Transboundary Transitional Waters: Arenas for Cross-Border Cooperation or Confrontation?

Abstract

The Russian Federation’s invasion of Ukraine in 2022 evoked academic interest in various aspects of international security and stability. The main objective of this study was to elicit the essential features and indicators for gauging the current state of cross-border cooperation and the risks of confrontation in and around transboundary transitional waters, i.e., those transitional waters where two or more countries share an estuary, delta, or lagoon while having a contiguous border in the territorial waters. The Cooperation and Confrontation Index comprises five facets and 25 indicators spread equally among these five facets. The following are the facets based on the integrated values with which the Cooperation and Confrontation Index for each transboundary transitional water body is calculated: 1. Socioeconomic Cohesion; 2. Environmental Coherence; 3. Cross-border Connectivity; 4. Cross-border Co-operation; and 5. Confrontation Risk. A comprehensive worldwide inventory of transboundary transitional waters was created. The investigation results show that eight transboundary transitional water areas are under high confrontation risk, while six areas are under no confrontation risk. The conclusion is that this risk of confrontation is closely correlated with the level of corruption in a more bellicose country sharing the transboundary transitional water body.

1. Introduction

The term “transitional waters” (TW) was introduced in 2000 by the Water Framework Directive (WFD) of the European Communities (WFD, 2000/60/EC) to delineate the space between freshwaters, coastal waters, and marine waters. TW are defined in the Official Journal of the European Communities 43 (L327) as “bodies of surface water in the vicinity of river mouths which are partly saline in character as a result of their proximity to coastal waters but which are substantially influenced by freshwater flows” [1]. The WFD obliges the EU Member States to prepare and regularly update six-year river basin management plans, including coastal and TW up to 1 nautical mile (NM).

River estuaries and coastal lagoons are the most typical TW. However, the term also refers to a vast range of littoral aquatic habitats with varying physical and ecological characteristics, including lakes, rias, fjards and fjords, hypersaline ecosystems and brackish wetlands, and freshwater offshore discharge plumes, to mention just the most common types. TW ecosystems are the ecotones between freshwater basins and the open sea, and the resulting gradients make them some of the world’s most productive ecosystems [2].

Despite their limited scale, due to their specific locations and stressors, TW contain ecological communities with a rich biodiversity and complicated self-regulation mechanisms [3]. However, also due to these reasons, TW suffer from more impacts, resulting in a worse ecological status than lakes and coastal waters. The worst areas in Europe concerning these ecological pressures and the environmental status of TW and coastal waters are located along the Greater North Sea and Baltic Sea coastlines [4].

Human settlements on TW shores have been documented since prehistoric times, providing loci rich in living aquatic resources for developing early human societies and later civilizations [5,6,7,8,9,10]. TW’s enduring economic role prompted this paper’s authors to suggest and elaborate on the Emerald Growth concept as a novel approach to addressing TW’s ecological integrity and ecosystem services. The concept relies on native knowledge of TW environments as a sustainable development “tool chest” and offers innovative measures for revitalizing these areas [10].

Many TW worldwide are estuaries of large rivers or straits and conveniently serve as international boundaries. There are 108 transboundary TW (TTW) worldwide. We consider these TW to be transboundary when two or more countries share an estuary, delta, lagoon, strait, or another TW while having a contiguous border in the adjacent territorial maritime waters. The terms “transitional” and “transboundary” may look confusing, as there has been much attention paid to managing transboundary river basins and conflicts around sharing water resources. Although TTW may be an integral part of these transboundary river basins, the coastal locations of TTW pose very different management issues and challenges compared to inland river basin management.

The Russian Federation’s invasion of Ukraine in 2022 evoked academic interest in various aspects of international security and stability [11,12,13,14]. Due to their rich natural resources, TTW, disproportionally to their size, may contribute to achieving these goals through cross-border cooperation or heightening the confrontation between the countries sharing them., e.g., the Han River estuary, the TTW shared by North and South Korea, is part of the world’s most heavily militarized political border.

The ever-increasing attention paid by researchers from diverse research fields to TW is primarily related to the inclusion of TW into the regulations imposed by the WFD on the EU Member States with regard to a good ecological status, its criteria, and its indicators [15]. The majority of papers on these issues have been published by authors from Mediterranean countries, i.e., Italy [16,17,18], Greece [19,20,21], and Spain [22,23,24] in particular. The investigated and assessed good ecological status indicators range from invertebrates [25,26,27] to fish [28,29,30] and aquatic macrophytes [31,32,33]. Some recent papers on TW biodiversity have considered the role of aquatic meiofauna diversity in achieving TW with good ecological statuses [34,35].

Pitacco et al. [36] summarized the variation patterns of macrobenthic invertebrates in 18 Italian TW. Considering the threats posed to the native biodiversity of TW by alien species, the primary concerns for researchers and TW managers are the invasions and proliferation of alien invertebrates [37,38,39], fish [40,41], and aquatic macrophytes [42,43,44]. These concerns are closely related to research on the economy and sustainable development in TW areas [45,46,47,48,49]. A few case studies of TW in Portugal have dealt with aspects of Emerald Growth, including TW aquaculture, tourism, and management planning [50,51,52].

Due to their disproportionate role compared to their size, TTW, as a specific research area, need more consideration. Many high-level publications on the various management aspects of TTW have focused on the Baltic Sea area [53]. This is no surprise, since 8 of 31 TTW in Europe are around the Baltic Sea and 4 are along its southern rim [54,55]. There is a particular focus on cross-border cooperation for the environmental protection of TTW [56,57]. Several investigations have discussed the hydropolitical relationships involved in cross-border cooperation practices for transboundary river basin and adjacent TTW management, considering various aspects of cross-border politics and the power center stage in water issues [58,59,60].

Hence, the main objective of our study was to elicit the essential features and indicators for gauging the current state of cooperation and the confrontation risks in and around the TTW between the neighboring countries sharing them. Besides our ambition to deliver an inventory of all the TTW and their current situations worldwide, the main research question we focused on was to obtain an insight into the mechanisms governing the cross-border politics and policies on water issues in these TTW, mainly focusing on power struggles and confrontation risks.

2. Materials and Methods

2.1. Study Area

Defining the limits of TTW as areas of cross-border collaboration or, eventually, confrontation, is challenging. In a strict sense, a TTW body covers an aquatic area, tidal marshes, floodplains, and a possible freshwater discharge plume into the sea up to the 12 NM limit of territorial waters with a contiguous border (Figure 1). However, in a broad sense, particularly considering socioeconomic aspects, a TTW area should also cover the land directly adjacent to this TTW. In this case, boundaries are drawn arbitrarily. Typically, they follow the boundaries of riparian and the littoral local, municipal, or regional administrative units on both sides of the border.

2.2. Methods

Achieving the objectives and research results of the study comprised five steps:

• Obtaining a thorough inventory of the TTW worldwide using Google Earth^TM as a tool;
• Designing an integrated TTW Cooperation and Confrontation Index (the Index) to gauge the level of cross-border cooperation versus the confrontation in and around TTW;
• Validating the statistical independence of the selected indicators comprising the Index and checking if collinearity was excluded from the data set;
• Quarrying raw and derivative socioeconomic data on TTW areas and the countries sharing them to elicit the values of the indicators comprising the CCI as a pilot test of the approach;
• In the next stage of our study, we conducted two comprehensive case studies: of the Curonian Lagoon, shared by Lithuania and the Russian Federation, and the Zwin tidal inlet, shared by Belgium and the Netherlands, in order to elicit the current CCI values for both of these TTW and compare their situations regarding their levels of cross-border cooperation and its perspectives.

