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Article

Climate Change in the Biodiversity and Forest Strategies in Greece Using Discourse Analysis and Text Mining: Is an Integration into a Cost-Efficient Natural Resources Policy Feasible?

by
Konstantinos G. Papaspyropoulos
1,*,
Harikleia Liakou
2 and
Panayotis Dimopoulos
3
1
Laboratory of Forest Economics, School of Forestry and Natural Environment, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
2
MSc Applied Policies and Techniques of Environmental Protection, University of West Attica, 12243 Egaleo, Greece
3
Laboratory of Botany, Department of Biology, University of Patras, 26504 Patras, Greece
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(7), 6127; https://doi.org/10.3390/su15076127
Submission received: 13 February 2023 / Revised: 16 March 2023 / Accepted: 27 March 2023 / Published: 2 April 2023
(This article belongs to the Special Issue Revealing the Nexus between Biodiversity and Climate Change: Impacts and Opportunities)
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Abstract

:
Climate change poses major threats to biodiversity and ecosystems. National policies on environmental issues address the problems created by these threats and set targets for their mitigation. In Greece, the National Biodiversity Strategy and the National Forest Strategy analyze, among others, the issue of climate change and how it is related to biodiversity conservation and forest management. As biodiversity and forests are interrelated, common strategies may be similar, overlapping, or opposite. In the present research, the issue of climate change is investigated in the two national strategies for finding out if an integration of policies is feasible. Such an integration may result in a cost efficient and smaller set of solutions that should be determined and may have important results in the funding of natural resources policy. Discourse analysis and content analysis with the text mining approach were used to compare the two policy texts. The results are both methodological and applied. Methodologically, text mining is confirmed in the present research to enhance the objectivity of discourse analysis, and it is recommended to complement it. In terms of the way the two policies deal with climate change, it is found that a future integration of a Biodiversity and Forest Strategy in Greece for climate change issues is relevant and may be applied.

1. Introduction

Discourse analysis is a method of analyzing language data that has been used vastly in the environmental policy discipline [1,2,3,4,5]. Researchers try to find the motivations and purposes of the text that is in focus and underline the main meanings that are hidden behind the words [6]. On the other hand, computational text mining has started being used in environmental research with the same objectives as discourse analysis, having as an advantage that it is a more independent way for studying environmental policy documents [7,8,9,10]. However, both methods have rarely been used simultaneously in the environmental policy literature. To our knowledge, only [11,12] seem to have used discourse analysis and text mining to analyze environmental policy in the last three years. However, such a combined use may have several positive implications, such as a deeper, correct, and objective understanding of the meaning that a policy has for the environment, the comparative understanding of two or more policy texts, and the integration of policies if it is discovered by the analysis that there are common objectives in such texts. This may be especially important in critical environmental sectors, such as biodiversity conservation, where there is low funding, and common solutions need to be implemented.
As it is globally accepted, biodiversity conservation is one of humanity’s greatest goals, both from the point of view of organizations dealing with the natural environment and at a political–economic level. Ref. [13] recognized biodiversity loss as one of the greatest risks for humanity. However, this has been recognized for more than three decades now by the United Nations (UN). In 1992, the United Nations signed the Convention on Biological Diversity (CBD, or Biological Diversity) in Rio de Janeiro [14,15]. Parts of forests are also part of the planet’s biodiversity. According to [16], forest biological diversity is a broad term that refers to all forms of life found in forests, but also to the ecological functions they perform.
Greece is one of the richest countries in biodiversity and has extensive forest cover. According to the publication on the state of the environment in Greece [17], Greece is considered one of the world’s centers of biodiversity, and its importance for the conservation of European biological capital is high. In total, the forest covers more than 60% of the country [18].
Climate change poses severe threats for biodiversity, as they are described comprehensively in [19,20,21]. On the other hand, forest biodiversity can serve as a mitigation mechanism of the phenomenon [22]. This relationship of biodiversity to climate change has been described and directed in several environmental policy documents [23,24,25].
In Greece, the Ministry of Environment and Energy is responsible for the conservation and management of biodiversity, forests, and climate change. The Public Services that deal with these sectors are a) the General Directorate of Environmental Policy and b) the General Secretary of Forests. From there comes the policy and legislation that must normally be implemented by the Natural Environment and Climate Change Agency (NECCA) and the Public Forest Service to achieve sustainable use. NECCA is a Legal Entity of private law supervised by the above Ministry, whereas the Forestry Service is a public service belonging to it.
The current National Strategies for Biodiversity and Forests were issued by the above Ministry in the previous decade. Firstly, the National Biodiversity Strategy (NBS) [26] has been institutionalized since 2014, which arose as an obligation for the country from its participation in CBD [27]. Since 2018, a National Forest Strategy (NFS) [28] has also been instituted, not as an obligation, but was influenced by the publication of the European Strategy for Forests in 2013 [29]. In these two texts, visions, general objectives, and policy directions are presented for topics that are often common. In between, in 2016, the Ministry issued the National Climate Change Adaptation Strategy [30].
Taking the above into account, the main objective of this research is to compare the texts of the NBS and NFS in order to find out if there are (a) similarities, (b) overlaps, and (c) differences in these two policy texts in the way they deal with climate change and the targets they set in order to manage its consequences for biodiversity and in order to mitigate it through forest biodiversity.
Based on the results obtained from the above, another objective of the present research is to establish whether the two texts could be unified in a common natural resources strategy in Greece. In the context of policy making, an integrated natural resource policy can help policy makers define a smaller set of solutions that can be implemented in the two areas of biodiversity and forests. This may also lead to a more efficient allocation of natural resource funding.

