Digital Geography, AI and Emerging Economic Zones

Introduction

Geography has traditionally explained economic activity through distance, accessibility, natural resources, transport costs, labour markets, and institutional boundaries. Digital technologies do not make geography irrelevant. Instead, they reorganize the meaning of distance and create new forms of spatial dependence. A software firm may sell globally from a small office, yet it still depends on data centres, submarine cables, electricity grids, cloud availability zones, cybersecurity institutions, skilled labour, payment systems, and regulatory access. A digital platform may connect distant buyers and sellers, but the quality of that connection varies by bandwidth, latency, trust, language, law, and purchasing power.

The central proposition of this chapter is that digitalization produces a layered geography. Physical geography remains important, while network geography, data geography, institutional geography, and algorithmic geography become increasingly influential. Economic zones therefore include not only export-processing parks, logistics corridors, and special economic zones, but also cloud regions, technology clusters, innovation districts, digital free zones, data centres, smart-city ecosystems, and cross-border digital-service corridors.

For international business, the strategic question is not whether location still matters. It is which dimensions of location matter for a particular value chain, technology, market, and regulatory environment.

Learning Objectives

By the end of this chapter, students should be able to:

1. Explain how e-commerce, platforms, cloud computing, and digital trade reshape distance and market access.
2. Distinguish physical, network, data, institutional, and algorithmic geographies.
3. Evaluate the location requirements of cloud regions, data centres, AI systems, and smart-city infrastructures.
4. Apply location and competitiveness frameworks to digital economic zones.
5. Analyse data-localization, cybersecurity, sovereignty, labour, and ethical issues.
6. Formulate a balanced strategy for firms and governments seeking digital regional competitiveness.

1. From Territorial Geography to Layered Digital Geography

Classical economic geography commonly treats distance as a source of friction. Transport costs, delivery time, information asymmetry, and coordination difficulties tend to increase with physical separation. Digital communication reduces some of these frictions, especially for information-intensive goods and services. It does not eliminate all costs. Instead, it changes their composition.

A digitally enabled transaction may involve:

• a customer in one country;
• a platform legally incorporated in another;
• a cloud workload hosted in a third;
• a payment processor in a fourth;
• a fulfilment centre near the customer;
• data analysts and software engineers distributed across several regions; and
• regulators whose authority is territorially bounded.

This arrangement creates at least five interacting geographies:

Digital geography is therefore not placelessness. It is the spatial organization of digital infrastructures, capabilities, rules, and effects.

Digital distance

Physical distance remains relevant, but digital distance may be measured through latency, bandwidth, reliability, interoperability, regulatory friction, and trust. Two cities separated by a small physical distance may be digitally distant if they have incompatible regulations or poor connectivity. Conversely, two distant cities may be digitally close when connected by high-capacity networks and interoperable institutions.

Latency is the time required for a signal or transaction to travel through a network. It matters greatly for financial trading, interactive gaming, remote control, industrial automation, and some AI applications. Bandwidth is the volume of data that can be transmitted over a period. Reliability concerns the continuity and predictability of service. These dimensions are distinct: a network may offer high bandwidth but poor reliability, or low latency but insufficient capacity.

2. E-Commerce, Platforms, and the Reorganization of Markets

E-commerce changes the relationship between market access and physical presence. A firm can reach foreign customers without opening a conventional retail outlet in every country. However, successful international e-commerce still requires localized logistics, payment methods, customer service, consumer protection, returns management, taxation, and compliance.

Digital platforms create multi-sided markets. They connect groups such as consumers and merchants, drivers and passengers, advertisers and audiences, or software developers and users. The platform may benefit from indirect network effects: the value to one user group increases as participation by another group expands. More buyers can attract more sellers, while more sellers can attract more buyers.

Platforms also shape geography through ranking and recommendation systems. A merchant is not merely located in a city; it is positioned within a platform’s search results, delivery radius, reputation system, and advertising market. Algorithmic visibility becomes a new form of commercial accessibility.

Platform concentration and regional dependence

Platforms can lower entry barriers for small firms while simultaneously creating dependence on private infrastructure. A local business may gain international customers but become vulnerable to changes in commission rates, search-ranking rules, account suspension, data access, or platform ownership. Regions may therefore experience digital growth without developing equivalent control over the infrastructure and data that support it.

For policymakers, the issue is not simply whether a platform operates in a region. It is whether local firms acquire capabilities, retain value, and develop alternatives. Measures may include competition policy, open standards, digital identity, interoperable payments, training, and support for domestic innovation.

