Evolving landscape of US-China science collaboration: Convergence and divergence

TL;DR

This study analyzes long-run US–China science collaboration trends across disciplines using OpenAlex data. It uses two complementary measures—the paper-based Collaboration Distance and the author-based Knowledge Flow Rate—to quantify interactions and their evolution from 1970–2021 and into projections. Results show rapid convergence through the late 2000s/early 2010s, followed by divergence, especially in natural sciences, producing a time-reversed J-curve pattern. These findings highlight geopolitical factors shaping global science and the need for policy and diplomacy responses, while demonstrating the value of open bibliometric data for informing decisions.

Abstract

International research collaboration among global scientific powerhouses has exhibited a discernible trend towards convergence in recent decades. Notably, the US and China have significantly fortified their collaboration across diverse scientific disciplines, solidifying their status as a national-level duopoly in global scientific knowledge production. However, recent reports hint at a potential decline in collaboration between these two giants, even amidst the backdrop of advancing global convergence. Understanding the intricate interplay between cooperation and disparity within the US-China relationship is vital for both academia and policy leaders, as it provides invaluable insights into the potential future trajectory of global science collaboration. Despite its significance, there remains a noticeable dearth of quantitative evidence that adequately encapsulates the dynamism across disciplines and over time. To bridge this knowledge gap, this study delves into the evolving landscape of interaction between the US and China over recent decades. This investigation employs two approaches, one based on paper identifiers and the other on researcher identifiers, both obtained from bibliometric data sourced from OpenAlex. From both approaches, our findings unveil the unique and dynamic nature of the US-China relationship, characterised by a collaboration pattern initially marked by rapid convergence, followed by a recent phase of divergence.

Figures (7)

• Figure 1: Changes in the mutual distance between the four parties over time. The four parties included in the analysis are the US, China, EU27&UK and Japan. The changes over the period of 1970 to 2021 are displayed in five-year intervals. Each coloured sphere's volume is proportional to the number of scientific publications produced by each party, and the sizes can be compared within and across snapshots. The distance between the centres of each sphere represents the level of closeness in international collaboration between the corresponding parties, with shorter distances indicating closer collaboration. The calculation methodology for these distances is described in Section \ref{['sec:Methods']}.
• Figure 2: Change in the Collaboration Distance among the scientific powerhouses in natural sciences over time. In addition to the observed values for 1990--2021, simulation results until 2030 based on specific scenarios (A--C) are shown for the US--China pair.
• Figure 3: 'Shrinking (and-possibly-Polarising) World'. The data prior to 2021 represents actual values obtained from observational data, while the data from 2022 onwards illustrates results derived from simulations based on Scenario B. The abbreviation 'RoW' stands for 'Rest of the World's top 50', comprising the top 50 countries, following the US and China in terms of work production. The decreasing trend in the area of the triangle formed by the US, China and RoW signifies the visualisation of the 'Shrinking World'.
• Figure 4: Change in the Knowledge Flow Rate between the US and China in natural sciences over time.
• Figure S1: Change in the Collaboration Distance among the five parties over time. The changes in the distance from 1970 to 2021 between the five parties---the US, China, EU27, the UK and Japan---are shown for each of OpenAlex's 19 level-0 categories.