Satellite mapping reveals extensive industrial activity at sea.

The world's population increasingly relies on the ocean for food, energy production and global trade1-3, yet human activities at sea are not well quantified4,5. We combine satellite imagery, vessel GPS data and deep-learning models to map industrial vessel activities and offshore energy infrastructure across the world's coastal waters from 2017 to 2021. We find that 72-76% of the world's industrial fishing vessels are not publicly tracked, with much of that fishing taking place around South Asia, Southeast Asia and Africa. We also find that 21-30% of transport and energy vessel activity is missing from public tracking systems. Globally, fishing decreased by 12 ± 1% at the onset of the COVID-19 pandemic in 2020 and had not recovered to pre-pandemic levels by 2021. By contrast, transport and energy vessel activities were relatively unaffected during the same period. Offshore wind is growing rapidly, with most wind turbines confined to small areas of the ocean but surpassing the number of oil structures in 2021. Our map of ocean industrialization reveals changes in some of the most extensive and economically important human activities at sea.

Country-specific net-zero strategies of the pulp and paper industry.

The pulp and paper industry is an important contributor to global greenhouse gas emissions1,2. Country-specific strategies are essential for the industry to achieve net-zero emissions by 2050, given its vast heterogeneities across countries3,4. Here we develop a comprehensive bottom-up assessment of net greenhouse gas emissions of the domestic paper-related sectors for 30 major countries from 1961 to 2019-about 3.2% of global anthropogenic greenhouse gas emissions from the same period5-and explore mitigation strategies through 2,160 scenarios covering key factors. Our results show substantial differences across countries in terms of historical emissions evolution trends and structure. All countries can achieve net-zero emissions for their pulp and paper industry by 2050, with a single measure for most developed countries and several measures for most developing countries. Except for energy-efficiency improvement and energy-system decarbonization, tropical developing countries with abundant forest resources should give priority to sustainable forest management, whereas other developing countries should pay more attention to enhancing methane capture rate and reducing recycling. These insights are crucial for developing net-zero strategies tailored to each country and achieving net-zero emissions by 2050 for the pulp and paper industry.

The role of modifiable risk factors in incident cancer in transport, rescue, and security industries.

PURPOSE: Examine the risk for site-specific incident cancer across representative transport, rescue, and security industries. METHODS: This Danish nationwide register-based study included all 302,789 workers from transport, rescue and security industries in 2001-2015 and 2,230,877 individuals aged 18-64 years from a total sample of the economically active population for comparison. We used Cox models to estimate the hazard ratios (HRs) of incident cancers. We categorized site-specific cancers by using population-attributable fraction (PAF) estimates from the previous literature. RESULTS: During an average follow-up of 13.4 years, 22,116 incident cancer cases were recorded in these industries. Compared with the reference population, the age-adjusted cancer incidence with a high PAF was higher among men in seafaring (HR 1.28; 95% CI 1.14-1.43), and land transport (HR 1.32; 95% CI: 1.26-1.37), and among women in seafaring (HR 1.26; 95% CI: 1.01-1.57), land transport (HR 1.21; 95% CI: 1.12-1.32), aviation (HR 1.22; 95% CI: 1.05-1.41), and police force (HR 1.21; 95% CI: 1.04-1.40). Overall, tobacco and physical inactivity were the most significant risk factors of cancer. CONCLUSIONS: Regardless of considerable disparities in incident cancer attributable to modifiable risk factors across industries, the total incident cancer rate was elevated in all industries in both sexes.

Characteristics of workers' compensation claim applications for COVID-19 infections in South Korea.

This study aimed to identify the major industries and jobs with the highest proportion of workers' compensation (WC) claims for COVID-19, characterize COVID-19 WC claims in terms of their demographic properties and disease severity, and identify factors influencing the approval of COVID-19 WC claims as occupational disease. A total of 488 workers who submitted COVID-19-related claims to the Korea Workers' Compensation and Welfare Service (KWCWS) from January 2020 to July 2021 were analyzed. A Fisher's exact test was employed to associate the severity of COVID-19 infection with demographic properties. The highest proportion of all COVID-19 WC claims compensated as occupational disease (N=462) were submitted by healthcare workers (HCW=233, 50%), while only 9% (N=41) of the total originated from manufacturing industries. The 5% (N=26) of the COVID-19 WC claims accepted were evaluated as severe (N=15) and acute respiratory distress syndrome (N=9). A total of 71% (N=329) of the COVID-19 patients compensated (N=462) were from workplaces with infection clusters. A total of 26 WC cases were rejected for various reasons, including unclear infection routes, infection at private gatherings (including within families), no diagnosis, and more. Given our findings, we suggest an official system should be established to detect and compensate more job-associated infectious diseases like COVID-19.

