Resource Policy Research Articles

Pengaruh Kompetensi dan Kompensasi Terhadap Kinerja Karyawan Airport Security PT. Angkasa Pura I Bandara Internasional Juanda Surabaya

Study aims to determine the effect of competence and compensation on the performance of Airport Security employees of PT. Angkasa Pura I Juanda International Airport Surabaya using quantitative methods with a casual relationship approach. The sampling technique used is Stratified Random Sampling. The total population is 111 employees with a sample of 53 employees of the Airport Security section of PT. Angkasa Pura I Juanda International Airport Surabaya. The results of this study proved that the partial test (t-test) of the Competency variable (X1) obtained a significant value of 0.000 < 0.05 and tcount 3,824 > table 2,009 which had a significant effect, while the partial test (t-test) of the Compensation variable (X2) obtained a significant value of 0.000 < 0.05 and the tcount test of 7,942 > table 2,009 which had a significant effect. Simultaneously (Test F) by obtaining significant values of 0.000 > alpha 0.05 and Fcalculate 108. 844 > a value of Ftable 3.18 means that it has a significant effect on employee performance. The importance of attention to employee competency development and compensation system improvement as a strategy to improve performance in the Airport Security sector of PT. Angkasa Pura I Juanda International Airport Surabaya. This research is expected to provide guidance for PT Angkasa Pura in designing more effective human resource policies, focusing on developing employee competencies and improving the compensation system.

A STUDY ON THE PERFORMANCE OF HR POLICIES AND ITS IMPLEMENTATION OF DELTA WEAR TECH ENGINEER’S

Human resource management is concerned with people element in management. Since every organization is made up of people, acquiring their services, developing their skills/ motivating to high level of performances and ensuring that they continue to maintain their commitments to the organization which are essential to achieve organizational objectives. This project is meant to know the Human Resource Policies in the organization. The HR Policies are a tool to achieve employee satisfaction and thus highly motivated employees. The main objective of various HR Policies is to increase efficiency by increasing motivation and thus fulfill organizational goals and objectives. The objective is to provide the reader with a framework of the HR Policy Manual and the various objectives that the different policies aim to achieve. The main focus was on the managerial levels of employees in Keywords: HR policies, Employee Relationship, Organization.

Policy and governance of maritime resources in the Riau Islands Province

ABSTRACT This research refers to the policies carried out and the existing management in Riau Islands Province. The implementation process in managing the maritime sector must refer to Law Number 23 of 2014 which regulates Regional Government. The purpose of this research also explains what steps must be taken in resolving problems in managing natural resources that occur. This research method uses qualitative methods with qualitative analysis, the data analysis process is used purposively, namely by consideration. The results showed that the policy carried out by the Riau Islands Provincial Government through the Marine and Fisheries Service, was found to have no definite authority regarding the process of managing maritime resources in Riau Islands Province, besides there was an overlap of authority, especially for its state civil servants, who had training related to capacity building and how to manage marine resources. The conclusion in this research is to respond to the applicable law, namely Number 23 of 2014, which clearly gives heavy authority to the water area. Thus, the management that occurs is less than optimal.

Impact of motivation on the job performance of public sector employees: the case of Morocco

This study investigates the driving forces behind job performance in Morocco's public sector amid its latest reforms. Despite extensive research on motivation and job performance, the unique dynamics within this sector, particularly following recent legislative changes, still need to be explored. This study links job performance to an incentive-based system by examining the roles of both intrinsic and extrinsic motivations in enhancing employee performance. Employing qualitative methodology, this study explores how incentives and motivation impact job performance. Data collected via a structured questionnaire are analyzed using structural equation modeling to elucidate the connections between work performance, motivation, and incentives. These findings highlight the significance of transparent human resource policies, comprehensive incentive systems, appropriate performance metrics, and robust leadership in cultivating a motivated and high-performing workforce.

A Novel Deep Learning Approach for Forecasting Myocardial Infarction Occurrences with Time Series Patient Data.