2. Materials and Methods

To achieve the objectives of this research, the methodology of text analysis was used. According to [31], text analysis is “any scientific technique that aims to detect messages in recorded communication through the processing and interpretation of a text”. Two methodological approaches appear in text analysis:
Discourse analysis (qualitative research): According to discourse analysis, discourse can be understood as the systematic use of language in a text with specific rules, terminology, and conventions and can refer to a cognitive field (in the case of the present research, the cognitive field of climate change science). Discourse analysis aims to delineate the contexts in which the text is included and encourages the understanding of the motivations and purposes of the texts [6,31]. According to the comparative method [32], “…the discovery of the similarities and differences that exist in the laws under comparison requires the explanation of the factors that cause them, affecting the structure, content and evolution of law. The most important factors differentiating law are: history, the economy, the political system and the role of the legislator...”. For this reason, the comparative overview of the two legal texts of Greece (the National Biodiversity Strategy and National Forest Strategy) for the qualitative characteristics of the two strategies which could reveal the objectives set by this research was undertaken. Differences were defined as differences both in the substance of the text itself and in the approach with which the two National Strategies may have been drafted. Similarities were defined as issues that may be addressed simultaneously in both policies, as well as the similar ways of preparing the strategies. Finally, topics that are common to the two strategies but with which the approach to one overlaps the approach to the other were defined as overlaps. The discourse analysis is presented in the results chapter, mainly descriptively, while there are also some quantitative data that highlight the qualitative characteristics of the two National Strategies.
Content analysis (quantitative research): According to [33], content analysis is the “...systematic and identical examination of communication symbols, which are given numerical values according to rules of valid measurement, as well as the analysis of relationships involving these values using statistics methods for describing the transmission of information and drawing conclusions about its meaning”. Content analysis [34] has been widely used in the environmental sciences [35,36]. In recent years, with the development of computing machines and their learning, the modern tool of knowledge mining from texts (text mining) has arisen and is used for content analysis.
According to [31], discourse analysis works in collaboration with content analysis, since the latter creates an objective and stable framework within which the former places meanings in groups consisting of suspicions and assumptions, however important, for a full understanding of the text. Because the present research dealt with more than one policy text, a comparative analysis of the texts was carried out according to the standards of the science of comparative law [32].

2.1. Text Mining

Text mining is a research field that uses techniques dedicated to extracting information from text data that do not have any apparent structure [37]. Text mining has gained great interest in both academic research and business intelligence applications over the past 25 years. There is a vast amount of text data in computer-readable format that is easily accessible via the internet or databases. Such texts can be scientific articles, abstracts, and books to memos, letters, online forums, mailing lists, blogs, and other means of communication that provide logical information. Text mining is an interdisciplinary research field that uses techniques from computer science, linguistics, and statistics [38]. According to [39], text mining can include, among others, word frequency analysis and the relationships between words.

