Policy Model Research Articles

Human Capital Management Development and Environment Policy Model on The Performance of State Civil Apparatus in Riau Province Government

This study aims to develop a conceptual framework for Competence-based Human Capital Management (HCM) and Religiosity to the Performance of the State Civil Apparatus (ASN) in the Riau Provincial Government. This study is to find the most appropriate way to develop ASN competence and fill in the limitations of previous research and gaps in competency research on ASN performance. This study uses a quantitative approach by distributing questionnaires to officials related to the development of ASN competencies, both Structural Officials ranging from Middle High Officials, Primary High Officials, Administrator, and Supervisory Officers, as well as Functional Officers related to competency development, as many as 114 respondents with using a purposive sampling approach, data collection techniques using a questionnaire with a scale of 1-5, which then uses Structural Equation Modeling-Partial Least Square (PLS) analysis with the outer Model, and inner model and processed using the SmartPLS 3. Capital Management and Supported by Religiosity. This study examines the development of competence based on Human Capital Management and Religiosity as a moderator of ASN performance. This research has never been conducted simultaneously.

The essence and assessment of modern industrial policy

The state of modern economy is exposed to significant challenges from geopolitical risks and transformation of the process of industrial development, which requires determining the conditions for implementing modern industrial policy and changing directions and priorities of industrial development. Improvement of the ways of production, development of the system of social institutions takes place when solving the issues related to the development of science underlying the formation of new industries. The subject of the study is the transformation of industrial policy features in modern economy at various levels of interaction between government and business. The article examines the changes in industrial development and corresponding transformation of industrial policy tools in import substitution development. The study aims at determining the relationships between the directions and levels of implementing industrial policy in accordance with system analysis and identifying the key features in the relationship between business structures and the state. The theories of industrial development and the concepts of resource and process approaches to industrial development and implementation of innovative industrial policy provide the methodological basis of the study. The study uses the methods of system analysis and synthesis in identifying the key relationships between business structures and the state in achieving the goals of industrial development. The study defines the transformation of types, tools and system of indicators of industrial policy under the sanction pressure on the economy, identifies the possibilities of using the matrix system in managing industrial development, implemented at three levels (macro-, meso- and microeconomic) in interaction of business structures and public authorities, assesses the possibility of forming and conducting conservative industrial policy in the lack of scientific research funding. The findings can be used in assessing the priorities, directions and transformation of the tools used to implement an innovative model of industrial policy in the Russian economy, in the development of import substitution and formation of new industries, to form an effective interaction of innovation process and the process of industrial development of the national economy.

Socio-economic Development and Carbon Productivity: A Panel Data Analysis of the World’s Largest Carbon-Emitting Countries

AbstractAs global concerns about climate change intensify, assessing the environmental efficiency of production processes through carbon productivity has become increasingly important. This study examines the impact of socio-economic development on carbon productivity in the top 18 CO2-emitting countries, which contribute approximately 82% of global CO2 emissions, over the period 1990–2019. Using a class of econometric tests to address heterogeneity and cross-sectional dependence, we employ the Cross-Sectionally Augmented Autoregressive Distributed Lag (CS-ARDL) model for final estimation, ensuring robustness with Common Correlated Effects Mean Group (CCEMG) and Augmented Mean Group (AMG) estimations. The empirical findings reveal that GDP per capita, Trade, and FDI increase carbon productivity while energy consumption and urbanization curtail carbon productivity. The country-specific effects indicated that 83% of the sample countries exhibit positive relationships between socio-economic development and carbon productivity, suggesting that these nations can serve as models for effective low-carbon policies. Moreover, the results demonstrate bi-directional relationships for GDP per capita, FDI, and energy use with carbon productivity and uni-causal relationship for trade and urbanization. The study highlights the need for implementing stricter regulations to improve energy efficiency and promote the adoption of renewable energy sources such as wind, solar, hydro, and nuclear power. Additionally, countries should incentivize green technology investments through tax breaks and subsidies, enhance international trade agreements that support the exchange of clean technologies, and develop sustainable urban planning initiatives to mitigate the negative impact of urbanization on carbon productivity.