Myocardial Infarction (MI) commonly referred to as a heart attack, results from the abrupt obstruction of blood supply to a section of the heart muscle, leading to the deterioration or death of the affected tissue due to a lack of oxygen. MI, poses a significant public health concern worldwide, particularly affecting the citizens of the Chittagong Metropolitan Area. The challenges lie in both prevention and treatment, as the emergence of MI has inflicted considerable suffering among residents. Early warning systems are crucial for managing epidemics promptly, especially given the escalating disease burden in older populations and the complexities of assessing present and future demands. The primary objective of this study is to forecast MI incidence early using a deep learning model, predicting the prevalence of heart attacks in patients. Our approach involves a novel dataset collected from daily heart attack incidence Time Series Patient Data spanning January 1, 2020, to December 31, 2021, in the Chittagong Metropolitan Area. Initially, we applied various advanced models, including Autoregressive Integrated Moving Average (ARIMA), Error-Trend-Seasonal (ETS), Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend and Seasonal (TBATS), and Long Short Time Memory (LSTM). To enhance prediction accuracy, we propose a novel Myocardial Sequence Classification (MSC)-LSTM method tailored to forecast heart attack occurrences in patients using the newly collected data from the Chittagong Metropolitan Area. Comprehensive results comparisons reveal that the novel MSC-LSTM model outperforms other applied models in terms of performance, achieving a minimum Mean Percentage Error (MPE) score of 1.6477. This research aids in predicting the likely future course of heart attack occurrences, facilitating the development of thorough plans for future preventive measures. The forecasting of MI occurrences contributes to effective resource allocation, capacity planning, policy creation, budgeting, public awareness, research identification, quality improvement, and disaster preparedness.

Ecological Economics and Dynamic Games: A Systematic Literature Review

Ecological and environmental economics are inherently dynamic systems requiring dynamic optimization tools. Understanding the interplay between environmental economics, ecological economics, and dynamic games is crucial. This paper presents a systematic literature review on ecological and environmental economics modeled with dynamic games. To be more specific, this systematic literature review analyzes a dataset of 88 peer-reviewed articles from international journals. This study identifies clusters related to resource management, taxation, and policy. It also reveals niche and motor research themes, such as policy, biological invasions, pollution, taxation, abatement, and efficiency, paving the way for future research avenues. By comprehensively examining the literature, this review provides insights into current and future challenges faced by companies, consumers, regulators, and society. It contributes to a deeper understanding of the complex relationship between ecological and environmental dynamics and the field of dynamic games in economics.

How many friends at work are too many? The nonlinear association between the number of friends, social support and mental well-being

PurposeWe grounded this study in the Too-Much-of-a-Good-Thing (TMGT) meta-theoretical framework to disentangle the costs and benefits associated with workplace friendship in a military setting.Design/methodology/approachWe collected data cross-sectionally through self-reports from 287 employees from the Romanian Air Force.FindingsThe number of friends had an inverted U-shaped association with perceived social support. Our results show that as the number of friends increases from 9 to 10, so does the social support. However, as the number of friends further increases above 10, social support tends to decrease rather than increase. Furthermore, we found that social support and all dimensions of mental well-being (emotional, social and psychological well-being) were positively associated. Moreover, social support mediated the relationship between the number of friends and the three dimensions of mental well-being.Research limitations/implicationsOur findings can help human resources policies in military organizations foster an organizational climate that cultivates friendship ties between employees, which is crucial for their social support and overall mental well-being.Originality/valueThis work provides additional information about the specific mechanisms through which the effects of workplace friendships on mental well-being occur.

Does new-type infrastructure improve total factor carbon productivity? Experimental evidence from China

The implementation of new-type infrastructure plays a crucial role in driving low-carbon economic development in China and worldwide. This work employs fixed-effects model, mediated-effects model, spatial Durbin model, and meta-analysis to thoroughly examine the influence of new-type infrastructure on total factor carbon productivity using panel data of 274 Chinese cities spanning from 2011 to 2021. The findings indicate that: firstly, new-type infrastructure has a notable and beneficial impact on total factor carbon productivity, but there is heterogeneity in resource endowment, city scale, and policy intensity. New-type infrastructure can only promote total factor carbon productivity enhancement in non-resource cities and large cities. Under the influence of smart city policies, new-type infrastructure has a stronger effect on total factor carbon productivity enhancement. Secondly, digital finance and marketization are important ways for new-type infrastructure to improve total factor carbon productivity. Thirdly, via spatial spillover effect, new-type infrastructure may boost total factor carbon productivity not only in the local region but also in places that are spatially connected. Accordingly, this paper offers policy recommendations for the government to encourage the investment and development of new-type infrastructure to support total factor carbon productivity growth.

