{"content_id":"xwkefvppxh","slug":"japan-2026-growth-strategy-17-investment-sectors","locale":"en","schema_type":"Report","category":"report","category_name":"Report","title":"Japan's 2026 Growth Strategy Explained: Public-Private Investment Roadmap for 17 Strategic Fields","summary":"On July 21, 2026, the Japanese government finalized a growth strategy and public-private investment roadmap covering 17 strategic fields, including AI and semiconductors, digital technology and cybersecurity, biotechnology, and energy. The key is to link crisis-management investment for economic security with growth investment that expands productivity and exports, while converting government support into actual private investment and industrial outcomes.","author":{"name":"Injoys Editorial Team","url":"https://injoys.com/ko/about"},"key_points":["The 17 strategic fields combine not only the development of advanced technologies but also supply chains, energy, logistics, defense, and disaster response into a single industrial policy framework.","Crisis-management investment aims to reduce vulnerabilities in critical supply chains and infrastructure, while growth investment seeks to expand productivity, markets, and exports.","The success of AI and semiconductor policy depends not only on computing resources and chip production capacity but also on electricity, data, talent, and the software ecosystem.","Policies for manufacturing data and robotics foundation models must also address rules for intercompany data sharing, safety verification, and adoption costs for small and medium-sized enterprises.","Policy evaluations should focus on outcome indicators such as additional private investment, commercialization, supply chain diversification, and productivity gains rather than government budgets or announced investment amounts."],"content_markdown":"Japan’s 2026 growth strategy goes beyond allocating subsidies to individual industries. It is an attempt to connect economic security and growth policy through a unified public-private investment framework. On July 21, 2026, the Japanese government finalized a new growth strategy that includes investment roadmaps for 17 strategic fields. This article outlines the strategy’s structure, technology-specific challenges, evaluation metrics, and constraints to monitor during implementation.\n\n## Core Structure of the 2026 Growth Strategy\n\nThe strategy’s basic logic is for the government to bear some of the initial risks and public costs, thereby encouraging companies to invest in facilities, R\u0026D, and talent. Its main targets include fields with large investment requirements and long payback periods, such as semiconductor plants and power grids; fields with a high probability of failure, such as new drugs and synthetic biology; and fields where investment by individual companies alone cannot reduce nationwide risks, such as cybersecurity.\n\nThe following four elements should be distinguished when reading the roadmaps.\n\n1. **Policy objectives:** Intended outcomes such as supply chain stability, higher productivity, creation of new markets, and export growth\n2. **Government measures:** Subsidies, procurement, regulatory reform, standardization, R\u0026D support, and provision of public data\n3. **Private-sector role:** Capital investment, commercialization, hiring, data provision, and overseas market development\n4. **Verification metrics:** Figures showing whether announced investments led to actual production, sales, exports, productivity gains, and risk reduction\n\nNot all support necessarily translates into growth outcomes. Budgets announced by the government are input metrics, while additional investment actually executed by companies and commercialization performance are outcome metrics. These two types must be separated to assess the additionality of the policy.\n\n## 17 Strategic Fields and Policy Objectives\n\nThe official strategy addresses both technology industries and the foundations of economic security. The Korean names in the following table are translated to make the Japanese government’s field classifications easier to understand. Detailed project names, schedules, and figures should be verified against the original roadmap for each field.\n\n| Strategic field | Main policy objective | Key variables to analyze |\n|---|---|---|\n| AI and semiconductors | Strengthen computing resources, advanced and general-purpose semiconductors, and the foundation for AI use | Power supply, production yield, customers, talent |\n| Shipbuilding | Restore shipbuilding capacity and productivity and strengthen the maritime supply chain | Order backlog, automation, skilled workers, equipment and materials |\n| Quantum | Commercialize computing, communications, and sensing technologies | Error rates, use cases, researchers, international standards |\n| Synthetic biology and biotechnology | Develop biomanufacturing and high-value-added materials and products | Scale-up of cultivation, raw materials, safety, market demand |\n| Aviation and space | Expand the aviation supply chain and satellite and launch vehicle industries | Certification, launch frequency, domestic production of components, demand |\n| Digital and cybersecurity | Strengthen digital infrastructure and security capabilities | Specialists, incident response, supply chain security, procurement |\n| Content | Expand overseas distribution and revenue from intellectual property | Rights protection, localization, distribution agreements, overseas sales |\n| Food tech | Address food challenges and advance the food industry | Costs, regulations, consumer acceptance, supply stability |\n| Resources, energy security, and GX | Pursue energy supply stability and decarbonization investment in parallel | Electricity prices, grid, fuel dependence, emissions-reduction effects |\n| Disaster prevention and national resilience | Reduce disaster damage and the risk of infrastructure disruption | Prevention effects, recovery time, maintenance costs |\n| New drugs and advanced medicine | Connect research outcomes to clinical trials, approval, and production | Clinical success rates, review periods, manufacturing capacity, accessibility |\n| Fusion energy | Secure long-term energy technologies and related supply chains | Technology maturity, costs, component and material base |\n| Advanced materials | Strengthen competitiveness in critical materials for semiconductors, batteries, aviation, and other industries | Supply concentration, recycling, performance, mass-production capacity |\n| Ports and logistics | Improve logistics efficiency and supply chain resilience | Processing time, automation, transport workforce, connectivity |\n| Defense industry | Maintain the production base and supply chains and strengthen technological capabilities | Procurement predictability, production capacity, export controls, workforce |\n| Information and communications | Strengthen next-generation networks and communications infrastructure | Coverage, equipment supply chains, energy efficiency, standards |\n| Maritime | Expand capabilities in marine resources, observation, and equipment industries | Exploration technology, data, environmental impact, commercial viability |\n\nThe 17 fields are not independent of one another. AI is commonly used in manufacturing, new drugs, logistics, and disaster prevention, while semiconductors and advanced materials provide the foundation for information and communications, space, and the defense industry. Power grids and cybersecurity are also common constraints across nearly all fields.