LAND-POLICY

Land degradation, a multifaceted and urgent challenge, results in a chain reaction of catastrophic consequences. It drives climate change, biodiversity loss, water pollution, and diminishes global agricultural productivity. To tackle this issue, the ERC-funded SHARE-CTD project develops a holistic approach. By combining global measurement, econometric research designs, and innovative machine learning, the project will provide valuable insights for academics, policymakers and the public. Specifically, it will shed light into the causal impacts of public policies on land conditions, comparing their cost-benefit ratios, and identifying factors influencing their performance. This will improve policies and mitigate the damaging impacts of land degradation.

Land degradation is one of the major sustainability challenges of our time. It is a driver of climate change, biodiversity loss, and water pollution, and reduces global agricultural productivity. This requires effective and economically efficient policies.

Here, I outline a project that combines the global measurement and modelling of land degradation trends with econometric research designs to estimate policy effectiveness, their benefit cost ratios, and how design features and contextual factors explain policy performance. This research builds on the unique expertise I have developed over the last 5 years.

The project consists of four work packages. In the first WP, global datasets will be build, including a new database of public policies relevant to land conditions, maps of different land degradation indicators, such as soil productivity trends, vegetation and agricultural yield changes, soil erosion and pollution, and land cover changes, such as cropland expansion and forest loss.

In the second WP, econometric research designs (such as difference-in-differences, difference-in discontinuities, and synthetic control) will be used to estimate the causal effect(s) of public policies on land conditions. The comprehensiveness and global scope of the analysis means that for the first time, we will have the full picture, largely free of selection and publication biases, and methodologically unified.

In the third WP, all the policies costs and benefits will be compared to each other and we will quantify how much benefit each policy has been generating per its costs.

In the fourth WP, we will use both conventional econometric techniques and novel machine learning approaches to systematically explain when and why some public policies perform better than others.

This research will generate new insights on how to improve public policies to mitigate and reverse land degradation. I expect it will generate high interest among academics, policy makers, and the public.