Efficiency Evaluation and Regional Disparities of Green Agricultural Product Supply Chains: A Case Study of Hebei Province, China

Abstract

Building a sustainable and efficient green agricultural product supply chain (GASC) is crucial for ensuring global food security and promoting environmental sustainability. However, at the regional level, the spatial differentiation patterns of its efficiency and underlying driving mechanisms—particularly the synergistic relationship between technical efficiency and scale efficiency—remain to be elucidated. This study focuses on Hebei Province, a key agricultural region in China. By constructing a multidimensional evaluation index system and employing a two-stage approach combining Principal Component Analysis (PCA) with Data Envelopment Analysis (DEA), we measure and analyze the operational efficiency and regional disparities of green agricultural product supply chains across 11 prefecture-level cities. Findings revealed that the overall efficiency of Hebei’s green agricultural product supply chains required improvement and exhibited a distinct spatial pattern characterized by “high-efficiency dominance with localized lags.” The core bottleneck lies in the failure of most regions to achieve effective synergy between technology and scale, resulting in widespread resource misallocation—either “technology without scale” or “scale without technology”—and causing some areas to experience diminishing returns to scale. Furthermore, excessive reliance on single factor advantages in many cities reveals structural vulnerabilities within their supply chain systems. This study’s primary contribution lies in deepening the understanding that efficiency cannot be driven by technology or scale alone. It theoretically emphasizes that the synergistic coupling of “technology-scale” is key to enhancing the efficiency of regional green agricultural product supply chains. These findings provide empirical evidence and policy insights for building a more resilient and balanced regional green agricultural system.

1. Introduction

Against the global macro-background of pursuing sustainable development goals and addressing climate change, the green transformation of the agricultural sector has become a core issue. Unlike traditional supply chains focused on cost and output, the green agricultural product supply chain is a complex network that balances economic efficiency, ecological integrity, and social value. It demands ecological production processes, intensive resource utilization, and full traceability of quality [1]. As the world’s largest producer and consumer of agricultural products, China’s green agricultural product supply chain efficiency is not only crucial for ensuring domestic supply security and meeting upgraded consumption demands but also a vital pathway for achieving the national strategy of high-quality development. Scientifically evaluating the efficiency of green agricultural product supply chains and accurately identifying systemic bottlenecks holds significant theoretical and practical implications for advancing the sustainable transformation of agriculture in China and globally.

As a major agricultural production base in China, Hebei Province shoulders the vital mission of securing the “vegetable baskets” for Beijing and Tianjin. Its diverse topography—encompassing plains, mountains, and coastal areas—coupled with uneven economic development and pronounced regional disparities, provides an ideal “natural laboratory” for studying regional variations in supply chain efficiency. However, the development of Hebei’s green agricultural products industry does not align with its status as a major agricultural province. By the end of 2024, the province’s certified green food products accounted for only 2.91% of the national total, while standardized production bases for green food raw materials covered less than 1.75% of its cultivated land. This indicates potential deep-seated efficiency bottlenecks in the green agricultural product supply chain across production organization, technology application, processing, and distribution.

Most academic research focuses on the national macro level or emphasizes the perspective of individual enterprises, paying insufficient attention to the patterns of efficiency differentiation at the municipal level within provinces. More importantly, existing studies often examine the impact of technology or scale on efficiency in isolation, overlooking the complex interactions and synergistic mechanisms between technical efficiency and scale efficiency [2]. Is inefficiency rooted in outdated technical management, inappropriate industrial scale, or the failure to effectively align the two? Exploring this core mechanism is essential for formulating precise optimization strategies. To address these research gaps, this study aims to delve into the following three core questions:

RQ1: What spatial distribution characteristics does the efficiency of green agricultural product supply chains exhibit across cities in Hebei Province? What factors contribute to these patterns?

RQ2: How do technical efficiency and scale efficiency interact within supply chain systems across different cities? What impact does this relationship have on overall supply chain performance?

RQ3: What factor allocation structures have emerged as cities enhance supply chain efficiency? What implications do these structural characteristics hold for supply chain stability and sustainability?

2. Literature Review

2.1. The Essence and Efficiency Dimensions of Green Agricultural Product Supply Chains

Research on green agricultural product supply chains logically originates from a critical reexamination of traditional supply chain theory. Traditional theories, often grounded in Porter’s value chain concept [3], tend to view supply chains as linear value-transfer pipelines centered on cost minimization and speed [4]. However, the strong environmental externalities inherent in the production process of green agricultural products, the information asymmetry surrounding their quality attributes (trust goods), and the complexity arising from multi-stakeholder participation have driven a fundamental epistemological shift in academic understanding of their essence. Current scholarship posits that the green agricultural product supply chain is fundamentally a collaborative value network aimed at maximizing the integrated economic, environmental, and social value [5,6]. This conceptual evolution is reflected in the expansion and integration across three dimensions.

First, full-chain greening serves as the fundamental prerequisite for ensuring the quality attributes of green agricultural products. Its essence lies in embedding environmentally friendly principles throughout every stage from production to consumption, thereby guaranteeing that final products meet “green” standards. This process relies on green technology investments at the production end [7], innovative reuse of byproducts at the processing end [8,9], low-carbon intensive distribution models [10], and information traceability systems at the consumption end [11].

Second, multi-stakeholder coordination serves as the governance structure sustaining the stable operation of green agricultural product value networks. The complexity of green agricultural production and its quality-trust attributes have driven supply chains to evolve into networked systems encompassing governments, core enterprises, farmers, and platforms. Within this network, core enterprises play a pivotal role in standard-setting and technology diffusion, while government regulation and platform empowerment provide indispensable support, collectively upholding the order and efficiency of value creation [12,13,14].

Third, the diversification of efficiency dimensions serves as a comprehensive benchmark for measuring the value realization of green agricultural products. Evaluations of supply chain efficiency for green agricultural products often pursue the goal of maximizing overall value. This has driven the evolution of supply chain efficiency assessment systems from early single-dimensional perspectives based on the SCOR model and focused on finance and operations [15] toward integrated frameworks encompassing economic, environmental, and social dimensions [16,17,18], moving beyond traditional metrics of cost and speed. Recent research has further expanded from the enterprise level to regional coordination, examining the influence of macro and regional factors such as policy systems, the digital economy [19,20] and geographical indication products [21,22]. Greater emphasis has been placed on supply-demand alignment and the enabling effects of digitalization on overall value [23,24], signaling the increasing maturity and refinement of evaluation systems in this field.

2.2. Methods for Measuring Supply Chain Efficiency and Research Findings

In the empirical measurement of green agricultural product supply chain efficiency, two mainstream paradigms have emerged: multi-indicator comprehensive evaluation and frontier analysis. While addressing the limitations of each approach, a frontier trend toward combined models is emerging. The multi-indicator comprehensive evaluation model constructs a multidimensional indicator system encompassing economic, environmental, and social dimensions. It employs subjective weighting methods such as the Analytic Hierarchy Process (AHP) or objective weighting methods like entropy weighting to determine weights, and combines techniques like TOPSIS and grey relational analysis for comprehensive ranking [25]. While these approaches enable multidimensional efficiency assessment, the objectivity of their outcomes heavily depends on weighting methodologies. Subjective weighting may introduce expert bias, while objective weighting may overlook intrinsic conversion efficiencies between inputs and outputs due to data distribution characteristics [26]. Consequently, they inherently fall short in revealing the relative effectiveness of resource allocation.

