Disasters Weaken the Synergy Effects among the Global Sustainable Development Goals
Description
This research dataset aims to systematically analyze the impact of natural disasters on the synergy effects among the Sustainable Development Goals (SDGs), providing scientific evidence for understanding how disaster shocks disrupt the global sustainable development process. The dataset is constructed from panel data covering 137 countries with relatively complete cross-national data over the period 2010–2022, encompassing multiple dimensions including sustainable development performance, exposure to natural disasters, socioeconomic characteristics, and governance capacity. The data show that scores for 13 SDGs (SDG1, 2, 3, 5, 7, 8, 9, 11, 12, 13, 14, 15, and 17), calculated using the entropy method, are used to construct two types of synergy indicators: first, the composite synergy index among 13 SDGs (CSSDGs), which measures the overall coupling coordination degree of the 13 goals and reflects the systemic synergy level of national sustainable development; and second, the synergy index between pair of SDGs (PSSDGs), comprising 78 pairwise combinations, used to identify interactive relationships between specific goal pairs. The SDG indicators data are sourced from the World Bank database and the Global SDG Indicators Database, while the mediating and control variables are primarily obtained from the World Bank database. Natural disaster data are drawn from the EM-DAT International Disaster Database, focusing on nine severe disaster types: floods, droughts, earthquakes, storms, extreme temperatures, wildfires, epidemics, landslides, and volcanic activity. The core explanatory variable is the logarithm of disaster duration (LnDuration), supplemented by the logarithm of death tolls (LnDeath) and the logarithm of affected population (LnAffected) for robustness checks. Key findings from the study include: natural disasters significantly weaken the synergy effects among SDGs, and this negative impact persists for up to two years, with droughts and extreme temperatures having particularly pronounced effects. Mechanism analysis reveals that human capital and government governance mitigate these negative impacts, whereas trade openness exacerbates them. Heterogeneity analysis shows that lower-middle-income countries, as well as countries in Europe and Central Asia, South Asia, East Asia and the Pacific, and Sub-Saharan Africa, are more vulnerable to the disruptive effects of disasters on SDG synergies. Furthermore, disasters particularly weaken the synergies between SDG1 (No Poverty) and other goals, and between SDG3 (Good Health and Well-being) and other goals in low-income countries.
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Steps to reproduce
First, raw data on multiple Sustainable Development Goals SDGs related indicators were collected from the World Bank database and the UN Global SDG Indicators Database. After systematic organization, a panel dataset was constructed comprising 39 SDG indicators for 137 countries with relatively complete cross-national data over the period 2010 to 2022. Meanwhile, comprehensive disaster records were obtained from the EM DAT International Disaster Database, and based on the original data, the duration, number of deaths, and number of affected people for nine major disaster types including floods, droughts, earthquakes, storms, extreme temperatures, wildfires, epidemics, landslides, and volcanic activity were manually compiled for each country year within the study period from 2010 to 2022. Additionally, data on control and mediating variables including GDP per capita, population density, industry value added as a share of GDP, general government final consumption expenditure, gross capital formation, life expectancy at birth, tertiary education enrollment rate, and trade openness trade as a percentage of GDP were collected from the World Bank and other public sources. The Worldwide Governance Indicators WGI were sourced from the PPData database and aggregated into a composite governance index using the entropy method. Second, raw indicators were cleaned, missing values were imputed using linear interpolation, and all variables were standardized according to their directional nature positive or negative. Next, the entropy method was applied to calculate indicator weights, which were then used to derive individual scores for each of the 13 SDGs. Subsequently, the coupling coordination degree model was employed to compute two synergy indices: the composite synergy index among the 13 SDGs CSSDGs and the pairwise synergy index between each pair of SDGs PSSDGs, totaling 78 pairs. Finally, a panel two-way fixed effects model was estimated using Stata 17, with the logarithm of disaster duration (LnDuration) as the core explanatory variable and country and time fixed effects controlled for, to examine the impact of disasters on the composite synergy effects among the 13 SDGs. Robustness checks and mechanism analyses were conducted through alternative variable specifications, subgroup regressions, and mediation tests. Complete code files for all computational steps are provided to ensure full reproducibility of the results.