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The development and utilization of biomass energy based on the thermochemical conversion of crop biomass to produce hydrogen are of great significance for promoting China's ecological civilization construction, energy revolution, and low-carbon economic development. Henan province is one of the largest agricultural and pasturage provinces in China. Based on the analysis of the status and trends of Henan's biomass energy (BE) development, this paper summarizes the present status of the construction of ecological civilization (CEC) and the factors restricting its development. Challenges in developing biomass energy are analyzed systematically, and strategies and key technical directions for future biomass energy development are discussed. Finally, the paper presents countermeasures and suggestions for CEC based on the development of BE, which will vigorously promote the development and utilization of BE and the process of CEC. This research provides a reference for the further development of BE and CEC in the future. [ABSTRACT FROM AUTHOR]

To comprehend city-level driving mechanisms of carbon emissions, this paper utilizes spectral cluster and two-layer LMDI (logarithmic mean divisia index) method to systematically assess the contribution values of correlative factors from each cluster to Henan's carbon emissions increments, and accordingly comes up with more strategies about how to reduce carbon emissions for each cluster on the basis of driving forces of carbon emissions. The results of clustering and the decomposition are as follows: (1) the 18 prefecture-level cities in Henan were divided into five categories by spectral clustering, and there are similar development patterns within each category, so they can learn from each other to improve their own defects of development; (2) this paper utilizes the two-layer LMDI method to divide the factors affecting each cluster of carbon emissions into four types, which includes energy structure, energy intensity, per capita GDP, and population, and calculates the contribution value of each factor. It was concluded that the contribution value of per capita GDP dominantly drove up carbon emissions, while energy intensity played a significant role in offsetting them. Therefore, it is important for Henan's low-carbon development to control the expansion of economic activity and improve energy efficiency in the future. [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 China's traditional agricultural province, the rural areas of Henan have experienced phenomenal economic growth while also consuming too much energy. However, few studies have focused on the analysis of rural energy economic development in Henan. For the first time, this paper used the combination of symbolic regression and tapio decoupling analysis, and introduce six factors that best reflect the characteristics of rural energy consumption (E) in Henan Province, such as effective irrigated area (EIA), per capita living space (PLS), total power of agricultural machinery (PAM), Population (POP), per capita income (PI) and total value of agricultural (TVA). This paper collected data from 2000 to 2015 to fit the formula for rural energy consumption in Henan Province based on the Eureqa-formulize software. The conclusion shows that: (1) The most critical factors are affecting the energy consumption in rural is effective irrigated area, total power of agricultural machinery, rural per capita income, per capita living space and total value of agricultural, but the population is not the main factor. (2) The E-EIA decoupling and the E-PLS decoupling have two decoupling status: weak decoupling and expansive negative decoupling. The E-PAM decoupling has three decoupling status: weak decoupling, expansive coupling, and expansive negative decoupling. The E-PI decoupling and the E-TVA decoupling have one decoupling status: weak decoupling. (3) According to the incidence of each influencing factors, from strong to weak is EIA> PLS> PAM> TVA> PI. The five influencing factors have all shown a positive decoupling effect on energy consumptions. [ABSTRACT FROM AUTHOR]

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The sustainable development of China's economy and society has been restricted by energy and environmental issues. With the implementation of the Rise of Central China strategy, Henan, a populous province in China, is facing the threat of an energy shortage as its economy grows steadily and the urbanization process accelerates. Using panel data of 18 cities in Henan Province for the period of 2006–2018, this study investigates the relationship between energy consumption and economic and social development variables based on the extended Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model and threshold regression method. The empirical results indicate that economic and social development are significantly positively correlated with total electricity consumption, although there is regional heterogeneity in this relationship. Moreover, there is evidence for a nonlinear relationship between economic and social development and urban energy consumption. These conclusions not only promote the healthy and sustainable economic development of Henan Province but also provide a reference for other provinces and cities that are experiencing rapid development in China. [ABSTRACT FROM AUTHOR]

