Lizhen Wei’s research while affiliated with Huaqiao University and other places

Buildings consume large amounts of energy resources and emit considerable amounts of greenhouse gases, especially existing buildings that do not meet energy standards. Building retrofitting is considered one of the most promising and significant solutions to reduce energy consumption and greenhouse gas emissions. However, finding suitable energy efficiency measures for existing buildings is extremely difficult due to the existence of thousands of retrofit measures and the need to meet various objectives. In this paper, a multi-stage decision framework, including a multi-objective optimization model, and a ranking method are proposed to help decision-makers select the optimal energy efficiency measures. The multi-objective optimization model considers the economic and environmental objectives, expressed as the retrofit cost and energy consumption, respectively. The entropy weight ideal point ranking method, an evaluation and ranking method that combines the entropy weight method and ideal point method, is adopted to sort the Pareto front and make a final decision. Then, the proposed decision framework was implemented for the retrofit planning of an educational building in Chongqing, China. The results show that decision-makers can quickly identify near-optimal energy efficiency measures through multi-objective optimization and can select suitable energy efficiency measures using the ranking method. Moreover, energy consumption can be reduced by building retrofitting. The energy consumption of the case building was 64.20 kWh/m2 before retrofitting, and the value can be reduced by 6.79% through retrofitting. Furthermore, the reduction in building energy consumption was significantly improved by applying the decision framework. The highest value of energy consumption was 59.84 kWh/m2, while the lowest value was 27.11 kWh/m2 when implementing the multi-stage decision framework. Thus, this paper provides a useful decision framework for decision-makers to formulate suitable energy efficiency measures.

Most Chinese cities have spent decades achieving urbanisation. So far, rural urbanisation has shifted to urban renewal. However, the distinction between a rapidly changing social environment and the establishment of an institution has led to the failure of urban renewal policies to sustainably achieve complete transformation through urban modernisation involving many stakeholders. Owing to the top-down political system in China, the formulation and implementation of urban renewal policies are carried out in a closed-loop process in which “decisions are issued by the central government to the local government which gives feedback to the centre”. This seems to affect urban renewal through a transfer of renewal policies in a local area. Therefore, it is essential to explore the differences between the urban renewal policies at different government levels and to analyse these policies in diverse urban contexts with multiple stakeholders. Based on the policy transfer theory, this paper selects 216 core policy texts at the state level and at the level of four first-tier cities (Beijing, Shanghai, Guangzhou, and Shenzhen), and uses the methods of text mining and semantic analysis to form open coding, axial coding, and selective coding. Furthermore, it discusses the policy transfer and impact mechanism of urban renewal policy at different levels with diverse characteristics of policy subsystems. We found that the transfer of urban renewal policy occurred in China through top-down coercive vertical transfer, bottom-up combination reverse transfer, and voluntary horizontal transfer among cities. Finally, we suggest that “inspiration”-type policy transfer is an effective method to promote urban renewal in China.

Buildings consume large amounts of energy resources and emit considerable amounts of greenhouse gas: especially existing buildings which do not meet energy standards. Building retrofitting is considered one of the most promising and significant solutions to reduce energy consumption and greenhouse gas emissions. However, finding suitable energy efficiency measures for existing buildings is extremely difficult due to the existence of thousands of retrofit measures and the need to meet various objectives. In this paper, a multi-stage decision framework including a multi-objective optimization model and a ranking method is proposed to help decision-makers select optimal energy efficiency measures. The multi-objective optimization model takes into account the economic objectives and the environmental objectives, expressed as retrofit cost and energy consumption, respectively. The entropy weight ideal point ranking method is adopted to sort the Pareto front and make a final decision. Then, the proposed decision framework is implemented for the retrofit planning of an educational building in Chongqing, China. The results show that decision-makers can identify near-optimal energy efficiency measures quickly through multi-objective optimization and can select suitable energy efficiency measures by the ranking method. Moreover, energy consumption can be reduced by building retrofitting. The energy consumption of the case building is 64.20 kWh/m2 before retrofitting, and the value can be reduced by 6.79% through retrofitting. Furthermore, the reduction of building energy consumption was significantly improved by applying the decision framework. The highest value of energy consumption is 59.84 kWh/m2, while the lowest value is 27.11 kWh/m2 when implementing the multi-stage decision framework. Thus, this paper provides a useful decision framework for decision-makers to formulate suitable energy efficiency measures.