Dataset on Regulatory Frameworks and AHP-Based Multi-Criteria Evaluation of Investment Attractiveness in Therapeutic Forests
Description
Research Hypothesis and Objectives: This dataset was compiled to test the hypothesis that the investment attractiveness of therapeutic forests is driven not only by natural conditions, but largely by regulatory, economic, and institutional factors. These factors can be systematically evaluated through the comparative analysis of international legal acts and the application of multi-criteria decision-making models. The primary objective is to provide a transparent and reproducible foundation for assessing the economic, institutional, regulatory, organizational, and natural factors influencing the development of therapeutic forests. Data Collection and Content: Data were gathered via a systematic comparative review of 27 legal and regulatory documents, national standards, certification systems, and practical case studies of therapeutic forests across seven countries (Japan, South Korea, Germany, Austria, Slovenia, the USA, and Ukraine). The repository contains structured files comprising: An inventory of regulatory documents across the studied countries, complete with references; Analytic Hierarchy Process (AHP) pairwise comparison matrices and priority weight calculations for socio-economic, natural, regulatory, and organizational criteria (representing the baseline scenario); Scenario modeling datasets demonstrating shifts in investment attractiveness priorities under various strategic and conceptual approaches to therapeutic forest development. Notable Findings and Interpretation: The presented data demonstrate that institutional support, financial-economic incentives, and regulatory foundations are key determinants of the investment attractiveness of therapeutic forests. Comparative analysis reveals varying levels of regulatory maturity across the studied countries, along with the presence or complete absence of state support and financial-economic incentives for forest owners. These factors are directly linked to the maturity of therapeutic forests as well as the prevalence and popularity of forest therapy practices. As evidenced by international experience, therapeutic forests typically evolve within the framework of public well-being enhancement, niche tourism, and the recreation sector, serving as a marketing tool to improve the economic viability of forestry enterprises and local communities. Consequently, successful therapeutic forest development relies on integrating their concept into local and regional socio-economic development programs, supported by appropriate financial, economic, and institutional mechanisms. Usage and Reusability: This dataset can be directly utilized by researchers, foresters, policymakers, and investors to adapt Analytic Hierarchy Process (AHP) calculations and conduct further economic assessments in the therapeutic forestry sector. All calculations are fully transparent and formatted for secondary analysis.
Files
Steps to reproduce
Step 1: Systematic review of the regulatory framework Protocol: Conducted a comparative analysis of 27 legal acts and national standards across seven countries (Japan, South Korea, Germany, Austria, Slovenia, the USA, and Ukraine). Data Organization: Regulatory documents governing the development and use of therapeutic forests for each country were selected and compiled in Regulatory_Frameworks.docx. Step 2: AHP model and hierarchy structuring Protocol: Developed a multi-criteria decision-making hierarchy divided into primary criteria groups (Legal and regulatory factors; Certification; Organizational factors; Socio-economic factors; Financial and economic factors; Natural, climatic and medical factors) and specific sub-criteria influencing the investment attractiveness of therapeutic forests. Step 3: Pairwise comparisons and baseline calculations Tools and Software: Baseline calculations and alternative scenario simulations were performed using the AHP Online System, an online calculator for the AHP method (https://bpmsg.com/ahp/). Protocol: Executed AHP pairwise comparisons and determined priority weights. Matrix consistency was verified, with the consistency ratio remaining within acceptable thresholds (CR < 0.1). Decision matrices and aggregated priority weights are presented in AHP_Scenario_Analysis_and_Data.docx.Step 4: Scenario simulation for testing the robustness of baseline priorities Protocol: Alternative scenario models were developed to evaluate how investment priorities shift under various conceptual approaches to therapeutic forest organization. The simulation was conducted at the first level of the hierarchy – meaning that group weights were varied, while sub-criteria weights within the groups were maintained from the baseline calculation. Data Organization: AHP pairwise comparison datasets for the simulated scenarios are also presented in AHP_Scenario_Analysis_and_Data.docx.