Hasil untuk "Industries. Land use. Labor"

Yurui Li, Yan-sui Liu, H. Long et al.

محمدرضا سرتیپی اصفهانی

نطنز یکی از قدیمی‌ترین زیستگاه‌های کویری ایران، شهری با سازمان فضایی مبتنی‌بر باغ‌شهر بوده که به‌دلایل مختلفی ازجمله دورافتادن از آزادراه جدید شرق اصفهان، خشکسالی، تغییرات اقلیمی و توسعة صنعتی غیرمکانمند مبتنی‌بر سود اقتصادی صرف، رونق خود را از دست داده است. این پژوهش قصد دارد با روش مطالعة اسناد کتابخانه‌ای و بهره‌مندی از بازدید میدانی و مصاحبة آزاد با کارشناسان، شهروندان و مدیران شهری، دلیل ناسازگاری توسعه با بستر میزبان آن را بررسی کند. یافتة حاصل نشان می‌دهد در صورتی می‌توان توسعة صنعتی پایدار و متوازن داشت که به تطبیق مقیاسِ توسعة صنعتی با مقیاس پتانسیل‌ها و ظرفیت‌های بستر آن، اعم از ظرفیت‌های کالبدی-مادی بستر و هم ظرفیت اذهان جامعة محلی در پذیرابودن توسعه، به‌عنوان یکی از ارکان اصلی توسعة مکانمند توجه ویژه شود، در غیر این صورت توسعة زیان‌های جبران ناپذیری به بستر خود خواهد زد. آن‌چه در نطنز باعث تخریب باغات و به‌تبع آن سازمان فضایی مبتنی‌بر ساختار باغ‌شهری آن شد.

Hussein A. Kazem, Miqdam T. Chaichan, Ali H.A. Al-Waeli et al.

Integrating the photovoltaic/thermal (PV/T) system in green hydrogen production is an improvement in sustainable energy technologies. In PV/T systems, solar energy is converted into electricity and thermal energy simultaneously using hot water or air together with electricity. This dual use saves a significant amount of energy and officially fights greenhouse gases. Different cooling techniques have been proposed in the literature for improving the overall performance of the PV/T systems; employing different types of agents including nanofluids and phase change materials. Hydrogen is the lightest and most abundant element in the universe and has later turned into a flexible energy carrier for transportation and other industrial applications. Issues, including the processes of Hydrogen manufacturing, preservation as well as some risks act as barriers. This paper provides an analysis of several recent publications on the efficiency of using PV/T technology in the process of green hydrogen production and indicates the potential for its increased efficiency as compared to conventional systems that rely on fossil fuels. Due to the effective integration of solar energy, the PV/T system can play an important role in the reduction of the levelized cost of hydrogen (LCOH) and hence play an important part in reducing the economic calculations of the decarbonized energy system.

David Marguerit

Artificial intelligence (AI) is reshaping the labor market by changing the task content of occupations. This study investigates the impact of AI development on the emergence of new work, employment, and wages in the United States from 2015 to 2022. I develop innovative methods to measure occupational and industry exposure to AI technologies that substitute labor (automation AI ) or enhance workers' output (augmentation AI), and to identify new work (i.e., new job titles). To address endogeneity, I use instrumental variable estimators, leveraging AI development in countries with limited economic ties to the United States. The findings indicate that automation AI negatively impacts new work, employment, and wages in low-skilled occupations, while augmentation AI fosters the emergence of new work and raises wages for high-skilled occupations. These results suggest that AI may contribute to rising wage inequality.

Harshitha Reddy Vaddi, Guru Keerthi Mandadhi, S. Ameer et al.

In India, the economy relies on the agriculture, which creates jobs for a sizable section of the labor force and contributes significantly to GDP. The agricultural industry is crucial, but it faces challenges exacerbated by a lack of knowledge about which crops should be planted where due to the unpredictable changes in climate that impact food supply and livelihood. Due to the changes in the climatic conditions, farmers should select the best crop to grow for maximum yield. Even if they select the best crop, they are not aware of the fertilizers to be used based on the soil characteristics of their land. Farmers are not aware of the yield they are going to get if they grow a particular crop based on the season and area. Therefore, we suggested a system using Random Forest, Decision Tree, Gradient Boosting Regressor, XGBooster, KNN and other simulated intelligence techniques and calculations. This helps the farmers in decision-making and increases the food production. In addition to recommending crops that can flourish in variable environmental conditions to maximize yield, this approach assists in the prediction of agricultural yields as well as recommends the fertilizers to get more yield.

