Water covers most of the Earth's surface and is nowhere near a good ecological or recreational state in many areas of the world. Moreover, only a small fraction of the water is potable. As climate change-induced extreme weather events become ever more prevalent, more and more issues arise, such as worsening water quality problems. Therefore, protecting invaluable and usable drinking water is critical. Environmental agencies must continuously check water sources to determine whether they are in a good or healthy state regarding pollutant levels and ecological status. The currently available tools are better suited for stationary laboratory use, and domain specialists lack suitable tools for on-site visualisation and interactive exploration of environmental data. Meanwhile, collecting data for lab analysis requires substantial time and significant effort. We, therefore, investigate using an augmented reality system with a Microsoft HoloLens 2 device to explore the visualisation of water quality and status in situ. The developed prototype visualises geo-referenced sensor measurements incorporated into the perspective of the surroundings. Any users interested in water bodies' conditions can quickly examine and retrieve an overview of water body status using augmented reality and then take the necessary steps to address the current situation.
This study aims to investigate the dynamic correlations among carbon emission reduction, total cost savings, and asset investments in the industrial sector in China. This study uses the panel vector autoregressive (PVAR) model and the generalized method of moments (GMM) model to obtain three conclusions based on Chinese industrial industry data from 2005–2019. (1) The interaction between carbon emission reduction and cost reduction is bidirectional. A carbon emission decrease can result in persistent cost cutting, while measures in shrinking costs lead to reducing carbon emissions with lasting effects. Moreover, carbon emission decline has strong inertia, while cost reduction is softer. (2) Green investment promotes reducing carbon emissions and is efficient and sustainable. Conversely, completing carbon reduction milestones will inhibit asset expansion in the subsequent period. (3) China’s industrial sector has already achieved the “synergy of emission reduction and cost decrease” development model. The transmission chain “asset investment–carbon emission decline–cost decrease–carbon emission abatement” has been established. Nonetheless, a gap remains between the mature cycle of decarbonization, cost saving, and effectiveness. Finally, it is recommended that the government focuses on the synergistic effect of carbon and cost reduction, encourages continuous green investment, and systematically organizes decarbonization actions. This study provides a basis for increasing the interest of companies in transitioning to a low-carbon economy, contributing to the simultaneous realization of green development and economic benefits.
Fund investment is a hot issue in today’s society. How to choose a project for investment is affected by many factors. In view of this problem, this paper starts from the granular computing point of view and combines the multigranulation rough set decision‐making method to construct a fund investment decision information system; then, the fund investment decision information system is reduced under different thresholds, and the decision rules are extracted through reduction. And from the aspects of decision accuracy and rule accuracy, the rules are analyzed. Finally, decision rules are used to give the decision of the fund investment project. This study provides a new approach to fund management.
This paper adopts the super-efficient DEA (data envelopment analysis) model to measure the forestry eco-efficiency (FECO) of 30 Chinese provinces and cities from 2008 to 2021, and then introduces the Tobit model to explore the influencing factors of FECO to better understand the sustainable development level of forestry. It draws the following conclusions: (1) The average value of FECO in China is 0.504, which is still at a low level, and the FECO of each region has significant regional heterogeneity; the provinces with higher FECO are mainly concentrated in the eastern region, while the FECO of the central and western regions is lower; (2) In terms of the main factors affecting FECO in China, the regression coefficients of market-based environmental regulations are significantly positive in the national, eastern and central regions, while they are significantly negative in the western region. The coefficient of impact of scientific research funding investment on forestry industry eco-efficiency is negative and shows a significant promotion effect in the eastern region, but the elasticity coefficient in the central and western regions is negative but not significant. Economic development has a positive but insignificant effect on FECO, with the eastern region showing a positive correlation, while the central and western regions are insignificant. Industrial structure has a significant negative effect on FECO in the national, eastern and central regions, but the effect of industrial structure on FECO in the western region is not significant. The effect of foreign direct investment on FECO was negative for the national, central and western regions, but the central region did not pass the significance test, while the eastern region reflected a significant promotion effect.
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