The Property Rights Index in Forestry (PRIF): a new governance indicator with predictive capacity for forest status and dynamic across Europe

The Property Rights Index in Forestry (PRIF): a new governance indicator with predictive capacity for forest status and dynamic across Europe | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The Property Rights Index in Forestry (PRIF): a new governance indicator with predictive capacity for forest status and dynamic across Europe Richard Rimoli, Jean-Daniel Bontemps, Laura Bouriaud, Aditya Acharya, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7683489/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Understanding how forest governance shapes environmental outcomes is critical for sustainable land-use policies. Yet, existing governance indicators are often too broad to reflect sector-specific legal frameworks that govern natural resources. This study assessed the relevance of an existing governance index for forestry sector (Property Rights Index in Forestry - PRIF), correlating it with recognised governance metrics (e.g., rule of law, corruption perception, economic freedom) and forest status indicators (e.g., reforestation, afforestation, and deforestation rates). The PRIF was found to have strong associations with national governance quality, economic development, and forest dynamics, capturing both enabling and constraining aspects of owner freedom. Countries with higher PRIF scores tend to experience higher rates of both deforestation and reforestation, suggesting that increased owner autonomy drives more dynamic and potentially polarized forest outcomes. A Principal component analysis further reveals that PRIF aligns with major gradients in forest management intensity, ownership structure, and ecological change. These findings demonstrate that PRIF is a sensitive, interpretable, and scalable indicator for evaluating forest governance and its environmental implications. The index offers a valuable tool for policymakers seeking to balance property rights with sustainability goals across diverse institutional contexts. Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental social sciences Social science/Environmental studies Scientific community and society/Geography Social science/Geography Forestry policy property rights PRIF resources ecosystem services European forests Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction The governance of forest resources in Europe, traditionally managed through command and control by regulatory policies and dominated by state forestry [ 1 ], [ 2 ] is facing significant challenges due to various factors such as increasing societal pressure, developmental projects, and climate change [ 3 ], alongside with the multiplication of social and policy expectations upon the forest management [ 2 ], [ 4 ]. Implementing the principles of sustainable forest management (SFM) implies the choice of a set of policy instruments and a certain level of governmental coercion, differentiating therefore various governance arrangements and models [ 5 ], [ 6 ]. For example, there are systems defined by a strong emphasis on command-and-control instruments (also known as “sticks”) that create a highly restrictive regulatory framework [ 9 ]. This is often the case in the post-socialist countries from Central, East and South Europe, in which the state prescribes the normative set of mandatory rules for SFM [ 10 , p. 201]. There are many definitions of the term governance that can be found in the literature, each with its own variation on the nature of the governance or on its components [ 7 ], [ 8 ]. For example, for [ 11 ], forest governance is a complex system composed by: “a) all formal and informal, public and private regulatory structures, i.e. institutions consisting of rules, norms, principles, decision procedures, concerning forests, their utilisation and their conservation, b) the interactions between public and private actors therein and c) the effects of either on forests.”. A simplified but systemic framework that characterizes the use and management of natural resources is the Socio Ecological System (SES) framework, proposed by Ostrom [ 12 ]. Within this framework, forest management can be conceptualised as an action situation where governance factors, resource factors and actors interact (Fig. 1). In the Action Situation interaction (I), participants receive information, select actions according to that first input, engage in patterns of interactions, and analyse the outcomes (O) of these interactions [ 13 ], [ 14 ]. The resource system (RS) is the biophysical system from which resource units (RU) are consumed, used as inputs of production or other goods and services. The prevailing set of processes, in which the rules shape the behaviour of actors, can be defined as a governance system (GS) [ 14 ]. Potential exogenous influences from a broader socio-environmental context are classified as related ecosystems (ECO, Fig. 1). Therefore, the SES framework allows the appraisal of systems in which users extract resource units from a system and share the responsibility for the system’s maintenance, according to rules and procedures determined within a governance system [ 15 ], and other external forces. Therefore, an accurate capture of the broader context, featuring governance characteristics, is necessary for a better understand the mechanisms and influences in place. Governance systems can, to some extent, be characterised with the help of indicators. According to [ 16 ] “the term governance indicator refers to an eclectic set of measures covering the wide range of governance topics”. Indicators are continuously created, tested and updated, depicting the evolution of social, political, legal, economic or environmental aspects of governance [ 16 ], [ 17 ], [ 18 ]. Increasing importance is paid to specialised forest governance indicators, at local level or for specific governance purposes [ 19 ]. Freedom in decision-making regarding forest resource and services (RS) management, as an outcome of formal rules governing forest management [ 11 ], can be described with the Property Rights Index in Forestry (PRIF) [ 20 ]. The index is based on the de jure rights of forest owners over their forest property. It provides an aggregate score on the forest owners’ rights, in different countries and jurisdictions, as well as scores on different bundles of rights. It has been shown to applicable in a variety of institutional contexts across the globe, including, for example, community forests [ 21 ]. While this quantification has proved to be useful for comparative purposes, the index capacity to depict the impacts of forest governance has not yet been investigated. Furthermore, it is of equal interest to evaluate the accuracy and sensitivity of this index on a more continuous gradient of forest governance and associated forest dynamic. This paper focuses on 31 countries of the European continent, aiming to analyse: 1) to what extent the PRIF, and its components, correlates with consolidated governance indicators (reflecting socio-economic, policy and environmental development); 2) to what extent the PRIF is able to capture and explain the current state and trends of forest resources, environmental services as well as if it can be used for predictive purposes. 2 Material The core indicator under investigations is the Property Right Index in Forestry. The data collection was based on the 31 European countries used by [20]. Additional data encompassed in this study included (i) governance indicators , representing distinct aspects (economic, social, environmental, political) of the SES framework, including more specific indicators for forests as part of the environment, (ii) an array of 30 forest reporting variables describing status and trends of the countries’ forests, collected from both the Forest Resource assessment (FRA) of the UN/FAO [22], and the State of Europe's forests assessment of Forest Europe [23]. Most of the data used is relative to 2015, the year in which the PRIF data was calculated (Annex1). 2.1 The Property Rights Index for Forestry (PRIF) Based on the framework proposed by [24], the Property Rights Index for Forestry (PRIF) has the objective of assessing and quantifying the rights of forest owners. It is structured according to the 5 Property Rights Categories (PRC): Access, Management, Alienation, Exclusion and Withdrawal rights. The PRIF scores the freedom, of each PRC as well as an overall score, that forest owners enjoy in each analysed jurisdiction. Since different states have different regulations regarding natural resources in their territory, the index is useful to compare rights and obligations of owners and users of forest resources across countries. The dataset is composed of 31 European jurisdictions [20]. While data is available for countries in non-European jurisdictions [21], they remain too scarce to be embraced in the present study. Since its conceptualization, the PRIF has been applied in many cases including (i) the analysis of the evolution of forestry property rights over different periods [25], [26], [27]; (ii) the assessment and comparison of regulatory frameworks between different countries and how they affect cooperation [28]; (iii) the analysis of how different political backgrounds impacted on current forestry property rights [25]; and (iv) it has also been used as one of the explanatory factors of forest expansion trends in Europe [29]. 2.2 Governance indicators Governance indicator aggregation has been shown to be effective in the estimation of governance trends at country level, providing enhanced reliability and precision [30]. The SES framework [14] distinguishes between indicators concerning the governance system, actors, and resource system, leading to the following selection of indicators: (i) Governance : International Property Rights Index (IPRI), Rule of Law Index (RLI), Corruption Perception Index (CPI), Index of Economic Freedom (IEF), (ii) Actors : Human Development Index (HDI), (iii) Resource system: GDP per capita, Environmental Performance Index (EPI) . Of note, these different indices can overlap some of these different fields, also between each other, and they are classified here for the sake of simplicity. These indicators and their methodology are further described in Annex 2. Table 1: governance indicators Index Field Organisation Year International Property rights Index Governance Property Rights Alliance 2022 Rule of Law Index Governance World Justice project 2022 Corruption perception index (CPI) Governance Transparency International 2022 Index Economic freedom Governance The Heritage Foundation 2022 Human Development Index Actors United Nations Development Programme 2021-2022 GDP per capita Resource system National statistical agencies 2021-2022 Environmental Performance Index Resource system Yale Centre for Environmental Law & Policy 2022 2.3 Forest indicators A set of indicators of forest dynamics, environmental performance, silviculture/production, and ownership was selected (Table 2). Sources for each of the variables were indicated on Annex 1. Table 2: Indicators of forest status and trends, categorised. Indicator Indicator Category SES Category Unit/Scale Relative forest cover 2015 Forest dynamic Resource units Percentage Reforestation 2010-2015 Forest dynamic Outcome %/year naturally regenerated forest (%) Forest dynamic Outcome %/year planted forest (%) Forest dynamic Outcome %/year Relative forest expansion 2010-2015 Forest dynamic Outcome %/year ...of which afforestation Forest dynamic Outcome %/year ...of which natural expansion (%/year) Forest dynamic Outcome %/year Deforestation rate 2010-2015 Forest dynamic Outcome %/year Forest area net change 2010-2015 Forest dynamic Outcome %/year Rate of forest area designated to protection of soil and water management 2015 environmental performance Resource systems Percentage Rate of forest area designated to conservation management 2015 environmental performance Resource systems Percentage Rate of protected area relative to forest cover 2015 environmental performance Resource systems Percentage Rate of forest with a long-term management plan 2015 silviculture/production Resource systems Percentage Rate of forest area only used for production 2015 silviculture/production Resource systems Percentage Rate of Gross Value-Added from forestry to GDP 2015 silviculture/production Outcome Percentage Gross Value-Added form forestry per 1000ha production managed Forest 2015 silviculture/production Outcome Million € / 1000ha Gross Value-Added form forestry per 1000ha of forest cover 2015 silviculture/production Outcome Million € / 1000ha Employment in forestry and logging/production area 2015 silviculture/production Resource units Workers/ha Average forest density 2015 silviculture/production Resource units m³/1000ha Forest Growing Stock in 2015 silviculture/production Resource units million m³ over bark/1000ha …of which naturally regenerating forest silviculture/production Resource units million m³ over bark/1000ha …of which planted forest silviculture/production Resource units million m³ over bark/1000ha Felling to increment rate 1990 silviculture/production Outcome Percentage Felling to increment rate 2000 silviculture/production Outcome Percentage Felling to increment rate 2010 silviculture/production Outcome Percentage Felling to increment rate 2015 silviculture/production Outcome Percentage Average annual change in growing stock 2005-2015 silviculture/production Outcome Percentage Rate of public forest ownership in 2015 ownership Resource systems Percentage Rate of private forest ownership in 2015 ownership Resource systems Percentage Unknown/other ownership in 2015 ownership Resource systems Percentage The data collected are relative to the year 2015, as the original PRIF dataset was calculated based on laws in force in 2015. However, there were a few exceptions including: (i) growing stock change as an annual average and was taken between 2005 and 2015, (ii) indicators of forest area dynamic from 2010 to 2015, (iii) felling-to-net increment ratios from 1990, 2000, and 2010 were also included in the dataset. 3 Methods 3.1 Correlation tests Systematic correlation tests were performed between the PRIF index and the sets of governance and forest indicators. Since the PRIF index relies on an additive aggregation of different bundles of rights, that have no prior reason of being substitutable, the 5 components of the PRIF index were also correlated to these indicators. The Spearman's rank correlation test was used, as an efficient method to test the null hypothesis (no relationship between the 2 tested variables), resilient to the presence of outliers and not assuming any linearity in the relationships. Also, since governance indices are defined at broad scale, and inherently subjected to measurement and estimate errors [30], limits of test significance for detecting relevant correlations tests were relaxed with respect to standard practices, and p-values smaller than 0.1 (‘) were considered [31], alongside the traditional limits of p < 0.05 (*), < 0.01(**), < 0.001 (***) and < 0.0001(****). The initial hypotheses evaluated were that the PRIF should be closest to RLI, IPRI and IEF indicators, as they feature values related to rule of law and freedom of action, i.e. what the PRIF intends to measure for the forestry sector, in particular. In the cases where the PRIF was calculated on a state legislation scale (Germany, Spain, Switzerland, and Great Britain) due to data availability, these values were also compared to the national variables. 3.2 Principal Component Analysis (PCA) The PCA analysis was conducted with 33 variables (Table 2 plus growing stock values of 2010) of the 31 jurisdictions analysed to further explore and synthetise the trends already uncovered partially by correlation analysis. The PCA was calculated using a weighted dataset, indexed to the forest area in 2015, intended to avoid the excessive load of small countries To inquire whether and how variations in these indicators across countries can align with the PRIF index or its components, this data was introduced as supplementary variables (not contributing to the calculation of principal components) in the analysis. The PCA was calculated using the R software along with the FactoMineR [32] package. In order to calculate the PCA, missing values were imputed, using the missMDA package, employing the leave-one-out (loo) cross-validation method, to estimate the number of dimensions of the PCA [33]. 