using standardized temperature [61] and precipitation based on a 20-year historical reference period.

3.2.2. Data Sources

This study focuses on 61 county-level administrative units in Fujian Province, including 9 municipal districts and 52 counties, from 2005 to 2021. Fujian is located in a subtropical monsoon climate zone with favorable hydrothermal conditions. The arable land in the province is spatially distributed with a greater concentration in the southeastern region and a more fragmented pattern in the northwest (see Figure 1). The agricultural production-related and economic data for this study are primarily drawn from the Fujian Statistical Yearbook and the Fujian Rural Statistical Yearbook of the corresponding years. Missing values are supplemented using interpolation methods. Data on land use types, net primary productivity of vegetation, climate, and farmland production potential are obtained from the Resource and Environmental Science and Data Center of the Chinese Academy of Sciences (http://www.resdc.cn/), with spatial resolutions of 30 m, 1 km, 1 km, and 1 km, respectively. The land use categories include cultivated land, forest land, grassland, wetlands, water bodies, built-up areas, and unused land. Using ArcMap 10.2, a vector database of land use for each county in Fujian Province is constructed. All price-related variables are deflated to constant prices, with 2005 as the base year.

4. Results

In this section, we present the empirical findings of our study, encompassing the characteristics of agricultural transformation, the state of the agricultural environment, the dynamics of coupling coordination, and the econometric analysis of the influence mechanisms.

4.1. Analysis of Agricultural Transformation and Environmental Systems

4.1.1. Agricultural Transformation Characteristics

Based on a natural breaks classification of the agricultural transformation evaluation index, from 2005 to 2021, Fujian Province’s agricultural transformation exhibited three distinct levels: low, medium, and high. By further integrating the score characteristics of the “elements–structure–function” dimensions with regional economic and social foundations, agricultural resource endowments, and primary functional orientations, we identified four distinct types of agricultural transformation. Using data from 2021 as an illustrative example, these types are visualized in Figure 2 (e.g., a map showing distribution) and summarized below:

(1) Small-scale specialized with multifunctional disadvantages (Type 1): This type is predominantly found in 12 counties and cities in the northeast and southeast. It is characterized by small areas of per capita cultivated land, moderate specialization, and grain-sowing area, with relatively weak production, social security, and urban–rural transformation support functions. These regions face challenges in achieving economies of scale and diversifying their income, leading to a less robust agricultural system and potentially higher environmental costs per unit of output.

(2) Small-scale diversified with multifunctional improvement (Type 2): This type is primarily located in 12 counties dominated by municipal districts. Features small areas of per capita cultivated land, low specialization, and a small grain-sowing area, alongside a weak cultivated land production function. However, it exhibits moderate social security, ecological conservation, and urban–rural transformation support functions, indicating a strategic shift towards high-value, diversified agriculture influenced by urban market demand and an emphasis on environmental quality.

(3) Small-scale specialized with multifunctional improvement (Type 3): This type is mainly distributed across 26 counties in central Ningde; northern and southern Fuzhou; Quanzhou; and Sanming. These regions are characterized by limited per capita cultivated land, moderate specialization, and an average grain-sowing area, along with medium performance across various agricultural functions. They represent a transitional stage that balances traditional grain production with emerging specialized agriculture, reflecting a gradual process of structural adjustment.

(4) Medium-scale specialized with multifunctional advantages (Type 4): This type is mainly found in western Sanming and western Longyan. It features a larger area of per capita cultivated land, higher specialization, and a greater grain-sowing area, alongside strong grain production, social security, and ecological conservation functions. These regions benefit from favorable land endowments, enabling larger-scale operations and an integrated approach to agricultural development, contributing significantly to regional food security and environmental protection.

This typology offers a nuanced understanding of agricultural transformation pathways in hilly regions, moving beyond a monolithic view and highlighting the importance of context-specific analysis for policy formulation.

4.1.2. Agricultural Environment Analysis

From 2005 to 2021, the intensity of agricultural carbon emissions produced in Fujian Province showed a steady downward trend, indicating progress towards environmental sustainability in the agricultural sector (see Figure 3). The proportion of counties with carbon emission intensity below 50 tons per CNY 10,000 of agricultural GDP significantly increased from 6.5% to 52.5% during the study period. Spatially, low-intensity areas were concentrated along the coast, whereas high-intensity areas were located inland, reflecting regional disparities in agricultural development, resource endowments, and the adoption of green technologies (see Figure 3).