Enhanced temperature regulation in compound heating systems: Leveraging guided policy search and model predictive control

With the widespread application of solar-energy-air-source heat pump composite heating, optimizing the regulation of air temperature in heating zones, enhancing system thermal comfort, and reducing energy consumption have become crucial. To ensure residential comfort while minimizing HVAC system energy costs, a control method combining guided policy search (GPS) with model predictive control (MPC) is proposed for composite heating systems. This method replaces state estimation in MPC with policy search and substitutes the offline trajectory optimization used in guided policy search with MPC, thus integrating the strengths of both approaches. This strategy not only improves control precision but also reduces energy consumption and enhances system robustness. The GPS-MPC control algorithm was validated against reinforcement learning and MPC. A simulation model was developed and validated on a real physical platform. Simulations compared the control effects of on-off control and MPC on pump frequency, flow rate, stratified thermal storage tank temperature, component, and system energy consumption. The data results indicate that the GPS-MPC algorithm offers superior predictive accuracy, efficiency, and robustness in composite heating control systems compared to conventional methods. Under the GPS-MPC strategy, indoor temperature fluctuations, pump frequency response speed, and energy consumption were significantly improved, with temperature fluctuation limited to only 0.8 °C, and the system achieving energy savings of over 12 %.

Adaptive edge security framework for dynamic IoT security policies in diverse environments

The rapid expansion of Internet of Things (IoT) technologies has introduced significant cybersecurity challenges, particularly at the network edge where IoT devices operate. Traditional security policies designed for static environments fall short of addressing the dynamic, heterogeneous, and resource-constrained nature of IoT ecosystems. Existing dynamic security policy models lack versatility and fail to fully integrate comprehensive risk assessments, regulatory compliance, and AI/ML (artificial intelligence/machine learning)-driven adaptability. We develop a novel adaptive edge security framework that dynamically generates and adjusts security policies for IoT edge devices. Our framework integrates a dynamic security policy generator, a conflict detection and resolution in policy generator, a bias-aware risk assessment system, a regulatory compliance analysis system, and an AI-driven adaptability integration system. This approach produces tailored security policies that adapt to changes in the threat landscape, regulatory requirements, and device statuses. Our study identifies critical security challenges in diverse IoT environments and demonstrates the effectiveness of our framework through simulations and real-world scenarios. We found that our framework significantly enhances the adaptability and resilience of IoT security policies. Our results demonstrate the potential of AI/ML integration in creating responsive and robust security measures for IoT ecosystems. The implications of our findings suggest that dynamic and adaptive security frameworks are essential for protecting IoT devices against evolving cyber threats, ensuring compliance with regulatory standards, and maintaining the integrity and availability of IoT services across various applications.

Dynamic control of ecological restoration in abandoned mines under the background of ecology-oriented development in China

PurposeThis study examines how local governments and enterprises can implement ecological restoration of abandoned mines based on ecology-oriented development (EOD), which will be more beneficial to local environmental protection and economic development under the central government’s policy of outcome incentives or process subsidies.Design/methodology/approachWe construct a dynamic differential game model to simulate the interactions between local governments and enterprises during the ecological restoration of abandoned mines from an EOD perspective.FindingsThe findings suggest that under the central government’s outcome incentive policy, cooperation between local governments and enterprises is an optimal strategy. Under the process subsidy policy, while neither cooperative nor non-cooperative models significantly affect the investment levels of local governments and enterprises, a cooperative approach ensures optimal investments from both without solely relying on the process subsidy. Additionally, incorporating altruistic preferences can lead to Pareto improvements in economic and environmental results under central government outcome incentives.Practical implicationsThis research offers a policy foundation for governments to encourage the EOD model in the ecological restoration of abandoned mines. It provides theoretical support for achieving environmental sustainability and high-quality economic development, and is particularly significant for resource-depleted cities seeking to transform their development strategies.Originality/valueThrough a dynamic differential game model involving government agencies and enterprises to simulate decision-making in the ecological restoration of abandoned mines, incorporating altruistic preferences into this restoration process, and identifying optimal strategies and policies for ecological restoration.

