Action Policies Research Articles

From developmental neuroscience to policy: A novel framework based on participatory research

Insights from developmental neuroscience are not always translated to actionable policy decisions. In this review, we explore the potential of bridging the gap between developmental neuroscience and policy through youth participatory research approaches. As the current generation of adolescents lives in an increasingly complex and rapidly changing society, their lived experiences are crucial for both research and policy. Moreover, their active involvement holds significant promise, given their heightened creativity and need to contribute. We therefore advocate for a transdisciplinary framework that fosters collaboration between developmental scientists, adolescents, and policy makers in addressing complex societal challenges. We highlight the added value of adolescents' lived experiences in relation to two pressing societal issues affecting adolescents’ mental health: performance pressure and social inequality. By integrating firsthand lived experiences with insights from developmental neuroscience, we provide a foundation for progress in informed policy decisions.

Neural Action Policy Safety Verification: Applicablity Filtering

Neural Action Policy Safety Verification: Applicablity Filtering

Comparative Study of Indonesian and Malaysian Immigration Action Policies Towards Immigration Permit Abuse

The era of globalization, driven by advancements in science and information technology, has created a borderless world, transforming human relationships from national to international, and significantly impacting various aspects of human life. Globalization has promoted the movement of people between countries, including Indonesia and Malaysia, affecting the demographics, economy, culture, and social aspects of both countries. In this context, immigration regulations play a crucial role in controlling the flow of people and reflecting a country's legal sovereignty within its territorial boundaries. This study is a normative juridical research focusing on the analysis and comparison of immigration regulations and policies in Indonesia and Malaysia, using a Statutory and Comparative approach. Indonesian immigration policy, regulated in Law Number 6 of 2011 on Immigration, focuses on controlling the flow of people entering and leaving Indonesian territory to maintain state sovereignty. Meanwhile, Malaysian immigration policy, regulated in Malaysian Law Number 155 of 1959/63 on Immigration, covers crucial aspects such as entry and exit requirements, monitoring of foreigners, and law enforcement related to immigration violations. The immigration action policies of Indonesia and Malaysia towards immigration permit abuse have several similarities and differences. Although both countries have similar policies in handling immigration permit abuse, the implementation and law enforcement may differ depending on the specific context and conditions in each country.

ОСОБЕННОСТИ РЕАЛИЗАЦИИ РОССИЙСКОЙ ФИНАНСОВОЙ ПОЛИТИКИ В УСЛОВИЯХ САНКЦИОННОГО ДАВЛЕНИЯ

In 2022, Russia experienced maximum sanctions pressure from the EU and the United States, which had a strong impact on the country's economy, and also showed the shortcomings of the previous mechanisms of its functioning. Despite the difficult economic situation, it is necessary to rebuild the Russian financial system based on the needs of an innovative oriented market economy in order to overcome the consequences associated with the introduction of anti-Russian economic sanctions. During the period of structural transformation of the Russian economy, the monetary policy of the state will have to adapt to the changing external and internal conditions of development. A balanced financial policy, effective management and coordination of actions by all stakeholders will contribute to improving the activities of the institutions of the national financial system and meeting the needs of all economic entities in financial resources. The article analyzes financial policy in Russia under the conditions of sanctions restrictions (2021-2023), and also provides forecasts for the draft monetary policy of 2023-2025. The content of Russia's financial policy in modern conditions is revealed. The main directions of using financial resources have been identified. The problems and prospects of further development of the economy, its stabilization and restructuring are analyzed. Economic sanctions are considered, as well as the reasons why they were imposed on Russia. The results of the sanctions pressure on the banking sector are investigated. The measures and actions of the Central Bank aimed at stabilizing and increasing financial stability in the country have been identified. The statistics of inflation and the Central Bank's key rate have been studied. The paper considers the dynamics of real GDP growth in the Russian Federation and the main indicators of the banking sector. The risks of lending and their methods of overcoming are assessed.

