Green Light for Green Agricultural Policies? An Analysis at Regional and Global Scales

Abstract

This paper analyses the effects of introducing biodiversity‐targeted ecological focus area (EFA) requirements on all farms with arable land in the EU by quantifying their global, regional, economic and environmental impacts in a mutually consistent way. To capture these impacts, different spatial scales need to be considered – ranging from on‐farm decisions regarding the EFA in the EU, to supply response around the world. In order to address this challenge, we combine the supply side of the CAPRI model, which offers high spatial, farm and policy resolution in the EU, with the GTAP model of global trade and land use. Both models are linked through a multi‐product, restricted‐revenue function for the EU crop sector. The results predict improved environmental status in the high‐yielding regions of the EU. However, output price increases lead to intensification in the more marginal areas of the EU where little or no additional land is taken out of production. The decrease in arable land in the EU is partially compensated by an increase of crop land, as well as increased fertiliser applications, in other regions of the globe. Thus, the improvement of environmental status in the EU comes at the price of global intensification, as well as the loss of forest and grassland areas outside the EU. Overall, we find that every hectare of land that is taken out of production in the EU increases greenhouse gas emissions in the rest of the world by 20.8 tonnes CO2 equivalent.

Linking global CGE models with sectoral models to generate baseline scenarios: Approaches, opportunities and pitfalls

“…The link between AGLINK and MAGNET thus provides an example how borrowing from a sectoral model implicitly transfers expert knowledge and detailed plausibility assessments into CGE baseline construction. Britz and Hertel (2011) and Pelikan et al (2015) combine bottom-up the PE model CAPRI (Britz and Witzke, 2014) and the GTAP model in a comparativestatic analysis of policy shocks. Their approach is subsequently explored by Philipidis et al (2017) in baseline generation.…”

“…In most one-way linking approaches, linkages are simply calibration exercises with little effort to make responses between models more similar. A best practice example for one-way linking is the link developed by Pelikan et al (2015) to render the behavioral responses between a PE and CGE model consistent. By using a response-surface approach instead of simple point calibration, the authors move towards harmonized model behavior to decrease divergence in overlapping endogenous variables.…”

A review of global-local-global linkages in economic land-use/cover change models

“…In later work, the same authors extended this summary function approach to incorporate a specific, land-based policy lever-in this case, an index of the stringency of the EU agricultural land set-aside program. This allowed them to simulate the spillover effects of this very specific EU-land use policy in a global model, and then link the EU market price results back to the disaggregated CAPRI model in order to assess the detailed effects of this intervention at the regional level (Pelikan et al 2014). Summary functions have also been used to represent land use change in hydrological models (Hundecha and Bárdossy 2004).…”

Biodiversity impacts of bioenergy production: Microalgae vs. first generation biofuels

“…Indirect land-use change occurs when agricultural lands are displaced into regions previously occupied by native ecosystems or non-intensive production systems including extensive pastures and agroforestry systems [17,[96][97][98][99]. For example, the European Union biofuel policies are expected to lead to increased land-use changes outside Europe and transfer environmental impacts to more biodiverse regions [100][101][102]. Biofuel cropping has also been related to indirect land-use change as a result of complex interactions between economic factors, including increases in food prices and economic incentives for biofuel production [103][104][105].…”