This paper resulted from our dedicated work on this subject over several decades. We systematically surveyed the world’s coastal areas in detail on large-scale charts and maps, using Google Earth^TM and published materials compiling the worldwide database of the TTW. Google Earth^TM is widely applied as a free Graphic Satellite Interface for the inventory and a semi-instrumental spatial analysis of the land and coastal features [61]. We thoroughly examined the world’s coastline by scrutinizing the sites where sovereign states’ borders or their overseas dependencies meet the sea and checked additional materials in dubious cases. In several cases, we conducted fieldwork in Europe and the Americas.

The Index gauges the socioeconomic and environmental policies and factors that facilitate or hinder the cross-border cooperation in TTW areas and measures the confrontation risks between the countries sharing these areas. It demonstrates the need for and ways of securing peace and stability in TTW areas, which may contribute to or delay achieving the United Nations Sustainable Development Goals by 2030 in the countries sharing these TTW. The Index comprises five facets and 25 indicators are spread equally among these facets.

The methodology for eliciting the indicator values for computing the Index followed the quantitative part of the methodology used by the World Development Forum to compute the global Travel & Tourism Development Index [62,63]. The following are the five facets based on the values with which the total values of the CCI for each TTW are computed:

• Socioeconomic Cohesion;
• Environmental Coherence;
• Cross-border Connectivity;
• Cross-border Cooperation;
• Confrontation Risk.

After carefully selecting and comprehensively analyzing the collected material, we chose these five facets, relying on our more than twenty years of experience in conducting complex international environmental cooperation studies on TTW in Europe, especially in the South Baltic area and the Americas. We organized a focus group at Klaipeda University early in 2022 to verify this choice. In addition to the authors of this article, five other experts on environmental studies on TTW also took part. These five aspects did not cause disagreement among the experts and were approved unanimously. However, the selection of the 25 indicators raised more discussions, as we needed to maintain them within a reasonable number, have them be based on available and reliable data, and have them be representative.

We estimated the confrontation risk values for all the TTW areas worldwide as the fifth facet of the Index. We subtracted the last facet value from the other four facet values’ averages. Each facet comprised five indicators whose values were quarried directly from open sources, derived from spatial data, processed using GIS, and normalized from 1 to 7 (

Table 1. Description of the 25 indicators of the five facets of the Index.

Indicator Number	Indicator Title	Indicator Units	Range Used for Normalization	Guiding Principle	Description
Socioeconomic Cohesion
1.01	Relative difference in GDP per capita (PPP) between both parts of the area	%	1/7 (equal)	Winsorization	Data on GDP per capita on the regional or national level depending on data availability
1.02	Relative difference in night time light intensity between both parts of the area	Scores	1/7 (equal)	Range of relative values	Data on average night time light intensity in the riparian and littoral areas
1.03	Difference in road network density between both parts of the area	km/surface area	1/7 (equal)	Winsorization	The road network includes highways, national, and secondary or regional roads
1.04	Difference in poverty rate between both parts of the area	%	1/7 (equal)	Winsorization	Multidimensional poverty headcount ratio (% of population)
1.05	Corruption perception of a more corrupt neighbor	Scores	13/88	Natural min max	Data on the national-level corruption perceptions
2. Environmental Coherence
2.01	Difference in relative acreage of natural habitats between both parts of the area	%	0/100	Range of possible values	Data on forest and wetland acreage in the riparian and littoral areas
2.02	Difference in deforestation and wetland reclamation rates between both parts of the area	Scores	0/10	Range of possible values	Data on forest and wetland area changes in the last 25 years in the riparian and littoral areas
2.03	Difference in relative acreage of protected areas between both parts of the area	%	0/100	Range of possible values	Data on protected areas acreage in the riparian and littoral areas
2.04	Difference in the treatment rate of wastewater discharged between both parts of the area	%	0/100	Range of possible values	Data on the treatment rate of wastewater discharged in the riparian and littoral municipal or regional administrative entities
2.05	Difference in poaching rate of TW resources between both parts of the area	Scores	1/7 (equal)	Range of relative values	Average score across the components of the available data from reports
3. Cross-border Connectivity
3.01	Strictness of the cross-border visa regime	Scores	1/7 (visa-free)	Range of relative values	Data on the strictness of the visa regime between both countries
3.02	Quantity of border checkpoints and their regime	Scores	1/7 (highest)	Range of relative values	Data on the quantity of border checkpoints and their regime
3.03	Intensity of regular cross-border passenger traffic	Scores	1/7 (highest)	Range of relative values	Data on the intensity of regular cross-border passenger traffic
3.04	Number of annual border crossings	Persons	0/200	Winsorization	Data on annual border crossings adjusted to the diversity of TTW
3.05	Cross-border infrastructure (power grid, cables, pipelines, and water canals)	Scores	1/7 (highest)	Range of relative values	Number of points within the area where technical infrastructure crosses the border
4. Cross-border Cooperation
4.01	Availability and viability of bilateral mechanisms	Scores	1/7 (best)	Range of relative values	This indicator refers to any bilateral mechanisms for controlling the quantities of harvested resources
4.02	Availability and viability of cross-border cooperation agreements	Scores	1/7 (best)	Range of relative values	This indicator gauges the number and viability of cross-border cooperation agreements
4.03	Availability and viability of a bilateral body functioning on routine basis	Scores	1/7 (best)	Range of relative values	This indicator refers to the availability and viability of any bilateral body functioning
4.04	Availability and viability of NGO collaboration networks	Scores	1/7 (best)	Range of relative values	This indicator gauges the number and viability of NGO collaboration networks
4.05	Availability and viability of mutually agreed platforms to resolve disputes	Scores	1/7 (best)	Range of relative values	This indicator refers to the availability and viability of any platform to resolve disputes
5. Confrontation Risk
5.01	Presence and severity of territorial disputes between the countries sharing TTW	Scores	1/5.9 (worst)	Range of relative values	Summing of relative score values reflecting territorial disputes and armed conflicts
5.02	Index of Despotism of a more despotic neighbor	Scores	1.9/6.2 (worst)	Range of relative values	Calculated from the Index of Democracy
5.03	Index of Bellicosity of a more bellicose neighbor	Scores	2.9/6.7 (worst)	Range of relative values	Calculated from the Global Peace Index
5.04	Value of human life in a more bellicose neighbor	Scores	1/7 (worst)	Winsorization	Intentional homicide rate per 100,000 inhabitants of the country
5.05	Population ratio between a more bellicose and less bellicose neighbor	Scores	1/7 (worst)	Winsorization	Shows the potential threat in terms of manpower from a more bellicose neighbor

).

To be selected for the Index, the indicators had to meet the following criteria [63]:

• Regional or national coverage: Appropriate indicator data must be available for all the TTW covered by the CCI;
• Relevance: The indicator must represent a suitable metric for the addressed facet;
• Comparability: The data must be comparable between similar-level administrative units and throughout the time series;
• Update regularity: The indicator is recurrently updated for the foreseeable future;
• Source objectivity: The indicator must be quarried from a single objective source. The first-to-quarry sources include reputable international bodies such as the World Bank or International Monetary Fund.

In this study, we applied vector GIS to calculate the lengths and areas of six parameters: These were: 1.02, the relative difference in the nighttime light intensity between both parts of the area; 1.03, the difference in the road network density between both parts of the area; 2.01, the difference in the relative acreage of the natural habitats between both parts of the area; 2.02, the difference in the deforestation and wetland reclamation rate between both parts of the area; 2.03, the difference in the relative acreage of the protected areas between both parts of the area and 3.05, the cross-border infrastructure (power grid, cables, pipelines, and water canals).

