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This study focuses on the carbon emissions of key industries in Henan Province, employing techniques of seasonal adjustment, frequency transformation, and statistical modeling to construct an industry-level "Electricity–Energy–Carbon" model to aid in the high-frequency monitoring of carbon emissions in the province's industries. Based on relevant data, this research performs high-frequency calculations of carbon emissions from energy consumption in 34 typical industries and from the production processes of 53 typical sub-categories in the industrial sector of Henan. The findings reveal the following: Firstly, industrial energy consumption in Henan accounts for over half of the total provincial energy consumption, with most months seeing proportions around 60%. Industries such as energy, non-ferrous metals, building materials, steel, chemicals, petrochemicals, and paper making contribute to over 80% of the industrial energy consumption's carbon emissions, often nearing 90% in most months. Secondly, among the major industries, such as non-ferrous metals, chemicals, building materials, and steel, there is a dual challenge of being restricted under the "high energy consumption and high emissions" project while also being required to build key industrial bases, leading to fluctuating trends in historical annual carbon emissions data. Thirdly, six sub-categories, namely plastic products, cement, flat glass, steel, ten types of non-ferrous metals, and alumina, have significant carbon emissions in their production processes, accounting for about 72.3% of the total production-related emissions. [ABSTRACT FROM AUTHOR]

In the context of carbon neutrality, the phase-out of coal from the energy structure has resulted in numerous old coal mines that possess abundant underground space resources suitable for underground pumped hydroelectric energy storage (UPHES). Site selection and estimation of potential are critical to the planning and implementation of UPHES in old coal mines. This paper introduces a two-step site selection concept, including a screening assessment followed by a comprehensive assessment, to determine suitable locations for UPHES. The screening indicators in the screening assessment comprise geological features, mine water disasters, and minimum installed capacity, while the analytic hierarchy process (AHP) is applied in the comprehensive assessment. Additionally, coal mines in Henan Province are preliminarily screened through the screening assessment and the potential for UPHES is thoroughly investigated. The estimated volume of the drifts and shafts in old coal mines is approximately 1.35 × 107 m3, while in producing coal mines, it is around 2.96 × 107 m3. Furthermore, the corresponding annual potential for UPHES is 1468.9 GWh and 3226.3 GWh, respectively. By consuming surplus wind and solar power, UPHES is able to reduce 4.68 × 105 tonnes of carbon dioxide (CO2) emissions. The study provides preliminary guidance for policy-makers in developing UPHES in old coal mines. [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]

Building integrated photovoltaics (BIPV) contributes to promoting green and low-carbon transformation in rural areas. In order to better guide rural households to invest in BIPV projects and promote the goal of "carbon neutrality" in China's building sector, this study integrates the theory of planned behavior (TPB), the social cognitive theory (SCT), and the PEST analysis framework. It constructs a theoretical model from the perspective of "External Factors-Internal Psychology-Investment Intention" to investigate rural households' investment intentions toward BIPV projects and their influencing factors. Basic data were collected from 488 valid questionnaires from rural households in Henan Province, and the theoretical model was empirically tested using structural equation modeling. The results show that the model constructed from both internal and external factors effectively explains rural households' investment intentions (II) toward BIPV projects (R2 = 0.89), with investment attitude (IA) being the strongest psychological motivation leading to their II. All four external factors—policy, economic, social, and technological—positively influence II with diminishing effects. Additionally, the policy factor has the most significant effect on IA, while the economic factor has a more prominent effect on perceived behavioral control (PBC), and the technological factor has a relatively weaker effect on the two psychological factors. Furthermore, the four external factors indirectly influence investment intentions through the two psychological factors of IA and PBC, with the mediating effect of IA being higher than PBC. Based on the findings, this study proposes effective suggestions to enhance rural households' investment intentions toward BIPV projects. [ABSTRACT FROM AUTHOR]

