Modeling the Economic Impacts of Indonesia’s Zero Over-Dimension Over-Load Policy: A Logistics System Governance Perspective

Robust logistics management is widely recognized as a critical determinant of supply chain efficiency, business performance, and broader economic competitiveness [1], [2]. The logistics and transportation industry acts as the fundamental pillar of both domestic and cross-border trade, dictating the efficiency of national supply chains [3]. In the context of global trade integration, the capacity to streamline goods movement is directly correlated with a nation’s competitive advantage. High logistics costs relative to GDP are widely recognized in global literature as a primary barrier to a nation’s trade competitiveness [4], [5]. However, Indonesia currently grapples with structural inefficiencies, evidenced by a logistics cost-to-GDP ratio of 14.29%, which significantly hampers national competitiveness [6]. Previous research suggests that sustainable logistics performance depends on coordinated governance across infrastructure, institutions, and transport operations [7], a framework supported by global logistics assessments [5], yet Indonesia faces challenges in optimizing this sector relative to its regional peers. This structural gap is also illustrated in Figure 1, which compares Indonesia’s logistics performance and cost burden with those of selected ASEAN peers.

However, existing studies have largely examined logistics efficiency from operational, infrastructural, or transport-policy perspectives, while relatively limited attention has been paid to how compliance-oriented regulation reshapes the logistics system as a whole. As noted by the analysis [8], land transportation accounts for a substantial portion of domestic logistics expenses, meaning inefficiencies in this mode reverberate throughout the entire economy. This is consistent with macroeconomic measurements indicating that land transport inefficiencies reverberate throughout the entire economy as well [4]. Previous research suggests that logistics inefficiencies are often associated with weak institutional coordination, which reinforces broader structural weaknesses in the sector [9]. A prominent example is the widespread use of Over-Dimension Over-Load (ODOL) vehicles. From a transport economics perspective, while overloading reduces unit transport costs for private operators, it represents a market failure that transfers substantial external costs to the public sector through accelerated pavement degradation [10]. Although operators often use overloading to reduce unit transport costs, the practice imposes substantial external costs on infrastructure and road safety [11]. As a result, private cost savings are effectively sustained by losses borne by the public sector.

The economic ramifications of these externalities are profound. Excessive loads cause infrastructure deterioration estimated to cost the state up to IDR 43 trillion annually and contribute to 22.4% of traffic accidents involving freight transport on key corridors. Studies on pavement engineering globally confirm that overloaded vehicles drastically reduce the technical lifespan of flexible pavements [12]. The study [13] found that ODOL vehicles drastically reduce the technical lifespan of roads from a planned 10 years to merely 3 years, imposing a perpetual fiscal burden for maintenance. Furthermore, recent statistics indicate that 73% of freight transport violations in 2024 were related to dimension and load infractions, signaling a systemic market failure where private gains are subsidized by public losses in safety and infrastructure [14].

In response to these challenges, the government has committed to a full implementation of the Zero ODOL policy as part of a comprehensive logistics reform. This initiative includes rigorous enforcement via Weigh-in-Motion (WIM) systems, regulatory harmonization, and digital supervision [15]. However, its implementation remains contested because the short-term economic burden on transport operators and commodity prices may be substantial [16]. On one hand, strict enforcement is predicted to escalate freight rates and trigger cost-push inflation due to reduced effective carrying capacity [17], [18]. On the other hand, the policy promises long-term efficiency gains through infrastructure preservation and enhanced safety [19].

Existing research on ODOL has mainly focused on transport regulation, WIM enforcement, and logistics efficiency [15], [20]. Less attention has been given to the wider economic effects of this type of regulation, particularly those related to rising transport costs, infrastructure burdens, and changes in fleet investment [21]. From this perspective, the logistics sector should not be viewed only as a transport service, since regulatory intervention can also influence capital renewal and public expenditure. To examine these broader effects, this study uses the CGE IndoTERM model together with Leontief price analysis and social cost estimation to assess whether the long-term economic benefits of compliance are sufficient to offset the short-term increase in logistics costs. This study tests two primary hypotheses:

H1: The implementation of the Zero ODOL policy is expected to increase logistics costs and short-term inflationary pressure [22], [23].

H2: In the long run, the Zero ODOL policy is expected to support GDP growth through fleet investment and lower social and infrastructure-related costs [24], [25].