4 Results 4.1 PRIF aligns with governance indicators As a first scrutiny, the PRIF scores was correlated to its own components. The PRIF was found mostly influenced by the withdrawal, management, and exclusion right, while access and alienation were the least correlated to the overall score (Table 3 ). Management and withdrawal right scores were themselves strongly correlated; this thus implied a control of the PRIF score by these two variables. The weak correlations between other components justified the analysis of each individual component of the index in subsequent correlation analyses. Table 3: Correlation matrix of the PRIF index for 31 European countries PRIF access withdrawal management exclusion Access 0.45 * Withdrawal 0.90 **** 0.39 * Management 0.87 **** 0.43 * 0.87 **** Exclusion 0.57 *** 0.12 0.31 ' 0.19 Alienation 0.44 * 0.28 0.28 0.21 0.33 ' Countries as listed in [20] . Spearman correlation coefficient. p-value: <0.1 ', <0.05 *, <0.01 **, <0.001 ***, <0.0001 **** Remarkably, the PRIF and its components correlated strongly with most governance indicators evaluated (Table 4). The strongest correlations found were with the IPRI, RLI and CPI. As notable exceptions, EPI’s ecosystem service (EPI_ECO) and climate change policy (EPI_CCP) did not correlate with the PRIF, nor with any of its components. However, the PRIF correlated positively with the global EPI index. Exclusion and alienation rights were found to show the weakest correlations with governance indices, exclusion rights being negatively correlated with EPI’s tree cover loss subcategory, while alienation rights were positively linked with the index of economic freedom (IEF), showing a strong relationship of the component’s score and its definition. The strongest correlations were found using the PRIF, withdrawal and management. The correlations found were strong across three SES categories of indicators, resource, actors, and governance, and as such, did not allow any significant differentiation between them. The results also confirmed the hypothesis of stronger associations to IPRI, RLI and IEF (correlations > 0.8). Nevertheless, IEF was slightly less correlated than the other two, indicating a tighter association of the PRIF with right-oriented rather than economy-oriented indicators. CPI and HDI were also found to be highly correlated with PRIF, withdrawal and management rights. Table 4: Correlation values - PRIF components and governance indicators PRIF Access Withdrawal Management Exclusion Alienation Resource system EPI 0.49 ** 0.26 0.63 *** 0.57 *** -0.01 0.16 EPI_ECO 0.17 0.04 0.2 0.13 0.1 -0.06 EPI_ECO_tcl -0.55 ** -0.23 -0.52 ** -0.43 * -0.36 * -0.35 ' Governance EPI_CCP -0.04 -0.06 0.11 -0.01 -0.18 0.12 Actors HDI 0.68 **** 0.52 ** 0.73 **** 0.8 **** 0.01 0.3 ' Resource system GDP per capita 0.75 **** 0.52 ** 0.76 **** 0.78 **** 0.18 0.35 ' Governance CPI 0.78 **** 0.48 ** 0.84 **** 0.85 **** 0.14 0.34 ' Governance IPRI 0.76 **** 0.44 * 0.86 **** 0.81 **** 0.13 0.29 Governance IEF 0.51 ** 0.21 0.56 *** 0.47 ** 0.13 0.41 * Governance RLI 0.79 **** 0.41 * 0.87 **** 0.86 **** 0.18 0.29 This statistical analysis is based on Spearman’s rho correlation test. It displays the value of the coefficient of correlation for the compared variables, and the associated p-value: <0.1 ', <0.05 *, <0.01 **, <0.001 ***, <0.0001 ****. The PRIF and its 5 subsequent property rights categories (Access, Management, Withdrawal, Exclusion and Alienation) are correlated to 7 governance indicators: Environmental performance Index (EPI), Human development Index (HDI), GDP per capita (HDI_gdpcap), Corruption perception Index (CPI), International Property Rights Index (IPRI), Rule of law index (RLI), Index of Economic Freedom (IEF) as well as some specific components of these indices EPI_ ECO (ecosystem services) , EPI_ECO_tcl (tree cover loss) and EPI_CCP (Climate change policy) . 4.2 PRIF and its components explain the forest status and the deforestation rates The correlation analysis of the PRIF index revealed a set of significant links, including positive correlations with annual reforestation rates (p<0.01), deforestation rates (p<0.01), rate of planted forests (p<0.01), private ownership fraction (p<0.01) and forest expansion rate via afforestation (p<0.01). That both deforestation and reforestation rates positively correlated with the PRIF index emphasizes well the opposed effects that freedom of action can yield and suggested the polyvalence of this index in this respect. Negative correlations were also identified, with the fraction of naturally regenerated forests (p<0.01), ration of public forest ownership (p<0.01) and relative forests cover with water and soil management plans (p<0.05). While current levels of felling-to-increment ratios did not correlate with PRIF (see also [29]), felling rates in the 2000s were found to be fairly, and positively, correlated to PRIF. In view of the positive correlations of PRIF with the rate of planted forests and expansion via afforestation, these suggested that more intensive harvests are compensated by a more proactive forest renewal, indicating compatibility with sustainable forest management principals. Overall the PRIF index clearly defined a broad gradient contrasting public forestry (favouring natural ecological regeneration processes and ecosystem services) and an intensive and private forestry (favouring felling and plantation). The highlighted positive correlation of PRIF with deforestation suggests a possible adverse effects of freedom of action (Table 5). Table 5: PRIF and SES indicators correlation values SES variables PRIF Relative forest cover -0.2 Reforestation 2010-2015 0.46 ** Naturally regenerated forest (%) -0.55 ** Planted forest (%) 0.55 ** Relative forest expansion 2010-2015 0.28 ...of which afforestation 0.5 ** ...of which natural expansion (%/year) -0.25 Deforestation rate 2010-2015 (%/year) 0.59 ** Forest area net change 2010-2015 -0.11 Rate of forest area designated to protection of soil and water management -0.47 * Rate of forest area designated to conservation management 0.13 Rate of protected area relative to forest cover -0.26 Rate of forest with a long-term management plan -0.48 * Rate of forest area only used for production -0.13 Rate of Gross Value-Added form forestry to GDP -0.23 Gross Value-Added form forestry per 1000ha production managed Forest -0.02 Gross Value-Added form forestry per 1000ha of forest cover 0.31 Employment in forestry and logging/production area 0 Average forest density 0.1 Forest GS 0.07 Naturally regenerating forest -0.42 * Planted forest 0.51 ** Felling to increment rate 1990 0.21 Felling to increment rate 2000 0.48 * Felling to increment rate 2010 -0.1 Felling to increment rate 2015 0.02 Average annual change in growing stock 2005-2015 -0.03 Rate of public forest ownership -0.48 ** Rate of private forest ownership 0.49 ** Rate of Unknown/other forest ownership -0.02 This statistical analysis is based on Spearman's rho correlation test. It displays the value of the coefficient of correlation for the compared variables, and the associated p-value: <0.1 ', <0.05 *, <0.01 **, <0.001 ***, <0.0001 ****. In the cases where the PRIF was calculated based on state legislation, the values were also compared to the national variables. In general, correlations with the PRIF components were weaker than those calculated with the aggregated index, further consolidating the relevance of an aggregated index. Specific indicators targeting forest protection, economic return or employment did not correlate to any of the individual components ( Table 6). Access : A positive correlation (p<0.05) with deforestation was identified, stressing the relationship between forest degradation and access. A weak positive trend with the private ownership fraction (p<0.1) was also found. Withdrawal : Withdrawal rights were positively correlated with the fraction of planted forests (p<0.01), the share of private forests (p<0.01) in the country, annual reforestation rate (p<0.05), and deforestation rate (p<0.05). They were found to be negatively correlated with naturally regenerated forest (p<0.01) and share of public ownership (p<0.01). These relationships further corroborate the existence of a gradient of forestry intensity across European countries, associated to the freedom of forest ownership. Weak trends (p<0.1) were also found with reforestation types, as mirrored in the correlations of the global PRIF index (see 3.3). Management : the management right component was found to be directly related to distribution of forest ownerships, being positively correlated with the fraction of private forests (p<0.01) and deforestation rates (p<0.05), while negatively correlated with the fraction of public forests (p<0.01). These links also consolidate the correlation between management and withdrawal rights (Table 3). Exclusion : this component was found to be negatively correlated with the forest cover fraction (p<0.05), suggesting a higher degree of restriction in countries’ where the relative forest area is reduced. It was positively correlated with forest expansion rates (p<0.001), forest expansion by afforestation (p<0.01) and fraction of planted forests (p < 0.05). To a smaller extent, it was negatively correlated with the fraction of forests managed with water and soil protection goals (p<0.05), of naturally regenerated forests (p<0.05). Weak correlation trends (p<0.1) were found with annual reforestation rates and rates of forests with a protection management plan, positive and negative, respectively. This correlation pattern indicated that high exclusion rights are associated with plantation forestry, and low exclusion rights are found in areas with protections goals, multifunctional and more natural forests. Alienation: The strongest correlation found in this dataset was the positive association between alienation rights and the annual rate of reforestation (p<0.001), particularly with active plantation rather than natural regeneration. Other positive correlations found were with the rate of planted forests (p<0.01), deforestation (p<0.01) and a weak trend with forest gross value added (p<0.1). Table 6: PRIF components and SES indicators correlation values SES variables Access Withdrawal Management Exclusion Alienation Relative forest cover 0.03 -0.01 -0.03 -0.38 * -0.3 ' Reforestation 2010-2015 0.09 0.41 * 0.26 0.33 ' 0.59 *** …Naturally regenerated forest (%) 0.01 -0.5 ** -0.36 ' -0.43 * -0.5 ** …Planted forest (%) -0.01 0.5 ** 0.36 ' 0.43 * 0.5 ** Relative forest expansion 2010-2015 0.18 0.12 -0.11 0.62 *** 0.41 * ...of which afforestation 0.12 0.3 ' 0.32 ' 0.51 ** 0.17 ...of which natural expansion (%/year) 0.19 -0.4 ' -0.42 ' 0.2 -0.02 Deforestation rate 2010-2015 (%/year) 0.44 * 0.44 * 0.42 * 0.3 0.51 ** Forest area net change 2010-2015 0.01 -0.16 -0.32 ' 0.28 -0.03 Rate of forest area designated to protection of soil and water management 0.18 -0.35 -0.37 -0.47 * -0.26 Rate of forest area designated to conservation management 0.17 0.21 0.18 -0.03 0.04 Rate of protected area relative to forest cover 0.08 -0.08 -0.16 -0.34 ' -0.12 Rate of forest with a long-term management plan 0.02 -0.4 * -0.41 * -0.36 ' -0.11 Rate of forest area only used for production 0.05 -0.04 -0.09 -0.32 0.04 Rate of Gross Value-Added form forestry to GDP -0.02 -0.13 -0.22 -0.14 -0.06 Gross Value-Added from forestry per 1000ha production managed Forest 0.1 0 -0.07 0.13 0.1 Gross Value-Added form forestry per 1000ha of forest cover 0.12 0.33 ' 0.19 0.19 0.34 ' Employment in forestry and logging/production area 0.12 -0.11 -0.19 0.33 0.01 Average forest density m³/1000ha 0.01 -0.01 -0.06 0.21 0.14 Forest GS (million m³ over bark/1000ha)2015 -0.01 0.02 -0.05 0.04 0.26 Naturally regenerating forest GS (million m³ over bark/1000ha) 2015 -0.32 -0.39 * -0.39 * -0.23 -0.19 Planted forest GS (million m³ over bark/1000ha) 2015 0.16 0.45 * 0.29 0.34 ' 0.4 * Felling to increment rate 1990 0.02 0.24 0.23 -0.12 0.15 Felling to increment rate 2000 0.31 0.36 0.26 0.2 0.41 ' Felling to increment rate 2010 -0.17 -0.06 -0.06 -0.26 0.13 Felling to increment rate 2015 -0.24 -0.03 -0.06 -0.08 0.18 Average annual change in growing stock 2005-2015 -0.09 0.08 0.11 0.02 -0.31 ' Rate of public forest ownership -0.23 -0.59 ** -0.49 ** -0.12 -0.12 Rate of private forest ownership 0.35 ' 0.52 ** 0.49 ** 0.16 0.07 Rate of Unknown/other forest ownership 0.05 0.08 -0.08 0.09 -0.06 This statistical analysis is based on Spearman's rho correlation test. It displays the value of the coefficient of correlation for the compared variables, and the associated p-value: <0.1 ', <0.05 *, <0.01 **, <0.001 ***, <0.0001 **** 4.3 Principal component analysis of forest status, trend indicators and relationships with the PRIF index The inertia analysis revealed that 79.4% of the total variability was spread over the first five principal components of the PCA. The first 3 components represented 67,3% of the inertia and called for an in-depth interpretation. Principal component 1 – (Felling intensity and expansion) Felling to net increment values (1990, 2015, 2010 and 2005), forest cover, share of forests with management plan (including conservation and production management plans) were the indicators with the higher positive correlation on dimension 1. On the other hand, natural expansion, forest area expansion and net change were the most negatively correlated (Figure 2). Exclusion rights scores were the highest correlation value from the supplementary dataset followed by withdrawal and management (Table 7) Table 7: Correlation of PRIF components to PC1 Cor Cos² Access 0.040 0.002 Withdrawal 0.343 0.118 Management 0.276 0.076 Exclusion -0.660 0.436 Alienation 0.027 0.001 PRIF 0.081 0.007 Principal component 2 (growing stock and density – Forest management) Overall growing stock, from 2015 and 2010 (correlation of 0.92 and 0.91 respectively), planted forest growing stock from 2010, GVA and planted growing stock in 2015 are the most correlated variables to dimension 2, all of which a positive The supplementary variable that is best represented on this dimension is alienation (Table 8). Table 8: Correlation of PRIF components to PC2: Cor Cos² Access 0.090 0.008 Withdrawal -0.198 0.039 Management -0.271 0.074 Exclusion 0.171 0.029 Alienation 0.429 0.184 PRIF -0.163 0.027 Principal component 3 (production versus conservation) The third principal component of the PCA accounts for 16.63% of variation and represents mostly the opposition between production and conservation values. Deforestation rate, afforestation rate, forest GVA per hectare of production managed forests, reforestation rate, employment and planted forests rates are all positively related, in opposition to the rate of areas managed for protection of soils, natural regeneration and rate of forests with protection management. High deforestation and reforestation rates indicate a highly dynamic land use, concurrently favouring expansion using planted forests. Higher GVA per hectare of production managed forests indicate a higher aggregated value production per hectare and a higher density of workforce. This component is most correlated with the PRIF aggregated score, followed by alienation, withdrawal and management, respectively (Table 9). Table 9: Correlation of PRIF components to PC3 Cor Cos² Access 0.070 0.005 Withdrawal 0.311 0.097 Management 0.297 0.088 Exclusion 0.137 0.019 Alienation 0.349 0.122 PRIF 0.380 0.145 Dimensions 1 and 2 Variable correlations were first analysed on a plane representing the first two principal components which covered 50.6% of the total dataset variability. When analysing the first component plane, withdrawal and management rights followed similar directions to forest cover rates and rate of forest managed for conservation, while being the opposite directions of naturally regenerating growing stock (2010 and 2015) and relative forest area net change. Alienation rights scores are mostly represented by dimension 2 in this plane, following the similar trends to forest growing stock (2010 and 2015) and average density, in opposition to rate of private forest ownership. Lastly, exclusion rights scores were found to mostly oppose felling to net inclement rates (especially from 1990 and 2015). The correlations show that higher private property shares are correlated with a more dynamic land use, favouring reforestation using planted species and lower growing stocks overall. The distribution of variables on first plane showed a continuous spread, suggesting that cross-country variability in these indicators was hard to reduce to privileged directions. Countries best represented by dim1 (Figure 3) were Sweden (cos² 0.76) and Finland (cos² 0.43), on the right side of the distribution graph, and Italy (cos² 0.86) and France (cos² 0.73) on the left half. These were mostly characterized by fling to net increment rates versus forest net change and forest expansion rates. Meanwhile on dim 2, countries are mostly differentiated by density variables (growing stock/ha, GVA/ha) with Czechia (cos² 0.69), Germany (cos² 0.79), Slovakia (cos² 0.46) and Poland (cos² 0.65) being the best represented in the upper half, in contrast to Greece (cos² 0.70) Spain (cos² 0.65) and Norway (cos² 0.46) bellow. While not particularly well represented in this plane, it is possible to observe the Netherlands, Scotland and, to a certain degree, Romania are discriminated due to their lower-than-average forest cover, in opposition to countries like Finland and Sweden. Dimensions 1 and 3 The third dimension was analysed along with the first on the plane represented by Figure 4. This plane covers 42.95% of the dataset variability. Except for exclusion rights, which was mostly negatively correlated to dimension 1, supplementary were positively correlated to dimensions 3 and 1 (1 st quadrant). In this plane it is possible see how countries with more liberal approaches to forestry policy (exclusion rights to a lesser extent) are more focused on productivity and intensive management (represented by variables such as Forest GVA, reforestation and afforestation, employment), in opposition to extensive, naturally regenerative forests. Meanwhile on the sample distribution chart of plane 1-3 (Figure 5), countries with aggressive afforestation policies, and initially smaller forest area, the Netherlands, Denmark, Scotland, Ireland, Portugal and Belgium were the most differentiated by dimension 3. By contrast, countries of central Europe are all aggregated at the bottom, reflecting more concern for ecosystem maintenance. 