Coupling dynamics of SDGs in Tajikistan from 2001 to 2023

doi:10.1016/j.regsus.2026.100295

Abstract

Abstract:

Since the United Nations launched the Sustainable Development Goals (SDGs) in 2015, global implementation has steadily advanced, yet prominent challenges persist. Progress has been uneven across regions and countries, with Tajikistan representing a typical example of such disparities. Based on 81 SDG indicators for Tajikistan from 2001 to 2023, this study applied a three-level coupling network framework: at the microscale, it identified synergies and trade-offs between indicators; at the mesoscale, it examined the strength and direction of linkages within four SDG-related components (society, finance, governance, and environment); and at the global level, it focused on the overall SDG interlinkages. Spearman’s rank correlation, sliding window method, and topological properties were employed to analyze the coupling dynamics of SDGs. Results showed that over 70.00% of associations in the global SDG network were of medium-to-low intensity, alongside extremely strong ones (|r| value approached 1.00, where r is the correlation coefficient). SDG interactions were generally limited, with stable local synergy clusters in core livelihood sectors. Network modularity fluctuated, reflecting a cycle of differentiation, integration, and fragmentation, while coupling efficiency varied with the external environment. Each component exhibited distinct functional characteristics. The social component maintained high connectivity through the “poverty alleviation-education-healthcare” loop. The environmental component shifted toward coordinated eco-economic governance. The governance-related component broke interdepartmental barriers, while the financial component showed weak links between resource-based indicators and consumption/employment indicators. Tajikistan’s SDG coupling evolved through three phases: survival-oriented (2001-2012), policy integration (2013-2018), and shock adaptation (2019-2023). These phases were driven by policy changes, resource industries, governance optimization, and external factors. This study enriches the analytical framework for understanding the dynamic coupling of SDGs in mountainous resource-dependent countries and provides empirical evidence to support similar countries in formulating phase-specific SDG promotion strategies.

Key words: Sustainable, Development, Goals (SDGs), Climate change, Coupling network analysis, Spearman’s rank correlation, Synergic relationships, Trade-off relationships, Tajikistan

Ranna HAZIHAN, DU Hongru, HE Chuanchuan, Kobiljon Khushvakht KHUSHVAKHTZODA, Bobozoda KOMIL. Coupling dynamics of SDGs in Tajikistan from 2001 to 2023[J]. Regional Sustainability, 2026, 7(1): 100295.

Figures/Tables 9

Table 1

Description of the selected indicators and identifiers."

