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The aim of this paper is to analyze how historical maps could be used in the context of SEEA EA and what information could be derived from historical maps to facilitate mapping and assessment of ecosystem extent and condition in according to the SEEA EA guidelines. The source materials were maps documenting the landscape in 1883, 1936, 1977 and 2011 of the case study area of Ślęża Landscape Park in Poland, Central Europe. The landscape level characteristics of naturalness and diversity were used to understand the condition. The Ślęża Landscape Park’s ecosystem extent results show trends of increase in forest cover of about 700 ha driven by afforestation and decrease in the agricultural share of land. In 1883, the biggest forested areas were not connected to each other, but on the other hand, forest areas were more compact, as diversity of boundaries was lowered inside forests. The least natural areas were scarce and so human pressure and impacts were lower. Humans have significantly influenced this landscape by expanding mineral extraction activities and built-up areas. We discuss the challenges of local scale SEEA EA applications and present lessons learned from this process.

Ecosystem services are essential for human survival and wellbeing, and the quantity and quality of the services delivered by an ecosystem are dependent on its underlying condition. With many ecosystems degraded or in poor condition, the capacity to deliver key ecosystem services can be affected. The System of Environmental Economic Accounting – Ecosystem Accounting (SEEA EA) is the international statistical framework developed to estimate and monitor changes in ecosystem extent, condition, and flow of ecosystem services. Although the condition of ecosystems underpins their capacity to supply a service, the connection between ecosystem condition and ecosystem services accounts is underdeveloped. This is because condition is usually assessed relative to an ideal reference state, whereas capacity to supply ecosystem services depends on both the condition of the ecosystem and the ecosystem service being considered. For example, a forest in one condition may have very different capacities to supply ecosystem services such as timber provisioning and recreational services. We propose a methodology to better integrate condition and ecosystem services accounts through development of an ecosystem capacity index. Building on condition accounts, we derived a capacity index and related capacity accounts, reflecting the capacity of each ecosystem asset to support the delivery of selected ecosystem services. We show that an ecosystem with a particular condition profile may have different capacity index values depending on the ecosystem service that is expected to be delivered. We provide case studies from two sites in the Republic of Ireland to illustrate the integration of capacity indices into ecosystem services accounts. This approach is an extension to ecosystem accounting that enables more rigorous connection of condition to ecosystem service supply by accounting for capacity.

In this paper, we demonstrate value generalisation from a sample of ecosystem assets – municipally managed trees - to all tree assets within an urban ecosystem accounting area. A Bayesian network model is used to machine-learn non-parametric correlation patterns between biophysical site condition variables and output variables of an ecosystem service model – here iTree Eco for modelling the regulating services of urban forests. The paper also demonstrates the use of spatial Bayesian network modelling to quantify the reliability of value generalisation for accounting purposes. Value generalisation entails inferring ecosystem service values for all locations in an ecosystem accounting area, where the accounting practitioner has less information about the asset and its context, than in an available sample of managed sites within the accounting area. The modelling is carried out as a “proof-of-principle” of potential value generalisation and uncertainty analysis methods for ecosystem accounting. It does not cover all regulating ecosystem services of urban forests, nor cultural services. While noting that wide confidence intervals for generalised values pose challenges for using monetary accounts for the accounting purpose of change detection, we find that tree-specific asset valuation is possible in an urban accounting setting. Our findings serve the purpose of raising awareness about asset values of urban green infrastructure, to bring them more on a par with grey infrastructure in urban planning. We also argue that the reliability of the asset value of individual trees is also good enough to be used for non-accounting purposes, such as municipal tree damage assessments.

The European Commission developed an amendment to Regulation 691/2011 on European environmental economic accounts to include reporting on ecosystem accounts compliant to the United Nations Statistical Commission System of Environmental-Economic Accounts – Ecosystem Accounts (SEEA-EA) standard. To support Member States implementing this regulation, an open source tool, known as INCA-tool, to generate ecosystem service accounts has been developed, based on the Knowledge Innovation Project on Integrated Systems of Naural Capital and Ecosystem Services Accounting (KIP-INCA) methodologies. The INCA-tool was developed by taking into account the FAIR principle for software and data, as well as existing interoperability standards by the SEEA community. Three types of users were identified with their specific needs, interactions and skills. To meet their needs, the INCA-tool was split into two parts, a python package to perform the calculations and an acessible and easy-to-use user interface in QGIS to integrate national information. With a first version of the toolkit in place, improvements to the existing calculation methods and alignment with the upcoming EU regulation can be achieved. Further, feedback from Member States beta-tests and their experiences is currently collected and implemented and the full public roll-out is planned for the end of 2022. The software packages in the toolkit were already used to extend the existing nine INCA European wall-to-wall account series with the year 2018.

