Resources Article

Targeting Forest Conservation Investments: An Interview with Allen Blackman

Jan 15, 2015 | Allen Blackman

Researchers at RFF are developing a free, Web-based interactive decision tool to help policymakers in Mexico, Central America, and the Dominican Republic decide where to invest scarce forest conservation resources. The tool uses rich spatial data on deforestation risk, forest ecosystem services, and the cost of conservation to map trade-offs in conservation investments.

During the past two years, a prototype has been used by The Nature Conservancy to help target conservation investments for the US Agency for International Development’s $29 million program, Mexico Reducing Emissions from Deforestation and Degradation (MREDD). RFF Thomas Klutznick Senior Fellow and project director Allen Blackman recently talked with Resources about how the tool empowers conservation decisionmakers.

RESOURCES: What was the impetus for developing the Forest Conservation Targeting Tool?

ALLEN BLACKMAN: Deforestation and degradation continue to be severe problems in developing countries. They cause all kinds of local and global environmental problems, from soil erosion and flooding to biodiversity loss and climate change. But resources available to address those problems are scarce. That means it is critically important that the resources we devote to forest conservation have the biggest bang for the buck possible.

Juha Siikamäki [RFF associate research director and senior fellow] and I conceived of the Forest Conservation Targeting Tool to do that. The goal is to help decisionmakers choose locations where conservation has the greatest payoff. Building the tool has been a team effort, and I think Juha and I have put together a really good one. RFF Research Assistant Len Goff is responsible for programming the model, and it looks good and works well because of him. Jessica Chu is our geographical information systems expert. We’ve just hired a post-doc named Joe Maher who will play a big role over the next couple of years. And Matt Hansen and Peter Potopov at the University of Maryland are creating our forest loss data using satellite images.

The targeting tool is actually one of two forest conservation decision tools we’re developing as part of a larger project funded by the National Aeronautics and Space Administration’s SERVIR program and the Moore Foundation. While the targeting tool can be used to plan future conservation investment, the other tool is for evaluating the effectiveness of specific policies, such as protected areas and payments for ecosystems, that already are in use.

Looking ahead, we hope to combine these two approaches. We want to enable policymakers to get answers to questions such as, “Where are payments for ecosystem services likely to be particularly effective?” and “Where are protected areas likely to work best?”

RESOURCES: An important feature of the Forest Conservation Targeting Tool is that it allows users to choose which conservation goals are most important to them and gauge the consequences of those judgments. What factors come into play when estimating the return on investment for a given conservation investment, and how does the tool account for these?

BLACKMAN: The model works by dividing up forested land into parcels. The user can define these parcels in a number of ways. Where we have data on property boundaries, they can be used. In other places, fine-scale administrative boundaries or simple one-kilometer cells can be used.

For each of these parcels, the model estimates the conservation return on investment, which depends on three factors. The first is the risk of deforestation in the parcel. If you have very low risk, investment in conservation is not going to buy you anything additional.

The second factor is how much it costs to conserve forests in the parcel.

The third factor is the level of ecosystem services the forests in the parcel produce. We look at three forest ecosystem services—carbon storage; hydrological services, such as flood prevention and replenishing of aquifers; and the provision of biodiversity habitat.

All users of the tool will not place the same weights on these factors. For example, managers of REDD programs may care more about carbon than biodiversity. And managers of well-funded projects may care less about conservation costs than benefits. So the tool allows users to weight these factors as they please, with an easy-to-use slider graphic. In general, we have tried to make the tool as flexible as possible so that users can adapt it to their specific needs.

The Forest Conservation Targeting Tool maps areas that will have the highest (red) to lowest (blue) return on conservation investment, as defined by the user’s particular budget and goals.

RESOURCES: So this rich dataset is the first contribution, and the second is that you have developed a user-friendly, Web-based interface that allows a non-technical person to use these data. When a user sits down at this application, what does that person see?

BLACKMAN: The main way that data is conveyed is through a map. At the simplest level, the model does two things. First, it maps deforestation risk, conservation costs, and ecological services. And second, it tells you where the greatest conservation bang for the buck is.

A variety of pull-down menus and selection tools allow the user to define the study area and parcels and to control how bang for the buck is calculated, what the conservation budget is, what information is displayed, and what information is outputted. The maps are designed to be intuitive; the data are displayed in a color-coded format, where red areas typically are high values and blue areas are low ones. The model allows the user to take advantage of rich onboard data and to download results in map or tabular form.

RESOURCES: Where are you in the process of developing the Forest Conservation Targeting Tool?

BLACKMAN: We are two years into a four-year project. The goal, at the end of the project, is to launch both the targeting tool and the evaluation tool, make them publicly available on the Web, and train an initial set of users.

We are on track to meet that goal. The tool is basically ready to be used for Mexico. We are close to being at that stage for Central America and the Dominican Republic. We’ve been taking trips to the region to lay the groundwork for training and dissemination in the last year of the project.

The software architecture is more or less where we need it to be except for one thing: creating the ability for users to upload and use their own data. We think that the onboard data we have now is pretty good given the challenge of covering all of Mesoamerica. But users may be interested in conservation efforts targeting just a small part of that region, and they may be interested in a very specific question for which they have better data.

For example, let’s say they’re interested in conserving habitat for jaguars in northern Guatemala. While our data on species richness are reasonably good given our large geographic scope, they are not the most accurate data if you are interested in one particular place or species. If users have better data on jaguars, or some other species, we want to enable them to upload and use those data.