Mapping the attractiveness of globally important agricultural heritages to digital nomads: a stakeholder-driven, multi-criteria evaluation framework

Abstract

Global agricultural heritage systems (GIAHS), recognized for their biocultural significance, is facing a critical challenge of balancing preservation with development. Despite digital nomads’ potential to generate low-impact heritage revitalization, current research lacks robust methodologies to globally quantify compatibility between agro-cultural assets and digital nomads’ needs. This study pioneers the Digital Nomad-Friendliness (DNF) Index, a stakeholder-driven, multi-criteria framework that spatially evaluates the attractiveness of 86 GIAHS sites to digital nomads. Integrating natural, cultural, and socioeconomic dimensions through hybrid AHP-CRITIC weighting, the index reveals a dominance of biocultural capital: natural landscapes (51.0%) and cultural value (27.9%) outweighing economic factors for digital nomads. Results show significant spatial polarization, with High-DNF sites in East Asia and Western Europe achieving balanced development, while low-DNF African sites face affordability-authenticity traps. The framework positions GIAHS as sustainable digital nomad hubs, offering a novel tool for adaptive governance that reconciles heritage conservation with economic-cultural co-benefits in the Anthropocene.

Introduction

The Anthropocene epoch have witnessed profound shifts in global work and lifestyle paradigms, catalyzing the rise of digital nomadism as a defining feature of 21st-century labor mobility. Digital nomads are location-independent professionals sustain, often prioritizing affordability, natural scenery, and cultural immersion. Digital nomads are location-independent professionals who sustain nomadic lifestyle their location-independent livelihoods through remote work opportunities while enjoying the advantages of affordable living, beautiful scenery, and cultural immersion¹. Previous research can help us meticulously craft a portrait of a digital nomad: they constitute a demographic of predominantly highly-educated young individuals engaged in various knowledge-intensive field, such as information technology, digital marketing, design, online education, and social media influencer^2,3. Unlike conventional tourists or remote workers, their distinctive spatiotemporal patterns integrate elements of slow tourism (prolonged stays longer than 3 months) and geo-arbitrage (low-cost destination)^4,5, driven not solely by economic stimulus, but also by quests for experiential enrichment and identity reconstruction through social engagement and cultural adventure⁶.

Digital nomadism is thriving due to globalization and significant changes including the rapid expansion of online income opportunities, the increasing prevalence of post-pandemic remote working, and the growing youth unemployment^7,8,9, with estimates indicating a 131% surge to 35 million individuals in recent three years¹⁰. Projections also suggest the population of digital nomads could reach 1 billion within a decade¹¹. This trend has made destinations now compete to attract digital nomads: over 40 countries to establish specialized digital nomad visa¹², reflecting a growing recognition of their potential as a source of local economic stimulus through extended stays spending on housing, cultural experiences, and co-working infrastructure¹³.

Paradoxically, while initial digital nomad flows concentrated in urban hubs, rising costs and overcrowding in metropolitan areas are now driving interest in alternative rural destinations. Concurrently, Globally Important Agricultural Heritage Systems (GIAHS), as designated by the UN Food and Agriculture Organization (FAO)¹⁴, represent landscapes of remarkable biocultural diversity in rural areas. These are not merely historical relics but living, evolving systems that demonstrate sustainable, co-evolved relationships between human communities and their environment^15,16. Emerging evidence suggests digital nomads increasingly favor these rural GIAHS destinations for affordable living costs compared to urban hubs¹⁷, aligning with nomads’ geoarbitrage strategies. In addition, the human-environment dynamics in GIAHS provide authentic experiences that satisfy nomads’ desire for natural-based exploration and meaningful cultural engagement^18,19. Also, nature immersion in agricultural heritage sites correlates with enhanced the wellbeing of digital nomads such as social interactions and creative inspiration^20,21. GIAHS destinations, with their inherent offerings of affordability, natural richness, social well-being, and cultural authenticity, present a theoretically ideal “digital nomad-friendly (DNF)” destinations, yet empirically underexplored fit for the digital nomads^22,23.

More importantly, this convergence of digital nomads and agricultural heritages creates co-benefits: Digital nomads could become a central driver of economic recovery in those “DNF” destinations^24,25, since naturally evolved on-site tourism has long been recognized as one of the dynamic approaches to heritage conservation²⁶. Digital nomads contribute to agricultural heritages not only through its financial inflows but also by building partnership: First, extended stays of digital nomads generate stable revenue streams for local communities via housing rentals, food consumption, and cultural experience tourism, thus creating job opportunities and enhancing the well-being of local populations^27,28. Second, digital nomads facilitate technological bridging through market integration for traditional products while promoting resource-efficient innovations^29,30. Third, digital nomads could amplify global attractiveness of agricultural heritage sites through social media, fostering cross-cultural exchanges that reinforce community identity^31,32,33. In essence, with those diversified collaborative efforts, innovative approaches, and supplementary livelihoods, the integration of digital nomads and agricultural heritages could serve as a powerful catalyst for the sustainable management of agricultural heritages^34,35,36.

This potential convergence, however, presents a critical tension central to the Anthropocene: the conflict between hyper-mobility and place-based sustainability. Digital nomads’ “work from anywhere” ethos can both threaten and revitalize fragile socio-ecological systems like GIAHS, creating a duality that demands urgent theoretical and empirical exploration. While emerging evidence suggests nomads are increasingly drawn to GIAHS for their authentic experiences and well-being benefits^15,17, a significant research deficit persists.

Criteria and indicators are employed as powerful tools for understanding and quantifying the composite image of this complex context through subjective and objective parameters, providing a simplified and comparable perspective on the multifaceted phenomena. Existing tools in relevant research for evaluating destination attractiveness, such as Nomad List (NL), the Global Remote Work Index (GRWI), Global Life-Work Balance Index, and the Global Liveability Index (GLI), selected the aspects determining the choice of a specific city were distinguished³⁷: social management, cultural capital, and environmental resource. Others tested the quality of sustainable tourism destinations using accessibility, attraction, amenity, activity, accommodation indexes³⁸, providing us with valuable references. Although integrating multi-dimensional crowdsourced, dynamic-updated databases based on user-generated content to provide a comprehensive ranking of destination profiles for digital nomads³⁹, they are fundamentally ill-suited to this context (Table 1). They suffer from a pronounced urban bias, rely on generic, often compensatory commercial metrics (e.g., cost, internet speed), and operate at spatial scales (city or country-level) that obscure the unique attributes of specific agricultural heritage destinations. Consequently, they fail to capture the core biocultural capital of agricultural heritage destinations—such as agricultural biodiversity, traditional knowledge, cultural vitality, and landscape aesthetics—that defines GIAHS value^16,22. Additionally, Nomad List’s UGC-dependent data risks subjective bias, useless for rural GIAHS destinations, serving individual destination choices with lack of granularity to guide site-specific heritage management.