To more directly measure resource allocation efficiency, frontier analysis models emerged and diverged into two paths: parametric and nonparametric. Parametric Stochastic Frontier Analysis (SFA) can separate random errors from managerial inefficiency and incorporate environmental variables to analyze their impact, making it suitable for analyzing segments significantly influenced by exogenous factors such as nature and policy [27]. Non-parametric Data Envelopment Analysis (DEA), however, has gained widespread application in this field due to its unique advantages of not requiring a predefined production function form and its ability to handle multiple inputs and outputs [28]. Its practicality has been thoroughly validated in agricultural eco-efficiency research. For instance, Golas et al. (2020) integrated traditional inputs and environmental variables into the DEA framework, effectively quantifying agricultural eco-efficiency [29]. However, traditional DEA models have inherent limitations: they cannot directly address undesirable outputs such as environmental pollution, and their discriminative power significantly diminishes under the “curse of dimensionality” when evaluation indicators are excessive or highly correlated [30]. This restricts their direct application in complex green supply chain contexts.

To overcome the limitations of single methodologies, combined evaluation models have emerged as a leading trend in current research innovation. Researchers are committed to integrating methods to leverage their respective strengths and address weaknesses. For instance, addressing the identification capability issue of DEA models in high-dimensional data, researchers combined Principal Component Analysis (PCA) with DEA. By applying PCA to reduce the dimensionality and eliminate multicollinearity among highly correlated original indicators, the extracted principal components were then used as input-output variables in DEA. This approach significantly enhanced the robustness and interpretability of evaluation results [31]. Similarly, to address undesirable outputs, scholars have introduced models such as the Directional Distance Function (DDF) and Slackness Measure (SBM) to refine DEA [32]. This “methodological combination” approach preserves the objective core of frontier analysis models in efficiency measurement while enhancing their capacity to handle real-world complexity through the incorporation of other statistical or modeling techniques. It represents the mainstream direction of methodological development in this field.

2.3. Technical Efficiency and Scale Efficiency: Decomposition and Synergy of Efficiency Sources

Within the framework of cutting-edge analytical methods, the exploration of efficiency has deepened to diagnose its sources—specifically, distinguishing between technical efficiency and scale efficiency. This decomposition is crucial for understanding efficiency bottlenecks in green agricultural product supply chains: Does inefficiency stem from inadequate technical management capabilities, or from improper industrial organization scale? Answering this question is a prerequisite for formulating precise optimization strategies.

At the theoretical level, both are components of overall efficiency. Banker et al. (1984) pioneered research by introducing the variable returns to scale (VRS) hypothesis, successfully decomposing the comprehensive technical efficiency proposed by Charnes et al. (1978) into the product of pure technical efficiency (PTE) and scale efficiency (SE), i.e., TE = PTE × SE [33,34]. Pure technical efficiency reflects the ability to allocate and manage resources effectively at a given scale, while scale efficiency measures the gap between actual production scale and optimal scale [35]. In green agricultural product supply chain research, pure technical efficiency manifests as the application level of green production technologies such as precision fertilization [36] and the operational management capabilities of the supply chain. Scale efficiency relates to factors like the concentration of land management, the capacity utilization rate of processing enterprises, and the service coverage radius of logistics networks.

In terms of their mechanisms of action, the two exhibit a complex dynamic synergy. On one hand, improvements in technical efficiency can facilitate the realization of scale efficiency [37]. For instance, the application of advanced technologies such as digital twins and the Internet of Things (which enhance PTE) can reduce the management complexity of large-scale production, thereby enabling larger-scale operations. On the other hand, moderate-scale operations (which enhance SE) can spread the costs and pool resources for the research, development, and application of new technologies [38], thereby creating conditions for pure technical efficiency gains. However, extensive empirical research indicates that this synergy is not linear, particularly in agriculture, where an inverted U-shaped relationship is commonly observed [39]. This implies the existence of an “optimal operating scale” range: scales too small fail to realize economies of scale, while scales too large may exceed management capacity boundaries [40], thereby inhibiting the realization of pure technical efficiency.

In recent years, with the evolution of technology and business models, our understanding of their relationship has entered a more profound and dialectical phase. The interaction between technological efficiency and economies of scale is far from a simple complementarity or substitution; its core mechanism lies in the dynamic equilibrium between capability building and resource allocation.

On the one hand, leaps in technological efficiency can transcend the boundaries of traditional economies of scale, reshaping their pathways to realization. Technological advances represented by digital technologies and precision agriculture derive their revolutionary significance not from directly expanding production scale, but from fostering “non-scale economies of scale” [41] by enhancing the precision of resource allocation and utilization. This enables small and medium-sized enterprises or smallholder farmers to achieve exceptionally high operational efficiency through technological empowerment despite physical limitations, thereby reducing path dependence on pure scale expansion and redefining the concept of scale efficiency.

However, the realization of technical efficiency is not unconditional; it is highly dependent on the application scenarios and cost-sharing foundations provided by economies of scale [42]. Particularly in heterogeneous agricultural supply chains, government green subsidy policies can create a more favorable scale environment for technological efficiency by adjusting the relationship between scale thresholds and technological investment costs [43], further validating the necessity of scale support. This constitutes the other side of the interactive relationship: the support and constraints of scale efficiency on technological efficiency. Empirical studies consistently validate the existence of a threshold effect: the economic viability of any technology depends on a minimum scale threshold. Below this threshold, fixed inputs cannot be sufficiently amortized, and the technology’s efficiency potential is stifled by insufficient scale. Thus, scale efficiency provides an indispensable stage for technological efficiency. Conversely, however, overly rapid scale expansion that outpaces management capabilities creates knowledge gaps, thereby inhibiting the release of technological potential [44].

The net effect of their interactive relationship is moderated by key variables such as technological attributes, institutional environments, and management capabilities [45,46,47]. Different technology types exhibit varying degrees of scale dependence. Mechanization technologies typically demonstrate significant scale preferences, whereas biochemical and digital technologies may exhibit greater scale neutrality [48]. Concurrently, robust regional infrastructure and agricultural extension services can compensate for insufficient land scale through service scaling, achieving external synergies between technological efficiency and scale efficiency [49]). General-purpose technologies are more likely to drive scale boundary expansion [50], whereas specialized technologies rely more heavily on scale support. Robust digital infrastructure, collaborative platforms, and policy support can effectively lower scale thresholds. Thus, pursuing synergies between technical efficiency and scale efficiency fundamentally involves precisely aligning the pace of technological iteration with the trajectory of scale expansion. The optimal strategy lies not in blindly pursuing cutting-edge technology or massive scale, but in constructing context-specific adaptive models where technological capabilities and scale configurations mutually empower each other while sharing costs [51]. This provides more refined guidance for efficiency optimization in theory and practice, and offers a core theoretical lens for analyzing regional disparities within Hebei Province in this study.