These indicators’ values were elicited from the GIS-based calculations [64]. For achieving this aim, various spatial information sources were used—from using the baseline vector landcover map of the GIS software ArcGIS^® 10.8 by ESRI^TM to open-source GIS-based vector data sets (HELCOM GIS, EU Digital Observatory of Protected Areas Explorer, Marine Regions, OpenStreetMap, Ramsar Sites Information Service, Google Night Lights) and digitizing the available topographical maps and nautical charts. For our investigation, we created additional ArcGIS layers and overlayed them. Afterwards, we accomplished the necessary calculations for the indicators.

For the six indicators, where the range of the elicited values was extensive, deleting data from each distribution end was applied to the sample minimum and maximum. This procedure is named winsorization and is necessary for reducing the effect of outliers and obtaining robust averages. This means that we used winsorized data sets for further normalization. We applied a typical winsorization strategy to set all the outliers to a specified percentile of the data, i.e., we winsorized the data at 1.5 standard deviations (σ), meaning that about 6.5% of the numbers from the minimum and maximum tails were deleted from the process of eliciting the indicator values. For example, if there were no border crossings in the Han River estuary and 2 Mio border crossings in the Bidasoa Estuary (Spain/France) and Tornio Estuary (Sweden/Finland), these outliers were deleted from the calculation.

The scores for each indicator were normalized and rated using a mathematical normalization from 1 (highest difference) to 7 (equal values). The World Development Forum applied this approach to elicit the global Travel & Tourism Development Index [62,63]. In addition to converting the data sets to a 1-to-7 scale for the indicators of the fifth facet, we adjusted the data so that 1 and 7 corresponded to best and worst, respectively. Finally, the CCI for each TTW was calculated as an average of the four cross-border cooperation facet value averages, from which the average of the confrontation facet was subtracted.

2.3. Statistical Analysis

To validate this choice of indicators and the necessity of including every single indicator in the calculation of each of the facets or “composite indicators”, we applied a principal component analysis (PCA) using multivariate covariance and correlation interpretation, focusing on the total inertia analysis of the first and further dimensions. This showed if strong relationships existed between the variables and suggested the number of dimensions that should be studied. We analyzed the data set containing 108 individuals (TTWs) and 25 variables (cooperation and confrontation risk indicators).

The results of the PCA and necessity of including every single indicator in the calculation of each of the facets or “composite indicators” was additionally performed by the expert focus group according to six criteria, such as statistical independence or multicollinearity, relevance, robustness, transparency, parsimony, and data reliability, following the methodological principles outlined and recommended by the Organization for Economic Cooperation and Development [65]. The focus group comprised the same team of experts, with many years of research experience in international environmental cooperation studies on TTW, and the authors of this paper.

Finally, we performed an additional PCA for analyzing the selected indicator data set to illustrate the suitability of the socioeconomic cohesion indicators for the confrontation risk. We plotted the selected data in two dimensions to elicit the clusters of the closely related data points. The data set contained 108 individuals and four variables for the PCA demonstration based on the correlation analysis between the confrontation risk index and socioeconomic cohesion indicators. The variables included: 1.01, the relative difference in GDP per capita (PPP); 1.05, the corruption perception of a more corrupt neighbor; 5, and the confrontation risk and continent as a qualitative variable. We included the latest variable for illustrative purposes.

3. Results

3.1. Global Database of TTW Bodies and Their Characteristics

TW cover 13% of the global coastline. In Europe, this percentage is significantly lower (8%) due to the predominantly indented coastline of the continent. However, the distribution of TTW illustrates a different picture. After a thorough inventory of the world’s coasts, we amassed a global database of 108 TTW bodies: 31 are in Europe, 27 are in Asia, 21 are in Africa, 18 are in North America and the Caribbean, and 11 are in South America (

Table A1. TTW areas of the world listed for each continent in the alphabetical order.