Table 1 Comparative Framework: DNF Index and Established Indices

Furthermore, the scholarly literature on digital nomadism remains dominated by small-sample qualitative studies and urban case studies, lacking a transregional, quantitative framework capable of assessing site-specific attractiveness within rural and heritage context. Studies involving in economic, environmental, and social sustainability, provide references that could piece together the full picture of digital nomads’ destination preference. In the environmental aspect, studies focus on the ecological footprint, geo-arbitrage opportunities, and the technological infrastructure for digital nomadism⁴⁰. In the economic aspect, research examines the available economic opportunities, considering employment, cost of living, and the supportive policies^41,42. In the social aspect, the framework is expanded to integrate cultural, community, and knowledge-sharing dimensions⁴³. However, given the substantial population of digital nomads, significant gaps persist⁴⁴: First, there is urban bias because most studies focus on metropolitan destinations and cultural heritage^4,45,46, neglecting natural heritage in rural contexts⁴⁷. Second, methodologies are limited to small-sample qualitative designs, including case comparisons³⁸, descriptive literature reviews⁴⁸, and in-depth interviews^4,37, lacking transregional quantitative frameworks^49,50.

Critically, despite the growing evidence of digital nomads flowing into GIAHS destinations, a significant and specific research deficit persists: there is a pronounced lack of systematic, empirical studies that explicitly connect the unique attributes of GIAHS to the specific needs, preferences, and impacts of the digital nomad demographic. This gap is particularly salient given the potential of GIAHS to serve as ideal ‘DNF’ destinations and the critical need for evidence-based strategies to harness this convergence for sustainable heritage management and rural revitalization. This research bridges a critical gap by providing a dedicated, scalable framework to evaluate GIAHS destinations for digital nomads—transcending the urban-centric, generic, and policy-agnostic limitations of current indices (Table 2).

Table 2 Profile of digital nomad participants

In response, this study pioneers the Digital Nomad-Friendliness (DNF) Index, a novel, stakeholder-driven, multi-criteria decision analysis (MCDA) framework specifically designed to bridge this gap. This research seeks to evaluate how well agricultural heritages satisfy to the needs of digital nomads on a global scale, trying to find co-beneficial, sustainable pathway between them. By employing evaluation systematic indicators specifically tailored to 86 GIAHS destinations, the key variables for digital nomads’ choices on GIAHS destination are weighted and integrated into the DNF index, grounded in the combination of subjective and objective method. Our research aims to:

• (1) Establish a robust evaluation framework that integrates natural, socio-economic, and cultural dimensions to quantify the DNF of GIAHS destinations.

• (2) Apply this framework to conduct a systematic, comparative assessment and ranking of all 86 designated GIAHS sites worldwide.

• (3) Generate evidence-based insights into the global spatial patterns of GIAHS attractiveness, providing actionable recommendations for adaptive heritage management and sustainable rural development.

Methods

The primary goal of this study is to develop a methodological framework to evaluate the potential of agricultural heritage sites as digital nomads’ destinations. To gain deeper insights into digital nomads’ preferences for agricultural heritage and the factors that shape these preferences, the research was conducted in three phases, as illustrated in Fig. 1:

Fig. 1

Schematic illustration of the research framework and process.

Preference Identification: Establishing digital nomads’ destination selection criteria through needs-wants theory and destination imagery analysis;

Establishment of DNF Evaluation System: Creating the DNF evaluation system via multi-criteria method: MIVES integrating subjective and objective weighting;

DNF Assessment and Spatial Analysis of GIAHS: Mapping geographical patterns of GIAHS attractiveness using GIS techniques.

Preference identification based on Digital Nomads’ needs and destination imagery

Destination choices widely recognized as a dynamic process, fundamentally driven by the interplay between tourists’ internal needs (“push” factors) and the external perceptions of a destination’s attributes (“pull” factors), commonly termed destination image^51,52,53,54. The “push-pull” factor framework remains a cornerstone in understanding travel motivation⁵⁵. “Push” factors originate from the tourist’s intrinsic, socio-psychological desires (relaxation, self-exploration. or learning), while “pull” factors are associated with the destination-specific distinctive attractions (e.g., cultural and historical assets, landscapes, or unique products and activities)⁵⁶.

This study conceptualizes the digital nomads’ push motivations primarily as work-life balanced living, relaxation and recreation, novelty-seeking. For digital nomads specifically, these tourism motivations were identified through structured online interviews, which directly formed the basis for the Requirements Tree Establishment in the MIVES approach (Fig. 2).

Fig. 2

From the theoretical constructs to the MIVES Requirements Tree.

To complement the motivation framework, the concept of destination image, a multifaceted, composite, and dynamic perceptual construct, plays a critical role in identifying “pull” factors. Destination image, closely tied to a place’s environmental characteristics, extends beyond natural attractions (e.g., landscapes, biodiversity, geomorphology) to encompass social factors such as cultural appeal, safety, and socio-economic conditions/tourist services, as well as infrastructure elements^{57,58,59,60,61,62,63}. These dimensions collectively form key determinants in the travel decision-making process of digital nomads.

Building on this theoretical grounding, the present study adopts a cognitive perspective to categorize destination image into four dimensions: natural environment, cultural environment, social environment, and infrastructure (Fig. 2). This framework allows for an empirical examination of the relationship between these specific dimensions of destination image and the revisitation intentions of digital nomads within GIAHS sites.

The 86 GIAHS sites designated by FAO (Food and Agriculture Organization of the United Nations) served as primary study units for digital nomad destinations. Destination imagery data were extracted through:

Systematic analysis of official GIAHS documentation (covering aspects such as food and livelihood, bio-ecosystem services, traditional knowledge, culture, beliefs, social organizations, and land and seascape features food systems, biocultural features, social organizations)¹⁴.

Scholarly literature review (articles, technical reports, etc.)

Visual photographic information depicting landscape characteristics as supplementary material.

Given that official FAO-provided GIS polygons are unavailable for the majority of the 86 GIAHS sites, we adopted a pragmatic and transparent hybrid strategy based on the nature of the input data:

For site-specific Indicator intrinsically tied to the GIAHS site’s core cultural and agricultural area, we relied on the best available local spatial definitions. This involved extracting information from the official FAO designation documents, supporting literature, and, where available, high-resolution local maps. The data for these indicators were collected and scored with this specific spatial context in mind. This approach ensures that the assessment of these qualitative and culturally-grounded indicators is contextually appropriate and relevant to the actual heritage landscape.

For global context Indicators, the analysis is designed to reveal broad-scale relative trends rather than precise local specifics. Thus, we established a consistent and replicable proxy boundary for all sites. We defined each GIAHS site by its geographic centroid (representative central point of its described core area) and applied a standardized 10-km buffer radius (functional region a digital nomad might engage with for daily activities and exploration while based in a heritage area) to create a circular area of influence. This approach provides a consistent and comparable spatial unit across all diverse sites. All relevant global datasets were then overlaid with these buffer zones. The indicator value was calculated by extracting and averaging the pixel values (for rasters) or aggregating features (for vectors) within each buffer zone.

For indicators that could not be directly quantified (e.g., aesthetic features), descriptive and graphic data were manually converted into quantitative scores using a 0–1 scale. This conversion was based on expert evaluations and cross-referenced with crowd-sourced data from platforms like website of GIAHS and Nomad List.