Zhengzhou of Henan province is an important agricultural production base. To improve the performance of the combined drying system and promote the development of agricultural products in Zhengzhou area, a solar assisted multi-source heat pump (SMSHP) drying system was proposed based on a traditional solar assisted air source heat pump (SASHP) drying system. The simulation models were developed for the air source heat pump (ASHP), SASHP, and SMSHP drying system respectively. The coefficient of performance, energy consumption and specific moisture extraction rate of the drying systems under different seasons were analyzed. Moreover, to validate the simulation models, an experimental platform of the combined drying system was designed and set up, and the experimental tests were carried out on the ASHP, SASHP, and SMSHP drying system. The simulation results revealed that the SMSHP drying system had lower energy consumption, higher coefficient of performance and specific moisture extraction rate than that of the SASHP drying system. The most significant performance enhancement was observed in spring, where the SMSHP system demonstrated a 22.69 % increase in coefficient of performance, a 19.2 % increase in specific moisture extraction rate, and a 16.11 % decrease in energy consumption compared to the SASHP drying system. Comparing with the experimental data, the simulation model errors of the energy consumption, coefficient of performance, and specific moisture extraction rate of the SMSHP drying system were 3.19 %, 0.28 %, and 2.27 %, respectively. Therefore, the accuracy of the simulation results was confirmed. To better calculate the heat provided by the solar collection system, the concept of the tank heating guarantee rate was first introduced, and it was found to be more scientific than the solar energy guarantee rate. At last, The economic and environmental benefit of the drying systems were analyzed during the life cycle, and the SMSHP drying system exhibited better comprehensive performance than the SASHP drying system. This paper provides valuable insights into the comprehensive utilization of solar energy and the control logic of solar assisted air source heat pump combined drying system under different seasons. [ABSTRACT FROM AUTHOR]

With the increase in energy demand, environmental issues such as carbon emissions are becoming more and more prominent. China will scale its intended nationally determined contributions by adopting more vigorous policies and measures. China aims to have CO2 emissions peak before 2030 and achieve carbon neutrality before 2060. The current challenge and priority of China's high-quality development is to ensure a harmonious balance between the ecological environment and the economy. The South-to-North Water Diversion Project passes through Beijing, Tianjin, Henan, and Hebei, which were chosen as the study sites. The carbon emission data was from the China Carbon Emission Database 2000–2019. Decoupling modeling using statistical yearbook data from four provinces and municipalities. KMO and Bartlett's test used SPSS 27 software. The selection of indicators was based on relevance. Analyses were performed using the extended STIRPAT model and ridge regression. Moreover, projections of carbon peaks in the study area for 2020–2035 under different rates of change were simulated by the scenario analysis method. The results show that: (1) The decoupling analysis of the four provinces and cities from 2000-2019 gradually shifts to strong decoupling; (2) Resident population, energy structure, and secondary industry as a proportion of GDP significantly impact carbon emissions; (3) From 2000–2035, Beijing and Henan experienced carbon peaks. The peak time in Beijing was 96.836 million tons in 2010. The peak time in Henan was 654.1004 million tons in 2011; (4) There was no peak in Hebei from 2000–2035. [ABSTRACT FROM AUTHOR]

The main objective of this paper was to analyze the status quo of energy consumption in rural Henan and identify the driving forces governing energy consumption based on the STIRPAT model. Potential driven factors of energy consumption including power of agricultural machinery, effective irrigated area, investment, income, total value, and per capital living space were selected to build the extended stochastic impacts by regression on population, affluence, and technology (STIRPAT) model, where ridge regression was applied to eliminate multicollinearity. The estimation results show that all the factors are positively related to rural energy consumption in Henan. The effective irrigated area is the maximal driven factor of energy consumption in rural Henan. Per capital living space, peasant household investment, power of agricultural machinery, total value of agricultural output, and per capita income all contribute to rural energy consumption growth in Henan. Finally, we propose policies that can be applied to mitigate future energy consumption in rural Henan. [ABSTRACT FROM AUTHOR]

• Deep reinforcement learning (DRL) based models are proposed for HVAC system energy consumption prediction. • Autoencoder (AE) algorithm is used to extract the high-level features of state space of DDPG. • The DDPG based models are applied for energy consumption forecasting over short-term time horizon. • Improvement in prediction performance is observed when combine AE with DDPG. • The DDPG based prediction models, outperform common supervised models like BP neural network and support vector machine. Short-term energy consumption prediction has fundamental importance in many HVAC system management tasks, such as demand-side management, short-term maintenance, etc. Currently, the prevailing data-driven techniques, especially supervised machine learning methods, are widely applied for short-term energy consumption prediction. Deep reinforcement learning (DRL), as the state-of-the-art machine learning techniques, have been applied for HVAC system control, but rarely for energy consumption prediction. In this paper, a DRL algorithm, namely Deep Deterministic Policy Gradient (DDPG), is firstly introduced for short-term HVAC system energy consumption prediction. Moreover, Autoencoder (AE), which is powerful in processing data in their raw form, is incorporated into DDPG method to extract the high-level features of state space and optimize the prediction model. The operation data of the ground source heat pump (GSHP) system of an office building in Henan province, China is used to train and assess the proposed models. The results demonstrate that the proposed DDPG based models can achieve better prediction performance than common supervised models like BP Neural Network and Support Vector Machine. This study is an enlightening work which may inspire other researchers to tap the potential of DRL algorithms in this field. [ABSTRACT FROM AUTHOR]

