Integrating place-specific livelihood and equity outcomes into global assessments of bioenergy deployment

Recent efforts to model climate change mitigation within possible future energy systems have suggested that bioenergy can play a major role. The special report on renewable energy sources (SRREN) (IPCC 2011) indicates a global potential deployment of biomass for bioenergy in the range of 100–300 EJ yr⁻¹ in 2050, with possibly more than 100 EJ yr⁻¹ coming from dedicated bioenergy crops—compared to about 11 EJ yr⁻¹ in dedicated bioenergy crops in 2008 (Chum et al 2011). Similarly, the Global Energy Assessment suggests that the production of bioenergy crops may increase by a factor of 10 in 2050 (Coelho et al 2012). Such a high deployment of biomass for bioenergy would require a massive agricultural transformation with significant implications for both environmental and social change. The global warming effects of induced land-use change and carbon stock dynamics can negate mitigation from fuel substitution (Fargione et al 2008, Searchinger 2012), a result that has more recently been taken up in assessment models (Wise et al 2009, Creutzig et al 2012a). Models also consider potential widespread impacts that such an agricultural transformation would have on environmental resources, e.g. water, soil and biodiversity (Erb et al 2012, Gerbens-Leenes et al 2009, Popp et al 2011). But the global competition for land use (Lambin and Meyfroidt 2011) is intensified by higher demand for biofuels, and impacts not only ecological dimensions but also directly about 1.5 billion smallholders⁷ and agro-pastoralists. Hence, the aforementioned studies and assessments remain deficient in two ways. First, they operationalize social impacts as economic efficiency, economic growth, and sometimes food prices, ignoring other important dimensions of human wellbeing such as change in socio-economic and health conditions. Second, the high level of spatial aggregation of the assessments makes them blind to place-specific drivers and distribution of impacts among different social groups and geographical areas within countries.

As an example of a global energy assessment, the SRREN mentions livelihoods as a relevant category (p 58 and p 90), indicating positive outcomes of bioenergy deployment for livelihoods (Chum et al 2011). However, it neither considers livelihood impacts as a constraint to deployment nor identifies factors shaping the interaction between bioenergy and livelihoods. The mandate of the SRREN focuses on cost-effective climate change mitigation and does not extend to evaluating goals such as poverty alleviation, other livelihood improvements, and ecological sustainability. But climate change mitigation and economic growth are ultimately not goals on their own but rather means to achieve human wellbeing. Place-specific livelihood impacts of bioenergy schemes are nuanced, differentiated and insufficiently reflected in current bioenergy assessments (German et al 2012, Creutzig et al 2012b). As a result, the livelihoods of workers, smallholder farmers and local populations, as well as distributional considerations are systematically underexplored (Corbera and Pascual 2012, Creutzig et al 2012b). It is these gaps—livelihoods and equity—that we seek to narrow.

In this letter, we systematically identify routes by which bioenergy deployment can influence livelihoods, influenced by global market dynamics and place-specific production models. We analyze case studies that investigate livelihood effects of cultivating the most common feedstocks for first-generation biofuels, as these constitute the vast majority of bioenergy schemes. Among these schemes, we investigate specifically those in developing countries where agriculture often remains a predominant source of livelihoods en large. We focus on the cultivation of oil palm, jatropha, soy, cassava and sugarcane. Section 2 addresses both global and local drivers and effects of biofuel deployment on livelihoods. Section 3 summarizes the livelihood effects in a conceptual figure and an example. We then discuss how these insights could be better incorporated in integrated assessments of bioenergy deployment and provide an outlook on the potential of second-generation biofuels for improving livelihoods in section 4.