Digital trade

Digital trade includes digitally ordered goods and services, digitally delivered services, cross-border data transfers, online intermediation, cloud services, and other transactions enabled by digital technologies. Its governance is fragmented across trade agreements, privacy regimes, consumer law, intellectual-property rules, tax systems, cybersecurity regulation, and sector-specific requirements.

A useful distinction is between:

• digitally ordered trade, in which an order is placed electronically but physical delivery may follow; and
• digitally delivered trade, in which the service itself crosses borders electronically, such as cloud computing, online consulting, streaming, or remote education.

This distinction matters because digitally ordered goods remain dependent on ports, roads, warehouses, and customs, whereas digitally delivered services depend more heavily on connectivity, interoperability, legal recognition, and data governance.

3. Cloud Regions, Data Centres, and Digital Infrastructure Location

Cloud computing is geographically distributed but physically grounded. Major cloud providers organize infrastructure into regions and availability zones. A region generally corresponds to a geographic area in which cloud services are hosted; availability zones are separated facilities or data-centre clusters designed to improve resilience. Exact architecture varies by provider, and commercial terminology should not be treated as universally standardized.

Location decisions for data centres typically consider:

1. Electricity availability and price. Computing facilities require continuous power and may need renewable-energy contracts or backup generation.
2. Connectivity. Proximity to fibre routes, internet exchanges, and submarine-cable landing points reduces network friction.
3. Latency to users. Applications requiring rapid interaction benefit from proximity to demand centres.
4. Climate and cooling. Temperature, humidity, water availability, and cooling technology affect operating costs and environmental impact.
5. Disaster risk. Floods, earthquakes, heatwaves, storms, and wildfire exposure affect resilience.
6. Political and legal stability. Investors evaluate property rights, data rules, taxation, sanctions exposure, and regulatory predictability.
7. Skilled labour and suppliers. Construction, maintenance, security, telecommunications, and engineering capabilities are essential.
8. Social licence. Communities may object to land use, water consumption, electricity demand, or limited local employment.

The economics of latency and replication

A firm may place data and applications near customers to reduce latency, while replicating workloads across locations to improve resilience. These objectives can conflict with cost, data-localization rules, energy constraints, and operational complexity.

A simplified expected annual cost model can clarify the decision:

Here, C_total is total expected cost; C_compute is processing cost; C_network is data-transfer cost; C_storage is storage and replication cost; C_compliance is the cost of meeting legal and contractual requirements; and C_risk is the expected cost of outages, breaches, and other disruptions. The model is a managerial framework rather than a universal accounting identity. Its terms should be estimated consistently over the same time period and currency.

Expected risk cost can be represented as:

where p_i is the probability of disruption type i during the chosen period and L_i is the monetary loss if that disruption occurs. The expression assumes that the relevant risks can be identified and that expected-value analysis is appropriate. It does not capture all systemic or non-monetary consequences.

Edge computing

Edge computing places processing closer to users or devices rather than relying exclusively on distant centralized facilities. It is useful where latency, bandwidth, privacy, or operational continuity is important. Examples include connected factories, autonomous systems, telemedicine, smart traffic management, and augmented reality.

Edge computing does not eliminate cloud computing. It creates a distributed architecture in which some data are processed locally, some at regional edge facilities, and some in centralized cloud environments. The location strategy becomes an architectural decision rather than a single-site decision.

4. Data Localization, Sovereignty, and Digital Regulation

Data localization refers broadly to requirements that certain data be stored, processed, or transferred within a specified jurisdiction. Such requirements vary considerably. Some laws require local copies; others restrict transfers unless adequacy, contractual, certification, or security conditions are satisfied. The legal meaning must therefore be examined in the relevant jurisdiction rather than assumed from the label.

Governments may pursue localization for several reasons:

• national security and law-enforcement access;
• protection of personal privacy;
• continuity of critical services;
• development of domestic digital industries;
• regulatory supervision; and
• political control over strategic information.

Localization can support trust and domestic capability, but it may also increase costs, fragment markets, reduce economies of scale, and encourage retaliatory restrictions. For international firms, the central task is to distinguish data categories and apply proportionate controls.

A practical data-governance architecture commonly includes:

1. data classification by sensitivity and business purpose;
2. mapping of collection, storage, processing, and transfer locations;
3. access controls and identity management;
4. encryption in transit and at rest;
5. retention and deletion rules;
6. vendor and cloud-contract review;
7. incident-response procedures; and
8. auditable accountability for automated processing.

