Trend Analysis and Megatrends: Identifying Patterns of Change in Complex Systems

Last Updated June 2, 2026

Trend analysis and megatrends are core tools in futures thinking used to identify patterns of change and understand the forces shaping long-term transformation. Trend analysis examines observable developments over time, while megatrends refer to large-scale, persistent forces that influence multiple systems across decades. Together, they help decision-makers distinguish between surface movement and deeper structural change.

In complex systems, change rarely occurs randomly. It emerges through patterns, interactions, feedback loops, institutional choices, cultural shifts, ecological pressures, technological diffusion, demographic change, political conflict, and structural dynamics that unfold over time. Understanding those patterns is essential for anticipating future developments, identifying risk, recognizing opportunity, and designing strategies that remain viable under uncertainty.

At a deeper level, trend analysis is not merely about observing movement. It is about interpreting whether visible developments represent fluctuation, temporary noise, durable pattern formation, structural transition, or the early stages of systemic transformation. The real challenge is not detecting that something is changing. The real challenge is determining what kind of change is taking place, how it interacts with other shifts, and whether it is likely to persist, accelerate, fragment, stall, reverse, or cross a threshold.

This is what turns descriptive pattern recognition into strategic foresight. A list of trends may tell an institution what appears to be changing. A serious trend analysis asks why the change is occurring, what forces sustain it, who benefits, who is harmed, what assumptions it challenges, and what future pathways it may open or close.

What Is Trend Analysis?

Trend analysis is the systematic examination of patterns in data, behavior, institutions, culture, technology, ecology, or system dynamics over time. It seeks to identify the direction, speed, scale, durability, and meaning of change within a given domain. Trends may appear in technological development, economic structure, market behavior, labor systems, demography, environmental conditions, political institutions, public health, education, governance, culture, or social values.

Trend analysis provides one of the empirical foundations for futures thinking because it identifies what is currently changing and how those changes may continue, interact, or transform. Yet trend analysis is not simply pattern detection. It is also an act of interpretation. Analysts must decide whether a pattern is temporary or durable, local or systemic, reactive or structural, linear or nonlinear, reversible or path-dependent, visible or still emergent.

This makes trend analysis valuable and difficult at the same time. The method does not merely ask whether change exists. It asks what the pattern means, what conditions sustain it, what forces amplify it, what might cause it to stall, and what would happen if it interacted with other trends or shocks.

A simple example is remote work. Before it became widespread, it could be read as a weak signal, a niche work arrangement, a technology-enabled trend, a labor-market shift, or a cultural transformation depending on the time period and evidence available. Once pandemic shock, broadband infrastructure, management norms, urban housing patterns, worker expectations, and digital collaboration tools interacted, the pattern changed. Trend analysis had to shift from counting adoption to interpreting structural implications.

Trend analysis becomes strategic when it moves from describing movement to interpreting the architecture of change.

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What Are Megatrends?

Megatrends are large-scale, long-term forces that shape multiple systems and persist over extended periods. They operate at a global, civilizational, ecological, technological, demographic, or institutional level and influence economic, social, political, cultural, environmental, and infrastructural dynamics across decades.

Commonly discussed megatrends include technological acceleration, digital transformation, demographic aging, urbanization, climate change, ecological degradation, energy transition, geopolitical realignment, migration, inequality, public-health transition, automation, platformization, and changing forms of governance. These do not simply describe narrow movements in one sector. They alter the wider context in which institutions, markets, infrastructures, communities, and ecological systems operate.

More precisely, megatrends are not just “big trends.” They are structuring conditions: long-wave transformations that change the environment of action itself. A megatrend does not merely influence one domain. It reshapes the conditions under which multiple domains interact.

Climate change, for example, is not simply an environmental trend. It affects food systems, water systems, insurance markets, housing, public health, infrastructure, labor, migration, energy, finance, urban planning, national security, geopolitical relations, and democratic legitimacy. Likewise, digital transformation is not simply a technology trend. It changes labor, education, media, surveillance, public administration, warfare, culture, finance, and knowledge systems.