This study adopts a mixed-methods approach focused on DKI Jakarta and West Java, two of Indonesia’s most important logistics regions. These provinces were selected as the main study area because they account for a large share of freight movement and make a substantial contribution to national GDP. While the concentration on Java limits direct generalization to archipelagic regions with different infrastructure disparities (e.g., Sumatra or Sulawesi), this sample serves as a robust proxy for the national logistics ecosystem’s response to regulatory pressure. To ensure empirical validity and representativeness, primary data were gathered in 2025 using a stratified purposive sampling technique. The survey targeted 500 respondents, comprising logistics service providers, trucking operators, and cargo owners, including producers and traders, selected to cover various fleet scales ranging from owner-operators to corporate fleets. This stratification makes it possible to distinguish between small operators that may exit the market and corporate fleets with greater investment capacity. Data collection was conducted through online surveys and supplemented by Focus Group Discussions with logistics associations and regulators to refine assumptions regarding cost structures, trip patterns, and investment plans. The analysis also drew on secondary data, including the 2020 Indonesian Input-Output Table, regional GDP data, accident records from the National Police (Korlantas), and estimates of infrastructure losses from the Ministry of Public Works [6].

Quantitative analysis was conducted in three stages. First, the input-output analysis based on the Leontief price model was used to estimate the direct and indirect effects of rising transportation costs on the prices of 27 strategic commodities. The Leontief price model is expressed as follows:

where, $p$ denotes the price vector, $A$ represents the matrix of technical coefficients, and $v$ is the value-added vector [26].

First, within this framework, changes in primary input costs such as transportation are propagated throughout the economic sectors [26]. The output of this model serves as an exogenous price shock for the CGE simulation, allowing the macroeconomic effects to be evaluated without endogenizing the initial transport cost adjustment. Second, social cost analysis based on the human capital approach was used to estimate the economic savings associated with fewer accidents, including vehicle repair costs, productivity losses, and medical expenses [27]. To ensure robustness, parameter ranges (e.g., productivity loss of 17–50 days) were used rather than single point estimates, as detailed in the Appendix. Third, the CGE IndoTERM model, a dynamic multi-regional model, was used to simulate full macroeconomic impacts. Computable General Equilibrium models are widely acknowledged as the standard for evaluating the wider economic impacts of transport policies and infrastructure investments [28], [29]. It integrates shocks from fleet investment, operational costs, and budget reallocation under a budget-neutral reallocation assumption [24]. In practice, bureaucratic delays may affect the timing of such reallocation; however, the model assumes an efficient transfer in order to estimate the maximum potential opportunity cost of the policy.

The survey data indicate that compliance with fleet capacity limits requires logistics operators to increase trip frequency in order to maintain distribution volumes. This operational shift precipitates a non-linear escalation in variable costs, as the restriction on overloading necessitates the utilization of additional fleet units or increased rotation for the same aggregate cargo. This pattern is illustrated in Figure 2.

Furthermore, the data indicate clear differences in how individual cost components respond to the policy. While fixed costs such as truck rental increase moderately, variable costs associated with vehicle movement, particularly fuel and toll expenses, account for most of the increase in logistics operating costs. Taken together, the weighted effect of these cost components results in an overall increase of approximately 7.73% in transport operating costs. This aggregated cost increase is then introduced into the macroeconomic model as the main operating-cost shock in the subsequent simulation.

Despite these cost pressures, the IndoTERM simulation indicates a positive long-term macroeconomic outcome. The simulation shows that the policy increases GDP by 0.05% relative to the baseline, equivalent to a net output gain of IDR 14.3 trillion.

Although the policy raises national logistics costs by 4.58% and increases the logistics cost-to-GDP ratio from 14.29% to 14.94% in the short term, these effects are gradually moderated by higher fleet investment and lower road preservation costs. As shown in Table 1, fleet investment increases by 6.74%, equivalent to IDR 42.4 trillion, while road preservation savings amount to IDR 2.84 trillion. Together, these factors support a modest but positive increase in aggregate output.

