Agricultural land use is an important source of CO₂ emissions. Therefore, taking the CO₂ emissions of China’s agricultural land use during 1995–2020 as a case, we firstly calculated its composition and analyzed the spatiotemporal evolution characteristics. Then, the Tapio decoupling model and logarithmic mean Divisia index (LMDI) were, respectively, used to identify the decoupling relationship between the CO₂ emission change and economic growth, and analyze the driving factors for CO₂ emissions. (1) The CO₂ emissions of China’s agricultural land use were composed of two main phases (fluctuating growth phase (1995–2015) and rapid decline phase (2016–2020)). The total CO₂ emissions exhibited a non-equilibrium spatial distribution. The inter-provincial CO₂ emissions differences first expanded and then shrank, but the inter-provincial differences of CO₂ emissions intensity continuously decreased. (2) The total CO₂ emissions of China’s agricultural land use increased from 50.443 Mt in 1995 to 79.187 Mt in 2020, with an average annual growth rate of 1.82%. Fertilizer, agricultural diesel and agricultural (plastic) film were the main sources of anthropogenic agricultural-land-use CO₂ emissions. Controlling the use of fertilizer and agricultural diesel and improving the utilization efficiency of agricultural (plastic) film could be an effective way to reduce CO₂ emissions. (3) The Tapio decoupling relationship between the CO₂ emission change and economic growth was a weak decoupling state during 1995–2015 and a strong decoupling state during 2016–2020. This result indicates that China’s agricultural land use can be effectively controlled. (4) The agricultural economic level is the decisive factor in promoting CO₂ emissions increase, and its cumulative contribution was 476.09%. Inversely, the CO₂ emission intensity, agricultural structure and agricultural labor force were three key factors, with cumulative contributions of −189.51%, −16.86% and −169.72%, respectively. Collectively, based on the findings obtained from the present research, we have proposed some suggestions to promote the sustainable use of agriculture lands in China.

Currently, little attention has been paid to reducing carbon dioxide (CO₂) emissions of Gansu, and the two-dimensional decoupling model has been rarely used to study the relationship between the economic development and CO₂ emissions, especially in western China (e.g., Gansu). Thus, here, we first used the Logarithmic Mean Divisia Index (LMDI) to decompose the driving factors of Gansu’s CO₂ emissions between 2000–2017 and then analyzed the decoupling relationship by using the two-dimensional model. Results showed: (1) Gansu’s CO₂ emissions increased from 7805.70 × 10⁴ t in 2000 to 19,896.05 × 10⁴ t in 2017. The secondary industry accounted for the largest proportion in Gansu’s CO₂ emissions, followed by the tertiary industry and the primary industry. (2) The economic output showed the dominant driving effect on Gansu’s CO₂ emissions growth with the cumulative contribution rate of 201.94%, followed by the effects of industrial structure, population size, and energy structure, and their cumulative contribution rates were 9.68%, 7.81%, and 3.05%, respectively. In contrast, the energy intensity effect presented the most obvious mitigating effect with the cumulative contribution rate of −122.49%. (3) The Environmental Kuznets Curve (EKC) between CO₂ emissions and economic growth was demonstrated the inverted U-shape in Gansu. The two-dimensional decoupling status was the low level-weak decoupling (WD-LE) during 2000–2017. Thus, dropping the proportion of the secondary industry, reducing the use of carbon-intensive fuel like coal, introducing advanced technologies, and increasing the investment of new energy might effectively restrain the growth of Gansu’s CO₂ emissions.

Renewable energy consumption (REC) has an important significance in mitigating CO₂ emissions. However, currently, few scientists have analyzed the underlying impact of REC from a global geographic perspective. Thus, here, we divide the world into seven regions to study this impact during the period 1971–2016 using the logarithmic mean Divisia index (LMDI). These regions were East Asia and the Pacific (EAP), Europe and Central Asia (ECA), Latin America and the Caribbean (LAC), Middle East and North Africa (MENA), North America (NA), South Asia (SA), and Sub-Saharan Africa (SSA). The results showed that ECA had the most obviously mitigating effect of −10.13%, followed by NA and MENA (−3.91% and −3.87%, respectively). Inversely, EAP had the largest driving effect of 4.12%, followed by SA (3.43%) and the others. Globally, REC had an overall mitigating contribution of −11.04% to total CO₂ change. These results indicate that it is still important to exploit and utilize renewable energy, especially in presently developing or underdeveloped countries. Moreover, for some countries at a certain stage, their REC effects were negative, but, concurrently, their energy intensity effects were positive. These results show that some developing countries recently reduced carbon emissions only by extensively using renewable energy, not by enhancing energy-use efficiency. Finally, some policy implications for reducing CO₂ in different countries are recommended.