5 Discussion 5.1 The PRIF is a reliable governance indicator The property rights index for forests is significantly correlated with every other governance indicator analysed in this research. Spearman’s correlation between the EPI and PRIF shows that countries that provide greater freedom to forest owners in decision making are, overall, better performers in the EPI, meaning higher protection of health and ecosystems (i.e., biodiversity, water resources, habitats). Similar findings show that political freedom [34] and income (e.g. Environmental Kuznets curve) can have a positive effect on environmental performance. Nevertheless, in the forest-specific category of tree cover loss, the correlation was reversed, implying that higher PRIF scores are correlated to higher rates of tree cover loss, likely due to anthropic pressure and trade openness [35]. Studies have also found that there is a critical level of economic freedom and environmental performance in which more economic freedom can be detrimental to environmental performance [36]. Withdrawal agreement are the rights that showed the most robust correlation with governance indices, with Withdrawal having the highest coefficient. Exclusion rights are not correlated with any of the governance indicators other than EPI’s tree cover loss indicator. The corruption perception score (CPI) and PRIF were positively correlated. It is worth mentioning again that high CPI scores indicate lower perceived corruption; therefore, this correlation indicates that higher PRIF scores tend to accompany lower perceived levels of corruption. In the case of the correlation of PRIF categories with CPI, management rights and withdrawal rights showed the highest correlation coefficients (r = 0.837 and 0.850, respectively) while being strongly significant (both with p<0.0001), suggesting that the freedom of owners to manage their assets and extract products are key issues as regards corruption matters. While greater economic freedom has been correlated to lower corruption levels [37], this correlation is not a consensus [38], as it can also be affected by other variables such as the wealth of the country [39], as well as by micro level characteristics, including gender, religion, and marital status [40], [41], [42]. In general, it is accepted that convoluted legislation and a lack of enforcement will increase corruption [43], [44]. The PRIF and HDI also presented a strong positive link, possibly explained by its strong association with economic freedom [45], [46]. Management and withdrawal rights have the highest correlation with HDI. GDP per capita and the PRIF are positively correlated, which is not surprising given the positive correlation between the PRIF and the EFI, CPI and HDI. The findings of [47] reinforce this correlation, as their results found that the lower de GDP per capita, the higher the deforestation rates. The turning point found, where deforestation and afforestation tend to equalise, is about $6,500.00 (USD) per capita, while $19,500.00 (USD) was found to be the peak value for afforestation rates. As expected, a positive correlation between the PRIF and IEF was found, suggesting that high economic freedom in a country also translates to high freedom in the exercise of forest property rights, especially regarding resource withdrawal rights, though correlations were weaker than with the other governance indicators used. 5.2 The PRIF helps to indicate how specific policies can affect forest resource characteristics The PCA analysis enabled a better understanding of how the indicators interact with each other, as well as demonstrating how the PRIF is expected to correlate with the aggregated forest indicators. Overall, the PCA results corroborate with the trends found with the simple correlations. While this research is aimed at inferring the link between forest policy and forest resources, not causal effects, the PRIF and its components were found to be highly correlated with forest cover indicators. Scores on withdrawal, exclusion, and alienation rights, as well as the aggregated indicator were strongly correlated with the promotion of planted forests (be it through reforestation or afforestation) over natural regeneration/expansion. A possible explanation for this phenomenon would be that countries with higher freedom tend to attract more investments for industrial forests, as bureaucracy and costs linked to compliance with legislation are lower [48], as well as possibly avoiding land abandonment (somewhat represented by natural forest expansion). Strong exclusion rights may increase the attractiveness of investing in forest production as it: 1) lowers uncertainty [49], [50]; 2) preserves the owners’ position as “exclusive agenda setters” for their forest and its resources [51]; 3) and gives them authority to make decisions as to how they derive income therefrom [52]. On the other hand, the results found on the PCA analysis shows that, considering the aggregated dataset, exclusion rights are negatively correlated to production intensity variables (e.g. felling to increment rates, percentage of forests area managed for production) and positively correlated with natural expansion of forests. This is most likely caused by data from countries such as Finland and Sweeden, with large forest areas, high forest GVA [53] and low exclusion rights, due to “everyman’s rights” policies (Jokamiehenoikeus in Finland and Allemansrätten in Sweden), that grants recreation use of all forests to the general public [54]. Forest economic indicators were not found to be directly correlated to PRIF nor its components, though Forest GVA/ha has high correlation values to components 2 and 3 in the PCA. Resource availability was also found to be decoupled from resource exploitation. One could argue that the effects that forest policies have, on the production and economic outputs of forests, are complex and not direct, these being influenced by many other external factors. This would explain how policies that try to increase production and harvest, have great difficulties in meeting their objectives [55]. Higher deforestation rates were found to be correlated to jurisdictions with higher PRIF scores (in both PCA and simple correlation test), while a positive trend between forest area net change and exclusion rights were only found in the PCA analysis. The overall dynamic found was that, even though countries with higher PRIF scores tend to have higher deforestation rates, they also tend to reforest at a similar proportion. This is especially clear on the third component of the PCA, where liberalization of forestry law goes hand in hand with the intensification of land use change and economic activity, in opposition to the rate of areas reserved for the protection of soil and water as well as the growing stock of naturally regenerated forests. While aggregated governance indicators are useful in many situations, the use and comparison between disaggregated data can offer important insights into the analysis of complex systems [31]. 5.3 Future developments A temporal analysis of the evolution of the PRIF index indicators, coupled with forestry indicators, might allow one to further understand how PRIF variations impact these indicators, as well as how long it might take for these impacts to occur after changes in the law. This would be especially interesting, given the context of liberalization of forest policies in East European countries over recent decades [26]. The consolidation of the PRIF’s predictive potential depends on further clarification of its component’s correlation with various forest-based indicators. In cases where the PRIF was calculated for an infra-statal jurisdiction (e.g. Germany), governance indices and SES data are at a national level and not of that specific jurisdiction. This means that national variations in the legislation might not represent the local legislation perfectly, and correlations with SES indicators might not be fully representative in these situations. Some limitations of the study are due to the shortcomings of each indicator used. The CPI, for example, is based on “expert assessments” of corruption, representing the views of a small number of people. It is conducted by expatriates of the countries and, the longer the period they have been away from their country, the less likely they are to accurately understand the situation. Moreover, expatriates’ judgement may be biased due to their economic or social conditions [56]. Several technical problems could put into question the validity of this index, including large standard errors, overly complex standardization procedures, measurement errors and biased perceptions of corruption. Then again, corruption cannot be directly measured, as it is an illegal activity, and its prevalence can only be measured with proxies such as perceptions and/or regulatory measures in place to avoid the occurrence of this activity [30], [31]. The EPI (Environmental Performance Index) was chosen to test the correlation of environmental indicators with the PRIF, but other ecological indices such as the CIEP (Composite Index of Environmental Performance) or EF (Ecological Footprint) could have been used, though giving different information, due to the use of different variables and methodology (missing imputation, normalization, weighting, and aggregation). This concern is valid for every index used in this study. Another characteristic of dealing with many indicators is that they can overlap in some respects, which could explain their strong correlation, more so than the dataset. However, with the IPRI and the PRIF, even though they have similar goals (to quantify property rights), the methodologies and datasets are quite different. Therefore, the strong correlation found between these two indices confirms the validity of the PRIF as a property rights index . Governance indices are imperfect proxies of what they are intended to measure, with inherent uncertainty that should be considered when analysing their outcomes. As for the analysis of the text of the law alone, it can have limited explanatory value, as in many cases there are major gaps between what is written and what is implemented [31]. Governance indicators based on subjective perceptions have been found to be just as important, and complementary, when analysing legal realities [30]. Despite these theoretical limitations, correlations found with the PRIF index show that aspects of the written law are linked to forest dynamics. Our data only represents European countries, therefore, given the diverse cultural, economic, and environmental contexts, the correlations found in this paper are not likely to apply outside of this context. While the PRIF has been shown to be applicable to jurisdictions outside of Europe [21], the available data was too limited at the time of writing to be included in this study. Nevertheless, the contradictory trends found in the correlations of SES variables with the PRIF index show that the pivotal range of freedom of action, as captured by the PRIF, is indeed represented in this European gradient. A working assumption for future research is therefore that countries outside Europe may extend the index’s gradient. Lastly, deciding on an adequate method when developing a composite index can be a complex issue, as it will have an influence on the quality of the information produced. The PRIF calculation is a simple average of the indicators of the bundle of rights. This implies that the original methodology rates each indicator as equally impactful on the overall index and further assumes that these categories of rights are perfectly substitutable in terms of overall freedom of action (additive model). However, this has limited significance, and other aggregation operators may be better suited to the construction of the PRIF, e.g. representing the limiting or essential nature of some categories of rights, such as limiting factor or essential factor models identified in economic and agronomic modelling studies [57], [58]. It is possible that withdrawal and management indicators express, in a satisfactory way, the essence of the PRIF’s construction. Furthermore, correlations with both governance and development indicators, as well as with SES variables, clearly suggested that these components cover distinct aspects of forest governance. Future research in this respect may try exploring in two possible directions: 1) a validation of this (exploratory) causal theoretical understanding of the interplay between categories of rights and forest status, or dynamics using case studies and National forest inventory data; 2) a correlation-based approach to a set of candidates of PRIF aggregation formulations, based on the aforementioned models. 6 Conclusion The Property Rights Index for Forestry (PRIF) is a robust method to measure the legal rules on access, management, withdrawal, exclusion, and alienation of forests, with great potential as an indicator of governance quality, as well as a relevant policy indicator for modelling. Using its individual components can help to evaluate how specific policies are linked to forest dynamics and resources. Further studies on how policy variation influences forest indicators (production, growth, and other socio-economic and ecological features) may help to highlight the dynamics related to legislation major amendments in time. Declarations Funding sources Richard Rimoli acknowledges financing from INFORMA project (Science-based integrated Forest Management for Climate Mitigation), grant agreement N°101060309, EU Horizon Research and Innovation Programme. Laura Bouriaud and Liviu Nichiforel acknowledges financing from Wildcard project (Effects of Rewilding in Forests and Agricultural Lands on Carbon Sequestration and Diversity), grant agreement N°101081177, European Union’s HORIZON Research and Innovation Actions. Author Contribution RR and JB wrote the main manuscript text. LB, AA, AN, BS LA and LC, helped develop the concept, initial methodology and on the first draft of the paper. RR, JB, LB and LN reviewed the manuscript. 