The UN System of Environmental-Economic Accounting Ecosystem Accounting (SEEA EA) aims at regular and standardised stocktaking of the extent of ecosystems, their condition, and the services they provide to society. Recording the condition of ecosystems is one of the most complex pieces in this exercise and needs to be supported by consistent guidelines. SEEA EA defines the condition of an ecosystem as its overall quality, measured in terms of quantitative metrics describing its abiotic and biotic characteristics. One of the key challenges lies in identifying the most appropriate metrics for each ecosystem type that capture these essential characteristics.The objective of this paper is to create a well-defined framework for transparent and operative development of ecosystem condition indicators, which can be used in ecosystem accounting and ecosystem assessment studies. Starting from the SEEA EA documentation and a small targeted systematic review, we identified 12 key criteria, which we grouped according to their roles during the indicator development process. Five conceptual criteria (intrinsic relevance, instrumental relevance, sensitivity, directional meaning, and framework conformity) outline the priorities for identifying relevant characteristics of the ecosystems. Five practical criteria (validity, reliability, availability, simplicity, and compatibility) provide guidance on identifying concrete quantitative metrics for the selected characteristics. Finally, two ensemble criteria (comprehensiveness, and parsimony) ensure the completeness and complementarity of the final set of metrics.To tackle the climate and biodiversity crises, the condition of ecosystems needs to be better recognized in national economic planning. The proposed framework supports the selection of a concise set of salient and credible ecosystem condition indicators through a transparent, repeatable and scientific process. This can make the compilation of ecosystem condition accounts more accessible and more standardised on a global level, which is a key prerequisite for the success of SEEA EA. Additionally, the framework presented in this paper may be useful in other contexts where ecological, environmental, or sustainability indicators need to be identified.

The System of Environmental-Economic Accounting - Ecosystem Accounting (SEEA EA) provides an integrated statistical framework which organizes spatially explicit data on environmental quality, natural capital and ecosystem services and links this information to economic activities such as agriculture. In this paper we assess how the SEEA EA can support the monitoring and evaluation of environmental objectives of the Common Agricultural Policy (CAP). We focus on the Netherlands, for which an elaborate set of SEEA EA accounts has been published, and the themes of nitrogen pollution and farmland biodiversity. We studied the completeness of indicators included in the accounts, their quality and analysed how the accounts could support agri-environmental reporting, agri-environmental measures effectiveness assessments, and results-based payments to farmers. As a reference we used the Driving forces – Pressures – State – Impacts - Responses (DPSIR) framework. The Dutch SEEA EA accounts only include half of the indicators which we considered essential to assess the effects of farming on natural capital and ecosystem services for the two studied environmental themes. However, most gaps in the accounts could be filled with other publicly available environmental monitoring data. Regarding N pollution, the availability and reliability of indicators at landscape and farm scales are not sufficient to support the assessment of agri-environmental measures effectiveness and results-based payments to decrease N pollution. The accounts have a higher potential to support the assessment of measures to conserve farmland biodiversity, in particular due to high resolution maps of ecosystem extent and ecosystem services flows. The potential of the SEEA EA accounts may be more limited in other countries where ecosystem accounting has only recently started. However, the SEEA EA is also implemented at the European Union scale, so that SEEA EA indicators will gradually become available for all European countries. To enhance the relevance of the SEEA EA in the agri-environmental policy area, we recommend to integrate information on farming emissions (externalities) recorded in the SEEA Central Framework with SEEA EA accounts and evaluate the applicability of SEEA EA accounts for case studies at landscape and farm scales. Our research shows that the Dutch SEEA EA accounts, complemented with other data sources, have potential to strongly enhance the CAP monitoring and evaluation framework but further steps need to be taken to fill data gaps.