How do resource dependence and technological progress affect carbon emissions reduction effect of industrial structure transformation? Empirical research based on the rebound effect in China.

Under the guidance of carbon peak and carbon neutral targets, the industrial structure transformation is vital for carbon emissions reduction in China. However, there is a rebound effect of carbon emissions during the industrial structure transformation. Resource dependence and technological progress have significant impacts on industrial structure transformation and its carbon reduction effect. This paper explores how industrial structure transformation under resource dependence causes the rebound effect from a technological progress perspective. The key results indicate that (1) resource dependence distorts the carbon emissions reduction effect of industrial structure transformation; (2) with the development of technology, the industrial structure upgrading under resource dependence could cause an increase on carbon emissions at the beginning, but the increase would be weakened subsequently, displaying a two-stage feature; (3) the industrial structure rationalization under resource dependence reduces carbon emissions at first, but the reduction would be weakened as the technology develops, then industrial structure's rationalization shows an insignificant impact on carbon emissions, and finally reduces carbon emissions again, presenting a four-stage characteristic; (4) environmental protection technology can correct the distortion effect of resource dependence on the industrial structure rationalization and amplify the industrial structure rationalization's reduction effects on carbon emissions; (5) with the development of energy-saving technology, industrial structure rationalization has a paradoxical impact on carbon emissions, the industrial structure rationalization first reduces, then increases, and finally reduces carbon emissions, indicating an inverted "N" relationship. Finally, policy recommendations for carbon emissions reduction are proposed from the perspective of industrial structure transformation and technological progress.

The multimarket model of the veterinary industry.

Within this article, a graphical discussion of the veterinary industry is constructed and discussed. This viewpoint centers around the 4 main markets that operate in veterinary medicine: the market for veterinary education, the market for veterinarians, the market for animals, and the market for veterinary services. Moreover, how each market within veterinary medicine is interconnected is the important point of the article. The purpose of the article is to provide a mechanism for discussion on how changes in one market will affect other markets. From this view, I argue that the market for veterinary services is completely dependent on the other 3 markets in terms of defining supply, demand, and market equilibrium. The remainder of the article discusses how to use the multimarket graphical model to aid in discussions around policy changes and provides an example. Some concluding remarks about this view on the interconnectedness of markets within the industry are provided. Again, the intended purpose of this viewpoint is to provide a graphical understanding of the multimarket nature of the industry and a way to discuss any proposed changes to a market based on literature that empirically estimates many aspects of these various markets. I highly encourage any proposed changes to the industry be supported by mathematical/statistical modeling, but the graphical model can aid in a conceptual understanding of those same changes.

On the estimation of the fill rate for the continuous (s, S) inventory system for the lost sales context.

In the continuous review reorder point, base-stock (s, S) policy, the replenishment order is launched when the inventory position reaches the reorder point, s. It is commonly assumed that the inventory position is exactly equal to the reorder point at the moment the order is launched, when actually it could be lower at that moment. This implies neglecting the possible undershoots at the reorder point, which has a direct impact on the calculation of the expected shortages per replenishment cycle. This article presents a method for an exact calculation of the fill rate (fraction of demand that is immediately satisfied from shelf) which takes explicit account of the existence of undershoots and is applicable to any discrete demand distribution function in a context of lost sales. This method is based on the determination of the stock probability vector at the moment the replenishment order is launched. Furthermore, neglecting the undershoots is shown to lead to an overestimation of the fill rate, particularly when we move farther away from the unitary demand assumption. From a practical point of view, this behaviour involves underestimating the base-stock level, S, when a target fill rate is set for its determination. The method proposed in this paper overcomes these shortcomings.

U.S. postdoctoral careers in life sciences, physical sciences and engineering: Government, industry, and academia.