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17:21:41","extension":"html","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":178230,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7683489/v1/4285068c418a6827b978e2f2.html"},{"id":94788341,"identity":"7fcbe4a3-ffe3-48c4-96d0-dd3bfddca4c1","added_by":"auto","created_at":"2025-10-30 17:21:41","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":243155,"visible":true,"origin":"","legend":"\u003cp\u003eRevised socio-ecological system (SES) framework by [13] with multiple first-tier components with examples within the forestry sector.\u003c/p\u003e\n\u003cp\u003eSolid boxes denote first-tier categories: Resource Units, Governance Systems and Actors, which contain multiple variables in the second tier as well as lower tiers. Action situations are those in which all the actions take place as inputs and are then transformed, by the interactions of multiple actors, into outcomes. Dashed arrows represent feedback from action situation to each of the top-tier categories. The dotted-and-dashed line that surrounds the elements inside the figure shows that the focal SES can be considered as a whole, but that exogenous influences can affect any component of the SES. Those influences might emerge from the dynamic operation of processes on larger or smaller scales than that of the focal SES.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7683489/v1/a1aad44b4e0e5bf62666d8ad.jpeg"},{"id":94825826,"identity":"20c040ab-a376-45eb-8b05-b243b78b6318","added_by":"auto","created_at":"2025-10-31 06:50:46","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":304575,"visible":true,"origin":"","legend":"\u003cp\u003ePCA correlation circle in the first component plane (1-2). Variables plotted are those with higher quality of representation. Black: forest status and trend indicators contributing to the PCA. Blue: PRIF components projected on the correlation circle (supplementary variables).\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7683489/v1/46fcea31de6aca16fffbb970.jpeg"},{"id":94825535,"identity":"5d30e6a4-75e4-4489-821a-352ed416b737","added_by":"auto","created_at":"2025-10-31 06:50:23","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":152966,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of individuals (countries) in the first plane of the PCA analysis (1-2)\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7683489/v1/40d3a1e0eca463db8c492ab5.jpeg"},{"id":94825482,"identity":"790869c2-3a56-46f0-905e-f94d0a339010","added_by":"auto","created_at":"2025-10-31 06:50:21","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":265912,"visible":true,"origin":"","legend":"\u003cp\u003ePCA correlation circle in the first component plane (1-3). Variables plotted are those with higher quality of representation. Black: forest status and trend indicators contributing to the PCA. Blue: PRIF components projected on the correlation circle (supplementary variables)\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7683489/v1/18ac1b83e211252c8131010e.jpeg"},{"id":94788347,"identity":"8a529304-ae06-4fae-bb1f-257c865dd12c","added_by":"auto","created_at":"2025-10-30 17:21:41","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":136622,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of individuals (countries) in the Dim 1-3 plane of the PCA analysis\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7683489/v1/e973f3879834bc7dce791d4e.jpeg"},{"id":94827374,"identity":"db34ff02-5508-433e-8a4f-97da042addb7","added_by":"auto","created_at":"2025-10-31 06:57:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2315894,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7683489/v1/8e0c19c0-1881-4f23-93e5-04e341529f45.pdf"},{"id":94788338,"identity":"a8c6031c-48ed-42e0-810d-f1cc0d561846","added_by":"auto","created_at":"2025-10-30 17:21:41","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":31336,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7683489/v1/c8edd54980df7668bd089135.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Property Rights Index in Forestry (PRIF): a new governance indicator with predictive capacity for forest status and dynamic across Europe","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe governance of forest resources in Europe, traditionally managed through command and control by regulatory policies and dominated by state forestry [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] is facing significant challenges due to various factors such as increasing societal pressure, developmental projects, and climate change [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], alongside with the multiplication of social and policy expectations upon the forest management [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Implementing the principles of sustainable forest management (SFM) implies the choice of a set of policy instruments and a certain level of governmental coercion, differentiating therefore various governance arrangements and models [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. For example, there are systems defined by a strong emphasis on command-and-control instruments (also known as \u0026ldquo;sticks\u0026rdquo;) that create a highly restrictive regulatory framework [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This is often the case in the post-socialist countries from Central, East and South Europe, in which the state prescribes the normative set of mandatory rules for SFM [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, p. 201].\u003c/p\u003e\u003cp\u003eThere are many definitions of the term governance that can be found in the literature, each with its own variation on the nature of the governance or on its components [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. For example, for [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], forest governance is a complex system composed by: \u0026ldquo;a) all formal and informal, public and private regulatory structures, i.e. institutions consisting of rules, norms, principles, decision procedures, concerning forests, their utilisation and their conservation, b) the interactions between public and private actors therein and c) the effects of either on forests.\u0026rdquo;.\u003c/p\u003e\u003cp\u003eA simplified but systemic framework that characterizes the use and management of natural resources is the Socio Ecological System (SES) framework, proposed by Ostrom [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Within this framework, forest management can be conceptualised as an action situation where governance factors, resource factors and actors interact (Fig.\u0026nbsp;1). In the Action Situation interaction (I), participants receive information, select actions according to that first input, engage in patterns of interactions, and analyse the outcomes (O) of these interactions [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The resource system (RS) is the biophysical system from which resource units (RU) are consumed, used as inputs of production or other goods and services. The prevailing set of processes, in which the rules shape the behaviour of actors, can be defined as a governance system (GS) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Potential exogenous influences from a broader socio-environmental context are classified as related ecosystems (ECO, Fig.\u0026nbsp;1). Therefore, the SES framework allows the appraisal of systems in which users extract resource units from a system and share the responsibility for the system\u0026rsquo;s maintenance, according to rules and procedures determined within a governance system [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and other external forces. Therefore, an accurate capture of the broader context, featuring governance characteristics, is necessary for a better understand the mechanisms and influences in place.\u003c/p\u003e\u003cp\u003eGovernance systems can, to some extent, be characterised with the help of indicators. According to [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] \u0026ldquo;the term governance indicator refers to an eclectic set of measures covering the wide range of governance topics\u0026rdquo;. Indicators are continuously created, tested and updated, depicting the evolution of social, political, legal, economic or environmental aspects of governance [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Increasing importance is paid to specialised forest governance indicators, at local level or for specific governance purposes [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFreedom in decision-making regarding forest resource and services (RS) management, as an outcome of formal rules governing forest management [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], can be described with the Property Rights Index in Forestry (PRIF) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The index is based on the \u003cem\u003ede jure\u003c/em\u003e rights of forest owners over their forest property. It provides an aggregate score on the forest owners\u0026rsquo; rights, in different countries and jurisdictions, as well as scores on different bundles of rights. It has been shown to applicable in a variety of institutional contexts across the globe, including, for example, community forests [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhile this quantification has proved to be useful for comparative purposes, the index capacity to depict the impacts of forest governance has not yet been investigated. Furthermore, it is of equal interest to evaluate the accuracy and sensitivity of this index on a more continuous gradient of forest governance and associated forest dynamic. This paper focuses on 31 countries of the European continent, aiming to analyse: 1) to what extent the PRIF, and its components, correlates with consolidated governance indicators (reflecting socio-economic, policy and environmental development); 2) to what extent the PRIF is able to capture and explain the current state and trends of forest resources, environmental services as well as if it can be used for predictive purposes.\u003c/p\u003e"},{"header":"2 Material","content":"\u003cp\u003eThe core indicator under investigations is the Property Right Index in Forestry. The data collection was based on the 31 European countries used by [20]. Additional data encompassed in this study included (i) governance indicators , representing distinct aspects (economic, social, environmental, political) of the SES framework, including more specific indicators for forests as part of the environment, (ii) \u0026nbsp;an array of 30 forest reporting variables describing status and trends of the countries\u0026rsquo; forests, collected from both the Forest Resource assessment (FRA) of the UN/FAO [22], and the State of Europe\u0026apos;s forests assessment of Forest Europe [23]. \u0026nbsp;Most of the data used is relative to 2015, the year in which the PRIF data was calculated (Annex1).\u003c/p\u003e\n\u003ch2\u003e2.1 \u0026nbsp; \u0026nbsp; The Property Rights Index for Forestry (PRIF)\u003c/h2\u003e\n\u003cp\u003eBased on the framework proposed by [24], the Property Rights Index for Forestry (PRIF) has the objective of assessing and quantifying the rights of forest owners. It is structured according to the 5 Property Rights Categories (PRC): Access, Management, Alienation, Exclusion and Withdrawal rights.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe PRIF scores the freedom, of each PRC as well as an overall score, that forest owners enjoy in each analysed jurisdiction. Since different states have different regulations regarding natural resources in their territory, the index is useful to compare rights and obligations of owners and users of forest resources across countries. The dataset is composed of 31 European jurisdictions\u0026nbsp;[20]. While data is available for countries in non-European jurisdictions\u0026nbsp;[21], they remain too scarce to be embraced in the present study.\u003c/p\u003e\n\u003cp\u003eSince its conceptualization, the PRIF has been applied in many cases including (i) the analysis of the evolution of forestry property rights over different periods\u0026nbsp;[25], [26], [27]; (ii) the assessment and comparison of regulatory frameworks between different countries and how they affect cooperation\u0026nbsp;[28]; (iii) the analysis of how different political backgrounds impacted on current forestry property rights\u0026nbsp;[25]; and (iv) it has also been used as one of the explanatory factors of forest expansion trends in Europe\u0026nbsp;[29].\u003c/p\u003e\n\u003ch2\u003e2.2 \u0026nbsp; \u0026nbsp; Governance indicators\u003c/h2\u003e\n\u003cp\u003eGovernance indicator aggregation has been shown to be effective in the estimation of governance trends at country level, providing enhanced reliability and precision [30]. \u0026nbsp;The SES framework [14] distinguishes between indicators concerning the governance system, actors, and resource system, leading to the following selection of indicators: (i) \u003cstrong\u003eGovernance\u003c/strong\u003e: International Property Rights Index (IPRI), Rule of Law Index (RLI), Corruption Perception Index (CPI), Index of Economic Freedom (IEF), (ii) \u003cstrong\u003eActors\u003c/strong\u003e: Human Development Index (HDI), (iii) \u003cstrong\u003eResource system:\u0026nbsp;\u003c/strong\u003eGDP per capita, Environmental Performance Index (EPI) .\u003c/p\u003e\n\u003cp\u003eOf note, these different indices can overlap some of these different fields, also between each other, and they are classified here for the sake of simplicity. These indicators and their methodology are further described in Annex 2.\u003c/p\u003e\n\u003cp\u003eTable 1: governance indicators\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"605\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cem\u003eIndex\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cem\u003eField\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cem\u003eOrganisation\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003eYear\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cem\u003eInternational Property rights Index\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eGovernance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eProperty Rights Alliance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cem\u003eRule of Law Index\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eGovernance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eWorld Justice project\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cem\u003eCorruption perception index (CPI)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eGovernance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eTransparency International\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cem\u003eIndex Economic freedom\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eGovernance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eThe Heritage Foundation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2022\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cem\u003eHuman Development Index\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eActors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eUnited Nations Development Programme\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2021-2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cem\u003eGDP per capita\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eResource system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eNational statistical agencies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2021-2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cem\u003eEnvironmental Performance Index\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eResource system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eYale Centre for Environmental Law \u0026amp; Policy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e2.3 \u0026nbsp; \u0026nbsp; Forest indicators\u003c/h2\u003e\n\u003cp\u003eA set of indicators of forest dynamics, environmental performance, silviculture/production, and ownership was selected (Table 2). Sources for each of the variables were indicated on Annex 1.\u003c/p\u003e\n\u003cp\u003eTable 2: Indicators of forest status and trends, categorised.