Assessing the Economic Impact of the IMO Mid-Term Measures on the Korean Economy

Abstract

The International Maritime Organization (IMO) established an initial strategy for maritime decarbonization and later specified its long-term target of achieving net-zero strategy by 2050. The institutional framework for mid-term measures was introduced by IMO, and mid-term measures were originally scheduled to be adopted at the end of 2025, but will be re-discussed in 2026 due to opposition from some current member states; South Korea relies on maritime transport for over 99% of its total import/export volume, meaning that the national shipping sector constitutes a core infrastructure supporting trade-driven economic activity. However, mid-term measures are expected to increase logistics costs and weaken route competitiveness and contract markets, affecting individual shipping companies and the entire export–import industrial base. However, quantitative analyses of the cross-industry ripple effects remain limited. Existing studies assess regulatory burdens on shipping but rarely estimate economy-wide spillovers or provide empirical guidance for policy strategies. Therefore, research is needed to move beyond regulatory interpretation, assess domestic response capabilities, and quantitatively analyze the macroeconomic impacts of mid-term measures to support sound policy decision-making. This study aims to quantitatively evaluate the nationwide economic impact of the IMO mid-term measures and propose strategic policy solutions for effective domestic responses.

1. Introduction

The IMO (International Maritime Organization) has been striving to construct the structures required to decrease maritime greenhouse gas (GHG) emissions. For example, the IMO prepared an initial strategy for maritime decarbonization and stated its long-term target to achieve net-zero emissions by 2050. According to the IMO’s official announcement, Member States adopted the 2023 IMO Strategy on the reduction in GHG emissions from ships, which includes “a common ambition to reach net-zero GHG emissions from international shipping by or around 2050” (Revised GHG reduction strategy for global shipping adopted) [1]. The institutional framework for mid-term measures for reducing GHG emissions from international shipping was originally scheduled to be adopted at the end of 2025, but will be re-discussed in 2026 due to opposition from some current member states. This framework builds upon the IMO strategy, which also specifies interim checkpoints for 2030 and 2040 and promotes the uptake of zero- and near-zero-emission fuels, thereby providing a structured regulatory pathway for implementation (IMO net-zero shipping talks to resume in 2026) [2].

Kök et al. [3] argue that the IMO’s GHG mid-term measures are not merely environmental regulations but rather a transformative policy instrument that restructures both competitive dynamics and the underlying cost architecture of the global maritime industry. As noted by Cret et al. [4], earlier short-term technical measures primarily targeted enhancements in vessel design and operational efficiency, whereas mid-term measures necessitate a comprehensive industry response encompassing the imposition of tangible carbon-cost signals, incentivized fuel transition, and the introduction of a market-based trading framework to verify emission-reduction performance.

Lehmann et al. [5] further emphasize that mid-term measures should establish reduction requirements for all vessels based on well-to-wake (full lifecycle) emissions. The framework adopts a multi-layered performance evaluation system aligned with Base and Direct Targets while also mandating the purchase of remedial units (RUs) for non-compliance. Collectively, these mechanisms represent the first institutionalized form of global maritime carbon pricing. Consequently, compliance can no longer be achieved through technological upgrades alone; instead, regulatory outcomes hinge on strategic choices spanning voyage optimization, fuel procurement, fleet investment, and emission performance management. Recent policy discussions and regulatory proposals at the IMO level further support this interpretation, highlighting the introduction of market-based mechanisms and carbon-cost signals within the sector (IMO approves net-zero regulations for global shipping) [6].

The implications for Korea are particularly significant given that more than 80% of its import–export logistics depend on seaborne transportation. Shipping is widely recognized as accounting for over 80% of global trade volume, underscoring the structural dependence of trade-oriented economies such as Korea on maritime transport (Net-zero by 2050: Achieving shipping decarbonization through industry momentum and the new ambition at IMO) [7]. Although shipping is a core infrastructure within Korea’s trade-driven economy, the national fleet—dominated by small- and medium-sized bulk carriers—has limited capacity to undertake large-scale fuel transition or high-efficiency fleet renewal.

Beyond firm-level compliance challenges, the mid-term measures are expected to trigger economy-wide spillovers, including increased logistics costs, weakened route competitiveness, and potential contractions in trade-linked industries. Such effects are consistent with broader findings in the literature on carbon regulation, where cost pass-through and supply-chain impacts are commonly observed following the introduction of carbon pricing mechanisms. Despite the importance of these impacts, quantitative research examining the policy structure, cost transmission, and inter-industry ripple effects of mid-term measures remains scarce. Existing studies largely confine their analysis to shipping-sector outcomes, and no rigorous assessment has yet quantified the economy-wide or societal implications at the national level. Accordingly, there is a pressing need for research that moves beyond the descriptive interpretation of regulations to evaluate the adaptive capacity of domestic shipping and quantitatively assess macroeconomic impacts, thereby providing empirical evidence that strengthens the validity and effectiveness of national policy decisions.

Therefore, this study estimates the broader economic effects of the IMO’s mid-term measures on the Korean economy and presents a structured policy framework for national response and institutional improvement. By employing Input–Output (IO) analysis to measure not only direct impacts on shipping but also indirect and downstream effects across the national production network, this study quantifies how maritime decarbonization regulations propagate through the economy and provide robust, evidence-based insights for parties involved in global maritime affairs.

The composition of this paper is as follows. Section 2 reviews previous studies that performed IO analysis and previous studies that performed analysis on decarbonization. Section 3 presents the IO analysis methodology used in this study and presents the definition of the shipping industry as defined in this study. Section 4 quantitatively presents the impact of IMO mid-term measures on the Korean shipping industry and the Korean economy based on the analysis results of IO in this study. Section 5 summarizes the research results and proposes future research directions.

2. Literature Review

2.1. Preliminary Studies Using IO Analysis

Bunsen and Finkbeiner [8] emphasize that IO analysis is a well-established tool in research, commonly applied to macro-level footprint assessments that distribute economic externalities across agents according to intersectoral linkages. Yet, IO databases are typically constructed using aggregated data, which unavoidably reduces information and may distort analytical outcomes. This paper presents a concise, practice-oriented numerical introduction to IO analysis, with particular emphasis on the effects of data aggregation. Using a stylized example, it first derives production-based and consumption-based inventories from a simple 2 × 2 IO table, and then contrasts these results with those obtained from a corresponding disaggregated 4 × 4 table. The comparison clearly illustrates how aggregation can lead to misallocation and potentially misleading policy conclusions. Overall, the study provides a compact, numerically grounded introduction to IO analysis for sustainability practitioners, distinguishing itself through its intuitive and non-econometric approach to the issue of data aggregation.

Achieving carbon neutrality requires coordinated action across countries, regions, and industrial sectors, and IO analysis serves as a key framework for macro-scale carbon emission research. Sheng et al. [9] systematically reviews 1156 IO-based studies on carbon emissions and applies bibliometric techniques to examine publication trends, scholarly influence, international and institutional collaboration, and keyword clustering. The results indicate rapid growth in this research field, with China, the United States, and the United Kingdom identified as leading contributors. Beyond quantitative mapping, the review qualitatively assesses research themes and methodological approaches, revealing strong attention to carbon footprint inequality and trade-related embodied carbon flows, alongside notable gaps in city-level analyses and emerging industries. The paper highlights the need for more detailed and comprehensive IO-based carbon studies and outlines future research directions in data development, thematic focus, methodological advancement, and policy application to support effective carbon reduction strategies and global carbon neutrality efforts.

Attinasi et al. [10] proposes a new approach to increase the sectoral detail of Inter-Country Input–Output (ICIO) tables, mitigating aggregation bias that is especially severe for low-substitutability goods critical to the green transition. Applying this method, the paper constructs a disaggregated ICIO table that separates 129 energy-transition-related products into eight green sectors, including batteries, vehicles, rare earths, and renewable energy technologies. Using a calibrated multi-country, multi-sector trade model, the analysis simulates East–West decoupling in green supply chains. The results indicate sizable economic impacts, with welfare losses of up to 3% and a 20% decline in inter-bloc trade despite partial trade diversion. Furthermore, the findings show that fragmentation of green supply chains raises emissions intensity, highlighting that trade barriers in clean-energy sectors simultaneously weaken economic efficiency and climate mitigation goals.

