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Against the backdrop of increasingly serious global climate change and the development of the low-carbon economy, the coordination between energy consumption carbon emissions (ECCE) and regional population, resources, environment, economy and society has become an important subject. In this paper, the research focuses on the security early warning of ECCE in Hebei Province, China. First, an assessment index system of the security early warning of ECCE is constructed based on the pressure-state-response (P-S-R) model. Then, the variance method and linearity weighted method are used to calculate the security early warning index of ECCE. From the two dimensions of time series and spatial pattern, the security early warning conditions of ECCE are analyzed in depth. Finally, with the assessment analysis of the data from 2000 to 2014, the prediction of the security early warning of carbon emissions from 2015 to 2020 is given, using a back propagation neural network based on a kidney-inspired algorithm (KA-BPNN) model. The results indicate that: (1) from 2000 to 2014, the security comprehensive index of ECCE demonstrates a fluctuating upward trend in general and the trend of the alarm level is "Severe warning"-"Moderate warning"-"Slight warning"; (2) there is a big spatial difference in the security of ECCE, with relatively high-security alarm level in the north while it is relatively low in the other areas; (3) the security index shows the trend of continuing improvement from 2015 to 2020, however the security level will remain in the state of "Semi-secure" for a long time and the corresponding alarm is still in the state of "Slight warning", reflecting that the situation is still not optimistic. [ABSTRACT FROM AUTHOR]

This paper aims to optimize the vent design for additional sunspace of passive houses in rural areas. First, a typical rural house in northern China's Hebei was selected as the research object. Then, a total of twelve models with different vent length and vent number were designed to allow the warm air move from the sunspace to the house. All the vents are assumed to be square in shape and manually controllable according to the sunspace temperature. Through Fluent simulation, the twelve models were compared based on different variables, and the models with the best and worst results were determined. The results show that the addition of vents facilitates the exchange between the warm air in the sunspace and the cool indoor air, improving the warming speed and efficiency indoor; the highest indoor temperature was achieved at the vent length of 0.3m, using three inlet vents and two outlet vents. On this basis, the best and worst performing models were further compared to identify the influencing factors of sunspace and optimize the vent design. The energy-saving, emission reduction, and environmental impacts of the vent design were also discussed. The research results lay a theoretical basis for reducing the energy consumption of winter heating in rural houses, and promoting the sunspace design of passive houses. [ABSTRACT FROM AUTHOR]

[Display omitted] • Carbon emissions are explored in China's building operations from provincial perspective. • GDIM is employed to factorize ten affecting factors of CO 2 emissions in building sub-sectors. • CO 2 emissions of building sub-sectors are predicted with dynamic simulation. • The contribution of provinces to the change and peak of national emissions are discussed. Carbon emissions from building operations have a critical impact on achieving carbon peaking goal in China. However, the provincial emission performance in building operations are quite different and it is still unidentified how they affect the national emission change and peaking target, making national emission reduction target hard to implement at the provincial level. This paper explores the driving factors leading to emission changes and examines the prospective emission peaks in China and China's provinces by adopting generalized Divisia index method (GDIM) and the combination of scenario analysis and Monte Carlo simulation. Furthermore, the regional emission heterogeneity and the contribution of provinces to the change and peak of national emissions are discussed to identify regions and provinces that should be focused on emission monitoring. Results show that in China and most Chinese provinces, energy consumption of commercial buildings, energy consumption and floor space of urban residential buildings besides income improvement, and the per capita CO 2 emissions of rural residential buildings contributed greatly to emission growth. Under current policies, commercial and urban residential buildings in most provinces would fail to reach their emission peaks ahead of schedule. The urban residential buildings in the northern region, commercial and rural residential buildings in the eastern region caused higher CO 2 emissions. At the provincial level, Shandong, Shanxi and Hebei made the top contribution to total emissions of commercial, urban residential and rural residential buildings, respectively. Shandong and the provinces in north China of urban residential buildings and four provinces including Shandong, Guangdong, Hebei and Zhejiang of commercial and rural residential buildings had the significant impact on the uncertainty of the peaking time of CO 2 emissions. These findings provide a strong decision-making reference for Chinese government to formulate targeted regional emission reduction policies in building operations. [ABSTRACT FROM AUTHOR]

With the development of urbanization, the demand for bricks continues to increase. However, traditional brick production methods result in significant energy consumption and environmental pollution. In Hebei Province, China, historical mineral extraction activities have left behind substantial iron ore tailings (IOT). With the objective of recycling IOT resources and promoting ecological restoration and sustainable development, the feasibility of producing alkali-activated bricks using iron ore tailings was explored. This study primarily utilized IOT supplemented with ground granulated blast-furnace slag (GGBS) to successfully synthesize sustainable eco-friendly alkali-activated high-content IOT bricks. Experimental investigations were conducted to explore the effects of the raw material mixing ratio, content, and modulus of the alkaline activator, molding pressure, and grain size distribution on the strength. The research demonstrated that the IOT: GGBS ratio of 85:15 met the requirements of the Chinese JC/T422-2007 MU25 standard, resulting in compressive and flexural strengths of 31.72 MPa and 2.83 MPa, respectively. Increasing the alkali activator content enhanced the brick strength, with an optimal alkali activator modulus of 1 M. Moreover, the molding pressure significantly improved brick strength and also enhanced the particle-to-particle contact density. Bricks prepared using finer particle size IOT exhibited higher compressive strength, whereas flexural strength remained relatively unaffected by particle size distribution. Furthermore, a comprehensive analysis of the microstructure and alkali activation mechanism of IOT-GGBS bricks was performed using XRD, SEM, FTIR, and AFM techniques. The results indicated that IOT primarily acted as an aggregate and partially participated in the reaction, whereas GGBS reacted extensively, generating C-S-H gel and C-A-H, providing robust bonding strength. Additionally, the increase in GGBS content led to the partial disintegration of some IOT particles, forming more stable aggregates under the influence of the C-S-H gel. This study offers theoretical guidance for the efficient utilization of IOT in construction materials, thereby contributing to the promotion of sustainable development and environmental conservation. [ABSTRACT FROM AUTHOR]