Scaling carbon removal systems: deploying direct air capture amidst Canada’s low-carbon transition

Carbon dioxide removal (CDR) technologies, such as direct air carbon capture and storage (DACCS), will be critical in limiting the rise of the average global temperature over the next century. Scaling up DACCS technologies requires the support of a complex array of policies and infrastructure across multiple overlapping policy areas, such as climate, energy, technology innovation and resource management. While the literature on DACCS and other CDR technologies acknowledges the path-dependent nature of policy development, it has tended to focus on abstract policy prescriptions that are not rooted in the specific political, social and physical (infrastructural) context of the implementing state. To address this gap, this paper provides a country-level study of the emerging DACCS policy regime in Canada. Drawing on the existing literature that identifies idealized (acontextual) policy objectives that support DACCS development and effective regulation, we identify the actionable policy objectives across six issue domains: general climate mitigation strategies; energy and resource constraints; carbon storage and transport regulation and infrastructure; financing scale-up and supporting innovation; removal and capture technology availability and regulation; and addressing social acceptability and public interest. Using a database of Canadian climate policies (n = 457), we identify policies within the Canadian (federal and provincial) policy environment that map to the idealized policy objectives within each of these domains. This exercise allows us to analyze how key policy objectives for DACCS development are represented within the Canadian system, and enables us to identify potential niches, and landscape influences within the system, as well as gaps and potential barriers to the system transition process. This paper contributes to our understanding of national DACCS policy development by providing a framework for identifying components of the DAC system and linking those components to desired policy outcomes and may provide a basis for future cross-country comparisons of national-level DACCS policy.

Can affirmative action policies be inefficiently persistent?

We develop a dynamic model where successive, decentralized policy makers must decide whether to implement affirmative action policies aimed at improving the performance of future generations of a targeted group. Employers do not perfectly observe if a worker benefited from affirmative action, but take that possibility into account, resulting in the devaluation of the worker’s credentials and an associated feeling of injustice. We establish that, in equilibrium, affirmative action is implemented perpetually by benevolent policy makers, despite the feeling of injustice that eventually dominates the anticipated benefits. This contrasts with the first best, which requires affirmative action to be temporary.

Navigating the green growth spectrum: Exploring the synergy between geopolitical risk, environmental policy stringency, and green production practices

The deepening understanding of the environment has heightened our perception of the causes of environmental damage, promoting a shift from combating pollution to preventing it with a core emphasis on environmental protection. Academicians and governance authorities have shifted their focus from traditional production practices to green production practices (GPP). The current body of literature has explored the impact of GPP on environmental quality. Whereas, the implications of geopolitical risk (GPR) and environmental policy stringency (EPS) on GPP are still unexplored. Keeping in view these gaps in existing research, this research is the earliest endeavor to examine the impact of GPR and EPS on GPP for the group of seven (G-7) countries from 1990 to 2020. In this regard, we have applied panel quantile regression (PQR) which allows for a more flexible treatment of heterogeneity and is robust to deal with outliers and skewed distributions compared to traditional panel data techniques. The empirical findings reveal that GPR has a significant destructive impact on GPP whereas, the EPS upsurge the GPP in G-7 countries. Founded on the factual outcomes, we recommend policy recommendations to achieve the objectives of SDG 07 (affordable and clean energy), SDG 08 (exports for decent economic growth), SDG 09 (industry innovation and infrastructure), SDG 11 (sustainable cities and societies), SDG 12 (responsible consumption and production), SDG 13 (environmental policies for climate action), and SDG 16 (conflict, peace, and justice strong institutions).

Exploring the dynamics of urban energy efficiency in China: A double machine learning analysis of green finance influence

Analysing the impact of green finance policies on urban energy efficiency contributes to finding solutions to promote the development of China's low-carbon energy system and the global reduction of greenhouse gas emissions. Utilizing Chinese urban data from 2006 to 2021, the study employs an enhanced non-radial distance function (NDDF) model to evaluate the energy efficiency of 282 prefecture-level cities. It examines the 'Green Finance Reform and Innovation Pilot Zone' policy as a quasi-natural experiment. It uses a double machine learning model to analyze its effect on urban energy efficiency and its mechanisms. The research demonstrates that green finance policies significantly improve energy efficiency, mainly through strengthening environmental regulation, fostering green technology innovation, and optimizing industrial structure. Additionally, an in-depth analysis of regional heterogeneity reveals variations in the policy's effectiveness across eastern, central, and western regions and between resource-rich and resource-poor cities. The findings suggest that the efficacy of green finance policies, while generally positive, is influenced by regional economic development and resource endowment differences. The study suggests targeted strategies to improve the green finance system, customize policies according to regional specifics, incentivize corporate green innovation, and foster cross-sectoral collaboration. By offering novel insights into sustainable development and providing actionable policy recommendations, this research contributes to advancing Chinese urban centres' economic and environmental sustainability through the strategic application of green finance.