Yue et al. [11] examines the allocation of global biodiversity conservation responsibilities amid accelerating biodiversity loss under economic globalization. Using an environmentally extended multi-regional IO model, it estimates biodiversity loss footprints, distinguishing internal and external impacts across regions. Structural path analysis and social network analysis are further applied to trace transmission mechanisms and identify international biodiversity protection communities. The results reveal pronounced cross-country differences in biodiversity loss footprints, with Indonesia, China, the United States, Mexico, and Brazil showing the largest impacts through distinct pathways. While most biodiversity loss occurs domestically, over one-quarter is driven by global production chains, ultimately linked to major economies such as the United States, Japan, India, and China. Agriculture generates the largest direct footprint, whereas the food and beverage sectors dominate indirect impacts. The analysis identifies eight global biodiversity protection communities and suggests differentiated conservation strategies tailored to their structural roles.

Yu [12] reviews the use of multi-sector IO models in analyzing transportation–economic interactions at urban and regional scales. IO modeling is widely applied due to its ability to capture inter-industry linkages and its intuitive multiplier framework. The study introduces core IO concepts and surveys transportation-related IO research published since 2000, covering single-region, multi-region, and random utility-based multi-region models. Key methodological characteristics and modeling challenges are identified, including restrictive assumptions, limited treatment of spatial dynamics, and static economic representations. The review highlights the need for improved handling of sectoral aggregation, household sector specification, and stronger integration with transportation forecasting, and concludes with directions for future IO-based transportation research.

2.2. Preliminary Studies on Decarbonization

Plachinda et al. [13] emphasize the need for cross-sectoral integration in modeling carbon emissions to support effective net-zero strategies. Using the Idaho national laboratory campus as a case study, it develops a web-based net-zero engineering support tool (NEST) that applies historical data to project CO₂ emissions under different decarbonization pathways. The tool enables stakeholders to visualize emissions, electricity use, and costs, supporting strategic decision-making. A demonstration focusing on vehicle electrification shows that aggressive replacement schedules before 2030 can impose substantial capital constraints, highlighting the value of NEST in testing alternative timelines and prioritizing options that balance emissions targets with budget feasibility.

Bach and Hansen [14] explain that the international shipping accounts for roughly 3% of global greenhouse gas emissions and faces increasing pressure to decarbonize. Despite the adoption of the IMO’s Initial GHG Strategy in 2018, existing policy instruments remain fragmented and insufficient to meet stated emission-reduction targets. This outcome is puzzling given that shipping is governed by a single global regulator, which in principle enables coherent and comprehensive regulation. This paper examines the underlying reasons for this gap and identifies three key challenges: limited institutional capacity within the IMO to regulate diverse and emerging technologies, ambiguity surrounding the scope of the IMO’s regulatory mandate, and persistent political disagreement among member states. Addressing these challenges is essential if the IMO is to develop a more consistent and effective policy mix capable of delivering meaningful emission reductions.

Karmaker et al. [15] examine the role of renewable energy in accelerating hydrogen production as a pathway to decarbonizing energy systems. While hydrogen offers a potential substitute for fossil fuels, its current production remains largely fossil-based. Using a random-effects regression with global panel data optimized for cost and carbon constraints, the analysis evaluates three hydrogen-related policy regimes under geographically differentiated carbon reduction targets. The results show that greater deployment of renewable energy significantly enhances future hydrogen production across all regimes, suggesting that coordinated renewable investment and equitable carbon reduction policies are essential for fostering a sustainable, low-carbon hydrogen economy.

Psaraftis et al. [16] analyze policy instruments aimed at reducing greenhouse gas emissions in international shipping, with particular attention to market-based measures such as carbon pricing and fuel levies. The study reviews alternative regulatory approaches and evaluates their effectiveness in achieving emission-reduction targets while maintaining economic efficiency. The findings suggest that although market-based mechanisms can provide cost-effective incentives for emission reduction, their implementation is complicated by uncertainties related to fuel price volatility, regulatory harmonization, and distributional impacts across countries. The study highlights the importance of designing policy frameworks that balance environmental effectiveness with economic feasibility in the global maritime sector.

Zincir [17] assesses the environmental and economic performance of an ammonia–diesel dual-fuel engine using real voyage data from a ship. Thirteen scenarios are examined based on different ammonia energy shares and production pathways (brown, blue, and green). The results show that emission outcomes vary widely by ammonia type: brown ammonia can generate higher or slightly lower CO₂ emissions than marine diesel oil, while blue ammonia achieves a 42.8% CO₂ reduction consistent with the IMO 2030 target. Green ammonia produced from wind energy delivers up to 79.2% CO₂ reduction, meeting the 2050 target, whereas solar-based green ammonia shows more limited benefits. Ammonia use substantially reduces SO_x and particulate matter emissions, but high ammonia fractions increase NO_x emissions, necessitating additional control technologies. From a cost perspective, brown ammonia is cheaper than marine diesel oil, blue ammonia is moderately more expensive, and green ammonia remains economically unviable due to its high cost.

Cariou et al. [18] investigate how increases in maritime transport costs, particularly those associated with environmental regulations, are transmitted to international trade. Using empirical analysis of shipping costs and trade flows, the study shows that higher maritime transport costs lead to measurable reductions in trade volumes, with varying sensitivity across commodities and regions. The results indicate that transport cost increases are partially passed through to freight rates and ultimately to import prices, thereby affecting global supply chains and trade competitiveness. This finding underscores the broader economic implications of maritime decarbonization policies beyond the shipping sector itself.

3. Methodology

3.1. Concept of the IO Table

An IO table is a comprehensive, matrix-structured statistical account that systematically records all inter-industry transactions of goods and services produced within a nation over a one-year period in accordance with standardized classification and accounting principles. IO analysis quantitatively identifies interdependencies among industries using the IO Table to examine the flows of production, supply, and use of goods and services across the national economy [19].

This method enables a detailed analysis of an economic system by assuming a relatively stable flow of goods and services between industrial sectors. In contrast to macro-level models—which often lack sector-specific resolution—IO analysis is advantageous for identifying production linkages among industries, clarifying the structural characteristics of the economy, and estimating the ripple effects of final demand on production, employment, income, and other macroeconomic variables. Accordingly, IO analysis is a highly valuable analytical tool for economic planning, forecasting, and policy evaluation. In rapidly shifting economies, such as Korea, where production technologies and industrial structures evolve quickly, IO-based structural analysis is most effective when applied as a complement to traditional macroeconomic models.

3.2. Methodological Framework for IO-Based Analysis

An IO table organizes, in matrix form and under a unified accounting framework, all transactions that occur within a given year—including inter-industry exchanges of goods and services, transactions between industrial sectors and final demand sectors, and transactions between primary factor inputs (labor and capital) and industry sectors.

Table 1. Basic structure of IO table.

		Intermediate Demand						Final Demand	Imports	Total Output
		1	2	$\dots$	$\mathit{j}$	$\dots$	$\mathit{n}$
Intermediate Input	1	$x_{11}$	$x_{12}$	$\dots$	$x_{1j}$	$\dots$	$x_{1n}$	$Y_1$	$M_1$	$X_1$
	2	$x_{21}$	$x_{22}$	$\dots$	$x_{2j}$	$\dots$	$x_{2n}$	$Y_2$	$M_2$	$X_2$
	$⋮$	$⋮$	$⋮$		$⋮$		$⋮$	$⋮$	$⋮$	$⋮$
	$i$	$x_{i1}$	$x_{i2}$	$\dots$	$x_{ij}$	$\dots$	$x_{in}$	$Y_i$	$M_i$	$X_i$
	$⋮$	$⋮$	$⋮$		$⋮$		$⋮$	$⋮$	$⋮$	$⋮$
	$n$	$x_{n1}$	$x_{n2}$	$\dots$	$x_{nj}$	$\dots$	$x_{nn}$	$Y_n$	$M_n$	$X_n$
Value Added		$V_1$	$V_2$	$\dots$	$V_j$	$\dots$	$V_n$
Total Input		$X_1$	$X_2$	$\dots$	$X_j$	$\dots$	$X_n$

shows the basic structure of the IO table.

