"

10. Measuring the Immeasurable: Advanced Data Extraction Methods in Cost-Benefit Analysis

Valuing Social, Health, and Environmental Impacts in the Absence of Markets

Taha Chaiechi

10.1. Introduction: Why Valuation Gets Harder When It Matters Most

🔍 What It Is

Measuring the Immeasurable explores advanced techniques for valuing non-market impacts in Cost-Benefit Analysis (CBA)—outcomes like human life, health improvements, ecosystem services, or social cohesion that lack direct prices but are crucial for public investment decisions. This chapter provides a practical guide to the methods, tools, and frameworks used to assign economic value to these intangible benefits, helping analysts ensure that projects addressing social equity, climate resilience, health, and disaster risk reduction are fully and fairly evaluated. By doing so, it bridges the gap between economic appraisal and real-world priorities, equipping decision-makers to justify investments that serve the public good, even when markets fall silent.

Cost-Benefit Analysis (CBA) traditionally relies on the assumption that all relevant costs and benefits of a project can be clearly defined and valued in monetary terms. However, as public policy increasingly tackles challenges involving health, climate change, disaster risk reduction, and social equity, the limitations of this assumption become evident. Many of the most important outcomes in these domains—such as lives saved, mental well-being, biodiversity preserved, or avoided catastrophe—have no direct market price. Yet they are precisely the benefits that often drive public investment.

This mismatch raises a fundamental challenge: how do we rigorously evaluate projects when the most valued impacts are intangible, uncertain, or distributed across future generations? The problem is not merely technical—it has direct implications for equity, accountability, and investment prioritisation. Without robust valuation, socially beneficial projects risk being sidelined in favour of those with easier-to-measure, market-driven returns.

In response, economists and policy analysts have developed a diverse set of methods to extract value from complex, non-market impacts. These include direct elicitation techniques (e.g. Contingent Valuation), indirect behavioural approaches (e.g. Revealed Preference and Averting Behaviour), and probabilistic tools (e.g. Monte Carlo simulation). Others, like Choice Modelling or Benefit Transfer, offer flexibility when original valuation is impractical or when time and data are constrained. These methods are not substitutes for each other, but components of an increasingly sophisticated toolkit for evidence-based decision-making.

Importantly, this chapter focuses on the application of these tools in public sector contexts, where the need to justify interventions with social, environmental, and intergenerational impacts is especially high. Whether evaluating health system reform, climate adaptation infrastructure, or digital public goods, the methods covered here enable analysts to bring previously immeasurable benefits into the economic appraisal process.

The sections that follow provide a deep dive into each method, including:

  • Its theoretical foundation

  • Practical steps for application

  • Use cases across sectors

  • Limitations that must be carefully managed.

In doing so, we aim to equip practitioners, policy analysts, and researchers with a rigorous but pragmatic guide to valuing what matters—especially when markets fall silent.

10.2. Contingent Valuation (CV): Capturing the value of intangibles through stated preferences

🔍 What It Is

Contingent Valuation (CV) is a stated preference method used to estimate the economic value of goods and services that are not traded in traditional markets. It directly asks individuals their willingness to pay (WTP) for specific benefits, or willingness to accept (WTA) compensation for losses. This makes it uniquely suited for valuing non-market impacts such as public safety, environmental protection, cultural assets, or health outcomes—benefits that are central to many public sector investments.

CV is typically implemented through carefully designed surveys that present respondents with a hypothetical scenario, such as a policy that reduces the risk of flooding or improves access to emergency health services. Respondents are then asked how much they would be willing to pay for the improvement, or the minimum amount they would accept to forgo it.

Surveys can use open-ended questions, payment cards, or dichotomous choice formats (yes/no to a specific amount). To be valid, CV requires realistic framing, a plausible payment mechanism (e.g. tax levy), and clearly described outcomes.

📌Example

A local government wants to assess public support for a new early warning system for bushfires. A CV survey presents households with a scenario describing the system, its risk-reduction benefits, and its cost (e.g., an annual household levy). Respondents indicate whether they would be willing to pay that amount. Aggregated responses provide an estimate of total societal WTP, which can be compared to project costs.

Use in Public Policy

Contingent Valuation has been widely used in areas such as:

  • Environmental valuation (e.g. wetlands, biodiversity, clean air)

  • Public health (e.g. risk reduction from vaccines, emergency preparedness)

  • Cultural heritage (e.g. museums, indigenous language preservation)

  • Emergency services (e.g. fire and flood protection, pandemic response).

Benefits of Contingent Valuation (CV) Methods

✨Captures Non-Market Values: CV is one of the few methods that can directly estimate the value of non-market goods, such as peace of mind, ecosystem services, or lives saved. These values are central to public investments in health, safety, and the environment, and are often overlooked by traditional CBA.

📌Example

Valuing a faster emergency response system includes intangible benefits like anxiety reduction or feelings of safety—measurable only through CV.

✨Flexible and Context-Specific: CV is adaptable to almost any policy domain, including disaster management, healthcare, climate adaptation, and public infrastructure. It can be tailored to local priorities and values, enabling context-sensitive decision-making.

✨ Reveals Distributional Preferences: By analysing responses by subgroup (e.g. income, region, vulnerability), CV provides insight into who values what and how much, aiding equity-sensitive policy design.

✨Useful Where Behavioural Data Don’t Exist: For emerging policies or one-off events (like disaster mitigation or new pandemic response systems), where historical data are limited, CV fills a vital gap by eliciting preferences directly.

Challenges of Contingent Valuation Method and How to Overcome Them

⚠️Challenge 1: Hypothetical Bias

Respondents may overstate or understate their WTP because the scenario is not real—they know they won’t actually have to pay. This introduces hypothetical bias.

✅ Solution:

  • Use cheap talk scripts to explicitly warn respondents against overstatement.

  • Include certainty scales where respondents rate how certain they are about their stated value.

  • Employ follow-up questions to probe the reasoning behind their answers.

⚠️Challenge 2: Strategic Bias

If respondents believe their answer will influence real policy or fees, they may strategically understate WTP to avoid higher costs, or overstate WTP to signal strong support.

✅ Solution:

  • Clarify that individual responses are anonymous and won’t affect personal costs.

  • Use dichotomous choice (yes/no) formats instead of open-ended amounts to reduce strategic gaming.

⚠️Challenge 3:  Framing Effects and Survey Design Sensitivity

The way the question is framed—including the payment vehicle, wording, or order of questions—can significantly alter results.

✅ Solution:

  • Conduct pilot testing to refine questions.

  • Use neutral, non-leading language.

  • Consider running split samples to test sensitivity to framing.

⚠️Challenge 4: WTP vs. WTA Disparity

Empirical studies show that WTA (willingness to accept) is usually higher than WTP, sometimes by a factor of 2 or more. This discrepancy reflects loss aversion, a cognitive bias where people value avoiding losses more than gaining equivalent benefits.

✅ Solution:

  • Use behaviourally adjusted utility models (see Chapter 9).

  • Include both WTP and WTA questions to triangulate values.

  • Use anchoring checks to identify whether stated values are overly influenced by suggested prices.

⚠️Challenge 5: Ethical and Emotional Complexity

Valuing life, safety, or health in monetary terms can feel morally uncomfortable for respondents. This can lead to protest answers or item non-response.

✅ Solution:

  • Emphasise that valuation does not imply commodification, but helps inform resource allocation.

  • Frame the question as part of trade-offs faced by policymakers.

  • Allow respondents to express non-monetary values, and consider these qualitatively in reporting.

📌Example: Case Study

Rolfe & Windle (2012) used CV to evaluate public support for bushfire mitigation strategies in Queensland. Residents were asked about their WTP for enhanced fire protection through prescribed burns and buffer zones. Results informed regional fire management plans and were integrated into state-level investment decisions.


10.3. Hedonic Pricing (HP): Valuing Emergency Services through Market Behaviour

🔍 What It Is

Revealed Preference (RP) methods infer economic value from actual consumer behaviour in related markets. Rather than asking individuals what they would be willing to pay, these methods observe the decisions people already make, assuming that preferences are revealed through their choices.

Hedonic Pricing is the most widely used RP technique in public sector evaluation. It estimates the value of specific non-market attributes (like proximity to fire stations or low crime rates) by analysing how they influence market prices, especially of housing.

Originally formalised by Rosen (1974), hedonic pricing decomposes the price of a good—usually a house—into the implicit value of its characteristics, including those related to public safety and emergency services.

A hedonic pricing model typically uses regression analysis to estimate how various attributes affect housing prices. These include:

  • Structural characteristics (e.g., size, number of bedrooms)

  • Neighbourhood factors (e.g., school quality, public amenities)

  • Safety attributes (e.g., crime rate, distance to fire/police stations).