In a global context where sustainable growth is imperative, understanding carbon emissions in significant regions is essential. Henan Province, being a vital region in China for population, agriculture, industry, and energy consumption, plays a crucial role in this understanding. This study, rooted in the need to identify strategies that not only meet China's broader carbon neutrality objectives but also offer insights regarding global sustainability models, utilizes the STIRPAT model combined with scenario analysis. The aim was to forecast carbon emission trajectories from 2020 to 2060 across the key industries—electricity, steel, cement, transportation, coal, and chemical—that are responsible for over 80% of the total emissions in Henan. The findings suggest a varied carbon peak timeline: the steel and cement industries might achieve their peak before 2025, and the transportation, coal, and chemical sectors might achieve theirs around 2030, whereas that of the power industry could be delayed until 2033. Significantly, by 2060—a landmark year for Chinese carbon neutrality ambitions—only the electricity sector in Henan shows potential for zero emissions under an extreme scenario. This study's results underscore the importance of region-specific strategies for achieving global carbon neutrality and offer a blueprint for other populous, industrialized regions worldwide. [ABSTRACT FROM AUTHOR]

In view of the current situation of the global energy crisis and environmental pollution, the energy industry transition and environmental governance are urgently needed. To deal with the problem above, the construction of a power system dominated by renewable energy (PSDRE) with wind turbine (WT), photovoltaic (PV), biomass power (BP), and other clean, low-carbon, renewable energy sources as the principal part has become a consensus all over the world. However, the random and uncertain power output of renewable energy will not only put pressure on the power system but also lead to the unreasonable and insufficient usage of renewable energy. In this context, the energy storage (ES) effects of flexible resources, such as physical energy storage of batteries and demand response (DR), are analyzed first. Next, a modeling method for the operational characteristics of physical and virtual shared energy storage (PVSES) in regional PSDRE (RPSDRE) is proposed. Finally, an optimal scheduling strategy for RPSDRE that considers PVSES is proposed to achieve coordination of WT, PV, PVSES, and other flexible resources. The case study on RPSDRE in Lankao county, Kaifeng city, Henan province of China verifies the effectiveness and practicability of the proposed strategy. [ABSTRACT FROM AUTHOR]

Distributed Energy System (DES), a comprehensive energy utilization system distributed on user side, has been recognized as a promising energy utilization method that can improve energy efficiency, reduce greenhouse gas emissions, and achieve sustainable development. However, the DES is usually driven by various energy sources, and it is a complex issue to decide the composition of the system. To improve the incompleteness of a single subjective or objective assessment. So, it is urgent to find a comprehensive and efficient decision-making method for different systems. This paper states a total of 23 indicators in 4 criterion group: technology, economy, environment, and society. Based on the combination of the order relation analysis method (G1) and the anti-entropy weighting method (a-EWM), a comprehensive evaluation model, order relation-anti-entropy weight model (G1-aEWM), of distributed energy is established. This comprehensive evaluation model is used to analyze a hospital in Henan and find the final solution for the distributed energy system of the hospital. The empirical analysis results verify the rationality of the comprehensive evaluation model and provide an evaluation basis for the establishment of distributed energy systems in the future. [ABSTRACT FROM AUTHOR]

Active frequency response (AFR) control is needed in current power systems. To solve the over-frequency problems of generators connected to non-disturbed buses during the AFR control period, the generators should be clustered into coherent groups. Thus, the control efficiency can be improved on the premise of ensuring control accuracy. Since the influencing factors (such as the model parameters, operation modes, and disturbance locations, etc.) of power system operation can be comprehensively reflected by the generator frequency, which is easily collected and calculated, the generator frequency can be used as the coherency identification input. In this paper, we propose a coherency identification method of AFR control based on support vector clustering for a bulk power system. By mapping data samples from the initial space to the high-dimensional feature space, the radius of the minimal enclosing sphere that can envelop all the data samples is obtained. Moreover, the coherency identification of generators is determined for AFR control according to the evaluating method of AFR clustering control effects and the evaluating index of cluster compactness and separation. The simulation results for the modified New England IEEE 10-generator 39-bus system and Henan power grid show that the proposed method is feasible and effective. [ABSTRACT FROM AUTHOR]

