Aerosols and clouds greatly affect the Earth’s radiation budget and global climate. Light detection and ranging (lidar) has been recognized as a promising active remote sensing technique for the vertical observations of aerosols and clouds. China launched its first space-borne aerosol-cloud high-spectral-resolution lidar (ACHSRL) on April 16, 2022, which is capable for high accuracy profiling of aerosols and clouds around the globe. This study presents a retrieval algorithm for aerosol and cloud optical properties from ACHSRL which were compared with the end-to-end Monte-Carlo simulations and validated with the data from an airborne flight with the ACHSRL prototype (A2P) instrument. Using imaging denoising, threshold discrimination, and iterative reconstruction methods, this algorithm was developed for calibration, feature detection, and extinction coefficient (EC) retrievals. The simulation results show that 95.4% of the backscatter coefficient (BSC) have an error less than 12% while 95.4% of EC have an error less than 24%. Cirrus and marine and urban aerosols were identified based on the airborne measurements over different surface types. Then, comparisons were made with U.S. Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) profiles, Moderate-resolution Imaging Spectroradiometer (MODIS), and the ground-based sun photometers. High correlations (R > 0.79) were found between BSC (EC) profiles of A2P and CALIOP over forest and town cover, while the correlation coefficients are 0.57 for BSC and 0.58 for EC over ocean cover; the aerosol optical depth retrievals have correlation coefficient of 0.71 with MODIS data and show spatial variations consistent with those from the sun photometers. The algorithm developed for ACHSRL in this study can be directly employed for future space-borne high-spectral-resolution lidar (HSRL) and its data products will also supplement CALIOP data coverage for global observations of aerosol and cloud properties.

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Aerosols and clouds greatly affect the Earth's radiation budget and global climate. Light detection and ranging (lidar) has been recognized as a promising active remote sensing technique for the vertical profiles of aerosols and clouds. China is developing its first space-borne aerosol-cloud high-spectral-resolution lidar (ACHSRL) for global high accuracy observations of aerosols/clouds profiles. This study presents a retrieval algorithm for aerosol and cloud optical properties from ACHSRL which was compared with the end-to-end Monte-Carlo simulations and validated with the data from an airborne flight with the ACHSRL prototype (A2P) instrument. Using imaging denoising, threshold discrimination, and iterative reconstruction methods, this algorithm was developed for calibration, feature detection, and extinction coefficient retrievals. The simulation results show that 95.4% of the backscatter coefficient have an error less than 12% while 95.4% of extinction coefficient have an error less than 24%. Cirrus and marine and urban aerosols were identified based on the airborne measurements over different surface types. Then, comparisons were made with U.S. Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) profiles, Moderate-resolution Imaging Spectroradiometer (MODIS), and the ground-based sun photometers. High correlations (R > 0.79) were found between backscatter (extinction) coefficient profiles of A2P and CALIOP over forest and town cover; the aerosol optical depth retrievals have correlation coefficient of 0.71 with MODIS data and show spatial variations consistent with those from the sun photometers. The algorithm developed for ACHSRL in this study can be directly employed for future space-borne high-spectral-resolution lidar (HSRL) and its data products will also supplement CALIOP data coverage for global observations of aerosol and cloud properties.

Taking the data of various sectors and three industries from 1980 to 2019 as the research object, the LMDI-I (Logarithmic Mean Divisia Index) multiplicative decomposition model, which is based on the principle of decomposing the change in energy consumption into the contribution of each factor, was used to decompose the carbon emission intensity into technological progress effect and economic structure changing effect. Meanwhile, quantitative econometric models of energy price, economic growth, energy consumption structure, and the two effects were also established. The empirical results showed that energy price, economic growth, and energy consumption structure significantly influenced the reduction in carbon emission intensity. A positive U-shaped relationship between energy prices and carbon emission intensity was overserved, and the rise of energy prices mainly drive the decline of carbon emission intensity through the effect of technological progress. However, the effect of economic structure driven by the rise of energy prices was limited; thus, further optimization of economic structure is needed. Additionally, the proportion of coal consumption was positively correlated with the technological progress effect and economic structure change effect, while the decrease in coal consumption proportion promoted the decline of carbon emission intensity. Finally, three recommendations based on the analysis were proposed.