Operationalising ‘One Health’ through Primary Healthcare Approach

Abstract The countries worldwide have recognised the importance of the One Health (OH). This review examines the need and potential for the integration of OH into Primary Health Care (PHC) Approach. One Health through PHC has been implemented in many. Successful initiatives include mobile phone-based surveillance for zoonotic diseases in Kenya, innovative vaccination campaigns in Chad and measures to combat antimicrobial resistance in several nations. Collaboration and partnerships play a pivotal role in overcoming challenges such as resource constraints, policy alignment and limited intersectoral collaboration. The review emphasises integrating OH in primary healthcare so that the healthcare providers can contribute to addressing complex global health challenges and promoting the well-being of individuals, animals and the environment. The sustainable strategies, capacity building, advocacy and technological innovations to effectively implement the OH approach through PHC, ultimately improving health outcomes and ensuring a sustainable planet.

The DPSIR Model-Based Sustainability Assessment of Urban Water Resources: A Comparative Study of Zhuhai and Macao

Based on the driving force-pressure-state-impact-response (DPSIR) model, 19 indicators were selected to construct a comparison between Zhuhai and Macao, two adjacent cities at the estuary of the Pearl River in China, which have different development models and water resource sustainable development strategies. Factors that may affect the sustainability of water resources were screened and placed according to the relationships of the five subsystems in the DPSIR model, establishing a sustainable evaluation model for water resources in the two cities. The results analyzed by Principal Component Analysis and Entropy methods showed that (1) Zhuhai City was greatly affected by the driving force, while Macao was greatly affected by the state system from 2012 to 2021. (2) From the trend changes, it can be seen that, in recent years, the water resources of the two cities have been moving towards sustainable development, and the management and protection of water resources have achieved remarkable results. From the evaluation results, it can be seen that implementing urban water-saving activities, strengthening the proportion of environmental water conservancy, public measures in public investment, upgrading sewage treatment machinery to improve sewage treatment rates, and other measures can effectively improve the current situation of water resources in both regions. In the future, the Zhuhai and Macao cities may continue to face a series of water resource pressures brought on by socio-economic developments. Therefore, an active adjustment of the development of the measurement of controlling wastewater discharge and saving water resources was proposed, adhering to the direction of sustainable development, and ensuring the benign development of socio-economic conditions and the ecological environment. This study can provide data to support regional water resource security and policy formulation with different political systems.

Management of Natural Resources at Batu Pinagut Beach, North Bolaang Mongondow Regency

The research was carried out in Boroko Village, North Bolang Mongodow Regency with the aims of: (1) collecting data on natural resources on the Batu Pinagut coastline (2) Describing the potential for developing natural resources on the Batu Pinagut coastline (3) Analyzing policies on damage control. Determination of the sample using purposive sampling technique. Data was collected using observation, interview, documentation and document recording techniques. The results of the research show: (1) Batu Pinagut Beach has coastal resource potential that has been developed, namely biological resources and non-biological resources (2) management of coastal resources in Pemuteran Village includes tourism coral reef conservation in terms of tourism service training (3) Resource policy needs regulations are enforced, so that there is balance, preservation and continuity of resources. The rules do not apply to all areas, but to areas needed for the spawning and breeding of marine biota. To manage this area, it is necessary to have a conservation area that is equally understood and recognized by every stakeholder, stakeholders, fishermen, government, community and government. The rules and policies that are implemented involve stakeholders which become a culture that develops in society so that protection/conservation areas become a necessity and are implemented consciously by the community.

Water Quality Analysis Through Satellite Images

This project report presents a comprehensive study on water quality analysis using satellite images. The increasing global demand for water resources and the growing impact of human activities on water ecosystems underscore the need for efficient and scalable methods for water quality assessment. Leveraging remote sensing technology, particularly satellite imagery, provides a promising solution for monitoring large water bodies at a regional and global scale. The project focuses on the development and implementation of a robust methodology for water quality analysis through the utilization of satellite images. Key objectives include the extraction of relevant water quality indicators such as turbidity, chlorophyll-a concentration, and dissolved organic matter. Image processing techniques, including spectral analysis and machine learning algorithms, are employed to derive quantitative information from satellite data. The study employs a multi-sensor approach, utilizing data from various satellite platforms, to enhance the accuracy and reliability of the water quality assessments. Additionally, ground truth data collected from in situ measurements and laboratory analysis are integrated into the analysis to validate and calibrate the remote sensing results. The project contributes to the advancement of remote sensing applications in environmental monitoring, providing a cost-effective and timely method for water quality assessment. The findings of this research have implications for water resource management, environmental conservation, and policy formulation. Furthermore, the developed methodology can be applied to monitor changes in water quality over time, supporting sustainable water resource management practices. The report concludes with recommendations for further research and the potential integration of the developed methodology into existing water quality monitoring systems. Overall, the project highlights the significance of satellite-based approaches in addressing contemporary challenges related to water quality on a global scale.