Sitting on all sides of the table? OECD’s role in Icelandic Education Policy 2030

ABSTRACT The OECD is an inevitable force in contemporary education. This paper illustrates how the OECD affects recent education policy-making in Iceland with a particular focus on recent documents establishing the new Education Policy 2030 in Iceland and on OECD reports that directly relate to this policy. We illustrate the relationship between these documents both topically and chronologically and use Marilyn Strathern’s (2006) reasoning about policy making by bullet-points to understand these policy products. Our conclusions are that education policy 2030 is a clear step into a more Nordic tradition of policymaking, in terms of basing policy in previous works and official reports as well as increasing involvement by stakeholders. However, they are characterized by politically admirable, albeit lofty, formulations of ambitions and focus areas, rather than measurable, actionable and implementable policies. The OECD is given a great influence not only as a referential point and a tool to legitimate particular policies, but also a post-hoc policy-implementation adviser and auditor of the policy. Even though the OECD is asked for evaluation of the visionary policy and seems to be the solution to the perceived lack of professional procedure in Icelandic governance, the time-frame seems not to have allowed a thorough use of their advice.

Reinforcement Learning for Efficient Power Systems Planning: A Review of Operational and Expansion Strategies

The efficient planning of electric power systems is essential to meet both the current and future energy demands. In this context, reinforcement learning (RL) has emerged as a promising tool for control problems modeled as Markov decision processes (MDPs). Recently, its application has been extended to the planning and operation of power systems. This study provides a systematic review of advances in the application of RL and deep reinforcement learning (DRL) in this field. The problems are classified into two main categories: Operation planning including optimal power flow (OPF), economic dispatch (ED), and unit commitment (UC) and expansion planning, focusing on transmission network expansion planning (TNEP) and distribution network expansion planning (DNEP). The theoretical foundations of RL and DRL are explored, followed by a detailed analysis of their implementation in each planning area. This includes the identification of learning algorithms, function approximators, action policies, agent types, performance metrics, reward functions, and pertinent case studies. Our review reveals that RL and DRL algorithms outperform conventional methods, especially in terms of efficiency in computational time. These results highlight the transformative potential of RL and DRL in addressing complex challenges within power systems.

Making MOVES move: Fast emissions estimates for repeated transportation policy scenario analyses

Efforts to reduce US emissions from transportation rely greatly on the Environmental Protection Agency's MOVES software to estimate pollutant emissions, a high-precision, computationally intensive tool with many required inputs. To compare numerous scenarios at scale, decision-makers need a faster, user-friendly, and credible emissions estimator. This study introduces MOVESLite, a framework for making fast, highly accurate predictions (with respect to MOVES) of emissions tailored to a user's county, with a limited, more user-friendly number of inputs. MOVESLite uses county- and pollutant-specific models trained on previous MOVES estimates to estimate transportation emissions immediately. While MOVES may take minutes-to-hours to estimate one county-year's emissions, MOVESLite takes approximately 1 ms, with acceptable accuracy. We test MOVESLite performance, comparing thousands of possible models' accuracy for random samples of US counties, and we implement a case study of public transportation planning in Tompkins County, NY. MOVESLite aims to help decision-makers compare and select effective climate action policies.

Evidence-informed stakeholder consultations to promote rights-based approaches for children with disabilities.

To strengthen the translation of evidence to actionable policy, stakeholder engagement is necessary to synthesize, prioritize and contextualize the academic research content into accessible language. In this manuscript we describe a multi-level evidence-based stakeholder consultation process and related outcomes proposed to promote awareness of and foster cross-sectorial collaborations towards human rights-based approaches for children with disabilities. Mixed-methods participatory action research done in three steps: (1) A literature review of peer-reviewed evidence on rights-based approaches in childhood disabilities; (2) Consultation with researchers in diverse fields, grassroot organizations, caregivers, and youth with disabilities; (3) A constructive dialogue with decision makers at federal and provincial levels in Canada to discuss consultations results. Stakeholders value human rights approaches that can have a direct impact on practical aspects of their daily living. Organizations give high importance to adopting rights-based approaches to measure policy outcomes, while parents value service provision and youth emphasize accessibility. The implementation of rights-based approaches in childhood disabilities can support policy, services, and daily lives of children with disabilities and the ecosystems around them. It can also guide research priorities, and create a common language to foster collaborations across sectors and interested parties.

Drivers of Sustainability Credentialling in the Red Meat Value Chain—A Mixed Methods Study

Sustainability credentialling is the communication of environmental, social, economic, or animal-welfare-related information about a producer or product. Demand for sustainability credentials has been increasing and the aim of this study was to describe the main drivers for this kind of information in Australian red meat value chains that reach consumers across Australia and internationally, mainly in Asia, the USA, and the Middle East. The mixed methods approach included consultation with red meat processors. Desk-based research explored drivers from outside the value chain identified in the consultation. Little evidence was found that consumers are a driver of sustainability credentialling. The main drivers were in the global financial system, expressed in coordinated climate action policies by financial service providers and emerging government climate-related financial legislation. The inclusion of Scope 3 emissions extends coverage to most value chain participants. Net zero transitioning presents many risks to red meat value chains, potentially involving costly interventions and greater difficulty accessing financial services, with direct implications for production costs and asset values. Urgent action is recommended to achieve the formal recognition and use of climate metrics that differentiate the management strategies that are applicable to short-lived biogenic methane compared to CO2 to achieve the Paris Agreement goals.

