To facilitate the correct implementation of the right levers, TCA should be incentivized for policymaking, production processes and business management. As explained earlier in the report, TCA can facilitate a comprehensive understanding of impacts and dependencies and enable better decision-making to transition towards sustainable and resilient agrifood systems. This is currently not the case, despite perceptible progress. A number of players, including governments, academia, businesses, financial institutions, and intergovernmental and international agencies, are experimenting with innovative methods and frameworks to uncover the hidden costs of agrifood systems to guide actions towards sustainability. These efforts have spurred a positive change in agrifood systems, including businesses, but further development is still needed in many areas to fully realize TCA’s potential. So, what steps must be taken to mainstream TCA into decision-making? And what is the potential role of the different actors in supporting the creation of an enabling environment for TCA use?
Most importantly, scaling up the adoption of TCA cannot be achieved by a single set of actors; it requires complementary contributions from different stakeholders that influence the functioning of agrifood businesses. Governments, with their policies, funds, investments, laws and regulations, play the central role in creating a conducive environment for the scaling up of TCA to transform agrifood systems. Research institutions and academia are also central, as the different tools and indicators used in these studies need to be properly backed by rigorous methodologies and accurate databases informed by research. To this end, research organizations can be fundamental to mainstreaming TCA through various channels, including the development of: (i) (interdisciplinary) indicators, especially social and human ones, and their respective valuation factors; (ii) accounting mechanisms and reporting formats reflecting the principles of TCA; and (iii) case studies that inform businesses about sustainable practices (see Box 14, in which a TCA study reveals the value of climate-smart coffee production in Colombia).17
Research organizations and standard setters are also key to advancing methodologies and setting standards for data to be collected and used in TCA assessments. This is essential to guarantee the transparency of the true costs and benefits of agrifood systems. The applications of TCA studies will largely be facilitated by accounting firms and business consultancies, which advise and support agrifood producers, businesses and other relevant stakeholders in their sustainability transition. Financial institutions and credit rating agencies could be instrumental if they favour sustainable production, business and investment. Ultimately, it is the producers and businesses – and the alliances they create – that will make the change and implement new standards, in particular, voluntary standards.
The need to advance TCA methodology and data
Any TCA study typically requires a substantial amount of data to assess the costs and benefits in scope. The obvious goal is that data be fit for purpose, in terms of both quality and detail needed to appropriately inform decision-makers. So far, data collection related to food and agriculture concerns the visible flows and impacts, which are mostly related to produced capital and some elements of human capital (see Figure 1). Data on other aspects of human capital, such as working conditions, are generally lacking. What is more challenging is to find data on social capital, such as social networks and cultural knowledge. The challenges of finding data for use in TCA studies also include how to easily quantify some variables, as explained in Figure 2.
The lack of such data at low cost is potentially the main barrier to scaling up TCA.43 This is particularly pressing in middle- and low-income countries, where secondary data are scarce and primary data collection is costly due to limited resources. In view of the data-scarcity bottleneck, the scaling up of TCA will be informed by the following questions: How can the resource intensity of data collection be reduced? How can estimates of missing data be used in TCA? Can data of “insufficient” quality be included in TCA and ultimately inform policy decision-making?
Data can be obtained from three sources:44, 45 (i) primary data collected specifically for the TCA study, such as surveys, physical measurements and field experiments; (ii) secondary data originally collected and published for another purpose or a different study, but approximating the information required;46 and (iii) estimated data using models based on primary and secondary data from different contexts.
Needless to say, public sector-funded data collection, research and analysis are required to fill the data gaps. Unarguably, limitations relating to data scarcity and poor data quality pose an immensely pressing problem, particularly in low- and middle-income countries. Data gaps or poor-quality data are likely to cause high uncertainty in the assumptions required to perform a TCA study. This trade-off should be addressed in two broad ways.