Megatrends matter because they help explain why isolated trends often cannot be understood alone. A local change may be part of a wider transformation. A sectoral trend may be driven by pressures outside the sector. A short-term indicator may be the visible edge of a deeper structural shift.

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Trend vs Megatrend

The distinction between trends and megatrends is essential. A trend describes directional movement within a domain. A megatrend describes a large-scale structuring force that shapes many domains at once. The distinction matters because institutions can misread change in both directions: treating a temporary fluctuation as a megatrend, or treating a systemic transformation as a narrow sector trend.

A useful way to think about the distinction is this: a trend describes movement within a system; a megatrend helps define the conditions under which multiple systems are moving. That distinction matters because strategy can fail when institutions mistake a temporary movement for a structuring condition, or fail to see how a domain-level trend is nested inside a larger long-wave transformation.

For example, rising electric vehicle adoption is a trend. The broader energy transition is a megatrend. Increased telehealth visits may be a trend. The digitization of health systems is a megatrend. Growth in extreme heat alerts may be a trend. Climate-driven adaptation pressure is a megatrend. New worker preferences around flexibility may be a trend. The restructuring of labor and organizational life around digital, demographic, and cultural change may be a megatrend.

The distinction also matters for evidence. Trends can often be measured directly. Megatrends often require synthesis across many indicators, disciplines, and time horizons. The more structural the change, the less likely it is to be captured by one metric.

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Why Trend Analysis Matters

Trend analysis is essential for understanding how systems evolve over time. It allows decision-makers to detect patterned change, assess momentum, identify emerging shifts, distinguish short-term fluctuations from durable forces, and support scenario development. It connects directly to strategic foresight because it provides a disciplined way to interpret the present before developing plausible futures.

By identifying trends, organizations can anticipate changes in markets, technologies, governance systems, labor conditions, climate risk, public health, infrastructure, and social expectations. But the deeper value of trend analysis lies in helping institutions distinguish between what is merely new and what is genuinely consequential.

In strategic settings, that distinction can determine whether organizations respond too late, respond to the wrong thing, or misread continuity as transformation. Some changes look dramatic but fade quickly. Others appear small but accumulate into system-level transformation. Trend analysis helps identify which is which.

Trend analysis matters because institutions often fail not from lack of information, but from misreading the significance of information already visible.

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These dimensions help prevent trend analysis from becoming shallow pattern description. They turn trend analysis into structured strategic interpretation.

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How Megatrends Shape Complex Systems

Megatrends operate across multiple domains simultaneously, creating structural conditions that influence how systems evolve. Technological advancement affects labor markets, governance, infrastructure, culture, education, finance, and geopolitical competition. Climate change affects food systems, migration, insurance, energy systems, public health, housing, and institutional legitimacy. Demographic aging affects welfare systems, labor markets, fiscal capacity, healthcare, housing, caregiving, and political priorities.

This reflects core principles of systems thinking: interconnected systems influence one another through feedback loops, delays, thresholds, and dynamic interactions. Megatrends are not isolated forces. They reinforce one another, collide with one another, reshape incentives, and generate second-order effects that may be strategically more important than the visible first-order pattern.

For example, digital transformation interacts with labor-market restructuring. Climate stress interacts with migration, food systems, housing, and infrastructure vulnerability. Demographic aging interacts with fiscal pressure, healthcare demand, labor supply, and family care systems. Energy transition interacts with industrial strategy, geopolitics, mineral supply chains, land use, and grid modernization.

These interactions mean megatrends are rarely additive. They are combinational and often nonlinear. Their effects can accelerate when they reinforce one another, or produce instability when they collide. A city facing climate stress, housing unaffordability, aging infrastructure, migration pressure, and declining public trust is not facing five separate trends. It is facing a combined megatrend environment.

To understand megatrends well is to understand not only large forces, but the architecture of interaction among them.

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Trend Analysis and Uncertainty

Trend analysis provides valuable insight, but it does not eliminate uncertainty. Trends can accelerate, slow down, reverse, fragment, diffuse, stall, or interact in unexpected ways. A trend observed under one set of conditions may not continue if policies change, technologies mature, social backlash emerges, institutions fail, ecological thresholds are crossed, or new incentives appear.