In summary, while existing literature has made significant progress in conceptual definitions and measurement methodologies, systematic empirical evidence remains lacking regarding the specific interaction patterns between technical efficiency and scale efficiency within regional green agricultural product supply chains, as well as how to formulate differentiated “technical-scale” matching strategies based on regional heterogeneity. This study aims to construct an improved combined DEA model to delve into the underlying mechanisms driving regional efficiency disparities.

3. Materials and Methods

3.1. Research Area and Data Sources

This study selected Hebei Province, China as the research area. Hebei Province is located in the North China Plain, geographically surrounding Beijing, the capital of China, and Tianjin, a municipality directly under the Central Government. This unique location has endowed Hebei Province with a key strategic role: as the most important agricultural product supply base for the Beijing-Tianjin-Hebei urban agglomeration. The province governs 11 prefecture-level cities, which are taken as the basic decision-making units (DMUs) for evaluation in this study.

3.1.1. The Selection of the Research Area

The selection of Hebei Province as the research object is mainly based on the following three significant characteristics:

First, the paradox between production scale and green development. According to the third national agricultural census, small-scale farmers account for over 98% of agricultural business entities in China. Hebei Province, as a major agricultural province, also conforms to this feature. In 2021, the proportion of small-scale farmers receiving socialized services in agricultural production in Hebei Province exceeded 76%, further confirming the dominant position of small-scale farmers in agricultural production. This seriously restricts the scale of production and the popularization of green technologies. This contradiction is directly reflected in the lagging development of its green agriculture. By the end of 2024, there were only 10 national-level standardized production bases for green food raw materials that had been certified in the province, with another one under construction, covering a total area of 1.5704 million mu. This proportion is less than 1.75% of the total cultivated land of approximately 90 million mu in the province. This reveals that there is huge room for improving the efficiency of the green agricultural product supply chain in Hebei Province.

Second, the efficiency bottleneck of the green agricultural product supply chain. There are serious efficiency bottlenecks in key links such as processing and circulation in the green agricultural product supply chain of Hebei Province. At the processing end, the industrial chain is short. Most agricultural products are sold in their primary form. Insufficient deep processing leads to low added value of products, and lagging brand building weakens market competitiveness. At the circulation end, infrastructure is a key shortcoming, especially the severe shortage of cold chain logistics. For instance, there are only two fixed railway cold chain freight trains in the province, and the proportion of railway cold chain transportation volume is less than 3%, resulting in high transportation costs and a high product loss rate. These bottlenecks collectively contributed to efficiency losses in the supply chain.

Thirdly, weak support system and unbalanced internal development. The external support system of the green agricultural product supply chain in Hebei Province is relatively weak, facing the dual pressure of limited capital investment and a shortage of high-quality professional talents. There are still significant geographical and economic development differences in Hebei Province. The northern part is an ecological function zone, the central part is the core area of the metropolitan area, and the southern part is a traditional plain agricultural area. This imbalance indicates that there may be significant spatial differentiation in the resource endowment, efficiency level and development model of its green agricultural product supply chain, providing a typical sample for the regional difference analysis of this study.

3.1.2. Data Source

The data sources for this study are divided into two categories: one is non-modeling data used for research background, regional selection, and problem description; the other is modeling data used for the construction of the PCA-DEA model and efficiency evaluation.

(1) Non-modeling data is mainly used to analyze the development background, structural characteristics, and bottleneck problems of the green agricultural product supply chain in Hebei Province and does not participate in model calculations. The relevant data is sourced from the China Agricultural Network and publicly released statistical information and policy reports by the Ministry of Agriculture and Rural Affairs, specifically including:

Structural characteristic data, such as the proportion of small-scale farmers’ operations, the number of green food certifications, and the number of standardized raw material production bases;

Bottleneck data in the circulation link, such as the number of railway cold chain trains in the cold chain logistics infrastructure;

Support system status data, involving the coverage rate of agricultural socialized services and the advancement of green agriculture policies.

The above data reflects the overall development status and regional differences in the green agricultural product supply chain in Hebei Province, providing a realistic basis for the problem positioning and analysis framework of this study.

(2) This study constructs a two-stage PCA-DEA model based on cross-sectional data from 11 prefecture-level cities in Hebei Province for the year 2022. The selection of this year’s data is primarily driven by three considerations: First, constrained by the publication process of statistical yearbooks, 2022 represents the most recent systematic data currently available, ensuring consistent statistical definitions across decision units and the fairness and validity of results. Second, 2022 represents a period of relative policy and economic stability, facilitating the capture of structural characteristics while minimizing the impact of short-term anomalies. Finally, cross-sectional analysis aims to establish precise benchmarks for regional efficiency evaluation, providing reliable reference points for subsequent dynamic studies and policy assessments. Specifically:

The proportion of rural population in the region, number of reservoirs, total telecommunications business volume, freight volume, freight turnover, per capita road length, agricultural fertilizer application, ammonia nitrogen emissions, electricity consumption in transportation, warehousing, and postal services (used for carbon emissions calculation) are sourced from the 2023 Statistical Yearbook of Hebei Province and its municipalities, as well as the 2022 Statistical Bulletin on National Economic and Social Development. Data from the 2023 Statistical Yearbook reflects 2022 figures. The number of green agricultural product enterprises in 2022 was sourced from the China Green Food Development Center and retrieved in 2025. The number of nationally certified geographical indication agricultural products was sourced from the National Agricultural Product Geographical Indication Query System and relevant government open databases, retrieved in 2025. The land area occupied by transportation facilities was sourced from Zhongli Data Network and retrieved in 2025. The number of cold storage enterprises was sourced from Aiqi Cha and retrieved in 2025.

In terms of data quality control, this study implemented the following measures to address potential data limitations: For individual missing data points, such as the cargo turnover volume of Qinhuangdao City, mean imputation was employed to ensure dataset integrity. For data from different sources, consistency was verified through cross-validation, with priority given to official statistical yearbooks and bulletin data.

3.2. The Construction of the Evaluation Index System

In selecting indicators for this study, we adhered to the principle of data availability, ensuring all metrics have reliable, publicly accessible data sources for quantitative analysis. We applied the principle of representativeness, choosing typical indicators that accurately reflect key characteristics of green agricultural product supply chains. The principle of relevance was applied: indicators are highly correlated with green supply chain efficiency evaluation objectives. The principle of scientific rigor was upheld: the indicator system was constructed based on theoretical analysis and literature review. It also incorporates input-output variable definition theories from production economics [35], ensuring the selected indicators accurately reflect the intrinsic operational efficiency of the supply chain without being confounded by external environmental factors.