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" title=\"Related social, economic systems, forestry social indicators (table 1)\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 257px;\"\u003e\n \u003cp\u003e\u003cem\u003eIndicator\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cem\u003eIndicator Category\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003eSES Category\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cem\u003eUnit/Scale\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 257px;\"\u003e\n \u003cp\u003e\u003cem\u003eRelative forest cover 2015\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eForest dynamic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eResource units\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 257px;\"\u003e\n \u003cp\u003e\u003cem\u003eReforestation 2010-2015\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eForest dynamic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e%/year\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 257px;\"\u003e\n \u003cp\u003e\u003cem\u003enaturally\u003c/em\u003e\u003cem\u003e\u0026nbsp;regenerated forest (%)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eForest dynamic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e%/year\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 257px;\"\u003e\n \u003cp\u003e\u003cem\u003eplanted forest (%)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eForest dynamic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e%/year\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 257px;\"\u003e\n \u003cp\u003e\u003cem\u003eRelative forest expansion 2010-2015\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eForest dynamic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eOutcome\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e%/year\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 257px;\"\u003e\n \u003cp\u003e\u003cem\u003e...of which afforestation\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eForest dynamic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e%/year\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 257px;\"\u003e\n \u003cp\u003e\u003cem\u003e...of which natural expansion (%/year)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eForest dynamic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e%/year\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 257px;\"\u003e\n \u003cp\u003e\u003cem\u003eDeforestation rate 2010-2015\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eForest dynamic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e%/year\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 257px;\"\u003e\n \u003cp\u003e\u003cem\u003eForest area net change 2010-2015\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eForest dynamic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e%/year\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 257px;\"\u003e\n \u003cp\u003e\u003cem\u003eRate of forest area designated to protection of soil and water management 2015\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eenvironmental performance\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eResource systems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 257px;\"\u003e\n \u003cp\u003e\u003cem\u003eRate of forest area designated to conservation management 2015\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eenvironmental performance\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eResource systems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 257px;\"\u003e\n \u003cp\u003e\u003cem\u003eRate of protected area relative to forest cover 2015\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eenvironmental performance\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eResource systems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 257px;\"\u003e\n \u003cp\u003e\u003cem\u003eRate of forest with a long-term management plan 2015\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003esilviculture/production\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eResource systems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 257px;\"\u003e\n \u003cp\u003e\u003cem\u003eRate of forest area only used for production 2015\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003esilviculture/production\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eResource systems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 257px;\"\u003e\n \u003cp\u003e\u003cem\u003eRate of Gross Value-Added from forestry to GDP 2015\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003esilviculture/production\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 257px;\"\u003e\n \u003cp\u003e\u003cem\u003eGross Value-Added form forestry per 1000ha production managed Forest 2015\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003esilviculture/production\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eMillion \u0026euro; / 1000ha\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 257px;\"\u003e\n \u003cp\u003e\u003cem\u003eGross Value-Added form forestry per 1000ha of forest cover 2015\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003esilviculture/production\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eMillion \u0026euro; / 1000ha\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 257px;\"\u003e\n \u003cp\u003e\u003cem\u003eEmployment in forestry and logging/production area 2015\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003esilviculture/production\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eResource units\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eWorkers/ha\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 257px;\"\u003e\n \u003cp\u003e\u003cem\u003eAverage forest density 2015\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003esilviculture/production\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eResource units\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003em\u0026sup3;/1000ha\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 257px;\"\u003e\n \u003cp\u003e\u003cem\u003eForest Growing Stock in 2015\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003esilviculture/production\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eResource units\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003emillion m\u0026sup3; over bark/1000ha\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 257px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026hellip;of which naturally regenerating forest\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003esilviculture/production\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eResource units\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003emillion m\u0026sup3; over bark/1000ha\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 257px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026hellip;of which planted forest\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003esilviculture/production\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eResource units\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003emillion m\u0026sup3; over bark/1000ha\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 257px;\"\u003e\n \u003cp\u003e\u003cem\u003eFelling to increment rate 1990\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003esilviculture/production\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 257px;\"\u003e\n \u003cp\u003e\u003cem\u003eFelling to increment rate 2000\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003esilviculture/production\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 257px;\"\u003e\n \u003cp\u003e\u003cem\u003eFelling to increment rate 2010\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003esilviculture/production\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 257px;\"\u003e\n \u003cp\u003e\u003cem\u003eFelling to increment rate 2015\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003esilviculture/production\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 257px;\"\u003e\n \u003cp\u003e\u003cem\u003eAverage annual change in growing stock 2005-2015\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003esilviculture/production\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 257px;\"\u003e\n \u003cp\u003e\u003cem\u003eRate of public forest ownership in 2015\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eownership\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eResource systems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 257px;\"\u003e\n \u003cp\u003e\u003cem\u003eRate of private forest ownership in 2015\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eownership\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eResource systems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 257px;\"\u003e\n \u003cp\u003e\u003cem\u003eUnknown/other ownership in 2015\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eownership\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eResource systems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe data collected are relative to the year 2015, as the original PRIF dataset was calculated based on laws in force in 2015. However, there were a few exceptions including: (i) growing stock change as an annual average and was taken between 2005 and 2015, (ii) indicators of forest area dynamic from 2010 to 2015, (iii) felling-to-net increment ratios from 1990, 2000, and 2010 were also included in the dataset.\u003c/p\u003e"},{"header":"3 Methods","content":"\u003ch2\u003e3.1 \u0026nbsp; \u0026nbsp; Correlation tests\u003c/h2\u003e\n\u003cp\u003eSystematic correlation tests were performed between the PRIF index and the sets of governance and forest indicators. Since the PRIF index relies on an additive aggregation of different bundles of rights, that have no prior reason of being substitutable, the 5 components of the PRIF index were also correlated to these indicators.\u003c/p\u003e\n\u003cp\u003eThe Spearman\u0026apos;s rank correlation test was used, as an efficient method to test the null hypothesis (no relationship between the 2 tested variables), resilient to the presence of outliers and not assuming any linearity in the relationships. Also, since governance indices are defined at broad scale, and inherently subjected to measurement and estimate errors\u0026nbsp;[30], limits of test significance for detecting relevant correlations tests were relaxed with respect to standard practices, and p-values smaller than 0.1 (\u0026lsquo;) were considered\u0026nbsp;[31], alongside the traditional limits of \u0026nbsp;p \u0026lt; 0.05 (*), \u0026lt; 0.01(**), \u0026lt; 0.001 (***) and \u0026lt; 0.0001(****).\u003c/p\u003e\n\u003cp\u003eThe initial hypotheses evaluated were that the PRIF should be closest to RLI, IPRI and IEF indicators, as they feature values related to rule of law and freedom of action, i.e. what the PRIF intends to measure for the forestry sector, in particular. In the cases where the PRIF was calculated on a state legislation scale (Germany, Spain, Switzerland, and Great Britain) due to data availability, these values were also compared to the national variables.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e3.2 \u0026nbsp; \u0026nbsp; Principal Component Analysis (PCA)\u003c/h2\u003e\n\u003cp\u003eThe PCA analysis was conducted with 33 variables (Table 2 plus growing stock values of 2010) of the 31 jurisdictions analysed to further explore and synthetise the trends already uncovered partially by correlation analysis. The PCA was calculated using a weighted dataset, indexed to the forest area in 2015, intended to avoid the excessive load of small countries\u003c/p\u003e\n\u003cp\u003eTo inquire whether and how variations in these indicators across countries can align with the PRIF index or its components, this data was introduced as supplementary variables (not contributing to the calculation of principal components) in the analysis.\u003c/p\u003e\n\u003cp\u003eThe PCA was calculated using the R software along with the FactoMineR [32] package. In order to calculate the PCA, missing values were imputed, using the missMDA package, employing the leave-one-out (loo) cross-validation method, to estimate the number of dimensions of the PCA [33].\u003c/p\u003e"},{"header":"4 Results","content":"\u003ch2\u003e4.1 \u0026nbsp; \u0026nbsp; PRIF aligns with governance indicators\u003c/h2\u003e\n\u003cp\u003eAs a first scrutiny, the PRIF scores was correlated to its own components. The PRIF was found mostly influenced by the withdrawal, management, and exclusion right, while access and alienation were the least correlated to the overall score (Table 3\u003cstrong\u003e).\u003c/strong\u003e Management and withdrawal right scores were themselves strongly correlated; this thus implied a control of the PRIF score by these two variables. The weak correlations between other components justified the analysis of each individual component of the index in subsequent correlation analyses.\u003c/p\u003e\n\u003cp\u003eTable 3: Correlation matrix of the PRIF index for 31 European countries\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"442\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003ePRIF\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cem\u003eaccess\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003ewithdrawal\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003emanagement\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cem\u003eexclusion\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cem\u003eAccess\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.45 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cem\u003eWithdrawal\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.90 ****\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.39 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cem\u003eManagement\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.87 ****\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.43 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.87 ****\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cem\u003eExclusion\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.57 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.31 \u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cem\u003eAlienation\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.44 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.33 \u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 442px;\"\u003e\n \u003cp\u003e\u003cem\u003eCountries as listed in\u0026nbsp;\u003c/em\u003e\u003cem\u003e[20]\u003c/em\u003e\u003cem\u003e. Spearman correlation coefficient. p-value: \u0026lt;0.1 \u0026apos;, \u0026lt;0.05 *, \u0026lt;0.01 **, \u0026lt;0.001 ***, \u0026lt;0.0001 ****\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv align=\"center\"\u003e\u003cbr\u003e\u003c/div\u003e\n\u003cp\u003eRemarkably, the PRIF and its components correlated strongly with most governance indicators evaluated (Table 4). The strongest correlations found were with the IPRI, RLI and CPI. As notable exceptions, EPI\u0026rsquo;s ecosystem service (EPI_ECO) and climate change policy (EPI_CCP) did not correlate with the PRIF, nor with any of its components. However, the PRIF correlated positively with the global EPI index. Exclusion and alienation rights were found to show the weakest correlations with governance indices, exclusion rights being negatively correlated with EPI\u0026rsquo;s tree cover loss subcategory, while alienation rights were positively linked with the index of economic freedom (IEF), showing a strong relationship of the component\u0026rsquo;s score and its definition.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe strongest correlations were found using the PRIF, withdrawal and management. The correlations found were strong across three SES categories of indicators, resource, actors, and governance, and as such, did not allow any significant differentiation between them. The results also confirmed the hypothesis of stronger associations to IPRI, RLI and IEF (correlations \u0026gt; 0.8). Nevertheless, IEF was slightly less correlated than the other two, indicating a tighter association of the PRIF with right-oriented rather than economy-oriented indicators. CPI and HDI were also found to be highly correlated with PRIF, withdrawal and management rights.