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Abstract Background China and other developing countries in Asia follow similar economic growth patterns described by the flying geese (FG) model, which explains the "catching-up" process of industrialization in latecomer economies. Japan, newly industrialized economies, and China have followed this path, with similar economic development trajectories. Based on the FG model, we postulated a "flying S" hypothesis stating that if a country is located within an FG region and its energy matrix is relatively constant, its per capita CO 2 emission curve will mirror that of "leading geese" countries in the same FG group. Method Historical CO 2 emissions data were obtained from literature review and national reports and were calculated using bottom-up methods. A sigmoid-shaped, non-linear mixed effect model was applied to examine ex post data with 1000 simulated predictions to construct 95% empirical bands from these fits. By multiplying by estimated population, we predicted total emissions of selected FG countries. Results Per capita CO 2 emissions from the same FG group mirror each other, especially among second and third industrial sectors. We estimated an annual 18,252.24 million tons of CO 2 emissions (MtCO 2) (95% CI = 9458.88–23,972.88) in China and 8281.76 MtCO2 (95% CI = 2765.68–14,959.12) in India in 2030. Conclusion This study bridges the macroeconomic FG paradigm to study climate change and proposes a "flying S" hypothesis to predict greenhouse gas emissions in East Asia. By applying our theory to empirical data, we provide an alternative framework to predict CO 2 emissions in 2030 and beyond. Highlights • Per capita CO 2 emissions from developing countries in Asia follow economic growth patterns, described as flying geese model. • Per capita CO 2 emissions trajectory in one country can forecast the emissions in another, under two assumptions. • Our flying-S model provides business-as-usual scenario, predicting counterfactual CO 2 emissions without any intervention. • In 2030, we estimated more than 18 billion tons of CO 2 (BtCO 2) emission in China, and 8 BtCO 2 in India. [ABSTRACT FROM AUTHOR]

China is a world leader on agriculture production; with only 8% of global cropland it feeds 20% of the world's population. However, the increasing production capacity comes with the cost of greenhouse gas (GHG) emissions. As a populous country with the highest GHG emissions in the world, determining how to achieve the dual goals of mitigating climate change and ensuring food security is of great significance for the agricultural sector. This requires assessing the spatial variation in agricultural greenhouse gases (GHGs) and their drivers. In this study, we conduct a spatial assessment of agricultural GHGs at the provincial level in China for the years 1997–2017, and then explore the effects of related factors on GHG emissions using a geographically weighted regression (GWR) model. The results suggest the following. 1) There have always been significant interprovincial variations, whether in the total amount, structure, intensity, or per capita level of agricultural GHG emissions. 2) The directions of the effects of selected factors on GHG intensity fall broadly into three categories: negative effects (urbanization, intensity of agricultural practices, and agricultural structure), positive effects (agricultural investment and cropland endowments), and mixed effects, with factors leading to reductions in some provinces and increases in others (economic level, frequency and intensity of disasters, and the level of mechanization). 3) The magnitude of the effects varies by factor and also by province. The results suggest synergetic province- or state-specific reduction policies in agricultural GHG for China, as well as for other developing and emerging economies. • Systematic assessment of spatial variation in agricultural GHGs is conducted. • Spatial variation of agricultural GHGs is presented in scale and intensity. • The directions and magnitude of the factors' effects vary by province and factor. • Synergetic province-specific policies are critical for developing economies. [ABSTRACT FROM AUTHOR]