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To achieve carbon neutrality, energy efficiency improvement is an essential step. This paper measures the Malmquist energy productivity index (MEPI) to model the dynamics of total factor energy productivity utilizing the stochastic frontier analysis (SFA) method. We found evidence that city-level total factor energy efficiency (TFEE) has been greatly improved in China. However, there are significant regional differences among different cities. Then, this paper empirically examines the heterogeneous impacts of green technology innovation (GTI) on energy efficiency, and takes environmental regulation into consideration. The results show that GTI contributes to improving energy efficiency in cities with higher environmental regulations. Further, green innovation mainly improves the total factor energy productivity through technological progress instead of the catch-up effect.

The resource endowments, development levels and emission reduction potential of different provinces in China are different. Accurately judging regional differences of carbon neutrality potential of each province is helpful to analyze the low-carbon development strategy. In this research, a comprehensive evaluation index system for carbon neutrality potential is constructed, and the carbon neutrality potential score of China from 2003 to 2019 is calculated by sparse principal components(SPC), and dynamically evaluated by combining time ordered weighted average operator (TOWA operator). The results show that: (1) The evaluation score of carbon neutrality potential is greatly affected by the sustainable driving force, and the overall trend is on the rise. But there is a significant gap between provinces, showing the distribution characteristics of “high in the east and low in the central and western regions”. (2) The upward trend of sustainable driving force is obvious, there is a momentum of accelerated development in recent years. The gap between provinces is large. (3) The resilient driving force showed an increasing trend, but some provinces have experienced a decline since 2017. (4) The growth trend of coordination and transformational resilience is flat. The development of provinces is relatively balanced. This provides a reference for the analysis of each region to formulate low-carbon development strategies tailored to local conditions.

In this paper, the carbon-neutral process is constructed to measure the development level of a low-carbon economy in 30 provinces of China. The entropy weight method is used to measure the carbon neutralization level. The Functional Data Analysis and various curves are used to investigate potential dynamic changes. Finally, the heterogeneity analysis is carried out on the sample provinces. We find: (1) The carbon-neutral process’s performance of provinces is significantly different in China, the top three are: Guangzhou (0.6556), Shanghai (0.5559), Jiangsu (0.5095), and the last three are: Xinjiang (0.1583), Gansu (0.1475), Guizhou (0.1453); (2) The carbon-neutral process’s performance in China shows an obvious upward trend and the growth rate has usually been positive; (3) Through heterogeneity analysis, the performance of carbon neutralization process is different in the five regions. But all regions are currently in an upswing or recovery phase. These findings are significant for China to achieve’Carbon Peak in 2030, Carbon Neutrality in 2060’ as scheduled.

This paper constructs an index system for high-quality economic development in China from four dimensions: economic system, social system, environment system, and power system. The LASSO method was presented as screen indicators, then we use the panel fixed effect model to investigate the specific impact of key factors on high-quality economic development. The results indicate that: (1) Innovation-driven, infrastructure construction, green technology development, power supply, and efficient utilization of electrical equipment are the most important influencing factors to promote high-quality economic development. (2) Increasing financial expenditures for pollution prevention and control and strengthening the application of green technology will help to adjust the economic structure. (3) Building a complete green energy system will be an essential part of high-quality economic development. On this basis, heterogeneity analysis is carried out according to regional economic differences and power supply and demand positioning. These findings have great significance in high-quality economic development of China.

To promote the construction of ecological civilization in the Fourteenth Five-Year Plan, energy planning is conducive to making full use of Guangxi’s rich renewable energy, promoting the process of energy conservation and emission reduction, improving the low-carbon power structure, and promoting the development of intelligent green energy. This paper proposes a new energy planning scheme for Guangxi Province of China. The 2035 energy planning scheme can help users understand the basic issues of how to reduce emissions and meet energy demand, and provide new energy planning demonstration for the 14th five-year plan by using the scenario demonstration method so that the energy and emission data under different scenarios can actively reflect the future investment income, the proportion of non-fossil energy, carbon emission reduction, energy efficiency, and security; The scheme provides reference scenarios for future energy planning, such as minimum emission reduction, maximum energy efficiency, minimum cost and maximum utilization of renewable energy; In addition, genetic algorithm is used to optimize the path in various scenarios to select the optimal energy planning scheme for users.

Real-time carbon emissions monitoring at the enterprise level is a crucial tool in shifting macrolevel carbon peak and carbon neutrality plans toward micro-level implementations. This study extends the existing CO2 emissions accounting framework to enterprise emissions monitoring. We analyze the correlation mechanism between electricity consumption and CO2 emissions by industries, calculate the electricity–CO2 coefficients, and finally model an enterprise-level real-time carbon emissions monitoring method based on electricity big data. Taking Guangxi region as a sample, the results show that (1) the proportion of electricity-related emissions is on the rising stage in Guangxi, with 441 g CO2/KWh emitted from electricity consumption in 2020, (2) the carbon emissions from the energy-intensive industries account for over 70% of the whole society, and they all have high electricity–CO2 coefficients, far exceeding the industry average of 1129 g/kWh, and (3) the monitoring method is applied to 1338 enterprises from over 40 industries. The emission characteristics reflect the regional and industrial heterogeneity. This enterprise-level monitoring method aims to optimize the carbon emissions calculation method toward higher temporal and spatial resolutions, so as to provide an important numerical basis for promoting carbon emission reduction and sustainable development.