Proactive Policy Research Articles

The Port of Suakin in the Late 19th Century: Trade and Politics

Suakin, a strategically significant port on the Red Sea, came under the Ottoman control without direct military confrontation following Yavuz Sultan Selim’s conquest of Egypt in 1517. Strategically located, it became a key hub for the Ottoman trade and especially in the 19th century, a focal point of colonial competition. The opening of the Suez Canal in 1869 changed trade dynamics and increased British influence in the region. Britain has now begun to pursue a proactive policy in the region. Although the Ottoman presence in Suakin and the entire Abyssinian geography effectively ended with the British occupation of Egypt in 1882, relations with the region, which was officially considered Turkish territory, continued in some way until 1917. This study focuses on the strategic significance of the Port of Suakin, a key maritime hub that has retained its geopolitical relevance from the Ottoman period to the present. Suakin’s role as a major center of trade in the Red Sea positioned it at the intersection of global economic and political transformations, particularly during the era of European colonial expansion. The research examines the policies pursued by the Britain to assert its influence in Sudan and the broader region, with a particular focus on the instrumentalization of trade as a mechanism for political and territorial consolidation. Furthermore, the study assesses the interplay between trade, diplomacy, and military intervention in shaping Suakin’s historical trajectory, offering insights into the dynamics of colonial competition and the long-term implications of the British policies for Sudanese sovereignty.

AI-enhanced scenario planning for U.S. food trade policy: Anticipating global supply chain shocks and food insecurity risks

The increasing frequency of global supply chain disruptions—exacerbated by pandemics, geopolitical tensions, climate-related events, and economic volatility—has exposed critical vulnerabilities in U.S. food trade policy. As the United States navigates complex interdependencies in agricultural imports and exports, traditional scenario planning methods often fall short in addressing the velocity and uncertainty of modern supply chain shocks. To strengthen national food security and resilience, there is an urgent need for intelligent, data-driven frameworks that can anticipate risks and support proactive policy formulation. This paper investigates the role of artificial intelligence (AI)-enhanced scenario planning in transforming U.S. food trade policy amid escalating global uncertainty. We present a multi-layered framework that integrates machine learning, agent-based modeling, and geospatial analytics to simulate diverse trade disruption scenarios—ranging from port closures and export bans to climate-induced yield losses. The proposed system leverages real-time data inputs such as trade flows, climate projections, and geopolitical signals to model cascading impacts across domestic supply chains and global food markets. Case studies illustrate how AI-enhanced tools can identify early warning signs, quantify ripple effects of trade policies, and optimize contingency strategies. Special focus is given to evaluating implications for low-income and food-insecure populations within the U.S., ensuring equitable outcomes in policy response. The study also discusses the importance of ethical AI governance, data transparency, and public-private collaboration in shaping responsive and inclusive food trade policy. In conclusion, AI-enhanced scenario planning offers a strategic imperative for safeguarding U.S. food systems against emergent threats, while fostering adaptive, forward-looking trade policy in an increasingly volatile global landscape.

The impact of prudent financial policies on the urban–rural household health expenditure disparity: evidence from China

IntroductionWith the rapid development of China’s financial markets, the impact of proactive financial policies on household health investment has drawn significant attention, yet little is known about the effects of prudent financial policies on household health expenditures. We examine the effects of a prudent financial policy called the New Asset Management Regulation (NAMR). The policy aims to reduce systemic financial risks by breaking rigid payments and regulating shadow banking. The introduction of the policy expands research in this field.MethodsThis study employs the intensity difference-in-differences (intensity DID) approach, utilizing data from the China Family Panel Studies (2012–2020), to assess the effects of the NAMR. We further construct interaction terms between household existing capital (both tangible and intangible) and the key explanatory variables to test the heterogeneity of the baseline results.ResultsThe results show that the regulation significantly affects rural households’ medical expenditures (with a 20.5% decrease in guaranteed investment and a 1.3% increase in out-of-pocket payments), but has no significant impact on urban households. Heterogeneity analysis shows urban households’ health investment reduces health spending by 0.067%, moderated negatively by tangible assets (property) and positively by intangible assets (business experience, social ties). For households with business experience, a 10% treatment intensity increase raises health spending by 5.4%. A simultaneous 10% treatment intensity and 1-unit transport spending increase boosts health expenditures by 5.8%, revealing how asset types shape urban–rural health spending differences. Furthermore, post-implementation, urban households’ education expenditures significantly increased, while migrant households experienced a rise in guaranteed investment and a decline in out-of-pocket payments, though not statistically significant.DiscussionThis study highlights the multifaceted impact of prudent financial policies on household health expenditures across urban and rural areas, underscoring the need for policy formulation to address the distinct needs of urban, rural, and migrant populations, thereby laying a more solid institutional foundation for universal health coverage.

