Effective Policymaking Research Articles

Persuasions in United Nations Climate Change Speeches

This paper examines the persuasive rhetoric used in political speeches at United Nations Climate Change Conferences. It addresses the significant global challenge of climate change and the pivotal role of the United Nations Climate Change Conference (UNCCC) and related events like the United Nations Climate Action Summit and World Earth Day in formulating policies and action plans through international cooperation. The study focuses on the rhetorical techniques employed in these speeches which not only enhance the persuasive impact of the speeches but also contribute to their artistic and expressive quality. Despite extensive research on political speeches within the UN context, there’s a noticeable research gap in examining persuasive rhetoric beyond the UN platform. This paper seeks to bridge this gap by providing a comprehensive analysis of persuasive rhetoric and discourse analysis in political speeches on climate change in various international and national contexts. The methodology includes a thorough review of related literature on rhetoric, communication, discourse analysis, and writing strategies. The study employs qualitative research methods, focusing on discourse and rhetorical analysis of speeches by world leaders from the seven continents at the United Nations Climate Change Conference. The research aims to understand the structures, key elements, and dominant rhetorical appeals used in these speeches. It also seeks to develop an instructional plan to enhance skills in writing persuasive texts, with significant implications for policymakers, communication specialists, and researchers in the field of climate change. In summary, this paper provides valuable insights into the persuasive strategies used in climate change speeches, offering guidance for effective communication and policy-making in addressing the challenges of climate change.

Psychological distress in adolescence and later economic and health outcomes in the United States population: A retrospective and modeling study.

Federal policy impact analyses in the United States do not incorporate the potential economic benefits of adolescent mental health policies. Understanding the extent to which economic benefits may offset policy costs would support more effective policymaking. This study estimates the relationship between adolescent psychological distress and later health and economic outcomes and uses these estimates to determine the potential economic effects of a hypothetical policy. This analysis estimated the relationship between psychological distress in those aged 15 to 17 years in 2000 and economic and health outcomes approximately 10 years later, accounting for an array of explanatory variables using machine learning-enabled methods. The cohort was from the National Longitudinal Study of Youth 1997 and nationally representative of those aged 12 to 18 years in 1997. The cohort included 3,343 individuals under age 18 years in round 4 who completed the Mental Health Inventory-5 (MHI-5). Round 1 captured 50 explanatory variables that covered domains of potential confounders, including basic demographics, neighborhood environment, family resources, family processes, physical health, school quality, and academic skills. The exposure included a binary variable of clinically significant psychological distress (MHI-5 score of less than or equal to 3) and a categorical variable of symptom severity on the MHI-5. Outcomes covered domains of employment, income, total assets at age 30 years, education, and health approximately 10 years later. Forty-seven percent of the cohort were black and Hispanic, and 4.4% had past-month clinically significant psychological distress. Past-month clinically significant psychological distress in adolescence led to a 6-percentage-point (95% confidence interval [CI] [-0.08, -0.03]) reduction in past-year labor force participation 10 years later and $5,658 (95% CI [-6,772, -4,545]) USD fewer past-year wages earned. We used these results to model the labor market impacts of a hypothetical policy that expanded access to mental health preventive care and reached 10% of youth who would have otherwise developed clinically significant psychological distress. We found that the hypothetical policy could lead to $52 (95% credible interval [51,54]) billion USD in federal budget benefits over 10 years from labor supply impacts alone. This study faced limitations, including potential unmeasured confounding, missing data, and challenges to generalizability. Our findings showed the impacts of adolescent mental health policies on the federal budget and found potentially large effects on the economy if policies achieve population-level change.

Improve carbon dioxide emission prediction in the Asia and Oceania (OECD): nature-inspired optimisation algorithms versus conventional machine learning

This paper investigates the application of three nature-inspired optimisation algorithms – SHO, MFO, and GOA – combined with four machine learning methods – Gaussian Processes, Linear Regression, MLP, and Random Forest – to enhance carbon dioxide emission prediction in the OECD – Asia and Oceania region. The study uses historical carbon dioxide emissions data, socioeconomic indicators such as GDP, population density, energy consumption, and urbanisation rates, and environmental indicators such as temperature, precipitation, and forest cover. Through comprehensive experimentation, the study evaluates the performance of each combination, revealing varying effectiveness levels. The MFO-MLP combination achieved the highest accuracy with R2 values of 0.9996 and 0.9995 and RMSE values of 11.7065 and 12.8890 for the training and testing datasets, respectively. The GOA-MLP configuration achieved R2 values of 0.9994 and 0.99934 and RMSE values of 15.01306 and 14.59333. The SHO-MLP combination, while effective, showed lower performance with R2 values of 0.9915 and 0.9946 and RMSE values of 55.4516 and 41.575. The findings suggest hybrid techniques can significantly enhance prediction accuracy compared to conventional methods. This research provides valuable insights for policymakers and stakeholders, indicating that optimised machine learning models can support more informed and effective environmental policy-making and sustainability efforts in the OECD – Asia and Oceania region. Future research should explore additional optimisation algorithms and ensemble techniques to improve prediction robustness and accuracy. These findings offer a robust tool for policymakers to forecast emissions more accurately, aiding in developing targeted strategies to reduce carbon footprints and achieve climate goals.

