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Most good quality overseas oil projects, high in investment returns and abundant in resources, are located in politically unstable regions, where competing objectives present great challenges for investors to make informed decisions. Moreover, most of the existing models are single objective and do not adequately incorporate the unique characteristics of overseas oil investment. To bridge these gaps, this study develops a Non-linear Multi-objective Binary Program (NMBP) to optimize the investment portfolios under three competing objectives. A solution algorithm is developed to solve this multiple objective program by integrating Non-dominated Sorting Genetic Algorithm II (NSGA-II) with Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). NSGA-II searches for the pareto set of optimal investment portfolios and TOPSIS determines the best compromise solution based on the investors’ preferences. Finally, China’s oil investment in the Belt and Road Initiative countries is taken as a case study to demonstrate the feasibility and effectiveness of the proposed approach.
Hao Chen; Xi-Yu Li; Xin-Ru Lu; Ni Sheng; Wei Zhou; Hao-Peng Geng; Shiwei Yu. A multi-objective optimization approach for the selection of overseas oil projects. Computers & Industrial Engineering 2020, 151, 106977 .
AMA StyleHao Chen, Xi-Yu Li, Xin-Ru Lu, Ni Sheng, Wei Zhou, Hao-Peng Geng, Shiwei Yu. A multi-objective optimization approach for the selection of overseas oil projects. Computers & Industrial Engineering. 2020; 151 ():106977.
Chicago/Turabian StyleHao Chen; Xi-Yu Li; Xin-Ru Lu; Ni Sheng; Wei Zhou; Hao-Peng Geng; Shiwei Yu. 2020. "A multi-objective optimization approach for the selection of overseas oil projects." Computers & Industrial Engineering 151, no. : 106977.
Promoting the decarbonisation of buildings requires effective policy measures. An integral part of policy design is ex-ante evaluation of possible policy options and effects. System Dynamics, one of a range of potential modelling paradigms, emphasises the dynamic complexity arising from stock-and-flow structures, feedbacks, non-linearities and time lags of the system in question. It is therefore well placed to model building stock turnover dynamics and the associated energy use and carbon emissions, in order to conduct scenario analysis for policy evaluation. Previous efforts to employ System Dynamics based models in buildings in various national contexts are found to have some common fundamental structural and behavioural limitations. We present an improved formulation that includes both building stock disaggregation and dynamics of energy-related retrofits. The model is characterised by greater transparency facilitating reproducibility and further improvements, high structural and functional flexibility for either extensions or reductions depending upon needs, and high generality and adaptability in diverse applications. It can be used as a stand-alone model or as part of a larger model for policy evaluation and scenario analysis exploring the transformation of building stock from improving energy efficiency and shifting towards low-carbon development.
Wei Zhou; Alice Moncaster; David Reiner; Peter Guthrie. Developing a generic System Dynamics model for building stock transformation towards energy efficiency and low-carbon development. Energy and Buildings 2020, 224, 110246 .
AMA StyleWei Zhou, Alice Moncaster, David Reiner, Peter Guthrie. Developing a generic System Dynamics model for building stock transformation towards energy efficiency and low-carbon development. Energy and Buildings. 2020; 224 ():110246.
Chicago/Turabian StyleWei Zhou; Alice Moncaster; David Reiner; Peter Guthrie. 2020. "Developing a generic System Dynamics model for building stock transformation towards energy efficiency and low-carbon development." Energy and Buildings 224, no. : 110246.
Knowing the size of building stock is perhaps the most basic determinant in assessing energy use in buildings. However, official statistics on urban residential stock for many countries are piecemeal at best. Previous studies estimating stock size and energy use make various debateable methodological assumptions and only produce deterministic results. This paper presents a Bayesian approach to characterise stock turnover dynamics and estimate stock size uncertainties, applied here to China. Firstly, a probabilistic dynamic building stock turnover model is developed to describe the building aging and demolition process, governed by a hazard function specified by a parametric survival model. Secondly, using five candidate parametric survival models, the building stock turnover model is simulated through Markov Chain Monte Carlo to obtain posterior distributions of model-specific parameters, estimate marginal likelihood, and make predictions of stock size. Thirdly, Bayesian Model Averaging is applied to create a model ensemble that combines model-specific posterior predictive distributions of the recent historical stock evolution pathway in proportion to posterior model probabilities. Finally, the Bayesian Model Averaging model ensemble is extended to forecast future trajectories of residential stock development through 2100. The modelling results suggest that the total stock in China will peak around 2065, at between 42.4 and 50.1 billion m2. This Bayesian modelling framework produces probability distributions of annual total stock, age-specific substocks, annual new buildings and annual demolition rates. This can support future analysis of policy trade-offs across embodied-versus-operational energy consumption, in the context of sector-wide decarbonisation.