Discussions about science and engineering postdoctoral researchers focus almost exclusively on academic postdocs and their chances of eventually securing tenure-track faculty positions. Further, biological sciences dominate policy research and published advice for new PhDs regarding postdoctoral employment. Our analysis uses the Survey of Earned Doctorates and Survey of Doctorate Recipients to understand employment implications for physical sciences and engineering (PSE) and life sciences (LS) graduates who took postdoctoral positions in government, industry, and academic sectors. We examine postdoc duration, reasons for staying in a postdoc, movement between sectors, and salary implications. There is considerable movement between employment sectors within the first six years post-PhD. Additionally, postdocs in PSE are shorter, better paid, and more often in nonacademic sectors than postdocs in LS. These results can help science and engineering faculty discuss a broader range of career pathways with doctoral students and help new PhDs make better informed early career decisions.

Green urbanization.

Large cities are more productive and generate more output per person. Using data from the UK on energy demand and waste generation, we show that they are also more energy-efficient. Large cities are therefore greener than small towns. The amount of energy demanded and waste generated per person is decreasing in total output produced, that is, energy demand and waste generation scale sublinearly with output. Our research provides the first direct evidence of green urbanization by calculating the rate at which per capita electricity use and waste decrease with city population. The energy demand elasticity with respect to city output is 83%: as the total output of a city increases by one percent, energy demand increases less than one percent, and the Urban Energy Premium is therefore 17%. The energy premium by source of energy demand is from households (13%), transport (20%), and industry (16%). Similarly, we find that the elasticity of waste generation with respect to city output is 90%. For one percent increase in total city output, there is a less than one percent increase in waste, with an Urban Waste Premium of 10%. Because large cities are energy-efficient ways of generating output, energy efficiency can be improved by encouraging urbanization and thus green living. We perform a counterfactual analysis in a spatial equilibrium model that makes income taxes contingent on city population, which attracts more people to big cities. We find that this pro-urbanization counterfactual not only increases economic output but also lowers energy consumption and waste production in the aggregate.

Skill-driven recommendations for job transition pathways.

Job security can never be taken for granted, especially in times of rapid, widespread and unexpected social and economic change. These changes can force workers to transition to new jobs. This may be because new technologies emerge or production is moved abroad. Perhaps it is a global crisis, such as COVID-19, which shutters industries and displaces labor en masse. Regardless of the impetus, people are faced with the challenge of moving between jobs to find new work. Successful transitions typically occur when workers leverage their existing skills in the new occupation. Here, we propose a novel method to measure the similarity between occupations using their underlying skills. We then build a recommender system for identifying optimal transition pathways between occupations using job advertisements (ads) data and a longitudinal household survey. Our results show that not only can we accurately predict occupational transitions (Accuracy = 76%), but we account for the asymmetric difficulties of moving between jobs (it is easier to move in one direction than the other). We also build an early warning indicator for new technology adoption (showcasing Artificial Intelligence), a major driver of rising job transitions. By using real-time data, our systems can respond to labor demand shifts as they occur (such as those caused by COVID-19). They can be leveraged by policy-makers, educators, and job seekers who are forced to confront the often distressing challenges of finding new jobs.

Recommending blue ocean technologies for subcontractors: A framework based on business reports of prime contractors and patents.

Subcontractors depend heavily on their prime contractor and thus find it very risky to enter a new business on their own. This study proposes a framework for these subcontractors to develop blue ocean technologies related to their prime contractor. First, the primary technologies predicted to be promising are extracted from the business reports of the prime contractor. Sub-technologies are then selected through a patent-based search using keywords and International Patent Classification codes of the primary technologies. From them, blue ocean technologies are proposed by optimizing the weighted mean of the min-max normalized market value, degree of competition in the technology market, and subcontractors' potential technological capabilities for each sub-technology. This study shows that subcontractors can enhance their technology competitiveness by finding a low-risk blue ocean technology. Our empirical research on the subcontractors of a semiconductor firm identified technological patent fields for them to pursue. From our framework, subcontractors can identify blue ocean technologies by considering their prime contractor's future industrial areas and technologies of interest as well as their own technological capabilities. Furthermore, the prime contractors can gain the synergy effect of technology expansion through cooperation.

Performance evaluation of e-commerce firms in China: Using three-stage data envelopment analysis and the Malmquist productivity index.

As a new business form of international trade and electronic commerce, e-commerce has been a controversial topic that has attracted the attention of scholars and industry professionals. This study estimated the operating efficiency and total factor productivity (TFP) of listed e-commerce firms in China from 2015 to 2019. Three related methodologies were applied: data envelopment analysis (DEA), the Malmquist TFP index, and stochastic frontier analysis. The DEA analysis results showed that environmental variables exerted a substantial effect on technical efficiency. Most firms demonstrated effective technical efficiency after adjustment for input variables. Business-to-business firms had the highest operating efficiency, followed by business-to-consumer and production-to-consumer firms. Technical progress and scale were identified as two major factors affecting improvement in TFP. Hence, e-commerce firms should make full use of advanced technology and aim to achieve economies of scale.