In terms of framework design, this study draws upon and integrates research by Zhu Qinghua et al. (2005) on green supply chain management, Ding Baocheng et al. (2023) on agricultural supply chain efficiency, Reshad et al. (2023) on brand value, and Zhu Haihua (2023) on supply chain security [52,53,54,55]. It constructs a comprehensive evaluation system encompassing four dimensions: green production, distribution efficiency, market value, and support and assurance. Input indicators within this system are strictly limited to resources that are directly controllable or consumed during supply chain operations (e.g., fertilizers, labor, infrastructure). Output indicators are defined as the direct economic, social, and brand outcomes generated by supply chain activities.

The system comprises 4 primary indicators and 13 secondary indicators, as shown in

Table 1. Evaluation System of Green Agricultural Products Supply Chain Efficiency in Hebei Province.

First-Level Indicator	Second-Level Indicator	Indicator Nature
Green production indicators	Number of Green Agricultural Product Enterprises/units	Expected output
	Ammonia Nitrogen Emissions/tons	Undesirable output
	Chemical Fertilizer Application per Hectare/tons	Input indicator
Circulation efficiency indicator	Carbon Emissions from Transport, Storage, and Postal Services/tons	Undesirable output
	Land Area for Road Traffic Facilities/km2	Input indicator
	Number of Cold Storage Enterprises	Input indicator
	Road Length per Capita/meters	Input indicator
Market consumption indicators	Freight Volume/10,000 tons	Expected output
	Cargo Turnover/10,000 ton-kilometers	Expected output
	Telecommunications Business Volume/10,000 yuan	Expected output
	Number of National GI Agricultural Product Certifications	Expected output
Support and guarantee indicators	Rural Population Proportion/%	Input indicator
	Number of Reservoirs/units	Input indicator

. Environmental burden indicators (e.g., ammonia nitrogen emissions) are treated as undesirable outputs to more accurately reflect the requirements of green sustainable development.

To ensure the accuracy of the indicator system, this study provided theoretical explanations for each secondary indicator to clarify their specific attributes and functional roles in the green agricultural product supply chain.

3.2.1. Green Production Indicators Reflect the Production Capacity of Green Agricultural Products and the Degree of Greenness in the Production Process

(1) Number of Green Agricultural Product Enterprises: This represents the number of enterprises that have passed audits covering production environment, processes, packaging, and other full-process aspects, obtaining green food production qualifications. It indicates the scale and industrialization level of green production entities within the region. Enterprises are key nodes in the supply chain, and their number and quality directly reflect the organized application level of green production technologies and market supply capacity.

(2) Ammonia Nitrogen Emissions: This is a key indicator reflecting agricultural non-point source pollution, primarily originating from chemical fertilizer application. Its concentration directly affects water environment quality and indirectly constrains the certification pass rate for green agricultural products, directly reflecting the negative environmental externality of the production link.

(3) Chemical Fertilizer Application per Hectare: Calculated as total chemical fertilizer amount divided by total sown area. This is a key indicator measuring the intensity of chemical fertilizer use in agricultural production, reflecting the input intensity and dependence on chemicals. High input intensity is a key cost that needs to be controlled for achieving green transformation and is also an input factor affecting whether products meet standards.

3.2.2. Circulation Efficiency Indicators Reflect the Promoting Effect of Infrastructure and Informatization Level on Circulation Efficiency

(1) Carbon Emissions from Transport, Storage, and Postal Services: Calculated as electricity consumption in transport, storage, and postal services multiplied by 0.7252 [56]. This indicator assesses the energy consumption intensity and environmental impact of green agricultural product circulation activities, serving as a key metric for evaluating the environmental footprint of the green agricultural product supply chain.

(2) Land Area for Road Traffic Facilities: Represents the physical infrastructure capital stock invested to ensure smooth logistics, constituting a fundamental resource input for achieving efficient transportation.

(3) Number of Cold Storage Enterprises: Characterizes the input level of cold chain logistics infrastructure. Sufficient cold chain facilities are a key input for reducing losses of high-value-added fresh agricultural products, ensuring quality, and realizing value.

(4) Road Length per Capita: Reflects the density of the regional transportation network and is an important infrastructure input indicator for measuring regional accessibility and logistics convenience.

3.2.3. Market Value Indicators Primarily Measure the Market Performance and Value Realization Level of Green Agricultural Products

(1) Freight Volume: Freight volume provides a direct measure of the flow of market entities within a region, serving not only as a core indicator of the scale of movement but also as the fundamental physical metric for assessing the realization of its market value.

(2) Cargo Turnover: This indicator comprehensively reflects the scale of spatial allocation undertaken to meet cross-regional market demand. Efficient transshipment and distribution serve as the key enablers for green agricultural products to overcome geographical constraints and realize broader market value.

(3) Telecommunications Business Volume: This metric serves as a key indicator of market informatization, representing the information infrastructure that underpins online transactions, brand dissemination, and value transmission. Efficient information flow directly empowers the creation and enhancement of market value by reducing transaction costs and elevating brand recognition.

(4) Number of National GI Agricultural Product Certifications: Represents the number of geographical indication products with regional characteristics and traditional ecological cultivation practices. Their certification requirements are highly aligned with green production standards. Their quantity represents the intangible asset output of agricultural products in terms of brand value, market differentiation, and consumer trust.

3.2.4. The Support and Guarantee Indicators Mainly Refer to the Investment and Guarantee Provided by External Conditions of the Supply Chain

(1) Rural Population Proportion: Reflects the human resource base for green agricultural product production. A moderate rural population proportion can both preserve traditional farming experience and provide manpower support for the promotion of green technologies, thereby maintaining the production stability of the green agricultural product supply chain.

(2) Number of Reservoirs: Represents the input level of regional water conservancy infrastructure. A stable irrigation water source is a key natural resource and engineering input for resisting climate risks and ensuring the stability of agricultural production (especially green agriculture).

3.3. Analysis Methods and Model Construction

This study adopted the PCA-DEA two-stage model to overcome the problems of index collinearity and subjective weighting. The process is shown in Figure 1.

Phase One: Principal Component Analysis. The principal components of 6 input indicators and 7 output indicators were extracted, respectively, using SPSS 26.0 software. The number of principal components was determined based on the principle that the cumulative variance contribution rate was ≥80%, and uncorrelated principal components were screened out. The principal component scores were used as the new input-output variables of DEA.

Phase Two: Data Envelopment Analysis. The input-oriented BCC model was adopted, and the DEAP software was used to calculate the efficiency levels of each decision-making unit in the green agricultural product supply chain of Hebei Province, identifying the existing deficiencies in the current supply chain and the future optimization directions. The specific model construction is as follows:

Suppose there are n decision units (DMU_j, j = 1, 2, ... n), each consuming m types of inputs and producing s types of outputs.

