Spatial Heterogeneity in Temperature Elasticity of Agricultural Economic Production in Xinjiang Province, China

Shiwei Liu, Yongyu Yue (), Lei Wang and Yang Yang
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Shiwei Liu: State Key Laboratory of Earth Surface Processes and Disaster Risk Reduction, Beijing Normal University, Beijing 100875, China
Yongyu Yue: State Key Laboratory of Earth Surface Processes and Disaster Risk Reduction, Beijing Normal University, Beijing 100875, China
Lei Wang: Key Laboratory of Mountain Surface Processes and Ecological Regulation, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China
Yang Yang: Wu Jinglian School of Economics, Changzhou University, Changzhou 213159, China

Sustainability, 2025, vol. 17, issue 17, 1-24

Abstract: Agricultural production is significantly impacted by climate change. Owing to its arid and warm climate, investigating the impacts of climate change on agricultural production in Xinjiang Province can help improve resilience and designate adaptive responses for the agricultural sector. On the basis of agricultural output data at the county level in Xinjiang from 1990–2019, we used the feasible generalized least squares (FGLS), panel-corrected standard errors (PCSE), and double machine learning (DML) model to study the spatial heterogeneity in temperature elasticity of agricultural economic production. The results revealed that there is an inverted U-shape of temperature impact on agricultural economic production. The presented temperature elasticity in county level showed that regions with negative temperature elasticities are primarily located in the mainstream of the Tarim basin and the Turpan basin in southern Xinjiang. The SHapley Additive exPlanations (SHAP) analysis was further incorporated to elucidate the impact of different factors on the spatial heterogeneity in temperature elasticity. The results indicated that temperature is the most substantial factor influencing temperature elasticity, with labor and precipitation following in sequence. In particular, increased precipitation in arid and hot regions could alleviate the heat stress and lead to a positive temperature elasticity prediction. These findings provide a scientific basis for spatial heterogeneity in the response of agricultural economic production to climate change, and help identify priority regions for achieving Sustainable Development Goals (SDGs) 1 and 2.

Keywords: climate change; agricultural economic production; temperature elasticity; double machine learning model; Xinjiang Province (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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