As China’s second largest energy-use sector, residential consumption has a great potential for carbon dioxide (CO₂) reduction and energy saving or transition. Thus, here, using the methods of social network analysis (SNA) and geographically weighted regression (GWR), we investigated the spatiotemporal evolution characteristics of China’s residential CO₂ emissions (RCEs) from direct energy use and proposed some policy suggestions for regional energy transition. (1) From 2000 to 2019, the total direct RCEs rose from 396.32 Mt to 1411.69 Mt; the consumption of electricity and coal were the primary sources. Controlling coal consumption and increasing the proportion of electricity generated from renewable energy should be the effective way of energy transition. (2) The spatial associations of direct RCEs show an obvious spatial network structure and the number of associations is increasing. Provinces with a higher level of economic development (Beijing, Shanghai, and Jiangsu) were at the center of the network and classified as the net beneficiary cluster in 2019. These provinces should be the priority areas of energy transition. (3) The net spillover cluster (Yunnan, Shanxi, Xinjiang, Gansu, Qinghai, Guizhou) is an important area to develop clean energy. People in this cluster should be encouraged to use more renewable energy. (4) GDP and per capita energy consumption had a significant positive influence on the growth of direct RCEs. Therefore, the national economy should grow healthily and sustainably to provide a favorable economic environment for energy transition. Meanwhile, residential consumption patterns should be greener to promote the use of clean energy.

Residential energy consumption (REC) is now the second largest energy sector in China. However, decoupling analysis of REC has not received enough attention. Here, we explore the decoupling relationship between REC and economic growth in urban and rural Guangdong from 2000 to 2017. First, we use the Tapio decoupling model to study the decoupling state. Then, key drivers affecting the decoupling were explored by combining the Tapio decoupling model with the Log-mean Divisa Index (LMDI) method. Finally, the decoupling efforts were evaluated by using the decoupling efforts model. The main results are as follows: (1) The decoupling state in urban regions was better than that in rural regions. Urban regions experienced weak decoupling (WD) in most years during 2000–2017, while expansive negative decoupling (END) mostly was seen in rural regions. (2) The per capita income effect was the most important driver inhibiting decoupling in Guangdong urban and rural regions. The energy intensity effect was the biggest driver promoting decoupling in urban Guangdong, followed by the family size effect. In contrast, the family size effect exerted the dominant influence on accelerating the decoupling in rural Guangdong. Overall, the household effect inhibited decoupling in urban regions, but promoted decoupling in rural regions. (3) The decoupling efforts results suggest that the energy efficiency improvement played a major role in the decoupling efforts of urban Guangdong, while the decrease of family size in rural Guangdong was the leading contributor to the decoupling efforts.

Our objective has been to decompose the energy-related industrial carbon emissions (ERICE) from both the macroeconomic and the microeconomic scales using an extended logarithmic mean Divisia index (LMDI), which few scientists have applied, for Jiangxi, China, over the period of 1998–2015. The macroeconomic factors were output, industrial structure, energy intensity, and energy structure. The microeconomic factors were investment intensity, R&D intensity, and R&D efficiency. It was found that output, R&D intensity, and investment intensity were mainly responsible for the increase of the ERICE, and their average annual contribution rates were 33.212%, 9.537%, and 4.200%, respectively. However, considering the infeasibility of decelerating industrial activities related to these three drivers, the development pattern of a circular economy was promoted. Then, the driving effect of the energy structure was the weakest (0.017%). Nevertheless, the potential of energy structure optimization to improve energy efficiency in Jiangxi should be given sufficient attention, e.g., greatly reducing the use of coal. Inversely, the R&D efficiency, energy intensity, and industrial structure presented obvious mitigating effects on the ERICE (− 13.737%, − 11, 652%, and − 7.804%, respectively). Therefore, some regulatory policy instruments have been recommended. For example, carbon reduction liability and carbon labels related to R&D investment should be implemented to encourage industrial firms to improve their energy efficiency. Then, reducing the energy intensity unceasingly while inhibiting the possible rebound effect should serve as a long-term strategy for the local government. Last, the potential mitigation effect of industrial structure optimization should be given sufficient attention when designing related reduction policies. Particularly, the top five energy-intensive subsectors S33 (Production and Supply of Electric Power and Heat Power), S23 (Smelting and Pressing of Ferrous Metals), S17 (Processing of Petroleum, Coking, and Processing of Nuclear Fuel), S22 (Manufacture of Non-metallic Mineral Products), and S1 (Mining and Washing of Coal) should be given priority.