Climate change and economic productivity have long been in tension. Industrial expansion is seen as essential to economic growth, but it also drives up carbon emissions and contributes to global warming. The environmental consequences of these emissions are distributed globally while the economic gains remain largely concentrated in the countries responsible for the emissions. This asymmetry poses a recurring challenge in climate governance: those who benefit most are not the ones who pay the price. Despite global agreements, accountability mechanisms have repeatedly broken down.

My project introduces a machine learning model that quantifies the relationship between a country’s carbon emissions and the economic value it generates through exports. The foundational concept is straightforward:  countries that generate high export value with relatively low emissions are seen as carbon efficient, while those that produce high emissions with minimal trade value are considered carbon inefficient.