Based on the current practice of the “dual carbon” strategy and the promotion of green and high-quality development of the planting industry, it needs to clarify the current situation, spatio-temporal patterns and dynamic evolution features of planting industry eco-efficiency, so as to provide references for accelerating the green and low-carbon transformation of planting industry.

Based on the scientific reconstruction of the evaluation index system, firstly, the SBM-Undesired model was employed to measure planting industry eco-efficiency in China and analyze its current characteristics. Then, the Dagum Gini coefficient was used to clarify the regional differences of planting industry and the causes, and next, the spatial autocorrelation model was used to analyze its spatial agglomeration characteristics. Finally, this paper used kernel density estimation and Markov chain method to investigate the characteristics of its dynamic evolution.

From 2005 to 2023, the overall planting industry eco-efficiency in China was significantly improved, with the average value of provincial efficiency as high as 87.95% and has gradually evolved from the initial obviously regionally differences to a new pattern of “higher level and simultaneous progress”. Specifically, among the three major functional areas, the increase of planting industry eco-efficiency from high to low was the main grain selling area, the main producing area, and the balanced production and sales area, respectively. The overall difference of planting industry eco-efficiency in China has been greatly reduced and moved towards uniformity. The sources of regional differences were mainly attributed to hypervariable density, followed by intra-group differences, and inter-group differences. Since 2009, the planting industry eco-efficiency in China has exhibited spatial agglomeration characteristics and also obviously spatial clustering characteristics. The number of high-high-agglomeration provinces has increased, while the number of low-low-agglomeration provinces has decreased, showing a good development trend of the overall spatial agglomeration pattern. With the passage of time, planting industry eco-efficiency in the whole country and in three functional areas has all been in a rising trend and has gradually changed from multipolar to unipolar. At the same time, the eco-efficiency level was relatively stable, with the characteristics of “club convergence”. In consideration of spatial factors, the stability of the ecological efficiency level of planting industry in each province was affected, but high-level provinces could usually release positive spillover effects.

The overall planting industry eco-efficiency in China has significantly improved, but there were some differences in different functional areas. The overall difference has shown an obvious downward trend, and the main source of the difference was lied in the hyper-variable density. The eco-efficiency also showed certain spatial dependence and heterogeneity. There were similarities and differences in the dynamic evolution characteristics of planting industry eco-efficiency in China and in three functional areas. It needed to establish and improve the policy support system, clarify the key influencing factors, strengthen regional exchanges and mutual assistance, and build a risk prevention mechanism, so as to ensure the planting industry eco-efficiency at a high level.

In the context of the “dual carbon” strategy, clarifying the current characteristics, spatial-temporal pattern and influencing factors of rural energy carbon emissions can provide important support for effectively promoting rural low-carbon development.

Carbon emission factor method is used to measure rural energy carbon emissions in China effectively, and analyze its temporal and spatial characteristics. Then, the autocorrelation model is used to explore its spatial correlation pattern. Finally, the introduction of STIRPAT extended model is used to analyze the main factors affecting its intensity changes and the spatial spillover effect.

China's total rural energy carbon emissions are in a continuous upward trend, with an increase of 77.55% in 2019 compared with 2005, which is mainly attributed to the increase in rural residents' domestic energy consumption. Rural energy carbon emission intensity has increased slightly during the investigation period. Although there are some inter-annual fluctuations, the overall fluctuations are small. In 2019, there were significant inter-provincial differences in rural energy carbon emissions, with Hebei leading the way and Ningxia at the bottom. Compared with 2005, only 5 provinces were in a downward trend. In 2019, Beijing ranked first in rural energy carbon emission intensity, while Hainan ranked last, with the latter even less than one tenth of the former. Since 2008, China's rural energy carbon emissions have shown obvious and stable spatial dependence, as well as local spatial clustering, with a small and relatively stable number of high-high concentration provinces and a lager and growing number of low-low concentration provinces. Among the social factors, the increase of rural affluence can lead to an increase of rural energy carbon emission intensity, while agricultural technology progress and rural labor force structure variables have a dampening effect, with only rural affluence showing a spatial spillover effect in a negative direction. Among the economic factors, the increase in the rural financial agglomeration and the improvement of agricultural development level both lead to the increase of rural energy carbon emission intensity, and both have spatial spillover effects, with the former positive and the latter negative. While agricultural financial investment does not have a direct effect but shows a negative spatial spillover effect. Among the industry-level factors, the increase of agricultural industry agglomeration leads to the increase of rural energy carbon emission intensity, but at the same time, it also presents a negative spatial spillover effect.

The total amount and intensity of rural energy carbon emissions in China are on the rise, with significant inter-provincial differences. China's rural energy carbon emissions show obvious spatial dependence and spatial heterogeneity. Rural energy carbon emissions are affected by a combination of social, economic and industrial factors.

Based on the current “dual carbon” strategic goal, this study aimed to clarify the current characteristics, spatio-temporal pattern and influencing factors of agricultural net carbon sink, so as to provide the important support for accelerating agricultural sink increase and emission reduction.

Based on the scientific reconstruction of the index system, the carbon sink/carbon emission factor method was used to measure and analyze the current situation of China’s agricultural net carbon sink. Then the spatial autocorrelation model was used to discuss the spatial dependence and spatial heterogeneity. Finally, the least-squares method was used to analyze the main factors affecting the change of its intensity.

From 2005 to 2022, the total amount of agricultural net carbon sink in China was in an obvious upward trend, although there were some interannual fluctuations, and its evolutionary characteristics could be roughly divided into four stages, namely, “continuous rise”, “fluctuating decline”, “rapid rise”, and “slow rise”; the intensity of agricultural net carbon sink was also in an obvious upward trend, with only a slight difference in the trajectory of the evolution, and the difference in its growth rate could be roughly categorized into four stages: “continuous rapid growth”, “slow growth”, “fluctuating ups and downs”, and “slow growth”. 2022, the amount of agricultural net carbon sink had a large interprovincial difference, with Inner Mongolia being the first and Shanghai being the last, and compared with the year of 2005, all the provinces had a significant increase. In 2022, the net carbon sink intensity of agriculture would be the highest in Henan and the lowest in Qinghai, with all provinces showing different degrees of increase compared with 2005. China’s provincial agricultural net carbon sink intensity as a whole showed obvious spatial dependence, but there was also a local spatial clustering phenomenon, more than 70% of the provinces showed obvious spatial clustering characteristics, and the number of provinces located in the high-high clustering and the low-low clustering was approaching. The structure of arable land use, urbanization level, rural residents' income level and the internal industrial structure of agriculture all had a significant impact on the intensity of agricultural net carbon sink; specifically, the higher the ratio of sown area of grain crops, or the higher the urbanization rate, or the higher the income level of rural residents, or the larger the ratio of plantation industry to animal husbandry, the higher the intensity of net carbon sink in agriculture.

The total amount and intensity of China’s agricultural net carbon sink were in a fluctuating upward trend and there were obvious inter-provincial differences. The intensity of China’s agricultural net carbon sink showed obvious spatial dependence and spatial heterogeneity. The intensity of the agricultural net carbon sink was affected by the structure of arable land use, the level of urbanization, the level of rural residents' income, and the structure of the internal industries of agriculture. The measures should be taken to promote the enhancement of sink and emission reductions and to promote the enhancement of agricultural net carbon sink in agriculture, such as establishing a sound policy support system for the development of low-carbon agriculture, strengthening inter-provincial exchanges and cooperation, and increasing financial support for agriculture.