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Climate change in agriculture has important impacts on human life and health, so it is crucial to explore the process of carbon peaking in China's agricultural resource inputs and to provide data support for greenhouse gas (GHG) emission reduction in agriculture. The LMDI model and Tapio decoupling model were used in this study, and the interrelationship between carbon emissions from agricultural resource inputs and agricultural output was analyzed deeply. Meanwhile, global and local Moran's I indices were introduced to further analyze the degree of spatially significant correlation. The results showed that China's total agricultural carbon emissions from 2003 to 2021 showed an"inverted U-shaped"curve, indicating a first growth and then a downward trend, and reached a peak in 2015. Carbon emissions in east China have always been in the first place, followed by central China. However, the Northwest region, which has the lowest carbon emissions among the seven regions, has risen significantly to become the third largest source of agricultural carbon emissions by 2021. The use of fertilizers was the major factor contributing to agricultural carbon emissions, with an average annual contribution of 58%. while pesticides, agricultural films and diesel use were secondary factors. The global Moran' s I index showed significant spatial correlation in agricultural carbon emissions, but the degree of spatial aggregation gradually weakened over time. The local Moran' s I showed that the high-high aggregation was mainly concentrated in east China and central China. According to the results of the LMDI model, agricultural production efficiency, regional industrial structure, and the size of the agricultural labor force play an inhibitory role in the growth of agricultural carbon emissions, of which agricultural production efficiency is the main inhibitory factor, with an annual average emission reduction of 6.475 7 million tons. Agricultural industrial structure, the level of regional economic development, and the level of urbanization, on the other hand, promote the growth of agricultural carbon emissions, and the level of regional economic development is the main factor that promotes the growth of agricultural carbon emissions, with an annual average of about 8.91 million tons. The Tapio decoupling model showed that all regions achieved strong decoupling after 2016, and that agricultural economic growth was accompanied by a reduction in agricultural carbon emissions, which reached a desirable state, indicating that the effect of emission reduction after 2016 was significant. [ABSTRACT FROM AUTHOR]

There is unequal spatial distribution of resource endowment, population density, industrial structure, and economic development with diverse differences in labor, energy, and capital productivities in China. However, previous studies paid little attention to the determinants of CO 2 transfers embodied in electricity trade. In this study, we use both the absolute and comparative advantage theories to reveal the determinants of embodied CO 2 transfers through electricity trade within China. Results show that China's electricity sector has higher labor productivity but lower asset efficiency and energy productivity than that of mining and manufacturing sectors. The large-scale electricity trade alleviates the shortage of electricity supply in developed regions by outsourcing to the less-developed regions, reduces the unequal spatial distribution of coal and natural gas reserves, and changes CO 2 flow embodied in power grid. Econometric analysis shows that coal reserve contributes to the increase of embodied CO 2 emission, while natural gas reduces the embodied CO 2 emission. The regional differences in the opportunity cost of labor productivity of non-electricity sector are the dominant factor of the embodied CO 2 transfers through electricity trade within China, while asset efficiency and energy productivity are not significant in the regressions. Our findings could provide details about China's power grid expansion when confronting climate mitigation in the future. • Absolute and comparative advantage theories in determining the embodied CO 2 transfers through electricity trade. • Electricity sector has higher labor productivity but lower asset efficiency and energy productivity than other sectors. • The electricity trade alleviates the shortage of electricity supply in developed regions by outsourcing. • Coal reserve increases the embodied CO 2 emission, while natural gas reduces the embodied CO 2 emission. • The opportunity cost of labor productivity of non-electricity sector is the dominant factor in the embodied CO 2 transfers. [ABSTRACT FROM AUTHOR]

[Display omitted] • Estimated relationship between nighttime stable light data and carbon emissions. • Reference to shared social economy and representative concentration to set scenarios. • Classification of cities based on industry output and GDP. • The carbon emission pathway of the city from 2021 to 2060 was dynamically simulated. • Service cities are more likely to have autonomous carbon peaks. As cities are critical actors in mitigating climate change and achieving the "3060″ target, multi-scenario studies on urban carbon emissions can provide a scientific basis for formulating urban carbon peaking action plans. To remedy the problems of missing regional statistics, inconsistent caliber, and lack of city-scale studies in carbon emission research, this paper uses the sparrow optimization neural network algorithm to fit carbon emission data with nighttime stable light for training. Carbon emission data were obtained for 281 cities in China during 2000–2020. The rates of change of influencing factors are set based on shared socioeconomic pathways (SSPs) and representative concentration pathways (RCPs) for different periods and different scenarios. The carbon emission and carbon peaking evolution paths of service, industrial and comprehensive cities from 2021 to 2060 are dynamically simulated. The results show that (1) service cities are significantly higher than industrial and comprehensive cities in population, GDP, secondary industry output, and energy consumption. (2) The economic development effect, as the primary driver of carbon emission growth, increases and then decreases in all five categories of cities, with 2010 as the inflection point. Industrial structure improvement has an increasingly strong offsetting effect on carbon emissions and is one of the critical directions for future carbon emission reduction. (3) Service cities such as Beijing and Shanghai are already at the completion stage of urban transformation and are more likely to reach the carbon peak on their own than other types of cities. In the low carbon following scenario, comprehensive cities such as Kaifeng, Rizhao, and Jilin can achieve their carbon peaking targets efficiently. The findings of this paper can provide valid theoretical support for carbon peaking action programs in China and other countries. [ABSTRACT FROM AUTHOR]