Implications of a Carbon Tax Mechanism in Remanufacturing Outsourcing on Carbon Neutrality

Many governments have imposed methods such as a carbon tax that aim to even out the negative effects of carbon emissions. The taxes levied on different agents lead to different make–buy decisions for production structures and different environmental outcomes. Some original equipment manufacturers (OEMs) outsource remanufacturing to independent remanufacturers (IRs). Thus, a question arises: What are the implications of carbon taxes levied on different agents on remanufacturing outsourcing decisions? To answer this question, we developed two models: (1) acting as common brand owners, OEMs can be taxed for both new and remanufactured products, or (2) acting as different emitters for production and remanufacturing, OEMs are taxed for new products; however, all carbon taxes related to remanufacturing are levied on IRs. Our analysis reveals that, regarding economic performance, firms should undertake a carbon emission tax on their own initiative because this allows the taxpayer to choose more units for its preferred products and leaves its rivals at a huge disadvantage. Moreover, regarding environmental sustainability, carbon emission taxes indeed lead to mitigating the effects of carbon emissions per unit; however, environmental agencies should also pay attention to reducing the total carbon emissions by limiting the volume effects

Research on non-linear visual matching model under inherent constraints of images

Visual matching of plane images has promoted the development of artificial intelligence and digital vision. High-precision visual matching can promote the innovation of geometric measurement, visual navigation and other fields. Therefore, a non-linear visual matching model with inherent constraints is established in this paper. First, according to the principle of visual imaging, a non-linear conversion model of visual point coordinates is proposed, and the deviation of coordinate points is proofread. Then, inherent boundary constraints are introduced into the model to improve the accuracy of visual matching. Finally, through analysis and evaluation of error, results are generated showing that the visual matching model can effectively solve the shortcoming of low-matching accuracy in feature points, and provide more accurate data support for 3D calculation of images

Research on the generality of icon sizes based on visual attention

Abstract Icon size is one of the key factors affecting the efficiency of information search. The advent of the era of intelligent interaction has made it difficult for icon design to meet the requirements of noncontact, large information volume, and high precision proposed by natural interaction technology in the future. At the same time, with the continuous improvement of display technology, the display resolution has been increased from 720 P to 8 K. Different sizes of display carriers use different resolutions. In order for icons to have efficient recognition at different display resolutions, it is necessary to obtain the best proportional relationship between icon size and display resolution. This paper summarizes the existing relevant research, calculates the ratio of recommended icon size and display resolution as the research variables, and comprehensively evaluates the recommended optimal ratio range of 1:641–1:334 through eye movement, EEG and behavioral response experiments and entropy‐weight TOPSIS method, providing a reference for icon design in various forms of interactive interfaces in the future

Attribution of Lake Warming in Four Shallow Lakes in the Middle and Lower Yangtze River Basin

Many physical, chemical, and biological processes in lakes depend on lake water temperature. However, attribution of the warming rate in a shallow lake is not well understood yet. Here, we evaluated a one-dimensional lake model FLake by observed daily lake surface water temperature (LSWT) at four typical lakes in the Middle and Lower Yangtze River basin and then attributed LSWT warming to climate variables during the period 1979-2017. We found that FLake could capture well the seasonal/interannual variation of observed LSWT. During the 39-year study period, LSWT significantly warms at a rate of 0.26-0.28 degrees C per decade, 24-35% slower than the air temperature. Increased solar radiation and air temperature contributed to most (>80%) of the LSWT warming. The warming trend of LSWT in the spring is the largest among the four seasons, 2-4 times the warming rate of the other seasons. Brightening in the spring contributes 50-64% of the largest spring warming. The future air warming plus the brightening trend with the Clean Air Act in China would amplify LSWT warming and, thus, advance and/or deteriorate algae blooms, especially in spring

Temporal and Spatial Variations of Chlorophyll a Concentration and Eutrophication Assessment (1987-2018) of Donghu Lake in Wuhan Using Landsat Images