Do Green Innovation, Environmental Governance, and Renewable Energy Transition Drive Trade-Adjusted Resource Footprints in Top Sub-Saharan African Countries?

This study examined how green innovation (GIN), environmental governance (EGR), and renewable energy transition (RET) influence trade-adjusted resource footprints (proxied by material footprint, MFP) in the top 15 Sub-Saharan African (SSA) countries from 1970 to 2022. After confirming the cross-sectional dependence and slope heterogeneity in the dataset, second-generation panel econometric techniques, including the cross-sectionally augmented Dickey–Fuller (CADF) unit root test and Kao cointegration test, were employed to establish stationarity and long-run equilibrium relationships. The Method of Moments Quantile Regression (MMQR) revealed heterogeneous effects across quantiles: GIN exhibited a positive impact on MFP, intensifying at higher quantiles (resource-intensive economies), while GDP per capita (GDPC) reduced MFP, with effects strengthening as quantiles raised. Conversely, EGR and RET exacerbated MFP across all quantiles, suggesting that governance and energy policies may inadvertently spur resource exploitation in SSA’s transitional economies. The Dumitrescu–Hurlin (D-H) causality test confirmed a unidirectional relationship from all independent variables to MFP, highlighting the need for integrated policies to decouple growth from material consumption. This suggests that while governance, innovation, and renewable energy transition influence resource footprints, MFP does not, in turn, impact these factors, reinforcing the importance of proactive policy interventions.

Policy Interventions for Climate Change: Assessing Global Strategies to Reduce Carbon Footprint

Climate change is still a critical issue in the world, and some trends should be combat with international efforts in the regions and some sectors. The purpose of this research is to evaluate the efficiency of policy measures in fostering energy efficiency (EE) and reducing deforestation from 2000 to 2020 for Europe, California, Brazil, China, India, Indonesia, and others. The work focuses on assessing savings from EE and demand, and reductions in deforestation. The changes on EE on facilities were as percent changes while on the other hand deforestation was compared on the number of hectares of forest covers. These two areas with policy interference captured here as European union and California depicted sharp improvements in EE and had reduced the figure of energy consumption per GDP by 28 and 29 respectively. Where an implementation of a control on deforestation was done in Brazil, deforestation was reduced by 37.5%. China and India had no policy change, and they witnessed a magnitude of 10% increase in the efficiency of energy use and 5% increase in deforestation. It was observed that proactive policy areas recorded substantial positive changes while the lack of such measures incurred small gains. The study indicates the need to integrate climate policies to achieve climate sensitive development.

ENHANCING THE BANKABILITY OF GREEN ENERGY PROJECTS IN KUWAIT

Objective: The purpose of this research paper is to empirically analyse how conventional economic and financial metrics influence investment in solar energy projects in the state of Kuwait. The empirical analysis is based on 13 years of data covering the period from 2010 to 2023. Theoretical Framework: This literature review examines the factors influencing bankability and explores strategies to enhance financial viability. The authors of the research designed a financial model to produce some necessary financial metrics. Method: A multiple linear regression model is used to study the relationship of total solar investment in MWh and six independent variables, such as Weighted average capacity factor, WAAC weighted average cost of capital internal rate of return (IRR), Payback period, and PVOUT. Along with statistical tests such as ANOVA and OLS regression, used to evaluate the strength and significance of the relationship between the dependent and independent variables. Results and Discussion: The result of the analysis identifies only LCOE as the only marginally significant factor influencing investment in solar energy projects in Kuwait, with a negative coefficient (-454.79) and a p-value of 0.0822. Other independent variables, including the weighted-average capacity factor, WACC, payback period, and IRR, are shown to be statistically insignificant, indicating limited influence on investment decisions. Research Implications: This study has limitations that should be acknowledged. First, these findings show that investment in solar energy relies especially on conventional economic and financial investment metrics which are not enough to rapidly advance renewable energy development in Kuwait. Proactive public policy with attractive tools that stimulate investor confidence and promote a sustainable energy transition is not included. Originality/Value: This study provides original empirical evidence on the economic and financial factors influencing investment in solar energy projects in the State of Kuwait. As a relatively underexplored topic in the renewable energy finance literature, particularly within the context of Kuwait.