💡Formula: Hedonic Pricing

The hedonic price function is usually specified as:

P = f(X₁, X₂, …, Xₙ)

Where P is the property price, and X₁ to Xₙ are the attributes of the property, including location-specific features like emergency services.

By isolating the effect of proximity to emergency services, we can estimate the monetary value people implicitly place on those services.

Key Benefits of Hedonic Pricing (Explained)

✨ Based on Real Behaviour, Not Hypotheticals

Unlike survey-based methods, hedonic pricing reflects actual purchasing decisions. This removes hypothetical bias and strategic misreporting.

📌Example

If homebuyers consistently pay more for homes near fire stations, this price premium reflects their revealed valuation of faster emergency response.

✨ Credible for Capital Infrastructure Valuation

Because housing markets are sensitive to safety, noise, and risk factors, hedonic pricing is a credible tool for valuing public safety investments, such as:

  • Fire and police station upgrades

  • Flood control infrastructure

  • Emergency access roads.

✨ Useful for Urban and Regional Planning

By mapping how public services affect property values, hedonic models support evidence-based urban development and equity analysis (e.g., identifying underserved areas with lower access to emergency services).

Challenges of Hedonic Pricing and How to Overcome Them

⚠️Challenge 1: Data Requirements

Hedonic pricing requires detailed property-level data, including location, characteristics, sale prices, and distances to service points. These data are not always accessible.

✅ Solution:

  • Collaborate with municipal agencies or private data providers (e.g. CoreLogic, real estate databases)

  • Use GIS software to match spatial data (e.g., distance to the nearest fire station) to sales records

⚠️Challenge 2: Omitted Variable Bias

If the model omits key housing attributes (e.g., school quality or noise levels), the estimated effect of emergency services may be confounded or biased.

✅ Solution:

  • Include a broad set of controls in the regression model

  • Use instrumental variables if endogeneity is suspected

  • Perform robustness checks with alternative model specifications

⚠️Challenge 3: Limited to Tangible, Perceived Effects

Hedonic pricing only captures the value people consciously include in their housing decisions. It may miss less visible or indirect benefits—like reduced anxiety from being near a hospital—or undervalue services that are not well known or understood.

✅ Solution:

  • Combine hedonic pricing with stated preference methods (e.g., CV or choice modelling) for a more complete valuation

  • Use focus groups or qualitative research to identify which attributes influence housing decisions but are under-acknowledged

⚠️Challenge 4: Market Constraints

In some markets, property prices may not fully adjust to reflect service quality (e.g., due to rent control, information asymmetry, or liquidity issues).

✅ Solution:

  • Validate findings with cross-city comparisons

  • Focus on open-market transactions in high-information settings

📌Example: Real-world Use Case

Simons, Quercia, and Maric (1998) studied the value impact of residential proximity to police and fire stations in several U.S. cities. Using hedonic regression, they found that homes within 1 km of a fire station had 5–8% higher sale prices compared to similar homes further away. The results were used to support additional station investments in underserved urban neighbourhoods.

10.3. Cost of Illness (COI)

🔍 What It Is

The Cost of Illness (COI) method is a widely recognised approach in health economics used to estimate the total economic burden of disease or injury. It calculates both direct costs (e.g., medical treatment, hospitalisation, pharmaceuticals) and indirect costs (e.g., lost productivity, premature death, long-term disability). In the context of public policy, COI is especially valuable for understanding the economic benefits of risk reduction and prevention—such as those delivered by emergency services, early warning systems, or public health interventions.

Originally developed by Dorothy P. Rice in 1967, COI has evolved into a robust framework applied across fields including epidemiology, environmental health, climate resilience, and occupational safety. It plays a pivotal role in cost-benefit analysis (CBA) by providing the “cost side” of avoided illness or injury, helping to justify investments in public infrastructure, health systems, and safety initiatives.

The COI method follows a structured approach to quantify the economic costs associated with a specific health condition or hazard.

💡 Formula

COI=Direct Costs (DC)+Indirect Costs (IC)+Intangible Costs (ITC)

The values can be calculated retrospectively (based on actual health outcomes) or projected using incidence/prevalence models and economic multipliers.

The typical steps include:

  1. Identify the Disease or Risk Event: Define the scope, such as heat-related illness, respiratory disease from pollution, or injury from flood events.

  2. Calculate Direct Costs: Gather data on medical treatment, hospital admissions, ambulance usage, medication, and long-term care. These costs are typically obtained from public health systems or insurance claims.

  3. Calculate Indirect Costs: Estimate lost income due to work absences, premature death, or long-term disability. This often includes the Value of Statistical Life (VSL) or productivity estimates based on wage data.

  4. Aggregate and Adjust: Combine all cost components across the affected population. Adjust for inflation, regional differences, or demographic profiles.

  5. Compare Pre- and Post-Intervention: To assess the benefits of an intervention (e.g. installing heat shelters), compare COI estimates before and after the policy implementation, or between intervention and control areas.

Benefits of the COI Method

✨ Captures the Full Economic Burden of Illness: COI goes beyond hospital bills—it includes the economic ripple effects of illness, disability, and death. This is crucial for policymakers aiming to understand the true cost of inaction or underinvestment in risk prevention.

✨ Supports Preventive Investment Logic: By demonstrating the economic savings from avoided illness, COI helps build a compelling case for proactive measures—from improving air quality to upgrading emergency response systems.

✨ Widely Recognised by Institutions: COI is a globally accepted method used by the World Health Organisation (WHO), OECD, and many national governments, making it credible and policy-friendly.

✨ Utilises Routine Administrative Data: Much of the data required for COI is already collected by health departments and insurance systems, reducing the burden of new data collection and enhancing feasibility.

✨ Supports Equity Analysis: By disaggregating costs across demographic groups, COI can reveal who bears the greatest burden of illness—enabling more equitable planning and resource allocation.

✨Links to Other Health Metrics: COI is often used alongside QALYs and DALYs, making it adaptable to mixed-method CBAs and long-term health planning.

✨ Applicable Across Sectors: Though rooted in healthcare, COI is increasingly used in climate resilience, disaster preparedness, urban planning, and transport safety, making it a flexible valuation tool.

 Challenges of COI and How to Overcome Them

⚠️Challenge 1: Limited Scope of Intangibles

COI may underestimate the full impact of illness by excluding non-economic costs like emotional distress or loss of quality of life.

✅ Solution: Pair COI with stated preference methods (e.g., WTP surveys) or health utility weights (e.g., QALYs) to incorporate intangible impacts.

⚠️Challenge 2: Data Availability and Quality

Some COI components—especially indirect costs like informal caregiving—can be difficult to quantify accurately.

✅ Solution: Use national health accounts, employer surveys, or proxy measures. Expert panels (via the Delphi method) can also estimate where data is sparse.

⚠️ Challenge 3: Static Snapshot vs. Dynamic Change

COI traditionally offers a static view, which may not reflect changing risk patterns, such as increasing heatwaves or evolving disease burdens.

✅ Solution: Apply Dynamic CBA or model disease progression over time using epidemiological forecasting and time series data.

⚠️ Challenge 4: Attribution of Benefits

When evaluating interventions, it can be challenging to isolate how much of the reduced illness burden is due to the intervention itself.

✅ Solution: Use before-after comparisons, control groups, or statistical matching to improve attribution in the analysis.

⚠️Challenge 5: Monetisation of Life and Disability

Using values like the VSL can raise ethical concerns and may vary significantly across regions or income groups.

✅ Solution: Clearly communicate assumptions, use sensitivity analysis with multiple valuation benchmarks, and contextualise the ethical rationale.

📌Example

Heatwave Emergency Response
Emergency services implemented a public heat alert system with improved EMS deployment and community cooling shelters. A COI analysis estimated:

  • $2.1 million saved in reduced hospital admissions

  • $4.7 million in avoided productivity losses

  • 15% reduction in emergency department visits.

These findings were used to justify the permanent expansion of the heat alert system and were integrated into future CBAs on urban resilience.

10.4. Benefit Transfer

🔍 What It Is

Benefit Transfer is a practical method used in Cost-Benefit Analysis to estimate the economic value of non-market goods or services in a new context by leveraging results from previous studies. It is particularly useful when time, budget, or logistical constraints prevent the collection of original valuation data. In public policy contexts—especially for large-scale infrastructure, environmental, or public safety projects—benefit transfer allows analysts to apply existing valuation estimates to similar regions or interventions.