Green Economy Transition and Sustainable Development: Implication for Developing Countries

The green economy has received prominence owing to environmental concerns and the need for sustainable growth. For developing nations in the Global South, shifting to a green economy is crucial to tackling climate change, poverty, and unemployment through renewable energy, sustainable agriculture, and green technology. Effective policy-making, financial assistance, and international collaboration are vital to overcoming hurdles and achieving a successful green economic transition, contributing to global sustainability objectives. This paper investigates the green economic transition, stressing regulatory structures and stakeholder interaction. This study uses secondary data analysis, focused on a literature examination of over 50 sources, including government files and academic journals. It studied case studies, policy frameworks, stakeholder engagement tactics, and the advantages, obstacles, and success of green economy initiatives in developing nations. The study discussed engaging stakeholders in developing nations' green economy transitions involving inclusive policies, multi-stakeholder platforms, public awareness, financial incentives, and capacity building. Despite constraints like limited budgets and inadequate institutions, effective transitions depend on clear, integrated policies, cross-sector coordination, and sustainable financing. Strengthening stakeholder participation and harnessing international assistance can generate effective transformation. In conclusion, the study highlights that transitioning to a green economy in undeveloped nations brings both possibilities and problems that require specific measures. Effective policies must incorporate environmental sustainability into wider economic objectives, address capacity-building and funding restrictions, and foster cross-sector collaboration. By building institutions, motivating green investments, and engaging stakeholders, developing nations may achieve sustainable growth and contribute to global sustainability goals.

A Stewardship Approach to Policy Practice and Capacity Renewal in Canada

AbstractOne of Canada's most significant public administration challenges is ensuring the public service possesses the capacity and modern practices needed for effective policymaking. This article argues that Canada's policy renewal efforts have been hampered by a lack of focused and coherent public service reform more generally. Informed by international best practice and an appreciation for Canada's unique public administration context and history, I argue that Canada must adopt a stewardship approach. I provide five actionable recommendations targeted at renewing and modernizing policy capacity. These include: creating a new unit responsible for managing public service renewal to be headed by a new deputy Clerk of the Privy Council, mandating regular systematic reviews of public service policy capacity, expanding the substantive focus of the Clerk's annual reports, developing an enterprise‐wide training strategy for policy professionals, and standardizing modern policymaking practices by embedding them in formal policymaking processes and instruments.

Spatial analysis of mangrove ecosystem dynamics in Banyuwangi: a geographically weighted regression approach

This study examines the dynamics of mangrove land change in Banyuwangi, East Java, employing Geographically Weighted Regression (GWR) to investigate the spatial variability in the drivers of mangrove deforestation. Our analysis identifies significant anthropogenic and natural elements that influence mangrove ecosystems, with findings indicating considerable variability in mangrove growth predictions across the region, ranging from approximately 0.10 to 18.67. The mean predicted growth rate is 3.68, with local R-squared values varying from 0.003 to 0.547, highlighting the model’s differential explanatory power across diverse locales. The results underscore the complex interplay between environmental conditions and human activities, revealing that proximity to agricultural areas, urban development, and infrastructure significantly impact mangrove deforestation rates. This study advocates for implementing spatially tailored conservation strategies that accommodate local ecological dynamics and anthropogenic pressures. By enhancing our understanding of these localized influences, our research supports more effective policy-making and land management practices to sustain mangrove ecosystems, which are vital for biodiversity, coastal protection, and carbon sequestration.