\n\n## Difference Between Risk-Management Investment and Growth Investment\n\n**Risk-management investment** refers to investment that reduces the risk of economic and social disruption. This includes projects that diversify supply chains concentrated in specific countries or companies, secure production capacity for critical components, and prepare for cyberattacks and natural disasters.\n\n**Growth investment** refers to investment that increases productivity, market size, added value, and exports. Representative fields include AI adoption, automation, new drug commercialization, overseas content distribution, and next-generation communications.\n\nThe two categories are not mutually exclusive. For example, domestic semiconductor production can reduce the risk of supply disruptions while promoting manufacturing investment and technological accumulation. However, policy evaluation should vary according to the objective.\n\n| Nature of investment | Primary question | Appropriate outcome metrics |\n|---|---|---|\n| Risk-management investment | Has the risk of disruption actually decreased? | Supplier concentration, stockpile duration, recovery time, production capacity for critical items |\n| Growth investment | Have additional private-sector activity and market outcomes emerged? | Additional capital investment, productivity, sales, exports, commercialization rate |\n| Combined investment | Is there an appropriate balance between risk reduction and industrial growth? | Supply stability, cost competitiveness, private investment, long-term fiscal burden |\n\nCost verification should not be omitted merely because an investment is classified as risk management. Conversely, the public value of supply chains, security, and disaster response should not be ignored merely because short-term profitability is low.\n\n## Priorities for AI and Semiconductors\n\nThe AI and semiconductor field is not limited to chip manufacturing and AI services. It simultaneously requires data centers, power, cooling, networks, software, researchers, and industrial data.\n\n### Computing Resources and Semiconductor Supply\n\nTo assess policy effects, the following items should be examined in addition to factory construction starts and subsidy amounts.\n\n- Whether the target processes and products correspond to actual demand within Japan\n- Whether production yields and utilization rates rise to competitive levels\n- Whether equipment, materials, design, and packaging are interconnected\n- Whether customers and cash flow can be secured even after support ends\n- Whether data-center power demand exceeds the capacity of regional power grids\n\nFocusing only on advanced chips may overlook supply risks involving general-purpose and power semiconductors needed for automobiles and industrial machinery. Conversely, pursuing only expanded domestic production may weaken connections with overseas technologies and markets.\n\n### Use of Manufacturing Data for AI\n\nJapan’s manufacturing sector has accumulated data related to equipment conditions, quality inspections, work processes, and maintenance. However, differences in format and issues involving trade secrets and security make it difficult to use this data immediately for joint learning among companies.\n\nThe following policy foundations are needed.\n\n- Standardization of data formats and metadata\n- Contractual rules reflecting trade secrets, personal information, and usage rights\n- Federated learning and secure analytical environments that do not move original data externally\n- Data cleansing and security support accessible to small and medium-sized enterprises\n- Verification of the traceability, quality, and scope of responsibility for model outputs\n\nMore important than the scale of a data platform are operational outcomes such as actual reductions in defect rates, increased uptime, and energy savings.\n\n## Robotics Foundation Models and Industrial Automation\n\nRobotics foundation models are general-purpose models designed to learn jointly from visual, language, sensor, and motion data and apply that learning to multiple tasks. While generative AI handles text or images, robotics models must perform perception, planning, and control in physical environments.\n\nJapan has a manufacturing base and robotics companies, but general-purpose robotics AI requires large-scale motion data, simulation, safety verification, and high-performance computing resources. Support policies should cover not only model development itself but also the following foundations.\n\n- Motion-data standards usable by multiple manufacturers\n- Testing and simulation environments resembling actual factories\n- Safety assessments for collisions, malfunctions, and cyber intrusions\n- Liability and operating standards for collaboration with human workers\n- Reduced adoption and maintenance costs for small and medium-sized manufacturers\n\nCommercial viability is difficult to assess based solely on the number of successful demonstrations. The time required to switch tasks, error rates, utilization rates, frequency of human intervention, and payback period for adoption costs must also be measured.\n\n## Challenges in the Digital and Cybersecurity Field\n\nAs industries become more digitalized, security becomes an operating condition for production and supply chains rather than a separate sector. When factory equipment, ports, hospitals, and energy facilities are connected, operational disruption becomes a major risk alongside information leaks.\n\nThe priorities are as follows.\n\n- Strengthening incident detection and recovery capabilities for critical infrastructure\n- Managing vulnerabilities in software and equipment supply chains\n- Improving the basic security level of small and medium-sized enterprises\n- Training security specialists and conducting practical exercises\n- Spreading safety standards through government procurement\n- Responding to attacks against AI models and training data\n\nEvaluation metrics should not be limited to the number of people trained or spending on security products. Vulnerability remediation time, breach detection time, recovery time, repeat incident rates, and the rate at which companies participating in supply chains meet standards are more direct outcome metrics.\n\n## Conditions for Commercializing Synthetic Biology and New Drugs\n\nSynthetic biology is a technological field that designs biological systems to produce pharmaceuticals, materials, chemicals, food ingredients, and other products. Expanding laboratory-stage success into industrial production requires cultivation facilities, process control, quality management, raw material procurement, and regulatory compliance.