Conflicts of Interest in the Assessment of Chemicals, Waste, and Pollution.

Pollution by chemicals and waste impacts human and ecosystem health on regional, national, and global scales, resulting, together with climate change and biodiversity loss, in a triple planetary crisis. Consequently, in 2022, countries agreed to establish an intergovernmental science-policy panel (SPP) on chemicals, waste, and pollution prevention, complementary to the existing intergovernmental science-policy bodies on climate change and biodiversity. To ensure the SPP's success, it is imperative to protect it from conflicts of interest (COI). Here, we (i) define and review the implications of COI, and its relevance for the management of chemicals, waste, and pollution; (ii) summarize established tactics to manufacture doubt in favor of vested interests, i.e., to counter scientific evidence and/or to promote misleading narratives favorable to financial interests; and (iii) illustrate these with selected examples. This analysis leads to a review of arguments for and against chemical industry representation in the SPP's work. We further (iv) rebut an assertion voiced by some that the chemical industry should be directly involved in the panel's work because it possesses data on chemicals essential for the panel's activities. Finally, (v) we present steps that should be taken to prevent the detrimental impacts of COI in the work of the SPP. In particular, we propose to include an independent auditor's role in the SPP to ensure that participation and processes follow clear COI rules. Among others, the auditor should evaluate the content of the assessments produced to ensure unbiased representation of information that underpins the SPP's activities.

A Horizon Scan to Support Chemical Pollution-Related Policymaking for Sustainable and Climate-Resilient Economies.

While chemicals are vital to modern society through materials, agriculture, textiles, new technology, medicines, and consumer goods, their use is not without risks. Unfortunately, our resources seem inadequate to address the breadth of chemical challenges to the environment and human health. Therefore, it is important we use our intelligence and knowledge wisely to prepare for what lies ahead. The present study used a Delphi-style approach to horizon-scan future chemical threats that need to be considered in the setting of chemicals and environmental policy, which involved a multidisciplinary, multisectoral, and multinational panel of 25 scientists and practitioners (mainly from the United Kingdom, Europe, and other industrialized nations) in a three-stage process. Fifteen issues were shortlisted (from a nominated list of 48), considered by the panel to hold global relevance. The issues span from the need for new chemical manufacturing (including transitioning to non-fossil-fuel feedstocks); challenges from novel materials, food imports, landfills, and tire wear; and opportunities from artificial intelligence, greater data transparency, and the weight-of-evidence approach. The 15 issues can be divided into three classes: new perspectives on historic but insufficiently appreciated chemicals/issues, new or relatively new products and their associated industries, and thinking through approaches we can use to meet these challenges. Chemicals are one threat among many that influence the environment and human health, and interlinkages with wider issues such as climate change and how we mitigate these were clear in this exercise. The horizon scan highlights the value of thinking broadly and consulting widely, considering systems approaches to ensure that interventions appreciate synergies and avoid harmful trade-offs in other areas. We recommend further collaboration between researchers, industry, regulators, and policymakers to perform horizon scanning to inform policymaking, to develop our ability to meet these challenges, and especially to extend the approach to consider also concerns from countries with developing economies. Environ Toxicol Chem 2023;42:1212-1228. © 2023 Crown copyright and The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC. This article is published with the permission of the Controller of HMSO and the King's Printer for Scotland.

Analysis of the Relationship between Economic Development and Water Resources-Ecological Management Capacity in China Based on Nighttime Lighting Data.