FUTURE OF UNEMPLOYMENT IN JAPAN: AN ARTIFICIAL NEURAL NETWORK FORECAST UTILISING ARTIFICIAL INTELLIGENCE AND MACROECONOMIC DYNAMICS

Since the unemployment rate is a critical factor that directly affects a country's economic performance and social health, reducing unemployment with effective policies is of great importance for sustainable development and prosperity. Therefore, precise forecasting of the unemployment rate is pivotal to effective policymaking and planning, especially in Japan, where unique demographic structures and economic challenges prevail. This study aims to estimate the unemployment rate in Japan using an Artificial Neural Network (ANN) model with the annual data for the period 1985-2017. Key factors shaping Japan's labour market dynamics, such as artificial intelligence-related technology patent applications, inflation rate, population growth rate, and labour productivity, are used to estimate the unemployment rate. The findings indicate that the Japanese unemployment rate is expected to increase gradually until 2030. This research provides significant insights to the Japanese government and policymakers through a non-linear forecasting model that includes the variable of artificial intelligence, which has not previously been used in the literature.

Open Economy Macroeconomics: Developments in Theory and Policy Using the TOPSIS Methodology

Open economy macroeconomics examines the complex interplay between a nation's economic policies and global financial markets. This field of study addresses critical issues such as exchange rate determination, balance of payments, and the effects of trade and capital flows on a country's economic performance. The increased integration of economies through globalization has made it essential to understand how domestic economic policies, such as monetary and fiscal policies, interact with international economic environments. This paper explores the key concepts and models in open economy macroeconomics, including the Mundell-Fleming model, purchasing power parity, and the dynamics of exchange rate regimes. The study of open economy macroeconomics has gained prominence with the growing interconnectedness of global markets. Unlike a closed economy, where all economic transactions occur within national borders, an open economy engages in trade and financial transactions with other countries. This openness introduces new challenges and opportunities for policymakers, as domestic economic conditions are increasingly influenced by external factors such as foreign exchange rates, international trade agreements, and global capital flows. Open economy macroeconomics seeks to understand how these external factors affect key economic variables, such as output, inflation, and employment.The significance of studying open economy macroeconomics lies in its profound implications for both national and global economic stability and growth. As economies become increasingly interconnected, understanding the mechanisms by which international trade, capital flows, and exchange rates influence domestic economies is essential for effective policymaking. This field of study is particularly relevant in today’s world, where financial crises, trade wars, and global economic shocks can have ripple effects across borders, impacting nations' economic well-being regardless of their size or level of development.. Free Trade Agreements, Currency Pegging, Capital Controls, Trade Tariffs, Exchange Rate Floating. GDP Growth Rate (%),Trade Balance (Billion $), Inflation Rate (%),Unemployment Rate (%). The results indicate that Free Trade Agreements achieved the highest rank, while Capital Controls had the lowest rank being attained. The value of the dataset for Corporate Open Economy Macroeconomics according to the topsis Method, Integrated Pest Management achieves the highest ranking.

How the development of chronic morbidity and multimorbidity depends on natural age of menopause: Results from nationally representative cross-sectional Indian study.

Multimorbidity development is linked with the age at menopause. Fewer studies are available to support the findings. This study was conducted to find, how multimorbidity is associated with the natural age of menopause. LASI-1, a longitudinal study, collected detailed information on the psychological, social, economic, and health aspects of aging in India. Wave-1 data collection was done in all 35 states and union territories in India. Baseline data was collected from year 2017-2019. As the current study was a secondary data analysis to find the association between multimorbidity and age of menopause, only eligible women's relevant data was analyzed. A total of 25,256 women were analyzed, 67.8% of participants had at least one comorbidity. The mean age was 58.5 ± 10.17 years. 57.04% and 13.45% women had optimal or suboptimal menopause, while 7.4%, 17.5%, and 4.4% had premature, early, and delayed menopause. A significant association was found for the presence of multimorbidity and premature (AOR 1.19 (1.07-1.32)), early menopause (AOR 1.18 (1.10-1.27)), and optimal age of menopause (AOR 0.83 (0.78-0.88)). There is a high burden of multimorbidity and it is associated with the natural age of menopause. This study would be helpful for effective policymaking and a better primary healthcare approach to deal with the condition.