No.	TTW	Countries Sharing TTW	UNSD Region	WWF Marine Ecoregion
Africa
1.	Akpa Yafe Estuary	Cameroon, Nigeria	Middle Africa/West Africa	Gulf of Guinea Central
2.	Allahein Estuary	The Gambia, Senegal	West Africa	Sahelian Upwelling
3.	Benzú Bay	Morocco, Spain	North Africa	Alboran Sea
4.	Bujejete Estuary	Guinea Bissau, Senegal	West Africa	Sahelian Upwelling
5.	Cavally Estuary	Ivory Coast, Liberia	West Africa	Gulf of Guinea West
6.	Congo Estuary	Angola, Congo DR	Middle Africa	Angolan
7.	Jinnak/Sina-Niji Delta and Estuary	The Gambia, Senegal	West Africa	Sahelian Upwelling
8.	Juen Lagoon	Ghana, Ivory Coast	West Africa	Gulf of Guinea Upwelling
9.	Katchek Estuary	Guinea, Guinea Bissau	West Africa	Gulf of Guinea West
10.	Kis Oued (Wadi)	Morocco, Algeria	North Africa	Alboran Sea
11.	Kunene Estuary	Namibia, Angola	South Africa/Middle Africa	Namib
12.	Loema Lagoon	Congo, Angola	Middle Africa	Gulf of Guinea South
13.	Loyada Bight	Djibouti, Somalia	East Africa	Gulf of Aden
14.	Mano Estuary	Liberia, Sierra Leone	West Africa	Gulf of Guinea West
15.	Melilla Harbour	Morocco, Spain	North Africa	Alboran Sea
16.	Muni Estuary	Gabon, Equatorial Guinea	Middle Africa	Gulf of Guinea Central
17.	Ntem (Campo) Estuary	Equatorial Guinea, Cameroon	Middle Africa	Gulf of Guinea Central
18.	Orange Estuary	South Africa, Namibia	South Africa	Namaqua
19.	Ruvuma Estuary	Tanzania, Mozambique	East Africa	East African coral coast
20.	Senegal Estuary	Senegal, Mauritania	West Africa	Sahelian Upwelling
21.	Vanga Bay	Kenya, Tanzania	East Africa	East African coral coast
Asia
22.	Abd Allah & Būbiyān Khawr	Kuwait, Iraq	West Asia	Persian (Arabian) Gulf
23.	al Adaid Khawr	Saudi Arabia, Qatar	West Asia	Persian (Arabian) Gulf
24.	Aqaba/Eilat Gulf	Egypt, Israel, Jordan, Saudi Arabia	West Asia	Northern and Central Red Sea
25.	Astara Estuary	Azerbaijan, Iran	West Asia/South Asia	Caspian Sea
26.	Bahrain Gulf	Saudi Arabia, Qatar, Bahrain	West Asia	Persian (Arabian) Gulf
27.	Beilun/Ka Long Delta	Vietnam, China	Southeast Asia/East Asia	Gulf of Tonkin
28.	Brunei Bay	Brunei, Malaysia	Southeast Asia	Palawan/North Borneo
29.	Duweihin Khawr	Saudi Arabia, UAE	West Asia	Persian (Arabian) Gulf
30.	Golok/Bang Nara Estuary	Malaysia, Thailand	Southeast Asia	Sunda Shelf/Java Sea
31.	Gwadar/Jiwani Bay	Iran, Pakistan	South Asia	Gulf of Oman
32.	Han Estuary	North Korea, South Korea	East Asia	Yellow Sea
33.	Hari(a)bhanga Estuary	India, Bangladesh	South Asia	Northern Bay of Bengal
34.	Johor Strait	Malaysia, Singapore	Southeast Asia	Malacca Strait
35.	Kep Bay	Cambodia, Vietnam	Southeast Asia	Gulf of Thailand
36.	Kutch Rann	Pakistan, India	South Asia	Western India
37.	Kyan/Kra Buri Estuary	Myanmar, Thailand	Southeast Asia	Andaman Sea Coral Coast
38.	Masin Bight	Indonesia, East Timor	Southeast Asia	Lesser Sunda
39.	Matsu Bay	China, Taiwan	East Asia	East China Sea
40.	Mota Bight	Indonesia, East Timor	Southeast Asia	Lesser Sunda
41.	Naaf Estuary	Bangladesh, Myanmar	Southeast Asia	Northern Bay of Bengal
42.	Nahr el Kabir Estuary	Syria, Lebanon	West Asia	Levantine Sea
43.	Northeast Strait of Malacca	Malaysia, Thailand	Southeast Asia	Andaman Sea Coral Coast
44.	Sebuku Bay	Indonesia, Malaysia	Southeast Asia	Palawan/North Borneo
45.	Shatt-al-Arab/Arvan Rud Estuary	Iraq, Iran	West Asia/South Asia	Persian (Arabian) Gulf
46.	Tumen Estuary	North Korea, Russia	East Asia	Sea of Japan
47.	Xiamen (Amoy) Bay	China, Taiwan	East Asia	Southern China
48.	Yalu Estuary	China, North Korea	East Asia	Yellow Sea
Europe
49.	Bidasoa Estuary	Spain, France	South Europe/West Europe	South European Atlantic Shelf
50.	Bojana/Buna Delta	Albania, Montenegro	South Europe	Adriatic Sea
51.	Carlingford Lough	Ireland, UK	North Europe	Celtic Seas
52.	Curonian Lagoon	Russia, Lithuania	East Europe/North Europe	Baltic Sea
53.	Danube Delta	Ukraine, Romania	East Europe	Black Sea
54.	Evros/ Meriç Delta	Turkey, Greece	West Asia/South Europe	Aegean Sea
55.	Foyle Lough	Ireland, UK	North Europe	Celtic Seas
56.	Ftelia Bight	Albania, Greece	South Europe	Ionian Sea
57.	Gibraltar/Algeciras	Spain, UK	South Europe	Alboran Sea
58.	Guadiana Estuary	Spain, Portugal	South Europe	South European Atlantic Shelf
59.	Idde Fjord & Svinesund	Sweden, Norway	North Europe	North Sea
60.	Jakobselv/Voryema	Russia, Norway	East Europe/North Europe	Northern Norway and Finnmark
61.	Kigach Estuary	Kazakhstan, Russia	East Europe/Central Asia	Caspian Sea
62.	Kotor (Outer) Bay	Montenegro, Croatia	South Europe	Adriatic Sea
63.	Mali Ston Bay & Strait	Bosnia and Herzegovina, Croatia	South Europe	Adriatic Sea
64.	Minho/ Miño Estuary	Spain, Portugal	South Europe	South European Atlantic Shelf
65.	Narva Estuary	Russia, Estonia	East Europe/North Europe	Baltic Sea
66.	Oder/Odra Lagoon	Germany, Poland	West Europe/East Europe	Baltic Sea
67.	Piran Bay	Croatia, Slovenia	South Europe	Adriatic Sea
68.	Psou River mouth	Georgia, Russia	East Europe/West Asia	Black Sea
69.	Rezovo/Mutludere	Bulgaria, Turkey	East Europe/West Asia	Black Sea
70.	San Bartolomeo Bay	Slovenia, Italy	South Europe	Adriatic Sea
71.	Scheldt Estuary	Belgium, The Netherlands	West Europe	North Sea
72.	Southern Belt Sea	Germany, Denmark	West Europe/North Europe	Baltic Sea
73.	The Sound	Sweden, Denmark	North Europe	Baltic Sea
74.	The Zwin	Belgium, The Netherlands	West Europe	North Sea
75.	Tornio/Torne Delta	Finland, Sweden	North Europe	Baltic Sea
76.	Virolahti Bight	Russia, Finland	East Europe/North Europe	Baltic Sea
77.	Vistula Lagoon	Poland, Russia	East Europe	Baltic Sea
78.	Wadden Sea	The Netherlands, Germany	West Europe	North Sea
79.	Wadden Sea	Germany, Denmark	West Europe/North Europe	North Sea
North and Central America and the Caribbean
80.	Amatique Bay	Belize, Guatemala	Centr. America	Western Caribbean
81.	Chetumal Bay	Mexico, Belize	Centr. America	Western Caribbean
82.	Coco Estuary	Honduras, Nicaragua	Centr. America	Southwest. Caribbean
83.	Dajabón Estuary	Haiti, Dominican Republic	Caribbean	Greater Antilles
84.	Fonseca Gulf	El Salvador, Nicaragua, Honduras	Centr. America	Chiapas-Nicaragua
85.	Fortune Bay	Canada, France	North America	Gulf of St. Lawrence
86.	Los Portillos Lagoon	Nicaragua, Costa Rica	Centr. America	Southwest. Caribbean
87.	Motagua Estuary	Guatemala, Honduras	Centr. America	Western Caribbean
88.	Oyster Bay, St. Martin Island	The Netherlands, France	Caribbean	Eastern Caribbean
89.	Paz Estuary	El Salvador, Guatemala	Centr. America	Chiapas-Nicaragua
90.	Pedernales Estuary	Haiti, Dominican Republic	Caribbean	Greater Antilles
91.	Portland Canal	US, Canada	North America	North American Pacific Fjordland
92.	Quoddy Bay, St. Croix	US, Canada	North America	Gulf of Maine/Bay of Fundy
93.	Rio Grande Estuary, Laguna Madre	US, Mexico	North America/Centr. America	Northern Gulf of Mexico
94.	Salish Sea	US, Canada	North America	Puget Trough/Georgia Basin
95.	Simpson Bay, St Martin Island	The Netherlands, France	Caribbean	Eastern Caribbean
96.	Sixaola Estuary	Costa Rica, Panama	Centr. America	Southwestern Caribbean
97.	Suchiate Estuary	Guatemala, Mexico	Centr. America	Chiapas-Nicaragua
South America
98.	Barima Delta	Venezuela, Guyana	South America	Guianan
99.	Beagle Channel	Argentina, Chile	South America	Channels and Fjords of Southern Chile
100.	Canal de Capones	Peru, Ecuador	South America	Guayaquil
101.	Chuy Stream	Brazil, Uruguay	South America	Uruguay-Buenos Aires Shelf
102.	Courantyne Estuary	Guyana, Surinam	South America	Guianan
103.	La Plata Estuary	Uruguay, Argentina	South America	Rio de la Plata
104.	Maroni Estuary	Surinam, French Guiana	South America	Guianan
105.	Mataje Estuary	Ecuador, Colombia	South America	Panama Bight
106.	Mirim/Merin Lagoon	Brazil, Uruguay	South America	Uruguay-Buenos Aires Shelf
107.	Oyapock Bay	French Guiana, Brazil	South America	Amazonia
108.	Paria Gulf	Trinidad and Tobago, Venezuela	Caribbean/South America	Southern Caribbean

). Two TTW are shared by three countries: El Salvador, Honduras, and Nicaragua share the Gulf of Fonseca, whereas Bahrain, Qatar, and Saudi Arabia share the Gulf of Bahrain. Four countries, Egypt, Israel, Jordan, and Saudi Arabia, share the Gulf of Aqaba.