The Shortest Duration Constrained Hidden Markov Model: Data denoise and forecast optimization on the country-product matrix for the Fitness-Complexity Algorithm.

The Economic Fitness Index describes industrial completeness and comprehensively reflects product diversification with competitiveness and product complexity in production globalization. The Fitness-Complexity Algorithm offers a scientific approach to predicting GDP and obtains fruitful results. As a recursion algorithm, the non-linear iteration processes give novel insights into product complexity and country fitness without noise data. However, the Country-Product Matrix and Revealed Comparative Advantage data have abnormal noises which contradict the relative stability of product diversity and the transformation of global production. The data noise entering the iteration algorithm, combined with positively related Fitness and Complexity, will be amplified in each recursion step. We introduce the Shortest Duration Constrained Hidden Markov Model (SDC-HMM) to denoise the Country-Product Matrix for the first time. After the country-product matrix test, the country case test, the noise estimation test and the panel regression test of national economic fitness indicators to predict GDP growth, we show that the SDC-HMM could reduce abnormal noise by about 25% and identify change points. This article provides intra-sample predictions that theoretically confirm that the SDC-HMM can improve the effectiveness of economic fitness indicators in interpreting economic growth.

Impact of the implementation of carbon emission trading on corporate financial performance: Evidence from listed companies in China.

With the development of ecological paradigm coupled with the relentless implementation of myriad environmental policies in China, the rapid development of carbon emission trading and carbon trading market has had a vital impact on the financial performance of enterprises at the microlevel. This study has sampled the A-share listed companies in China, from 2009 to 2018, and adopted the difference-in-difference (DID) method to investigate the effect of the carbon emission trading on corporate financial performance from the microlevel. Evidence showed that the implementation of carbon emission trading effectively improved the total asset-liability ratio of enterprises, though it reduced the value of the current capital market. Moreover, in the regions under strict legal environment, the enhancement effect of the total asset-liability ratio was more obvious, whereas in the regions under loose legal environment, the reduction effect of the value of the capital market was more obvious. Further analysis showed that the implementation of carbon emission trading could not promote Chinese enterprises to increase R&D investment. Hence the implementation of carbon emission trading has improved the level of non-business income of enterprises incorporated into the trading system, but its impact on the investment income of enterprises was not significant.

Assessing disabling and non-disabling injuries and illnesses using accepted workers compensation claims data to prioritize industries of high risk for Oregon young workers.

INTRODUCTION: Young workers are especially vulnerable to occupational injuries and illnesses. There is a continued need to investigate injury burden among young workers across demographics and industry to inform targeted interventions. Workers compensation (WC) claims are important for quantifying work-related injuries and illnesses, however published studies have focused on disabling claims. This study extended previous research on Oregon young workers by including the most recent WC claims data to identify patterns of injury and high risk industries. METHODS: We obtained all accepted disabling claims (N = 13,360) and a significant portion of non-disabling claims (N = 24,660) on workers aged 24 years and under from 2013 to 2018. Claim count, rate and cost were calculated by year, age, gender, industry, and injury type. A prevention index (PI) method was used to rank industries in order to inform prevention efforts. RESULTS: Average annual disabling and non-disabling claim rates were 111.6 and 401.3 per 10,000 young workers. Workers aged 19-21 (disabling: 119.0 per 10,000 and non-disabling: 429.3) and 22-24 years (115.7 and 396.4) and male workers (145.3 and 509.0) had higher claim rates than workers aged 14-18 (80.6 and 297.0) and female workers (79.8 and 282.9). The most frequent injury types were "struck by/against" (35.6%) and "work-related musculoskeletal disorders (WMSDs)" (19.5%). High risk industries included agriculture, construction, and manufacturing for both genders combined. For female young workers, the highest risk industry was healthcare. CONCLUSIONS: This study demonstrated the added value of non-disabling WC claims data. Using both disabling and non-disabling data and PI method, agriculture, construction, manufacturing and healthcare industries were identified as priority workplaces to prevent common and costly injuries among Oregon young workers. Practical Applications: While the industries identified are considered hazardous for all workers, findings in this study can guide targeted research and prevention efforts specific to young workers.