This is why trend analysis is often combined with scenario planning. Scenarios explore how trends and uncertainties may interact to produce different futures under changing conditions. Trend analysis identifies directional movement. Scenario planning asks how that movement might unfold differently if critical uncertainties resolve in different ways.

This point is crucial: a trend is not destiny. It is an interpreted pattern observed under current conditions. If those conditions shift through policy, crisis, innovation, backlash, conflict, or threshold effects, the trend itself may shift with them.

There are several kinds of uncertainty in trend analysis. Data uncertainty concerns whether the pattern is accurately measured. Interpretation uncertainty concerns what the pattern means. Structural uncertainty concerns whether the forces behind the trend will continue. Interaction uncertainty concerns how the trend will combine with other changes. Normative uncertainty concerns whether the trend is desirable, harmful, just, or legitimate.

Understanding trends is necessary but not sufficient for anticipating the future. The strategic task is to understand both patterned direction and the conditions under which that direction may fail.

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Weak Signals and Emerging Trends

Not all trends are immediately visible. Some begin as weak signals: early, ambiguous, marginal, or low-visibility developments that may later become significant. Weak signals may appear in academic research, activist movements, fringe technologies, regulatory experiments, local policy pilots, cultural shifts, ecological anomalies, unusual market behavior, youth practices, or community responses to stress.

Trend analysis occupies a middle position within the foresight chain:

• Horizon Scanning identifies emerging developments.
• Weak Signals and Early Indicators interprets uncertain signs.
• Trend analysis detects directional pattern formation.
• Megatrend analysis situates those patterns in larger structural change.
• Scenario planning explores how those trends may interact under different futures.

This layered logic matters because misclassification can distort strategy. Treating a weak signal as a trend can produce overreaction. Treating a trend as a temporary anomaly can produce delay. Treating a trend as a megatrend can exaggerate its significance. Treating a megatrend as a sector issue can produce systemic blindness.

Trend analysis becomes strongest when it is treated as one stage in a wider architecture of interpretation rather than as a standalone verdict on the future.

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Trend Analysis in Practice

Trend analysis is widely used across business strategy, public policy, technology assessment, innovation management, sustainability planning, climate adaptation, infrastructure governance, education, public health, and environmental monitoring. In each context, it helps decision-makers understand how current developments may shape future conditions.

But the strongest applications do more than list trends. They ask which trends are structurally significant, which are interacting, which are reversible, which are being amplified by institutions or infrastructures, and which may generate second-order effects that matter more than the visible first-order pattern.

In business, trend analysis may examine customer behavior, technological adoption, market restructuring, regulatory change, labor expectations, and supply-chain risk. In public policy, it may examine demographic shifts, service demand, fiscal stress, public trust, health burdens, infrastructure condition, migration, and climate exposure. In sustainability planning, it may examine emissions, resource use, ecological stress, land-use change, energy transition, biodiversity risk, and justice implications.

This is where trend analysis becomes strategic rather than descriptive. A list of trends can be useful. A structured interpretation of how trends change the environment of action is much more powerful.

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Trend Analysis, Institutions, and Power

Trend analysis is often presented as a neutral reading of change, but in practice it is shaped by institutional perspective, power, and interpretive choice. Organizations decide which signals to collect, which metrics to prioritize, which time horizons to privilege, and which developments count as strategically significant. Those decisions are never fully neutral.

What one institution treats as a major trend, another may ignore. What one actor calls technological progress, another may interpret as labor displacement, surveillance expansion, ecological burden, cultural loss, governance risk, or public harm. What appears as efficiency to one sector may appear as fragility or dispossession to another.

This matters because trend analysis can reinforce dominant narratives. A corporate trend report may highlight automation, productivity, consumer personalization, and growth while underplaying worker insecurity, data extraction, public accountability, or ecological costs. A government trend report may highlight fiscal sustainability while underplaying structural inequality. A technology forecast may frame adoption as inevitable while ignoring democratic contestation.

