Unveiling the nexus: exploring the influence of terrorism on energy trade in China and the Belt and Road countries

The paper examines how terrorism affects China’s energy trade with Belt and Road (B&R) countries, against the backdrop of energy’s central role in geopolitics and the concentration of oil and gas reserves and transport chokepoints along B&R routes. Terrorist activities have been frequent in these regions, threatening energy production, supply, and transportation. The study addresses whether and how terrorism—and its spatial spillovers to neighboring countries—shape China’s energy imports and exports with B&R partners. It contributes by: (1) quantitatively mapping spatial characteristics of energy trade and terrorism; (2) using spatial econometric models to disentangle direct and spatial spillover effects on imports versus exports; and (3) employing disaggregated terrorism indicators (incidents, deaths, injuries). Theoretical hypotheses posit that terrorism inhibits partner countries’ energy exports (reducing China’s imports) and inhibits partners’ energy imports (reducing China’s exports), while spillovers may increase China’s imports from neighbors of attacked countries (substitution) and suppress China’s exports to neighbors (security and displacement effects).

Prior work shows energy trade is spatially structured, with production belts (e.g., Russia, Middle East, Central Asia, North America) and import centers (Asia, Europe, North America). Gravity-model evidence highlights transport distance, domestic economic conditions, bilateral agreements, and geopolitical risks (including terrorism) as key determinants of energy trade. Studies document spatial correlations and spillovers in B&R energy markets. Terrorism generally depresses trade, with heterogeneous effects by attack type, product category, and timing; impacts can differ for imports versus exports. Spillover literature finds terrorism in one country reduces neighboring countries’ trade through heightened security costs and perceived risk, though energy’s regional concentration and limited substitutability may yield distinct patterns versus general merchandise trade. The paper identifies a gap: limited evidence on terrorism’s spatial spillovers specifically for China–B&R energy trade, motivating the present spatial panel analysis.

Study scope: China’s energy trade with B&R countries spanning Northeast Asia (Mongolia), Southeast Asia (11 countries), South Asia (8), West Asia & North Africa (19), Central and Eastern Europe (20), and Central Asia (5). Period: 2008–2019. Data: Dependent variables are bilateral energy import and export values (coal, natural gas, oil, other fossil fuels) from UN Comtrade. Terrorism metrics include a composite Terrorism Index (0–10) and sub-indicators (incidents, deaths, injuries) from the Global Terrorism Index Report and Global Terrorism Database. The Terrorism Index weights: incidents 1, deaths 3, injuries 0.5, property damage 0.5, then normalized. Controls: partner GDP, Liner Shipping Connectivity Index (LSCI), and energy export dependence (REP), from World Bank. Missing bilateral trade observations (2.54% imports; 0.90% exports) were linearly interpolated using averages of adjacent years. To harmonize scales, all independent variables use ln(x+0.1). Spatial analysis: Exploratory Spatial Data Analysis (ESDA) computes global Moran’s I and local indicators of spatial association (LISA) to assess clustering. Spatial weights W are contiguity-based (wij=1 if countries share borders; otherwise 0); for island countries, wij=1 to their two nearest continental neighbors. Econometric model: Spatial Durbin Models (SDM) are estimated for imports and exports. Two specifications separate the composite Terrorism Index from sub-indicators (incidents, deaths, injuries) to mitigate multicollinearity; VIF tests confirm acceptable collinearity. The SDM includes: spatial lag of the dependent variable, spatial lags of covariates (to capture spillovers), partner and year fixed effects, and maximum likelihood estimation (MATLAB). Robustness checks include: (1) alternative distance-based spatial weight matrix; (2) one-period lag of explanatory and control variables; and (3) a dynamic SDM with temporal lag of the dependent variable to address endogeneity. Model diagnostics (LR, Wald, Hausman; spatial and time fixed effects) support the SDM choice.