Livelihoods include 'flows', like income and food availability, and 'stocks' such as land tenure, social and financial assets. Gaining an income through a variety of on-farm and off-farm activities constitutes a key pillar of most rural livelihoods, allowing households and individuals to participate in markets and to benefit from resource use (Ellis 2000, Tesfaye et al 2011). Food access is an obvious livelihood dimension, which is often closely related to income but can also be decoupled, particularly in non-market economies and subsistence farming. Access to land and natural resources is another crucial capability because it enables the production of food for subsistence or sale, and provides other goods and services including firewood and medicinal plants. Access to land can also translate into land rents and flows of income. Such access is mediated by tenure regimes, which encompass property rights, and both formal and informal social relations and systems of authority that influence who gets access to and exercises control over land resources (Ribot and Peluso 2003). Health constitutes a particularly important asset that we include in our analysis as it is influenced both by poverty levels and livelihood strategies, including agricultural practices. We discuss each dimension in turn, highlighting what has been covered in macro-economic studies and contrasting with insights from bottom-up studies of bioenergy feedstock cultivation.

2.1. Bioenergy and income

2.2. Bioenergy and food security

2.3. Bioenergy and land tenure

2.4. Bioenergy and health

Section 2 has reviewed studies that illustrate actual or potential impacts of bioenergy feedstock deployment on rural livelihoods, considering global and local dynamics. Global and regional models can already capture potentially relevant income and food security effects, and can identify groups that profit or lose from bioenergy deployment. However, they fall short of scrutinizing changes in capabilities and assets, and remain restricted in their treatment of distributional equity due to crude resolution of distributional dynamics. At the micro-scale, research reveals that the production model plays a key role in determining income gains or losses, food security, land tenure conflicts and health effects for workers and communities. The smallholder production model benefits livelihoods more when it is labor intensive, when it enables smallholders to participate in value creation along the supply chain, and when it is pursued alongside food production. Large-scale production may involve higher land productivity, but also requires considerable financial and human capital, increasing the risk that income inequalities will result, sometimes mirrored in health impacts and social exclusion. While plantation workers usually experience increased income, reductions in food production and community activities due to time constraints must also be accounted for. Those who obtain or keep formal land rights tend to profit from bioenergy deployment by reaping a higher land rent, while those who lose formal or—more often—informal land rights and access to land and its ecosystem services see a reduction in their land-related livelihoods. This can be particularly acute if deployment takes place through plantations that do not offer new opportunities to the dispossessed. As those with formal land rights usually have more capital to start with, and those without rights have less access to institutions and money, distributional inequities are often increased by formalization and consolidation of land rights. These main effects are summarized in the conceptual diagram of figure 1.

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Assessing how these effects are distributed is crucial. As an example, a recent study of oil palm production in Indonesia by independent growers and outgrowers contracted to nucleus estates reveals aggregate and distributional effects that generate winners and losers (Obidzinski et al 2012). Smallholders reported increased income from producing oil palm and non-participants benefited from spillover demand effects in other economic sectors. However, these economic benefits were unequally distributed: 'former land owners' and 'customary land users' lost while (migrant) landowners with formal title gained. Access to capital reportedly served as a barrier to participation and whilst most stakeholders reported improved access to food, oil palm replaced cash crops and some food crops on smallholder land. Land values also increased, benefiting those with plots, but conflicts over land occurred, both between communities and companies and between traditional landowners and migrants. The loss of customary rights to cropland, mentioned by over half (53%) of respondents in one site, was more likely to affect households without formal land titles. The expansion of oil palm also reduced access to forest products and services—including fallows for shifting agriculture— affecting women's livelihoods more than men's. Oil palm production reduced the quality and quantity of available fresh water, caused soil erosion and water logging, and increased flooding. If one looked only at aggregate outcomes, this case would suggest that mildly positive outcomes occurred in three of the four livelihood dimensions. But once distributional outcomes are also considered, the analysis would suggest that inequities were perpetuated or widened in all four livelihood dimensions. Particularly for land and income, gains (for some) have been offset by increased inequality in the distribution of outcomes (figure 2).