Why it is used in CBA

Benefit Transfer enables policymakers to evaluate social, environmental, and health-related benefits without the expense or delay of primary data collection. It is especially relevant for non-market outcomes such as reduced mortality, improved ecosystem services, or enhanced public safety where direct market valuations don’t exist. For example, when assessing the value of early warning systems for floods, rather than running a full contingent valuation survey in each region, analysts can apply results from a previously studied, comparable location.

How It Works

Benefit transfer can represent a viable substitute for applying one of the principal stated or revealed preference research approaches outlined in this chapter. The approach uses data from other existing research employing alternative approaches instead of obtaining primary data. To conduct a benefit transfer, analysts must first locate relevant studies through a comprehensive literature search, including published work, grey literature, and expert consultation. Next, these studies should be critically reviewed for both methodological quality and relevance to the current policy context, as the reliability of the transfer depends heavily on the quality and applicability of the source data. The next step involves the actual transfer of values.  In the transfer stage of benefit transfer, analysts apply value estimates from existing studies to a new policy context using one of four methods: point estimate (or unit value transfer), benefit function (or function transfer), and meta-analysis, or Bayesian approaches.

1. Unit Value Transfer

The most common method, involving direct transfer of WTP (willingness to pay) or benefit estimates per unit (e.g., per household).

💡 Formula

BT=UV×(YtYs)ϵBT = UV \times \left( \frac{Y_t}{Y_s} \right)^\epsilon

Where:

  • BT = Benefit transferred to the target context
  • UV = Unit Value from source study (e.g. WTP per household/year)
  • Yt​ = Per capita income at the target site
  • Ys= Per capita income at the source site
  • ϵ = Income elasticity of WTP (typically 0.3–1.0)

This equation adjusts for income differences between the original and target populations, assuming WTP changes proportionally with income levels.

2. Function Transfer

Instead of transferring a single value, the functional relationship between benefits and explanatory variables (e.g., income, population density, hazard exposure) from the source study is applied to the target setting. This approach is more flexible but requires access to the original benefit function and more contextual data.

💡 Formula: Example Structure

WTPt=α+β1⋅ln⁡(Yt)+β2⋅Rt+β3⋅AtWTP_t = \alpha + \beta_1 \cdot \ln(Y_t) + \beta_2 \cdot R_t + \beta_3 \cdot A_tWhere:

    • WTPt​ = Estimated willingness to pay in the target site
    • Yt​ = Per capita income in the target site
    • Rt= Risk level or hazard exposure
    • At = Other attributes (e.g., age, awareness, region type)
    • α,β1,β2,β3= Coefficients from the original benefit function
Applications in CBA

  • Environmental Projects: Estimating the value of wetland preservation, clean air, or ecosystem services by transferring values from similar biomes.

  • Disaster Mitigation: Applying flood mitigation benefit estimates from one country to another with a similar climate and urban structure.

  • Public Health: Transferring the value of reducing respiratory illness from urban pollution between comparable urban centres.

📌Example

If a study in the Netherlands found a WTP of €150 per household per year to reduce flood risk, and the target region is a UK coastal town with higher per capita income, the adjusted WTP can be calculated using the unit transfer formula to support flood infrastructure investment decisions.

3. Meta-Analytic Function Transfer

A more sophisticated version of function transfer, where values are predicted using meta-regression models based on multiple source studies. This allows broader generalisability.

💡 Formula

WTP^t=α+∑i=1nβi⋅Xi,t\hat{WTP}_t = \alpha + \sum_{i=1}^{n} \beta_i \cdot X_{i,t}

Where:

  • WTP^t= Predicted WTP at the target site
  • Xi= Site- or population-specific variables (e.g., income, environmental quality, policy type)
  • βi​ = Estimated meta-regression coefficients from pooled literature
  • n = Number of explanatory variables

Benefits

✨ Cost-effective: Avoids the need for time-consuming and expensive primary valuation studies.

✨ Timely: Enables faster decision-making in policy and emergency planning contexts.

✨ Scalable: Can be used for regional or national assessments by aggregating local values.

✨Useful in Emergency Contexts: Where rapid assessments are needed (e.g., post-disaster investments), benefit transfer allows practical estimation of social value.

Challenges of Benefit Transfer and How to Overcome Them

 Challenge Description Mitigation Strategy
Context Mismatch Source and target sites may differ in socio-economic, environmental, or cultural characteristics. Select source studies that closely resemble the target context in geography, income, demographics, or risk exposure.
Income Sensitivity WTP values often vary with income. Failing to adjust may lead to under- or over-valuation. Apply the unit value transfer formula to scale WTP using income elasticity. Use localised income data.
Data Transparency Original studies may lack clear documentation, making adjustment difficult. Prioritise well-documented, peer-reviewed studies with transparent methods and valuation context.
Outdated Values Valuations may not reflect current economic or social conditions. Adjust for inflation, exchange rates, or other temporal factors using official indices.
Aggregation Errors Scaling up individual WTP to population-level benefits may overstate impact. Use conservative assumptions, apply appropriate scaling methods, and conduct sensitivity analyses.

10.5. Choice Modelling (CM)

🔍 What It Is

Choice Modelling (CM), also known as Discrete Choice Experiments (DCE), is a stated preference technique that estimates the value individuals place on the attributes of a good or service by observing the trade-offs they are willing to make in hypothetical scenarios. Unlike contingent valuation, which focuses on a single overall WTP figure, CM reveals the relative importance of multiple features simultaneously.

CM is grounded in random utility theory, where each individual is assumed to choose the option that provides the highest utility. This method is highly effective for valuing non-market goods with multiple attributes, such as safety, response time, quality of service, and cost.

How It Works

Participants are presented with a series of hypothetical scenarios (choice sets), each composed of two or more alternatives. Each alternative is described by several attributes with varying levels (e.g., response time: 5 vs. 10 minutes; cost: $10 vs. $15). Respondents are asked to choose their preferred option in each set.

These choices are then analysed using econometric models (typically multinomial logit or mixed logit) to estimate the marginal willingness to pay (MWTP) for each attribute.

Utility Model Formula

The individual utility derives from choosing an option, and it is typically modelled as:

💡 Formula

Uij=Vij+εijU_{ij} = V_{ij} + \varepsilon_{ij}Where:

  • Uij= total utility of alternative j for individual i
  • Vij= systematic (observable) component of utility
  • εij= random (unobservable) component

The deterministic component, and:

Vij=β1x1ij+β2x2ij+⋯+βkxkijV_{ij} = \beta_1 x_{1ij} + \beta_2 x_{2ij} + \cdots + \beta_k x_{kij}Where:

  • xkij: Attribute k level of alternative j for respondent i
  • βk: Coefficient showing the weight or marginal utility of that attribute

The marginal willingness to pay (MWTP) for attribute is calculated as:

MWTPk=−βkβprice

Applications in Policy and Investment Appraisal

Choice Modelling is widely used in transport, health, environment, and public infrastructure appraisal, especially where:

  • Multiple non-market benefits exist (e.g. clean air, risk reduction, convenience)

  • Stakeholders value features differently

  • Policymakers want to test different investment scenarios before implementation

📌Example

Suppose a health agency wants to design an improved ambulance service. Using CM, the agency can assess public preferences over response times, staffing quality, and out-of-pocket costs. Participants may choose between options like:

  • Option A: 10-minute response, standard staff, $5 per month

  • Option B: 5-minute response, advanced staff, $15 per month

By analysing choice patterns, policymakers can derive MWTP values and simulate optimal service configurations.

Benefits of Choice Modelling in Economic Evaluation

✨ Attribute-Level Valuation: Unlike simpler stated preference methods, CM provides separate valuation for each project attribute, such as improved service speed, reliability, or safety. This enables more detailed, transparent CBAs where each component can be justified.

For example, in evaluating a flood warning system, CM can reveal that people value timeliness more than geographic coverage — a crucial insight for cost-effective design.

✨Captures Trade-Offs Reflecting Real-World Behaviour: CM mimics actual decision-making, where individuals weigh multiple factors simultaneously. By forcing respondents to choose under constraints (e.g., cost vs service level), it provides more realistic data about stakeholder preferences.

This is particularly useful in health and safety, where projects often include competing priorities (e.g., cost vs. mortality risk reduction).

✨Supports Scenario Testing: CM allows analysts to simulate different investment configurations and test how changes in project design affect utility and public acceptance. This supports evidence-based planning and adaptive policymaking.

E.g., what happens to public preference if cost increases but safety also improves? CM can quantify this directly.

✨Applicable Across Sectors: Choice Modelling is highly adaptable; it has been used in transport, health care, emergency services, education, and environmental management, making it a powerful multi-sector tool for public investment analysis.

📌Example

In a study on ambulance services in rural Australia, CM was used to evaluate preferences for response times, treatment quality, and service coverage. It found that while fast response was highly valued, many residents were willing to accept slightly longer delays for higher-skilled staff or expanded geographic coverage, helping policymakers optimise investment distribution.

