Between Nature and Nourishment

Abstract

Celsius or 1085 GtC. This thesis explores this significance and points toward prioritizing ecosystem conservation as a highly effective strategy for mitigating climate change. Initiatives such as carbon offsetting, performed by organizations like ‘8 billion trees’ and ‘cool earth’, could provide feasible solutions to enhance carbon sequestration. The EU's Nature Restoration Law is a step in the right direction, advocating for the conservation of existing ecosystems and restoring deteriorated systems, specifically increasing organic stocks in forests and restoring and rewetting drained peatlands of agricultural use and peat extraction sites seem to be compelling aspects in terms of improving the carbon system proposed in this law. I encourage the exploration of similar policy changes globally, underscoring the importance of taking immediate action and ensuring no further deterioration of existing terrestrial carbon systems.

Following the importance of terrestrial carbon storage, the debate remains about who should sacrifice their land. This research has displayed a high sensitivity in land allocation for terrestrial carbon storage when comparing ethical perspectives of justness, also defined as climate justice principles. This sensitivity can be seen as normative uncertainties and represent potential tensions in developing a coherent global policy. In my research, certain countries are more sensitive to these normative uncertainties than others. The Netherlands, Pakistan, China, and Yemen are highly sensitive to terrestrial carbon storage obligations if islands and smaller states are not considered. In the case of the Netherlands, this sensitivity is caused by a high GDP and, therefore, ability-to-pay. Still, when following efficiency prioritized, Dutch agricultural land should be kept for its high agricultural productivity. China is expected to reach a high level of domestic food security. Therefore, they would have to sacrifice their land for terrestrial carbon storage according to a different interpretation of ability-to-pay. Also, the efficiency prioritized principle for China would be ideal because, with their economic growth and innovation, they are expected to reach a high level of agricultural productivity. In the case of Yemen, it is different, Yemen has low agricultural productivity, and efficiency-prioritized will lead to them converting agricultural land to forests. The best interpretation of ability-to-pay for Yemen is the available land that could be converted for terrestrial carbon storage.

In my analysis, I showed proof of principle to include these normative uncertainties in policy analysis to explore areas of consensus. Through this approach, I found countries that should convert or conserve their land for terrestrial carbon storage and countries that arguably could still replace their forests and wetlands for other land covers such as urban- and agricultural land. I propose that climate justice principles should be included as uncertainties in policy-analysis, thus arguing for – additional – simplified models to allow for easy implementation of these uncertainties in the analysis.

To come to these findings, I performed an extensive analysis of the global food-, land cover-, and carbon system, simulating a wide range of scenarios up to the year 2100. The model I have developed operates at a national-detail level and includes 173 countries worldwide. The model considers several factors for each country, such as socioeconomic development, current land cover, agricultural productivity, and food demand. The national systems interact globally to represent global systems such as food change and the carbon cycle.

I tested a wide range of distribution methods on this system for terrestrial carbon storage burden, represented by five policy levers. Additionally, I considered 21 uncertainties in the model and performed 10.000 experiments to ensure the robustness of the proposed policies. In the last phase of the analysis, I created world maps that display the land countries should convert for terrestrial carbon storage according to climate justice principles and the sensitivity per country. These maps allow for an easy understanding and interpretation of the climate justice principles.

I started the research by exploring frequently used climate change policy evaluation in ethics. Ethics literature discusses two main social justice types: distributive and procedural justice. In this study, I focus on distributive justice for its applicability in modelling solutions. I utilize pre-defined climate justice principles and introduce a new principle – efficiency prioritized, based on the utilitarian principle. The ‘principle efficiency prioritized’ allocates agricultural land to countries with the highest agricultural productivity and terrestrial carbon storage burden to the less-productive countries. Selected principles are Ability to Pay (based on available land for terrestrial carbon storage, domestic food supply, and GDP per capita), You-Broke-It-You-Fix-It (based on historical emissions), and Efficiency Prioritized, with different interpretation methods considered for Ability to Pay.

I modeled the system using Vensim, a System Dynamics modeling software. System Dynamics is a quantitative modeling formalism suitable for dealing with complex systems, allowing for exploring relations between system structure and behavior. It helps to understand the complex land use, land-use change, and forestry, food, and carbon system. It also enables the incorporation of parametric and structural uncertainties, testing a wide range of scenarios in systems with deep uncertainty. The model consists of several sub-systems, leveraging existing models from prior research by Auping (2018), and takes data from sources such as the World Bank, FAOSTAT, OECD, and IPCC as input.

To find robust policies in the face of deep uncertainty, I adopted an Exploratory Modelling and Analysis (EMA) approach using the open-source EMA workbench for Python. This approach involves performing a broad range of computational experiments, analyzing the results, and identifying robust policies based on these findings. EMA workbench has a special connection built-in to run experiments with Vensim efficiently. It includes built-in functions for sampling, such as Latin Hypercube Sampling, and scenario discovery, such as PRIM, used in my analysis to find the scenarios of interest.

I visualized the results with Basemap, NumPy, Pandas, and the EMA workbench, among other tools. Creating maps allows for a simple data representation, enhancing understanding and communicating the findings. This approach enables a thorough exploration of climate justice principles, modeling, and experimentation, providing valuable insights for decision-makers facing complex and uncertain climate change challenges.

To summarize, I applied System Dynamics modeling and scenario discovery to explore the global food and carbon system. I proved the importance of terrestrial carbon storage, especially conserving existing forests and wetlands. Terrestrial carbon storage policy also faces policy challenges in implementation, mainly the distribution of the terrestrial carbon storage burden. Because land for terrestrial carbon storage goes at the cost of agricultural and urban land that could provide economic prosperity and food security, there is a trade-off between different Sustainable Development Goals. Several distribution conventions have been evaluated following climate justice principles to find overlapping policies between the principles. I found a significant difference in the distribution of terrestrial carbon storage obligations between the principles, displaying potential complexities and tensions in policy-making. I also found consensus between the principles, defined as non-discriminatory policies. Policy-makers should start with non-discriminatory policies to ensure swift implementation. I recommend that other policy modelers also include ethical perspectives as uncertainties in their models to find non-discriminatory policies and, if necessary, develop simplified models to enable this.