Prevention and Mitigation of Disruptions in Medical Device Supply Chains: A Policy Perspective

New technologies and their connectivity to the internet render medical devices and their supply chains a target for worldwide criminal attacks. Disruptions to a medical device supply chain (MDSC), including cybersecurity attacks, have increased rapidly, with some sophisticated attacks compromising the availability and operation of life-critical devices. Understanding the impact of disruptions on MDSCs is difficult due to the attendant supply chain complexity. The lack of a systematic classification of disruptions to MDSCs limits the transferability of mitigation strategies. Thus, there is a need for evidence-based, actionable policy guidance for corporations and government agencies that address disruption risks to MDSCs. This paper first presents a disruption classification scheme based on historical and anticipated disruptions to MDSCs. Then, it introduces a model, dubbed the Focal-firm Supply-chain Integrated Model (FSIM), that integrates a focal-firm view of supply chains and attendant disruptions, illustrated through an MDSC example. Based on lessons learned from constructing a disruption classification and creating an FSIM map, we describe the following actionable insights: a) implementing procedures and technologies that enable early-detection of disruptions, b) accurate and timely information flows, c) continual monitoring of physical inventory levels and comparing them with digital data, d) enhanced and repeated testing at various supply chain testing sites, e) standardized metrics that measure the impact of disruptions, f) developing in advance a preparedness strategy and a planning process to allocate resources effectively, equitably, and transparently during disruptions, and g) the need for a common framework that bridges the medical device regulatory gaps across countries. These insights can guide MDSC stakeholders, including manufacturers, suppliers, service providers, healthcare providers, policymakers, and government agencies.

Human decision making balances reward maximization and policy compression.

Policy compression is a computational framework that describes how capacity-limited agents trade reward for simpler action policies to reduce cognitive cost. In this study, we present behavioral evidence that humans prefer simpler policies, as predicted by a capacity-limited reinforcement learning model. Across a set of tasks, we find that people exploit structure in the relationships between states, actions, and rewards to "compress" their policies. In particular, compressed policies are systematically biased towards actions with high marginal probability, thereby discarding some state information. This bias is greater when there is redundancy in the reward-maximizing action policy across states, and increases with memory load. These results could not be explained qualitatively or quantitatively by models that did not make use of policy compression under a capacity limit. We also confirmed the prediction that time pressure should further reduce policy complexity and increase action bias, based on the hypothesis that actions are selected via time-dependent decoding of a compressed code. These findings contribute to a deeper understanding of how humans adapt their decision-making strategies under cognitive resource constraints.

HiSOMA: A hierarchical multi-agent model integrating self-organizing neural networks with multi-agent deep reinforcement learning

Multi-agent deep reinforcement learning (MADRL) has shown remarkable advancements in the past decade. However, most current MADRL models focus on task-specific short-horizon problems involving a small number of agents, limiting their applicability to long-horizon planning in complex environments. Hierarchical multi-agent models offer a promising solution by organizing agents into different levels, effectively addressing tasks with varying planning horizons. However, these models often face constraints related to the number of agents or levels of hierarchies. This paper introduces HiSOMA, a novel hierarchical multi-agent model designed to handle long-horizon, multi-agent, multi-task decision-making problems. The top-level controller, FALCON, is modeled as a class of self-organizing neural networks (SONN), designed to learn high-level decision rules as internal cognitive codes to modulate middle-level controllers in a fast and incremental manner. The middle-level controllers, MADRL models, in turn receive modulatory signals from the higher level and regulate bottom-level controllers, which learn individual action policies generating primitive actions and interacting directly with the environment. Extensive experiments across different levels of the hierarchical model demonstrate HiSOMA’s efficiency in tackling challenging long-horizon problems, surpassing a number of non-hierarchical MADRL approaches. Moreover, its modular design allows for extension into deeper hierarchies and application to more complex tasks with heterogeneous controllers. Demonstration videos and codes can be found on our project web page: https://smu-ncc.github.io.