Wei Zhou; Eoghan O'Neill; Alice Moncaster; David Reiner; Peter Guthrie. Forecasting urban residential stock turnover dynamics using system dynamics and Bayesian model averaging. Applied Energy 2020, 275, 115388 .
AMA StyleWei Zhou, Eoghan O'Neill, Alice Moncaster, David Reiner, Peter Guthrie. Forecasting urban residential stock turnover dynamics using system dynamics and Bayesian model averaging. Applied Energy. 2020; 275 ():115388.
Chicago/Turabian StyleWei Zhou; Eoghan O'Neill; Alice Moncaster; David Reiner; Peter Guthrie. 2020. "Forecasting urban residential stock turnover dynamics using system dynamics and Bayesian model averaging." Applied Energy 275, no. : 115388.
Building lifetime and stock turnover are both key determinants in modelling building energy and carbon. However in China, aside from anecdotal claims that urban residential buildings are generally short-lived, there are no recent official statistics, and empirical data are extremely limited. We present a system dynamics model where survival analysis is used to characterise the dynamic interplay between new construction, aging, and demolition of residential buildings in urban China. The uncertainties associated with building lifetime were represented using a Weibull distribution, whose shape and scale parameters were calibrated based on official statistics on floor area up to 2006. The calibrated Weibull lifetime distribution allowed us to estimate the dynamic stock turnover of Chinese urban residential buildings for 2007 to 2017. We find that the average lifetime of urban residential buildings was around 34 years, and the overall residential stock size reached 23.7 billion m2 in 2017. The resultant age-specific sub-stocks provide a baseline for the overall stock, which—along with the calibrated Weibull lifetime distribution—can be used in further modelling and for analysis of policies to reduce the whole-life embodied and operational energy and CO2 emissions in Chinese residential buildings.
Wei Zhou; Alice Moncaster; David M Reiner; Peter Guthrie. Estimating Lifetimes and Stock Turnover Dynamics of Urban Residential Buildings in China. Sustainability 2019, 11, 3720 .
AMA StyleWei Zhou, Alice Moncaster, David M Reiner, Peter Guthrie. Estimating Lifetimes and Stock Turnover Dynamics of Urban Residential Buildings in China. Sustainability. 2019; 11 (13):3720.
Chicago/Turabian StyleWei Zhou; Alice Moncaster; David M Reiner; Peter Guthrie. 2019. "Estimating Lifetimes and Stock Turnover Dynamics of Urban Residential Buildings in China." Sustainability 11, no. 13: 3720.
The performance of a circular perforated panel (CPP) air terminal device for a personalized ventilation (PV) system operating under two levels of turbulent intensity (Tu) was examined. The impact of Tu on spatial distribution of the cooling effect on the facial region and whole body were studied through experiments carried out in an indoor environment chamber using a breathing thermal manikin and 24 tropically acclimatized subjects. The PV system was adjusted to deliver treated outdoor air over a range of conditions, which were presented blind to the subjects in a balanced order. Over a 15-min exposure, subjects voted their thermal sensation experienced at the facial region and whole body. At each of the conditions, the near body flow field characteristics and heat loss rate on each of the 26 body segments of the manikin were measured. The results indicate that over the range of PV air supply volume studied, by controlling the temperature and velocity of PV air supply at 15 cm from the face, PV air supplied at lower Tu, when compared against that supplied at higher Tu:
Wei Sun; Kwok Wai Tham; Wei Zhou; Nan Gong. Thermal performance of a personalized ventilation air terminal device at two different turbulence intensities. Building and Environment 2007, 42, 3974 -3983.
AMA StyleWei Sun, Kwok Wai Tham, Wei Zhou, Nan Gong. Thermal performance of a personalized ventilation air terminal device at two different turbulence intensities. Building and Environment. 2007; 42 (12):3974-3983.
Chicago/Turabian StyleWei Sun; Kwok Wai Tham; Wei Zhou; Nan Gong. 2007. "Thermal performance of a personalized ventilation air terminal device at two different turbulence intensities." Building and Environment 42, no. 12: 3974-3983.