Robust trend analysis therefore requires reflexivity. Analysts must ask not only what is changing, but how institutional assumptions shape what is being seen, how it is being framed, and what remains outside the frame. Without that reflexivity, trend analysis can reinforce dominant narratives rather than reveal structural reality.

Trend analysis is stronger when it treats power not as an external concern, but as part of how trends are produced, measured, interpreted, and acted upon.

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Limitations of Trend Analysis

Trend analysis has real limitations. It may assume continuity where discontinuity occurs. It may overlook emerging disruptions. It depends on the availability and quality of data. It can mistake correlation for structural significance. It may understate feedback loops, tipping points, path dependency, institutional resistance, social backlash, and second-order interaction effects.

Trend analysis is also vulnerable to recency bias. Recent changes may appear more important than they are because they are visible, emotionally salient, or heavily covered. It is vulnerable to data bias because the most measurable trends are not always the most important. It is vulnerable to institutional bias because organizations may prefer trends that confirm existing strategy. It is vulnerable to linearity bias because patterns are often extrapolated without asking what might break them.

These limitations do not weaken the method. They define the discipline with which it should be used. Trend analysis belongs in dialogue with horizon scanning, weak signals analysis, scenario planning, systems modeling, resilience thinking, participatory foresight, and assumption review.

The purpose of trend analysis is not to eliminate uncertainty. It is to reason more carefully about patterned change while remaining alert to disruption, interaction, and structural shift.

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A Practical Trend Analysis Workflow

A practical trend analysis workflow should move from scanning to interpretation, classification, interaction mapping, scenario input, strategic translation, and monitoring. The goal is not to produce a static trend list, but to create a living system for understanding change.

The strongest trend analysis workflows include both evidence and interpretation. They show not only what is changing, but why it matters, what remains uncertain, and what decisions should be affected.

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Mathematical Lens: Direction, Momentum, and Structural Change

A stylized representation of a trend can be written as the movement of a variable over time under current conditions:

A more useful conceptual form introduces interacting drivers:

Momentum can be represented as a function of persistence, acceleration, and reinforcement:

Megatrends can be treated conceptually as long-wave structural conditions that shape many domain-level trajectories at once:

A structural shift can be represented as the moment when trend interaction crosses a threshold:

These equations are conceptual tools. They do not turn trend analysis into deterministic prediction. They help clarify the difference between movement, momentum, interaction, and structural change.

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Computational Modeling for Trend and Megatrend Analysis

Computational modeling can support trend and megatrend analysis by making assumptions, indicators, interactions, classifications, and monitoring routines more transparent. It should not replace judgment, historical awareness, public participation, or ethical interpretation. Its purpose is to help analysts reason more carefully about pattern formation.

A useful computational workflow may include:

• Trend inventory: a structured list of candidate trends with domains, descriptions, evidence, and sources.
• Trend profiling: scores for momentum, scale, durability, uncertainty, reversibility, and cross-system influence.
• Megatrend classification: thresholds for distinguishing ordinary trends from larger structuring forces.
• Interaction mapping: pairwise or network-based mapping of how trends reinforce or constrain one another.
• Signal monitoring: indicators that track acceleration, slowdown, fragmentation, reversal, or threshold crossing.
• Scenario inputs: outputs that feed trend evidence into scenario planning and strategic foresight.
• Equity and power review: documentation of beneficiaries, burdens, missing perspectives, and distributional effects.

Computational trend analysis is strongest when it documents uncertainty rather than hiding it. A model should show how trend scores were created, which assumptions were used, what data are synthetic or real, and how classifications might change if evidence changes.

The goal is not to automate interpretation. The goal is to make interpretation structured, auditable, and easier to revise.

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Advanced R Workflow: Comparing Trend and Megatrend Profiles

The R workflow below compares several stylized long-term change profiles across momentum, structural depth, cross-system influence, reversibility, uncertainty, and distributional sensitivity. It is designed as an evergreen illustration of how domain-level trends can be compared while also signaling when they begin to approach megatrend status.

