An ongoing series on the value of ecological data.
(See Part 1 here)
This post addresses the value of ecological data used to support regenerative land management practices. We describe an approach to estimate current values of ecological data based on what we know about expenditures on payment for ecosystem services. The methods can apply from a global scale all the way down to individual projects.
This article focuses on activities like crop- and rangeland restoration projects, changes in land management practices, and the factors that enable these activities. We describe an outcome-based approach to ecological regeneration that is concerned with a measurable set of conditions we would like to achieve. These conditions can be set through any number of processes, such as multi-criteria decision making, democratic voting institutions, and even command-and-control approaches. Decision making processes are very important to outcomes, but the focus of this post will be on what happens after these decisions are made.
Knowledge and information are some of the key ingredients of Regen Network’s economic approach to achieve desirable ecological outcomes. Implementing regenerative projects or activities requires many types of resources: financial, social capital, built capital, labor, technical expertise, and data. As we will explain in our forthcoming economic technical paper, we consider “data” to mean not only raw signals, but a broader category of data resources, which we define in more detail below.
What we are really looking to develop is a Knowledge Production System (hereafter abbreviated as KPS) that provides infrastructure and guidance for achieving ecological outcomes. Data resources allow us to marshal and coordinate resources, identify needs for regenerative practices, guide the implementation, and ensure desirable outcomes are maintained over time.
A KPS contains the elements we need to acquire and manage the data resources we need to solve problems and process them into more valuable information products. It also includes the tools embodied in software code and the know-how required to use them effectively. While all these components may exist independently of one another — a data set here, an image repository there — they must be conceptually tied together, and eventually we hope to see an integrated system arising from the universe of valuable tools that have already been developed in the ecosystem services field.
The notion of knowledge production is inspired by the work of Robert S. Taylor, one of the early proponents of the field of information science (see here and this informative PDF here for more background). Generalizing from Taylor’s value-added model of information, it may be useful to consider data as part of a knowledge production system consisting of entities that contribute (in roughly increasing order of value):
- Raw, unprocessed data
- Filtered, aggregated, and processed data
- Data management tools
- Information products, such as descriptive studies and reports
- Decision support services
Categories of data resources
When enterprises start to develop their service systems, with the aim to support value co-creation, they will also need to do infrastructural investments [such as] …cultural/knowledge assets…institutions…as well as technological assets such as shared technology platforms.
Henderik A. Proper, Marija Bjekovic, Christophe Feltus, and Ivan Razo-Zapata. 2018. “On the Development of a Modelling Framework for Value Co-creation.” (PDF)
The article quoted above highlights the importance of know-how and information infrastructures embedded in software libraries, personnel, and processes created to handle data. This leads us to the following categorization of data resources in the data economy:
- Data inputs: raw or processed data used to create or update an existing asset, or trigger a process defined in an ecological contract.
- Data assets: a compilation of data that has some enduring value. For example, a baseline conditions report of a restoration site.
- Data infrastructure: software used to manage, process, and add value to data products. Also the ability to use and interpret data and information.
The article also mentions the important notion of value co-creation, a topic to which we will turn in the next essay in this series.
A budget-based approach
Some data resources may have intrinsic values, but for this exercise, they are assumed to be valuable mostly in service of real world ecological objectives as implemented through projects or in management processes. This budget-based approach is a cost accounting model used to measure the use of data resources in an activity. When a project or management practice is proposed, we (implicitly or explicitly) must consider resource availability and constraints. While these are often expressed in monetary terms, like the costs required to undertake a study or pay for on-site monitoring, they may also include non-monetary units like time. This framework is useful for several reasons:
- Projects generally require budgets.
- A budget-based approach allows us to use standard financial metrics in addition to ecological measures.
- We can achieve greater conservation and restoration impact by considering ecological benefits and economic costs together rather than as separate concerns. Paying close attention to project budgets and cost accounting can make this happen.
The data resource life cycle
There are also life cycles associated with data resources. Efforts implemented through ESPs and Ecological Contracts will require different types of data resource at different times, and the value of those resources at any time will vary. For example, data used in the planning stages of a project may require a high degree of processing and interpretation while the plan is being developed, but becomes less relevant in later stages of the project. This is important to the allocation of payments and other resources among the system’s participants, which will be explained in more detail in the forthcoming economic technical paper.
What is the size of the ecological data economy? A project-level approach
Our approach to creating a budget framework for ecological regeneration practices involves three steps:
- Breaking project costs into categories based on the general type of activity.
- Estimating the data intensity of each type of activity — how much does the end state depend on data?
- Estimating the the relative contributions of data in the form of inputs, assets, and infrastructure.
To illustrate, we use species conservation banking in the United States as an example. Conservation banking offsets the adverse impacts on endangered species caused by human development by creating permanently protected habitat elsewhere. More helpful information can be found here.
In a recent paper, Ecosystem Marketplace reported five cost categories in an analysis of species conservation banking in the United States:
- Design and planning
- Land costs
- Construction costs
- Maintenance and monitoring
- Long-term stewardship
For data intensity and relative contributions (steps two and three), we will make some assumptions about data intensity and the combination of data inputs, which are illustrated with a hypothetical species conservation bank, as shown in the table below.
Based on figures reported in a survey conducted in 2013 (PDF), the average cost of a species conservation bank in the United States is $35 million. This yields the following contribution of data resources to the project:
In this example, roughly 16% of expenditures associated with creating a species bank can be attributed to data resources. While each project will face its own data resource requirements, the cost accounting framework can be universally applied.
The size of the global ecological data economy
Now let’s take a look at the global ecological data economy. The following estimates are based on Payments for Ecosystem Services (PES), which represent only the actual money spent on these activities. They do not address the total economic value of ecosystem services, nor do they address indirect and unreported expenditures on ecosystem service provision. These are important questions we save for another time.
A recent paper on global PES by James Salzman and others has estimated the level of global PES at $36 billion. Plugging this figure into the budget model we described above yields an estimate of $5.65 billion per year in the value of PES that can be attributable to data resources.
This is obviously very sensitive to the assumptions one makes: how similar are are watershed protection projects in Asia or forestry projects in Europe to the biodiversity markets in the U.S.? While we are doing our best to estimate these figures, we warmly welcome your help on this issue. In one sense, every ecosystem restoration project or land management practice implementation will be different, but developing a standardized set of cost accounting measures will provide a tremendous public good that can improve environmental decision making across a wide range of product and practice types.
This article was originally intended to provide a discussion of how economic networks provide the means to generate surplus value — in other words, where working cooperatively can provide greater benefits to society than just going at it alone.
In writing that article, it became clear that other elements of the system deserved some attention, and now that we have laid out the thinking behind our current estimates of the value of ecological data, we can move on to topics such as the value of information, and the conditions where the improved use of data resources can open up entirely new opportunities to create value — which we see as one of the major growth stories behind Regen Network and the Regen Consortium.
Stay tuned for more articles in this series:
- How economic networks allow us to generate surplus value (next time)
- Allocating the surplus generated by cooperation
- The role of accounting models in the ecological data economy
- Using economic elements in an Ecological State Protocol / Ecological Contract framework
- “Eye-opening metaphors” in ecological economics: beyond stocks and flows