Considering the geographical diversity of these TTW bodies, as well as the delineation of the borders separating the neighboring countries sharing the TTW, the following patterns can be distinguished: transboundary estuaries with a border stretching along the thalweg is by far the most popular pattern, being demonstrated by 34 of such TTW bodies around the world. The borders along the thalwegs dividing tectonic inlets form the second most popular pattern, with 19 such TTW showing this worldwide. Other frequent patterns include an asymmetric division of estuaries and tectonic inlets. The Zwin, the Curonian Lagoon, and the Hondo estuary with the Corozal Lagoon do not fall into any of these elicited categories.

3.2. Independence of the Indicators

The results of the PCA analysis aimed to validate the choice of the indicators and the necessity of including all the indicators in the calculation for each of the facets, as the correlation plot of all the variables indicated that the collinearity was minimized in the data set.

Table 2. Pearson’s R² of all investigated variables resulting from PCA analysis (the values in the cells indicate the correlation degree).

Indicator Number	1.01	1.02	1.03	1.04	1.05	2.01	2.02	2.03	2.04	2.05	3.01	3.02	3.03	3.04	3.05	4.01	4.02	4.03	4.04	4.05	5.01	5.02	5.03	5.04	5.05
1.01	1.00	0.01	0.02	0.12	0.10	0.00	0.00	0.00	0.14	0.13	0.29	0.09	0.06	0.06	0.08	0.11	0.06	0.02	0.01	0.03	0.03	0.13	0.08	0.00	0.00
1.02	0.01	1.00	0.22	0.00	0.00	0.00	0.01	0.00	0.04	0.00	0.02	0.00	0.02	0.00	0.00	0.00	0.00	0.00	0.01	0.00	0.00	0.01	0.00	0.00	0.02
1.03	0.02	0.22	1.00	0.02	0.00	0.04	0.07	0.01	0.09	0.03	0.08	0.01	0.03	0.02	0.01	0.03	0.06	0.05	0.03	0.04	0.01	0.06	0.04	0.00	0.06
1.04	0.12	0.00	0.02	1.00	0.24	0.02	0.00	0.01	0.01	0.09	0.06	0.07	0.06	0.12	0.13	0.11	0.07	0.07	0.04	0.06	0.00	0.15	0.05	0.13	0.00
1.05	0.10	0.00	0.00	0.24	1.00	0.00	0.00	0.01	0.10	0.14	0.31	0.47	0.40	0.48	0.46	0.51	0.53	0.62	0.45	0.56	0.02	0.64	0.42	0.22	0.00
2.01	0.00	0.00	0.04	0.02	0.00	1.00	0.00	0.00	0.04	0.00	0.01	0.02	0.00	0.00	0.02	0.00	0.01	0.00	0.01	0.02	0.03	0.00	0.00	0.05	0.00
2.02	0.00	0.01	0.07	0.00	0.00	0.00	1.00	0.00	0.00	0.01	0.02	0.00	0.00	0.00	0.00	0.01	0.02	0.01	0.00	0.01	0.02	0.01	0.02	0.03	0.00
2.03	0.00	0.00	0.01	0.01	0.01	0.00	0.00	1.00	0.02	0.05	0.01	0.01	0.00	0.02	0.01	0.00	0.01	0.00	0.00	0.00	0.01	0.00	0.03	0.01	0.02
2.04	0.14	0.04	0.09	0.01	0.10	0.04	0.00	0.02	1.00	0.35	0.27	0.07	0.06	0.03	0.05	0.03	0.05	0.08	0.02	0.05	0.04	0.06	0.10	0.00	0.01
2.05	0.13	0.00	0.03	0.09	0.14	0.00	0.01	0.05	0.35	1.00	0.17	0.03	0.03	0.11	0.07	0.06	0.07	0.07	0.06	0.08	0.03	0.09	0.10	0.00	0.00
3.01	0.29	0.02	0.08	0.06	0.31	0.01	0.02	0.01	0.27	0.17	1.00	0.25	0.19	0.13	0.21	0.27	0.26	0.24	0.15	0.20	0.08	0.41	0.38	0.00	0.00
3.02	0.09	0.00	0.01	0.07	0.47	0.02	0.00	0.01	0.07	0.03	0.25	1.00	0.57	0.65	0.66	0.31	0.21	0.26	0.15	0.23	0.00	0.33	0.27	0.15	0.00
3.03	0.06	0.02	0.03	0.06	0.40	0.00	0.00	0.00	0.06	0.03	0.19	0.57	1.00	0.52	0.55	0.33	0.30	0.36	0.18	0.21	0.00	0.31	0.16	0.08	0.00
3.04	0.06	0.00	0.02	0.12	0.48	0.00	0.00	0.02	0.03	0.11	0.13	0.65	0.52	1.00	0.55	0.25	0.30	0.29	0.20	0.30	0.01	0.29	0.18	0.13	0.00
3.05	0.08	0.00	0.01	0.13	0.46	0.02	0.00	0.01	0.05	0.07	0.21	0.66	0.55	0.55	1.00	0.36	0.21	0.29	0.19	0.23	0.00	0.30	0.15	0.15	0.00
4.01	0.11	0.00	0.03	0.11	0.51	0.00	0.01	0.00	0.03	0.06	0.27	0.31	0.33	0.25	0.36	1.00	0.58	0.61	0.38	0.38	0.07	0.46	0.19	0.07	0.00
4.02	0.06	0.00	0.06	0.07	0.53	0.01	0.02	0.01	0.05	0.07	0.26	0.21	0.30	0.30	0.21	0.58	1.00	0.64	0.48	0.54	0.07	0.43	0.20	0.04	0.00
4.03	0.02	0.00	0.05	0.07	0.62	0.00	0.01	0.00	0.08	0.07	0.24	0.26	0.36	0.29	0.29	0.61	0.64	1.00	0.64	0.66	0.05	0.44	0.21	0.08	0.00
4.04	0.01	0.01	0.03	0.04	0.45	0.01	0.00	0.00	0.02	0.06	0.15	0.15	0.18	0.20	0.19	0.38	0.48	0.64	1.00	0.64	0.01	0.40	0.16	0.01	0.02
4.05	0.03	0.00	0.04	0.06	0.56	0.02	0.01	0.00	0.05	0.08	0.20	0.23	0.21	0.30	0.23	0.38	0.54	0.66	0.64	1.00	0.02	0.34	0.25	0.11	0.01
5.01	0.03	0.00	0.01	0.00	0.02	0.03	0.02	0.01	0.04	0.03	0.08	0.00	0.00	0.01	0.00	0.07	0.07	0.05	0.01	0.02	1.00	0.02	0.01	0.04	0.01
5.02	0.13	0.01	0.06	0.15	0.64	0.00	0.01	0.00	0.06	0.09	0.41	0.33	0.31	0.29	0.30	0.46	0.43	0.44	0.40	0.34	0.02	1.00	0.39	0.05	0.00
5.03	0.08	0.00	0.04	0.05	0.42	0.00	0.02	0.03	0.10	0.10	0.38	0.27	0.16	0.18	0.15	0.19	0.20	0.21	0.16	0.25	0.01	0.39	1.00	0.08	0.04
5.04	0.00	0.00	0.00	0.13	0.22	0.05	0.03	0.01	0.00	0.00	0.00	0.15	0.08	0.13	0.15	0.07	0.04	0.08	0.01	0.11	0.04	0.05	0.08	1.00	0.01
5.05	0.00	0.02	0.06	0.00	0.00	0.00	0.00	0.02	0.01	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.02	0.01	0.01	0.00	0.04	0.01	1.00

shows that two facets—3 (cross-border connectivity) and 4 (cross-border cooperation)—showed a certain degree of inner correlation, with Pearson’s R² reaching 0.66. However, such a statistical level of correlation is insufficient for reducing the number of indicators and merging some indicators into larger “composite” ones, or using easier available or more reliable indicators as proxies for less reliable indicators.

