Structural Evolution and Determinants of the Agricultural Machinery Trade Network Among RCEP Members

1. Introduction

Agricultural machinery provides an essential material and technological foundation for food security, higher agricultural productivity, and the timely completion of farm operations [1,2,3]. Unlike conventional approaches to mechanization that emphasize labor substitution and scale expansion, sustainable agricultural mechanization gives greater weight to economic viability, environmental compatibility, social inclusion, and adaptation to local farming systems. Appropriate mechanization can reduce drudgery, improve land and labor productivity, support conservation tillage, post-harvest handling, and agro-processing, and thereby enhance the operational efficiency of food systems [2,4,5,6].

The sustainability effects of agricultural machinery are determined not by machinery inputs alone but by the production technologies and practices they embody. Precision seeding, variable-rate input application, water-efficient irrigation, reduced or zero tillage, energy-efficient drying, and digital control can lower the use of fuel, water, fertilizers, and pesticides while maintaining or increasing output [2,7]. International trade can broaden access to relevant equipment, components, and technical knowledge. The extent of diffusion nevertheless depends on equipment prices, financing conditions, skills training, maintenance capacity, and the compatibility of machinery with local crop types, farm sizes, water conditions, and climate risks [4,5,6,8].

RCEP entered into force on 1 January 2022. Its member economies differ substantially in manufacturing capacity, agricultural production structures, and market demand [9,10,11,12,13]. Available industrial and trade patterns suggest that Japan and the Republic of Korea have relatively strong capabilities in high-value equipment, precision components, and intelligent control systems; China combines a broad manufacturing base with scale and cost advantages; Singapore is well positioned to support logistics organization and supply-chain services; ASEAN members represent expanding markets for scale-appropriate agricultural machinery; and Australia and New Zealand have demand associated with large-scale cropping and livestock production. These production-demand complementarities provide an economic and functional basis for regional agricultural machinery trade and potential cross-border technology flows [8].

Regional trade integration generates both efficiency gains and concentration risks. Dense agricultural machinery trade ties and specialized supply systems can reduce procurement costs, shorten delivery times, and facilitate technological learning. Conversely, excessive dependence on a single country or a small number of producers of critical components may allow localized shocks to spread rapidly through the trade network [14,15]. The sustainability of agricultural machinery trade therefore depends not only on cost and efficiency improvements but also on diversified sources of supply, interoperable technical standards, inventories of critical components, maintenance capacity, and coordination across communities. Because agricultural machinery is a durable capital good, sustainable governance should extend across the product lifecycle, including environmental performance assessment, durable and repairable design, component reuse, remanufacturing, recycling, and regulated end-of-life treatment [16,17,18,19].

Against this background, this study addresses three questions. How has the structure of the agricultural machinery trade network among RCEP members evolved? How have the relative positions of individual members changed within the regional network? How are economic, resource, geographical, cultural, and institutional differences associated with bilateral agricultural machinery trade ties, and what possible implications follow for sustainable technology diffusion and regional supply-chain resilience? This study provides significant insights into the development of the agricultural machinery trade network within the RCEP region, the influencing factors, the promotion of sustainable technology diffusion, and the regional supply chain resilience of agricultural machinery.

2. Literature Review

The literature relevant to this study comprises three strands. The first examines the structure and evolution of international trade networks. As global trade has shifted from linear bilateral relationships toward interactions among multiple actors, conventional trade analysis has become less able to capture structural dependence among countries. Complex network analysis and social network analysis have therefore been widely applied to international trade. These studies typically treat countries as nodes and bilateral trade flows as directed weighted edges, and investigate the topology and evolution of global trade networks in terms of connectivity, node centrality, and community structure [20,21,22,23,24,25,26,27]. Related research on international economic integration and the dynamics of global trade communities has documented core–periphery structures and regional differentiation [28,29,30]. Within the RCEP context, recent studies have examined the structural evolution and formation mechanisms of manufacturing trade networks [31,32]. This work provides an important methodological foundation, but network research remains limited for agricultural machinery, which simultaneously functions as a capital good, a carrier of technology, and an agricultural input.

The second strand concerns agricultural mechanization and sustainable development. Early research focused mainly on the economic effects of machinery through labor substitution, productivity growth, and technology adoption [1]. More recent work has expanded to resource use, climate adaptation, social inclusion, and food-system transformation [2,3]. Farm size and land fragmentation directly affect machinery adoption and operating efficiency [33], whereas machinery-service outsourcing and specialization can ease the fixed-investment constraints faced by smallholders [34]. Studies further show that scale-appropriate equipment, leasing services, and cooperative use can improve smallholder access to technology, although diffusion still depends on financing, skills training, local repair networks, and supportive institutions [4,5,6,8]. Regarding environmental outcomes, precision agriculture can improve input-use efficiency and offer emissions-reduction potential [7]. Durable design, repair, remanufacturing, and material recovery are also integral to sustainable lifecycle governance of agricultural machinery [16,17,18,19].

The third strand addresses trade in agricultural machinery products and its determinants. Existing studies examine trade scale and product structure [35], export flows and trade potential [36,37,38], intra-industry trade [39], developments in agricultural machinery markets in major advanced economies [40], and the comparative competitiveness of agricultural machinery products from China and India [41]. Research on export competitiveness evaluates Chinese agricultural machinery products in terms of comparative advantage, market share, export technological sophistication, and product quality. It generally finds continued growth in China’s agricultural machinery exports but substantial heterogeneity in competitive advantage, quality tier, and market performance across products [42,43,44,45]. Trade-policy shocks can also affect agricultural machinery trade through import costs, product substitution, and supply-chain adjustment [46]. Overall, this literature relies largely on gravity models, constant-market-share models, and competitiveness indices to explain bilateral trade volumes or export performance, while paying less attention to the overall regional structure of agricultural machinery trade from a relational-network perspective.