The impact of coronavirus disease 2019 (COVID-19) pandemic experiences on attitudes towards vaccinations: on the social, cultural and political determinants of preferred vaccination organization models in Poland

BackgroundThe article describes attitudes towards vaccinations in Poland in relation to issues such as voluntary versus compulsory vaccinations, the method of financing vaccinations, the method of organizing and carrying out vaccinations, the cognitive and educational aspect of vaccines (how to obtain knowledge about vaccines) and the preferred model of work and research on new vaccines. Taking into account these issues, the authors have created four ideal models of preferred vaccination policies: (a) the market model; (b) the state model; (c) the vaccine hesitancy model; and (d) the civic–social model. This perspective makes it possible to better understand and learn about the various motives behind the attitudes of anti-vaccination movements, as well as to notice cracks and divisions among vaccination supporters and their attitudes towards the financing and organization of vaccinations.MethodsThe study was carried out using the CATI method on a representative random-quota sample of Polish society of 1000 people aged 18 and over. The study took age, sex, education and the size of the place of residence into account.Additionally, in the Socio-demographic factors influencing attitudes towards vaccination practices in Poland section, we used the chi-squared test and regression analysis of factors influencing vaccination practices in Poland. PASW Statistics 18 (a version of SPSS) software was used for statistical analysis. Significant correlations were demonstrated at a significance level of 0.05% Pearson.ResultsThis article has shown that attitudes towards vaccinations are embedded in broader divisions and orientations related to the vision of the social order: the role of the state, the organization of healthcare and payments for vaccinations and medical services, as well as preferred ways of knowledge production in society and work on new vaccines. The political sympathies and the age of the respondents were the most important variables influencing vaccination behaviour. The education of the respondents was less important.ConclusionsA few years after the peak of the pandemic, the scope of anti-vaccination attitudes in Polish society ranges from 20% of the population (dogmatic anti-vaxxers) to 30% (vaccine hesitancy occurring depending on attitudes towards vaccinations).

A Deep Reinforcement Learning Method Based on a Transformer Model for the Flexible Job Shop Scheduling Problem

The flexible job shop scheduling problem (FJSSP), which can significantly enhance production efficiency, is a mathematical optimization problem widely applied in modern manufacturing industries. However, due to its NP-hard nature, finding an optimal solution for all scenarios within a reasonable time frame faces serious challenges. This paper proposes a solution that transforms the FJSSP into a Markov Decision Process (MDP) and employs deep reinforcement learning (DRL) techniques for resolution. First, we represent the state features of the scheduling environment using seven feature vectors and utilize a transformer encoder as a feature extraction module to effectively capture the relationships between state features and enhance representation capability. Second, based on the features of the jobs and machines, we design 16 composite dispatching rules from multiple dimensions, including the job completion rate, processing time, waiting time, and manufacturing resource utilization, to achieve flexible and efficient scheduling decisions. Furthermore, we project an intuitive and dense reward function with the objective of minimizing the total idle time of machines. Finally, to verify the performance and feasibility of the algorithm, we evaluate the proposed policy model on the Brandimarte, Hurink, and Dauzere datasets. Our experimental results demonstrate that the proposed framework consistently outperforms traditional dispatching rules, surpasses metaheuristic methods on larger-scale instances, and exceeds the performance of existing DRL-based scheduling methods across most datasets.

Modern state migration policy models: migration protectionism as a factor of migration attractiveness

The choice of the model of migration policy of a modern state depends on various aspects, both in the economic, demographic and political planes. A rationally chosen model of migration policy allows you to ensure the effectiveness of managing migration flows and achieving optimal socio-economic development of the state, minimizing risks in the field of national security. The study examines various approaches to typologizing the models of migration policy of a modern state, and also provides an author's classification of migration policy models characteristic of states receiving migrants. Migration protectionism is cited as a key criterion for the author's typology of models, which, within the framework of the migration regime of a particular state, has a significant impact on the level of its migration attractiveness, based on the norms of acts of national migration legislation and ratified international agreements. Migration protectionism is considered as a result of the activities of the authorities of the receiving state of migrants, directly forming the migration regime. At the same time, the level of preferences in relation to various categories of migrants and types of migration acts as a mechanism for differentiating external migration according to various criteria, among which one can single out the purpose of entry, the state of origin, the level of qualifications of external migrants, knowledge of the language, which makes it possible to stimulate the demanded migration and limit unwanted. The study of migration protectionism as one of the instruments of the migration policy of the state makes it possible to identify the conceptual foundations of the formed migration regime, determine the types of foreign migration in demand and predict the trends in further political and legal regulation of state migration policy.