Data sovereignty is broader than storage location. It concerns the authority, rights, and control associated with data. A dataset stored locally may still be controlled by a foreign firm, processed by foreign software, or subject to extraterritorial legal claims. Sovereignty should therefore be analysed through ownership, access, jurisdiction, infrastructure, capability, and governance.

5. Smart Cities and Emerging Economic Zones

A smart city uses digital technologies, sensors, connectivity, analytics, and institutional coordination to improve urban services and decision-making. Applications include transport management, utilities, public safety, environmental monitoring, land administration, health services, and municipal finance.

Smart-city projects can strengthen regional competitiveness by reducing congestion, improving reliability, attracting firms, and generating data for innovation. Yet technology alone does not create a competitive city. Effective governance, maintenance, affordability, public trust, and inclusion are equally important.

Emerging forms of digital economic zones

An economic zone is competitive when it creates durable productivity advantages, not merely when it offers tax incentives. Digital zones need complementary assets: reliable power, open connectivity, human capital, legal certainty, cybersecurity, finance, research institutions, and links to local firms.

Agglomeration in the digital era

Digital technologies can decentralize production, but they can also intensify agglomeration. Firms cluster where specialized labour, venture capital, universities, suppliers, investors, and professional services are available. Knowledge often travels through social interaction and labour mobility, which remain spatially concentrated.

At the same time, remote work and cloud services may allow secondary cities to participate in global value chains. This outcome is not automatic. It depends on broadband quality, education, language skills, managerial capacity, affordable housing, and the credibility of institutions.

6. Automation, Artificial Intelligence, and Geospatial Intelligence

Automation changes the geography of work by substituting for some routine tasks and complementing other forms of expertise. The effects differ by occupation, industry, skill level, and institutional context. A region may lose routine back-office employment while gaining demand for data engineering, system integration, cybersecurity, maintenance, and AI governance.

Artificial intelligence changes location strategy in at least four ways:

1. Demand for compute. Training and operating advanced models require specialized chips, data centres, cooling, and electricity.
2. Demand for data. Firms need legally usable, high-quality, representative, and well-governed data.
3. Demand for talent. Research scientists, engineers, domain specialists, auditors, and managers are unevenly distributed.
4. Demand for institutional trust. Customers and regulators require explainability, safety, privacy, and accountability.

AI can reduce some information costs while increasing dependence on infrastructure and specialized ecosystems. It may therefore reinforce leading clusters rather than distribute opportunity evenly.

Geospatial intelligence

Geospatial intelligence combines location data, imagery, remote sensing, geographic information systems, and analytical methods to support decisions. Commercial applications include site selection, agricultural monitoring, insurance, logistics, infrastructure management, disaster response, and market analysis.

A location strategy based on geospatial intelligence should distinguish correlation from causation. A map showing high sales in an area does not prove that a new store caused regional growth. Analysts must consider population, income, transport access, competition, seasonality, and data quality. Spatial data may also encode historical discrimination or omit informal activity.

A location-scoring framework

A firm can compare candidate regions using a weighted score:

where S_j is the score for location j, w_k is the weight assigned to criterion k, and x_jk is the normalized performance of location j on criterion k. Weights should sum to 1, and all criteria should be scaled in a comparable direction. The method is useful for screening, but it can hide uncertainty, correlations among criteria, and political or ethical considerations.

For example, suppose three criteria are resilience, talent, and operating cost, with weights 0.4, 0.35, and 0.25. If a region has normalized scores 0.8, 0.7, and 0.6, then:

The score is 0.715 on a zero-to-one scale. It does not mean that the region is objectively “71.5 percent competitive”; it means that the region achieves that weighted score under the stated criteria and assumptions.

7. Digital Regional Competitiveness

Regional competitiveness should be assessed through capabilities and outcomes rather than technology adoption alone. A useful framework contains six dimensions:

1. Infrastructure: broadband, mobile networks, cloud access, data centres, power, transport, and digital identity.
2. Human capital: technical skills, management, research, vocational education, and lifelong learning.
3. Institutions: rule of law, regulatory quality, privacy protection, competition policy, and public-sector competence.
4. Innovation system: universities, finance, incubators, intellectual property, procurement, and firm networks.
5. Market connectivity: access to customers, payment systems, logistics, standards, and trade agreements.
6. Inclusive resilience: affordability, accessibility, cybersecurity, disaster preparedness, and social legitimacy.