Challenges and How to Address Them

⚠️ Challenge 1: Complex Survey Design

Designing realistic and cognitively manageable choice sets is challenging. If scenarios are too abstract or difficult to understand, data quality may suffer.

✅ Solution: Pilot test extensively with diverse users, use visuals or simplified language, and include practice questions. Always pretest for cognitive load.

⚠️Challenge 2: Econometric Complexity

Analysing CM data requires advanced statistical modelling (e.g. mixed logit, latent class analysis), which may be beyond the capacity of many organisations.

✅ Solution: Partner with academic or technical experts, or use user-friendly CM platforms (e.g., Ngene, Sawtooth, R packages like ‘mlogit’) that offer guided modelling workflows.

⚠️ Challenge 3: Hypothetical Bias

Although CM mimics real-world trade-offs better than other methods, it’s still based on hypothetical scenarios, which may diverge from actual behaviour.

✅ Solution: Enhance realism by grounding scenarios in policy-relevant details, framing trade-offs clearly, and calibrating models with real-world data when possible.

⚠️Challenge 4: Sampling and Representation

Accurate valuation depends on representative respondent samples, especially in diverse or vulnerable populations (e.g., rural, low-income, or Indigenous communities).

✅ Solution: Use stratified sampling and adjust weights to reflect demographic characteristics. Incorporate culturally adapted survey designs when needed.

10.6. The Delphi Method

🔍 What It Is

The Delphi Method is a structured, iterative process for gathering expert opinion through multiple rounds of questionnaires. It is especially valuable in complex decision-making environments where empirical data is limited or uncertain, such as estimating the social or intangible benefits of public investments. The method facilitates expert consensus by allowing participants to revise their views in light of group feedback—thereby increasing the reliability of subjective estimates.

Originally developed during the Cold War for forecasting technological developments, the Delphi Method has evolved into a respected technique in economics, healthcare, disaster risk management, and strategic policy planning (Linstone & Turoff, 2002).

The Delphi Method unfolds through a series of structured steps:

  1. Expert Panel Selection: A diverse group of subject matter experts is recruited. For emergency or public health CBAs, this could include economists, public health officials, emergency planners, and community leaders.

  2. Round 1 – Open-Ended Survey: Experts are asked to provide qualitative estimates on hard-to-measure variables (e.g., the value of a life saved, improved response times, or psychological well-being). Responses are anonymised to reduce social bias.

  3. Synthesis & Feedback: The facilitator aggregates and summarises responses. Experts review the summary to identify areas of agreement and disagreement.

  4. Round 2+ – Revision of Judgements: Experts revise their answers based on group feedback. This process continues until convergence or acceptable consensus is reached—often within 2–3 rounds.

  5. Final Aggregation: The refined outputs (e.g., median values or confidence intervals) can now be incorporated into a CBA model.

Applications in CBA

The Delphi Method is especially powerful in contexts where quantitative data are incomplete, sensitive, or not yet observable. This includes:

  • Disaster Preparedness Projects: Estimating the Value of Statistical Life (VSL) for communities vulnerable to floods or bushfires where no local empirical studies exist.

  • Emergency Communication Upgrades: Assessing the indirect social benefits of improved coordination systems that reduce response times but don’t yet have operational data.

  • Health Infrastructure: Evaluating the long-term value of mental health outreach programs in underserved areas, where effects are diffuse and under-reported.

Benefits of the Delphi Method

✨ Captures Informed Judgment
Allows for expert input in situations where no direct data exists—such as valuing intangible outcomes like social cohesion or mental health improvement.

✨ Mitigates Groupthink and Bias
Anonymity and iteration help reduce dominant voices or political influence, ensuring balanced perspectives.

✨ Produces Structured, Defensible Estimates
The iterative nature of the process yields a transparent and methodologically sound estimate, improving legitimacy and credibility.

Challenges of Delphi Method and How to Address Them

⚠️Challenge 1: Time and Resource Intensive

Delphi rounds can take weeks or months to complete, depending on expert availability.

✅ Solution: Use digital platforms to streamline data collection and automate feedback loops.

⚠️ Challenge 2: Risk of Expert Dominance or Misalignment

If the panel lacks disciplinary diversity or local knowledge, the results may be skewed.

✅ Solution: Ensure inclusive panel composition and apply local weighting factors in final estimates.

⚠️ Challenge 3: Convergence May Be Artificial

Consensus may arise not from shared understanding but from social pressure or fatigue.

✅ Solution: Track the variance of responses, not just the mean, and apply sensitivity analysis to capture uncertainty.

Common Use in CBA: Valuation of Statistical Life (VSL)

One of the most frequent uses of the Delphi Method in public economics is estimating the Value of a Statistical Life (VSL)—an essential input for CBAs in emergency, transport, or health sectors.

💡Formula: VSL Delphi estimate

VSLDelphi=Median(V1,V2,...,Vn)\text{VSL}_{\text{Delphi}} = \text{Median}(V_1, V_2, …, V_n)Where:

  • Vi = estimate provided by expert i
  • n= number of experts

These values are then integrated into risk-reduction benefit calculations (e.g., fatalities avoided × VSL).

📌Example

During the COVID-19 pandemic, several national governments used Delphi panels to estimate the social and economic value of lockdown interventions, particularly where hard evidence was lacking on indirect effects (e.g., mental health, productivity losses). In Australia, Delphi methods were also applied in regional planning for bushfire risk mitigation, where expert consensus helped determine priority investments in early warning systems and evacuation infrastructure.

Benefits of Delphi Method

✨Applicable in Data-Scarce Contexts:

  • Especially valuable for intangible or non-market valuations (e.g., human life, mental health, disaster trauma).

  • Useful in developing contexts or forward-looking policies where no baseline data exist.

✨Cross-Disciplinary Validity:

  • Synthesises knowledge from economics, epidemiology, engineering, and climate science.

  • Supports more nuanced cost-benefit frameworks.

✨Reduces Individual Bias:

  • Structured anonymity reduces groupthink and social pressure.

  • Iterative feedback refines judgment.

✨Flexible Across Sectors:

  • Useful in healthcare planning, infrastructure resilience, education, and digital governance.

Challenges

⚠️Challenge 1: Resource Intensive

Delphi requires coordination across multiple experts and rounds of engagement.

✅ Solution: Limit to 2–3 critical variables or use digital tools to streamline participation.

⚠️Challenge 2: Panel Selection Bias

Results may be skewed by the expertise or homogeneity of the panel.

✅ Solution: Diversify the panel and pre-screen for experience and regional/contextual knowledge.

⚠️Challenge 3: Lack of Precision

The output is not a statistically sampled value, but rather an expert-informed approximation.

✅ Solution: Use Delphi outputs to complement, not replace, empirical models—ideally triangulated with scenario or sensitivity analysis.

10.7. Averting Behaviour Method (ABM)

🔍 What It Is

The Averting Behaviour Method (ABM) is an economic valuation approach that estimates how much individuals are willing to pay to avoid exposure to risks or negative outcomes by observing the real costs they incur to protect themselves. These actions—such as installing air purifiers, buying flood insurance, or purchasing fire-retardant materials—reflect individuals’ implicit valuation of risk reduction.

According to Cropper and Oates (1992), ABM assumes that expenditures made by individuals to reduce harm are proxies for the value they assign to avoiding those risks. This method is particularly useful when market prices for a good or service do not exist, as is often the case with public safety, environmental risks, or health hazards.

How It Works:

  1. Risk Identification: A specific environmental or public health risk (e.g., bushfire exposure, air pollution, flood hazard) is identified.

  2. Observation of Behaviour: Researchers measure how individuals alter their behaviour or spend money to avoid this risk—such as using bottled water in contaminated areas or retrofitting homes in fire-prone zones.

  3. Calculation of Avoidance Costs: The costs of these actions are used to estimate the willingness to pay (WTP) for risk reduction. This assumes rational behaviour—individuals act in ways that maximise their perceived utility.

💡Formula: ABM

WTPABM=ΔCΔRWTP_{ABM} = \frac{\Delta C}{\Delta R}

Where:

  • WTPABM​ = Implied willingness to pay for risk reduction
  • ΔC = Change in cost from averting behaviour (e.g., installing filters, buying insurance)
  • ΔR = Change in risk exposure resulting from the behaviour

The Averting Behaviour Method offers a pragmatic way to estimate the value of public interventions by revealing how much people already pay to protect themselves. While it doesn’t capture all social benefits, its grounding in real expenditures makes it a valuable tool in emergency preparedness, environmental health, and infrastructure resilience appraisals.