Taken together, the literature provides a strong basis for understanding trade-network structures, the effects of agricultural mechanization, and trade in agricultural machinery, but three gaps remain. First, trade-network studies focus mainly on global trade or manufacturing as a whole, whereas research on agricultural machinery trade concentrates on export competitiveness, trade potential, and bilateral determinants. The overall evolution of the RCEP agricultural machinery trade network has not been systematically identified. Second, few studies examine agricultural machinery trade simultaneously through the lenses of structural readiness for technology diffusion and exposure to supply-chain concentration. Consequently, they reveal little about how connectivity, centralization, and community structure may enable cross-border technology flows while also creating concentration risk. Third, conventional regression models cannot adequately account for the structural dependence inherent in relational data between countries. This article attempts to provide a supplement. This study makes three contributions: first, it constructs a directed weighted agricultural machinery trade network among RCEP member countries from 2004 to 2023, characterizing its evolution at the levels of the overall network, nodes, and communities; second, it evaluates this network as critical infrastructure facilitating technology flow, supply chain coordination, and inclusive mechanization, exploring its potential links to sustainable technology diffusion and regional supply chain resilience; third, it employs QAP methods to identify statistical associations between factors such as economic scale, agricultural resources, geography and culture, exchange rates, trade structure, and institutional distance, and bilateral trade relationships.

3. Evolution of the Agricultural Machinery Trade Network Among RCEP Member States

3.1. Data and Product Coverage

This study uses bilateral agricultural machinery trade data for 2004–2023 from the United Nations Comtrade Database (UN Comtrade) [47]. Because statistical definitions and product coverage differ across members, this study follows the function- and production-stage-based classification adopted in related research [46] and groups six-digit HS products into nine categories: tillage and land preparation, planting and fertilizing, harvesting, post-harvest handling, primary processing of agricultural products, agricultural transport, livestock machinery, agricultural power machinery, and other agricultural machinery (Table 1). This classification broadly covers the pre-production, production, and post-production stages of agriculture and permits the network analysis to capture trade channels through which machinery, components, and related technical knowledge may move.

Table 1. Classification and HS Codes of Agricultural Machinery Products.

3.2. Node Selection and Network Construction

In the trade network, countries constitute nodes and bilateral trade relationships form directed edges; trade among multiple countries therefore creates a directed weighted network [20,21,22,23]. The sample comprises all 15 RCEP members: China, Japan, the Republic of Korea, Australia, New Zealand, and the ten ASEAN members. Bilateral 15 × 15 trade matrices are constructed from data for 2004–2023. To represent changes across stages of network development, 2004, 2010, 2015, 2020, and 2023 are selected as benchmark years. A bilateral trade value of USD 1 million is used as the edge threshold to identify economically substantive capital-goods relationships and to prevent low-frequency, very small, or one-off transactions from mechanically inflating network density. The cutoff is an operational definition of the high-value trade backbone, not a minimum level required for technology diffusion. It may understate fragmented participation by smaller economies, including Laos, Cambodia, Myanmar, and Brunei; consequently, findings concerning peripheral participation and network density are conditional on this threshold. Alternative cutoffs could change the number of low-value ties and should be examined in future sensitivity analyses. Network visualization and indicator calculation are performed in Gephi 0.9.2 and UCINET 6, respectively [47,48].

3.3. Evolution of Agricultural Machinery Trade Network Patterns

Gephi is used to visualize the agricultural machinery trade network among RCEP members. Nodes represent member states, and node size reflects a country’s share of total regional agricultural machinery trade. Directed edges represent export relationships and point from the exporter to the importer; edge thickness reflects bilateral trade flows. Figure 1 presents the network structures in 2004, 2010, 2015, and 2023. China, Japan, the Republic of Korea, Singapore, Thailand, Vietnam, Malaysia, and Australia display relatively high node weights in the selected benchmark years, whereas Laos, Myanmar, Cambodia, Brunei, and New Zealand display relatively low weights under the USD 1 million threshold. The visualized network therefore exhibits a marked hierarchy in high-value trade participation.

Figure 1. Evolution of the Agricultural Machinery Trade Network, 2004, 2010, 2015, and 2023. Note: In Figure 1, from left to right, these represent the years 2004, 2010, 2015, and 2023.

China’s observed position in the regional agricultural machinery trade network strengthened over the sample period. In 2004, China already had substantial trade ties with the Republic of Korea, Japan, Thailand, Vietnam, and Malaysia, with the China–Republic of Korea relationship particularly prominent. China’s node size subsequently expanded, and its connections with Japan, the Republic of Korea, Vietnam, Malaysia, Thailand, and Singapore became markedly thicker. By the later part of the sample period, a larger share of high-value trade flows involved China, which is consistent with an expanding hub position in regional agricultural machinery imports, exports, and industrial-chain linkages.

Japan, the Republic of Korea, and Singapore retained comparatively important network positions throughout the sample period. Japan and the Republic of Korea consistently ranked among the members with high node weights and maintained strong trade ties with China, Vietnam, Malaysia, and Thailand. In conjunction with the industrial patterns discussed in the literature, these network positions are consistent with important roles in high-value equipment and component supply. The China–Republic of Korea link was promi