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In spite of this growing evidence that bioenergy deployment has effected or may effect income, food security, land tenure and other assets, global integrated assessment studies consider livelihoods only generically and are operationalized by focusing on global market effects. Golub et al (2012) investigate the livelihood impact of global land-based climate policies (not bioenergy deployment), focusing on income and food security. They find that restricting deforestation and agricultural expansion for food production leads to increased income and food availability in farm households due to price appreciation, but mostly a decrease in real income and food availability for the non-farm poor. As biofuel production puts similar pressure on arable land and food production, it is likely to produce similar livelihood effects on the agricultural food-sector and non-farming livelihoods. Golub et al's study is the first to try to quantify livelihood effects of climate policies systematically and globally. However, by modeling a representative farming household it assumes complete and equal value capture by rural households, and underestimates the distribution of benefits and costs at the micro-scale, e.g. by not representing livelihood assets.

Our analysis demonstrates that deployment schemes do not only affect aggregate income and food availability. Schemes also impact land tenure and the quality or availability of other assets, particularly finance and health. More importantly, the design of deployment schemes also has distributional consequences across the four outcome dimensions we considered (income, food, land, and health). We therefore argue that to be policy relevant, integrated assessments should comprehensively consider livelihood outcomes of bioenergy deployment, summarized in table 2. Our analysis suggests a likely tension between aggregate and equity impacts that needs to be made explicit in bioenergy assessments to better support decision-making.

A first attempt to bring livelihoods into integrated assessment models could include introducing distributional parameters. For example, distinct parameters could represent the fractions of households (percentage of affected households) with improved or reduced (A) income, (B) food access, (C) land tenure and (D) health as a result of deployment schemes. We are aware that such parameters are highly uncertain and would still be insufficient to cover all place-specific effects. But the current practice of ignoring the distributional outcome is also a strong normative choice that can lead to misguided policy. Integrated assessment would turn from optimization models towards multi-criteria assessments that rely on scenarios that take into account potential distributional consequences at household and community levels: the combined outcome in efficiency, GHG emissions, livelihoods, and equity could be the more complex but also the more comprehensive basis for policy decisions (Creutzig and Kammen 2009). One could still object that aggregate livelihood and equity issues are problems that can be analyzed and solved independently from each other (this is the main claim of the second welfare theorem), but our analysis clearly highlights that these issues remain entangled. The choice of production model, the underlying and evolving land tenure regimes, global biofuel markets and equilibrium effects on food markets, all affect both aggregate and distributional outcomes. Hence, equity should be included in a full set of criteria for bioenergy assessments. It is likely that this conclusion is also valid more generally for other climate and energy assessments, such as those focusing on other technological but also behavioral options.

Our analysis remains limited in focusing on first-generation crop-based biofuels. Jatropha is however sometimes considered a second-generation biofuel, but is similarly prone to detrimental distributional issues (Findlater and Kandlikar 2011) that need to be explicitly considered in comprehensive assessments. More generally, we suspect that second-generation bioenergy plantations would on the one hand provide higher income and land rent with higher yields compared to first-generation biofuels but again would marginalize local populations that live from informal land tenure. The utilization of residues and the cascade utilization of biomass is however also seen as a promising route for providing energy (Haberl and Geissler 2000, WBGU 2009) and could improve existing livelihoods. Future research should explore this direction.

Finally, we recognize that our analysis does not tackle a key conundrum: how can livelihoods be assessed globally if each deployment scheme has effects that depend on various place-specific factors? A comprehensive assessment by human geographers and agricultural economists could elucidate which kind of distributional livelihood effects need to be accounted for, and which biofuel deployment schemes (crops, institutional arrangements, land tenure schemes) have more advantageous outcomes. The diversity of approaches, methods and analytical categories could be roughly mapped using integrated livelihood assessment figures resembling figure 2, and assessments could rank deployment scenarios in terms of their impact on livelihood dimensions. We see ample opportunity to soft-couple integrated assessment models with local livelihood analyses and CGE and partial equilibrium sector models. This could elucidate both risks and opportunities of bioenergy deployment for livelihoods. The ultimate art is to ensure that pathways and production models not only produce positive aggregate outcomes, but that they simultaneously respect and improve place-specific livelihoods. If specific deployment schemes are unlikely to achieve these goals, they should not be pursued.