\n\nIn the new drug field, performance is also difficult to assess based only on the number of papers or candidate compounds. Significant costs and failure risks arise throughout the process from preclinical work and clinical trials to approval, manufacturing, insurance coverage, and market access.\n\nBiotechnology policy should therefore distinguish and evaluate the following.\n\n- Research outcomes: Papers, patents, candidate compounds, and technology validation\n- Development progress: Entry into clinical stages, trial completion, and approval applications\n- Production capabilities: Process scale-up, compliance with quality standards, and stable manufacturing\n- Market outcomes: Approvals, sales, exports, technology transfers, and patient accessibility\n\nAI-based drug discovery can accelerate the identification of candidates, but it does not guarantee clinical success. The performance of AI use should be verified not only by predictive accuracy but also by total development time, costs, and the rate of transition to clinical trials.\n\n## Metrics Needed to Evaluate the Roadmaps\n\nIt is useful to structure policy evaluation in the order of inputs, outputs, outcomes, and long-term impacts.\n\n| Stage | Example metrics | Cautions when interpreting |\n|---|---|---|\n| Input | Government budget, tax support, personnel, research funding | The scale of execution alone does not indicate performance |\n| Output | Factories, testing facilities, patents, training data, prototypes | Facility utilization and quality must also be verified |\n| Outcome | Additional private investment, productivity, commercialization, exports, supply chain diversification | Investments that would have occurred even without the policy must be excluded |\n| Impact | Potential growth rate, high-wage jobs, economic security, regional spillover effects | The effects of other factors, such as economic conditions, exchange rates, and international affairs, must be separated |\n\nIn particular, **the amount of induced private investment** should be disclosed separately as announced and executed amounts. Double counting that reclassifies existing investment plans as results of government support must also be prevented. For support provided to individual companies, support conditions, performance criteria, clawback provisions, and ex-post evaluation results should be disclosed to the extent possible.\n\n## Major Constraints During Implementation\n\n### Workforce Shortages\n\nIn addition to AI researchers, semiconductor process engineers, electrical and construction workers, biomanufacturing personnel, security officers, and robot operators are needed. If multiple strategic fields simultaneously demand the same workers, wages and project costs may rise and schedules may be delayed.\n\n### Power and Infrastructure\n\nSemiconductor plants and data centers require large amounts of power and cooling resources. Simply expanding generation facilities is not sufficient; transmission grids, substations, site approvals, backup power, and local acceptance must also be considered.\n\n### Supply Chain Dependence\n\nEven if domestic production expands, critical equipment, raw materials, software, or intellectual property may remain dependent on specific overseas suppliers. Dependence should be assessed by examining not only the country producing the final product but also second- and third-tier supply chains and the time required to secure alternatives.\n\n### Concentration of Support and Market Distortion\n\nSupport for large-scale projects can easily become concentrated among a small number of companies. Stage-specific conditions and exit criteria are needed to prevent competition for support from creating excess capacity or sustaining low-productivity projects over the long term.\n\n### Interministerial Coordination\n\nAI, energy, biotechnology, defense, and communications span multiple ministries and regulatory agencies. If the schedules and metrics of field-specific roadmaps are not interconnected, common bottlenecks such as power grids or permits and approvals may delay the entire plan.\n\n## Matters for Companies and Research Institutions to Verify\n\nRather than focusing simply on whether they are included in a strategic field, companies and research institutions should specifically verify the support measures and mandatory conditions.\n\n- Whether the support targets basic research, demonstration, facilities, or commercialization\n- The company’s required contribution ratio and the period for which investment must be maintained\n- Whether there are conditions concerning data sharing, disclosure of outcomes, and domestic production\n- Whether international joint research and overseas procurement are permitted\n- Whether clawback conditions apply if targets are not met or a project is discontinued\n- Whether power, personnel, land, and permits and approvals can actually be secured\n\nThe roadmaps provide direction but do not automatically guarantee support for individual projects. Actual participation conditions must be verified in project-specific documents announced by the responsible ministries and agencies.\n\n## Overall Assessment\n\nA defining feature of Japan’s 2026 growth strategy is its integration of advanced technologies such as AI and semiconductors with industrial foundations such as shipbuilding, logistics, energy, and disaster prevention. The strategy’s success or failure depends not on the number of fields or the announced scale of investment, but on how effectively common bottlenecks are resolved.\n\nGoing forward, it will be necessary to continuously compare the execution rate of private investment, actual utilization rates for data and infrastructure, productivity, commercialization, exports, supply chain concentration, and the status of power supply. The most important verification criteria are whether government support accelerated investment in the short term and whether competitiveness and markets remain sustainable after support ends in the long term.","content_html":"\u003cp\u003eJapan’s 2026 growth strategy goes beyond allocating subsidies to individual industries. It is an attempt to connect economic security and growth policy through a unified public-private investment framework. On July 21, 2026, the Japanese government finalized a new growth strategy that includes investment roadmaps for 17 strategic fields. This article outlines the strategy’s structure, technology-specific challenges, evaluation metrics, and constraints to monitor during implementation.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#core-structure-of-the-2026-growth-strategy\" class=\"anchor\" id=\"core-structure-of-the-2026-growth-strategy\"\u003e\u003c/a\u003eCore Structure of the 2026 Growth Strategy\u003c/h2\u003e\n\u003cp\u003eThe strategy’s basic logic is for the government to bear some of the initial risks and public costs, thereby encouraging companies to invest in facilities, R\u0026amp;D, and talent. Its main targets include fields with large investment requirements and long payback periods, such as semiconductor plants and power grids; fields with a high probability of failure, such as new drugs and synthetic biology; and fields where investment by individual companies alone cannot reduce nationwide risks, such as cybersecurity.