2.2. Statistical Content Analysis

Text mining can be performed with statistical analysis, including basic descriptive statistics as well as more applied analyses such as correlation analysis and significance testing. Descriptive statistics are mainly expressed by graphs and tables of the frequencies of occurrence of the words that prevail in the texts. Applied statistical analysis involves analyzing correlations between words, using appropriate correlation coefficients [40,41].

2.3. Data Collection

Data for the research were obtained by the texts of the two National Strategies. For the discourse analysis, the two texts were used in the Greek language. Regarding the content analysis, the two texts were used in the English language. This is because the text mining methodology includes packages in R [42] that work significantly better with the English language in relation to lesser-used languages like Greek, although there are also studies that have directly used the Greek language [43]. The National Biodiversity Strategy was readily available in English, but the National Forest Strategy had to be translated first. For this reason, the website translate.google.com was used, which is considered a reliable language translator [44]. However, the text was reviewed after translation, and necessary corrections were made, where necessary.
To achieve a homogeneous content analysis in the two texts, a smaller part of the NBS was used, which corresponded to the text of the NFS. This was Section A1_Definitions, Chapter C_Vision, Purpose and Objectives of the National Biodiversity Strategy, Chapter D_Monitoring, and the evaluation of the implementation of the National Strategy for Biodiversity.

2.4. Data Analysis

For the discourse analysis, the two texts were used episcopally. For the content analysis, the two texts were processed with the R programming language. Specifically, R Studio and the following packages were used: (tm) to remove numbers, special characters, accents, and other deprecated words (and, or, etc.) [45], (snowballc) for the truncation of words, i.e., reducing them to their root, for example, the words “forestry”, “forested”, “forests”, and “forester” were truncated to the word “forest” [46], and (ggplot2) to generate graphs [47].
Based on the above procedure, only the so-called meaningful words were used, which were examined both with descriptive statistical methods (frequencies) and graphs, with the use of the correlation coefficient Spearman, the Sign test, and the Wilcoxon Signed Ranks test, with a = 0.05 as the significance level [48]. The R code used was written by [49].
The flowchart in Figure 1 sums up the methodology process.

3. Results and Discussion

Firstly, the results from the discourse analysis are presented, followed by the text mining approach.

3.1. Discource Analysis Results

The NBS was established in Greece in 2014 and is valid until 2029. The time span for the NFS is 20 years, from 2018 until 2038. Thus, there is a common period of 11 years that the strategies are simultaneously applied, with 6 remaining until 2029. There are some differences in the way that the two strategies have been built. The NBS, as a strategy that resulted from the country’s commitment to the CBD, presents almost 60 pages with the current (in 2014) status of biodiversity in Greece. On the other hand, the NFS is mainly a regulatory text, which in 25 pages presents what must be undertaken until 2038 for the development of forest resources in Greece.
The four years, from 2014 to 2018, that passed until the NFS was established seem to have had an impact on the way that climate change is represented in the strategy’s text. When the NBS was established, there was no Paris Agreement yet (2015), and the first strategy about climate change in Greece (the National Climate Change Adaptation Strategy) was presented in April 2016. This is apparent in the vision of the NBS where climate change is not mentioned, and there is mainly a protection/conservation approach to biodiversity:
“By 2050, biodiversity in Greece and the ecosystem services it provides—the country’s natural capital—are protected. This protection is warranted because of the intrinsic value of biodiversity, along with its essential contribution to human well-being and economic prosperity and aims to avoid catastrophic changes caused by the loss of biodiversity. In this context, the value of ecosystem services and functioning are highlighted and the functions that have been degraded are restored.”
(Page 71)
In the NFS, climate change is included in the vision as follows:
“Ensuring sustainability and increasing the contribution of forest ecosystems to the country’s economy through multifunctionality, adaptability and strengthening their socio-economic role, in the light of climate change.”
(Page 2)
One can see here that the whole vision of the NFS, which is a managerial–economic vision mainly, is influenced by the presence of climate change, while adaptation is additionally included as a term that arises from the climatic problem.
The two national strategies have different methods in dealing with the general objectives that they set. In the NBS, there are thirteen general objectives that are not classified into any subjects. In the NFS, there are twenty-two general objectives that are divided into seven policy axes, three horizontal and four vertical ones. One of the axes in the NFS is named “Climate Change”.
The NBS, in the seventh general objective talks about “General Target 7: Prevention and minimization of the impacts of climate change on biodiversity” and specifies four special targets. Although these four special targets include both adaptation and mitigation objectives, the general target is dedicated to adaptation. The NFS, on the other hand, dedicates an analysis to climate change in the vertical axe (VA) 2 “VA2: Climate Change” where it specifies the general objectives for the issue.
Both strategies consider vulnerability, i.e., the effects of climate change on biodiversity, ecosystem functions, and forests. The NBS presents two special targets for biodiversity adaptation to climate change: “7.1 Studying the effects of climate change on biodiversity and ecosystem functions” and “7.2 Taking action so that the components of biodiversity will be able to adapt to climate change”. The NFS includes two general objectives for climate change adaptation: “2.1.1. Assessing the vulnerability of forest ecosystems to climate change” and “2.1.2. Management for the adaptation of forest ecosystems to climate change”. These objectives are similar in their discourse, and the NBS overlaps with the objectives of the NFS as biodiversity includes the forest ecosystems.
Regarding the mitigation of the effects of climate change, the NBS presents the role of forests and demands: “7.4 Enhancing the role of forests in mitigating the effects of climate change”. Obviously, the NFS includes this issue in its general objectives; that is the general objective “2.1.3 Contribution to the mitigation of climate change by increasing carbon sequestration and storage in forest ecosystems”. These objectives are exactly similar in their discourse as they set forest ecosystems as the mechanism to mitigate climate change.
The NBS, additionally, presents the perspective of the impacts that climate change adaptation infrastructure can have on biodiversity, and the strategy calls for this factor to be considered when dealing with climate change: “7.3 Reducing the impact of actions established to address climate change on biodiversity”. There is no such prediction in the NFS.
Table 1 presents the comparison of the two strategies in some general issues and in climate change.