The conceptualisation of cardiometabolic disease policy model in the UK

BackgroundDecision models are increasingly used to inform policy-making processes, and there is a need to improve their credibility. The estimation of health and economic outcomes generated from decision models is influenced by the development process itself. This paper aims to present the conceptual model development process of cardiometabolic disease (CMD) policy models in the UK setting.MethodsThis conceptual model followed the International Society of Pharmacoeconomics and Outcomes Research-Society of Medical Decision Making (ISPOR-SMDM) Modelling Good Research Practices Task Force-2.ResultsFirst, for the conceptualisation of the problem, the CMD disease staging, progression and current clinical guidelines were summarised, followed by a systematic review of published policy models. We critically appraised policy models such as cardiovascular disease and type 2 diabetes. Key messages from the review emphasised the importance of understanding various determinants influencing model development, including risk factors, model structure, models’ parameters, data utilisation, economic perspective, equality/equity consideration, transparency and validation process. Second, as a sequential process, is model conceptualisation, to determine which modelling types and their attributes best represent the defined problem. Expert opinions, including a clinician and experienced modellers, provided input on the state transition model to ensure the structure is clinically relevant. From this stage, the consideration and agreement to establish a disease state in a state transition model was discussed.ConclusionThis conceptual model serves as a basis for representing the systematic process for structuring a CMD policy model to enhance its transparency and credibility.

Development and calibration of a mathematical model of HIV outcomes among Rwandan adults: informing equitable achievement of targets in Rwanda.

We developed and calibrated the Central Africa-International epidemiology Databases to Evaluate AIDS (CA-IeDEA) HIV policy model to inform equitable achievement of global goals, overall and across sub-populations, in Rwanda. We created a deterministic dynamic model to project adult HIV epidemic and care continuum outcomes, overall and for 25 subpopulations (age group, sex, HIV acquisition risk, urbanicity). Data came from the Rwanda cohort of CA-IeDEA, 2004-2020; Rwanda Demographic and Health Surveys, 2005, 2010, 2015; Rwanda Population-based HIV Impact Assessment, 2019; and the literature and reports. We calibrated the model to 47 targets by selecting the 50 best-fitting parameter sets among 20,000 simulations. Calibration targets reflected epidemic (HIV prevalence, incidence), global goals (percentage on antiretroviral therapy (ART) among diagnosed, percentage virally suppressed among on ART) and other (number on ART, percentage virally suppressed) indicators, overall and by sex. Best-fitting sets minimized the summed absolute value of the percentage deviation (AVPD) between model projections and calibration targets. Good model performance was mean AVPD ≤5% across the 50 best-fitting sets and/or projections within the target confidence intervals; acceptable was mean AVPD >5% and ≤15%. Across indicators, 1,841 of 2,350 (78.3%) model projections were a good or acceptable fit to calibration targets. For HIV epidemic indicators, 256 of 300 (85.3%) projections were a good fit to targets, with the model performing better for women (83.3% a good fit) than for men (71.7% a good fit). For global goals indicators, 96 of 100 (96.0%) projections were a good fit; model performance was similar for women and men. For other indicators, 653 of 950 (68.7%) projections were a good or acceptable fit. Fit was better for women than for men (percentage virally suppressed only) and when restricting targets for number on ART to 2013 and beyond. The CA-IeDEA HIV policy model fits historical data and can inform policy solutions for equitably achieving global goals to end the HIV epidemic in Rwanda. High-quality, unbiased population-based data, as well as novel approaches that account for calibration target quality, are critical to ongoing use of mathematical models for programmatic planning.

Urban Greening and Ecological Balance in the United States: Policies, Practices, and Challenges

Urbanization is a significant hallmark of modern societal development. However, the environmental issues that accompany it, especially the disruption of ecological balance, have become a global challenge. Urban greening, as an essential means of improving the urban ecological environment and promoting ecological balance, is increasingly recognized for its importance. The United States, with one of the highest levels of urbanization globally, serves as a model for urban greening policies and practices worldwide. This paper provides an in-depth analysis of the current state, policies, practices, and challenges of urban greening in the United States, aiming to offer strategies and recommendations for sustainable urban greening development.