Chlorophyll a (Chl-a) concentration, which reflects the biomass and primary productivity of phytoplankton in water, is an important water quality parameter to assess the eutrophication status of water. The band combinations shown in the images of Donghu Lake (Wuhan City, China) captured by Landsat satellites from 1987 to 2018 were analyzed. The (B4 - B3)/(B4 + B3) [(Green-Red)/(Green+Red)] band combination was employed to construct linear, power, exponential, logarithmic and cubic polynomial models based on Chl-a values in Donghu Lake in April 2016. The correlation coefficient (R-2), the relative error (RE) and the root mean square error (RMSE) of the cubic model were 0.859, 9.175% and 11.194 mu g/L, respectively and those of the validation model were 0.831, 6.509% and 19.846 mu g/L, respectively. Remote sensing images from 1987 to 2018 were applied to the model and the spatial distribution of Chl-a concentrations in spring and autumn of these years was obtained. At the same time, the eutrophication status of Donghu Lake was monitored and evaluated based on the comprehensive trophic level index (TLI). The results showed that theTLI( n-ary sumation ) of Donghu Lake in April 2016 was 63.49 and the historical data on Chl-a concentration showed that Donghu Lake had been eutrophic. The distribution of Chl-a concentration in Donghu Lake was affected by factors such as construction of bridges and dams, commercial activities and enclosure culture in the lake. The overall distribution of Chl-a concentration in each sub-lake was higher than that in the main lake region and Chl-a concentration was highest in summer, followed by spring, autumn and winter. Based on the data of three long-term (2005-2018) monitoring points in Donghu Lake, the matching patterns between meteorological data and Chl-a concentration were analyzed. It revealed that the Chl-a concentration was relatively high in warmer years or rainy years. The long-term measured data also verified the accuracy of the cubic model for Chl-a concentration. The R-2, RE and RMSE of the validation model were 0.641, 2.518% and 22.606 mu g/L, respectively, which indicated that it was feasible to use Landsat images to retrieve long-term Chl-a concentrations. Based on longitudinal remote sensing data from 1987 to 2018, long-term and large-scale dynamic monitoring of Chl-a concentrations in Donghu Lake was carried out in this study, providing reference and guidance for lake water quality management in the future

Human Activities Aggravate VOC Pollution in the Huangshui River of the Tibetan Plateau

Many xenobiotic compounds can threaten human health and natural ecosystems. The ability to predict the level of human activities and identify major impact factors is crucial for the design of pollutant risk-reduction plans. In this study, a total of 25 volatile organic compounds (VOCs) including eight alkenes, six alkanes, and eleven aromatics were identified at 11 monitoring locations along the Huangshui River of the Tibetan Plateau. GC-MS analysis was applied to detect the concentrations of the VOCs. The results showed that the alkene, alkane, and aromatic concentrations in the sediment were significantly higher than in the water in all seasons (p < 0.001). The VOC concentrations in summer were significantly higher than in spring and winter (p < 0.01). In addition, several VOCs were found to surpass the national standard, i.e., bromoform reached 312.43 μg/L in water during the summer (the national standard is 100 μg/L), carbon tetrachloride was 209.58 μg/L (the national standard is 2 μg/L), and vinyl chloride was 10.99 μg/L (the national standard is 5 μg/L), which were all related to human activities. Principal component analysis (PCA) was used to comprehensively evaluate the water quality and the VOCs. The total organic carbon (TOC) was found to be responsible for the presence of the VOCs in the river, accounting for 77.93%, 81.97%, and 82.13% of the total variance in the datasets in spring, summer, and winter, respectively

Use the predictive models to explore the key factors affecting phytoplankton succession in Lake Erhai, China