When the IO table is interpreted column-wise, each industry records its intermediate input ($x_{ij}$), value added ($V_j$), and total input ($X_j$). The structural relationship between them is expressed as follows:

$$X_j={\displaystyle \sum _{i=1}^n{x}_{ij}+V_j=}{\displaystyle \sum _{i=1}^n{r}_{ij}{X}_i+V_j}$$

(1)

In Equation (1), $r_{ij}$ represents the direct output coefficient, calculated as the ratio of the intermediate input to the total output ($r_{ij}=x_{ij}/X_i$). The total input of sector $j$ is equal to the sum of all intermediate purchases made from other sectors plus the value added generated within sector $j$.

When the IO table is viewed row-wise, it records each sector’s intermediate demand ($x_{ij}$), final demand ($Y_i$), imports ($M_i$), and total output ($X_i$). Accordingly, the output structure of industry can be expressed as shown in Equation (2).

$$X_i={\displaystyle \sum _{j=1}^n{x}_{ij}+Y_i-M_i=}{\displaystyle \sum _{j=1}^n{a}_{ij}{X}_j+Y_i-M_i}$$

(2)

Here, $a_{ij}$ represents the proportion of input $i$ from sector $j$ required for the production of sector $a_{ij}=x_{ij}/X_j$, reflecting the inter-industry technical relationship. The total output is equal to the sum of inputs supplied to all other sectors for their production, plus final demand net of imports.

The demand-driven model estimates the level of output required to satisfy final demand and is defined as follows:

$$X={\left(I-A\right)}^{-1}\left(Y-M\right)$$

(3)

In Equation (3), ${\left(I-A\right)}^{-1}$ denotes the Leontief inverse matrix and each element ${\alpha }_{ij}$ represents the change in the total output of sector $i$ induced directly and indirectly by a one-unit increase in the final demand for sector $j$. The demand-driven model can measure the impacts of the final demand activity on all the sectors of the economy.

The supply-driven model estimates the level of output required to satisfy value added and is defined as follows:

$$X^{\prime }=V^{\prime }{\left(I-R\right)}^{-1}$$

(4)

In Equation (4), a prime means the transpose of the presented matrix and $R$ denotes the output coefficient matrix. Moreover, ${\left(I-R\right)}^{-1}$ means the output inverse matrix and each element ${\gamma }_{ij}$ means the total requirements of sector $j$ by a one-unit increase in value added of sector $i$. The supply-driven model will be suitable for measuring the effect of primary supply and input-oriented activities [20]. Equations (3) and (4) can be interpreted as the production-inducement effects according to the scenarios of the final demand or value added.

The value-added coefficient is defined as the ratio of value added ($V_j$) to total input ($X_j$), indicating the value added directly generated by one unit of production. The value-added coefficient matrix is obtained by converting the vector of the value-added coefficients into a diagonal matrix. By multiplying the value-added coefficient matrix by the production-inducement effect, the value-added inducement effect can be obtained. In such a way, we can measure the employment-inducement effect by multiplying the employment coefficient matrix by the production-inducement effect.

3.3. I-O Table and Data Transformation

To evaluate the economic impact of the IMO mid-term measures, we employ the original benchmark 2023 I-O table published by Bank of Korea [21]. For analyzing the IMO mid-term measure-based scenarios, we aggregate the original I-O table into 35-sector tables, including shipbuilding industry and shipping industry, as depicted in the first column of

Table 2. Industrial spillover effects of the shipping industry (supply-driven IO model).

Industry		Production Inducement		Value-Added Inducement		Employment Inducement (Persons per KRW 1 Billion)
1	Agriculture, Forestry & Fisheries	18	0.0165	17	0.0078	27	0.0203
2	Mining Products	34	0.0039	32	0.0016	32	0.0072
3	Food & Beverage Manufacturing	5	0.0479	7	0.0113	11	0.0765
4	Textiles & Leather Products	17	0.0182	25	0.0033	23	0.0306
5	Wood, Paper & Printing	24	0.0092	28	0.0027	26	0.0216
6	Coal & Petroleum Products	22	0.0103	31	0.0019	34	0.0007
7	Chemical Products	3	0.0943	4	0.0228	8	0.1101
8	Non-metallic Mineral Products	12	0.0303	16	0.0080	18	0.0532
9	Basic Metal Products	10	0.0345	18	0.0067	25	0.0236
10	Fabricated Metal Products	11	0.0312	9	0.0101	12	0.0684
11	Computers, Electronics & Optical Devices	8	0.0398	6	0.0122	19	0.0456
12	Electrical Equipment	14	0.0271	20	0.0059	22	0.0364
13	Machinery & Equipment	9	0.0397	10	0.0100	10	0.0807
14	Transportation Equipment	7	0.0424	14	0.0084	20	0.0454
15	Other Manufactured Goods	25	0.0089	29	0.0024	24	0.0287
16	Contract Manufacturing & Industrial Repair Services	21	0.0123	21	0.0058	16	0.0553
17	Electricity, Gas & Steam Supply	31	0.0054	34	0.0011	33	0.0029
18	Water Supply, Waste Management & Recycling Services	16	0.0185	15	0.0083	13	0.0679
19	Construction	6	0.0455	5	0.0190	4	0.2013
20	Wholesale, Retail & Brokerage Services	2	0.0967	2	0.0518	2	0.4565
21	Transport Services	4	0.0928	3	0.0436	3	0.4558
22	Food Service & Accommodation	13	0.0302	11	0.0099	6	0.1404
23	Information, Communication & Broadcasting Services	23	0.0103	23	0.0054	21	0.0385
24	Finance & Insurance Services	32	0.0045	27	0.0028	29	0.0128
25	Real Estate Services	26	0.0085	19	0.0059	30	0.0124
26	Professional, Scientific & Technical Services	15	0.0202	8	0.0105	9	0.0919
27	Business Support Services	20	0.0143	13	0.0087	7	0.1181
28	Public Administration, Defense & Social Security	27	0.0076	22	0.0056	14	0.0589
29	Education Services	29	0.0067	24	0.0045	17	0.0540
30	Health & Social Welfare Services	19	0.0152	12	0.0089	5	0.1479
31	Arts, Sports & Leisure Services	33	0.0040	30	0.0022	28	0.0184
32	Other Services	28	0.0071	26	0.0032	15	0.0567
33	Others	35	0.0036	35	0.0000	35	0.0000
34	Shipbuilding Industry	30	0.0063	33	0.0013	31	0.0090
35	Shipping Industry	1	1.0225	1	0.3444	1	1.7710
Total		1.8865		0.6583		4.4187

Unit: index, persons per KRW 1 billion.

. To lessen voluntariness in aggregation, we comply with Bank of Korea’s 33-sector classification method.

4. Results

4.1. Results of the IO Analysis

4.1.1. Results from the Supply-Driven Model

The supply-driven model estimates the economic spillover generated across related industries when the value added of a given sector increases exogenously by one unit. This approach is particularly suitable for policy analysis, as it decomposes the sequential transmission of production, value-added, and employment effects, allowing the inter-industry dependency structure to be quantified in detail.

This study treats the shipping industry as an exogenous sector. This reflects an analytical perspective that views shipping not only as a transportation activity but also as a core industrial base with strong forward and backward linkages to shipbuilding, repair/maintenance, port handling, and marine fuel supply, generating broad spillover effects across the economy.

The results of the analysis are in Table 2:

-

Production-inducement effect: 1.8865

-

Value-added inducement effect: 0.6583

-

Employment-inducement effect: 4.4187 persons per KRW 1 billion

The analysis indicates that the production-inducement coefficient of the shipping industry is 1.8865, the value-added inducement coefficient is 0.6583, and the employment-inducement effect corresponds to 4.4187 workers per KRW 1 billion in output. This implies that an incremental unit of value added in the shipping sector does not result solely in increased shipping service output but also triggers additional economic activity across upstream and downstream industries, including shipbuilding, port handling and transportation services, marine fuel, and equipment supply.

