Backcasting and Strategic Planning: Designing Pathways from Desired Futures to Present Action

Last Updated June 2, 2026

Backcasting is a strategic planning method that begins with a desired future and works backward to identify the decisions, capacities, investments, reforms, and transition pathways required to reach it. Unlike forecasting, which projects present trends forward, backcasting starts from a future condition judged to be necessary, desirable, legitimate, or ethically required, then asks what must change in the present to make that future possible.

This makes backcasting especially valuable when current trajectories are inadequate. Climate stabilization, ecological restoration, resilient infrastructure, public-health preparedness, just energy transition, institutional reform, democratic renewal, care-system redesign, and sustainable development cannot always be achieved by extending existing patterns. In many cases, the present path is precisely what must be changed.

At its deepest level, backcasting represents a shift from predictive reasoning to normative design. It does not ask only what is likely to happen. It asks what future should exist, what conditions would make that future viable, what barriers stand in the way, and what sequence of actions could move a system from current conditions toward a preferred state.

Backcasting is therefore one of the most important bridges between futures thinking and strategy. Scenario planning explores alternative futures. Horizon scanning and weak signal analysis identify emerging change. Trend analysis and megatrends clarify long-term structural forces. Backcasting then turns future-oriented insight into pathway design: a structured attempt to connect long-range purpose to present-day action.

What Is Backcasting?

Backcasting is a planning and foresight method that starts with a defined future state and works backward to identify the actions required to reach that state. It is most useful when the desired future cannot be reached by simply extending current trends or making small incremental adjustments. Instead of asking what the future is likely to be, backcasting asks what future should be pursued and what present decisions would make it possible.

The method begins with a normative or strategic target. That target may be a low-carbon energy system, a resilient food and water system, a just housing future, a decarbonized transportation network, a public-health system prepared for climate stress, a regenerative land-use pattern, a democratic technology governance framework, or an institution capable of long-term responsibility. Once the future target is defined, the analyst works backward to identify the necessary preconditions, milestones, policies, investments, behavioral shifts, institutional reforms, and monitoring systems.

Backcasting is often used in sustainability science, transition studies, public policy, infrastructure planning, organizational strategy, and long-range governance. It is especially valuable when a future goal is clear enough to orient action, but the pathway toward it is uncertain, politically difficult, institutionally fragmented, or blocked by current system dynamics.

This makes backcasting different from ordinary goal setting. A goal says what should be achieved. Backcasting asks what must be true before that goal becomes achievable. It examines the gap between present reality and desired future conditions, then decomposes that gap into stages of action.

Backcasting turns a preferred future from an abstract aspiration into a structured pathway problem.

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Backcasting vs Forecasting

Backcasting and forecasting are complementary, but they reason in opposite directions. Forecasting begins with present data and trends, then projects forward. Backcasting begins with a desired future, then reasons backward to the present. Forecasting is especially useful when systems are relatively stable, historical patterns are informative, and the goal is to estimate likely outcomes. Backcasting is especially useful when existing trajectories are inadequate, undesirable, unjust, unstable, or incapable of producing the required future.

The distinction matters because many long-term challenges cannot be solved by better projection alone. If present trends are leading toward ecological instability, infrastructure failure, deepening inequality, institutional distrust, or unsustainable resource use, then projecting those trends forward may clarify the problem but not solve it. Backcasting asks how the system must be changed.

That does not mean forecasting is useless. Forecasts can help identify the consequences of current trajectories. They can show why transformation is necessary. But backcasting adds a different kind of reasoning: not “where are we headed?” but “what must we do to move somewhere better?”

Forecasting clarifies probable direction. Backcasting clarifies necessary transformation.

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Why Backcasting Matters

Backcasting matters because many of the most important future-oriented problems are not problems of prediction alone. They are problems of transformation. Climate stabilization, biodiversity recovery, public health resilience, equitable infrastructure, sustainable cities, just energy transition, responsible technology governance, institutional legitimacy, and long-term social wellbeing all require intentional design rather than passive extrapolation.

In such contexts, the present is not a neutral starting point. It is a structured system of incentives, infrastructures, laws, capital stocks, habits, institutions, political interests, cultural assumptions, and path dependencies. Existing arrangements often reproduce themselves. They make some futures easier and others harder. Backcasting helps reveal what must change if a different future is to become plausible.