When returns to scale are constant, the production possibility set is:

$${{\displaystyle T}}_{CCR}=\{(X,Y) | X\ge {\displaystyle \sum _{\mathrm{j}=1}^{\mathrm{n}}{{\displaystyle \lambda }}_{\mathrm{j}}}Xj, Y\le {\displaystyle \sum _{\mathrm{j}=1}^{\mathrm{n}}{{\displaystyle \lambda }}_{\mathrm{j}}}Yj, {{\displaystyle \lambda }}_{\mathrm{j}}\ge 0\}$$

(1)

However, in practical applications, economies of scale are often variable. By adding convexity constraints and setting ${\displaystyle \sum _{\mathrm{j}=1}^{\mathrm{n}}\lambda \mathrm{j}}$ = 1, the production possible set is:

$${{\displaystyle T}}_{BCC}=\{(X,Y) | X\ge {\displaystyle \sum _{\mathrm{j}=1}^{\mathrm{t}}{{\displaystyle \lambda }}_{\mathrm{j}}}Xj, Y\le {\displaystyle \sum _{\mathrm{j}=1}^{\mathrm{t}}{{\displaystyle \lambda }}_{\mathrm{j}}}Yj, {\displaystyle \sum _{\mathrm{j}=1}^{\mathrm{t}}{{\displaystyle \lambda }}_{\mathrm{j}}}=1, {{\displaystyle \lambda }}_{\mathrm{j}}\ge 0\}$$

(2)

To evaluate the efficiency of the k-th DMU, the objective function is to maximize the efficiency value θ_kk. The constraint conditions are all input-output combinations of DMU. After the Charnes-Cooper transformation, the dual programming form of the BCC model is:

$$\begin{gathered} \min \mathsf{\theta } \\ \mathrm{s}.\mathrm{t}.{\displaystyle {\sum }_{\mathit{j}=1}^{\mathit{n}}}{\mathsf{\lambda }}_{\mathrm{j}}{\mathrm{X}}_{\mathrm{j}}\le \mathsf{\theta }{\mathrm{X}}_{\mathrm{k}} \\ {\displaystyle {\sum }_{\mathit{j}=1}^{\mathit{n}}}{\mathsf{\lambda }}_{\mathrm{j}}{\mathrm{Y}}_{\mathrm{j}}\le {\mathrm{Y}}_{\mathrm{k}} \\ {\displaystyle {\sum }_{\mathit{j}=1}^{\mathit{n}}}{\mathsf{\lambda }}_{\mathit{j}}=1 \\ {\mathsf{\lambda }}_{\mathrm{j}}\ge 0,\mathrm{with}\mathrm{no}\mathrm{constraint}\mathrm{on}\mathsf{\theta } \end{gathered}$$

(3)

In practical computation, it is necessary to introduce slack variables S⁻ and S⁺. The model is extended as:

$$\begin{gathered} \min \mathsf{\theta }-\mathsf{\epsilon }\left({\displaystyle {\sum }_{\mathit{i}=1}^{\mathit{m}}}{\mathrm{S}}_{\mathrm{i}}^{-}+{\displaystyle {\sum }_{\mathit{r}=1}^{\mathit{s}}}{\mathrm{S}}_{\mathrm{r}}^{+}\right) \\ \mathrm{s}.\mathrm{t}.{\displaystyle {\sum }_{\mathit{j}=1}^{\mathit{n}}}{\mathsf{\lambda }}_{\mathrm{j}}{\mathrm{X}}_{\mathrm{j}}+{\mathrm{S}}^{-}=\mathsf{\theta }{\mathrm{X}}_{\mathrm{k}} \\ {\displaystyle {\sum }_{\mathit{j}=1}^{\mathit{n}}}{\mathsf{\lambda }}_{\mathrm{j}}{\mathrm{Y}}_{\mathrm{j}}-{\mathrm{S}}^{+}={\mathrm{Y}}_{\mathrm{k}} \\ {\displaystyle {\sum }_{\mathit{j}=1}^{\mathit{n}}}{\mathsf{\lambda }}_{\mathit{j}}=1,{\mathsf{\lambda }}_{\mathrm{j}}\ge 0,{\mathrm{S}}^{-}\ge 0{,\mathrm{S}}^{+}\ge 0 \end{gathered}$$

(4)

Among them, X_j = (x_1j, x_2j, ..., x_mj)^T is the input vector of the JTH DMU; Y_j = (y_1j, y_2j, ..., y_mj)^T is the output vector of the JTH DMU; λ_j is the weight coefficient of each DMU; θ is the relative efficiency value, θ ∈ [0, 1], which is a non-Archimedean infinitesimal.

If there exists an optimal solution θ* = 1 and all slack variables are 0, that is, S⁺* = 0 and S⁻* = 0, then DMU is DEA efficient. If θ* = 1 but there is a non-zero slack variable, it is weakly DEA efficient. If θ* < 1, the DMU is considered inefficient.

4. Results

4.1. Principal Component Analysis Extracts Core Indicators Reflecting Supply Chain Efficiency

The KMO and Bartlett’s sphericity tests confirmed the data were suitable for PCA (input indicator KMO = 0.64, output indicator KMO = 0.62; both p-values < 0.005). The analysis results are shown in

Table 2. Results of Principal Component Extraction.

Principal Component	Characteristic Root	Variance Interpretation Rate %	Cumulative %
P1	3.47	57.88	57.88
P2	1.16	19.26	77.14
P3	0.72	11.92	89.06
Q1	4.17	59.52	59.52
Q2	1.51	21.61	81.14

. Three principal components (P₁–P₃) were extracted from the input end, with a cumulative variance contribution rate reaching 89.06%. Two principal components (Q₁–Q₂) were extracted from the output end, with a cumulative variance contribution rate of 81.14%, which effectively captures the information of the original output indicators.

Through the analysis of the load matrix (

Table 3. Input Index Load Coefficient Table.

Indicator Name	Loading Coefficients			Communality
	P1	P2	P3
Chemical Fertilizer Application per Hectare/tons	0.789	−0.233	−0.005	0.677
Land Area for Road Traffic Facilities/km2	0.797	0.466	−0.183	0.885
Number of Cold Storage Enterprises	0.635	0.711	−0.124	0.924
Road Length per Capita/meters	0.837	−0.361	0.354	0.956
Rural Population Proportion/%	−0.527	0.491	0.666	0.963
Number of Reservoirs/units	0.913	−0.086	0.311	0.938

and

Table 4. Output Index Load Factor Table.

Indicator Name	Loading Coefficients		Communality
	Q1	Q2
Number of Green Agricultural Product Enterprises/units	0.627	0.699	0.881
Ammonia Nitrogen Emissions/tons	0.85	−0.187	0.758
Freight Volume/10,000 tons	0.946	−0.073	0.9
Cargo Turnover/10,000 ton-kilometers	0.862	−0.19	0.779
Telecommunications Business Volume/10,000 yuan	0.798	−0.263	0.706
Carbon Emissions from Transport, Storage, and Postal Services/tons	0.856	−0.034	0.733
Number of National GI Agricultural Product Certifications	0.205	0.937	0.921

), the principal components are interpreted and named:

P₁ exhibits extremely high positive loadings on road traffic facility land area, per capita road length, and reservoir quantity, indicating it primarily represents the sophistication of regional hardware infrastructure in transportation and water conservancy. Simultaneously, the high load of agricultural fertilizer application signifies modern agricultural production methods. The negative load of “rural population proportion” further confirms that regions with higher scores in this component exhibit greater urbanization and infrastructure modernization levels. Hence, it is named “Infrastructure and Agricultural Modernization Level”.