📌Example

For example, if households spend $200 to reduce heat exposure and that action lowers heat stroke risk by 10%, then:

WTPABM=2000.10=2000WTP_{ABM} = \frac{200}{0.10} = 2000

This implies a $2000 value placed on avoiding the full risk.

Applications in Practice:

1. Bushfire Risk: Homeowners in bushfire-prone areas often invest in fire-resistant materials, maintain defensible space, and install ember screens. The costs of these efforts reflect how much they value protection from fire damage. These values can be aggregated to inform public investment in fire prevention programs like controlled burns or water bombing capacity.

2. Urban Heat Risk: During extreme heat events, households may purchase air conditioners or modify windows and roofing to reduce indoor temperatures. ABM can estimate the economic value of heat mitigation based on these expenditures.

3. Flood Protection: Residents in flood-prone areas may raise their homes, install sump pumps, or buy sandbags. These actions provide insight into the perceived risk and are useful in valuing government-led flood control programs.

4. Health Risks (Air Pollution): Purchasing masks, air purifiers, or relocating to areas with better air quality offers a clear monetary expression of WTP to avoid respiratory harm—essential in evaluating the social benefit of clean air regulation.

Benefits of ABM

✨Grounded in Actual Behaviour: Relies on observed expenditures, which reduces the risk of hypothetical bias found in stated preference methods like surveys.

✨Captures Individual Risk Perception: Provides real-world insight into how different people value risk—allowing for differentiated policy responses.

✨Applies to Diverse Risk Contexts: Can be used across environmental, health, and safety domains, especially when preventive actions are common.

Challenges and How to Overcome Them:

⚠️Challenge 1: Not all individuals take averting action—may underestimate total social value.

✅ Solution: Combine ABM with stated preference methods (e.g., CV or CM) to capture broader population values.

⚠️Challenge 2: Varied perception of risk affects expenditure patterns.

✅ Solution: Use stratified sampling and control for socioeconomic factors to better understand heterogeneity in behaviour.

⚠️Challenge 3: May miss non-monetary or passive avoidance behaviours.

✅ Solution: Supplement ABM with qualitative insights and indirect valuation techniques (e.g., Delphi or benefit transfer) to capture a fuller picture.

⚠️Challenge 4: Only captures direct costs—not emotional, social, or long-term impacts.

✅ Solution: Use ABM in combination with COI or Monte Carlo simulation to integrate intangible and long-term effects.

10.8. Travel Cost Method (TCM)

🔍 What It Is

The Travel Cost Method (TCM) is a revealed preference technique used to estimate the economic value of non-market goods—especially natural, recreational, and public safety sites—based on the time and money people spend to access them. The idea is simple: the more people are willing to spend to visit a location, the more they implicitly value it.

TCM is particularly useful in valuing services or spaces that don’t have a market price, such as national parks, coastal evacuation areas, public health zones, or recreational trails that offer cooling or flood refuge. By turning behavioural data into economic valuation, it transforms public usage into policy-relevant benefit figures.

Why It Matters

Governments often need to justify investments in assets like coastal parks, emergency infrastructure, or public safety areas, where no direct revenue is generated. TCM fills this gap by using real-world travel behaviour to infer the willingness to pay for access—thus assigning a value to benefits that would otherwise go unmeasured.

It is especially valuable for:

  • Valuing environmental and safety infrastructure (e.g., flood levees with public access).

  • Informing access equity across urban and regional populations.

  • Supporting budget allocations where usage rates are a strong proxy for value.

How It Works

Travel cost models often start by examining visits to a single recreational site. These models rely on user surveys that capture where visitors are coming from, allowing the data to be sorted by travel distance. Typically, the farther people have to travel, the fewer trips they make—indicating an inverse relationship between distance and visitation. By converting distance into a travel cost (accounting for vehicle expenses, time value, etc.), analysts can construct a demand curve that reflects how the number of trips varies with the cost of reaching the site. The basic assumption is that the travel cost acts as a “price” people pay to use the site.

There are two common approaches:

  1. Single-site TCM

    • Focuses on one site and examines how visitation varies with travel cost across different users.

    • A demand curve is estimated, and the consumer surplus (area under the curve) is used as a measure of total benefit.

  2. Zonal or Multi-site TCM

    • Divides the region into zones (e.g., by distance from the site) and estimates how visit rates decrease as travel costs increase.

    • Adjustments are made for population, access to substitutes, and income.

Data Required:

  • Number of visits per person or household.

  • Distance from home to site.

  • Travel cost (fuel, tolls, parking, etc.).

  • Time cost (e.g., opportunity cost of travel time).

  • Demographics: income, age, household size.

Analytical Steps:

  1. Estimate travel cost per visitor.

  2. Model visitation as a function of travel cost (e.g., using regression analysis).

  3. Derive a demand curve.

  4. Calculate the area under the demand curve (consumer surplus) to estimate total benefit.

This demand curve reflects how sensitive people are to travel cost—thus revealing the value they place on the site.

💡 Formula

The basic functional form of TCM is:

 

Vi=f(Ci,Xi) 

Where:

  • Vi= number of visits by individual or household i
  • Ci= travel cost (transport + time)
  • Xi= individual characteristics (income, age, etc.)

The estimated demand function gives us a curve of price (cost) versus quantity (visits).

The consumer surplus (CS) is:

 

CS=∫0QP(Q)dQ 

Where:

  • P(Q) = inverse demand function (price as a function of quantity)
  • Q = quantity of visits

This CS is then aggregated across all users to calculate the total social value of the site.

📌Example: Enhancing Flood Zones

Scenario: A regional government is assessing whether to invest in enhancing a flood refuge zone that provides safety, shade, and water access during high-risk events (e.g., heatwaves, floods). As part of the CBA, analysts use the Travel Cost Method to estimate the value residents place on access to the site.

Data Collected:

  • Number of visits to the site from different regions.

  • Travel distance and associated costs (fuel, public transport, time).

  • Demographic details like income, age, and household size.

Analysis: A regression is run to estimate the relationship between travel cost and visitation frequency. Using the derived demand function, the analyst calculates the average consumer surplus per visit and multiplies it by the number of users to estimate annual benefit.

Benefits of Travel Cost Method

✨Based on actual behaviour: Since TCM uses observed data on travel patterns, it reduces hypothetical bias common in stated preference methods like contingent valuation.

✨Valuable for physical public goods: Particularly effective for estimating benefits from parks, recreational areas, disaster evacuation zones, or accessible emergency hubs.

✨Supports equitable investment planning: Disaggregating by location or income reveals which groups bear more travel burden, helping target improvements where they matter most.

✨Easily integrated with GIS tools: Geographic Information Systems (GIS) can enhance spatial accuracy of travel estimates, especially in infrastructure planning.

Challenges and How to Overcome Them:

⚠️ Challenge 1: Only captures use value
Non-users (e.g., people who value a site but do not travel to it) are excluded.

✅ Solution: Combine TCM with contingent valuation to estimate non-use value.

⚠️ Challenge 2: Assumes travel cost = WTP
In reality, people may enjoy the travel itself (e.g., scenic drives), inflating the estimated value.

✅ Solution: Adjust for travel enjoyment or include purpose-of-trip variables.

⚠️ Challenge 3: Data intensive
Requires large, reliable datasets on travel frequency, costs, and demographics.

✅ Solution: Use household travel surveys, or digital mobility data (e.g., from mobile apps or transit authorities).

⚠️ Challenge 4: Substitute sites complicate estimation
If multiple sites offer similar services, it’s harder to isolate demand for one site.

✅ Solution: Use multi-site TCM or control for substitute availability in regression models.

10.9. Life Satisfaction / Subjective Wellbeing (SWB) Valuation

🔍 What It Is

Subjective Wellbeing (SWB) Valuation is an emerging method in economic appraisal that uses self-reported measures of life satisfaction, happiness, or mental health to estimate the value of non-market goods and services. Instead of inferring value from behaviour (as in revealed preference) or hypothetical choices (as in stated preference), SWB directly measures how different policies or conditions affect people’s overall wellbeing.

The central idea is to estimate the change in life satisfaction associated with a policy or environmental change and then translate that change into monetary terms by comparing it to the effect of income on wellbeing. This allows analysts to assign economic value to intangible or hard-to-measure outcomes, such as mental health programs, social inclusion initiatives, or access to green spaces.

💡 Formula

The money-equivalent value of a wellbeing improvement is calculated by:

Monetary Value of Policy Impact=Effect of Policy on SWBEffect of Income on SWB×Average Income\text{Monetary Value of Policy Impact} = \frac{\text{Effect of Policy on SWB}}{\text{Effect of Income on SWB}} \times \text{Average Income}

Where:

  • Effect of Policy on SWB = Change in subjective wellbeing score (e.g., 0.2 increase on a 1–10 scale)

  • Effect of Income on SWB = The estimated marginal effect of income on wellbeing (from regression models)

  • Average Income = Mean income level of the population

Applications in Policy and CBA:

  • Mental health programs: Valuing improvements in emotional wellbeing or reductions in anxiety/depression.

