Defining the scope of targeted assessments
Unlike the broad national-level estimates provided in Chapter 2, targeted assessments enable evaluation of the impacts of specific agrifood systems policies or agrifood business operations. They can also reveal the values of ecosystem services – often neglected by wider assessments due to data limitations (see Chapter 2) – so that these can be factored into decisions, as well as provide recommendations on how to change to practices that deliver more equitable and sustainable agrifood systems. Such an example can be found in Indonesia, where a TCA study convinced the government to include cacao agroforestry in its 2020 Five-Year Development Plan.6, 7
The same TCA study relied on the TEEBAgriFood Evaluation Framework, which, as noted in Chapter 1, has the broadest support when it comes to targeted assessments and has been applied numerous times in both the public and the private sectors in various countries. Box 10 discusses the framework’s three guiding principles – universality, comprehensiveness and inclusiveness – in which targeted TCA assessments should be rooted.
BOX 10GUIDING PRINCIPLES OF THE TEEBAgriFood EVALUATION FRAMEWORK
The TEEBAgriFood Evaluation Framework has three guiding principles:8 universality – the Framework is applicable to evaluate agrifood systems in any geographical, ecological or social context; comprehensiveness – covering all agrifood systems components; and inclusiveness – supportive of multiple analytical methods.
The universality principle ensures that the elements considered and evaluated in each assessment are defined and described in a uniform, methodical and consistent manner. This is essential to avoid the limitations of siloed assessment models, such as those only assessing agricultural systems on the basis of land productivity, or water- or energy-use efficiency. These models neglect other aspects of sustainability or equity that are related to, but not determined by, the issues studied in siloed assessments.
The principle of comprehensiveness ensures that all (relevant) hidden costs and benefits, including dependencies and impacts upstream and downstream affecting different stakeholders, are part of the assessment.
The principle of inclusiveness recognizes that several market and non-market valuation tools and methods, including in quantitative and qualitative terms,8 can assess the hidden costs of agrifood systems. While many flows and stocks can be measured in monetary terms, this is not possible for all aspects of human well-being. Indeed, in different contexts, monetary valuation may not be possible or ethically appropriate, and measurement in qualitative, physical or non-monetary terms may provide important insights.9
Thus, the TEEBAgriFood Evaluation Framework allows for a plurality of value perspectives and assessment techniques. Consequently, it can accommodate national-level assessments (as presented in Chapter 2), but expand the analysis with more targeted assessments that recognize local contexts within countries.
NOTE: TEEB = The Economics of Ecosystems and Biodiversity.
An important part of setting up a targeted assessment is to establish the boundary of analysis to keep the scope of the study feasible while allowing it to sufficiently meet its goal. This starts with choosing the functional unit of analysis, that is, what is being assessed and measured.10 Figure 12 describes the scopes of the different functional units – agrifood systems, dietary patterns, investment, organization and product – and their relevance to the transformation of agrifood systems towards sustainability.
FIGURE 12 Five commonly used functional units, their scope and relevance
The scope of the analysis is further defined by geographical and temporal boundaries. Geographical boundaries set the study within a defined geographical area such as a country or subregion. Examples include a study that assesses different dietary patterns in the United States,11 one that analyses meat produced in Germany,12 and another that studies rice production in Thailand.13 The temporal boundaries in a TCA study refer to the time span of the results, including the baseline of the data used and the policies assessed, as well as the timeline for scenario analysis.14 In essence, any targeted assessment will inevitably be a partial and incomplete snapshot of reality, limited by a given set of boundaries over a given period of time.
The chosen functional unit will depend on the policy focus or research question. Generally, boundaries of analysis that incorporate the higher level of agrifood systems and include various actors are most suitable for policymaking, as they are more holistic and consider the potential to steer systemic impact.14 Chapter 2 relied on the highest functional unit (national agrifood systems) to estimate the hidden costs of entire systems for 154 countries. Despite their importance in catalysing change, analyses at a systemic level remain aggregated and do not allow great detail.