A production-inducement effect of 1.8865 implies that, when the shipping industry produces one unit of output, approximately 1.89 units of additional output is generated throughout the economy. This reflects the role of shipping as an inducing sector—not merely a transport function within the logistics chain but also a structurally embedded node in the national supply network that activates value creation across the broader industrial system.

Table 2 provides comparative production, value added, and employment-inducement effects by industry. The aggregate figures presented for the shipping sector at the bottom of the table do not represent a rank relative to other industries; instead, they reflect the total economy-wide ripple effect induced by a one-unit increase in shipping value-added. These results demonstrate that the shipping industry stimulates diverse industrial activities through interconnected supply chains and that its economic impact extends well beyond its direct transport role in the broader industrial ecosystem.

4.1.2. Results from the Demand-Driven Model

Economic Spillover Effects in the Financial Industry

This study estimates the direct and indirect economic ripple effects generated when final demand in the financial sector increases by one unit. The financial industry performs the essential functions of capital intermediation, investment decision support, and risk management, thereby coordinating resource allocation across the entire economy. This stabilizes real-sector activities and enhances the efficiency of economic transactions. Owing to these structural characteristics, changes in the final demand for financial services are not confined to a single sector but rather propagate simultaneously across multiple industrial domains.

According to

Table 3. Industrial spillover effects of the financial industry (demand-driven model).

Industry		Production Inducement		Value-Added Inducement		Employment Inducement (Persons per KRW 1 Billion)
1	Agriculture, Forestry & Fisheries	18	0.0041	16	0.0019	25	0.0051
2	Mining Products	35	0.0001	34	0.0000	34	0.0002
3	Food & Beverage Manufacturing	10	0.0109	12	0.0026	13	0.0175
4	Textiles & Leather Products	27	0.0023	29	0.0004	26	0.0039
5	Wood, Paper & Printing	8	0.0139	9	0.0041	10	0.0329
6	Coal & Petroleum Products	16	0.0047	24	0.0009	33	0.0003
7	Chemical Products	11	0.0105	13	0.0025	17	0.0123
8	Non-metallic Mineral Products	31	0.0008	32	0.0002	31	0.0015
9	Basic Metal Products	25	0.0026	28	0.0005	30	0.0017
10	Fabricated Metal Products	20	0.0037	22	0.0012	22	0.0081
11	Computers, Electronics & Optical Devices	13	0.0087	11	0.0027	20	0.0099
12	Electrical Equipment	14	0.0076	18	0.0017	19	0.0102
13	Machinery & Equipment	29	0.0014	31	0.0003	28	0.0028
14	Transportation Equipment	24	0.0026	26	0.0005	27	0.0028
15	Other Manufactured Goods	26	0.0025	25	0.0007	21	0.0081
16	Contract Manufacturing & Industrial Repair Services	19	0.0038	17	0.0018	14	0.0169
17	Electricity, Gas & Steam Supply	9	0.0119	15	0.0024	23	0.0063
18	Water Supply, Waste Management & Recycling Services	21	0.0034	20	0.0015	16	0.0126
19	Construction	23	0.0027	23	0.0011	18	0.0119
20	Wholesale, Retail & Brokerage Services	7	0.0151	7	0.0081	6	0.0713
21	Transport Services	12	0.0096	8	0.0045	8	0.0469
22	Food Service & Accommodation	5	0.0285	6	0.0093	5	0.1323
23	Information, Communication & Broadcasting Services	2	0.0604	3	0.0316	3	0.2261
24	Finance & Insurance Services	1	1.2128	1	0.7576	1	3.4517
25	Real Estate Services	6	0.0258	5	0.0181	9	0.0378
26	Professional, Scientific & Technical Services	4	0.0414	4	0.0215	4	0.1882
27	Business Support Services	3	0.0541	2	0.0331	2	0.4471
28	Public Administration, Defense & Social Security	33	0.0007	27	0.0005	24	0.0053
29	Education Services	28	0.0020	21	0.0014	15	0.0163
30	Health & Social Welfare Services	22	0.0028	19	0.0016	11	0.0271
31	Arts, Sports & Leisure Services	17	0.0045	14	0.0025	12	0.0210
32	Other Services	15	0.0064	10	0.0029	7	0.0512
33	Others	32	0.0008	35	0.0000	35	0.0000
34	Shipbuilding Industry	34	0.0003	33	0.0001	32	0.0004
35	Shipping Industry	30	0.0012	30	0.0004	29	0.0020
Total		1.5647		0.9203		4.8896

Unit: index, persons per KRW 1 billion.

, the analysis finds that a one-unit increase in the final demand for financial services generates a production-inducement effect of 1.5647, a value-added inducement effect of 0.9203, and an employment-inducement effect of 4.8896 persons per KRW 1 billion. A production multiplier of 1.5647 indicates additional output creation in various related industries—such as ICT systems, business administration and support services, real estate, and professional consulting—which are required to facilitate financial transactions. Despite its service-oriented nature, this finding demonstrates that the financial industry is closely connected to multiple upstream and support sectors through an intermediate input structure.

The relatively high value-added inducement coefficient of 0.9203 reflects the knowledge-intensive nature of finance, which is driven by human capital, expertise, and information-based decision systems. This suggests that financial industry growth contributes not only to higher sales but also to income generation and capital accumulation, reinforcing long-term growth potential at the macroeconomic level.

Furthermore, the employment-inducement effect of 4.8896 persons signifies that increased demand for financial services expands labor requirements across multiple occupational categories, including professional analysts, managerial staff, IT engineers, and advisory personnel. Given the sector’s high skill and expertise intensity, the employment effect is significant not only in terms of job quantity but also in the qualitative improvement of employment, driven by the creation of high-skilled professional positions.

-

Production-inducement effect: 1.5647

-

Value-added inducement effect: 0.9203

-

Employment-inducement effect: 4.8896 persons per KRW 1 billion

Table 3 compares the production, value-added, and employment-inducement coefficients across industries. Although the finance sector displays the highest coefficient values, these figures should not be interpreted as simple rankings among all industries. Rather, they represent the total magnitude of economy-wide changes generated both within the financial sector and through linked upstream and downstream industries.

In other words, an increase in the final demand for financial services induces widespread spillovers across the service economy—propagating through information and communication technology, real estate, professional and business support services, and hospitality and food-related sectors via the circulation of capital flows. Such forward- and backward-linkage structures reaffirm that the financial sector functions as a fundamental pillar supporting the operational stability of the national economy and real-sector activity, rather than an isolated industrial domain.

Economic Spillover Effects of the Shipbuilding Industry

This study estimates the direct and indirect economic spillover effects generated when final demand in the shipbuilding industry increases by one unit using the demand-driven input–output model. The shipbuilding sector is characterized by a high intermediate-linkage structure, as ship construction requires substantial intermediate inputs from various manufacturing industries, including steel, machinery, electronic components, electrical equipment, and industrial repair services. Accordingly, changes in the final demand within the shipbuilding sector tend to propagate widely throughout the economy, generating extensive chain reactions.

This structural feature of the shipbuilding industry is quantitatively captured and more precisely illustrated through the demand-driven analysis conducted in

Table 4. Industrial spillover effects of the shipbuilding industry (demand-driven model).

