EN中文

Acta Geographica Sinica >

2025 , Vol. 80 >Issue 5: 1353 - 1369

DOI: https://doi.org/10.11821/dlxb202505013

Environmental Change and Agricultural Development

Spatio-temporal impacts of extreme heat on economic growth in the Yellow River Basin

  • ZHANG Hang , 1, 2, 3 ,
  • GUO Yuchen 1, 2 ,
  • ZHANG Hongjuan 1, 2 ,
  • GAO Wenkai 1, 2 ,
  • LI Yurui 4 ,
  • DONG Guanpeng , 1, 2, 3
Expand
  • 1. Climate Change and Carbon Neutrality Lab, Henan University, Kaifeng 475001, Henan, China
  • 2. Key Research Institute of Yellow River Civilization and Sustainable Development, Henan University, Kaifeng 475001, Henan, China
  • 3. Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River, Ministry of Education, Henan University, Kaifeng 475001, Henan, China
  • 4. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China

Received date: 2024-09-23

  Revised date: 2025-04-23

  Online published: 2025-05-23

Supported by

National Natural Science Foundation of China(42471467)

National Natural Science Foundation of China(42001115)

Fold

Abstract

Since the 1950s, the frequency, intensity, and spatial extent of extreme heat events have increased significantly. Beyond the broader economic consequences of global warming, extreme heat imposes independent and substantial influences on economic growth. Neglecting its effects could lead to substantial underestimations of climate-driven economic losses and regional disparities. The Yellow River Basin (YRB), a crucial ecological security barrier and key pilot region for high-quality development, is home to predominantly energy- and labor-intensive industries that are particularly vulnerable to extreme heat. Understanding the historical impacts of extreme heat on economic growth is essential for assessing the region's climate resilience and formulating adaptation strategies. To address this, this study constructed a county-level GDP dataset for the YRB spanning 30 years (1992-2021) by integrating nighttime light data and machine learning techniques. Extreme heat, average temperature, and temperature variability were systematically incorporated into a climate econometric model to analyze the nonlinear and persistent effects of extreme heat on economic growth. Additionally, projections from the Coupled Model Inter-comparison Project Phase 6 (CMIP6) further enable the quantification of economic losses attributable to anthropogenic extreme heat. The key findings reveal that: (1) As a county's annual average temperature rises, the marginal effect of extreme heat shifts from being statistically insignificant to significantly negative, with an inflection point at 6.7 ℃. Counties experiencing significant economic losses due to extreme heat constitute approximately 94.9% of the YRB. (2) The marginal effect of extreme heat tends to accumulate and intensify within the first three years' post-event, may rebound in the fourth year, and typically dissipate by the fifth year. However, when a county's annual average temperature exceeds 11.1 ℃, extreme heat can cause permanent damage to economic growth. (3) Economic losses attributed to anthropogenic extreme heat totaled approximately 2 billion yuan in 2010 but surged to around 142 billion yuan by 2020, representing about 1.8% of that year's GDP：A nearly 70-fold increase. The cumulative economic loss from 1998 to 2020 amounted to approximately 566 billion yuan.

Key words： climate econometrics; spatial heterogeneity; time-persistent effects; Yellow River Basin; machine learning

Cite this article

ZHANG Hang , GUO Yuchen , ZHANG Hongjuan , GAO Wenkai , LI Yurui , DONG Guanpeng . Spatio-temporal impacts of extreme heat on economic growth in the Yellow River Basin[J]. Acta Geographica Sinica, 2025 , 80(5) : 1353 -1369 . DOI: 10.11821/dlxb202505013

References

Publishing order | Descend order by publishing year | Descend order by cited within
[1]
IPCC. Climate Change 2023:Synthesis Report. Contribution of Working Groups I, II, and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Geneva: IPCC, 2023.

[2]
Zhou Botao, Qian Jin. Changes of weather and climate extremes in the IPCC AR6. Climate Change Research, 2021, 17(6): 713-718.

[周波涛, 钱进. IPCC AR6报告解读: 极端天气气候事件变化. 气候变化研究进展, 2021, 17(6): 713-718.]