The agricultural carbon emission growth rate generally exhibited a trend of increasing and then decreasing across three phases: 5.18% during 2006–2011; 6.03% during 2011–2016; and 1.27% during 2016–2021. Spatially, counties showed significant variations in carbon emission growth rates, with the proportion of counties experiencing negative growth steadily increasing from 23.0% to 36.1%. Declining carbon emission rates were initially concentrated in eastern Fujian (e.g., Ningde, Fuzhou, Putian) and southern cities (e.g., Xiamen, Zhangzhou) from 2005 to 2011, expanding into northern and western parts from 2011 to 2016, and further increasing in western and southern regions by 2016–2021. This spatial pattern corresponds to the distribution of agricultural resource endowments and economic development conditions, underscoring their critical role in shaping environmental outcomes.

4.1.3. Coupling Coordination Degree Analysis

From 2005 to 2021, the overall coupling coordination degree between agricultural transformation and the environmental system in Fujian steadily improved, progressing from low-level to basic coordination (see Figure 4 for overall trend). This indicates an increasingly positive interaction and synergistic development, with the coupling stage progressing from an antagonistic phase to a running-in phase, reflecting significantly enhanced coordination. This improvement highlights the effectiveness of integrated policies that balance agricultural growth with environmental protection.

In an analysis based on the agricultural transformation type, all four categories showed an upward trend in the degree of coupling coordination, albeit with varying rates and levels (see Figure 5 for type-specific trends):

Type 1 (small-scale specialized with multifunctional disadvantages): An increase from 0.563 to 0.771 was observed. Despite exhibiting a slower transformation, the focus on food security and productive services promoted specialization and the adoption of green technologies, resulting in reduced agricultural carbon emission intensity and improved coordination. Targeted interventions appear to be effective in these disadvantaged regions.

Type 2 (small-scale diversified with multifunctional improvement): An increase from 0.660 to 0.787 was recorded. These municipal districts, with commercialized and high-value agriculture, enhanced multifunctionality. Effective environmental policies led to notable improvements and a relatively high degree of coordination, though with the lowest average annual growth rate, suggesting a more mature, slower path towards high-quality coordination.

Type 3 (small-scale specialized with multifunctional improvement): We recorded an increase from 0.545 to 0.717. Agricultural production shifted toward modern, input-intensive systems. Although the overall pace of transformation was relatively slow, a notable decline in grain-sown area and a modest reduction in chemical inputs substantially lowered the agricultural carbon emission intensity. Nevertheless, the overall coordination level remained relatively low, underscoring the challenges of balancing intensification with environmental objectives.

Type 4 (medium-scale specialized with multifunctional advantages): An increase from 0.521 to 0.717 was observed. As a key grain and economic crop production area, this region benefited from favorable land endowments and national emphasis on food production. Accelerated agricultural transformation and green technologies enhanced scale effects and significantly reduced agricultural carbon emission intensity. Although the coordination level remained low, it experienced the highest average annual growth rate, indicating rapid progress from a lower starting point.

4.2. Econometric Regression Results

The Hausman test indicated that the random effects model is more efficient for our panel data analysis. We thus adopted the random effects model as the baseline and conducted empirical analysis using a stepwise regression approach (see

Table 2. Regression results of agricultural productive services and system coupling and coordination.

Variable	Cc	Cc	Cc	Cc
Aps	0.002 **	0.003 **	0.005 ***	0.004 ***
	(0.001)	(0.002)	(0.002)	(0.002)
Ecd		0.011 ***	0.009 ***	0.011 ***
		(0.003)	(0.003)	(0.003)
Nfe		0.148 ***	0.143 ***	0.130 ***
		(0.017)	(0.017)	(0.017)
Agf		0.467 ***	0.470 ***	0.459 ***
		(0.060)	(0.065)	(0.065)
Clpt			0.007	0.003
			(0.006)	(0.006)
Flc			−0.043 ***	−0.050 ***
			(0.014)	(0.014)
Temp				0.001
				(0.001)
Perc				−0.009 ***
				(0.002)
_cons	0.362 ***	0.236 ***	0.197 ***	0.252 ***
	(0.007)	(0.015)	(0.053)	(0.058)
N	1037	1037	1037	1037

Note: *** and ** denotes statistical significance at the 1% and 5% level; and the values in parentheses are the corresponding standard errors.

for full regression results).

4.2.1. Direct Effect of Agricultural Productive Services (APSs)

The coefficient of agricultural productive services (APSs) is consistently positive and statistically significant across all model specifications (e.g., 0.005 *** in the full model, as shown in Table 2). This robust finding provides strong empirical support for Hypothesis 1 (H1), which posits that agricultural productive services positively influence the coupling coordination degree between agricultural transformation and environmental systems. This suggests that by facilitating efficient resource allocation, promoting moderate-scale farming, and enabling green technology adoption, APSs are a crucial economic driver for achieving synergistic development in agriculture.

