IELTS Jan March 2026 Reading passages for practice
READING PASSAGE 1
Artificial Intelligence and the Transformation of White-Collar Employment
For much of the twentieth century, automation was associated primarily with factory floors, where machines replaced manual labour in repetitive physical tasks. White-collar occupations, by contrast, were largely insulated from such disruption due to their reliance on cognitive skills, judgement, and specialised knowledge. However, advances in artificial intelligence (AI) over the past two decades have begun to challenge this assumption, raising concerns about the future of professional and administrative work.
AI systems are no longer confined to rule-based programming that follows predetermined instructions. Modern machine-learning models are capable of identifying patterns in vast datasets, making predictions, generating written content, and even engaging in complex decision-making. As a result, tasks once thought to require human intelligence such as legal document review, financial analysis, and medical diagnostics are increasingly being performed, or at least assisted, by algorithms.
One of the earliest white-collar sectors affected by AI was finance. Algorithmic trading systems now execute a significant proportion of transactions in global stock markets, often reacting to market changes faster than any human trader could. In banking, AI-driven software is used to assess credit risk, detect fraudulent activity, and manage customer service inquiries through automated chat systems. While these technologies have improved efficiency and reduced operational costs, they have also diminished the demand for entry-level analysts and clerical staff.
The legal profession has experienced similar changes. Traditionally, junior lawyers spent large amounts of time reviewing contracts, searching for precedents, and conducting due diligence. Today, AI tools can scan thousands of legal documents within minutes, identifying relevant clauses and potential risks with remarkable accuracy. Although senior legal professionals argue that strategic reasoning and courtroom advocacy remain firmly human domains, the reduction in routine tasks has altered career pathways for young practitioners.
Healthcare presents a more complex picture. AI applications have demonstrated high levels of accuracy in areas such as medical imaging, where algorithms can detect early signs of diseases including cancer and neurological disorders. Rather than replacing doctors entirely, these systems are often positioned as decision-support tools that enhance diagnostic precision. Nevertheless, their growing reliability has prompted debate over the extent to which certain medical roles may become redundant, particularly in diagnostic specialties.
Despite widespread concern about job losses, some economists argue that AI will not simply eliminate white-collar employment but transform it. Historical evidence suggests that technological innovation often creates new roles even as it renders others obsolete. For example, the introduction of computers reduced the need for typists but generated demand for software developers, IT specialists, and digital content creators. Proponents of this view contend that AI will similarly lead to the emergence of occupations focused on system oversight, ethical regulation, and human-machine collaboration.
However, critics point out that the current wave of automation differs fundamentally from previous technological shifts. Unlike earlier tools that complemented human labour, AI has the potential to replicate core cognitive functions across multiple domains simultaneously. This raises the possibility of widespread displacement occurring faster than the labour market can adapt. Moreover, the new jobs created may require advanced technical skills, leaving large segments of the workforce unable to transition without substantial retraining.
Education systems therefore face mounting pressure to adapt. Universities and professional training institutions are increasingly emphasising skills that are less susceptible to automation, such as critical thinking, creativity, and emotional intelligence. At the same time, there is growing recognition of the need for lifelong learning, as individuals may be required to reskill multiple times over the course of their careers.
Governments also play a crucial role in shaping the outcome of AI-driven transformation. Policy responses range from investment in education and workforce development to proposals for universal basic income as a means of mitigating economic insecurity. The effectiveness of these measures remains uncertain, particularly given the uneven pace of AI adoption across industries and regions.
In conclusion, artificial intelligence is reshaping white-collar employment in ways that challenge long-held assumptions about the nature of professional work. While AI offers undeniable benefits in terms of efficiency and accuracy, it also raises profound questions about job security, skill requirements, and social equity. The ultimate impact will depend not only on technological capabilities but also on how societies choose to manage and distribute the consequences of this transformation.
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QUESTIONS 1–13
Questions 1–5
Do the following statements agree with the information given in the passage?
Write:
TRUE if the statement agrees with the passage
FALSE if the statement contradicts the passage
NOT GIVEN if there is no information on this
1. Early forms of automation mainly affected physical labour rather than professional occupations.
2. Modern AI systems rely exclusively on pre-programmed instructions.
3. AI has reduced the need for junior legal professionals to perform routine tasks.
4. Doctors are being fully replaced by AI systems in medical diagnostics.
5. Governments worldwide have agreed on a single policy approach to AI-related job losses.
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Questions 6–9
Match each sector with the effect of AI mentioned in the passage.
