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[Display omitted] • Vegetation has acted as a carbon sink in most areas in China over the past two decades. • Historical drivers and future changes of vegetation productivity revealed by explainable neural network. • Nonlinear response of vegetation productivity to climatic factors and topography revealed by SHAP. • Vegetation productivity is at risk of declining in the near future under high emission scenarios. • The work was supported by the National Natural Science Foundation of China (Grant No. 4217011432) Net primary productivity (NPP) is a crucial component of the terrestrial carbon cycle and plays a crucial role in assessing ecological security. Although machine learning models have been widely used in research on ecosystem modelling, however, the performance of deep learning models with interpretability in predicting NPP are still very limited. In this study, we developed an interpretable deep learning predictive model to uncover the historical drivers and future changes of NPP in China using remote sensing data, climate data and topography. The results show that more than three quarters (88 %) of China's regional NPP have shown an increasing trend over the past 20 years, with an overall trend of 2.46 gC/m2/yr. This extensive range of dynamic processes is a nonlinear response to climatic conditions and topographic factors. The constructed convolutional neural network (CNN) model showed good predictive skill for historical NPP, and revealed that temperature is the main controlling factor, highlighting its importance in vegetation growth in China. Compared to the historical period (2001–2020), in the context of future climate change, the risk of carbon sinks decreasing in China is highly likely in the imminent near term (2021–2039), especially under high emission scenarios. These results suggest that interpretable deep learning method is a useful tool for revealing vegetation productivity drivers and estimating vegetation productivity under a nonstationary climate background. [ABSTRACT FROM AUTHOR]

It still remains uncertainty whether China's future investment is conducive to carbon reduction of Belt and Road (B&R) region or not. Here, the possible impact of China's investment was revealed by comparing the CO 2 emissions of B&R region in different Chinese investment scenarios. The results showed that CO 2 emissions of B&R region will cumulatively increase by 1973 Gt in 2018–2100 in the "No-Chinese Investment Scenario (NIS)". Regionally, the India (IND), Middle East Central Asia (MECAsia), and Southeast Asia and its surrounding (SEAsia) has the largest increment. Comparing with the NIS, the CO 2 emissions of B&R region will cumulatively decrease by 44.16 Gt and 79.48 Gt, respectively, in the "Business-as-usual Chinese Investment Scenario (BIS)" and "Strengthening Chinese Investment Scenario (SIS)". Regionally, the cumulative CO 2 emissions of all subregions will decrease in BIS and SIS, of which SEAsia and MECAsia have the largest decrement. Technique and structural effects of China's investment have positive impact on cumulative CO 2 emission reduction of B&R region, especially in the structural effect. The scale effect has the negative contribution, but it cannot neutralize the positive impact induced from technique and structural effects. As a result, China's future investment is conductive to reducing CO 2 emission of B&R region and holding the temperature increase below 2 °C. [ABSTRACT FROM AUTHOR]