ARTIFICIAL INTELLIGENCE AND LABOR MARKET DYNAMICS: EMPLOYMENT PROBLEMS AND DEVELOPMENT TRENDS

This paper explores the dual impact of Artificial Intelligence (AI) on employment, examining both the job creation opportunities and the displacement risks AI brings to labor markets. As AI technologies advance across sectors like manufacturing, healthcare, finance, and services, they create new industries while automating routine tasks. However, the effects on employment are uneven, with high-skilled workers benefiting more than those in low-to-medium-skilled roles, contributing to greater economic inequality. The paper discusses the challenge of skill mismatches between traditional education systems and the evolving needs of the AI-driven economy. It highlights the importance of reskilling and lifelong learning initiatives to address these gaps. The study also reviews government responses to AI’s impact, with proactive policies on digital infrastructure, education, and innovation better positioning countries to capitalize on AI’s potential. Conversely, regions slow to adopt AI risk economic stagnation. The paper presents a framework for understanding AI’s diverse effects on employment and suggests a balanced approach to harness AI’s benefits while ensuring social equity. It concludes with policy recommendations to support a resilient, inclusive workforce capable of thriving in the AI era. Keywords: Artificial Intelligence, Employment Dynamics, Job Creation, Job Displacement, Workforce Skills, Economic Policy, Reskilling Programs, Labor Market Development Trends.

Exploring Educational Systems: A Comparative Systematic Review of Costa Rica, the Philippines, and Singapore

This study systematically reviews the educational systems of Costa Rica, the Philippines, and Singapore, focusing on governance and management, curriculum development, learner development, teacher development, pedagogical approaches, community engagement, support systems, and the role of private schools. Utilizing a qualitative thematic approach, the paper synthesizes scholarly research, government publications, and policy reports to evaluate how these nations manage structural challenges and educational reforms. Findings indicate that while Costa Rica demonstrates relatively stable governance and proactive policy responses, the Philippines faces systemic constraints, and Singapore exemplifies how strategic investments and coherent policy alignment produce world-class outcomes. Comparative PISA scores from 2018 and 2022 reveal significant disparities in student achievement, with Singapore consistently excelling, Costa Rica achieving moderate results, and the Philippines demonstrating critical challenges. These insights contribute to the development of equity-driven and resilient education policy frameworks for developing and middle-income countries.

Shooting the Messenger? Harassment and Hate Speech Directed at Journalists on Social Media

Journalists have incorporated social networks into their work as a standard tool, enhancing their ability to produce and disseminate information and making it easier for them to connect more directly with their audiences. However, this greater presence in the digital public sphere has also increased their exposure to harassment and hate speech, particularly in the case of women journalists. This study analyzes the presence of harassment and hate speech in responses (n = 60,684) to messages that 200 journalists and media outlets posted on X (formerly Twitter) accounts during the days immediately preceding and following the July 23 (23-J) general elections held in Spain in 2023. The results indicate that the most common forms of harassment were insults and political hate, which were more frequently aimed at personal accounts than institutional ones, highlighting the significant role of political polarization—particularly during election periods—in shaping the hostility that journalists face. Moreover, although, generally speaking, the total number of harassing messages was similar for men and women, it was found that a greater number of sexist messages were aimed at women journalists, and an ideological dimension was identified in the hate speech that extremists or right-wing populists directed at them. This study corroborates that this is a minor but systemic issue, particularly from a political and gender perspective. To counteract this, the media must develop proactive policies and protective actions extending even to the individual level, where this issue usually applies.