Write the correct letter A–F next to Questions 6–9.
A. Faster reaction to market changes
B. Reduced demand for entry-level workers
C. Improved accuracy in image analysis
D. Increased reliance on courtroom advocacy
E. Creation of new technical job roles
F. Elimination of human decision-making
6. Finance
7. Law
8. Healthcare
9. Technology sector (historical example)
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Questions 10–13
Complete the sentences below.
Choose NO MORE THAN TWO WORDS from the passage for each answer.
10. Critics argue that AI differs from previous technologies because it can replicate __________ functions.
11. Some new jobs created by AI may require __________ skills.
12. Education systems are placing greater emphasis on skills such as creativity and __________ intelligence.
13. The long-term impact of AI will depend partly on how its consequences are __________.
READING PASSAGE 2
Ethical Concerns Surrounding the Use of Artificial Intelligence in Healthcare
The integration of artificial intelligence into healthcare systems has accelerated rapidly in recent years, driven by advances in computing power, data availability, and machine-learning techniques. AI is now used in areas ranging from diagnostic imaging and predictive analytics to patient monitoring and administrative management. While these developments promise improved efficiency and accuracy, they have also raised significant ethical concerns that challenge traditional medical principles.
One of the primary ethical issues relates to decision-making responsibility. In conventional medical practice, healthcare professionals are accountable for diagnoses and treatment decisions. However, when AI systems are used to assist or even guide clinical judgement, determining responsibility becomes more complex. If an algorithm produces an incorrect diagnosis or treatment recommendation, it is often unclear whether liability lies with the physician, the hospital, or the developers of the technology. This ambiguity complicates existing legal and ethical frameworks.
Bias in AI systems represents another major concern. AI algorithms are trained on large datasets, which often reflect existing inequalities within healthcare systems. If training data is skewed towards certain populations, such as specific age groups or ethnic backgrounds, the resulting AI models may perform poorly when applied to underrepresented groups. This can lead to misdiagnosis, unequal treatment outcomes, and the reinforcement of health disparities rather than their reduction.
Data privacy is also a critical issue in AI-driven healthcare. Medical AI systems rely heavily on vast amounts of personal health information, including medical histories, genetic data, and real-time physiological measurements. Although data is frequently anonymised, concerns persist about the potential for re-identification and misuse. Large-scale data breaches or unauthorised access could undermine patient trust and discourage individuals from sharing information essential for effective treatment.
Transparency further complicates the ethical landscape. Many advanced AI models operate as so-called “black boxes,” producing outputs without clear explanations of how conclusions were reached. In medicine, where informed consent and clear communication are fundamental, this lack of interpretability presents a problem. Patients may find it difficult to accept treatment recommendations when neither they nor their healthcare providers fully understand the reasoning behind them.
The use of AI in resource allocation has also attracted ethical scrutiny. In some healthcare systems, AI tools are employed to prioritise patients for treatment, predict hospital readmission risks, or allocate limited medical resources. While such systems aim to improve efficiency, critics argue that reducing human judgement in these contexts may lead to overly rigid decision-making that fails to account for individual circumstances or social factors.
Another concern involves the potential erosion of the doctor-patient relationship. Medicine has traditionally relied on human interaction, empathy, and trust. Increased reliance on AI systems may shift attention away from patient-centred care towards data-driven processes. Some practitioners worry that this could diminish the role of interpersonal communication, particularly in fields such as mental health and palliative care, where emotional understanding is essential.
Despite these concerns, proponents argue that ethical challenges can be mitigated through appropriate regulation and oversight. They emphasise the importance of diverse training datasets, transparent algorithm design, and clear guidelines defining the role of AI in clinical decision-making. Ethical review boards and interdisciplinary collaboration between technologists, clinicians, and policymakers are increasingly viewed as essential components of responsible AI deployment.
In addition, ongoing education for healthcare professionals is crucial. Clinicians must understand both the capabilities and limitations of AI tools in order to use them responsibly. Rather than replacing human judgement, AI is most effective when used as a supportive tool that enhances, rather than overrides, clinical expertise.
In conclusion, while artificial intelligence offers considerable potential to improve healthcare outcomes, its ethical implications cannot be ignored. Issues of accountability, bias, privacy, transparency, and human interaction must be carefully addressed to ensure that AI contributes positively to medical practice. The challenge lies not in rejecting technological innovation, but in integrating it in a manner that upholds the core ethical values of healthcare.