[3]
Fischer E M, Sippel S, Knutti R. Increasing probability of record-shattering climate extremes. Nature Climate Change, 2021, 11(8): 689-695.

DOI PMID

[4]
Hu Ting, Sun Ying, Zhang Xuebin. Temperature and precipitation projection at 1.5 ℃ and 2 ℃ increase in global mean temperature. Chinese Science Bulletin, 2017, 62(26): 3098-3111.

[胡婷, 孙颖, 张学斌. 全球1.5 ℃和2 ℃温升时的气温和降水变化预估. 科学通报, 2017, 62(26): 3098-3111.]

[5]
Swain D L, Singh D, Touma D, et al. Attributing extreme events to climate change: A new frontier in a warming world. One Earth, 2020, 2(6): 522-527.

[6]
Huang Manjie, Li Yanzhong, Bai Peng, et al. Spatial evolution of heat stroke risk exposure and its driving factors in southeast China. Geographical Research, 2023, 42(8): 2121-2134.

[黄曼捷, 李艳忠, 白鹏, 等. 中国东南部人口中暑风险暴露度空间演变特征及其驱动因素. 地理研究, 2023, 42(8): 2121-2134.]

[7]
Wang Chunchao, Lin Qianqian. How does bad weather affect labor productivity? Evidence from the adaptation behavior of couriers. China Economic Quarterly, 2021, 21(3): 797-818.

[王春超, 林芊芊. 恶劣天气如何影响劳动生产率? 基于快递业劳动者的适应行为研究. 经济学(季刊), 2021, 21(3): 797-818].

[8]
Tol R S J. A meta-analysis of the total economic impact of climate change. Energy Policy, 2024, 185: 113922. DOI: 10.1016/j.enpol.2023.113922.

[9]
Zheng S Q, Wang J H, Sun C, et al. Air pollution lowers Chinese urbanites' expressed happiness on social media. Nature Human Behaviour, 2019, 3(3): 237-243.

DOI PMID

[10]
Li Yang, Liu Changxin, Song Yao. Research progress of the impact of frequent high temperature heat waves on economic system. Climate Change Research, 2021, 17(1): 121-130.

[李扬, 刘昌新, 宋瑶. 高温热浪频发对经济系统影响的研究进展. 气候变化研究进展, 2021, 17(1): 121-130.]

[11]
Auffhammer M. Quantifying economic damages from climate change. Journal of Economic Perspectives, 2018, 32(4): 33-52.

[12]
Kalkuhl M, Wenz L. The impact of climate conditions on economic production. Evidence from a global panel of regions. Journal of Environmental Economics and Management, 2020, 103: 102360. Doi: 10.1016/j.jeem.2020.102360.

[13]
Callahan C W, Mankin J S. Globally unequal effect of extreme heat on economic growth. Science Advances, 2022, 8(43): eadd3726. DOI: 10.1126/sciadv.add3726.

[14]
Kotz M, Levermann A, Wenz L. The economic commitment of climate change. Nature, 2024, 628(8008): 551-557.

[15]
Dong Wenjie, Yuan Wenping, Teng Fei, et al. Coupling Earth system model and integrated assessment model. Advances in Earth Science, 2016, 31(12): 1215-1219.

DOI

[董文杰, 袁文平, 滕飞, 等. 地球系统模式与综合评估模型的双向耦合及应用. 地球科学进展, 2016, 31(12): 1215-1219.]

DOI

[16]
Wang Zheng, Gu Gaoxiang, Wu Jing, et al. CIECIA: A new climate change integrated assessment model and its evaluation of global cooperative emission reduction programs. Scientia Sinica (Terrae), 2015, 45(10): 1575-1596, 1-4.

[王铮, 顾高翔, 吴静, 等. CIECIA: 一个新的气候变化集成评估模型及其对全球合作减排方案的评估. 中国科学: 地球科学, 2015, 45(10): 1575-1596, 1-4.]

