Integrating Econometrics and Artificial Intelligence to Assess the Impact of Trade on Nuclear Proliferation

Given the rise in global interest in nuclear energy, the spread of nuclear technological capabilities and their potential impact on nuclear nonproliferation are of significant interest. This study examines the utility of open-source international trade data along with demand and supply-side data as a means by which to assess the potential nuclear proliferation risk related to nuclear power development. The proliferation risk assessment involves the use of machine learning, deep learning, traditional econometric methods, and big data. The results of the analysis indicated that using trade data can assist with nuclear proliferation risk predictions. Key items of importance in relation to nuclear trade were found to be the Harmonized Commodity Description and Coding System (HS) code 360300 (explosives for signaling, the most significant feature), followed by HS codes 282590 (inorganic bases) and 841350 (reciprocating positive displacement pumps for liquids). Other important items were HS codes 722810 (stainless steel products), 391721 (tubes, pipes, and hoses of plastic), 840120 (nuclear reactors and their parts), and 722830 (bars and rods of alloy steel).