The method is especially important under conditions of lock-in. Energy systems are locked in through infrastructure, regulation, investment, land use, industrial interests, and consumer practices. Transportation systems are locked in through roads, zoning, vehicle dependence, financing, and cultural expectations. Institutions are locked in through rules, professional identities, budgets, accountability systems, and power relations. Backcasting asks what sequence of changes can loosen or redirect these structures.

Backcasting matters because it converts long-term responsibility into present-day strategic design.

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The backcasting process is strongest when it treats future vision, present constraints, political reality, and adaptive learning as parts of one pathway architecture.

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Backcasting and Strategic Foresight

Backcasting is closely linked to strategic foresight because it turns future-oriented analysis into pathway design. Foresight methods help actors understand possible futures, emerging signals, uncertainties, risks, opportunities, and structural change. Backcasting then asks how a preferred or necessary future could be built from the present.

The relationship is especially clear when backcasting is combined with scenario planning. Scenario planning explores multiple plausible contexts. Backcasting can then be used to identify pathways toward a preferred outcome across those contexts. A pathway that works only under one scenario may be fragile. A pathway that remains viable across several plausible futures is more strategically robust.

Horizon scanning and weak signal analysis also strengthen backcasting. They help identify early developments that may create opportunities, barriers, risks, or inflection points along the pathway. Trend analysis and megatrends provide the structural context within which the desired future must be pursued. Together, these methods prevent backcasting from becoming detached from actual system change.

Without backcasting, foresight can remain interpretive. With backcasting, foresight becomes actionable.

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Backcasting and Systems Thinking

Backcasting requires systems thinking because desired futures usually depend on interactions among many components. Infrastructure, institutions, finance, law, technology, behavior, culture, ecology, labor, power, and public legitimacy rarely change independently. A pathway that treats them as separate variables may fail even if each component appears plausible in isolation.

Systems thinking helps backcasting identify feedback loops, delays, reinforcing dynamics, balancing constraints, thresholds, path dependency, bottlenecks, and leverage points. For example, an energy transition pathway is not simply a question of replacing one technology with another. It involves grid capacity, permitting, land use, labor skills, mineral supply chains, public trust, affordability, regulatory design, storage, demand management, finance, and community consent.

Backcasting also benefits from understanding slow variables and delays. Infrastructure turnover may take decades. Institutional reform may lag public need. Cultural norms may shift slowly, then suddenly. Ecological systems may absorb stress until thresholds are crossed. These dynamics shape the timing and sequencing of pathways.

Backcasting is not merely a planning tool. It is a method for designing system transformation under constraint.

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Backcasting and Socio-Technical Transitions

Backcasting is widely used in sustainability science and transition studies because many desired futures require socio-technical change. Socio-technical systems include not only technologies, but also institutions, infrastructures, skills, markets, regulations, cultural meanings, user practices, supply chains, and political coalitions. Energy, mobility, housing, food, water, health, and digital systems are all socio-technical systems.

Transition theory often distinguishes among niches, regimes, and landscape pressures. Niches are experimental spaces where alternatives develop. Regimes are dominant systems of rules, practices, infrastructures, and interests. Landscape pressures are wider forces such as climate change, demographic transition, geopolitical disruption, ecological stress, or cultural change. Backcasting helps identify how niche innovations might scale, how regimes might be reformed or destabilized, and how landscape pressures create openings for transition.

This perspective is important because desired futures rarely emerge from technical innovation alone. A low-carbon energy future requires not only renewable generation, but grid modernization, storage, demand flexibility, industrial policy, environmental justice, community consent, financing, labor transition, permitting reform, public legitimacy, and governance capacity. A resilient public-health future requires not only medical technology, but prevention, care work, surveillance ethics, climate adaptation, workforce investment, trust, and equitable access.

Backcasting is especially powerful when the future requires system transition rather than incremental optimization.

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Pathways, Milestones, and Transition Logic

Backcasting becomes practical when it translates a desired future into pathways and milestones. A pathway describes the sequence of changes required over time. A milestone marks a condition that must be achieved by a particular phase. Together, pathways and milestones prevent the future vision from remaining abstract.

Good milestones are not merely symbolic. They represent necessary conditions. For a climate-resilient city, milestones may include updated heat-risk maps, public cooling infrastructure, housing retrofits, tree canopy expansion, grid reliability improvements, public-health staffing, tenant protections, insurance reform, and emergency-response coordination. For a responsible AI governance system, milestones may include procurement standards, audit capacity, public accountability rules, worker protections, data governance, civil-rights review, and appeals mechanisms.