Advancing Electricity Storage Technologies with a System Dynamics Approach

Abstract – Climate neutrality targets and strategies show that the share of renewables in energy generation is set to increase, nevertheless their variability, seasonality and lack of reliability pose significant challenges. With the continued growth in energy consumption, demand for energy storage is becoming more evident. The integration of renewable energy and the reliability of the grid depend on the installed storage capacity. However, the overestimation of the amount of storage needed can result in wasted investment. Therefore, to enable proactive energy policy – making and efficient energy system planning, it is necessary to be able to predict how the installed capacity of specific energy storage applications will develop over time. This study conducts an electricity storage technology assessment to investigate feasibility and technological progress, compare available technologies and conclude the main drivers and barriers affecting the adoption rate. A multi-criteria analysis approach is applied to compare the different types of energy storage technologies available. TOPSIS analysis, a ranking method based on distance from the ideal solution, was used to identify the electricity storage technologies that are closest to the ideal solution, followed by a more in-depth analysis – a systems dynamics approach. The system dynamic model was built in Stella Architect to further assess the viability of these technologies, predict and compare installed capacities. The results reveal various critical factors that influence the rate of deployment of electricity storage technologies: capital and operating costs, storage installation time, market distribution constraints, storage outages, lifetime, etc. This paper concludes which technologies should be given more consideration for deployment and what are the key factors affecting their installed capacity over time. The findings of this study underline that energy storage is one of the key steppingstones towards the energy transition, providing insights into technology prioritization and driving factors identified.

Macroeconomic Information Sentiment Biases in Derivatives Market: Asymmetry Evidence in Case of India

This research attempts to scrutinize the dynamic nexus between the aggregate derivatives market trading activity/volume and the macroeconomic information. We track the nonlinear impact of money supply, interest rates and gold prices along with the stock index, volatility indices and economic policy uncertainty index on India’s NSE derivative market and its sub-segments trading activity using a nonlinear autoregressive distributed lag model. This article makes contributions by filling the deficit in the available literature with reference to the understanding of the nexus of derivatives trading activity and macroeconomic information. First, we found evidence of the long-run equilibrium cointegration between money supply, gold prices, interest rates and aggregate derivatives market trading activity. Second, we found that derivatives market segments vary in macroeconomic variable sensitivity. Third, identifying macroeconomic variable-induced long- and short-term asymmetry across segments. Recognizing the asymmetrical relationship between macroeconomic information and derivatives trading activity helps investors formulate trading strategies and policymakers mitigate stock market volatility through proactive policy adjustments. Key Takeaways Evidence of the long-run equilibrium cointegration between money supply, gold prices, interest rates and aggregate derivatives market trading activity. Derivative market segments vary in macroeconomic variable sensitivity. Identifying macroeconomic variables-induced long- and short-term asymmetry across segments.

AI and the Future of Work

This paper explores the transformative impact of Artificial Intelligence (AI) on various sectors, particularly focusing on its implications for industries, employment, aìnd workplace practices. Through comprehensive analysis and case studies, the research highlights how AI technologies are reshaping productivity, job roles, and economic landscapes. While AI offers significant advantages such as increased efficiency and the creation of new jobs, it also presents challenges including job displacement, ethical concerns, and a shift in required skill sets. The paper discusses both the opportunities provided by AI and the strategic responses needed at governmental and organizational levels to mitigate risks and ensure equitable benefits. Key findings suggest that proactive policy frameworks, ongoing workforce training, and ethical AI deployment are crucial to harnessing AI’s potential responsibly. This research contributes to the broader understanding of AI’s role in modern industries and provides a foundation for future studies on sustainable AI integration.

Central Asia in the Geopolitical Crossfire: Strategic Alignments Amidst the Russo-Ukrainian Conflict

Abstract This study explores the evolving cooperation perspectives of Central Asian states against the backdrop of an ongoing war between Russia and Ukraine. This protracted conflict has triggered regional and global geopolitical shifts, prompting reassessment of the roles and strategies of the various global and regional actors. Focusing on Kazakhstan, Kyrgyzstan, Uzbekistan, Turkmenistan and Tajikistan, this research employs the argumentative framework of Political Discourse Analysis to examine the prospects for cooperative actions drawing insights from five Central Asian leaders’ 2022 speech in Cholpan-Ata. Guided by the shelter-seeking foreign policy strategy, the findings reveal Central Asian leaders’ commitment to pursuing a cohesive regional strategy as an immediate necessity to address shared challenges, including security, economic and infrastructural development and water resource management to promote enhanced cooperation within the region. The study advocates for taking proactive, tangible, and assertive policy measures to realize the proposed actions, emphasizing the importance of a unified approach in navigating the complex geopolitical dynamics.

Fiscal Implications of Aging in Andorra

Andorra has the highest life expectancy and the lowest fertility rate in Europe, resulting in one of the fastest-aging populations on the continent. This study assesses the long-term fiscal implications of demographic change for Andorra’s pension and healthcare systems. Without reform, public pension and healthcare expenditures are expected to rise significantly by 2050. Comprehensive pension and healthcare reforms, alongside proactive immigration policies, are essential to mitigate demographic pressures and safeguard Andorra’s macroeconomic stability over the long term.

