Stories 1 - MAgPIE stories #1

In this story, I would like to explain what’s behind sticky and also give examples on the improvements it brings to spatially-explicit data. If time allows (and also if it’s need), I could also deepen on adaptation and climate impacts reduction for the agricultural sector.

In this study, we follow Wang et al. (2020) to combine an agro-economic land system model (MAgPIE) with a well-recognized TFP calculation method (DEA) to measure China’s TFP changes in two distinct steps. First, this study uses MAgPIE to simulate the production outcomes based on the endogenous TC and different future socioeconomic scenarios. Second, we employ DEA to estimate TFP changes in China and further analysis the impact of TFP on the food and environment.

This paper will demonstrate the growth of China’s agricultural TFP and its impact on the food and environment.Our results show that agricultural TFP in China will continue to grow but vary across different scenarios.The growth of TFP ensures food security, decreases the consumer prices and increases self-sufficiency. The development of TFP also has important implications for the environment. Scarcity of agricultural water is alleviated as the increase in TFP gives room for adjustment of the spatial planting structure.

The Global Innovation Needs Assessments (GINAs) project, funded by ClimateWorks, provides an analytical framework for why and how much governments should spend on innovation to reach net zero emissions. MAgPIE was used together with Vivid’s in-house energy model to quantify the benefits associated with innovation. We modelled five scenarios:

• A central 1.5C scenario
• Four “innovation” scenarios, each adding a different innovation to the central scenario.

The emissions reductions achieved by each innovation scenario relative to the central scenario represented the innovation’s mitigation benefits. To estimate the economic value of those benefits, we assumed that any mitigation in the land use sector would directly result in a higher carbon budget for the energy sector, reducing energy system costs. MAgPIE outputs also informed the estimation of the GVA and jobs associated with greater innovation, and the innovation investment need to support a Net Zero pathway.

As foundation for future work, we are coupling MAgPIE and the MESSAGEix framework. Initially, we’re using a soft-link approach similar to the MESSAGEix-GLOBIOM land-use emulator matrix. In this approach, a set of land-use scenarios is integrated into the MESSAGE energy model via some of their core outputs relevant for the energy system, e.g., bioenergy production potential and associated AFOLU GHG emissions. Scenarios are defined based on 2100 target prices for bioenergy per GJ produced and GHG emissions per tCO2eq. To achieve such a soft link for MAgPIE, we modify the model to take bioenergy prices as input instead of demands, linearly implemented between 2020 and 2100. Similarly, for GHG emissions, we create a new module realization implementing linear price trajectories for an easy to change target setting. In this MAgPIE story, we will present our approach, issues we ran into, and first results.

Scenarios limiting climate change to 2°C or even 1.5°C frequently require large-scale forestation efforts to buy precious time for the phase-out of fossil fuels. However, in addition to removing CO2, forests change their local climate. This secondary, physical effect is routinely overlooked in scenarios despite studies demonstrating its impact to be similar to the CO2 removal for 20 years. One reason for this neglect is the mismatch between drivers of the physical Earth system and the economic world of scenario-building models. Here I show how I bridged this gap to consider local temperature changes in the cost optimization of MAgPIE.