# ------------------------------------------------------------
# R Workflow: Comparing Trend and Megatrend Profiles
# Purpose:
#   Build stylized profiles across long-term change patterns
#   using momentum, structural depth, cross-system influence,
#   reversibility, uncertainty, and distributional sensitivity.
#
# Optional dependency:
#   install.packages(c("tidyverse"))
# ------------------------------------------------------------

library(tidyverse)

patterns <- tibble(
  pattern_type = c(
    "Digital Labor Restructuring",
    "Climate Risk Intensification",
    "Population Aging",
    "Distributed Energy Transition",
    "Public Trust Fragmentation",
    "Urban Heat and Housing Stress"
  ),
  momentum = c(0.78, 0.74, 0.69, 0.72, 0.64, 0.70),
  structural_depth = c(0.71, 0.86, 0.81, 0.67, 0.72, 0.82),
  cross_system_influence = c(0.76, 0.88, 0.79, 0.73, 0.78, 0.84),
  reversibility = c(0.34, 0.22, 0.28, 0.41, 0.46, 0.26),
  uncertainty = c(0.58, 0.52, 0.46, 0.61, 0.66, 0.54),
  distributional_sensitivity = c(0.72, 0.86, 0.70, 0.68, 0.82, 0.90)
)

patterns <- patterns %>%
  mutate(
    long_term_change_profile =
      0.20 * momentum +
      0.22 * structural_depth +
      0.22 * cross_system_influence -
      0.12 * reversibility -
      0.14 * uncertainty +
      0.10 * distributional_sensitivity,
    classification = case_when(
      long_term_change_profile >= 0.55 ~ "Megatrend-level structural force",
      long_term_change_profile >= 0.45 ~ "Established strategic trend",
      TRUE ~ "Emerging or domain-specific trend"
    )
  ) %>%
  arrange(desc(long_term_change_profile))

print(patterns)

patterns_long <- patterns %>%
  pivot_longer(
    cols = c(
      momentum,
      structural_depth,
      cross_system_influence,
      reversibility,
      uncertainty,
      distributional_sensitivity
    ),
    names_to = "dimension",
    values_to = "value"
  )

ggplot(patterns_long, aes(x = dimension, y = value, fill = pattern_type)) +
  geom_col(position = "dodge") +
  labs(
    title = "Stylized Trend and Megatrend Dimensions",
    x = "Dimension",
    y = "Value",
    fill = "Pattern Type"
  ) +
  theme_minimal(base_size = 12) +
  coord_flip()

ggplot(patterns, aes(x = reorder(pattern_type, long_term_change_profile), y = long_term_change_profile)) +
  geom_col() +
  coord_flip() +
  labs(
    title = "Stylized Long-Term Change Profile",
    x = "Pattern Type",
    y = "Profile Score"
  ) +
  theme_minimal(base_size = 12)

dir.create("outputs", showWarnings = FALSE)

write_csv(patterns, "outputs/trend_megatrend_profiles.csv")
write_csv(patterns_long, "outputs/trend_megatrend_profiles_long.csv")

This workflow is not intended to produce final truth about megatrends. It is a transparent way to compare patterns, document assumptions, and support structured discussion about which changes deserve strategic attention.

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Advanced Python Workflow: Simulating Trend Momentum and Structural Shift

The Python workflow below simulates stylized long-term change under different assumptions about momentum, reinforcing feedback, threshold sensitivity, and reversibility. It is useful for showing why some patterns remain ordinary trends while others grow into larger structural forces.

