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Estimating Global Dependence of Livelihoods on Forests and the Impacts of Forest Investments

Estimating Global Dependence of Livelihoods on Forests and the Impacts of Forest Investments

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Funder: UK’s Department for International Development (DFID)
Project Period: September 2014-August 2019

The project’s aim is to assess and evaluate forest-sector investment impacts, with a focus on 10 programs supported by three agencies in the UK investing in sustainable forest based livelihoods outcomes through the International Climate Fund: DFID, DECC, and DEFRA. The research focuses on program implementation in five countries (Brazil, Ethiopia, Indonesia, Ghana, and Nepal).

The core objectives of this research project are:

  • Estimate, attribute, and explain the impact of key interventions in DFID’s current forest investments on the livelihoods of forest-dependent people
  • Compare and explain livelihood impacts across different intervention strategies, countries, forest types and farmer characteristics to identify the conditions under which projects generate greater positive impacts
  • Develop robust outcome and causal process indicators, flexible and cost effective data collection procedures, and attribution strategies to enhance the cost-effectiveness of, program targeting, real-time monitoring of program outcomes, and impact evaluation
Quantifying spatial dimensions of forest livelihoods: Estimating how many people live in or near forests?

Development agencies, NGOs, and donors – including DFID and the ICF – concerned with forest sector conservation and development policy frequently refer to large numbers of people (sometimes referred to as ‘forest-dependent people’) that live in and around forests and that rely on forests for livelihoods. Many forest sector development programs have a stated aim of improving the livelihoods of such people. For example, the ICF Key Performance Indicator 3 explicitly aims to measure the “number of forest dependent people with livelihoods benefits protected or improved as a result of ICF support”.

Although there are variations in definitions of ‘forest-dependent people’ (Newton et al. 2016), many include criteria of spatial relationships between people and forests: the typical referent for forest-dependent people are rural people living in and around forests. Claims regarding the number of people living in and around forests vary (including a commonly-cited but empirically unsupported claim from the World Bank (2002) that 1.6 billion people depend on forests for their livelihoods). However, there are few quantitative analyses of the spatial relationships between people and forests nor are consistent data available to indicate the numbers of people living in and around forests that directly derive benefits from forests. Yet such information is essential, if agencies such as DFID and the ICF wish to spatially target interventions to areas where people and forests interact so as to generate and measure the impacts of such interventions. This work therefore aims to help fill this knowledge gap by quantifying the spatial relationship between people and forests.

To date we have combined recent forest cover datasets with human population density data to map the spatial relationship between people and forests globally. We estimate that in 2012 there were 1.6 billion ‘forest-proximate people’ (defined as rural people living within 5km of a forest). A large proportion were in countries entirely or partially in the tropics. A large proportion also lived in relatively densely populated areas. Between 2000 and 2012, the number of forest-proximate people increased in some places but declined in others. We are continuing our analyses to overlay indicators of poverty (using night lights data) onto our maps of forests and people, to estimate numbers of poor forest-proximate people in several individual countries.

Our initial results can be interpreted in two ways. First, the number of forest-dependent people may have previously been over-estimated. If forest proximity is a necessary but insufficient condition for forest dependence, then only a subset of the 1.6 billion forest-proximate people are also forest dependent. It is not possible to know what proportion of forest-proximate people this subset represents: no consistent global data are available on most dimensions of forest livelihoods. But common interpretations of forest dependence do not include people living in relatively densely-populated areas, nor those living in more-developed countries; they instead refer to indigenous, “traditional,” and other peoples living in the more remote forested regions in tropical developing countries. Since a large proportion of forest-proximate people live in relatively densely populated areas, and because our analyses included more- as well as less-developed countries, it is likely that a relatively small proportion of forest-proximate people are also forest dependent, at least according to the most common definitions. Second, much of the spatial interaction between people and forests may occur in relatively densely-populated areas. These findings have substantial implications for individuals and agencies concerned with policies that affect human populations in and around forests.

References

Newton, P., Miller, D. C., Byenkya, M. A. A., & Agrawal, A. (2016). Who are forest-dependent people? A taxonomy to aid livelihood and land use decision-making in forested regions. Land Use Policy, 57: 388-395

World Bank (2002). A revised forest strategy for the World Bank group_31 October 2002. World Bank, Washington, D.C. 

 

 

The first impact assessment, of the Multi-Stakeholder Forestry Program in Nepal, was completed in 2016.

Based on a survey of ~5,000 households in 23 districts across the country, we found that: 

  1. The number of households that MSFP programs covered 344,583 households in the 23 districts covered by our survey (derived from sample proportion of households covered by MSFP in each district, and multiplied by district population).
  2. The mean positive impact on household forest incomes that can be attributed to MSFP: $17.27 per household in 2015 dollars (Note that this estimate does not include changes in asset ownership or in other sources of income).
  3. The estimated total increase in forest incomes for all covered households (simply the multiplication of 1 and 2 above): US$ 5.951M in 2015 dollars.
  4. The number of households covered by MSFP whose total incomes registered an improvement or no decline: 139,437 in 2015.

Read more about the work in the MSFP Impact Estimation Brief  

 

The second assessment, of the ICF Cerrado Program in Brazil (Bahia and Piauí states), was completed in 2017.

The research assessed the ProCerrado program, which aims to build capacity among farmers and provide knowledge for Rural Environmental Register (CAR).

Analyses suggest:

  1. While the CAR and programs facilitating CAR do not have explicit livelihood impact goals, they nonetheless affect livelihoods, both positively and negatively. These impacts depend on the initial amount of natural vegetation on farmers’ properties, farmers’ access to credit and infrastructure, and changing market conditions.
  2. Upon CAR registration, smallholders may actually intend to deforest their property, depending on existing percentage of native vegetation on the property, the use of agricultural loans, property owner’s age, and livestock production experience.

Read more about this work: 

Research Brief: Hastened deforestation as a potential outcome of environmental regulation? KPI-3 Forests and Livelihoods

Research Brief: Brazil’s national environmental registry of rural properties: Implications for livelihoods KPI-3 Forests and Livelihoods:

Rasmussen, L.V., Jung, S., Brites, A.D., Watkins, C., Agrawal, A. 2017 Understanding smallholders’ intended deforestation behavior in the Brazilian Cerrado following environmental registry. Environmental Research Letters 12:9

Jung, S., Rasmussen, L.V., Watkins, C., Newton, P., Agrawal, A. 2017. Brazil’s National Environmental Registry of Rural Properties: Implications for Livelihoods. Ecological Economics 137: 53-61 

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Impact assessments are also being undertaken in the following countries/programs:

Credit: Jennifer Zavaleta

Brazil (The Rural Sustentável project) 

Read about this DEFRA project here:

Newton, P., Gomez, A.E.A., Jung, S., Kelly, T., de Araújo Mendes, T., Rasmussen, L.V., dos Reis, J.C., Rodrigues, R.D.A.R., Tipper, R., van der Horst, D. and Watkins, C., (2016). Overcoming barriers to low carbon agriculture and forest restoration in Brazil: The Rural Sustentável project. World Development Perspectives, 4, pp.5-7.

Ghana (FIP program, specifically in Western and Brong Ahafo regions)

FLARE researchers recently completed a training for a team of enumerators to undertake household surveys in ~1800 households. The team will use FLARE’s LivWell tool to collect data by the end of 2017.

Ghana (FGMC-VPA program, specifically in Western and Brong Ahafo regions)

Indonesia (FLAG program, specifically in South Sumatra)

For more information on these projects, contact Cristy Watkins (watkinsc@umich.edu)