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4: Correlation values - PRIF components and governance indicators\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"597\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003ePRIF\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cem\u003eAccess\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cem\u003eWithdrawal\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003eManagement\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003eExclusion\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003eAlienation\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cem\u003eResource system\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eEPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.49 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e0.63 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.57 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eEPI_ECO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eEPI_ECO_tcl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.55 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e-0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e-0.52 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.43 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.36 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.35 \u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cem\u003eGovernance\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eEPI_CCP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cem\u003eActors\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eHDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.68 ****\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e0.52 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e0.73 ****\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.8 ****\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.3 \u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cem\u003eResource system\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eGDP per capita\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.75 ****\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e0.52 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e0.76 ****\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.78 ****\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e0.35 \u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cem\u003eGovernance\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eCPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.78 ****\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e0.48 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e0.84 ****\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.85 ****\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.34 \u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cem\u003eGovernance\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eIPRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.76 ****\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e0.44 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e0.86 ****\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.81 ****\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cem\u003eGovernance\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eIEF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.51 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e0.56 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.47 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.41 *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cem\u003eGovernance\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eRLI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.79 ****\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e0.41 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e0.87 ****\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.86 ****\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThis statistical analysis is based on Spearman\u0026rsquo;s rho correlation test. It displays the value of the coefficient of correlation for the compared variables, and the associated p-value: \u0026lt;0.1 \u0026apos;, \u0026lt;0.05 *, \u0026lt;0.01 **, \u0026lt;0.001 ***, \u0026lt;0.0001 ****.\u003c/p\u003e\n\u003cp\u003eThe PRIF and its 5 subsequent property rights categories (Access, Management, Withdrawal, Exclusion and Alienation) are correlated to 7 governance indicators: Environmental performance Index (EPI), Human development Index (HDI), GDP per capita (HDI_gdpcap), Corruption perception Index (CPI), International Property Rights Index (IPRI), Rule of law index (RLI), Index of Economic Freedom (IEF) as well as some specific components of these indices EPI_ ECO (ecosystem services) , EPI_ECO_tcl (tree cover loss) and EPI_CCP (Climate change policy) .\u003c/p\u003e\n\u003ch2\u003e4.2 \u0026nbsp; \u0026nbsp; PRIF and its components explain the forest status and the deforestation rates\u003c/h2\u003e\n\u003cp\u003eThe correlation analysis of the PRIF index revealed a set of significant links, including positive correlations with annual reforestation rates (p\u0026lt;0.01), deforestation rates (p\u0026lt;0.01), rate of planted forests (p\u0026lt;0.01), private ownership fraction (p\u0026lt;0.01) and forest expansion rate via afforestation (p\u0026lt;0.01). That both deforestation and reforestation rates positively correlated with the PRIF index emphasizes well the opposed effects that freedom of action can yield and suggested the polyvalence of this index in this respect.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNegative correlations were also identified, with the fraction of naturally regenerated forests (p\u0026lt;0.01), ration of public forest ownership (p\u0026lt;0.01) and relative forests cover with water and soil management plans (p\u0026lt;0.05). While current levels of felling-to-increment ratios did not correlate with PRIF (see also [29]), felling rates in the 2000s were found to be fairly, and positively, correlated to PRIF. In view of the positive correlations of PRIF with the rate of planted forests and expansion via afforestation, these suggested that more intensive harvests are compensated by a more proactive forest renewal, indicating compatibility with sustainable forest management principals.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOverall the PRIF index clearly defined a broad gradient contrasting public forestry (favouring natural ecological regeneration processes and ecosystem services) and an intensive and private forestry (favouring felling and plantation). The highlighted positive correlation of PRIF with deforestation suggests a possible adverse effects of freedom of action (Table 5).\u003c/p\u003e\n\u003cp\u003eTable 5: PRIF and SES indicators correlation values\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"513\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003eSES variables\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cem\u003ePRIF\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003eRelative forest cover\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e-0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003eReforestation 2010-2015\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.46 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003eNaturally regenerated forest (%)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e-0.55 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003ePlanted forest (%)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.55 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003eRelative forest expansion 2010-2015\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003e...of which afforestation\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.5 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003e...of which natural expansion (%/year)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e-0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003eDeforestation rate 2010-2015 (%/year)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.59 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003eForest area net change 2010-2015\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003eRate of forest area designated to protection of soil and water management\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e-0.47 *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003eRate of forest area designated to conservation management\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003eRate of protected area relative to forest cover\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e-0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003eRate of forest with a long-term management plan\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e-0.48 *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003eRate of forest area only used for production\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003eRate of Gross Value-Added form forestry to GDP\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e-0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003eGross Value-Added form forestry per 1000ha production managed Forest\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003eGross Value-Added form forestry per 1000ha of forest cover\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003eEmployment in forestry and logging/production area\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003eAverage forest density\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003eForest GS\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003eNaturally regenerating forest\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e-0.42 *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003ePlanted forest\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.51 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003eFelling to increment rate 1990\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003eFelling to increment rate 2000\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.48 *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003eFelling to increment rate 2010\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e-0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003eFelling to increment rate 2015\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003eAverage annual change in growing stock 2005-2015\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003eRate of public forest ownership\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e-0.48 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003eRate of private forest ownership\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.49 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003eRate of Unknown/other forest ownership\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThis statistical analysis is based on Spearman\u0026apos;s rho correlation test. It displays the value of the coefficient of correlation for the compared variables, and the associated p-value: \u0026lt;0.1 \u0026apos;, \u0026lt;0.05 *, \u0026lt;0.01 **, \u0026lt;0.001 ***, \u0026lt;0.0001 ****. In the cases where the PRIF was calculated based on state legislation, the values were also compared to the national variables.\u003c/p\u003e\n\u003cp\u003eIn general, correlations with the PRIF components were weaker than those calculated with the aggregated index, further consolidating the relevance of an aggregated index. Specific indicators targeting forest protection, economic return or employment did not correlate to any of the individual components (\u003c/p\u003e\n\u003cp\u003eTable 6).\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAccess\u003c/u\u003e: A positive correlation (p\u0026lt;0.05) with deforestation was identified, stressing the relationship between forest degradation and access. A weak positive trend with the private ownership fraction (p\u0026lt;0.1) was also found.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eWithdrawal\u003c/u\u003e: Withdrawal rights were positively correlated with the fraction of planted forests (p\u0026lt;0.01), the share of private forests (p\u0026lt;0.01) in the country, annual reforestation rate (p\u0026lt;0.05), and deforestation rate (p\u0026lt;0.05). They were found to be negatively correlated with naturally regenerated forest (p\u0026lt;0.01) and share of public ownership (p\u0026lt;0.01). These relationships further corroborate the existence of a gradient of forestry intensity across European countries, associated to the freedom of forest ownership. Weak trends (p\u0026lt;0.1) were also found with reforestation types, as mirrored in the correlations of the global PRIF index (see 3.3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eManagement\u003c/u\u003e: the management right component was found to be directly related to distribution of forest ownerships, being positively correlated with the fraction of private forests (p\u0026lt;0.01) and deforestation rates (p\u0026lt;0.05), while negatively correlated with the fraction of public forests (p\u0026lt;0.01). These links also consolidate the correlation between management and withdrawal rights (Table 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eExclusion\u003c/u\u003e: this component was found to be negatively correlated with the forest cover fraction (p\u0026lt;0.05), suggesting a higher degree of restriction in countries\u0026rsquo; where the relative forest area is reduced. It was positively correlated with forest expansion rates (p\u0026lt;0.001), forest expansion by afforestation (p\u0026lt;0.01) and fraction of planted forests (p \u0026lt; 0.05). To a smaller extent, it was negatively correlated with the fraction of forests managed with water and soil protection goals (p\u0026lt;0.05), of naturally regenerated forests (p\u0026lt;0.05). Weak correlation trends (p\u0026lt;0.1) were found with annual reforestation rates and rates of forests with a protection management plan, positive and negative, respectively. This correlation pattern indicated that high exclusion rights are associated with plantation forestry, and low exclusion rights are found in areas with protections goals, multifunctional and more natural forests.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAlienation:\u003c/u\u003e The strongest correlation found in this dataset was the positive association between alienation rights and the annual rate of reforestation (p\u0026lt;0.001), particularly with active plantation rather than natural regeneration. Other positive correlations found were with the rate of planted forests (p\u0026lt;0.01), deforestation (p\u0026lt;0.01) and a weak trend with forest gross value added (p\u0026lt;0.1).