Industry		Production Inducement		Value-Added Inducement		Employment Inducement (Persons per KRW 1 Billion)
1	Agriculture, Forestry & Fisheries	27	0.0038	25	0.0018	32	0.0047
2	Mining Products	35	0.0008	34	0.0003	33	0.0014
3	Food & Beverage Manufacturing	21	0.0086	24	0.0020	24	0.0138
4	Textiles & Leather Products	22	0.0086	27	0.0016	23	0.0144
5	Wood, Paper & Printing	24	0.0075	23	0.0022	20	0.0176
6	Coal & Petroleum Products	17	0.0128	20	0.0024	34	0.0009
7	Chemical Products	10	0.0498	12	0.0120	13	0.0581
8	Non-metallic Mineral Products	26	0.0049	28	0.0013	30	0.0086
9	Basic Metal Products	2	0.1599	7	0.0312	9	0.1094
10	Fabricated Metal Products	3	0.1477	3	0.0480	6	0.3237
11	Computers, Electronics & Optical Devices	11	0.0421	11	0.0129	14	0.0483
12	Electrical Equipment	15	0.0175	18	0.0038	18	0.0235
13	Machinery & Equipment	8	0.0648	9	0.0164	8	0.1319
14	Transportation Equipment	18	0.0112	22	0.0022	26	0.0120
15	Other Manufactured Goods	30	0.0021	33	0.0006	31	0.0068
16	Contract Manufacturing & Industrial Repair Services	4	0.1257	2	0.0593	2	0.5628
17	Electricity, Gas & Steam Supply	9	0.0528	13	0.0108	17	0.0281
18	Water Supply, Waste Management & Recycling Services	20	0.0099	17	0.0044	16	0.0363
19	Construction	31	0.0020	32	0.0008	29	0.0087
20	Wholesale, Retail & Brokerage Services	6	0.0733	6	0.0393	5	0.3461
21	Transport Services	12	0.0313	10	0.0147	7	0.1535
22	Food Service & Accommodation	14	0.0185	16	0.0061	11	0.0861
23	Information, Communication & Broadcasting Services	16	0.0167	14	0.0087	12	0.0626
24	Finance & Insurance Services	13	0.0309	8	0.0193	10	0.0879
25	Real Estate Services	19	0.0110	15	0.0077	21	0.0162
26	Professional, Scientific & Technical Services	5	0.0811	4	0.0421	4	0.3681
27	Business Support Services	7	0.0680	5	0.0416	3	0.5619
28	Public Administration, Defense & Social Security	33	0.0012	30	0.0009	28	0.0097
29	Education Services	34	0.0012	31	0.0008	27	0.0099
30	Health & Social Welfare Services	29	0.0022	29	0.0013	19	0.0212
31	Arts, Sports & Leisure Services	28	0.0028	26	0.0016	25	0.0130
32	Other Services	25	0.0051	21	0.0023	15	0.0408
33	Others	32	0.0014	35	0.0000	35	0.0000
34	Shipbuilding Industry	1	1.1394	1	0.2339	1	1.6131
35	Shipping Industry	23	0.0085	19	0.0029	22	0.0148
Total		2.2251		0.6372		4.8157

Unit: index, persons per KRW 1 billion.

.

-

Production-inducement effect: 2.2251

-

Value-added inducement effect: 0.6372

-

Employment-inducement effect: 4.8157 persons per KRW 1 billion

The results indicate that when final demand in the shipbuilding industry increases by one unit, the production-inducement effect equals 2.2251, value-added inducement effect equals 0.6372, and employment-inducement effect corresponds to 4.8157 workers per KRW 1 billion. A production multiplier of 2.2251 signifies that an expansion in demand stimulates not only direct output within shipbuilding but also substantial additional production across upstream manufacturing sectors, including steel, metal fabrication, machinery, and electrical and electronic components. This reflects the integrated nature of shipbuilding as a composite assembly industry situated at the center of the manufacturing value chain, where raw material sourcing, component fabrication, and system integration occur simultaneously.

The value-added inducement effect of 0.6372 demonstrates that shipbuilding is not merely a heavy-assembly industry but relies on advanced skills, engineering capabilities, and precision component processing, highlighting the knowledge-intensive characteristics embedded in its production structure. This suggests that strengthening competitiveness in shipbuilding can directly enhance national manufacturing capacity, technological sophistication, and value-creation potential.

Moreover, the employment effect of 4.8157 persons confirms that shipbuilding generates multi-layered labor demand not only through direct employment but also via subcontracting in component production, design and engineering services, and industrial equipment maintenance. This finding reaffirms shipbuilding as an industry that supports stable employment bases across regional economies and broader industrial ecosystems.

The comparative table illustrates the production, value-added, and employment-inducement effects across industries. The results indicate particularly strong interlinkages between the shipbuilding, steel, and metal fabrication sectors. This implies that revitalizing shipbuilding does not confine benefits within the industry alone but generates cascading spillover effects along the manufacturing value chain, which has significant implications for industrial policy and supply chain strategy.

4.2. Summary of Input–Output Analysis Results

The aggregated inducement coefficients are summarized in

Table 5. Summary of induced coefficients.

Industry	Model Type	Output Inducement	Value-Added Inducement	Employment Inducement (Persons per KRW 1 Billion)
Shipping Industry	Supply-Driven Model	1.8865	0.6583	4.4187
Financial Industry	Demand-Driven Model	1.5647	0.9203	4.8896
Shipbuilding Industry	Demand-Driven Model	2.2251	0.6372	4.8157

Unit: index.

.

First, the shipping industry was analyzed using a supply-driven model, producing a production-inducement coefficient of 1.8865, a value-added inducement coefficient of 0.6583, and an employment-inducement effect of 4.4187 persons per KRW 1 billion. These results indicate that an increase in value added within shipping generates broad spillover effects in associated industries, particularly those linked to transportation, port handling, vessel maintenance, and marine fuel supply. This demonstrates the industry’s structural role as a logistics-based hub sector that disperses economic effects throughout the supply chain.

By contrast, the financial industry was assessed using a demand-driven model, yielding a production-inducement coefficient of 1.5647, a value-added coefficient of 0.9203, and an employment-inducement effect of 4.8896 persons per KRW 1 billion. A comparatively high value-added coefficient reflects the sector’s capital- and knowledge-intensive nature, which relies on expertise, information processing, and human capital. Similarly, the employment effect indicates high-skill job creation centered on professional and administrative labor.

The shipbuilding industry, also evaluated using a demand-driven model, exhibited a production-inducement coefficient of 2.2251, one of the highest among the sectors examined. This illustrates the deeply integrated value-chain linkages with steel, metal fabrication, machinery, and electronic equipment manufacturing. Meanwhile, a value-added coefficient of 0.6372 and an employment-inducement effect of 4.8157 persons highlight the combined demand for skilled labor and engineering capabilities across the design, assembly, component production, and maintenance processes.

Taken together, these results confirm the distinct, yet complementary, economic roles of these three industries. Shipping functions as a logistics-driven diffusion sector, activating supply-chain-wide effects; finance, as a high-value, knowledge-based service sector, enhances resource allocation and employment quality; and shipbuilding, as a structurally integrated manufacturing industry, generates strong production spillovers through the national value chain. Despite their differing operational logics, all three sectors produce significant economy-wide multiplier effects, demonstrating their strategic importance as core drivers of national economic activity.

The empirical findings derived from the IO analysis can be theoretically interpreted through the lens of inter-industry linkage theory and structural propagation mechanisms within production networks. In particular, the shipping industry’s high production-inducement coefficient (1.8865) indicates its role as a forward-linkage-dominant diffusion sector, in which shocks originating in shipping are transmitted widely across downstream industries such as manufacturing, trade, and logistics services.

From a theoretical perspective, this aligns with the concept of key sector theory [22], where industries with strong forward and backward linkages amplify economic fluctuations across the entire system. In this context, the observed decline in shipping value added under IMO mid-term measures is not merely a sector-specific contraction but represents a systemic negative shock propagated through the national production network.

Furthermore, the scenario analysis results demonstrate a clear cost transmission mechanism, whereby increased compliance costs in shipping are passed through to freight rates, subsequently affecting export-oriented manufacturing sectors. This empirical observation is consistent with cost-push inflation theory and supply-chain transmission models [23], in which upstream cost increases cascade into broader economic contraction.

Based on this theoretical interpretation, the proposed policy pathways—such as integrating shipping, shipbuilding, and financial sectors—can be understood as attempts to internalize external shocks within the domestic industrial system. In particular, linking shipping decarbonization with domestic shipbuilding investment reflects a structural coupling strategy, which seeks to transform regulatory costs into domestic industrial demand, thereby mitigating negative spillover effects.

Therefore, the empirical findings of this study not only quantify the magnitude of economic impacts but also provide theoretical support for policy interventions aimed at restructuring inter-industry linkages and enhancing systemic resilience under carbon regulatory regimes.

The use of mixed modeling approaches in this study—namely the supply-driven model for the shipping industry and the demand-driven model for the financial and shipbuilding industries—reflects the fundamentally different economic roles and shock transmission mechanisms associated with each sector.