Sustainable Renewable Energy Resources in Comoros Islands

Sustainable Renewable Energy Resources in Comoros Islands

Expression of concern: Resource curse hypothesis and economic growth: A global analysis using bootstrapped panel quantile regression analysis [Resources Policy, Volume 85, Part A, August 2023, 103790]

Expression of concern: Resource curse hypothesis and economic growth: A global analysis using bootstrapped panel quantile regression analysis [Resources Policy, Volume 85, Part A, August 2023, 103790]

Investigating the spatiotemporal patterns and clustering of attendances for mental health services to inform policy and resource allocation in Thailand

BackgroundMental illness poses a substantial global public health challenge, including in Thailand, where exploration of access to mental health services is limited. The spatial and temporal dimensions of mental illness in the country are not extensively studied, despite the recognized association between poor mental health and socioeconomic inequalities. Gaining insights into these dimensions is crucial for effective public health interventions and resource allocation.MethodsThis retrospective study analyzed mental health service utilization data in Thailand from 2015 to 2023. Temporal trends in annual numbers of individuals visiting mental health services by diagnosis were examined, while spatial pattern analysis employed Moran’s I statistics to assess autocorrelation, identify small-area clustering, and hotspots. The implications of our findings for mental health resource allocation and policy were discussed.ResultsBetween 2015 and 2023, mental health facilities documented a total of 13,793,884 visits. The study found anxiety, schizophrenia, and depression emerged as the top three illnesses for mental health visits, with an increase in patient attendance following the onset of the COVID-19 outbreak. Spatial analysis identified areas of significance for various disorders across different regions of Thailand. Positive correlations between certain disorder pairs were found in specific regions, suggesting shared risk factors or comorbidities.ConclusionsThis study highlights spatial and temporal variations in individuals visiting services for different mental disorders in Thailand, shedding light on service gaps and socioeconomic issues. Addressing these disparities requires increased attention to mental health, the development of appropriate interventions, and overcoming barriers to accessibility. The findings provide a baseline for policymakers and stakeholders to allocate resources and implement culturally responsive interventions to improve mental health outcomes.

Leveraging diversity through purposeful flexibility: towards a redefinition of the international classroom

ABSTRACT To date, participation in an international classroom has been positioned as a means to prepare most Dutch higher education students to live and work in a globalised world. Nevertheless, it is not clear how the international classroom is defined and how lecturers should be equipped to deliver on its potential for Internationalisation at Home, especially within culturally diverse, practice-oriented universities of applied sciences. Through a Delphi study engaging 28 expert lecturers and policy makers from five Dutch universities of applied sciences, this study offers a new definition of the international classroom, framing it as an approach that centralises diversity across the classroom as an asset in the design and facilitation of education. Drawing on the panel’s collective experience, it proposes eight competences that lecturers need to implement this approach, providing a framework to rethink professional development, curriculum innovation and human resource policies within and beyond Dutch universities of applied sciences.

Research on economic growth in smart cities based on wireless sensor networks

With the acceleration of urbanization, the concept of smart city is rising gradually. Wireless sensor network as an important technical support of smart city, its application in environmental monitoring and water resources management has a profound impact on economic growth. Water resource is one of the most dependent resources for human beings. With the growth of world population and the rapid development of economy, water resource crisis is constant, water pollution, water shortage and water waste coexist. How to build a perfect water resource economic policy is a worldwide problem at present. At present, the formulation of water resources policies is often based on experience or the knowledge system of decision makers. Due to the dynamic nature of water resources utilization and the incomplete information of decision makers, there are often policy failures, which affect economic growth. Based on this, this paper uses system dynamics model to study the mechanism of water resources management policies affecting economic growth by taking Gansu, Tianjin and Zhejiang as three qualitatively representative arid areas, transitional areas and water-rich areas. The research results show that under the same water resources policy coupling, different regions also have different eco-economic effects. The effect of coupled water resources policy is better than that of single water resources management policy.