P₂ exhibits the highest load on the number of cold storage enterprises. Cold storage facilities serve as core infrastructure for agricultural product warehousing, processing, and logistics, with their quantity directly reflecting the vibrancy of local agricultural commerce and logistics industries. Concurrently, the area of road transportation facilities provides positive support, indicating that logistics development is inseparable from transportation infrastructure. It is noteworthy that the “rural population proportion” component exhibits a positive load in this model, contrasting with P1. This indicates that the commercial logistics activities represented by this component are more closely integrated with rural areas themselves. Therefore, it is named “Rural Commercial Logistics Activity.”

P₃ is primarily driven by the proportion of rural population, reflecting the basic structure of the rural population as a reserve of agricultural labor force. It is named “Agricultural Labor Security Level”.

Q₁ has high loadings on economic and logistics indicators such as freight volume, cargo turnover, and telecommunications business volume. It embodies the synergistic relationship between regional economic scale, logistics efficiency, and industrial chain circulation. It is therefore named “Economic Scale and Logistics Efficiency”.

Q₂ is highly correlated with variables such as the number of nationally certified geographical indication agricultural products and the number of green agricultural product enterprises. It centrally reflects the level of green certification, brand building, and sustainable development in the agricultural product sector. It is thus named “Agricultural Product Greenness and Brand Building”.

Take the principal component score data of each city as the input and output variables of the DEA model. Due to the presence of negative values in the principal component scores, in order to meet the DEA model’s requirement for non-negative data, the Min-Max normalization method was used to normalize all principal component data and convert them into a unified non-negative interval. The processing results are shown in

Table 5. Normalization Results of New Indicators.

DMU	P1	P2	P3	Q1	Q2
Shijiazhuang	1.01	1.01	0.59	1.01	0.01
Tangshan	0.69	0.34	0.48	0.94	0.83
Handan	0.48	0.60	0.49	0.48	0.63
Qinhuangdao	0.90	0.01	1.01	0.04	0.07
Baoding	0.58	0.30	0.01	0.50	0.64
Zhangjiakou	0.31	0.32	0.35	0.25	0.96
Chengde	0.19	0.39	0.85	0.01	0.58
Langfang	0.20	0.39	0.17	0.07	0.07
Cangzhou	0.01	0.66	0.76	0.57	0.01
Hengshui	0.11	0.46	0.70	0.01	0.48
Xingtai	0.19	0.58	0.74	0.35	1.01

.

4.2. Evaluation Results of the Efficiency of the Green Agricultural Product Supply Chain in Hebei Province Based on the DEA Model

The principal component scores obtained from the above processing were taken as the input and output variables of the DEA model, and the input-oriented BCC model was adopted for calculation. The efficiency values of the green agricultural product supply chain in 11 cities of Hebei Province were obtained, including comprehensive efficiency, pure technical efficiency and scale efficiency. The specific results are shown in

Table 6. Processing Results of the BCC Model.

City	Pure Technical Efficiency (PTE)	Scale Efficiency (SE)	Comprehensive Efficiency (OE/θ)	Slack Variable S−	Slack Variable S+	Effectiveness
Shijiazhuang	1.00	0.78	0.78	0.00	0.86	DEA Inefficient
Tangshan	1.00	1.00	1.00	0.00	0.00	DEA Strongly Efficient
Handan	0.75	0.99	0.74	0.15	0.00	DEA Inefficient
Qinhuangdao	1.00	1.00	1.00	0.00	0.00	DEA Strongly Efficient
Baoding	1.00	1.00	1.00	0.00	0.00	DEA Strongly Efficient
Zhangjiakou	1.00	1.00	1.00	0.00	0.00	DEA Strongly Efficient
Chengde	1.00	0.72	0.72	0.26	0.17	DEA Inefficient
Langfang	1.00	0.27	0.27	0.07	0.00	DEA Inefficient
Cangzhou	1.00	1.00	1.00	0.00	0.00	DEA Strongly Efficient
Hengshui	1.00	0.80	0.80	0.30	0.15	DEA Inefficient
Xingtai	1.00	1.00	1.00	0.00	0.00	DEA Strongly Efficient

Note: Slack variable S– denotes the potential reduction in input levels while maintaining current outputs; slack variable S+ denotes the potential augmentation in output levels while maintaining current inputs.

and

Table 7. Analysis of Returns to Scale.

City	Coefficient of Return to Scale	Type
Shijiazhuang	1.93	Decreasing Returns to Scale
Tangshan	1.00	Constant Returns to Scale
Handan	0.80	Increasing Returns to Scale
Qinhuangdao	1.00	Constant Returns to Scale
Baoding	1.00	Constant Returns to Scale
Zhangjiakou	1.00	Constant Returns to Scale
Chengde	0.58	Increasing Returns to Scale
Langfang	0.09	Increasing Returns to Scale
Cangzhou	1.00	Constant Returns to Scale
Hengshui	0.47	Increasing Returns to Scale
Xingtai	1.00	Constant Returns to Scale

.

4.3. Spatial Differentiation Characteristics of the Efficiency of the Green Agricultural Product Supply Chain in Hebei Province

To visually present the regional distribution pattern of efficiency, the evaluation results of comprehensive efficiency, pure technical efficiency, and scale efficiency were spatially visualized, resulting in Figure 2, Figure 3 and Figure 4.

5. Discussion

This study measures and analyzes the efficiency of green agricultural product supply chains in Hebei Province, revealing that overall supply chain efficiency across the province requires improvement and exhibits spatial differentiation characterized by “high efficiency in dominant areas and localized lagging regions.” It identifies the causes of efficiency disparities as the lack of effective synergy between technical efficiency and scale efficiency, coupled with excessive reliance on single factor advantages in certain regions. This has led to resource misallocation to some extent and weakened the overall resilience and sustainability of the supply chain.

5.1. The Causes of Spatial Differentiation in Supply Chain Efficiency

The overall spatial pattern of Hebei Province’s green agricultural product supply chain is characterized by “high efficiency in dominant areas and localized lagging regions.” Six DEA strongly efficient prefecture-level cities—Tangshan, Baoding, Qinhuangdao, Zhangjiakou, Cangzhou, and Xingtai (OE = 1.000)—have formed a multipolar leadership pattern. This finding indicates that despite significant disparities in resource endowments among cities, precise alignment between input factor structures and functional positioning can also lead to high efficiency.