Strategically, in the long term, data needed for TCA studies should be included in the systematic censuses and surveys conducted by public statistical agencies. For this to happen, an easy-to-use tool needs to be developed and tested, which can then be used to establish a standard procedure for accounting that can generate data on the hidden costs and benefits of agrifood systems, that is, on their impacts on social, human and environmental capitals (see Chapter 1). While acknowledging that this is a challenging task, requiring time and resources, it can pay off substantially in the long term by reducing the financial and human resources needed to collect data and perform true cost calculations at a later stage.
Governments can facilitate the process by developing reporting mechanisms and making them mandatory. Examples include the EU Taxonomy, which creates a common classification system for sustainable economic activities,47 and the EU Corporate Sustainability Reporting Directive, which requires large and listed companies to publish regular reports on the social and environmental risks they face and on how their activities impact people, including human rights, and the environment.48
However, due to the pressing need to address the issue of hidden costs, decision-makers should not wait, but rather use what is available, provided its limitations are well understood. In the short-to-medium term, they should take advantage of all available secondary and estimated data. Modelling techniques and sensitivity analysis can then be used to identify data points that produced results that deviated substantially from the average and should be targeted for primary data collection. The prioritization of evidence can also be aided by a variety of different tools, such as Evidence Gap Maps, which visually represent the quantity and quality of available evidence on, for example, possible policy interventions and their outcomes. These maps make it possible to see at a glance the interventions for which there is strong evidence and those that have not been studied at all or only partially.49
Here, shared data directories for secondary data and standardized collection tools for primary data can greatly reduce the resources required to perform a TCA study.46 In France, for example, public-sector efforts led to the creation of Agribalyse – a harmonized database of life cycle assessments for 2 500 food products – which, in turn, has been used to develop environmental impact labelling schemes.49 These initiatives should be a collective effort of the TCA community, as they enable data gaps to be filled at a reduced cost, which is essential for scaling up TCA studies.
It is important to emphasize that the different tools and indicators used in TCA studies need to be backed by rigorously conducted research and accurate databases.8 There is a lot of research to be done on TCA to narrow the currently wide gaps in data availability. Recent tools and models developed by FAO exemplify the role of research in facilitating the mainstreaming of TCA in agrifood systems. For example, FAO’s Ex-Ante Carbon-balance Tool (EX-ACT) and its complementary tools – the Biodiversity Integrated Assessment and Computation Tool (B-INTACT) and the Ex-Ante Carbon-balance Tool for Value Chains (EX-ACT VC) – allow the consistent estimation and tracking of outcomes of agricultural interventions on GHG emissions and biodiversity.50 The tools can be used individually or together to focus on specific elements of projects and policies, or to develop a holistic overview of their environmental impacts. Another example is the Global Livestock Environmental Assessment Model (GLEAM), which is based on life cycle assessment (LCA) and can be used to assess alternative scenarios towards more sustainable livestock production. GLEAM can be used to generate scenarios on the potential impacts of biological shocks (driven by disease) on livestock production and related GHG emissions (Box 21), or to conduct comparative analysis of different production systems in terms of productivity and sustainability indicators (Box 22).
Box 22Greenhouse gas emissions from egg and milk production – evidence from two life cycle assessments
FAO’s Global Livestock Environmental Assessment Model (GLEAM) uses life cycle assessments to quantify GHG emissions generated along livestock supply chains. Two examples are given to illustrate the variability in emissions across different livestock production systems and regions.
Example 1. Intensive versus extensive egg production in South-eastern Asia
Emission intensities per egg in an extensive system are much lower than in an industrial/intensive system.* This is partly because the feed used in the backyard/extensive system is produced locally and consists mostly of crop residues and food waste. Emissions related to these residues were already allocated to their main purpose (food production), so are not considered. In addition, no emissions occur from energy use in either on- or post-farm operations.
Industrial/intensive systems, in contrast, generate energy-related GHG emissions from packing and processing activities. Furthermore, these systems often import feed grown in areas that were cleared for this purpose and which emit GHGs from land-use conversion processes (for example, deforestation to grow soybeans). Figure A quantifies GHG emissions (in CO2 equivalent) per kilogram of eggs along extensive (top) and intensive (bottom) value chains in South-eastern Asia. As expected, total emissions per kilogram of eggs are much lower in the extensive system. Emissions related to manure, however, are significantly higher due to the type of breed and feed used.