Gender Disparity in Principal Officers’ Positions in Nigerian Universities: Implications for Educational Planners and Administrators

The study investigates the gender disparities in leadership positions within Nigerian universities, focusing on principal officers such as Vice-Chancellors, Deputy Vice-Chancellors, Registrars, Bursars, and Librarians. Analyzing data from the 2019 Nigerian University System Statistical Digest. The study employs ANOVA statistics to establish the statistical significance of these disparities and highlights the need for targeted interventions to rectify these imbalances. The research reveals striking imbalances in gender representation across these roles. The findings demonstrate that, despite notable progress in some areas, women remain significantly underrepresented in key leadership positions, with a pronounced gender gap persisting across various types of universities (federal, state, and private). The recommendations offered encompass gender-neutral selection processes, affirmative action policies, leadership development programs, mentorship initiatives, educational campaigns, and regular data collection to promote gender equity. These proposed strategies aim to create a more inclusive academic environment where leadership positions are accessible to all based on qualifications and merit, ultimately fostering diversity and enriching the higher education landscape in Nigeria.

Understanding Students’ Attitudes Towards Affirmative Action Policy in Higher Education in India

To mitigate the severe educational horizontal inequalities in India, affirmative action (AA) measures in higher education (HE) have been implemented for socially excluded groups, such as the Scheduled Castes (former “untouchables”), the scheduled tribes (whose status resembles indigenous groups in other countries), and other classes lower in the caste hierarchy. Despite the introduction of AA measures, societal attitudes generally remain resistant to caste‐based reservation policies. Interestingly, very few studies in India have examined AA support among the most directly affected group of people when it comes to AA measures in HE—college students. The current article aims to fill this gap. It asks: Which factors (such as students’ background characteristics, pre‐college credentials, experience in college, and caste‐based beliefs) underlie college students’ attitudes (support or resistance) towards AA? This study builds on a large‐scale survey conducted among 3200 students studying in 12 public higher education institutions across six provinces in India. The results of the empirical analysis indicate that students’ attitudes towards AA are shaped and influenced by their social identity and educational experiences in college. It is also noteworthy that caste‐based biases and prejudices affect students’ attitudes particularly and may explain opposition to AA.

AoI minimization of ambient backscatter-assisted EH-CRN with cooperative spectrum sensing

To evaluate the timeliness and freshness of information from energy-constrained devices, we minimize the weighted average age of information (AoI) of the ambient backscatter-assisted energy harvesting cognitive radio network (AB-EH-CRN), where secondary transmitters (STs) follow the non-orthogonal multiple access (NOMA) strategy to transmit status updates. The secondary receiver (SR) schedules the selected actions for STs to perform spectrum access, and receives status updates from them. We formulate the AoI minimization problem where the action selection and transmission power of STs are considered as variables. To exploit spectrum resources in the AB-EH-CRN with unknown signal-to-noise ratios (SNRs) at the STs, we propose the deep neural network (DNN)-based cooperative spectrum sensing (CSS), and adopt an energy threshold approach for STs to determine the energy allocation between spectrum sensing (SS) and packet transmission. Moreover, we address the AoI minimization problem by the proposed hybrid action and energy penalty deep deterministic policy gradient (HAEP-DDPG) algorithm, which adapts to the hybrid continuous and discrete action spaces. Simulation results validate the advantage of the proposed HAEP-DDPG algorithm compared with comparison schemes in terms of AoI.

Configurational pathways to innovation capability enhancement in high-tech enterprises: a perspective from technological innovation network

PurposeHigh-tech enterprises, as the national innovation powerhouses, have garnered considerable interest, particularly regarding their technological innovation capabilities. Nevertheless, prevalent research tends to spotlight the impact of individual factors on innovative behavior, with only a fraction adopting a comprehensive viewpoint, scrutinizing the causal amalgamations of precursor conditions influencing the overall innovation proficiency of high-tech enterprises.Design/methodology/approachThis paper employs a hybrid approach integrating necessary condition analysis (NCA) and fuzzy-set qualitative comparative analysis (fsQCA) to examine the combinatorial effects of antecedent factors on high-tech enterprises' innovation output. Our analysis draws upon data from 46 listed Chinese high-tech enterprises. To promote technological innovation within high-tech enterprises, we introduce a novel perspective that emphasizes technological innovation networks, grounded in a network agents-structure-environment framework. These antecedents are government subsidy, tax benefits, customer concentration, purchase concentration rate, market-oriented index and innovation environment.FindingsThe findings delineate four configurational pathways leading to high innovative output and three pathways resulting in low production.Originality/valueThis study thereby enriches the body of knowledge around technological innovation and provides actionable policy recommendations.