Ulysses M. Montojo, R. J. Banicod, Gezelle C. Tadifa et al.

Despite being archipelagic, the Philippines heavily relies on salt imports to meet its annual demand. There is a dearth of literature on the country’s salt industry, including verified production data, current practices, and factors affecting declining domestic production. This paper seeks to bridge these knowledge gaps, generating baseline data to provide applicable policy direction and sustainable development strategies for the Philippine salt industry. Contrary to the industry report, local salt production is estimated at 114,623.29 MT, or 16.78% of the country’s annual salt requirement. Occidental Mindoro is the biggest salt-producing province, with a 57.43% share in production. A myriad of factors has contributed to the decline in domestic production, such as failure to adapt to the changes brought by climate change, passage of ASIN Law, profitable land-use conversion, market competition, and stringent food safety standards and product quality requirements. Moreover, limited government policies that can be associated with the lack of agency tasked to oversee the industry, unattractive business environment, and limited access to government support services have pushed local salt producers into obscurity. The labor-intensive and seasonal production, unreasonable labor practices, and small economic returns have steered the growing disinterest among younger generations, which may indicate a total demise of the industry in the long run. The country should maximize its inherent natural advantages to scale up domestic salt production and lower importation. This could be done by institutionalizing an orchestrated approach to set forth holistic solutions to the multifaceted challenges for the sustainable development of the Philippine salt industry.

As the first major developing country heavily struck by the COVID-19 pandemic, China adopted the world's most stringent lockdown interventions to contain the virus spread. Using macro- and micro-level data, this paper shows that both the pandemic and lockdown policies have had negative and significant impacts on the economy. Gross regional product (GRP) fell by 9.5 and 0.3 percentage points in cities with and without lockdown interventions, respectively. These impacts represent a dramatic recession from China's average growth of 6.74% before the pandemic. The results indicate that lockdown explains 2.8 percentage points of the GDP loss. We also document significant spill-over effects of the pandemic in adjacent areas but no such effects of lockdown. Reduced labor mobility, land supply, and entrepreneurship are among the most significant mechanisms underpinning the impacts of the pandemic and lockdown. Cities with higher share of secondary industry, higher traffic intensity, lower population density, lower internet access, and lower fiscal capacity suffered more. However, these cities seem to have recovered well from the recession and quickly closed the economic gap in the aftermath of the pandemic and city lockdown. Our findings have broader implications for the global interventions in pandemic containment.

K. Bharathi B, S. S, Parthiban Kt et al.

In India, mechanization has emerged as the key component of modern agriculture because of factors like rising demand, declining production, a manpower shortage, etc. Improving farmers understanding of farm mechanization is crucial to maintain sustainability in the agriculture industry. The primary obstacles to farmers adopting mechanical alternatives are ignorance and limited resources, including land, labor, and equipment. The aim of this research is to examine the necessity and obstacles of agricultural mechanization, as well as the present advancements in this domain in India. According to the study's findings, India's agriculture produces 18.3% (2022-23) of the country's GDP. Nonetheless, 58% of people in the population make their living from agriculture. Tractor sales and demand, power tiller sales, and farm power availability are all rising year on year. This brings us to the conclusion that it is critical to use mechanical agricultural alternates. This report outlines some of the issues with agricultural mechanization as well as steps that should be made to raise awareness and promote farm mechanization.

Muhammad Joni Iskandar, Rini Endang Prasetyowati, M. Anwar

Corporate farming is a strategic step to increase the production and productivity of subsistence lowland rice farming. In addition, the use of corporate farming model agriculture is a new opportunity to overcome the risk of national rice production. Especially at this time, the rice fields are getting narrower due to the conversion of functions into agro-tourism, housing, industry, and services. The purpose of this research is to estimate the production risk and farmers' behavior towards the risk of rice farming with the corporate farming model in Sukoharjo Regency, Central Java. The result of this research shows that the risk of production of the corporate farming model is high with a variability of 50.45. This means that the corporate farming model of rice farming faces the risk of major crop failure. Factors that influence the increase in production risk are SP-36 fertilizer and pesticides, while land area, Urea fertilizer, NPK fertilizer, and labor have the potential to reduce production risk but have no significant effect. Farmers' behavior towards the risk of farming with the corporate farming model is risk aversion. The attitude of reluctant farmers is shown by their behavior in facing the intermediate risk of 32 farmers