Important indicators include broadband adoption, average and peak latency, outage frequency, cloud availability, electricity reliability, digital-service exports, start-up survival, research intensity, AI talent, female participation in technology, and the distribution of productivity gains. No single index adequately captures regional competitiveness.

A region may have excellent infrastructure but weak institutions. Another may possess strong universities but inadequate commercialization. A third may attract data centres but generate limited local value because equipment, expertise, and profits are imported. Evaluation should therefore examine additionality: what new capabilities would not have emerged without the zone or investment?

8. Case Application: The Meridian Digital Corridor

This is an illustrative case, not a report of an actual named project.

Background

The fictional country of Meridian consists of a coastal metropolitan area, an inland university city, and a port region. The government proposes a digital corridor linking these areas through fibre, renewable-energy projects, logistics platforms, cloud facilities, and an innovation district. It offers tax incentives to attract international cloud providers and encourages the use of AI in customs, traffic management, and agriculture.

Problem

The proposal faces four challenges. First, the coastal region has reliable connectivity, but the inland city suffers from power interruptions. Second, data-protection rules are unclear, creating uncertainty for foreign investors. Third, the port authority wants automated customs processing, while small logistics firms fear exclusion from a proprietary platform. Fourth, local communities are concerned about water consumption and surveillance in the proposed smart-city district.

Analysis

A narrow strategy would select locations based on tax incentives and land cost. A broader analysis identifies interdependencies:

• Cloud facilities require reliable electricity and diverse network routes.
• Customs AI requires high-quality data, legal authorization, human oversight, and interoperability.
• Local firms need open application interfaces and training to participate.
• Smart-city sensors require procurement transparency, cybersecurity, privacy safeguards, and public consultation.
• Regional competitiveness depends on local spillovers, not only foreign capital expenditure.

The government uses a weighted location model, but it also conducts scenario analysis for drought, cyberattack, cable failure, and sudden regulatory change. It separates critical public data from commercially sensitive data and establishes rules for cross-border transfers.

Approach and solution

The adopted strategy has five elements:

1. Build two geographically separated data-centre sites rather than one concentrated site.
2. Require renewable-energy reporting, water-use disclosure, and emergency power planning.
3. Create interoperable digital customs standards so that smaller firms can connect without adopting one private platform.
4. Establish an independent data-protection and AI-oversight authority.
5. Link tax incentives to measurable outcomes: local employment, supplier development, skills transfer, research collaboration, and service reliability.

The corridor also introduces a public-interest data trust for approved research and urban planning. Access is controlled, purposes are documented, and sensitive personal data are protected.

Outcome

In the first phase, the corridor attracts cloud investment and improves port processing. However, employment growth is concentrated among highly skilled workers, and smaller firms require additional support. The government therefore expands technical colleges, funds cybersecurity training, and reviews the distributional effects of automation. It also postpones facial-recognition deployment until a rights-impact assessment is completed.

Learning points

The case illustrates that digital zones are socio-technical systems. Their success depends on infrastructure, governance, talent, inclusion, and legitimacy. A location can be attractive for a data centre but unsuitable for a broad innovation ecosystem. Incentives are most defensible when they produce verifiable public benefits and remain conditional on performance.

9. Ethical, Political, and Environmental Issues

Digital geography creates significant ethical concerns. Automated systems may reproduce discriminatory patterns in lending, recruitment, policing, insurance, or service allocation. Location data can reveal movements, religious practices, health conditions, or political participation. Smart-city systems may increase surveillance without meaningful consent.

Environmental impacts include electricity demand, embodied carbon in equipment, water use for cooling, electronic waste, and the construction of new transmission and network infrastructure. The appropriate assessment should consider the full life cycle rather than operational energy alone.

Political risks include digital dependency, foreign control of critical infrastructure, cyber conflict, platform monopolization, and unequal bargaining power between governments and technology firms. National security concerns may justify safeguards, but broad restrictions can reduce competition and inhibit beneficial data flows.

Good governance principles include:

• necessity and proportionality in data collection;
• transparency about automated decisions;
• human review for high-impact decisions;
• cybersecurity by design;
• independent auditing;
• accessibility and non-discrimination;
• clear responsibility across vendors and public agencies; and
• meaningful remedy for affected individuals.