The successful conversion of advantageous resource elements. Represented by Tangshan and Baoding, these cities rank highest in the P1 principal component (normalized scores of 0.69 and 0.58) reflecting infrastructure and urban-rural development, and the P₂ principal component (normalized scores of 0.34 and 0.30) indicating commercial logistics activity. This has enabled efficient outputs in economic scale (Q₁) and green branding (Q₂). This demonstrates that leveraging robust industrial foundations, port logistics systems (Tangshan), and proximity to Beijing-Tianjin (Baoding) has successfully translated into high supply chain efficiency, driving both scale expansion and green brand enhancement.

Value Transformation of Ecological and Distinctive Resources. Represented by Zhangjiakou, its score in P₁ (infrastructure) is not dominant. However, leveraging the ecological brand effect accumulated from the Winter Olympics, it achieved one of the highest normalized scores in the province (0.96) for Q₂, reflecting green brand development, ultimately attaining DEA efficiency. Zhangjiakou’s high altitude, significant diurnal temperature variation, and abundant sunlight have enabled concentrated development of off-season vegetables and specialty grains. This provides a practical pathway for regions rich in ecological resources but with relatively weak foundations to achieve efficiency leaps through differentiated competition.

Resource crowding out and efficiency losses in urbanization. In stark contrast are less efficient regions represented by Shijiazhuang and Langfang. Taking Shijiazhuang as an example, as the provincial capital, its urban development (with the highest normalized P₁ score in the province at 1.01, see Table 5) has significantly crowded out agricultural production space, leading to fragmented farming practices that hinder the formation of economies of scale. Simultaneously, the developed secondary and tertiary industries have elevated the opportunity cost of labor. Consequently, despite possessing the most advantageous market location, Shijiazhuang ultimately becomes an efficiency lowland due to low scale efficiency (SE = 0.78, see Table 6). This reveals the real challenges facing agricultural modernization amid rapid urbanization.

5.2. Categorization and Causal Analysis of Efficiency Bottlenecks in Non-DEA-Efficient Cities

The efficiency losses in non-DEA-efficient cities are not attributable to a single factor but stem from resource misallocation across different dimensions.

Scale Diseconomies Type: Shijiazhuang represents a case of scale diseconomies. It has the highest infrastructure investment (P₁ = 1.01) and the largest economic output scale (Q1 = 1.01) in the province, with a pure technical efficiency (PTE) of 1.00. However, its overall efficiency loss is entirely due to low scale efficiency (SE = 0.78), and it is in a stage of decreasing returns to scale. Excessive resource concentration has led to an overextended management radius, soaring factor costs (such as land rent and labor), and significant encroachment of urban functions on agricultural space. The inputs have not yielded proportional green brand outputs (Q₂ is only 0.01), indicating that its supply chain is trapped in a predicament of diminishing marginal returns from extensive expansion.

Technical Efficiency Lag Type: Handan is a typical example of this pattern. Its scale efficiency (SE) is as high as 0.99, approaching the optimal scale, but its pure technical efficiency (PTE) is only 0.75, making it the only city in the province with PTE inefficiency. This suggests that although this traditional industrial hub has strong potential for scale expansion, it lacks innovation vitality due to deep-seated path dependency in traditional industries. As a result, its modern infrastructure (P₁) scores only 0.48, placing it in the middle to lower tier in the province, and it fails to translate potential business vitality (P₂) into economic scale (Q₁), resulting in a pattern of being “large in scale but weak in competitiveness.”

Scale Threshold Constraint Type: Langfang is a typical case of scale threshold constraints. It has the lowest efficiency (TE = 0.27) in the province, primarily due to low scale efficiency (SE = 0.27), despite achieving a pure technical efficiency (PTE) of 1.00 and being in a stage of increasing returns to scale. However, Langfang is located in the Beijing-Tianjin corridor and has long been constrained by the “siphoning effect” of megacities, causing high-quality production factors to flow toward Beijing. The lack of sufficient land space and an independent industrial hinterland has prevented Langfang’s agriculture from forming an independent economy of scale (Q₁ = 0.07), and its development space is structurally constrained.

5.3. The Deep-Rooted Constraint on Efficiency Improvement Lies in the Lack of a Technology-Scale Synergy Mechanism

The efficiency bottlenecks in the green agricultural product supply chain of Hebei Province ultimately stem from the failure to achieve synergistic coupling between technical efficiency and scale efficiency during their dynamic evolution. Specifically:

Type 1: Technically Efficient but Scale-Inefficient with Decreasing Returns. The core issue lies in the fact that the iteration speed of management technologies and production models lags behind the pace of scale expansion. In Shijiazhuang, the massive industrial scale has failed to drive the upgrading of green technologies and brand value, resulting in a severe disconnection between scale and technology in terms of development quality.

Type 2: Scale Potential Constrained by Technical Bottlenecks. In traditional agricultural regions like Handan, the scale efficiency and increasing returns indicate that conditions for expansion are already in place. However, many large-scale agricultural parks, despite achieving physical spatial aggregation, lack refined environmental control and supply chain coordination technologies. This extensive scale expansion has failed to deliver a leap in efficiency.

Type 3: Efficiency Loss Due to Insufficient Scale Support. For cities that are technically efficient but scale-inefficient with increasing returns, the crux lies in the limitation of technical potential by insufficient scale. In cities such as Chengde, Hengshui, and Langfang, technical efficiency cannot be translated into actual market competitiveness and economic benefits due to the lack of a necessary scale platform.

5.4. Homogeneity of Development Models and Potential Vulnerability of the Supply Chain System

Further analysis reveals that even in DEA-efficient cities, there exists a risk of over-reliance on a single efficiency source. For instance, Cangzhou’s efficiency heavily depends on logistics turnover (Q₁-driven), with minimal contribution from its green brand output (Q₂ = 0.01). In contrast, Zhangjiakou relies predominantly on brand value (Q₂-driven), demonstrating weak logistics and economic scale (Q₁ = 0.25). While such development models focusing on a singular strength may prove effective in the short term, they significantly undermine the supply chain’s resilience to external shocks. Cangzhou remains highly vulnerable to fluctuations in logistics costs, whereas Zhangjiakou faces risks associated with unsustainable brand premiums. In comparison, Tangshan achieves balanced and high scores across P₁, P₂, Q₁, and Q₂, demonstrating stronger risk resilience and sustainability.

5.5. Limitations of This Study and Directions for Future Improvement

This study has several limitations. First, the analysis primarily relies on cross-sectional data from 2022, representing a cross-sectional research design that inherently limits the ability to reflect temporal changes in efficiency. Future research utilizing multi-year panel data for longitudinal dynamic analysis would help systematically reveal the evolution pathways and inherent patterns of efficiency. Second, there are methodological limitations. Principal Component Analysis may lose subtle features of the original information during dimensionality reduction, and its linear assumptions might not fully capture complex nonlinear relationships between variables. Meanwhile, Data Envelopment Analysis is sensitive to outliers and lacks a statistical inference foundation, particularly with a limited sample size (11 decision-making units), which may lead to insufficient discriminative power in efficiency evaluation. Finally, the selected indicators in this study predominantly consist of macro-level statistical variables, leaving relatively inadequate exploration of qualitative dimensions such as specific policy implementation mechanisms, and the perceptions and responses of stakeholders like farmers and enterprises. Subsequent research could integrate qualitative methods such as field surveys and in-depth interviews to incorporate micro-level actor behaviors and institutional practices into the analytical framework, thereby enhancing the interpretive depth and practical implications of the study.