Prevalence of Dementia in the Geriatric Population Residing in a Long-term Care Facility: An Updated Systematic Review and Meta-Analysis

Background: The global rise in the geriatric population is increasing dementia cases, imposing significant burdens on families and society, making long-term care facilities (LTCFs) essential. Thus, updated dementia prevalence data are crucial for effective health-care planning and policy-making. Methodology: A systematic review and meta-analysis, updating Fagundes et al. (2021), included articles post-August 2, 2020, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Studies were selected based on predefined criteria, and data extraction was independently performed by two reviewers, focusing on dementia prevalence rates, assessment tools used, and regional variations. Meta-analytic techniques estimated pooled dementia prevalence rates and assessed study heterogeneity, focusing on assessment tools and regional variations. Results: The review included 38 studies, with 18 new ones since the previous review. The pooled dementia prevalence in LTCFs was 57.8% (confidence interval 51.63–63.98; P < 0.01), up from 53%. Europe had the highest rate, while other regions showed variability. High heterogeneity suggests diverse factors, such as diagnostic criteria and population characteristics, influence prevalence estimates. Discussion: The rising dementia prevalence in LTCFs highlights the need for specialized care, improved health policies, and resource allocation. Regional prevalence variations may be due to differences in diagnostic criteria, health-care infrastructure, and societal factors, with high heterogeneity reflecting diverse methodologies and populations. Increased public awareness and ongoing research are essential for developing innovative, tailored care models for dementia patients in LTCFs. Conclusion: This updated review shows a higher dementia prevalence in LTCFs, highlighting the need for targeted interventions and policy adjustments to improve care quality and outcomes. Further research is needed to explore regional prevalence factors and develop standardized care practices.

Oral Health Data Gaps and Needs in North Carolina.

Effective oral health-related policymaking and program implementation rely on comprehensive, accurate, and timely data. This commentary will address the lack of data available and the need for a robust data dashboard, in addition to the importance of community water fluoridation.

Remote Sensing Techniques for Assessing Snow Avalanche Formation Factors and Building Hazard Monitoring Systems

Snow avalanches, one of the most severe natural hazards in mountainous regions, pose significant risks to human lives, infrastructure, and ecosystems. As climate change accelerates shifts in snowfall and temperature patterns, it is increasingly important to improve our ability to monitor and predict avalanches. This review explores the use of remote sensing technologies in understanding key geomorphological, geobotanical, and meteorological factors that contribute to avalanche formation. The primary objective is to assess how remote sensing can enhance avalanche risk assessment and monitoring systems. A systematic literature review was conducted, focusing on studies published between 2010 and 2025. The analysis involved screening relevant studies on remote sensing, avalanche dynamics, and data processing techniques. Key data sources included satellite platforms such as Sentinel-1, Sentinel-2, TerraSAR-X, and Landsat-8, combined with machine learning, data fusion, and change detection algorithms to process and interpret the data. The review found that remote sensing significantly improves avalanche monitoring by providing continuous, large-scale coverage of snowpack stability and terrain features. Optical and radar imagery enable the detection of crucial parameters like snow cover, slope, and vegetation that influence avalanche risks. However, challenges such as limitations in spatial and temporal resolution and real-time monitoring were identified. Emerging technologies, including microsatellites and hyperspectral imaging, offer potential solutions to these issues. The practical implications of these findings underscore the importance of integrating remote sensing data with ground-based observations for more robust avalanche forecasting. Enhanced real-time monitoring and data fusion techniques will improve disaster management, allowing for quicker response times and more effective policymaking to mitigate risks in avalanche-prone regions.

Understanding the user perspective on urban public spaces: A systematic review and opportunities for machine learning

With people-centered approaches gaining prominence in urban development, studying urban public spaces from the user's perspective has become crucial for effective urban design, planning, and policy-making. The rapid advancement of Machine Learning (ML) techniques has enhanced the ability to analyze and understand user data in urban public spaces, such as usage patterns, activities, and public opinions. However, limited efforts have been made on a structured understanding of urban public spaces from the user's perspective. These knowledge gaps have also hindered the full realization of ML's potential in describing and analyzing urban public spaces. After systematically reviewing 319 relevant papers, this study analyzes ten dimensions of the user's perspective on urban public spaces and identifies three unaddressed issues: (1) interpretation of user's perception, (2) overlooked user demographics, and (3) data acquisition. In addition, this review also examines the applications of ML to these dimensions and their potential to tackle the three issues, and highlights two main opportunities to integrate ML for more rigorous and data-driven public spaces studies: (1) combining Computer Vision and Natural Language Processing in public spaces quality measurement and (2) investing in high-quality user data.