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QUESTIONS 1–13
Questions 1–5
Do the following statements agree with the information given in the passage?
Write:
TRUE if the statement agrees with the passage
FALSE if the statement contradicts the passage
NOT GIVEN if there is no information on this
1. AI is currently used only for administrative tasks in healthcare.
2. It is always clear who is responsible when an AI system makes an error.
3. Biased training data can negatively affect healthcare outcomes for certain groups.
4. All medical AI systems clearly explain how their conclusions are reached.
5. AI-based resource allocation may overlook individual patient circumstances.
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Questions 6–9
Match each ethical issue with the correct description.
Write the correct letter A–F next to Questions 6–9.
A. Risk of unequal medical treatment
B. Difficulty understanding algorithmic reasoning
C. Loss of patient trust due to data misuse
D. Reduced need for medical professionals
E. Uncertainty about accountability
F. Increased emotional connection with patients
6. Decision-making responsibility
7. Bias in training data
8. Data privacy
9. Transparency of AI systems
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Questions 10–13
Complete the sentences below.
Choose NO MORE THAN TWO WORDS from the passage for each answer.
10. Some AI models are described as __________ because their reasoning is unclear.
11. AI tools are sometimes used to __________ patients for medical treatment.
12. Critics fear that AI may weaken the __________ relationship.
13. Supporters believe ethical risks can be reduced through regulation and __________.
READING PASSAGE 3
Automation and Its Influence on Workplace Productivity
Automation has long been associated with increased efficiency in manufacturing, where machines replaced manual labour to speed up production and reduce error rates. In recent decades, however, automation has expanded beyond factory environments into offices, service industries, and knowledge-based workplaces. Software automation, robotics, and algorithmic management systems are now reshaping how work is organised and measured, with significant implications for productivity.
One of the most widely cited benefits of automation is its ability to perform repetitive tasks with consistency and speed. In administrative roles, automated systems handle data entry, scheduling, payroll processing, and document management with minimal human intervention. This has allowed organisations to reduce processing times and operational costs while reallocating human employees to tasks requiring judgement or interpersonal skills.
Despite these advantages, the relationship between automation and productivity is not always straightforward. While output per worker may increase in highly automated environments, overall productivity gains depend heavily on how technology is integrated into existing workflows. Poorly implemented systems can disrupt communication, create bottlenecks, and increase employee frustration, ultimately undermining efficiency rather than enhancing it.
Another factor influencing productivity outcomes is employee adaptation. Automation often requires workers to learn new systems and adjust to changing job roles. During transitional periods, productivity may temporarily decline as employees undergo training and adapt to unfamiliar processes. Organisations that fail to invest in adequate training frequently experience resistance to automation, which can further reduce its effectiveness.
Automation also affects how productivity is measured. Traditional metrics such as hours worked or tasks completed are increasingly supplemented by data-driven performance indicators generated by digital monitoring tools. In some workplaces, algorithms track keystrokes, response times, and workflow patterns to assess employee performance. While such systems can provide detailed insights, critics argue that excessive monitoring may increase stress and reduce motivation, negatively impacting long-term productivity.
In knowledge-based sectors, automation has enabled faster decision-making by providing real-time data analysis and predictive modelling. Managers can identify trends, forecast demand, and allocate resources more efficiently than before. However, over-reliance on automated recommendations may discourage critical thinking, leading to suboptimal decisions when systems fail to account for contextual or human factors.
The impact of automation on productivity also varies across industries. In logistics and manufacturing, productivity gains are often immediate and measurable. In contrast, sectors such as education, healthcare, and creative industries experience more complex outcomes, as productivity in these fields is closely tied to quality, human interaction, and innovation rather than speed alone.
Some economists argue that automation contributes to long-term productivity growth by enabling firms to scale operations and compete globally. Others caution that productivity gains may be unevenly distributed, benefiting organisations more than individual workers. In such cases, increased output does not necessarily translate into improved job satisfaction or higher wages.
In summary, automation has the potential to enhance workplace productivity, but its success depends on implementation quality, workforce adaptation, and the nature of the work involved. Productivity gains are not automatic and require careful alignment between technology, organisational goals, and human capabilities.
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QUESTIONS 1–13
Questions 1–5
Match each heading with the correct paragraph.
Write the correct letter A–H next to Questions 1–5.