The integrated energy system (IES) can improve energy efficiency and promote the use of renewable energy. It is of great significance to find the optimal alternative from many IES alternatives. However, most studies focused on the IES evaluation with complete information. Given this background, this paper proposes an evaluation method based on generalized regression neural network (GRNN) and probabilistic neural network (PNN) to address the issue of IES evaluation with incomplete information, which takes the running speed of the algorithm and the dynamic updating of the indexes into account. Firstly, the missing data is predicted via GRNN, and a complete evaluation matrix is obtained. Then, the evaluation indexes are clustered by affinity propagation algorithm, and the evaluation matrix of the cluster centers is determined, which can reduce the dimension of the initial matrix and improve the speed of the IES evaluation method. For the newly added evaluation indexes, the PNN is used to classify them, and the new cluster center is calculated, which can address the issue of the indexes updating. Based on the data of a hospital in Henan, case studies were carried out to evaluate the performance of the proposed method. The results show that the proposed method performs better than the existing IES evaluation method. • GRNN is used to address the IES evaluation problem with incomplete information. • The accuracy of GRNN is verified by comparing with common prediction methods. • It is the first work to deal with the issue of indexes updating in IES evaluation. • Compared with the existing evaluation method, the proposed method performs better. [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 the context of global climate change and energy security pressures, biomass power generation has been regarded as an effective way to supplement the energy supply, reduce fossil fuel consumption, and alleviate environmental pollutants. This paper presents an integrated modeling framework to address the regional biomass power generation planning to facilitate biomass utilization in developing countries. By combining the geographic information system, multi-criteria decision-making approach, as well as the type-2 fuzzy inexact programming method, the proposed integrated framework could assist the optimal decision-making for the specific site and size selection of biomass power plants in the whole region as well as the optimal operation strategies under deep uncertainties. The proposed modeling framework is applied to the particular context of Henan, an agricultural province in China with great biomass potential. The results suggested that biomass power plants with a total capacity of 2.4 GW could be established, which could satisfy 6% of local electricity consumption and avoid 18.51 Mt of carbon emissions. The sensitivity analysis of the main economic parameters indicated that lower investment and operation costs were essential to stimulate the utilization of local biomass energy. A higher feed-in tariff is not encouraged due to the limited incentive effect but is a heavy burden for the government. Furthermore, the optimal strategies for biomass power generation planning under uncertainties could be suggested for different decision makers with various risk preferences to achieve a tradeoff between system cost and risk. [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]

As an institutional innovation in the market-oriented reform of China's energy sector, the policy effect of energy consumption license transaction (ECPT) is still lack of research. The pilot provinces of energy market-oriented reform are relatively scattered. Therefore, this paper aims to evaluate the spillover effect and regional differences of ECPT, so as to make up for the lack of research on China's market-oriented energy reform. Results show that ECPT has inhibitory effects on energy consumption and energy intensity. In addition, ECPT can reduce energy consumption and energy intensity in neighboring areas. The policy effect of ECPT shows the characteristics of strengthening with the evolution of time. Foreign trade, industrial structure and technological progress are the paths for ECPT policies to promote energy transformation. The implementation effect of ECPT shows significant regional differences, which has a significant role in promoting the energy transformation of Henan and Sichuan. Therefore, the central government must ensure the continuity of energy market-oriented reforms to release long-term policy dividends. Pilot provinces should not only play the role of domestic regional energy-saving cooperation, but also strengthen foreign trade and technological innovation to promote energy transformation. • ECPT has an obvious inhibitory effect on energy consumption and energy intensity. • ECPT has the spillover effect of energy saving on neighboring provinces. • The long-term effects of ECPT are stronger than the short-term effects. • Foreign trade, industrial upgrading and technological progress are the mechanisms. • The effect of ECPT shows significant heterogeneity in different pilot provinces. [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]

