Bivalve shellfish farming is expected to be performed as the effective mitigator of the growing pressure for global animal protein demand. Carbon emission reduction in the whole farming process of the bivalve shellfish has great potential in reducing carbon emissions in food production in the future. However, the hatchery stage of oyster, with high energy input, may be a high carbon emission process and necessitates effective carbon emission reduction. This study uses life cycle assessment (LCA) to analyze the carbon footprint of the farming of Pacific oyster (Crassostrea gigas), a shellfish with the largest farming yield in the world, in Fujian province in China. The results show that the total carbon footprint, from cradle to gate is 70.81 kgCO2-eq/tonne fresh oyster, which suggested that oyster farming perform favorably against livestock farming for protein products and can justifiably be promoted as a low- carbon food product. Hatchery culture contributed 62.2% of the total carbon emission. The feed production in the hatchery culture stage, account for about 2.27 % of carbon emission, were not the major emission factor. Carbon emission form energy consumption and material inputs are about half and half. The carbon footprint of Pacific oyster can be reduced by employing energy- conserving transport technology and utilizing renewable energy. The improvements could be helpful for sustainable production of Pacific oyster farming.
The Chinese government plans to implement Carbon cap-and-trade mechanisms and Renewable portfolio standards in order to promote the transition of low-carbon. Hence, this paper constructs a monopolistic utility company model based on four policy scenarios namely: Reference scenario (FREE), Carbon cap-and-trade mechanisms (CAP), Renewable portfolio standards 𝑐𝑛𝑟 (RPS), and Mixed scenario (MIX). The mentioned policy scenarios were used to test the impact of various policy situations on utility company investment decision considering the existence of renewables, new conventional energy sources (such as natural gas) and conventional energy sources (such as coal) in the market. In addition, social welfare under each scenario through numerical simulations, together with the impact of key parameters related to emission reduction mechanism on social welfare were compared. The results show both CAP and RPS can effectively reduce carbon emissions. In terms of renewable energy promotion, the effect under MIX is the best comparatively. Further, MIX recorded the highest level of social welfare during the present stages of China’s low-carbon transition. The study provides some new suggestions for policy-makers.
Reducing building energy use and the associated greenhouse gas emissions is becoming increasingly important. Since occupants’ behaviour has significant impacts on building energy performance and occupant comfort, and it varies with an individual’s age, sex, background, and other personal factors, it is important to understand the critical links between people’s lifestyles and energy consumption. Most studies of the relationship between occupancy behaviour and energy consumption focus on public buildings like office buildings and commercial buildings. Research for dwellings is limited since the information is difficult to collect, and detailed knowledge of individual homes is needed. This paper conducted a detailed survey to gain information on thermal satisfaction levels, occupancy equipment ownership and their using patterns of 112 urban households who lived in a typical booming city in southeast China. Based on the collected data, an energy simulation software program was used to investigate the main factors of occupancy behaviour, which affect energy consumption. The results lead to the internal gains profiles and window-opening profiles, which reflect the lifestyle in the target area who lived in an urban high- rise building. The simulation of typical households indicated that occupancy behaviour only occupied a small scale compared to equipment but still significant to improve.
Recently, advanced prediction tools based on artificial intelligence are increasingly being employed for predicting occupancy patterns in buildings. The present work carries out a comprehensive review of studies using artificial intelligence and machine learning models to predict occupancy and its applications, covering studies about energy consumption, thermal comfort, lighting use and indoor air quality. The analyses show that while these studies have revealed that occupancy is a critical contributor to the energy prediction model, they have not paid enough attention to the thermal condition, air quality and their effect on occupant productivity and quality of life. In this study, occupancy detection with the vision-based camera is employed, which captures specific occupancy activities and other related behaviour like window opening behaviour. These activities will generate real-time deep learning influenced profile formation, which can train the prediction model. The results showed that the current CNN model framework provided an initial average detection accuracy of 84.48%. The ability of the deep learning detection to inform HVAC systems with significant help towards reducing building energy loads with the temperature setpoint changed. Therefore, it is important to develop an effective solution to increase the performance of buildings by assisting the HVAC control system in providing adequate indoor thermal comfort and air quality, while improving the building energy performance.
This paper focuses on the impact of energy prices in the process of monetary policy influencing macroeconomics. We employ monthly data spanning the period of January 2006 to June 2021 for China for money supply amount, energy price index and purchasing managers index (PMI). Two independent empirical studies for mediation and moderation effects are introduced. We firstly apply the newly proposed Granger mediation model to link the three variables. The results document that energy prices may be impacted by changes in monetary policy, which will ultimately damage the effectiveness of monetary policy in promoting economic prosperity. Then, we obtain the time-varying causality strength from monetary policy to economic boom through the TV-GC model, and detect the influence of energy prices on such strength dynamics. The results indicate a significant effect of the energy price on the time-varying causal impact strength from the money supply on PMI in both the static and regime-switching framework. In another word, the energy price could also play a moderating effect in the process of monetary policy driving the economic boom. At last, some targeted policy recommendations are also summarized based on our findings.