  • Urban green spaces: Estimating the wellbeing value of access to parks or natural areas.

  • Social cohesion initiatives: Measuring the value of community-building policies or interventions.

Benefits of SWB Valuation:

✨Captures Emotional and Social Outcomes: Goes beyond financial metrics to value mental health, social connection, or environmental quality.

✨Applicable Across Sectors: Useful for health, environment, urban planning, and social policy domains where psychosocial benefits matter.

✨Aligns with Wellbeing Frameworks: Supports government wellbeing initiatives (e.g., New Zealand’s Wellbeing Budget) by integrating subjective welfare measures into CBAs.

Challenges of SWB Valuation and How to Overcome Them:

⚠️Challenge 1: Data Availability

Requires large-scale, representative SWB surveys linked to policy variables and income.

✅ Solution: Use national wellbeing datasets (e.g., OECD, Gallup, UK ONS) or commission targeted surveys.

⚠️Challenge 2: Causality Issues

Correlation between SWB and policy factors may not imply causation.

✅ Solution: Use quasi-experimental designs (e.g., difference-in-differences) or instrumental variables to strengthen causal claims.

⚠️Challenge 3: Variability in SWB Measures

Different scales (e.g., life satisfaction vs. happiness) can produce different results.

✅ Solution: Standardise scales and report sensitivity analyses across different SWB measures.

10.10. Multi-Attribute Utility Theory (MAUT)

🔍 What It Is

Multi-Attribute Utility Theory (MAUT) is a structured decision-making approach that evaluates options based on multiple criteria, formalising trade-offs between competing objectives. Similar to Multi-Criteria Decision Analysis (MCDA), MAUT uses utility functions to quantify preferences across different attributes (e.g., cost, risk, equity), assigning weights based on stakeholder values.

Like Multi-Criteria Decision Analysis (MCDA), MAUT considers diverse project attributes (e.g., cost, risk, equity, environmental impact), but it goes further by quantifying how decision-makers value trade-offs among these attributes—especially under uncertainty. MAUT assigns utility scores to different levels of each attribute, reflecting how desirable or beneficial each outcome is to stakeholders. These utilities are then combined using weighted aggregation, but unlike MCDA, MAUT incorporates risk preferences (e.g., risk aversion or risk-seeking behaviour) into the calculation. This allows for a more realistic evaluation of projects where outcomes are uncertain and where stakeholders have different attitudes toward risk.

By using utility functions (which may be linear or non-linear), MAUT provides a structured, mathematically robust approach to decision-making, ensuring that both preferences and uncertainties are reflected in the final evaluation.

💡 Formula

For each alternative, total utility is calculated as:

Ui=∑j=1nwj⋅uj(xij)U_i = \sum_{j=1}^{n} w_j \cdot u_j(x_{ij})Where:

  • Ui = Total utility for alternative i
  • wj= Weight for attribute j
  • uj(xij)= Utility function for attribute j evaluated at level xij

Utility functions can reflect diminishing returns, risk aversion, or thresholds—capturing preferences more accurately than simple linear scores.

📌Example: Healthcare Infrastructure Planning

Scenario:
A government must choose between three healthcare infrastructure projects:

  1. Upgrade urban hospitals (high cost, high capacity impact).

  2. Expand rural clinics (moderate cost, improves access equity).

  3. Invest in telemedicine platforms (lower cost, scalable, uncertain adoption).

Each option is evaluated across three key criteria:

  • Cost (minimise).

  • Access equity (maximise).

  • Technological risk (minimise).

Step 1: Define Utility Functions for Each Criterion
For cost, the utility decreases non-linearly (i.e., small cost increases are acceptable, but large costs sharply reduce utility).
For equity, the utility increases linearly with service coverage in underserved areas.
For technological risk, the utility decreases with higher risk but reflects that some risk is tolerable if benefits are large.

Step 2: Assign Weights (w₁, w₂, w₃)
Stakeholders prioritise access equity (50%), followed by cost (30%), and risk (20%).

Step 3: Calculate Utilities for Each Option

 

Option Cost Utility Equity Utility Risk Utility Total Utility Calculation Total Utility
Urban Hospitals 0.4 0.5 0.9 (0.3 × 0.4) + (0.5 × 0.5) + (0.2 × 0.9) = 0.12 + 0.25 + 0.18 0.55
Rural Clinics 0.7 0.9 0.7 (0.3 × 0.7) + (0.5 × 0.9) + (0.2 × 0.7) = 0.21 + 0.45 + 0.14 0.80
Telemedicine Platforms 0.9 0.7 0.5 (0.3 × 0.9) + (0.5 × 0.7) + (0.2 × 0.5) = 0.27 + 0.35 + 0.10 0.72

Result:
Rural Clinics score highest on total utility (0.80) due to their strong performance on equity and moderate risk profile, even though they are not the lowest-cost option.

Why MAUT Matters in Public Decision-Making

  1. Captures Risk Preferences Explicitly: Unlike MCDA, MAUT accommodates risk aversion or tolerance directly within its utility functions, which is crucial for uncertain, high-stakes investments (e.g., disaster resilience, healthcare, climate adaptation).

  2. Handles Complex Trade-offs Transparently: MAUT formalises how decision-makers weigh competing objectives (e.g., cost vs. risk vs. equity) in a consistent, quantitative framework.

  3. Supports Adaptive and Robust Decisions: The use of utility curves allows for dynamic adjustments based on shifting stakeholder priorities or emerging risks.

  4. Encourages Participatory Decision-Making: Stakeholders are engaged in defining criteria weights and utility curves, ensuring that diverse values and preferences are reflected in the analysis.

Benefits of MAUT

✨Incorporates Risk Preferences: Utility functions allow for modelling non-linear preferences (e.g., risk aversion) directly.

✨Handles Complex Trade-offs: Suitable for multi-dimensional decisions with competing goals (e.g., cost vs. equity).

✨Mathematically Robust: Provides a formalised, consistent framework grounded in utility theory.

Challenges of MAUT and How to Address Them

⚠️Challenge 1: Data and Elicitation Intensive

Requires detailed stakeholder input to define utility functions and weights.

✅ Solution: Use structured elicitation techniques (e.g., expert panels, stakeholder workshops) and simplify functions where feasible.

⚠️Challenge 2: Technical Complexity

May be hard for non-experts to understand.

✅ Solution: Use visual aids (e.g., utility curves) and simplify outputs for broader communication.

10.11. Dynamic Adaptive Policy Pathways (DAPP)

🔍 What It Is

Dynamic Adaptive Policy Pathways (DAPP) is a strategic planning approach designed for long-term, uncertain futures, particularly where climate change, technology, or social conditions evolve unpredictably. Rather than selecting a single static policy, DAPP develops flexible sequences of options, allowing decision-makers to adapt as conditions change.

DAPP maps out flexible policy pathways that allow for adjustment over time, using specific “signposts” or “triggers” to guide decision-making. A signpost is a monitored indicator that signals whether current conditions are evolving as expected, while a trigger is a predetermined threshold or event that prompts a policy action or shift. For example, in coastal defence planning, a signpost could be the rate of sea-level rise or frequency of storm surges, while a trigger might be sea levels exceeding 0.5 meters above a baseline. When the trigger is reached, the plan adapts—such as expanding flood barriers, relocating infrastructure, or implementing new technologies.

By embedding these adaptive mechanisms, DAPP ensures that policy pathways remain robust under a wide range of future conditions. Instead of committing to a fixed, long-term strategy based on uncertain projections, decision-makers can implement actions incrementally, pivoting when signposts indicate emerging risks or opportunities. This approach reduces the likelihood of over-investing too early or under-preparing for future changes, making it particularly valuable for climate adaptation, energy transition, and infrastructure resilience, where uncertainty is high and long-term impacts are profound.

This dynamic structure helps align public investment with evolving risks, societal values, and technological advancements, ensuring that resources are allocated efficiently while maintaining flexibility to respond to the unknown.

Applications in Policy and CBA:

  • Climate adaptation: Coastal protection, flood defence systems, drought management.

  • Technology infrastructure: Planning for digital resilience, energy transition, or transport futures.

Benefits of DAPP:

✨Manages Deep Uncertainty: Allows for flexible decision-making that adapts as future conditions unfold.

✨Aligns with Dynamic CBA: Complements probabilistic modelling, scenario analysis, and real options in CBAs involving long-term, uncertain outcomes.

