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Carbon neutrality is an important environmental management tool to deal with the global greenhouse effect. Addressing climate change is an important starting point for promoting the high-quality development of China's economy and the construction of ecological civilization, as well as an important area for participating in global governance and adhering to multilateralism. In this paper, from the perspective of carbon neutrality, taking China's Jiangsu Province as an example, the super-efficiency DEA model is selected to measure the level of ecological efficiency in Jiangsu, and on this basis, the Tobit model is used to verify the main factors affecting regional ecological efficiency. The results are as follows: (1) The regional eco-efficiency in Jiangsu shows a trend of decreasing from south to north, with obvious phenomenon of "club convergence", with significant spatial correlation and agglomeration. (2) The industrial structure is negatively related to the regional eco-efficiency of Jiangsu regions; Energy consumption structure has a positively impact in southern Jiangsu,but negatively in central and northern Jiangsu; FDI has a positively impact in central and northern Jiangsu, but negatively in southern Jiangsu; Regional economic development has a positively impact in southern and northern Jiangsu, but negatively in central Jiangsu; The progress in the technological level has positive effects on the eco-efficiency of all regions of Jiangsu. The final paper puts forward some suggestions based on the carbon neutral target Provide theoretical reference for achieving the goal of carbon neutrality. • Construct an regional ecological efficiency evaluation index system. • Use super-DEAmethod to measure ecological efficiency level. • Study from the perspective of carbon neutrality. • Propose a reference for achieving carbon neutrality. [ABSTRACT FROM AUTHOR]

The remarkable prediction performance of electricity consumption has always assumed particular importance for electric power utility planning and economic development. On account of the complexity and uncertainty of the electricity system, this paper establishes a self-adaptive grey fractional weighted model to predict Jiangsu's electricity consumption, which efficiently enhances the prediction quality of electricity consumption. This newly constructed grey model introduces the fractional weighted coefficients to design a novel initial condition. Compared with the old one in the conventional grey models, the newly optimized initial condition has a flexible structure, which has advantages in capturing the dynamic characteristics of the electricity consumption observations. In addition, to further promote the forecasting precision, the adjustable fractional weighted coefficients and corresponding time parameter of the initial condition are estimated by utilizing the Particle Swarm Algorithm (PSO). Furthermore, five competing models are employed to forecast Jiangsu's electricity consumption in China, which certifies the validity of the established model. Experimental results illustrate that the newly designed model has significant advantages over other five competing models. According to the forecasted results, electricity consumption in Jiangsu Province is expected to reach 6778 billion kilowatt-hours in 2020, while the growth rate will fall down by 1.11%. Therefore, several proposals are made for decision-makers. • A self-adaptive fractional weighted grey model is developed for electricity demand forecast. • The novel initial condition having dynamic fractional order weighted coefficients is proposed. • The PSO algorithm is applied to estimate the weighted coefficients and time parameter. • Jiangsu's electricity consumption is predicted from 2018 to 2020. [ABSTRACT FROM AUTHOR]