3.2. Content Analysis Results

After applying the text mining approach, almost 1000 meaningful words remained in each strategy text. There were 926 words in the NBS and 1023 in the NFS. The word clouds that are presented in Figure 2 give an overview of the terms that have the main presence in each strategy. The bigger the word fonts, the bigger the representation of the word in the text.
The most frequent 10 words that are present in each text are shown in Figure 3.
As it was expected, for the NBS the word “biodivers” and for the NFS the word “forest” were the most frequent. There were no common words in the 10 most frequent words, except from ecosystems, and no obvious word related to climate change. In the NBS, the main 10 words were related to the protection and conservation of biodiversity, the protected areas, ecosystems, and the implementation of the general objectives of the strategy. In the NFS, the main 10 words were related to the management of forests and their production. This difference among the two strategies, the protective role of the NBS and the productive role of the NFS, was evident in the vision of the two strategies as presented previously in Section 3.1.
Table 2 shows the correlation of the word “climat” in the two national strategies. Both texts, as expected, strongly related “climat” to “chang” (r > 0.7). In general, the NBS seems to focus more on adaptation issues and less on mitigation, while the opposite is happening in the NFS. This was not as apparent with the discourse analysis as there were two objectives in both strategies dedicated to climate change adaptation. However, the fact that the NFS dedicates more text explaining the relations of climate change with forests, may have an impact on the revealed correlations, due to a little more focus given in the mitigation issues included in the strategy.
Although all the correlations in the NBS are greater than those in the NFS, except from the word “mitig” which is greater in the latter, the statistical comparison tests showed that there is no statistically significant difference between the correlations existing in the NBS with the correlations that exist in the NFS with the same set of “climat”-related words. Both the Wilcoxon signed ranks test and Sign test had p values greater than 0.05 (Wilcoxon signed ranks test Z = −1.859, asymptotic p = 0.063 > 0.05-exaxt p = 0.078 > 0.05 and Sign test exact p = 0.125 > 0.05).
This shows that although the NBS was established earlier than the Paris Agreement, the National Strategy for Climate Change Adaptation, and other climate-related international and national policies, the climate change issues are dealt properly, similarly, and sometimes more than those in the NFS. On the other hand, the NFS seems to pay slightly more attention in issues related to mitigation compared to adaptation. The mitigating role of forests is more evident in the NBS, too. Indeed, the main correlations that the word “forest” has with other words in the NBS are as follows: sea: 0.5, regul: 0.36, and r = 0.35 the words: botan, atmoshper, carbon, concentr, dioxid, gas, greenhous, and sequestr. Forests in the NBS are mainly seen as regulatory ecosystems which sequester carbon dioxide and reduce the carbon concentration in the atmosphere.