Activating levers for change usually requires analysis on a more granular level. This often requires product or investment to be the functional units necessary to inform concrete decisions. For instance, Box 11 assesses the impact of changes in rice production, and thus product is chosen as the functional unit. However, the assessment could also have been conducted at a territorial level to complement farm-level results, to capture the full range of impacts, externalities and dependencies taking place beyond the farm gate, such as the impact on food security.13
BOX 11TEEBAgriFood EVALUATION OF RICE PRODUCTION IN NORTHEASTERN THAILAND
The TEEBAgriFood Evaluation Framework was used to identify and measure the diverse costs and benefits of expanding organic rice production in Thailand. The aim was to pinpoint options for promoting the long-term sustainability of production and management of rice landscapes. The analysis was concluded in June 2022 and considered hidden costs across all four capitals: natural (greenhouse gas [GHG] emissions and biodiversity), human (effects of air pollution and pesticides on health, happiness and well-being), social (cooperation, trust and pro-social or voluntary behaviour) and produced (revenues and expenditures of conventional versus organic rice).
Taking into account government policies and targets, as well as the views of local stakeholders – including local agricultural officers, farmers and banks – the analysis proposed four scenarios to demonstrate the potential synergies and trade-offs of different rice practices in Thailand over 2019–2035. One was the baseline business-as-usual (BAU) scenario (S1), while the other three scenarios (S2, S3 and S4) assumed the progressive adoption of organic rice production and other sustainable practices. Each scenario was measured over three time frames: short (2025), medium (2030) and long (2035).
Applying cost–benefit analysis to the results of the four scenarios, the study found that the expansion of organic rice area under S2, S3 and S4 (compared with S1) generated benefits for the environment (as a result of lower GHG emissions) and human health (thanks to reduced exposure to pesticides and air pollution). The human health net benefits ranged from USD 438 million in S2 to USD 4 146 million in S4. The net environmental benefits were between USD 2 million in S2 and USD 16 million in S4. However, this same expansion caused a net loss of revenue – from USD 29 million in S2 to USD 389 million in S4. Putting this in perspective, this loss is less than 1 percent of the total BAU scenario projection of net revenue of USD 57 billion.
However, it was estimated that the revenue lost as a result of the decline in yield would be offset if organic rice were priced 3.5 percent higher than conventional rice. Given these findings, the assessment recommended that subsidies be reoriented to induce farmers to adopt sustainable agricultural practices, including organic rice growing. This was particularly pertinent to the transitional period, when farmers would need more support, as organic rice yields could be expected to fall slightly in the short to medium term. Furthermore, to boost demand for the increased output of organic rice, export promotion might be needed, for example, policies and standards for certification, such as policies to promote the grouping of farmers into discreet areas certified as organic to share the cost.
NOTE: TEEB = The Economics of Ecosystems and Biodiversity.
SOURCE: Khon Kaen University. 2022. Measuring What Matters in Rice Systems: TEEBAgriFood Assessment Thailand, focus on the Northeast region. Key messages, August 2022. TEEB. https://teebweb.org/wp-content/uploads/2022/09/5-TEEBAgriFood-IKI-Key-messages.pdf
Furthermore, if the policy concern is to promote healthy diets, then choosing dietary patterns as the functional unit would be more appropriate. Choosing organization as the functional unit might also be suitable in certain cases. While it is mostly used for the private sector, organization as the functional unit can produce valuable insights if the policy goal is to identify areas in which businesses need support either to conduct TCA themselves or to reduce their negative impacts.14
Policy and scenario analyses: their fundamental and complementary roles in targeted TCA assessments
Scenario analysis is a critical feature of any TCA exercise, regardless of the boundaries of the analysis. In this report, scenarios are defined as representations of possible futures for one or more components of the studied system, based on alternative policy or management options. Whether the domain of a TCA application is national agrifood systems, local dietary patterns, a public investment or a value chain, the analysis of these scenarios involves the comparison of potential future paths and assesses the impact and effectiveness of different policies and management options.15 Scenario analysis aims to answer the following questions: What will happen if no action is taken? Will the problem worsen and how quickly? What will the cost of inaction be? In answering these questions, scenario analysis identifies emerging issues from inaction and explores alternative options for action that can potentially lead to improved outcomes, as well as synergies and trade-offs. Such trade-offs can then be carefully weighed to formulate stronger strategies and assess the effectiveness of different potential actions.
