Ex ante appraisal of policy options in the Circular Economy Data Observatory page on plastic packaging is implemented through a systems dynamics (SD) model. The SD model consists of an overarching material flow layer made up of individual sub-models for each polymer and packaging application (a total of 32 combinations).
Figure 1. Structure of the material flow layer in the systems dynamics model
flowchart LR
A(Net Trade) --> B(Placed on market)
C(Domestic production) --> B(Placed on market/<br>sales)
B-- Product lifetime --> D(Waste generated)
D --> E(Waste collected)
D --> F(Littering)
E --> G(Formal domestic treatment)
E ---> H(Treated overseas)
G --> I(Recycling)
G --> J(Residual treatment)
J --> K(Incineration)
J --> L(Landfill)
I --> M(Mechanical)
I --> N(Chemical)
E --> O(Mismanaged e.g. <br> dumping)
Assumptions around the impacts of policies are applied to this material flow model when a website user toggles on the policy from the scenario interface. There, the user is shown the against-baseline impact of policies including based on the parameters they define around the year of introduction and in some cases, scope of policy.
Counterfactual construction
Ex ante policy assessments requires baseline projections against which to compare scenarios with policies introduced. While a robust and credible baseline is sought to be established here, it does not necessarily attempt to provide a forecast of future developments, but rather a neutral viewpoint to provide a point of comparison against.
A counterfactual is constructed for key variables in the model - the quantity of plastic packaging ‘placed on the market’ (POM) and transfer coefficients which define how the materials placed on the market then flow through the economy.
The counterfactual POM is calculated through the following steps:
A correlation analysis between key determinants of plastic packaging POM highlighted in the literature such as population and final demand is undertaken to select drivers of greatest influence;
A ratio is calculated between the exogenous factor showing a strongest correlation and outturn values for POM (technology coefficient);
This ratio is forecasted into the future using a range of parametric time-series forecasting approaches;
The forecasted ratio is multipled by published projections for the exogenous variable e.g. as published by the ONS for population or OECD for GDP to approximate future POM values.
Transfer coefficients used in the dynamic material flow model are derive from a static material flow analysis undertaken for plastic packaging for the years 2014-2023. A naive approach is used to forecast these variables into the future, with transfer coefficients in all future years within the scope of the model (up to 2042) reflecting the transfer coefficient from the latest year of the static analysis.
Policy modelling
As part of the modelling framework, the impacts of a given policy instrument (and scenarios consisting of multiple instruments introduced alongside one another) is a function of:
User supplied parameters
These parameters are passed to the model from the site interface, and can include:
The year of policy introduction;
The scope of a lever such as the scope of materials and applications the lever is applied to; and
Where applicable, at what level a policy is set e.g. the level of a charge.
Relationships in the model
Relationships captured within the SD model, formalised within the model through mathematical functions
Literature and data-derived effect sizes
Potential effect sizes of policies are estimated through an evidence review which accounts for:
Likely penetration rates within the target group;
The potential immediacy pathway of impact following introduction of a policy; and
Interaction effects between instruments, including based on their sequencing.
Impact assumption development
We undertake a systematic review of the evidence regarding the likely type and scale of effects of shortlisted policies. An attempt is made to be as systematic as possible in reviewing the literature to be able to make a legitimate statement of what the evidence on policy instruments in scope says to date overall and to do so in a clear and consistent way that is accessible to a range of readers (DFID, 2010). Sources are reviewed and synthesised in line with guidance under the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, and information is provided about the policy instruments against the selection criteria listed on this site.
Extending the analysis to other appraisal frameworks
Cost-benefit analysis
Outputs of the modelling such as physical impacts of policies on material flows and emissions can be monetised to input to cost-benefit analysis - which has become the default standard for policy appraisal when formulating most new government regulation in the UK (Sunstein 2000). The general steps of a cost-benefit analysis (CBA) are (Boardman et al., 2006; Atkinson et al. 2018):
Select the portfolio of options (projects or policies);
Establish a baseline for comparison to indicate the counterfactual;
Determine who has ‘standing’ and we are interested in counting the benefits and costs to;
Identify, list and quantify potential physical impacts for the life of the project or policy (this is where outputs of the DO input directly);
Monetize impacts for each time period;
Benefits - Outputs of the project which increase human welfare, the value of which are assessed based on how much someone is willing to pay for that benefit, even for the likes of avoided costs.