3.3. Comparison of the Two Methods and Integration Implications

Based on the above results, the comparison of discourse analysis and text mining shows that they work complementary to each other and help researchers and policy makers to better understand and process environmental policy. This is in accordance with the previous literature stating that the combination of text mining with discourse analysis offers a deeper understanding of the discourses and an avoidance of over- or under-interpretations of their meanings [50]. Furthermore, this combination reduces the subjectivism from which the discourse analysis suffers [51] and makes the implicit understanding that the discourse analysis offers an explicit understanding that is encoded into statistical, i.e., objective approaches [52].
In terms of comparing the results of the implementation of the two methods, the above analysis showed that concerning the vision of the two strategies, text mining confirms discourse analysis about the character of the vision, that is conservation in the NBS and managerial/economic in the NFS. However, unless one does text mining only on the two visions of the strategy, text mining is not able to reveal the fact that in the NFS the whole vision is influenced by the presence of climate change. On the other hand, the fact that the NBS was established before major changes in climate change policy internationally and nationally, and this has impacted its vision, is not apparent when using text mining. The latter succeeds in showing a significant reference of the NBS to climate change. Additionally, it shows that the NBS does not statistically differ from the NFS when compared in terms of correlations of basic terms that are used in climate change terminology.
By using discourse analysis, one can understand that the NBS is more focused on the adaptation of biodiversity to climate change. However, both the NBS and NFS have the same objectives in terms of climate change. There are, equally, two targets for climate change adaptation and one target for climate change mitigation in each strategy, with a difference being that in the NBS these targets are special and not general targets as in the NFS. Text mining confirms statistically that the NBS is more dedicated to climate change adaptation, whereas the NFS is to climate change mitigation. However, text mining fails to reveal the fact that the NBS cautions about the impacts on biodiversity that climate change adaptation infrastructure may have, unless one considers the r = 0.21 of the word “infrastructur” with “climat” and the fact that the NFS lacks such words.
Regarding the issue of the integration or not of the two strategies:
For the time being, such an integration is impossible because the NBS ends in 2029, whereas the NFS ends in 2038. If the country wanted to do something like this, it would have to harmonize the years of validity, which also needs a legislative provision.
The revealed overlaps and similarities could be dealt with in a unified way. Climate change mitigation could be obtained from the NFS because it is a part of policy distinct from the NBS. Climate change adaptation in the NBS overlaps with that in the NFS, but the former represents the whole approach in the issue and could be obtained by this policy.
The above findings show that, indeed, the two methods can work complementarily, and in the present research, discourse analysis was on some occasions more efficient than text mining. However, in this case there was a limitation of only two texts for analysis, while the method of text mining has been developed for analyzing hundreds, or thousands of texts, whereas discourse analysis would not be able to reveal the meanings of these texts.
Future research to ascertain the above findings, as well as additional issues, should move into the direction of topic modeling, which is a more advanced method in the science of extracting information from texts using statistical programs [53,54]. Thematic modeling could be implemented in multiple climate change, biodiversity, and forestry policy texts from the globe to reveal common approaches, overlaps, and differentiations in the strategies.
Finally, topic modelling supported by text mining could be used in future research where a vast number of technical policy documents, the press, and other written sources would be examined for finding out if the general objectives of the policies referring to climate change, biodiversity, and forests are really implemented. Testing the implementation and the trends of environmental policy through text mining has been used in several scientific papers [55,56,57].
In any case, qualitative and quantitative research with discourse analysis and text mining in environmental policy texts is a new scientific field that has the potential to reveal opportunities to reduce the political tools that need to be used and the better distribution of the scarce financial resources available.