Garrett Graddy-Lovelace, Antonio Roman-Alcalá

Agroecology—with its diverse, multifaceted, and liberatory principles, methods, and commitments—seems incommensurate with the U.S. Department of Agriculture (USDA), with its settler colonial origins, imperial histories, racist legacies, neoliberal hegemonies, and contemporary reproduction of the unjust and ecocidal agricultural status quo. And yet, is it possible to make use of what the behemoth department has to offer, in its attempts, albeit paltry, at reform and restitution? More pressingly, can we engage and demand more from the non-monolithic ministry—call for it to stave off further corporate capture of markets, land, germplasm, data, and water? Can we pressure the USDA to protect farmworkers from exploita­tion, animals from abuse, cooperatives from corpo­rate co-optation, and small-scale farmers from farmgate price degradation? Is abandoning the USDA tantamount to ceding its resources to agro-industries intent on dispossessing Black, Indige­nous, and other essential agricultures? Shouldn’t we at least attempt to obstruct the USDA’s obstruc­tionist international stance, as it thwarts the right to food, climate justice, labor rights, and redistributive reforms globally? . . .

Atsuki Matsui, Ryoto Ishibashi, Lin Meng

Japan’s aging population has led to significant labor shortages, particularly in agriculture. An aging workforce and a lack of successors have exacerbated this issue. Additionally, agricultural processes, such as land preparation, seeding, and regular maintenance, require significant manual labor. This further contributes to the labor shortage. To address this issue, process automation through Information Technology (IT) solutions like “factory Automation” have been increasingly adopted in Japan. Automating simple, repetitive tasks can significantly reduce the need for human labor. Among IT solutions, Artificial Intelligence (AI) excels at automating tasks and is widely applied in labor-intensive industries. In this research, we focus on using an Object Detection model, specifically YOLO, to automate the inspection of harvested products for defects. This reduces labor demands in the agricultural sector. This study aims to improve YOLO’s detection accuracy to make it more effective in real-world applications. We propose enhancing the Loss function, a critical component in AI training. Specifically, we improve the L(obj) component, called SBCE, to account for the higher prevalence of small defects compared to larger ones. By scaling the Loss scores for small defects, our model becomes better at detecting small objects. As a result, our improved model demonstrates higher accuracy in object detection. We observe improvements in the mean Average Precision (mAP) of YOLOv7-tiny from 81.51% to 82.13%, as well as increases in Recall (from 68.97% to 72.55%) and mean Intersection over Union (mIoU) (from 52.91% to 53.48%) on the PASCAL VOC dataset.

Sentagi Sesotya Utami, Winny Setyonugroho, Moch Zihad Islami et al.

Introduction: Ship-to-shore (STS) crane operators strive for efficiency in their work, but they must take a hard look at their high-risk jobs. It is necessary to learn how to improve occupational safety and health. This study aims to investigate the problems faced by STS crane operators working in container ports and to understand the importance of fit-for-work monitoring procedures, particularly for individuals working in high-risk industries such as STS operators. Methods: This study used a qualitative approach, and data were collected through interviews and observations of STS operators and in-house clinic staff. Nine STS operators, two in-house clinic staff, and two safety, health, and environment (SHE) staff were interviewed. Results: This study found that container terminal companies emphasise two critical aspects for STS operators: productivity and occupational safety and health. STS operators face health problems, including physical and psychological problems, due to the fast-paced work system, sleep patterns, daily activities, and thoughts that are difficult to control. Employees have coping mechanisms to deal with fatigue, and stakeholders have effectively communicated the company's safety and health culture. Most stakeholders in a container terminal company want a fit-for-work monitoring system to make the business efficient and sustainable. Conclusion: The STS industry faces a significant problem with operator fatigue, which can negatively impact safety and productivity. This issue requires a comprehensive strategy, including legislation to regulate working hours and shift patterns, technology to combat fatigue, and operator education and training.