The supply-driven model is applied to the shipping industry because the primary shock under the IMO mid-term measures originates from the cost side, specifically through increased compliance costs that directly reduce value added. In input–output theory, supply-driven models are more appropriate for analyzing exogenous changes in primary inputs or value-added components, as they capture how reductions in productive capacity propagate through downstream industries via forward linkages. Given that shipping functions as a logistics infrastructure sector, a contraction in its value added is transmitted across the economy through supply constraints rather than demand expansion.

In contrast, the demand-driven model is employed for the financial and shipbuilding industries because the corresponding shocks arise from investment demand expansion. In these cases, increased financial flows (e.g., green financing) and shipbuilding orders represent exogenous increases in final demand. Demand-driven models are theoretically suited to capturing such shocks, as they estimate how increases in final demand stimulate production, value added, and employment through backward linkages across upstream industries.

Accordingly, the coefficients derived from the supply-driven and demand-driven models represent different economic interpretations and should not be directly compared as simple rankings. The supply-driven coefficients reflect the propagation of cost-induced contractions, whereas the demand-driven coefficients capture the expansionary effects of investment-driven demand shocks. Therefore, comparisons across sectors in Table 5 should be understood as illustrating distinct transmission mechanisms rather than relative magnitudes under a unified framework.

4.3. Economic Impact Assessment of IMO Mid-Term Measures on the Korean National Economy

4.3.1. Case 1. Cost Burden Applied to the Shipping Industry

In this scenario, it is assumed that compliance costs incurred during the implementation of IMO Mid-term Measures lead to an increase in operational expenses for the shipping industry, resulting in reduced profit margins and a decline in value added. Although the shipping sector generates substantial spillover effects across related industries as the backbone of import–export logistics, increased compliance costs reverse this linkage and transmit negative effects throughout the economy. A reduction in shipping value added translates to lower production, a contraction in domestic industrial activity, and a decline in employment. These structural adjustments can be quantified through input–output analysis.

BAU Scenario

The BAU (business as usual) scenario assumes that existing fuel use and operational structures within international shipping are partially maintained and that regulatory compliance progresses incrementally rather than rapidly. Under these conditions, shipping companies undertake measures, such as moderate improvements in ship efficiency, operational adjustments, and limited adoption of alternative fuels, resulting in compliance costs emerging at a manageable but notable level.

Such costs weaken profitability within the shipping industry and simultaneously influence economic activity in associated sectors, including fuel supply, ship management, and port stevedoring. This cascade effect places gradual downward pressure on production and employment across the wider economy. Therefore, the BAU scenario represents an adjustment pathway in which a regulatory response occurs alongside continued reliance on current operating structures, reflecting the transitional economic impacts that may arise during the phased compliance with mid-term decarbonization measures.

Table 6. Economic impact under the BAU scenario.

Year	Scenario	Economy-Wide Spillover Effects
	Shipping Industry Value Added	Output Inducement	Value-Added Inducement	Employment Inducement
2028	−50.3	−94.8	−33.1	−22.2
2029	−62.0	−117.0	−40.8	−27.4
2030	−52.0	−98.1	−33.2	−23.0
2035	−99.9	−188.5	−65.8	−44.2

Unit: KRW 100 billion, thousand persons.

shows the economic impact under the BAU scenario.

A reduction of KRW 5.03 trillion in value added within the shipping industry in 2028 is estimated to translate into approximately KRW 9.48 trillion in lost production across the economy. This outcome reflects the industry’s structural role as a demand generator for port stevedoring; ship management, repair, and maintenance; and marine fuel supply. In other words, losses originating in a single sector are transmitted to multiple industries through forward and backward-linkage channels, representing a typical inter-industry spillover effect.

Furthermore, a decline of KRW 3.31 trillion in induced value added implies reduced corporate earnings and lower aggregate wages, which may weaken household consumption capacity and potentially lead to broader slowdowns in regional consumption and investment.

By 2029, cumulative compliance costs will begin to materialize more prominently; production losses will widen to KRW 11.7 trillion, while employment losses will expand to approximately 27,400 workers. This suggests a transition from short-term cost effects to sustained structural pressure on freight rates and fixed-cost operating systems. In 2030, the decline in value added in shipping industry appears to be marginally alleviated (−KRW 5.2 trillion), but this should not be interpreted as a fundamental easing of cost burdens. Instead, the result likely reflects temporary market adjustments and gains from operational optimization. By 2035, the contraction intensifies once again—shipping value added falls by approximately KRW 10 trillion, total production losses reach KRW 18.85 trillion, and employment declines by approximately 44,200 workers, indicating a long-term accumulation of economic strain.

Base Compliance Scenario

The Base Compliance Scenario assumes that additional mitigation measures are required to meet regulatory obligations. In this case, the industry is assumed to achieve the Base Target but falls short of the Direct Target, implying a mid-range compliance trajectory where partial progress has been made but full regulatory alignment has not yet been realized.

Table 7. Economic impact under the Base Compliance Scenario.

Year	Scenario	Economy-Wide Spillover Effects
	Shipping Industry Value Added	Output Inducement	Value-Added Inducement	Employment Inducement
2028	−50.5	−95.2	−33.2	−22.3
2029	−61.6	−116.2	−40.6	−27.2
2030	−50.8	−95.8	−33.4	−22.4
2035	−95.7	−180.5	−63.0	−42.3

Unit: KRW 100 billion, thousand persons.

shows the economic impact under the base compliance scenario.

In 2028, value added in the shipping industry is projected to decline by approximately KRW 5.05 trillion, leading to an estimated KRW 9.52 trillion reduction in total production across the economy. By 2029, as cumulative compliance costs accelerate, the contraction intensifies—production losses widen to KRW 11.62 trillion, while employment losses reach approximately 27,200 workers. This reflects the direct link between shipping companies’ cost structure and freight competitiveness, indicating a potential transmission pathway for higher logistics costs→increased financial burden on shippers→upward pressure on export manufacturing cost structures. In 2030, the decline appears temporarily moderated (−KRW 5.08 trillion in shipping industry value added), yet without structural measures such as fleet renewal or fuel transition, the underlying cost burden is unlikely to ease. Indeed, by 2035, the impact resurfaces more sharply—value added in shipping industry decreases by KRW 9.57 trillion, production losses escalate to KRW 18.05 trillion, and employment reductions expand to 42,300 workers, indicating a resurgence of long-term economic strain.

Direct Compliance Scenario

The Direct Compliance Scenario refers to the condition under which the industry achieves not only the Base Target but also the Direct Target. In other words, ship efficiency and emission performance reached the highest standards among the three scenarios.

Table 8. Economic impact under the Direct Compliance Scenario.

Year	Scenario	Economy-Wide Spillover Effects
	Shipping Industry Value Added	Output Inducement	Value-Added Inducement	Employment Inducement
2028	−58.5	−110.4	−38.5	−25.8
2029	−70.1	−132.2	−46.1	−31.0
2030	−59.7	−112.6	−39.3	−26.4
2035	−106.3	−200.5	−70.0	−47.0

Unit: KRW 100 billion, thousand persons.

shows the economic impact under the direct compliance scenario.

In 2028, the decline in shipping industry value added (−KRW 5.85 trillion) is projected to lead to approximately KRW 11.04 trillion in reduced production, accompanied by an estimated employment loss of about 25,800 workers. By 2029, the contraction deepens, with total production losses expanding to KRW 13.22 trillion and employment losses increasing to 31,000 workers. By 2035, the reduction in shipping industry value added (−KRW 10.63 trillion) is estimated to result in KRW 20.05 trillion in foregone production and a decline of approximately 47,000 jobs, representing the greatest macroeconomic impact among the three scenarios.

4.3.2. Case 2. Shipping Sector Cost Burden + Financial Investment Effects

Case 2 incorporates both (1) the negative economic effects stemming from value-added losses in the shipping industry and (2) the positive counter-effects generated through new investment inflows into the financial sector associated with green shipbuilding and retrofit financing.

In this scenario, while compliance costs reduce value added in shipping, financial institutions benefit from the increased demand for ship financing, leasing, and guarantees, allowing a partial economic offset.