\u003c/p\u003e\n\u003cp\u003eThe following four elements should be distinguished when reading the roadmaps.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003ePolicy objectives:\u003c/strong\u003e Intended outcomes such as supply chain stability, higher productivity, creation of new markets, and export growth\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eGovernment measures:\u003c/strong\u003e Subsidies, procurement, regulatory reform, standardization, R\u0026amp;D support, and provision of public data\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePrivate-sector role:\u003c/strong\u003e Capital investment, commercialization, hiring, data provision, and overseas market development\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eVerification metrics:\u003c/strong\u003e Figures showing whether announced investments led to actual production, sales, exports, productivity gains, and risk reduction\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eNot all support necessarily translates into growth outcomes. Budgets announced by the government are input metrics, while additional investment actually executed by companies and commercialization performance are outcome metrics. These two types must be separated to assess the additionality of the policy.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#17-strategic-fields-and-policy-objectives\" class=\"anchor\" id=\"17-strategic-fields-and-policy-objectives\"\u003e\u003c/a\u003e17 Strategic Fields and Policy Objectives\u003c/h2\u003e\n\u003cp\u003eThe official strategy addresses both technology industries and the foundations of economic security. The Korean names in the following table are translated to make the Japanese government’s field classifications easier to understand. Detailed project names, schedules, and figures should be verified against the original roadmap for each field.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eStrategic field\u003c/th\u003e\n\u003cth\u003eMain policy objective\u003c/th\u003e\n\u003cth\u003eKey variables to analyze\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Strategic field\"\u003eAI and semiconductors\u003c/td\u003e\n\u003ctd data-label=\"Main policy objective\"\u003eStrengthen computing resources, advanced and general-purpose semiconductors, and the foundation for AI use\u003c/td\u003e\n\u003ctd data-label=\"Key variables to analyze\"\u003ePower supply, production yield, customers, talent\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Strategic field\"\u003eShipbuilding\u003c/td\u003e\n\u003ctd data-label=\"Main policy objective\"\u003eRestore shipbuilding capacity and productivity and strengthen the maritime supply chain\u003c/td\u003e\n\u003ctd data-label=\"Key variables to analyze\"\u003eOrder backlog, automation, skilled workers, equipment and materials\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Strategic field\"\u003eQuantum\u003c/td\u003e\n\u003ctd data-label=\"Main policy objective\"\u003eCommercialize computing, communications, and sensing technologies\u003c/td\u003e\n\u003ctd data-label=\"Key variables to analyze\"\u003eError rates, use cases, researchers, international standards\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Strategic field\"\u003eSynthetic biology and biotechnology\u003c/td\u003e\n\u003ctd data-label=\"Main policy objective\"\u003eDevelop biomanufacturing and high-value-added materials and products\u003c/td\u003e\n\u003ctd data-label=\"Key variables to analyze\"\u003eScale-up of cultivation, raw materials, safety, market demand\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Strategic field\"\u003eAviation and space\u003c/td\u003e\n\u003ctd data-label=\"Main policy objective\"\u003eExpand the aviation supply chain and satellite and launch vehicle industries\u003c/td\u003e\n\u003ctd data-label=\"Key variables to analyze\"\u003eCertification, launch frequency, domestic production of components, demand\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Strategic field\"\u003eDigital and cybersecurity\u003c/td\u003e\n\u003ctd data-label=\"Main policy objective\"\u003eStrengthen digital infrastructure and security capabilities\u003c/td\u003e\n\u003ctd data-label=\"Key variables to analyze\"\u003eSpecialists, incident response, supply chain security, procurement\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Strategic field\"\u003eContent\u003c/td\u003e\n\u003ctd data-label=\"Main policy objective\"\u003eExpand overseas distribution and revenue from intellectual property\u003c/td\u003e\n\u003ctd data-label=\"Key variables to analyze\"\u003eRights protection, localization, distribution agreements, overseas sales\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Strategic field\"\u003eFood tech\u003c/td\u003e\n\u003ctd data-label=\"Main policy objective\"\u003eAddress food challenges and advance the food industry\u003c/td\u003e\n\u003ctd data-label=\"Key variables to analyze\"\u003eCosts, regulations, consumer acceptance, supply stability\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Strategic field\"\u003eResources, energy security, and GX\u003c/td\u003e\n\u003ctd data-label=\"Main policy objective\"\u003ePursue energy supply stability and decarbonization investment in parallel\u003c/td\u003e\n\u003ctd data-label=\"Key variables to analyze\"\u003eElectricity prices, grid, fuel dependence, emissions-reduction effects\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Strategic field\"\u003eDisaster prevention and national resilience\u003c/td\u003e\n\u003ctd data-label=\"Main policy objective\"\u003eReduce disaster damage and the risk of infrastructure disruption\u003c/td\u003e\n\u003ctd data-label=\"Key variables to analyze\"\u003ePrevention effects, recovery time, maintenance costs\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Strategic field\"\u003eNew drugs and advanced medicine\u003c/td\u003e\n\u003ctd data-label=\"Main policy objective\"\u003eConnect research outcomes to clinical trials, approval, and production\u003c/td\u003e\n\u003ctd data-label=\"Key variables to analyze\"\u003eClinical success rates, review periods, manufacturing capacity, accessibility\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Strategic field\"\u003eFusion energy\u003c/td\u003e\n\u003ctd data-label=\"Main policy objective\"\u003eSecure long-term energy technologies and related supply chains\u003c/td\u003e\n\u003ctd data-label=\"Key variables to analyze\"\u003eTechnology maturity, costs, component and material base\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Strategic field\"\u003eAdvanced materials\u003c/td\u003e\n\u003ctd data-label=\"Main policy objective\"\u003eStrengthen competitiveness in critical materials for semiconductors, batteries, aviation, and other industries\u003c/td\u003e\n\u003ctd data-label=\"Key variables to analyze\"\u003eSupply concentration, recycling, performance, mass-production capacity\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Strategic field\"\u003ePorts and logistics\u003c/td\u003e\n\u003ctd data-label=\"Main policy objective\"\u003eImprove logistics efficiency and supply chain resilience\u003c/td\u003e\n\u003ctd data-label=\"Key variables to analyze\"\u003eProcessing