Extreme comovements and downside/upside risk spillovers between oil prices and exchange rates

Abstract This paper examines the dependence structure and risk spillovers between oil prices and exchange rates in both oil-exporting and oil-importing countries. Using a flexible dependence switching copula model, we analyze both positive and negative dependence and transitions between the dependence regimes. Additionally, we investigate the directional risk spillovers between oil and currency markets in both their downsides and upsides. Based on empirical data from 1999 to 2024 for major oil-exporting and oil-importing countries, we find that oil price-currency dependence is predominantly positive for oil-exporting countries, with infrequent transitions, but mainly negative for oil-importing countries, with frequent transitions between the two dependence regimes. These transitions often occur around crisis or war times. Furthermore, we observe that during downturns in the oil market, tail dependence between oil prices and currencies becomes more pronounced than during upturns. Our results indicate the presence of risk spillovers between oil and currency markets, with the downside spillover effects outweighing the upside ones. Moreover, we find that risk spillover is stronger from oil markets to currency markets than the reverse direction. These insights substantially enrich the existing literature and would offer valuable implications for effective risk management strategies and policymaking.

Violence Against Women and Girls in Zimbabwe: A Review of a Decade of the Empirical Literature.

Violence against women and girls (VAWG) is a complex social problem affecting many women globally. In Zimbabwe, intimate partner violence (IPV) and child marriages remain persistent public health problems with detrimental effects on the health and well-being of women and girls. Statistics show that Zimbabwe has one of the highest rates of IPV and child marriage in sub-Saharan Africa. Given this background, this paper systematically reviewed published research on VAWG in Zimbabwe from 2012 to 2022 to identify research gaps. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, we systematically searched for literature across five electronic databases: Web of Science, Medline, Psych-Info via EBSCO-Host, and Google Scholar. Our initial search yielded 261 articles, of which only 45 met our inclusion criteria. We summarized these studies using thematic analysis and performed a quality assessment using the Mixed Methods Appraisal Tool. The findings revealed several gaps, including a limited focus on relational and perpetrator perspectives, insufficient attention to other forms of VAWG such as non-partner sexual violence, rape, and trafficking, and a lack of studies on marginalized groups such as people with disabilities, sex workers, and same-sex couples. In addition, there were no longitudinal studies examining trends and dynamics of VAWG over extended periods or comparing different geographical regions. Few studies also focused on the evaluation of interventions. Although significant progress has been made in addressing VAWG, this review underscores the need for more research to fill these gaps for effective and evidence-based policymaking and response strategies.

Perspectives on the quality of climate information for adaptation decision support

We summarise the contributions to the Topical Collection on quality of climate information for adaptation decision support. Based on these contributions, we draw some further lessons for the development of high-quality climate information and services, bridging between a “credibility-first” paradigm (exemplified by top-down information provision from systematic downscaling or impact projections) and a “salience-first” paradigm (exemplified by user-led tailored information products or storylines) by looking to identify their respective strengths and use cases. We emphasise that a more nuanced collective understanding of the dimensions of information quality in climate information and services would be beneficial to users and providers and ultimately support more confident and effective climate adaptation decisions and policy-making.

How spousal cognitive functioning affects the level of depression in middle-aged and older adults: An instrumental variable study based on CHARLS in China.

A better understanding of the causal relationship between spousal cognitive functioning and depression levels among middle-aged and older adults is vital for effective health policymaking under the globally severe aging challenge. However, the related evidence is often limited by potential omitted-variable bias and reverse causation. This study uses an instrumental variables approach, namely the two-stage least squares (2SLS) method, to examine the impact of spousal cognitive functioning on depression levels among middle-aged and older adults in China. The data were sourced from the China Health and Retirement Longitudinal Study (CHARLS) of 2020, including a total of 3,710 couples aged 45 years and above. Depression levels were measured using the Center for Epidemiologic Studies Depression Scale (CES-D-10), while cognitive functioning was assessed using the Mini-Mental State Examination (MMSE). Spousal social participation was employed as the instrumental variable to address omitted-variable bias and reverse causation. Additionally, an interaction effect test between gender and spousal cognitive functioning was conducted. The results show that for each one-point increase in the spouse's MMSE score, the CES-D-10 score of middle-aged and older adults decreased by 17.1% to 68.2%. The OLS results indicated that women, rural residents, and middle-aged individuals were more sensitive to these changes. The interaction effect test results confirmed that women were more affected by changes in spousal cognitive functioning. However, after a more reliable 2SLS analysis, the results for age groups shifted, showing that middle-aged individuals were more sensitive to these changes, with a decrease in depression levels reaching 70.0%, compared to 60.2% for the elderly group. Nonetheless, given the prevalence of depression among the elderly, the impact of spousal cognitive decline on depression in this group should not be overlooked. Our findings highlight the importance of spousal cognitive health in managing depression among both middle-aged and older adults, with particular attention to women and rural populations.

