To identify appropriate strategies for temperature change adaptation, the economic impacts of temperature changes are critical to be understood. Despite a wide investigation about this issue, the obtained evidence is still mixed, including positive linear, negative linear, U-shaped, inverted U-shaped, or even irrelevant relationships. To address this question, we investigated the findings of collected studies through a meta-analysis based on 87 studies with 2977 estimates. We first examined the genuine effects between temperature changes and economic impacts based on statistical models (i.e., funnel plots, MST and FAT-PET-PEESE tests). Then, we adopted the meta-regression method to identify the sensitive modeling characteristics influencing the research outcomes. The results illustrate four major conclusions. First, there is a negative relationship between temperature changes and economic outputs in linear regression analysis, and an inverted U-shaped relationship in quadratic regression specifications. Second, research areas and temperature variables involved in individual studies have significant effects on current economic consequence analysis. Particularly, rich countries located in colder climates can even benefit from temperature changes, whereas poor countries located in hotter climates suffered adverse impacts. Third, the resilience factors should be involved in future prediction models to investigate the mitigation effects. Fourth, the sensitive modeling specifications were different according to different research sub-objects. The results obtained can provide implications for the sustainable development of the economy and human society caused by climatic change, and can also make contributions to advance the theory and practice of future temperature change consequence analysis.
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The datasets generated during the current study are available from the corresponding author on reasonable request.
Primary sectors, secondary sectors, and tertiary sectors in this paper referred to agriculture, manufacturing, and services, respectively.
If there were no clear t-values in the original studies, but both the regression coefficient and the standard error were presented, then t-value = regression coefficient/standard error. For ease of discussion, this paper reports t-values always in a way that “negative” impact means that the costs of temperature changes were larger. However, if the studies investigated the relationship between temperature changes and diseases or deaths, then the positive t-values indicated that temperature changes were positively correlated with the effects of diseases and deaths. Therefore, we changed the signs of the coefficients to evaluate them between different studies.
Two stepwise regression models were performed (forward-stepwise and backward-stepwise) to extract variables at 5% level of significance.
Due to the small number of estimates from quadratic regression analysis, the funnel plots of quadratic regression analysis were performed between temperature changes and aggregate output, human capital, and the output of primary sectors. Also, the meta-regression analysis on different research objects was performed based on estimates derived from linear regression analysis.
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The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The first author would like to acknowledge the financial support from the China Scholarship Council (CSC) scholarship, No. 201908320518, the National Social and Scientific Fund Program of China (16ZDA047, 18ZDA052).
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Tan, L., Zhou, K., Zheng, H. et al. Revalidation of temperature changes on economic impacts: a meta-analysis. Climatic Change 168, 7 (2021). https://doi.org/10.1007/s10584-021-03213-x
- Temperature change
- Economic impact
- Empirical research