Agricultural intensification and greenhouse gas emissions in Saudi Arabia: evidence from linear and nonlinear ARDL models
サウジアラビアにおける農業集約化と温室効果ガス排出：線形・非線形ARDLモデルによる証拠 (AI 翻訳)
Hazrat Hassan, Muhammad Saeed Ashraf, Agyemang Kwasi Sampene
🤖 gxceed AI 要約
日本語
サウジアラビアの農業活動（肥料消費、作物生産、森林レント）が総GHG排出に与える長期的・非線形な影響を、2000〜2023年の年次データを用いてARDLおよびNARDLモデルで分析。肥料消費と作物生産は排出を有意に増加させ、正のショックの影響が負のショックより大きい非対称性を確認。持続的集約化理論に整合し、Vision 2030に沿った気候スマート農業政策への示唆を提供。
English
This study analyzes the long-run and nonlinear effects of agricultural activities (fertilizer consumption, crop production, forest rents) on total GHG emissions in Saudi Arabia using ARDL and NARDL models with annual data from 2000-2023. Results confirm that fertilizer and crop production significantly increase emissions, with asymmetric effects where positive shocks have stronger impact. Findings align with Sustainable Intensification Theory and offer policy insights for climate-smart agriculture under Vision 2030.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本では農業部門のGHG排出削減が課題であり、非線形・非対称な排出応答の分析手法は、国内の農業政策や土地利用計画に示唆を与える。ただし、サウジアラビア特有の状況であり、直接適用には注意が必要。
In the global GX context
This paper contributes to global literature on agricultural GHG emissions by applying nonlinear ARDL methods to an emerging economy context. It provides evidence on asymmetric responses of emissions to agricultural inputs, relevant for designing climate-smart agriculture policies in arid regions and other developing countries.
👥 読者別の含意
🔬研究者:Methodological insights on applying NARDL to agricultural emissions data, useful for similar studies in other regions.
🏢実務担当者:Limited direct applicability for corporate sustainability teams, but may inform agricultural supply chain strategies in Middle East.
🏛政策担当者:Evidence for designing agricultural policies that balance productivity and emissions reduction, relevant for Vision 2030 and similar strategies.
📄 Abstract（原文）
Introduction This study examines the long-run and non-linear effects of key agricultural activities on total greenhouse gas (GHG) emissions in Saudi Arabia, a country pursuing rapid agricultural modernization under Vision 2030 amid rising environmental pressures. Although agriculture is increasingly recognized as a significant contributor to GHG emissions, existing evidence for Saudi Arabia remains limited, with little attention to asymmetric and non-linear responses of emissions to agricultural inputs and land-use dynamics. To address this gap, the study investigates how fertilizer consumption, forest rents, and crop production influence total GHG emissions, while accounting for the roles of arable land, agricultural land, and total fisheries production as control variables. Methods Annual data spanning 2000–2023 are analyzed using both linear ARDL and non-linear ARDL (NARDL) frameworks to capture long-run relationships, short-run adjustments, and asymmetric effects. Results The results confirm a stable long-run cointegrating relationship among the variables and reveal that fertilizer consumption and crop production significantly increase GHG emissions, with positive shocks exerting stronger environmental pressure than negative shocks. Forest rents also intensify emissions, reflecting land-use and resource-extraction effects, while control variables exhibit heterogeneous influences. Discussion These findings are consistent with Sustainable Intensification Theory, highlighting trade-offs between productivity gains and environmental sustainability. The study’s novelty lies in its integrated linear–non-linear analysis of agricultural drivers of total GHG emissions in Saudi Arabia. Practically, the results offer targeted policy insights for designing climate-smart agriculture, land-use regulation, and input-management strategies aligned with Vision 2030 and applicable to other emerging economies.
🔗 Provenance — このレコードを発見したソース
- crossref https://doi.org/10.3389/fenvs.2026.1830288first seen 2026-07-02 07:49:05 · last seen 2026-07-15 06:05:41
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