Policy analysis builds on and complements scenario analysis – to evaluate and compare the different proposed policy options, as well as their relative potential in achieving specific policy goals. In other words, policy analysis uses scenarios to identify, from the pre-screened policies, those options most likely to be economically viable and effective in achieving the desired policy outcome, given the estimated resources required for implementation against available resources. In a policymaking context, scenario analysis is applied in relation to the decision-making process depicted in Figure 13.15 Problem identification (scenario of inaction), policy formulation and policy assessment (scenarios of action for policy analysis) are stages of the decision-making process that take place before implementation, which is followed by monitoring and evaluation.
FIGURE 13 The role of scenarios in informing policymaking
To use scenarios in policymaking, the first stage is problem identification. Here, exploratory scenarios can examine a range of plausible futures based on potential trends in drivers such as climatic, socioeconomic, biophysical and technological factors. These scenarios enable policymakers to be aware of the baseline (that is, the current situation) and the main drivers of change in a scenario of inaction (the business-as-usual [BAU] scenario). These scenarios rely on input from a multistakeholder approach that involves the various actors in question and, thus, incorporates different perspectives and expertise, promoting a more comprehensive understanding of agrifood systems. The objective of this stage is to map the relationships between agrifood systems and the four capitals, represented by the most important flows in the specific content, such as the impacts of agrifood systems on GHG emissions, human health and income distribution.8
Box 12 describes a scenario analysis used to compare current and future food consumption – following the BAU scenario – and alternative consumption scenarios that have been devised as being healthier and more sustainable.
Box 12Scenario analysis to uncover the health and environmental hidden costs of different diets
An analysis by Springmann (2020)16 as a background paper for FAO et al. (2020)17 estimated health- and climate-related hidden costs of dietary patterns by the year 2030. It compared the continuation of current dietary patterns (see Figure 12) with four alternative consumption scenarios that had been devised as healthier and more sustainable (flexitarian, pescatarian, vegetarian and vegan). The objective was to measure by how much these costs could be reduced and, thus, inform food policy to incentivize dietary changes towards healthy diets that were more environmentally sustainable.
The results showed that if current food consumption patterns continued, diet-related health costs linked to non-communicable diseases and their mortality would likely exceed USD 1.3 trillion per year by 2030. In contrast, shifting to healthy diets would lead to an estimated reduction of up to 97 percent in direct and indirect health costs, generating significant savings that could be invested to lower the cost of nutritious foods. As for climate-related costs, greenhouse gas emissions associated with current dietary patterns were projected to exceed USD 1.7 trillion per year by 2030. The adoption of alternative diets, however, would reduce this cost by an estimated 41–74 percent in 2030, depending on the scenario.
These exploratory scenarios can help to reframe the problem in order to set a policy agenda more effectively. They typically have both qualitative and quantitative components and are often combined with participatory approaches involving local and regional stakeholders. For example, population growth projections can be used to estimate expected land-cover changes when investigating trends in agricultural expansion or urbanization.
The next stage of the decision-making process is policy formulation, which is critical if a targeted assessment is to be impactful. Based on input from the BAU scenario in the problem identification stage, targets can be set to drive change towards more desirable outcomes, again based on national objectives. Target-seeking scenarios can then be used to examine and formulate policy targets, depending on their viability and effectiveness.
These identified policies are then pre-screened in the policy assessment stage, using policy-screening scenarios that assess how a policy instrument (or set of instruments, such as incentives, mandates, direct investments or awareness raising) can modify the future.18 This enables better understanding and forecasting of the outcomes of implementing a specific policy, by exploring the interlinkages and interdependencies within and between the systems targeted by the policy. Criteria that might be considered for the selection of specific policy instruments include: (i) the extent to which reaching the stated target is economically viable and whether new valuation evidence might support the adoption of a new policy; (ii) political economy – who favours the change, who is against it and what the influence of each group is; and (iii) who might gain and who might lose from the change, and whether the new policy would provide livelihood options to communities or sectors of society that have few alternatives. Considerations can be informed by the use of qualitative and quantitative methods, including simulation models, as well as stakeholder and expert consultation workshops. Box 13 provides an example from Indonesia on how policy-screening scenarios can be used in a real policy context (see Box 11 for another example in Thailand).