✨ Visualises Policy Paths: Helps stakeholders understand the timing and conditions under which different options become optimal.

Challenges of DAPP and How to Address Them:

⚠️Challenge 1: Strategic, not purely economic

Focuses more on pathways and timing than direct monetary valuation.

✅ Solution: Combine DAPP with Dynamic CBA for integrated planning.

⚠️Challenge 2: Requires long-term commitment

Needs sustained monitoring of triggers and signposts.

✅ Solution: Institutionalise monitoring systems and adaptive governance mechanisms.

10.12. Social Cost of Carbon (SCC)

🔍 What It Is

The Social Cost of Carbon (SCC) estimates the monetary value of damages caused by emitting one additional ton of CO₂ into the atmosphere. It reflects the global economic harm from climate impacts such as sea-level rise, health risks, and agricultural losses. SCC is essential in energy, transport, and climate-related CBAs, allowing policymakers to internalise the external costs of carbon emissions.

The Social Cost of Carbon (SCC) is a critical metric used in environmental economics to quantify the monetary value of the long-term damage caused by emitting one additional ton of carbon dioxide (CO₂) into the atmosphere. This value reflects the global economic costs associated with climate-related impacts, including sea-level rise, increased frequency and severity of extreme weather events, agricultural productivity losses, health risks from heatwaves and disease spread, biodiversity decline, and damage to infrastructure. By assigning a dollar value to these future harms, SCC provides a mechanism to internalise the external costs of carbon emissions—costs that would otherwise be borne by society and future generations but are not accounted for in market prices.

The SCC is widely applied in energy, transport, and climate-related Cost-Benefit Analyses (CBAs), ensuring that the environmental costs of carbon emissions are incorporated into policy and investment decisions. For example, when evaluating whether to build a new coal-fired power plant versus investing in renewable energy, the inclusion of the SCC helps reveal the true societal cost of each option by factoring in the future climate damages from carbon emissions. This supports more informed, responsible decision-making aligned with climate mitigation goals.

How SCC Works in CBA:

When conducting a CBA for projects that involve greenhouse gas emissions, policymakers multiply the estimated emissions (in tons of CO₂) by the SCC value to calculate the total climate-related cost of those emissions. This cost is then added to the project’s total costs, enabling a more comprehensive comparison between alternatives that may have different emission profiles.

💡 Formula

Total Carbon Cost=Emissions (tons of CO₂)×SCC (per ton)\text{Total Carbon Cost} = \text{Emissions (tons of CO₂)} \times \text{SCC (per ton)}

Where:

  • Emissions represent the project’s carbon output.

  • SCC reflects the cost per ton of CO₂ emissions, typically ranging between $50 and $150 per ton depending on assumptions about climate sensitivity, discount rates, and future damage projections.

📌Example: Evaluating an Offshore Wind Farm vs. Natural Gas Plant

Suppose a government is comparing two energy projects:

  1. Natural Gas Power Plant: Expected emissions = 1 million tons CO₂ per year.

  2. Offshore Wind Farm: Zero direct emissions.

If the SCC is set at $85 per ton, the climate-related cost of emissions from the natural gas plant would be:

1,000,000tons×85$ per ton=85million $ per year

This $85 million annual cost is included in the CBA for the natural gas plant but not for the wind farm, shifting the cost-benefit comparison toward the renewable option. Over a 30-year lifespan, this could translate into $2.55 billion in avoided climate damages for the wind farm, strongly influencing investment decisions.

Why SCC Matters:

  • Internalises environmental externalities: Without SCC, the true societal costs of carbon emissions are ignored, leading to suboptimal decisions that exacerbate climate change.

  • Promotes climate-responsible investment: SCC helps governments and businesses weigh low-carbon technologies against high-emission alternatives.

  • Aligns with international climate targets: Including SCC in CBA supports policies aimed at limiting global temperature rise, as outlined in agreements like the Paris Accord.

  • Reflects intergenerational fairness: By valuing future damages, SCC ensures that the well-being of future generations is considered in today’s decisions.

However, the choice of SCC value is subject to debate and uncertainty. It depends on assumptions about climate sensitivity, economic growth, discount rates, and geographic equity (since climate impacts are not evenly distributed). The US Interagency Working Group interim Social Cost of Carbon (SCC) values are much lower than what’s needed to achieve climate goals, such as limiting warming to well below 2°C or reaching net-zero emissions by 2050. The IAWG estimates the SCC at $85 in 2050, using a 3% discount rate. These figures are significantly below the required SCC levels to achieve broader climate targets. However, using updated models and adjusted discount rates could push SCC values closer to or even above $100–$150 per ton of CO₂, as some progressive policy models suggest, compared to lower traditional estimates (e.g., $50–$60 per ton)(Stern et al., 2022).

Real-World Application: The U.S. Clean Power Plan (CPP)

The Clean Power Plan (CPP), proposed by the U.S. Environmental Protection Agency (EPA), used the Social Cost of Carbon to assess the economic benefits of reducing emissions from power plants. By incorporating the SCC into the plan’s cost-benefit analysis, the EPA demonstrated that the health and climate benefits of reducing CO₂ emissions outweighed the compliance costs for utilities. This approach helped justify regulatory action by quantifying the avoided damages from climate change, such as reduced healthcare costs from air pollution and fewer climate-induced disasters.

In this way, SCC acts as a cornerstone in climate policy and sustainable economic evaluation, ensuring that decisions today account for the long-term consequences of carbon emissions.

Benefits of SCC

✨ Internalises Climate Externalities: Ensures that carbon-intensive projects account for their global environmental costs.

✨Policy Standardisation: Provides a consistent benchmark for integrating climate impacts into economic decisions.

Challenges of SCC and How to Address Them

⚠️Challenge 1: Value Variability

SCC estimates range from $10 to over $200/ton depending on assumptions.

✅ Solution: Use sensitivity analysis with different SCC values and disclose assumptions transparently.

⚠️Challenge 2: Ethical Considerations in Discounting

High discount rates may undervalue future generations’ welfare.

✅ Solution: Apply declining discount rates or ethical frameworks (e.g., Stern Review recommends low discount rates).

⚠️Challenge 3: Uncertainty in Climate Modelling

Damage projections are inherently uncertain.

✅ Solution: Combine SCC with probabilistic models and scenario analysis to capture uncertainty.

10.13. Monte Carlo Estimation

🔍 What It Is

Monte Carlo Estimation is a probabilistic modeling technique that uses random sampling to simulate thousands (or millions) of possible outcomes based on uncertainty ranges for key variables. Rather than relying on single-point estimates (e.g., for costs, benefits, discount rates), Monte Carlo models generate probability distributions for outcomes like Net Present Value (NPV) or Benefit-Cost Ratio (BCR). This approach captures the inherent uncertainty and risk in complex projects, providing a range of possible results and their likelihood.

Monte Carlo Estimation is especially valuable in Cost-Benefit Analysis (CBA) for projects involving climate change, infrastructure, disaster resilience, or health, where uncertainty about future conditions (e.g., sea-level rise, technological change, economic growth) can significantly affect outcomes. By integrating this uncertainty into the CBA process, Monte Carlo simulation helps policymakers make more robust, risk-informed decisions.

For instance, instead of assuming a fixed value for future carbon prices or flood risk exposure, a Monte Carlo model allows these inputs to vary across a defined probability distribution (e.g., normal, triangular, or uniform). The model then runs thousands of iterations to simulate a full range of possible scenarios, calculating NPV or BCR for each one. The result is a distribution of outcomes—not a single estimate—offering insights into the likelihood of achieving a positive return or the risk of loss.

How Monte Carlo Works in CBA:

  1. Define Key Variables and Uncertainty Ranges: Identify critical inputs such as future costs, benefits, discount rates, carbon prices, or hazard probabilities. Assign probability distributions to these variables based on historical data, expert input, or scenario assumptions.

  2. Random Sampling: Use random sampling techniques to select values from each distribution across thousands of iterations. Each iteration represents one possible future scenario.

  3. Simulate Project Outcomes: For each scenario, calculate the NPV, BCR, or other CBA metrics based on the sampled inputs.

  4. Aggregate Results: Generate a distribution of outcomes, showing the probability of different NPV or BCR levels.

💡 Formula 

While Monte Carlo does not have a fixed formula, its logic can be represented as:

NPV = ∑ [(Benefitᵢ – Costᵢ) / (1 + rᵢ)ᵗ] for i iterations

Where:

  • Benefitᵢ, Costᵢ, and rᵢ are sampled from their respective probability distributions in each iteration.

  • t = time period

After n simulations, the outputs (NPVs or BCRs) form a probability distribution.