Transition logic matters because some steps depend on others. Workforce training may need to precede technology deployment. Public trust may need to precede major infrastructure siting. Legal authority may need to precede implementation. Monitoring systems may need to precede adaptive regulation. If the sequence is wrong, the pathway may fail even if each individual action is reasonable.

Backcasting is not only about working backward from the future. It is about building a credible sequence of forward action.

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Constraints, Power, and Political Economy

Backcasting is not a purely technical exercise. It operates in systems shaped by power, money, law, institutional incentives, political conflict, public legitimacy, and unequal exposure to risk. A desired future may be technically possible but politically contested, financially constrained, institutionally fragmented, or opposed by actors who benefit from current arrangements.

This is especially important in sustainability transitions. Incumbent industries may resist change. Public agencies may lack capacity. Communities may distrust planning processes because of past harm. Workers may fear displacement. Investors may resist stranded assets. Governments may face short election cycles. Regulatory systems may be fragmented. Transition costs may fall unevenly on those least able to bear them.

Backcasting becomes more realistic when it treats these conditions as central rather than secondary. A pathway that does not account for power is not a strategy. It is a diagram. Effective backcasting must examine who benefits from the current system, who would bear transition costs, who has veto power, who is excluded from decision-making, what coalitions are needed, and what forms of compensation, protection, or redistribution are required for legitimacy.

Backcasting becomes credible only when it treats constraint, conflict, and power as part of the pathway.

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Participation, Legitimacy, and Preferred Futures

Because backcasting begins with a desired future, it must ask who defines that future. Preferred futures are not neutral. They reflect values, priorities, institutional interests, public needs, ethical commitments, and political choices. A future that appears desirable to one group may impose burdens on another. A transition that appears efficient from a technical standpoint may be unjust if it ignores workers, tenants, Indigenous peoples, disabled people, caregivers, low-income communities, or those already exposed to environmental harm.

Participation matters because preferred futures require legitimacy. If affected groups are excluded from defining the future, the pathway may reproduce inequality or fail in implementation. Participation also improves the quality of backcasting. Communities often understand barriers, risks, and practical constraints that formal models miss. Workers understand implementation realities. Local governments understand capacity limits. Youth understand intergenerational stakes. Frontline communities understand exposure and harm before aggregate indicators reflect them.

Participatory backcasting should influence the target future, the criteria for success, the identification of barriers, the sequencing of actions, the distribution of costs and benefits, and the monitoring of outcomes. Participation that does not alter the pathway is not meaningful participation. It is consultation without power.

Backcasting is strongest when the preferred future is not imposed from above, but built through legitimate, informed, and accountable deliberation.

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Applications of Backcasting

Backcasting is used across many domains because it is useful wherever long-term goals require present-day transformation. It is especially common in sustainability planning, but its logic applies more broadly to public policy, institutional reform, infrastructure strategy, organizational change, technology governance, public health, education, and community resilience.

In each domain, backcasting shifts planning from vague aspiration to staged transition design. Its usefulness depends on whether future targets are legitimate, pathways are realistic, and institutions are willing to revise strategy as conditions change.

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Strengths and Limitations

Backcasting has several major strengths. It aligns present strategy with long-term purpose. It helps institutions escape path dependency. It supports transformation rather than mere optimization. It encourages systems thinking, sequencing, and cross-domain integration. It also creates a practical structure for turning values into action.

But backcasting also has limitations. Desired futures may be contested or vague. Pathways may underestimate uncertainty, political resistance, institutional fragmentation, funding constraints, technological limits, or implementation capacity. Backcasting can become idealistic if it does not account for power, conflict, and material conditions. It can also become technocratic if experts define preferred futures without meaningful participation.

Backcasting is strongest when it is treated not as a fixed blueprint, but as an adaptive pathway discipline.

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A Practical Backcasting Workflow

A practical backcasting workflow should move from future definition to system diagnosis, gap analysis, pathway design, stress testing, implementation, and learning. The workflow below can be adapted for public agencies, organizations, universities, civic institutions, sustainability teams, infrastructure planners, and strategy groups.

A practical backcasting process should be rigorous enough to shape decisions and flexible enough to adapt. The desired future provides direction; the pathway provides structure; monitoring provides learning.

The method works best when it combines long-term ambition with present-day accountability.