FIGURE A GREENHOUSE GAS emissions along extensive (top) and intensive (bottom) egg production value chains in South-eastern Asia
SOURCE: FAO. 2023. GLEAM 3.0 Assessment of greenhouse gas emissions and mitigation potential. In: Global Livestock Environmental Assessment Model (GLEAM). [Cited 28 April 2023]. https://www.fao.org/gleam/dashboard/en
Example 2. Milk-related GHG emissions in Eastern Africa and Northern America
The intensity of GHG emissions also differs across world regions. In Eastern Africa, for example, most emissions per unit of milk are related to enteric fermentation, whereas in Northern America, emissions are further associated with post-farm activities and energy use. However, because emissions associated with enteric fermentation are lower in the latter – due to higher output per animal, and different breeds, feed inputs and management practices – total emissions per unit of milk are lower in Northern America.
This can be seen in Figure B, which breaks down the milk value chain in Eastern Africa (top) and Northern America (bottom) and quantifies the GHG emissions (in CO2 equivalent) associated with each stage.
FIGURE B Milk-related GREENHOUSE GAS emissions in Eastern Africa (top) and Northern America (bottom)
Decision-makers wishing to monetize GHG emissions from an economic activity (for example, egg or milk production) can do so by multiplying emissions with a social cost of GHG emissions, which may vary by context. However, looking at emissions alone is misleading, as this ignores important trade-offs and costs in other dimensions, such as costs related to land use, deforestation or the production and use of fertilizer and pesticides.
These tools are valuable in accounting for the different impacts and in assessing scenarios that can feed into policy analysis in the context of TCA (see Chapter 3). They also help to fill the data gaps currently hindering TCA mainstreaming and to make scientific findings and data available, accessible and comprehensible to policymakers and other stakeholders.
Life cycle assessment, which sheds light on the environmental impact of certain value chains or single products, can be a valuable input to TCA and should be used to scale up TCA studies.51 For example, LCAs have been used to compare the environmental costs of animal-source food production for livestock, aquaculture and capture fisheries, finding that the lowest-impact production methods were small pelagic fisheries and mollusc aquaculture, whereas the highest-impact production methods were beef production and catfish aquaculture.52 Caution should be exercised, however, with current LCA methodology and studies that tend to favour high-input intensive agricultural systems and misrepresent less intensive agroecological systems, such as organic agriculture.53 Furthermore, any evidence gaps that affect LCA in agrifood systems can carry over to TCA estimates.54 Nevertheless, LCA can be used as a starting point for TCA analysis, with impacts that are typically reported in physical units being converted into monetary terms (as in the case of GHG emissions). Box 22 describes two LCA analyses used to compare different egg and milk production systems in terms of GHG emissions. The two analyses, however, focus on emissions and disregard the other environmental impacts of livestock, so can only provide a partial representation of the environmental impact of livestock production systems. Thus, the analysis provides key inputs for a comprehensive TCA analysis, but needs to be complemented by other major impacts, including deforestation, biodiversity loss, nitrogen leakages, land change, water use and pollution.
Where data for such systemic TCA analyses are lacking, therefore, policymakers and stakeholders should start with data that are currently available. In this regard, FAO’s GLEAM tool is already an important step, as it provides detailed, evidence-based information to key stakeholders on the impacts of the livestock sector on health and the environment (see Box 21 and Box 22). The results of the model can feed into hotspot analyses that can provide alternatives to full quantification when this cannot be achieved due to data scarcity. In a hotspot analysis, the relative importance of the different indicators is made explicit without fully quantifying them. It can be used when data are scarce, but also in other contexts where quantification is not possible, for example, if there are no methods to assess, measure or value certain variables, such as the dependencies and impacts related to some aspects of social capital.