Headings
A. Automation and performance measurement
B. Short-term productivity losses during adjustment
C. Uneven productivity effects across industries
D. Automation as a source of workplace stress
E. Benefits of automation in routine tasks
F. Risks of poor system integration
G. Automation and long-term economic growth
H. The changing definition of productivity
Paragraphs
1. Paragraph 2
2. Paragraph 3
3. Paragraph 4
4. Paragraph 5
5. Paragraph 7
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Questions 6–9
Complete the summary below.
Choose NO MORE THAN TWO WORDS from the passage for each answer.
Automation allows organisations to reduce operational costs by handling repetitive administrative tasks. However, productivity gains depend on effective integration into existing (6) __________. During periods of change, productivity may decline as employees require (7) __________ to adapt. In some workplaces, digital monitoring tools generate detailed (8) __________ indicators, although excessive monitoring may reduce employee (9) __________.
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Questions 10–13
Complete each sentence with the correct ending, A–F.
Sentence beginnings
10. Productivity gains from automation are not guaranteed because
11. In knowledge-based sectors, automation supports decision-making by
12. Some critics argue that algorithmic monitoring can
13. Economists disagree on automation because
Sentence endings
A. providing real-time data analysis and forecasts.
B. implementation quality plays a critical role.
C. increase stress and lower motivation.
D. productivity improvements may not benefit workers equally.
E. productivity is measured primarily by speed.
F. employees are unwilling to learn new skills.
READING PASSAGE 4
Climate Change Adaptation in Coastal Cities
Coastal cities have historically developed around access to trade routes, natural harbours, and marine resources. Today, however, these same locations face increasing risks due to climate change. Rising sea levels, more frequent storm surges, and coastal erosion are placing growing pressure on urban infrastructure and populations. As a result, adaptation strategies have become a central focus of urban planning in coastal regions.
One of the most immediate challenges is sea-level rise. Even modest increases in average sea levels can significantly increase the likelihood of flooding during high tides or storms. Low-lying cities are particularly vulnerable, as drainage systems, transport networks, and residential areas are often located only marginally above current sea levels. In response, many cities have invested in physical barriers such as sea walls, flood gates, and reinforced embankments.
While engineered solutions offer visible protection, they are expensive and require continuous maintenance. In some cases, such structures can even worsen erosion in neighbouring areas by altering natural sediment flows. Consequently, urban planners are increasingly exploring nature-based solutions, including the restoration of mangroves, wetlands, and coral reefs. These ecosystems act as natural buffers, absorbing wave energy and reducing flood impact while also providing environmental benefits.
Urban design also plays a crucial role in adaptation. Cities are revising building codes to require elevated foundations, flood-resistant materials, and flexible infrastructure systems. In newly developed areas, planners may restrict construction in high-risk zones altogether. However, implementing such measures in established urban centres is often politically and socially challenging, particularly where relocation of residents is involved.
Economic inequality further complicates adaptation efforts. Wealthier neighbourhoods typically receive greater protection due to higher property values and political influence, while lower-income communities are more likely to be located in vulnerable areas. This uneven distribution of risk raises concerns about climate justice, as disadvantaged populations often have fewer resources to recover from climate-related damage.
In addition to physical measures, coastal cities are increasingly relying on data-driven forecasting systems. Advances in climate modelling allow authorities to predict storm patterns, sea-level changes, and flood risks with greater accuracy. These tools support early warning systems and emergency planning, enabling cities to respond more effectively to extreme weather events.
Despite these advances, uncertainty remains a defining feature of climate adaptation. Future climate impacts depend on complex global processes and long-term emission trends. This uncertainty makes it difficult for city governments to justify large investments, particularly when immediate economic pressures compete for limited public funding.
Some experts argue that successful adaptation requires a shift in perspective. Rather than attempting to control natural forces entirely, cities must learn to coexist with them. This approach emphasises flexibility, long-term planning, and community involvement. Public participation is increasingly recognised as essential, as local knowledge can inform practical and socially acceptable adaptation strategies.
In summary, climate change adaptation in coastal cities involves a combination of engineering, ecological restoration, policy reform, and social engagement. While no single strategy offers complete protection, integrated approaches can reduce risk and enhance resilience. The effectiveness of these efforts will depend on political commitment, financial capacity, and the ability to balance short-term needs with long-term environmental realities.
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QUESTIONS 1–13
Questions 1–5
Match each heading with the correct paragraph.
Write the correct letter A–H next to Questions 1–5.