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Mathematical Lens: Pathways from Desired Futures to Present Action

A simple stylized representation of backcasting treats a desired future state \(F^*\) as the target and the current state \(S_0\) as the starting point:

A sequenced pathway can be represented as a set of staged interventions:

Under uncertainty, backcasting often works with multiple possible pathways toward the same target future:

A pathway viability score can combine feasibility, alignment, resilience, speed, and political friction:

Adaptive backcasting can also be represented using trigger points:

These equations are conceptual tools. They do not reduce transformation to mathematics. They clarify the logic of strategic gap analysis, pathway design, pathway comparison, and adaptive revision.

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Computational Modeling for Backcasting

Computational modeling can support backcasting by making future targets, present conditions, strategic gaps, pathway options, milestones, constraints, and monitoring indicators more transparent. It should not replace public deliberation, political judgment, institutional knowledge, or ethical reasoning. The purpose is to support pathway reasoning, not automate transformation.

A useful computational backcasting workflow may include:

• Target future profiles: structured descriptions of desired outcomes, principles, thresholds, and indicators.
• Baseline datasets: present-state indicators describing current system conditions.
• Gap analysis: comparison between current state and target future.
• Pathway libraries: possible sequences of action, milestones, dependencies, and enabling conditions.
• Constraint scoring: assessment of political, institutional, financial, technical, and social barriers.
• Pathway viability scoring: comparison of alternative pathways across feasibility, alignment, resilience, speed, and friction.
• Scenario stress testing: evaluation of pathways under different plausible futures.
• Monitoring dashboards: review indicators that show whether the transition remains on track.

Computational backcasting should document assumptions clearly. It should show how targets were defined, how pathways were scored, what constraints were included, whose values shaped the preferred future, and how results should be revised as conditions change.

The goal is not to mechanize transformation. The goal is to make pathway assumptions visible, testable, and accountable.

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Advanced R Workflow: Comparing Backcasting Pathways Across Strategic Futures

The R workflow below compares several stylized backcasting pathways toward a desired future across feasibility, institutional difficulty, transition speed, stakeholder alignment, resilience, justice, and adaptability. It is designed as an evergreen illustration of how alternative routes to the same future can differ substantially in profile and viability.

# ------------------------------------------------------------
# R Workflow: Comparing Backcasting Pathways
# Purpose:
#   Build stylized pathway profiles toward a desired future
#   using feasibility, institutional difficulty, transition speed,
#   stakeholder alignment, resilience, justice, and adaptability.
#
# Optional dependency:
#   install.packages(c("tidyverse"))
# ------------------------------------------------------------

library(tidyverse)

pathways <- tibble(
  pathway = c(
    "Rapid Top-Down Transition",
    "Incremental Coordinated Transition",
    "Innovation-Led Transition",
    "Participatory Just Transition",
    "Crisis-Driven Accelerated Transition"
  ),
  feasibility = c(0.46, 0.71, 0.58, 0.64, 0.39),
  institutional_difficulty = c(0.78, 0.49, 0.56, 0.62, 0.82),
  transition_speed = c(0.84, 0.55, 0.68, 0.52, 0.88),
  stakeholder_alignment = c(0.38, 0.74, 0.51, 0.86, 0.33),
  resilience = c(0.52, 0.79, 0.61, 0.82, 0.47),
  justice = c(0.36, 0.68, 0.54, 0.90, 0.40),
  adaptability = c(0.42, 0.76, 0.64, 0.80, 0.38)
)

pathways <- pathways %>%
  mutate(
    backcasting_profile =
      0.18 * feasibility -
      0.16 * institutional_difficulty +
      0.14 * transition_speed +
      0.18 * stakeholder_alignment +
      0.16 * resilience +
      0.12 * justice +
      0.14 * adaptability,
    pathway_class = case_when(
      backcasting_profile >= 0.62 ~ "Strong adaptive pathway",
      backcasting_profile >= 0.50 ~ "Potential pathway with constraints",
      TRUE ~ "Fragile or high-risk pathway"
    )
  ) %>%
  arrange(desc(backcasting_profile))

print(pathways)

pathways_long <- pathways %>%
  pivot_longer(
    cols = c(
      feasibility,
      institutional_difficulty,
      transition_speed,
      stakeholder_alignment,
      resilience,
      justice,
      adaptability
    ),
    names_to = "dimension",
    values_to = "value"
  )

ggplot(pathways_long, aes(x = dimension, y = value, fill = pathway)) +
  geom_col(position = "dodge") +
  labs(
    title = "Stylized Backcasting Pathway Dimensions",
    x = "Dimension",
    y = "Value",
    fill = "Pathway"
  ) +
  theme_minimal(base_size = 12) +
  coord_flip()

ggplot(pathways, aes(x = reorder(pathway, backcasting_profile), y = backcasting_profile)) +
  geom_col() +
  coord_flip() +
  labs(
    title = "Stylized Backcasting Pathway Profile",
    x = "Pathway",
    y = "Profile Score"
  ) +
  theme_minimal(base_size = 12)