The expert group rectified the results of the PCA, agreeing that, although our chosen “five-by-five” approach may have looked very formal, the process of selection was transparent and careful, and, as noted by the OECD [65] (p. 138): “subjectivity and fitness need not be antithetical. They are both at play when constructing and adopting a composite indicator, where inter-subjectivity may be at the core of the exercise”. Indicator 1.5. The corruption perception of a more corrupt neighbor was exceptional, correlating with various indicators. Although the correlation was insufficient for using indicator 1.5 as a proxy, it validated the role of spin dictatorships in shaping the collaboration and confrontation in TTW.

3.3. TTW Areas in the World under High Confrontation Risk

When studying the distribution of the world’s TTW according to confrontation risk (Figure 2), the results of the investigation showed that 8 TTW areas are under a high confrontation risk (average score 5.1–6.0), 23 TTW areas are under a significant confrontation risk (average score 4.1–5.0), 41 areas are under a medium confrontation risk (average score 3.1–4.0), and 29 TTW areas are under a low confrontation risk (average score 2.1–3.0). Six transboundary transitional water areas have little or no confrontation risk (average score 1.0–2.0). These six TTW areas are in Northern and Western Europe. No single TTW area has a very high confrontation risk (average score 6.1–7.0).

Currently, the highest confrontation risk among the TTW areas worldwide is in the Psou River mouth shared by the Russian Federation and, de jure, Georgia, although de facto, a breakaway region of Abkhazia controls the southern part of the Psou River mouth (

Table 3. TTW areas of the world under high confrontation risk.

No.	Average Score	TTW	Countries Sharing TTW	Description
1.	5.5	Psou River mouth	Georgia, Russia	Russia, a more bellicose and populous neighbor, recognizes and militarily protects the independence of a breakaway region of Abkhazia, internationally recognized as part of Georgia, resulting in a military conflict
2.	5.5	Barima Delta	Venezuela, Guyana	Venezuela, a more corrupt, bellicose, and populous neighbor, notorious for its high homicide rate and endemic civic unrest, claims all of Guyana’s territory west of the Essequibo River
3.	5.3	Paria Gulf	Trinidad and Tobago, Venezuela	Venezuela, a more corrupt, bellicose, and populous neighbor, notorious for its high homicide rate, shares a TTW rich in hydrocarbons
4.	5.3	Akpa Yafe Estuary	Cameroon, Nigeria	Nigeria, a more corrupt, bellicose, and populous neighbor, notorious for its high homicide rate, shares the estuary in a region rich in hydrocarbons
5.	5.3	Ntem (Campo) Estuary	Equatorial Guinea, Cameroon	Sovereignty dispute between two corrupt countries with authoritarian regimes over an island at the river mouth in a region rich in hydrocarbons
6.	5.2	Narva Estuary	Russia, Estonia	Russia, a more corrupt, bellicose, and populous neighbor sharing the estuary, did not ratify the border treaty and demands better treatment for the Russian-speaking population in Estonia
7.	5.1	Abd Allah & Būbiyān Khawr	Kuwait, Iraq	In 1990, Iraq invaded Kuwait, causing the Gulf War. Although the border was settled in 2004, political instability in Iraq makes the situation in the area rich in hydrocarbons precarious
8.	5.1	Congo Estuary	Angola, DR Congo	Military involvement of the Angolan government in the civil war in the DRC in 1997 and 1998 made the situation in the area rich in hydrocarbons volatile

). Russia has recognized and protects the independence of Abkhazia. Hence, Georgia’s efforts to take back Abkhazia resulted in a military conflict between Georgia and Russia in 2008. Russia won the conflict as a more bellicose and populous country. However, if Russia loses the invasion of Ukraine, Georgia may be tempted to take back Abkhazia, leading to military clashes between Russia and Georgia, also in the Psou River mouth.

The borders between the Russian Federation and most of its neighbors with whom it shares TTW, except Estonia, are demarcated, and there are no formal territorial disputes. However, the Russian Federation, an authoritarian [66], corrupt, and bellicose country that is more populous than all its littoral neighbors, poses a high confrontation risk on the eight TTW shared with its neighbors. Any of these TTW may become an area of a military conflict, especially the Narva River estuary on the Russian border with Estonia (Table 3).

Another TTW with a high confrontation risk is in South America, in the Barima River delta shared by Venezuela and Guyana. Venezuela, a bellicose, autocratic, and more populous neighbor, is notorious for its high homicide rate and endemic civil unrest. To make the situation worse, it claims all the territories of Guyana west of the Essequibo River, i.e., half of its neighbors’ territory, preventing any settlement of a maritime boundary, including the Barima River delta. Therefore, the average of the five confrontation indicator values showed that this TTW area is under a high confrontation risk.

However, on the world’s scale, the distribution of TTW according to the confrontation risk index shows normality (Figure 2). The median value of the distribution was 3.5, i.e., lying almost in the middle between 1.7, i.e., the lowest value, and 5.5, the highest value. The TTW with confrontation risk scores of 3.5 are in various regions of the world: the Kyan/Kra Buri Estuary, shared by Myanmar and Thailand, in southeast Asia; the Katchek Estuary (Guinea and Guinea Bissau) in western Africa; the Los Portillos Lagoon (Nicaragua and Costa Rica) in Central America; and Rio de la Plata (Uruguay and Argentina) in South America.

Three of the twenty-one African TTW have a high confrontation risk: the Ntem (Campo) Estuary, shared by Equatorial Guinea and Cameroon (confrontation risk score—5.3); the Akpa Yafe Estuary (Cameroon and Nigeria, score—5.3); and the Congo Estuary (Angola and DR Congo, score—5.1). Another five TTW are under a significant confrontation risk: the Orange River Estuary (South Africa and Namibia, score—4.6); the Muni River Estuary (Gabon and Equatorial Guinea, score—4.6); Lawyacado Bay (Djibouti and Somalia, score—4.5); Mekoundji Lagoon (Congo and Gabon, score 4.4); and the Kunene River Estuary (Namibia and Angola, score—4.1).

Three of the ten Central American TTW are under a significant confrontation risk: Amatique Bay (Belize and Guatemala, score—4.8); Chetumal Bay (Mexico and Belize, score—4.7); and the Suchiate River Estuary (Guatemala and Mexico, score—4.7). Notably, both TTW shared by Belize with its neighbors are under a high confrontation risk. Until 1981, Belize (British Honduras) was the only British colony in Central America. Guatemala has territorial claims to half of Belize, while Mexico and Belize have border demarcation issues in a TTW area with complicated physical features (Figure 3). The Suchiate River Estuary, shared by Mexico and Guatemala, is a porous border used by drug smugglers and illegal migrants heading to the US, causing potential conflict risks.

Three TTW areas in eastern Asia were presumed to showcase a very high confrontation risk. The People’s Republic of China and Taiwan (China) share the Xiamen Bay and Matsu Bay, and North Korea and the Republic of Korea share the Han River estuary. The PRC and ROC claim sovereignty over each other’s territory, as do North and South Korea. Xiamen Bay, Matsu Bay, and the Han River Estuary are heavily militarized few-NM-wide TW bodies separating these confronting polities. However, despite militant rhetoric and occasional warmongering by more bellicose neighbors, none of these polities intend to escalate tensions. The average of the confrontation indicator values showed that these TTW areas are under a significant but not high confrontation risk.