Headings
A. Inequality in climate risk exposure
B. Limits of prediction and planning
C. Nature-based alternatives to engineering
D. Sea-level rise as an urban threat
E. The importance of community participation
F. Costs and side effects of hard infrastructure
G. Climate modelling and early warning systems
H. Rethinking the relationship between cities and nature
Paragraphs
1. Paragraph 2
2. Paragraph 3
3. Paragraph 5
4. Paragraph 6
5. Paragraph 8
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Questions 6–9
Complete the summary below.
Choose NO MORE THAN TWO WORDS from the passage for each answer.
Rising sea levels increase the risk of flooding in coastal cities, particularly in (6) __________ areas. Although physical barriers provide protection, they are costly and require ongoing (7) __________. As an alternative, planners are restoring natural ecosystems that function as (8) __________ buffers. However, uncertainty about future climate conditions makes long-term (9) __________ difficult.
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Questions 10–13
Complete each sentence with the correct ending, A–F.
Sentence beginnings
10. Nature-based solutions are increasingly favoured because
11. Implementing adaptation measures in existing cities is difficult since
12. Climate adaptation efforts often disadvantage poorer communities because
13. Some experts believe cities should adapt by
Sentence endings
A. construction restrictions face political resistance.
B. they reduce wave energy and provide ecological benefits.
C. adaptation requires abandoning coastal locations.
D. they are more likely to live in high-risk areas.
E. learning to coexist with natural forces.
F. climate models are completely reliable.
READING PASSAGE 5
Renewable Energy Storage Technologies and Their Role in Energy Systems
The rapid expansion of renewable energy sources such as wind and solar power has transformed modern energy systems. Unlike fossil fuels, these sources produce electricity without direct carbon emissions. However, their dependence on natural conditions introduces a major challenge: variability. Wind does not always blow, and sunlight is not constant throughout the day. As a result, effective energy storage has become essential to ensure reliable power supply.
Energy storage technologies allow excess electricity generated during periods of high production to be stored and used later when demand exceeds supply. Without such systems, renewable energy generation can lead to inefficiencies, including wasted electricity and instability in power grids. Storage therefore plays a critical role in balancing supply and demand, particularly as renewable sources account for a growing share of global electricity production.
One of the most widely used storage technologies is battery storage. Lithium-ion batteries, originally developed for consumer electronics, are now commonly deployed in large-scale energy storage facilities. These batteries offer high energy density and fast response times, making them suitable for stabilising grids and supporting short-term energy needs. However, concerns remain regarding their cost, limited lifespan, and reliance on rare materials.
Pumped hydro storage represents another established method. This system stores energy by pumping water to a higher elevation during periods of low demand and releasing it through turbines when electricity is needed. Pumped hydro is highly efficient and can store large amounts of energy for extended periods. Despite these advantages, its deployment is limited by geographical requirements, as suitable sites with appropriate terrain are not universally available.
Emerging technologies aim to address some of the limitations of existing systems. Thermal energy storage, for example, captures excess energy in the form of heat, which can later be converted back into electricity. This approach is particularly effective when integrated with concentrated solar power plants. Similarly, hydrogen storage involves using surplus electricity to produce hydrogen through electrolysis, which can then be stored and used as a fuel or converted back into electricity.
While technological innovation continues, economic considerations remain central to storage adoption. Energy storage systems often require significant upfront investment, and their financial viability depends on electricity prices, policy incentives, and grid regulations. In regions with unstable grids or high renewable penetration, storage can offer long-term savings by reducing reliance on fossil-fuel-based backup systems.
Energy storage also influences grid resilience. During extreme weather events or system failures, stored energy can provide backup power, reducing the likelihood of large-scale outages. This capability is increasingly valued as climate-related disruptions become more frequent. Consequently, policymakers are beginning to view storage not only as a technical solution but also as a component of energy security.
Despite growing interest, large-scale deployment of storage technologies faces challenges. Standardisation, recycling of storage materials, and integration with existing infrastructure require coordinated planning. Furthermore, different storage technologies are suited to different time scales, ranging from seconds to seasonal storage, making a single universal solution unlikely.
In conclusion, renewable energy storage technologies are fundamental to the transition towards low-carbon energy systems. While no single technology meets all requirements, a combination of storage solutions can enhance grid stability, efficiency, and resilience. Continued investment, supportive policies, and technological advances will determine how effectively storage supports the future of renewable energy.
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QUESTIONS 1–13
Questions 1–5
Match each heading with the correct paragraph.
Write the correct letter A–H next to Questions 1–5.