\u003c/p\u003e\n\u003cp\u003eTable 6: PRIF components and SES indicators correlation values\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eSES variables\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eAccess\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eWithdrawal\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eManagement\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eExclusion\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eAlienation\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eRelative forest cover\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.38 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.3 \u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eReforestation 2010-2015\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.41 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.33 \u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.59 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026hellip;Naturally regenerated forest (%)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.5 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.36 \u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.43 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.5 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026hellip;Planted forest (%)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.5 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.36 \u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.43 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.5 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eRelative forest expansion 2010-2015\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.62 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.41 *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e...of which afforestation\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.3 \u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.32 \u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.51 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e...of which natural expansion (%/year)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.4 \u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.42 \u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eDeforestation rate 2010-2015 (%/year)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.44 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.44 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.42 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.51 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eForest area net change 2010-2015\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.32 \u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eRate of forest area designated to protection of soil and water management\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.47 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eRate of forest area designated to conservation management\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eRate of protected area relative to forest cover\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.34 \u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eRate of forest with a long-term management plan\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.4 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.41 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.36 \u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eRate of forest area only used for production\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eRate of Gross Value-Added form forestry to GDP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eGross Value-Added from forestry per 1000ha production managed Forest\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eGross Value-Added form forestry per 1000ha of forest cover\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.33 \u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.34 \u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eEmployment in forestry and logging/production area\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eAverage forest density m\u0026sup3;/1000ha\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eForest GS (million m\u0026sup3; over bark/1000ha)2015\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eNaturally regenerating forest GS (million m\u0026sup3; over bark/1000ha) 2015\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.39 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.39 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ePlanted forest GS (million m\u0026sup3; over bark/1000ha) 2015\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.45 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.34 \u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.4 *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eFelling to increment rate 1990\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eFelling to increment rate 2000\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.41 \u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eFelling to increment rate 2010\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eFelling to increment rate 2015\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eAverage annual change in growing stock 2005-2015\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.31 \u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eRate of public forest ownership\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.59 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.49 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eRate of private forest ownership\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.35 \u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.52 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.49 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eRate of Unknown/other forest ownership\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThis statistical analysis is based on Spearman\u0026apos;s rho correlation test. It displays the value of the coefficient of correlation for the compared variables, and the associated p-value: \u0026lt;0.1 \u0026apos;, \u0026lt;0.05 *, \u0026lt;0.01 **, \u0026lt;0.001 ***, \u0026lt;0.0001 ****\u003c/p\u003e\n\u003ch2\u003e4.3 \u0026nbsp; \u0026nbsp; Principal component analysis of forest status, trend indicators and relationships with the PRIF index\u003c/h2\u003e\n\u003cp\u003eThe inertia analysis revealed that 79.4%\u003csup\u003e\u0026nbsp;\u003c/sup\u003eof the total variability was spread over the first five principal components of the PCA. The first 3 components represented 67,3% of the inertia and called for an in-depth interpretation.\u003c/p\u003e\n\u003cp\u003ePrincipal component 1 \u0026ndash; (Felling intensity and expansion)\u003c/p\u003e\n\u003cp\u003eFelling to net increment values (1990, 2015, 2010 and 2005), forest cover, share of forests with management plan (including conservation and production management plans) were the indicators with the higher positive correlation on dimension 1. On the other hand, natural expansion, forest area expansion and net change were the most negatively correlated (Figure 2). Exclusion rights scores were the highest correlation value from the supplementary dataset followed by withdrawal and management (Table 7)\u003c/p\u003e\n\u003cp\u003eTable 7: Correlation of PRIF components to PC1\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cem\u003eCor\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cem\u003eCos\u0026sup2;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cem\u003eAccess\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cem\u003eWithdrawal\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e0.343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cem\u003eManagement\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e0.276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cem\u003eExclusion\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e-0.660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e0.436\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cem\u003eAlienation\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cem\u003ePRIF\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ePrincipal component 2 (growing stock and density \u0026ndash; Forest management)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOverall growing stock, from 2015 and 2010 (correlation of \u0026nbsp;0.92 and 0.91 respectively), planted forest growing stock from 2010, GVA and planted growing stock in 2015 are the most correlated variables to dimension 2, all of which a positive The supplementary variable that is best represented on this dimension is alienation (Table 8).\u003c/p\u003e\n\u003cp\u003eTable 8: Correlation of PRIF components to PC2:\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cem\u003eCor\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cem\u003eCos\u0026sup2;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cem\u003eAccess\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cem\u003eWithdrawal\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e-0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cem\u003eManagement\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e-0.271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cem\u003eExclusion\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cem\u003eAlienation\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e0.429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cem\u003ePRIF\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e-0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ePrincipal component 3 (production versus conservation)\u003c/p\u003e\n\u003cp\u003eThe third principal component of the PCA accounts for 16.63% of variation and represents mostly the opposition between production and conservation values. Deforestation rate, afforestation rate, forest GVA per hectare of production managed forests, reforestation rate, employment and planted forests rates are all positively related, in opposition to the rate of areas managed for protection of soils, natural regeneration and rate of forests with protection management. High deforestation and reforestation rates indicate a highly dynamic land use, concurrently favouring expansion using planted forests. Higher GVA per hectare of production managed forests indicate a higher aggregated value production per hectare and a higher density of workforce. This component is most correlated with the PRIF aggregated score, followed by alienation, withdrawal and management, respectively (Table 9).\u003c/p\u003e\n\u003cp\u003eTable 9:\u0026nbsp;Correlation of PRIF components to PC3\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cem\u003eCor\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cem\u003eCos\u0026sup2;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cem\u003eAccess\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cem\u003eWithdrawal\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e0.311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cem\u003eManagement\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e0.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cem\u003eExclusion\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cem\u003eAlienation\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e0.349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cem\u003ePRIF\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e0.380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eDimensions 1 and 2\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eVariable correlations were first analysed on a plane representing the first two principal components which covered 50.6% of the total dataset variability.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhen analysing the first component plane, \u003cu\u003ewithdrawal and management\u003c/u\u003e rights followed similar directions to forest cover rates and rate of forest managed for conservation, while being the opposite directions of naturally regenerating growing stock (2010 and 2015) and relative forest area net change. \u003cu\u003eAlienation\u003c/u\u003e rights scores are mostly represented by dimension 2 in this plane, following the similar trends to forest growing stock (2010 and 2015) and average density, in opposition to rate of private forest ownership. Lastly, \u003cu\u003eexclusion\u003c/u\u003e rights scores were found to mostly oppose felling to net inclement rates (especially from 1990 and 2015). The correlations show that higher private property shares are correlated with a more dynamic land use, favouring reforestation using planted species and lower growing stocks overall.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe distribution of variables on first plane showed a continuous spread, suggesting that cross-country variability in these indicators was hard to reduce to privileged directions.\u003c/p\u003e\n\u003cp\u003eCountries best represented by dim1 (Figure 3) were Sweden (cos\u0026sup2; 0.76) and Finland (cos\u0026sup2; 0.43), on the right side of the distribution graph, and Italy (cos\u0026sup2; 0.86) and France (cos\u0026sup2; 0.73) on the left half. These were mostly characterized by fling to net increment rates versus forest net change and forest expansion rates. Meanwhile on dim 2, countries are mostly differentiated by density variables (growing stock/ha, GVA/ha) with Czechia (cos\u0026sup2; 0.69), Germany (cos\u0026sup2; 0.79), Slovakia (cos\u0026sup2; 0.46) \u0026nbsp;and Poland (cos\u0026sup2; 0.65) being the best represented in the upper half, in contrast to Greece (cos\u0026sup2; 0.70) Spain (cos\u0026sup2; 0.65) and Norway (cos\u0026sup2; 0.46) \u0026nbsp;bellow. While not particularly well represented in this plane, it is possible to observe the Netherlands, Scotland and, to a certain degree, Romania are discriminated due to their lower-than-average forest cover, in opposition to countries like Finland and Sweden.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDimensions 1 and 3\u003c/p\u003e\n\u003cp\u003eThe third dimension was analysed along with the first on the plane represented by Figure 4. This plane covers 42.95% of the dataset variability. Except for exclusion rights, which was mostly negatively correlated to dimension 1, supplementary were positively correlated to dimensions 3 and 1 (1\u003csup\u003est\u003c/sup\u003e quadrant).\u003c/p\u003e\n\u003cp\u003eIn this plane it is possible see how countries with more liberal approaches to forestry policy (exclusion rights to a lesser extent) are more focused on productivity and intensive management (represented by variables such as Forest GVA, reforestation and afforestation, employment), in opposition to extensive, naturally regenerative forests.\u003c/p\u003e\n\u003cp\u003eMeanwhile on the sample distribution chart of plane 1-3 (Figure 5), countries with aggressive afforestation policies, and initially smaller forest area, the Netherlands, Denmark, Scotland, Ireland, Portugal and Belgium were the most differentiated by dimension 3. By contrast, countries of central Europe are all aggregated at the bottom, reflecting more concern for ecosystem maintenance.\u003c/p\u003e"},{"header":"5 Discussion","content":"\u003ch2\u003e5.1 The PRIF is a reliable governance indicator\u003c/h2\u003e\n\u003cp\u003eThe property rights index for forests is significantly correlated with every other governance indicator analysed in this research.\u003c/p\u003e\n\u003cp\u003eSpearman\u0026rsquo;s correlation between the EPI and PRIF shows that countries that provide greater freedom to forest owners in decision making are, overall, better performers in the EPI, meaning higher protection of health and ecosystems (i.e., biodiversity, water resources, habitats). Similar findings show that political freedom [34] and income (e.g. Environmental Kuznets curve) can have a positive effect on environmental performance. Nevertheless, in the forest-specific category of tree cover loss, the correlation was reversed, implying that higher PRIF scores are correlated to higher rates of tree cover loss, likely due to anthropic pressure and trade openness [35]. Studies have also found that there is a critical level of economic freedom and environmental performance in which more economic freedom can be detrimental to environmental performance [36].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWithdrawal agreement are the rights that showed the most robust correlation with governance indices, with Withdrawal having the highest coefficient. Exclusion rights are not correlated with any of the governance indicators other than EPI\u0026rsquo;s tree cover loss indicator.\u003c/p\u003e\n\u003cp\u003eThe corruption perception score (CPI) and PRIF were positively correlated. It is worth mentioning again that high CPI scores indicate lower perceived corruption; therefore, this correlation indicates that higher PRIF scores tend to accompany lower perceived levels of corruption. In the case of the correlation of PRIF categories with CPI, management rights and withdrawal rights showed the highest correlation coefficients (r = 0.837 and 0.850, respectively) while being strongly significant (both with p\u0026lt;0.0001), suggesting that the freedom of owners to manage their assets and extract products are key issues as regards corruption matters. While greater economic freedom has been correlated to lower corruption levels\u0026nbsp;[37], this correlation is not a consensus\u0026nbsp;[38], as it can also be affected by other variables such as the wealth of the country\u0026nbsp;[39], as well as by micro level characteristics, including gender, religion, and marital status\u0026nbsp;[40], [41], [42]. In general, it is accepted that convoluted legislation and a lack of enforcement will increase corruption\u0026nbsp;[43], [44].\u003c/p\u003e\n\u003cp\u003eThe PRIF and HDI also presented a strong positive link, possibly explained by its strong association with economic freedom\u0026nbsp;[45], [46]. Management and withdrawal rights have the highest correlation with HDI. GDP per capita and the PRIF are positively correlated, which is not surprising given the positive correlation between the PRIF and the EFI, CPI and HDI. The findings of\u0026nbsp;[47]\u0026nbsp;reinforce this correlation, as their results found that the lower de GDP per capita, the higher the deforestation rates. The turning point found, where deforestation and afforestation tend to equalise, is about $6,500.00 (USD) per capita, while $19,500.00 (USD) was found to be the peak value for afforestation rates.