BAU Scenario

Under the BAU scenario, value added in the shipping industry continues to decline, whereas a financial sector investment of approximately KRW 1.58 trillion provides a partial buffering effect in 2028. However, given that the production and employment-inducement coefficients of the financial sector are lower than those of shipping, an increase in financial activity cannot fully compensate for economy-wide losses.

For example, in 2028, a −KRW 5.03 trillion reduction in shipping value added results in a −KRW 7.02 trillion contraction in induced production. Financial investment mitigates a portion of this decline, making the reduction less severe than in Case 1. However, by 2035, as compliance intensifies and losses accumulate, economy-wide spillover production declines to −KRW 16.02 trillion, with employment losses reaching approximately 35,300. Thus, under the BAU compliance scenario, financial investment provides short-run cushioning but structural economic pressure persists in the long term.

Table 9. Economic impact under the BAU scenario: with financial investment.

Year	Scenario		Economy-Wide Spillover Effects
	Shipping Industry Value Added	Financial Industry Investment	Output Inducement	Value- Added Inducement	Employment Inducement
2028	−50.3	15.8	−70.2	−18.6	−14.5
2029	−62.0	19.0	−87.2	−23.3	−18.1
2030	−52.0	14.2	−75.9	−21.2	−16.0
2035	−99.9	18.1	−160.2	−49.1	−35.3

Unit: KRW 100 billion, thousand persons.

shows the economic impact under the BAU scenario with financial investment.

Base Compliance Scenario

Under the Base Compliance Scenario, the estimated reduction in induced production reaches −KRW 15.22 trillion by 2035, accompanied by a decline in employment of approximately 33,400 workers.

Table 10. Economic impact under the Base Compliance Scenario: with financial investment.

Year	Scenario		Economy-Wide Spillover Effects
	Shipping Industry Value Added	Financial Industry Investment	Output Inducement	Value- Added Inducement	Employment Inducement
2028	−50.5	15.8	−70.5	−18.7	−14.6
2029	−61.6	19.0	−86.5	−23.1	−17.9
2030	−50.8	14.2	−73.6	−20.4	−15.5
2035	−95.7	18.1	−152.2	−46.3	−33.4

Unit: KRW 100 billion, thousand persons.

shows the economic impact under the base compliance scenario with financial investment.

Direct Compliance Scenario

Under the Direct Compliance Scenario, the reduction in value-added for the shipping industry is the largest among all three scenarios, resulting in the most severe economy-wide decrease in production and employment across the linked sectors.

Although additional financial investments occur, they remain largely confined to accounting-level capital flows, such as ship financing and operating leases. As such, the compensation for real-sector contractions is insufficient. In essence, financial investment represents a movement of capital, whereas a decline in shipping value added represents a reduction in real economic activity. The depth of the macroeconomic impact between the two is fundamentally different.

By 2035, the decline in induced entire social production reaches approximately KRW 17.22 trillion, signaling not merely an industry-level shock but also a broader macroeconomic burden transmitted through the pathway:

higher logistics costs→increased manufacturing cost pressures→higher final-goods prices→weakened consumer demand.

Employment losses are also significant, estimated at approximately 38,100 jobs, indicating simultaneous labor displacement not only among maritime transport workers but also across port stevedoring, ship management (MRO), marine supplies, bunker fuel services, and port service industries, reflecting multi-layered forward and backward linkages.

In summary, despite the partial positive effects generated through financial sector investment, the contraction in shipping-based real economic activity dominates the overall outcome, making the Direct Compliance Scenario the most damaging to the national economy. The results quantitatively demonstrate that if environmental compliance relies solely on market autonomy or shipping-company-borne costs, both industrial performance and national logistics competitiveness may deteriorate concurrently.

Table 11. Economic impact under the Direct Compliance Scenario: with financial investment.

Year	Scenario		Economy-Wide Spillover Effects
	Shipping Industry Value Added	Financial Industry Investment	Output Inducement	Value- Added Inducement	Employment Inducement
2028	−58.5	15.8	−85.7	−24.0	−18.1
2029	−70.1	19.0	−102.5	−28.6	−21.7
2030	−59.7	14.2	−90.4	−26.2	−19.4
2035	−106.3	18.1	−172.2	−53.3	−38.1

Unit: KRW 100 billion, thousand persons.

shows the economic impact under the direct compliance scenario with financial investment.

4.3.3. Case 3. Shipping Sector Cost Burden + Shipbuilding Investment Effects

In this case, it is assumed that the increase in operating costs arising from compliance with IMO Mid-term Measures reduces the value added in the shipping industry, while simultaneously generating new domestic investment inflows into shipbuilding through orders for newbuilds and retrofits intended for green-fleet conversion. This implies that although the shipping sector experiences negative cost pressure in the short term, expanded investment and production within the domestic shipbuilding supply chain—covering ship construction, marine equipment manufacturing, and yard facility operations—can partially offset these losses. In other words, Case 3 represents a condition in which the shipping industry bears cost-driven contractions, whereas the shipbuilding industry gains from compliance-driven investments, with the model quantifying the net interaction of both forces.

BAU Scenario

Under the BAU scenario, shipping industry value-added declines by −KRW 5.03 trillion in 2028, yet the simultaneous inflow of shipbuilding investment (KRW 4.26 trillion) limits economy-wide spillover value-added losses to approximately −KRW 590 billion won. This outcome reflects the high domestic production-chain integration in shipbuilding, where the procurement of engines, steel plates, and marine components generates immediate increases in output and employment across related industries, highlighting the sector’s short-term buffering effect.

However, although this compensatory effect is evident in the early phase, it weakens over time as the pace of fleet replacement accelerates and compliance-related cost burdens accumulate. By 2035, the contraction in shipping is projected to outweigh the offsetting impact of shipbuilding investment, resulting in −KRW 3.46 trillion in reduced value-added and an estimated employment loss of approximately 20,600 workers.

Table 12. Economic impact under the BAU scenario: with shipbuilding investment.

Year	Scenario		Economy-Wide Spillover Effects
	Shipping Industry Value Added	Shipbuilding Industry Investment	Output Inducement	Value- Added Inducement	Employment Inducement
2028	−50.3	42.6	0.0	−5.9	−1.7
2029	−62.0	51.4	−2.7	−8.1	−2.7
2030	−52.0	38.3	−12.8	−9.8	−4.5
2035	−99.9	49.0	−79.6	−34.6	−20.6

Unit: KRW 100 billion, thousand persons.

shows the economic impact under the BAU scenario with shipbuilding investment.

Base Compliance Scenario

Even under the Base Compliance Scenario, while large-scale orders for eco-friendly vessels stimulate shipbuilding investment and generate spillover effects, the simultaneous deterioration of profitability in the shipping sector leads to renewed increases in production, value-added, and employment losses by 2035.

Table 13. Economic impact under the Base Compliance scenario: with shipbuilding investment.

Year	Scenario		Economy-Wide Spillover Effects
	Shipping Industry Value Added	Shipbuilding Industry Investment	Output Inducement	Value- Added Inducement	Employment Inducement
2028	−50.5	42.6	−0.4	−6.1	−1.8
2029	−61.6	51.4	−1.9	−7.8	−2.5
2030	−50.8	38.3	−10.5	−9.0	−4.0
2035	−95.7	49.0	−71.5	−31.8	−18.7

Unit: KRW 100 billion, thousand persons.

shows the economic impact under the base compliance scenario with shipbuilding investment.

Direct Compliance Scenario

Under the Direct Compliance Scenario, the decline in value-added within the shipping industry was the largest among all three scenarios. Although expanded newbuilding investment generates additional output and employment in the shipbuilding sector, the scale of this positive effect remains insufficient to offset the losses originating from shipping.

This asymmetry arises because production and employment gains in shipbuilding are investment-driven, temporary, and stimulus-oriented, whereas a reduction in shipping value-added triggers structural and persistent contraction through the following pathway: declining freight profitability→reduced sailing frequency→downsized liner schedules→weakening demand for port-linked services.

Consequently, even if shipbuilding maintains growth momentum for a period, the rapid deterioration in shipping profitability creates a non-symmetrical shock profile, ultimately imposing long-term negative impacts on real-sector output and employment bases.