A situational analysis of model holistic-comprehensive and integrated approaches to handling stunting in Buleleng Regency, Bali, Indonesia

Background: Policies and strategies for handling stunting cases effectively and efficiently vary in different regions, depending on their situation and conditions. There has yet to be an empirical study of stunting management with a holistic-comprehensive and integrated approach in Indonesia, especially in Buleleng Regency, Bali. Thus, conducting initial research to analyze the policy and determine the right stunting management strategy to implement is essential.Purpose: This study aims to map stunting cases, explore their causal factors, analyze the suitable policy model for overcoming stunting and describe the effective and efficient stunting management strategy in Buleleng Regency based on a holistic, comprehensive, and integrated approach.Methods: This is a situation analysis case study. Respondents were stunted children and related governmental agencies in Buleleng Regency, Bali. The incidence of stunting, risk factors suspected as the cause, policy models and appropriate stunting handling strategies were analyzed in depth using qualitative and quantitative methods.Results: There were 1040 cases of stunting in the regency, with the highest cases in Banjar District. Significant risk factors influencing the stunting incidence in the region include the lack of good complementary feeding (OR: 2.6722; p <0.05) and low family income (OR: 7.6667; p <0.0001). The results of the analysis of stunting handling policies, based on a holistic-comprehensive and integrated approach model by regional needs, are categorized into two policy areas: Policies in the Medical and Non-Medical fields. In contrast, the handling strategy is divided into two stages: the screening and the handling stages.Conclusion: Poor-quality complementary foods and low family income are the main risk factors for high stunting rates in Buleleng Regency, Bali. These factors must be considered in an effective and efficient policy and strategy model for handling stunting cases in the regency.

METI and Japanese scramble: re-definition of Japan’s African policy under the second Abe administration and future of African summit diplomacy

During the Second Abe Administration (2012-2020), Japan intensified its efforts to strengthen economic ties with Africa. This paper explores the reasons behind this shift and its implications for Japan’s African policy. Challenging the prevailing ‘Reactive State’ model of Japanese African policy, the study argues that the shift resulted from political and bureaucratic reforms that centralized power in the Prime Minister’s office (Kantei) and Abe’s favoritism toward the Ministry of Economy, Trade and Industry (METI). As a result, METI gained greater influence over African policy. Unlike the Ministry of Foreign Affairs, METI views Africa as a promising market with a limited Japanese presence and advocates for a more business-oriented approach. The paper concludes that Japan’s new policy provides a convincing reason why Japan must engage with Africa. However, Japanese companies do not always follow as the Government intended. Despite government efforts, the Japanese private sector remains hesitant to expand investment in Africa due to challenging business environments and the absence of legal frameworks between Japan and African countries.

On Epidemiological and Actuarial Analyses of Health Insurance Models for Communicable Diseases

This article examines an SIDRS disease model design for insurance contracts, in which the insured population is exposed to the risks of communicable diseases. For the epidemiological models, the population is structured into four demographic groups, namely: susceptibles (S), infected PHs undergoing treatment (I), infected PHs who are diseased (D), and infected PHs who recovered (R) from the disease, with part of the latter thereafter re-entering the group of susceptibles (S) and continuing with the insurer by paying premiums until he or she is infected again. The infective class is a union of the class of those undergoing treatment, deceased policy holders (PHs), and those who have recovered. The number of deceased PHs is counted as part of the insured population because the number PHs who die of the disease must be accounted for and are entitled to claims. The insurance policies are designed for the members of the susceptible group: the latter constantly face the risk of infection and death due to the disease. The probability of PHs remaining susceptible for the SIDRS disease model is formulated. The probability density function (PDF) and cumulative distribution function (CDFs) for the SIDRS disease model for an insurance policy are obtained. For the actuarial part, we used actuarial techniques and principles to determine the financial obligations of the parties and the insurance amounts for the insurer and PHs. The cumulative insurance reserve for an annuity and a lump sum plan are also considered. To ascertain the risk of insolvency, premium adjustment for the PHs is considered. Some numerical computations of the results arising from our model analyses are also considered in this article. This article has practical applications in health insurance schemes. It is also applicable in the determination of insurance premiums payable by PHs who did not have the disease and insurance claims receivable by those who are hospitalized or die of the disease. Our model can also be practically applicable in the determination of health insurance reserves for communicable diseases.