3.4. Correlation between the Confrontation Risk Index and the Socioeconomic Cohesion Indicators

There was no correlation between the confrontation risk index and GDP (PPP) per capita difference between the countries sharing TTW (Figure 4A, Pearson’s R was—0.2965). This may indicate that differences in living standards on both border sides do not play any role in increasing tensions between these countries. However, they facilitate illegal migration, the smuggling of drugs and migrants, and raise cross-border tensions [67]. On the other hand, applying the purchasing power parity concept distorts this socio-economic comparison of neighboring countries, albeit being applied by the European Commission. Additionally, our research used regional rather than national GDP (PPP) per capita data, where available.

Instead, the correlation between the confrontation risk and corruption perception of a more corrupt neighbor was quite strong (Figure 4B, R = −0.6642), i.e., the more corrupt one of the countries sharing a TTW with another was, the greater the confrontation risk was. The correlation between the confrontation risk and corruption perception was negative, because the corruption perception index calculated by Transparency International ranks countries from the least corrupt (highest values) to most corrupt (lowest values). This correlation leads to the spin dictatorship concept [66].

The Principal Component Analysis (PCA) was accomplished for all 108 worldwide TTW, with four variables being analyzed:

  i.

The confrontation risk index (labelled as Conflicting.index);

 ii.

The relative difference in GDP per capita (PPP) between both parts of the area (labelled as GDP.similarity);

iii.

The corruption perception of a more corrupt neighbor (labelled as Corruption.index);

iv.

The continent where the TTW is located (labelled as Continent.index).

The fourth variable (Continent.index) was interpreted as qualitative and considered to be illustrative. The two dimensions of the PCA explained 88.86% of the total variability in all the TTW cases regarding the multiple variations in the factors. This value was greater than the reference value of 75.57%. The variability explained by the plane was thus significant (the reference value was the 0.95-quantile of the inertia percentages’ distribution, obtained by simulating 1000 data tables of equivalent sizes based on a normal distribution). This was coherent with the results presented in Table 2.

The first factor (Dimension 1) was major: it explained 62.78% of the data variability. The Dimension 1 axis presented an inertia amount greater than that obtained by the 0.95-quantile of random distributions (62.78% against 42.91%). This observation suggests that only this axis carried factual information. The second factor (Dimension 2) was minor: it explained just 26.08% of the data variability. The variables factor map of the PCA (Figure 5B) indicated that the Dimension 1 axis featured the corruption perception of a more corrupt neighbor and the Dimension 2 axis featured the relative difference in GDP per capita (PPP) between both parts of the area.

3.5. Case Studies of the Curonian Lagoon and the Zwin

The results of our comprehensive study on the Curonian Lagoon, which is shared by Lithuania and the Russian Federation, showed that, just a couple of years ago, the level of cross-border cooperation in and around the lagoon was robust, with no confrontation risk. However, the Russian Federation’s invasion of Ukraine in 2022 and Lithuania’s stance on Ukraine’s side have led to a nearly complete deterioration of this cross-border cooperation around the Curonian Lagoon.

Table 4. Cross-border cooperation and confrontation index for the Curonian Lagoon and the Zwin.

Socioeconomic Cohesion
TTW	1.01	1.02	1.03	1.04	1.05	Avg. 1
Curonian Lagoon	3.5	2.2	7.0	6.5	2.7	4.4
The Zwin	4.8	7.0	7.0	6.9	5.6	6.3
2. Environmental Coherence
TTW	2.01	2.02	2.03	2.04	2.05	Avg. 2
Curonian Lagoon	3.6	6.5	3.2	3.0	5.4	4.3
The Zwin	7.0	7.0	7.0	7.0	7.0	7.0
3. Cross-border Connectivity
TTW	3.01	3.02	3.03	3.04	3.05	Avg. 3
Curonian Lagoon	2.0	1.5	1.0	1.4	4.5	2.1
The Zwin	7.0	7.0	7.0	7.0	7.0	7.0
4. Cross-border Cooperation
TTW	4.01	4.02	4.03	4.04	4.05	Avg. 4
Curonian Lagoon	5.4	3.4	2.4	1.2	1.3	2.7
The Zwin	7.0	7.0	7.0	7.0	7.0	7.0
5. Confrontation Risk
TTW	5.01	5.02	5.03	5.04	5.05	Avg. 5
Curonian Lagoon	1.0	5.7	6.6	2.4	6.7	4.5
The Zwin	1.0	2.7	3.1	1.3	1.7	2.0

Scores for facets 1 to 4: 1.1–2.0—virtually no cohesion, coherence, vonnectivity, or cooperation; 2.1–3.0—weak cohesion, coherence, connectivity, or cooperation; 3.1–4.0—mediocre cohesion, coherence, connectivity, or cooperation; 4.1–5.0—quite close cohesion, coherence, connectivity, or cooperation; 5.1–6.0—close cohesion, coherence, connectivity, or cooperation; and 6.1–7.0—very close cohesion, coherence, connectivity, or cooperation. Scores for facet 5: 1.0–2.0—no confrontation risk; 2.1–3.0—low confrontation risk; 3.1–4.0—medium confrontation risk; 4.1–5.0—significant confrontation risk; 5.1–6.0—high confrontation risk; 6.1–7.0—very high confrontation risk.

shows the current situation in the Curonian Lagoon area.

The non-tidal Curonian Lagoon is Europe’s largest coastal lagoon, situated on the southeast rim of the Baltic Sea. It is a semi-locked, shallow, and almost freshwater TW due to discharge from the Nemunas River. Politically, the southern two-thirds of the Curonian Lagoon and the adjacent area belong to Kaliningrad Oblast, an exclave territory of the Russian Federation. The northern part of the lagoon, the Strait of Klaipeda, and the Baltic Sea nearshore belong to Lithuania (Figure 6). This is a relatively recent division (since the collapse of the Soviet Union in 1991). The entire area was part of Prussia and Germany for centuries and part of the Soviet Union after World War II.

The close cohesion, e.g., of the road network, between the Russian and Lithuanian parts of the Curonian Lagoon area results from the long history of the area being within a single country. The much more intensive nighttime light in the Lithuanian part of the area is determined by Klaipeda City hosting the largest eastern Baltic seaport. Although Lithuania is richer in GDP per capita (PPP) than Kaliningrad Oblast of the Russian Federation, the difference in the official poverty rate between Lithuania and the Russian Federation is negligible. The most striking difference was in the Corruption Perceptions Index between Lithuania (the country rank determined by Transparency International was 34 for 2021) and the Russian Federation (136).

The close environmental coherence in the TTW area results from the prudence of both countries towards using natural resources with low rates of deforestation, wetland reclamation, and the poaching of resources. However, the natural habitats are better protected in the Lithuanian part of the area [57]. Additionally, the treatment of the wastewater discharged into the Curonian Lagoon is more advanced in the Lithuanian part, due to extensive EU subsidies.

Assessed as weak, the cross-border connectivity between the Lithuanian and Russian parts of the Curonian Lagoon area has declined due to the recent confrontation between NATO and the Russian Federation. Since March 2022, it has become problematic for Russian Federation citizens to obtain visas to enter Lithuania. Any regular cross-border passenger traffic between both parts of the area was cancelled and monthly border crossings are rapidly declining. However, the cross-border infrastructure (power grid, pipelines, cables, and water canals), which was created during the Soviet Union period (1945 to 1990), when the entire Curonian Lagoon area belonged to a single country, is still functioning.