6. Conclusions and Implications

6.1. Main Research Conclusions

This study empirically analyzed the efficiency of the green agricultural product supply chain in Hebei Province by constructing a PCA-DEA two-stage model, and drew the following core conclusions:

First, the efficiency of the green agricultural product supply chain in Hebei Province exhibits significant non-equilibrium spatial heterogeneity. The formation of this spatial pattern stems from differences in regional resource endowments and development pathways. Empirical results indicate that supply chain efficiency is not determined by the absolute abundance of resources, but rather by the precision of alignment between resource structure and urban functional positioning. Tangshan leverages its industrial foundation and port logistics, while Baoding capitalizes on its proximity to Beijing and Tianjin, successfully transforming infrastructure advantages into efficient outputs. In contrast, Zhangjiakou and Xingtai have achieved DEA efficiency by focusing on ecological agriculture and geographical indication products, following distinctive, differentiated pathways. This reveals that regional heterogeneity is the fundamental force shaping the spatial pattern of efficiency.

Second, this study identifies the developmental imbalance between technical efficiency and scale efficiency as the core bottleneck constraining supply chain improvement. This finding provides an important supplement to the traditional resource-based view. The competitive advantage of a regional supply chain derives not only from factor endowments but more critically from the capacity for synergistic integration of various factors. In practice, the disconnection between technology and scale has led to three typical types of resource misallocation: (1) Scale Diseconomies Type, characterized by excessive infrastructure investment but lagging green technology upgrades—exemplified by Shijiazhuang, which faces diminishing returns and weak green brand output despite its large scale; (2) Scale Threshold Constraint Type, where effective technical management is undermined by insufficient circulation facilities, suppressing economic output—as seen in Chengde and Hengshui; and (3) Technical Efficiency Lag Type, where potential for scale expansion is hindered by outdated technology and management capabilities, typified by Handan.

Third, the operational models of currently high-efficiency cities exhibit path dependence on singular factor advantages, resulting in a supply chain system lacking sufficient structural resilience. Whether in Cangzhou or Zhangjiakou, neither has achieved a balanced development across the entire chain of production, circulation, and value creation. This renders the supply chain system highly vulnerable to fluctuations when facing external shocks.

6.2. Policy Implications

6.2.1. Implement Differentiated and Targeted Regional Synergy Policies

Formulate precise measures based on the types and causes of efficiency imbalances.

For regions with scale diseconomies, such as Shijiazhuang, the core strategy should focus on optimizing the structure of resource allocation. Policy priorities must shift from supporting physical capital expansion to enhancing total factor productivity. By establishing special funds, resources should be directed toward green certification, brand marketing, and high-value-added processing sectors to improve output per unit and alleviate the challenge of diminishing returns to scale.

For regions constrained by scale thresholds, such as Langfang, efforts should be made to actively integrate into the Beijing-Tianjin metropolitan supply chain. Encouraging the adoption of a “headquarters + base” model, these regions can relocate production segments to resource-rich neighboring areas through cross-regional collaboration. They should vigorously develop urban and contract agriculture tailored to high-end and personalized demands.

For regions with technological inefficiency, such as Handan, differentiated technical solutions should be designed according to the scale of operations. For smallholder farmers, provide user-friendly micro-agricultural machinery and biological control technologies; for large agricultural enterprises, supply IoT monitoring and full-industry-chain digital systems. Increase fiscal subsidies for projects with high applicability and alignment.

6.2.2. Build a “Technology-Scale” Synergistic Mechanism

Establish a two-way resource allocation mechanism guided by fitness-for-purpose principles. For large-scale intelligent agricultural machinery and automated cold chain systems characterized by high fixed costs, calculate the minimum effective scale threshold required to achieve break-even points. In formulating policies for land transfer and high-standard farmland development, prioritize support for new types of business entities whose operational areas meet this threshold. This ensures advanced technologies have necessary application platforms, thereby avoiding technical underutilization due to insufficient scale. Establish a regionally appropriate technology access catalog. In areas with complex topography and highly fragmented land, prioritize screening and promoting compact, lightweight, and bio-based technologies while restricting the entry of unsuitable large-scale, capital-intensive equipment.

Resolve the structural contradiction between small-scale, dispersed operations and the economies of scale inherent in modern agricultural technologies through innovative agricultural production organization models. Cultivate specialized agricultural social service organizations to support comprehensive operational services across all production stages. Utilize service outsourcing to enable shared use of advanced technologies and equipment among different agricultural operators. This allows small-scale farmers to gain efficiency dividends from scaled operations without altering land contracting relationships, effectively substituting service scaling for land consolidation. Establish tightly integrated industrial consortiums. Leverage the technology spillover effects of leading enterprises to disseminate standardized varieties, technical standards, and management protocols to cooperatives and farmers, ensuring effective penetration of technological elements into production. Simultaneously, utilize the organizational strengths of cooperatives to achieve contiguous standardized production, guaranteeing the implementation of these technologies.

6.2.3. Strengthen the Resilience of Regional Supply Chain Networks Based on Functional Complementarity

Based on the differences in resource endowments and efficiency advantages revealed by this study’s measurements, clearly define the core functional positioning of each node city within the provincial supply chain network to avoid resource wastage caused by homogeneous competition. Leveraging the advantages of coastal cities like Tangshan, Cangzhou, and Qinhuangdao in infrastructure and circulation efficiency, establish their functional positioning as regional hubs for agricultural product logistics distribution and export. Prioritize strengthening their port logistics, bonded warehousing, and international trade services to enhance the supply chain’s outward reach. Leverage the comparative advantages of northern Hebei cities like Zhangjiakou and Chengde in ecological environments and green brand development to establish their roles as suppliers of high-quality green agricultural products and ecological barriers for the Beijing-Tianjin-Hebei region. Focus on developing off-season vegetables, eco-farming, and certified organic agriculture while strengthening brand outreach capabilities. Leverage the resource advantages of cities like Shijiazhuang and Baoding in research institute clusters and technological R&D to establish their role as a source of supply chain technological innovation. Prioritize the development of seed industry R&D, smart agriculture technology integration, and supply chain financial services to export technological and capital elements throughout the province.

Establish a cross-regional supply chain coordination mechanism. Create a long-term, stable “production area-sales area” interest linkage mechanism. Establish direct procurement and supply relationships between provincial production bases and Beijing, Tianjin, and Xiongan. Through the “contract farming + joint base development” model, achieve precise matching between production and market ends to reduce market volatility risks. Accelerate the construction of a province-wide green agricultural product supply chain big data center to enable real-time data sharing for production monitoring, logistics tracking, inventory alerts, and price announcements.