Proponents of distributive justice with regard to energy hold that every household has the right to affordable access to energy. However, when passively participating in clean energy reform policies, households, and especially low-income households, are often forced to use energy beyond the scope of affordability, resulting in distributive injustice. However, research on the quantitative estimation of injustice in energy distribution is still rare. In this paper, an evaluation model of energy vulnerability and distributive justice is established through the "reverse deduction method," and the change value of energy vulnerability before and after energy reform is used to calculate the loss value of distributive justice, which is used as the basis for energy policy evaluation. Taking the policy of replacing coal with natural gas for rural heating in Gaocheng District, Hebei Province, China as a case study, the value of lost distributive justice is calculated. The results show that 34.51% of households experienced distributive injustice after the energy reform, while 19.34% of households improved their energy vulnerability and achieved energy justice through energy subsidy policies. However, 46.15% of households were able to adapt to the energy reform independently without subsidies, which indicates a waste of resources in the energy subsidy policy. The policy implication of these results is that differentiated energy subsidy policies should be implemented according to household energy vulnerability, so as to maximize the policy benefits on the premise of ensuring justice in household energy distribution and minimizing injustice. The empirical study proves that the method of assessing injustice in energy distribution based on the "reverse deduction method" of energy vulnerability is feasible and a new attempt. • The passive participation of low-income households in clean energy reform policies results in distributive injustice. • Apply the vulnerability framework to calculate energy distribution injustice. • The implementation of differentiated subsidies can effectively improve the vulnerability of rural households energy poverty. • It is feasible and a new attempt to measure energy distributive injustice by using the "reverse deduction method". [ABSTRACT FROM AUTHOR]

With the advantages of avoiding land-use competition, biomass from agricultural residues is a promising solution to alleviate the energy crisis and environmental problems. In the traditional biomass utilization management problem, few studies have considered that land and water are key resources for the sustainable development of biomass from a life cycle view. Concerning the complex food-water-energy nexus, this paper focuses on the synergetic management of crop planting structure and biomass utilization as a whole system. An integrated framework is proposed for the co-optimization of cropland distribution and biomass utilization pathway under multiple uncertainties. Base on interval programming and fuzzy set theory, a type-2 fuzzy interval linear programming model is developed to handle multiple uncertainties with various characteristics and provide the optimum strategies for decision makers with different risk preferences. The proposed framework is verified by a case study of Hebei Province, China. Moreover, the impacts of water shortage and the implementation of the carbon price on the optimized strategies, economic and environmental benefits are investigated to provide deeper management insights when facing complex external factors. The obtained results suggest that corn and wheat will still be the primary crops, and bioethanol production will gain priority in the biomass utilization pathways for economic purposes. Water resources availability will greatly affect the crop planting allocation as well as total benefits but barely influence the biomass utilization pathways. Furthermore, with the implementation of the carbon trade market, a higher carbon price will stimulate biomass pellets production to replace fossil fuel consumption and improve the economic benefits directly. • Synergetic management of crop planting and biomass utilization is proposed. • A novel hybrid inexact programming approach is developed to address uncertainties. • The impacts of water resources availability are analyzed from a nexus perspective. • The influences of carbon price on the biomass utilization pathway are explored. [ABSTRACT FROM AUTHOR]

Abstract: Energy is the essential material for human survival, and it is the important resource for economic development and social progress. Making a reasonable and reliable development plan by forecasting the energy consumption scientifically and effectively can ensure economic healthily and orderly developing. The combination forecast method based on inducted ordered weighted average operator is used widely in these years. The difference between this model and the traditional combination forecast method is that the empowerment coefficient of the combination forecast has nothing to do with the monomial prediction model, but it is closely related to forecast precision''s size of the monomial prediction model at each time-point, it is an invariable empowerment method. In this paper we adopt the time series tendency forecast model and the gray system GM (1, 1) forecast model in order to analysis the energy consumption of the Hebei Province; then, it introduces the energy consumption combination forecast model based on the IOWA operator. Empirical analysis indicates that this model can enhance the forecast precision of the combination and it is an effective combination forecast method. [Copyright &y& Elsevier]

Abstract: This article evaluated the energy effeciency in Hebei Province using some indicators such as comprehensive energy consumption, efficiency of energy transformation. Then it makes a comparative analysis about the HeBei Province''s status in the country''s energy conservation. The final part of the paper predicts energy consumption indicators in HeBei province from 2009 to 2013. [Copyright &y& Elsevier]

Abstract: This paper uses the methods of co-integration analysis and Granger Causality test, analyses the relationship between energy consumption and economic growth of Hebei Province from 1980 to 2008. The empirical results show that there is a stable causal relation between TEC and GDP in the long run. Hebei''s economic growth contributes to energy consumption growth. To change economic development mode and save energy, reduction of pollutant emission will not only impact the economic growth, but also achieve energy-saving, emission reduction and sustainable development. [Copyright &y& Elsevier]