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Carbon emission reduction is an important part of regional low-carbon economic development. In this paper, gray correlation analysis, neural network model, Gaussian multi-mode fitting and other methods were used to analyze the relationship between total carbon emissions and regional economic development, industrial structure, and energy consumption in Henan Province. On this basis, the future development of carbon emissions is predicted. The calculation results showed that the correlation between the three industries and carbon emissions in Henan Province is more than .7, among which the secondary industry has the highest correlation (.77). In the secondary industry, the correlation coefficient between coal and carbon emissions is the highest .87, while the correlation coefficient between other energy sources is about .5. In the neural network prediction model, the correlation coefficient between the prediction curve and the actual total carbon emission curve is .989, and the prediction results have a good degree of fit. The carbon emission prediction curve was divided into two parts: a linear decline stage from 2018 to 2024, and a rapid decline stage after 2024.The results showed that more efforts should be made in industrial structure, energy consumption structure and environmental protection to achieve low-carbon development in Henan province. [ABSTRACT FROM AUTHOR]

Near-zero carbon emission power (NZCEP) plants, consisting of gas-fired units; wind turbines; power-to-gas (P2G); and carbon capture, utilization and storage (CCUS) systems, have recently received a lot of attention due to their enormous benefits in reducing carbon emissions and increasing the consumption of renewable energy. However, a complex environment of interest and a combination of risks makes their development very slow. This paper establishes a risk analysis framework for NZCEP considering multi-stakeholder participation. Firstly, a synthetic risk factor system was constructed based on stakeholders' interests. Subsequently, interval type II trapezoidal fuzzy numbers were used and final weights were calculated from both subjective and objective aspects. Finally, we applied an acronym in Portuguese of the interactive and multi-criteria decision-making (TODIM) method to site selection to achieve a balance of interests of all stakeholders. In addition, a case study was conducted. The case result demonstrates that Zhengzhou in Henan Province is the best choice for a NZCEP power plant. A further finding is that government plays an important role in the development of NZCEP plants, with site selection results being the most sensitive to changes in the government's risk appetite. Moreover, human resources are an important factor in the siting of an NZCEP plant. [ABSTRACT FROM AUTHOR]

Clarifying the influencing factors of energy consumption in underground production processes of coal mine is the premise and foundation for the coal mining industry to control the energy consumption and intensity to achieve the goal of carbon peak and carbon neutrality. This paper aims to investigate and compare the differences in energy consumption and carbon emissions between a mining‐separating‐backfilling integrated coal mine (MSBICM) and a traditional coal mine and explore the effects of the main production links underground on carbon emission intensity in coal mines. Hence, taking an MSBICM in Henan province as an example, this study constructed an energy consumption and carbon emission model of the mine using system dynamics (SD) method. Based on this model, the energy consumption of the main production logistics system and auxiliary production system in MSBICM was simulated and analyzed. In addition, this study made a comparative analysis of the energy consumption of the different production links in the two types of coal mines. The results showed that: (1) the comprehensive mechanized coal mining with solid filling system and ventilation system were the subsystems with the highest and lowest energy consumption in MSBICM respectively. (2) The influence rates of raw coal mining, gangue filling, coal‐gangue separating, and material transportation on carbon emission intensity in coal mine were 1.09%, 1.34%, 1.09%, and 5.13%, respectively. (3) Under the same production and filling targets, energy consumption of underground transportation systems in MSBICM was found to be 336.66 tons of standard coal less than that in traditional coal mine. This study has helped clarify the complex relationships among the factors that influence energy consumption in coal mines and provided a reference for implementing dual control of energy consumption to promote low‐carbon mining of coal resources. [ABSTRACT FROM AUTHOR]