📌Example: Coastal Flood Defence Investment

Scenario
A government is evaluating a coastal flood defence project designed to protect a vulnerable urban area. Key uncertainties include:

  • Sea-level rise projections (e.g., 0.5m to 2m by 2100),

  • Future storm intensity,

  • Construction costs (subject to inflation or material price volatility),

  • Discount rates (ranging from 2% to 5%).

Monte Carlo Simulation Process

  • Assign distributions to each uncertain variable (e.g., sea-level rise = triangular distribution, discount rate = uniform distribution).

  • Run 10,000 simulations sampling different combinations of inputs.

  • Generate a distribution of NPVs for the flood defence project.

Result

  • 80% of simulations show a positive NPV, with a mean NPV of $150 million.

  • However, 10% of scenarios reveal potential losses due to extreme sea-level rise or higher-than-expected costs.

This analysis allows policymakers to visualise risk and quantify uncertainty, aiding in decisions about insurance mechanisms, design adjustments, or phased implementation.

Benefits of Monte Carlo Estimation:

✨Captures Uncertainty: Provides a probabilistic view of project outcomes, avoiding overconfidence in single-point estimates.

✨Supports Risk Management: Identifies the likelihood of both favourable and unfavourable scenarios, helping policymakers plan for contingencies.

✨Enhances Decision Robustness: Ensures that investments are stress-tested across a wide range of possible futures, promoting resilient decision-making.

✨Improves Stakeholder Confidence: Demonstrates a transparent, quantitative approach to handling uncertainty, building trust in complex evaluations.

Challenges of Monte Carlo Estimation and How to Address Them:

⚠️ Challenge Description ✅ Solution
Data Requirements Requires well-defined probability distributions for inputs. Use expert elicitation, historical data, or scenario analysis to define input ranges. Validate assumptions through stakeholder engagement.
Model Complexity Building and interpreting models can be technically demanding. Leverage user-friendly tools (e.g., @Risk, Crystal Ball) or partner with modelling experts for complex CBAs.
Interpretation for Non-Experts Probabilistic results (e.g., distributions, percentiles) can confuse stakeholders. Use clear visualisations (e.g., histograms, fan charts) and explain confidence intervals to convey results effectively.
Choice of Distributions and Correlations Results can be sensitive to the choice of input distributions or correlations between variables. Conduct sensitivity analysis on distribution types and assumptions. Test with alternative correlation scenarios.

Real-World Application: Infrastructure Victoria (Australia)

Infrastructure Victoria used Monte Carlo Estimation in evaluating transport demand for future infrastructure projects, including major road and rail expansions. By modeling a range of population growth, urban development patterns, and climate scenarios, the analysis provided probability distributions for NPV and BCR. This allowed policymakers to assess the risk of underutilisation or cost overruns, enhancing the resilience and flexibility of infrastructure investments.

Monte Carlo Estimation has become a cornerstone of dynamic, uncertainty-aware Cost-Benefit Analysis, ensuring that public investments are not only efficient under average conditions, but also robust in the face of future unknowns.

📝Key Takeaways

  • Non-market impacts are often the most significant drivers of public value—from saving lives to preserving ecosystems—but they require specialised tools to capture and quantify.

  • A one-size-fits-all approach doesn’t work. The choice of valuation method depends on the context, data availability, stakeholder priorities, and the type of benefit (e.g., health, environment, social cohesion).

  • Combining methods enhances robustness. Hybrid approaches, like pairing stated preference surveys with revealed preference data or combining expert judgment with probabilistic modeling, improve the accuracy and defensibility of CBAs.

  • Incorporating uncertainty and risk is essential. Techniques like Monte Carlo Estimation, DAPP, and Real Options Analysis ensure that CBAs remain relevant and adaptable in uncertain futures, particularly in climate and disaster contexts.

  • Valuation is not just technical—it’s ethical and political. Discount rates, equity considerations, and the treatment of future generations fundamentally shape policy outcomes. Analysts must make these choices transparent and aligned with societal goals.

  • These advanced methods empower policymakers to make better-informed, socially responsible, and future-proof decisions, even when traditional markets fall silent.

📚References

Cropper, M. L., & Oates, W. E. (1992). Environmental economics: A survey. Journal of Economic Literature, 30(2), 675–740. https://www.jstor.org/stable/2727701

Linstone, H. A., & Turoff, M. (Eds.). (2002). The Delphi method: Techniques and applications. Portland State University & New Jersey Institute of Technology. https://web.njit.edu/~turoff/pubs/delphibook/delphibook.pdf

Rolfe, J., & Windle, J. (2012). Testing benefit transfer of reef protection values between local case studies. Ecological Economics, 81, 60–69.

Rosen, S. (1974). Hedonic prices and implicit markets: Product differentiation in pure competition. Journal of Political Economy, 82(1), 34–55. https://doi.org/10.1086/260169

Simons, R. A., Quercia, R. G., & Maric, I. (1998). The value impact of new residential construction and neighborhood disinvestment on residential sales price. Journal of Real Estate Research, 15(2).

Stern, N., Stiglitz, J., Karlsson, K. & Taylor, C. (2022). A social cost of carbon consistent with a net-zero climate goal. Roosevelt Institute & Grantham Research Institute at the London School of Economics. https://rooseveltinstitute.org/wp-content/uploads/2022/01/RI_Social-Cost-of-Carbon_202201-1.pdf

Rice, D. P. (1967). Estimating the cost of illness (Health Economics Series No. 6). U.S. Department of Health, Education, and Welfare.

📚Further Reading

Basu, R., & Samet, J. M. (2002). Relation between elevated ambient temperature and mortality: A review of the epidemiologic evidence. Epidemiologic Reviews, 24(2), 190–202. https://doi.org/10.1093/epirev/mxf007

Brouwer, R., Akter, S., Brander, L., & Haque, E. (2007). Estimating the value of the environmental benefits of reducing climate-related risk in Bangladesh. Risk Analysis, 27(2), 313–326. https://doi.org/10.1111/j.1539-6924.2007.00884.x

Carson, R. T. (2012). Contingent valuation: A practical alternative when prices aren’t available. Journal of Economic Perspectives, 26(4), 27–42. https://doi.org/10.1257/jep.26.4.27

Champ, P. A., Boyle, K. J., & Brown, T. C. (Eds.). (2017). A primer on nonmarket valuation (2nd ed.). Springer. https://doi.org/10.1007/978-94-007-7104-8

Freeman III, A.M., Herriges, J.A., & Kling, C.L. (2014). The measurement of environmental and resource values: Theory and methods (3rd ed.). Routledge. https://doi.org/10.4324/9781315780917

Haab, T. C., & McConnell, K. E. (2002). Valuing environmental and natural resources: The econometrics of non-market valuation. Edward Elgar Publishing.

Hensher, D. A., Rose, J. M., & Greene, W. H. (2005). Applied choice analysis: A primer. Cambridge University Press.

Johnston, R. J., Rolfe, J., Rosenberger, R. S., & Brouwer, R. (2015). Benefit transfer of environmental and resource values: A guide for researchers and practitioners. Springer.

Lazarus, J. V., Romero, D., Kopka, C. J., Karim, S. A., Abu-Raddad, L. J., Almeida, G., Baptista-Leite, R., Barocas, J. A., Barreto, M. L., Bar-Yam, Y., Bassat, Q., Batista, C., Bazilian, M., Chiou, S. T., Del Rio, C., Dore, G. J., Gao, G. F., Gostin, L. O., Hellard, M., … El-Mohandes, A. (2022). A multinational Delphi consensus to end the COVID-19 public health threat. Nature, 611, 332–345. https://doi.org/10.1038/s41586-022-05398-2

Lucas, R. C. (1968). Book reviews: Economics of Outdoor Recreation by M. Clawson & J. L. Knetsch. Natural Resources Journal, 8(4), 738–743. http://www.jstor.org/stable/24880022

Louviere, J. J., Hensher, D. A., & Swait, J. D. (2000). Stated choice methods: Analysis and applications. Cambridge University Press.

Navrud, S., & Ready, R. (Eds.). (2007). Environmental value transfer: Issues and methods. Springer.

Ryan, M., Gerard, K., & Amaya-Amaya, M. (2008). Using discrete choice experiments to value health and health care. Springer.

U.S. Environmental Protection Agency. (2000). Guidelines for preparing economic analyses (EPA 240-R-10-001). National Center for Environmental Economics, Office of Policy.

definition

License

Icon for the Creative Commons Attribution-NonCommercial 4.0 International License

Cost-Benefit Analysis: A Practical Guide for Decision-Making Copyright © 2025 by Taha Chaiechi is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, except where otherwise noted.

Share This Book