Drug Shop Intervention to Enhance Knowledge, Attitude, and Practices of Patent Medicine Vendors for the Control of COVID-19 In Southeastern Nigeria.

Drug shops are the first point of care for most community members in low-resource countries. Because of symptomatic similarities with common illnesses such as malaria, probable coronavirus disease 2019 (COVID-19) cases may seek care at drug shops, where the knowledge and skills required to handle it may be lacking, thereby fostering community spread of the disease. This single-arm study provided an intervention to improve COVID-19-related knowledge, attitude, and practices of patent medicine vendors (PMVs) in 97 participating drug shops selected through cluster sampling in Owerri, southeastern Nigeria. The intervention involved a drug shop sensitization using information, education, and communication material, as well as training on the use of a risk assessment checklist to identify probable COVID-19 cases and to take appropriate action. Data were collected to determine the effect of this intervention using a pre-tested questionnaire and practice observation checklist, first at baseline and then 3 months post-intervention. Data analysis involved exploratory analysis and the t-test to determine pre- and post-intervention mean score differences at the 5% α level. There was post-intervention knowledge improvement on the COVID-19 causative pathogen (98.1% post-intervention versus 61.9% pre-intervention) and disease transmissibility from person to person (95.9% post-intervention versus 81.4% pre-intervention) among other knowledge domains. There was significant post-intervention improvement for positive attitude, with a mean gain score of 2.8 ± 1.7 (t = 4.4, P = 0.005), and preventive practices, with a mean gain score of 6.0 ± 4.7 (t = 4.1, P = 0.007). Engaging patent medicine vendors in the pandemic response plans through targeted interventions such as drug shop intervention could prove vital in the fight against COVID-19.

Analyzing the regional economic changes in a high-tech industrial development zone using machine learning algorithms.

The aims are to improve the efficiency in analyzing the regional economic changes in China's high-tech industrial development zones (IDZs), ensure the industrial structural integrity, and comprehensively understand the roles of capital, technology, and talents in regional economic structural changes. According to previous works, the economic efficiency and impact mechanism of China's high-tech IDZ are analyzed profoundly. The machine learning (ML)-based Data Envelopment Analysis (DEA) and Malmquist index measurement algorithms are adopted to analyze the dynamic and static characteristics of high-tech IDZ's economic data from 2009 to 2019. Furthermore, a high-tech IDZ economic efficiency influencing factor model is built. Based on the detailed data of a high-tech IDZ, the regional economic changes are analyzed from the following dimensions: economic environment, economic structure, number of talents, capital investment, and high-tech IDZ's regional scale, which verifies the effectiveness of the proposed model further. Results demonstrate that the comprehensive economic efficiency of all national high-tech IDZs in China is relatively high. However, there are huge differences among different regions. The economic efficiency of the eastern region is significantly lower than the national average. The economic structure, number of talents, capital investment, and economic efficiency of the high-tech IDZs show a significant positive correlation. The economic changes in high-tech IDZs can be improved through the secondary industry, employee value, and funding input. The ML technology applied can make data processing more efficient, providing proper suggestions for developing China's high-tech industrial parks.

Beta decoupling relationship between CO2 emissions by GDP, energy consumption, electricity production, value-added industries, and population in China.

The credible sources of fossil energy efficiently are a vital cause of economic growth and considerable influence on adequate security. Whereas radiant energy positively enhances or ostensibly promotes socio-economic stability and the controlled environment. The fossil energy sources supply has become progressively stern in China and reconnoitering the beta decoupling relationships between CO2 emissions, GDP, energy consumption, electricity consumption, value-added industries, and population. The results will be favorable for illustrative the security of the valuable resources. This study adopts the extended stochastic model (STIRPAT) with Beta Decoupling Techniques (BDT). This modern technique merely employs the decoupling situation by the alpha and beta effects from 1989 to 2018 and calculates the % change in CO2 emissions by GDP growth and energy consumption. The estimated results represent negative and economic growth depends on coal and natural gas. First, CO2 emissions annually increasing cause of rapid growth, energy consumption, and electricity production, and the structural contradiction of energy remained static. Second, the Value-added industries estimated that CO2 emissions reduce by primary industries. Third, the decoupling states of CO2 emissions and population show an inverse relationship. This paper tentatively suggests China is sustainable, naturally strengthens energy output, transmutes the energy consumption structure, and advances development policies under environmental circumstances.