Elia Moretti, Michael Benzaquen

Biodiversity loss driven by agricultural intensification is a pressing global issue, with significant implications for ecosystem stability and human well-being. Existing policy instruments have so far proven insufficient in halting this decline, which raises the need to explore the possible feedback loops that are pivotal to ecosystem degradation. We design a minimal integrated bio-economic agent-based model to qualitatively explore macro-level biodiversity trends, as influenced by individual farmer behavior within simple decision-making processes. Our model predicts further biodiversity decline under a business-as-usual scenario, primarily due to intensified land consolidation. We evaluate two policy options: reducing pesticide use and subsidizing small farmers. While pesticide reduction rapidly benefits biodiversity in the beginning, it eventually leads to increased land consolidation and further biodiversity loss. In contrast, subsidizing small farmers by reallocating a small fraction of existing subsidies, stabilizes farm sizes and enhances biodiversity in the long run. The most effective strategy results from combining both policies, leveraging pesticide reduction alongside targeted subsidies to balance economic pressures and consistently improve biodiversity.

Jean-Gabriel Cuby, Christine Matsuda, Rich Matsuda et al.

Astronomy is at a turning point in its history and in its relations with the Indigenous peoples who are the generational stewards of land where several of our main observatories are located. The controversy regarding the further development of astronomy facilities on Maunakea is probably the most significant and publicized conflict about the use of such land in the name of science. Thousands have stood in resistance, elders were arrested, and the community is divided. Astronomy's access to one of its most emblematic sites is at risk. This situation challenges our professional practice, the projects we build on Indigenous lands, and our relationships with the people who live within these lands and with society in general. This paper attempts to share the perspective of the authors on the historical events, including the very recent past, through the lens of our understanding and opinions; to provide transparency, with humility, into our process of introspection and transformation; and to share our hopes and ambitions as leaders from Maunakea Observatories for the future of astronomy in Hawai'i, as advocated by the Astro2020 report from the U.S. National Academies of Sciences, Engineering, and Medicine; and to suggest ways for the profession to commit to this long-term vision.

Huiran Yi, Lu Xian

This paper critically examines flexible content creation conducted by Key Opinion Consumers (KOCs) on a prominent social media and e-commerce platform in China, Xiaohongshu (RED). Drawing on nine-month ethnographic work conducted online, we find that the production of the KOC role on RED is predicated on the interactions and negotiations among multiple stakeholders -- content creators, marketers, consumer brands (corporations), and the platform. KOCs are instrumental in RED influencer marketing tactics and amplify the mundane and daily life content popular on the platform. They navigate the dynamics in the triangulated relations with other stakeholders in order to secure economic opportunities for producing advertorial content, and yet, the labor involved in producing such content is deliberately obscured to make it appear as spontaneous, ordinary user posts for the sake of marketing campaigns. Meanwhile, the commercial value of their work is often underestimated and overshadowed in corporate paperwork, platform technological mechanisms, and business models, resulting in and reinforcing inadequate recognition and compensation of KOCs. We propose the concept of ``informal labor'' to offer a new lens to understand content creation labor that is indispensable yet unrecognized by the social media industry. We advocate for a contextualized and nuanced examination of how labor is valued and compensated and urge for better protections and working conditions for informal laborers like KOCs.

Qiao He, Ying Xue

China's economic growth has reached a new plateau. It is no longer appropriate to use the old economic growth model, which relied on labor, land resources, mineral resources, and other economic considerations. Under the background of artificial intelligence, high-quality economic development is an inevitable trend. A new financial paradigm called "digital finance" integrates financial services with information technologies. Digital financial technology is thought to be a crucial foundation for fostering high-quality and sustainable economic and social development since it may offer more economic entities reduced cost of capital and more realistic financial service skills than in traditional financial models. In the era of artificial intelligence, how to reasonably release the momentum of digital finance for China's sustained economic growth has become a hot topic of discussion at this stage. This paper studies the impact of digital finance on the economic efficiency of the energy industry in the context of artificial intelligence. Relevant metrics were also calculated. The findings revealed that: The benchmark regression result of digital finance on the efficiency of the green economy was 0.4685 before adding the main restrictions; the benchmark regression result of digital finance on the efficiency of the green economy was 0.2243 after adding the main constraints. As a result, data finance had a favorable impact on the effectiveness of the green economy.