time, automation, transport workforce, connectivity\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Strategic field\"\u003eDefense industry\u003c/td\u003e\n\u003ctd data-label=\"Main policy objective\"\u003eMaintain the production base and supply chains and strengthen technological capabilities\u003c/td\u003e\n\u003ctd data-label=\"Key variables to analyze\"\u003eProcurement predictability, production capacity, export controls, workforce\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Strategic field\"\u003eInformation and communications\u003c/td\u003e\n\u003ctd data-label=\"Main policy objective\"\u003eStrengthen next-generation networks and communications infrastructure\u003c/td\u003e\n\u003ctd data-label=\"Key variables to analyze\"\u003eCoverage, equipment supply chains, energy efficiency, standards\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Strategic field\"\u003eMaritime\u003c/td\u003e\n\u003ctd data-label=\"Main policy objective\"\u003eExpand capabilities in marine resources, observation, and equipment industries\u003c/td\u003e\n\u003ctd data-label=\"Key variables to analyze\"\u003eExploration technology, data, environmental impact, commercial viability\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003cp\u003eThe 17 fields are not independent of one another. AI is commonly used in manufacturing, new drugs, logistics, and disaster prevention, while semiconductors and advanced materials provide the foundation for information and communications, space, and the defense industry. Power grids and cybersecurity are also common constraints across nearly all fields.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#difference-between-risk-management-investment-and-growth-investment\" class=\"anchor\" id=\"difference-between-risk-management-investment-and-growth-investment\"\u003e\u003c/a\u003eDifference Between Risk-Management Investment and Growth Investment\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eRisk-management investment\u003c/strong\u003e refers to investment that reduces the risk of economic and social disruption. This includes projects that diversify supply chains concentrated in specific countries or companies, secure production capacity for critical components, and prepare for cyberattacks and natural disasters.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGrowth investment\u003c/strong\u003e refers to investment that increases productivity, market size, added value, and exports. Representative fields include AI adoption, automation, new drug commercialization, overseas content distribution, and next-generation communications.\u003c/p\u003e\n\u003cp\u003eThe two categories are not mutually exclusive. For example, domestic semiconductor production can reduce the risk of supply disruptions while promoting manufacturing investment and technological accumulation. However, policy evaluation should vary according to the objective.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eNature of investment\u003c/th\u003e\n\u003cth\u003ePrimary question\u003c/th\u003e\n\u003cth\u003eAppropriate outcome metrics\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Nature of investment\"\u003eRisk-management investment\u003c/td\u003e\n\u003ctd data-label=\"Primary question\"\u003eHas the risk of disruption actually decreased?\u003c/td\u003e\n\u003ctd data-label=\"Appropriate outcome metrics\"\u003eSupplier concentration, stockpile duration, recovery time, production capacity for critical items\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Nature of investment\"\u003eGrowth investment\u003c/td\u003e\n\u003ctd data-label=\"Primary question\"\u003eHave additional private-sector activity and market outcomes emerged?\u003c/td\u003e\n\u003ctd data-label=\"Appropriate outcome metrics\"\u003eAdditional capital investment, productivity, sales, exports, commercialization rate\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Nature of investment\"\u003eCombined investment\u003c/td\u003e\n\u003ctd data-label=\"Primary question\"\u003eIs there an appropriate balance between risk reduction and industrial growth?\u003c/td\u003e\n\u003ctd data-label=\"Appropriate outcome metrics\"\u003eSupply stability, cost competitiveness, private investment, long-term fiscal burden\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003cp\u003eCost verification should not be omitted merely because an investment is classified as risk management. Conversely, the public value of supply chains, security, and disaster response should not be ignored merely because short-term profitability is low.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#priorities-for-ai-and-semiconductors\" class=\"anchor\" id=\"priorities-for-ai-and-semiconductors\"\u003e\u003c/a\u003ePriorities for AI and Semiconductors\u003c/h2\u003e\n\u003cp\u003eThe AI and semiconductor field is not limited to chip manufacturing and AI services. It simultaneously requires data centers, power, cooling, networks, software, researchers, and industrial data.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca href=\"#computing-resources-and-semiconductor-supply\" class=\"anchor\" id=\"computing-resources-and-semiconductor-supply\"\u003e\u003c/a\u003eComputing Resources and Semiconductor Supply\u003c/h3\u003e\n\u003cp\u003eTo assess policy effects, the following items should be examined in addition to factory construction starts and subsidy amounts.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eWhether the target processes and products correspond to actual demand within Japan\u003c/li\u003e\n\u003cli\u003eWhether production yields and utilization rates rise to competitive levels\u003c/li\u003e\n\u003cli\u003eWhether equipment, materials, design, and packaging are interconnected\u003c/li\u003e\n\u003cli\u003eWhether customers and cash flow can be secured even after support ends\u003c/li\u003e\n\u003cli\u003eWhether data-center power demand exceeds the capacity of regional power grids\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFocusing only on advanced chips may overlook supply risks involving general-purpose and power semiconductors needed for automobiles and industrial machinery. Conversely, pursuing only expanded domestic production may weaken connections with overseas technologies and markets.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca href=\"#use-of-manufacturing-data-for-ai\" class=\"anchor\" id=\"use-of-manufacturing-data-for-ai\"\u003e\u003c/a\u003eUse of Manufacturing Data for AI\u003c/h3\u003e\n\u003cp\u003eJapan’s manufacturing sector has accumulated data related to equipment conditions, quality inspections, work processes, and maintenance. However, differences in format and issues involving trade secrets and security make it difficult to use this data immediately for joint learning among companies.\u003c/p\u003e\n\u003cp\u003eThe following policy foundations are needed.