The impact of digital platforms on the creativity of remote workers through the mediating role of explicit and tacit knowledge sharing

PurposeRemote working has brought forward many challenges for employees as the phenomenon is still new for most employees across the globe. Some of these challenges may be addressed by the recent adoption of digital technologies by organizations. In this vein, our study explores the impact of digital platform capability on the creativity of employees through the mediating mechanism of explicit and tacit knowledge sharing.Design/methodology/approachThe data were gathered from higher education institutes (HEIs) in a developing country, Pakistan which recently saw a major disruption during the Covid-19 pandemic. The proposed hypotheses were tested through Structural Equational Modeling (SEM) and the results confirmed our hypotheses.FindingsThe findings confirmed that the digital platform capabilities impact both tacit and explicit knowledge sharing among these remote employees. Likewise, the results also supported the mediating role of both explicit and tacit knowledge sharing on the creativity of these remote workers.Originality/valueOur results are significant as they confirm the impact of digitalization on remote workers’ creativity predisposition. We thus advance the academic debate on the problems of knowledge sharing in remote working. We prove that digital capabilities outweigh the challenges created due to new forms of work driven by the pandemic. It further highlights the important areas to focus on while planning human resource policies in the new normal.

Whitlam's Economic (Inter)Nationalism

By his own admission, Australian Prime Minister Gough Whitlam was not much of an economist, a disclosure that has fuelled criticisms of his government's performance during the economic crises of the 1970s. By contrast, Whitlam was a self‐declared internationalist who promoted the domestic and global possibilities of the international system. Of course, twentieth‐century economics and internationalism were mutual rather than dichotomous. Accordingly, Whitlam's internationalism provides a vantage point to re‐evaluate his economics. This article focusses on how one strand of Whitlam's internationalism — his Third World sympathies and alignments — informed his government's resource policy, as designed by Minister for Minerals and Energy, Rex Connor. Rather than seeking to redeem Whitlam's economic credentials via this internationalism, however, I argue Whitlam's appeal to Third Worldism sought to infuse an anti‐economics — or, more precisely, a critique of mainstream economic thinking — into Australian resources policy. The legacies of this critique have been enduring. Whitlam and Connor's attempts to establish export controls, foreign investment regulations, and state‐owned enterprise galvanised a fierce backlash from miners and libertarian economists. This backlash has helped shape the neoliberal framing of Australian mining and energy policy over the past 40 years. At the same time, with climate change and energy transitions again illuminating the politics of natural resources, Whitlam's Third World critique remains salient.

Enhancing the exploitation of natural resources for green energy: An application of LSTM-based meta-model for aluminum prices forecasting

Efficient resource allocation for electric car production can be achieved by anticipating aluminum future pricing. For the electric car sector to maintain a steady supply chain, efficient production, and long-term growth, accurate forecasts are essential for policymakers to plan resource policies. The accuracy of ensemble learning models in predicting Aluminum prices was analyzed using Gene Expression programming and long short-term memory (LSTM) method. The analysis was based on monthly frequency spot settlement price data of Aluminum, copper, silver, and crude oil prices, as well as currency inflation rates of USA, China, and Peru. The study period spanned from January 1, 1994 to December 31, 2022, and also included the gross domestic product (GDP) of USA, China, and other metal minerals. Initially, we assess the level of multicollinearity between each chosen input parameter and Aluminum price volatility using Variance inflation factor-based multicollinearity. The primary innovation of this study lies in the creation of a highly precise model that utilizes the LSTM algorithm to estimate monthly Aluminum prices. This model serves as a revolutionary forecasting method. An extensive evaluation of various ensemble learning models reveals that XGBoost is the optimal model for accurately forecasting monthly Aluminum prices. The LSTM-based recurrent neural network model exhibited the lowest error, with a Mean Relative Error (MRE) ranging from 0.001 to 0.008, RMSE ranging from 54.527 to 136.044, and R2 ranging from 0.95 to 0.983. The findings demonstrated that the LSTM-based super learner models had superior performance compared to the CatBoost, LightBoost, Random Forest, XGBoost, and AdaBoost models, as evidenced by higher values of the determination coefficient (R2), value account for (VAF), and other 10 error analysis evaluators. Therefore, the utilization of an LSTM-based super learner model can serve as a dependable approach for forecasting future monthly Aluminum prices. In addition, this study introduces a logical mathematical model utilizing gene expression programming (GEP) to anticipate future Aluminum prices, which can be utilized by other researchers. Thus, we conclude that both the GEP and ensemble learning models, particularly the LSTM-based Super learner model, are appropriate for precise metal price predictions, which can benefit policymakers and assist in resource policy planning.