Statistical analysis of the impact of profitability in the manufacturing and service sectors on the UK Economy’s Growth

The UK economy's performance is closely tied to the profitability of its Manufacturing Sector (MS) and Service Sector (SS). Understanding these sectors' dynamics is crucial for effective policymaking. Integrating sustainable practices in both sectors enhances profitability and operational efficiency while contributing to economic growth and reducing environmental impact. This study investigates the relationship between UK economic growth and profitability in the MS and SS from 1998 to 2022. The primary objective is to examine how profitability influences the UK's growth trajectory. Employing various statistical methods, including correlation, regression, cointegration, and variance decomposition analyses, the study aims to provide insights into the interplay between GDP and sector profitability. A quantitative approach using secondary data on GDP and profitability from reliable sources is adopted. Correlation analysis explores relationships between these variables, while regression analysis assesses the impact of MS and SS profitability on GDP. Cointegration tests identify long-term equilibrium relationships, and variance decomposition analysis reveals the variability of GDP and sector profitability over time. The correlation matrix indicates positive associations between GDP and both sectors, with the SS showing a stronger correlation. Regression analysis confirms significant effects of both sectors on GDP, with the SS exerting a positive influence and the MS a negative impact. Cointegration tests highlight long-term stability, and variance decomposition analysis emphasizes the dynamic nature of these relationships. This study elucidates the intricate link between UK economic growth and sector profitability, underscoring the SS's critical role in driving expansion while addressing challenges in the MS. Policy recommendations include enhancing productivity and innovation in both sectors, informed by evidence-based decision-making. Overall, the study contributes valuable insights into the UK economy's growth dynamics, guiding sustainable economic development strategies.

9.L. Round table: “What” and “How” for building competencies for effective policy making in and for public health

Abstract Policymakers operating within the field of public health must regularly draw on their skills or competencies to facilitate engagement in and between policy sectors and with the public, to navigate the larger landscape of public health institutions and governance levels across the policy cycle, to generate, understand, and apply evidence for decision making, as well as to negotiate the demands of diverse stakeholders. What exactly are the competencies needed and how can they be cultivated to enhance the collective capacity of policymaking institutions? The increasing strain placed on public health systems worldwide, owing specially to challenges in population ageing, the burden of non-communicable diseases, and the exigencies introduced by pandemics, environmental disasters, and geo-political instabilities push us to expand the scope of public health. A look to the policy environs and outcomes seen within and across countries demonstrates that the quality of policy is both highly variable and exceedingly subject to the quality of public health leadership itself. These developments shed light on the need for greater investment in the development of competencies needed by policymakers to engage in effective decision making on key issues impacting population health and the resilience of public health systems. This includes efforts to strengthen the abilities of policymakers to identify problems, recognize opportunities, and put forth solutions in line with robust data and evidence, principles of public health ethics, gaps and priorities in public health needs, and in accordance with financial and political realities. The proposed roundtable will examine the state-of-the-art on policymaking competencies in public health, and how to build them by examining the barriers and opportunities. The panel will convene leading experts in the field of public health research and practice to explore: 1) the conceptual definition of competencies for policymaking at different levels of governance and across the policy cycle; 2) the strengths and limits of competency frameworks and their utility in guiding assessment and training tools, as well as policy practice; 3) the challenges associated with developing, maintaining and transferring competencies in fast-changing, multi-level policy making environments; 4) the challenges for public health experts and institutions to be trusted advisors both by policy makers and other stakeholders and the population; and 5) the opportunities to harness the unexploited power of professional networks in some countries of Europe. Key messages • An enhanced research agenda and investment are needed for the development of competencies - individual and collective - for effective public health policy-making and stronger governance. • Innovative approaches to policy-making are needed, and this requires building competencies, including leadership, for addressing the complex nature of public health.

An Improved Neural Network Algorithm for Energy Consumption Forecasting

Accurate and efficient forecasting of energy consumption is a crucial prerequisite for effective energy planning and policymaking. The BP neural network has been widely used in forecasting, machine learning, and various other fields due to its nonlinear fitting ability. In order to improve the prediction accuracy of the BP neural network, this paper introduces the concept of forecast lead time and establishes a mathematical model accordingly. Prior to training the neural network, the input layer data are preprocessed based on the forecast lead time model. The training and forecasting results of the BP neural network when and when not considering forecast lead time are compared and verified. The findings demonstrate that the forecast lead time model can significantly improve the prediction speed and accuracy, proving to be highly applicable for short-term energy consumption forecasting.