Component type	Indicators and identifiers
Social component	The proportion of the population below the poverty line of 6.85 USD/d at 2017 purchasing power parity (SDG 1_S01), dietary energy supply (SDG 2_S02), prevalence of malnutrition (SDG 2_S03), the percentage of stunted children under 5 years of age (SDG 2 _S04), prevalence rate of anemia among pregnant women (SDG 2_S05), infant mortality rate (SDG 2_S06), adolescent fertility rate (SDG 2_S07), maternal mortality ratio (SDG 2_S08), tuberculosis incidence rate (SDG 3_S09), life expectancy at birth (SDG 3_S10), preschool enrollment rate (SDG 4_S11), primary school enrollment rate (SDG 4_S12), secondary school enrollment rate (SDG 4_S13), tertiary education enrollment rate (SDG 4_S14), male-female enrollment ratio in primary and secondary schools (SDG 4_S15), overall youth literacy rate (SDG 4_S16), higher education, teaching staff (SDG 5_S17), access to clean fuels and cooking technologies (SDG 7_S18), urban population growth rate (SDG 11_S19), the proportion of slum dwellers within the urban population (SDG 11_S20), and population using the internet (SDG 17_S21).
Financial component	Household final consumption expenditure (SDG 1_F01), current health expenditure (SDG 3_F02), female labor force participation rate (SDG 5_F03), female employment in industry (SDG 5_F04), primary energy intensity (SDG 7_F05), renewable energy consumption (SDG 7_F06), GDP per employed person (SDG 8_F07), GDP growth rate (SDG 8_F08), GDP per capital growth (SDG 8_F09), total unemployed persons (SDG 8_F10), total unemployed women (SDG 8_F11), international tourism receipts (SDG 8_F12), merchandise trade (SDG 8_F13), employees in the service sector (SDG 8_F14), industrial value added (SDG 9_F15), manufacturing value added (SDG 9_F16), research and development expenditure (SDG 9_F17), railway passenger traffic (SDG 9_F18), air transport passenger volume (SDG 9_F19), ratio of actual government expenditure to original approved budget (SDG 16_F20), net barter terms of trade index (SDG 17_F21), merchandise exports to high-income economies (SDG 17_F22), exports of goods and services (SDG 17_F23), merchandise imports from developing economies in East Asia and the Pacific (SDG 17_F24), net official development assistance received (SDG 17_F25), net bilateral aid flows from Development Assistance Committee donors, Republic of Korea (SDG 17_F26), net inflows of foreign direct investment (SDG 17_F27), and net foreign assets (SDG 17_F28).
Governance-related component	Grain import dependency ratio (SDG 2_G01), Political Stability and Absence of Violence/Terrorism (SDG 2_G02), physician density (SDG 3_G03), hospital beds (SDG 3_G04), out-of-pocket expenditure as a percentage of current health expenditure (SDG 3_G05), professional and technical institutions (SDG 4_G06), tertiary education institutions (SDG 4_G07), proportion of women in national parliaments (SDG 5_G08), Women, Business and Law Index score (SDG 5_G09), population using at least basic drinking water services (SDG 6_G10), population using safely managed drinking water services (SDG 6_G11), population using at least basic sanitation services (SDG 6_G12), population practicing open defecation (SDG 6_G13), electrification rate (SDG 7_G14), scientific and technical journal articles (SDG 9_G15), railway line density (SDG 9_G16), standard deviation of the Control of Corruption index (SDG 16_G17), and tax revenue (SDG 16_G18).
Environmental component	Grain production (SDG 2_E01), per capita food supply variability (SDG 2_E02), proportion of freshwater withdrawal to available water resources (SDG 6_E03), total renewable internal freshwater resources (SDG 7_E04), per capita forest area (SDG 11_E05), adjusted net savings, excluding particulate emission damages (SDG 12_E06), total natural resource rents (SDG 12_E07), adjusted savings: mineral resource depletion (SDG 12_E08), total greenhouse gas emissions (SDG 13_E09), annual average exposure to PM_2.5 air pollution (SDG 13_E10), total emissions from crop residue burning (SDG 13_E11), renewable internal freshwater resources per capita (SDG 15_E12), permanent cropland (SDG 15_E13), and permanent meadows and pastures (SDG 15_E14).

Table 1

Fig. 1.

Table 2

Connotations and calculation methods of topological properties."

Property	Connotation	Calculation formula	Reference
Connectivity (C)	Network coupling strength (reflecting overall connection tightness)	$C=\frac{{\displaystyle \sum {w}_{\text{observed}}}}{{\displaystyle \sum {w}_{\text{max}}}}$, where ∑w_observed is the sum of the weights of all edges observed in practice; and ∑w_max is the theoretical maximum total link weight for the same number of nodes (assuming all possible edge weights are 1).	Wu et al. (2022); Hu et al. (2023)
Transitivity (T)	Local stability of the network	$T=\frac{3 \times \text { number of triangles }}{\text { Number of connected triples }}$, where number of triangles is the number of actual triangles formed (where all three nodes are pairwise connected); and number of connected triples is the number of possible triangles (triplets of nodes that could potentially form a triangle).	Wu et al. (2022); Hu et al. (2023)
Modularity (Q)	Network inhomogeneity	$Q=\frac{1}{2m}{\displaystyle \sum _{\begin{array}{c}i=1\\ j=1\end{array}}^{n}\left({A}_{ij}-\frac{{k}_{i}{k}_{j}}{2m}\right)}\delta ({c}_{i},{c}_{j})$ , where m is the total edge weight of the network; n is the total number of nodes; A_ij is the edge weight between nodes i and j (0 if not connected); k_i and k_j are the total connection weight of nodes i and j belong, respectively; c_i and c_j are the subset or community to which nodes i and j belongs, respectively; and δ(c_i, c_j) is the Kronecker delta function. If c_i=c_j, the value equals to 1; otherwise, it is 0.	Hu et al. (2023)
Coupling efficiency (Coupling E)	Transmission speed of coupling effects	$\text{Coupling }E=\frac{{t}_{\text{ideal}}}{{t}_{\text{observed}}}=\frac{n(n-1)}{{\displaystyle \sum _{i=1}^{n}\mathrm{min}}\left({\displaystyle \sum _{i\ne j\in G}\frac{1}{{w}_{ij}}}\right)}$ , where t_idealis the ideal traversal time; t_observed is the actual traversal time; n is the number of nodes; G represents the network under study; w_ij is the edge weight between nodes i and j; and 1/w_ij represents the transmission time between nodes i and j.	Yuan et al. (2021)

Table 2

Fig. 2.

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Fig. 7.

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