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

write_csv(pathways, "outputs/backcasting_pathway_profiles.csv")
write_csv(pathways_long, "outputs/backcasting_pathway_profiles_long.csv")

This workflow is not a claim that pathway design can be reduced to a score. It is a transparent way to compare assumptions, expose tradeoffs, and support structured discussion about which transition pathway deserves further development.

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Advanced Python Workflow: Simulating Transition Pathways from a Desired Future

The Python workflow below simulates stylized transition pathways toward a desired future under different assumptions about institutional capacity, coordination, transition speed, friction, and adaptive revision. It is useful for showing why pathways to the same future can diverge sharply in viability.

# ------------------------------------------------------------
# Python Workflow: Simulating Backcasting Pathways
# Purpose:
#   Compare stylized pathways toward a desired future under
#   different institutional, coordination, speed, and friction
#   assumptions.
#
# 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, 37)

pathways = [
    {
        "pathway": "Coordinated Transition",
        "capacity": 0.76,
        "coordination": 0.74,
        "speed": 0.62,
        "friction": 0.34,
        "adaptability": 0.72
    },
    {
        "pathway": "Fragmented Transition",
        "capacity": 0.48,
        "coordination": 0.39,
        "speed": 0.58,
        "friction": 0.64,
        "adaptability": 0.42
    },
    {
        "pathway": "Participatory Just Transition",
        "capacity": 0.68,
        "coordination": 0.70,
        "speed": 0.52,
        "friction": 0.38,
        "adaptability": 0.82
    },
    {
        "pathway": "Crisis-Driven Transition",
        "capacity": 0.52,
        "coordination": 0.44,
        "speed": 0.86,
        "friction": 0.72,
        "adaptability": 0.36
    }
]

def simulate_pathway(
    capacity,
    coordination,
    speed,
    friction,
    adaptability,
    initial_gap=1.0
):
    gap = np.zeros(len(time_steps))
    gap[0] = initial_gap

    for t in range(1, len(time_steps)):
        progress = (
            0.24 * capacity +
            0.24 * coordination +
            0.18 * speed +
            0.18 * adaptability
        )

        friction_penalty = 0.18 * friction
        review_bonus = 0.035 if (t + 1) % 6 == 0 else 0.0
        shock_penalty = 0.025 if (t + 1) % 8 != 0 else 0.085

        gap[t] = (
            gap[t - 1]
            - progress / 4
            + friction_penalty / 4
            - review_bonus
            + shock_penalty
        )

        gap[t] = max(0, gap[t])

    return gap

rows = []

for pathway in pathways:
    path = simulate_pathway(
        pathway["capacity"],
        pathway["coordination"],
        pathway["speed"],
        pathway["friction"],
        pathway["adaptability"]
    )

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

df = pd.DataFrame(rows)

summary = (
    df.groupby("pathway")["remaining_gap"]
    .agg(
        final_gap="last",
        mean_gap="mean",
        max_gap="max"
    )
    .reset_index()
    .sort_values("final_gap", ascending=True)
)

print("\nBackcasting transition pathway summary:")
print(summary)

df.to_csv(OUTPUT_DIR / "backcasting_transition_paths.csv", index=False)
summary.to_csv(OUTPUT_DIR / "backcasting_transition_summary.csv", index=False)

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

plt.xlabel("Time Step")
plt.ylabel("Remaining Strategic Gap")
plt.title("Backcasting Pathways Toward a Desired Future")
plt.legend()
plt.tight_layout()
plt.savefig(OUTPUT_DIR / "backcasting_transition_paths.png", dpi=150)
plt.close()

plt.figure(figsize=(10, 6))
plt.barh(summary["pathway"], summary["final_gap"])
plt.xlabel("Final Remaining Gap")
plt.title("Final Strategic Gap by Backcasting Pathway")
plt.tight_layout()
plt.savefig(OUTPUT_DIR / "backcasting_final_gap_summary.png", dpi=150)
plt.close()

This workflow demonstrates a central lesson of backcasting: pathways do not succeed only because the desired future is compelling. They succeed when capacity, coordination, legitimacy, adaptability, and friction are addressed together.