Establishment of DNF evaluation system for GIAHSs using integrated value Model for Sustainable Assessment (MIVES)

The DNF index assessment methodology employs the MIVES (Integrated Value Model for Sustainability Assessment) approach, a well-defined, robust decision-making framework. MIVES combines the principles of multi-criteria decision-making with the methodologies of value engineering. Although it is an infrastructure-based utility model initially implemented occurred in the construction^64,65, MIVES was developed to introduce environment and social indicators to construction decisions. Lately, it was adapted for general evaluation and prioritization of investments and alternatives focusing on sustainability. MIVES has proved to be a coherent and straightforward methodology for evaluating, prioritizing, and selecting alternatives towards sustainable development, being applied in various scientific fields related to social assessment, risk analysis, policy-making and project management^66,67, such as climate change effects on sustainable cities development and cultural heritage^68,69,70. The widespread utilization of MIVES is owing to the fact that it allows for diverse and dimensionless measurement considering economic, social, and environmental requirements—both quantitative and qualitative data⁷¹, resulting in a unique, comparable index. Also, it reduces subjectivity in the evaluation process through value functions and expert workshops.

Given its versatility and flexibility, MIVES can be effectively applied to assess a wide range of products and services⁷¹. In our study, digital nomads’ destination choice is an activity that depends on a diversity of services the destination could provide⁷². Creating distinctive experiences through exceptional service quality, thereby enhancing the destination’s attractiveness and competitiveness. Since MIVES is an effective tool to measure sustainability, the DNF can be seen as a partnership with sustainability, because the experience that agricultural heritages provide could foster the relationship between digital nomads and the environmental social, and economic setting⁷³, conforming to the requirement of sustainability. During their stay, digital nomads have actively engaged with agricultural heritage sites, fostering a sense of attachment and connection to these unique cultural and natural landscapes⁷⁴. Thus, indicators that affect the choice of digital nomads should consider comprehensive aspect including economic, social, and environmental requirements.

The MIVES method can be effectively implemented by following these structured steps:

First, construct the requirements tree by create a hierarchical information diagram to outline essential requirements, considering all relevant aspects including both qualitative and quantitative variables.

Second, determine the relative weight of each variable to ensure a balanced and systematic comparison.

Third, establish value functions by converting qualitative and quantitative data into standardized variables with consistent scales and units.

Fourth, evaluate the alternatives using the previously established model to derive the value index.

The indicators were selected based on an initial literature review, focusing on the advantages of agricultural heritage associated with the digital nomad lifestyle. Digital nomads often begin by choosing destinations that offer geo-arbitrage opportunities⁴³, allowing them to benefit from lower living costs²⁸. Similarly, desirable climates, picturesque landscapes, and vibrant cultures enhance the appeal of specific agricultural heritage destinations for digital nomads by cultivating a sense of belonging and improving well-being, significantly shaping the digital nomads’ experience^28,75. In addition to natural and cultural attractions, the presence of services and amenities plays a pivotal role in appealing to digital nomads. Key elements include dependable public transportation systems²⁸, access to modern infrastructure and daily conveniences⁸, well-developed workspaces and recreational facilities⁴⁰. However, beyond these practical considerations, factors like robust social safety, are fundamental in creating an environment that supports extended remote work and living²⁸. These multifaceted conditions collectively ensure that digital nomads can maintain their productivity while also enjoying a fulfilling and high-quality lifestyle.

As the digital nomads’ destination preference driven by their needs and wants, stakeholder engagement was essential to be integrated in this comprehensive research. On 13 June 2024, an online workshop with 38 digital nomads, engaging in a social media influencer, education, creative industries (design, writing), digital marketing, and information technology, was conducted focusing on the environmental, economic, and social factors influencing their destination preferences, with in-depth discussions for proposing a comprehensive indicator system. During workshop, we maintained a global perspective on the problem while facilitating the understanding of the model for all stakeholders involved in the decision-making process, to promote transparency by stakeholder understanding. More importantly, we implemented a strict data protection protocol that all recordings were anonymized, stored on encrypted servers, and will be destroyed 3 years post-publication.

The database of 62 indicators served as our initial foundation. These were then thoroughly analyzed and evaluated by a panel of experts. Through iterative expert consultations (n = 10 field specialists), we refined 62 candidate indicators to a final list of 19 core indicators (Table 3) using five selection criteria: (a) Contextual Relevance: indicators must directly measure a key attribute of agricultural heritage systems (GIAHS) or a fundamental need of digital nomads within this specific context (applicable only to urban or generic tourism destinations were discarded), representative at site or reginal scale. (b) Measurability: indicators should provide measurable or quantifiable using reliable, accessible, and standardized world-wide/global data source (vague or purely qualitative descriptors that are difficult to be transformed into quantitative terms were removed). (c) Minimal inter-indicator correlation: those which were differentiating and complementary, capturing a unique dimension and avoiding the repetition of certain aspects already represented by others (redundant indicators were identified for elimination). (d) Communicability: indicators need to be easily understandable and actionable for policymakers, site managers, and nomads in order to process effectively (overly technical or obscure metrics were excluded). e) Availability and Accessibility: the data and information required must be publicly available or feasibly collected/can be obtained using appropriate methods for the vast majority (around 90%) of the 86 GIAHS sites.

Table 3 Systematic assessment of candidate indicators by selection criteria

The proposed requirements tree adopts a hierarchical structure, comprising general qualitative criteria and specific, measurable indicators. In this context, criteria refer to the destination preferences of digital nomads. Accordingly, the requirements tree is structured around three main requirements, twelve criteria, and sixteen indicators, as illustrated in Fig. 3.

Fig. 3

Requirements tree and subjective weight of the DNF evaluation.

Regarding the data sources listed in Table 4, we gathered information from reputable platforms. Furthermore, we thoroughly consulted databases provided by various multidisciplinary institutions. This diverse data collection with cross-referenced sources ensured a comprehensiveness and validity of the evaluation results, thereby preventing oversimplification and bias.

The qualitative indicators—Aesthetic Richness, and Existed Community—were assessed by a panel of 7 independent experts in landscape architecture, cultural geography and sustainable tourism. To ensure consistency and minimize subjectivity, prior to assessment, the panel was trained using a detailed rubric that defined clear descriptive anchors for each score level based on visual evidence and workshop transcripts. Aesthetic Richness was assessed based on Landscape Coherence and Visual Integrity, Distinctness of Landscape Features, and Diversity of Landscape Elements, while Existed Community was evaluated through Evidence of Community Organization, Visible Cultural Identity and Activities, and Presence and Quality of Public Space.

Table 4 Data source for the GIAHSs’ DNF indicators system

A 3-point Likert scale was employed for each dimension, with scores of 0.1 (Low/None), 0.2 (Medium), and 0.3 (High). Descriptive benchmarks for each score level were defined to guide the expert panel (Table 5). To further enhance scoring consistency, Table 5 also includes reference photographs for each indicator, providing visual anchors of high, medium, and low scores based on real-world examples from our dataset.

Table 5 Scoring criteria for qualitative indicators

By comparing their scores for a pilot set of 10 GIAHS sites (excluded from the final analysis), discrepancies were discussed to align scoring standards. For the final 86 sites, each expert assigned scores for indicators independently based on high-resolution satellite imagery, photographic databases, and official reports. The final score for each site-indicator pair was taken as the average of the experts’ scores.

To quantitatively assess the consistency of the expert panel, the Intraclass Correlation Coefficient (ICC) was calculated using a Two-Way Mixed-Effects Model for absolute agreement. The analysis demonstrated good to excellent inter-rater reliability. The ICC for Aesthetic Richness was 0.73, and for Existed Community, it was 0.85. Both values exceed the conventional threshold of 0.70 for good inter-rater reliability, confirming that the qualitative scoring was consistent and non-arbitrary.

The weights of DNF indicators are determined with combination weighting method. Initially, this study employed Analytic Hierarchy Process (AHP) and Delphi Method (DM)to determine weights of evaluation indicators^76,77, reflecting the subjective preference information of evaluators. Qualitative questionnaires were individually distributed to evaluators, fifteen digital nomads (n = 15) and twelve experts (n = 12) in the field tourism, heritage, and sustainability via DM. The weighting process was informed by a diverse yet relevant range of professional expertise to ensure methodological credibility. The 12 involved experts are from diverse disciplinary backgrounds, including seven specialists in Cultural Heritage Management, four in Regional and Rural Planning, and one in Sustainable Tourism.

The Analytic Hierarchy Process (AHP) was employed to assign weights, thereby establishing the relative significance of each branch of the requirement tree. The weight assignment process began with the indicators, proceeded to the criteria, and concluded with the requirements. Weights were determined by comparing elements at the same level and within the same branch of the requirements tree. Evaluators utilized a 1–9 scale to compare the relative importance of each indicator, informed by feedback from participants.

The final AHP results were constructed based on the geometric mean of all individual pairwise comparisons from the expert panel. This process synthesizes the judgments of all experts into a single, representative matrix for calculation, where the resulting values (often non-integer decimals) reflect the precise, consolidated expert opinion. After seven rounds of anonymous consultations and adjustments, a high level of consensus was achieved, as evidenced by consistency ratio (CR) results ranging from 0.013 to 0.058, all well below the 0.10 threshold⁷⁸. The subjective weight ${W}_{j}^{{subj}}$ for each criterion was then derived from these consolidated matrices.

To balances the strengths of human expertise with statistical rigor, CRITIC (Criteria Importance Through Intercriteria Correlation) method was selected to process objective information. This information refers to the observed values of the evaluation objects under each indicator⁷⁹. CRITIC method is an effective objective weighting method suitable for multi-indicator comprehensive evaluation problems in this study. This method reflects the relative importance of indicators by leveraging comparative and conflicting information among them. By eliminating the influence of some strongly correlated indicators and reducing the overlap of information between indicators, the CRITIC method makes the allocation of weights more reasonable, scientific, and credible (Table 6).

Table 6 CRITIC method criteria evaluation and weight allocation

The information value reflects the degree of variation of the criterion and indicator, that is, the contrast intensity of the indicator. The greater the information value, the higher importance of the indicator. For each criterion, we calculate its information value, by computing information intensity (${\sigma }_{j}$) and conflict index $\left(1-{\rho }_{{ij}}\right)$ using the following formula:

$${C}_{j}={\sigma }_{j}\mathop{\sum }\limits_{i\ne j}\left(1-{\rho }_{{ij}}\right)$$

(1)

where ${C}_{j}$ represents the information value of the indicator j, ${\sigma }_{j}$ is the standard deviation (measure of contrast intensity) of the indicator j, and ${\rho }_{{ij}}$ indicates the correlation coefficient between the i-th and j-th indicators (measure of conflict degree). Ultimately, the objective weight ${W}_{j}^{{obj}}$ for each criterion can be measured using the following formula:

$${W}_{j}^{obj}=\frac{{C}_{j}}{\sum {C}_{j}}$$

(2)

where the weight ${W}_{j}^{\mathrm{obj}}$ of an indicator is obtained by dividing its information carrying capacity Cj by the sum of the information carrying capacities of all indicators.

To combine both expert judgment and data-driven insights, this study integrate the weights derived from both the AHP-DM and the CRITIC methods to determine the final index weights. The subjective (${W}_{j}^{\mathrm{subj}}$) and objective (${W}_{j}^{\mathrm{obj}}$) weights are integrated through multiplicative normalization using the formula:

$${W}_{j}^{final}=\frac{{W}_{{\rm{j}}}^{subj}\cdot {W}_{j}^{obj}}{\sum \left({w}_{j}^{subj}\cdot {W}_{J}^{obj}\right)}$$

(3)

Combining weights with subjective and objective methods not only reduces biases and instabilities associated with single-method approaches, such as the subjectivity of AHP or the data requirements of CRITIC, but also enhances the scientific and rational basis of weight allocation. This combination promotes the credibility and reliability of DNF evaluation results by effectively handling interdependencies and conflicts among incorporated contextual factors and indicators. Additionally, it demonstrates robustness and generalizability by improving the applicability and validity of evaluation results across different contexts. Below are the AHP-CRITIC weighting results for criteria (Table 7).

Table 7 AHP-CRITIC weighting results for criteria

A value function serves as an instrument designed to standardize individual indicators. Its purpose is to convert the raw data of each indicator into a unified, dimensionless score ranging from 0 to 1, allowing for the equivalences among the diverse units of measurement within the indicators. The transformation of raw indicator values into normalized utilities (0–1) via value functions is a critical step in the MIVES model. The diverse indicators incorporated in this study can be effectively represented through either increasing (I) or decreasing (D) functions, which can be further categorized as linear (Lr), convex (Cx), concave (Ce), Sigmoid (S-shape) (S) shape based on the economic and behavioral theory governing each indicator’s relationship with digital nomads. Specifically, the decision protocol adhered to the following principles (Table 8):

Table 8 Rationale for the Selection of value function types

S-shaped (S) functions were applied to indicators with clear psychophysical or perception thresholds (e.g., Aesthetic Richness, Climate Suitability and Existed community), where utility increases slowly at extremes and rapidly around a critical midpoint, following the Weber-Fechner law.

Concave (Ce) functions were used for resource-based indicators (e.g., Cost of Living, Public security and Access to cities), where initial improvements yield high utility gains that diminish after a satiation point.

Convex (Cx) functions model indicators with increasing marginal utility (e.g., Community atmosphere, Infrastructure), where DNF accelerates as values improve.

Linear (Lr) functions were reserved for indicators where utility is assumed to change proportionally across the observed range (e.g., Agricultural Biodiversity and Mean species abundance).

Step Function is adopted for discrete or categorical indicators like visa for digital nomads, which require binary or tiered classification.

By rationally selecting function types, we can more accurately reflect the relationship between indicator values and attractiveness, thereby enhancing the scientific rigor and practical applicability of the evaluation model. Consequently, Fig. 4 illustrates the specific form of each value function.

Fig. 4

Value function of each indicator.

This value function not only mirrors preferences based on the underlying data distribution characteristics but also enhances interpretability and understandability, thereby ensuring transparency during the decision-making procedure. Furthermore, it might be easily used for weighted summation and indicator calculations thus facilitating comprehensive evaluation.

A sensitivity analysis was performed to evaluate the robustness of the model’s conclusions to the specification of value functions. To this end, the DNF scores and site rankings generated by our theoretical model—which employs S-shaped, convex, and linear functions as justified in Section 2.2.3—were compared against a baseline scenario where all indicators used simple linear value functions.

The results demonstrate a strong and statistically significant Spearman’s rank correlation of 0.705 (p < 0.05) between the two ranking lists. This finding indicates that the relative attractiveness of GIAHS sites is largely insensitive to the specific parameterization of the value functions. The primary contribution of the theoretically nuanced, non-linear functions is therefore not to redefine the ordinal ranking, but to offer a more psychologically grounded and interval-scaled measurement of utility. This refinement is crucial for the accurate aggregation of preferences and for enhancing the validity of future scenario analyses. Consequently, the core comparative findings of this study can be considered highly stable.

The DNF assessment and spatial analysis of GIAHSs

The third phase of this study involved accessing the DNF score of GIAHS sites. To ensure accuracy and objectivity, this research relied on valid quantitative datasets verified by authoritative platforms or qualified scholars.

The overall DNF score for each GIAHS site was aggregated and then calculated as subjective-objective-combined weighted sum of normalized indicator value:

$$DN{F}_{i}=\mathop{\sum }\limits_{j=1}^{m}\left({x}_{{i}_{j}}^{{\prime} }\cdot {W}_{j}^{final}\right)$$

(4)

where ${x}_{{i}_{j}}^{{\prime} }$ is the normalized value of indicator j for site i, and ${W}_{j}^{\mathrm{final}}$ is the combined weight of indicator j.

This study utilized GIS and statistical techniques to examine the spatial distribution of the DNF score of GIAHSs. First, precise latitude and longitude coordinates for all 86 GIAHS sites were obtained from the FAO Geoportal and verified using Google Earth.

Next, the indicator values were linked to each site’s geographical location in a GIS-compatible format. DNF scores, the aggregated value data using hybrid weighting, were obtained to create a global map for the analysis in ESRI ArcGIS 10.8 software, depicting the character of regional variation of the GIAHSs’ DNF.

Finally, we symbolized sites based on their DNF scores: higher scores were represented by larger circles and darker shades of purple, while lower scores used smaller circles and lighter shades.

Through the spatial analysis, high-DNF GIAHS clusters are driven by balanced development across all dimensions, while low-DNF GIAHS clusters reflecting infrastructural and socioeconomic challenges. This significant difference in DNF regional variations highlights the need for targeted interventions in underperforming areas.

Results

Indicator weight analysis

The DNF indicator system reveals distinct priorities among digital nomads (Fig. 5). At the factor level, “Natural Landscape Value” (0.510) and “Cultural Value” (0.279) outweigh “Economic and Social Value” (0.211), suggesting that biocultural attributes are prominent considerations in destination selection. This prioritization likely due to the scarcity of natural scenery and cultural characteristics in urban areas where digital nomads typically reside.

Fig. 5

Assigned weights for the DNF indicators including three dimensions.

At the indicator level, the findings collectively depict a preference profile that prioritizes experiential and environmental factors over purely economic considerations. Some of the key drivers emerge:

Aesthetic Landscape Features (0.253): The highest-weighted indicator, underscoring the importance of visual appeal and natural scenic beauty in destination selection. This aligns with previous studies emphasizing the role of picturesque environments in enhancing well-being and productivity (Thompson⁸⁰; Schwarz et al.⁷⁵).

Climatic Suitability (0.185) and Surface comfort (0.057)]: The practical need for comfortable working and living conditions is important to digital nomads. Moderate climates and natural environment with minimal seasonal variability are particularly favored, as they support year-round outdoor activities and reduce logistical challenges (Zhou et al.²⁸).

Community Atmosphere (0.170): Community Atmosphere highlights the importance of social connectivity and local engagement for digital nomadic lifestyles. Owing to the risk of multigroup identity caused by excessive freedom, digital nomads value destinations that foster a sense of belonging and offer opportunities for cultural immersion.

Additionally, the moderate weight assigned to “Local Living Cost” (0.119) confirms digital nomads’ strategic exploitation of geo-arbitrage opportunities, allowing for balancing affordability with quality of life. Interestingly, indicators such as “Transportation” (0.060) and “Infrastructure” (0.020) also received notable weights, reflecting the dual need for reliable connectivity and secure environments. This can be attribute to the mobility of the digital nomads.

Top twenty DNF List of GIAHs

Figure 6 presents a list of the twenty most prominent DNF GIAHSs (DNF Score > 0.7). The results show a distinct geographical concentration, with all top twenty sites located in Europe and Asia (including several in China and Spain). This is probably because those areas have a long history of agriculture, with many agricultural heritages remaining, relatively suitable climate due to their medium latitudes, and relatively prosperous economies and social development.

Fig. 6

Top twenty DNF GIAHs.

The ranking of the top twenty DNF-scoring GIAHS sites (Fig. 6) reveals a pronounced geographical concentration. This spatial clustering can be attributed to three synergistic factors:

Historical Agriculture: These regions boast millennia-old farming traditions, preserving rich agroecological practices and cultural landscapes. For instance, Huzhou Mulberry-dyke and Fish Pond System in China, Valle Salado de Añana and Malaga Raisin Production System in Spain exemplify the integration of traditional knowledge with sustainable land use, with over two thousand years agricultural history.

Climatic Advantages: Their mid-latitude locations offer temperate conditions conducive to year-round nomadic stays. This contrasts with tropical regions, where extreme weather can hinder long-term residency.

Socioeconomic Development: Robust infrastructure and tourism ecosystems enhance destination accessibility and livability. For example, the presence of co-working spaces, high-speed internet, and cultural festivals in these regions aligns well with digital nomads’ needs.

When examining the structure of DNF scores of diverse indicators for the top twenty sites, we found that they share common strengths, with “Aesthetic features” all scoring above 0.7, achieving the highest safety degree, mostly (fifteen of twenty) excelling in “Tourism popularity”, “World heritage”, and “Climate suitability”.

These results suggest that successful digital nomad destinations integrate natural beauty, cultural depth, and functional amenities. However, the dominance of East Asia and Europe also highlights the need for greater diversification in promoting GIAHS sites globally.

Spatial distribution and variations in DNF of GIAHs

The global distribution of DNF scores across 86 GIAHS sites exhibits marked spatial heterogeneity (Fig. 7). High-DNF clusters, those areas with darker purple and bigger circles, are concentrated in Western Europe, East Asia, and Latin America, regions characterized by: (1) Long agricultural histories: Sustained human-environment coevolution has yielded diverse and resilient landscapes. For example, most of the in Western Europe and East Asia have an over one-thousand-year long agricultural history, and the majority of GIAHS in Spain are two thousand years old. (2) Balanced development integrating natural, cultural, and economic assets to creates holistic destination appeal. The Hani Rice Terraces, for instance, combines terraced landscapes with vibrant local traditions and modern tourism infrastructure.

Fig. 7

The map of the country-level and site-level 86 GIAHSs’ DNF.

At the country level, China, Spain, and Mexico emerge as leaders in DNF GIAHS sites, while regions like Africa, Near East and North Africa show lower DNF scores due to infrastructural and socioeconomic constraints. For example, the Siwa Oasis in Egypt, despite their cultural significance, score lower due to limited digital infrastructure, accessibility challenges and uncomfortable environment and climate.

Considering the board natural and cultural contexts across the global scale, this study delves into the distinct regional variations in DNF characteristics among GIAHS sites. A deeper examination of regional variations reveals three distinct patterns, as shown in Fig. 7.

Statistical tests confirm the significant spatial clustering of DNF scores. The Global Moran’s I index of 0.258 (p < 0.01) for the composite score confirms positive spatial autocorrelation, rejecting the hypothesis of a random spatial distribution.

Decomposing this overall pattern, the constituent indicators all show significant spatial clustering, but with varying intensity. Moran’s I values range from 0.224 (Climate) to 0.713 (Safety), demonstrating that cultural and economic indicators related to safety, intangible culture (0.628), and transportation (0.618) have particularly strong spatial disparities over those natural indicators, which are key drivers of the overall regional differentiation. This quantitatively demonstrates that the observed overall spatial pattern is driven by the pronounced spatial dependency of these specific factors.

The Getis-Ord Gi* hotspot map (Fig. 8) localizes these clusters, identifying specific regions of high and low values with statistical confidence. Specifically, significant hot spots (indicating clusters of high DNF scores) were identified in Southeast Asia at the 95% confidence level, and East Asia at the 90% confidence level, while cold spots (clusters of low DNF scores) were found in Western Asia and Africa, showing difference in Global South.

Fig. 8

Spatial Clustering analysis of DNF.

High-DNF regions Exhibit balanced performance across all dimensions, with particularly strong natural and cultural values (Fig. 9). Hani Rice Terraces, for instance, scores highly for its terraced rice fields and traditional festivals.

Low-DNF regions Display significant imbalances, with economic and social values compensating for weaker natural and cultural dimensions (Fig. 9). The ksour of Figuig in Morocco, for instance, offers low living costs but faces challenges in tourism development and connectivity. Notably, low-DNF regions often benefit from lower living costs, suggesting potential for future development through targeted investments in biocultural conservation and digital infrastructure.

Fig. 9

DNF variation according to three dimensions in GIAHS sites.

A Pearson correlation analysis was conducted across 86 GIAHS to examine inter-indicator relationships within the Digital Nomad Friendliness (DNF) framework (Fig. 10). The results revealed significant negative correlations (−0.436 < P < − 0.333, p < 0.05) between living cost indicators and key natural, cultural metrics, including surface comfort and tourism popularity. This inverse relationship suggests a systemic trade-off mechanism wherein economically disadvantaged GIAHS sites (characterized by lower living costs) demonstrate constrained capacity for infrastructure development (P = −0.602, p < 0.01), cultural preservation funding allocation (P = −0.333, p < 0.05), and digital nomad community cultivation (P = −0.374, p < 0.01).

Fig. 10

Pearson’s correlation coefficient of the DNF indicators.

Notably, advanced infrastructural development exhibited paradoxical associations with ecological degradation, as evidenced by the inverse correlation between modernization levels and landscape diversity (P = −0.353, p < 0.05). This finding aligns with the “developmental paradox” hypothesis, where techno-economic progress may affect biocultural heritage through standardized urban sprawl and habitat fragmentation. More importantly, the analysis further uncovered notable correlations suggestive of incipient gentrification dynamics: Community atmosphere showed significant negative association with Heritage resource (P = −0.321, p < 0.05) and Cultural resource (P = −0.529, p < 0.01). These patterns require further investigation into the relationship between community development and cultural preservation in heritage sites.

DNF parallel comparison with established indices

To quantitatively assess the distinctiveness and policy value of the DNF Index, a comparative analysis against two established benchmarks—Nomad List (NL), and GRWI—was conducted. Acknowledging the disparity in spatial scale, DNF site-level scores were aggregated to their national means to enable a direct comparison. This approach reveals not only convergent validity but, more importantly, the critical divergences that underscore DNF’s novel conceptualization of destination attractiveness.

Statistical analysis indicates a moderate positive correlation between the national mean DNF score and the other indices (Spearman’s ρ with NL = 0.62, with GRWI = 0.58, with GLI = 0.55). This suggests a shared understanding of baseline development prerequisites, such as fundamental infrastructure and safety.

However, the substantial unexplained variance highlights the unique explanatory power of the DNF framework. The core of the disparity is visualized in the radar chart (Fig. 11), which plots the normalized scores of 27 representative countries across the four indices. This comparison reveals a systematic biocultural valuation gap:

Fig. 11

Radar Chart Comparison of the DNF Index with Benchmark Indices(Nomad List and GRWI).

Global South Nations with High Biocultural Capital: Countries such as the Philippines, Iran, Ecuador, and Tanzania consistently demonstrate a significant advantage in their DNF scores showing a stark contrast to their mid-to-lower-tier ranking positions in urban-centric indices such as NL and GRWI. This pattern indicates that the economic and infrastructural metrics dominant in conventional indices systematically undervalue the exceptional natural and cultural capital inherent in these nations’ GIAHS sites. The DNF index, by assigning decisive weight to biocultural factors (78.9%), successfully corrects this bias.

Developed Nations with Lower Biocultural Salience: Conversely, nations including Austria, Algeria, and Egypt exhibit the inverse pattern. Their robust performance in NL, and GRWI—driven by high scores in internet infrastructure, economic safety, and urban services—is moderated in the DNF index. This adjustment reflects a key finding: high levels of general development and urban livability do not automatically translate into high-quality agricultural heritage experiences for digital nomads. The DNF index thus provides a more nuanced and context-sensitive assessment, preventing the oversight of culturally rich but less developed sites.

This comparative validation yields two principal conclusions. First, the DNF index provides a distinct approach to assessing the attractiveness of rural biocultural landscapes to the digital nomad demographic, precisely because it integrates criteria that are otherwise overlooked. Second, and more importantly for governance, the DNF framework generates policy-ready, dimension-specific diagnostics. Unlike the generic rankings of commercial indices, it can identify whether a site’s low score is due to inadequate infrastructure (a common development challenge) or a degraded cultural landscape (a conservation emergency), thereby enabling targeted interventions tailored for local sustainable development.

Empirical validation with real-World Data

To provide an initial ground-truthing of the DNF Index, we correlated its scores with two independent, real-world metrics that capture core aspects of the digital nomad ecosystem: Airbnb listing density (reflecting accommodation flexibility) and coworking space count (representing professional infrastructure) within the regions of the GIAHS sites. This multi-proxy approach allows for a more robust assessment of the index’s construct validity than any single metric could provide.