Headings
A. Economic barriers to storage adoption
B. The problem of renewable energy variability
C. Battery storage advantages and limitations
D. Long-established storage solutions
E. Storage as part of energy security
F. Emerging alternatives to conventional storage
G. The absence of a universal solution
H. The role of storage in grid efficiency
Paragraphs
1. Paragraph 1
2. Paragraph 3
3. Paragraph 4
4. Paragraph 6
5. Paragraph 8
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Questions 6–9
Complete the summary below.
Choose NO MORE THAN TWO WORDS from the passage for each answer.
Renewable energy production is affected by natural conditions, leading to supply (6) __________. Battery storage systems respond quickly but raise concerns about cost and material availability. Pumped hydro storage is efficient but depends on suitable (7) __________. New methods such as thermal and hydrogen storage aim to overcome the (8) __________ of existing systems, although high (9) __________ costs remain a challenge.
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Questions 10–13
Complete each sentence with the correct ending, A–F.
Sentence beginnings
10. Energy storage systems help stabilise power grids by
11. Lithium-ion batteries are widely used because
12. Hydrogen storage converts excess electricity by
13. Large-scale storage deployment is complex because
Sentence endings
A. turning it into a fuel through electrolysis.
B. balancing electricity supply and demand.
C. they have fast response times.
D. suitable locations are not always available.
E. multiple technologies serve different time scales.
F. electricity prices are uniform worldwide.
READING PASSAGE 6
Urban Heat Islands and the Role of City Planning
As cities expand and densify, they increasingly modify their local climates. One of the most widely observed consequences of urbanisation is the urban heat island effect, a phenomenon in which urban areas experience higher temperatures than surrounding rural regions. This temperature difference is primarily caused by human activities and the physical characteristics of urban environments.
Urban heat islands form when natural land surfaces are replaced with buildings, roads, and other infrastructure that absorb and retain heat. Materials such as concrete and asphalt store solar energy during the day and release it slowly at night, preventing cities from cooling effectively. In addition, reduced vegetation limits the natural cooling effects of shade and evapotranspiration, further intensifying urban temperatures.
The impact of urban heat islands extends beyond discomfort. Higher temperatures increase energy demand for cooling, placing strain on electricity systems and raising greenhouse gas emissions. Heat stress also poses serious health risks, particularly for elderly populations, outdoor workers, and individuals with pre-existing medical conditions. During heatwaves, urban heat islands can significantly worsen mortality rates.
City planning plays a critical role in mitigating these effects. One commonly adopted strategy is increasing urban greenery through parks, street trees, and green roofs. Vegetation cools the surrounding air by providing shade and releasing moisture, reducing surface and air temperatures. Studies have shown that neighbourhoods with higher tree cover can be several degrees cooler than those without.
Building design and materials also influence urban temperatures. Reflective or “cool” roofing materials reduce heat absorption, while improved insulation limits the transfer of heat into buildings. Urban planners increasingly encourage the use of lighter-coloured surfaces and permeable materials that allow heat to dissipate more effectively.
The layout of cities further affects heat distribution. Narrow streets and closely packed buildings can restrict airflow, trapping heat between structures. In contrast, well-planned street orientation and open spaces allow for improved ventilation. Some cities now incorporate wind corridors into planning frameworks to enhance natural cooling.
Despite available solutions, implementation remains uneven. Economic constraints, competing land-use priorities, and limited public awareness often slow progress. In rapidly growing cities, particularly in developing regions, immediate housing and infrastructure needs may take precedence over long-term climate considerations.
Addressing urban heat islands also raises questions of equity. Lower-income communities frequently live in areas with fewer green spaces and higher exposure to heat. As a result, heat mitigation strategies must be integrated with broader social planning to avoid reinforcing existing inequalities.
In conclusion, urban heat islands are a direct consequence of modern urban development, but they are not inevitable. Through thoughtful city planning, material choices, and equitable policy measures, cities can reduce heat-related risks and improve urban livability. As climate change intensifies, addressing urban heat will become an increasingly urgent priority for planners worldwide.
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QUESTIONS 1–13
Questions 1–5
Match each heading with the correct paragraph.
Write the correct letter A–H next to Questions 1–5.
Headings
A. Health and energy consequences of urban heat
B. Social inequality and heat exposure
C. Causes of the urban heat island effect
D. Planning challenges in fast-growing cities
E. The role of vegetation in cooling cities
F. Urban layout and airflow management
G. Construction materials and temperature control
H. Urban heat as a modern planning priority
Paragraphs
1. Paragraph 2
2. Paragraph 3
3. Paragraph 4
4. Paragraph 6
5. Paragraph 8
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Questions 6–9
Complete the summary below.