Targeted TCA assessments in data-scarce contexts should also leverage existing tools in the field of sustainability. For example, the FAO Sustainable Food Value Chain framework, although it is not classified as a TCA framework, is very aligned conceptually with the TCA approach. It has been used to analyse food value chains along the three dimensions of sustainability: economic, social and environmental.55 A prominent example of its use in agrifood systems transformation comes from the EU-funded FISH4ACP Project (2020–2024), providing a rigorous standardized approach for value-chain analysis and development in the capture fisheries and aquaculture subsectors. This methodology was field-tested in 12 countries in Africa, the Caribbean and the Pacific.56 The FISH4ACP methodology starts with a functional analysis of the value-chain structure and dynamics, considering all the relevant elements, actors and stakeholders. Then, a sustainability assessment is conducted to assess the value chain’s economic, social and environmental impacts and to identify critical sustainability hotspots. A value chain development plan is subsequently developed to address the identified hotspots,57 including aspects such as capacity building, women’s empowerment, responsible fish stock management, compliance with fisheries legislation and improvements in working conditions.58
The complementary role of standards and accounting services and the need for capacity building
Governments, research institutions and other agencies involved in setting standards for TCA studies play an important role in mainstreaming TCA through a combination of requirements and incentives. Governments can facilitate the integration of TCA into existing and upcoming sustainability and impact reporting mechanisms, as mentioned in the previous section. The recent approval of the Kunming-Montreal Global Biodiversity Framework by 196 countries is a positive step towards enhancing reporting obligations on sustainability challenges resulting from business activities. For example, Target 15 commits governments to requiring all large business and financial institutions to assess and disclose their risks, impacts and dependencies on biodiversity, while Target 18 promises comprehensive reform of environmentally harmful subsidies.61
However, these mechanisms and directives need to be supported by appropriate standards and indicators in order to be implemented successfully. Internationally agreed standards, such as those of the International Organization for Standardization (ISO), allow businesses, for example, to communicate transparently to external stakeholders about the claims they make about their operations.62 Examples of such standards would be how climate change affects the value of the company and how company activities contribute to climate change.63
Another positive development regards the standards for corporate sustainability reporting elaborated by the Taskforce on Climate-related Financial Disclosures (TCFD). The TCFD has further developed recommendations for companies on disclosing climate-related risks and opportunities, which have been widely adopted by businesses and investors, with more than 1 700 organizations endorsing them as of 2021.17 Similarly, the Taskforce on Nature-related Financial Disclosures, a new global initiative, is drawing up recommendations, which are expected by September 2023.64 While not a standard, the taskforce will provide a global framework designed to inform standards about nature-related risk management and disclosure. Therefore, while standard setters play a key role in drawing up standardization for various aspects of TCA, the extent to which these standards are implemented by agrifood systems actors (mainly producers and businesses) will depend on many factors, including the capacity of actors to implement them. Capacity building is certainly needed in this regard, particularly in middle- and low-income countries. The process can be facilitated by governments, who may decide to adopt the standards as mandatory, and could play a critical part in enabling capacity-building programmes in a period of transition.
Complementary to setting standards is the role of accounting services. Accounting firms and business consultancies have a function in developing assessment tools and accounting rules for TCA. By working closely with agrifood producers, businesses and other stakeholders, accounting firms and business consultancies can identify relevant hurdles in the application of TCA and support stakeholders in overcoming them. Here, again, capacity-building and knowledge transfer programmes are needed to adapt accounting services and business consultancies to the norm of TCA.
Financial institutions can facilitate the process through lending policies that increasingly favour sustainable agrifood businesses with the aim of mainstreaming them. In this context, the way credit rating agencies categorize companies based on creditworthiness must also be adapted to the new realities; costs, benefits, risks and assets from a non-financial perspective must be included in any assessment. An example of how this could work in practice is the Agri3 Fund for supporting sustainable agriculture and forest conservation.65 The fund aims to mobilize up to USD 1 billion of public and private financing by “providing credit enhancement tools and technical assistance to enable a transition to more sustainable practices in agricultural value chains and avert deforestation”.66 However, the possibility of scaling up similar initiatives requires the availability of tools and data backed by robust research for evaluating the performance of potential beneficiaries.