- Temporal trends: China’s energy imports from B&R partners rose from USD 98.8B (2008) to 207.3B (2014), fell to 107.4B (2016), and reached 211.3B (2019). Exports rose steadily from USD 6.6B (2008) to 21.2B (2019). Terrorism indicators peaked circa 2014–2015 then declined by 2019. - Spatial clustering: Moran’s I indicates significant positive spatial autocorrelation for most years. Imports’ Moran’s I reached 0.304 (2014); exports up to 0.220 (2017); Terrorism Index rose to 0.360 (2019), evidencing clustering. - Spatial patterns: High-high import clusters in West Asia and North Africa (e.g., Kuwait, Oman, Saudi Arabia, UAE; later adding Iraq, Qatar); exports cluster in Southeast Asia (e.g., Singapore, Philippines). High terrorism clusters in South Asia, Central Asia, West Asia & North Africa. - SDM baseline (imports): Direct effect of Terrorism Index on imports is negative and significant (−0.339 at 1%). Sub-indicators show frequency (incidents) and injuries suppress imports (e.g., incidents direct −0.359; injuries −0.719). Spatial spillover: Terrorism Index’s indirect effect on imports is positive (+0.207), implying substitution toward neighboring energy suppliers when a country faces higher terrorism. Spatial autoregressive parameter ρ positive and significant. - SDM baseline (exports): Terrorism Index is not directly significant for exports, but exhibits a negative spatial spillover (indirect −0.774), indicating that higher terrorism in neighbors reduces China’s exports to a given country. Frequency of attacks raises exports (direct +0.125; indirect +0.335), while deaths reduce exports (indirect −0.398; total −0.485). ρ positive and significant. Models fit well (R² ≈ 0.86–0.91). - Total effects (exports): Terrorism Index total effect −0.785 (1%); incidents total effect +0.460 (5%); deaths total effect −0.485 (5%). - Regional heterogeneity: Terrorism Index negatively affects China’s imports across regions—central (South/Central/West Asia & North Africa) strongest (e.g., −0.515), followed by eastern (−0.480) and western (−0.383). For exports, negative effects are more evident in central and western regions. LSCI positively influences trade in eastern and some western contexts; REP raises imports (notably in central region) but can reduce exports. - Controls: Partner GDP generally increases both China’s imports and exports; LSCI often boosts exports; energy export dependence (REP) increases China’s imports. - Robustness: Results persist under distance-based W, lagged covariates, and dynamic SDM, with some coefficients becoming stronger under distance weights.

The results confirm that terrorism materially reshapes China–B&R energy trade, with distinct direct and spatial spillover channels. For imports, terrorism in a supplier country reduces China’s purchases due to disrupted production, higher risk premia, and transport insecurity. Yet, because energy resources are geographically concentrated and not easily substitutable globally, terrorism in one country shifts China’s import sourcing toward neighboring energy producers (positive spillovers on imports), consistent with a regional substitution mechanism. For exports, terrorism raises security costs and border frictions in neighboring countries, limiting China’s export flows (negative spillovers). Heterogeneity across regions aligns with geopolitical realities: central B&R regions (South/Central/West Asia & North Africa) exhibit the strongest negative import effects given heightened conflict exposure and energy resource concentration. The differentiated impacts of incidents versus deaths imply that frequent, lower-fatality events can stimulate substitution and export opportunities, while high-fatality events heighten geopolitical risk, close corridors, and depress exports. These findings refine the literature by showing that energy trade, unlike general goods trade, exhibits substitution-driven spatial responses under terrorism due to resource immobility and regional clustering, underscoring the importance of spatially aware risk management and regional cooperation.

The study documents pronounced spatial clustering in China’s energy trade with B&R countries and in regional terrorism. Using a Spatial Durbin Model with disaggregated terrorism indicators, it shows: (1) terrorism reduces China’s energy imports directly, with positive spillovers on imports via substitution to neighbors; (2) terrorism has limited direct effects on exports but negative spillovers on exports to neighbors; (3) incidents can increase China’s exports while deaths reduce them; and (4) partner GDP, maritime connectivity, and export dependence significantly shape trade. Policy implications include: strengthening China’s strategic energy reserves; diversifying import sources with emphasis on geopolitically stable partners; advancing domestic and offshore resource development; deepening cross-border infrastructure (e.g., pipelines and corridors with Russia, Central Asia, Pakistan); and instituting coordinated counter-terrorism mechanisms (data sharing, joint exercises, harmonized legal frameworks) with key B&R partners. Future research could integrate price dynamics, firm-level trade data, alternative spatial networks (e.g., shipping routes), and micro-level security incidents to further parse mechanisms and endogeneity.