Regarding the control variables (Table 2), we drew the following conclusions.

Socioeconomic development variables: Higher levels of economic development (Ecd), non-agricultural employment (Nfe), and public financial support for agriculture (Agf) significantly and positively influence coupling coordination. These factors facilitate agricultural transformation, drive changes in production structure, encourage professional farmers to adopt green technologies, and strengthen their capacity for sustainable practices.

Agricultural land conditions: Higher land production potential (Clpt) tends to lead farmers to increase external input use, potentially leading to excessive chemical use and negatively affecting system coordination. Conversely, better farmland cultivation conditions (Flcs) enable farmers to optimize production structures and diversify agricultural functions, helping to reduce carbon intensity and promote coordination.

Climate change variables: Precipitation variability (Perc) negatively impacts agricultural planning and slows transformation, hindering coordination. The coefficient for standardized accumulated temperature (Temp) is positive but not statistically significant.

The impact of agricultural productive services on coupling coordination exhibits a lagged effect (

Table 3. Robustness tests.

Variable	Cc.L	Cc
		First-Stage Regression	Second-Stage Regression
Asp	0.003 *		0.008 **
	(0.002)		(0.012)
Mpper		0.835 ***
		(0.000)
_cons	0.259 ***	0.360 ***	0.481 ***
	(0.062)	(0.000)	(0.003)
Control variable	Yes	Yes	Yes

Note: ***, ** and * denotes statistical significance at the 1%, 5% and 10% level; and the values in parentheses are the corresponding standard errors.

Column 2), providing further confirmation of its long-term positive influence. To ensure robustness and address potential endogeneity, an instrumental variable (per capita mechanical power in 1991) approach was employed. Using two-stage least squares (2SLSs), the regression coefficient of the instrumental variable was significant at the 1% level, and the APS coefficient remained significant at the 5% level (Table 3 Column 3–4). The estimated coefficient increased relative to the baseline, indicating that the baseline model may have underestimated the positive effect of APS due to endogeneity.

4.2.2. Heterogeneity Analysis of APS Impact

To further explore the heterogeneous impact of APS on coupling coordination across different agricultural transformation categories, Equation (7) was analyzed for each of the four identified types (see

Table 4. Heterogeneity tests.

Variable	Cc	Cc	Cc	Cc
Aps	0.006 **	0.003 ***	0.002 **	0.002 **
	(0.003)	(0.001)	(0.001)	(0.001)
Controls	Yes	Yes	Yes	Yes
_cons	−0.012	0.390 ***	0.162 ***	−1.023 ***
	(0.134)	(0.105)	(0.011)	(0.321)
N	204	204	442	187

Note: *** and ** denotes statistical significance at the 1% and 5% level; and the values in parentheses are the corresponding standard errors.

for type-specific regression results). The regression coefficients of APSs on coupling coordination are all significant at the 5% level or higher across all types. Notably, their promoting effect is stronger in agricultural transformations characterized by a small scale and diversification (Type 1 and Type 2) than in those characterized by a medium scale and specialization (Type 3 and Type 4). This finding provides novel insights into the context-specific effectiveness of APSs.

Specifically, we determined the following:

Type 1 (small-scale specialized with multifunctional disadvantages): The estimated coefficient is 0.006 and significant at the 5% level. APSs development has facilitated mechanized production, promoted specialization and moderate-scale operations, optimized resource allocation, and reduced agricultural carbon emission intensity. This pronounced effect indicates that APSs are especially effective at overcoming inherent disadvantages in these regions, where market failures or resource constraints make their benefits more prominent.

Type 2 (small-scale diversified with multifunctional improvement): The estimated coefficient is 0.003 and significant at the 1% level. This type, often comprising urban districts, benefits from locational advantages that promote diversified planting and agricultural multifunctionality. Farmers have greater access to and acceptance of green technologies, which helps reduce agricultural carbon emission intensity. The positive effect, while significant, is slightly less pronounced than in Type 1, possibly due to the higher baseline coordination levels and more developed agricultural structure.

Type 3 (small-scale specialized with multifunctional improvement): The estimated coefficient is 0.002 and is significant at the 1% level. Although agricultural specialization levels vary, this transformation zone’s better economic foundation leads to a “de-grainization” trend focusing on higher-value economic crops, somewhat reducing agricultural carbon emission intensity. The positive impact of APSs is still present, but its marginal effects might be less significant in these more economically developed areas.