The cross-border cooperation between Lithuania and the Russian Federation in the Curonian Lagoon area has declined dramatically. Lithuania suspended EU-funded cross-border NGO, academic, and business collaboration projects. Formal cross-border cooperation agreements are still valid, but their viability is weak, except for the regulation of the fishing quotas in the Curonian Lagoon. The border delimitation treaty between Russia and Lithuania was ratified in 2003, with no territorial disputes. Nevertheless, the Russian Federation poses a high confrontation risk. We estimated the total ICC value for the Curonian Lagoon to be 0.7 (cohesion and cooperation counterbalanced by confrontation).

In contrast, the Zwin tidal inlet shared by Belgium and the Netherlands is a tiny TTW (area—1.58 km², 1.25 km² being on the Belgian side and 0.32 km² on the Dutch side, Figure 7), which enjoys a long tradition of close cross-border cooperation between the neighboring countries sharing it (Table 4). The cross-border management of the Zwin started in 1873 with the construction of the Willem–Leopold-polder, named after William III of the Netherlands and Leopold II of Belgium, stretching across the border. The transboundary nature reserve was established in 1952, and in March 1986, it was designated as a Ramsar wetland of international importance.

The International Zwin Commission (IZC) has been operating since the creation of the Willem–Leopold-polder in 1872. Its main task is to preserve the Zwin as a tidal inlet and Ramsar wetland. Recently, the expansion of the natural inlet and facilitation of the tidal regime was agreed upon by both countries sharing the Zwin. The renaturalization of the Willem–Leopold-polder, combined with the widening and deepening of the Zwin inlet, was chosen as the scenario that best matched the managed semi-natural coastal landscape type set as a target for the ecosystem vision of the coast.

After completing these renaturalization works in 2019, the intertidal area was doubled and a new dyke was constructed surrounding the expanded natural tidal inlet and adjacent mudflats, dunes, canal system, and water pumping stations. The institutes from Belgium and the Netherlands carry out a multidisciplinary cross-border monitoring program to record the evolution in the morphology of the Zwin, its tidal regime, and changes in target species and habitats within the nature reserve [68].

Good road and cycling path networks facilitate the visa-free movement of people across the border in the Zwin area, ensured by the EU Schengen regime. This free movement, however, was hampered in 2020 and 2021 by the anti-COVID quarantine regulations in both Belgium and the Netherlands. Considering this and other minor obstacles, and because the Economist Intelligence Unit regards Belgium as a country with a flawed democracy, we have estimated the total ICC value for the Zwin to be 4.8 (prevailing close cooperation and coherence). Despite it narrowly missing the ICC’s top value (+7.0), the Zwin can be a good benchmark of cross-border cooperation in TTW conservation and management (Figure 8).

4. Discussion

As can be seen from our investigation results, the main methodological advantage of the proposed Index is in the transparent and comprehensive systematization of such diverse and vaguely defined physical, geographical, ecological, and political territories, with all 108 of them being shared by several countries and covering the continuum between terrestrial and marine spheres. Such a comprehensive approach facilitates a broader application of the Emerald Growth concept in the contexts of different UN Sustainable Development regions and marine ecoregions. Of course, like with every hybrid survey, the results’ applicability and validity depend on the investigators’ alertness.

Naturally, this investigation is still in its pilot phase, and by far, only some of the 108 TTW have received the attention they deserve. There exist many possibilities for potential improvements. More attention should be paid to fine-tuning and the potential application of weights to the selected indicators. Additionally, there is room for expanding the existing indicator list to address the challenge of meeting the UN SDGs in these TTW areas, facilitating cross-border collaboration between the countries sharing the TTW and, eventually, involving additional countries in the basins of the rivers forming these TTW.

As mentioned in the introduction, due to their disproportionate socioeconomic and ecological roles in comparison to their sizes, TTW, as a specific research area, offer some advantages in the ways of measuring the confrontational risks between countries in coastal and maritime areas and areas upstream, especially regarding their competition over water resources and the protection of their quality. Our investigation offers additional perspectives for studying the hydropolitical relationships involved in transboundary cooperation practices for river basin and adjacent TTW management, regarding various aspects of cross-border politics and the political power in water issues [69].

For this discussion, the concept of “spin dictatorship”, coined by Guriev and Treisman [66], aptly fits when explaining the transboundary cooperation risks in the TTW identified in our investigation. Authoritarian spin dictators of the 21st century, in contrast to totalitarian “fear dictators” of the 20th century, hold on to power not by spreading fear through mass repression, but by using sophisticated leverage: “manipulating the media, engineering popularity, faking democracy, limiting public violence, and opening up to the world” [66] (p. 13). Hence, the term “spin dictators” is an analogy of “spin doctors”.

Spin dictators tend to show an eagerness and readiness for international cooperation. Guriev and Treisman argued that, in normal circumstances, they “welcome flows of people, capital, and data and find ways to profit from them” [66] (p. 17). However, if economic problems arise and the population’s support dwindles, they declare that this poor economic performance is the fault of external conditions and enemies. Spin dictators may consider a small victorious war with a presumably weaker neighbor as the means to restoring popular support through rallying around their flag. TTW may provide an appealing arena for such an undertaking, especially in resource-rich and disputed areas.

Particularly, when one neighboring country is dominant, even if there is an established formal cooperation structure, e.g., a bilateral commission or a committee for sharing resources, it may turn into an arena of domination rather than cooperation, like in several cases of sharing water resources at the river catchment area level. Several case studies from the Middle East, where water resources are particularly scarce and shared by several countries, show how this cooperation may turn into domination and “bullying” by the much stronger partner, becoming a potential cause of conflict [69,70,71]. In the case of TTW areas, fish resources and reserves of hydrocarbons are the most apparent causes for domination and tensions in the world.

On the other hand, TTW areas, which typically comprise a maze of water bodies, wetlands, river branches, and oxbow lakes, are ideal places for local grassroots cross-border collaboration, usually in the form of smuggling. As our ethnographic studies and interviews from the early 1990s with the oldest local inhabitants of the Nemunas River Delta and Curonian Lagoon shores revealed, they eagerly engaged in the smuggling of goods across the newly established border between the newly independent Republic of Lithuania and Germany in the pre-WW II period, between 1923 and 1939, for rounding their income [72]. Sporadic evidence hints that this is still the case in many TTW areas with porous borders worldwide.

5. Conclusions

The main conclusion of our investigation is that risks of confrontation are closely correlated with the level of corruption in a more bellicose country. As our results show, whether or not a TTW serves as an arena for cross-border cooperation or confrontation depends on a plethora of critical factors, first of all, on the goodwill of both (or all) the countries sharing it. Admittedly, it is hard to unequivocally judge if cross-border tensions inevitably lead to confrontation. However, the rapid deterioration of the cross-border cooperation in the Curonian Lagoon area shows that good neighborhood prospects with a corrupt, authoritarian, and bellicose neighbor should not be taken for granted.

Therefore, it is difficult to draw any firm conclusion about under what conditions these confrontation risks can be avoided. Our long-term experience of the cross-border cooperation with the Russian Federation in the Curonian and Vistula lagoons has taught us that only firmly rooted democratic institutions on both sides of a border at the local, regional, and national levels, which cherish bottom-up democratic decision making and respect the rule of law, can reduce these confrontation risks in the long run. Open visa-free exchange across borders, joint transboundary ventures, or ad hoc collaboration platforms such as joint commissions or forums cannot sufficiently moderate or mediate these confrontation risks.

The study’s main objective was to elicit the essential features and indicators for gauging the cross-border cooperation and risks of confrontation in and around TTW, particularly regarding the international aspiration to reach the UN SDGs by 2030. The investigation enabled us to develop and test a comprehensive CCI, which covered the essential facets of this complex issue. However, further research is necessary for validating and fine-tuning the indicators comprising the Index on a more extensive and diverse sample of TTW.