The goal of “carbon peak” and “carbon neutrality” is China’s long-term development strategy for low greenhouse gas emissions in the 21st century (hereinafter referred to as the “double carbon” goal), with the goal that carbon dioxide emissions strive to reach an emissions peak by 2030 and strive to achieve carbon neutrality by 2060. Achieving the goal of “double carbon” is an inherent requirement for promoting high-quality development. Conforming to the development trend of the times, the “double carbon” goal is the necessary path of industrial transformation. The current situation of China’s energy production, consumption, and utilization in recent years shows that China’s energy has a simple structure, heavy external dependence, and low utilization efficiency which severely restricts the realization of China’s “double carbon” goal. Commencing the analysis of the current situation, this paper summarizes the implementation path from a “carbon emission reduction” and “carbon-negative technology” to accelerate the realization of the “double carbon” goal. First, statistical methods are used to analyze the implementation of a “carbon emission reduction” based on industrial technology reform, an energy structure adjustment, market mechanisms, public participation, and international cooperation. Second, the “carbon negative technology” path is explored in terms of afforestation, carbon capture, utilization and sequestration (CCUS), ecological management, and other technologies. In addition, feasible suggestions are made for four relationships, namely, the short and long-term, overall and local, development and emissions reduction, and government and market. This paper contributes to the study of energy development in the world and serves as a reference for follow-up studies.

Energy-saving and emission reduction are recognized as the primary measure to tackle the problems associated with climate change, which is one of the major challenges for humanity for the forthcoming decades. Energy modeling and process parameters optimization of machining are effective and powerful ways to realize energy saving in the manufacturing industry. In order to realize high quality and low energy consumption machining of computer numerical control (CNC) lathe, a multi-objective optimization of CNC turning process parameters considering transient-steady state energy consumption is proposed. By analyzing the energy consumption characteristics in the process of machining and introducing practical constraints, such as machine tool equipment performance and tool life, a multi-objective optimization model with turning process parameters as optimization variables and high quality and low energy consumption as optimization objectives is established. The model is solved by non-dominated sorting genetic algorithm-II (NSGA-II), and the pareto optimal solution set of the model is obtained. Finally, the machining process of shaft parts is studied by CK6153i CNC lathe. The results show that 38.3% energy consumption is saved, and the surface roughness of workpiece is reduced by 47.0%, which verifies the effectiveness of the optimization method.

Enhanced oil recovery (EOR) is a complex process which has high investment cost and involves multiple disciplines including reservoir engineering, chemical engineering, geological engineering, etc. Finding the most suitable EOR technique for the candidate reservoir is time consuming and critical for reservoir engineers. The objective of this research is to propose a new methodology to assist engineers to make fast and scientific decisions on the EOR selection process by implementing machine learning algorithms to worldwide EOR projects. First, worldwide EOR project information were collected from oil companies, the extensive literature, and reports. Then, exploratory data analysis methods were employed to reveal the distribution and relationships among different reservoir/fluid parameters. Random forest, artificial neural networks, naïve Bayes, support vector machines, and decision trees were applied to the dataset to establish classification models, and five-fold cross-validation was performed to fully apply the dataset and ensure the performance of the model. Utilizing random search, we optimized the model’s hyper parameters to achieve optimal classification results. The results show that the random forest classification model has the highest accuracy and the accuracy of the test set increased from 88.54% to 91.15% without or with the optimization process, achieving an accuracy improvement of 2.61%. The prediction accuracy in the three categories of thermal flooding, gas injection, and chemical flooding were 100%, 96.51%, and 88.46%, respectively. The results also show that the established RF classification model has good capability to make recommendations of the EOR technique for a new candidate oil reservoir.