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eStandardization of data formats and metadata\u003c/li\u003e\n\u003cli\u003eContractual rules reflecting trade secrets, personal information, and usage rights\u003c/li\u003e\n\u003cli\u003eFederated learning and secure analytical environments that do not move original data externally\u003c/li\u003e\n\u003cli\u003eData cleansing and security support accessible to small and medium-sized enterprises\u003c/li\u003e\n\u003cli\u003eVerification of the traceability, quality, and scope of responsibility for model outputs\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eMore important than the scale of a data platform are operational outcomes such as actual reductions in defect rates, increased uptime, and energy savings.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#robotics-foundation-models-and-industrial-automation\" class=\"anchor\" id=\"robotics-foundation-models-and-industrial-automation\"\u003e\u003c/a\u003eRobotics Foundation Models and Industrial Automation\u003c/h2\u003e\n\u003cp\u003eRobotics foundation models are general-purpose models designed to learn jointly from visual, language, sensor, and motion data and apply that learning to multiple tasks. While generative AI handles text or images, robotics models must perform perception, planning, and control in physical environments.\u003c/p\u003e\n\u003cp\u003eJapan has a manufacturing base and robotics companies, but general-purpose robotics AI requires large-scale motion data, simulation, safety verification, and high-performance computing resources. Support policies should cover not only model development itself but also the following foundations.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eMotion-data standards usable by multiple manufacturers\u003c/li\u003e\n\u003cli\u003eTesting and simulation environments resembling actual factories\u003c/li\u003e\n\u003cli\u003eSafety assessments for collisions, malfunctions, and cyber intrusions\u003c/li\u003e\n\u003cli\u003eLiability and operating standards for collaboration with human workers\u003c/li\u003e\n\u003cli\u003eReduced adoption and maintenance costs for small and medium-sized manufacturers\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eCommercial viability is difficult to assess based solely on the number of successful demonstrations. The time required to switch tasks, error rates, utilization rates, frequency of human intervention, and payback period for adoption costs must also be measured.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#challenges-in-the-digital-and-cybersecurity-field\" class=\"anchor\" id=\"challenges-in-the-digital-and-cybersecurity-field\"\u003e\u003c/a\u003eChallenges in the Digital and Cybersecurity Field\u003c/h2\u003e\n\u003cp\u003eAs industries become more digitalized, security becomes an operating condition for production and supply chains rather than a separate sector. When factory equipment, ports, hospitals, and energy facilities are connected, operational disruption becomes a major risk alongside information leaks.\u003c/p\u003e\n\u003cp\u003eThe priorities are as follows.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eStrengthening incident detection and recovery capabilities for critical infrastructure\u003c/li\u003e\n\u003cli\u003eManaging vulnerabilities in software and equipment supply chains\u003c/li\u003e\n\u003cli\u003eImproving the basic security level of small and medium-sized enterprises\u003c/li\u003e\n\u003cli\u003eTraining security specialists and conducting practical exercises\u003c/li\u003e\n\u003cli\u003eSpreading safety standards through government procurement\u003c/li\u003e\n\u003cli\u003eResponding to attacks against AI models and training data\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eEvaluation metrics should not be limited to the number of people trained or spending on security products. Vulnerability remediation time, breach detection time, recovery time, repeat incident rates, and the rate at which companies participating in supply chains meet standards are more direct outcome metrics.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#conditions-for-commercializing-synthetic-biology-and-new-drugs\" class=\"anchor\" id=\"conditions-for-commercializing-synthetic-biology-and-new-drugs\"\u003e\u003c/a\u003eConditions for Commercializing Synthetic Biology and New Drugs\u003c/h2\u003e\n\u003cp\u003eSynthetic biology is a technological field that designs biological systems to produce pharmaceuticals, materials, chemicals, food ingredients, and other products. Expanding laboratory-stage success into industrial production requires cultivation facilities, process control, quality management, raw material procurement, and regulatory compliance.\u003c/p\u003e\n\u003cp\u003eIn the new drug field, performance is also difficult to assess based only on the number of papers or candidate compounds. Significant costs and failure risks arise throughout the process from preclinical work and clinical trials to approval, manufacturing, insurance coverage, and market access.\u003c/p\u003e\n\u003cp\u003eBiotechnology policy should therefore distinguish and evaluate the following.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eResearch outcomes: Papers, patents, candidate compounds, and technology validation\u003c/li\u003e\n\u003cli\u003eDevelopment progress: Entry into clinical stages, trial completion, and approval applications\u003c/li\u003e\n\u003cli\u003eProduction capabilities: Process scale-up, compliance with quality standards, and stable manufacturing\u003c/li\u003e\n\u003cli\u003eMarket outcomes: Approvals, sales, exports, technology transfers, and patient accessibility\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAI-based drug discovery can accelerate the identification of candidates, but it does not guarantee clinical success. The performance of AI use should be verified not only by predictive accuracy but also by total development time, costs, and the rate of transition to clinical trials.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#metrics-needed-to-evaluate-the-roadmaps\" class=\"anchor\" id=\"metrics-needed-to-evaluate-the-roadmaps\"\u003e\u003c/a\u003eMetrics Needed to Evaluate the Roadmaps\u003c/h2\u003e\n\u003cp\u003eIt is useful to structure policy evaluation in the order of inputs, outputs, outcomes, and long-term impacts.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eStage\u003c/th\u003e\n\u003cth\u003eExample metrics\u003c/th\u003e\n\u003cth\u003eCautions when interpreting\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Stage\"\u003eInput\u003c/td\u003e\n\u003ctd data-label=\"Example metrics\"\u003eGovernment budget, tax support, personnel, research funding\u003c/td\u003e\n\u003ctd data-label=\"Cautions when interpreting\"\u003eThe scale of execution alone does not indicate performance\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Stage\"\u003eOutput\u003c/td\u003e\n\u003ctd data-label=\"Example metrics\"\u003eFactories, testing facilities, patents, training data, prototypes\u003c/td\u003e\n\u003ctd data-label=\"Cautions when interpreting\"\u003eFacility utilization and quality must also be verified\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Stage\"\u003eOutcome\u003c/td\u003e\n\u003ctd data-label=\"Example metrics\"\u003eAdditional private investment, productivity, commercialization, exports, supply chain diversification\u003c/td\u003e\n\u003ctd data-label=\"Cautions when interpreting\"\u003eInvestments that would have occurred even without the policy must be excluded\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Stage\"\u003eImpact\u003c/td\u003e\n\u003ctd data-label=\"Example metrics\"\u003ePotential growth rate, high-wage jobs, economic security, regional spillover effects\u003c/td\u003e\n\u003ctd data-label=\"Cautions when interpreting\"\u003eThe effects of other factors, such as economic conditions, exchange rates, and international affairs, must be separated\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003cp\u003eIn particular, \u003cstrong\u003ethe amount of induced private investment\u003c/strong\u003e should be disclosed separately as announced and executed amounts. Double counting that reclassifies existing investment plans as results of government support must also be prevented. For support provided to individual companies, support conditions, performance criteria, clawback provisions, and ex-post evaluation results should be disclosed to the extent possible.