The weather affects air conditioner purchases to fill the energy efficiency gap

Energy efficiency improvement is often hindered by the energy efficiency gap. This paper examines the effect of short-run temperature fluctuations on the Energy Star air conditioner purchases in the United States from 2006 to 2019 using transaction-level data. Results show that the probability of purchasing an Energy Star air conditioner increases as the weekly temperature before the transaction deviates from 20–22 °C. A larger response is related to fewer cooling degree days in the previous years, higher electricity prices/income/educational levels/age/rate of owners, more common use of electricity, and stronger concern about climate change. 1 °C increase and decrease from 21 °C would lead to a reduction of total energy expenditure by 35.46 and 17.73 million dollars nationwide (0.13% and 0.06% of the annual total energy expenditure on air conditioning), respectively. Our findings have important policy implications for demand-end interventions to incorporate the potential impact of the ambient physical environment

The weather affects air conditioner purchases to fill the energy efficiency gap

Energy efficiency improvement is often hindered by the energy efficiency gap. This paper examines the effect of short-run temperature fluctuations on the Energy Star air conditioner purchases in the United States from 2006 to 2019 using transaction-level data. Results show that the probability of purchasing an Energy Star air conditioner increases as the weekly temperature before the transaction deviates from 20–22 °C. A larger response is related to fewer cooling degree days in the previous years, higher electricity prices/income/educational levels/age/rate of owners, more common use of electricity, and stronger concern about climate change. 1 °C increase and decrease from 21 °C would lead to a reduction of total energy expenditure by 35.46 and 17.73 million dollars nationwide (0.13% and 0.06% of the annual total energy expenditure on air conditioning), respectively. Our findings have important policy implications for demand-end interventions to incorporate the potential impact of the ambient physical environment

Evaluating the Public Participation Processes in Community Regeneration Using the EPST Model: A Case Study in Nanjing, China

Public participation is increasingly becoming a necessary content in community regeneration in China, though there is a lack of evaluation of the public participation process. This study explores a method for evaluating the public participation process, with the aim of improving the effectiveness of public participation. Based on the American Customer Satisfaction Index (ACSI) and using the analysis method of structural equation modeling, this study has preliminarily established the evaluation model of public participation processes represented by “Expectation–Perception–Satisfaction–Trust (EPST)”, while taking a case study in Nanjing for empirical study. The results show that to improve general satisfaction and public trust in those activities participated in, it is necessary to upgrade public expectation and the public perceived quality at the same time. This study believes that the entire investigation and understanding of public demands before regeneration is the premise to improving the regeneration effect. For community regeneration in Chinese cities, the key is to mobilize public participation, while a detailed understanding of residents’ needs for community environment and community services is an important part of enhancing the effectiveness of regeneration. This study believes that in community regeneration, actively organizing various participation activities and giving full play to the role of local government and third-party organizations are conducive to enhancing the public’s satisfaction with regeneration

Evaluating the Public Participation Processes in Community Regeneration Using the EPST Model: A Case Study in Nanjing, China

Public participation is increasingly becoming a necessary content in community regeneration in China, though there is a lack of evaluation of the public participation process. This study explores a method for evaluating the public participation process, with the aim of improving the effectiveness of public participation. Based on the American Customer Satisfaction Index (ACSI) and using the analysis method of structural equation modeling, this study has preliminarily established the evaluation model of public participation processes represented by “Expectation–Perception–Satisfaction–Trust (EPST)”, while taking a case study in Nanjing for empirical study. The results show that to improve general satisfaction and public trust in those activities participated in, it is necessary to upgrade public expectation and the public perceived quality at the same time. This study believes that the entire investigation and understanding of public demands before regeneration is the premise to improving the regeneration effect. For community regeneration in Chinese cities, the key is to mobilize public participation, while a detailed understanding of residents’ needs for community environment and community services is an important part of enhancing the effectiveness of regeneration. This study believes that in community regeneration, actively organizing various participation activities and giving full play to the role of local government and third-party organizations are conducive to enhancing the public’s satisfaction with regeneration

UniInst: Unique Representation for End-to-End Instance Segmentation

Existing instance segmentation methods have achieved impressive performance but still suffer from a common dilemma: redundant representations (e.g., multiple boxes, grids, and anchor points) are inferred for one instance, which leads to multiple duplicated predictions. Thus, mainstream methods usually rely on a hand-designed non-maximum suppression (NMS) post-processing step to select the optimal prediction result, which hinders end-to-end training. To address this issue, we propose a box-free and NMS-free end-to-end instance segmentation framework, termed UniInst, that yields only one unique representation for each instance. Specifically, we design an instance-aware one-to-one assignment scheme, namely Only Yield One Representation (OYOR), which dynamically assigns one unique representation to each instance according to the matching quality between predictions and ground truths. Then, a novel prediction re-ranking strategy is elegantly integrated into the framework to address the misalignment between the classification score and the mask quality, enabling the learned representation to be more discriminative. With these techniques, our UniInst, the first FCN-based end-to-end instance segmentation framework, achieves competitive performance, e.g., 39.0 mask AP using ResNet-50-FPN and 40.2 mask AP using ResNet-101-FPN, against mainstream methods on COCO test-dev. Moreover, the proposed instance-aware method is robust to occlusion scenes, outperforming common baselines by remarkable mask AP on the heavily-occluded OCHuman benchmark. Our codes will be available upon publication.Comment: This work is in the revision phase of the journal Neurocomputing. Codes will be available upon publicatio