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Urban sprawl in Latin America is described as one of the major problems of 'the growth machine'. As a reaction, most planning policies are based on anti-sprawl narratives, while in practice, urban sprawl has been thoroughly consolidated by all tiers of government. In this paper – and using the capital city of Chile, Santiago, as a case study – we challenge these anti-sprawl politics in light of the emerging environmental values and associated meanings of the interstitial spaces resulting from land fragmentation in contexts of urban sprawl. Looking at the interstitial spaces that lie between developments becomes relevant in understanding urban sprawl, considering that significant attention has been paid to the impact of the built-up space that defines the urban character of cities and their governance arrangements. We propose that looking at Santiago's urban sprawl from the interstitial spaces may contribute to the creation of more sustainable sprawling landscapes and inspire modernisations beyond anti-sprawl policies. Finally, it is suggested that a more sustainable urban development of city regions might include the environmental values of suburban interstices and consider them as assets for the creation of more comprehensive planning and policy responses to urban sprawl.

Purpose
Mean absolute percentage error (MAPE) is the primary forecast evaluation metric in hospitality and tourism research; however its main shortcoming is that it is asymmetric. The asymmetry occurs due to over or under forecasts that introduce bias into forecast evaluation. This study aims to explore the nature of asymmetry and designs a new measure, one that reduces the asymmetric properties while maintaining MAPE's scale-free and intuitive interpretation characteristics.


Design/methodology/approach
The study proposes and tests a new forecasting accuracy measure for hospitality revenue management (RM). A computer simulation is used to assess and demonstrate the problem of asymmetry when forecasting with MAPE, and the new measures' (MSapeMER, that is, Mean of Selectively applied Absolute Percentage Error or Magnitude of Error Relative to the estimate) ability to reduce it. The MSapeMER's effectiveness is empirically validated by using a large set of hotel forecasts.


Findings
The study demonstrates the ability of the MSapeMER to reduce the asymmetry bias generated by MAPE. Furthermore, this study demonstrates that MSapeMER is more effective than previous attempts to correct for asymmetry bias. The results show via simulation and empirical investigation that the error metric is more stable and less swayed by the presence of over and under forecasts.


Research limitations/implications
It is recommended that hospitality RM researchers and professionals adopt MSapeMER when using MAPE to evaluate forecasting performance. The MSapeMER removes the potential bias that MAPE invites due to its calculation and presence of over and under forecasts. Therefore, forecasting evaluations may be less affected by the presence of over and under forecasts and their ability to bias forecasting results.


Practical implications
Hospitality RM should adopt this measure when MAPE is used, to reduce biased decisions driven by the "asymmetry of MAPE."


Originality/value
The MAPE error metric exhibits an asymmetry problem, and this paper proposes a more effective solution to reduce biased results with two major methodological contributions. It is first to systematically study the characteristics of MAPE's asymmetry, while proposing and testing a measure that considerably reduces the amount of asymmetry. This is a critical contribution because MAPE is the primary forecasting metric in hospitality and tourism studies. The second methodological contribution is a procedure developed to "quantify" the asymmetry. The approach is demonstrated and allows future research to compare asymmetric characteristics among various accuracy measures.

AbstractIn the context of the global economy's green and low‐carbon development, companies are paying increasing attention to environmental management. This study compiles statistics on Chinese listed firms from 2010 to 2020 and examines the influence of CEOs' environmental background on corporate environmental management information disclosure based on imprinting theory. It found that CEOs with environmental backgrounds are more willing to disclose corporate environmental management information, and the level of disclosure is high. By attracting media attention and reducing the company's financing constraints, CEOs with an environmental background can enhance the level of corporate environmental management information disclosure, according to the analysis of the impact mechanism. Further research revealed that CEO environmental background promoted corporate environmental management information disclosure more significantly in state‐owned and heavily polluting enterprises.

Sentiment expression in microblog posts can be affected by user's personal character, opinion bias, political stance and so on. Most of existing personalized microblog sentiment classification methods suffer from the insufficiency of discriminative tweets for personalization learning. We observed that microblog users have consistent individuality and opinion bias in different languages. Based on this observation, in this paper we propose a novel user-attention-based Convolutional Neural Network (CNN) model with adversarial cross-lingual learning framework. The user attention mechanism is leveraged in CNN model to capture user's language-specific individuality from the posts. Then the attention-based CNN model is incorporated into a novel adversarial cross-lingual learning framework, in which with the help of user properties as bridge between languages, we can extract the language-specific features and language-independent features to enrich the user post representation so as to alleviate the data insufficiency problem. Results on English and Chinese microblog datasets confirm that our method outperforms state-of-the-art baseline algorithms with large margins.