4. Conclusions

The present research attempted to compare two environmental policy texts, the National Biodiversity Strategy and the National Forestry Strategy in Greece in terms of the way they deal with climate change. The main goal was the examination of whether these two texts can be integrated in the future in a common natural resources policy, because consolidating policies in their formation can offer a reduction in the number of solutions that decision makers need to find, while also reducing the search for funding sources. Such a solution reduction with an integrated policy can be cost-effective and may more easily win the focus of governments compared to two policy documents.
The comparison was made using two scientific tools: the qualitative method of discourse analysis and the statistical–quantitative method of content analysis with the approach of text mining.
From the results of the application of the two research methods we conclude the following:
  • Discourse analysis and text mining may and must work complementarily for comparing and analyzing environmental policy documents;
  • Discourse analysis may be enhanced by text mining when there are few documents for analysis;
  • Text mining revealed that the NBS and NFS are statistically similar in the way they approach climate change issues;
  • Although not in the NBS vision, climate change is of focus in the strategy, irrelevant of the fact that the NBS was established before major climate change policies;
  • Text mining succeeded in finding that the NBS focuses on climate change adaptation, whereas the NFS is focused on climate change mitigation;
  • The latter was not as apparent via discourse analysis, and without text mining one could have underestimated it;
  • The NBS climate change adaptation target overlaps with the similar ones in the NFS;
  • The NFS climate change mitigation objective is similar to the one of the NBS;
  • An integration of the two natural resources policies on climate change issues seems relevant, and this could possibly be part of a future climate change policy in Greece.
Overall, general directorates responsible for the continuous design and control of climate change and environmental policies could use these tools, discourse analysis and text mining, to improve and update strategies that have been developed independently from specialized policy makers belonging to different fields. This may indeed offer fewer solutions for dealing with climate change, which will succeed in effectively allocating the funds for tackling this problem.

Author Contributions

Conceptualization, K.G.P.; methodology, K.G.P. and H.L.; software, K.G.P.; validation, K.G.P., H.L. and P.D.; formal analysis, K.G.P. and H.L.; data curation, K.G.P.; writing—original draft preparation, K.G.P. and H.L.; review and editing, P.D.; visualization, K.G.P.; supervision, P.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are available upon request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 15 06127 g001 550
Figure 1. Flowchart of the methodology approach.
Figure 1. Flowchart of the methodology approach.
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Figure 2. Word clouds for (a) National Biodiversity Strategy; and (b) National Forest Strategy.
Figure 2. Word clouds for (a) National Biodiversity Strategy; and (b) National Forest Strategy.
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Figure 3. Word frequency in (a) National Biodiversity Strategy; and (b) National Forest Strategy.
Figure 3. Word frequency in (a) National Biodiversity Strategy; and (b) National Forest Strategy.
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Table
Table 1. Comparison of the NBS and NFS in some general issues and in climate change.
Table 1. Comparison of the NBS and NFS in some general issues and in climate change.
SubjectNBSNFS
Pages11925
Time span15 years (2014–2029)20 years (2018–2038)
Current statusYesNo
Action planYesNo
FundingNoYes
Policy axesNoYes
General objectives1322
VisionNo reference on climate changeWhole vision under the light of climate change
Climate changeSimilarities in mitigation and overlaps in adaptation to climate change
Climate changeImpacts on biodiversity by climate change adaptation infrastructureNo
Table
Table 2. Spearman correlations (r) among the word “climat” and relevant to climate change words in the two strategies.
Table 2. Spearman correlations (r) among the word “climat” and relevant to climate change words in the two strategies.
ClimatrNBSrNFS
chang0.920.75
adapt0.730.32
mitig0.330.45
carbon0.250.18
dioxid0.250.14
gase0.250.05
sequestr0.250.06
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Papaspyropoulos, K.G.; Liakou, H.; Dimopoulos, P. Climate Change in the Biodiversity and Forest Strategies in Greece Using Discourse Analysis and Text Mining: Is an Integration into a Cost-Efficient Natural Resources Policy Feasible? Sustainability 2023, 15, 6127. https://doi.org/10.3390/su15076127

AMA Style

Papaspyropoulos KG, Liakou H, Dimopoulos P. Climate Change in the Biodiversity and Forest Strategies in Greece Using Discourse Analysis and Text Mining: Is an Integration into a Cost-Efficient Natural Resources Policy Feasible? Sustainability. 2023; 15(7):6127. https://doi.org/10.3390/su15076127

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Papaspyropoulos, Konstantinos G., Harikleia Liakou, and Panayotis Dimopoulos. 2023. "Climate Change in the Biodiversity and Forest Strategies in Greece Using Discourse Analysis and Text Mining: Is an Integration into a Cost-Efficient Natural Resources Policy Feasible?" Sustainability 15, no. 7: 6127. https://doi.org/10.3390/su15076127

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