The findings support Hypothesis 1 (H1). The Zero ODOL policy generates a substantial cost shock, increasing operating costs by 7.73% and raising the logistics cost-to-GDP ratio to 14.94%. This is consistent with earlier concerns about the structural burden of logistics costs in Indonesia [17]. The results suggest that part of the sector’s earlier cost efficiency was achieved by shifting infrastructure and safety-related costs to the public sector. Once these external costs are brought back into the cost structure, the apparent efficiency of overloading becomes less sustainable. The transmission of these cost increases is not uniform across sectors. In line with system-level economic analyses, sectors with strong intersectoral linkages are highly exposed to transport cost shocks [29]. In line with previous work on input-output price propagation [30], sectors with strong intersectoral linkages, such as construction and manufacturing, are more exposed to rising transport costs. This is particularly relevant for commodities such as beef, where earlier supply chain studies have shown high sensitivity to logistics conditions [31]. Operators respond to load restrictions by increasing the number of operating vehicles, which in turn raises fuel and toll expenses. The stronger price response observed in sand and cement also points to limited modal substitution for bulk commodities, which is consistent with earlier research on transport rigidities [32].

The CGE IndoTERM simulation also supports Hypothesis 2 (H2). Contrary to the concern that higher logistics costs would weaken overall economic performance, the simulation indicates a long-term GDP increase of 0.05%. This positive outcome appears to be driven by two main channels: stronger capital formation and the reallocation of fiscal resources, as illustrated in Figure 3.

First, the policy appears to accelerate fleet modernization, which is consistent with an investment-led adjustment process [33]. The results suggest that approximately 35% of operators, mainly larger corporate fleets with better access to capital, are prepared to modernize their operations. By contrast, smaller operators are more likely to delay adjustment or exit the market. This regulatory pressure encourages capital renewal, as reflected in the projected 6.74% increase in fleet investment, equivalent to IDR 42.4 trillion. This rise in investment may also create additional demand for the domestic automotive sector and related upstream industries.

Second, the simulation suggests an improvement in the allocation of public resources. Previous research has shown that, although infrastructure maintenance is essential for development, excessive spending on premature road damage represents an inefficient use of public funds [25]. Relocating these funds to productive investments fosters spatial equity and efficiency [29]. The results indicate that the Zero ODOL policy generates road preservation savings of IDR 2.84 trillion. This creates fiscal space that can be redirected from repair expenditure to more productive forms of infrastructure investment. When combined with lower social costs, particularly those associated with fewer traffic accidents [34], the overall effect is a positive net economic outcome. This result is also consistent with earlier evidence that infrastructure compliance can support regional economic growth [35].

The findings have several practical implications for logistics governance and engineering management. First, the implementation of the Zero ODOL policy should be carefully timed to reduce short-term price pressures, particularly in food-related sectors such as rice [36]. This is important because the simulation results indicate that logistics cost pressures become more pronounced during periods of high demand. Second, the projected investment requirement of IDR 42.4 trillion suggests that fiscal incentives or concessional financing may be needed to support fleet renewal. Without such support, smaller operators may struggle to adjust and may postpone modernization or leave the market [16]. Third, the effectiveness of the policy will depend on reliable enforcement technologies, including WIM systems, to strengthen compliance and improve the allocation of enforcement resources [37].

This study finds that the Zero ODOL policy can generate a positive net economic effect despite short-term adjustment costs. The results indicate that implementation is likely to increase national logistics costs by 4.58% and create inflationary pressure in several sectors in the short term. At the same time, the CGE IndoTERM analysis suggests that the policy can support GDP growth by 0.05%, equivalent to IDR 14.3 trillion, through higher fleet investment, improved infrastructure efficiency, and lower social costs. In this sense, the transition to a compliant logistics system should be understood not only as a regulatory adjustment, but also as a longer-term investment in system efficiency and competitiveness. The findings also suggest that implementation should be carefully timed to avoid peak demand periods and accompanied by fiscal support for vulnerable operators.

This study has several limitations. First, the primary data are concentrated in DKI Jakarta and West Java. Although these provinces account for a substantial share of national GDP and truck activity, the results may not fully reflect the characteristics of inter-island transport in other regions, such as Sumatra or Sulawesi. Second, the inflation simulation applies a ceteris paribus assumption to provinces outside the study area, which may understate spillover effects across the archipelago. Third, the model assumes a stable global energy price environment. Given the large share of fuel in transport operating costs, volatility in global crude oil prices could affect the estimated economic balance of the policy. Future research could extend the analysis to Outer Java corridors in order to provide a broader picture of how the policy may affect Indonesia’s archipelagic logistics system.