Carbon emission is further intensified as urbanization and industrialization continue to accelerate. China has maintained its rapid economic development and urbanization in the last 2 decades. The development of the construction industry has not only consumed a large number of energy sources but also resulted in significant carbon emissions, causing some environmental damage. Recognizing the major influencing factors of carbon emissions in the construction industry has become a research hotspot to alleviate environmental pollution caused by the construction industry and meet industrial demands for energy saving and emission reduction. In this study, the factors that influence annual carbon emissions of different building types in China from 2011 to 2018 were decomposed by Logarithmic Mean Divisia Index (LMDI) through a case study in Henan Province. The major influencing factors of carbon emissions have been identified. Results demonstrate that the per capita carbon emission in the construction industry in Henan Province remains high from 2011 to 2018, but it decreases year by year. Carbon emissions from the construction industry in Henan Province increase due to economic development and energy structure. Energy efficiency can inhibit carbon emissions from the construction industry in Henan Province. The obtained conclusions have a positive effect on analyzing annual variations in carbon emissions from the construction industry in a region, identifying influencing factors, and proposing specific countermeasures of energy saving and emission reduction. [ABSTRACT FROM AUTHOR]

Low energy utilization is observed in China due to the extensive economic growth mode, which further leads to considerable energy wastes and environmental pollution. The construction industry plays an important role in the national economic development of China and consumes tremendous materials; thus, this industry discharges abundant CO2. The energy consumption growth rate of the construction industry in China is far higher than the national energy consumption growth rate, resulting in the prominent situation of high energy consumption and low yield. A case study based on Henan Province, China, was conducted to further analyze the environmental efficiency of the construction industry. An index system was established by using the Super-slack-based model (Super-SBM). This system chooses the following: labor, energy, capital, and technology of the construction industry as the input variables, economic output as the output variable, and carbon emissions as the unexpected output. This system was also used to investigate the energy efficiency of the construction industry in Henan Province from 2008 to 2019. Results demonstrated that the construction industry in Henan Province has failed to eliminate the extensive development mode thus far. The environmental efficiency of the construction industry presents a fluctuating growth with a mean of 1.048, which generally remains at a relatively low level. The numbers of construction machines and enterprises in the construction industry have redundancy in approximately 50% of the years. Thus, this study can provide some positive references to enrich the evaluation index system and estimation model of energy efficiency of the construction industry, which includes unexpected output. Moreover, the current study can provide a comprehensive understanding of the environmental efficiency of the construction industry in a province in China and realize reasonable allocation of construction industrial resources. [ABSTRACT FROM AUTHOR]

Industrial parks play an extremely important role in the rapid development of China's economy. However, as the backbone of China's economic development, industrial parks also consume huge energy resources and cause serious pollution to the environment, making China face greater pressure on environmental issues. This article takes the Yongcheng Economic and Technological Development Area, a typical energy-intensive industrial park in Henan Province, as the research object to analyze its energy saving and emission reduction potential. Three scenarios (baseline scenario, energy cascade utilization scenario, and energy efficiency technology enhancement scenario) are set to quantify the energy-saving potential and air pollutant emission reduction of the park under different scenarios. The results show that in the energy cascade utilization scenario, by realizing the recycling of waste heat resources from heat source enterprises, it can bring energy saving of 6385 TJ, and reduce 0.35 kt SO2, 0.79 kt NOx, 0.067 kt PM10, and 0.035 kt PM2.5. And CO2 emission reductions have reached 604 kt. In the energy efficiency technology enhancement scenario, by eliminating relatively backward technologies and adding advanced energy-saving technologies, 7306 TJ energy saving could be achieved. SO2, NOx, PM10, PM2.5, and CO2 emission reductions are 0.37, 0.82, 0.038, 0.071, and 719 kt, respectively. The results of the CALPUFF model indicate that the pollutant concentrations of SO2, NOx, PM10, and PM2.5 in the spring and autumn are relatively high, while those in the summer and winter seasons are relatively low. In four seasons, the highest 1-h average concentration and dispersion range of four pollutants have been reduced both in the energy cascade utilization scenario and in the efficiency technology enhancement scenario. [ABSTRACT FROM AUTHOR]