Type 4 (medium-scale specialized with multifunctional advantages): The estimated coefficient is 0.002 and significance is at the 1% level. As a major production area for grain and economic crops, the development of APSs has further concentrated agricultural input factors, ensuring both production and social security functions, while effectively promoting the adoption of green agricultural technologies and significantly reducing agricultural carbon emission intensity.

The consistent positive effect across all types underscores the universal importance of APSs, while the varying magnitudes highlight the need for differentiated policy approaches tailored to specific agricultural transformation contexts.

4.2.3. Moderating Effect of Industrial Agglomeration

To investigate the moderating role of industrial agglomeration in the impact of APSs on coupling coordination, a moderation effect model based on Equation (9) was constructed (see

Table 5. Moderation effect tests.

Variable	Cc	Cc
Aps	0.004 **	0.006 ***
	(0.002)	(0.002)
Lq	−0.020 ***	−0.015 ***
	(0.004)	(0.005)
Aps × Lq		0.002 **
		(0.001)
_cons	0.254 ***	0.245 ***
	(0.056)	(0.057)
N	1037	1037

Note: *** and ** denotes statistical significance at the 1% and 5% level; and the values in parentheses are the corresponding standard errors.

for moderation effect results).

After controlling for the moderating variable of industrial agglomeration, APSs still have a significant positive effect on the system’s coupling coordination degree. Industrial agglomeration itself also exerts a significant influence. Crucially, the interaction term between productive services and industrial agglomeration (Aps × Lq) is positive and statistically significant (0.002 **). This finding provides strong empirical support for Hypothesis 2 (H2), which posits that agricultural industrial agglomeration positively moderates the effect of agricultural productive services on the coupling coordination degree.

The underlying mechanism is that, during the development of agricultural industrial agglomeration in Fujian, challenges such as imperfect infrastructure and difficulties in promoting new technologies may exist. However, the growth of productive services within agglomerated areas enhances scale effects, technological demonstration, and diffusion, reducing both farmers’ operating costs and the costs of experimenting with new technologies. This, in turn, promotes agricultural transformation, lowers agricultural carbon emission intensity, and improves the system’s coupling coordination. These findings highlight a critical synergistic mechanism: the benefits of agricultural productive services are amplified within a strong industrial agglomeration context, offering a pathway for more efficient and sustainable agricultural development. This interaction effect represents a novel contribution, demonstrating how spatial economic organization can enhance the effectiveness of agricultural support services.

5. Conclusions and Policy Implications

This study rigorously examines the dynamic coupling coordination between agricultural transformation and environmental systems across 61 county-level administrative units in Fujian Province from 2005 to 2021. Employing a comprehensive “elements–structure–function” framework and advanced econometric models, we analyze the influence mechanisms of agricultural productive services (APSs) and the crucial moderating role of agricultural industrial agglomeration.

5.1. Key Findings

Our key findings are as follows:

First, the coupling coordination degree between agricultural transformation and environmental systems in Fujian has steadily improved throughout the study period, progressing from a low-level to a basic coordination. This indicates a positive trend towards synergistic development between agricultural modernization and ecological protection in this representative hilly region. Concurrently, agricultural carbon emission intensity has shown a consistent decline, reflecting progress in green agricultural development.

Second, our analysis reveals four distinct agricultural transformation types in Fujian: small-scale specialized with multifunctional disadvantages (Type 1), small-scale diversified with multifunctional improvements (Type 2), small-scale specialized with multifunctional improvements (Type 3), and medium-scale specialized with multifunctional advantages (Type 4). This typology provides a nuanced understanding of the diverse pathways of agricultural modernization in resource-constrained environments.

Third, agricultural productive services (APSs) are found to positively influence the coupling coordination between agricultural transformation and the environmental system. However, this promoting effect varies considerably across different agricultural transformation types, being more pronounced in regions characterized by small-scale and diversified features (Type 1 and Type 2). This highlights the critical role of APS as a crucial link for smallholder farmers, enabling them to overcome scale disadvantages and adopt more sustainable practices.

Fourth, and importantly, agricultural industrial agglomeration significantly enhances the positive effects of productive services on coupling coordination. In regions with higher agglomeration, the scale economies, technological diffusion, and demonstration effects of productive services are amplified. This synergistic interaction lowers farmers’ operating costs and the costs of experimenting with new technologies, thereby accelerating agricultural transformation, reducing agricultural carbon emission intensity, and ultimately improving the system’s coupling coordination.