# ------------------------------------------------------------
# Python Workflow: Simulating Trend Momentum and Structural Shift
# Purpose:
#   Compare stylized long-term patterns under different levels
#   of momentum, feedback, threshold sensitivity, and reversibility.
#
# Optional dependencies:
#   pip install pandas numpy matplotlib
# ------------------------------------------------------------

from pathlib import Path

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

OUTPUT_DIR = Path("outputs")
OUTPUT_DIR.mkdir(exist_ok=True)

time_steps = np.arange(1, 41)

patterns = [
    {
        "pattern": "Ordinary Sector Trend",
        "momentum": 0.54,
        "feedback": 0.42,
        "threshold": 0.18,
        "reversibility": 0.62
    },
    {
        "pattern": "Emerging Structural Megatrend",
        "momentum": 0.72,
        "feedback": 0.74,
        "threshold": 0.32,
        "reversibility": 0.28
    },
    {
        "pattern": "Climate-Linked System Stress",
        "momentum": 0.76,
        "feedback": 0.82,
        "threshold": 0.40,
        "reversibility": 0.20
    }
]

def simulate_pattern(momentum, feedback, threshold, reversibility, initial_state=0.40):
    state = np.zeros(len(time_steps))
    state[0] = initial_state

    for index in range(1, len(time_steps)):
        growth = 0.18 * momentum + 0.24 * feedback
        threshold_effect = threshold if (index + 1) % 10 == 0 else threshold / 4
        reversibility_drag = 0.08 * reversibility

        state[index] = (
            state[index - 1]
            + growth / 4
            + threshold_effect / 6
            - reversibility_drag / 8
        )

        state[index] = np.clip(state[index], 0, 2.5)

    return state

rows = []

for pattern in patterns:
    path = simulate_pattern(
        pattern["momentum"],
        pattern["feedback"],
        pattern["threshold"],
        pattern["reversibility"]
    )

    for time, value in zip(time_steps, path):
        rows.append({
            "pattern": pattern["pattern"],
            "time": time,
            "structural_shift_index": value
        })

df = pd.DataFrame(rows)

summary = (
    df.groupby("pattern")["structural_shift_index"]
    .agg(
        final_value="last",
        mean_value="mean",
        max_value="max"
    )
    .reset_index()
    .sort_values("final_value", ascending=False)
)

print("\nTrend structural shift summary:")
print(summary)

df.to_csv(OUTPUT_DIR / "trend_structural_shift_paths.csv", index=False)
summary.to_csv(OUTPUT_DIR / "trend_structural_shift_summary.csv", index=False)

plt.figure(figsize=(10, 6))
for pattern_name in df["pattern"].unique():
    subset = df[df["pattern"] == pattern_name]
    plt.plot(
        subset["time"],
        subset["structural_shift_index"],
        marker="o",
        linewidth=1.5,
        label=pattern_name
    )

plt.xlabel("Time step")
plt.ylabel("Structural shift index")
plt.title("Trend Momentum and Structural Shift")
plt.legend()
plt.tight_layout()
plt.savefig(OUTPUT_DIR / "trend_structural_shift_paths.png", dpi=150)
plt.close()

plt.figure(figsize=(10, 6))
plt.barh(summary["pattern"], summary["final_value"])
plt.xlabel("Final structural shift index")
plt.title("Final Structural Shift by Pattern")
plt.tight_layout()
plt.savefig(OUTPUT_DIR / "trend_structural_shift_summary.png", dpi=150)
plt.close()

This workflow shows why trend analysis must consider more than direction. Momentum, feedback, threshold sensitivity, and reversibility help distinguish ordinary movement from deeper structural transformation.

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GitHub Repository

The companion repository for this article contains computational examples for trend analysis, megatrend profiling, structural-shift scoring, trend interaction, signal monitoring, and long-term change interpretation.

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Why This Matters

Trend analysis and megatrends provide a structured way to understand how change unfolds over time. They offer insight into the direction, momentum, drivers, interactions, and structural implications of transformation, helping decision-makers navigate complexity and uncertainty with more discipline than impressionistic reading alone.

But their highest value lies in helping institutions distinguish between signal and momentum, pattern and structure, temporary movement and durable transformation. That distinction is increasingly important in a world shaped by climate instability, artificial intelligence, demographic transition, public-health risk, infrastructure stress, geopolitical fragmentation, ecological limits, and contested institutions.

Trend analysis helps institutions see the present more clearly. Megatrend analysis helps them understand the wider environment in which the present is changing. Together, they support scenario planning, strategic foresight, resilience thinking, public policy, sustainability planning, and long-term responsibility.