Water resources are important factors limiting social and economic development, so how to ensure the coordination between economic development and water resources-ecological management capacity has become a key issue that needs to be addressed urgently for China's high-quality economic development. This paper used nighttime light data as proxy variables of economic development to calculate the coupling coordination degree between provincial economic development and water resources-ecological management capacity in China from 2004 to 2019 based on the coupling coordination degree model; w constructed a spatial econometric model to explore the spatial correlation and influencing factors between economic development and water resources-ecological management capacity. The results are shown in the following: (1) The overall level of China's economic development is on an upward trend, but the regional development is unbalanced, showing a decreasing spatial pattern distribution of the eastern coastal region-mid-western region-far-western region. (2) The level of water resources-ecological management capacity is low, and the spatial distribution shows a decreasing trend in the far west-central and western-eastern coastal regions. (3) The level of coupling and coordination between economic development and water resources-ecological management capacity rises from a mild imbalance level to a little imbalance level, and the spatial distribution is consistent with the spatial distribution of economic development. (4) The factors influencing the level of coupling and coordination of inter-provincial economic development and water resources-ecological management capacity in China mainly involve the population scale, technological progress, affluence, and foreign direct investment. Each province and city should take into account its own actual situation and develop targeted measures to promote the coordinated development of economic development and the water resources-ecological management capacity.

Does Water Matter? The Impact of Water Vulnerability on Corporate Financial Performance.

This study aims to understand the potential relationship between water vulnerability and corporate financial performance for listed companies in China. Studies have argued that water risk has begun to affect the sustainability of firms, but few studies have included water conditions in the research framework to examine whether and how water conditions have a direct impact on firms. In addition, studies on environment governance have emphasized the impact of government environmental regulation on firms. This study focuses on both regulation and government investments that have been previously neglected. Using a sample of Chinese listed companies from 2016 to 2020, this paper uses pooled cross-sectional regressions with year and industry fixed effects to examine the effects of water vulnerability on corporate financial performance and analyze the mechanism of government water governance (which can be divided into water regulation and water investment) on the relationship between water vulnerability and corporate financial performance. This study finds that water vulnerability could negatively impact corporate financial performance, and water regulation can intensify but water investment couldn't significantly relieve the negative impact. The relationships above differ between SOEs and non-SOEs and water-intensive and non-water-intensive industries.

A New Fault Diagnosis of Rolling Bearing Based on Markov Transition Field and CNN.

The rolling bearing is a crucial component of the rotating machine, and it is particularly vital to ensure its normal operation. In addition, the selection of different category features will add uncertainty and bias to the classification results. In order to decrease the interference of these factors to fault diagnosis, a new method that automatically learns the features of the data combined with Markov transition field (MTF) and convolutional neural network (CNN) is proposed in this paper, namely MTF-CNN. The MTF contributes to convert the original time series into corresponding figures, and the CNN is used to extract the deep feature information in the figure to complete the fault diagnosis. The effectiveness of the proposed method is verified by two public data sets. The experimental results show that MTF-CNN can classify different types of faults, and the highest accuracy rate can reach 100%. Likewise, the classification accuracy of this method is higher than some existing methods.

Identifying the spatial heterogeneity in the effects of the construction land scale on carbon emissions: Case study of the Yangtze River Economic Belt, China.

Low-carbon emissions are a major research focus to solve the problem of global warming and an important area that China needs to focus on to achieve high-quality development. Construction land scale is a non-negligible factor affecting carbon emissions. However, carbon emission impacts of county-scale spatial heterogeneity in construction land scale are under addressed in contemporary research. To address this gap, this paper took 1042 counties in China's Yangtze River Economic Belt (YREB) and developed datasets of the influencing factors including the construction land scale, GDP, secondary industry output proportion in GDP, residential population, and fixed asset investment. After comparing the ordinary least squares and geographically weighted regression (GWR) models, we applied GWR for more in-depth analyses. The global regression model results showed that the effect of the scale of construction land on carbon emissions was exceedingly significant and that the directions of the impacts coincided with the predictions. Further, local regression model results showed that construction land scale had significant spatial heterogeneity in the impact on carbon emissions and most counties (69.58%) showed significant positive correlations. The counties with significant construction land scale impacts on carbon emissions were concentrated and contiguous in spatial distribution and spatially clustered areas varied, with the clearest impact in the downstream region. The findings help to further identify the spatial heterogeneity of construction land scale impacts on carbon emissions, which provides evidence-based and theoretical support for policymakers to develop spatially differentiated emission reduction measures.