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GitHub Repository

The companion repository for this article contains computational examples for backcasting, strategic gap analysis, pathway comparison, transition milestones, feasibility scoring, scenario stress testing, and adaptive pathway monitoring.

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Why This Matters

Backcasting provides a practical way to translate futures thinking into action. It allows organizations, governments, communities, and institutions to move beyond understanding possible futures toward actively shaping preferred ones. In a world defined by uncertainty, lock-in, ecological risk, technological acceleration, institutional stress, and long-term public responsibility, the ability to design pathways toward desired outcomes is a critical strategic capability.

The method matters because many of the futures worth pursuing will not arrive by default. Sustainable infrastructure, accountable technology, resilient public systems, ecological restoration, just transition, and long-term social wellbeing require choices that current trajectories may not produce. Backcasting helps actors ask what must change now for those futures to become possible.

Its strongest use is not as a rigid master plan, but as an adaptive pathway discipline. It combines vision with constraint, ambition with sequencing, ethics with implementation, and long-term responsibility with present action.

Backcasting is the bridge between foresight and execution: a method for turning desired futures into accountable pathways of change.

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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
• Trend Analysis and Megatrends
• Horizon Scanning
• Weak Signals and Early Indicators
• Systems Modeling
• Resilience Thinking

Further Reading

• Dreborg, K.H. (1996) ‘Essence of backcasting’, Futures, 28(9), pp. 813–828.
• Holmberg, J. and Robèrt, K.-H. (2000) ‘Backcasting from non-overlapping sustainability principles: A framework for strategic planning’, International Journal of Sustainable Development & World Ecology, 7(4), pp. 291–308.
• Robinson, J. (1990) ‘Futures under glass: A recipe for people who hate to predict’, Futures, 22(8), pp. 820–842.
• Robinson, J. (2003) ‘Future subjunctive: backcasting as social learning’, Futures, 35(8), pp. 839–856.
• Quist, J. and Vergragt, P. (2006) ‘Past and future of backcasting: The shift to stakeholder participation and a proposal for a methodological framework’, Futures, 38(9), pp. 1027–1045.
• Vergragt, P.J. and Quist, J. (2011) ‘Backcasting for sustainability: Introduction to the special issue’, Technological Forecasting and Social Change, 78(5), pp. 747–755.
• Geels, F.W. (2002) ‘Technological transitions as evolutionary reconfiguration processes: A multi-level perspective and a case-study’, Research Policy, 31(8–9), pp. 1257–1274.
• Geels, F.W. and Schot, J. (2007) ‘Typology of sociotechnical transition pathways’, Research Policy, 36(3), pp. 399–417.
• 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.
• United Nations Development Programme (UNDP) (2018) Foresight Manual: Empowered Futures for the 2030 Agenda. New York: UNDP. Available at: UNDP.

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References

• Dreborg, K.H. (1996) ‘Essence of backcasting’, Futures, 28(9), pp. 813–828.
• Geels, F.W. (2002) ‘Technological transitions as evolutionary reconfiguration processes: A multi-level perspective and a case-study’, Research Policy, 31(8–9), pp. 1257–1274.
• Geels, F.W. and Schot, J. (2007) ‘Typology of sociotechnical transition pathways’, Research Policy, 36(3), pp. 399–417.
• 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.
• Holmberg, J. and Robèrt, K.-H. (2000) ‘Backcasting from non-overlapping sustainability principles: A framework for strategic planning’, International Journal of Sustainable Development & World Ecology, 7(4), pp. 291–308.
• 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.
• Quist, J. and Vergragt, P. (2006) ‘Past and future of backcasting: The shift to stakeholder participation and a proposal for a methodological framework’, Futures, 38(9), pp. 1027–1045.
• Robinson, J. (1990) ‘Futures under glass: A recipe for people who hate to predict’, Futures, 22(8), pp. 820–842.
• Robinson, J. (2003) ‘Future subjunctive: backcasting as social learning’, Futures, 35(8), pp. 839–856.
• 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.
• Vergragt, P.J. and Quist, J. (2011) ‘Backcasting for sustainability: Introduction to the special issue’, Technological Forecasting and Social Change, 78(5), pp. 747–755.

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