10. Strategic Guidance for Firms and Governments

Firms evaluating digital locations should begin with the value chain rather than with a technology label. They should identify which activities require physical proximity, which require low latency, which depend on specialized talent, and which are constrained by law. A portfolio of locations may be superior to a single global hub.

A practical decision process is:

1. Map customers, suppliers, data, workloads, skills, and regulatory obligations.
2. Classify activities by latency, resilience, confidentiality, and scalability requirements.
3. Compare locations using transparent criteria and sensitivity analysis.
4. Test scenarios involving outages, cyber incidents, energy-price changes, and regulatory shifts.
5. Evaluate local stakeholder effects and environmental constraints.
6. Design exit, substitution, and recovery options before deployment.
7. Monitor outcomes rather than assuming that investment automatically creates competitiveness.

Governments should avoid treating digital zones as substitutes for broad institutional reform. Tax incentives cannot compensate indefinitely for unreliable electricity, weak education, insecure data, or unpredictable regulation. The strongest zones combine infrastructure with local capability formation and public accountability.

Contemporary Developments

Several developments are likely to influence digital geography over the coming decade. Generative AI is increasing demand for compute, specialized chips, and high-quality data. Edge and distributed computing are expanding as firms seek lower latency and greater resilience. Satellite connectivity may improve access in remote regions, although affordability and capacity remain concerns. Digital public infrastructure, including interoperable identity and payment systems, may reduce transaction costs. At the same time, export controls, technology rivalry, data restrictions, and supply-chain security concerns may produce more regionalized digital ecosystems.

The likely outcome is neither complete globalization nor complete digital fragmentation. Instead, firms will operate within partially connected regional systems. Competitive advantage will belong to locations that combine connectivity with trust, energy, skills, institutional quality, and the capacity to adapt.

Chapter Summary

Digitalization reshapes but does not abolish geography. E-commerce reduces some market-access barriers while retaining dependence on logistics, regulation, payments, and trust. Platforms create network effects and algorithmic visibility, but can also generate concentration and dependency. Cloud regions and data centres demonstrate that digital activity remains grounded in electricity, connectivity, land, cooling, skills, and political institutions.

Data localization and digital sovereignty influence where firms store, process, and transfer information. Smart cities and digital economic zones can improve services and attract investment, but their success depends on governance, inclusion, environmental responsibility, and local spillovers. AI, automation, and geospatial intelligence alter the geography of work, infrastructure, and decision-making while creating new risks involving bias, surveillance, cybersecurity, and inequality.

For international business, location strategy should evaluate layered geographies and use transparent, scenario-based analysis. Regional competitiveness is strongest when digital investment develops durable local capabilities rather than creating isolated enclaves. The central managerial and policy challenge is to combine efficiency, resilience, sovereignty, innovation, and public legitimacy.

Key Terms

Digital geography: The spatial organization of digital infrastructures, data, capabilities, rules, and effects.

Digital distance: The effective separation between locations measured through latency, bandwidth, reliability, trust, and regulatory friction.

Platform: A digital intermediary that facilitates interactions among two or more user groups.

Network effect: A change in the value of a service resulting from changes in participation by users or complementary groups.

Digital trade: Cross-border trade involving digital ordering, digital delivery, digital intermediation, or data-enabled services.

Cloud region: A geographic area containing infrastructure used to provide cloud services.

Availability zone: A separated cloud-infrastructure location designed to support resilience and continuity.

Edge computing: Processing data near the point where it is generated or consumed.

Data localization: Requirements concerning the domestic storage, processing, or transfer of specified data.

Data sovereignty: Authority and control over data, including jurisdiction, access, ownership, and governance.

Smart city: An urban system using digital technologies and data to improve services and decision-making.

Digital economic zone: A geographically or institutionally defined area designed to attract digital activity and investment.

Agglomeration: The concentration of economic activity and capabilities in a location.

Artificial intelligence: Computational systems able to perform tasks commonly associated with human cognitive capabilities.

Geospatial intelligence: Analysis of location-based information, imagery, and spatial data for decision-making.

Latency: The time required for data or a transaction to travel through a network.

Digital sovereignty: The capacity of a state or organization to govern and control critical digital resources.

Algorithmic geography: The spatial effects produced by automated ranking, routing, pricing, classification, and allocation.

Regional competitiveness: The capacity of a region to attract, retain, and create productive, inclusive, and resilient economic activity.