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#major-constraints-during-implementation\" class=\"anchor\" id=\"major-constraints-during-implementation\"\u003e\u003c/a\u003eMajor Constraints During Implementation\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca href=\"#workforce-shortages\" class=\"anchor\" id=\"workforce-shortages\"\u003e\u003c/a\u003eWorkforce Shortages\u003c/h3\u003e\n\u003cp\u003eIn addition to AI researchers, semiconductor process engineers, electrical and construction workers, biomanufacturing personnel, security officers, and robot operators are needed. If multiple strategic fields simultaneously demand the same workers, wages and project costs may rise and schedules may be delayed.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca href=\"#power-and-infrastructure\" class=\"anchor\" id=\"power-and-infrastructure\"\u003e\u003c/a\u003ePower and Infrastructure\u003c/h3\u003e\n\u003cp\u003eSemiconductor plants and data centers require large amounts of power and cooling resources. Simply expanding generation facilities is not sufficient; transmission grids, substations, site approvals, backup power, and local acceptance must also be considered.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca href=\"#supply-chain-dependence\" class=\"anchor\" id=\"supply-chain-dependence\"\u003e\u003c/a\u003eSupply Chain Dependence\u003c/h3\u003e\n\u003cp\u003eEven if domestic production expands, critical equipment, raw materials, software, or intellectual property may remain dependent on specific overseas suppliers. Dependence should be assessed by examining not only the country producing the final product but also second- and third-tier supply chains and the time required to secure alternatives.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca href=\"#concentration-of-support-and-market-distortion\" class=\"anchor\" id=\"concentration-of-support-and-market-distortion\"\u003e\u003c/a\u003eConcentration of Support and Market Distortion\u003c/h3\u003e\n\u003cp\u003eSupport for large-scale projects can easily become concentrated among a small number of companies. Stage-specific conditions and exit criteria are needed to prevent competition for support from creating excess capacity or sustaining low-productivity projects over the long term.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca href=\"#interministerial-coordination\" class=\"anchor\" id=\"interministerial-coordination\"\u003e\u003c/a\u003eInterministerial Coordination\u003c/h3\u003e\n\u003cp\u003eAI, energy, biotechnology, defense, and communications span multiple ministries and regulatory agencies. If the schedules and metrics of field-specific roadmaps are not interconnected, common bottlenecks such as power grids or permits and approvals may delay the entire plan.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#matters-for-companies-and-research-institutions-to-verify\" class=\"anchor\" id=\"matters-for-companies-and-research-institutions-to-verify\"\u003e\u003c/a\u003eMatters for Companies and Research Institutions to Verify\u003c/h2\u003e\n\u003cp\u003eRather than focusing simply on whether they are included in a strategic field, companies and research institutions should specifically verify the support measures and mandatory conditions.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eWhether the support targets basic research, demonstration, facilities, or commercialization\u003c/li\u003e\n\u003cli\u003eThe company’s required contribution ratio and the period for which investment must be maintained\u003c/li\u003e\n\u003cli\u003eWhether there are conditions concerning data sharing, disclosure of outcomes, and domestic production\u003c/li\u003e\n\u003cli\u003eWhether international joint research and overseas procurement are permitted\u003c/li\u003e\n\u003cli\u003eWhether clawback conditions apply if targets are not met or a project is discontinued\u003c/li\u003e\n\u003cli\u003eWhether power, personnel, land, and permits and approvals can actually be secured\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe roadmaps provide direction but do not automatically guarantee support for individual projects. Actual participation conditions must be verified in project-specific documents announced by the responsible ministries and agencies.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#overall-assessment\" class=\"anchor\" id=\"overall-assessment\"\u003e\u003c/a\u003eOverall Assessment\u003c/h2\u003e\n\u003cp\u003eA defining feature of Japan’s 2026 growth strategy is its integration of advanced technologies such as AI and semiconductors with industrial foundations such as shipbuilding, logistics, energy, and disaster prevention. The strategy’s success or failure depends not on the number of fields or the announced scale of investment, but on how effectively common bottlenecks are resolved.\u003c/p\u003e\n\u003cp\u003eGoing forward, it will be necessary to continuously compare the execution rate of private investment, actual utilization rates for data and infrastructure, productivity, commercialization, exports, supply chain concentration, and the status of power supply. The most important verification criteria are whether government support accelerated investment in the short term and whether competitiveness and markets remain sustainable after support ends in the long term.\u003c/p\u003e\n","tags":["Industrial policy","AI chips","Robots","Japan Growth Strategy","Biotechnology","Economic Security"],"faqs":[{"question":"When was Japan's 2026 growth strategy finalized?","answer":"On July 21, 2026, the Japanese government finalized a new growth strategy and public-private investment roadmaps for 17 strategic sectors. Detailed schedules and project conditions for each sector may be further specified in subsequent announcements by the ministries and agencies in charge."},{"question":"What common criteria were used to select the 17 strategic sectors?","answer":"The common assessment factors are their importance to economic security, Japan's existing industrial and technological base, the initial risks that are difficult for private investment alone to bear, and the potential to increase productivity and exports. Foundational sectors with significant social benefits, such as supply chains, energy, and disaster response, are also included."