Costs - Outputs of a project that decrease human welfare and which include the costs of inputs.
Aggregation then involves all benefits and costs being summed individually, while discounting for future benefits and costs depending on the social discount rate;
Sensitivity analysis can then be used as an indication of uncertainty by testing the sensitivity of the net benefit to changes in its determinants;1
Results are presented either as a net benefit (B-C) or a benefit-cost ratio (B/C);
Options are selected based on the following decision-rules:
Where options are mutually exclusive, the policy should be chosen that has the highest Net-Benefit.
If options can be chosen independently with no resource constraints, all options with a net benefit greater than 0 can be chosen.
If options can be chosen independently but resources are limited, then options can be ranked by the B/C ratio and those yielding the greatest net-benefit, selected.
Risk-opportunity analysis
When standard assumptions of welfare economics and CBA are met, optimisation models can play an important role in analysing the potential impact of policies or projects on the allocation of existing economic resources. However, where assumptions for welfare economics CBA are not met, Sharpe et al.(2021) recommend the use of ‘risk opportunity analysis’ (ROA) which is a more general form of cost-benefit analysis. ROA steps involve:
System boundaries are delimited, and all relevant interactions and positive and negative feedbacks are identified (suitable models, if required, are chosen or designed);
The potential effects (intended and unintended) of policy options in the economy are assessed, and uncertainty ranges estimated;
Mapping the relationships between components of the economic system of concern, in terms of reinforcing and balancing feedbacks
Identifying the likely effect of policy interventions on system behaviour, based on changes to the structure of relationships between components (including relationships created by other policies that already exist or are under consideration). This may be extended to include the creation of a range of scenarios and storylines of cumulative causation that result from policy action, where longer-term effects are likely to be important to policy objectives;
Comparing likely effects in terms of:
Direction of change (of any variables of policy interest)
Magnitude of change (which may or may not be quantifiable)
Pace of change
Possible accumulation of risk and opportunity (option generation)
Confidence, or range of uncertainty, in each of i to iv above.
The risks and opportunities of options (including most likely, best-case and worst-case outcomes) are compared along multiple relevant metrics and dimensions (where probabilities may be quantifiable or unquantifiable). This includes consideration of systemic risk (breakdown of an existing system), and systemic opportunity (where policy generates a whole new system, or set of opportunities);
The preferred option is determined by the decision-maker based on a qualitative judgment of the scale of the opportunities and risks, compared to the cost of the intervention. This will necessarily be a subjective judgment (since it incorporates a weighing of outcomes in different dimensions), informed by an objective assessment of likelihood and magnitude of possible outcomes in each of the relevant dimensions; and
A clear statement of the reasoning behind the decision is recorded including the decisionmaking body’s assessment of the risks and opportunities in their various dimensions. (This can be helpful for transparency and for learning from experience).
Key differences in ROA to CBA are that multiple metrics are used, the focus is on expected processes that drive change rather than outcomes, unquantified risks and opportunities are central to consideration so expected values are not used and that policies are recommended that aim for maximum and self-reinforcing leverage in the desired direction of change.
Sensitivity analysis requires that the CBA is computed using different values of the parameters about which there is uncertainty. Such procedures require some assumption about likely minima and maxima, but do not necessarily make assumptions about the distribution of values between these limits. For example, if a discount rate of 4% is chosen as the central case, then 2 and 6% could also be chosen for a sensitivity analysis. One possible outcome is that the sign of the net benefits will be unaffected by these alternatives, in which case the analysis is said to be “robust” with respect to these assumptions. In other cases, changing assumptions may alter the CBA result to such an extent that the option is no longer net-welfare-enhancing.↩︎