Choose NO MORE THAN TWO WORDS from the passage for each answer.
Urban heat islands occur when natural surfaces are replaced by heat-absorbing materials, while reduced vegetation limits cooling through (6) __________. Higher temperatures increase energy use and pose risks to vulnerable (7) __________. City planners reduce heat by expanding green spaces and improving building (8) __________. However, limited resources and competing priorities often delay effective (9) __________.
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Questions 10–13
Complete each sentence with the correct ending, A–F.
Sentence beginnings
10. Urban areas cool more slowly at night because
11. Green roofs and trees reduce temperatures by
12. Poorly planned street layouts can increase heat since
13. Heat mitigation policies must consider equity because
Sentence endings
A. vegetation provides shade and moisture.
B. heat is trapped between closely packed buildings.
C. vulnerable communities face higher exposure.
D. concrete and asphalt release stored heat slowly.
E. cities prioritise housing over climate planning.
F. reflective materials are rarely effective.
READING PASSAGE 7
The Role of Smart Sensors in Sustainable Agriculture
Agriculture faces increasing pressure to produce more food while reducing environmental impact. Population growth, climate variability, and resource constraints have intensified the need for farming practices that are both productive and sustainable. In response, smart sensor technologies are being adopted across agricultural systems to improve efficiency, reduce waste, and support data-driven decision-making.
Smart sensors are devices that collect real-time data on environmental and biological conditions such as soil moisture, nutrient levels, temperature, and crop health. When integrated with digital platforms, these sensors allow farmers to monitor fields continuously rather than relying on periodic manual inspections. This shift enables more precise management of resources, particularly water and fertilisers.
One of the most significant benefits of sensor-based agriculture is improved water efficiency. Traditional irrigation methods often apply water uniformly across fields, regardless of variations in soil conditions. Smart irrigation systems, guided by soil moisture sensors, deliver water only where and when it is needed. This targeted approach reduces water consumption and minimises runoff, which can carry pollutants into nearby ecosystems.
Nutrient management also benefits from sensor technology. Sensors capable of detecting soil nutrient levels help farmers apply fertilisers more accurately. Over-application of fertilisers not only wastes resources but also contributes to soil degradation and water pollution. By adjusting inputs based on real-time data, farmers can maintain soil health while reducing environmental harm.
Crop health monitoring is another area where sensors play a critical role. Optical and thermal sensors can identify signs of plant stress, disease, or pest infestation at early stages. Early detection allows for timely intervention, often reducing the need for widespread pesticide use. This supports both environmental sustainability and food safety.
Despite these advantages, the adoption of smart sensors is not without challenges. High initial costs remain a barrier, particularly for small-scale farmers. In addition, effective use of sensor data requires technical knowledge and reliable digital infrastructure, which may be lacking in rural or developing regions.
Data management and ownership also raise concerns. Large volumes of agricultural data are often processed by technology providers, leading to questions about who controls this information and how it may be used. Some farmers express hesitation about sharing sensitive data related to yields or land conditions, particularly when regulatory or commercial consequences are unclear.
Another limitation is that sensor systems may not fully account for complex ecological interactions. While sensors provide valuable quantitative data, they cannot replace experiential knowledge gained through years of farming. As a result, experts emphasise that sensor technology should complement, rather than replace, human judgement.
Governments and research institutions are increasingly supporting sensor-based agriculture through subsidies, training programmes, and pilot projects. These initiatives aim to make technology more accessible and demonstrate its long-term benefits. As costs decline and digital literacy improves, wider adoption is expected.
In conclusion, smart sensors offer significant potential to enhance agricultural sustainability by improving resource efficiency and environmental management. However, their effectiveness depends on affordability, data governance, and integration with traditional farming knowledge. Sustainable agriculture is therefore most likely to succeed when technological innovation and human expertise work together.
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QUESTIONS 1–13
Questions 1–5
Match each heading with the correct paragraph.
Write the correct letter A–H next to Questions 1–5.
Headings
A. Barriers to widespread adoption
B. Combining technology with traditional knowledge
C. Precision in water use
D. Environmental pressures on modern agriculture
E. Managing fertiliser application more accurately
F. Concerns about agricultural data control
G. Early detection of crop problems
H. Institutional support for sensor technology
Paragraphs
1. Paragraph 1
2. Paragraph 3
3. Paragraph 4
4. Paragraph 6
5. Paragraph 8
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Questions 6–9
Complete the summary below.