[17]
Rising J A, Taylor C, Ives M C, et al. Challenges and innovations in the economic evaluation of the risks of climate change. Ecological Economics, 2022, 197: 107437. DOI: 10.1016/j.ecolecon.2022.107437.

[18]
Stern N, Stiglitz J E. Climate change and growth. Industrial and Corporate Change, 2023, 32(2): 277-303.

[19]
Chang J J, Mi Z F, Wei Y M. Temperature and GDP: A review of climate econometrics analysis. Structural Change and Economic Dynamics, 2023, 66: 383-392.

[20]
Hsiang S. Climate econometrics. Annual Review of Resource Economics, 2016, 8(1): 43-75.

[21]
Kotz M, Levermann A, Wenz L. The effect of rainfall changes on economic production. Nature, 2022, 601(7892): 223-227.

[22]
Kotz M, Wenz L, Stechemesser A, et al. Day-to-day temperature variability reduces economic growth. Nature Climate Change, 2021, 11(4): 319-325.

[23]
Dell M, Jones B F, Olken B A. What do we learn from the weather? The new climate-economy literature. Journal of Economic literature, 2014, 52(3): 740-798.

[24]
Wooldridge J M. Econometric Analysis of Cross Section and Panel Data. Cambridge: MIT Press, 2010.

[25]
He Canfei, Zhu Yangang. An empirical study on the geographical distribution of resource-based industries in China: A case study of the oil processing and coking industry and the ferrous metal smelting and rolling processing industry. Journal of Natural Resources, 2010, 25(3): 488-501.

DOI

[贺灿飞, 朱彦刚. 中国资源密集型产业地理分布研究: 以石油加工业和黑色金属产业为例. 自然资源学报, 2010, 25(3): 488-501.]

DOI

[26]
Lu Dadao, Sun Dongqi. Development and management tasks of the Yellow River Basin: A preliminary understanding and suggestion. Acta Geographica Sinica, 2019, 74(12): 2431-2436.

DOI

[陆大道, 孙东琪. 黄河流域的综合治理与可持续发展. 地理学报, 2019, 74(12): 2431-2436.]

DOI

[27]
Ma Guangming. Research on the classification and its influencing factors for domestic location transfer of China's low-end manufacturing industry. Inquiry into Economic Issues, 2022(1): 112-133.

[马光明. 中国低端制造业的分类筛选、国内转移及其影响因素研究. 经济问题探索, 2022(1): 112-133.]

[28]
Burke M, Davis W M, Diffenbaugh N S. Large potential reduction in economic damages under UN mitigation targets. Nature, 2018, 557(7706): 549-553.

[29]
Burke M, Hsiang S M, Miguel E. Global non-linear effect of temperature on economic production. Nature, 2015, 527(7577): 235-239.

[30]
Jing Cheng, Jiang Tong, Wang Yanjun, et al. A study on regional extreme precipitation events and the exposure of population and economy in China. Acta Meteorologica Sinica, 2016, 74(4): 572-582.

[景丞, 姜彤, 王艳君, 等. 中国区域性极端降水事件及人口经济暴露度研究. 气象学报, 2016, 74(4): 572-582.]

[31]
Li Minna, Cai Shu, Zhang Huirong, et al. Factor endowment and economic spatial differences in the Yellow River Valley. Economic Geography, 2011, 31(1): 14-20.

[李敏纳, 蔡舒, 张慧蓉, 等. 要素禀赋与黄河流域经济空间分异研究. 经济地理, 2011, 31(1): 14-20.]

[32]
Chen Shiqiang, Zhang Hang, Qi Ying, et al. Spatial spillover effect and influencing factors of haze pollution in the Yellow River Basin. Economic Geography, 2020, 40(5): 40-48.

DOI

[陈世强, 张航, 齐莹, 等. 黄河流域雾霾污染空间溢出效应与影响因素. 经济地理, 2020, 40(5): 40-48.]

DOI

[33]
Zhang Pengyan, Li Yanyan, Kang Guohua, et al. A study on estimates and spatial differentiation of economic density at county level in Yellow River Basin. China Population, Resources and Environment, 2017, 27(8): 128-135.

