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With the development of green energy, the penetration rate of new energy in the power grid is rising rapidly. Incorporating a large amount of new energy into the power grid will affect the power supply balance of the power grid, and even in severe cases will affect the safe and stable operation of the power grid. The information park has various small and medium data centers. The adjustable load of the data center can be transferred in time and space through the optical fiber and other communication networks, which can enhance the controllability of the power grid to a certain extent and promote the consumption of new energy. This paper proposes a collaborative planning method for information parks that considers the temporal and spatial transfer characteristics of data center loads, builds a load transfer model for data centers and builds a park power grid expansion model with the goal of minimizing the total cost of construction and operation. In this paper, an example of a park is used to verify the calculation example, and it is compared with other schemes to verify the feasibility of the method in this paper. [ABSTRACT FROM AUTHOR]

The high consumption of energy resources but low utilization, low factor allocation, and unreasonable economic structure affect the sustainability of economic development. Based on the energy factor theory from a low-carbon perspective, this paper constructs an impact model of green total point productivity and conducts a detailed analysis of the resource mismatch in the energy industry. Under the premise of efficient resource allocation, the factor input of an enterprise is positively correlated with total point productivity. Second, the model adds environmental constraints, considers the undesired output of carbon dioxide, and considers the input of energy factors, and reduces the undesired output as much as possible while increasing the expected output. The model realizes the update estimation of each component of the stationary part of a class of nonstationary stochastic processes and can use the information at a certain time and its previous time in a cycle to forecast all subsequent times. In the simulation process, in order to make the results more objective and accurate, this paper calculates the index and its decomposition results under two conditions, and uses MaxDEA 6.0 professional software to measure the regional industry total point productivity index. The experimental results show that the effective factor of low-carbon data is 0.91, and the relative error is less than 3.11%, which meets the test threshold and effectively improves the robustness of the model. [ABSTRACT FROM AUTHOR]

In recent years, solar and wind energy have been increasingly abandoned due to the blind expansion of the new energy industry. Due to the competitive relationships between different types of new energy, reasonable industrial development planning needs to be implemented to not only save the cost of government subsidies but also clarify the investment direction of social capital. Based on the panel data of OECD countries between 2006 and 2018, the stochastic frontier analysis (SFA) was used to measure the efficiency of new energy generation (NEG) and the influencing factors were analyzed in this paper. Results were as follows: the efficiency of NEG in OECD countries is improving; the efficiency of NEG is positively correlated with technical innovation, government policies, economic level, and education level and negatively correlated with urbanization. Based on the empirical results of this study, problems in the development of the new energy industry have been discussed and suggestions to improve the efficiency of NEG have been proposed. [ABSTRACT FROM AUTHOR]

This study uses the super efficiency DEA model to evaluate the logistics industry's total factor energy efficiency in various Chinese provinces, autonomous regions, and municipalities, and examines the relationship between environmental regulations and the total factor energy efficiency. The results show that environmental regulations can help improve the logistics industry's total factor energy efficiency. This study further analyzes the threshold effect of environmental pollution control investment level, quality of labor, and logistics industry development level in environmental regulations on the logistics industry's total factor energy efficiency. The results showed that the level of investment in environmental pollution control in environmental regulations has a significant single threshold effect on logistics industry's total factor energy efficiency. The quality of labor has a significant double threshold effect on environmental regulation's impact on the logistics industry's total factor energy efficiency. Logistics industry development level has a significant double threshold effect on the impact of environmental regulation on logistics industry's total factor energy efficiency. [ABSTRACT FROM AUTHOR]