Understanding trends is therefore not just a matter of observing change. It is a matter of learning how to read the architecture of change before it fully reorganizes the system.

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Related Articles

• Futures Thinking
• What Is Futures Thinking?
• Forecasting, Foresight, and Futures Studies
• Futures Literacy and Anticipatory Capacity
• Possible, Plausible, Probable, and Preferable Futures
• Scenario Planning
• Strategic Foresight Methods
• Horizon Scanning
• Weak Signals and Early Indicators
• Systems Modeling
• Resilience Thinking

Further Reading

• Bengston, D.N. (2019) ‘Horizon scanning for environmental foresight: a review of issues and approaches’, Futures, 113. Available at: ScienceDirect.
• European Environment Agency (2015) Assessment of Global Megatrends: An Update. Copenhagen: European Environment Agency. Available at: European Environment Agency.
• Government Office for Science (2024) The Futures Toolkit: Tools for Futures Thinking and Foresight Across UK Government. London: Government Office for Science. Available at: UK Government.
• Hines, A. and Bishop, P. (2015) Thinking About the Future: Guidelines for Strategic Foresight. 2nd edn. Houston: Hinesight.
• Inayatullah, S. (2008) ‘Six pillars: futures thinking for transforming’, Foresight, 10(1), pp. 4–21. Available at: Emerald.
• Naisbitt, J. (1982) Megatrends: Ten New Directions Transforming Our Lives. New York: Warner Books.
• Organisation for Economic Co-operation and Development (OECD) (2021) Strategic Foresight for Better Policies: Building Effective Governance in the Face of Uncertain Futures. Paris: OECD Publishing. Available at: OECD.
• Organisation for Economic Co-operation and Development (OECD) (2025) Foresight Toolkit for Resilient Public Policy. Paris: OECD Publishing. Available at: OECD.
• United Nations (no date) Global Issues. Available at: United Nations.
• World Economic Forum (2024) The Global Risks Report 2024. Geneva: World Economic Forum. Available at: World Economic Forum.

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References

• Bengston, D.N. (2019) ‘Horizon scanning for environmental foresight: a review of issues and approaches’, Futures, 113. Available at: ScienceDirect.
• European Environment Agency (2015) Assessment of Global Megatrends: An Update. Copenhagen: European Environment Agency. Available at: European Environment Agency.
• Government Office for Science (2024) The Futures Toolkit: Tools for Futures Thinking and Foresight Across UK Government. London: Government Office for Science. Available at: UK Government.
• Government Office for Science (2025) A Brief Guide to Futures Thinking and Foresight. London: Government Office for Science. Available at: UK Government.
• Inayatullah, S. (2008) ‘Six pillars: futures thinking for transforming’, Foresight, 10(1), pp. 4–21. Available at: Emerald.
• Naisbitt, J. (1982) Megatrends: Ten New Directions Transforming Our Lives. New York: Warner Books.
• Organisation for Economic Co-operation and Development (OECD) (no date) Strategic Foresight. Available at: OECD.
• Organisation for Economic Co-operation and Development Observatory of Public Sector Innovation (OECD OPSI) (no date) Futures & Foresight. Available at: OECD OPSI.
• Organisation for Economic Co-operation and Development (OECD) (2021) Strategic Foresight for Better Policies: Building Effective Governance in the Face of Uncertain Futures. Paris: OECD Publishing. Available at: OECD.
• Organisation for Economic Co-operation and Development (OECD) (2025) Foresight Toolkit for Resilient Public Policy. Paris: OECD Publishing. Available at: OECD.
• United Nations (no date) Global Issues. Available at: United Nations.
• United Nations Development Programme (UNDP) (2018) Foresight Manual: Empowered Futures for the 2030 Agenda. New York: UNDP. Available at: UNDP.
• United Nations Educational, Scientific and Cultural Organization (UNESCO) (no date) Futures Literacy & Foresight. Available at: UNESCO.
• World Economic Forum (2024) The Global Risks Report 2024. Geneva: World Economic Forum. Available at: World Economic Forum.

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