Land Expansion, Fertilizer Use, and Agricultural Greenhouse Gas Emissions in ASEAN: A Dynamic Panel ARDL Analysis 10.32526/ennrj/25/20260078
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Abstract
The agricultural sector of the Association of Southeast Asian Nations (ASEAN) was identified as a significant yet under-researched source of non-CO₂ greenhouse gases, nitrous oxide (N₂O), and methane (CH₄). This study applied a Panel Autoregressive Distributed Lag (Panel ARDL) Mean Group (MG) estimator on 166 country-year observations across eight ASEAN member states (2000-2020) to test two hypotheses: H₁ (land area expansion is positively associated with long-run emissions) and H₂ (agricultural value-added intensity is positively associated with emissions). A fertilizer control variable, ln(Fert), was included to correct omitted variable bias. The error correction term (φ̄ = -0.672, p<0.001) confirmed long-run cointegration. The long-run land-area elasticity was not robustly identified: the pooled MG estimate (θ_Land=0.151, p=0.815) was statistically indistinguishable from zero, and this remained the case under estimators that accounted for cross-sectional dependence (CCEMG θ_Land= -0.479, p=0.311; AMG θ_Lan= -0.552, p=0.210). Only the Fixed Effects within estimator, which did not model cross-sectional dependence, yielded a positive and marginally significant coefficient under cluster-robust inference (β_Land=0.412, p=0.092); H₁ was therefore not robustly supported. H₂ was rejected across MG, CCEMG, and AMG estimators (θ_VA/β_VA not statistically distinguishable from zero in any specification), reported here as a null result. In the short run, fertilizer use showed the largest point estimate among the differenced regressors (δ_Fert=0.057, p=0.114), though this did not reach conventional significance at the pooled MG level. Adjustment speeds varied from Indonesia (φᵢ= -1.199, fastest) to Vietnam (φᵢ= -0.393, slowest). Policy priorities include fertilizer input management, scaling biogas and Alternate Wetting and Drying (AWD) irrigation, and harmonising ASEAN emission monitoring frameworks.
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