Story | 17 Jan, 2018

An economist's view of forest landscape restoration

The Restoration Opportunities Assessment Methodology has proven effective for countries in planning their restoration actions. A large part of this process consists of comparing the costs and benefits of different restoration options, the consideration of the carbon balance and the analysis of financing mechanisms. All of these components are essential to understanding the advantages and disadvantages of each potential restoration action, and how their implementation can be financed. 

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Photo: IUCN

We asked Leander Raes, an economist at the IUCN Regional Office for Mexico, Central America and the Caribbean, to explain the functioning and the importance of different analyses for decision-making around forest landscape restoration (FLR). 

Q. As an economic specialist, what is your role in the Restoration Opportunities Assessment Methodology (ROAM)?

I carry out economic analyses and analyse financing mechanisms for FLR in four Central American countries. Basically, that means looking at the costs and benefits of the different restoration actions proposed in each country, comparing them to one another and with current land uses to evaluate which ones will have the greatest impact. This is based on a set of priority criteria and analysing the potential sources of funding to implement these restoration actions.

These analyses generate diverse data, some monetary, some non-monetary. Most people think economics is only about money; however, economics is about resources. In the field of landscape management, the analyses we run are very broad. We study the cash flows (the investment we will need to make and what we will gain from it) but also the environmental and social advantages and disadvantages of restoration actions. The results of these analyses will tell us where to restore and how to potentially finance these actions to optimise our resources and efforts according to our restoration goals.

Q. What are these analyses about?

The impact of restoration actions may not all be positive. With the cost-benefit analysis, we estimate the pros and cons for each proposed restoration intervention. We go beyond the monetary costs and benefits by including environmental and social aspects to the equation. We include in this analysis the different implementation costs (for example: labour costs, purchase of fertilisers or seeds, etc.), the financial benefits (whether the action will enhance timber or crop production for instance, and what would the financial gain be), but also the environmental benefits (such as how sedimentation reduction enhances water quality, or the improvement of ecosystem connectivity) and the social ones (job creation and the improvement of food security, for example). We do the same analysis for current land uses, since the idea is to compare them to one another. We want to know, for example from a financial perspective, whether a restoration action is profitable or not; whether it’s more profitable than the current land use, or not—that’s the first thing we want to know.

These analyses are essential to understand the trade-offs of FLR, since certain restoration actions, such as specific conservation actions may not generate cash flows. For example, the restoration of riparian forests may not generate direct monetary benefits for landholders, but may certainly have a positive impact on sediments and nutrient retention, for instance, and thus on water quality and public goods.

We also calculate the carbon balance measure, which shows us the differences with and without restoration actions in terms of carbon emissions, carbon sequestration and storage. It allows us to predict if the restoration actions proposed will increase carbon emissions or sequester carbon, and provides an estimate of how much the carbon balance is.

All the previous analyses are part of the overall economic analysis and work together with a multi-criteria analysis to provide us with an understanding of which restoration actions have, on average, the best performance. Considering different units (monetary, carbon, job creation, etc.), this last analysis allows us to estimate the overall impact of different restoration actions.

Then, we try to connect, as far as possible, the impact of the different restoration actions with those areas of the country where we can potentially achieve the highest benefit, conducting a spatial multi-criteria analysis. For example, you want to create jobs in areas with the highest unemployment or you want to reduce erosion in areas with the most fertile soils, etc. With the help of spatial models, we can « superimpose » each of these criteria maps together, resulting in a map that will show us which areas are prioritised based on a series of overlapping indicators.

In addition to spatial prioritisation, we use data from the economic analysis to evaluate the different financing options. This analysis of financing mechanisms allows us to identify potential sources of funding in a country to implement these restoration actions, taking into account existing and potential financing instruments, such as governmental subsidies for FLR, private investment and payments for ecosystem services programmes, among others.

Q. What kinds of data are needed to conduct these analyses?

First of all, we need to have a clear idea of the restoration actions the country is proposing to achieve its restoration goals. What species of trees might they add to the landscape (what density)? Will they use fertilisers (how much, which types)? Will there be cattle on the site? We need to have this information prior to starting any of these analyses.

We will also need to gather data related to crops and livestock prices, yield production, etc. For example, historical data (when predictions of future yields or prices are not available) can be used as they include information on past price and production fluctuations. Climate change predictions are also very useful, but unfortunately not always available for all the different production systems considered. 

We can find historical data in national statistics and in databases generated by international organisations, such as The United Nations Food and Agriculture Organization (FAO) or the World Bank, among others. We can also generate information based on existing data. In El Salvador, there was no land use map available when we started the ROAM assessment so one was created during the process. We can also generate new information, for example, in Costa Rica we created a map of rainfall erosivity based on a national formula which uses spatial rainfall data and elevation.

Q. How do these analyses work?

Different software can be used to conduct economic and spatial multi-criteria analyses. We used the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) tool in Costa Rica, El Salvador and Honduras. It was used to estimate the impact on erosion, sediment and nutrient export of different restoration actions. This tool uses spatial models to map and value the different ecosystem services and goods.

In El Salvador and Honduras, we used spatial multi-criteria models built with the ArcGIS software. In Costa Rica, we used the Restoration Opportunities Optimisation Tool (ROOT), software that helps with visualising where restoration efforts should be made to optimise benefits for multiple landscape goals. It is a very useful instrument to identify the areas where the impact on a series of ecosystem services would be the highest. In the case of Costa Rica, where there are governmental programmes implementing or encouraging FLR such as the national “Payments for Environmental Services Program” and the “NAMAs” (Nationally Appropriate Mitigation Actions, from the United Nations Framework Convention on Climate Change) for coffee and livestock, we used the results of the ROOT analysis to define where these programmes should prioritise the implementation of restoration actions in the country – to optimise the impact on the provision of the ecosystem service in consideration. In Guatemala, we are currently implementing the Resource Investment Optimatization System (RIOS) tool, which can be an interesting tool for countries which have a specific budget allocated for an FLR related programme.

To measure carbon balance, we used the Ex-Ante Carbon-balance Tool (EX-ACT) created by FAO. It calculates the impact of restoration actions on the carbon balance considering different types of gas emissions (carbon equivalents).

All these tools we are using have been developed by experts and tested in the field—they have a lot of capabilities. They are all available online and free to use.

Q. How do the results of these analyses affect decision making for FLR?

When we talk about landscape restoration, it includes many different interests. Some stakeholders will be more concerned about the impact of restoration actions on water quality, others on timber production, while others may have biological corridors as a priority area for restoration actions. There is never only one criterion to consider, so we have to conduct these different analyses to be able to compare the advantages and disadvantages of each restoration option proposed—to make informed decisions.

Moreover, FLR is a means used by many countries to catalyse their efforts to achieve their part of international commitments around global development and conservation goals, such as: Aichi Target 15 from the Convention on Biological Diversity, the REDD+ goal of the United Nations Framework Convention on Climate Change (UNFCCC), The Sustainable Development Goals and the Rio+20 land degradation neutrality goal. It is then imperative to link financial profitability of restoration actions with environmental and social impacts.

The information generated by these analyses can also influence policy making around FLR. Having a clear idea of what, where, how and at what cost FLR should be implemented in a country may help its government to develop new policies or to adapt the existing ones to ensure restoration goals will be achieved.

Thanks very much for your time, Leander.

Related links:

infoFLR –  

Bonn Challenge: