6.1. General Statements and Findings

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Hungarian sustainability law and governance is multilayered, hierarchical, and functionally dispersed over various areas of law and governance. It draws upon international, EU, constitutional and national law and makes Hungary’s commitments to global and regional sustainability legally recognised. At the international level, Hungary joined and promulgated (incorporated to national level) a number of relevant environmental and sustainability conventions, treaties (e.g. the UN Framework Convention on Climate Change, the Convention on Biological Diversity, and the Paris Agreement), which were ratified by Hungary and became an organic part of domestic law. Sustainability is written into the Fundamental Law of Hungary (Constitution) and as a principle, Hungary must retain the country’s resources, biodiversity, and cultural heritage for the generations to come. The Fundamental Law is a constitutional provision that serves as a guiding norm for subsequent legislation within a sectoral context.

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A vast range of sectoral laws and government decrees include sustainability across the statutory and regulatory levels, such as laws and regulations relating to environmental protection, water management, forest management, energy, finance, agriculture, education, public procurement, cultural heritage. The formulisation of a myriad of laws demonstrates a horizontal understanding of sustainability across policy sectors.1 Competences are shared among diverse ministries (e.g., agriculture, economic development, energy, education), and institutions (e.g., Hungarian National Bank), with distinct sustainability functions related to their sectoral obligations. This generates functional specialisation and aids coordinated governance. Moreover, Hungary implements a life-cycle-driven, systems-based approach to sustainability, by integrating sustainability considerations into its planning, licensing, and operations. Additionally, oversight processes such as environmental impact assessments and sustainability strategies also support integration.

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The term frequency analysis through the examined corpus (relevant legislation) gives us insight into the central conceptual and thematic framework of a given text, especially as it relates to sustainability and legal discourse. Grounded in corpus linguistics and computational text, term frequency is one of the most basic indicators of how often specific terms or term sets occur throughout a document or set of documents. Term frequency, therefore, allows researchers to identify themes and legal concepts and terminology important for policy, relevant to the legal and regulatory framework being investigated.
 
Figure 42. Term Frequency. Source: Compiled by the author
 

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In the present corpus, the pair of words ‘sustainable, sustainability’ appears most frequently, appearing 237 times, emphasising the significance of these words, both in the story of and legal framing in the Hungarian sustainability discourse. This frequency suggests the kind of relevance that sustainability lends to a guiding principle. Moreover, it also indicates that these words can be found in a variety of arguments referencing legal, economic, and environmental contexts of the documents analysed.

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Next in frequency are terms like ‘act, decree, law’ (141), which exemplify legalism in the documents and reflect the incorporation of sustainability into formal legislative documents. Similarly, the frequency of the terms ‘development’ (118 occurrences) and “economic, financial” (99 occurrences) suggests that sustainability has been integrated into social planning and economic and financial policymaking. These numbers demonstrate that sustainability in legal theory and practice in Hungary is not seen as a standalone environmental issue, but as an integrated part – a governance paradigm – excited of sustainability.

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Several other frequent terms, such as ‘management,’ ‘resources,’ ‘climate,’ and ‘nature’ reflect the multi-dimensional nature of sustainability law, where environmental stewardship, institutional prescription and safeguarding nature, intersect. The pattern of vocabulary also suggests a discourse based on systems-thinking and policy coherence, where different ‘sectors’- ecological, economic, legal- are valued as interrelated or interdependent.
 

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The comparative analysis of the topic modelling techniques of Latent Dirichlet Allocation (abbr. LDA) and Non-negative Matrix Factorization (abbr. NMF) demonstrates both convergences and divergences in the extraction of latent themes from the legal and policy-oriented text regarding sustainability in Hungary.2 While both models reveal relevant and interpretable themes, they exhibit differences with respect to semantic granularity, thematic cohesion, and redundancy.
 

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Figure 43. Topic distribution. Source: Compiled by the author
LDA Topics
 
NMF Topics
Mostly temporal and procedural references – may relate to legislation timelines or efficiency programmes.
Topic 1: period, contracting, 2015, provides, efficient, 2016, 2014, contribution, 2003, optimal
 
Education and cultural sustainability; sustainability as a societal and curricular value.
Topic 1: exam, environmental, protection, values, use, cultural, development, heritage, sustainable, sustainability
A legal-administrative theme focusing on sustainable governance and statutory requirements.
Topic 2: management, article, protection, act, government, sustainability, use, shall, development, sustainable
 
Natural resource governance – land, water, game management.
Topic 2: government, game, use, land, act, section, protection, management, sustainable, water
Environmental and resource governance – particularly water and energy sustainability.
Topic 3: use, production, development, protection, energy, management, act, section, sustainable, water
 
Public services, energy, and sustainable waste/resource management.
Topic 3: public, natural, service, sustainability, waste, act, development, section, sustainable, energy
(Same as Topic 1 – suggesting a duplicated thematic cluster in the model).
Topic 4: period, contracting, 2015, provides, efficient, 2016, 2014, contribution, 2003, optimal
 
Sustainable finance, EU regulations, green mortgages.
Topic 4: management, financial, European, mortgage, public, EU, sustainability, act, section, sustainable
(Another repetition – may reflect consistent use of fixed legal boilerplate language across sections).
Topic 5: period, contracting, 2015, provides, efficient, 2016, 2014, contribution, 2003, optimal
 
Legislative and administrative obligations for sustainability.
Topic 5: article, act, management, natural, government, section, minister, development, shall, sustainable
Focuses on economic and financial instruments related to sustainability – e.g., green bonds, public finance, EU regulations.
Topic 6: economic, mortgage, European, financial, EU, public, sustainability, section, act, sustainable
 
International treaties and frameworks on climate change and biodiversity.
Topic 6: convention, economic, change, resources, use, climate, article, biological, development, sustainable
 

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The LDA provided multiple topics (most notably topic 1, 4, and 5) that were defined by keyword sets that overlapped substantially- especially “period, contracting, 2015, 2016, efficient,” etc. This observed pattern appears to indicate a selective modelling bias towards formal, temporal, and procedural language due to potentially repetitive boilerplate legal references throughout the statutes. As would be expected, redundancy dilutes the discriminative utility of LDA in this case, especially due to the repetitive, legal stylised nature of the text. In contrast, NMF exhibited fewer duplicative topics with more meaningful differences and interpretations. For example, the NMF topic 1 clearly defines an education and cultural heritage perspective on sustainability with the keywords “exam, cultural, heritage, protection, sustainable,” a perspective not present in the LDA’s themes. This indicates NMF’s better ability to produce niche but meaningful patterns in normatively rich documents.

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In both models, a legal-administrative axis of sustainability is identified. LDA Topic 2 and NMF Topic 5 feature high-frequency terms such as "management, article, government, shall, act, sustainable," signalling the place of statutory and regulatory backing of sustainability mandates. These topics identify the formal expression of sustainability by way of laws and government decrees; nevertheless, NMF provided somewhat more specific clustering for the message, which grouped specific actors as thematic owners (e.g., ministry, public finance institutions, or EU agencies) whereas LDA remained on a broad conceptualisation of framing. (NMF Topic 4: “mortgage, European, public, financial, sustainability”)

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A theme of resource use and sectoral governance – particularly in respect to water, energy, land, waste and biodiversity – appears prominently in both models; LDA Topic 3 and NMF Topic 2. The NMF model appropriately referred to a just distinction between natural resource law (game, land, water) and public utility and waste management as the latter was emphasised within the NMF model (and Topic 3). This potentially indicates that the NMF model is able to depict the sectoral segmentation of sustainability policies embedded within Hungary’s legislative structure.
1 Iván, D., Boros, A., & Hegedüs, V. (2020). A fenntarthatóság társadalompolitikai indikátorai és azok hazai teljesülése. [Socio-political indicators of sustainability and their domestic fulfillment]. Pro Publico Bono–Public Administration, 8(2), 162-193.
2

LDA is a generative probabilistic model for discrete datasets. It is a three-level hierarchical Bayesian model, where each collection item is represented as a finite mixture over an underlying set of topics, and each topic is represented as an infinite mixture over a collection of topic probabilities. Since the number of topics need not be predefined, LDA provides researchers with an efficient resource to obtain an explicit representation of a document. In contrast to LDA, NMF is a decompositional, non-probabilistic algorithm using matrix factorization and belongs to the group of linear-algebraic algorithms. NMF works on TF-IDF-transformed data by breaking down a matrix into two lower-ranking matrices. Specifically, NMF decomposes its input, which is a term-document matrix (A), into a product of a terms-topics matrix (W) and a topics-documents matrix (H). W contains the basis vectors, and H contains the corresponding weights.

Egger, R., & Yu, J. (2022). A topic modeling comparison between lda, nmf, top2vec, and bertopic to demystify twitter posts. Frontiers in sociology, 7, 886498.

A Topic Modeling Comparison Between LDA, NMF, Top2Vec, and BERTopic. URL: https://www.datascience-in-tourism.com/?p=550 (accessed: 29 October 2024).

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