Indirect Land-Use Change: A Persistent Challenge for Modeling and Policy – Resources for the Future

Report on Modeling Approaches to Indirect Land Use Change (ILUC) and Their Implications for Sustainable Development Goals (SDGs)

Overview of Modeling Frameworks

Modeling indirect land use change (ILUC) is critical for understanding environmental impacts linked to land use, which directly relates to several Sustainable Development Goals (SDGs), including SDG 13 (Climate Action), SDG 15 (Life on Land), and SDG 2 (Zero Hunger). Two primary modeling frameworks are used:

1. General Equilibrium Models: These models capture interconnections across all markets in the economy, allowing for broad behavioral adjustments in response to supply and demand shocks. They typically produce lower ILUC projections due to their comprehensive scope.
2. Partial Equilibrium Models: These focus on specific markets or sectors, holding conditions in others fixed. They often yield higher ILUC estimates but vary widely in sectoral coverage and complexity.

For example, the GTAP model, a widely used computable general equilibrium model, consistently results in significantly lower ILUC predictions compared to partial equilibrium models such as those used by Searchinger et al. (2008). However, differences in model structure and assumptions play a larger role in explaining result variations.

Model Complexity and Sectoral Coverage

• Sector Representation: Models differ in the number and detail of sectors represented. GLOBIOM models three land-related sectors, whereas GCAM, a partial equilibrium model, includes more industries plus earth systems dynamics.
• Time Dynamics: Some models are comparative static, abstracting from time dynamics, while others explicitly model land-use changes over time. Comparative static models, such as GTAP, benefit from computational simplicity but face challenges in interpreting adjustment periods, affecting parameterization.
• Land Supply Depiction: Models vary in how they represent land supply and land-use change. For instance, GLOBIOM explicitly models land use, while GTAP uses a Constant Elasticity of Transformation factor, which may overlook land-use constraints, potentially leading to unexpected predictions about land availability.

Parameterization and Critical Assumptions

Model outcomes are highly sensitive to key parameters and assumptions, which influence projections relevant to SDG targets on sustainable agriculture, climate mitigation, and ecosystem preservation:

1. Price Elasticity of Food Demand: Determines how consumption responds to price changes, impacting food security (SDG 2).
2. Price Elasticity of Yield: Captures productivity response to price changes, influencing sustainable agricultural intensification (SDG 2, SDG 12).
3. Choice of Crops: Different crops vary in productivity per hectare, affecting land use and biodiversity (SDG 15).
4. Utilization of Co-products: Use of by-products like distillers’ grains can reduce pressure on feed sources, supporting resource efficiency (SDG 12).
5. Price Elasticity of Cultivated Area: Dictates cropland expansion in response to price changes, with implications for deforestation and land degradation (SDG 15).
6. Carbon Stock of Converted Land: Determines emissions effects per hectare, critical for climate action (SDG 13).

Focus on Yield-Price Elasticity

Yield-price elasticity is particularly influential yet contested. For instance:

• Searchinger et al. (2008) assumed zero net yield-price elasticity, implying no yield increase with higher prices.
• GTAP models assume a yield-price elasticity of 0.25, indicating a 1% price increase leads to a 0.25% yield increase.
• Recent studies suggest smaller short-run elasticities, but long-run elasticities remain uncertain.

The ambiguity in yield-price elasticity values affects model calibration and ILUC predictions. Consistent parameter calibration aligned with the model’s time horizon is essential to avoid bias in estimating the contributions of yield, land expansion, and demand adjustments.

Implications for Sustainable Development Goals

• SDG 2 (Zero Hunger): Accurate modeling of food demand elasticity and yield responses supports sustainable food production strategies.
• SDG 13 (Climate Action): Understanding carbon stock changes from land conversion informs mitigation policies.
• SDG 15 (Life on Land): Modeling land-use change helps protect forests and grasslands, preserving biodiversity.
• SDG 12 (Responsible Consumption and Production): Efficient use of co-products and sustainable land management reduce environmental footprints.

Conclusion

Modeling ILUC requires balancing complexity, parameter accuracy, and computational feasibility. Given the direct links to multiple SDGs, improving model transparency and empirical support for key parameters is vital for informed policy-making that promotes sustainable land use, climate mitigation, and food security.

1. Sustainable Development Goals (SDGs) Addressed or Connected

1. SDG 2: Zero Hunger
   • The article discusses food demand, crop yields, and land use, which are directly related to food security and sustainable agriculture.
2. SDG 13: Climate Action
   • Land-use change and carbon stock of converted land relate to greenhouse gas emissions and climate change mitigation.
3. SDG 15: Life on Land
   • The article addresses land-use change, forests, grasslands, and land conversion, which impact terrestrial ecosystems and biodiversity.

2. Specific Targets Under Those SDGs Identified

1. SDG 2: Zero Hunger
   • Target 2.3: By 2030, double the agricultural productivity and incomes of small-scale food producers.
   • Target 2.4: Ensure sustainable food production systems and implement resilient agricultural practices.
2. SDG 13: Climate Action
   • Target 13.2: Integrate climate change measures into national policies, strategies, and planning.
   • Target 13.3: Improve education, awareness-raising, and human and institutional capacity on climate change mitigation.
3. SDG 15: Life on Land
   • Target 15.2: Promote the implementation of sustainable management of all types of forests, halt deforestation, restore degraded forests.
   • Target 15.3: Combat desertification, restore degraded land and soil.

3. Indicators Mentioned or Implied to Measure Progress

1. Indicators Related to SDG 2
   • Yield-price elasticity: Measures how crop yields respond to price changes, indicating productivity changes.
   • Price elasticity of food demand: Indicates how food consumption changes with price fluctuations.
   • Price elasticity of cultivated area: Reflects the extent of cropland expansion in response to price changes.
2. Indicators Related to SDG 13
   • Carbon stock of converted land: Measures emissions effects per hectare of land conversion.
   • ILUC (Indirect Land Use Change) projections: Indicate emissions related to land-use changes.
3. Indicators Related to SDG 15
   • Land-use change rates: The extent of conversion of forests, grasslands, and managed lands.
   • Types of land available for conversion: Managed land versus unmanaged forests and grasslands.

4. Table of SDGs, Targets, and Indicators

Source: rff.org