DYNAMIC SPATIAL-TEMPORAL EVOLUTION OF THE CONSTRUCTION INDUSTRY’S SUSTAINABLE DEVELOPMENT IN CHINA BASED ON THE COUPLING COORDINATION MODEL

ABSTRACT Ensuring effective coordination among the economy, society, and environment is an essential issue in fostering the sustainable development of the construction industry (SDCI). However, previous studies on sustainability assessment within the construction sector have largely overlooked this aspect and inadequately addressed its spatial-temporal dynamic evolution. In this research, we propose an assessment framework of SDCI from the industry perspective and develop a sophisticated assessment model using a coupling coordination degree approach. By analyzing data from 30 provinces in China, we conduct an extensive spatial-temporal assessment of SDCI. The results reveal significant progress in China’s SDCI, with a notable transition from a mildly imbalanced state to a basic level of coordination. The coordination levels of SDCI exhibit remarkable spatial heterogeneity, not only in the east-west direction but also in the south-north direction. Overall, spatial disparities across the country are observed to be increasing. The inter-regional disparities serve as the primary source, while the intra-regional disparities demonstrate an upward trend. Furthermore, the coordination of SDCI in China tends to remain at the current level, indicating a characteristic of path dependence. Meanwhile, the phenomenon of spatial differentiation is likely to intensify. Based on these findings, we recommend that the Chinese government prioritize the coordination of SDCI and implement proactive policies to address the imbalances in development and path dependence. The insights provided by this study into the dynamic spatial-temporal evolution of SDCI offer a robust scientific basis for formulating effective SDCI policies.

دور الإعلام الرقمي في إدارة الأزمات في القطاع الخاص بالمملكة العربية السعودية

Research Title: The Role of Digital Media in Crisis Management in the Private Sector in the Kingdom of Saudi Arabia. The study aimed to identify the role of digital media in crisis management in the Saudi private sector. The study seeks to identify how digital media tools—such as social media platforms, websites, and interactive applications—can be used to monitor, interact with, and handle crises, in terms of speed of response, information transparency, and internal and external communication strategies. The study adopted a descriptive analytical approach by reviewing previous literature and analyzing several real-life examples of crises experienced by the Saudi private sector in recent years. It concluded that digital media can be an effective tool in crisis management if employed within clear and integrated institutional communication plans, while emphasizing the importance of building specialized digital communication teams within institutions. The study recommends developing digital infrastructure, enhancing the skills of communication teams, and formulating proactive media policies capable of efficiently addressing future challenges.

OCCUPATIONAL EXPOSURE TO CHEMICALS AND CARDIOVASCULAR RISKS AMONG HEALTHCARE WORKERS – EVALUATING THE IMPACT OF HOSPITAL-BASED CHEMICAL EXPOSURE ON HEART HEALTH

Background: Occupational exposure to hazardous chemicals in healthcare settings poses a significant, yet often underrecognized, threat to cardiovascular health. Despite increasing global attention to occupational health, evidence linking chemical exposure to cardiovascular risk among healthcare professionals remains limited, particularly in resource-constrained countries. Objective: To evaluate the impact of hospital-based chemical exposure on cardiovascular risk among healthcare workers in tertiary care hospitals in Punjab, Pakistan. Methods: This cross-sectional study was conducted over eight months (July 2024 to February 2025) in tertiary care hospitals located in Lahore, Rawalpindi, Faisalabad, and Multan. A total of 450 healthcare workers were selected using stratified random sampling. Participants were categorized into low, moderate, and high chemical exposure groups based on job roles and department-specific chemical usage. Cardiovascular risk was assessed using the Framingham Risk Score (FRS), blood pressure measurements, lipid profiles, fasting glucose, and ECG evaluations. Data were analyzed using SPSS version 26.0. One-way ANOVA and multivariate regression were applied to determine associations between exposure levels and cardiovascular risk markers. Results: FRS scores increased with exposure level, from 6.8 ± 2.1 in the low group to 12.3 ± 3.5 in the high exposure group (p < 0.001). Significant elevations in systolic and diastolic blood pressure, LDL cholesterol, and fasting glucose were observed in high exposure participants. Multivariate analysis confirmed chemical exposure as an independent predictor of elevated cardiovascular risk (β = 0.37, p < 0.001). Conclusion: Healthcare workers exposed to hospital-based chemicals are at a heightened risk of cardiovascular disease. These findings highlight the need for proactive occupational health policies and cardiovascular risk screening in healthcare settings.