By 2035, the cumulative effect becomes evident; induced economy-wide spillover value-added losses reach approximately KRW 3.88 trillion, while employment declines by approximately 23,400 jobs. This outcome indicates that, despite continued shipbuilding investment, the rising cost burden in shipping produces a downward cascade across related industries. In other words, because the shipping sector underpins Korea’s import–export logistics, production losses propagate through manufacturing, port services, logistics, and distribution systems via the following chain: increased export logistics cost→higher manufacturing cost burden→weakened national logistics competitiveness.

Such dynamics pose risks of slower GDP expansion, reduced port-regional economic activity, and heightened instability in employment for seafarers and port-related service workers.

Accordingly, the Direct Compliance Scenario represents not merely a high-cost case but also a high-risk scenario, demonstrating quantitatively that weakening shipping performance directly threatens the efficiency and competitiveness of the national economic system. The results imply that shipbuilding investment alone cannot sufficiently buffer mid-term policy shocks. Instead, an integrated response is needed—encompassing shipping–shipbuilding–finance linkage policies, freight-rate stabilization tools, fuel-transition infrastructure support, and incentive frameworks for green-vessel investment.

Table 14. Economic impact under the direct scenario: with shipbuilding investment.

Year	Scenario		Economy-Wide Spillover Effects
	Shipping Industry Value Added	Shipbuilding Industry Investment	Output Inducement	Value- Added Inducement	Employment Inducement
2028	−58.5	42.6	−15.6	−11.4	−5.3
2029	−70.1	51.4	−17.9	−13.4	−6.2
2030	−59.7	38.3	−27.3	−14.9	−7.9
2035	−106.3	49.0	−91.5	−38.8	−23.4

Unit: KRW 100 billion, thousand persons.

shows the economic impact under the direct compliance scenario with shipbuilding investment.

5. Conclusions

5.1. Research Conclusions

This study quantitatively assessed the economy-wide impacts of the IMO GHG mid-term measures on the Korean economy using an input–output analytical framework and examined the structural transmission mechanisms across key industries, including shipping, shipbuilding, and finance.

The empirical results reveal several critical findings. First, the shipping industry functions as a core diffusion sector within the national economy, with a production-inducement coefficient of 1.8865, a value-added inducement coefficient of 0.6583, and an employment-inducement effect of 4.4187 persons per KRW 1 billion. These results indicate that changes in shipping value added are not confined to the sector itself but propagate widely through forward and backward linkages across logistics, port services, fuel supply, and manufacturing activities.

Second, the shipbuilding industry exhibits the strongest production linkage (2.2251), confirming its role as a central manufacturing hub that activates upstream industries such as steel, machinery, and equipment. In contrast, the financial sector demonstrates a high value-added inducement effect (0.9203), reflecting its function as a knowledge-intensive industry that enhances resource allocation and supports investment-driven economic activity.

Third, scenario-based analysis shows that the IMO mid-term measures generate substantial negative macroeconomic impacts when compliance costs are borne primarily by the shipping sector. Under the Direct Compliance Scenario, production losses expand to approximately KRW 20.05 trillion by 2035 (Table 8), accompanied by significant declines in value added and employment. Although financial and shipbuilding investments provide partial short-term mitigation, these effects are insufficient to offset long-term structural losses in real economic activity.

Taken together, these findings demonstrate that the IMO mid-term measures are not merely environmental regulations affecting a single industry but constitute a structural economic shock that reshapes national supply chains, trade competitiveness, and industrial linkages.

5.2. Managerial Implications

Based on the empirical results, several policy implications emerge. First, a coordinated national response framework is required that integrates shipping, shipbuilding, and finance. Relying solely on market-based compliance or cost pass-through mechanisms may lead to increased logistics costs, weakened export competitiveness, and broader industrial contraction. Second, policy support should focus on facilitating the transition to eco-friendly vessels through integrated instruments, including green financing, fleet renewal incentives, and risk-sharing mechanisms. Linking shipping decarbonization with domestic shipbuilding demand can enhance industrial spillover benefits and partially internalize regulatory costs within the national economy.

Third, it is necessary to establish institutional mechanisms that mitigate cost transmission to shippers and exporters. Without such measures, increased freight rates may propagate through supply chains, resulting in higher production costs and reduced global competitiveness of export industries. To enhance policy effectiveness, the recommendations should be differentiated according to the roles of key stakeholders.

5.2.1. Shipping Companies

Shipping firms should prioritize fleet modernization and fuel-transition strategies to reduce long-term compliance costs. In addition, they need to adopt digital optimization technologies, such as voyage optimization and emissions monitoring systems, to enhance operational efficiency under carbon constraints.

5.2.2. Government (Policy Authorities)

The government should establish a comprehensive policy framework that integrates industrial policy and environmental regulation. This includes providing financial subsidies, tax incentives, and regulatory flexibility to support the transition toward low-carbon shipping. Furthermore, policies to stabilize freight rates and mitigate cost pass-through to exporters are essential.

5.2.3. Shipbuilding Industry

Shipbuilding firms should expand their capabilities in eco-friendly vessel construction, including ammonia-, methanol-, and hydrogen-powered ships. Strengthening R&D investment and securing technological leadership in green shipbuilding will be critical to capturing new demand generated by IMO regulations.

5.2.4. Financial Institutions

Financial institutions should develop tailored financing solutions, such as green ship funds, leasing programs, and sustainability-linked loans, to support large-scale investment in fleet renewal. In addition, risk-sharing mechanisms and public–private partnerships should be expanded to reduce financial uncertainty associated with new technologies.

5.3. Literature Contributions and Future Research

This study contributes to the literature in three main aspects. First, it extends existing research beyond firm-level or sector-specific analyses by quantifying economy-wide spillover effects using an input–output framework. Second, it provides an integrated analytical perspective by jointly examining shipping, shipbuilding, and financial industries, thereby capturing the structural interdependencies embedded in maritime decarbonization policies. Third, it offers empirical evidence on how global environmental regulations translate into macroeconomic impacts at the national level, contributing to the emerging literature on carbon policy transmission mechanisms in trade-dependent economies.

Despite its contributions, this study has several limitations. First, the analysis is based on a static input–output framework, which does not fully capture dynamic adjustments such as technological innovation, behavioral responses, or long-term structural transformation. Second, key parameters—such as compliance costs, fuel-transition pathways, and market-based mechanism prices (e.g., remedial units)—are treated under scenario assumptions rather than endogenously modeled, which may limit the precision of long-term projections. As an initial analytical approach, a static single-country input–output model provides a reasonable basis for estimating ripple effects; however, over the 2028–2035 period, it is unable to endogenously capture broader equilibrium responses, technological adjustment, foreign shipyard utilization, import leakage, freight-rate pass-through, or international substitution. Therefore, this paper currently presents the conclusion that the Direct scenario is “most damaging” among the three scenarios: ① BAU, ② Base Compliance Scenario, and ③ Direct Compliance Scenario, but the analysis results may vary when analyzing through dynamic models.

Third, the aggregation of industries into a 35-sector structure may obscure more granular inter-industry heterogeneity, particularly within energy supply chains and maritime services.

Future research should address these limitations by incorporating dynamic modeling approaches, such as computable general equilibrium (CGE) models or system dynamics frameworks. IO analysis, the primary methodology employed in this study, relies on data from a specific year and thus exhibits certain static characteristics. However, the prices of eco-friendly alternative fuels widely used in the shipping industry—such as biofuel oil, ammonia, methanol, and hydrogen—are expected to fluctuate over time. Therefore, the application of dynamic models, such as CGE models, is expected to enhance the accuracy of long-term forecasts. In future research, a dynamic modeling approach beyond IO analysis will be adopted to better capture changes in alternative fuel prices.

In addition, further studies should explore RU market price simulations, well-to-wake (WtW) emission pathways under alternative fuels, and the development of an integrated maritime carbon economic model that jointly considers shipping, shipbuilding, and fuel supply systems. Such extensions would enhance the robustness of policy evaluation and provide a more comprehensive foundation for national GX (Green Transformation) strategies in the maritime sector.