A Spearman’s rank-order correlation analysis revealed statistically significant positive relationships (Fig. 12). The DNF Index showed a strong correlation with coworking space count (ρ = 0.514, p < 0.001) and a modest yet significant correlation with Airbnb listing density (ρ = 0.262, p < 0.020). Furthermore, the significant correlation between coworking space count and Airbnb listing density (ρ = 0.533, p < 0.001) suggests these proxies capture related but distinct aspects of a location’s appeal to digital nomads. The strength of this correlational pattern offers critical insights. The robust association with coworking spaces provides supporting evidence for the index’s validity, as it directly links our measure of destination attractiveness to a key infrastructural element for professional remote work.

Fig. 12

Spearman’s correlations between the DNF Index with Airbnb Listing Density and Coworking Space Count.

The weaker, though still significant, correlation with Airbnb density is equally informative, underscoring the inherent complexity of digital nomad destination choice. On the one hand, Airbnb listing density likely reflects a broader short-term rental market, which only partially overlaps with the specific needs of digital nomads seeking longer-term accommodations. On the other hand, while the DNF index captures key destination-level attributes, digital nomadic mobility is also influenced by a multitude of factors not fully captured in our model, such as dynamic social networks and fleeting online recommendations. Therefore, the DNF index should be interpreted as a robust measure of a destination’s potential attractiveness or inherent suitability, rather than a deterministic predictor of nomad numbers. This finding reinforces the need for multi-faceted models to understand contemporary mobility patterns.

Despite their different nature, the fact that both proxies show significant positive correlations creates a powerful “triangulation” effect, substantially strengthening the case for the DNF Index’s construct validity and mitigating the risk of bias from relying on a single metric. Consequently, the DNF Index is best interpreted as a robust measure of a destination’s structural suitability for digital nomads.

Discussion

The rapid expansion of digital nomad communities since the late 2010s reflects their growing emblematic status—combining freedom, self-determined work, independence and flexibility in this new information era. This trend has spurred academic interest in their transnational mobility patterns³. However, this excessive freedom and lack of fixed affiliation can also lead to a lack of group sense of belonging, driving a need for community spaces where they can express identity^8,23, and fully engage with the opportunities of a destination³. Consequently, destination selection has become an increasingly significant and deliberate process for digital nomads. Nevertheless, the overwhelming range of options and the scarcity of reliable, authoritative structured information often make this decision-making process complex and uncertain⁸¹.

Although it seems that digital tools appear well-suited to providing precise information for destination choice, analyses of user-generated content on social media suggest that lifestyle and cultural factors—though highly valued—are less discussed in online platforms³⁹. This suggests that the lifestyle and culture desired by digital nomads are more evident in their everyday behavior and experiences, rather than expressions on social networks. In this context, indicator-based frameworks serve as powerful tools for understanding and quantifying the complexities of multifaceted preference of digital nomads, integrating subjective judgment values and objective data to outline a contextual picture of strengths and weaknesses. The findings of this study offer valuable insights for digital nomads, enabling not only an in-depth understanding of trade-offs implicit in their lifestyle, but also supporting more informed, efficient staycation planning.

Moreover, digital nomads not only adapt to but also actively catalyze a broader socio-cultural shift: from economic globalization toward deeper, cross-cultural engagement in a hyper-connected world. Digital nomads, in their pursuit of diverse natural and cultural experiences, exemplify how adaptability and intercultural understanding have become essential in a globalized world. This can be proven by the high weight assigned to the cultural dimension (0.31) from our evaluation model in this study. As the focus evolves from tangible material such as ecology and economics towards intangible and spiritual values, future research should investigate digital nomads’ role in reshaping cultural exchange and international relations through approaches such as cultural mapping and social dynamic analysis. By exploring these areas, we can better understand how digital nomads contribute to cultural exchange, international relations, and the evolution of global dynamics. Such inquiry would not only illuminate contemporary human landscapes but also help anticipate future trajectories of human connectivity, global connectivity and cultural evolution.

Tourism has been widely employed as a dynamic conservation strategy for GIAHS. However, current prevailing approaches, including cultural festivals, agricultural experiences, and heritage-themed commercialization, often yield only short-term economic benefits. These interventions remain limited in addressing deeper structural challenges such as labor shortages caused by rural depopulation, youth disengagement from traditional practices, the loss of livelihood and economic vitality.

In this context, digital nomadism emerges as a promising yet underutilized catalyst for sustainable revitalization. Unlike conventional tourists, digital nomads typically engage in longer-term stays, stimulating more sustained localized expenditure and deeper cultural exchanges with local communities²⁸. Their presence supports a shift from transient, extractive tourism models toward more embedded, stewardship-oriented relationships with agricultural heritages.

Our proposed agricultural heritage value assessment framework informed by the DNF index, delineates two strategic pathways for adaptive management of GIAHS:

For High-DNF Sites: GIAHS with superior natural landscapes and cultural capital (DNF score > 0.6) should prioritize holistic destination ecosystems for digital-nomad-oriented tourism development. This involves co-designing integrated workspaces with local amenities, curating community programs that bridge digital nomads and indigenous populations, and supporting the sustainable commodification of natural and cultural assets for tourism. Business opportunities would be obtained by commodifying natural elements into profitable products, making those sites tourism destinations. The collaborative efforts are also needed through tailored policies, subsidies, platforms, and mechanisms, aligning with the unique contexts of each GIAHS site.

For Low-DNF Sites: For areas scoring lower on nomadic appeal, a more viable strategy focuses on strengthening the agri-product value chain. This involves modernizing traditional cultivation with nomad-driven innovations while rigorously preserving biocultural authenticity.

In terms of regional differences, for sites in Global South regions, the primary development imperative lies in leveraging their biocultural wealth while cautiously improving digital and physical infrastructure without compromising heritage values. For sites in high-income countries, the challenge is to deepen the authenticity and immersion of the cultural experience, moving beyond generic service provision.

To enhance the attractiveness of select destinations and optimize the holistic experience of digital nomads, effective implementation requires coordinated action across sectors including public institutions, tourism agencies, local government, and the digital nomads themselves.

Public institutions can capitalize on digital nomads’ high demand for aesthetic feature and community atmosphere by fostering dedicated hubs for them. These should integrate visually appealing living environments with high-quality coworking spaces, amenities, and recreational facilities. Concurrently, supportive social integration initiatives, such as skill-sharing workshops, and community gatherings, can further enhance a sense of belonging and mitigate the isolation sometimes experienced by digital nomad individuals.

Tourism agencies are encouraged to rebrand destinations using the social capital of digital nomads. Initiatives could include geo-tagged Instagram trails curated by resident nomads, virtual reality (VR) previews of local agricultural heritage, and other digitally-enabled storytelling formats that highlight the uniqueness of each site.

Local governments could utilize the findings of this study by introducing targeted policies interventions. These may include optimizing specialized visa schemes, improving digital and physical infrastructure, and incentivizing telecom investments through public-private partnerships to ensure robust connectivity.

Digital nomad communities could contribute to local sustainability by establishing self-regulatory charters that encourage expenditure on local agro-products and mandatory participation in heritage preservation activities. Such mechanisms help align nomadic presence with long-term local development goals.