T. Werner, A. Bebbington, G. Gregory

Abstract Mining produces several environmental, social, and economic impacts which can be analysed spatially using remote sensing (RS) and geographical information systems (GIS). This paper provides an overview of recent studies using these techniques to assess mining impacts on water, land, and society. It also highlights the geographic complexities of these impacts via mining case studies, and discusses spatial research methods, data sources, and limitations. Despite noted simplifications, risks, and uncertainties of mapping the impacts of mining, the cases included in our overview illustrate that there are clearly beneficial applications. At a local level, these include environmental and socioeconomic risk assessments, disaster mitigation, and adjudication on mine-related conflicts. At a regional level, spatial analyses can support cumulative and strategic impact assessments. At a global level, spatial analyses can reveal industry-wide land use trends, and provide key land use data for comparative analyses of mining impacts between commodities, locations, and mine configurations. The degree to which such benefits are realised will likely depend on the resources afforded to what is a growing field of study.

Aristides Moustakas, Panagiotis Georgiakakis, Elzbieta Kret et al.

Wind turbines (WT) cause bird and bat mortalities which depend on the WT and landscape features. The effects of WT features and environmental variables at different spatial scales associated to bat deaths in a mountainous and forested area in Thrace, NE Greece were investigated. Initially, we sought to quantify the most lethal WT characteristic between tower height, rotor diameter and power. The scale of interaction distance between bat deaths and the land cover characteristics surrounding the WTs was quantified. A statistical model was trained and validated against bat deaths and WT, land cover and topography features. Variance partitioning between bat deaths and the explanatory covariates was conducted. The trained model was used to predict bat deaths attributed to existing and future wind farm development in the region. Results indicated that the optimal interaction distance between WT and surrounding land cover was 5 km, the larger distance than the ones examined. WT power, natural land cover type and distance from water explained 40 %, 15 % and 11 % respectively of the total variance in bat deaths by WTs. The model predicted that operating but not surveyed WTs comprise of 377.8% and licensed but not operating yet will contribute to 210.2% additional deaths than the ones recorded. Results indicate that among all WT features and land cover characteristics, wind turbine power is the most significant factor associated to bat deaths. Results indicated that WTs located within 5 km buffer comprised of natural land cover types have substantial higher deaths. More WT power will result in more deaths. Wind turbines should not be licensed in areas where natural land cover at a radius of 5km exceeds 50%. These results are discussed in the climate-land use-biodiversity-energy nexus.

Naoto Yokoya, Junshi Xia, Clifford Broni-Bediako

Deep learning has shown promising performance in submeter-level mapping tasks; however, the annotation cost of submeter-level imagery remains a challenge, especially when applied on a large scale. In this paper, we present the first submeter-level land cover mapping of Japan with eight classes, at a relatively low annotation cost. We introduce a human-in-the-loop deep learning framework leveraging OpenEarthMap, a recently introduced benchmark dataset for global submeter-level land cover mapping, with a U-Net model that achieves national-scale mapping with a small amount of additional labeled data. By adding a small amount of labeled data of areas or regions where a U-Net model trained on OpenEarthMap clearly failed and retraining the model, an overall accuracy of 80\% was achieved, which is a nearly 16 percentage point improvement after retraining. Using aerial imagery provided by the Geospatial Information Authority of Japan, we create land cover classification maps of eight classes for the entire country of Japan. Our framework, with its low annotation cost and high-accuracy mapping results, demonstrates the potential to contribute to the automatic updating of national-scale land cover mapping using submeter-level optical remote sensing data. The mapping results will be made publicly available.

Suryadeepto Nag

Using a panel of 1,171 villages in rural India that were surveyed in the India Human Development Surveys, I perform a difference-in-differences analysis to find that improvements in electricity reliability have a negative effect on the increase in casual agricultural labor wage rates. Changes in men's wage rates are found to be affected more adversely than women's, resulting in a smaller widening of the gender wage gap. I find that better electricity reliability reduces the time spent by women in fuel collection substantially which could potentially increase labor supply. The demand for labor remains unaffected by reliability, which could lead the surplus in labor supply to cause wage rates to stunt. However, I show that electrical appliances such as groundwater pumps considerably increase labor demand indicating that governments could target increasing the adoption of electric pumps along with bettering the quality of electricity to absorb the surplus labor into agriculture.