\u003c/p\u003e\n\u003cp\u003eAs expected, a positive correlation between the PRIF and IEF was found, suggesting that high economic freedom in a country also translates to high freedom in the exercise of forest property rights, especially regarding resource withdrawal rights, though correlations were weaker than with the other governance indicators used.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e5.2 The PRIF helps to indicate how specific policies can affect forest resource characteristics\u003c/h2\u003e\n\u003cp\u003eThe PCA analysis enabled a better understanding of how the indicators interact with each other, as well as demonstrating how the PRIF is expected to correlate with the aggregated forest indicators. Overall, the PCA results corroborate with the trends found with the simple correlations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile this research is aimed at inferring the link between forest policy and forest resources, not causal effects, the PRIF and its components were found to be highly correlated with forest cover indicators.\u003c/p\u003e\n\u003cp\u003eScores on withdrawal, exclusion, and alienation rights, as well as the aggregated indicator were strongly correlated with the promotion of planted forests (be it through reforestation or afforestation) over natural regeneration/expansion. A possible explanation for this phenomenon would be that countries with higher freedom tend to attract more investments for industrial forests, as bureaucracy and costs linked to compliance with legislation are lower [48], as well as possibly avoiding land abandonment (somewhat represented by natural forest expansion). Strong exclusion rights may increase the attractiveness of investing in forest production as it: 1) lowers uncertainty [49], [50]; 2) preserves the owners\u0026rsquo; position as \u0026ldquo;exclusive agenda setters\u0026rdquo; for their forest and its resources [51]; 3) and gives them authority to make decisions as to how they derive income therefrom [52].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOn the other hand, the results found on the PCA analysis shows that, considering the aggregated dataset, exclusion rights are negatively correlated to production intensity variables (e.g. felling to increment rates, percentage of forests area managed for production) and positively correlated with natural expansion of forests. This is most likely caused by data from countries such as Finland and Sweeden, with large forest areas, high forest GVA [53] and low exclusion rights, due to \u0026ldquo;everyman\u0026rsquo;s rights\u0026rdquo; policies (Jokamiehenoikeus in Finland and Allemansr\u0026auml;tten in Sweden), that grants recreation use of all forests to the general public [54].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eForest economic indicators were not found to be directly correlated to PRIF nor its components, though Forest GVA/ha has high correlation values to components 2 and 3 in the PCA. Resource availability was also found to be decoupled from resource exploitation. One could argue that the effects that forest policies have, on the production and economic outputs of forests, are complex and not direct, these being influenced by many other external factors. This would explain how policies that try to increase production and harvest, have great difficulties in meeting their objectives\u0026nbsp;[55].\u003c/p\u003e\n\u003cp\u003eHigher deforestation rates were found to be correlated to jurisdictions with higher PRIF scores (in both PCA and simple correlation test), while a positive trend between forest area net change and exclusion rights were only found in the PCA analysis. The overall dynamic found was that, even though countries with higher PRIF scores tend to have higher deforestation rates, they also tend to reforest at a similar proportion. This is especially clear on the third component of the PCA, where liberalization of forestry law goes hand in hand with the intensification of land use change and economic activity, in opposition to the rate of areas reserved for the protection of soil and water as well as the growing stock of naturally regenerated forests.\u003c/p\u003e\n\u003cp\u003eWhile aggregated governance indicators are useful in many situations, the use and comparison between disaggregated data can offer important insights into the analysis of complex systems [31].\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e5.3 \u0026nbsp; \u0026nbsp; Future developments\u003c/h2\u003e\n\u003cp\u003eA temporal analysis of the evolution of the PRIF index indicators, coupled with forestry indicators, might allow one to further understand how PRIF variations impact these indicators, as well as how long it might take for these impacts to occur after changes in the law. This would be especially interesting, given the context of liberalization of forest policies in East European countries over recent decades\u0026nbsp;[26].\u0026nbsp;The consolidation of the PRIF\u0026rsquo;s predictive potential depends on further clarification of its component\u0026rsquo;s correlation with various forest-based indicators.\u003c/p\u003e\n\u003cp\u003eIn cases where the PRIF was calculated for an infra-statal jurisdiction (e.g. Germany), governance indices and SES data are at a national level and not of that specific jurisdiction. This means that national variations in the legislation might not represent the local legislation perfectly, and correlations with SES indicators might not be fully representative in these situations. Some limitations of the study are due to the shortcomings of each indicator used. The CPI, for example, is based on \u0026ldquo;expert assessments\u0026rdquo; of corruption, representing the views of a small number of people. It is conducted by expatriates of the countries and, the longer the period they have been away from their country, the less likely they are to accurately understand the situation. Moreover, expatriates\u0026rsquo; judgement may be biased due to their economic or social conditions\u0026nbsp;[56]. Several technical problems could put into question the validity of this index, including large standard errors, overly complex standardization procedures, measurement errors and biased perceptions of corruption. Then again, corruption cannot be directly measured, as it is an illegal activity, and its prevalence can only be measured with proxies such as perceptions and/or regulatory measures in place to avoid the occurrence of this activity\u0026nbsp;[30], [31].\u003c/p\u003e\n\u003cp\u003eThe EPI (Environmental Performance Index) was chosen to test the correlation of environmental indicators with the PRIF, but other ecological indices such as the CIEP (Composite Index of Environmental Performance) or EF (Ecological Footprint) could have been used, though giving different information, due to the use of different variables and methodology (missing imputation, normalization, weighting, and aggregation). This concern is valid for every index used in this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnother characteristic of dealing with many indicators is that they can overlap in some respects, which could explain their strong correlation, more so than the dataset. However, with the IPRI and the PRIF, even though they have similar goals (to quantify property rights), the methodologies and datasets are quite different. \u003cu\u003eTherefore, the strong correlation found between these two indices confirms the validity of the PRIF as a property rights index\u003c/u\u003e.\u003c/p\u003e\n\u003cp\u003eGovernance indices are imperfect proxies of what they are intended to measure, with inherent uncertainty that should be considered when analysing their outcomes. As for the analysis of the text of the law alone, it can have limited explanatory value, as in many cases there are major gaps between what is written and what is implemented [31]. Governance indicators based on subjective perceptions have been found to be just as important, and complementary, when analysing legal realities [30]. Despite these theoretical limitations, \u003cu\u003ecorrelations found with the PRIF index show that aspects of the written law are linked to forest dynamics.\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eOur data only represents European countries, therefore, given the diverse cultural, economic, and environmental contexts, the correlations found in this paper are not likely to apply outside of this context. While the PRIF has been shown to be applicable to jurisdictions outside of Europe\u0026nbsp;[21], the available data was too limited at the time of writing to be included in this study. Nevertheless, the contradictory trends found in the correlations of SES variables with the PRIF index show that the pivotal range of freedom of action, as captured by the PRIF, is indeed represented in this European gradient. A working assumption for future research is therefore that countries outside Europe may extend the index\u0026rsquo;s gradient.\u003c/p\u003e\n\u003cp\u003eLastly, deciding on an adequate method when developing a composite index can be a complex issue, as it will have an influence on the quality of the information produced. The PRIF calculation is a simple average of the indicators of the bundle of rights. This implies that the original methodology rates each indicator as equally impactful on the overall index and further assumes that these categories of rights are perfectly substitutable in terms of overall freedom of action (additive model). However, this has limited significance, and other aggregation operators may be better suited to the construction of the PRIF, e.g. representing the limiting or essential nature of some categories of rights, such as limiting factor or essential factor models identified in economic and agronomic modelling studies\u0026nbsp;[57], [58]. It is possible that withdrawal and management indicators express, in a satisfactory way, the essence of the PRIF\u0026rsquo;s construction.\u003c/p\u003e\n\u003cp\u003eFurthermore, correlations with both governance and development indicators, as well as with SES variables, clearly suggested that these components cover distinct aspects of forest governance. Future research in this respect may try exploring in two possible directions: 1) a validation of this (exploratory) causal theoretical understanding of the interplay between categories of rights and forest status, or dynamics using case studies and National forest inventory data; 2) a correlation-based approach to a set of candidates of PRIF aggregation formulations, based on the aforementioned models.\u003c/p\u003e"},{"header":"6 Conclusion","content":"\u003cp\u003eThe Property Rights Index for Forestry (PRIF) is a robust method to measure the legal rules on access, management, withdrawal, exclusion, and alienation of forests, with great potential as an indicator of governance quality, as well as a relevant policy indicator for modelling. Using its individual components can help to evaluate how specific policies are linked to forest dynamics and resources. Further studies on how policy variation influences forest indicators (production, growth, and other socio-economic and ecological features) may help to highlight the dynamics related to legislation major amendments in time.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding sources\u003c/h2\u003e\u003cp\u003eRichard Rimoli acknowledges financing from INFORMA project (Science-based integrated Forest Management for Climate Mitigation), grant agreement N\u0026deg;101060309, EU Horizon Research and Innovation Programme.\u003c/p\u003e\u003cp\u003eLaura Bouriaud and Liviu Nichiforel acknowledges financing from Wildcard project (Effects of Rewilding in Forests and Agricultural Lands on Carbon Sequestration and Diversity), grant agreement N\u0026deg;101081177, European Union\u0026rsquo;s HORIZON Research and Innovation Actions.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eRR and JB wrote the main manuscript text. LB, AA, AN, BS LA and LC, helped develop the concept, initial methodology and on the first draft of the paper. RR, JB, LB and LN reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided within the manuscript or supplementary information files\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eArts, B. Assessing forest governance from a Triple G perspective: Government, governance, governmentality⁎. \u003cem\u003ePolicy Econ.\u003c/em\u003e \u003cb\u003e49\u003c/b\u003e, 17\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.forpol.2014.05.008\u003c/span\u003e\u003cspan address=\"10.1016/j.forpol.2014.05.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (Dec. 2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSergent, A., Arts, B. \u0026amp; Edwards, P. 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Figure legends (these are limited to 350 words per figure).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e\u0026bull; Tables (maximum size of one page).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Forestry policy, property rights, PRIF, resources, ecosystem services, European forests","lastPublishedDoi":"10.21203/rs.3.rs-7683489/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7683489/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnderstanding how forest governance shapes environmental outcomes is critical for sustainable land-use policies. Yet, existing governance indicators are often too broad to reflect sector-specific legal frameworks that govern natural resources. This study assessed the relevance of an existing governance index for forestry sector (Property Rights Index in Forestry - PRIF), correlating it with recognised governance metrics (e.g., rule of law, corruption perception, economic freedom) and forest status indicators (e.g., reforestation, afforestation, and deforestation rates). The PRIF was found to have strong associations with national governance quality, economic development, and forest dynamics, capturing both enabling and constraining aspects of owner freedom. Countries with higher PRIF scores tend to experience higher rates of both deforestation and reforestation, suggesting that increased owner autonomy drives more dynamic and potentially polarized forest outcomes. A Principal component analysis further reveals that PRIF aligns with major gradients in forest management intensity, ownership structure, and ecological change. These findings demonstrate that PRIF is a sensitive, interpretable, and scalable indicator for evaluating forest governance and its environmental implications. The index offers a valuable tool for policymakers seeking to balance property rights with sustainability goals across diverse institutional contexts.\u003c/p\u003e","manuscriptTitle":"The Property Rights Index in Forestry (PRIF): a new governance indicator with predictive capacity for forest status and dynamic across Europe","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-30 17:21:36","doi":"10.21203/rs.3.rs-7683489/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-20T04:36:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-13T15:46:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"226458298296283945443582004983191437639","date":"2025-10-21T12:12:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"195959236975427180474877124406025613637","date":"2025-10-20T16:11:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-20T16:03:08+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-07T05:45:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-25T10:17:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-24T12:06:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-22T11:35:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"159e84f3-8066-48da-bbba-906aa905ec73","owner":[],"postedDate":"October 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":56883035,"name":"Biological sciences/Ecology"},{"id":56883036,"name":"Earth and environmental sciences/Ecology"},{"id":56883037,"name":"Earth and environmental sciences/Environmental sciences"},{"id":56883038,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":56883039,"name":"Social science/Environmental studies"},{"id":56883040,"name":"Scientific community and society/Geography"},{"id":56883041,"name":"Social science/Geography"}],"tags":[],"updatedAt":"2026-03-23T14:24:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-30 17:21:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7683489","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7683489","identity":"rs-7683489","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}