},{"question":"How does crisis management investment differ from growth investment?","answer":"Crisis management investment focuses on reducing risks such as supply disruptions, cyberattacks, energy instability, and disaster damage. Growth investment aims to expand productivity, markets, added value, and exports. Many projects, including those in semiconductors and energy, serve both purposes simultaneously."},{"question":"What is a robot foundation model?","answer":"A robot foundation model is a general-purpose AI model that jointly learns from video, language, sensor, and motion data and applies that learning to a variety of physical tasks. In actual industrial settings, it is necessary to verify not only task success rates but also error rates, safety, the frequency of human intervention, and the time required to recoup implementation costs."},{"question":"What is the biggest obstacle to using manufacturing data for AI?","answer":"Major obstacles include differences in data formats and quality among companies, as well as issues involving trade secrets, security, and usage rights. Standardization, secure analytical environments, clear contractual rules, and support for data cleansing by small and medium-sized enterprises are all needed."},{"question":"Can a policy be considered successful if the amount of government support is large?","answer":"No. The amount of support is only an input indicator. Actual additional private investment, facility utilization rates, productivity, commercialization, exports, supply chain diversification, and the potential for self-sufficiency after support ends must also be assessed."},{"question":"What are the key constraints on AI and semiconductor investment?","answer":"Advanced talent, power grids, cooling and water, production yields, and customer acquisition are the main constraints. If only chip production capacity is expanded without securing power for data centers or a software and data ecosystem, the impact of the investment may be limited."},{"question":"If a sector is included in the growth strategy, do companies automatically receive support?","answer":"No. Designation as a strategic sector indicates a policy direction and does not guarantee support for individual companies. Eligible technologies, cost-sharing requirements, domestic investment, disclosure of results, project continuation, and clawback conditions must be checked in the project-specific announcements issued by each ministry and agency."}],"sources":[{"url":"https://www.cas.go.jp/jp/seisaku/nipponseichosenryaku/index.html","title":"Japan's Growth Strategy, Cabinet Secretariat of Japan","type":"source"},{"url":"https://www.meti.go.jp/english/press/2026/0514_001.html","title":"Ministry of Economy, Trade and Industry Press Release, May 14, 2026","type":"source"},{"url":"https://www.jst.go.jp/EN/news_topics/","title":"Japan Science and Technology Agency News \u0026 Topics","type":"source"}],"images":[{"id":491,"url":"https://injoys.com/rails/active_storage/blobs/proxy/eyJfcmFpbHMiOnsiZGF0YSI6NTg0MiwicHVyIjoiYmxvYl9pZCJ9fQ==--847e0031c5a59361f8cf45dddf4a4bc065a78ed3/ai-ec85c01e.webp","is_representative":true,"generation_method":"ai_image","license":"ai_generated","mime_type":"image/webp","translations":{"ko":{"alt":"일본 지도 주위에 반도체, 로봇, 공장, 바이오, 에너지, 물류가 배치된 성장전략 도식","caption":"일본의 전략산업과 민관 투자 분야를 연결해 보여주는 일러스트다.","description":null},"en":{"alt":"Japan map surrounded by icons for chips, robotics, factories, biotech, energy and logistics","caption":"The illustration connects Japan’s strategic industries with areas for public-private investment.","description":null},"ja":{"alt":"日本地図の周囲に半導体、ロボット、工場、バイオ、エネルギー、物流を配した成長戦略図","caption":"日本の戦略産業と官民投資の対象分野を結び付けて示している。","description":null},"es":{"alt":"Mapa de Japón rodeado de iconos de chips, robótica, fábricas, biotecnología, energía y logística","caption":"La ilustración conecta las industrias estratégicas de Japón con áreas de inversión público-privada.","description":null},"id":{"alt":"Peta Jepang dikelilingi ikon cip, robotika, pabrik, bioteknologi, energi, dan logistik","caption":"Ilustrasi ini menghubungkan industri strategis Jepang dengan bidang investasi publik-swasta.","description":null},"pt":{"alt":"Mapa do Japão cercado por ícones de chips, robótica, fábricas, biotecnologia, energia e logística","caption":"A ilustração conecta as indústrias estratégicas do Japão às áreas de investimento público-privado.","description":null},"zh-hant":{"alt":"日本地圖周圍排列晶片、機器人、工廠、生技、能源與物流圖示","caption":"此插圖呈現日本戰略產業與公私協力投資領域之間的連結。","description":null},"de":{"alt":"Japan-Karte mit Symbolen für Chips, Robotik, Fabriken, Biotechnologie, Energie und Logistik","caption":"Die Illustration verknüpft Japans strategische Industrien mit Bereichen öffentlich-privater Investitionen.","description":null}}},{"id":492,"url":"https://injoys.com/rails/active_storage/blobs/proxy/eyJfcmFpbHMiOnsiZGF0YSI6NTg0OCwicHVyIjoiYmxvYl9pZCJ9fQ==--6ecaeba8f0960b14f351300733c5cfae5449a8d4/ai-4f7bfa2a.webp","is_representative":false,"generation_method":"ai_image","license":"ai_generated","mime_type":"image/webp","translations":{"ko":{"alt":"재해 지역과 연결된 반도체 공장, 데이터센터, 재생에너지, 스마트 항만의 산업 생태계","caption":"재해 대응부터 첨단 제조, 청정에너지, 물류까지 연결된 성장전략을 시각화했다.","description":null},"en":{"alt":"Industrial ecosystem with disaster zones, chip plants, data centers, renewables, and a smart port","caption":"The illustration connects resilience, advanced manufacturing, clean energy, and global logistics.","description":null},"ja":{"alt":"被災地域と半導体工場、データセンター、再生可能エネルギー、スマート港湾を結ぶ産業基盤","caption":"防災から先端製造、クリーンエネルギー、国際物流までを結ぶ成長戦略を表している。","description":null},"es":{"alt":"Ecosistema industrial con zonas dañadas, fábricas de chips, centros de datos, renovables y puerto inteligente","caption":"La ilustración conecta resiliencia, manufactura avanzada, energía limpia y logística global.","description":null},"id":{"alt":"Ekosistem industri dengan zona bencana, pabrik cip, pusat data, energi terbarukan, dan pelabuhan pintar","caption":"Ilustrasi ini menghubungkan ketahanan, manufaktur maju, energi bersih, dan logistik global.","description":null},"pt":{"alt":"Ecossistema industrial com áreas afetadas, fábricas de chips, data centers, renováveis e porto inteligente","caption":"A ilustração conecta resiliência, manufatura avançada, energia limpa e logística global.","description":null},"zh-hant":{"alt":"連結災區、晶片工廠、資料中心、再生能源與智慧港口的產業生態系","caption":"圖中呈現防災韌性、先進製造、潔淨能源與全球物流相互連結的成長策略。","description":null},"de":{"alt":"Industrieökosystem mit Katastrophengebiet, Chipfabriken, Rechenzentren, erneuerbarer Energie und Smart Port","caption":"Die Grafik verbindet Resilienz, moderne Fertigung, saubere Energie und globale Logistik.","description":null}}}],"published_at":"2026-08-05T21:18:18+09:00","updated_at":"2026-08-05T21:18:18+09:00","license":"cc_by","translation_status":"reviewed","available_locales":["ko","en","ja","es"],"data_locales":["ko","en","ja","es","id","pt","zh-hant","de"],"url":"https://injoys.com/en/articles/japan-2026-growth-strategy-17-investment-sectors"}