Burden of disease of Alzheimer disease in Italy: a real-world data analysis

BackgroundAlzheimer Disease (AD) represents a growing global health concern with profound socioeconomic implications, with predictions indicating a potential 50% increase in AD cases in Italy over the next 30 years. Timely diagnosis remains challenging due to the slow progression of symptoms and limited accessibility to advanced diagnostic tools, yet it remains one of the few tools available to prevent and alter the clinical course of the disease. The aim of this study is to build a cost-of-illness model to estimate the number of AD patients managed by the National Health Service, analyzing their use of hospital care, and estimating the social costs through real-world data.MethodsThe analysis encompassed a multifaceted approach, combining real-world data analysis from different sources for the period 2014–2019. Health direct costs related to AD in Italy were estimated thanks to the Italian database of all hospital discharges and a Local Health Unit database (400,000 residents) collecting all information on resource consumption related to AD. The National Social Security System database was used to estimate social security costs (disability compensations) related to Attendance Allowance (AA) recognitions.ResultsIn Italy a prevalence of 413,715 AD patients was estimated, with annual health direct costs per patient equal to €3,779. Annual social security costs related to AA recognitions amounted to 240 million euros. Overall, the analysis estimated an annual total cost exceeding 1.8 billion euros.ConclusionsThis study provides a comprehensive exploration of the multifaceted burden of AD in Italy, shedding light on its economic dimensions. The results underscore the urgency of prioritizing AD on political agendas, especially in the face of the projected global surge in AD cases. The study advocates for proactive policy interventions and informed healthcare decision-making to address the complex challenges posed by AD.

Diplomatic training and Sovereignty: Insights from Non-Western States

The paper examines how the sovereignty of national diplomatic training schools in the Global South influences their standing within the regional subsystems. The study is relevant in light of the growing clout of non-Western countries in global politics and economy, as well as their ever-increasing significance for Russia. We focus on countries that seek to pursue especially proactive foreign policies: Egypt, Iran, Saudi Arabia, and Turkey in the Middle East; Nigeria, Ethiopia, and South Africa in Sub-Saharan Africa; Brazil, Cuba, and Mexico in Latin America. The authors employ a cross- regional comparative analysis of diplomatic training there, supplemented by a biographical method that traces the career trajectories of prominent foreign ministry officials. Our research rests on selected post-structuralist, post-colonial, neo-Marxist, and post-critical approaches to sovereignty in states deemed peripheral or semi-peripheral. Additionally, we take stock of various approaches to diplomacy as an institution and further conceptualize how the indigenous diplomatic practices of individual states shape their role in the region. Our findings partly support the hypothesis that higher levels of sovereignty (sophistication) in national diplomatic training schools help these states to set the (sub)regional agenda. However, high levels of sovereignty in diplomatic training, as opposed to sophistication, do not necessarily translate into a country’s ability to set the regional agenda.

Simulation-Free Hierarchical Latent Policy Planning for Proactive Dialogues

Recent advancements in proactive dialogues have garnered significant attention, particularly for more complex objectives (e.g. emotion support and persuasion). Unlike traditional task-oriented dialogues, proactive dialogues demand advanced policy planning and adaptability, requiring rich scenarios and comprehensive policy repositories to develop such systems. However, existing approaches tend to rely on Large Language Models (LLMs) for user simulation and online learning, leading to biases that diverge from realistic scenarios and result in suboptimal efficiency. Moreover, these methods depend on manually defined, context-independent, coarse-grained policies, which not only incur high expert costs but also raise concerns regarding their completeness. In our work, we highlight the potential for automatically discovering policies directly from raw, real-world dialogue records. To this end, we introduce a novel dialogue policy planning framework, LDPP. It fully automates the process from mining policies in dialogue records to learning policy planning. Specifically, we employ a variant of the Variational Autoencoder to discover fine-grained policies represented as latent vectors. After automatically annotating the data with these latent policy labels, we propose an Offline Hierarchical Reinforcement Learning (RL) algorithm in the latent space to develop effective policy planning capabilities. Our experiments demonstrate that LDPP outperforms existing methods on two proactive scenarios, even surpassing ChatGPT with only a 1.8-billion-parameter LLM.