Box 13Using scenario analysis in a real policy context: an example from Indonesia
In scenario analysis for agrifood systems transformation, a key policy question is: how can sectoral sustainability be enhanced? Such was the question in Indonesia, where cacao is an important crop, contributing to export earnings and job creation, but where current monoculture practices threaten its sustainability.19, 20 The use of scenario analysis in a TEEBAgriFood study of North Luwu Regency, South Sulawesi focused on the impacts and dependencies of cacao production, including processing, distribution and consumption activities and their relationships with ecosystems.7 It compared the social and environmental impacts of monoculture cacao production and agroforestry cacao production systems to develop agriculture and land-use policies that will build its resilience and economic viability.
Specifically, the study determined the total economic value (TEV) of cacao production under monoculture and agroforestry practices. It further evaluated the consequences of scenarios of cacao agroforestry expansion. To achieve this goal, a set of dynamic simulation models was applied to evaluate the TEV of particular areas between 2021 and 2050.
The assessment compares the potential costs and benefits of a business-as-usual scenario (monoculture) with a simple agroforestry and a complex agroforestry (CAF) scenario. For the implementation of the CAF scenario, two policy interventions are considered and tested in policy-screening scenarios: (i) providing seedlings for the agroforestry system along with targeted extension services and training on good agricultural practices; and (ii) promoting certification and eco-labelling. These cacao production scenarios were generated using a comprehensive suite of environmental, biophysical, statistical and socioeconomic models.
The results of this exercise show that cacao agroforestry provides higher total economic value than both cacao monoculture and cacao intercropping. The benefits derive from a variety of sources, including lower rates of erosion and nutrient leaching, and higher rates of carbon storage in the hypothetical agroforestry systems, leading to both social and private benefits (fewer greenhouse gas emissions and higher crop productivity). In addition, farmers would improve their private income when measuring all possible agroforestry products and could enhance their resilience through income diversification.
Despite these benefits, the adoption of cacao agroforestry is still very limited. While the study identifies the need for capacity building on good agricultural practices as a major priority, it also points to the need to create incentives for producing premium-quality agroforestry systems.
Lastly, the policy-screening scenarios need to be ranked so that they can inform decisions. Ranking can be informed by a cost–benefit analysis or a cost-effectiveness analysis, coupled with a multicriteria analysis. While a cost–benefit analysis compares the benefits and costs of different interventions and determines their economic and financial viability, a cost-effectiveness approach compares the costs of meeting a given objective when using different intervention options, such as the cost per tonne of avoided emissions through energy efficiency, renewable energy and reduced deforestation. These ways of ranking results are particularly relevant when examining different options for reducing the hidden costs of agrifood systems, because the cost of transformation (that is, the abatement cost), despite being necessary for effective decision-making, is not always visible.
In some cases, certain hidden costs cannot be valued in monetary terms, but are material to a policy decision – in other words, meaningful in a given decision-making context (see Glossary for a definition of “materiality”). For these, both a cost-effectiveness analysis and a multicriteria analysis (which couples qualitative and quantitative indicators) can be used to determine the extent to which an intervention option generates societal value and is worth implementing. Ultimately, TCA analyses should consider all material indicators, including monetizable and non-monetizable impacts. The aim is to account for all costs and benefits of any proposed investment or policy change over the foreseeable future, so an assessment can be made as to where the benefits exceed the costs.
Based on the outcomes of the scenario analysis, policy decisions are made and implemented, as illustrated in Figure 13. This should be followed by monitoring and evaluation to assess past efforts to achieve policy goals in all stages of the policy cycle and decision-making context. These assessments also rely on exploratory, target-seeking and policy-screening scenarios to assess: (i) whether the identified problem has been solved; (ii) whether the set targets have been achieved; and (iii) how each intervention performed against specific indicators.