Choose NO MORE THAN TWO WORDS from the passage for each answer.
Smart sensors enable farmers to collect (6) __________ data on soil and crop conditions. This allows irrigation systems to reduce water use and limit (7) __________ into surrounding environments. Sensors also help avoid excessive fertiliser use, protecting long-term (8) __________ health. However, high costs and limited digital infrastructure restrict adoption, particularly among (9) __________ farmers.
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Questions 10–13
Complete each sentence with the correct ending, A–F.
Sentence beginnings
10. Smart sensors improve irrigation efficiency by
11. Early detection of crop stress reduces the need for
12. Some farmers are concerned about sensor data because
13. Experts argue that sensor technology should
Sentence endings
A. complement rather than replace human judgement.
B. identifying soil conditions in real time.
C. large-scale pesticide application.
D. it may be controlled by external organisations.
E. increasing fertiliser application frequency.
F. eliminating traditional farming practices.
READING PASSAGE 1 – ANSWERS
Questions 1–5 (TRUE / FALSE / NOT GIVEN)
1. TRUE
2. FALSE
3. TRUE
4. FALSE
5. NOT GIVEN
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Questions 6–9 (Matching)
6. Finance → A (Faster reaction to market changes)
7. Law → B (Reduced demand for entry-level workers)
8. Healthcare → C (Improved accuracy in image analysis)
9. Technology sector → E (Creation of new technical job roles)
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Questions 10–13 (Sentence completion)
10. cognitive
11. technical
12. emotional
13. distributed
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READING PASSAGE 2 – ANSWERS
Questions 1–5 (TRUE / FALSE / NOT GIVEN)
1. FALSE
2. FALSE
3. TRUE
4. FALSE
5. TRUE
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Questions 6–9 (Matching ethical issues)
6. Decision-making responsibility → E
7. Bias in training data → A
8. Data privacy → C
9. Transparency of AI systems → B
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Questions 10–13 (Sentence completion)
10. black boxes
11. prioritise
12. doctor-patient
13. oversight
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READING PASSAGE 3 – ANSWERS
Questions 1–5 (Matching headings)
1. Paragraph 2 → E
2. Paragraph 3 → F
3. Paragraph 4 → B
4. Paragraph 5 → A
5. Paragraph 7 → C
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Questions 6–9 (Summary completion)
6. workflows
7. training
8. performance
9. motivation
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Questions 10–13 (Sentence endings)
10. → B
11. → A
12. → C
13. → D
READING PASSAGE 4
Climate Change Adaptation in Coastal Cities
Questions 1–5 (Matching Headings)
1. Paragraph 2 → D
2. Paragraph 3 → F
3. Paragraph 5 → A
4. Paragraph 6 → G
5. Paragraph 8 → H
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Questions 6–9 (Summary Completion)
6. low-lying
7. maintenance
8. natural
9. planning
---
Questions 10–13 (Sentence Endings)
10. B
11. A
12. D
13. E
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READING PASSAGE 5
Renewable Energy Storage Technologies
Questions 1–5 (Matching Headings)
1. Paragraph 1 → B
2. Paragraph 3 → C
3. Paragraph 4 → D
4. Paragraph 6 → A
5. Paragraph 8 → G
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Questions 6–9 (Summary Completion)
6. variability
7. terrain
8. limitations
9. upfront
---
Questions 10–13 (Sentence Endings)
10. B
11. C
12. A
13. E
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READING PASSAGE 6
Urban Heat Islands and City Planning
Questions 1–5 (Matching Headings)
1. Paragraph 2 → C
2. Paragraph 3 → A
3. Paragraph 4 → E
4. Paragraph 6 → F
5. Paragraph 8 → H
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Questions 6–9 (Summary Completion)
6. evapotranspiration
7. populations
8. materials
9. implementation
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Questions 10–13 (Sentence Endings)
10. D
11. A
12. B
13. C
READING PASSAGE 7
Sustainable Agriculture Using Smart Sensors
Questions 1–5 (Matching Headings)
1. Paragraph 1 → D
2. Paragraph 3 → C
3. Paragraph 4 → E
4. Paragraph 6 → A
5. Paragraph 8 → B
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Questions 6–9 (Summary Completion)
6. real-time
7. runoff
8. soil
9. small-scale
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Questions 10–13 (Sentence Endings)
10. B
11. C
12. D
13. A