A paramount theoretical contribution of this study is the empirical identification of a biocultural non-compensability effect within agricultural heritage landscapes, challenging a foundational premise of conventional established standard MCDA operated on the principle of full compensability (Gómez-Navarro et al., 2021; Greco et al., 2019). This principle assumes that a deficit in one natural or cultural criterion/dimension can be fully offset by an excess in another socio-economic criterion/dimension (e.g., low cost or high-speed internet).

Our findings fundamentally contest this assumption in the context of biocultural heritage. This study has demonstrated that for GIAHS sites, natural and cultural capital forms a non-negotiable foundational layer. Specifically, when the aggregate score of natural and cultural dimensions falls below a critical threshold of 0.615 (on a 0–1 scale), economic and infrastructural factors lose their compensatory power to elevate overall DNF beyond 0.6/1. This phenomenon can be conceptualized as a utility threshold, beyond which the site cannot be perceived as an authentic, immersive biocultural destination, regardless of its economic advantages (Brand, 2009; Throsby, 2017), differentiating the DNF framework from urban-centric indices. For example, a city might rank highly on Nomad List due to excellent infrastructure and affordability, compensating for a lack of cultural depth. Conversely, a GIAHS site like the Ifugao Rice Terraces might be penalized by such indices for its relative remoteness but is correctly identified by the DNF model as a high-potential destination due to its unparalleled biocultural assets.

The biocultural non-compensability effect is not merely a statistical nuance but a critical theoretical lens. Not all forms of capital are substitutable particularly in the realm of cultural and natural heritage. It provides a validated model/empirical evidence for governing the delicate interplay between conservation and tourism development in fragile heritage systems, ensuring that strategies aimed at attracting digital nomads do not inadvertently accelerate the erosion of the very values that make these destinations unique. Thus, it necessitates a paradigm shift in how we model destination choice for niche, values-driven tourism segments like the culture-seeking digital nomads we study.

The DNF framework reframes the concept of carrying capacity beyond ecological or physical limits to include heritage authenticity capacity. A declining biocultural score signals an impending loss of attractiveness to the very demographic that could contribute sustainably to the local economy. Therefore, high-performing sites on biocultural criteria can justifiably leverage their non-compensable assets for branding and need not engage in a “race to the bottom” on cost, attracting nomads seeking resilience and unique cultural immersion over convenience.

This insight has profound consequences for sustainable heritage tourism policy and management in terms of targeted investment. It argues against generic, one-size-fits-all development strategies. For sites below the threshold (e.g., those with severely degraded landscapes or eroded cultural practices), policy must prioritize investments in biocultural restoration (e.g., revitalizing traditional practices, ecosystem rehabilitation) before or investments in digital infrastructure or marketing alone, which will yield minimal returns if the core heritage value is lost.

The rise of digital nomadism, as a salient feature of contemporary digital transformation, presents a paradoxical relationship with regional inequality. While critics argue that it exacerbates spatial inequalities, our findings reveal a more complex picture and nuanced insights, suggesting that digital nomadism can also function as a potential catalyst for reconfiguring geographic flows of capital and attention.

Conventional critiques emphasize that digital nomads may reinforce existing disparities through market concentration effects. Digital nomads, cluster in well-connected urban hubs or established tourist circuits, may inadvertently amplify the economic dominance of developed regions while further marginalizing rural and under-resourced areas³. This potential dynamic process has been conceptualized as a form of “digital gentrification”, where platform capitalism mechanisms, such as short-term rental markets and location-based service algorithms, disproportionately benefit technology corporations and destination marketing organizations, thereby intensifying wealth polarization⁸¹.

However, our analysis identifies countervailing trends that complicate this narrative. Notably, 55% (eleven out of twenty) of top-twenty-ranked DNF GIAHS destinations are located in Global South countries (see Fig. 6), with China’s emergence as a major destination hub marking a significant shift in traditional Global North dominance of digital nomad flows. This geographical reconfiguration implies that digital nomads might, under certain conditions, serve as unconventional development driver of development. Extended stays facilitate deeper cultural immersion, enabling the translation of intangible heritage value into tangible economic value through sustained local consumption, co-created experiences, and informal skill-sharing.

Thus, the role of digital nomadism in regional inequality is not monolithic, but context-dependent. Its impact as either a catalyst for inclusive development or a culprit of disparity is mediated by destination-specific attributes, local governance, and the capacity of communities to leverage nomadic presence into sustainable, place-based value creation.

While this study provides a systematic assessment of DNF across GIAHS sites, several limitations should be considered when interpreting the results: First, the static nature of the DNF framework offers a cross-sectional evaluation of destination potential but does not capture the dynamic spatiotemporal patterns of digital nomad mobility, influenced by visiting patterns and cultural attraction. Although we considered using dynamic data sources such as Instagram geotag density, we found that publicly available data could not reliably distinguish between tourists, local residents, and long-term digital nomads. Employing such undifferentiated data would have introduced substantial noise and compromised validation integrity. Therefore, the present model should be interpreted as an interpretable, static assessment of structural suitability rather than a predictor of short-term fluctuations in the presence of digital nomads. Moving forward, we plan to integrate dynamic mobility dataset of digital nomads with agricultural heritages observations to develop a spatially and temporally explicit model of human-heritage interaction in today’s digital age.

Second, the study faces certain data and objectivity constraints. The lack of clearly delineated GIAHS boundaries, and limited site-specific data pose challenges to quantitative model. Future efforts should incorporate more spatially explicit indicators to improve accuracy and conduct deeper research. Additionally, while Airbnb density served as a useful initial validation proxy, it remains an imperfect measure of digital nomad presence. Future studies should seek more direct behavioral data to strengthen validation.

Third, the subjective dimensions of our framework—including indicator design and weighting—reflect the perspectives of a limited expert panel. The panel’s composition introduced two specific biases: (1) geographical and cultural concentration, with approximately 80% of participants from Asia-Pacific and North America, potentially overemphasizing criteria relevant to these regions and constraining the direct generalizability of our findings to the global digital nomads; and (2) professional blind spots, with a high proportion of social media influencers and educators (44.7%), which may underrepresent other digital nomad subgroups, such as information technology or marketing professionals. Consequently, the current weights should be viewed as a first-generation model, and future studies should expand the stakeholder sample to better capture these diversities, aiming to enhance the global robustness and achieve a more comprehensive understanding of digital nomads’ opinions. We recommend a structured validation strategy: (1) constituting expert panels from underrepresented regions (e.g., Europe, Africa, South America); (2) deriving regional weight sets by re-running the AHP, and systematically comparing these with our global weights to quantify regional variation; (3) calibrating future index versions toward either region-specific or balanced global models. This process will enhance the index’s cross-cultural validity and socio-economic applicability.

Finally, given the wide variety and unique combinations of historical landscapes, livelihoods, and cultural practices in GIAHS sites, the DNF evaluation approach in this study appears somewhat overgeneralized. A uniform model risks overlooking local specificities. We recommend that future applications adapt the indicator system to local contexts by adding, modifying, or removing criteria based on site-specific intelligence. Meanwhile, subsequent research would be worthy to explore this spatial variation in site-specific case studies involving thorough investigations, and accommodating comprehensive contexts. Fine-scale, place-based case studies will be essential to translate global assessment frameworks into locally meaningful insights.