5.2. Theoretical Contributions

Beyond its practical implications, this study makes several significant theoretical contributions to the agricultural economics and development economics literature:

Integrated assessment framework: By constructing a comprehensive evaluation system and applying a modified coupling coordination model, we provide a robust framework for quantifying and dynamically assessing the intricate interplay between agricultural transformation and environmental systems, moving beyond fragmented analyses.

Validation of the direct promoting effect of APSs: Our findings empirically validate the direct positive role of agricultural productive services in enhancing this coupling coordination, enriching the understanding of sustainable agricultural development drivers.

Elucidation of moderating mechanisms of industrial agglomeration: Crucially, demonstrating agricultural industrial agglomeration’s moderating effect extends our understanding of how agglomeration economies and knowledge diffusion amplify productive services’ benefits, offering a refined view of their synergy.

Unveiling contextual transformation pathways: Lastly, the identification of four distinct agricultural transformation types and the heterogeneity analysis of APS’s impact reveal context-specific agricultural modernization pathways, demonstrating that intervention effectiveness depends on regional endowments and development traits. This advances a more refined, contingency-based theory of agricultural transformation.

5.3. Policy Implications

Based on these conclusions, this study offers valuable policy implications for agricultural transformation in major East Asian economies with similar land endowments to China, as well as for hilly regions worldwide:

(1) Implement differentiated agricultural transformation strategies: Recognize that regional resource endowments are key drivers of agricultural transformation characteristics.

For municipal districts with low specialization and grain planting proportions (e.g., Type 2), prioritize multifunctional agricultural development, leveraging their proximity to urban markets for high-value, diversified agriculture.

In inland areas with a larger amount of per capita arable land and higher specialization (e.g., Type 4), reinforce their role as major grain-producing regions while integrating green technologies.

Coastal areas with moderate specialization and a trend toward modern intensive agriculture (e.g., Type 3) should strengthen sustainable and intensive agricultural management practices.

Tailoring transformation paths to unique regional characteristics is crucial for maximizing efficiency and sustainability.

(2) Optimize agricultural input management for environmental impact reduction: Fertilizers, pesticides, and energy are major sources of agricultural carbon emissions. While ensuring food security, it is necessary to continuously expand the application of soil-testing-based formula fertilization, integrated pest management (combining green control with coordinated prevention and treatment), and clean energy technologies. These measures aim to optimize fertilizer use, reduce pesticide application while improving effectiveness, and increase the use of green energy, thereby minimizing negative environmental impacts and promoting a low-carbon agricultural system.

(3) Broaden the application of agricultural productive services: Promoting agricultural productive services is a key factor in improving the coordination between agricultural transformation and environmental systems. Given that agricultural transformation is embedded within broader social transformation, policy attention should also extend to how economic development, fiscal support, and non-agricultural employment policies influence agricultural input, production organization, and operational modes. Agricultural transformation should be guided by regional resource endowments and industrial foundations, actively mitigating any negative impacts on agricultural carbon emissions to promote sustainable development.

(4) Leverage agricultural industrial clustering while mitigating risks: Given the significant spatial spillover effects of agricultural clusters, productive services in these areas can optimize the allocation of agricultural inputs, improve resource efficiency, and enhance economies of scale, knowledge spillovers, and demonstration effects. Therefore, agricultural industrial clustering should be strategically developed to avoid negative outcomes such as siphoning and congestion, which could hinder coupling coordination between agricultural transformation and environmental systems. Policies should promote well-managed agricultural industrial parks that integrate production, processing, and services, fostering a virtuous cycle of innovation and sustainable growth.

5.4. Limitations and Future Research

This study has the following limitations, which should be addressed in future research: First, in terms of carbon emission accounting, this study primarily considers crop production and does not include greenhouse gas emissions from animal husbandry and the entire food chain. Future research could expand this coverage to the entire food system to more comprehensively capture carbon emission patterns. Second, although the positive effects of APSs have been confirmed, their impacts vary across different types of agricultural transformation. This study fails to propose strategies to promote the coordinated coupling of agricultural transformation and the environment in regions where the impact of APSs is weaker. Future research should further analyze the structural barriers or policy transmission inefficiencies in these regions and explore more targeted interventions. Third, regarding the regulatory role of agricultural industrial agglomeration, this study emphasizes its positive synergistic effects. However, it fails to fully quantify the potential negative externalities that may arise in the early stages of agglomeration. Future research should more carefully assess these externalities and explore strategies to avoid or mitigate their impacts. Finally, due to the land size constraints in Fujian Province, there is no classification of large-scale agricultural production in this study. Future research could consider data from a wider geographic area or longer time series to capture agricultural production patterns of varying scales and types.