The concept of green building plays an important role in carbon emission reduction. This paper focuses on the energy conservation in the building operational phase to assess the carbon emission reduction potential of several major green building technologies. Energy use intensity is simulated using EnergyPlus with prototypical models of various green building scenarios for office buildings and hotels in Shanghai and Harbin. The carbon emission factor is calculated according to the energy structure in the building sector in China. Results shows that passive technologies such as daylighting and natural ventilation can bring significant reductions in carbon emissions followed by retrofitting of building envelopes and energy efficiency improvement of HVAC equipment. Covering all green building technologies considered, the total operational carbon emission reduction rate can reach nearly 19% for office buildings and 25% for hotels.
To ameliorate the climate change which caused by environmental problems and achieve the carbon neutrality proposed by Chinese government in 2020, renewable energy like photovoltaic and wind power play an increasingly important role in energy consumption. To address the random fluctuation and insufficient consumption brought by renewable energy, integrated energy system (IES) is one of the solutions to cope with these problems. This study focuses on the planning problem of IES and proposes a planning model, which takes both minimum total costs and CO2 emission as the objectives. To further reduce the carbon emission, fuel cells (FC) which uses hydrogen as fuel to provide both electricity and heat, as well as multi-energy storage systems (ESS) are considered as options in IES planning. A real multi-energy office building in Shanghai, in which photovoltaic, multi-energy storage equipment, fuel cell, electric vehicle (EV) and other equipment are included as planning options, is used as numerical example to verify the effectiveness of proposed planning method for building-level IES. Moreover, the operation scenarios and functions of ESS in IES are analyzed.
Zero/low energy buildings, as an important means to facilitate the achievement of carbon neutrality, are receiving increasing attention worldwide. Numerous studies have been conducted to identify the key design parameters for zero/low energy buildings. However, these studies mainly focus on low-rise buildings. Few study has comprehensively investigated the key design parameters of high-rise buildings and compared with that of low-rise buildings, which is essential to achieve the zero /low energy buildings in cities with many high-rises. In this study, the key design parameters of high-rise and low-rise buildings in subtropical regions are identified by sensitivity analysis (SA), and the impacts of building height on the key building design focus are investigated. The SA is performed using Morris, in which a comprehensive consideration of 34 parameters (in 5 PMVhourly categories) affecting building performance are taken. Symbols The key design parameters affecting building energy consumption and winter thermal discomfort of high-rise and low-rise buildings in subtropical regions are identified respectively. Remarkable finding is that the overhang is the most influential element of the high-rise buildings, while skylight is the most influential element of the low-rise buildings concerning building energy consumption. The studied results may offer valuable references for the building envelope design in subtropical regions.
With the Intelligent Connected Vehicle, Intelligent Transportation System and data mining technology, information sharing provides the feasibility for real-time application of global optimization energy management. To standardize the optimizing process, a framework of “Cyber-physical system – Dynamic Programming” (CPS- DP) is proposed. Based on the Internet of Vehicles, the information from various physical subjects (mainly refer to drivers, vehicles, and roads) can be acquired from different scenarios. For the stochastic information, the “drivers-vehicles-roads” co-constraint model is proposed to determine the speed limits. Based on the available information, optimal power distribution is determined in the control system. The keys are to determine feasible work modes based on the “kinetic/potential energy & onboard energy” conservation framework and develop an effective global domain-searching algorithm. To verify the proposed method, a case study (WLTP) is given. Simulation results demonstrate that the proposed method gains a better performance in both real-time performance and global optimality.
The life and durability problems of proton exchange membrane fuel cell (PEMFC) have limited the commercialization process. The main reason for the degradation of life due to the frequent occurrence of local gas starvation in the dynamic process. The existing research is mainly carried out by experiment or simulation to diagnose local gas starvation, there is almost no research using machine learning methods to predict the local gas starvation through operating parameters. To solve this problem, a snake-shaped five-channel PEMFC model is established in this paper, and obtained source data through CFD simulation. Principal component analysis and k-means clustering algorithm are used to effectively define the local gas starvation state of each sample point, and complete sample labeling. Five operating parameters (temperature, pressure, humidity, gas stoichiometric ratio and current density), were used as model inputs. Three machine learning methods are chosen for training and prediction, and compare their accuracy. The prediction accuracy rate based on the extreme learning machine regression model is the highest, which is 93.49%, and have a fast prediction speed. It can quickly and accurately predict the local gas starvation state under a certain working condition, which has guiding significance for the optimization of op