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Reducing carbon intensity is a key strategy in achieving carbon neutrality. Existing joint methods, including log-mean division index and production-theoretical decomposition analysis, were often linked to the Shepherd distance function to identify factors driving carbon intensity. However, this would overlook the biases in decomposition results caused by the disaggregated efficiency in different orientations. To gain a more comprehensive insight into determinants of China's aggregate carbon intensity from regional technological catch-up, technological innovation, technological leadership and low-carbon development mode, this study extended and modified the regional decomposition and attribution analysis framework by incorporating the two aforementioned approaches, the meta-frontier global non-radial Malmquist-Luenberger productivity index, and attribution analysis. The results demonstrated that China's aggregate carbon intensity declined by 56.03% during 2000–2019, which was primarily driven by the technological innovation category. Almost all provinces exhibited negative contributions to output-based technology progress, output-based technology gap, energy structure, and R&D intensity, all of which helped reduce the aggregate carbon intensity. However, this negative contribution was compensated for by the potential energy intensity, potential capital intensity, energy-based technology efficiency, energy-based technology progress, energy-based technology gap, and output-based technology efficiency. The decline of China's aggregate carbon intensity was attributed to the greatest negative effect of output-based technology progress in Shandong. The increase in energy-based technology gap led to a great increase in the aggregate carbon intensity, mainly owing to Liaoning. Our study not only advances research in the field of factor decomposition, but also is of special interests for policy makers tasked with ensuring carbon emission reduction in China. • We extended decomposition methods considering tech and development-mode factors. • Output-based technology progress was vital for decreasing China's carbon intensity. • Energy-based technology gap has great potential for carbon intensity reduction. • Policy should rationalize R&D expenditure and fixed-asset investment utilization. [ABSTRACT FROM AUTHOR]

• Decomposition analysis, SE-SBM model, and GTWR are employed. • A new decoupling index is compared with the original decoupling index. • The new decoupling index is better than the original index in some respects. • The influencing factors show spatial and temporal variability in new decoupling index. Economic development is accompanied by the production of pollutants. Coordinating the relationship between economic development and haze pollution is of great significance to future development in China. Based on the original decoupling index (Tapio decoupling index), this study developed a new decoupling index that overcomes some of the original defects and highlights the trends to a better extent. It also analyzes the transformation relationship among the new decoupling index, the original decoupling index and the EKC curve. Firstly, we compared the changing trends of the two decoupling indexes and then derived the factors that influence the new and original decoupling indexes based on C-D-Kaya equation. Finally, we compared the factors influencing the two decoupling indexes from the perspective of decomposition analysis and measurement. The conclusions are as follows: (1) Based on the limit theory, the boundary point for relative decoupling and absolute decoupling derived from the original decoupling index can be used to determine the inflection point of the EKC curve. The new decoupling index can also reflect this property. At the inflection point of the EKC curve, the ratio of the new decoupling index of two adjacent periods satisfies equation (12), which is only related to GDP. (2) In 2003, the new decoupling index showed a downward trend, and the fluctuation was smaller than that of the original decoupling index. The original decoupling index is more sensitive to short-term policies than the new decoupling index, but the new decoupling index is more conducive to the comprehensive analysis of China's decoupling trend. (3) After decomposing the new decoupling index and the original decoupling index, per capita GDP and the emission coefficient are the largest positive and negative driving forces, respectively. However, the trend of the contribution made by each factor was more obvious when the new decoupling index was used. The emission coefficient for Shandong made the largest negative contribution, and the per capita GDP for Shanxi made the greatest positive contribution. (4) The geographically and temporally weighted regression (GTWR) analysis showed that the fitting degree of the new decoupling index is much higher than that of the original decoupling index. The analysis of the influencing factors affecting the new decoupling index showed that there was temporal and spatial heterogeneity among the factors in east, middle, and west China, and there were significant differences in the impacts due to energy structure, energy intensity, technological progress, and urban population factors, respectively. These findings will significantly improve the formulation of efficient and pertinent policies for energy conservation and haze reduction. [ABSTRACT FROM AUTHOR]

Under the premise of ensuring economic growth, how to achieve the maximum reduction of PM 2.5 has become one of the important tasks in China. In this study, we investigated the influencing factors of PM 2.5 by employing the Logarithmic Mean Divisia Index (LMDI). The sensitivity of PM 2.5 to various emission reduction factors was also explored. Then, by constructing an indicator of PM 2.5 decoupling efforts (PDE), PDE of different regions and the degree of efforts caused by various factors were explored. Finally, the spatiotemporal heterogeneity of internal and external factors influencing PDE was explored using the Geographically and Temporally Weighted Regression Model (GTWR). The main results are as follows. (1) The emission coefficient was the main negative driving factor of changes in PM 2.5 , and per capita GDP was the main positive driving factor. (2) The sensitivity of PM 2.5 to emission reduction factors in most regions followed the ranking: emission coefficient > energy intensity > population. In the analysis of the sensitivity of PM 2.5 to various emission reduction factors throughout the country, the sensitivity of the emission coefficient was highest in Shandong province. (3) From 2000 to 2014, all provinces made decoupling efforts (PDE>0), among which PDE in Guizhou province were the largest. According to various indicators of decoupling efforts, the contribution of decoupling effort of emission coefficient was the largest. (4) All influencing factors exhibited spatiotemporal heterogeneity. In most regions, the interaction between environmental regulation and industrial structure had a positive impact on PDE, which verifies the existence of the "Porter Hypothesis". However, in some central and western regions, this interaction exhibited a dynamic change from negative to positive. • Decomposition, Sensitivity analysis, Decoupling and GTWR are employed. • The sensitivity of the emission coefficient is the strongest. • PDE in Guizhou province are the largest. • "Porter Hypothesis" exists in most regions. [ABSTRACT FROM AUTHOR]

• CEI is decomposed into 11 influencing factors. • The sensitivity changed from the initial stage to the later stage. • In the sensitivity analysis of CEI, the factors of the secondary industry have the greatest impact. • The same influencing factors have significant differences in different regions. Due to the Chinese reduction targets of CO 2 emission intensity (CEI), it is of great significance to explore its determinants and mechanism. Therefore, the purpose of this study is to find how to effectively reduce CEI. At the national level, we investigate the influencing factors of CEI by using the Logarithmic Mean Divisia Index method (LMDI). Then, this study examines the sensitivity of CEI change to various factors. At the regional level, we study the spatial-temporal heterogeneity of the influencing factors through Geographically and Temporally Weighted Regress (GTWR). The main results are as follows. (1) At the national level, the positive contribution of the energy mix is the largest. The energy intensity of the production sector is the main negative driving factor in the early stage, and in the later stage, the principal negative contribution comes from the combined action of the CO 2 emission coefficient and economic structure. (2) A dynamic change is observed in the sensitivity of CEI to various factors. (3) At the regional level, various determinants of CEI show spatial-temporal heterogeneity based on GTWR. For example, in analysing the impact of energy intensity in the industrial sector on CEI in various regions, Shandong Province has the largest coefficient. The findings are of considerable interest for China's policy makers to effectively formulate more appropriate emission-reduction measures for each region. [ABSTRACT FROM AUTHOR]