Enhancing energy efficiency is globally regarded as an effective way to reduce carbon emissions. In recent years, the energy efficiency of China has gradually improved; however, energy consumption and CO2 emissions are still increasing. To better understand the reasons for this, we evaluated the energy rebound effect (RE) of 30 provinces in China over the period 2001–2017 by employing stochastic frontier analysis (SFA) and the system generalized method of moments (system-GMM) approach, and explored the extent to which the RE affects CO2 emissions. Asymmetric and regional heterogeneity analyses were also conducted. The results indicate that the national average RE was 90.47% in the short run, and 78.17% in the long run, during the sample period. Most of the provinces experienced a partial RE, with a backfire effect occurring in some provinces such as Guangxi and Henan. The RE was associated with significant increases in CO2 emissions; specifically, a 1% increase in the short-run RE led to an increase in CO2 emissions of approximately 0.818%, and a 1% increase in the long-run RE resulted in an increase in CO2 emissions of approximately 0.695%. Moreover, significant regional differences existed in the impact of the RE on CO2 emissions; in regions with high emissions and a high RE, the CO2 reduction effect from the marginal decline in the RE was much more pronounced than that in other regions. [ABSTRACT FROM AUTHOR]

In order to investigate the difference of pore structure characteristics between mudstone and coal under different particle size conditions, samples acquired from Henan province were smashed and screened into three different particle sizes (20–40, 80–100, and >200 mesh) to conduct the experiments, using the high-pressure mercury intrusion porosimetry (MIP) and low-temperature N2 adsorption (LT-N2A) techniques. The results demonstrated that the proportion of open pores or semi-enclosed pores increased, and the pores became preferable contacted each other for both mudstone and coal during the crushing process. These variations of pore structure characteristics in the coal were beneficial to methane storage and migration. The total specific surface areas and pore volumes all showed a tendency of increasing continually for both mudstone and coal, as the particle sizes decreased from the LT-N2A test. The mudstone and coal were non-rigid aggregates with micropores, plate-shaped pores, and slit-shaped pores developed inside. The effect of the crushing process on the pore shape for the mudstone and coal was inappreciable. Moreover, the influence of the particle sizes on the mesopore was the most significant, followed by the macropore; and on the micropore, the influence was negligible for both mudstone and coal. The crushing process only had a significant impact on the pore structure of mudstone with a particle size of less than 100 mesh, while it could still alter the pore structure of coal with a particle size of larger than 100 mesh. It is believed that this work has a significant meaning to explore the diffusion and migration rules of coal-bed methane in coal. [ABSTRACT FROM AUTHOR]

Uniaxial compressive strength (UCS) and peak strain (PS) are essential indices for studying the mechanical properties of coal and rock masses, and they are closely related to mechanical parameters such as the elastic modulus (E), Poisson's ratio (υ), cohesion (C) and internal friction angle (Φ) of coal and rock masses. This study took the No. 2-1 coal seam of Zhaogu No. 2 Mine, in Henan Province, China, as the research object. An RMT-150B servo testing machine was used to test all mechanical parameters, including the E, υ, C and Φ of coal and rock masses. Based on the principle of orthogonal testing, Three Dimensions Fast Lagrangian Analysis of Continua (FLAC3D) was used to select E, υ, C, Φ, tensile strength (Rm) and dilation angle (Ψ) as initial participation factors. Using these six parameters and a five-level combination scheme (L25 (56)), the influence of coal mechanical parameters on UCS and PS was investigated, using the software SPSS for stepwise regression analysis, and a uniaxial pressure-resistant regression prediction equation was established. The research showed that, under uniaxial compression conditions, the main parameters controlling UCS of coal masses are C and Φ; conversely, the main parameters controlling PS are E and C. UCS and PS exhibit significant linear relationships with these main controlling parameters. Here, a stepwise regression prediction equation was established through reliability verification analysis using the main controlling parameters. This prediction method produces very small errors and a good degree of fit, thus allowing the rapid prediction of UCS. The precision of the stepwise regression model depends on the number of test samples, which can be increased in the later stages of a design project to further improve the precision of the projection model. [ABSTRACT FROM AUTHOR]