Review Questions

Short Answer Questions

1. Why does digitalization not eliminate the importance of geography?
2. Distinguish digitally ordered trade from digitally delivered trade.
3. Identify four factors influencing data-centre location.
4. What is data localization, and why might governments adopt it?
5. Explain how algorithmic visibility affects platform-based businesses.

Long Answer Questions

1. Evaluate how cloud computing and edge computing create different location requirements.
2. Discuss the advantages and disadvantages of digital economic zones for regional development.
3. Analyse the relationship between data sovereignty, digital trade, and international business strategy.
4. Explain how AI and automation may reinforce some regional inequalities while reducing others.
5. Assess the institutional conditions required for a smart city to generate durable competitiveness.

Analytical / Application Questions

1. Using the weighted-score framework in this chapter, compare two hypothetical locations for a regional cloud facility. Explain your choice of criteria, weights, assumptions, and limitations.
2. A government wants to attract a large data centre to a water-stressed region. Develop a policy recommendation that considers employment, energy, environmental impact, resilience, and public legitimacy.
3. An international platform plans to centralize customer data in one country while serving users worldwide. Prepare a governance and location strategy addressing latency, localization, privacy, cybersecurity, and operational continuity.

Multiple Choice Questions with Answers

Question 1

Which statement best describes digital geography?

A. It is the replacement of physical geography by cyberspace.

B. It is the spatial organization of digital infrastructures, data, capabilities, rules, and effects.

C. It is the study of only online consumer behaviour.

D. It is the measurement of national internet speeds alone.

Correct Answer: B. It is the spatial organization of digital infrastructures, data, capabilities, rules, and effects.

Question 2

Which activity is an example of digitally delivered trade?

A. Ordering a manufactured bicycle online for physical shipment

B. Purchasing imported furniture through a website

C. Receiving cloud-based consulting services across a border

D. Buying food from a local shop using cash

Correct Answer: C. Receiving cloud-based consulting services across a border

Question 3

Why is electricity availability strategically important for data-centre location?

A. Data centres require continuous and substantial power for computing and cooling.

B. Electricity determines the legal ownership of customer data.

C. Electricity eliminates the need for network connectivity.

D. Electricity guarantees a supply of specialized software engineers.

Correct Answer: A. Data centres require continuous and substantial power for computing and cooling.

Question 4

What is the principal purpose of edge computing?

A. To eliminate all centralized cloud infrastructure

B. To process data closer to users or devices

C. To require every firm to store data in one country

D. To increase the physical distance between users and servers

Correct Answer: B. To process data closer to users or devices

Question 5

Which of the following is a potential disadvantage of data localization?

A. It may increase infrastructure costs and fragment digital markets.

B. It always removes cybersecurity risks.

C. It prevents governments from regulating data.

D. It guarantees equal access to digital services.

Correct Answer: A. It may increase infrastructure costs and fragment digital markets.

Question 6

In a multi-sided platform, an indirect network effect occurs when:

A. A platform becomes slower as users leave it.

B. The value to one user group changes as participation by another group changes.

C. A government bans cross-border data flows.

D. A data centre reduces its electricity consumption.

Correct Answer: B. The value to one user group changes as participation by another group changes.

Question 7

Using the chapter’s weighted-score formula, a location with scores 0.8, 0.7, and 0.6 and weights 0.4, 0.35, and 0.25 receives what score?

A. 0.615

B. 0.685

C. 0.715

D. 0.815

Correct Answer: C. 0.715

Question 8

Which policy best supports inclusive digital-zone development?

A. Offering tax incentives without performance requirements

B. Limiting participation to multinational technology firms

C. Linking incentives to skills transfer, local suppliers, and measurable public benefits

D. Avoiding cybersecurity regulation to accelerate investment

Correct Answer: C. Linking incentives to skills transfer, local suppliers, and measurable public benefits

Question 9

Which is an example of algorithmic geography?

A. A mountain range affecting road construction

B. A platform ranking sellers and changing their commercial visibility

C. A port requiring physical cranes

D. A city’s annual rainfall pattern

Correct Answer: B. A platform ranking sellers and changing their commercial visibility

Question 10

Why should a region not be judged as competitive solely because it attracts a data centre?

A. Data centres never create economic value.

B. Data centres operate without electricity or networks.

C. Investment may generate limited local capability, employment, or spillovers.

D. Data centres cannot be located in economic zones.

Correct Answer: C. Investment may generate limited local capability, employment, or spillovers.