Increasing algae in Lake Erhai has resulted in frequent blooms that have not only led to water ecosystem degeneration but also seriously influenced the quality of the water supply and caused extensive damage to the local people, as the lake is a water resource for Dali City. Exploring the key factors affecting phytoplankton succession and developing predictive models with easily detectable parameters for phytoplankton have been proven to be practical ways to improve water quality. To this end, a systematic survey focused on phytoplankton succession was conducted over 2 years in Lake Erhai. The data from the first study year were used to develop predictive models, and the data from the second year were used for model verification. The seasonal succession of phytoplankton in Lake Erhai was obvious. The dominant groups were Cyanobacteria in the summer, Chlorophyta in the autumn and Bacillariophyta in the winter. The developments and verification of predictive models indicated that compared to phytoplankton biomass, phytoplankton density is more effective for estimating phytoplankton variation in Lake Erhai. CCA (canonical correlation analysis) indicated that TN (total nitrogen), TP (total phosphorus), DO (dissolved oxygen), SD (Secchi depth), Cond (conductivity), T (water temperature), and ORP (oxidation reduction potential) had significant influences (p &lt; 0.05) on the phytoplankton community. The CCA of the dominant species found that Microcystis was significantly influenced by T. The dominant Chlorophyta, Psephonema aenigmaticum and Mougeotia, were significantly influenced by TN. All results indicated that TN and T were the two key factors driving phytoplankton succession in Lake Erhai.</p

Correction: Development of Models for Predicting the Predominant Taste and Odor Compounds in Taihu Lake, China.

Taste and odor (T&O) problems, which have adversely affected the quality of water supplied to millions of residents, have repeatedly occurred in Taihu Lake (e.g., a serious odor accident occurred in 2007). Because these accidents are difficult for water resource managers to forecast in a timely manner, there is an urgent need to develop optimum models to predict these T&O problems. For this purpose, various biotic and abiotic environmental parameters were monitored monthly for one year at 30 sites across Taihu Lake. This is the first investigation of this huge lake to sample T&O compounds at the whole-lake level. Certain phytoplankton taxa were important variables in the models; for instance, the concentrations of the particle-bound 2-methylisoborneol (p-MIB) were correlated with the presence of Oscillatoria, whereas those of the p-β-cyclocitral and p-β-ionone were correlated with Microcystis levels. Abiotic factors such as nitrogen (TN, TDN, NO(3)-N, and NO(2)-N), pH, DO, COND, COD and Chl-a also contributed significantly to the T&O predictive models. The dissolved (d) T&O compounds were related to both the algal biomass and to certain abiotic environmental factors, whereas the particle-bound (p) T&O compounds were more strongly related to the algal presence. We also tested the validity of these models using an independent data set that was previously collected from Taihu Lake in 2008. In comparing the concentrations of the T&O compounds observed in 2008 with those concentrations predicted from our models, we found that most of the predicted data points fell within the 90% confidence intervals of the observed values. This result supported the validity of these models in the studied system. These models, basing on easily collected environmental data, will be of practical value to the water resource managers of Taihu Lake for evaluating the probability of T&O accidents

Eco-chemical mechanisms govern phytoplankton emissions of dimethylsulfide in global surface waters

The anti-greenhouse gas dimethylsulfide (DMS) is mainly emitted by algae and accounts for more than half of the total natural flux of gaseous sulfur to the atmosphere, strongly reducing the solar radiation and thereby the temperature on Earth. However, the relationship between phytoplankton biomass and DMS emissions is debated and inconclusive. Our study presents field observations from 100 freshwater lakes, in concert with data of global ocean DMS emissions, showing that DMS and algal biomass show a hump-shaped relationship, i.e. DMS emissions to the atmosphere increase up to a pH of about 8.1 but, at higher pH, DMS concentrations decline, likely mainly due to decomposition. Our findings from lake and ocean ecosystems worldwide were corroborated in experimental studies. This novel finding allows assessments of more accurate global patterns of DMS emissions and advances our knowledge on the negative feedback regulation of phytoplankton-driven DMS emissions on climate