[张鹏岩, 李颜颜, 康国华, 等. 黄河流域县域经济密度测算及空间分异研究. 中国人口·资源与环境, 2017, 27(8): 128-135.]

[34]
Chen X, Nordhaus W D. Using luminosity data as a proxy for economic statistics. PNAS, 2011, 108(21): 8589-8594.

DOI PMID

[35]
Henderson J V, Storeygard A, Weil D N. Measuring economic growth from outer space. The American Economic Review, 2012, 102(2): 994-1028.

[36]
Xu Kangning, Chen Fenglong, Liu Xiuyan. The truth of China economic growth: Evidence from global night-time light data. Economic Research Journal, 2015, 50(9): 17-29, 57.

[徐康宁, 陈丰龙, 刘修岩. 中国经济增长的真实性: 基于全球夜间灯光数据的检验. 经济研究, 2015, 50(9): 17-29, 57.]

[37]
Zhang H, Dong G P, Li B, et al. Developing an annual global Sub-National scale economic data from 1992 to 2021 using nighttime lights and deep learning. International Journal of Applied Earth Observation and Geoinformation, 2024, 133: 104086. DOI: 10.1016/j.jag.2024.104086.

[38]
Mueller B, Seneviratne S I. Hot days induced by precipitation deficits at the global scale. PNAS, 2012, 109(31): 12398-12403.

DOI PMID

[39]
Perkins-Kirkpatrick S E, Lewis S C. Increasing trends in regional heatwaves. Nature Communications, 2020, 11: 3357. DOI: 10.1038/s41467-020-16970-7.

PMID

[40]
An Ning, Zuo Zhiyan. Structural changes of heat waves in China from 1961 to 2017. Scientia Sinica (Terrae), 2021, 51(8): 1214-1226.

[安宁, 左志燕. 1961—2017年中国地区热浪的结构变化. 中国科学: 地球科学, 2021, 51(8): 1214-1226.]

[41]
Gillett N P, Shiogama H, Funke B, et al. The detection and attribution model intercomparison project (DAMIP v1.0) contribution to CMIP6. Geoscientific Model Development, 2016, 9(10): 3685-3697.

[42]
Gebrechorkos S, Leyland J, Slater L, et al. A high-resolution daily global dataset of statistically downscaled CMIP 6 models for climate impact analyses. Scientific Data, 2023, 10: 611. DOI: 10.1038/s41597-023-02528-x.

PMID

[43]
Waidelich P, Batibeniz F, Rising J, et al. Climate damage projections beyond annual temperature. Nature Climate Change, 2024, 14(1): 592-599.

[44]
Sen S, von Schickfus M T. Climate policy, stranded assets, and investors' expectations. Journal of Environmental Economics and Management, 2020, 100: 102277. DOI: 10.1016/j.jeem.2019.102277.

[45]
Jin Fengjun. Coordinated promotion strategy of ecological protection and high-quality development in the Yellow River Basin. Reform, 2019(11): 33-39.

[金凤君. 黄河流域生态保护和高质量发展的协调推进策略. 改革, 2019(11): 33-39.]

[46]
Moore F C, Diaz D B. Temperature impacts on economic growth warrant stringent mitigation policy. Nature Climate Change, 2015, 5(2): 127-131.

[47]
Sun Y D, Zhu S P, Wang D P, et al. Global supply chains amplify economic costs of future extreme heat risk. Nature, 2024, 627(8005): 797-804.

[48]
Hsiang S M, Jina A S. The Causal Effect of Environmental Catastrophe on Long-Run Economic Growth: Evidence from 6700 Cyclones. Cambridge: National Bureau of Economic Research, 2014. DOI: 10.3386/w20352.

[49]
Newell R G, Prest B C, Sexton S E. The GDP-temperature relationship: Implications for climate change damages. Journal of Environmental Economics and Management, 2021, 108: 102445. DOI: 10.1016/j.jeem.2021.102445.

Options
Outlines

/

〈 〉