As a major institutional innovation in the practice of the green development concept, the energy‑consuming transaction system is a major measure to promote the reform of the ecological civilization system. The implementation of the energy‑consuming transaction system is important for improving energy utilization efficiency and achieving ‘dual control’ of energy consumption and intensity. Using the energy‑consuming transaction system pilots as an entry point, and based on the panel data from 30 provinces in China from 2006 to 2019, this paper used the difference‑in‑differences method to examine the impact and mechanism of energy‑consuming transaction system on energy efficiency. The research results showed that the energy‑consuming transaction system could significantly improve energy utilization efficiency. Specifically, it could reduce the energy consumption per unit of GDP in the pilot provinces by 5.78%. The mechanism test showed that the energy‑consuming transaction system mainly improved energy utilization efficiency by promoting green technology innovation. Heterogeneity analysis showed that the energy‑consuming transaction system would have a diminishing effect on energy utilization efficiency along with the energy saving potential. The energy ‑consuming transaction system could play a significant role in promoting the energy utilization efficiency of Henan and Sichuan, while the impact on Zhejiang and Fujian was not significant. In addition, this article also found that the energy‑consuming transaction system had environmental dividends, which could effectively reduce sulfur dioxide and carbon dioxide emissions while promoting energy efficiency. After parallel trend test, structural breakpoint test, PSM‑DID, placebo test, and other robustness tests, the conclusion of this article was still valid. This study has the following implications: In the process of building the energy‑consuming transaction market, it is necessary to promptly summarize the pilot experience, gradually expand the piloting scope, constantly improve the regulatory mechanism to reduce institutional transaction costs, promote efficient energy allocation and rational use, and at the same time, adhere to local conditions and set out to formulate and implement supporting measures for energy‑consuming transactions with local characteristics. [ABSTRACT FROM AUTHOR]

Global warming caused by carbon emissions has become increasingly prominent. As the world's second-largest economy, China is under enormous pressure to cut down its carbon dioxide emissions. It is urgent to seek effective methods to forecast carbon emissions and put forward the pointed and effective measures to reduce emissions. In this paper, we first use the software Eviews to make an analysis of randomness on data of carbon emissions in the four representative provinces and city, Beijing, Henan, Guangdong, and Zhejiang, in terms of their carbon emissions data from 1997 to 2017. Then, according to their distinct characteristics, the methods of stationary processing of the difference, moving average, and substituting strong impact points, respectively, are adopted to perform the data preprocessing. Then, model identification, parameter estimation, and model test are carried out to establish the model of ARIMA(p , d, q) for the prediction of the carbon emissions of the four regions, respectively. Finally, the model is used to forecast the data and analyze their tendency for their carbon emissions in the next three years. The results can provide guidance for decision-makers to set reasonable carbon emission reduction targets and take appropriate energy conservation and emission reduction measures. [ABSTRACT FROM AUTHOR]

The carbon emission from major sectors and key industries is the main source of regional carbon emission, which has caused severe climate anomalies. Decoupling analysis and prediction of carbon emissions are helpful to formulate effective carbon emission reduction targets and coordinate the relationship between carbon emission and economic development. Considering the differences between sectors, this paper selects 12 major industrial carbon emitting sectors in Henan Province as the research object, decomposes the carbon emission driving factors by Logarithmic Mean Divisia Index (LMDI) method, analyzes the decoupling relationship of carbon emission and economic development by Tapio decoupling model, constructs a decoupling effort model by combining the LMDI method and Tapio decoupling model to analyze the influence of each effect on the decoupling relationship, and sets up baseline scenario, low carbon scenario and enhanced low carbon scenario to forecast carbon emission and its decoupling state with economic development by the Long-range Energy Alternatives Planning system (LEAP) model. It is concluded that the restraining effects of energy structure, energy intensity and industrial structure are increasing. The decoupling index between carbon emission and economic development of 12 sectors shows a downward trend. Both energy structure effect and energy intensity effect have made weak decoupling efforts, and the decoupling efforts of industrial structure effect were relatively small, especially in Ferrous metal smelting and calendering industry and other five sectors. In 2035, four sectors will achieve carbon neutralization and the overall decoupling effect of carbon emissions will be obvious. Finally, this paper puts forward some recommendations to promote carbon emission reduction. • Decomposition, decoupling and forecasting analysis of carbon emission. • Combining decomposition and decoupling analysis to construct decoupling effort model. • Differences between sectors of industry are considered. • The decoupling efforts of industrial structure effect were relatively small. • Four industrial sectors will achieve carbon neutralization in 2035. [ABSTRACT FROM AUTHOR]