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The concept of NZEB (Nearly Zero Energy Building) has a promising share to fulfill the future of sustainable development goals. However, exploiting renewable-based THESs (Trigeneration Hybrid Energy Systems) for providing the energetic demands of the building sector needs further attention. In this regard, the present paper proposes a sustainable THES driven by a biogas-fueled gas engine. The bottoming systems comprise absorption/electrical chillers (AC and EC), organic Rankine cycle (ORC), solar collector (PTC), and an auxiliary boiler. A detailed sustainability evaluation framework (technical and eco-environment) is derived to approve the performance of the THES. Within this procedure, the best working fluid of the ORC is firstly selected and then the sensitivity of the system performance to the design variables is assessed by a comprehensive parametric study. To make the derived framework more applicable, a case study building is considered and the proposed THES is sized to meet its electricity, heating, and cooling energies. The analyses proved that R123 shows the best techno-economic performance among six investigated ORC working fluids. The parametric study illustrated that the highest and lowest sensitivity of the THES's performance is related to the outlet temperatures of EC condenser and AC evaporator. Furthermore, the energy, exergy, and economic efficiencies of the THES for the case study building are 61.78%, 25.24%, and 44.77%. In this condition, discounted payback period is 7 years, demonstrating the feasibility of this proposal. The maximum CO 2 emission is released in November, with a total electricity generation of 320 MWh. [Display omitted] • Renewable-based hybrid system is introduced to produce cold, heat, and power. • Comprehensive sensitivity analysis is done to assess impact of some decision variables. • A case study building is considered to boost NZEB concept based on the proposed system. • Energy efficiency and payback period is 61.78% and 7 years for the case study building. • Using renewables in energy systems to reduce fossil fuel share has great significance. [ABSTRACT FROM AUTHOR]

Excessive energy-related carbon emission intensity will lead to deterioration of environmental quality and hinder green economic growth. This paper proposes a novel self-adaptive fractional order grey generalized Verhulst model (SAFGGVM) to predict energy-related carbon emission intensity in China, America, India, Russia, and Japan with nonlinear and complex characteristics effectively. Firstly, self-adaptive fractional order and dynamic background value coefficient are introduced to capture the nonlinear evolutionary trends. Subsequently, the optimal nonlinear parameters are determined using Grey Wolf Optimization algorithm through comprehensive comparison. Furthermore, the flexibility of SAFGGVM is verified by presenting its relevance to existing models. Finally, the validity, applicability, and robustness of SAFGGVM are confirmed by comparing with two machine learning models and four grey prediction models. The empirical results exhibit that the overall precision of SAFGGVM model significantly prevails over the others in five cases, with MAPE values less than 6% in both the simulation and prediction intervals. The out-of-sample forecast results indicate that the energy-related carbon emission intensity of five countries will fall into 5.7024, 1.6030, 7.3668, 8.1633, 2.0399 tons/10,000 USD by 2025, and the decreasing rate of developing countries tends to catch up with, or even exceed, developed countries. Projection results can serve as a reference point for countries to achieve green and sustainable development. • A novel self-adaptive grey generalized Verhulst model is proposed. • Energy-related carbon emission intensity (ECEI) of five countries is predicted. • Optimal nonlinear parameters are determined by Grey Wolf Optimization algorithm. • The novel model is superior to other competing models in accuracy and roubutness. • Decline rate of ECEI in developing countries will catch up with developed countries. [ABSTRACT FROM AUTHOR]

The construction industry is one of the most energy-intensive industries in China, which contributes to high carbon emissions and hinders efforts to reduce the emissions. The high energy consumption and emission have seriously constrained the growth of low carbon economy in China's construction industry. And reducing the carbon emission from construction industry (CECI) has become the key to sustainable development and the achievement of "double carbon target". The paper firstly decomposes the main influencing factors and calculates their effects by LMDI model. Then, the Tapio decoupling index is used to explore the decoupling relationship between carbon emissions and economic output. Finally, the spatial-temporal evolution of carbon emissions is analyzed through spatial autocorrelation theory. The results show that the carbon emissions from construction industry are highly increased by the construction employment rate (EM), and may be reduced by the energy intensity (EI) and carbon emission efficiency (CI). And the regions with better decoupling status are the eastern and central regions, which have relatively developed construction industry. Moreover, the spatial-temporal characteristic of carbon emissions is "high in the east and low in the west, high in the south and low in the north," showing obviously regional differences. [Display omitted] • Factors about production technology are taken into account, reflecting the characteristics of the construction industry. • Developed regions have better decoupling potential and more responsibility for CO 2. • The spatial distribution is "high in the east and low in the west, high in the south and low in the north". • The provinces in a region have similar decoupling status and carbon emission distribution. [ABSTRACT FROM AUTHOR]

Low carbonization is an inevitable pathway toward the sustainable development of gear machining. Reliable and reasonable prediction of hobbing parameters can effectively reduce energy consumption and carbon emissions. The initial value of process parameters is often generated in a large range, which has a negative effect on the subsequent parameter prediction and the improvement of carbon emission, cutting time and cost. In this paper, a three-stage parameter prediction approach is proposed based on the similarity retrieval method, ε-support vector regression (ε-SVR) and an improved Harris hawks optimization for reducing the cutting time, cost and carbon emissions under data-driven conditions. The first stage is responsible for searching past machining cases similar to hobbing process problems. The ε-SVR and Harris hawks optimization hybrid approach (SVR-HHO) is applied to predict the hobbing parameters in the second stage. Finally, the hobbing process parameters obtained in the second stage are revised using the improved multi-objective Harris hawks optimization for reducing carbon emissions, cutting time and cost. Compared with several well-established algorithms, SVR-HHO is evaluated against 9 benchmark datasets, performing better than 7 of them. A practical case of hobbing prediction is used to perform the feasibility verification and comparison study for the entire approach. The proposed approach achieved a relative carbon emission reduction of 9.3%, when compared to multi-objective HHO. The proposed approach obtained the optimum carbon emission, cutting time and cost compared with other approaches; therefore, it can adequately resolve the problem of the low-carbon hobbing parameter prediction. The performance of prediction stability and running time is slightly weak. Therefore, improving it is a major challenge for future research. Image 1 • Harris hawks optimization is applied to improve the parameters of ε-SVR and hobbing. • Prediction accuracy of ε-SVR is greatly improved with Harris hawks optimization. • Multi-objective Harris hawks optimization is realized using Pareto optimization. • Hobbing parameters are revised for carbon emissions, cutting time and cost. • The proposed method achieved a relative carbon emission reduction of 9.3%. [ABSTRACT FROM AUTHOR]