Green transformation of pastoral livestock systems in China: development level, efficiency mismatch, and carbon decoupling
Year: 2026
Type: article
Abstract: Pastoral livestock systems are expected to support food supply, rural livelihoods, ecological protection, and lower emissions intensity at the same time. Existing evaluations often consider composite green-development scores, production efficiency, or coordination separately, making it difficult to determine whether these dimensions improve together. This study develops an integrated diagnostic framework for Xinjiang, Inner Mongolia, Qinghai, and Tibet from 2006 to 2022. An entropy weight-TOPSIS level index is combined with super-efficiency EBM measures, bounded-efficiency coupling coordination, level-efficiency lag diagnosis, output-based CO 2 e intensity, Tapio decoupling, and obstacle-degree analysis. The baseline level index shows a clear hierarchy: Inner Mongolia has the highest mean score (0.679), followed by Xinjiang (0.414), Qinghai (0.219), and Tibet (0.198). Robustness tests preserve the upper tier (Inner Mongolia and Xinjiang) and lower tier (Qinghai and Tibet), although some within-tier ordering varies across specifications. Efficiency results qualify the composite-level ranking: Inner Mongolia has the highest scale efficiency but limited frontier years; Xinjiang reaches the PTE frontier most frequently but has the lowest scale efficiency; Qinghai has the highest mean TE; and Tibet has the highest mean PTE but lower scale efficiency. Coupling coordination improves most clearly in Inner Mongolia and Xinjiang and also rises from lower initial levels in Qinghai and Tibet. Estimated output-based CO 2 e intensity declines by 17.6 to 43.2% across the four regions. Obstacle analysis shows that the policy-round variable influences baseline rankings; excluding it shifts the leading diagnostic obstacles toward production, income, and environmental indicators. Livestock-system sustainability is therefore better understood as a joint livelihood-efficiency-emissions problem than as a single composite-ranking exercise.
Language: English
Field: Decision Sciences
Domain: Social Sciences
SDGs: Climate action, Zero hunger
Open Access status: gold
Estimated APC paid: $2,353
OpenAlex ID: W7214023782