Based on the data of Chinese A-share manufacturing firms from 2015 to 2021, this paper employs the multiple regression method to explore the relationship between ESG information disclosure and corporate green innovation, while also examining the impact of local government environmental attention on this relationship. The results indicate that: (1) ESG information disclosure significantly promotes corporate green innovation, particularly in the eastern and central regions of China, as well as areas with a strong Confucian cultural background; (2) Alleviating financing constraints and reducing agency costs are important channels through which ESG information disclosure influences corporate green innovation; and (3) Local government environmental attention exhibits an “inverted U-shaped” regulating effect on the relationship between ESG disclosure and corporate green innovation.

Accurately predicting wave height has a significant impact on offshore production and marine transportation. Numerical model predictions are the most commonly used wave height prediction method. But all numerical models are hard to describe the rules of ocean physics numerical model completely. Machine learning methods learn the inherent rules of data and are widely used in various predictions. In this paper, we try to use machine learning to correct errors in numerical model predictions. We count wave height numerical model predictions and observations for one year and find that the residuals of them are subject to a Gaussian distribution. Therefore, we propose a method for correcting wave height predictions from the Simulating Wave Nearshore (SWAN) model based on Gaussian process regression (GPR). To this end, we use residuals of wave height observations and the predictions of the SWAN model as input. We then train a GPR model for correcting the next time step wave height. Experimental results reveal that the proposed method predicts the wave height more accurately compared with the maritime numerical model.

Purpose: The purpose of this paper is to have a deeper understanding of the nonlinear relationship between the impact of climate change on tourism development. Current studies on the effects of climate change on tourism development primarily rely on linear correlation assumptions. Design/methodology/approach: Based on the New Institutional Economics theory, the institutional setting inherently motivates and ensures the growth of the tourism industry. For a precise evaluation of the nonlinear consequences of climate change on tourism, this paper concentrates on Chinese cities between 2011 and 2021, methodically analyzing the influence of climate change on tourism. Findings: The study findings suggest that there is an “inverse U”-shaped nonlinear relationship between climate change and tourism development, initially strengthening and subsequently weakening. Based on these findings, the research further delves into how institutional contexts shape the nonlinear association between climate change and tourism growth. It was found that in a higher institutional backdrop, the “inverse U” curve tends to flatten and surpass the curve adjusted for a lesser institutional context. Upon deeper mechanism analysis, it was observed that cities with more advanced marketization, improved industrial restructuring and enhanced educational growth exhibit a more evident “inverse U”-shaped nonlinear connection between climate change and tourism evolution. Originality/value: First, previous studies on climate change and tourism development largely rely on questionnaire data (Hu et al. , 2022). In contrast to these studies, this paper uses dynamic panel data, which to some extent overcomes the subjectivity and difficulty of causality identification in questionnaire data, making our research conclusions more accurate and reliable. Second, this study breaks through the linear relationship hypothesis of previous literature regarding climate change and tourism development. By evaluating the nonlinear relationship of climate change to tourism development from the institutional pressure perspective, it more intricately delineates their interplay mechanism, expanding and supplementing the research literature on the relationship mechanism between climate change and tourism development. Thirdly, the conclusions of this study are beneficial for policymakers to better understand and assess the scope of climate change impacts. It also aids relevant departments in clarifying the direction of institutional environment optimization to elevate the level of tourism development when faced with adverse impacts brought about by climate change.