How are green stocks and monetary policy related?

We propose and test the hypothesis that green stocks and monetary policies are interdependent. Using time-series data for two large economies (India and Indonesia), that have adopted inflation targeting regimes, we develop a structural vector autoregressive model of monetary policy and green stocks that combines with output. We find that green stock prices fall (rise) in response to inflation with a lagged effect and policy rate shocks for India (Indonesia). Currency depreciation, on the other hand, increases green stock prices for India but has a muted effect for Indonesia. We also discover that, for India, monetary policies hardly respond to green stock price shocks while, for Indonesia, green stock prices do influence the evolution of monetary price variables. Our main contribution is that we decipher the effects of green stocks from aggregate market shocks and show each of these market price shocks is related to the monetary aggregates.

Getting better at getting better: Advancing quality and safety in healthcare.

Scaling the innovative models of care, policies, practices, and technologies that deliver true value requires deliberate, focused effort. We need to simultaneously apply practical strategies that enable change agents to drive meaningful, sustainable impact to address particular challenges that a health system is facing, alongside implementing proven, evidence-informed approaches that broadly strengthen health system foundations. Both approaches matter; it's not about choosing between them. Rather, we need to respond to the health system's specific and immediate needs, while also growing culture, capacity, systems, and tools that enable transformation in quality and safety.

Policy design and implementation: a quantitative evaluation and empirical study of China's rural sports policy based on the PMC index model.

The formulation and implementation of a rural sports policy is an important means of promoting rural sports, improving the physical wellbeing of farmers, and enhancing the cohesion of rural communities. However, introducing such a policy faces problems in the process of specific policy practices, such as poor effective implementation, a lagging implementation effect, and goal cognitive bias. How to look at the current rural sports policy implementation blockage problem and the governance of the blockage, in order to improve the level of rural sports public service, is the focus of this paper's research. On this basis, this paper selects 56 policy texts, issued from 2002 to 2023, that are highly relevant to rural sports and have high timeliness and authority from the sports policies issued in China. Also, ROST CM6 software is used to count high-frequency words; this study then draws keyword social network mapping for the visual analysis of policy preferences and selects 20 rural sports policy texts as typical samples. Finally, a policy modeling research consistency (PMC) index model is used to evaluate the texts comprehensively and quantitatively. The results show that the overall design of China's rural sports policies is relatively reasonable. However, the consistency and effectiveness of their implementation need to be improved. Twenty representative policy texts have an average PMC index score of 5.96, with a concave index of 3.04 (which is good overall), with the highest mean value for rural sports policies at the national level. This is followed by the second highest value at the municipal and county levels, and the smallest at the provincial level. Therefore, in the future formulation and implementation of rural sports policies, a multi-dimensional rural sports policy system should be constructed. This would help to strengthen the consistency and effectiveness of the implementation of the policy system and promote the high-quality development of rural sports.

Community-based malaria prevention policy model in Rampah Village, Kutambaru District, Langkat Regency, Indonesia

Community-based malaria prevention policy model in Rampah Village, Kutambaru District, Langkat Regency, Indonesia

An emotion-sensitive dialogue policy for task-oriented dialogue system

Reinforcement learning (RL) is an effective method in training dialogue policies to steer the conversation towards successful task completion. However, most RL-based methods only rely on semantic inputs that lack empathy as they ignore the user emotional information. Moreover, these methods suffer from delayed rewards caused by the user simulator returning valuable results only at dialogue end. Recently, some methods have been proposed to learn the reward function together with user emotions, but they omit considering user emotion in each dialogue turn. In this paper, we proposed an emotion-sensitive dialogue policy model (ESDP), it incorporates user emotions information into dialogue policy and selects the optimal action by the combination of top-k actions with the user emotions. The user emotion information in each turn is used as an immediate reward for the current dialogue state to solve sparse rewards and the dependency on termination. Extensive experiments validate that our method outperforms the baseline approaches when combined with different Q-Learning algorithms, and also surpasses other popular existing dialog policies’ performance.